THERAPY AND DIAGNOSIS OF DISEASE CHARACTERIZED BY ALTERATIONS IN THE DNA DAMAGE RESPONSE

Abstract

The present invention relates to at least one modulator of PP2A or at least one modulator of PP2A-like phosphatase or at least one modulator of PP2A and PP2A-like phosphatase or a combination of said modulators for use in the treatment of a disease characterized by an alteration in the DNA damage response. The present invention also relates to a method to identify a subject to be treated with a PP2A modulator comprising detecting in the genome of said patient a mutation in PP2A.

Claims

1. (canceled)

2. The method according to claim 26, wherein said modulator modulates the PP2A-GSK3-MCL-1 axis.

3. The method at least one modulator or combination thereof for use according to claim 2, wherein said modulator is selected from the group consisting of: a) a small molecule; b) a polypeptide; c) an antibody or a fragment thereof; d) a polynucleotide coding for said antibody or polypeptide or a functional derivative thereof; e) a polynucleotide, such as antisense construct, antisense oligonucleotide, RNA interference construct or siRNA, f) a vector comprising or expressing the polynucleotide as defined in d) or e); and g) a host cell genetically engineered expressing said polypeptide or antibody or comprising the polynucleotide as defined in d) or e).

4. The method according to claim 3 wherein said modulator is selected from the group consisting of: a TORC1 inhibitor, a Ppm1 methyltransferase activator, a TOR inhibitor or wherein said modulator is an intervention and/or an agent that inhibits nutrient uptake (inhibition of nutrient uptake).

5. The method according to claim 7 wherein the ceramide is selected from the group consisting of: N-Acetyl-D-sphingosine c2 ceramide, C6-Ceramide, ceramidase inhibitor, such as D-e-MAPP and D-NMAPPD (B13).

6. The method according to claim 4 wherein the TORC1 inhibitor inhibits the TORC1-Tap42 pathway.

7. The method according to claim 3, wherein the modulator is selected from the group consisting of: metformin, thioridazine, perphenazine, ceramide, Irc21, rapamycin, caffeine, wortmannin, S-adenosyl methionine, FTY-720, fluphenazine, thiethylperazine, pimozide, clozapine, loratadine, promethazine, haloperidol, mersalyl acid, myriocin, fumonisin B1, okadaic acid, cardiolipin, thiethylperazine maleate.

8. The method according to claim 26, wherein said modulator or combination thereof is used in combination with low glucose and/or with at least one DNA damaging agent.

9. The method according to according to claim 26, wherein said DNA damaging agent is an agent selected from the group consisting of: hydroxyurea, gemcitabine, carboplatin, platin-based drug, camptotechin, topoisomerase inhibitors and other chemoterapic drugs or combination thereof.

10. The method according to claim 26, wherein said modulator or combination thereof is used in combination with an inhibitor of glycosidase and/or an inhibitor of amylase.

11. The method according to claim 26, wherein the combination is selected from the group consisting of the combination of: perphenazine and metformin; metformin and thioridazine; metformin and fasting; metformin and intermittent fasting; metformin and fasting mimicking diets; metformin and any form of fasting and at least one compound selected from table 1B such as fluphenazine, thiethylperazine, pimozide, clozapine, loratadine, promethazine, haloperidol; metformin and 2-Deoxy-Glucose; metformin and rapamycin; metformin and amylases and/or glycosidases inhibitors, such as acarbose, quercetin, 5,4-dihydroxy-3,7-dimethoxyflavone, flavone luteolin, luteolin-7-O-glucoside, eupafolin.

12. The method according to claim 26, wherein the disease characterized by an alteration in the DNA damage response is a cancer and the modulator is an activator of PP2A and/or of PP2A-like phosphatase.

13. The method according to claim 26 wherein the modulator is used in combination with low glucose and/or with at least one DNA damaging agent.

14. The method according to claim 26 wherein the PP2A activator is a compound able to form an active PP2A holoenzyme comprising the regulatory subunit B56 or an activator that induces a PP2A holoenzyme that includes the B56c subunit, such as PPZ and Thioridazine, or an activator that needs low glucose (or fasting) and metformin to achieve the formation of an active PP2A holoenzyme that includes B56.

15. The method according to 26 wherein said activator of PP2A and/or of PP2A-like phosphatase is selected from the group consisting of: metformin, thioridazine, perphenazine, ceramide, Irc21, a Ppm1 methyltransferase activator, TORC1 inhibitor, rapamycin, caffeine, wortmannin, S-adenosyl methionine, FTY-720, fluphenazine, thiethylperazine, pimozide, clozapine, loratadine, promethazine, haloperidol, and TOR inhibitors.

16. The method according to 12 wherein the cancer presents at least one defect in at least one DDR pathways gene.

17. The method according to 26 wherein the subjects to be treated were previously stratified by analysis of DDR markers.

18-19. (canceled)

20. An in vitro method to identify a subject to be treated with a modulator which is an activator of PP2A and/or of PP2A-like phosphatase or a combination thereof comprising detecting in the genome of said patient a mutation in PP2A and/or a mutation in PP2A-like phosphatase or measuring expression level variation of PP2A and/or PP2A-like phosphatase.

21. The in vitro method according to claim 20 wherein said patient is resistant to treatment with metformin.

22. An in vitro method to identify a subject to be treated with a modulator which is an activator of PP2A and/or of PP2A-like phosphatase or a combination thereof comprising detecting in the genome of said patient at least one mutation in at least one DDR pathways gene.

23-25. (canceled)

26. A method for the prevention and/or treatment of a disease characterized by an alteration in the DNA damage response (DDR) comprising administering to a subject in need thereof a modulator which is an activator of PP2A and/or of PP2A-like phosphatase or a combination thereof.

27. The method according to claim 26, wherein said modulator or combination thereof is administered in combination with a therapeutic agent.

Description

[0081] The present invention will be illustrated by means of non-limiting examples and figures as follows.

[0082] FIG. 1. IRC21 deletion rescue checkpoint mutants.

[0083] (A) Cells were grown on SD/-Ura plates containing glucose 2% or galactose 2% with or without hydroxyurea (HU). (B and C) Cells were grown on YPD plates with or without HU. (D) sml1, sml1 mec1, sml1 irc21 and sml1 mec1A irc21 cells were arrested in G.sub.1 with -factor (alpha) and released into YPD containing 0.2 M HU. After 3 hours, cells were released into YPD. Samples were collected at the indicated times to detect Rad53 by Western blot analysis. (E) Cells were arrested with -factor and released in YPD with/without 0.2 M HU. Cells were treated for 3 hours and samples collected to detect Rad53 and Mrel1.

[0084] FIG. 2. IRC21 deletion impairs Cytb5-dependent processes.

[0085] (A) Classification of putative Irc21 processes based on Gene OntologyPANTHER classification system. (B) Oxygen consumption rate of exponentially growing wt and irc21 cells. The results are shown as meansSD of triplicate. ****<0.0001. (C) Determination of ROS levels using the DCFH-DA assay in wt and irc21 cells, depending on incubation period. The results are shown as meansSD of triplicate. P values are indicated. (D) Spot assay of wt and irc21 cells on YPD plates with or without Paraquat, Mersalyl, t-BOOH, Terbinafine, Fluconazole, Cerulenin at the indicated concentrations. Drug mechanisms of action are illustrated.

[0086] FIG. 3. Irc21 interacts with PP2A and PP2A-like phosphatases.

[0087] (A) Comparison between the interactome of the irc21 array strain with the interactomes of 3884 mutant array strains by calculating the correlation (R) value of their interaction scores with the 1712 query mutants (datasets from (Costanzo et al., 2010)). (B) Heatmap representing pairwise interactome correlation values (R) of mutants with altered PP2A activity. (C) Heatmap representing SGA interaction scores between query mutants of the irc21A & rrd1A & tip41 signature (rows) and PP2A-related array mutants (columns). Gray fields indicate that the interaction score has not been determined. (D) Genetic interactions of IRC21 with PP2A components and regulators assessed by SGA screening. Left panel: Quantitative effect of PP2A component deletions (rows) on the growth of irc21 mutants vs. wt. Right panel: Summary of the confirmation of individual genetic interactions. SS=Synthetic sick, SL=synthetic lethal. Interactions with ppm1 and tor1 were confirmed by gene targeting. (E) Representation of PP2A and PP2A-like complex subunits and regulators (see text). Blue and the orange dotted lines indicate irc21 mutant genetic interactors found in Costanzo et al. 2010 and in the present study, respectively.

[0088] FIG. 4. Irc21 inhibits DDR through PP2A activation.

[0089] (A) Cells were treated with 200 ng/ml rapamycin. Bandshift assays following the phosphorylation of PP2A branch proteins Gln3, Nnk1, Npr1 and Rtg3, after 30 of rapamycin treatment. A horizontal line has been overlaid to assist in determining mobility shifts (left panel). Bandshift assays following the phosphorylation of Sch9, after 2 hours of rapamycin (right panel). (B) wt and irc21 cells were grown on YPD plates with or without rapamycin, metformin, caffeine and wortmannin (left panel). All drugs are inhibitors of the Torc11-Tap42 pathway, represented in the right panel. (C, E, F) Cells were grown on YPD plates with or without 3 mM HU. (D) Cells were arrested with -factor and released in YPD with/without 0.2 M HU. Cells were treated for 3 hours and harvested to detect Rad53. (G) Cells were arrested in G.sub.1 with -factor and released in YPD-0.2 M HU. After 3 hours, cells were released into YPD. Samples were collected at the indicated times to detect Rad53. (H) Cells were grown on YPD+HU plates with or without Okadaic acid (OA).

[0090] FIG. 5. Irc21 exerts PP2A-dependent and PP2A-activating metabolic regulations.

[0091] (A) Unsupervised hierarchical clustering of irc21 and rrd1 mutants (six replicates each) based on metabolome alterations during logarithmic growth in rich medium.

[0092] (B) Summary of metabolome alterations of irc21 and rrd1 mutants during logarithmic growth in rich medium. Left panel: Scatter plot of quantitative alterations of individual metabolites in irc21A and rrd1 mutants compared with a congenic wt identifies irc21A-specific (blue), rrd1A-specific (yellow), common (green, PP2A signature) and opposite (red) regulations. Right panel: Venn diagram representation of the intersection of metabolic alterations in irc21A and rrd1 mutants, and intersection significance p value determined by chi-squared test.

[0093] (C) Heatmap representation of altered metabolites by signature. Top panel: PP2A signature (common alterations in irc21 and rrd1A). Bottom panel: Specific regulations in irc21A and opposite regulations in irc21 and rrd1A. As indicated, metabolites were grouped by class.

[0094] (D) 3-keto-sphinganine, sphinganine, sphinganine-1p and dihydroceramide were quantified in wt and irc21 cells. Average values are shown, and error bars represent the standard error of the mean.

[0095] (E) Simplified scheme representing ceramide biosynthesis in S. cerevisiae. Colored metabolites indicate an increase (red) or a decrease (green) of their amount in irc21 cells.

[0096] (F) wt and irc21 cells were grown on YPD plates with or without myriocin.

[0097] (G) wt and irc21 cells were grown on YPD plates with or without syringomycin E.

[0098] (H) wt and irc21 cells were grown in SD medium with or without Fumonisin B1.

[0099] (I) Cells were grown on YPD+HU with or without ceramide.

[0100] (J) Cells were arrested in G.sub.1 with -factor and released in YPD with/without 0.2M HU, 15 M ceramide or 0.2M HU in combination with 15 M ceramide for 3 hours.

[0101] FIG. 6. Ceramides, TORC1, Irc21 and Ppm1 impact on the HU-induced DDR by modulating PP2A activity.

[0102] (A and D) Cells were grown on YPD with or without HU.

[0103] (B) Cells were grown on YPD. 1:10 dilutions were used to highlight growth rate differences.

[0104] (C) wt and irc21 cells were grown on YPD plates with or without ethionine or cycloleucine.

[0105] (E) Cells were arrested with -factor and released into YPD containing 0.2M HU alone or in combination with rapamycin 200 ng/ml, ceramide 15 M, rapamycin 200 ng/ml+ceramide 15 M. Cells were treated for 1 hour and harvested after 5 and 60 in order to detect Nnk1 and Rad53.

[0106] FIG. 7. Model: PP2A links DRR with cell metabolism.

[0107] PP2A and PP2A-like phosphatases are regulated by TORC1 (nitrogen availability), Snf1.sup.AMPK(carbon availability), ceramide (sphingolipids and fatty acids availability) and SAM (methionine availability). The two PP2A complexes integrate the metabolic input with the control of DDR. Irc21 acts upstream of PP2A: it shares PP2A signature but also display specific metabolic function (Irc21 signature).

[0108] FIG. 8. (A,B,D) Cells were grown on YPD plates with or without HU.

[0109] (C) Cells were arrested in G.sub.1 with -factor (F) and released into YPD containing 0.2 M HU. After 3 hours, cells were released into YPD. Samples were collected at the indicated times to determine DNA content by fluorescence-activated cell sorting (FACS) analysis (Related to FIG. 1D).

[0110] (E) Cells were arrested with -factor and released in YPD or YPD containing 0.2 M HU. Cells were treated for 3 hours and harvested to detect Dun1 and Rad53.

[0111] (F) Cells were arrested in G.sub.1 with -factor and released into YPD containing 0.2 M HU. After 3 hours, cells were released into YPD. Samples were collected at the indicated times to determine DNA content by fluorescence-activated cell sorting (FACS) analysis (left panel) and to detect Rad53, Dun1, P-H2A and H2A by Western blot analysis (right panel).

[0112] FIG. 9. (A) Top 10 array mutants with highest interactome similarity to irc21. The Pearson correlation (R) value was obtained by comparing the interactome of the irc21 array strain with the interactomes of 3884 mutant array strains with the 1712 query mutants (datasets from (Costanzo et al., 2010)). (Related to FIG. 3A).

[0113] (B) Negative and rescuing genetic interactions of IRC21 assessed by SGA screening. Left panel: Quantitative effect of array gene deletions (rows) on the growth of irc21 mutants vs. wt. Middle panel: Manual functional classification of IRC21 interactors. Right panel: Comparison with published SGA scores (Costanzo et al., 2010).

[0114] (C) Epistatic genetic interactions of IRC21 assessed by SGA screening. Left panel: Quantitative effect of array gene deletions (rows) on the growth of irc21 mutants vs. wt. Middle panel: Manual functional classification of IRC21 interactors. Right panel: Comparison with published SGA scores (Costanzo et al., 2010).

[0115] (D) Tetrad analysis of irc21 rrd1, irc21 ptc1, irc21 rts1, irc21 tip41, irc21 sap190 strains (Related to FIG. 3D).

[0116] (E) Confirmation of the genetic interactions between Irc21 and Rrd1, Ptc1 and Rts1 by random spore analysis (spore derived from the SGA screening, performed in S228C genetic background) (Related to FIG. 3D).

[0117] FIG. 10. (A) Cells were grown on SD/-Ura containing glucose 2% or galactose 2% with or without HU.

[0118] (B) Cells were grown on YPD plates with or without HU.

[0119] (C) Cells were grown on YPD plates with or without rapamycin or metformin.

[0120] (D) Cells were treated for 30 with 200 ng/ml rapamycin. Bandshift assays following the phosphorylation of PP2A branch proteins Gln3, Nnk1, and Npr1.

[0121] FIG. 11. (A) Quantification (pmol/mg) of the listed metabolites in wt and irc21 cells by TrueMass Ceramide analysis. Average values (AVG), standard deviation (SD) and standard error of the mean (SEM) are shown (Related to FIGS. 5D and E).

[0122] (B) TrueMass Ceramide panel quantification of the listed metabolites in irc21 cells. p-value and statistical significance are shown (Related to FIGS. 5D and E).

[0123] (C) Illustration of sphingolipid biosynthesis in S. cerevisiae. Colored metabolites indicate an increase (red) or a decrease (green) of their amount in irc21 cells (refer to FIG. 11B). Myriocin inhibits serine palmitoyl-CoA transferase. Fumonisin B1 inhibits ceramide synthase.

[0124] (D) Cell sensitivity to syringomycin in presence or absence of dihydroceramide. Cells were grown in SD medium. Average values are shown and error bars represent the standard deviation.

[0125] (E) mec1A sml1 irc21 cells were arrested in G.sub.1 with -factor and released into YPD containing 0.1 M HU. After 3 hours, cells were released into YPD or YPD with ceramide 15 M. Samples were collected at the indicated times to detect Rad53, by Western blot analysis.

[0126] FIG. 12. (A) Cells were treated with 200 ng/ml rapamycin. Bandshift assays following the phosphorylation of PP2A branch proteins Gln3, Nnk1, Npr1 and Rtg3, after 30 of rapamycin treatment.

[0127] (B) Cells were treated with 0.2M HU alone or in combination with rapamycin 200 ng/ml, ceramide 15 M, rapamycin 200 ng/ml+ceramide 15 M. Samples were collected at the indicated times to determine DNA content by FACS analysis, to detect Rad53 and Nnk1 by Western blot analysis, to evaluate the budding index.

[0128] FIG. 13. Intermittent fasting sensitizes tumor-bearing mice to metformin administered during hypoglycemic periods. (A) Schematic representation of the experimental design showing the feeding protocols and timing of metformin administration in different experimental groups. (B-D) The levels of blood glucose measured at the end of each feeding/fasting cycle in different experimental groups. Arrows (C-D) indicate timing of metformin administration. (E) In vivo growth of xenograft tumors as measured by tumor volume (lengthwidthwidth/2) in mice inoculated with HCT116 cells. Upon establishment of tumors, mice were kept on 24-hour feeding/fasting cycles as indicated above and treated with either vehicle or metformin (200 mg/kg) administered by oral gavage every 48 hours either during feeding or fasting cycles. Error bars indicate SEM. (n=5 per group). (F) Weight and images of tumors isolated from mice in different groups. student's t test ****: p<0.001, ns: non-significant.

[0129] FIG. 14. Glycolysis inhibition sensitizes cancer cells to metformin. (A) Images of HCT116 and HeLa cells cultured for 24 hours in either nutrient-rich DMEM (containing 10% FBS and 10 mM glucose), DMEM containing 2.5 mM glucose (glucose deprivation), DMEM with 0.1% serum (serum deprivation) or DMEM with no glutamine, no methionine and no cysteine (amino acids deprivation). Media were replenished every 6 hours. (B) Quantification of cell death of HCT116 and HeLa cells cultured as in A as measured by propidium iodide uptake using flow cytometry. (C, D) Percentage of cell death measured by propidium iodide uptake using flow cytometry (C) or growth rate as assessed by CellTiter-Glo assay (D) of HCT116 and HeLa cells cultured for 24 hours in DMEM containing the indicated amounts of glucose in the absence or presence of 5 mM metformin (C) or in DMEM containing the indicated concentration of glucose and treated with increasing concentrations of metformin for 24 h (D). (E) Percentage of cell death of HCT116 and HeLa cells cultured for 24 hours in DMEM containing either 10 mM glucose (Normal glucose) or 2.5 mM glucose (Low glucose) in combination with increasing concentrations of metformin for 24 h. (F) Percentage of cell death of HCT116 and HeLa cells cultured for 24 hours in DMEM containing either 10 mM glucose (Normal glucose) or 2.5 mM glucose (Low glucose) in combination with either Metformin (5 mM), SAHA (2.5 M) or Brefeldin A (10 M). Media were replenished every 6 hours.

[0130] FIG. 15. Synergistic cytotoxicity of low glucose and metformin is mediated by GSK3. (A) Percentage of cell death of HCT116 and HeLa cells treated with either GSK3P inhibitor xii (20 M), GSK3 inhibitor viii (25 M), ERK inhibitor UO126 (20 M), p38 inhibitor SB202190 (20 M) or JNK inhibitor SP600125 (20 M) and cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). Treatment with the inhibitors started 1 hour before metformin treatment. (B) Immunoblotting analysis of lysates derived from HCT116 and HeLa cells cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). (C) Immunoblotting analysis of lysates derived from HCT116 cells cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of the indicated concentrations of metformin. (D) Immunoblotting analysis of lysates derived from HCT116 cells cultured for 24 hours in DMEM (replenished every 6 hours) containing the indicated concentrations of glucose in the absence or presence of metformin (5 mM). (E) Immunoblotting analysis of lysates derived from HCT116 cells stably expressing either scrambled shRNA or shRNA against GSK3P3 and treated as in B. (F) Percentage of cell death of control or GSK3-depleted HCT116 and HeLa cells treated as in B. (G) Proliferation assessed by CellTiter-Glo assay of control or GSK3-depleted HCT116 and HeLa cultured in DMEM containing the indicated concentration of glucose and treated with increasing concentrations of metformin for 24 h

[0131] FIG. 16. GSK3-depleted MCL-1 degradation mediates synergistic cytotoxicity of low glucose and metformin. (A, B) Immunoblotting analysis of lysates derived from HCT116 and HeLa cells cultured for 24 hours in either nutrient-rich DMEM (containing 10% FBS and 10 mM glucose), DMEM containing 2.5 mM glucose (glucose deprivation), DMEM with 0.1% serum (serum deprivation) or DMEM with no glutamine, no methionine and no cysteine (amino acids deprivation). Media were replenished every 6 hours. (C) Immunoblotting analysis of lysates derived from HCT116 cells cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of the indicated concentrations of metformin. (D) Immunoblotting analysis of lysates derived from HCT116 cells cultured for 24 hours in DMEM (replenished every 6 hours) containing the indicated concentrations of glucose in the absence or presence of metformin (5 mM). (E) Immunoblotting analysis of lysates derived from HCT116 cells expressing either scrambled shRNA or shRNA against GSK3 and cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). (F) Percentage of cell death of HCT116 and HeLa cells expressing the indicated constructs and cultured as in D. (G) Proliferation assessed by CellTiter-Glo assay of control or MCL-1 overexpressing HCT116 and HeLa cells cultured in DMEM containing the indicated concentration of glucose and treated with increasing concentrations of metformin for 24 h.

[0132] FIG. 17. PP2A-regulated GSK3 dephosphorylation mediates synergistic cytotoxicity of low glucose and metformin. (A) Immunoblotting analysis of lysates derived from HCT116 cells expressing either scrambled shRNA or shRNA against PP2A and cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). (B) Percentage of cell death of control or PP2A-depleted HCT116 and HeLa cells treated as in A. (C) Proliferation assessed by CellTiter-Glo assay of control or PP2A-depleted HCT116 and HeLa cells cultured in DMEM containing the indicated concentration of glucose and treated with increasing concentrations of metformin for 24 h.

[0133] FIG. 18. Simultaneous CIP2A inhibition and B56 upregulation mediate synergistic cytotoxicity of low glucose/metformin combination. (A) Immunoblotting analysis of lysates derived from HCT116 and HeLa cells cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). (B) Immunoblotting analysis of lysates derived from HCT116 cells stably expressing either scrambled shRNA or shRNA against CIP2A and cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). (C) Proliferation assessed by CellTiter-Glo assay of control or CIP2A-depleted HCT116 and HeLa cells cultured in DMEM containing the indicated concentration of glucose and treated with increasing concentrations of metformin for 24 h. (D) Immunoblotting analysis of lysates derived from HCT116 cells stably expressing scrambled shRNA or shRNAs against wither B56 or B55 and cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). (E) Proliferation assessed by CellTiter-Glo assay of control or B56-depleted HCT116 and HeL cells cultured in DMEM containing the indicated concentration of glucose and treated with increasing concentrations of metformin for 24 h. (F) Immunoprecipitation analysis of PP2A A from cell lysates derived from HCT116 cells stably expressing scrambled shRNA or shRNAs against wither B56 or B55 and cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM).

[0134] FIG. 19. Modulation of GSK3-MCL-1 axis mediates tumor sensitization to metformin administered during fasting-induced hypoglycemia. (A,B) Immunohistochemical analysis and representative images (original magnification is 20) of MCL-1 and phosphorylated GSK3 in tissue samples isolated from mice treated as in FIG. 13. The bars represent the highest and lowest quartiles. (C) Immunoblotting analysis of tumor lysates derived from mice treated as in FIG. 13. (D) In vivo growth of tumors xenografts in mice inoculated with either control, GSk3-depleted or MCL-1-overexpressing HCT116 cells. Upon establishment of tumors, mice were kept on 24-hour feeding/fasting cycles and treated with metformin (200 mg/kg) administered by oral every 48 hours either during feeding cycles (Met/Fed) or during fasting cycle (Met/fast). Error bars indicate SEM. (n=5 per group). (E) Weight of tumors from D isolated at the end of the treatment. (F) Schematic representation of the molecular mechanism of targeting metabolic plasticity of tumor cells by low glucose-metformin combination.

[0135] FIG. 20. Targeting metabolic plasticity of cancer cells (A) Proliferation assessed by CellTiter-Glo assay of cell lines representative of different cancer types treated with increasing concentrations of metformin. (B, C) Quantification of lactate production normalized by cell numbers of cells treated with the indicated concentrations of metformin for 12 hours (A) or with 5 mM of metformin for the indicated time points (B). (D, E) Quantification of glucose consumption normalized by cell numbers of cells treated with the indicated concentrations of metformin for 12 hours (D) or with 5 mM of metformin for the indicated time points (E). (F, G) Quantification of oxygen consumption rate normalized by cell numbers of cells cultured in DMEM medium containing the indicated concentrations of glucose for 12 hours (F) or in DMEM medium containing with 2.5 mM glucose for the indicated time points (G).

[0136] FIG. 21. Synergistic cytotoxicity of simultaneous treatment with metformin and low glucose (A) Percentage of cell death of HCT116 and HeLa cells cultured for 12 or 24 hours in either nutrient-rich DMEM or in DMEM containing 2.5 mM glucose (Low Glu) in the presences or absence of metformin (5 mM). Alternatively, cells were sequentially treated for 12 hours with metformin followed by washing out and plating in low glucose medium or vice versa. (B) Proliferation of the indicated cancer cells cultured in media containing the indicated concentration of glucose and treated with increasing concentrations of metformin for 24 h as assessed by CellTiter-Glo assay.

[0137] FIG. 22. Synergistic cytotoxicity of low glucose and metformin is AMPK-independent. (A) Immunoblotting analysis of lysates derived from HCT116 and HeLa cells cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). (B) Immunoblotting analysis of lysates derived from HCT116 cells stably expressing either scrambled shRNA or shRNA against AMPK and treated as in A. (C) Percentage of cell death of control or AMPK-depleted HCT116 cells treated as in B. (D) Proliferation assessed by CellTiter-Glo assay of control or AMPK-depleted HCT116 cells cultured in DMEM containing the indicated concentration of glucose and treated with increasing concentrations of metformin for 24 h. (E) Immunoblotting analysis of lysates derived from HeLa stably expressing either vector or constitutively active form of AMPK and treated as in A. (F) Percentage of cell death of HeLa cells treated as in A. (G) Proliferation assessed by CellTiter-Glo assay of control or HeLa cells expressing constitutively active AMPK and cultured in DMEM containing the indicated concentration of glucose and treated with increasing concentrations of metformin for 24 hours.

[0138] FIG. 23. Pharmacological inhibition of glycolysis by 2-DG synergizes with metformin (A) Immunoblotting analysis of lysates derived from HCT116 and HeLa cells cultured in nutrient-rich DMEM and treated for 24 with either vehicle or metformin (5 mM) in the absence or presence of 2-DG (25 mM). (B) Percentage of cell death of HCT116 and HeLa cells cultured in nutrient-rich DMEM (containing 10% FBS and 10 mM glucose) and treated for 24 with either vehicle or metformin (10 mM) in the absence or presence of 2-DG (25 mM).

[0139] FIG. 24. GSK3-induced downregulation of MCL-1 mediates the cytotoxicity of metformin-low glucose combination (A) Percentage of cell death of patient-derived melanoma cells GaLa1948 and LuCa1973 expressing either scrambled shRNA or shRNAs against GSK3 and cultured for 72 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (10 mM). (B) Immunoblotting analysis of lysates derived from HCT116 cells treated with either vehicle or GSK3 inhibitor xii (20 M) and then cultured as in D. Treatment with GSK3 inhibitor xii started 1 hour before metformin treatment. Media were replenished every 6 hours.

[0140] FIG. 25. Validation of the mechanistic model in patient-derived melanoma cells (A) Percentage of cell death of patient-derived melanoma cells GaLa1948 and LuCa1973 expressing either vector or MCL-1 constructs and cultured for 72 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (10 mM). (B) Percentage of cell death of patient-derived melanoma cells GaLa1948 and LuCa1973 expressing either scrambled shRNA or shRNAs against PP2A and cultured for 72 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (10 mM).

[0141] FIG. 26. Modulation of MCL-1 or CIP2A oncoproteins impacts metformin/low glucose cytotoxicity (A) Percentage of cell death of HCT116, HeLa cells and patient-derived melanoma cells GaLa1948 and LuCa1973 expressing either vector or MCL-1 constructs and cultured for 72 hours (GaLa1948 and LuCa1973 cells) or 24 hours (HCT116 and HeLa cells) in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of 10 mM (GaLa1948 and LuCa1973 cells) or 5 mM (HCT116 and HeLa cells) metformin. (B) Immunoblotting analysis of lysates derived from HCT116 cells expressing either scrambled shRNA or shRNA against CIP2A and cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively). (C) Percentage of cell death of HCT116, HeLa cells expressing either scrambled shRNA or shRNA against CIP2A and cultured for 24 hours in DMEM (replenished every 6 hours) containing the indicated concentrations of glucose.

[0142] FIG. 27. Depletion of B56 impedes metformin-low glucose cytotoxicity.

[0143] Percentage of cell death of HCT116, HeLa cells and patient-derived melanoma cells GaLa1948 and LuCa1973 expressing either scrambled shRNA or shRNA against B56 and cultured for 72 hours (GaLa1948 and LuCa1973 cells) or 24 hours (HCT116 and HeLa cells) in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of 10 mM (GaLa1948 and LuCa1973 cells) or 5 mM (HCT116 and HeLa cells) metformin.

[0144] FIG. 28. Analysis of the role of the B56C regulatory subunit. (A) Immunoprecipitation analysis of PP2A A from cell lysates derived from HCT116 cells overexpressing either vector, B56 or B55 construct and cultured for 24 hours in nutrient-rich DMEM in the absence or presence of metformin (5 mM). (B) Immunoblotting analysis of lysates derived from HCT116 cells overexpressing either vector or B56 construct and cultured in nutrient-rich DMEM and treated for 24 with either vehicle or metformin (5 mM). (C) Percentage of cell death of HCT116 (A) and HeLa cells overexpressing either vehicle or B56 and treated for 24 hours with the indicated concentrations of metformin, SAHA or Brefeldin A.

[0145] FIG. 29. Enhanced recruitment of B56 to PP2A complex mediates metformin-low glucose cytotoxicity

[0146] (A) Immunoblotting analysis of total cell lysates used for immunoprecipitation (B). Immunoprecipitation analysis of PP2A A from cell lysates derived from PP2A-ablated HCT116 cells and reconstituted with vector, wild type PP2A A or PP2A A mutant S256F and cultured for 24 hours in DMEM (replenished every 6 hours) containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM).

[0147] FIG. 30. Immunoprecipitation analysis of PP2A A from tumor lysates used in FIG. 19C.

[0148] FIG. 31. PPZ synergizes with metformin and recapitulates the molecular effects of low glucose (A) Immunoprecipitation analysis of PP2A A from cell lysates derived from HCT116 cells treated with either vehicle or PPZ (10 M) and cultured for 24 hours in nutrient-rich DMEM in the absence or presence of metformin (5 mM). Treatment with PPZ started 1 hour before metformin treatment. (B) Immunoblotting analysis of total cell lysates used for immunoprecipitation in (A). (C, D) Percentage of cell death of HCT116 (C) and HeLa (D) cells treated for 24 hours with the indicated concentrations of metformin in the absence or presence of PPZ (10 M). (E) In vivo growth of HCT116 xenograft tumors as measured in mice treated with either dextrose water vehicle, metformin (200 mg/kg administered daily by oral gavage, PPZ (5 mg/kg administered daily by intra-peritoneal injection) or a combination of metformin and PPZ. Error bars indicate SEM. (n=5 per group). (F) Weight of tumors isolated from mice in different groups in (E).

[0149] FIG. 32. Readouts to monitor chronological lifespan in yeast.

[0150] A. Yeast cells were subjected to chronological aging growth conditions as described in Materials and Methods. Aliquots of cells were removed at Days 1, 4, 7 and 11 and proteins were extracted from untreated, 0 and 2 hours post UV treatment (40J/m.sup.2). Western blot analysis was performed and Rad53 was detected.

[0151] B. Samples were harvested from untreated, 0, 6 or 24 hours post UV (40 J/m.sup.2) at Days 1, 4 and 7. Genomic DNA was prepared and subjected to Southern Blot analysis. Filters were probed with an antibody that detects thymine dimers (top panel). As a loading control, blots were stripped and re-probed with an anti-single stranded DNA antibody (bottom panel).

[0152] C. Viability of cells undergoing chronological aging was monitored at Days 1, 4, 7 and 11. The ratio of UV-treated to untreated cells is depicted. Mean values+/St Dev on n=3 replicates are shown.

[0153] FIG. 33. Rapamycin-mediated inhibition of Tor1 and deletion of SCH9 improve DDR, NER and extend lifespan

[0154] A. DMSO or Rapamycin (2 ng/ml) were added to cells at Day 0 and CLS kinetic time course was carried similar to that described in FIG. 32. Western blot analysis on protein samples prepared from untreated, 0 and 2 hours post UV (40 J/m.sup.2) was performed using anti-Rad53 antibodies. B. Genomic DNA was prepared from untreated, 0, 6 and 24 hours after UV samples. Thymine dimers removal and total DNA content were detected with anti-dT dimer and anti-ss DNA antibodies respectively. C. Viability of DMSO- and Rapamycin-treated cells was monitored using spot assay analysis. Cells were harvested from the original cultures, serially-diluted and spotted on rich media. Plates were then subjected to 0 or 40 J/m.sup.2 UV. After incubation at room temperature for 3 days, plates were scanned. D. Viability curve representing DMSO and Rapamycin-treated wt cells at Days 1, 4, 7, 10 and 15. Mean values+/St Dev on three replicates are shown. E. wt and sch9 cells/+Rapamycin were subjected to CLS kinetic time course and proteins were extracted from untreated, 0 and 2 hours post UV (40 J/m.sup.2). Rad53 phosphorylation was monitored by Western blot analysis. F. Thymine dimer removal was assessed in wt and sch9 cells/+Rapamycin using same procedure as that described in B. G. Viability curve representing wt and sch9 cells undergoing CLS in control conditions (i.e. DMSO only) spanning Days 1, 4, 7, 11 and 15. Mean values+/St Dev on three replicates are shown. Inventors note that FIGS. 33D and G share the same reference (wt in DMSO), as they come from a single CLS kinetic here presented in two parts to emphasize the effects of Torc1 inhibition by Rapamycin and of SCH9 ablation, respectively. H. Untreated, 0 and 2 hr post UV protein samples were probed for the status of Sch9 phosphorylation using antibodies specific for p-Sch9 (above panel) and total Sch9 protein levels (bottom panel).

[0155] FIG. 34. Metformin and constitutively-active Snf1 improve DDR and promote longevity but not NER

[0156] A. Rad53 phosphorylation was monitored in untreated and Metformin (80 mM)-treated cells undergoing CLS at Days 1, 4 and 7.

[0157] B. Phosphorylation of Snf1 (T172) was detected throughout CLS (daily from 1-10) in untreated and Metformin-treated cells. Western blot filter was stripped and reprobed with anti-PGK as an equal loading control (top panel). Quantification of phospho-Snf1 levels relative to PGK is represented graphically (bottom panel).

[0158] C. Snf1 kinase activity was also monitored using ADH2-lacZ expression. lacZ expression was measured using P3-galactosidase assay as described in Materials and Methods in untreated (grey) and metformin-treated (black) cells at Days 1, 4, 7, 10, 15 and 20.

[0159] D. Thymine dimers and single-stranded DNAs were detected in samples harvested from untreated and metformin-treated cells at Days 1, 4, 7 and 11 that represent untreated, 0, 6 and 24 hours post UV (40 J/m.sup.2).

[0160] E. Viability of untreated and metformin-treated cells undergoing CLS was monitored using spot assay analysis at Days 1, 4, 8, 11 and 15/+UV (40 J/m.sup.2).

[0161] F. Cell viability of snf1 and cells expressing constitutively-active Snf1 (referred to as G53R) at Days 1 and 22. UV treatment of 0, 20 and 40 J/m.sup.2 was applied.

[0162] G. DDR activation was assessed using Rad53 phosphorylation in snf1 cells and hyperactive SNF1-G53R allele relative to wt cells undergoing CLS.

[0163] H. Cells were grown in 2% or 0.5% glucose and subjected to CLS kinetics. Protein extracts were prepared as described above and Rad53 phosphorylation was assessed by Western blot analysis.

[0164] I. Viability of cells grown in 2% or 0.5% glucose was monitored using spot assay analysis at Days 1, 4, 7, 11, 15 and 18.0, 40 or 80 J/m.sup.2 UV treatment was applied.

[0165] FIG. 35. Deletion of positive regulators of PP2A, Rrd1 and Tip41, ameliorates DDR, NER and extends lifespan.

[0166] A. RRD1 and TIP41 encode positive regulators of the Serine/Threonine phosphatase PP2A (left panel). A pharmacogenomic screen identified tip41 and rrd1 as resistant to rapamycin (5 ng/ml) and metformin (80 mM) treatment (right panel).

[0167] B-D. wt, tip41 and rrd1 cells/+Rapamycin were subjected to CLS kinetic time course and protein extracts were prepared from each strain at Days 1, 4, 7 and 11. Western blot analysis was performed to assess Rad53 phosphorylation in untreated, 0 and 2 hours post UV (40J/m.sup.2) (B). Thymine dimers removal and total DNA loading control were assessed in all 3 strains/+Rapamycin at Days 1, 4, 7 and 11 in untreated, 0, 6 and 24 hours post UV (40 J/m.sup.2) (C). Viability of wt, tip41 and rrd1/+Rapamycin was monitored using spot assay analysis. Results of Days 1 (young cells) and 27 (old cells) in 0 and 40 J/m.sup.2 are presented (D).

[0168] FIG. 36. Gcn2 kinase contribution to pro-longevity conditions.

[0169] A-C. Phosphorylation of eIF2 at S51 is assessed using an antibody specific for this site. Western blot analysis was performed on protein samples extracted from DMSO and Rapamycin-treated cells throughout CLS using anti-S51 eIF2 antibody (top panel). Filters were stripped and reprobed with an anti-total eIF2 antibody (bottom panel). The same procedure was repeated for wt and sch9 cells (B) as well as for wt rrd1 and tip41 cells (C).

[0170] D-E. Role of Gcn2 kinase during CLS was assessed. Deletion of GCN2 in wt and sch9 cells during CLS aging kinetics resulted in less efficient Rad53 activation as assessed by Western Blot analysis (D). Viability of gcn2 and sch9 gcn2 relative to wt and sch9 cells respectively at Days 1 and 22, in untreated and 20 J/m.sup.2 UV (E)

[0171] F-G. CLS kinetic time course to assess the contribution of Gcn2 in the extended lifespan of tip41 and rrd1 cells. gcn2tip41 and gcn2rrd1 cells were compared to tip41 and rrd1 cells respectively. Western blot analysis on Rad53 is shown in F, while viability by spot assays at Days 1 and 28/+20 J/m.sup.2 is depicted in G.

[0172] H-I. Rad53 phosphorylation was analyzed in wt and gcn2 cells undergoing CLS without (left panels) or with (right panels) 80 mM Metformin. (H). Viability of gcn2/+Metformin relative to wt, evaluated by serial dilution and spot assay/+40J/m.sup.2 UV (I).

[0173] FIG. 37. Absence of autophagy affects DDR and NER efficiency (in wt and sch9 cells)

[0174] A. wt cells, atg1 (autophagy defective) and sch9 single mutants and atg1sch9 double mutant were subjected to CLS kinetic. Proteins were extracted at Days 1, 4, 8 and 11 before and after UV treatment (40J/m.sup.2) and Rad53 phosphorylation was assessed by Western Blot analysis with EL7 antibodies.

[0175] B. NER efficiency was investigated using dT dimers removal as a readout (upper panels). Hybridization with anti-ssDNA antibodies was performed on stripped membranes as loading control (lower panels).

[0176] FIG. 38. Model. Top panel: Representation of relative activities of Torc1 (measured using Sch9 phosphorylation as a readout), Snf1 and PP2A during chronological aging. Activity of the Torc1 and Snf1 kinases and of PP2A phosphatase all fluctuate between a low (L) and high (H) level in the different stages of aging, and reciprocally influence each other.

[0177] Bottom panel: Cross-talks between metformin- and rapamycin-targeted pathways, affecting NER and DDR.

[0178] FIG. 39. Activation of the SNF1 pathway by either hyperactive Snf1-G53R or growth in low glucose does not improve NER during CLS

[0179] A: Genomic DNA was extracted before and after UV exposure (40J/m.sup.2) at Days 1, 4, 7 and 11 from cells of the indicated relevant genotypes. After Southern Blot transfer, membranes were incubated with anti-thymine dimers antibody (upper panels), stripped and re-probed with anti-ssDNA antibody as loading control (lower panels).

[0180] B: wt cells were subjected to CLS kinetic in SC medium containing 0.5% glucose (top part) or 2% glucose (bottom part). Total DNA was extracted before and after UV treatment and analysis of NER efficiency was performed with anti-TD (upper panels) and anti-ssDNA (lower panels) antibodies, as described in A.

[0181] FIG. 40. PP2A activity during CLS using phosphorylation status of the targets Gln3, Nnk1 and Np1.

[0182] A. Gln3-Myc, Nnk1-Myc and Npr1-Myc phosphorylation was monitored at Days 1, 4, 7 and 10 in untreated, Metformin, Rapamycin and low glucose (0.5%) treatments.

[0183] B. Targets above were assessed in snf1 cells/+Metformin treatment to monitor the contribution of Snf1 to their phosphorylation during CLS.

[0184] FIG. 41. Evidence of phospho-eIF2 independent manner to improve DDR and extend lifespan.

[0185] A. Rapamycin treatment rescues the DDR defect of gcn2 cells undergoing CLS. Rad53 phosphorylation is used as a measure of checkpoint activation in wt (top) and gcn2 (bottom) strains.

[0186] B. Viability using spot assay analysis is performed on wt and gcn2 cells/+Rapamycin. Results are presented for young (Day 1) and old cells (Day 23)/+UV (40 J/m.sup.2).

[0187] C-E. Monitoring eIF2 phosphorylation as described in FIG. 36, in the Metformin/Snf1 branch. Cells grown in 80 mM Metformin (C) or in low glucose (E), and strains carrying either the snf1A or the hyperactive SNF1-G53R allele (D) are all assessed for the status of eIF2 phosphorylation (top panel) as well as total eIF2 protein as a loading control (bottom panel).

[0188] FIG. 42. Metformin enhances the effect of DNA damaging agents.

[0189] HeLa cells were seeded in 6-well plates (200,00 cells/well), and left untreated, or treated with DNA damaging agents (hydroxyurea 10 mM+gemcitabine 10 nM), in high (10 mM) or low (2.5 mM) glucose, and treated with metformin (10 mM) as indicated. After 24 hrs treatment, cells are allowed to grow for further 24 hrs (replacing the medium with normal medium), before being harvested and counted with trypan blue to measure viable cells. As observed, not only metformin (as described in other parts of this application) cooperates with low glucose in inducing cell death, but also potentiates the effect of DNA damaging agents in low glucose.

[0190] FIG. 43. Perphenazine enhances the effect of DNA damaging agents.

[0191] HeLa cells were seeded in 6-well plates (200,00 cells/well), and left untreated, or treated with DNA damaging agents (hydroxyurea 10 mM+gemcitabine 10 nM), in high (10 mM) or low (2.5 mM) glucose, and treated with perphenazine (PPZ) as indicated. After 24 hrs treatment, cells are allowed to grow for further 24 hrs (replacing the medium with normal medium), before being harvested and counted with trypan blue to measure viable cells. PPZ (that activates PP2A, as also shown in other parts of this application where it is shown its ability to cooperate with metformin) cooperates with DNA damage, and this cooperation is further increased in low glucose conditions.

[0192] FIG. 44. FTY-720 enhances the effect of DNA damaging agents.

[0193] HeLa cells were seeded in 6-well plates (200,00 cells/well), and left untreated, or treated with DNA damaging agents (hydroxyurea 10 mM+gemcitabine 10 nM), in high (10 mM) or low (2.5 mM) glucose, and treated with FTY-720 (FTY) as indicated. After 24 hrs treatment, cells are allowed to grow for further 24 hrs (replacing the medium with normal medium), before being harvested and counted with trypan blue to measure viable cells. FTY-720 (a sphingosine analog that activates PP2A through multiple mechanisms, including suppression of the PP2A inhibitor SET) strongly cooperates with DNA damage.

[0194] FIG. 45. Ceramide enhances the effect of DNA damaging agents.

[0195] HeLa cells were seeded in 6-well plates (200,00 cells/well), and left untreated, or treated with DNA damaging agents (hydroxyurea 10 mM+gemcitabine 10 nM), in high (10 mM) or low (2.5 mM) glucose, and treated with Ceramide as indicated. After 24 hrs treatment, cells are allowed to grow for further 24 hrs (replacing the medium with normal medium), before being harvested and counted with trypan blue to measure viable cells. Ceramide (a known modulator of PP2A activity, that emerged as an important mediator from the yeast studies) strongly cooperates with DNA damage, especially in low glucose conditions.

[0196] FIG. 46. PP2A mediates the cooperative effect of several drugs with DNA damaging agents.

[0197] A) HeLa cells transduced by means of a lentiviral inducible construct (pLKO-Tet-On) carrying shRNA targeting sequences against the catalytic subunit c of PP2A (PP2Ac-kd) or control (no kd) were treated with doxycyclin to induce the transcription of shRNAs. 72 hrs post knockdown induction, cells are treated with DNA damaging agents (Hydroxyurea+gemcitabine), ceramide, or perphenazine (PPZ) as indicated for further 24 hours, then medium is replaced with fresh complete DMEM, cells are allowed to grow for further 24 hrs before being harvested and counted with trypan blue. B) Same as in (a), but AZD7762 (an inhibitor of the phosphorylation and activation of the checkpoint kinases involved in the modulation of DDR in mammalschk1 and chk2) was used in combination with DNA damaging agents. PP2Ac knockdown is able to completely rescue the cooperative reduction of cell viability observed by co-treatment of DNA damaging agents with known PP2A activators (ceramide, PPZ), showing that PP2A activity is required for the efficacy of the drug combination. Strikingly, PP2A knockdown is not protective against AZD7762 in combination with DNA damaging agents, in strong agreement with our hypothesis that PP2A acts upstream of the regulation of the DDR, while agents such as AZD7762, that act downstream, are not affected by PP2A down-modulation.

[0198] FIG. 47. PP2A activation counteracts the DNA damage response.

[0199] Western blot analysis of phosphorylated Chk1 and Chk2 (P-Chk1 and P-Chk2) from Hct116 cells treated with DNA damaging agents (hydroxyurea and gemcitabine) in combination with the indicated drugs (Met=Metformin, PPZ=Perphenazine, Ceramide), in the presence of high or low glucose conditions. Vinculin (Vinc) is used as loading control. Similar results were obtained in HeLa cells. Activation of PP2A triggers a reduction of Chk1/Chk2 phosphorylation (activation) in high glucose condition, that is dramatically amplified in low glucose condition, consistently with the greater antitumor effect observed in low glucose.

[0200] FIG. 48. Genetic impairment of the DDR is synthetic lethal to treatments able to activate PP2A

[0201] Knockdown of RAD51 in Bx-PC3 cells dramatically sensitizes them to PP2A-inducing treatment. Bx-PC3 cells, WT and transduced with a lentiviral construct (pLKO.1) carrying shRNA targeting sequences against RAD51, were treated with metformin in combination with low glucose for 24, 48 and 72 hrs. Cell viability is measured with Cell Titer Glo assay.

[0202] FIG. 49. Characterization of the activity of small molecules known as PP2A activators.

[0203] A panel of known PP2A activators were tested in combination with metformin. The assay was performed in 96-well plates (n=4) in HeLa cells, treating cells as described before, in high (10 mM) and low (2.5 mM) glucose conditions. Viability was measured by CellTiter Glo. Parallel assays (cell count with Trypan blue) confirmed the results. Perphenazine and thioridazine were the only drugs able to cooperate with metformin in reducing tumor cell viability in high glucose conditions, while 7 other compounds cooperated with metformin under low glucose conditions (see also table 1B).

[0204] FIG. 50. Combination of Metformin with Intermittent fasting works also in PDX models. In vivo growth of PDX models (patient-derived xenografts) derived from tumor samples isolated from two melanoma patients. Upon establishment of tumors, mice were kept on 24-hour feeding/fasting cycles and treated with either vehicle or metformin (200 mg/kg) administered by oral gavage every 48 hours either during feeding or fasting cycles. Tumor volume was calculated as (lengthwidthwidth)/2. Error bars indicate SEM. (n=5 per group).

[0205] FIG. 51. Combination of Metformin with low glucose occurs at very low doses of metformin

[0206] (Left panel) Percentage of cell death of HCT116 cells cultured for 72 hours in DMEM containing either 10 mM glucose (Normal glucose) or 2.5 mM glucose (Low glucose) in the presence or absence of the indicated concentrations of metformin.

[0207] (Right panel) Immunoblotting analysis of lysates derived from HCT116 cells cultured as in the left panel.

[0208] FIG. 52. Treatment with ultra-low doses of metformin (for longer durations) synergizes with low glucose through the PP2A-GSK3-Mcl1 axis

[0209] (A) Percentage of cell death of HCT116 cells cultured for 72 hours in DMEM as indicated. (B) Immunoblotting analysis of lysates derived from HCT116 cells cultured as in A.

[0210] FIG. 53. Cell death by Metformin/low glucose is mediated by caspases

[0211] Percentage of cell death of HCT116 cells treated with either DMSO or the caspase inhibitors zVAD-FMK (25 mM), zDEVD-FMK (25 mM) and cultured for 24 hours in DMEM containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). Treatment with the inhibitors started 1 hour before metformin treatment.

[0212] FIG. 54. Modulation of different components of the PP2A-GSK33-Mcl1 axis imparts long-term rescue against Metformin/low glucose in clonogenic survival assay

[0213] 500 HCT116 cells from different conditions were plated for four weeks in DMEM containing either 10 mM or 2.5 mM glucose (Normal or Low glucose respectively) in the absence or presence of metformin (5 mM). Mcl1-CIP2A: overexpression; shGSK3, shB560: knockdown

[0214] FIG. 55. Cell death by Metformin/low glucose in AML cell lines

[0215] Several AML cell lines were incubated in high (10 mM) or low (0 mM) glucose for 72h, in the absence or in the presence of the indicated concentrations of metformin. Cell viability was measured by the Cell Titer Glo assay. Examples of the results obtained are shown in the graphs.

[0216] FIG. 56. PP2A inhibition partially protects from DNA damaging agents.

[0217] HeLa cells were seeded in 6-well plates (200,00 cells/well), serum starved for 24 hrs to synchronize, then treated in high concentration of HU (20 mM) for 12 hrs in the presence or in the absence of increasing concentrations of okadaic acid (from 0.01 nM to 1 nM). After treatment, cells are allowed to grow for further 24 hrs (replacing the medium with normal medium), before being harvested and counted with trypan blue to measure viable cells.

[0218] Okadaic acid inactivation of PP2A results in a better survival of cells treated with HU.

[0219] FIG. 57. BON-1 neuroendocrine tumor cells show reduction in cell viability upon combination of metformin with low glucose. Bon-1 cells were treated with the indicated concentrations of metformin, either in high (10 mM) or low (2.5 mM) glucose. The assay as performed as described in the other examples.

[0220] FIG. 58. Thioridazine treatment increase B56 incorporation in PP2A holoenzyme. Immunoprecipitation and total cell lysate analysis of PP2A Aalpha from cell lysates derived from HCT116 cells treated with either DMSO, 10 uM PERPHENAZINE (PPZ) or 10 uM THIORIDAZINE (Thio) and cultured for 24 hours in high glucose DMEM in the absence or presence of metformin (5 mM).

DETAILED DESCRIPTION OF THE INVENTION

Example 1: PP2A Controls Genome Integrity by Integrating Nutrient Sensing and Metabolic Pathways with the DNA Damage Response

[0221] Material and Methods

S. cerevisiae Strains, Growth Conditions, Drug Sensitivity Assay

[0222] All the strains used in this study are listed in Table 2 and are W303 derivatives with the wild type RAD5 locus. The MATa deletion mutant array and the SGA MAT query strain (S288C) were purchased from OpenBiosystems. Deletion, MYC-tagged, PK-tagged strains were obtained by one-step PCR targeting method (Wach et al., 1994). Unless otherwise stated, yeast strains were grown in yeast extract/peptone with 2% glucose (YPD). YPD agar plates were supplemented with adenine. Cells were synchronized in G1 with -factor to a final concentration of 3 g/ml. For drug sensitivity assay, cells were grown overnight. Serial 1:5 dilutions of stationary cultures were made and one drop of each dilution was pin-spotted onto agar plates, containing drugs. Plates were incubated for 2-3 days at 28 C. For liquid drug sensitivity assay, yeast strains were grown in SD liquid medium at the initial concentration of 10.sup.5 cell/ml in microtiter wells. Cultures were either left untreated (control-solvent) or were treated with the drug of interest. The absorbance (OD.sub.595) of untreated and treated cultures was measured after 12-18 hours. 3 independent repeats were performed. For ceramide experiments, inventors noticed a rapid response (few minutes for PP2A activity, within one hour for DDR activity).

[0223] Chemicals

[0224] Hydroxyurea (Sigma H8627), Rapamycin (Sigma R0395), Metformin (Sigma D150959), Caffeine (Sigma C8960), Wortmannin (Sigma W1628), Okadayc acid (LC laboratories O-2220), C.sub.2 ceramide (Sigma A7191), dihydroceramide C.sub.2 (Sigma C7980), Myriocin (Sigma M1177), Paraquat dichloride hydrate (Sigma 36541), Mersalyl acid or in its sodium salt form (Sigma M9784, Pubchem 23690449), Tert-butyl hydroperoxide (Aldrich 416665), Terbinafine hydrochloride (Sigma T8826), Fluonazole (Sigma F8929), Cerulenin (Sigma C2389), Syringomycin E (kindly provided by Jon Takemoto, Utah State University), Fumonisin B1 (Enzo BML-SL220), L-Ethionine (Sigma E1260), Cycloleucine (Aldrich A48105)

[0225] Synthetic Genetic Array Screening

[0226] Synthetic genetic array (SGA) was carried out as described (Tong et al., 2001; Tong et al., 2004). Shortly, congenic irc21 (6 replicates) and wt (4 replicates) query strains were crossed with the haploid viable library (Tong, Evangelista et al. 2001). Colony sizes were quantified with the Colony Grid Analyzer (version 1.1.7) (Collins et al., 2010), and normalized to the intra-dish 80-percentile. A 1 separation of normalized wt and irc21 colonies sizes was used to call candidate hits.

[0227] Tetrad Dissection and Random Spore Analysis

[0228] Standard procedures were used for tetrad dissection and random spore analysis (Abdullah and Borts, 2001; Tong and Boone, 2006).

[0229] Protein Extraction and Western Blot Analysis

[0230] Crude protein extracts were prepared following TCA based protocol and analyzed by SDS-PAGE as previously described (Pellicioli et al., 1999).

[0231] Anti-Rad53 (EL7 antibody described in (Fiorani et al., 2008) produced by IFOM monoclonal facility), anti-Mrel1 (clone 263++, described in (Ira et al., 2004) produced by IFOM monoclonal facility), anti-H2AX (Abcam, ab15083), anti-H2A (Active motif, 39235), anti-c-myc (clone 9E10, produced by IFOM monoclonal facility), anti-PK (V5-TAG, MCA 1360 Bio-Rad) antibodies were used as primary antibodies during western blot procedure. Anti-mouse (Bio-Rad, 170-6510) and anti-rabbit (Bio-Rad, 170-6515) antibodies coupled with horseradish peroxidase enzyme were used as secondary antibodies. Detection was done through electrogenerated chemiluminescence (ECL, GE-Healthcare).

[0232] Fluorescence-Activated Cell Sorter (FACS) Analysis

[0233] Cell cycle analysis was conducted as previously described (Pellicioli et al., 1999).

[0234] Budding Index Analysis

[0235] After sonication, cells were fixed by the addition of 3.7% formaldehyde and 0.9% NaCl. Cells were examined under a light microscope, by counting 200 cells per time point.

[0236] Measurement of Intracellular Oxidation

[0237] ROS measurement was conducted as previously described (Rand and Grant, 2006).

[0238] Measurement of Oxygen Consumption

[0239] Respiration of log-phase S. cerevisiae cells was measured by polarographic analysis using a Clark's type oxygen electrode (Hansatech Instrument Ltd, Pentney UK) according to standard procedures, upon addition of dinitrophenol as uncoupling agent of respiration/ATP synthesis.

[0240] Search for Suppressors of Mec1-100 Sensitivity to HU

[0241] To search for suppressor mutations of the HU-sensitivity of mec1-100 mutant, 110.sup.6 mec1-100 cells were plated on YEPD in the presence of 25 mM HU. Survivors were crossed to wt cells to identify by tetrad analysis that the suppression events were due to single-gene mutations. Subsequent genetic analyses allowed grouping the single-gene suppression events in 4 classes. The class that showed the most efficient suppression was chosen and the mutations altering open reading frames within the reference S. cerevisiae genome were identified by next-generation Illumina sequencing (IGA technology services). To confirm that the tap42-G360R mutation was responsible for the suppression, a URA3 gene was integrated downstream of the tap42-G360R stop codon and the resulting strain was crossed to wt cells to verify by tetrad dissection that the suppression of the mec1-100 HU sensitivity co-segregated with the URA3 allele.

[0242] Metabolic Analysis

[0243] Yeast cells were grown to logarithmic phase (110.sup.7 cells/mL) in YPD medium in 6 replicates. 510.sup.8 cells per replicate were harvested by centrifugation, washed in water, snap-frozen in liquid nitrogen and stored at 80 C. Metabolite extraction and Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy analysis of 484 metabolites were performed by Metabolon, Inc. (Durham, N.C.) as previously described (Chaudhri et al., 2013). Missing metabolite raw intensity values were filled in with the lowest detectable intensity of the respective metabolite, and all raw intensities were normalized to the median intensity of the respective replicate. Fold changes and significant alterations were calculated with the LIMMA method implemented in MultiExperiment Viewer (MeV) software (version 4.9.0) (Saeed et al., 2003) using an adjusted p value of 0.05 and a minimum fold-change of 1.3.

[0244] Statistics and Data Visualization

[0245] Interactome correlation analysis of irc21 was performed with published genome-wide SGA scores (Costanzo et al., 2010). Unsupervised hierarchical clustering and heatmap representation by SGA scores and genome-wide SGA score Pearson correlation values were done in MeV. Unsupervised hierarchical clustering of metabolome samples by Pearson correlation coefficient and heatmap representation were performed in R using the pheatmap library (version 1.0.8). Significances of the intersections of metabolite alterations were calculated by chi-squared test. Heatmaps for visualization of altered metabolite classes were generated with MeV.

[0246] TrueMass Ceramide Panel

[0247] Metabolites were isolated from cell pellets by sequential chloroform/methanol extraction and aqueous potassium chloride liquid-liquid extraction. The chloroform/methanol solution contained internal standards (Cer12:0, Cer19:0, dhCer12:0, hexCer12:0, [Avanti Polar Lipids, Alabaster, Ala.]) The organic layer was evaporated in a stream of nitrogen, reconstituted and subjected to a solid phase extraction clean up step on silica [Si, 100 mg, Supelco, Bellefonte, Pa.]. The ceramide fraction was eluted, evaporated in a stream of nitrogen, reconstituted and an aliquot was injected onto an AB Sciex 4000 QTRAP (Sciex, Foster City, Calif.)/Acquity (Waters, Milford, Mass.) LC-MS/MS system equipped with a reversed phase UHPLC column [Zorbax Eclipse Plus C8, 2.1150 mm, 1.8 m, Agilent Technologies] using a gradient of 2 mM ammonium formate/0.2% formic acid in water and 1 mM ammonium formate/0.2% formic acid in Acetonitrile:Isopropanol (60:40). The mass spectrometer was operated in MRM mode using positive electrospray ionization. The peak areas of the analyte fragment ions were measured against the peak area of the respective fragment ions of the corresponding internal standards. For the purposes of this panel, the fragment ion m/z 264 was used for ceramides with a sphingosine backbone and the m/z 266 fragment was used for analytes with a sphinganine backbone. Quantitation was based on a series of five calibration standard samples that were included in each run. Calibration standards contained 26 reference compounds. For analytes for which calibration standards were not commercially available, a surrogate analyte from the same compound class was used for quantitation (e.g. quantitation of CER 22:1 is based on CER 20:0 calibration standards). A total of 56 analytes covering ceramides, dihydroceramides, hexosylceramides and lactosylceramides with different fatty acid composition (14:0, 16:0, 18:0, 18:1, 20:0, 20:1, 22:0, 22:1, 24:0, 24:1, 26:0, 26:1) were determined.

[0248] Results

[0249] Irc21 Influences the Response to Replication Stress.

[0250] Out of the class of rad53 suppressors partially rescuing the HU sensitivity of the dominant-negative rad53-D339A and the kinase deficient rad53-K227A mutations (Bermejo et al., 2011; Fay et al., 1997), inventors identified irc21A. The suppression was validated in the W303 (Thomas and Rothstein, 1989) genetic background (FIGS. 1A and B). irc21 also rescued the HU sensitivity of mec1A sml1, rad53 sml1, mec1 rad53 sml1, and chk1 rad53 sml1 strains (FIG. 1C). Sml1 inhibits ribonucleotide reductase (Chabes et al., 1999) and its ablation bypasses the essential functions of MEC1 and RAD53 by increasing dNTP levels (Desany et al., 1998; Huang et al., 1998; Zhao et al., 1998). IRC21 and SML1 double ablation caused an additive suppression in a rad53-K227A background; thus, unlikely, Irc21 influences dNTP pools (FIG. 8A). irc21 mutants were hypersensitive to high HU doses (FIG. 8B), suggesting that the irc21 suppression does not depend on dNTP levels or intrinsic HU resistance.

[0251] Inventors addressed whether IRC21 deletion influenced the Mec1-dependent Rad53 phosphorylation and dephosphorylation (Sanchez et al., 1996) during checkpoint activation and deactivation (FIG. 1D). sml1, mec1 sml1, irc21 sml1 and mec1 irc21 sml1 mutants were released from G1 into 0.2M HU to activate Mec1 and released into fresh medium without HU to allow cell cycle checkpoint deactivation and recovery (Pellicioli et al., 1999). In sml1 cells, Rad53 phosphorylation was obvious in HU and decreased during recovery. In mec1 sml1 mutants Rad53 phosphorylation was barely detectable, while in irc21 sml1 cells it was evident during HU treatment and persisted longer during recovery, compared to sml1 cells. irc21 restored Rad53 phosphorylation in a mec1 sml1 background. The persistence of Rad53 phosphorylation in mec1 irc21 sml1 mutants recovering from HU correlated with the inability to efficiently complete S phase (FIG. 8C). Inventors addressed whether irc21A-mediated rescue of Rad53 phosphorylation in mec1 cells was dependent on Tel1, which phosphorylates Mrel1 and shares overlapping functions with Mec1 (Usui et al., 2001). Rad53 was not phosphorylated in mec1 tel1 irc21 sml1 cells exposed to HU (FIG. 1E), suggesting that the irc21A-mediated rescue of Rad53 phosphorylation in mec1 cells depends on Tel1. irc21 suppression mechanism was not due to Tel1 hyperactivation as it is sufficient to ablate MEC1 to observe Tel1 activity (FIG. 1E).

[0252] Dun1 is phosphorylated by Rad53 (Bashkirov et al., 2003) and controls dNTP pools by inactivating Sml1 (Zhao and Rothstein, 2002). irc21 did not rescue the HU sensitivity of dun1A mutants (FIG. 8D), further suggesting that Irc21 does not cause an increase in dNTP pools. Moreover, IRC21 deletion failed to fully phosphorylate Dun1 in mec1 or rad53 mutants (FIG. 8E). Hence, the Tel1-mediated Rad53 phosphorylation in sml1 mec1 mutants is somewhat suboptimal as it prevents Rad53 from phosphorylating Dun1. This is consistent with the notion that certain phospho-isoforms of Rad53 are unable to phosphorylate Dun1 (Lee et al., 2003). Inventors monitored the capability of irc21 cells to recover from the HU-induced cell cycle block. irc21 mutants failed to efficiently recover from the HU treatment as visualized by FACS profile and by the delayed dephosphorylation of Rad53 and Dun1 (FIG. 8F).

[0253] Irc21 Affects Mitochondrial Functions and Lipid Biosynthesis.

[0254] Irc21 contains a unique domain: the NADH-cytochrome b.sub.5 reductase domain (CBR); CBRs are involved in mitochondrial functions and lipid biosynthesis (Gene OntologyPANTHER classification system) (FIG. 2A). Inventors therefore measured the oxygen consumption rate of logarithmically growing cells and found that irc21 mutants showed a higher respiration rate compared to wild type cells (FIG. 2B). During respiration, mitochondria generate reactive oxygen species (ROS). Using the oxidant-sensing probe 2,7-dichlorodihydrofluorescein diacetate (DCFH-DA) to measure ROS levels (Davidson et al., 1996), inventors found that exponentially growing irc21 cells accumulated more ROS than wt cells (FIG. 2C), according with previous findings (Neklesa and Davis, 2008). Inventors tested the oxidative stress resistance of irc21 cells using paraquat (1,1-dimethyl-4,4-bipyridinium dichloride) a redox cycler that stimulates superoxide production (Cocheme and Murphy, 2009). irc21 cells were hypersensitive to paraquat (FIG. 2D). Since respiration deficiency causes paraquat resistance (Blaszczynski et al., 1985), the higher respiration rates of irc21 cells may account for paraquat sensitivity. Mersalyl is a mercurial diuretic that affects mitochondrial functions and inhibits the NADH-cytochrome b.sub.5 reductase (Bemardi and Azzone, 1981). irc21 cells were resistant to mersalyl, in accordance with the putative Irc21 CBR activity (FIG. 2D). tert-butyl hydroperoxide (t-BOOH, an organic peroxide) produces ROS and damages a variety of cellular constituents, including lipids, causing lipid peroxidation, which results in the oxidative degeneration of cellular polyunsaturated fatty acids (Girotti, 1998). irc21 cells were resistant to t-BOOH (FIG. 2D). The cytochrome b.sub.5-dependent electron transport system is also involved in lipid metabolic processes, such as cholesterol/ergosterol biosynthesis and the desaturation and elongation of fatty acids (Aoyama et al., 1981; Osumi et al., 1979; Poklepovich et al., 2012; Tamura et al., 1976). In particular, Heme is required for the enzymatic activities of Erg3p (sterol C5-6 desaturase), Erg5p (sterol C22-23 desaturase), and Erg11p (sterol 14-demethylase) (Mallory et al., 2005). Inventors analyzed the sensitivity of irc211A cells to the ergosterol-depleting agents fluconazole and terbinafine (FIG. 2D); Cyb5-dependent Erg11 is the target of fluconazole, while Cyb5-independent Erg1 (squalene epoxidase) is inhibited by terbinafine (Kontoyiannis, 2000; Lamb et al., 1999; Petranyi et al., 1984). irc21 cells were specifically sensitive to fluconazole and not to terbinafine. Since Cyb5 contributes to Erg11 activation (Lamb et al., 1999), IRC21 ablation could exacerbate the inhibitory effect of fluconazole on ergosterol synthesis. Next, inventors tested Irc21 involvement in fatty acids biosynthesis by analyzing irc21 sensitivity to cerulenin, an inhibitor of fatty acid synthase (FAS), that prevents the synthesis of medium and long chain fatty acids (MCFA and LCFA) and of very long chain fatty acids (VLCFA) (Awaya et al., 1975; Kvam et al., 2005; Rossler et al., 2003). VLCFAs participate to the formation of sphingolipids (SL), ceramides, inositolglycerophospholipids (IGP), and the phosphatidylinositol moiety of GPI anchored proteins (Dickson, 1998; Kvam et al., 2005). irc21 mutants were hypersensitive to cerulenin (FIG. 2D), thus implying that Irc21 affects fatty acid synthesis. Hence, the sensitivity/resistance profile suggests that Irc21 influences CBR-dependent processes.

[0255] Irc21 and PP2A Mutants Genetically Interact and Exhibit Similar Interactome Profiles.

[0256] Inventors compared the irc21 interactome with the interactomes of 3884 mutant array strains (Costanzo et al., Science, 2010) by calculating the correlation (R) value of their interaction scores with the 1712 query mutants. Among the top 10 array strains with SGA interactomes most similar to irc21 mutants, inventors identified deletions in the RRD1 (R=0.39), TIP41 (R=0.33), SAP185 (R=0.19) and RRD2 (R=0.17) genes (FIGS. 3A and 9A), which encode PP2A and PP2A-like regulators (Luke et al., 1996; Van Hoof et al., 2005) (FIG. 3E). The interactome correlation between irc21 and rrd1 was quantitatively similar to the one between the two PP2A/PP2A-like activators rrd1 and tip41A (R=0.37). The second hit with an irc21 interactome correlation similar to rrd1 was imp2A (R=0.38) (FIG. 9A), in which the ORF next to RRD1 is disrupted; one possibility is that imp2A impairs RRD1 expression and thus PP2A-like functionality. This is consistent with the high similarity of rrd1 and imp2 interaction profiles (R=0.69). Inventors also identified deletions in the genes encoding Bck2, involved in the protein kinase C signaling pathway (Lee et al., 1993), Rrm3, a replicative DNA helicase targeted by the Mec1-Rad53 pathway (Rossi et al., 2015; Torres et al., 2004), Rnh201, a ribonuclease H2 involved in Okazaki fragment processing (Qiu et al., 1999) and the two phosphatases Ptp2 and Pph3 (Guan et al., 1992; O'Neill et al., 2007). Inventors compared the interactomes of irc21 and other PP2A and PP2A-like deletion mutants, by calculating the pairwise interactome correlation scores (R), and performed hierarchical clustering of R-values (FIG. 3B). The analysis revealed 2 clusters, the first containing pph21, ppm1 and rrd2 (loosely associated with rts1) and the second containing irc21, rrd1 and tip41 (loosely associated with sap155). Overall, these observations suggest that irc21 affects both PP2A and PP2A-like phosphatase activities (FIG. 3E). The signatures of genetic interactions shared between irc21 and either rrd1 or tip41 (Costanzo et al., 2010) (FIG. 3C) showed that common interactors were associated with PP2A/PP2A-like regulated processes, including mitosis, checkpoint recovery and adaptation, and mitochondrial maintenance. In addition, other PP2A mutant strains, including sap185, rrd2, rts1, pph21, and pph22 shared interactions with the irc21/rrd1/tip41 signatures to varying degrees (FIG. 3C).

[0257] Inventors conducted an independent synthetic genetic array (SGA) analysis mating the irc21 query strain to the haploid deletion library containing deletions of 4700 non-essential genes (FIG. 3D) (Tong et al., 2001). Inventors found 42 negative and 12 positive interactions, causing, respectively, synthetic growth defects or suppression of the mild slow growth phenotype of irc21l mutants (FIG. 9B). In addition, inventors identified 5 high confidence epistatic interactors of IRC21, by filtering potential epistatic interactors from our dataset, with the positive IRC21 interactors previously reported in a genome-wide high-throughput E-MAP screen (Costanzo et al., 2010) (FIG. 9C).

[0258] Several PP2A/PP2A-like components displayed negative interactions with irc21 (significance: ptc1, rts1; trend: tip41, ppm1, rrd1, rrd2) (FIGS. 3D and E). Some of these interactions were confirmed by random spore analysis and/or tetrad dissection (FIGS. 3D, 9D and E). The largest categories of irc21 negative genetic interactors (FIG. 9B) were metabolic (oxidative stress, TCA cycle, lipids), and chromatin/checkpoint pathways, which have been related to PP2A (Heideker et al., 2007; Hughes Hallett et al., 2014; Madeira et al., 2015; Rossetto et al., 2012). In accordance with previous observations (FIG. 2DCerulenin sensitivity assay), irc21 displayed negative genetic interaction with the deletion in FEN1 (FIG. 9B), encoding the fatty acid elongase, required for the biosynthesis of ceramide (Oh et al., 1997), a PP2A/PP2A-like activators (Nickels and Broach, 1996); moreover, ceramide hydroxylase Scs7, that contains a cytochrome b.sub.5 domain like Irc21 (Mitchell and Martin, 1997), was identified among the top 5 high confidence Irc21 epistatic interactors (FIG. 9C). Rescuing interactors (FIG. 9B) were involved in phospholipid (PGC1), sterol (NSG2) and respiratory (RG12, TRX3) metabolism, mitochondrial localization/inheritance (JSN1), cell morphology (MGAJ, DFG5), nuclear membrane (MLP2), and genome integrity (RAD51); Epistatic interactors (FIG. 9C) were involved in spindle and organelle positioning (DYN3, NIP100), mitochondrial localization/inheritance (MMRI) and ion transport (PMR1).

[0259] Overall, the IRC21 interactome analysis supports a function for Irc21 in mitochondrial and lipid metabolism and in influencing PP2A activity, nuclear morphology and genome integrity.

[0260] Irc21 is Involved in the TORC1-PP2A Regulatory Axis.

[0261] Inventors next characterized the role of Irc21 in regulating PP2A/PP2A-like activities, which are negatively regulated by the TORC1 pathway (Di Como and Arndt, 1996). Inventors found that tor1 and tco89, defective in TORC1 components, were positive interactors of irc21 (FIGS. 3C and D). TORC1 controls through phosphorylation two main downstream effectors, Tap42 and Sch9 (Huber et al., 2009; Jiang and Broach, 1999; Urban et al., 2007) (FIG. 4A). Sch9 influences ribosome biogenesis, translation initiation and G0 events (Pedruzzi et al., 2003; Urban et al., 2007; Wei and Zheng, 2009). Tap42 regulates PP2A and PP2A-like phosphatases, which control the phosphorylation state of Msn2/Msn4, involved in environmental stress response, Rtg1/3, implicated in the retrograde pathway, and Npr1 and Gln3, connected with the amino acid synthesis and nitrogen assimilation pathways (Crespo et al., 2002; Di Como and Arndt, 1996; Santhanam et al., 2004).

[0262] To probe the Tap42/PP2A signaling branch in irc21A mutants, inventors selected four PP2A targets that exhibit clear modifications following TORC1 inhibition through rapamycin treatment: Gln3, Nnk1, Npr1 and Rtg3 (Beck and Hall, 1999; Cox et al., 2004; Hughes Hallett et al., 2014; Schmidt et al., 1998) (FIG. 4A). As expected, rapamycin led to substantial dephosphorylation of the four PP2A targets (Hughes Hallett et al., 2014); in contrast, all four targets remained hyper-phosphorylated in irc21, rrd1 and tip41 cells, even in the presence of rapamycin (FIG. 4A, left panel). Thus, Irc21 participates in the activation of the PP2A/PP2A-like pathways like Rrd1 and Tip41. To discern whether Irc21 activates PP2A directly, or by inhibiting TORC1, inventors monitored the TORC1-Sch9 branch: Sch9 was normally dephosphorylated after rapamycin treatment in all the mutant strains (FIG. 4A, right panel). Hence, Irc21 is specifically involved in the activation of the PP2A/PP2A-like sub-pathways, which are also regulated by TORC1. Accordingly, irc21 mutants are resistant to treatments with a variety of TORC1 inhibitors, such as rapamycin, caffeine, metformin and wortmannin (FIG. 4B), in analogy to certain PP2A mutants (Jacinto et al., 2001; Rempola et al., 2000; Zheng and Jiang, 2005).

[0263] PP2A Influences the Checkpoint Response.

[0264] Altogether, the previous observations led inventors to test the hypothesis that, similarly to Irc21, PP2A and PP2A-like, control the Rad53-mediated response to replication stress. Ablation of the PP2A positive regulators RRD1 and TIP41 mimicked irc21 in suppressing the HU sensitivity of rad53-D339A, rad53-K227A, rad53A sml1 and mec1 sml1 mutant alleles (FIGS. 10A and B, 4C and E). Moreover, the deletions of either RRD1 or TIP41 in a mec1 sml1 background were able to rescue the crippled Rad53 phosphorylation (FIG. 4D), similarly to irc21 (FIG. 1D). rrd1 and tip41 also exhibited a delayed Rad53 deactivation, following recovery from HU, similarly to irc21 mutants.

[0265] Inventors performed a screen to find spontaneous extragenic suppressors of mec1-100 (Paciotti et al., 2001) cells lethality on HU 25 mM and inventors identified a mutation in TAP42. tap42-G360R rescued the HU sensitivity of mec1 and rad53A cells (FIGS. 4E and F) and abolished the defective Rad53 phosphorylation in HU-treated mec1 sml1 cells, mimicking the phenotype of irc21, rrd1 and tip41 (FIG. 4G).

[0266] A gain-of-function mutation could account for the above results and for the semi-dominance behavior of the tap42-G360R allele. To test whether the mutation caused a constitutive inhibition of PP2A, inventors analyzed rapamycin and metformin sensitivity of tap42-G360R cells. As expected, and similarly to irc21, rrd1 and tip41, tap42-G360R mutants were partially resistant to both drugs (FIG. 10C). Secondly, after rapamycin treatment, tap42-G360R cells showed a defective Gln3, Nnk1 and Npr1 dephosphorylation, which is mediated by PP2A (FIG. 10D) (Hughes Hallett et al., 2014). Hence, the hyperactive Tap42 allele resembles the absence of PP2A activators.

[0267] Since PP2A appeared to target Rad53, inventors predicted that chemical compounds acting on PP2A activity should also affect rad53 HU sensitivity. Low doses of okadaic acid (OA) cause a selective inhibition of PP2A (Zhang et al., 1994), and partially rescued the HU sensitivity of rad53K227A mutants (FIG. 4H).

[0268] Irc21 and PP2A Metabolic Signatures.

[0269] PP2A is a master metabolic regulator and is regulated by metabolic stimuli (Di Como and Arndt, 1996; Oaks and Ogretmen, 2014). To characterize the relationship between PP2A and Irc21, inventors compared the global mass spectrometry metabolic profile of wt, irc21 and rrd1 cells during logarithmic growth in rich media. Unsupervised clustering by metabolite fold changes clearly grouped the replicates of irc21 and rrd1 by genotype, but also revealed a degree of similarity between both mutants (FIG. 5A). From a total of 484 examined compounds, 172 and 111 were significantly changed in irc21 and rrd1 cells, respectively (LIMMA, p.sub.adj=0.05) (FIG. 5B). The two mutants shared a significant amount of metabolite alterations (79, p=510.sup.11). Importantly, nearly all of these were co-regulations (72/79), suggesting that these 72 alterations define a metabolic, shared Irc21-PP2A signature (FIG. 5B). As expected for low PP2A activity (Staschke et al., 2010; Wong et al., 2015), the PP2A signature was characterized by a reduction of amino acid biosynthesis intermediates and dipeptides (FIG. 5C, top panel). It also featured elevated levels of multiple lipids and lipid intermediates (long chain fatty acids, sterol biosynthesis intermediates, lyso-phospholipids, carnitine conjugates, sphingolipid precursors) and a shifted composition of phospholipids to shorter fatty acid chain length (less than C18). Low PP2A activity correlated with high GlcNAc (N-Acetylglucosamine) biosynthesis intermediates, high deoxy-nucleosides (but normal deoxy-nucleotides) and high levels of the methyl donor SAM.

[0270] The specific metabolite alterations in irc21 that are not shared by rrd1 represent PP2A-independent functions of Irc21. In particular, inventors found that several metabolites related to CBR function were altered in irc21 cells. Accumulation of TCA cycle intermediates (aconitate, -ketoglutarate, fumarate, malate) and reduction in late glycolysis intermediates and Ac-CoA were indicative of altered mitochondrial activity, in accordance with the notion that Cytb5 participates in mitochondrial electron transport chain (FIG. 2A, 5C bottom panel); in addition, the levels of several amino acids derived from glycolysis (Gly, Val) and TCA (Gln, Thr, Lys) and their derivatives were reduced, while urea cycle products accumulated (Ornithine, urea). Purine and pyrimidine ribonucleosides and ribonucleotides as well as CTP-dependent phospholipid precursors were also reduced, whereas the nucleotide synthesis substrate PRPP increased. Cytb5 is also involved in fatty acid metabolism (FIG. 2A) and, accordingly, inventors found that while fatty acid accumulation was common to irc21 and rrd1, very long chain fatty acids (VLCFAs), which are used in the synthesis of ceramides, accumulated specifically in irc21 cells. Accumulation of VLCFAs and the genetic interaction with the VLCFA synthesis enzyme FEN1 suggest that irc21 mutants inefficiently condense sphingolipids and VLCFAs into ceramides, and, thus, fail to promote PP2A activation, that depends on ceramide levels.

[0271] Inventors therefore performed a quantitative mass spectrometry analysis of ceramides and related lipid metabolites in wt and irc21 cells. Dihydroceramides (DHC) were more abundant than phytoceramides (PHC) in wt cells (0.35 pmol/mg and 0.01 pmol/mg respectively) (FIG. 11A). Furthermore, deletion of IRC21 significantly reduced the level of DHC, (FIGS. 5D, 11A-C). In contrast, the levels of DHC precursors, 3-ketodihydrosphingosine (3-keto-DHS), dihydrosphingosine (DHS), dihydrosphingosine-1-P (DHS-1-P) and VLCFA-CoA increased in irc21 cells (FIGS. 5C-D, 11A-C). These observations confirmed that IRC21 deletion impairs ceramide biosynthesis and the defective step corresponds to the DHS-DHC conversion (FIGS. 5E and 11C). According to this scenario, inventors found that irc21 mutants were resistant to Myriocin (FIG. 5F), an inhibitor of ceramide synthesis acting on serine palmitoyltransferase (SPT), the first enzyme in the sphingolipid biosynthesis pathway (FIG. 11C) (Huang et al., 2012). Moreover, irc21 cells were resistant to syringomycin E (SRE) (FIG. 5G) (Julmanop et al., 1993; Takemoto et al., 1993). SRE resistance has been used as a readout reflecting a defect in sphingolipid biosynthesis (Cliften et al., 1996; Stock et al., 2000; Taguchi et al., 1994). Addition of exogenous DHC restored irc21 sensitivity to SRE (FIG. 11D). Fumonisin B1, a ceramide synthase inhibitor, pheno-copies irc21 mutants, by causing an increase in DHS and PHS levels and a concomitant decrease of ceramide and therefore DHC (Wu et al., 1995). Accordingly, inventors found that irc21 mutants were resistant to fumonisin B1 (FIG. 5H). Inventors conclude that, Irc21 promotes the condensation reaction leading to the formation of DHC.

[0272] Ceramides, SAM-Mediated Methylation and TORC1 Inhibition Attenuate the Checkpoint Response by Promoting PP2A Activation.

[0273] irc21 partially rescues the HU sensitivity of checkpoint mutants and Rad53 phosphorylation in HU-treated mec1 sml1. Moreover, PP2A activity is defective in irc21, as well as in rrd1, tip41 and tap42-G360R mutants, and that attenuated PP2A activity is beneficial for checkpoint mutants exposed to replication stress. Since our observations also involve Irc21 in ceramide synthesis, inventors investigated the possibility to abrogate irc21 phenotypes by exogenously providing ceramide. It is known that the cell-permeable ceramide analog C.sub.2-ceramide induces a dose dependent activation of PP2A in yeast (Jiang, 2006; Nickels and Broach, 1996). Inventors first analyzed the effect of exogenous ceramide on the rescue of rad53K227A HU sensitivity by irc21, rrd1 and tip41. Intriguingly, the combination of HU treatment with sphingolipid was able to suppress the rescue in all mutants (FIG. 5I). Inventors then asked whether ceramide was also able to suppress the IRC21A-dependent rescue of Rad53 phosphorylation in mec1 cells. Cells were arrested in G1 and released in HU with or without ceramide for 3 hours. Ceramide-mediated PP2A activation abolished Rad53 phosphorylation, specifically in irc21 mec1 cells, and reduced it in wt and irc21 cells (FIG. 5J). In addition, ceramide caused Rad53 dephosphorylation during recovery from HU treatment in irc21 mec1 mutants (FIG. 11E). Sur2 and Scs7 are both required for ceramide hydroxylation and are members of the cytochrome b.sub.5-dependent enzyme family (FIG. 11C) (Haak et al., 1997). Scs7p contains a cytochrome b.sub.5-like domain (Dunn et al., 1998), while cytochrome b.sub.5 may function to transfer electrons to Sur2 (Haak et al., 1997). Both SCS7 and SUR2 deletions partially rescued mec1 sml1 HU sensitivities (FIG. 6A). Thus, defective cytochrome b.sub.5-dependent enzymes, involved in ceramide biosynthesis, have beneficial consequences for checkpoint mutants exposed to replication stress. SAM levels are critical for methylation and activation of PP2A; in this process, Ppm1 methylates the C terminus of PP2A catalytic subunit (Sutter et al., 2013; Wei et al., 2001; Wu et al., 2000). Accordingly, PPM1 ablation partially impaired dephosphorylation of PP2A targets (FIG. 12A). Inventors found that irc21 and rrd1 mutants accumulate high levels of SAM (FIG. 5C); accordingly, irc21 and rrd1 mutants were hypersensitive to SAM limitation caused by ethionine (toxic analogue of methionine) or cycloleucine (inhibitor of methionine adenosyl transferase) (FIG. 6C). In addition, inventors discovered a negative interaction between IRC21 and the PP2A methyltransferase PPM1 (FIG. 3D). Inventors confirmed that irc21 mutants are synthetic sick with ppm1 cells (FIG. 6B). These results suggest that Irc21 and Ppm1 positively regulate PP2A through different mechanisms. Interestingly PPM1 ablation, like IRC21 deletion rescued the HU sensitivity of mec1 sml1 mutants (FIG. 6D) and partially recovered the Rad53 defective phosphorylation in HU-treated mec1 cells.

[0274] Rapamycin inhibits TORC1 that represses PP2A complexes. Both rapamycin and ceramide have been showed to promote PP2A activity (Loewith et al., 2002; Nickels and Broach, 1996).

[0275] Inventors tested whether rapamycin and ceramide treatments could modulate the HU induced-DDR response. Cells were released from G1 in the presence of HU alone or combined with rapamycin and ceramide (FIG. 6E). After 5 minute-treatment, when Rad53 was still unphosphorylated, the concomitant presence of rapamycin and ceramide caused PP2A hyperactivation, as indicated by the Nnk1 phosphorylation status. At 60 minutes, Rad53 phosphorylation was evident in HU-treated cells, partial in HU+rapamycin and HU+ceramide and abolished in HU+rapamycin and ceramide. Inventors obtained analogous results by treating exponentially growing cells with HU, in combination with rapamycin and/or ceramide (FIG. 12B), thus ruling out the possibility that the effect of rapamycin and ceramide on Rad53 phosphorylation was due to a delayed entry into S phase.

[0276] Altogether these observations suggest that TORC1, Irc21 and Ppm1 influence the HU-induced DDR by regulating the activity of PP2A, and that ceramide levels, as well as SAM levels, are crucial for Mec1 and Rad53 activation.

[0277] Discussion

[0278] Activation of the Mec1.sup.ATR-mediated DNA damage response requires multiple post-translational modifications that integrate chromosomal signals and mechanical stimuli (Awasthi et al., 2016). Deactivation of the Mec1.sup.ATR pathway promotes cell cycle recovery or adaptation (Bartek and Lukas, 2007; Clemenson and Marsolier-Kergoat, 2009). A fine-tuning of the Mec1.sup.ATR cascade is required to prevent deleterious consequences of unscheduled checkpoint activation (Bastos de Oliveira et al., 2015; Harrison and Haber, 2006). Mec1 and ATR regulate nuclear and non-nuclear pathways (Hilton et al., 2015; Kumar et al., 2014; Matsuoka et al., 2007). In yeast, several phosphatases have been involved in DDR silencing, including PP2C (Ptc2/Ptc3) and PP4 (Pph3-Psy2) required for DSB recovery, and PP1 (Glc7) which promotes HU recovery (Bazzi et al., 2010; Keogh et al., 2006; Leroy et al., 2003; O'Neill et al., 2007). PP2A has been genetically linked to the RAD53-MEC1 pathway, but ruled out as one of the main phosphatases implicated in checkpoint control (Hustedt et al., 2015). In mammals, PP2A shows activity towards H2AX, ATM, p53, Chk1 and Chk2 (Chen et al., 2015; Dozier et al., 2004; Goodarzi et al., 2004). Here, inventors demonstrate that PP2A inactivation is beneficial when the Mec1-Rad53 axis is defective. Moreover, PP2A/PP2A-like act in a network with Irc21 and TORC1 to integrate metabolic signals with phosphorylation and dephosphorylation events outside and inside the nucleus and to attenuate the Mec1.sup.ATR cascade in cells experiencing replication stress.

[0279] IRC21 ablation rescues mec1, rad53A, chk1 sensitivity to low doses of HU and it is able to promote HU-induced Rad53 phosphorylation when Mec1 is absent, through a process mediated by Tel1. Irc21 was previously connected to the DDR, but the mechanism remained unclear (Guenole et al., 2013).

[0280] Irc21 is an uncharacterized protein that consists of a cytochrome b5 domain; in accordance, irc21 mutants influence the respiration rate and ROS levels, display resistance to mersalyl, and show a metabolic profile altered in CBR-related functions. Interestingly, Irc21 localization is mainly cytoplasmic (Guenole et al., 2013; Huh et al., 2003), although a fraction has been detected in the nucleus (Guenole et al., 2013), in mitochondria and vacuoles (CYCLoPs (Koh et al., 2015)). A key question is how does Irc21 influence the checkpoint response.

[0281] Inventors show that Irc21 positively regulates PP2A/PP2A-like activities. IRC21 ablation causes resistance to TORC1 inhibitors, but does not influence the TORC1-Sch9 axis, suggesting that Irc21 unlikely acts upstream of TORC1. The functional relationship between PP2A and TORC1 is rather complex and still controversial (Duvel et al., 2003). According to the traditional view, TORC1 negatively regulates PP2A/PP2A-like through Tap42 phophorylation (Di Como and Arndt, 1996), and, in the meantime, PP2A stimulates TORC1 through Npr2 phosphorylation (Laxman et al., 2014). Intriguingly, irc21 mutants exhibit a negative genetic interaction with PP2A/PP2A-like activators (Rrd1, Rrd2, Tip41, Saps and Ppm1) and positive genetic interactions with TORC1 components (Tco89 and Tor1) (FIG. 7). Inventors speculate that IRC21 ablation ameliorates TORC1-defective mutants by limiting PP2A/PP2A-like activities.

[0282] Hence, inventors propose that Irc21 may stimulate PP2A and therefore attenuate the DDR. Indeed, genetic and pharmacological inactivation of PP2A ameliorates the defective response to replication stress of checkpoint mutants. In addition, exogenous ceramide causes Rad53 dephosphorylation during recovery from HU treatment in mec1 irc21 sml1 mutants, and abolishes irc21 rescue of Rad53 phosphorylation in mec1 irc21 sml1 cells during HU treatment.

[0283] The next key question is how does Irc21 regulate PP2A activity. Among all metabolic alterations in irc21 mutants, elevated ROS are a potential contributor to PP2A suppression; indeed, ROS accumulation causes PP2A inactivation (Nakahata and Morishita, 2014; Shimura et al., 2016). Inventors excluded this hypothesis since ROS scavengers did not affect the capability of irc21 to rescue the HU sensitivity of checkpoint mutants.

[0284] IRC21 ablation causes alterations in the sphingolipid metabolism associated with a reduction of DHC and an accumulation of DHC precursors (3-keto-DHS, DHS, DHS-1-P and VLCFA-CoA). Hence, irc21 mutants are deficient in ceramide biosynthesis and the defective step corresponds to the DHS-DHC conversion. Inventors note that i) the hydroxylation of sphingolipid long chain bases and ceramides requires Sur2 and Scs7, two members of the cytochrome b.sub.5-dependent enzyme family (Haak et al., 1997) Mitchell and Martin, 1997), ii) Irc21 is a cytochrome b.sub.5-like enzyme and iii) Scs7 was identified among the top 5 high confidence Irc21 epistatic interactors, iiii) absence of either Scs7 or Sur2 ameliorates the replication stress-resistance of checkpoint mutants. Although these observations may suggest a role for Irc21 in ceramide hydroxylation, this is unlikely, as a defect in ceramide hydroxylation would not lead to diminished DHC levels (see FIG. 5C).

[0285] DHC is produced by the condensation reaction of DHS with VLCFAs; the reaction is catalyzed by the ceramide synthase (Lag1, Lac1, Lip1) and a defect in this reaction would cause accumulation of DHS and reduction in the levels of DHC, as inventors observe in the absence of Irc21. Accordingly, irc21 mutants are resistant to fumonisin B1, a ceramide synthase inhibitor (Wu et al., 1995). Hence inventors favor the hypothesis that Irc21 promotes the condensation reaction leading to the formation of DHC. One possibility is that Irc21 facilitates the activity of the ceramide synthase; intriguingly, LAC1 transcription is regulated by Rox1, a heme-dependent anaerobic repressor (Kolaczkowski et al., 2004). Another possibility is that Irc21 counteracts the activity of the Ydc1 and Ypc1 ceramidases that hydrolyze ceramides into sphingosine and fatty acid. Notably, Ydc1 or Ypc1 overepressions phenocopy irc21 mutants in accumulating DHS with a concomitants reduction in DHC (Mao et al., 2000a; Mao et al., 2000b). Interestingly, Irc21 binds cardiolipin (CL), a mitochondrial phospholipid (Gallego et al., 2010) that is known to activate ceramidases (El Bawab et al., 2001).

[0286] Ceramides activate PP2A in yeast and mammals (Dobrowsky et al., 1993; Nickels and Broach, 1996). Hence, Irc21 might directly stimulate PP2A and therefore attenuate the DDR by contributing to the production of ceramides. Interestingly, rapamycin and ceramide treatments cause a synergistic stimulation of PP2A activity, according to the view that TORC1 and Irc21 regulate PP2A activity in a negative and positive way, respectively (FIG. 7). Isc1, involved in the hydrolysis of complex sphingolipids to ceramides, has been also connected to the HU-induced replication stress (Matmati et al., 2013; Tripathi et al., 2011); ISC1 ablation confers HU sensitivity, and this phenotype is suppressed by Cdc55 overexpression, thus suggesting that Isc1 provides protection from HU through a mechanism requiring PP2A (Matmati et al., 2013).

[0287] irc21 mutants exhibit alterations in glucose homeostasis and glycolytic pathways, grow poorly in low glucose conditions and are synthetic sick in combination with mutations in SNF.sup.AMPK(FIG. 5C). Given that Snf1.sup.AMPK cross-talks at several levels with the PP2A and TORC1, it is possible that, in irc21 mutants, Snf1 activity may influence, at least in part, PP2A and DDR activities. However, this is unlikely as inventors found that IRC21 ablation rescues the HU sensitivity of checkpoint defective mutants in a SNF1 independent manner.

[0288] PP2A activity is stimulated by the SAM-Ppm1 axis (Laxman et al., 2014). The synthetic sickness between ppm1 and irc21 mutants may therefore result from the simultaneous ablation of two independent positive regulatory pathways leading to PP2A activation (FIG. 7). In this scenario, the low PP2A activity observed in ppm1 mutants would depend on the lack of PP2A methylation (Wu et al., 2000), while, in irc21 mutants, may result from limiting ceramide levels. Intriguingly, inventors show that irc21 mutants display elevated SAM levels, raising the possibility that Ppm1-mediated PP2A methylation may facilitate basal PP2A activity in the absence of ceramide-dependent PP2A activation; Accordingly, irc21 mutants are particularly sensitive to treatments that limit SAM availability. Alternatively, since low glucose levels cause increased SAM levels (Ogawa et al., 2016), irc21 SAM levels may reflect glucose homeostasis defects.

[0289] Inventors propose that DDR is attenuated by PP2A/PP2A-like, which are negatively regulated by the TORC1-Tap42 axis and positively regulated by the Irc21-Ceramide and SAM-Ppm1 pathways (FIG. 7).-

[0290] Nutrients not only supply energy and building blocks for cellular growth, but also exert crucial regulatory functions. Inventors show that PP2A represents a central hub in mediating a crosstalk between nutrients sensing, cell metabolism and the DDR. In fact, PP2A controls the phosphorylation status of several targets involved both in cell metabolism and DDR and it integrates the following nutritional pathways. i) Nitrogen and carbon metabolism: together with Sch9.sup.S6K, PP2A is one of the two crucial effectors of TORC1, which is activated by nitrogen and carbon metabolites and promotes anabolic processes (Hughes Hallett et al., 2014; Loewith and Hall, 2011; Orlova et al., 2006; Ramachandran and Herman, 2011) ii) Methionine metabolism: PP2A responds to S-adenosylmethionine levels, which depends on the availability of methionine (Sutter et al., 2013). iii) Sphingolipid metabolism: PP2A/PP2A-like are CAPP (Janssens and Goris, 2001; Nickels and Broach, 1996). Here inventors show that nutritional pathways impinge on the DDR by regulating PP2A, thus demonstrating a key role of PP2A in transducing metabolic signals to checkpoint kinases.

[0291] Besides linking ceramide levels with PP2A activity and DDR attenuation, our observations identify synergic combinations between ceramides, TORC1 inhibitors and SAM. Further characterization of the links between DDR, nutrient sensing pathways and cell metabolism may have relevant implications for exploiting novel therapeutic options as well as for repositioning/combining known drugs.

Example 2: Combination of Hypoglycemia and Metformin Impairs Tumor Metabolic Plasticity and Growth by Modulating PP2A-GSK3B3-MCL-1 Axis

[0292] Experimental Procedures

[0293] Reagents

[0294] Antibodies were purchased from the indicated sources and used at a dilution of 1:1000 unless otherwise described: anti-MCL-1 (Santa Cruz Biotechnology); anti-AMPK, anti-AMPK, anti-pACC, anti-ACC, anti-pGSK3P, anti-GSK3, anti-pERK (Cell Signaling Technology); anti-BCL-2 and anti-Bcl-xL (BD Biosciences); anti-Vinculin (SIGMA, dilution of 1:10000). Drugs were purchased from the following sources: Metformin (Sigma Aldrich), GSK31 inhibitor xii, GSK3P inhibitor viii, U0126, PD98059, SP600125 and SB 202190 (Selleck Chemicals).

[0295] Tissue Culture

[0296] HCT116, HeLa, MCF7, SK-MEL28 and A-549 cell lines were grown in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum and 2 mM L-glutamine unless otherwise indicated. Other cell lines were grown in RPMI medium supplemented with 10% fetal bovine serum and 2 mM L-glutamine unless otherwise indicated. For starvation experiments, cells were washed three times with PBS pH 7.2 and then incubated in the indicated starvation conditions. All cultures were maintained in a humidified tissue culture incubator at 37 C. in 5% CO2.

[0297] Immunoblotting

[0298] Whole cell lysates were prepared by directly lysing cells growing in culturing dishes or collected cell pellets in lysis buffer (40 mM Hepes pH 7.5, 120 mM NaCl, 1 mM EDTA, 10 mM pyrophosphate, 10 mM glycerophosphate, 50 mM NaF, 0.5 mM orthovanadate, and EDTA-free protease inhibitors (Roche) containing 0.3% CHAPS). Lysates were prepared from frozen tumors using GentleMACS dissociator. Lysates were cleared by centrifugation at 13000 g for 15 min. at 4 C., quantified using BioRad DC protein assay reagent followed by mixing 1:1 with 4% SDS, 100 mM Tris.Cl pH 6.8, 20% glycerol, 0.1% bromophenol blue and 5% -mercaptoethanol added immediately before use and heating at 94 C. for 7 min. Equal amounts of proteins were then electrophoresed on 8-15% SDS-PAGE gels. Gels were run at 100 V (stacking gel)/150 V (separation gel) on Protean III apparatus (BioRad). Gels were transferred onto nitrocellulose and probed with the appropriate primary antibody for a variable incubation time depending on the experimental design, followed by the corresponding secondary antibodies diluted 1:5000-10000. The proteins were visualized by enhanced chemiluminescence (ECL) using ChemiDoc apparatus (BioRad) according to the manufacturer's instructions.

[0299] RNA Interference

[0300] shRNA pLKO.1 lentiviral constructs were purchased from Open Biosystems. Target sequences are as follows:

TABLE-US-00001 Scrambled: GTGGACTCTTGAAAGTACTAT (SEQIDNO:1) GSK3#1: GCTGAGCTGTTACTAGGACAA (SEQIDNO:2) GSK3#2: CACTGGTCACGTTTGGAAAGA (SEQIDNO:3) PP2A#1: CAACAATTGCCCTAGCACTTG (SEQIDNO:4) PP2A#2: GACAACAGCACCTTGCAGAGT (SEQIDNO:5) B55#1: AGTCTGACTGAGCCGGTAATTC (SEQIDNO:6) B56#2: CACATCTCCAGCTCGTGTATGC (SEQIDNO:7) PP2AA#1: TTGCCAATGTCCGCTTCAATGC (SEQIDNO:8) PP2AA#2: CTACGCTCTTCTGCATCAATGC (SEQIDNO:9)

[0301] Lentiviral Transduction

[0302] The pLKO.1 vectors and package plasmids were co-transfected into packaging HEK293T cells and the viral supernatants were collected, supplemented with polybrene (8 ug/mL) and used to infect target cells in four 2-hour cycles of transduction over two consecutive days.

[0303] Quantification of Cell Proliferation

[0304] CellTiter Glo Luminescent Cell Viability Assay (Promega) was used according to manufacturer's protocol. Briefly, cells were plated in 96 well plates, treated 24h later with different doses of drugs in total volume of 100 l. 24h later, 100 l of CellTiter Glo reagent was added to the cells and incubated for 15 min at 37 C. and luminescence was measured using a Promega plate reader.

[0305] Quantification of Cell Death

[0306] Cells were harvested by trypsinization, washed in PBS (pH 7.2), and then stained with propidium iodide (10 mg/ml) added immediately prior to analysis. Cell fluorescence was then measured on a flow cytometer (FACSCalibur; Becton Dickinson, CA) and analyzed using CellQuest software.

[0307] Lactate Production Assay

[0308] Lactate production was measured using Lactate Assay Kit (Sigma Aldrich) according to manufacturer's instructions.

[0309] Oxygen Consumption Assay

[0310] Oxygen Consumption Rate was measured using Oxygen Consumption Rate Assay Kit (MitoXpress Xtra HS Method). (Cayman Chemical) according to manufacturer's instructions.

[0311] Xenografts

[0312] CD1 nude mice received single subcutaneous flank injections of 510.sup.6 HCT-116 cells or 110.sup.5 patient-derived melanoma cells suspended in 200 l saline. After the tumors were established, mice were randomized in different groups. Mice were kept on the feeding/fasting protocols described. Fasting cycles were achieved by complete removal of food while allowing free access to water. Metformin was administered via oral gavage at 200 mg/kg dissolved in water. Tumor growth was monitored by bi-dimensional measurements using a caliper. Experiments have been done in accordance with the Italian Laws (D.L.vo 116/92 and following additions), which enforces EU 86/609 Directive (Council Directive 86/609/EEC of 24 Nov. 1986 on the approximation of laws, regulations and administrative provisions of the Member States regarding the protection of animals used for experimental and other scientific purposes). Mice have been housed accordingly to the guidelines set out in Commission Recommendation 2007/526/ECJun. 18, 2007 on guidelines for the accommodation and care of animals used for experimental and other scientific purposes. Note that nude mice show a strain-specific decline in glucose levels upon fasting, of a higher degree as compared to other commonly used mouse strains (C57B6).

[0313] Immunohistochemistry

[0314] Formalin fixed paraffin embedded samples of tumors were cut 5 m thick on polarized glass; unmasking for both antigen was made with Citrate for 30 at 99 C.; anti-Mcl1 and anti-pGSK3 antibodies were used at 1:200 and 1:50 concentration respectively for two hours. LSAB 2 System-AP (DAKO) and Vulcan Fast Red Chromogen Kit 2 (Biocare Medical) were used as visualization system according to company working procedure. After hematoxilin and eosin review, the positivity of tumor cells was scored using a scoring system evaluating the staining pattern (homogeneousi.e. low power reproduce high power- or heterogeneous scoring respectively 0.1), the intensity of staining in the most reactive area (absent/weak/moderate/strong scoring respectively 0, 1, 2 or 3) and the percentage of most reactive cells/total cancer cells (10%; more than 10% but 50%; and >50% scoring respectively 0, 1 or 2).

[0315] Results

[0316] Cancer Cells Exhibit Metabolic Plasticity

[0317] Our initial observations showed that metformin exerts only weak anti-proliferative effects on an array of cancer cell lines representative of different cancer types as well as patient-derived melanoma cells when cells are kept in nutrient-rich conditions (FIG. 20A). Treatment with metformin was associated with a dose and time-dependent increase in glucose consumption and lactate production indicating a switch towards increased glycolysis (FIG. 20B-20E). Conversely, culturing cells in low glucose conditions induced a rapid increase in oxygen consumption (FIGS. 20F and 20G), suggesting a shift towards increased oxidative phosphorylation (OXPHOS). Taken together, these results suggest that cancer cells possess the capacity to shuffle between OXPHOS and glycolysis to circumvent the inhibition of either process, which may contribute to explaining both; the weak anti-proliferative effect of OXPHOS inhibition by metformin as well as the fact that targeting glycolysis alone has not provided the expected therapeutic benefit given the well-established glucose bias of tumors. The present findings are therefore in line with previous reports that hinted to the ability of tumor cells to adapt and switch among metabolic pathways. Specifically, several recent reports showed separately in different cell systems and models that inhibition of either glycolysis or OXPHOS triggers a compensatory increase in the other pathway (Chance, 2005; Hao et al., 2010; Jose et al., 2011; Lee and Yoon, 2015). Inventors hypothesized therefore that simultaneous targeting of those pathways may be a more rational approach to target tumor metabolism.

[0318] Hypoglycemia-Metformin Combination Effectively Restrains Tumor Growth.

[0319] Given the observed metabolic plasticity of cancer cells and to device an effective in vivo metabolic approach to target tumors, inventors aimed to simultaneously target parallel metabolic pathways. Particularly, inventors sought to examine the effect of a combination of fasting-induced hypoglycemia and metformin. To this end, mice bearing HCT116 xenografts were distributed into five groups as schematized in FIG. 13; the first two groups were kept on ad libitum feeding while the three other groups were subjected to 24-hour cycles of feeding-fasting. Fasting in those groups was achieved by complete withdrawal of food while allowing free access to water. To test the effect of metformin alone in the absence of intermittent fasting on tumor growth, one of the two groups on ad lib feeding received vehicle (Vehicle group), while the other received metformin (Met group). To examine the effect of intermittent cycles of fasting on the anti-neoplastic effects of metformin, vehicle or metformin was administered in the three other groups kept on feeding/fasting cycle. The first of those three groups (Fed/Fast group) received vehicle every 48h to assess the effect of fasting-feeding cycles alone on tumor growth. The two last groups received metformin every 48 hours administered either while the mice were fasted (Met/Fast group) or fed (Met/Fed group). In those three groups exposed to fasting-feeding cycles, all mice were fasted at the same time for 24 hours (6 pm-6 pm of the following day) and vehicle or metformin was administered (9 am of next day) as shown in FIG. 13A. In this way, metformin was administered following a period of 15 hours of either fasting (Met/Fast) or feeding (Met/Fed) and was allowed the 9 ensuing hours to act before the fasting or feeding cycle was terminated. Of note, the half-life of metformin in mice is around 2.7 hours (Jee et al., 2007) and it does not bind to plasma proteins or accumulate in the plasma (Greenblatt et al., 1977). As expected, fasting cycles resulted in a strong drop in blood glucose levels, which returned back to almost normal during the following cycle of feeding (FIG. 13B). Administration of metformin also reduced blood glucose levels but to a much less extent than that induced by fasting (FIG. 13B). Notably, it has been shown that while metformin can dramatically lower the high glucose levels in type II diabetes, it has relatively modest effects when administered to subjects with normal glucose levels at baseline (Bonanni et al., 2012)(Pollak, 2012; Sambol et al., 1996). As per design of the experiment, metformin was administered during the hypoglycemia periods in Met/Fast group (FIG. 13C) or near the normoglycemia periods in Met/Fed group (FIG. 13D). This design thus allows us to assess not only the gross effect of intermittent fasting on metformin's anti-neoplastic activities, but also the specific effect of the timing of metformin administration during the fasting-feeding cycles while tumor microenvironments are exposed to different nutritional conditions. Tumor growth was monitored in all groups. Our results show that in this setting, metformin alone did not exert any significant tumor restraining effect. Strikingly however, tumor growth was dramatically impeded in the group receiving metformin while fasting (Met/Fast) as compared to all other experimental groups (FIGS. 13E and 13F) indicating that the anti-proliferative effects of metformin were highly enhanced when it was administered during the hypoglycemia periods of a schedule of intermittent fasting.

[0320] Low Glucose Levels Sensitize Cancer Cells to Metformin

[0321] Fasting reduces the blood levels of glucose but it also results in a decrease in circulating growth factors and nutrients (Lee and Longo, 2011). To examine which of these factors contribute to the observed sensitization of tumor cells to metformin, HCT116 and HeLa cells were cultured under glucose, serum or amino acid deprivation conditions in the presence or absence of metformin. Cells cultured under nutrient-rich conditions with or without metformin served as control. In agreement with recent reports (Birsoy et al., 2014; Choi and Lim, 2014; Zhuang et al., 2014), deprivation of glucose, but not serum or amino acids, markedly sensitized cells to metformin as cells cultured under low glucose conditions showed marked cell death upon metformin treatment while cells deprived of glucose alone or treated with metformin alone for the same time periods did not show comparable levels of cell death (FIGS. 14A and 14B). Moreover, cells sequentially treated with metformin and low glucose did not show the same magnitude of cell death observed in cells treated simultaneously with combination of both (FIG. 21A). These results suggest that lowering glucose levels in tumor microenvironment upon fasting-induced hypoglycemia sensitizes tumor cells to metformin. The synergistic effect between metformin treatment and glucose deprivation was dependent on both metformin concentration and glucose levels (FIGS. 14C-14E) and was observed in several cancer cell lines as well as patient-derived melanoma cells (FIG. 21B), suggesting it is a general phenomenon not confined only to HCT116 and HeLa cells. This effect also seemed to be specific to metformin as glucose deprivation did not sensitize cells to other cytotoxic agents such as SAHA and Brefeldin A (FIG. 14F), confirming the specificity of this effect rather than predisposition to cytotoxicity per se.

[0322] Synergistic Cytotoxicity of Low Glucose/Metformin Combination is AMPK-Independent

[0323] Next, inventors analyzed the molecular mechanisms that mediate the synergistic cytotoxicity between metformin and glucose deprivation. The activation of the AMP-activated protein kinase (AMPK) is the most widely accepted mechanism to explain the anti-cancer effects of metformin (Cant and Auwerx, 2011). We, therefore initially aimed to examine whether AMPK contributes to the observed synergistic cytotoxicity of metformin and low glucose combination. Immunoblotting analysis of lysates derived from HCT116 or HeLa cells cultured either under normal or low glucose conditions in the presence or absence of metformin showed that while in HCT116 cells, AMPK phosphorylation was slightly enhanced by the metformin-low glucose combination, in HeLa cells the combination almost completely abolished AMPK phosphorylation (FIG. 22A). Since HCT116 and HeLa cells showed the same synergistic cytotoxicity in the case of the metformin-low glucose combination, differential AMPK phosphorylation detected in the two cell lines initially suggested that AMPK may not be mediating the observed phenotype. Importantly, depletion of AMPK in HCT116 (FIGS. 22B-22D) or expression of a constitutively active form of AMPK in HeLa cells (FIGS. 22E-22G) failed to modulate the synergistic cytotoxicity observed in both cell lines to any significant extent. In both cases, phosphorylation of acetyl CoA carboxylase (ACC), a known downstream target of AMPK was used as a control to verify AMPK activity (FIGS. 22B and 22E). Taken together, these results indicate that the observed synergistic cytotoxicity of the metformin/low glucose combination is AMPK-independent.

[0324] Activation of GSK3 Mediates the Synergistic Cytotoxicity of Low Glucose/Metformin Combination

[0325] Inventors next aimed to identify the signaling pathway(s) mediating the observed synergistic cytotoxicity between metformin and low glucose. To this end, inventors used pharmacological inhibitors of several major signaling pathways. Screening of a battery of kinase inhibitors showed that HCT116 and HeLa cells treated with GSK3 inhibitors were resistant to cell death triggered by low glucose/metformin combination (FIG. 15A). Glycogen Synthase Kinase 3 (GSK3) is a Ser/Thr kinase that is known to play crucial roles in the regulation of a wide variety of signaling pathways that control protein synthesis, cell proliferation, differentiation, motility and apoptosis and is involved in the pathogenesis of several diseases (Cohen and Frame, 2001; Frame and Cohen, 2001; Jope and Johnson, 2004). GSK3 activity is regulated by diverse stimuli and signaling pathways. Phosphorylation of its N-terminus serine 9 residue inhibits its activity and it is thus commonly used as a marker for the inactive kinase form. Immunoblotting analysis of lysates derived from HCT116 or HeLa cells cultured under either normal or low glucose conditions in the presence or absence of metformin revealed an almost completely abolished GSK3 phosphorylation (and thus hyperactivation) in the condition of low glucose-metformin combination (FIG. 15B). Notably, low glucose/metformin-induced GSK3 dephosphorylation was consistently observed in both HCT116 and HeLa cells and was specifically induced only by the combination, while either metformin or low glucose aloneif anythingslightly increased the level of GSK3 phosphorylation. Phosphorylation of ERK, similarly to what observed for AMPK, did not show a consistent pattern of phosphorylation between the two cell lines, further confirming the specificity of modulation of GSK3 phosphorylation (FIG. 16B). The effect on GSK33 phosphorylation was also dose-dependent on both metformin and glucose (FIGS. 15C and 15D). Furthermore, a combination of metformin and 2-Deoxy-Glucose (2-DG), a glucose analog that inhibits glycolysis via its actions on hexokinase, the rate limiting step of glycolysis, resulted in a similar dramatic reduction in GSK3 phosphorylation (FIG. 23A), which correlated with synergistic cytotoxicity of this combination (FIG. 23B), further confirming the synergistic cytotoxicity between metformin and inhibition of glycolysis even in cells cultured in glucose-rich conditions and suggesting a role for GSK33 in mediating this synergism. Importantly, GSK33-depleted HCT116, HeLa and patient-derived melanoma cells GaLa1949 and LuCa1973 cells cultured under low glucose conditions proliferated almost normally and did not show cell death upon metformin treatment (FIGS. 15E-16G and 24A) confirming the essential role of GSK30 in mediating the synergistic cytotoxicity between low glucose and metformin.

[0326] GSK3-Dependent Decline in MCL-1 Levels Mediates the Synergistic Cytotoxicity of Low Glucose/Metformin Combination

[0327] Among the downstream targets modulated by GSK3, it has been shown that GSK3 phosphorylates and subsequently enhances the proteasomal degradation of MCL-1, an anti-apoptotic member of the BCL-2 family of proteins (Ding et al., 2007a; Inuzuka et al., 2011; Maurer et al., 2006; Ren et al., 2013a). GSK3-mediated MCL-1 degradation has been shown to be an essential event in mediating cell death triggered by GSK30 activation (Magiera et al., 2012; Maurer et al., 2006; Morel et al., 2009; Ren et al., 2013b; Wang et al., 2012).

[0328] Inventors therefore sought to explore whether MCL-1 modulation plays a role in mediating cell death induced by the low glucose and metformin combination. Immunoblotting analysis of lysates derived from HCT116 and HeLa cells cultured under different nutrient deprivation conditions in the presence or absence of metformin showed that metformin treatment of cells cultured in low glucose (but not upon serum or amino acids starvation) resulted in a marked reduction in MCL-1 protein levels. The levels of the other members of the BCL-2 proteins BCL-2 and Bcl-xL were not affected by the various combinations, confirming the specificity of MCL-1 modulation (FIGS. 16A and 16B). The decline in MCL-1 levels was consistent with GSK30 activation in those cells as described before (FIGS. 16A and 16B) and was also dependent on both metformin and glucose concentrations (FIGS. 16C and 16D). To test whether the observed decline in MCL-1 levels was indeed a result of GSK33 activation, GSK3-depleted or control cells were cultured in normal or low glucose levels in the presence or absence of metformin. Immunoblotting results showed that unlike control cells expressing scrambled shRNA, metformin-low glucose combination did not result in reduction in MCL-1 levels in cells depleted of GSK3, indicating that the decline in MCL-1 levels is mediated by GSK3 (FIG. 16E). Consistently, cells treated with pharmacological inhibitor of GSK3 did not show the decline in MCL-1 levels observed in control cells upon treatment with metformin and low glucose combination (FIG. 24B).

[0329] Finally, inventors tested whether GSK3-mediated decline in MCL-1 level contributes to the synergistic cytotoxicity between metformin and low glucose. HCT116, HeLa and patient-derived melanoma cells GaLa1949 and LuCa1973 overexpressing MCL-1 (or as control BCL-2 and Bcl-xL) were cultured in a medium containing normal or low glucose in the presence or absence of metformin. Cells expressing MCL-1 were more resistant to cell death observed in control and BCL-2 or Bcl-xL-expressing cells upon treatment with metformin in low glucose conditions (FIGS. 16F, 16G and 25A).

[0330] PP2A Acts Upstream of GSK3 to Mediate the Synergistic Cytotoxicity of Low Glucose/Metformin Combination

[0331] Protein phosphatase 2A (PP2A) is a major serine-threonine phosphatase in mammalian cells that has been shown to act as a tumor suppressor through its ability to regulate a number of major molecular switches involved in tumorigenesis. Among those molecular switches, PP2A has been shown to regulate GSK-3 activity by removing phosphorylation at serine 9 as well as other regulatory residues (Bennecib et al., 2000; Kapfhamer et al., 2010; Kumar et al., 2012; Lin et al., 2007a, 2007b; Mitra et al., 2012; Wang et al., 2015). To examine whether modulation of GSK3 phosphorylation by PP2A contributes to the synergistic cytotoxicity of metformin and low glucose, control or PP2A-depleted HCT116, HeLa, and patient-derived melanoma cells GaLa1949 and LuCa1973 cells were cultured in a medium containing normal or low glucose in the presence or absence of metformin. Unlike control cells, PP2A-depleted cells did not show decline in GSK3 phosphorylation or MCL-1 levels in the metformin/low glucose combination (FIG. 17A) and consistently were more resistant to cell death readily observed in the control cells (FIGS. 17B, 18C and 25B).

[0332] B56 Upregulation and CIP2A Inhibition Mediate Modulation of GSK3-MCL-1 Axis by Low Glucose/Metformin Combination

[0333] As described earlier, GSK3 dephosphorylation by PP2A was triggered only by the combination while either metformin or low glucose aloneif anythingdid the opposite and increased slightly GSK3 phosphorylation. This ruled out the possibility that the effect could simply be an additive sum of two weaker effects combined together and raised the intriguing question as why this happens only in the case of the two treatments combined but not by either treatment alone. To answer this question and as our results implicate PP2A to be the key upstream regulator of the cytotoxicity of the combination, inventors sought to get deeper mechanistic insight into the modulation of PP2A by metformin, low glucose and the combination of both.

[0334] PP2A is a trimeric protein complex consisting of a catalytic subunit (PP2Ac or C), a scaffold subunit (PR65 or A), and one of several alternative regulatory B subunits (Janssens and Goris, 2001). Such variability in PP2A composition results in numerous PP2A holoenzymes, each with unique substrate specificities and different signaling functions in a wide variety of physiological processes and sometimes even in seemingly opposing ways (Sents et al., 2013). The determinants governing PP2A trimer assembly are significantly dependent on the regulatory B-type subunit. The specific B subunit incorporated into the complex modulates substrate specificity, subcellular targeting, and fine-tuning of phosphatase activity (Sents et al., 2013). The activity of PP2A holoenzyme is also regulated by upstream inhibitors, among which cancerous inhibitor of protein phosphatase 2A (CIP2A) is an endogenous PP2A inhibitor that is found overexpressed in several types of cancer and has been shown to contribute to malignant transformation through inhibition of PP2A and therefore evading tumor suppressor functions exerted by PP2A (Junttila et al., 2007; Sangodkar et al., 2016).

[0335] Immunoblotting analysis of lysates derived from HCT116 and HeLa cells treated with metformin, low glucose and the combination of both showed that metformin treatment led to reduction in the level of CIP2A while low glucose treatment specifically upregulated the levels of B56 regulatory subunit (FIG. 18A). Cells treated with metformin/low glucose combination therefore showed reduced CIP2A and enhanced B56 levels simultaneously. Interestingly GSK3 is an established substrate of PP2A complex containing B56 (Haesen et al., 2016; Houge et al., 2015; Louis et al., 2011; Bennecib et al., 2000; Kapfhamer et al., 2010; Kumar et al., 2012; Lin et al., 2007a, 2007b; Mitra et al., 2012; Wang et al., 2015).

[0336] These results therefore suggested a model in which PP2A is activated by metformin through inhibition of its suppressor CIP2A and when combined with low glucose-induced B56 upregulation, the combination favors the formation of an active PP2A complex containing B56 subunit which then targets GSK3 for dephosphorylation, ultimately leading to MCL-1 reduction and cell death.

[0337] To test this hypothesis, inventors first overexpressed CIP2A to examine whether metformin-induced downregulation of CIP2A contributes to the modulation of PP2A-GSK33-Mcl-1 axis and the synergistic cytotoxicity by the combination. Our results show that cells overexpressing CIP2A did not show the decline in phosphorylated GSK3 and Mcl-1 levels and the induction of cell death observed in control cells expressing vector upon treatment with metformin/low glucose combination (FIGS. 18B, 18C and 26A).

[0338] Furthermore, depletion of CIP2A using shRNA was sufficient to recapitulate the effect of metformin in the combination as a combination of CIP2A depletion and low glucose treatment triggered a decline in phosphorylated GSK3 and MCL-1 levels and evoked cell death similar to what is observed in the case of metformin/low glucose combination (FIGS. 26B and 26C). These results thus indicate that metformin-induced downregulation of CIP2A mediates PP2A activation and can be attributed forat least in a big partsensitization of cells to low glucose conditions. Next, inventors aimed to examine the contribution of low glucose-induced upregulation of B56 subunit to the synergetic cytotoxicity of low glucose/metformin combination. Initially, inventors examined the effect of B56 depletion. As shown in FIG. 18D, combination of low glucose and metformin did not result in reduction in the levels of phosphorylated GSK3 and MCL-1 in B56-depleted cells, unlike control cells or cells depleted of another B regulatory subunit B55 (FIG. 18D). Consistently, B56-depleted cells were more resistant to low glucose/metformin combination and showed markedly less cell death compared to control cells (FIGS. 18E and 27), confirming that B56 is required for GSK3 dephosphorylation, MCL-1 reduction and cytotoxicity in response to the combination.

[0339] Finally, overexpression of B56 and subsequent enrichment of B56-containing PP2A holoenzyme synergized with metformin to induce GSK3 dephosphorylation, MCL-1 downregulation and cell death, thus mimicking the effect of low glucose in the combination (FIG. 28A-28D).

[0340] Formation of an Active PP2A Holoenzyme Containing B56 Mediates Cytotoxicity of Low Glucose/Metformin Combination

[0341] Taken together, our results showed that metformin-elicited CIP2A downregulation as well as low glucose-induced B56 upregulation mediate modulation GSK3-MCL-1 axis by the combination and suggested that PP2A activation following CIP2A downregulation together with B56 upregulation mediate the cytotoxicity of the combination though the formation of an active PP2A holoenzyme incorporating B56 subunit with high substrate specificity towards GSK3. To further test this model, inventors aimed to examine the composition of PP2A holoenzyme under different conditions. To this end, scaffold PP2A A subunit was immunoprecipitated from cells treated with either low glucose, metformin or a combination of both. Immunoprecipitation analysis showed specifically enhanced recruitment of B56 subunit and GSK3 to the PP2A holoenzyme in cells treated with low glucose/metformin combination indicating that the combination indeed elicits the formation of a B56-containing PP2A holoenzyme that shows higher substrate affinity toward GSK3 (FIG. 18F). Finally, to further confirm these observations, inventors made use of a PP2A A subunit mutant (S256Z) that shows defective binding to B56 (Reference). PP2A Au-ablated cells were reconstituted with either an empty vector, wild type PP2A A or S256Z mutant. Our results show that metformin/low glucose combination failed to induce GSK3 dephosphorylation and MCL-1 downregulation in PP2A A ablated cells reconstituted with an empty vector. Reconstitution of those cells with wild type PP2A A restored GSK3 dephosphorylation and MCL-1 downregulation upon treatment with the combination, which correlated with enhanced binding between PP2A A and B56 as described earlier. Conversely, metformin/low glucose treatment failed to induce similar B56 recruitment in cells reconstituted with the PP2A A mutant (S256Z) deficient for binding B56 and consistently did not result in similar GSK30 dephosphorylation, MCL-1 reduction and cell death observed in WT PP2A Au-reconstituted cells. (FIGS. 28A and 28B).

[0342] Tumors Depleted of GSK3 or Overexpressing MCL-1 are Resistant to Metformin Administered During Fasting

[0343] Our in vitro results indicate that the combination of metformin and low glucose conditions exerts synergistic cytotoxic effects on cancer cells through the dephosphorylation, and thus activation, of GSK33 by an induced PP2A holoenzyme containing the B56 subunit. Active GSK3 in turn, leads to diminished pro-survival MCL-1 levels, and ultimately to cell death. Inventors then aimed to examine whether this mechanistic model accounts for the tumor-restraining effect of metformin-hypoglycemia in vivo. Immunohistochemistry analysis of tumor tissues derived from the in vivo experiment in FIG. 13 showed that the levels of MCL-1 and phosphorylated GSK3 in the tissues derived from mice treated with metformin while fasting (Met/Fast group) were markedly lower compared to the other experimental groups (FIGS. 19A and 19B). Furthermore, immunoblotting analysis of tumor lysates prepared from Met/Fed against Met/Fast mice showed that administration of metformin to hypoglycemic (fasting) mice resulted in decrease in GSK3 phosphorylation and MCL-1 levels and conversely increase in the B56 levels (FIG. 19C) and the recruitment of B56 and GSK3 to PP2A holoenzyme (FIG. 30). AMPK phosphorylation however, did not seem to vary greatly among the two groups (FIG. 19C). Taken together, these results indicate that metformin-hypoglycemia combination elicited molecular events in tumors similar to those observed in vitro in cancer cells treated with metformin-low glucose combination. Finally, to further confirm the involvement of GSK3 f activation and subsequent reduction in MCL-1 levels in restraining tumor growth by metformin-hypoglycemia combination in vivo, mice were inoculated with either control, GSK3-depleted or MCL-1-overexpressing HCT116 cells. Upon establishment of tumors, mice were kept on 24-hour feeding/fasting cycles with half the mice from each group receiving metformin every 48 hours either during feeding or during fasting cycle as previously explained (see FIG. 13). Monitoring tumor growth showed that unlike control tumors (in which metformin markedly inhibited growth when administered in hypoglycemic mice during fasting (but not during feeding cycles), tumor-derived from GSK3-depleted or MCL-1-overexpressing cells grew similarly in both conditions and metformin-hypoglycemia combination failed to exert similar growth inhibitory effect on tumor growth (FIGS. 19D and 19E). Collectively, these results further highlight the crucial role of modulation of the PP2A-GSK33-MCL-1 axis in mediating the synergistic anti-proliferative effect of metformin-low glucose in vitro and similarly metformin-hypoglycemia in vivo as an approach to tackle tumor metabolic plasticity according to the model depicted in FIG. 19F.

[0344] PP2A Inducer Perphenazine Synergizes with Metformin In Vitro and In Vivo

[0345] Inventors finally aimed to exploit the molecular insight gained by analyzing the PP2A-GSK3-MCL-1 axis in response to hypoglycemia/metformin combination to attempt a pharmacological approach that could mimic the effect of this combination with more clinical feasibilities. Inventors made use of perphenazine (PPZ), an FDA-approved anti-psychotic medication that has been shown to induce PP2A activity (Gutierrez et al., 2014; Research, 2014; Tsuji et al., 2016) and that our observations showed that it enhances the assembly of PP2A holoenzyme containing the B56 subunit and the therefore recruitment of GSK3 (FIG. 31A),

[0346] Our results show that similar to hypoglycemia-metformin combination, treatment with a combination of PPZ with metformin diminished the phosphorylated GSK3 and MCL-1 levels (FIG. 31B). Consistently, PPZ sensitized HCT116 and HeLa cells to metformin specifically but not to other cytotoxic agents such as SAHA and Brefeldin A (FIGS. 31C and 31D). PPZ also synergized with metformin in impeding xenograft tumor growth in vivo (FIGS. 31E and 31F). These results suggest that modulation of PP2A-GSK3-MCL-1 axis can be exploited pharmacologically to enhance the tumor-restraining effects of metformin.

[0347] Discussion

[0348] Despite the big leap in our understanding of the differences between metabolism in normal versus cancer cells, this understanding has not so far been translated into clinically feasible approaches for therapeutic intervention in cancer. Particularly, given the bias of tumor cells towards increased uptake of glucose and switch to aerobic glycolysis, it is puzzling that many agents designed to inhibit these processes failed to provide the expected therapeutic benefits when tested clinically (Rodriguez-enriquez et al., 2009). In the light of our observations and other emerging reports, a major obstacle for tackling cancer metabolism is the metabolic plasticity of cancer cells demonstrated by their ability to shuffle among different metabolic pathways and thus circumvent the inhibition of a single pathway. Inventors therefore hypothesized that a more rational approach would be to aim to simultaneously inhibit alternative metabolic pathways.

[0349] In the present study, inventors exploited intermittent fasting (IF) as a clinically feasible, safe and effective approach to lower glucose availability and explored the potential synergistic effect of a combination of IF with the OXPHOS inhibitor metformin on tumor growth. In glucose-starving tumor cells, OXPHOS becomes critical for survival, which may render those cells particularly sensitive to metformin. Alternatively, fasting-induced glucose limitation may serve to impede the compensatory increase in glycolysis upon OXPHOS inhibition by metformin.

[0350] Our results show that the tumor-restraining effect of metformin was dramatically enhanced when it was administered in mice subjected to alternating cycles of fasting/feeding specifically during the periods of fasting-induced hypoglycemia. Interestingly, our results therefore indicate that not only intermittent fasting, but also the timing of metformin administration during the fasting/feeding cycles dictates the sensitivity of tumors to metformin. Notably, in our experimental setting (nude mice), fasting induced a reduction in blood glucose levels by 40-60%. However, the reduction in glucose levels within the tumor microenvironment is likely to be greater, considering the considerable uptake and use of glucose by tumor cells, leading to quicker and more profound glucose depletion in the hypovascularized tumor microenvironment. This could contribute to the observation that while the fasting protocol was well-tolerated with no gross clinical signs of toxicity on mice in whole, the effect on tumor tissues was detrimental when combined with metformin treatment.

[0351] Mechanistic analysis of the molecular events triggered by the low glucose-metformin combination initially ruled out a central role for AMPK in mediating cell death in response to the combination. The unexpected independency on AMPK was intriguing given the well-established role of AMPK in mediating the response to either metformin or low glucose individually and indicated that the combination specifically triggers signaling events that are distinguished from those triggered in response to each of the two treatments alone and thus cannot simply be attributed to the sum of the effects of the two individual treatments.

[0352] Further mechanistic investigation showed that cell death in response to low glucose/metformin combination was a consequence of a tightly orchestrated signaling process mediated by specific modulation of the PP2A-GSK3-MCL-1 axis. Importantly, genetic manipulation of this pathway (through overexpression of MCL-1 or depletion of either PP2A or GSK3) abrogated the synergism and rendered tumor cells resistant to a combination of low glucose and metformin in vitro and fasting-induced hypoglycemia and metformin in vivo. As initially deduced from the independency on AMPK, the specific modulation of GSK3 phosphorylation and MCL-1 levels only in the case of the combination, but neither by metformin or low glucose alone, indicated once again that the combination triggers molecular events that are distinguished from those triggered by either of the treatments alone. Since modulation of PP2A seemed to be an early response to energetic stress triggered by the combination in our model, inventors carried out mechanistic analysis of the PP2A complex assembly and activity in order to get further insight into the specificity of the combination.

[0353] PP2A is an important and ubiquitously expressed serine threonine phosphatase that regulates the function of many crucial molecules through mediating their dephosphorylating. PP2A thereby plays important roles in diverse cellular processes including cell cycle progression, DNA replication, gene transcription and protein translation (Westermarck and Hahn, 2008). In tumorigenesis, PP2A has been established as a tumor suppressor and the inactivation of PP2A has become widely accepted as an important step towards full-blown transformation (Eichhorn et al., 2009; Janssens et al., 2005; Mumby, 2007; Perrotti and Neviani, 2008). Indeed, genetic and/or functional inactivation of different PP2A subunits and, therefore, loss of its phosphatase activity have been found in several types of tumors (mutations of PP2A subunits have been found at high frequency in lung, colorectal and breast cancers: Perrotti and Neviani 2013).

[0354] Our findings thus establish a novel role for PP2A as an early response sensor of the energetic stress triggered in our model by simultaneous inhibition of alternative metabolic pathways via a combination of metformin and low glucose. Interestingly, in line with our findings, a recent study showed that simultaneous targeting of multiple metabolic through inhibition of nutrient uptake triggered PP2A activation (Kim et al., 2016).

[0355] Structurally, PP2A holoenzyme is a heterotrimeric complex of a catalytic C subunit, a scaffolding A subunit, and one of several regulatory B-type subunits. The substrate specificity and activity of PP2A are highly regulated by the type of the regulatory B-type subunit incorporated in the complex. It is thus essential when analyzing the role of PP2A in any biological context, to identify which of the many alternative PP2A complexes is/are involved. Additionally, PP2A activity is regulated by upstream inhibitors among which, CIP2A is an important endogenous PP2A inhibitor in cancer cells.

[0356] Delving deep into the molecular mechanisms, our results showed that on the one hand, treatment with metformin diminished the levels of PP2A inhibitor CIP2A in cancer cells. On the other hand, culturing cancer cells in low glucose triggered an increase in the levels of PP2A regulatory subunit B56. Taken together, only cells treated with metformin and low glucose combination exhibit a simultaneous decline in CIP2A together with an enrichment in B56 subunit levels leading to the formation of an active PP2A complex containing the B56 subunit, which subsequently targets GSK3 for dephosphorylation and ultimately leading to reduction in MCL-1 levels and cell death. This detailed molecular insight explains the specific synergistic cytotoxicity of the combination. Importantly, analysis of tumor samples from our in vivo model indicated that the same molecular model accounts for the tumor-restraining effect of the combination of metformin and fasting-induced hypoglycemia.

[0357] The PP2A inhibitor CIP2A is an oncoprotein, originally identified as a binding partner of the PP2A A subunit. CIP2A is specifically overexpressed in numerous types of tumors while is barely detectable in normal cells, making it a potential therapeutic target. CIP2A overexpression has been shown to correlate with poor prognosis in lung cancer, breast cancer, pancreatic cancer, bladder cancer, osteosarcoma, esophageal cancer, gastric cancer, ovarian cancer, cervical cancer, prostate cancer, HCC and colorectal cancer (Haesen et al., 2014; Seshacharyulu et al., 2013). Inhibition of PP2A and thus evading its tumor suppressor actions accounts for a big part of tumorigenic potential of CIP2A. It is not known exactly how CIP2A inhibits PP2A activity however it has been proposed that CIP2A interacts with the A subunit and impedes binding of B subunits to PP2A complexes (Junttila et al., 2007; Khanna et al., 2013; Sangodkar et al., 2016). According to this model, it is possible that metformin-induced downregulation of CIP2A frees PP2A A and C subunits from the inhibitory interaction with CIP2A, which when combined with low-glucose B56 upregulation, allows the formation of an active complex of PP2A A, C and B56 subunits. PP2A regulatory subunit B56 plays a role in tumorigenesis through its established function in the regulation of GSK3 dephosphorylation (Haesen et al., 2016; Houge et al., 2015; Louis et al., 2011; Bennecib et al., 2000; Kapfhamer et al., 2010; Kumar et al., 2012; Lin et al., 2007a, 2007b; Mitra et al., 2012; Wang et al., 2015) and possibly other substrates. Besides its role in cancer, B56 dysregulation has also been associated with neurological disorders. Mice lacking B56 develop ataxia and tauopathy (Louis et al., 2011) and mutations in B56 encoding gene Ppp2r5d that render B56 deficient for binding PP2A A and C subunits have also been recently identified in patients with intellectual disability (Houge et al., 2015).

[0358] Downstream of the PP2A complex, our results also establish a crucial role for GSK3 in mediating the observed synergistic cytotoxicity. GSK3 has been shown to play both tumor suppressor and promoter roles in cancer. The modulation of cell death by GSK3 contributes to its dual role in tumorigenesis. GSK3 has been shown to regulate several targets promoting both cell death and survival. GSK3 has been shown to mediate cell death in response to a wide variety of conditions including DNA damage, hypoxia, endoplasmic reticulum stress, heat shock and growth factor withdrawal (Beurel and Jope, 2006; Bijur and Jope, 2000; Hongisto et al., 2003; Jacobs et al., 2012; King et al., 2001; Loberg et al., 2002; Pap and Cooper, 1998, 2002; Somervaille et al., 2001; Song et al., 2002)

[0359] Phosphorylation and subsequent degradation of MCL-1 has been shown to play an essential role in mediating GSK3-induced cell death in response to certain stimuli such as UV irradiation, anticancer drug treatment and inhibition of growth factor pathways (Magiera et al., 2012; Maurer et al., 2006; Morel et al., 2009; Ren et al., 2013b; Wang et al., 2012). Furthermore, the levels of MCL-1 correlate with phosphorylated GSK3 levels (the inactive form of GSK3) in multiple cancer cell lines and primary human cancer samples (Ding et al., 2007b).

[0360] MCL-1 is a pro-survival member of the BCL-2 family that is upregulated in several types of tumors and contributes to drug resistance and relapse in those tumors (Aichberger et al., 2005; Akgul, 2009; Boisvert-Adamo et al., 2009; Boisvert-adamo et al., 2009; Cho-Vega et al., 2004; Elgendy, 2017, 2017; Gores and Kaufmann, 2012; Gores et al., 2012; Jiang et al., 2008; Khoury et al., 2003; Oyesanya et al., 2012; Quinn et al., 2011; Robillard et al., 2005; Warr and Shore, 2008; Wuilleme-Toumi et al., 2005). MCL-1 plays a key role in the regulation of apoptosis and its tumor-promoting properties have been largely attributed to its anti-apoptotic functions. However, recent reports suggest that MCL-1 might also be involved in other cellular processes that may contribute to its tumorigenic potential. Besides the crucial roles of MCL-1 in apoptosis, apoptosis-independent functions of MCL-1 in the regulation of autophagy and cellular energetics are emerging (Elgendy and Minucci, 2015; Elgendy et al., 2014; Germain et al., 2011; Perciavalle et al., 2012). The short half-life of MCL-1 makes it a particularly potential mediator for the regulation of critical processes such as changes in cellular energetics that require prompt coordination of cellular responses. While metformin may exhibit single-agent activity in some contexts, there is generally more interest in exploring its potential use in combinatorial therapy. Many combinations have been proposed but few have been thoroughly examined. Collectively, our findings suggest that the combination of metformin with intermittent fasting or PP2A inducers may prove efficacious in targeting cancer cells and warrants further clinical evaluation Additionally, our results predict that the functional/genetic loss of PP2A will lead to loss of synergism in treatment, and suggest a potential strategy for stratification of patients.

[0361] Targeting PP2A has emerged as a promising therapeutic strategy in cancer potentially capable of overcoming drug-resistance induced in patients by continuous exposure to kinase inhibitors. Although phosphatases remain generally difficult to target by small molecules, PP2A activators such as phenothiazines, forskolin, 1,9-dideoxy-forskolin and FTY720 effectively have been shown to impede leukemogenesis in both in vitro and in vivo models (Perrotti and Neviani, 2008). Importantly, the drug exploited in this study (perphenazine) is approved for clinical use as an anti-psychotic: inventors therefore suggest to exploit its PP2A inducing activity for repurposing it as an anti-cancer agent (Gutierrez et al., 2014; Research, 2014; Tsuji et al., 2016).

[0362] Finally, the effects of metformin described here occur at doses that can be achieved clinically, and therefore our model appears to be immediately amenable to validation in clinical studies.

Example 3: DDR and NER During Chronological Aging

[0363] Materials and Methods

[0364] Strains and Growth Conditions:

[0365] Yeast strains were derived from W303 (Thomas and Rothstein, 1989), but RAD5+ background and are listed in Table 1A.

TABLE-US-00002 TABLE 1A Strains used herein Strain Relevant genotype Source SY 2080 W303-1a ade2-1 trp1-1 leu2-3,112 his3-11,15 H. Klein ura3 can1-100 RAD5+ GAL PSI+ CY11668 W303-1a sch9::KanMX6 This study CY14953 W303-1a atg1::KanMX6 This study CY11823 W303-1a snf1::NatMX6 This study CY12239 W303-1a SNF1-G53R This study CY15050 W303-1a pBGM18 [URA3, ADH2-lacZ] This study CY11697 W303-1a rrd1::NAT This study CY11900 W303-1a tip41::KAN This study CY14101 W303-1a GLN3::MYC13-KanMX6 This study CY14103 W303-1a NNK1::MYC13-KanMX6 This study CY14105 W303-1a NPR::MYC13-KanMX6 This study CY14167 W303-1a GLN3::MYC13-KanMX6 snf1::HPH This study CY14169 W303-1a NNK1::MYC13-KanMX6 snf1::HPH This study CY14171 W303-1a NPR1::MYC13-KanMX6 snf1::HPH This study SY2081 W303-1 ade2-1 trp1-1 leu2-3,112 his3-11,15 H. Klein ura3 can1-100 GAL PSI+ CY14598 W303-1 sch9::KanMX6 This study CY14595 W303-1 gcn2::KanMX6 This study CY14597 W303-1 gcn2::KanMX6 sch9::KanMX6 This study CY12015 W303-1a gcn2::KanMX6 This study CY13855 W303-1a gcn2::KanMX6 rrd1::Nat This study CY13857 W303-1a gcn2::KanMX6 tip41::Nat This study CY15106 W303-1a atg1::KanMX6 sch9::KanMX6 This study

[0366] Deletion and MYC-tagging were obtained using one-step PCR-targeting method (Longtine et al., 1998). The SNF1-G53R allele was constructed through the delitto perfetto strategy (Storici and Resnick, 2006). Strain CY14595 was obtained by tetrad dissection of the diploid generated by SY2081 and CY12015 mating. Strains CY14598 and CY14597 were derived from tetrad dissection of the mating between CY11668 and CY14595.

[0367] All CLS experiments were performed according to the protocol described (Hu et al., 2013). Rapamycin (Sigma-Aldrich) was used at a final concentration of 2 ng/ml while metformin (Sigma-Aldrich) at 80 mM. Both drugs were supplemented at the initial overnight culture stage (Day 0), prior to cells entering stationary phase. In experiments including rapamycin-treated cultures, DMSO (0.04%) was used in the untreated cultures. For each chronological aging experiment, cells were set up in synthetic complete medium (SDC) and allowed to grow overnight at 28 C. to reach exponentially growing conditions the next day (day 1) until the end of kinetics (day 10/11 for DDR and NER analysis, day 21-28 for viability). At each examined day, small aliquots of cells were removed, exposed to 40 J/m.sup.2 and further incubated in their exhausted medium. Samples were taken at untreated (prior to UV), 0 and 2 hours (for protein extraction) or 0, 6 and 24 hours (for genomic DNA extraction) after UV treatment.

[0368] Western Blot Analysis:

[0369] Protein extracts were prepared using TCA extraction as described (Chiolo et al., 2005). Protein samples were loaded on 10% SDS-PAGE, followed by western blot analysis using different antibodies: anti-Rad53 (EL7 antibody (Fiorani et al., 2008) produced by IFOM monoclonal facility), anti-phospho Thr 210 of Snf1 (Cell Signalling), anti-phospho-eIF2 (Cell Signalling), anti-total eIF2 (a gift from Dr. Tom Dever), anti Myc (clone 9E10) antibody and anti-PGK (Life Technologies-Novex) antibody. For Sch9, protein samples were run on 7.5% SDS-PAGE gels, subjected to Western Blot analysis with anti-phospho and total Sch9 antibodies, kindly provided by Dr. Maria E. Cardenas (Kingsbury et al., 2014). PP2A targets: Gln3-myc, Npr1-myc, Nnk1-myc were all separated on NuPage precast 3-8% SDS gels. PonceauS staining was used as a loading control.

[0370] Thymine Dimer Repair Assay:

[0371] Cells were fixed in one volume of ice-cold 100% ethanol and incubated on ice for 5. Genomic DNAs were then prepared following standard procedure. Equal amounts of DNA were loaded on 0.8% agarose gel, transferred by Southern blot onto nitrocellulose membrane (Amersham Protran 0.45) and cross-linked by baking 1 hour at 80 C.

[0372] Filters were then probed with an antibody that specifically recognizes thymine dimers (ab10347, Abcam), stripped and reprobed with an antibody that specifically recognizes ssDNAs (MAB3034, Millipore) as a control for total DNA amount loaded on the gel.

[0373] Chronological Lifespan Analysis:

[0374] Lifespan Viability Assays were Carried as Follows: Aliquots of Cells were Removed Throughout the CLS kinetics, serially diluted and spotted on YPD plates. The plates were then exposed to 40 J/m.sup.2 (or 20 J/m.sup.2 in some cases), incubated at 25 C. and scanned after 3 days. In FIGS. 33, 34 and 36 individual spot assay rows were cut out for space reasons, but the compared strains were always from the same plates.

[0375] For viability curves (FIG. 32C, 33D, 33G), cells were taken from the original flasks at the indicated days, counted, serially diluted and spread on YPD plates. Plates were UV-flashed in the range spanning 20-80 J/m.sup.2 to analyze sensitivity of cells to UV damage, or left untreated as a control. They were incubated at room temperature in the dark and after 3 days, colony-forming units (c.f.u.) were counted. To analyze cell survival in aging, the ratio between viable colonies grown on plates (multiplied by the dilution factor) and total cells present in the flasks was calculated and results were normalized to the Day1 values. To analyze UV sensitivity in aging, the number of colonies counted on UV-treated plates was divided by the number of colonies counted on untreated plates at each day. Values are average+/st dev on n=3 replicates.

[0376] Inventors note that the variety in the kinetics of aging is due to the nature of biological variability inventors consistently observed during CLS. Inventors emphasize that the pattern of phenotypes is consistent.

[0377] lacZ Activity Assay:

[0378] The ADH2-lacZ plasmid (YCpBGM18) was a gift from Ted Young (Young et al., 2000) and transformed into SY2080 to produce strain CY15050. URA+ colonies were set up to grow in CLS synthetic media as described above, except lacking Uracil, in untreated or Metformin-treated (80 mM) conditions. CLS kinetic time course was performed and samples (110.sup.8 cells) were harvested at Days 1, 4, 7, 10, 15 and 20, washed in dH.sub.2O+PMSF and pellets were frozen at 80 C. lacZ expression was assayed using a protocol described by Hepworth et al (Hepworth et al, 1995). Frozen pellets were resuspended in 200 l Z buffer and cells were broken by vortexing with 500 m diameter glass beads for 101 min pulses at 4 C. Z buffer (0.81 l) was added and the samples were adjusted to 0.05% SDS and 2% chloroform. Aliquots of the extracts were then added to Z buffer to give a final volume of 0.5 ml. The samples were prewarmed at 28 C. for 2 minutes before addition of 0.1 ml of ONPG (O-Nitrophenyl--galactoside) solution (4 mg/ml of 0.1M NaPO4 [pH7]). After incubation at 28 C., the reactions were stopped by addition of 0.25 ml Na.sub.2CO.sub.3. The samples were centrifuged briefly and the optical density at 420 nm of the supernatant was recorded. galactosidase activity is given as nanomoles of ONPG cleaved per minute per milligram of protein at 28 C. Protein concentration of the extract was determined by the Bradford assay. All the reported values are the average activities obtained from n=3 replicates representing each day in both /+Metformin.

Example 4

[0379] Experimental Procedures

[0380] Reagents

[0381] Antibodies were purchased from the indicated sources and used at a dilution of 1:1000 unless otherwise described: anti-pCHK1, anti-pCHK2, (Cell Signaling Technology); anti-Vinculin (SIGMA, dilution of 1:10000). Small molecule compounds were purchased from the following sources: Metformin, perphenazine, FTY-720, ceramide C2, thioridazine, fluphenazine, thiethylperazine, pimozide, clozapine, loratadine, promethazine, haloperidol (Sigma Aldrich).

[0382] Tissue Culture

[0383] HCT116 and HeLa cell lines were grown in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum and 2 mM L-glutamine unless otherwise indicated. Bx-PC3 cells were grown in RPMI medium supplemented with 10% fetal bovine serum and 2 mM L-glutamine unless otherwise indicated. For starvation experiments, cells were washed three times with PBS pH 7.2 and then incubated in the indicated starvation conditions. All cultures were maintained in a humidified tissue culture incubator at 37 C. in 5% C02.

[0384] Immunoblotting

[0385] Whole cell lysates were prepared by directly lysing cells growing in culturing dishes or collected cell pellets in lysis buffer (40 mM Hepes pH 7.5, 120 mM NaCl, 1 mM EDTA, 10 mM pyrophosphate, 10 mM glycerophosphate, 50 mM NaF, 0.5 mM orthovanadate, and EDTA-free protease inhibitors (Roche) containing 0.3% CHAPS). Lysates were cleared by centrifugation at 13000 g for 15 min. at 4 C., quantified using BioRad DC protein assay reagent followed by mixing 1:1 with 4% SDS, 100 mM Tris.Cl pH 6.8, 20% glycerol, 0.1% bromophenol blue and 5% (3-mercaptoethanol added immediately before use and heating at 94 C. for 7 min. Equal amounts of proteins were then electrophoresed on 8-15% SDS-PAGE gels. Gels were run at 100 V (stacking gel)/150 V (separation gel) on Protean III apparatus (BioRad). Gels were transferred onto nitrocellulose and probed with the appropriate primary antibody for a variable incubation time depending on the experimental design, followed by the corresponding secondary antibodies diluted 1:5000-10000. The proteins were visualized by enhanced chemiluminescence (ECL) using ChemiDoc apparatus (BioRad) according to the manufacturer's instructions.

[0386] RNA Interference

[0387] shRNA pLKO-Tet-On and pLKO.1 lentiviral constructs were purchased from Open Biosystems. Target sequences are as follows:

TABLE-US-00003 Scrambled: GTGGACTCTTGAAAGTACTAT (SEQIDNO:1) PP2AC#1: ACCGGAATGTAGTAACGATTT (SEQIDNO:10) PP2AC#2: GGCAAATCACCAGATACAAA (SEQIDNO:11) PP2AC#3: TGGAACTTGACGATACTCTAA (SEQIDNO:12) RAD51#1: GCTGAAGCTATGTTCGCCATT (SEQIDNO:13)

[0388] Lentiviral Transduction

[0389] The pLKO vectors and package plasmids were co-transfected into packaging HEK293T cells and the viral supernatants were collected, supplemented with polybrene (8 ug/mL) and used to infect target cells in four 2-hour cycles of transduction over two consecutive days.

[0390] Quantification of Cell Proliferation

[0391] CellTiter Glo Luminescent Cell Viability Assay (Promega) was used according to manufacturer's protocol. Briefly, cells were plated in 96 well plates, treated 24h later with different doses of drugs in total volume of 100 l. 24h later, 100 l of CellTiter Glo reagent was added to the cells and incubated for 15 min at 37 C. and luminescence was measured using a Promega plate reader.

[0392] Quantification of Cell Viability

[0393] Cells were harvested by trypsinization, washed in PBS (pH 7.2), and then stained with trypan blue solution 04% v/v (Sigma Aldrich) added immediately prior to analysis. Cells ware then counted on a TC30 automated cell counter (Biorad).

[0394] Results

[0395] the Modulation of the DNA Damage Response by the Signaling Pathways Regulated by PP2A and Nutrient Availability in Tumor Cells

[0396] To parallel the studies performed in yeast, showing a clear involvement of PP2A in regulating the DDR, and to integrate them with the data obtained in mammalian cells (metformin/low glucose impinging on a PP2A-regulated signaling pathway), inventors performed studies in tumor cells where they: [0397] combined metformin with DNA damaging agents (mainly a combination of hydroxyurea/gemcitabine), in conditions where those drugs have a minimal impact on cell viability; [0398] directly triggered PP2A activation through compounds known to modulate PP2A directly or indirectly (ceramide); [0399] studied the relevance of glucose concentrations in conjunction with DNA damaging agents; [0400] evaluated the relevance of PP2A in the observed response, by knocking down PP2A expression; [0401] studied the activation of the DDR by evaluating phosphorylation status of factors involved in the DDR (Chk1 and Chk2).

[0402] Inventors then observed that: [0403] several modulators of PP2A cooperate with DNA damaging agents in inducing cell death of tumor cells; [0404] while this effect is present also in normal medium, it is amplified at various extent in low glucose conditions, consistently with the different modalities of PP2A activation by the different compounds; [0405] the cooperation depends on PP2A, as shown by the knockdown experiments; [0406] the cooperations correlates with inhibition of the activation of at least two key components of the DDR (Chk1 and Chk2).

[0407] As a parallel to the experiments described above, treatment with okadaic acid (FIG. 56) inhibiting PP2A protects HeLa cells treated with high concentrations of hydroxyurea (HU), counteracting the activity of DNA damaging agents by inhibiting Pp2A and presumably enhancing the efficiency of DDR.

[0408] All experiments have been repeated at least 3 times with similar results.

Example 5

[0409] Genetic Impairment of the DDR is Synthetic Lethal to Treatments Able to Activate PP2A

[0410] Inventors reasoned thatgiven the proposed role of PP2A in the regulation of DDR tumor cells that are already defective for any component of the DDR itself should be more sensitive than cells that do not present this defect to treatments that activate PP2A, such as the metformin/low glucose treatment.

[0411] To demonstrate this concept in a stringent way, rather than comparing tumor cell lines from different sources, inventors engineered a pancreatic cancer cell line (Bx-PC3 pancreatic cancer cells) to down-regulate RAD51, a key component of the DDR. They then examined the response of the knocked-down cells compared to the parental ones to metformin-low glucose treatment, known from present studies to activate PP2A.

[0412] Indeed, knockdown of RAD51 in Bx-PC3 cells dramatically sensitizes them to PP2A-inducing treatment. They surmise therefore that tumors carrying defects in the DDR (that can be scored for instance by the presence of inactivating mutations in components of the DDR, such as RAD51, or BRCA1/2, or other DDR factors) can be especially suited for treatments activating PP2A, and therefore analysis of DDR markers can be a diagnostic tool to stratify patients for treatment (FIG. 48).

[0413] All experiments have been repeated at least 3 times with similar results.

Example 6

[0414] Characterization of the Activity of Small Molecules Known as PP2A Activators

[0415] In the past years, several small molecules were shown to have the ability to modulate PP2A, acting either directly on the holo-enzyme, or indirectly on regulatory factors. Inventors have therefore tested several small molecules for their ability to cooperate with metformin treatment. The assay was performed in 96-well plates (n=4) in HeLa cells, treating cells as described before, in high (10 mM) and low (2.5 mM) glucose conditions. Viability was measured by CellTiter Glo. Parallel assays (cell count with Trypan blue) confirmed the results.

[0416] Perphenazine and thioridazine were the only drugs able to cooperate with metformin in reducing tumor cell viability in high glucose conditions, while 7 other compounds cooperated with metformin only under low glucose conditions (see table 1B below). Inventors therefore speculated thatwhile perphenazine and thioridazione, as shown herein for perphenazine, can activate PP2A and recruit actively the B56 subunit to the PP2A holo-complexother drugs can activate PP2A, but require low-glucose conditions to increase the amount of the PP2A-B560 containing holoenzyme.

TABLE-US-00004 TABLE 1B name Active in high glucose Active in low glucose PERPHENAZINE + + THIORIDAZINE + + FLUPHENAZINE + THIETHYLPERAZINE + PIMOZIDE + CLOZAPINE + LORATADINE + PROMETHAZINE + HALOPERIDOL +

[0417] In fact, (see FIG. 58) inventors performed immunoprecipitation and total cell lysate analysis of PP2A Aalpha from cell lysates derived from HCT116 cells treated with either DMSO, 10 uM PERPHENAZINE (PPZ) or 10 uM THIORIDAZINE (Thio) and cultured for 24 hours in high glucose DMEM in the absence or presence of metformin (5 mM).

Example 7

[0418] Additional Data on the Combination of Metformin/Low Glucose

[0419] Inventors performed additional experiments to characterize present main findings, with the following results, summarized in FIGS. 50-55: [0420] The metformin-intermittent fasting combination (in vitro: metformin-low glucose) also works on PDX models of tumor. So, the data initially observed in cell lines can be reproduced in the in vivo model considered the golden standard; [0421] Low doses of metformin can achieve similar effects to those observed with higher doses shown herein (with an extended duration of treatment). This finding erases the concerns linked to the possibility that the doses of metformin used in some in vitro experiments cannot be reached in the patients. Inventors also show that low doses of metformin/low glucose trigger the same biochemical pathways that inventors have studied at higher doses; [0422] To better characterize the induction of cell death by metformin/low glucose, inventors show that it can be greatly inhibited by caspase inhibitors, linking the observed effect to induction of caspase-mediated cell death; [0423] Inventors have already herein shown that knockdown of several components of the identified PP2A signaling pathway abrogates the response to metformin-low glucose. Here, it is shown that this finding can be reproduced also in long-term clonogenic assays, complementing short-term assays that inventors have shown before.

[0424] Inventors have observed the cooperation metformin-low glucose in a comprehensive list of cell lines/primary tumors (see below), as an indication that it can be widely used for tumor treatment in solid tumors and hematological malignancies.

[0425] Summary of Cell Lines Analyzed and Responsive to Metformin/Low Glucose Treatment:

[0426] Solid Tumors [0427] Colon cancer: HCT116 [0428] Cervical cancer: HeLa [0429] Breast cancer: MCF7 [0430] Ovarian cancer: COLO704 [0431] Melanoma: SK-MEL28, GaLa 1949, LuCa 1970 [0432] Lung cancer: A549 [0433] Pancreatic cancer: Bx-PC3 [0434] Neuroendocrine tumors: BON-1 (see FIG. 57)

[0435] AMLs [0436] Eol1, PBL-985, HL-60, ML-2, PL-21, UF1, THP1, MOLM-14 cell lines

TABLE-US-00005 TABLE 2 Yeast strains herein used Name Genotype Reference SY2080 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, H. Klein, Lab collection GAL, PSI+, RAD5+ CY9145 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, irc21::KNMX6 CY11846 MAT a, ADE2+, ura3-1, his3-11, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, GAL1-rad53-D339A::URA CY11845 MAT a, ADE2+, ura3-1, his3-11, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, GAL1-rad53-D339A::URA, irc21::NAT CY11864 MAT a, ADE2+, ura3-1, his3-11, leu2-3,12, trp1-1, can1-100, This study GAL, PSI+, RAD5+, GAL1-rad53-D339A::URA, rrd1::NAT CY12137 MAT a, ADE2+, ura3-1, his3-11, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, GAL1-rad53-D339A::URA, tip41::NAT CY10043 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, Lab collection GAL, PS1+, RAD5+, rad53K227A::KANMX4 CY12352 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, rad53K227A::KANMX4, irc21::NAT CY13431 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, rad53K227A::KANMX4, rrd1::NAT CY13429 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, rad53K227A::KANMX4, tip41::NAT CY13156 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, GAL, PS1+, Lab collection RAD5+, rad53K227A::KANMX4, sml1::TRP1 CY12336 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, GAL, PSI+, This study RAD5+, rad53K227A::KANMX4, sml1::TRP1 irc21::NAT CY8998 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, Lab collection GAL, PSI+, RAD5+, sml1::TRP1 CY13439 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, Lab collection GAL, PSI+, RAD5+, sml1::HIS3 CY14094 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, Lab collection GAL, PSI+, RAD5+, sml1::HPH CY14095 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, Lab collection GAL, PSI+, RAD5+, sml1::HPH, mec1::NAT CY14097 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, Lab collection GAL, PSI+, RAD5+, sml1::HPH, rad53::NAT CY13441 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HIS3, irc21::KANMX6 CY13766 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HIS3, rrd1::NAT CY13770 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HIS3, tip41::KANMX6 CY14528 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::KANMX6, tap42-G360R-URA3 CY14499 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HPH, mec1::NAT, irc21::KANMX6 CY13797 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, GAL, PSI+, This study RAD5+, sml1::TRP1, mec1::URA3, rrd1::KANMX6 CY13772 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::TRP1, mec1::URA3, tip41::KANMX6 CY14532 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::KAN, mec1::NAT, tap42-G360R-URA3 CY14501 MAT a, ade2-1 ura3-1, his3-11,15 leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HPH, rad53::NAT, irc21::KANMX6 CY14063 MAT a, add2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::TRP1, rad53-NAT, rrd1-KANMX6 CY14061 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HIS, rad53::NAT, tip41::KANMX6 CY14530 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::KANMX6, rad53::NAT, tap42-G360R-URA3 CY14503 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HPH, mec1::NAT rad53::KANMX6 CY13988 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, GAL, PSI+, This study RAD5+, sml1::TRP1, mec1::URA3, irc21::NAT, rad53::KANMX6 CY14070 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HIS3, chk1::HPH CY14072 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HIS3, chk1::HPH, irc21::KAN CY14505 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HIS3, chk1::HPH, rad53::KAN CY13996 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, GAL, PSI+, This study RAD5+, sml1::TRP1, chk1::HIS3, irc21::NAT, rad53::KANMX4 CY13795 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, GAL, PSI+, This study RAD5+, sml1::TRP1, mec1::URA3, irc21::NAT, tel1::KAN CY13768 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, tel1::HIS3, sml1::KANMX6 CY13774 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, tel1::HIS3, sml1::KANMX6, irc211::NAT CY10263 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, Lab collection GAL, PSI+, RAD5+, sml1::TRP1, mec1::KANMX6, tel1::HIS3 CY12161 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1 can1-100, Lab collection GAL, PSI+, RAD5+, dun1::HIS3 CY13875 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+ dun1::HIS3, irc21:NAT CY13863 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::TRP1, Dun1-PK::HIS3 CY13873 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::TRP1, Dun1-PK::HIS3, irc21::NAT CY14507 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::TRP1, Dun1-PK::HIS3, mec1::KAN CY13867 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::TRP1, Dun1-PK::HIS3, rad53::NAT CY14509 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::TRP1, Dun1-PK::HIS3, irc21::NAT, mec1::KANMX6 CY14511 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::TRP1, Dun1-PK::HI3S, irc21::NAT, rad53::KANMX6 CY13824 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, M. P. Longhese, this study GAL, PSI+, RAD5+, hta1::KANMX4, hta2-S129A-URA3 CY13828 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, M. P. Longhese, this study GAL, PSI+, RAD5+, hta1::KANMX4, hta2-S129A-URA3, irc21::NAT CY10984 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, ptc1::KANMX6 CY9143 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, rts1::KANMX6 CY11914 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, BAD5+, sap190::KANMX6 CY11697 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, BAD5+, rrd1::NAT CY11900 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, tip41::KANMX6 CY14523 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, M. P. Longhese this study GAL, PSI+, RAD5+, tap42-G360R-URA3 CY10816 MAT alpha, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1- This study 100, GAL, PSI+, RAD5+, irc21::NAT CY12323 MAT alpha, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, irc21::HPH CY10363 MAT alpha, can1::STE2pr-Sp_his5, lyp1, ura30, leu20, his31, C. Boone, Lab collection met150 CY10678 MAT alpha, can1::STE2pr-Sp_his5, lyp1, ura30, leu20, his31, This study met150, irc21::NAT CY14101 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, GLN3::GLN3-MYC-KANMX6 CY14134 MAT a, ade2-1, uria3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, GLN3::GLN3-MYC-KANMX6, irc21::NAT CY14136 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, GLN3::GLN3-MYC-KANMX6, rrd1::NAT CY14138 MAT a, ade1-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, GLN3::GLN3-MYC-KANMX6, tip41::NAT CY14524 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, M. P. Longhese, this study GAL, PSI+, RAD5+, GLN3::GLN3-MYC-KANMX6, tap42-G360R-URA3 CY14103 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, NNK1::NNK1-MYC-KANMX6 CY14140 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, NNK1::NNK1-MYC-KANMX6, irc21::NAT CY14142 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, NNK1::NNK1-MYC-KANMX6, rrd1::NAT CY14144 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, NNK1::NNK1-MYC-KANMX6, tip41::NAT CY14525 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, M. P. Longhese, this study GAL, PSI+, RAD5+, NNK1::NNK1-MYC-KANMX6, tap42-G360R-URA3 CY14105 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, NPR1::NPR1-MYC-KANMX6 CY14146 MAT s, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, NPR1::NPR1-MYC-KANMX6, irc21::NAT CY14146 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, NPR1::NPR1-MYC-KANMX6, rrd1::NAT CY14150 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, NPR1::NPR1-MYC-KANMX6, tip41::NAT CY14526 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, M. P. Longhese, this study GAL, PSI+, RAD5+, NPR1::NPR1-MYC-KANMX6, tap42-G360R-URA3 CY14069 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, RTG3::RTG3-MYC-KANMX6 CY14107 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, RTG3::RTG3-MYC-KANMX6, irc21::NAT CY14109 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, RTG3::RTG3-MYC-KANMX6, rrd1::NAT CY14111 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, RTG3::RTG3-MYC-KANMX6, tip41::NAT CY11685 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, Lab collection GAL, PSI+, RAD5+, SCH9::SCH9-PK-HIS3 CY11779 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, SCH9::SCH9-PK-HIS3, irc21::KANMX6 CY12013 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, SCH9::SCH9-PK-HIS3, rrd1::NAT CY14497 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, SCH9::SCH9-PK-HIS3, tip41::NAT CY13998 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, ppm1::KANMX6 CY14000 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, irc21::NAT, ppm1::KANMX6 CY14617 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, rad53K227A::KANMX4, ppm1::HPH CY14631 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HPH, ppm1::KANMX6 CY14619 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HPH, mec1::NAT, ppm1::KANMX6 CY14621 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, GLN3::GLN3-MYC-KANMX6, ppm1::HPH CY14623 MAT a, ade2-1, ura3-1, hls3.11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, NNK1::NNK1-MYC-KANMX6, ppm1::HPH CY14625 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112 trp1-1, can1-100, This study GAL, PSI+, RAD5+, NPR1::NPR1-MYC-KANX6, ppm1::HPH CY14627 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, RTG3::RTG3-MYC-KANMX6, ppm1::HPH CY14197 MAT a, ade1-1, uram3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, scs7::NAT CY14195 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sur2::NAT CY14199 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sur2::NAT, scs7::HPH CY14203 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, rad53K227A::KANMX4, sur2::NAT CY14205 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, rad53K227A::KANMX4, scs7::NAT CY14207 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, rad53K227A::KANMX4, sur2::NAT, scs7::HPH CY14633 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112 trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HPH, scs7::KAN CY14635 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HPH, sur2::KAN CY14637 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HPH, scs7::KAN, mec1::NAT CY14639 MAT a, ade2-1, ura3-1, his3-11,15, leu2-3,112, trp1-1, can1-100, This study GAL, PSI+, RAD5+, sml1::HPH, sur2::KAN, mec1::NAT

TABLE-US-00006 TABLE3 Primersequenceshereinused. SEQ Primer ID Target Name PrimerSequence NO: IRC21 for- I21MXF ACGAATAAGCAGAATATAACATAT 14 dele- ward TAGCAGGTGCTTAGATTACACTCA tion TAGAGATACATGGAGGCCCAGAAT ACCCT re- I21MXR AGTGTTTTTATATCCTATGTAAGT 15 verse CTTCAAACTTTTTTTTTATCTCTG GTAACCTCAGTATAGCGACCAGCA TTCAC RRD1 fo- RRD1F1 AAAGAACGCACATATGAACAAGCA 16 dele- rward TTAAACGAGCAAAGAACGGATCCC tion CGGGTTAATTAA re- RRD1R1 TCATAATGCTTGTCATACACATTT 17 verse ATATGTTTAATTAATAGAATTCGA GCTCGTTTAAAC TIP41 for- TIP41F1 ACCTAAGGGCAGCTTTAGACACAA 18 dele- ward CAGCTCCCCAGAAAAACGGATCCC tion CGGGTTAATTAA re- TIP41R1 CGTGTATGTATTTGTACGTATTGT 19 verse TTTGTATATTTGATTGGAATTCGA GCTCGTTTAAAC SML1 for- SML1F1 TCTCACTAACCTCTCTTCAACTGC 20 dele- ward TCAATAATTTCCCGCTCGGATCCC tion CGGGTTAATTAA re- SML1R1 GGGAAATGGAAAGAGAAAAGAAAA 21 verse GAGTATGAAAGGAACTGAATTCGA GCTCGTTTAAAC MEC1 for- MEC1MXF AAGTGAGGCTGGACAACAAGAACG 22 dele- ward ACATACACCGCGTAAAGGCCCACA tion AGACTGCACATGGAGGCCCAGAAT ACCCT re- MEC1MXR AGTGATGGTTAGATCAAGAGGAAG 23 verse TTCGTCTGTTGCCGAAAATGGTGG AAAGTCGCAGTATAGCGACCAGCA TTCAC RAD53 for- RAD53MXF AGCTTTAAAAGAGAGAATAGTGAG 24 dele- ward AAAAGATAGTGTTACACAACATCA tion ACTAAAAACATGGAGGCCCAGAAT ACCCT re- RAD53MXR CTACCATCTTCTCTCTTAAAAAGG 25 verse GGCAGCATTTTCTATGGGTATTTG TCCTTGGCAGTATAGCGACCAGCA TTCAC CHK1 for- CHK1F1 GTATATCATAAGTTGCTGTATATG 26 dele- ward GGCAGCACGTATTACTCGGATCCC tion CGGGTTAATTAA re- CHK1R1 TGATCAGTGCATCTTAACCCTTCT 27 verse TTTGTCTCCATTTTTTGAATTCGA GCTCGTTTAAAC PTC1 for- PTC1F1 ATTTAGGCACTGCATTTATCTTTT 28 dele- ward AAAAATCATTATACGGATCCCCGG tion GTTAATTAA re- PTC1R1 GTCTATGCATAATTTTTGCGCGGT 29 verse TTATAACGGATCCTTCGAATTCGA GCTCGTTTAAAC TEL1 for- TEL1F1 AAGCCTTCAAAGAAAAAGGGAAAT 30 dele- ward CAGTGTAACATAGACGCGGATCCC tion CGGGTTAATTAA re- TEL1R1 TATAAACAAAAAAAAGAAGTATAA 31 verse AGCATCTGCATAGCAAGAATTCGA GCTCGTTTAAAC RTS1 for- RTS1F1 ATCATAGGCACGTGCTATTTTCGA 32 dele- ward ACATCCACTTTCAATCGGATCCCC tion GGGTTAATTAA re- RTS1R1 AAACTTCCTCACTTCTTCGAGCTT 33 verse GTAATGAATTGCTGTTGAATTCGA GCTCGTTTAAAC SAP190 for- SP190F1 CATTTCTTCATTTACTTAACTGCG 34 dele- ward AGAAGATTATAATAGCCGGATCCC tion CGGGTTAATTAA re- SP190R1 TGAATAAAGGGTGAAAATGTGACA 35 verse ATGTGAATGTTTTAGTGAATTCGA GCTCGTTTAAAC SUR2 for- SUR2F1 TTCTAGTCCGAAGAGGGTGTATAC 36 dele- ward GAAAAGAAAATATACGCGGATCCC tion CGGGTTAATTAA re- SUR2R1 TGCCTTTACCCAGCAATTGAACGG 37 verse GAGGTATGCAAAAGGGGAATTCGA GCTCGTTTAAAC SCS7 for- SCS7F1 CAGGCACTAAAAGCGGTGGTAAGC 38 dele- ward TAAAACTAGTACGAAGCGGATCCC tion CGGGTTAATTAA re- SCS7R1 TTTTCCTAGGTTGACAATTTTGGA 39 verse CGAGGCTGACCAATAAGAATTCGA GCTCGTTTAAAC PPM1 for- PPM1F1 TCCGCATAAACTAGATGATAAAGA 40 dele- ward GTACAAACAAGTCGCCCGGATCCC tion CGGGTTAATTAA re- PPM1R1 AGCATATTAAGATCAAATTAGTTG 41 verse AGGCTGTAAATAAAAAGAATTCGA GCTCGTTTAAAC Gln3- for- GLN3F2 AGCAATTGCTGACGAATTGGATTG 42 tag- ward GTTAAAATTTGGTATACGGATCCC ging CGGGTTAATTAA re- GLN3R1 TTATTAACATAATAAGAATAATGA 43 verse TAATGATAATACGCGGGAATTCGA GCTCGTTTAAAC Nnk1- for- NNK1F2 AATGAACCTAAGCGAGGCCATTCA 44 tag- ward CGATAATAATGGCTCACGGATCCC ging CGGGTTAATTAA re- NNK1R1 TATGTATTTTTTCAATGCAATCAA 45 verse TATCATTAATCATAAGGAATTCGA GCTCGTTTAAAC Npr1- for- NPR1F2 TGCAGGCCTAGAAAAGAAAAAGAA 46 tag- ward AAAGCAAAATAATCAACGGATCCC ging CGGGTTAATTAA re- NPR1R1 TACAAATGCTTGGAAAAGAAATAA 47 verse AAGTGGGGACGCTTATGAATTCGA GCTCGTTTAAAC Rtg3- for- RTG3F2 CTCTAATCCAGCTGACTATCTTTT 48 tag- ward AGAATTTGGTTCGGGGCGGATCCC ging CGGGTTAATTAA re- RTG3R1 TTTTTCAAATTTAATTTTTTCCCG 49 verse CTAATAAGACCATAAAGAATTCGA GCTCGTTTAAAC

TABLE-US-00007 TABLE 4 Chemical structures of the herein mentioned molecules. Name Structure Caffeine [00001] Cardiolipin [00002] Clozapine [00003] FLUPHENAZINE [00004] FTY720 [00005] FUMONISIN B1 [00006] HALOPERIDOL [00007] LORATADINE [00008] MERSALYL ACID [00009] METFORMIN HCl [00010] MYRIOCIN [00011] OKADAIC ACID [00012] PERPHENAZINE HCl [00013] PIMOZIDE [00014] PROMETHAZINE HCl [00015] RAPAMYCIN [00016] S-ADENOSYL METHIONINE [00017] THIETHYLPERAZINE MALEATE [00018] THIORIDAZINE HCl [00019] WORTMANNIN [00020] N-Acetyl-D- sphingosine CERAMIDE C2 PubChem CID: 5497136 [00021] C6-Ceramide [00022] D-e-MAPP [00023] D-NMAPPD (B13) [00024] [00025]

TABLE-US-00008 TABLE 5 NCBI Accession numbers of PP2A subunits. Name Gene Isoform Accession number version PP2A sub A alpha PPP2R1A NP_055040 NP_055040.2 PP2A sub A beta PPP2R1B A NP_002707 NP_002707.3 B NP_859050 NP_859050.1 C NP_859051 NP_859051.1 D NP_001171033 NP_001171033.1 E NP_001171034 NP_001171034.1 PP2A sub C alpha PPP2CA 1 NP_002706 NP_002706.1 2 NP_001341948 NP_001341948.1 PP2A sub C beta PPP2CB NP_001009552 NP_001009552.1 PP2A sub B56delta PPP2R5D 1 NP_006236 NP_006236.1 2 NP_851307 NP_851307 3 NP_851308 NP_851308.1 4 NP_001257405 NP_001257405.1

TABLE-US-00009 Standard name Systematic name IRC21 YMR073C RRD1 YIL153W TIP41 YPR040W PTC1 YDL006W SAP190 YKR028W PPM1 YDR435C TCO89 YPL180W TOR1 YJR066W

[0437] Further accession numbers of PP2A and PP2A-like may be found in Dvel K1, Broach J R. (2011). The role of phosphatases in TOR signaling in yeast. Curr Top Microbiol Immunol. 279:19-38, incorporated by reference and any ortholog thereof, preferably human ortholog.

[0438] TOR is a highly conserved protein kinase that is important in both fundamental and clinical biology. In fundamental biology, TOR is a nutrient-sensitive, central controller of cell growth and aging. In clinical biology, TOR is implicated in many diseases and is the target of the drug rapamycin. In the present invention TOR is any TOR as described in Loewith, R., and Hall, M. N. (2011). Target of rapamycin (TOR) in nutrient signaling and growth control. Genetics 189, 1177-1201, incorporated by reference and any ortholog thereof, preferably human ortholog.

REFERENCES

[0439] Alvaro, D., et al. (2007). PLoS Genet 3, e228. [0440] Aoyama, Y., et al. (1981). Biochim Biophys Acta 663, 194-202. [0441] Awasthi, P., et al. (2016). J Cell Sci 129, 1285. [0442] Awaya, J., et al. (1975). Biochim Biophys Acta 409, 267-273. [0443] Bartek, J., and Lukas, J. (2007). Current opinion in cell biology 19, 238-245. [0444] Bashkirov, V. I., et al. (2003). Molecular and cellular biology 23, 1441-1452. [0445] Bastos de Oliveira, F. M., et al. (2015). Molecular cell 57, 1124-1132. [0446] Bazzi, M., et al. (2010). Molecular and cellular biology 30, 131-145. [0447] Beck, T., and Hall, M. N. (1999). Nature 402, 689-692. [0448] Bermejo, R., et al. (2011). Cell 146, 233-246. [0449] Bernardi, P., and Azzone, G. F. (1981). J Biol Chem 256, 7187-7192. [0450] Blaszczynski, M., et al. (1985). Acta Microbiol Pol 34, 243-254. [0451] Chabes, A., et al. (1999). The Journal of biological chemistry 274, 36679-36683. [0452] Chen, L., et al. (2015). J Cell Sci 128, 421. [0453] Clemenson, C., and Marsolier-Kergoat, M. C. (2009). DNA repair 8, 1101-1109. [0454] Cliften, P., et al. (1996). Microbiology 142 (Pt 3), 477-484. [0455] Cocheme, H. M., and Murphy, M. P. (2009). Methods Enzymol 456, 395-417. [0456] Costanzo, M., et al. (2010). Science 327, 425-431. [0457] Cox, K. H., et al. (2004). The Journal of biological chemistry 279, 10270-10278. [0458] Crespo, J. L., et al. (2002). Proceedings of the National Academy of Sciences of the United States of America 99, 6784-6789. [0459] Davidson, J. F., et al. (1996). Proceedings of the National Academy of Sciences of the United States of America 93, 5116-5121. [0460] Desany, B. A., et al. (1998). Genes & development 12, 2956-2970. [0461] Di Como, C. J., and Arndt, K. T. (1996). Genes & development 10, 1904-1916. [0462] Dickson, R. C. (1998). Annual review of biochemistry 67, 27-48. [0463] Dobrowsky, R. T., et al. (1993). The Journal of biological chemistry 268, 15523-15530. [0464] Dozier, C., et al. (2004). Cell 96, 509-517. [0465] Dunn, T. M., et al. (1998). Yeast 14, 311-321. [0466] Duvel, K., et al. (2003). Molecular cell 11, 1467-1478. [0467] El Bawab, S., et al. (2001). The Journal of biological chemistry 276, 16758-16766. [0468] Fay, D. S., et al. (1997). Curr Genet 31, 97-105. [0469] Freeman, A. K., and Monteiro, A. N. (2010). Cell Commun Signal 8, 27. [0470] Gallego, O., et al. (2010). Mol Syst Biol 6, 430. [0471] Girotti, A. W. (1998). J Lipid Res 39, 1529-1542. [0472] Gonzalez, A., et al. (2009). Molecular and cellular biology 29, 2876-2888. [0473] Goodarzi, A. A., et al. (2004). The EMBO journal 23, 4451-4461. [0474] Guan, K., et al. (1992). The Journal of biological chemistry 267, 10024-10030. [0475] Guenole, A., et al. (2013). Molecular cell 49, 346-358. [0476] Haak, D., et al. (1997). The Journal of biological chemistry 272, 29704-29710. [0477] Harrison, J. C., and Haber, J. E. (2006). Annual review of genetics 40, 209-235. [0478] Healy, A. M., et al. (1991). Molecular and cellular biology 11, 5767-5780. [0479] Heideker, J., et al. (2007). Cell cycle 6, 3058-3064. [0480] Hilton, B. A., et al. (2015). Molecular cell 60, 35-46. [0481] Huang, M., et al. (1998). Cell 94, 595-605. [0482] Huang, X., et al. (2012). PLoS Genet 8, e1002493. [0483] Huber, A., et al. (2009). Genes & development 23, 1929-1943. [0484] Hughes Hallett, et al. (2014). Genetics 198, 773-786. [0485] Huh, W. K., et al. (2003). Nature 425, 686-691. [0486] Hustedt, N., et al. (2015). Molecular cell 57, 273-289. [0487] Jacinto, E., et al. (2001). Molecular cell 8, 1017-1026. [0488] Janssens, V., and Goris, J. (2001). The Biochemical journal 353, 417-439. [0489] Jiang, Y. (2006). Microbiol Mol Biol Rev 70, 440-449. [0490] Jiang, Y., and Broach, J. R. (1999). The EMBO journal 18, 2782-2792. [0491] Jordens, J., et al. (2006). The Journal of biological chemistry 281, 6349-6357. [0492] Julmanop, C., et al. (1993). J Gen Microbiol 139, 2323-2327. [0493] Keogh, M. C., et al. (2006). Nature 439, 497-501. [0494] Koh, J. L., et al. (2015). G3 (Bethesda) 5, 1223-1232. [0495] Kolaczkowski, et al. (2004). Eukaryotic cell 3, 880-892. [0496] Kontoyiannis, D. P. (2000). J Antimicrob Chemother 46, 191-197. [0497] Kumar, A., et al. (2014). Cell 158, 633-646. [0498] Kvam, E., et al. (2005). Molecular biology of the cell 16, 3987-3998. [0499] Lamb, D. C., et al. (1999). FEBS letters 462, 283-288. [0500] Laxman, S., et al. (2014). Sci Signal 7, ra120. [0501] Lee, K. S., et al. (1993). Molecular and cellular biology 13, 5843-5853. [0502] Lee, S. J., et al. (2003). Molecular and cellular biology 23, 6300-6314. [0503] Lee, W., et al. (2005). PLoS Genet 1, e24. [0504] Leroy, C., et al. (2003). Mol Cell 11, 827-835. [0505] Loewith, R., and Hall, M. N. (2011). Genetics 189, 1177-1201. [0506] Loewith, R., et al. (2002). Molecular cell 10, 457-468. [0507] Luke, M. M., et al. (1996). Molecular and cellular biology 16, 2744-2755. [0508] Madeira, J. B., et al. (2015). Molecular and cellular biology 35, 737-746. [0509] Mallory, J. C., et al. (2005). Molecular and cellular biology 25, 1669-1679. [0510] Mao, C., et al. (2000a). The Journal of biological chemistry 275, 6876-6884. [0511] Mao, C., et al. (2000b). The Journal of biological chemistry 275, 31369-31378. [0512] Matmati, N., et al. (2013). The Journal of biological chemistry 288, 17272-17284. [0513] Matsuoka, S., et al. (2007). Science 316, 1160-1166. [0514] Mitchell, A. G., and Martin, C. E. (1997). The Journal of biological chemistry 272, 28281-28288. [0515] Nakahata, S., and Morishita, K. (2014). Blood 124, 2163-2165. [0516] Neklesa, T. K., and Davis, R. W. (2008). Proceedings of the National Academy of Sciences of the United [0517] States of America 105, 15166-15171. [0518] Nickels, J. T., and Broach, J. R. (1996). Genes & development 10, 382-394. [0519] O'Neill, B. M., et al. (2007). Proceedings of the National Academy of Sciences of the United States of [0520] America 104, 9290-9295. [0521] Oaks, J., and Ogretmen, B. (2014). Front Oncol 4, 388. [0522] Ogawa, T., et al. (2016). Proceedings of the National Academy of Sciences of the United States of America. [0523] Oh, C. S., et al. (1997). The Journal of biological chemistry 272, 17376-17384. [0524] Orlova, M., et al. (2006). Eukaryotic cell 5, 1831-1837. [0525] Osumi, T., et al. (1979). J Biochem 85, 819-826. [0526] Paciotti, V., et al. (2001). Molecular and cellular biology 21, 3913-3925. [0527] Pedruzzi, I., et al. (2003). Molecular cell 12, 1607-1613. [0528] Pellicioli, A., et al. (1999). EMBO J 18, 6561-6572. [0529] Petranyi, G., et al. (1984). Science 224, 1239-1241. [0530] Poklepovich, T. J., et al. (2012). Steroids 77, 1313-1320. [0531] Qiu, J., et al. (1999). Molecular and cellular biology 19, 8361-8371. [0532] Ramachandran, V., and Herman, P. K. (2011). Genetics 187, 441-454. [0533] Rempola, B., et al. (2000). Molecular & general genetics: MGG 262, 1081-1092. [0534] Ronne, H., et al. (1991). Molecular and cellular biology 11, 4876-4884. [0535] Rossetto, D., et al. (2012). Epigenetics 7, 1098-1108. [0536] Rossi, S. E., et al. (2015). Cell Rep 13, 80-92. [0537] Rossler, H., et al. (2003). Mol Genet Genomics 269, 290-298. [0538] Sanchez, Y., et al. (1996). Science 271, 357-360. [0539] Santhanam, A., et al. (2004). Eukaryotic cell 3, 1261-1271. [0540] Schmidt, A., et al. (1998). The EMBO journal 17, 6924-6931. [0541] Shimura, T., et al. (2016). Oncotarget 7, 3559-3570. [0542] Shu, Y., et al. (1997). Mol Cell Biol 17, 3242-3253. [0543] Slater, M. L. (1973). J Bacteriol 113, 263-270. [0544] Sneddon, A. A., et al. (1990). The EMBO journal 9, 4339-4346. [0545] Sogo, J. M., et al. (2002). Science 297, 599-602. [0546] Staschke, K. A., et al. (2010). The Journal of biological chemistry 285, 16893-16911. [0547] Stock, S. D., et al. (2000). Antimicrob Agents Chemother 44, 1174-1180. [0548] Sun, Z., et al. (1996). Genes & development 10, 395-406. [0549] Sutter, B. M., et al. (2013). Cell 154, 403-415. [0550] Sutton, A., et al. (1991). Molecular and cellular biology 11, 2133-2148. [0551] Taguchi, N., et al. (1994). Microbiology 140 (Pt 2), 353-359. [0552] Takemoto, J. Y., et al. (1993). FEMS Microbiol Lett 114, 339-342. [0553] Tamura, Y., et al. (1976). Archives of biochemistry and biophysics 175, 284-294. [0554] Thomas, B. J., and Rothstein, R. (1989). Cell 56, 619-630. [0555] Tong, A. H., et al. (2001). Science 294, 2364-2368. [0556] Torres, J. Z., et al. (2004). Molecular and cellular biology 24, 3198-3212. [0557] Tripathi, K., et al. (2011). Genetics 189, 533-547. [0558] Urban, J., et al. (2007). Molecular cell 26, 663-674. [0559] Usui, T., et al. (2001). Molecular cell 7, 1255-1266. [0560] Van Hoof, C., et al. (2005). The Biochemical journal 386, 93-102. [0561] van Zyl, W., et al. (1992). IMolecular and cellular biology 12, 4946-4959. [0562] Wei, H., et al. (2001). The Journal of biological chemistry 276, 1570-1577. [0563] Wei, Y., and Zheng, X. F. (2009). Cell cycle 8, 4085-4090. [0564] Wong, P. M., et al. (2015). Nat Commun 6, 8048. [0565] Wu, J., et al. (2000). The EMBO journal 19, 5672-5681. [0566] Wu, W. I., et al. (1995). The Journal of biological chemistry 270, 13171-13178. [0567] Zhang, Z., et al. (1994). The Journal of biological chemistry 269, 16997-17000. [0568] Zhao, X., et al. (1998). Molecular cell 2, 329-340. [0569] Zhao, X., and Rothstein, R. (2002). Proc Natl Acad Sci USA 99, 3746-3751. [0570] Zheng, Y., and Jiang, Y. (2005). Molecular biology of the cell 16, 2119-2127. [0571] Aichberger, K. J., et al. (2005). Blood 105, 3303-3311. [0572] Akgul, C. (2009). Cell. Mol. Life Sci. 66, 1326-1336. [0573] Anisimov, V. N. (2014). Ann. Transl. Med. 2, 60. [0574] Articles, R. (2007). Alternate-day fasting and chronic disease prevention: a review of human and animal trials 1-3 INTRODUCTION. 7-13. [0575] Bennecib, M., et al. (2000). FEBS Lett. 485, 87-93. [0576] Beurel, E., and Jope, R. S. (2006). Prog. Neurobiol. 79, 173-189. [0577] Bijur, G. N., and Jope, R. S. (2000). J. Neurochem. 75, 2401-2408. [0578] Birsoy, K., et al. (2014). Nature 508 VN-, 108-112. [0579] Boisvert-Adamo, K., et al. (2009). Mol. Cancer Res. MCR 7, 549-556. [0580] Boisvert-adamo, K., et al. (2009). Mcl-1 Is Required for Melanoma Cell Resistance to Anoikis Mcl-1 Is [0581] Required for Melanoma Cell Resistance to Anoikis. 549-556. [0582] Bonanni, B., et al. (2012). Dual Effect of Metformin on Breast Cancer Proliferation in a Randomized Presurgical Trial. 30, 2593-2600. [0583] Cant, C., and Auwerx, J. (2011). Physiology 26, 214-224. [0584] Chance, B. (2005). Cancer Biol. Ther. 4, 125-126. [0585] Choi, Y. W., and Lim, I. K. (2014). Cancer Lett. 346, 300-308. [0586] Cho-Vega, J. H., et al. (2004). Hum. Pathol. 35, 1095-1100. [0587] Cohen, D. H., and LeRoith, D. (2012). Cancer 19, 27-45. [0588] Cohen, P., and Frame, S. (2001). Mol. Cell Biol. 2, 769-776. [0589] DeCensi, A., et al. (2014). Breast Cancer Res. Treat. 148, 81-90. [0590] Ding, Q., et al. (2007a). Cancer Res. 67, 4564-4571. [0591] Ding, Q., He, X., Xia, W., Hsu, J., Chen, C., Li, L., Lee, D., Yang, J., Xie, X., Liu, J., et al. (2007b). Myeloid Cell Leukemia-1 Inversely Correlates with Glycogen Synthase Kinase-3 B Activity and Associates with Poor Prognosis in Human Breast Cancer. 4564-4572. [0592] Dougan, S., et al. (2005). BMJ 330, 1303-1304. [0593] Dowling, R. J. O., Niraula, S., Stambolic, V., and Goodwin, P. J. (2012). Metformin in cancer: Translational challenges. J. Mol. Endocrinol. 48. [0594] Eichhorn, P. J. A., et al. (2009). Biochim. Biophys. ActaRev. Cancer 1795, 1-15. [0595] Elgendy, M. (2017). Mol. Cell. Oncol. 0, e1285385. [0596] Elgendy, M., and Minucci, S. (2015). Autophagy 11, 581-582. [0597] Elgendy, M., et al. (2014). Nat. Commun. 5, 5637. [0598] Frame, S., and Cohen, P. (2001). Biochem. J. 359, 1-16. [0599] Gandini, S., et al. (2014). Cancer Prev. Res. (Phila. Pa.) 7, 867-885. [0600] Garg, S. K., et al. (2014). Diabetes Obes. Metab. 16, 97-110. [0601] Gatenby, R. A., and Gillies, R. J. (2004). Nat. Rev. Cancer 4, 891-899. [0602] Germain, M., et al. (2011). EMBO J. 30, 395-407. [0603] Gores, G. J., and Kaufmann, S. H. (2012). Genes Dev. 26, 305-311. [0604] Gores, G. J., Kaufmann, S. H., Glaser, S. P., Lee, E. F., and Trounson, E. (2012). leukemia and solid tumors [0605] Selectively targeting Mcl-1 for the treatment of acute myelogenous leukemia and solid tumors. 305-311. [0606] Greenblatt, D. J., et al. (1977). J. Clin. Pharmacol. 29, 490-494. [0607] Gutierrez, A., et al. (2014). J. Clin. Invest. 124, 644-655. [0608] Haesen, D., Sents, W., Lemaire, K., Hoorne, Y., and Janssens, V. (2014). The Basic Biology of PP2A in [0609] Hematologic Cells and Malignancies. Front. Oncol. 4. [0610] Haesen, D., et al. (2016). Cancer Res. 76, 5719-5731. [0611] Hao, W., et al. (2010). J. Biol. Chem. 285, 12647-12654. [0612] Hongisto, V., et al. (2003). Mol. Cell. Biol. 23, 6027-6036. [0613] Houge, G., et al. (2015). J. Clin. Invest. 125, 3051-3062. [0614] Inuzuka, H., et al. (2011). Nature 471, 104-109. [0615] Jacobs, K. M., et al. (2012). Int. J. Cell Biol. 2012. [0616] Janssens, V., and Goris, J. (2001). Biochem. J. 353, 417-439. [0617] Janssens, V., et al. (2005). Curr. Opin. Genet. Dev. 15, 34-41. [0618] Jee, H. U., et al. (2007). J. Biol. Chem. 282, 20794-20798. [0619] Jiang, C. C., et al. (2008). Cancer Res. 68, 6708-6717. [0620] Jope, R. S., and Johnson, G. V. W. (2004). Trends Biochem. Sci. 29, 95-102. [0621] Jose, C., et al. (2011). Biochim. Biophys. ActaBioenerg. 1807, 552-561. [0622] Junttila, M. R., et al. (2007). Cell 130, 51-62. [0623] Kapfhamer, D., et al. (2010). J Neurosci 30, 8830-8840. [0624] Kasznicki, J., et al. (2014). Ann. Transl. Med. 2, 57. [0625] Khanna, A., et al. (2013). Cancer Res. 73, 6548-6553. [0626] Khoury, J. D., et al. (2003). J. Pathol. 199, 90-97. [0627] Kim, S. M., et al. (2016). J. Clin. Invest. 126, 4088-4102. [0628] King, T. D., et al. (2001). Brain Res 919, 106-114. [0629] Kumar, A., et al. (2012). Carcinogenesis 33, 1726-1735. [0630] Laplante, M., and Sabatini, D. M. (2012). Cell 149, 274-293. [0631] Lee, C., and Longo, V. D. (2011). Oncogene 30, 3305-3316. [0632] Lee, M., and Yoon, J.-H. (2015). World J. Biol. Chem. 6, 148-161. [0633] Lee, C., et al. (2012). Sci. Transl. Med. 4, 124ra27. [0634] Lin, C., Chen, C., Chiang, C., Jan, M., Huang, W., and Lin, Y. (2007a). GSK-3P3 acts downstream of [0635] PP2A and the PI 3-kinase-Akt pathway, and upstream of caspase-2 in ceramide-induced mitochondrial [0636] apoptosis. 2935-2943. [0637] Lin, C.-F., et al. (2007b). J. Cell Sci. 120, 2935-2943. [0638] Loberg, R. D., et al. (2002). J. Biol. Chem. 277, 41667-41673. [0639] Longo, V. D., and Mattson, M. P. (2014). Cell Metab. 19, 181-192. [0640] Louis, J. V., et al. (2011). Proc. Natl. Acad. Sci. 108, 6957-6962. [0641] Magiera, M. M., et al. (2012). Cell Death Differ. 20, 281-292. [0642] Martin, M. J., et al. (2012). Cancer Discov. 2, 344-355. [0643] Maurer, U., C et al. (2006). Mol. Cell 21, 749-760. [0644] Mitra, A., et al. (2012). Oncogene 31, 4472-4483. [0645] Morel, C., Carlson, S. M., White, F. M., and Davis, R. J. (2009). Mcl-1 Integrates the Opposing Actions of Signaling Pathways That Mediate Survival and Apoptosis . 29, 3845-3852. [0646] Mumby, M. (2007). Cell 130, 21-24. [0647] Oyesanya, R. A., Dasgupta, S., Dent, P., and Grant, S. (2012). Targeting Mcl-1 for the therapy of cancer. 20, 1397-1411. [0648] Pap, M., and Cooper, G. M. (1998). Role of Glycogen Synthase Kinase-3 in the Akt Cell Survival Pathway *. 19929-19932. [0649] Pap, M., and Cooper, G. M. (2002). Mol. Cell. Biol. 22, 578-586. [0650] Perciavalle, R. M., et al. (2012). Nat. Cell Biol. 14, 575-583. [0651] Perrotti, D., and Neviani, P. (2008). Cancer Metastasis Rev. 27, 159-168. [0652] Pollak, M. N. (2012). Cancer Discov. 2, 778-790. [0653] Qiu, X., et al. (2010). Cell Metab. 12, 662-667. [0654] Quinn, B. A., et al. (2011). Expert Opin. Investig. Drugs 20, 1397-1411. [0655] Raffaghello, L., et al. (2008). Proc. Natl. Acad. Sci. 105, 8215-8220. [0656] Ren, H., et al. (2013a). Mol. Cancer 12, 146. [0657] Ren, H., et al. (2013b). Cancer Lett. 338, 229-238. [0658] Research, A. A. for C. (2014). Activation of PP2A by Perphenazine Induces Apoptosis in T-ALL. Cancer [0659] Discov. 4, OF14-OF14. [0660] Robillard, N., Gomez, P., Moreau, P., Gouill, S. Le, Harousseau, J., and Amiot, M. (2005). Mcl-1 is [0661] overexpressed in multiple myeloma and associated with relapse and shorter survival. 45, 1248-1252. [0662] Rodrguez-enriquez, S., Marn-hernndez, A., Gallardo-prez, J. C., Carreo-fuentes, L., and Moreno-snchez, R. (2009). Review Targeting of cancer energy metabolism. 29-48. [0663] Safdie, M., Dorff, T., Quinn, D., Fontana, L., Wei, M., Cohen, P., and Longo, V. D. (2009). Fasting and cancer treatment in humans: A case series report. 1. [0664] Sambol, N.C., et al. (1996). J. Clin. Pharmacol. 36, 1012-1021. [0665] Sangodkar, J., et al. (2016). FEBS J. 283, 1004-1024. [0666] Sents, W., et al. (2013). FEBS J. 280, 644-661. [0667] Seshacharyulu, P., et al. (2013). Cancer Lett. 335, 9-18. [0668] Somervaille, T. C., et al. (2001). Blood 98, 1374-1381. [0669] Song, L., Sarno, P. De, and Jope, R. S. (2002). Central Role of Glycogen Synthase Kinase-3 in Endoplasmic Reticulum Stress-induced Caspase-3 Activation *. 277, 44701-44708. [0670] Suissa, S., and Azoulay, L. (2012). Diabetes Care 35, 2665-2673. [0671] Tsuji, S., et al. (2016). J. Vet. Med. Sci. 78, 1293-1298. [0672] Wang, R., et al. (2012). Leukemia 27, 315-324. [0673] Wang, Y., et al. (2015). Aging 36, 188-200. [0674] Warr, M. R., and Shore, G. C. (2008). Curr. Mol. Med. 8, 138-147. [0675] Westermarck, J., and Hahn, W. C. (2008). Trends Mol. Med. 14, 152-160. [0676] Wuillme-Toumi, S., et al. (2005). Leukemia 19, 1248-1252. [0677] Zhuang, Y., Chan, D. K., Haugrud, A. B., and Miskimins, W. K. (2014). Mechanisms by which low glucose enhances the cytotoxicity of metformin to cancer cells both in vitro and in vivo. PLoS ONE 9. [0678] Alexander, A., et al. (2010). Proc Natl Acad Sci USA 107, 4153-4158. [0679] Alvers, A. L., et al. (2009a). Aging Cell 8, 353-369. [0680] Alvers, A. L., et al. (2009b). Autophagy 5, 847-849. [0681] Andressoo, J. O., et al. (2006). Cell Cycle 5, 2886-2888. [0682] Aris, J. P., et al. (2013). Exp Gerontol 48, 1107-1119. [0683] Bazzi, M., et al. (2010). Mol Cell Biol 30, 131-145. [0684] Bertram, P. G., et al. (2002). Mol Cell Biol 22, 1246-1252. [0685] Bonawitz, N. D., et al. (2007). Cell Metab 5, 265-277. [0686] Braun, K. A., et al. (2014). Sci Signal 7, ra64. [0687] Burtner, C. R., et al. (2009). Cell Cycle 8, 1256-1270. [0688] Chen, Y., and Klionsky, D. J. (2011). J Cell Sci 124, 161-170. [0689] Cherkasova, V., et al. (2010). Mol Cell Biol 30, 2862-2873. [0690] Cherkasova, V. A., and Hinnebusch, A. G. (2003). Genes Dev 17, 859-872. [0691] Conn, C. S., and Qian, S. B. (2013). Sci Signal 6, ra24. [0692] Cosentino, G. P., et al. (2000). Mol Cell Biol 20, 4604-4613. [0693] Daly, M. J. (2012). DNA Repair (Amst) 11, 12-21. [0694] De Haes, W., et al. (2014). Proc Natl Acad Sci USA 111, E2501-2509. [0695] De Virgilio, C. (2012). FEMS Microbiol Rev 36, 306-339. [0696] Dever, T. E., et al. (1992). Cell 68, 585-596. [0697] Diderich, K., et al. (2011). DNA Repair (Amst) 10, 772-780. [0698] Donnelly, N., et al. (2013). Cell Mol Life Sci 70, 3493-3511. [0699] Dotiwala, F., et al. (2013). Proc Natl Acad Sci USA 110, E41-49. [0700] Eapen, V. V., and Haber, J. E. (2013). Autophagy 9, 440-441. [0701] Estruch, F., et al. (1992). Genetics 132, 639-650. [0702] Fabrizio, P., et al. (2004). FEBS Lett 557, 136-142. [0703] Fabrizio, P., et al. (2001). Science 292, 288-290. [0704] Fasolo, J., et al. (2011). Genes Dev 25, 767-778. [0705] Feng, J., et al. (2016). FEMS Yeast Res 16, fow009. [0706] Feng, Z., et al. (2007). Proc Natl Acad Sci USA 104, 16633-16638. [0707] Fiedler, D., et al. (2009). Cell 136, 952-963. [0708] Fiorani, S., et al. (2008). Cell Cycle 7, 493-499. [0709] Fontana, L., et al. (2010). Science 328, 321-326. [0710] Franzke, B., et al. (2015). Mutat Res Rev Mutat Res 766, 48-57. [0711] Freeman, A. K., et al. (2010). Cell Cycle 9, 736-747. [0712] Freeman, A. K., and Monteiro, A. N. (2010). Cell Commun Signal 8, 27. [0713] Galdieri, L., et al. (2010). Omics 14, 629-638. [0714] Gallinetti, J., et al. (2013). Biochem J 449, 1-10. [0715] Ghavidel, A., et al. (2007). Cell 131, 915-926. [0716] Gimeno-Alcaniz, J. V., and Sanz, P. (2003). J Mol Biol 333, 201-209. [0717] Goukassian, D., et al. (2000). Faseb J 14, 1325-1334. [0718] Hardie, D. G. (2014). Cell Metab 20, 939-952. [0719] Harrison, D. E., et al. (2009). Nature 460, 392-395. [0720] Hedbacker, K., and Carlson, M. (2008). Front Biosci 13, 2408-2420. [0721] Hinnebusch, A. G. (2005). Annu Rev Microbiol 59, 407-450. [0722] Hong, S. P., et al. (2003). Proc Natl Acad Sci USA 100, 8839-8843. [0723] Hu, J., et al. (2013). Methods Mol Biol 965, 463-472. [0724] Huber, A., et al. (2009). Genes Dev 23, 1929-1943. [0725] Hughes Hallett, J. E., et al. (2014). Genetics 198, 773-786. [0726] Hussain, S. G., and Ramaiah, K. V. (2007). Biochem Biophys Res Commun 355, 365-370. [0727] Jacinto, E., et al. (2001). Mol Cell 8, 1017-1026. [0728] Kaeberlein, M. (2010). Nature 464, 513-519. [0729] Keogh, M. C., et al. (2006). Nature 439, 497-501. [0730] Kingsbury, J. M., et al. (2014). Genetics 196, 1077-1089. [0731] Klermund, J., et al. (2014). Cell Rep 9, 324-335. [0732] Koga, H., et al. (2011). Ageing Res Rev 10, 205-215. [0733] Krisko, A., and Radman, M. (2013). PLoS Genet 9, e1003810. [0734] Kubota, H., et al. (2003). J Biol Chem 278, 20457-20460. [0735] Leroy, C., et al. (2003). Mol Cell 11, 827-835. [0736] Loewith, R., and Hall, M. N. (2011). Genetics 189, 1177-1201. [0737] Longo, V. D., et al. (2012). Cell Metab 16, 18-31. [0738] Longtine, M. S., et al. (1998). Yeast 14, 953-961. [0739] Lu, J. Y., et al. (2011). Cell 146, 969-979. [0740] Marteijn, J. A., et al. (2014). Nat Rev Mol Cell Biol 15, 465-481. [0741] Martin-Montalvo, A., et al. (2013). Nat Commun 4, 2192. [0742] McCormick, M. A., et al. (2015). Cell Metab 22, 895-906. [0743] Menacho-Marquez, M., et al. (2007). Cell Cycle 6, 2302-2305. [0744] Murguia, J. R., and Serrano, R. (2012). IUBMB Life 64, 971-974. [0745] Neecke, H., et al. (1999). Embo J 18, 4485-4497. [0746] O'Neill, B. M., et al. (2007). Proc Natl Acad Sci USA 104, 9290-9295. [0747] Orlova, M., et al. (2008). Yeast 25, 745-754. [0748] Powers, R. W., 3rd, et al. (2006). Genes Dev 20, 174-184. [0749] Powley, I. R., et al. (2009). Genes Dev 23, 1207-1220. [0750] Ptacek, J., et al. (2005). Nature 438, 679-684. [0751] Qiang, L., et al. (2016). Autophagy 12, 357-368. [0752] Reiling, J. H., and Sabatini, D. M. (2006). Oncogene 25, 6373-6383. [0753] Rempola, B., et al. (2000). Mol Gen Genet 262, 1081-1092. [0754] Reverter-Branchat, G., et al. (2004). J Biol Chem 279, 31983-31989. [0755] Robert, T., et al. (2011). Nature 471, 74-79. [0756] Rohde, J. R., et al. (2004). Mol Cell Biol 24, 8332-8341. [0757] Ruvolo, P., (2016). BBA Clin. 2016 December; 6: 87-99) [0758] Saha, A., et al. (2015). Cancer Prev Res (Phila) 8, 597-606. [0759] Selman, C., et al. (2009). Science 326, 140-144. [0760] Sertic, S., et al. (2012). Cell Cycle 11, 668-674. [0761] Smets, B., et al. (2008). FEMS Yeast Res 8, 1276-1288. [0762] Smith, D. L., Jr., et al. (2007). Aging Cell 6, 649-662. [0763] Steffen, K. K., and Dillin, A. (2016). Cell Metab 23, 1004-1012. [0764] Steffen, K. K., et al. (2008). Cell 133, 292-302. [0765] Steinkraus, K. A., et al. (2008). Annu Rev Cell Dev Biol 24, 29-54. [0766] Storici, F., and Resnick, M. A. (2006). Methods Enzymol 409, 329-345. [0767] Szyjka, S. J., et al. (2008). Genes Dev 22, 1906-1920. [0768] Tate, J. J., et al. (2009). J Biol Chem 284, 2522-2534. [0769] Thomas, B. J., and Rothstein, R. (1989). Genetics 123, 725-738. [0770] Tvegard, T., et al. (2007). Genes Dev 21, 649-654. [0771] Urban, J., et al. (2007). Mol Cell 26, 663-674. [0772] Vaidya, A., et al. (2014). PLoS Genet 10, e1004511. [0773] Viollet, B., et al. Clin Sci (Lond) 122, 253-270. [0774] Vlanti, A., et al. (2013). Aging (Albany N.Y.) 5, 584-585. [0775] Wang, Y., et al. (2014). Oncol Res 22, 193-201. [0776] Weinberger, M., et al. (2010). Aging (Albany N.Y.) 2, 709-726. [0777] Weinberger, M., et al. (2013). Cell Cycle 12, 1189-1200. [0778] Yao, Y., et al. (2015). PLoS Genet 11, e1004968. [0779] Young, E. T., et al. (2000). Gene 245, 299-309. [0780] Yu, G., et al. (2015). Oncotarget 6, 12748-12762. [0781] Zabrocki, P., et al. (2002). Mol Microbiol 43, 835-842. [0782] Zhang, J., et al. (2011). Mol Syst Biol 7, 545. [0783] Zhou, K., et al. (2011). Nat Genet 43, 117-120.