Screening method for micro-organisms and methods for the production of a product

Abstract

In one aspect the disclosure relations to means and methods for identifying a protein or a DNA encoding the protein, involved in the production of a product by a micro-organism. In the methods the micro-organism is cultured under different culture conditions each of which exhibit a different level of the product that is produced by the micro-organism. The genetic expression of the genes of the micro-organism is compared with the level of the product, and groups of DNAs are identified that are involved in the production of the product by the micro-organism.

Claims

1. A method for identifying a candidate protein, or a DNA encoding the candidate protein, said method comprising culturing said micro-organism under at least two different culture conditions, selecting from said different culture conditions at least two cultures in which the level of a product that is produced by said micro-organism is different than the other culture, preparing a protein and/or RNA sample from the selected cultures of micro-organisms, determining a sequence of at least part of the proteins and/or RNA in said samples, selecting sequences of proteins and/or sequences of RNA of which the amount differs between the samples of the selected cultures of micro-organisms, grouping selected sequences of proteins and/or sequences of RNA coded for by DNAs into a first group that comprises selected sequences that are separated by no more than 30 open reading frames (ORFs) on the genome of the micro-organism, grouping remaining selected sequences of proteins and/or sequences of RNA coded for by DNAs (if any) into a second group that comprises selected sequences that are separated by no more than 30 ORFs on the genome of the micro-organism group, identifying a group of selected sequences that contains the coding regions of at least two different RNAs or proteins of which the amount correlates with the level of the product that is produced by said micro-organism under said at least two different culture conditions, and identifying a protein or DNA that comprises a sequence of the identified group thereby identifying a candidate protein that is likely involved in the production of the product by said micro-organism.

2. A method according to claim 1, wherein said product is a metabolite or an enzyme.

3. A method according to claim 2, wherein said metabolite is a secondary metabolite.

4. The method according to claim 3, wherein said secondary metabolite is an antibiotic, an antibiotic resistance inhibitor, an anti-cancer compound, an enzyme-inhibitor, an antifungal, an antihelminthic, an immunostimulant, an immunosuppressant, an insecticide, or an herbicide.

5. The method according to claim 3, wherein the identity of the secondary metabolite is not known prior to preparing said samples.

6. The method according to claim 5, further comprising: identifying the secondary metabolite.

7. The method according to claim 1, wherein at least three cultures are selected in which the level of the product that is produced by the micro-organism is different in the different culture conditions.

8. The method according to claim 1, wherein said micro-organism belongs to the phylum Actinobacteria.

9. The method according to claim 1, wherein said culture conditions differ from each other in that the culture medium has a different pH at the start of the culture, the culture conditions differ in the presence, amount and/or type of soil in the culture, the culture conditions differ in the presence, amount and/or type of bacterial remains at the start of the culture, the culture conditions differ in amount or type of carbon source in the culture medium, the culture conditions differ in the amount or type of nitrogen source in the culture medium, the culture conditions differ in metal composition, the culture conditions differ in the presence, amount and/or type of a further micro-organism in the culture, the culture conditions differ in the temperature, and/or the culture conditions differ in the presence of a signal molecule.

10. The method according to claim 1, further comprising sequencing at least 50% of the genome of said micro-organism.

11. The method according to claim 1, further comprising isolating the identified gene from the genome of said micro-organism.

12. The method according to claim 11, further comprising: providing a micro-organism of a different species with said identified gene.

13. A method according to claim 12, comprising providing said micro-organism of a different species with the genes of a gene cluster comprising said identified gene.

14. The method according to claim 1, further comprising culturing said micro-organism or said micro-organism of a different species comprising the genes of a gene cluster comprising said identified gene.

15. A method for obtaining a product produced by a micro-organism, said method comprising: performing the method according to claim 3, and producing said secondary metabolite by said micro-organism or a micro-organism of a different species comprising the genes of a gene cluster comprising said identified gene and obtaining the produced product.

16. A method for identifying a protein, or a DNA encoding said protein, the method comprising: culturing a microorganism under at least two different culture conditions, selecting from the different cultures at least three cultures in which the production level of a product produced by the microorganism is different than the other, preparing a protein sample and/or RNA sample from each of the at least three selected cultures of microorganisms, sequencing at least part of the proteins and/or RNA in the samples, selecting sequences of proteins and/or sequences of RNA of which the amount differs between the samples of the selected cultures of microorganisms, grouping selected sequences of proteins and/or sequences of RNA encoded by DNAs into a first group comprising selected sequences separated by no more than thirty open reading frames (ORFs) on the microorganism's genome, grouping any remaining selected sequences of proteins and/or sequences of RNA encoded by DNAs into a second group comprising the selected sequences separated by no more than thirty ORFs on the microorganism's genome, and identifying a group of the selected sequences that contains the coding regions of at least two different RNAs or proteins of which the amount correlates with the production level of the product.

17. The method according to claim 16, wherein the product is a metabolite, enzyme, or secondary metabolite.

18. The method according to claim 16, wherein the product is an antibiotic, an antibiotic resistance inhibitor, an anti-cancer compound, an enzyme-inhibitor, an antifungal, an antihelminthic, an immunostimulant, an immunosuppressant, an insecticide, or an herbicide.

19. The method according to claim 16, wherein the microorganism belongs to the phylum Actinobacteria.

20. The method according to claim 19, wherein the microorganism is a Streptomyces bacterium.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1: Experimental approach for quantitative proteomics of the dasR and rok7B7 deletion mutants. Mutant and parent (WT) strain were grown in either .sup.14N or .sup.15N labeled cultures, and mixed for SDS-PAGE separation (a). Bands from SDS-PAGE gel were digested using trypsin and subjected to LC-MS/MS analysis. A typical MS spectrum for one peptide is shown (b). The label swap experiment is shown in grey.

(2) FIG. 2: Overlap of proteins that demonstrated significant changes in the dasR or rok7B7 null mutant as compared to the parent strain. Proteins were considered that demonstrated a statistically significant change (a) or proteins that demonstrated a statistically significant change of at least two-fold (b).

(3) FIG. 3: MALDI-ToF MS analysis of prodiginin production in S. coelicolor. Mycelial extracts of S. coelicolor parental strain M145 (WT), its deletion mutants dasR and rok7B7, and strain DM9, deficient in prodiginin synthesis, were subject to MALDI-TOF MS analysis. Production of prodoginins could be detected at m/z 392 and 394 as indicated by the shaded area.

(4) FIG. 4: MALDI-ToF MS analysis of mersacidin production. B. amyloliquefaciens HIL-Y85/54728 was grown on indicated media (production medium (PM), Lucia Broth (LB), or trypsinized soy broth (TSB)). a) After five days, spent media samples were subjected to MALDI-ToF MS analysis. Mersacidin production was observed when grown in PM only. The other peaks in the mass range shown corresponded to sodium and potassium adducts of these three isoforms.

(5) FIG. 5: Proteomining of Streptomyces sp. Che1. Streptomyces sp. Che1 was grown in liquid NMMP media for 5 days using 6 different additives: (A) NaOH to pH 9, (B) 25 mM N-acetylglucosamine, (C) 0.8% (w/v) Bacto peptone (Difco), (D) 0.5% (w/v) yeast extract, (E) 2% (w/v) NaCl, (F) 0.5% (w/v) soy flower. Supernatants were tested for antibiotics production using M. luteus as indicator strain (a). Protein levels in mycelia from five conditions (A-E) were compared using quantitative proteomics (b). Stable istope labeling was performed through dimethylation of tryptic peptides. Since this method allows the comparison of three samples simultaneously, two experiments were performed, using condition A as a shared condition.

(6) FIG. 6: MALDI-ToF MS analysis of culture supernatants of Streptomyces sp. Che1. After growth in conditions A-F as described in the legend of FIG. 5, culture supernatants were subject to MALDI-ToF MS analysis. Three isoforms of actinomycin (D, C2, and C3) could be identified for conditions A, C, and D, as indicated. The other peaks in mass range shown corresponded to sodium and potassium adducts of these three isoforms.

(7) FIG. 7: Proteomining of Streptomyces sp. HM151. Streptomyces sp. HM151 was grown in liquid NMMP media for 4 days using () no additive or with (B) 25 mM N-acetylglucosamine, (C) 0.8% (w/v) Bacto peptone (Difco), (D) 0.5% (w/v) yeast extract, (E) 1% (w/v) NaCl, added respectively. Supernatants were tested for antibiotics production using M. luteus as indicator strain.

(8) FIG. 8: Metabolomics analysis of Streptomyces sp. HM151. Five biological replicates of Streptomyces sp. HM151 were grown under the conditions as described for FIG. 7. .sup.1H-NMR spectra of EtOAc extracts of spent medium were subjected to partial least square modeling-discriminant analysis (PLS-DA) to obtain a score (a) and loading (b) plot. The ellipse represents the Hotelling T.sup.2 with 95% confidence. The arrow indicates the signal obtained for H-5 of naphthoquinone. c) HMBC NMR spectrum of condition C in the range of d 5.2-d 8.4 (horizontal axis for .sup.1H) and d 90-d 200 (vertical axis for .sup.13C). Again, the arrow indicates the signal obtained for H-5 of naphthoquinone.

(9) FIG. 9: Proteomining of Streptomyces sp. MBT-GE. Streptomyces sp. MBT-GE was grown in MM, SFM, MBT1, MBT2 or CM media for 3 days. Supernatants were tested for antibiotic production using M. luteus as indicator strain. Antibiotic production is visible as zone of clearing around the supernatant spots.

EXAMPLES

(10) Methods

(11) Strains and Growth Conditions

(12) Streptomyces coelicolor A3(2) M145 was obtained from the John Innes Centre strain collection. The dasR null mutant (SAF29) (Rigali et al., 2006) and rok7B7 null mutant (GAM33) (Swiatek et al., 2013) of wild-type S. coelicolor were described previously. B. amyloliquefaciens HIL-Y85/54728 was obtained from Novacta Biosystems (Welwyn Garden City, UK). Streptomyces strains Che1 and HM151 were obtained de novo from soil samples. All Streptomyces strains were grown as indicated according to routine methods (Kieser et al., 2000).

(13) S. coelicolor M145 and its congenic dasR and rok7B7 deletion mutants were grown in adapted NMMP medium for .sup.14N/.sup.15N-labeling (Swiatek et al., 2013). Samples were taken at late logarithmic phase when production of pigmented antibiotics became apparent. .sup.14N/.sup.15N-labelling experiments were performed in duplicate with a label swap to avoid that differences in media composition should affect the outcome of the proteomics experiments.

(14) A seed culture of B. amyloliquefaciens was grown in Tryptic Soy Broth (TSB) for 24 h as described (Appleyard et al., 2009), before transfer (1:50 (v/v)) to mersacidin production medium, LB, or fresh TSB. Cultures were grown for five days at 30 C. Proteomics samples were taken after 24 h as protein levels were too low after five days of growth.

(15) Streptomyces strains Che1 and HM151 were grown in liquid NMMP medium containing 1% (w/v) glycerol and 0.5% (w/v) mannitol as carbon sources for 4-6 days, using five different additives to create varying growth conditions: (), no additive, (A) NaOH to pH 9, (B) 25 mM N-acetylglucosamine, (C) 0.8% (w/v) Bacto peptone (Difco), (D) 0.5% (w/v) Bacto yeast extract (Difco), (E) 1 (HM151) or 2% (Che1) (w/v) NaCl. For antibiotic activity assays, Micrococcus luteus was spread on LB agar plates and 20 L, spent medium were placed on the plates. After growth at 30 C. 0/N, the growth inhibition zone was measured.

(16) MALDI-ToF MS Analysis

(17) In case of prefractionation of compounds, supernatants were acidified by adding trifluoroacetic acid to a concentration of 0.1% (v/v), and loaded on a Sep-Pak plus C18 cartridge (Waters). Stepwise elution was performed using 1 mL of 0-90% (v/v) acetonitrile in 0.1% (v/v) TFA. Fractions were concentrated using a vacuum concentrator. Spent medium or concentrated fractions were mixed 1:1 (v/v), or 1:10 (v/v) in case of B. amyloliquefaciens spent medium, with a saturated -cyano-4-hydroxycinnamic acid solution in 50% (v/v) acetonitrile/0.05% (v/v) trifluoroacetic acid, 1 L was spotted on a MALDI target plate, and samples were measured on a Bruker microflex LRF mass spectrometer in the positive ion reflectron mode using delayed extraction. For each spectrum, at least 1,000 shots were acquired at 60 Hz.

(18) Illumina Sequencing

(19) Illumina/Solexa sequencing on Genome Analyzer IIx was outsourced (ServiceXS, Leiden, the Netherlands). Hundred-nucleotide paired-end reads were obtained. Quality of the short reads was verified using FastQC located on the World Wide Web at bioinformatics.bbsrc.ac.uk/projects/fastqc/. Reads were trimmed to discard base-calls of low quality and filtered data were assembled using Velvet (Zerbino & Birney, 2008). The resulting contigs were analyzed using the GeneMark.hmm algorithm with the S. coelicolor genome as model for ORF finding (Lukashin & Borodovsky, 1998).

(20) Proteomics Sample Preparation

(21) Mycelia or Bacillus cells were harvested by centrifugation, washed, and sonicated for 5 min at 12 W output power using 5 s on/5 s off intervals in 100 mM Tris/HCl (pH 7.5), 10 mM MgCl.sub.2, 5 mM dithiothreitol (DTT). Debris was removed by centrifugation at 16,000 g for 10 min at 4 C. Protein concentration of the extracts was determined using a Bradford protein assay, using BSA as standard.

(22) .sup.14N-labeled and .sup.15N-labeled mycelial extracts were mixed 1:1 for protein content, and proteins separated on SDS-PAGE, followed by in gel-digestion, all as described (Swiatek et al., 2013). In-solution digestion and dimethyl labeling of Chet, HM151, and Bacillus extracts were performed as described (Gubbens et al., 2012), using 0.167 mg of total protein per sample. Labeled peptides were mixed 1:1:1 to yield mixtures containing 0.5 mg of protein each. Acetonitrile was removed using a vacuum concentrator and peptides were dissolved in 0.6 mL SCX buffer A for fractionation by Strong Cationic Exchange (SCX) on a polysulfoethyl A column (PolyLC, 1002.1 mm, particle size 5 m, average pore size 200 , column volume (CV) 0.346 ml). Mobile phases were: SCX A (10 mM KH.sub.2PO.sub.4, 20% acetonitrile, pH 3) and SCX B (10 mM KH.sub.2PO.sub.4, 20% acetonitrile, 0.5 M KCl, pH 3). Peptides were fractioned at a flow rate of 250 l/min with a gradient of 0-18% SCX B in 18 CV (HM151) or 30 CV (Che1), 18-30% SCX B in 6 CV, and 30-100% SCX B in 5 CV. In total, 24 (HM151) or 32 (Che1) peptide fractions were collected for LC-MS analysis.

(23) LC-MS/MS Proteomics Analysis

(24) LC-MS/MS analysis on an LTQ-Orbitrap (Thermo, Waltham, Mass.) for both gel-extracted peptides (.sup.14N/.sup.15N labeling) (Florea et al., 2010) and SCX fractions (dimethyl labeling) (Gubbens et al., 2012) was performed as described, respectively.

(25) Data analysis of .sup.14N/.sup.15N labeled samples using MSQuant (Mortensen et al., 2010) has been described elsewhere (Swiatek et al., 2013). Data analysis of dimethyl labeled samples was performed using MaxQuant 1.2.2.5 (Cox & Mann, 2008) as described (Gubbens et al., 2012). For B. amyloliquefaciens HIL-Y85/54728, the B. amyloliquefaciens FZB42 complete proteome set (Uniprot 2012_10) with 98.5% sequence identity (Herzner et al., 2011), was appended with the ten mersacidin-producing proteins annotated for B. amyloliquefaciens HIL-Y85/54728 (Uniprot). For the Streptomyces strains Che1 and HM151, ORFs identified by Genemark.hmm were translated to obtain a protein database, and the two mixtures obtained for each strain were analyzed in one MaxQuant run. Normalized protein expression ratios were split in three equally-sized quantiles (up, unchanged, or down). Expression ratio filtering was based on selection of the expected quantile for each comparison.

(26) NMR-Based Metabolomic Analysis

(27) For each condition, 20 mL spent medium of five biological replicates was liquid-liquid partitioned using the same amount of EtOAc. This was repeated two times, after which the combined EtOAc fractions were evaporated by rotary evaporator at 40 C. and reconstituted in 1 mL of CH.sub.3OH-d.sub.4 (CortecNet, Voisins Le Bretonneux, France).

(28) NMR parameters have been described previously (Kim et al., 2010). 1D-.sup.1H NMR spectra, 2D J-resolved spectra as well as .sup.1H.sup.1H homonuclear and inverse detected .sup.1H.sup.13C correlation experiments were recorded at 25 C. on a Bruker 500 MHz DMX NMR spectrometer (500.13 MHz proton frequency) equipped with TCI cryoprobe and Z-gradient system. CD.sub.3OD was used for internal lock purposes. 128 scans of a standard one-pulse sequence with 30 flip angle for excitation and presaturation during 1.5 s relaxation delay with an effective field of gB.sub.1=50 Hz for suppression of the residual H.sub.2O signal was employed. For heteronuclear multiple bond correlation (HMBC), spectra were measured on Bruker 600 MHz DMX NMR spectrometer (600.13 MHz for proton and 150.13 MHz for .sup.13C frequency) equipped with cryoprobe. A data matrix of 3002048 points covering 33201.96265.6 Hz was recorded with 256 scans for each increment. A relaxation delay of 1.5 s and a coherence transfer delay optimized for a long range coupling of 8 Hz were applied. Data was linear predicted to 6002048 points using 32 coefficients prior to echo-anti echo type 2D Fourier transformation and a sine bell shaped window function shifted by p/2 in the F1 dimension and p/6 in the F2 dimension was applied. The final spectrum was obtained by magnitude calculation along the F2 dimension.

(29) For data processing of multivariate data analysis .sup.1H NMR were automatically reduced to ASCII files using AMIX (v. 3.7, Bruker Biospin). Spectral intensities were scaled to TMSP and reduced to integrated regions of equal width (0.04 ppm) corresponding to the region of d 0.3-d 10.00. The region of d 4.7-d 5.0 and d 3.28-d 3.34 were excluded from the analysis because of the residual signal of H.sub.2O and CH.sub.3OH-d.sub.4, respectively. Partial least square-discriminant analysis (PLS-DA) was performed with the SIMCA-P software (v. 13.0, Umetrics, Ume, Sweden) with unit variance (UV) scaling methods.

(30) Results

(31) Filamentous micro-organisms are widely used as industrial producers of products such as antibiotics, anticancer agents, antifungicides and enzymes (Bennett, 1998, Demain, 1991, Hopwood et al., 1995). These organisms include the eukaryotic filamentous fungi (ascomycetes) and the prokaryotic actinomycetes (e.g., Amycolatopsis, Nocardia, Thermobifido and Streptomyces). The market capitalization for antibiotics and enzymes totals around 28 and 2 billion dollars per year, respectively. Once a product of interest has been discovered, it is typically a long and painstaking process to identify the gene (cluster) that codes for the biosynthetic machinery, in particular considering the large number of such clusters found in these bacteria. Therefore, a new method that allows the rapid linkage between gene (cluster) and product of interest is highly desirable from a biotechnological and cost perspective.

(32) The proteomining concept we have developed is based on the analysis of the production of a compound or protein of interest under conditions where production fluctuates, and the analysis of the concomitant changes in global expression profiles of the mRNA and/or proteome pool. We demonstrated previously that DasR globally represses antibiotic production in actinomycetes, and that deletion of the dasR gene (SCO5231 on the S. coelicolor genome) results in the relieve of this repression, resulting in the enhanced production of natural products (Rigali et al., 2008, Craig et al., 2012). We recently noticed another regulator that is involved in the control of antibiotic production, namely Rok7B7, encoded by SCO6008 on the S. coelicolor genome (Swiatek et al., 2013). These genes form ideal targets in approaches to obtain global changes in the production of antibiotics and other natural products.

(33) To study the effect of the global changes regulatory proteins DasR and Rok7B7 on protein expression, S. coelicolor M145 and its congenic dasR and rok7B7 deletion mutants were grown in liquid minimal media containing either .sup.14N or .sup.15N as the sole nitrogen source, until late logarithmic phase when production of pigmented antibiotics became apparent. All experiments were performed in duplicate with a label swap to avoid that differences in media composition should affect the outcome of the proteomics experiments (FIG. 1a). .sup.14N and .sup.15N-labeled proteins were extracted from the mycelium, mixed in a roughly 1:1 molar ratio, separated by SDS-PAGE, and in-gel digested with trypsin (FIG. 1a). Digests were analyzed by LC-MS/MS on an LTQ-orbitrap mass spectrometer with the orbitrap analyzer enabling high resolution quantitation of peptide intensity ratios (FIG. 1b). .sup.15N-incorporation was 99% based on the shape of the isotopical envelope.

(34) After elimination of all proteins that did not show the same response in the label swap, 346 proteins were found that demonstrate significantly changed levels in the dasR and/or rok7B7 null mutants (FIG. 2a). There is a substantial overlap between the significantly changed proteins in the dasR and rok7B7 deletion mutants (27%). However, when only the 97 proteins were considered whose levels changed at least two-fold, the overlap between both deletion mutants was reduced to only 11 proteins (FIG. 2b), whereas SCO3286 demonstrated opposite changes.

(35) Excitingly, the most strongly differentially expressed proteins included a large number of proteins involved in secondary metabolite production (Table 1). Proteins involved in the production of calcium-dependent antibiotic (CDA; SCO3230-3232, SCO3236), for undecylprodigiosin and other prodiginins (SCO5878-5896, eight proteins detected), and for the production of a yet uncharacterized non-ribosomal peptide (SCO6431, SCO6436) demonstrated increased expression levels in the rok7B7 mutant. Surprisingly, deletion of dasR resulted in reduced expression of the biosynthetic machinery for these secondary metabolites. However, in line with the previously described repression of the cpk gene cluster by DasR (Rigali et al., 2008), expression of Cpk biosynthetic proteins (SCO6272-6292, seven proteins detected) was highly upregulated in the dasR mutant, and the same was observed for the rok7B7 mutant. Both mutants also demonstrated strongly increased expression of biosynthetic proteins for the siderophores coelichelin (SCO0492, SCO0494, SCO0498, and SCO0499, two- to five-fold upregulated) and desferrioxamine (SCO2782 and SCO2785, two to eight-fold upregulated). These compounds bind extracellular iron, allowing their import via dedicated ABC transporters (Barona-Gomez et al., 2006, Patel et al., 2010). A component of one of these transporters, CdtC (SCO7400) was also upregulated (two- to three-fold) in both mutants.

(36) To analyze if indeed there was a direct correlation between the expression of the biosynthetic proteins and natural product formation, mycelial (biomass) and spent medium (supernatant) samples obtained from the same cultures as those used for the proteomics samples (Table 1) were analyzed by MALDI-ToF MS. Prodiginin (m/z 392 and 394) was readily detected in mycelial extracts (FIG. 3) and was found to be virtually absent in the dasR null mutant (<10% of wild-type levels) and the DM9 strain which is deficient in prodiginin production. Prodiginin levels were approximately four-fold higher in the rok7B7 mutant, compared to the parental strain. This is in perfect agreement with the proteomics data presented in Table 1, strongly suggesting that indeed protein expression levels can be directly correlated to the amount of secondary metabolite that is produced by the respective biosynthetic clusters.

(37) We then wondered if this observation could be extended to different types of secondary metabolites and to other microorganisms. Therefore, we analyzed Bacillus amyloliquefaciens HIL-Y85/54728 as a second test system, which produces the lantibiotic mersacidin. Lantibiotics are ribosomally encoded peptides that are subsequently modified via among others lanthionine-type thioether crosslinking (Willey & van der Donk, 2007). Mersacidin is a type-B lantibiotic, the synthesis of which is encoded by a gene cluster consisting of ten ORFs (Altena et al., 2000). MALDI-ToF MS analysis of spent medium revealed that it was produced when B. amyloliquefaciens was grown in a synthetic production medium but not in the rich media TSB or LB (FIG. 4). Protein extracts were prepared from the same cultures and expression profiles correlated to the levels of mersacidin. Since .sup.15N metabolic labeling could not be easily performed in this case, dimethyl labeling of peptides (Boersema et al., 2009) was used for quantitative proteomics. Labeled peptides were first fractionated by SCX-HPLC, followed by LC-MS analysis of each fraction. This resulted in the quantification of expression levels of six of the ten mersacidin producing proteins, including the prepeptide MrsA (Table 2). Expression of all proteins was upregulated in the production medium, exemplified by the immunity proteins MrsF and MrsG, and modification protein MrsM (at least twenty-fold upregulated). The cluster-specific regulator MrsR2 was less strongly upregulated (less than two-fold) than the biosynthetic proteins, which is in line with previous observations (van Wezel & McDowall, 2011).

(38) These surprising observations provide important leads for a new and very effective way to connect natural products to its biosynthetic gene cluster. When strains are grown under different growth conditions, production of the secondary metabolite will fluctuate, and along with it the biosynthetic proteins responsible for its production. With the genome sequence known, the proteome can be directly connected to the genome. Therefore, we hypothesized that if a sufficiently large number of different growth conditions is chosen, correlation of expression profiles allows the identification of unique combinations of proteins and metabolites. As an additional constraint, the correlation should preferentially identify multiple biosynthetic proteins encoded by an apparent gene cluster. In this way, proteomics may be used to identify which proteins are responsible and, therefore, which gene cluster belongs to a metabolite of interest, even in a previously uncharacterized organism. We designate this conceptual drug discovery pipeline proteomining.

(39) As a proof of principle, we isolated an uncharacterized Streptomyces strain from forest soil that could produce a yellow pigment with strong antimicrobial activity. This strain was designated Streptomyces sp. Che1 and was grown in NMMP for 5 days using six different additives to create varying growth conditions: (A) NaOH to pH 9, (B) 25 mM N-acetylglucosamine, (C) 0.8% (w/v) Bacto peptone (Difco), (D) 0.5% (w/v) yeast extract, (E) 2% (w/v) NaCl, (F) 0.5% (w/v) soy flower. Culture supernatants displayed strong variation in the degree of yellow pigmentation, indicative of strong variation in the production of the compound of interest. Each culture supernatant was tested for antibiotic activity against M. luteus (FIG. 5a). Supernatants obtained after growth under condition A had highest antimicrobial activity (hallo size of 27.5 mm), conditions C and D resulted in medium sized halos (14.5 mm and 11.5 mm, respectively), while conditions B, E, and F did not induce detectable antimicrobial activity. The antimicrobial activity of the extracts was directly proportional to the degree of yellow pigmentation.

(40) The genome sequence of Streptomyces sp. Che1 was obtained using a single run of paired end Illumina sequencing (100 bp reads) and the output was assembled in 919 contigs. Open reading frames were predicted using the genemark algorithm and a database of 8,812 putative (and possibly partial) protein sequences was derived, and served as the reference databases for proteomics. Because only three different labels are available in dimethyl labeling, the samples were compared in two independent quantitative proteomics experiments with one sample in common (A,B,C and A,D,E, respectively), and each experiment containing at least a sample of high activity, and a sample of low activity (FIG. 5b). Protein quantifications of the two experiments were combined, resulting in the identification of 2,645 proteins for Che1, with 1,863 proteins quantified in all comparisons with at least three independent events.

(41) To select proteins of interest, for each comparison, the expression ratios were divided into three similarly sized quantiles: upregulated, downregulated or unchanged. We applied filtering to the four comparisons that included the proteome for the culture grown under condition A, since this culture demonstrated very high activity as compared to the others. In total, seven contigs with at least five matching ORFs clustered together (<10 non-matching or undetected ORFs in between) were selected (Table 3). Closer inspection of these contigs by annotation based on BLAST similarity searches and antiSMASH biosynthesis cluster identification (Medema et al., 2011) suggested that five contained features related to natural product biosynthesis (Table 3), while the other two contained genes for the NADH-dehydrogenase complex. (Keller et al., 2010). In conclusion, proteomining yielded four potential secondary metabolites that correlated to the antimicrobial activity, namely actinomycin (Keller et al., 2010) (Genbank accession HM038106, 48 kb, two contigs), nonactin (Walczak et al., 2000) (Genbank accessions AF263011, 16 kb and AF074603, 16 kb), skyllamycin (Pohle et al., 2011) (Genbank accession JF430460, 87 kb), and an unknown NRPS product, the biosynthetic gene cluster of which was also found in Streptomyces species W007 (Genbank accession AGSW0100016). Since several of the candidate natural products were already known, MALDI-ToF MS analysis allowed positive identification of the bioactive compound. Three mono-isotopic masses (1,255 Da, 1,269 Da, and 1,283 Da; FIG. 6) corresponded exactly to the masses expected for actinomycin C2, C3 and D (C1) (Keller et al., 2010), and these were confirmed by proton NMR (data not shown). The additional higher molecular weight peaks were most probably Na+ or K+ adducts of these (+22 Da, and +38 Da, respectively). Signal intensities for all these peaks were strictly coregulated between conditions and demonstrated high correlation to antibiotic activity (high signal for condition A, low signal for conditions C and D, and no detectable signal for the other conditions). This strongly suggested that the observed antimicrobial activity corresponded to actinomycin.

(42) By using the known sequence of the actinomycin cluster as input, we could validate the accuracy of our technology, which was based on a single next generation sequencing run. Additional contigs (237, 414, 925, 793, and 1020, Table 4) were positively mapped to the actinomycin gene cluster by using the Nucmer algorithm (Kurtz et al., 2004), increasing sequence coverage of the cluster to 91%. With one exception, the additional ORFs also matched our filter criteria, but were not identified previously due to the fact that the contigs were too small (less than five ORFs) or, in case of contig 793, contained only a minor section of the actinomycin production cluster. Interestingly, when the threshold value was lowered to three matching ORFs per cluster, contig 237 was the only additional identified contig that clearly coded for biosynthetic activity (not shown). In total, the products of 28 potential ORFs were detected in our experiment (Table 4), 18 of which matched the expected expression pattern. This demonstrates that the assembled contigs obtained from a single run of next generation sequencing (Illumina paired end) provided more than sufficient information for the positive identification of the biosynthetic machinery responsible for actinomycin production.

(43) To further corroborate the applicability of the proteomining concept, we analyzed a second previously undescribed soil isolate, designated Streptomyces sp. HM151. Supernatants from cultures grown for four days under conditions C and E contained strong antimicrobial activity, while in cultures grown under condition D had slightly lower activity (FIG. 7). Minute activity was observed when no additive was used () and no detectable activity for growth under condition B.

(44) Sequencing of HM151 yielded 396 contigs coding for 8,449 potential protein sequences. Protein expression profiles of cultures grown under conditions (), C, and E (experiment 1), and B, C, and D (experiment 2) were compared by quantitative proteomics (FIG. 5b), yielding 2,132 protein identifications, with 1,087 proteins quantified in all comparisons. Similar filtering as described for Streptomyces sp. Che1 was applied to Streptomyces sp. HM151, using the four comparisons of conditions C and E with high antimicrobial activity to the other conditions with reduced or no activity. With three clustered matching hits, the only candidate stretch of the HM151 genome was found in contig 561 between ORFs 118-122 (Table 5). BLAST analysis revealed the candidate cluster to be highly similar (>98%) to a polyketide producing gene cluster in Streptomyces antibioticus (Genbank accession Y19177) (Colombo et al., 2001) that codes for the enzymes involved in the first, shared, steps in the synthesis of benzoisochromanequinones, a class of compounds that includes actinorhodin, granaticin, and medermycin (Hopwood, 1997, Ichinose et al., 2003).

(45) To link benzoisochromanequinone synthesis under different growth conditions to the observed bioactivity, NMR-based metabolomics (Kim et al., 2010) was applied to EtOAc extracts of spent medium. .sup.1H-NMR spectra of five replicates of each condition were analyzed by partial least square modeling-discriminant analysis (PLS-DA, FIGS. 8a and 8b). As shown by the score plot, conditions C and E were found to be quite distinguished from other conditions (FIG. 8a). Main contributors to this difference were several phenolic resonances (FIG. 8b). Particularly, the resonance in 7.5- 7.6 was identified as an H-5 of naphthoquinone type compounds, which was confirmed by the correlation between H-5 and C4 in a heteronuclear multiple bonds correlation (HMBC) spectrum (FIG. 8c). These data strongly support the synthesis of a medermycin-like compound under conditions C and E, in agreement with our proteomining results.

(46) To further corroborate the applicability of the proteomining concept, we analyzed a third strain designated Streptomyces sp. MBT-GE. Best results for this strain were obtained when comparing growth on MM, CM, SFM (all according to Kieser et al., 2000), MBT1 and MBT2 media. MBT1 contains glucose (10 g/l), soy flour (10 g/l) and NaCl (5 g/l) pH 7.5. MBT2 contains soy flour (10 g/l), glucose (25 g/l), peptone (4 g/l), NaCl (2.5 g/l) and CaCO3 (5 g/l), adjusted to pH7.6. Supernatants from cultures grown for three days in MBT1, MBT2 or SFM contained strong antimicrobial activity, while supernatants from cultures grown in MM or CM contained no detectable activity (FIG. 9).

(47) Genome sequencing of MBT-GE yielded 1,585 contigs coding for 8,532 potential protein sequences. Protein expression profiles of cultures grown in MM, MBT1, and MBT2 (experiment 1), and SFM, MBT2, and CM (experiment 2) were compared by quantitative proteomics (FIG. 5b), yielding 2,223 protein identifications, with 1,364 proteins quantified in all comparisons. Again, similar filtering as described for Streptomyces sp. Che1 and Streptomyces sp. HM151, using the four comparisons between high activity and low activity, was applied. Two candidate contigs were identified containing four and six matching ORFs, respectively. Using BLAST analysis, both contigs were found to be >99% identical to parts of the daunorubicin synthesis cluster of Streptomyces peucetius.

(48) In conclusion, the proteomining technology provides a novel concept for the connection of a bioactivity to a gene or gene cluster, using a proteomics approach combined with a (partial) genome sequence. Correlation between bioactivity assays and protein expression profiles under different growth conditions that allow the differential production of the natural product of interest is an efficient way to identify the gene (cluster) responsible for its production. Since this method does not require pre-identification of genes of interest, it should also allow identification of completely new types of natural products, even if the genes have no similarity to any natural product that has been identified so far. We expect that the proteomining technology will facilitate the identification of novel compounds of high medical relevance, such as antibiotics for treatment of the rapidly emerging multidrug resistant pathogens, and anticancer compounds.

(49) TABLE-US-00001 TABLE 1 Expression level changes of proteins involved in secondary metabolite synthesis in S. coelicolor. .sup.2log ratio (mutant/wt).sup.a DasR Rok7B7 SCO.sup.b name.sup.b function/pathway.sup.b 1.7 2.0 SCO0492 cchH coelichelin synthesis 1.0 1.4 SCO0494 cchF coelichelin synthesis 2.3 2.4 SCO0498 cchB coelichelin synthesis 1.7 1.9 SCO0499 cchA coelichelin synthesis 2.8 1.0 SCO2782 DesA desferrioxamine synthesis 3.0 1.9 SCO2785 DesD desferrioxamine synthesis 1.4 4.5 SCO3230 cdaPS1 CDA synthesis 1.1 4.2 SCO3231 cdaPS2 CDA synthesis 4.7 SCO3232 cdaPS3 CDA synthesis 3.9 SCO3236 asnO CDA synthesis 3.5 SCO3334 TrpS1 Antibiotic resistance 1.4 SCO5878 redX prodiginin synthesis 1.7 SCO5879 redW prodiginin synthesis 1.9 SCO5888 fabH3 prodiginin synthesis 1.8 1.9 SCO5890 prodiginin synthesis 2.1 SCO5891 redM prodiginin synthesis 2.1 SCO5892 prodiginin synthesis 2.2 1.9 SCO5895 prodiginin synthesis 2.0 SCO5896 prodiginin synthesis 0.1 1.6 SCO6431 NRPS cluster 0.6 2.0 SCO6436 NRPS cluster 2.6 7.8 SCO6272 cpk cluster 3.1 5.6 SCO6273 cpkC cpk cluster 2.8 6.6 SCO6274 cpkB cpk cluster 2.9 6.1 SCO6275 cpkA cpk cluster 3.3 6.2 SCO6276 cpk cluster 3.5 6.2 SCO6279 cpk cluster 3.5 2.8 SCO6282 cpk cluster 1.5 0.9 SCO7400 cdtC siderophore uptake .sup.aprotein expression level changes expressed as signal intensity in dasR or rok7B7 deletion mutant vs. signal intensity in parent strain (wt). Data are the average of two experiments, with one experiment using opposite labeling compared to the other experiment (label swap). Italic numbers indicate that the ratio could only be determined in of the two experiments or demonstrated opposing signs between the two experiments. These numbers are included only if the same protein could be quantified (detected in both experiments with same sign) for the other deletion mutant. .sup.bAnnotation based on StrepDB located on the World Wide Web at strepdb.streptomyces.org.uk.

(50) TABLE-US-00002 TABLE 2 Proteomics analysis of mersacidin production. normalized .sup.2log ratios.sup.a TSB/ PM/ PM/ gene LB LB TSB MrsG 0.3 4.9 4.4 MrsR2 0.4 0.9 0.4 MrsF 0.4 5.0 5.7 MrsM 0.1 5.1 5.6 Mrs T 1.0 3.6 2.1 MrsA 1.6 3.4 1.3 .sup.aB. amyloliquefaciens HIL-Y85/54728 was grown on indicated media (production medium (PM), Lucia Broth (LB), or trypsinized soy broth (TSB)). Protein extracts after one day of growth were subjected to proteomics analysis. All six detected proteins involved in mersacidin production demonstrated elevated levels in production medium.

(51) TABLE-US-00003 TABLE 3 Candidate clusters demonstrating expected expression level changes in Streptomyces sp. Che1. ORFs first last in Anti- contig.sup.a ORFs ID Match ORF.sup.b ORF cluster SMASH.sup.c Blast analysis.sup.d 42 30 9 5 3 8 6 + Streptomyces sp. W007, contig 00173 412 12 9 6 1 6 6 NADH dehydrogenase/ complex 1 419 19 13 6 1 8 8 NADH dehydrogenase/ complex 1 814 34 23 17 12 32 21 + nonactin 816 11 8 6 1 8 8 + actinomycin 981 33 12 5 1 12 12 + actinomycin 1256 26 13 8 3 20 18 + skyllamycin .sup.aProtein expression level changes for the protein products were compared between growth conditions (A-E, see main text and FIG. 5). Expression ratios were divided in three equally sized quantiles for each comparison and filtered based on the four comparisons with the largest change in antibacterial activity (see main text). Contigs with at least 5 matching ORFs in a cluster (max gap <10 ORFs) were selected. .sup.bThe region between the first matching ORF and last matching ORF was defined as a cluster as to compare the number of matching ORFs to the number of ORFs in the cluster. .sup.cContigs were analyzed with antiSMASH (Medema et al., 2011) for the presence of secondary metabolite biosynthesis clusters. A hit is indicated with +. .sup.dSequences were compared using BLAST analysis to known streptomycetes sequences in the NCBI nr/nt and WGS (genomic shotgun sequences) databases. Hits with more than 95% identity were used for annotation.

(52) TABLE-US-00004 TABLE 4 Expression level changes of ORFs coding for actinomycin biosynthesis. Normalized ratios (2log).sup.a Quantiles.sup.b contig ORF gene.sup.d B/A C/A C/B D/A E/A E/D B/A C/A C/B D/A 981 23 AcmrC 2.8 0.0 2.4 3.5 3.3 0.3 Q1 Q2 Q3 Q1 981 22 AcmrB 3.1 0.3 2.8 2.9 2.0 1.9 Q1 Q2 Q3 Q1 981 21 AcmrA 3.6 0.3 3.2 4.2 3.6 0.9 Q1 Q2 Q3 Q1 981 20 AcmQ 2.8 0.9 3.3 3.2 1.7 1.4 Q1 Q3 Q3 Q1 981 19 AcmQ 2.0 1.0 2.9 3.6 2.6 0.9 Q1 Q3 Q3 Q1 981 18 AcmP 1.7 0.9 2.4 1.3 1.6 0.1 Q1 Q3 Q3 Q2 981 12 2.8 2.2 0.4 2.1 3.8 0.8 Q1 Q1 Q3 Q1 981 11 2.6 2.3 0.4 3.5 3.0 0.3 Q1 Q1 Q3 Q1 981 8 2.5 1.2 1.2 3.7 3.0 0.9 Q1 Q1 Q3 Q1 981 4 3.6 3.1 0.5 4.0 4.7 0.4 Q1 Q1 Q3 Q1 981 1 2.1 1.2 0.6 4.0 4.6 1.0 Q1 Q1 Q3 Q1 237 4 3.1 2.6 0.6 3.9 3.1 1.0 Q1 Q1 Q3 Q1 237 6 3.4 2.4 0.9 3.8 3.2 0.2 Q1 Q1 Q3 Q1 237 7 3.0 2.4 0.4 4.3 2.5 1.9 Q1 Q1 Q3 Q1 1020 1 3.7 2.5 1.0 3.4 4.0 1.1 Q1 Q1 Q3 Q1 1020 2 3.3 2.5 0.6 3.7 2.3 0.5 Q1 Q1 Q3 Q1 414 1 1.4 2.0 0.7 3.6 4.0 0.0 Q1 Q1 Q1 Q1 925 1 3.8 2.7 1.0 1.7 1.6 0.4 Q1 Q1 Q3 Q1 816 1 3.5 2.4 0.8 3.7 3.9 0.4 Q1 Q1 Q3 Q1 816 2 2.9 1.8 1.0 3.2 3.1 0.7 Q1 Q1 Q3 Q1 816 3 2.2 2.7 0.1 3.2 2.1 0.6 Q1 Q1 Q2 Q1 816 4 2.8 3.5 0.5 4.9 3.0 1.8 Q1 Q1 Q1 Q1 816 7 2.0 2.6 0.2 2.3 2.3 0.3 Q1 Q1 Q2 Q1 816 8 3.2 2.8 0.4 4.3 3.8 0.5 Q1 Q1 Q3 Q1 816 10 AcmU 2.7 0.9 3.4 Q1 Q3 Q3 816 11 AcmV 2.7 0.1 2.8 3.6 3.5 0.3 Q1 Q3 Q3 Q1 793 16 AcmW 3.8 0.3 3.2 2.6 3.4 0.7 Q1 Q2 Q3 Q1 793 14 AcmY 1.7 0.4 1.9 Q1 Q3 Q3 Quantiles.sup.b Quantification events.sup.0 contig ORF gene.sup.d E/A E/D B/A C/A C/B D/A E/A E/D 981 23 AcmrC Q1 Q3 40 40 40 26 26 26 981 22 AcmrB Q1 Q3 7 7 7 9 9 9 981 21 AcmrA Q1 Q3 31 31 31 22 22 22 981 20 AcmQ Q1 Q3 60 60 60 62 60 60 981 19 AcmQ Q1 Q3 16 16 16 9 9 9 981 18 AcmP Q1 Q2 7 7 7 5 5 5 981 12 Q1 Q1 11 11 11 18 16 16 981 11 Q1 Q3 52 52 52 61 57 57 981 8 Q1 Q3 39 38 38 38 33 33 981 4 Q1 Q2 20 20 20 25 23 23 981 1 Q1 Q1 15 15 15 9 9 9 237 4 Q1 Q3 72 72 72 84 81 81 237 6 Q1 Q3 44 44 44 43 41 41 237 7 Q1 Q3 12 12 12 15 15 15 1020 1 Q1 Q3 23 23 23 14 14 14 1020 2 Q1 Q3 11 11 11 13 13 13 414 1 Q1 Q2 3 3 3 3 3 3 925 1 Q1 Q3 7 7 7 4 4 4 816 1 Q1 Q3 46 46 46 46 38 38 816 2 Q1 Q3 39 39 39 44 42 42 816 3 Q1 Q3 10 10 10 16 15 15 816 4 Q1 Q3 3 3 3 5 5 5 816 7 Q1 Q3 15 15 15 21 20 20 816 8 Q1 Q3 103 100 100 106 99 99 816 10 AcmU 4 4 4 2 2 2 816 11 AcmV Q1 Q3 40 40 40 32 32 32 793 16 AcmW Q1 Q1 14 14 14 10 10 10 793 14 AcmY 5 5 5 2 2 2 .sup.aProtein expression level changes observed for the protein products of the indicated ORFs when compared between growth conditions A-E (see main text and FIG. 5). .sup.bExpression ratios were divided in three equally sized quantiles for each experiment. In case the expression level change corresponded to the expected quantile this is indicated in bold. In case all four comparisons used for filtering (B/A, C/A, D/A, and E/A) matched to the expected quantile, the ORF number/gene name is also indicated in bold. .sup.cNumber of quantifications events used to calculate the expression ratios. Quantifications based on less than three events (italicized) were discarded. .sup.dGene name according to GenBank.

(53) TABLE-US-00005 TABLE 5 Expression level changes for proteomining hit in contig 561 of streptomyces sp. HM151 normalized ratios (2log).sup.a quantiles.sup.b quantification events.sup.c ORF /C E/C E/ B/C D/C D/B /C E/C E/ B/C D/C D/B /C E/C E/ B/C D/C D/B 118 0.6 1.3 1.9 2.7 3.7 0.5 Q1 Q3 Q3 Q1 Q1 Q2 21 21 21 7 7 7 119 0.6 1.1 1.7 3.1 5.7 1.7 Q1 Q3 Q3 Q1 Q1 Q1 16 16 16 4 4 4 121 1.6 0.2 2.2 Q1 Q2 Q3 5 5 5 2 2 2 122 0.8 0.4 1.5 3.5 5.8 1.9 Q1 Q2 Q3 Q1 Q1 Q1 31 31 31 13 13 13 .sup.aProtein expression level changes observed for the protein products of the indicated ORFs when compared between growth conditions B-E (see main text and FIG. 5) and without additive (). .sup.bExpression ratios were divided in three equally sized quantiles for each experiment. In case the expression level change corresponded to the expected quantile this is indicated in bold. In case all four comparisons used for filtering (/C, E/, B/C, D/C) matched to the expected quantile, the ORF number is also indicated in bold. .sup.cNumber of quantifications events used to calculate the expression ratios. Quantifications based on less than three events (italicized) were discarded.

(54) TABLE-US-00006 TABLE 6 Expression level changes for proteomining hits of streptomyces sp. MBT-GE Normalized Ratios (2log).sup.a Quantiles.sup.b MBT1/ MM/ MM/ SFM/ CM/ CM/ MBT1/ MM/ MM/ SFM/ ORF MBT2 MBT2 MBT1 MBT2 MBT2 SFM MBT2 MBT2 MBT1 MBT2 Contig 626 1 1.5 3.3 1.6 2.4 3.7 1.1 Q1 Q1 Q1 Q1 2 0.9 5.7 6.7 1.9 3.9 2.0 Q3 Q1 Q1 Q1 3 0.1 2.5 2.0 2.2 3.9 1.8 Q2 Q1 Q1 Q1 4 0.9 3.3 4.2 2.0 3.5 1.7 Q3 Q1 Q1 Q1 5 0.8 2.0 2.7 Q3 Q1 Q1 Contig 1265 1 0.6 4.4 4.6 1.4 3.1 2.7 Q3 Q1 Q1 Q1 2 0.2 3.4 3.8 1.9 2.4 0.3 Q2 Q1 Q1 Q1 3 0.5 5.0 4.7 2.0 3.5 1.3 Q2 Q1 Q1 Q1 4 0.7 4.3 4.8 2.2 3.7 1.7 Q3 Q1 Q1 Q1 5 0.9 3.1 3.8 1.4 3.5 1.7 Q3 Q1 Q1 Q1 7 0.0 4.4 3.3 2.0 4.2 2.4 Q2 Q1 Q1 Q1 Quantiles.sup.b Quantification Events.sup.c CM/ CM/ MBT1/ MM/ MM/ SFM/ CM/ CM/ ORF MBT2 SFM MBT2 MBT2 MBT1 MBT2 MBT2 SFM Contig 626 1 Q1 Q1 4 4 4 7 7 7 2 Q1 Q1 46 43 43 38 34 34 3 Q1 Q1 18 16 16 21 21 21 4 Q1 Q1 13 13 13 8 8 8 5 3 3 3 2 2 2 Contig 1265 1 Q1 Q1 31 29 29 41 35 35 2 Q1 Q1 9 8 8 9 8 8 3 Q1 Q1 7 4 4 20 19 19 4 Q1 Q1 53 49 49 51 44 44 5 Q1 Q1 12 12 12 16 16 16 7 Q1 Q1 37 35 35 39 39 39 .sup.aProtein expression level changes observed for the protein products of the indicated ORFs when compared between growth conditions (see main text and FIG. 9) .sup.bExpression ratios were divided in three equally sized quantiles for each experiment. In case the expression level change corresponded to the expected quantile this is indicated in bold. In case all four comparisons used for filtering (-MM/MBT2, MM/MBT1, CM/MBT2, CM/SFM) matched to the expected quantile, the ORF number is also indicated in bold. .sup.cNumber of quantifications events used to calculate the expression ratios. Quantifications based on less than three events (italicized) were discarded.

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