Method for analyzing water toxicity

Abstract

A method for analyzing water toxicity, the method including: exposure experiment, sample collection, transcriptome detection, metabolome detection, screening of differentially expressed genes, screening of differentially expressed metabolites, and identification of commonly changed biological pathways in both the transcriptome and the metabolome.

Claims

1. A method for analyzing water toxicity, the method comprising: 1) providing model animals, and separating the model animals into a first group and a second group; 2) providing a water sample comprising organic pollutants and heavy metals as drinking water to the first group; after a first period of time, extracting a first set of RNA samples from livers of the first group, and collecting a first set of blood samples from the first group and centrifuging the first set of blood samples to obtain a first set of serum samples; and providing purified water as drinking water to the second group; after a second period of time that is equal to the first period of time, extracting a second group of RNA samples from livers of the second group, and collecting a second set of blood samples from the second group and centrifuging the second set of blood samples to obtain a second set of serum samples; 3) analyzing the first set of RNA samples by a microarray method to obtain a first transcriptome data; and analyzing the second set of RNA samples by a microarray method to obtain a second transcriptome data; 4) analyzing the first set of serum samples and the second set of serum samples by nuclear magnetic resonance (NMR) method to yield a first set of NMR spectra and a second set of NMR spectra, respectively; 5) comparing the first transcriptome data and the second transcriptome data and deeming genes having an expression multiple >2.0 and a false discovery rate (FDR) <0.1 as differentially expressed genes; 6) integrating the first set of NMR spectra and the second set of NMR spectra to obtain a first integration data and a second integration data, respectively, and analyzing the first integration data and the second integration data by partial least squares discriminant analysis (PLS-DA), and deeming metabolites having variable influence on projection (VIP) >1.0 and FDR <0.01 as differentially expressed metabolites; and 7) performing biological pathway analyses on the differentially expressed genes and differentially expressed metabolites to identify the biological pathways that are affected by the water sample.

2. The method of claim 1, wherein integrating the first group of NMR spectra and the second group of NMR spectra in 6) is performed with an interval of 0.005 ppm in the first group of NMR spectra and the second group of NMR spectra.

3. The method of claim 1, wherein 7) comprises: identifying biological pathways that have a hypergeometric test p <0.05and contain more than four of the differentially expressed genes as affected transcriptome pathways; identifying biological pathways that have a pathway impact (PI) >0.1 as affected metabolite pathways; identifying the biological pathways which are both the affected transcriptome pathways and affected metabolite pathways as specific biological processes affected by the water sample, and identifying corresponding differentially expressed genes and metabolites as biomarkers of the water toxicity.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The invention is described hereinbelow with reference to the accompanying drawings, in which:

(2) FIG. 1 is a flow chart of a method for analyzing water toxicity in accordance with one embodiment of the invention;

(3) FIG. 2A is a picture showing detection result of a tissue section of liver in a blank control group in accordance with one embodiment of the invention;

(4) FIG. 2B is a picture showing detection result of a tissue section of liver in an experimental group in accordance with one embodiment of the invention;

(5) FIG. 3A is a picture showing detection result of a tissue section of liver in a blank control group in accordance with one embodiment of the invention; and

(6) FIG. 3B is a picture showing detection result of a tissue section of liver in an experimental group in accordance with one embodiment of the invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

(7) For further illustrating the invention, experiments detailing a method for analyzing water toxicity based on bio-omics integration are described below. It should be noted that the following examples are intended to describe and not to limit the invention.

EXAMPLE 1

(8) A method for analyzing water toxicity based on bio-omics integration, to assess the toxicity of micro-polluted drinking water source, as shown in FIG. 1, is conducted as follows:

(9) 1) Exposure experiment: a sample of drinking water source and a sample of purified water were administered to mice by freely drinking. The test mice were seven weeks old male Kunming mice (Mus musculus) having and a weight of between 17 and 31 g and were randomly divided into an experimental group and a control group with ten in each group. The mice were administered with the water sample of the drinking water source and the sample of the purified water. An exposure period was 90 days.

(10) 2) Sample collection: after 90 days of the exposure period, total RNA samples of livers were extracted from the experimental group and the control group, and serum samples were collected.

(11) 3) transcriptome detection: mice genome chip Mouse Genome 430A 2.0 produced by Affymetrix Company from the USA were used for the transcriptome detection. The experimental group and the control group were provided with three genome chips, respectively. Statics analyses were conducted, and the complete genome expression data of the test mice were obtained.

(12) 4) Metabolome detection: 300 L of the serum sample was collected from each mouse in the experimental group and the control group. 300 L of a phosphate buffer having a pH value of 7.4 was added to each serum sample and evenly mixed to yield a mixture. 550 L of a supernate was collected from the mixture and was performed with nuclear magnetic resonance (NMR) detection. Segmental integral was performed on the NMR spectra of serum (with an interval of 0.005 ppm), and the integral data were input into the multivariate data analysis software SIMCA-P 11.5 to establish a PLS-DA model.

(13) 5) Screening of differentially expressed genes: genes having an expression multiple >2.0 and a false discovery rate (FDR)<0.1 were screened as the differentially expressed genes, and 243 differentially expressed genes were screened from ten thousands of complete genomes of the experimental group.

(14) 6) Screening of differentially expressed genes: metabolites having the variable influence on projection (VIP) >1.0 and FDR <0.01 were screened as the differentially expressed metabolites, and 10 differentially expressed metabolites were screened from thousands of the experimental group.

(15) 7) Biological pathway analyses were performed on the differentially expressed genes and metabolites, respectively, and three biological pathways where both the transcriptomes and the metabolites are affected (as shown in Table 1).

(16) TABLE-US-00001 TABLE 1 Biological pathways of mice obviously affected by toxicity of drinking water Screening of differentially expressed metabolites Commonly changed biological pathways Screening of differentially Variable Number of expressed genes Influence on differentially Pathway Expression Projection expressed Hypergeometric Impact Genes FDR times Metabolite FDR (VIP) Pathways genes Test p PI Adh4 0.050 2.0 Glutathione 0.005 1.1 Exogenous 6 0.005 0.15 Cyp1a1 0.004 2.2 Glutamine 0.004 1.2 substances Cyp2b10 0.041 2.9 metabolic Cyp2c55 0.003 5.7 pathways Cyp2c65 0.040 3.9 involving Gstm4 0.005 2.3 cytochrome P450 Adh4 0.050 2.0 Choline 0.007 1.2 Bile acid 4 0.004 0.15 Akr1d1 0.035 2.0 Trimethylamine 0.005 1.3 biological N-oxide pathway Cyp7a1 0.005 3.0 Taurine 0.004 1.5 Hsd3b7 0.040 2.0 Pyruvic acid 0.003 1.4 Citric acid 0.001 1.3 Lipid 0.006 1.1 Acs14 0.050 2.0 Cholesterol 0.004 1.6 PPAR 5 0.005 0.19 Cyp7a1 0.005 3.0 Lipid 0.006 1.1 Signaling Fabp2 0.025 2.0 Pathway Hmgcs2 0.003 2.2 Sorbs1 0.006 2.0

(17) These three biological pathways comprised: cytochrome P450 involved in the metabolic pathway of exogenous substance (involving 6 differentially expressed proteins and 2 differentially expressed serum metabolites), biosynthetic pathway of fatty acid (involving 4 differentially expressed genes and 6 differentially expressed serum metabolites), and PPAR signal pathway (involving 5 differentially expressed genes and 2 differentially expressed serum metabolites).

(18) The toxicity effect of the test micro-polluted drinking water source primarily affects the metabolic pathway of the exogenous substance and the metabolic process of fatty acid, and the micro-polluted drinking water source results in liver damage and metabolic disorder of the mice. The finally screened differentially expressed genes and the serum metabolites can be used as potential biomarkers of the toxicity of the micro-polluted drinking water source.

(19) It has been found that 14 of 16 representative pollutants in the test water have concentrations of a nanogram level (as shown in Table 2), to which the conventional toxicity assessment methods were not responsive.

(20) TABLE-US-00002 TABLE 2 Concentration test results of representative pollutants in slightly-polluted drinking water (Unit: ng/L, Mean standard deviation, each target is tested thrice) Pollutant Concentration Pollutant Concentration Isophorone 11 2 Benzo (a) anthracene 15 2 Dimethyl 79 8 Phthalate-di 1820 281 phthalate (2-ethylhexyl) ester Chrysene 31 4 Benzo (b) fluoranthene 161 17 Anthracene 96 4 Benzo (k) fluoranthene 99 12 Phenanthrene 19 1 Benzo (a) pyrene 195 14 2-n-butyl 3391 1265 Benzo (g, h, i) perylene 2 1 phthalate Pyrene 14 6 Indeno (1,2,3-cd) pyrene 13 12 Bis 206 84 Dibenzo (a, h) anthracene 5 2 (2-ethylhexyl) adipate

(21) Histopathological test results did not find that the micro-polluted water source resulted in liver lesions (FIG. 2), and test results of 14 serum biochemical indicator did not find that the test water led to changes in serum pathological indicators (Table 3).

(22) TABLE-US-00003 TABLE 3 Biochemical test results of serum (Mean standard deviation, 10 animals/group) Biochemical Blank Experimental parameters of serum control group group Total protein (g/L) 55.3 4.0 56.0 3.5 Albumin (g/L) 29.3 2.1 30.1 1.2 Globulin (g/L) 26.0 2.6 26.0 2.9 Glutamic-pyruvic 95.5 40.2 168.6 162.9 transaminase (U/L) Glutamic-oxal(o)acetic 135 28 200 127 transaminase (U/L) Alkaline phosphatase (U/L) 56 21 52 20 Lactate dehydrogenase (U/L) 728 286 895 442 Blood urea nitrogen (mol/L) 7.3 0.7 7.8 1.1 Creatinine (mol/L) 23 2 23 4 Uric acid (mol/L) 116 37 .sup.78 26.sup.b Cholesterol (mmol/L) 2.81 0.39 2.92 0.42 Triglyceride (mmol/L) 3.19 1.10 4.52 2.16 High-density lipoprotein 1.89 0.21 1.88 0.25 cholesterol (mmol/L) Low-density lipoprotein 0.56 0.12 0.61 0.15 cholesterol (mmol/L)

(23) The method of the invention was capable of simultaneously detecting a plurality of toxicity effects of the micro-polluted water source on the liver damage and the metabolic disorder, thereby having high sensitivity and obvious advantage.

EXAMPLE 2

(24) A method for analyzing water toxicity based on bio-omics integration, to assess the toxicity of secondary effluent from a municipal sewage treatment plant is conducted as follows:

(25) 1) Exposure experiment: a sample of the effluent of the sewage treatment plant and a sample of purified water were administered to mice by freely drinking. The test mice were seven weeks old male kunming mice (Mus musculus) having a weight of between 18 and 25 g and were randomly divided into an experimental group and a control group with ten in each group. The mice were administered with the water sample of the drinking water source and the sample of the purified water. An exposure period was 90 days.

(26) 2) Sample collection: after 90 days of the exposure period, total RNA samples of livers were extracted from the experimental group and the control group, and serum samples were collected.

(27) 3) transcriptome detection: mice genome chip Mouse Genome 430A 2.0 produced by Affymetrix Company from the USA were used for the transcriptome detection. The experimental group and the control group were provided with three genome chips, respectively. Statics analyses were conducted, and the complete genome expression data of the test mice were obtained.

(28) 4) Metabolome detection: 300 L of the serum sample was collected from each mouse in the experimental group and the control group. 300 L of a phosphate buffer having a pH value of 7.4 was added to each serum sample and evenly mixed to yield a mixture. 550 L of a supernate was collected from the mixture and was performed with nuclear magnetic resonance (NMR) detection. Segmental integral was performed on the NMR spectra of serum (with an interval of 0.005 ppm), and the integral data were input into the multivariate data analysis software SIMCA-P 11.5 to establish a PLS-DA model.

(29) 5) Screening of differentially expressed genes: genes having an expression multiple >2.0 and a false discovery rate (FDR)<0.1 were screened as the differentially expressed genes, and 767 differentially expressed genes were screened from ten thousands of complete genomes of the experimental group.

(30) 6) Screening of differentially expressed genes: metabolites having the variable influence on projection (VIP) >1.0 and FDR <0.01 were screened as the differentially expressed metabolites, and 5 differentially expressed metabolites were screened from thousands of the experimental group.

(31) 7) Biological pathway analyses were performed on the differentially expressed genes and metabolites, respectively, and three biological pathways where both the transcriptomes and the metabolites are affected (as shown in Table 4).

(32) TABLE-US-00004 TABLE 4 Biological pathways of mice obviously affected by toxicity of secondary effluent from sewage treatment plant Screening of differentially expressed metabolites Variable Commonly changed biological pathways Screening of differentially Influence Number of expressed genes on differentially Pathway Expression Projection expressed Hypergeometric Impact Genes FDR times Metabolite FDR (VIP) Pathways genes Test p PI Cyp51 0.033 4.1 Choline 0.004 1.5 Steroid 7 0.004 0.14 Ggcx 0.052 3.3 Phosphocholine 0.003 1.7 biosynthesis Hmgcr 0.005 3.2 Lss 0.045 3.8 Sc4mo1 0.007 3.3 Sq1e 0.035 3.6 Tm7sf2 0.050 3.1 Acy3 0.008 3.8 Proline 0.005 1.9 Metabolism 4 0.005 0.15 Asns 0.054 3.1 of alanine, Aspa 0.034 3.6 aspartic acid Ddo 0.009 3.9 and glutamic acid Atp6v1c1 0.010 3.3 2-oxoglutaric 0.001 1.2 Oxidation 6 0.003 0.17 acid phosphorylation Cox4i1 0.075 3.1 Lactic acid 0.004 1.4 Cox5a 0.050 3.2 Cox5b 0.004 3.0 Ndufa2 0.065 4.1 Ndufc1 0.074 3.6

(33) These three biological pathways comprised: biosynthetic pathways of steroid hormone (involving 7 differentially expressed genes and 2 differentially expressed serum metabolites), Alanine, Aspartic acid, and Glutamate (involving 4 differentially expressed genes and 1 differentially expressed serum metabolite), and oxidative phosphorylation pathway (involving 6 differentially expressed genes and 2 differentially expressed serum metabolites).

(34) The toxicity effect of the test secondary effluent from the sewage treatment plant primarily affects the fat metabolism, amino acid metabolism, and energy metabolism. The secondary effluent of the sewage treatment plant has toxicity effects on the normal liver functions including metabolisms of fat, amino acid, and energy. The differentially expressed genes and the serum metabolites from the screening process can be used as potential biomarkers of the toxicity of the secondary effluent from the sewage treatment plant.

(35) It has been found that 13 trace organic pollutants were detected from 22 representative pollutants in the test water and had concentrations of a nanogram level (as shown in Table 5), to which the conventional toxicity assessment methods were not responsive.

(36) TABLE-US-00005 TABLE 5 Concentration test results of representative pollutants in slightly-polluted drinking water (Unit: ng/L, Mean standard deviation, each target is tested thrice) Pollutant Concentration Pollutant Concentration Acenaphthene 10.22 0.06 Phenanthrene 22.93 0.17 Anthracene 26.16 1.82 Pyrene 3.69 0.37 Benzo (a) Undetected Butyl benzyl phthalate 149.33 6.45 anthracene Benzo (a) Undetected Dioctyladipate 77.57 1.37 pyrene Benzo (b) Undetected Phthalate-di (2- 77.26 0.13 Fluoranthene ethylhexyl) phthalate Benzo (g, h, i) Undetected Diethyl phthalate 39.25 1.08 Perylene Benzo (k) 28.73 0.41 Dimethyl phthalate 51.73 1.46 Fluoranthene Chrysene 62.23 0.01 2-n-butyl phthalate 71.03 8.52 Dibenzo (a, h) Undetected Hexachlorobenzene Undetected Anthracene Fluorene Undetected Hexachlorocyclo- Undetected pentadiene Indeno (1,2, Undetected Pentachlorophenol 421.06 1.70 3-cd) Pyrene

(37) Histopathological test results did not find that the secondary effluent of the sewage treatment plant resulted in liver lesions (FIG. 2), and test results of 14 serum biochemical indicator did not find that the test water sample led to changes in serum pathological indicators (Table 6).

(38) TABLE-US-00006 TABLE 6 Biochemical test results of serum (Mean standard deviation, 10 animals/group) Biochemical Blank Experimental parameters of serum control group group Total protein (g/L) 62.5 3.0 62.9 4.1 Albumin (g/L) 29.9 2.5 29.1 2.2 Globulin (g/L) 30.9 1.4 33.7 3.8 Glutamic-pyruvic 59.7 4.6 57.0 8.0 transaminase (U/L) Glutamic-oxal(o)acetic 113.7 8.1 116.0 5.0 transaminase (U/L) Alkaline phosphatase (U/L) 43.1 9.9 56.0 9.0 Lactate dehydrogenase (U/L) 625.8 144.2 665.3 165.8 Blood urea nitrogen (mmol/L) 8.7 1.4 8.4 1.1 Creatinine (mol/L) 19.4 3.4 20.2 4.1 Total cholesterol (mmol/L) 2.4 0.2 2.3 0.2 Triglyceride (mmol/L) 0.6 0.1 1.0 0.2

(39) The method of the invention was capable of simultaneously detecting a plurality of toxicity effects of the secondary effluent of the sewage treatment plant on the liver damage and the metabolic disorder, thereby having high sensitivity and obvious advantage.

(40) In summary, the method for analyzing water toxicity of the invention is capable of comprehensively and accurately assessing toxicity of the water from the underlying genetic information to the final phenotype, simultaneously detecting a plurality of toxicities, and providing a new type of biomarker. Furthermore, the method of the invention has sensitive response to trace toxic substances in the water, thereby being much superior to the conventional toxicity assessment methods.

(41) While particular embodiments of the invention have been shown and described, it will be obvious to those skilled in the art that changes and modifications may be made without departing from the invention in its broader aspects, and therefore, the aim in the appended claims is to cover all such changes and modifications as fall within the true spirit and scope of the invention.