WO2016049917A1 - Biomarqueurs pour les maladies liées à l'obésité - Google Patents

Biomarqueurs pour les maladies liées à l'obésité Download PDF

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WO2016049917A1
WO2016049917A1 PCT/CN2014/088043 CN2014088043W WO2016049917A1 WO 2016049917 A1 WO2016049917 A1 WO 2016049917A1 CN 2014088043 W CN2014088043 W CN 2014088043W WO 2016049917 A1 WO2016049917 A1 WO 2016049917A1
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sample
obesity
gene
abnormal condition
subject
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PCT/CN2014/088043
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English (en)
Inventor
Qiang FENG
Dongya ZHANG
Longqing TANG
Jun Wang
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Bgi Shenzhen Co., Limited
Bgi Shenzhen
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Priority to CN201480082372.1A priority Critical patent/CN107075562B/zh
Priority to PCT/CN2014/088043 priority patent/WO2016049917A1/fr
Publication of WO2016049917A1 publication Critical patent/WO2016049917A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material

Definitions

  • the present invention relates to biomarkers and methods for predicting the risk of a disease related to microbes, in particular obesity or related diseases.
  • Obesity which is prevalent in developed countries, has increased considerably worldwide (de Carvalho Pereira et al., 2013) . It is reported that the prevalence of overweight and obesity combined rose by 27.5% for adults and 47.1% for children between 1980 and 2013 in the world. The number of overweight individuals increased from 857 million in 1980, to 2.1 billion in 2013, and of these, 671 million are affected by obesity. More than 50% of which live in ten countries, and USA has the largest number of obese individuals, followed by China (Ng et al., 2014) .
  • BMI body mass index
  • waist circumference can be considered a reliable and useful tool for epidemiological studies to assess abdominal adiposity, but this measurement seems to be harder to perform (Miguel ⁇ Etayo et al., 2014) .
  • ICD-9 International Classification of Diseases, Ninth Revision
  • NAMCS National Ambulatory Medical Care Survey
  • NHAMCS National Hospital Medical Care Survey
  • Embodiments of the present disclosure seek to solve at least one of the problems existing in the prior art to at least some extent.
  • the present invention is based on the following findings by the inventors:
  • GWAS Metagenome-Wide Association Study
  • the inventors developed a disease classifier system based on the 9 gene markers that are defined as an optimal gene set by a minimum redundancy -maximum relevance (mRMR) feature selection method. For intuitive evaluation of the risk of obesity disease based on these 9 gut microbial gene markers, the inventors calculated a healthy index.
  • the inventors'data provide insight into the characteristics of the gut metagenome related to obesity risk, a paradigm for future studies of the pathophysiological role of the gut metagenome in other relevant disorders, and the potential usefulness for a gut-microbiota-based approach for assessment of individuals at risk of such disorders.
  • the markers of the present invention are more specific and sensitive as compared with conventional markers.
  • analysis of stool promises accuracy, safety, affordability, and patient compliance. And samples of stool are transportable.
  • the present invention relates to an in vitro method, which is comfortable and noninvasive, so people will participate in a given screening program more easily.
  • the markers of the present invention may also serve as tools for therapy monitoring in cancer patients to detect the response to therapy.
  • a biomarker set for predicting a disease related to microbiota in a subject consisting of:
  • a gut biomarker comprises at least a partial sequence of SEQ ID NO: 1 to 9.
  • the disease is obesity or related disease.
  • some disease related to the related to microbiota in a subject may be analyzed, for example obesity or related disease may be determined based on some sample from the subject , for example, some fecal sample may be used.
  • kits for determining the gene marker set described above comprising primers used for PCR amplification and designed according to the DNA sequecne as set forth in at least a partial sequence of SEQ ID NO: 1 to 9.
  • kits for determining the gene marker set described above comprising one or more probes designed according to the genes as set forth in SEQ ID NO: 1 to 9.
  • a ij is the relative abundance of marker i in sample j, wherein i refers to each of the gene markers in said gene marker set; .
  • N is a first subset of all patient-enriched markers in selected biomarkers related to the abnormal condition
  • M is a second subset of all control-enriched markers in selected biomarkers related to the abnormal condition
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing abnormal condition.
  • the cutoff is 0.03519 to 0.1337.
  • a ij is the relative abundance of marker i in sample j, wherein i refers to each of the gene markers in said gene marker set; .
  • N is a first subset of all patient-enriched markers in selected biomarkers related to the abnormal condition
  • M is a second subset of all control-enriched markers in selected biomarkers related to the abnormal condition
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing abnormal condition.
  • the cutoff is 0.03519 to 0.1337.
  • a method of diagnosing whether a subject has an abnormal condition related to microbiota or is at the risk of developing an abnormal condition related to microbiota comprising:
  • the method comprises:
  • a ij is the relative abundance of marker i in sample j, wherein i refers to each of the gene markers in said gene marker set; .
  • N is a first subset of all patient-enriched markers in selected biomarkers related to the abnormal condition
  • M is a second subset of all control-enriched markers in selected biomarkers related to the abnormal condition
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing abnormal condition.
  • the cutoff is 0.03519 to 0.1337.
  • the abnormal condition related to microbiota is obesity or related disorder.
  • Fig. 1 The association analysis of Obese p-value distribution identified a disproportionate over-representation of strongly associated markers at lower P-values.
  • Fig. 2 The inventors performed incremental search in Obese-associated gene markers by the minimum redundancy maximum relevance (mRMR) methods, and generated sequential number of subsets. For each subset, the error rate was then estimated by a leave-one-out cross-validation (LOOCV) of a linear discrimination classifier. The optimum (lowest error rate) subset contains 9 gene markers.
  • mRMR minimum redundancy maximum relevance
  • LOOCV leave-one-out cross-validation
  • Example 1 Identifying biomarkers for evaluating obesity risk
  • DNA library construction was performed following the manufacturer ⁇ s instruction (Illumina, insert size 350bp, read length 100bp) .
  • the inventors used the same workflow as described previously to perform cluster generation, template hybridization, isothermal amplification, linearization, blocking and denaturation, and hybridiza-tion of the sequencing primers.
  • the inventors constructed one paired-end (PE) library with insert size of 350 bp for each sample, followed by a high-throughput sequencing to obtain around 30 million PE reads of length 2x100bp. High-quality reads were obtained by filtering low-quality reads with ambiguous ⁇ N'bases, adapter contamination and human DNA contamination from the Illumina raw reads, and by trimming low-quality terminal bases of reads simultaneously.
  • PE paired-end
  • the inventors totally output about 5.9 Gb per sample of fecal micbiota sequencing data (high quality clean data) (Table 1) from 158 samples (78 cases and 80 controls) on Illumina HiSeq 2000 platform.
  • Table 1 Summary of metagenomic data. Fourth column reports results from Wilcoxon rank-sum tests.
  • the average reads mapping rate was shown on Table 1. This mapping rate was close to the samples in Li, J. et al. 2014, supra, which indicated that this mapping rate was sufficient for the further study.
  • the inventors derived the gene profile (9.9Mb genes) from the mapping result using the same method as Li, J. et al. 2014, supra.
  • Taxonomic assignment of genes was performed using an in-house pipeline which had described in the published paper (Li, J. et al. 2014, supra) .
  • PERMANOVA permutational multivariate analysis of variance
  • the inventors performed the analysis using the method implemented in package ′′vegan′′ in R, and the permuted p-value was obtained by 10, 000 times permutations.
  • the inventors also corrected for multiple testing using ′′p. adjust′′ in R with Benjamini-Hochberg method to get the q-value for each test.
  • PERMANOA identified three significant factors associated with gut microbe (based on gene profiles) (q ⁇ 0.05, Table 2) .
  • FDR false discovery rate
  • Receiver Operator Characteristic (ROC) analysis The inventors applied the ROC analysis to assess the performance of the obesity classification based on metagenomic markers. The inventors then used the “pROC” package in R to draw the ROC curve.
  • ROC Receiver Operator Characteristic
  • mRMR minimum redundancy-maximum relevance
  • the inventors estimated the error rate by a leave-one-out cross-validation (LOOCV) of linear discrimination classifier.
  • LOCV leave-one-out cross-validation
  • the optimal selection of marker sets was the one corresponding to the lowest error rate.
  • the inventors made the feature selection on a set of 396, 100 obesity-associated gene markers. Since this was computationally prohibitive to perform mRMR using all genes, the inventors derived a statistically non-redundant gene set. Firstly, we selected 8010 genes (q ⁇ 0.0005) . Subsequently, the inventors applied the mRMR feature selection method and identified an optimal set of 9 gene biomarkers (lowest error rate, Fig. 2) that are strongly associated with obesity for obesity classification, which were shown on Table 3 and Table 4. The gene id is from the published reference gene catalogue as Li, J. et al. 2014, supra.
  • gene id SEQ ID NO: gene_id: 7860042 1 gene_id: 1208989 2 gene_id: 5243950 3 gene_id: 5042942 4 gene_id: 3104115 5 gene_id: 2285506 6 gene_id: 3581202 7 gene_id: 64552 8 gene_id: 6793200 9
  • the inventors developed a disease classifier system based on the 9 gene markers that the inventors defined. For intuitive evaluation of the risk of disease based on these gut microbial gene markers, the inventors calculated a gut healthy index (obesity index) .
  • the inventors defined and calculated the gut healthy index for each individual on the basis of the selected 9 gene markers as described above. For each individual sample, the gut healthy index of sample j that denoted by I j was calculated by the formula below:
  • a ij is the relative abundance of marker i in sample j.
  • N is a subset of all patient-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all obesity-enriched markers in these 9 selected gene markers) ,
  • M is a subset of all control-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all control-enriched markers in these 9 selected gene markers) ,
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing obesity.
  • the inventors computed a obesity index based on the relative abundance of these 9 gene markers, which clearly separated the obesity patient microbiomes from the control microbiomes (Table 5) .
  • Classification of the 78 obesity patient microbiomes against the 80 control microbiomes using the obesity index exhibited an area under the receiver operating characteristic (ROC) curve of 0.9763 (Fig. 3) .
  • ROC receiver operating characteristic
  • TPR true positive rate
  • FPR false positive rate
  • error rate was 8.23% (13/158) , indicating that the 9 gene markers could be used to accurately classify obesity individuals.
  • a ij is the relative abundance of marker i in sample j.
  • N is a subset of all patient-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all obesity-enriched markers in these 9 selected gene markers) ,
  • M is a subset of all control-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all control-enriched markers in these 9 selected gene markers) ,
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing obesity.
  • Table 6 shows the calculated index of each sample and Table 7 shows the relevant gene relative abundance of a representative sample DB78A.
  • the error rate was 21.42% (9/42) , validating that the 54 gene markers can classify obesity individuals.
  • most of obesity patients (16/17) were diagnosed as obesity correctly.
  • TPR true positive rate
  • FPR false positive rate
  • Case means before operation samples
  • control means after operation 1 month and 3 month.
  • a ij is the relative abundance of marker i in sample j.
  • N is a subset of all patient-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all obesity-enriched markers in these 9 selected gene markers) ,
  • M is a subset of all control-enriched markers in selected biomarkers related to the abnormal condition (namely, a subset of all control-enriched markers in these 9 selected gene markers) ,
  • an index greater than a cutoff indicates that the subject has or is at the risk of developing obesity.
  • Table 9 shows the calculated index of each sample and Table 10 shows the relevant gene relative abundance of a representative sample DB126.
  • the error rate was 22.72% (5/22) , validating that the 54 gene markers can classify obesity individuals.
  • most of obesity patients (8/9) were diagnosed as obesity correctly.
  • TPR true positive rate
  • FPR false positive rate
  • the inventors have identified and validated 9 markers set by a minimum redundancy -maximum relevance (mRMR) feature selection method based on 396, 100 obesity-associated markers. And the inventors have built a gut healthy index to evaluate the risk of obesity disease based on these 9 gut microbial gene markers.
  • mRMR minimum redundancy -maximum relevance

Abstract

La présente invention concerne des biomarqueurs et des procédés de prédiction du risque d'une maladie liée aux microbes, en particulier l'obésité ou les maladies liées à l'obésité.
PCT/CN2014/088043 2014-09-30 2014-09-30 Biomarqueurs pour les maladies liées à l'obésité WO2016049917A1 (fr)

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CN201480082372.1A CN107075562B (zh) 2014-09-30 2014-09-30 用于肥胖症相关疾病的生物标记物
PCT/CN2014/088043 WO2016049917A1 (fr) 2014-09-30 2014-09-30 Biomarqueurs pour les maladies liées à l'obésité

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Citations (3)

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Publication number Priority date Publication date Assignee Title
WO2006012586A2 (fr) * 2004-07-27 2006-02-02 Washington University In St. Louis Modulation de fiaf et de microbiote gastro-intestinal
WO2006102350A1 (fr) * 2005-03-23 2006-09-28 Washington University In St. Louis Utilisation d'archees pour moduler les fonctions de capture des nutriments par la microbiote gastro-intestinale
WO2011107482A2 (fr) * 2010-03-01 2011-09-09 Institut National De La Recherche Agronomique Méthode de diagnostic de l'obésité

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EP1978107A1 (fr) * 2007-04-03 2008-10-08 Centre National De La Recherche Scientifique (Cnrs) Polymorphismes de gènes FTO associés à l'obésité et/ou les diabètes de type II
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WO2006012586A2 (fr) * 2004-07-27 2006-02-02 Washington University In St. Louis Modulation de fiaf et de microbiote gastro-intestinal
WO2006102350A1 (fr) * 2005-03-23 2006-09-28 Washington University In St. Louis Utilisation d'archees pour moduler les fonctions de capture des nutriments par la microbiote gastro-intestinale
WO2011107482A2 (fr) * 2010-03-01 2011-09-09 Institut National De La Recherche Agronomique Méthode de diagnostic de l'obésité

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