WO2016008082A1 - Marqueur génétique de la cirrhose du foie et ses utilisations - Google Patents

Marqueur génétique de la cirrhose du foie et ses utilisations Download PDF

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WO2016008082A1
WO2016008082A1 PCT/CN2014/082182 CN2014082182W WO2016008082A1 WO 2016008082 A1 WO2016008082 A1 WO 2016008082A1 CN 2014082182 W CN2014082182 W CN 2014082182W WO 2016008082 A1 WO2016008082 A1 WO 2016008082A1
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genes
gene
seq
liver cirrhosis
reads
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PCT/CN2014/082182
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English (en)
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Lanjuan LI
Nan Qin
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Zhejiang University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
    • A61P1/16—Drugs for disorders of the alimentary tract or the digestive system for liver or gallbladder disorders, e.g. hepatoprotective agents, cholagogues, litholytics
    • 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
    • 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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to the field of biomedicine and biotech,specifically related to biomarker for liver cirrhosis and its applications.
  • Liver cirrhosis is an advanced liver disease resulting from acute or chronic liver injury of any origin,including alcohol abuse,obesity and hepatitis virus infection.
  • the prognosis for patients with decompensated liver cirrhosis is poor,and they frequently require liver transplantation 1 .
  • the liver interacts directly with the gut through the hepatic portal and bile secretion 2 systems.Enteric dysbiosis,especially the translocation of bacteria 3 and their products 4,5 across the gut epithelial barrier,is involved in the progression of liver cirrhosis.However,the phylogenetic and functional composition changes in the human gut microbiota that are related to this progression remain obscure 5 .Although some studies have revealed that alterations in the gut microbiota play an important role in complications of end-stage liver cirrhosis 6 (such as spontaneous bacterial peritonitis 7 and hepatic encephalopathy 8 ) and the induction and promotion of liver damage in early-stage liver disease 9 (such as alcoholic
  • gut microbiota in human health and disease 14 has received unprecedented attention over the past few years with the rapid development of next-generation sequencing technologies.
  • Several complex chronic diseases such as obesity 15-18 ,inflammatory bowel disease 19,20 ,diabetes mellitus 21 , metabolic syndrome 22 ,symptomatic atherosclerosis 23 and non-alcoholic fatty liver disease 10 ,have been associated to gut microbiota.
  • a metagenomic study of 345 Chinese individuals with type-2 diabetes (T2D) identified 60,000 T2D-associated genes 24 . This study also demonstrated that gene markers could be identified and used for diagnosis of the disease.
  • the NIH Human Microbiome Project (HMP) generated 3.5 Tb of metagenomic data from different anatomical sites among 242 healthy individuals and generated the largest human microbiome gene resource 25 ,which includes most of the genera,enzyme families and community configurations from the microbiota of healthy adults from Western countries 26 .
  • Our invention aims to provide additional knowledge of gut microbiota modifications in liver cirrhosis patients and to propose targeted biomarkers offering a non-invasive approach for early detection of the disease.
  • a biomarker for liver cirrhosis in a human comprising
  • the biomarker for liver cirrhosis in a human comprising
  • a method of treating/preventing liver cirrhosis in a human comprising administering to the human a therapeutically effective amount composition, wherein the composition reduces the amount of at least 10 genes listed in Table 1,and/or activity of at least 10 genes listed in Table 1,which are over-represented in gut microbiota of LC affected subjects as compared to healthy subjects.
  • composition (ii) reduces the amount of the 15 genes listed in Table 1, and/or activity of the 15 genes listed in Table 1.
  • a method for diagnosing of liver cirrhosis comprising the steps of:
  • step (b) comparing the amount obtained in step (a) with a preset threshold.
  • the method comprising the steps of:
  • step (2b) comparing the amount obtained in step (2a) with a preset threshold.
  • kits for diagnosing of liver cirrhosis comprising reagents for:
  • step (b) comparing the amount obtained in step (a) with a preset threshold.
  • the kit comprising the reagents for:
  • step (2b) comparing the amount obtained in step (2a) with a preset threshold.
  • a method for identifying genes affected by LC disease in a human comprising;
  • the method of determining the metagenome of a diseased microbiota sample and a control untreated/healthy sample from the human;wherein determining the metagenome involves high-throughput sequencing.
  • FIG. 1 illustrates diagram of the data analysis pipeline
  • Figure 2A,2B illustrates Venn diagram showing the overlap of the current major human microbiome gene set
  • 2A.Venn diagram of the four major human microbiome gene sets is shown. The total gene number in each gene set and the overlapping areas are indicated.2B.Venn diagram of the three major human gut gene sets is shown.(LC:liver cirrhosis gene set,T2D:type 2 diabetes gene set, MetaHIT:MetaHIT gene set,HMP:HMP gene set).
  • Figure 3 illustrates results of a PCA of biomarkers distributed between two groups
  • Figure 4 illustrates histogram of the P-values from a comparison of gene markers between Type 2 diabetes and liver cirrhosis samples
  • the length of the bar (y-axis) represents the number of genes,and the P-value in related range is shown on the x-axis.
  • the light color bar and the deep color bar show genes involved in type 2 diabetes and liver cirrhosis,respectively.
  • the insert shows the log P-value of the gene markers between the two studies.
  • Figure 5 illustrates estimating the optimum number of markers
  • ThemRMR method was used to identify the liver cirrhosis-associated markers.Sequential subsets were generated at 5-marker intervals.For each subset,the error rate was estimated using a leave-one-out cross-validation (LOOCV) of a linear discrimination classifier. The optimum (highest Matthews correlation coefficient value) subset contains 15 gene markers.
  • LOCV leave-one-out cross-validation
  • Example 1 Construction of a liver cirrhosis gut microbial gene set and comparison with previous gene sets
  • liver cirrhosis patients and healthy control adults were Han Chinese.In total,123 liver cirrhosis patients and 114 healthy control adults were enrolled in our cohort.Our investigation included two phases. The first phase was a discovery phase in which 98 liver cirrhosis patients and 83 healthy controls were enrolled to characterize gut microbial compositional and functional changes between the two groups. The second phase was a validation phase,in which an additional 25 liver cirrhosis patients and 31 controls were enrolled to validate the accuracy of the discovery phase findings.
  • the reads were assembled into contigs for all samples using the assembly software SOAPdenovo 29 .Unassembled reads from 166 samples were pooled and thede novo assembly process was performed again for these reads (see Methods and Fig.1).Finally, 61.68% of the total reads were used to generate 4.4million contigs without ambiguous bases (minimum length of 500 bp).These contigs had a total length of 11.1 Gb,an average N50 length of 8,644 bp and ranged from 1,673 to 48,822bp.
  • the MetaHIT catalogue contained 3,452,726 genes,HMP 4,768,112 genes,and T2D 2,148,029 genes.In total 674,131 genes were shared among all four catalogues (Fig.2A).
  • the LC,MetaHIT,HMP and T2D gene sets contained 794,647,1,419,517 2,620,096 and 623,570 unique genes,respectively.
  • the HMP gut gene set was not included,as it contained Sanger,454 or Illumina based 16S sequences,in addition to whole metagenomic data andit was generated from exclusively healthy individuals rather than from a disease cohort with accompanying healthy controls.
  • the merged gene catalogue contained 5,382,817 genes,of which 797,690 were shared between all three catalogues (Fig.2B).Of the genes in the LC gene set,63.9% were also present in either one or both of the remaining two,whereas 37.1% were unique.
  • the MetaHIT and T2D sets contained 57.7% and 33.9% unique genes respectively.Large differences were also observed in the two gene sets derived from Chinese cohorts,the LC and T2D sets (Fig.4).
  • PDI patient discrimination index
  • Table 1 The 15 gene markers identified by the mRMR feature selection methods
  • Each cirrhotic patient and healthy control subject provided a fresh stool sample that was delivered immediately from our hospital to the lab on ice bag using insulating polystyrene foam containers.In the lab it was divided into 5 aliquots of 200mg and immediately stored at -80°C.Afrozen aliquot (200 mg) of each faecal sample was processed by phenol Trichloromethane DNA extraction method 16 as previously described.DNA concentration was measured by nanodrop (Thermo Scientific) and its molecular size was estimated by agarose gel electrophoresis.
  • DNA libraries were constructed according to the manufacturer’sinstruction (Illumina).Same workflows from Illumina were used to perform cluster generation,template hybridization, isothermal amplification,linearization,blocking,denaturing and hybridization of the sequencing primers.Paired-end sequencing 2*100bp was performed for all libraries.
  • the base-calling pipeline (Casava 1.8.2 with parameters ---use-bases-mask y100n,I6n,Y100n,--mismatches 1, --adapter-sequence) was used to process the raw fluorescent images and call sequences. The same insert size inferred by Agilent 2100 was used for all libraries (ranging from 275 to 450).
  • Reads that mapped to human genome together with their mated/paired reads were removed from each sample using BWA with parameters -n0.2.Then quality control was preceded with following criteria:a) Reads containing more than 3 N bases were removed,b) Reads containing more than 50 bases with low quality (Q2) were removed,c) No more than 10 bases with low quality (Q2) or assigned as N in the tail of reads were trimmed.Sequences that lost their mated reads were considered as single reads and were used in the assembly procedure.Resulting filtered reads were considered for next step analysis.
  • MetaGeneMark 33 (prokaryotic GeneMark.hmm version 2.8) was used to predict ORFs in scaffolds without ambiguous bases.
  • the non-redundant human gut gene set was built by pair-wise comparison of all the predicted ORFs using blat and the redundant ORFs were removed using a criterion of 95% identity over 90% of the shorter ORF length,which is consistent with the criterion used for the non-redundant European human gut gene set 31 and T2D study 24 .
  • MetaGeneMark to predict genes in assembled contigs originally from MetaHIT and T2D study and merged these three gene sets into a single one with the above method.
  • SOAPalign 2.21 was used to align paired-end clean reads against reference genomes with parameters–r2–m200–x1000.Reads with alignments on same reference genomes might be assigned into two types:
  • M Multiple reads
  • Ab (U) and Ab (M) are abundance of unique and multiple reads respectively,l is length of relative genome.For each multiple read,there is a species specific coefficient Co;let us suppose one read in ⁇ M ⁇ has alignments with N different species,then Co was calculated as follows.
  • Reads were aligned against the gene set by using SOAPalign 32 with parameters “-r–m200–x 1000”.We counted gene’sabundance if both paired-end reads could be aligned on the same gene.If only one of the paired-end reads could be aligned on a gene,we aligned both reads against assembled contigs by checking if the previously not aligned read are in the non-translated region or not.If true,both reads will be validated for gene count,if not,then both reads were discarded.
  • Ab (U) and Ab (M) are abundance of unique and multiple reads respectively,l is length of gene G.
  • Co for each multiple reads we calculate a specific coefficient Co for this gene,let us suppose one read with multiple ⁇ M ⁇ alignments in N different genes,then Co was calculated as follows.
  • Genes from the gene-profile matrix were used in an association study aiming to identify those that are differentially abundant between the patient and the healthy groups.Wilcoxon tests were employed to compute the probabilities that frequency profiles do not differ between the patient and the healthy groups by chance alone.Benjamini Hochberg multiple test correction was applied to the p-values.By performing a selection only based on a p-value threshold of p ⁇ 0.01 we found 541,582 genes.For specificity and computational reasons we used a very stringent significance threshold of fdr ⁇ 0.0001. This process identified 75,245 genes that are differentially abundant between the groups (49,830 were more abundant in the liver cirrhosis patients and 25,415 in the healthy control group).Asimilar p-value and group enrichment method was calculated for the NOG/KO as well.

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Abstract

La présente invention concerne un marqueur génétique de la cirrhose du foie et ses utilisations. Une étude d'association d'échantillons de selles s'étendant au microbiome intestinal entier, provenant de 98 patients atteints de cirrhose du foie et de 83 témoins sains a été entreprise pour caractériser les communautés microbiennes fécales et leur composition fonctionnelle. L'analyse méta-génomique quantitative a révélé 75 245 gènes qui différaient de manière significative (fdr < 0,0001) en abondance entre des patients et des témoins. En se basant sur les biomarqueurs de la cirrhose du foie, un indice de discrimination hautement précis des patients a été créé à l'aide uniquement de 15 gènes. Les applications du biomarqueur sont prévues.
PCT/CN2014/082182 2014-07-15 2014-07-15 Marqueur génétique de la cirrhose du foie et ses utilisations WO2016008082A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1997011968A2 (fr) * 1995-09-27 1997-04-03 Cedars-Sinai Medical Center Gene associe a une maladie neoplasique du foie
WO2014019408A1 (fr) * 2012-08-01 2014-02-06 Bgi Shenzhen Biomarqueurs pour le diabète et leurs utilisations
CN104195145A (zh) * 2014-07-15 2014-12-10 浙江大学 肝硬化的生物标志物及其应用

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1997011968A2 (fr) * 1995-09-27 1997-04-03 Cedars-Sinai Medical Center Gene associe a une maladie neoplasique du foie
WO2014019408A1 (fr) * 2012-08-01 2014-02-06 Bgi Shenzhen Biomarqueurs pour le diabète et leurs utilisations
CN104195145A (zh) * 2014-07-15 2014-12-10 浙江大学 肝硬化的生物标志物及其应用

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
CHATELIER, E. L. ET AL.: "Richness of human gut microbiome correlates with metabolic markers.", NATURE, vol. 500, 29 August 2013 (2013-08-29), pages 541 - 546, XP055087499, DOI: doi:10.1038/nature12506 *
QIN, J. ET AL.: "A metagenome-wide association study of gut microbiota in type 2 diabetes.", NATURE, vol. 490, 26 September 2012 (2012-09-26), pages 55 - 60, XP055111695, DOI: doi:10.1038/nature11450 *
WU, Z. ET AL.: "Cirrhotic complications and intestinal microflora.", INTER. J. EPIDEMIOL. INFECT. DIS., vol. 33, no. 1, 28 February 2006 (2006-02-28), pages 34 - 37 *
WU, Z. ET AL.: "Investigation of intestinal bacterial translocation in 78 patients with cirrhosis after liver transplantation.", CHIN. J. SURG., vol. 44, no. 21, 30 November 2006 (2006-11-30), pages 1456 - 1459 *

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