WO2016008082A1 - Gene marker for liver cirrhosis and usages thereof - Google Patents
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- 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
The present application discloses gene marker for liver cirrhosis and usages thereof. A whole gut microbiome-wide association study of stool samples from 98 liver cirrhosis patients and 83 healthy controls were undertook to characterise the faecal microbial communities and their functional composition. Quantitative metagenomics analysis revealed 75,245 genes that differed significantly (fdr<0.0001) in abundance between patients and controls. Based on the liver cirrhosis biomarkers, a highly accurate patient discrimination index was created using only 15 genes. The applications of the biomarker are provided.
Description
The present invention relates to the field of biomedicine and biotech,specifically related to
biomarker for liver cirrhosis and its applications.
Liver cirrhosis (LC) 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 transplantation1.The liver
interacts directly with the gut through the hepatic portal and bile secretion2 systems.Enteric
dysbiosis,especially the translocation of bacteria3 and their products4,5 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
obscure5.Although some studies have revealed that alterations in the gut microbiota play an
important role in complications of end-stage liver cirrhosis6 (such as spontaneous bacterial
peritonitis7 and hepatic encephalopathy8) and the induction and promotion of liver damage in
early-stage liver disease9 (such as alcoholic liver disease10 and non-alcoholic fatty liver disease11),
definitive associations between alterations in gut microbiota and liver pathology in humans are still
lacking12.Studies of liver cirrhosis patients13 and of mouse models for alcoholic liver disease10 have
revealed a similar and substantial alteration in the gut microbiota,as measured by sequencing of
16S rRNA genes.How these phylogenetic alterations relate to changes in the functioning of the gut
microbiota is unclear.As clinical liver cirrhosis is primarily diagnosed by clinical symptoms,liver
biopsy and hepatic imaging changes,our research 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.
The role of gut microbiota in human health and disease14 has received unprecedented attention over
the past few years with the rapid development of next-generation sequencing technologies.Several
complex chronic diseases,such as obesity15-18,inflammatory bowel disease19,20,diabetes mellitus21,
metabolic syndrome22,symptomatic atherosclerosis23 and non-alcoholic fatty liver disease10,have
been associated to gut microbiota.A metagenomic study of 345 Chinese individuals with type-2
diabetes (T2D) identified 60,000 T2D-associated genes24.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 resource25,which includes
most of the genera,enzyme families and community configurations from the microbiota of healthy
adults from Western countries26.A quantitative metagenomics analysis27,28 of stool samples from
Chinese liver cirrhosis patients and their healthy counterparts with the objective of improving the
understanding of gut microbiota changes associated with liver cirrhosis was carried out.
SUMMARY
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.
According to one embodiment of present disclosure,a biomarker for liver cirrhosis in a human
comprising
(i) at least 10 genes selected from 15 genes listed in Table 1 consisting of
MH0008_gene_7932 (SEQ ID NO:1)、L38_gene_38350 (SEQ ID NO:2)、NLM003_gene_35418
(SEQ ID NO:3)、H16_gene_75905 (SEQ ID NO:4)、H50_gene_73395 (SEQ ID NO:5)、
NLF009_gene_80134 (SEQ ID NO:6)、MH0085_gene_62624 (SEQ ID NO:7)、
DOM014_gene_22875 (SEQ ID NO:8)、L106_gene_52730 (SEQ ID NO:9)、H67_gene_32100
(SEQ ID NO:10)、L74_gene_31448 (SEQ ID NO:11)、DOF013_gene_35560 (SEQ ID NO:12)、
H55_gene_127852 (SEQ ID NO:13)、NOF008_gene_3070 (SEQ ID NO:14)、
DOM016_gene_86198 (SEQ ID NO:15),over-represented in gut microbiota of LC affected
subjects as compared to healthy subjects；and/or
(ii) complementary sequences or Homologous sequences of said at least 10 genes in (i)；and/or
(iii) gene products of said at least 10 genes in (i).
According to one embodiment of present disclosure,the biomarker for liver cirrhosis in a human
comprising
(2i) 15 genes listed in Table 1,over-represented in gut microbiota of LC affected subjects as
compared to healthy subjects；and/or
(2ii) complementary sequences or homologous sequences of said at least 15 genes in (2i)；
and/or
(2iii) gene products of said at least 15 genes in (2i).
According to one embodiment of present disclosure,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.
Preferably,wherein said composition (ii) reduces the amount of the 15 genes listed in Table 1,
and/or activity of the 15 genes listed in Table 1.
According to one embodiment of present disclosure,a method for diagnosing of liver cirrhosis
comprising the steps of:
(a) determining the amount of at least one of the followings:
(i) at least 10 genes selected from 15 genes listed in Table 1,over-represented in gut
microbiota of LC affected subjects as compared to healthy subjects；and/or
(ii) complementary sequences or homologous sequences of said at least 10 genes in (i)；and/or
(iii) gene products of said at least 10 genes in (i).
(b) comparing the amount obtained in step (a) with a preset threshold.
According to one embodiment of present disclosure,the method comprising the steps of:
(2a) determining the amount of at least one of the followings:
(i) 15 genes listed in Table 1,over-represented in gut microbiota of LC affected subjects as
compared to healthy subjects；and/or
(ii) complementary sequences or homologous sequences of said 15 genes in (i)；and/or
(iii) gene products of said 15 genes in (i).
(2b) comparing the amount obtained in step (2a) with a preset threshold.
According to one embodiment of present disclosure,a kit for diagnosing of liver cirrhosis
comprising reagents for:
(a) determining the amount of at least one of the followings:
(i) at least 10 genes selected from 15 genes listed in Table 1,over-represented in gut
microbiota of LC affected subjects as compared to healthy subjects；and/or
(ii) complementary sequences or homologous sequences of said at least 10 genes in (i)；and/or
(iii) gene products of said at least 10 genes in (i)；
(b) comparing the amount obtained in step (a) with a preset threshold.
According to one embodiment of present disclosure,the kit comprising the reagents for:
(2a) determining the amount of at least one of the followings:
(i) 15 genes listed in Table 1,over-represented in gut microbiota of LC affected subjects as
compared to healthy subjects；and/or
(ii) complementary sequences or homologous sequences of said 15 genes in (i)；and/or
(iii) gene products of said 15 genes in (i)；
(b) comparing the amount obtained in step (2a) with a preset threshold.
According to one embodiment of present disclosure,a method for identifying genes affected by LC
disease in a human comprising；
a) determining the metagenome of a diseased microbiota sample from the human；
b) determining the metagenome of a control untreated/healthy sample；
c) identifying under-represented and over-represented genes in the treated or diseased microbiota
sample in relation to the control sample.
According to one embodiment of present disclosure,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.
Additional aspects and advantages of embodiments of present disclosure will be given in part in the
following descriptions,become apparent in part from the following descriptions,or be learned from
the practice of the embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE FIGURES
Figure 1 illustrates diagram of the data analysis pipeline；
The study included a discovery phase.Volunteers for both phases were recruited in the same
hospital.Both direct read mapping and de novo assembly were performed for each sample.
Taxonomy profiling table was established for taxonomy analysis.A novel gut gene set was
established,and annotated.Identification of the finding markers is shown.
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；
A visualisation of the PCA results for the LC-associated genes that differed significantly in the
discovery cohort (fdr<0.0001 Wilcoxon rank-sum test adjusted for multiple testing).The PCA is
build here using these genes in the validation cohort (25 liver cirrhosis patients in dark and 31
healthy control in gray).
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.
These and other features,aspects,and advantages of the present invention will become better
understood with regard to the following description,appended claims,and accompanying drawings.
The following embodiments described by reference drawings are exemplary,which only used to
explain the present invention,and not regarded as the limitations of the present invention.
Example 1 Construction of a liver cirrhosis gut microbial gene set and comparison with
previous gene sets
All 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.
Total DNA was extracted from the faecal samples of 98 Chinese liver cirrhosis patients and 83
healthy Chinese controls and sequenced using the Illumina HiSeq 2000 (Illumina,San Diego,CA).
This produced an average of 4.74 Gb (sd.±2.04 Gb) of high quality sequence for each sample,
providing a total of 858 Gb of sequence data.The reads were assembled into contigs for all samples
using the assembly software SOAPdenovo29.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.
To predict microbial genes for each of the 181 samples,we applied the methodology used in the
MetaHIT human gut gene catalogue study30.The MetaGene program predicted 13,371,697 open
reading frames (ORFs) using a 100-bp cut-off for prediction.The total length of the predicted ORFs
was 9,495,923,532 bp,representing 90.28％ of the total length of the contigs.Among the ORFs,
1,047,885 (54.6％) were complete genes,while 869,808 (45.4％) were incomplete.A non-redundant
“LC gene set”was established by removing redundant ORFs,defined as those sharing 95％ identity
over 90％ of the shorter ORF length in pair-wise alignments.The final non-redundant liver cirrhosis
gut gene set contained 2,688,468 ORFs,with an average length of 750bp and 42％ of reads could be
aligned to the gene catalogue.
We compared our LC gene set with the three other gut microbiota gene sets,MetaHIT31,HMP25,
and T2D24.To facilitate this comparison,all genes were predicted from the original contigs using
the same criteria.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.
Genes from theLC,T2D and MetaHIT gene catalogs were merged to create a non-redundant gene
set for subsequent analyses.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).
Example 2
Gut microbial species associated with liver cirrhosis
To investigate the relationship between the human gut metagenomes of healthy control individuals
(n＝83) and liver cirrhosis patients (n＝96),we performed an association study for all genes from the
merged gene set.Based on profiles of all 181 training samples,a Wilcoxon rank-sum test combined
with Benjamini Hochberg adjustment for multiple testing was performed to identify differentially
abundant genes.Using a very stringent significance threshold (fdr<0.0001),significant differences
were found for 75,245 genes between healthy and liver cirrhosis groups.Of these,49,830 were
more abundant in the liver cirrhosis patients and 25,415 in the healthy control group as determined
using a rank-median test (Methods).A PCA analysis on these 75,245 genes revealed clear
differences between liver cirrhosis patients and healthy individuals.This result was confirmed in the
validation samples as well (Fig.3).
Based on 181 training samples,we introduced a pattern recognition technique to investigate the
possibility of identifying patients using gut microbiota information.Considering our computing
capacity,we selected 23,000 genes enriched in the liver cirrhosis group as biomarker candidates；the
same number of genes was selected from the healthy control group.From these 46,000 genes,we
selected 15 optimal gene markers using a minimum redundancy-maximum relevance (mRMR)
method combined with an incremental feature search,which showed highest Matthews correlation
coefficient value (Fig.5).A support vector machine (SVM) discriminator was constructed using the
181 training samples and 15 gene markers (Table 1),with the training and leave-one-out
cross-validation AUC (area under the receiver operating characteristic curve) achieving 0.918
(confidence interval:0.881-0.955) and 0.838 respectively.Analysis of samples from an additional
31 healthy controls and 25 liver cirrhosis patients showed an AUC value of 0.836 (CI:
0.730-0.943) for these samples,confirming that the gut microbiota information could be applied to
accurately identify potential patients.
To facilitate the clinical application of the 15 optimal gene markers,we tentatively propose a patient
discrimination index (PDI).The high correlation coefficient value between the ratio of patients in
our cohort and the PDI gave a preliminary indication that the PDI could be used for discriminating
patients with and without LC.The discriminatory power of the PDI was then validated using an
independentvalidation group.The average PDI index between the control and the patient groups
was significantly different (p<8.18e-05,Wilcoxon rank-sum test),confirming the potential use of gut
microbiota information for identifying liver cirrhosis patients.
Table 1:The 15 gene markers identified by the mRMR feature selection methods
METHODS
Human faecal sample collection and DNA extraction
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℃.Afrozen aliquot
(200 mg) of each faecal sample was processed by phenol Trichloromethane DNA extraction
method16 as previously described.DNA concentration was measured by nanodrop (Thermo
Scientific) and its molecular size was estimated by agarose gel electrophoresis.
DNA library construction and sequencing
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).
Quality control of reads
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.
De novo assembly of the Illumina short reads
Considering that kmers with very low frequencies might arise from sequencing errors,they were
not used in assembly by SOAPdenovo29 (version1.05),which is based on De brujin graph
construction.SOAPdenovo (version1.05) was used in Illumina short reads assembly with
parameters-d1-M3.Then we removed ambiguous bases from assembled scaffolds (this could
divide one scaffold into multiple ones) and discarded scaffolds with length less than 500 bp.Finally
we tested series of kmer values (from 31 to 59),then chose one with the longest N50 value for the
remaining scaffolds.For each sample,we mapped clean data against scaffolds using SOAPalign
version 2.21with parameters-u-2-m200.Unused data from each sample were pooled and split into
4 parts (considering memory limit).Unused reads were repeatedly assembled with the same
parameters but only one kmer value –K 55 was chosen.
Gene prediction and non-redundant human gut gene set
MetaGeneMark33 (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 set31 and T2D study24.We checked the gaps
and frames in the blat results.If there were gaps or the frames were different in the alignments
result of two ORFs,the shorter one would not be removed as a redundancy.We used
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.
Organism abundance profiling
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:
a) Unique reads (U):reads have alignments with only one genome；these reads were denoted as
unique reads.
b) Multiple reads (M):reads have alignments with more than one genome,if these genomes
come from one species；we denote these reads as unique reads.If they are from more than one
species,we denote these reads as multiple reads.
For species S,if its abundance is Ab (S),and it might have alignments with U unique reads and
Mmultiple reads,computation is as follows.
Ab(S)＝Ab(U)+Ab(M)
Ab(U)＝U/l
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.
For these reads,we will add unique abundance of N species as denominator.Before we calculate
abundance of species S,we had calculated Ab (U) for all species as constants,if Ab (U) of species S
is 0,then Co will also be 0,and consecutively the abundance of species S is 0.Profiling table at
genus level was generated by adding abundance of species into its genera.For some species that do
not have a genus,they are denoted as unclassified genera.
Gene abundance profiling
Reads were aligned against the gene set by using SOAPalign32 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.
When calculating abundance of genes,we used same strategy as for the organisms'abundance
profiling.For a given gene G,its abundance is Ab (G),and it might have alignments with U unique
reads and M multiplereads,it goes as follows.
Ab(G)＝Ab(U)+Ab(M)
Ab(U)＝U/l
Ab (U) and Ab (M) are abundance of unique and multiple reads respectively,l is length of gene G.
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.
For these reads,we will add unique abundance of N species as denominator.
Population stratification
Population stratification involved in our metagenomic data was corrected with modified
EIGENSTART method shown as follows:firstly,singular value decomposition was carried to
obtain axes of variation,where the number of significant axes was determined according to
Tracy-Widom test at a significance level of P<0.05；each axes was then replaced with the residuals
of this axis from a regression to disease state；the corrected data was finally achieved by subtracting
from original dataset the information associated with the residuals of each axis.
Gene count determination
Gene counts were computed essentially as described by Le Chatelier et al (2013).Briefly,data were
downsized to adjust for sequencing depth and technical variability linked to different sequencing by
randomly selecting 6.2 million of reads mapped to the merged gene catalog for each sample and
then computing the mean number of genes over 30 random drawings.This was possible for all but 2
liver cirrhosis patients from the validation cohort (with not sufficient number of mapped reads),
who were excluded from this analysis.
Gene biomarker identification
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.
Although the present invention has been described in considerable detail with reference to certain
preferred versions thereof,other versions are possible.Therefore,the spirit and scope of the
appended claims should not be limited to the description of the preferred versions contained therein.
Reference
1 Fouts,D.E.,Torralba,M.,Nelson,K.E.,Brenner,D.A.& Schnabl,B.Bacterial
translocation and changes in the intestinal microbiome in mouse models of liver disease.
Journal of hepatology 56,1283-1292,doi:10.1016/j.jhep.2012.01.019 (2012).
2 Cesaro,C.et al.Gut microbiota and probiotics in chronic liver diseases.Digestive and liver
disease:official journal of the Italian Society of Gastroenterology and the Italian
Association for the Study of the Liver 43,431-438,doi:10.1016/j.dld.2010.10.015 (2011).
3 Wiest,R.& Garcia-Tsao,G.Bacterial translocation (BT) in cirrhosis.Hepatology 41,
422-433,doi:10.1002/hep.20632 (2005).
4 Nolan,J.P.The role of intestinal endotoxin in liver injury:a long and evolving history.
Hepatology 52,1829-1835,doi:10.1002/hep.23917 (2010).
5 Gill,S.R.et al.Metagenomic analysis of the human distal gut microbiome.Science 312,
1355-1359,doi:10.1126/science.1124234 (2006).
6 Garcia-Tsao,G.& Wiest,R.Gut microflora in the pathogenesis of the complications of
cirrhosis.Best practice & research.Clinical gastroenterology 18,353-372,
doi:10.1016/j.bpg.2003.10.005 (2004).
7 Wiest,R.,Krag,A.& Gerbes,A.Spontaneous bacterial peritonitis:recent guidelines and
beyond.Gut 61,297-310,doi:10.1136/gutjnl-2011-300779 (2012).
8 Bass,N.M.et al.Rifaximin treatment in hepatic encephalopathy.The New England journal
of medicine 362,1071-1081,doi:10.1056/NEJMoa0907893 (2010).
9 Benten,D.& Wiest,R.Gut microbiome and intestinal barrier failure--the "Achilles heel"in
hepatology？Journal of hepatology 56,1221-1223,doi:10.1016/j.jhep.2012.03.003 (2012).
10 Yan,A.W.et al.Enteric dysbiosis associated with a mouse model of alcoholic liver disease.
Hepatology 53,96-105,doi:10.1002/hep.24018 (2011).
11 De Filippo,C.et al.Impact of diet in shaping gut microbiota revealed by a comparative
study in children from Europe and rural Africa.Proceedings of the National Academy of
Sciences of the United States of America 107,14691-14696,doi:10.1073/pnas.1005963107
(2010).
12 Cho,I.& Blaser,M.J.The human microbiome:at the interface of health and disease.
Nature reviews.Genetics 13,260-270,doi:10.1038/nrg3182 (2012).
13 Chen,Y.et al.Characterization of fecal microbial communities in patients with liver
cirrhosis.Hepatology 54,562-572,doi:10.1002/hep.24423 (2011).
14 Nelson,K.E.et al.A catalog of reference genomes from the human microbiome.Science
328,994-999,doi:10.1126/science.1183605 (2010).
15 Ley,R.E.,Turnbaugh,P.J.,Klein,S.& Gordon,J.I.Microbial ecology:human gut
microbes associated with obesity.Nature 444,1022-1023,doi:10.1038/4441022a (2006).
16 Turnbaugh,P.J.et al.An obesity-associated gut microbiome with increased capacity for
energy harvest.Nature 444,1027-1031,doi:10.1038/nature05414 (2006).
17 Turnbaugh,P.J.et al.A core gut microbiome in obese and lean twins.Nature 457,480-484,
doi:10.1038/nature07540 (2009).
18 Ley,R.E.et al.Obesity alters gut microbial ecology.Proceedings of the National Academy
of Sciences of the United States of America 102,11070-11075,
doi:10.1073/pnas.0504978102 (2005).
19 Lepage,P.et al.Twin study indicates loss of interaction between microbiota and mucosa of
patients with ulcerative colitis.Gastroenterology 141,227-236,
doi:10.1053/j.gastro.2011.04.011 (2011).
20 Garrett,W.S.et al.Enterobacteriaceae act in concert with the gut microbiota to induce
spontaneous and maternally transmitted colitis.Cell host & microbe 8,292-300,
doi:10.1016/j.chom.2010.08.004 (2010).
21 Wen,L.et al.Innate immunity and intestinal microbiota in the development of Type 1
diabetes.Nature 455,1109-1113,doi:10.1038/nature07336 (2008).
22 Vijay-Kumar,M.et al.Metabolic syndrome and altered gut microbiota in mice lacking
Toll-like receptor 5.Science 328,228-231,doi:10.1126/science.1179721 (2010).
23 Karlsson,F.H.et al.Symptomatic atherosclerosis is associated with an altered gut
metagenome.Nature communications 3,1245,doi:10.1038/ncomms2266 (2012).
24 Qin,J.et al.A metagenome-wide association study of gut microbiota in type 2 diabetes.
Nature 490,55-60,doi:10.1038/nature11450 (2012).
25 A framework for human microbiome research.Nature 486,215-221,
doi:10.1038/nature11209 (2012).
26 Structure,function and diversity of the healthy human microbiome.Nature 486,207-214,
doi:10.1038/nature11234 (2012).
27 Le Chatelier,E.et al.Richness of human gut microbiome correlates with metabolic markers.
Nature 500,541-546,doi:10.1038/nature 12506 (2013).
28 Cotillard,A.et al.Dietary intervention impact on gut microbial gene richness.Nature 500,
585-588,doi:10.1038/nature 12480 (2013).
29 Li,R.et al.De novo assembly of human genomes with massively parallel short read
sequencing.Genome research 20,265-272,doi:10.1101/gr.097261.109 (2010).
30 Gautam,M.,Chopra,K.B.,Douglas,D.D.,Stewart,R.A.& Kusne,S.Streptococcus
salivarius bacteremia and spontaneous bacterial peritonitis in liver transplantation candidates.
Liver transplantation:official publication of the American Association for the Study of
Liver Diseases and the International Liver Transplantation Society 13,1582-1588,
doi:10.1002/lt.21277 (2007).
31 Qin,J.et al.A human gut microbial gene catalogue established by metagenomic sequencing.
Nature 464,59-65,doi:10.1038/nature08821 (2010).
32 Li,R.et al.SOAP2:an improved ultrafast tool for short read alignment.Bioinformatics 25,
1966-1967,doi:10.1093/bioinformatics/btp336 (2009).
33 Noguchi,H.,Park,J.& Takagi,T.MetaGene:prokaryotic gene finding from environmental
genome shotgun sequences.Nucleic acids research 34,5623-5630,doi:10.1093/nar/gkl723
(2006).
Claims (8)
- A biomarker for liver cirrhosis in a human comprising(i) at least 10 genes selected from 15 genes listed in Table 1 consisting of MH0008_gene_7932 (SEQ ID NO:1)、L38_gene_38350 (SEQ ID NO:2)、NLM003_gene_35418 (SEQ ID NO:3)、 H16_gene_75905 (SEQ ID NO:4)、H50_gene_73395 (SEQ ID NO:5)、NLF009_gene_80134 (SEQ ID NO:6)、MH0085_gene_62624 (SEQ ID NO:7)、DOM014_gene_22875 (SEQ ID NO: 8)、L106_gene_52730 (SEQ ID NO:9)、H67_gene_32100 (SEQ ID NO:10)、L74_gene_31448 (SEQ ID NO:11)、DOF013_gene_35560 (SEQ ID NO:12)、H55_gene_127852 (SEQ ID NO: 13)、NOF008_gene_3070 (SEQ ID NO:14)、DOM016_gene_86198 (SEQ ID NO:15)；and/or(ii) complementary sequences or homologous sequences of said at least 10 genes in (i)；and/or(iii) gene products of said at least 10 genes in (i).
- The biomarker of claim1,comprising(2i) 15 genes listed in Table 1,over-represented in gut microbiota of LC affected subjects as compared to healthy subjects；and/or(2ii) complementary sequences or homologous sequences of said at least 15 genes in (2i)；and/or(2iii) gene products of said at least 15 genes in (2i).
- 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.
- The method of claim3,wherein said composition 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:(a) determining the amount of at least one of the followings:(i) at least 10 genes selected from 15 genes listed in Table 1,over-represented in gut microbiota of LC affected subjects as compared to healthy subjects；and/or(ii) complementary sequences or homologous sequences of said at least 10 genes in (i)；and/or(iii) gene products of said at least 10 genes in (i)；(b) comparing the amount obtained in step (a) with a preset threshold.
- The method of claim 5 comprising the steps of:(2a) determining the amount of at least one of the followings:(i) 15 genes listed in Table 1；and/or(ii) complementary sequences or homologous sequences of said 15 genes in (i)；and/or(iii) gene products of said 15 genes in (i)；(2b) comparing the amount obtained in step (2a) with a preset threshold.
- Akit for diagnosing of liver cirrhosis comprising reagents for:(a) determining the amount of at least one of the followings:(i) at least 10 genes selected from 15 genes listed in Table 1；and/or(ii) complementary sequences or homologous sequences of said at least 10 genes in (i)；and/or(iii) gene products of said at least 10 genes in (i)；(b) comparing the amount obtained in step (a) with a preset threshold.
- The kit of claim 7 comprising the reagents for:(2a) determining the amount of at least one of the followings:(i) 15 genes listed in Table 1；and/or(ii) complementary sequences or homologous sequences of said 15 genes in (i)；and/or(iii) gene products of said 15 genes in (i)；(2b) comparing the amount obtained in step (2a) with a preset threshold.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997011968A2 (en) * | 1995-09-27 | 1997-04-03 | Cedars-Sinai Medical Center | A gene associated with liver neoplastic disease |
| WO2014019408A1 (en) * | 2012-08-01 | 2014-02-06 | Bgi Shenzhen | Biomarkers for diabetes and usages thereof |
| CN104195145A (en) * | 2014-07-15 | 2014-12-10 | 浙江大学 | Biomarker of liver cirrhosis, and application thereof |
-
2014
- 2014-07-15 WO PCT/CN2014/082182 patent/WO2016008082A1/en not_active Ceased
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997011968A2 (en) * | 1995-09-27 | 1997-04-03 | Cedars-Sinai Medical Center | A gene associated with liver neoplastic disease |
| WO2014019408A1 (en) * | 2012-08-01 | 2014-02-06 | Bgi Shenzhen | Biomarkers for diabetes and usages thereof |
| CN104195145A (en) * | 2014-07-15 | 2014-12-10 | 浙江大学 | Biomarker of liver cirrhosis, and application thereof |
Non-Patent Citations (4)
| 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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