Metagenome-wide Association Studies Research Articles

Metaanalysis of Public Wastewater Metagenomes: Revealing the Influence of Climatic Variations on the Abundance of the Bacterial Members

As per the present trend, earth may experience severe hike in temperature by 2040. Therefore, global surveillance is required to combat against the upcoming climatic extremities. Emerging and re-emerging infectious diseases are part of the key pressing issues which can affect millions of people worldwide. Wastewater associated metagenomic data representing the wastewater samples collected from sewer networks in Newcastle (United Kingdom); Zimbabwe (Africa) and West Bengal (India), available in public databases were collected. It was observed that of the top 10 abundant genera, the prevalent genera in samples from United Kingdom, Africa, India were Desulfitobacterium, Prevotella, Sulfurospirillum respectively. By comparing all the three samples under study; it was observed that three microbial genera Bacteroides, Dechloromonas and Faecalibacterium were common throughout the samples. Metagenome-wide association studies were conducted to link microbes in the sample to the host disorders. Our study could predict overexpression of several enriched pathways like Vancomycin resistance, Platinum drug resistance across the samples. These findings prove efficacies of the community genomics approach to serve as a public health monitoring tool through detection of spatial variation as well as degree of commonness in wastewater bacterial profiles and also through the prediction of the pathogenic quantum of wastewater systems.

Bacterial SNPs in the human gut microbiome associate with host BMI.

Genome-wide association studies (GWASs) have provided numerous associations between human single-nucleotide polymorphisms (SNPs) and health traits. Likewise, metagenome-wide association studies (MWASs) between bacterial SNPs and human traits can suggest mechanistic links, but very few such studies have been done thus far. In this study, we devised an MWAS framework to detect SNPs and associate them with host phenotypes systematically. We recruited and obtained gut metagenomic samples from a cohort of 7,190 healthy individuals and discovered 1,358 statistically significant associations between a bacterial SNP and host body mass index (BMI), from which we distilled 40 independent associations. Most of these associations were unexplained by diet, medications or physical exercise, and 17 replicated in a geographically independent cohort. We uncovered BMI-associated SNPs in 27 bacterial species, and 12 of them showed no association by standard relative abundance analysis. We revealed a BMI association of an SNP in a potentially inflammatory pathway of Bilophila wadsworthia as well as of a group of SNPs in a region coding for energy metabolism functions in a Faecalibacterium prausnitzii genome. Our results demonstrate the importance of considering nucleotide-level diversity in microbiome studies and pave the way toward improved understanding of interpersonal microbiome differences and their potential health implications.

MicroEXPERT: Microbiome profiling platform with cross-study metagenome-wide association analysis functionality.

The framework of the MicroEXPERT platform. Our Platform was composed of five modules. Data management module: Users upload raw data and metadata to the system using a guided workflow. Data processing module: Uploaded data is processed to generate taxonomical distribution and functional composition results. Metagenome-wide association studies module (MWAS): Various methods, including biomarker analysis, PCA, co-occurrence networks, and sample classification, are employed using metadata. Data search module: Users can query nucleotide sequences to retrieve information in the MicroEXPERT database. Data visualization module: Visualization tools are used to illustrate the metagenome analysis results.

A consortium of three-bacteria isolated from human feces inhibits formation of atherosclerotic deposits and lowers lipid levels in a mouse model

By a survey of metagenome-wide association studies (MWAS), we found a robust depletion of Bacteroides cellulosilyticus, Faecalibacterium prausnitzii, and Roseburia intestinalis in individuals with atherosclerotic cardiovascular disease (ACVD). From an established collection of bacteria isolated from healthy Chinese individuals, we selected B.cellulosilyticus, R.intestinalis, and Faecalibacterium longum, a bacterium related to F.prausnitzii, and tested the effects of these bacteria in an Apoe/- atherosclerosis mouse model. We show that administration of these three bacterial species to Apoe-/- mice robustly improves cardiac function, reduces plasma lipid levels, and attenuates the formation of atherosclerotic plaques. Comprehensive analysis of gut microbiota, plasma metabolome, and liver transcriptome revealed that the beneficial effects are associated with a modulation of the gut microbiota linked to a 7α-dehydroxylation-lithocholic acid (LCA)-farnesoid X receptor (FXR) pathway. Our study provides insights into transcriptional and metabolic impact whereby specific bacteria may hold promises for prevention/treatment of ACVD.

Metagenomics of Parkinson’s disease implicates the gut microbiome in multiple disease mechanisms

Parkinson’s disease (PD) may start in the gut and spread to the brain. To investigate the role of gut microbiome, we conducted a large-scale study, at high taxonomic resolution, using uniform standardized methods from start to end. We enrolled 490 PD and 234 control individuals, conducted deep shotgun sequencing of fecal DNA, followed by metagenome-wide association studies requiring significance by two methods (ANCOM-BC and MaAsLin2) to declare disease association, network analysis to identify polymicrobial clusters, and functional profiling. Here we show that over 30% of species, genes and pathways tested have altered abundances in PD, depicting a widespread dysbiosis. PD-associated species form polymicrobial clusters that grow or shrink together, and some compete. PD microbiome is disease permissive, evidenced by overabundance of pathogens and immunogenic components, dysregulated neuroactive signaling, preponderance of molecules that induce alpha-synuclein pathology, and over-production of toxicants; with the reduction in anti-inflammatory and neuroprotective factors limiting the capacity to recover. We validate, in human PD, findings that were observed in experimental models; reconcile and resolve human PD microbiome literature; and provide a broad foundation with a wealth of concrete testable hypotheses to discern the role of the gut microbiome in PD.

Overrepresentation of Enterobacteriaceae and Escherichia coli is the major gut microbiome signature in Crohn's disease and ulcerative colitis; a comprehensive metagenomic analysis of IBDMDB datasets.

ObjectivesA number of converging strands of research suggest that the intestinal Enterobacteriaceae plays a crucial role in the development and progression of inflammatory bowel disease (IBD), however, the changes in the abundance of Enterobacteriaceae species and their related metabolic pathways in Crohn’s disease (CD) and ulcerative colitis (UC) compared to healthy people are not fully explained by comprehensive comparative metagenomics analysis. In the current study, we investigated the alternations of the Enterobacterales population in the gut microbiome of patients with CD and UC compared to healthy subjects.MethodsMetagenomic datasets were selected from the Integrative Human Microbiome Project (HMP2) through the Inflammatory Bowel Disease Multi’omics Database (IBDMDB). We performed metagenome-wide association studies on fecal samples from 191 CD patients, 132 UC patients, and 125 healthy controls (HCs). We used the metagenomics dataset to study bacterial community structure, relative abundance, differentially abundant bacteria, functional analysis, and Enterobacteriaceae-related biosynthetic pathways.ResultsCompared to the gut microbiome of HCs, six Enterobacteriaceae species were significantly elevated in both CD and UC patients, including Escherichia coli, Klebsiella variicola, Klebsiella quasipneumoniae, Klebsiella pneumoniae, Proteus mirabilis, Citrobacter freundii, and Citrobacter youngae, while Klebsiella oxytoca, Morganella morganii, and Citrobacter amalonaticus were uniquely differentially abundant and enriched in the CD cohort. Four species were uniquely differentially abundant and enriched in the UC cohort, including Citrobacter portucalensis, Citrobacter pasteurii, Citrobacter werkmanii, and Proteus hauseri. Our analysis also showed a dramatically increased abundance of E. coli in their intestinal bacterial community. Biosynthetic pathways of aerobactin siderophore, LPS, enterobacterial common antigen, nitrogen metabolism, and sulfur relay systems encoded by E. coli were significantly elevated in the CD samples compared to the HCs. Menaquinol biosynthetic pathways were associated with UC that belonged to K. pneumoniae strains.ConclusionsIn conclusion, compared with healthy people, the taxonomic and functional composition of intestinal bacteria in CD and UC patients was significantly shifted to Enterobacteriaceae species, mainly E. coli and Klebsiella species.

Analysis and evaluation of different sequencing depths from 5 to 20 million reads in shotgun metagenomic sequencing, with optimal minimum depth being recommended.

Our study was to analyze and evaluate the impact of different shotgun metagenomic sequencing depths from 5 to 20 million in metagenome-wide association studies (MWASs), and to determine the optimal minimum sequencing depth. We included a set of 200 previously published gut microbial shotgun metagenomic sequencing data on obesity (100 obese vs. 100 non-obese). The reads with original sequencing depths>20million were downsized into seven experimental groups with depths from 5 to 20 million (interval 2.5 million). Using both integrated gene cluster (IGC) and metagenomic phylogenetic analysis 2 (MetaPhlAn2), we obtained and analyzed the read matching rates, gene count, species richness and abundance, diversity, and clinical biomarkers of the experimental groups with the original depth as the control group. An additional set of 100 published data from a colorectal cancer (CRC) study was included for validation (50 CRC vs. 50 CRC-free). Our results showed that more genes and species were identified following the increase in sequencing depths. When it reached 15 million or higher, the species richness became more stable with changing rate of 5% or lower, and the species composition more stable with ICC intraclass correlation coefficient (ICC) higher than 0.75. In terms of species abundance, 81% and 97% of species showed significant differences in IGC and MetaPhlAn2 among all groups with p<0.05. Diversity showed significant differences across all groups, with decreasing differences of diversity between the experimental and the control groups following the increase in sequencing depth. The area under a receiver operating characteristic curve, AUC, of the obesity classifier for running the obesity testing samples showed an increasing trend following the increase in sequencing depth (τ = 0.29). The validation results were consistent with the above results. Our study found that the higher the sequencing depth is, the more the microbial information in structure and composition it provides. We also found that when sequencing depth was 15 million or higher, we obtained more stable species compositions and disease classifiers with good performance. Therefore, we recommend 15 million as the optimal minimum sequencing depth for an MWAS.

Association of Intrauterine Microbes with Endometrial Factors in Intrauterine Adhesion Formation and after Medicine Treatment.

Intrauterine adhesions (IUAs) have caused serious harm to women’s reproductive health. Although emerging evidence has linked intrauterine microbiome to gynecological diseases, the association of intrauterine microbiome with IUA, remains unknown. We performed metagenome-wide association, metabolomics, and transcriptomics studies on IUA and non-IUA uteri of adult rats to identify IUA-associated microbial species, which affected uterine metabolites and endometrial transcriptions. A rat model was used with one side of the duplex uterus undergoing IUA and the other remaining as a non-IUA control. Both 16S rRNA sequencing and metagenome-wide association analysis revealed that instead of Mycoplasmopsis specie in genital tract, murine lung pathogen Mycoplasmopsis pulmonis markedly increased in IUA samples and displayed a distinct positive interaction with the host immune system. Moreover, most of the IUA-enriched 58 metabolites positively correlate with M. pulmonis, which inversely correlates with a mitotic progression inhibitor named 3-hydroxycapric acid. A comparison of metabolic profiles of intrauterine flushing fluids from human patients with IUA, endometritis, and fallopian tube obstruction suggested that rat IUA shared much similarity to human IUA. The endometrial gene Tenascin-N, which is responsible for extracellular matrix of wounds, was highly up-regulated, while the key genes encoding parvalbumin, trophectoderm Dkkl1 and telomerase involved in leydig cells, trophectoderm cells, activated T cells and monocytes were dramatically down-regulated in rat IUA endometria. Treatment for rat IUA with estrogen (E2), oxytetracycline (OTC), and a traditional Chinese patent medicine GongXueNing (GXN) did not reduce the incidence of IUA, though inflammatory factor IL-6 was dramatically down-regulated (96–86%) with all three. Instead, in both the E2 and OTC treated groups, IUA became worse with a highly up-regulated B cell receptor signaling pathway, which may be associated with the significantly increased proportions of Ulvibacter or Staphylococcus. Our results suggest an association between intrauterine microbiota alterations, certain uterine metabolites, characteristic changes in endometrial transcription, and IUA and the possibility to intervene in IUA formation by targeting the causal factors, microbial infection, and Tenascin-like proteins.

OMARU: a robust and multifaceted pipeline for metagenome-wide association study.

Microbiome is an essential omics layer to elucidate disease pathophysiology. However, we face a challenge of low reproducibility in microbiome studies, partly due to a lack of standard analytical pipelines. Here, we developed OMARU (Omnibus metagenome-wide association study with robustness), a new end-to-end analysis workflow that covers a wide range of microbiome analysis from phylogenetic and functional profiling to case–control metagenome-wide association studies (MWAS). OMARU rigorously controls the statistical significance of the analysis results, including correction of hidden confounding factors and application of multiple testing comparisons. Furthermore, OMARU can evaluate pathway-level links between the metagenome and the germline genome-wide association study (i.e. MWAS-GWAS pathway interaction), as well as links between taxa and genes in the metagenome. OMARU is publicly available (https://github.com/toshi-kishikawa/OMARU), with a flexible workflow that can be customized by users. We applied OMARU to publicly available type 2 diabetes (T2D) and schizophrenia (SCZ) metagenomic data (n = 171 and 344, respectively), identifying disease biomarkers through comprehensive, multilateral, and unbiased case–control comparisons of metagenome (e.g. increased Streptococcus vestibularis in SCZ and disrupted diversity in T2D). OMARU improves accessibility and reproducibility in the microbiome research community. Robust and multifaceted results of OMARU reflect the dynamics of the microbiome authentically relevant to disease pathophysiology.

Disease trends in a young Chinese cohort according to fecal metagenome and plasma metabolites

Most of the disease studies for the gut microbiome have collected cases and control samples from the elderly or the middle-aged. Despite general interest in microbiome health, it is not known how microbial biomarkers from metagenome-wide association studies (MWAS) would perform in a cohort of young individuals, who would be largely free of chronic diseases, as well as medication. Here we analyze high-depth fecal metagenomic shotgun sequencing for 2183 healthy adults with clinical parameters, diet, lifestyle, and metabolite measurements. We provide the first set of large-scale evidence for gut microbiome dysbiosis in hyperuricemia, which relates to meat intake. We build a cardiometabolic disease risk model based on gut microbes for initial screening in a young population and confirm the validity using external cohorts. Fecal bacteria that have been reported to be enriched in colorectal cancer (CRC) are found to correlate with methylhistidines, branched-chain amino acids (BCAA), aromatic amino acids and glutamic acid in these young individuals, which were validated by an additional cohort of 1404 individuals. Our comprehensive data suggest that the gut microbiome could show trends towards diseases years before onset, and the results lay the foundation for the design of larger screens for cardiometabolic diseases and CRC with clinically meaningful cutoffs.

Metagenome Analysis of Intestinal Bacteria in Healthy People, Patients With Inflammatory Bowel Disease and Colorectal Cancer.

ObjectivesSeveral reports suggesting that the intestinal microbiome plays a key role in the development of inflammatory bowel disease (IBD) or colorectal cancer (CRC), but the changes of intestinal bacteria in healthy people, patients with IBD and CRC are not fully explained. The study aimed to investigate changes of intestinal bacteria in healthy subjects, patients with IBD, and patients with CRC.MaterialsWe collected data from the European Nucleotide Archive on healthy people and patients with colorectal cancer with the study accession number PRJEB6070, PRJEB7774, PRJEB27928, PRJEB12449, and PRJEB10878, collected IBD patient data from the Integrated Human Microbiome Project from the Human Microbiome Project Data Portal. We performed metagenome-wide association studies on the fecal samples from 290 healthy subjects, 512 IBD patients, and 285 CRC patients. We used the metagenomics dataset to study bacterial community structure, relative abundance, functional prediction, differentially abundant bacteria, and co-occurrence networks.ResultsThe bacterial community structure in both IBD and CRC was significantly different from healthy subjects. Our results showed that IBD patients had low intestinal bacterial diversity and CRC patients had high intestinal bacterial diversity compared to healthy subjects. At the phylum level, the relative abundance of Firmicutes in IBD decreased significantly, while the relative abundance of Bacteroidetes increased significantly. At the genus level, the relative abundance of Bacteroides in IBD was higher than in healthy people and CRC. Compared with healthy people and CRC, the main difference of intestinal bacteria in IBD patients was Bacteroidetes, and compared with healthy people and IBD, the main difference of intestinal bacteria in CRC patients was in Fusobacteria, Verrucomicrobia, and Proteobacteria. The main differences in the functional composition of intestinal bacteria in healthy people, IBD and CRC patients were L-homoserine and L-methionine biosynthesis, 5-aminoimidazole ribonucleotide biosynthesis II, L-methionine biosynthesis I, and superpathway of L-lysine, L-threonine, and L-methionine biosynthesis I. The results of stratified showed that the abundance of Firmicutes, Bacteroidetes, and Actinobacteria involved in metabolic pathways has significantly changed. Besides, the association network of intestinal bacteria in healthy people, IBD, and CRC patients has also changed.ConclusionsIn conclusion, compared with healthy people, the taxonomic and functional composition of intestinal bacteria in IBD and CRC patients was significantly changed.

MB-GAN: Microbiome Simulation via Generative Adversarial Network.

BackgroundTrillions of microbes inhabit the human body and have a profound effect on human health. The recent development of metagenome-wide association studies and other quantitative analysis methods accelerate the discovery of the associations between human microbiome and diseases. To assess the strengths and limitations of these analytical tools, simulating realistic microbiome datasets is critically important. However, simulating the real microbiome data is challenging because it is difficult to model their correlation structure using explicit statistical models.ResultsTo address the challenge of simulating realistic microbiome data, we designed a novel simulation framework termed MB-GAN, by using a generative adversarial network (GAN) and utilizing methodology advancements from the deep learning community. MB-GAN can automatically learn from given microbial abundances and compute simulated abundances that are indistinguishable from them. In practice, MB-GAN showed the following advantages. First, MB-GAN avoids explicit statistical modeling assumptions, and it only requires real datasets as inputs. Second, unlike the traditional GANs, MB-GAN is easily applicable and can converge efficiently.ConclusionsBy applying MB-GAN to a case-control gut microbiome study of 396 samples, we demonstrated that the simulated data and the original data had similar first-order and second-order properties, including sparsity, diversities, and taxa-taxa correlations. These advantages are suitable for further microbiome methodology development where high-fidelity microbiome data are needed.

The Formation of Glycan-Specific Natural Antibodies Repertoire in GalT-KO Mice Is Determined by Gut Microbiota.

Gut commensal bacteria are known to have a significant role in regulating the innate and adaptive immune homeostasis. Alterations in the intestinal microbial composition have been associated with several disease states, including autoimmune and inflammatory conditions. However, it is not entirely clear how commensal gut microbiota modulate and contribute to the systemic immunity, and whether circulating elements of the host immune system could regulate the microbiome. Thus, we have studied the diversity and abundance of specific taxons in the gut microbiota of inbred GalT-KO mice during 7 months of animal life by metagenetic high-throughput sequencing (16S rRNA gene, variable regions V3–V5). The repertoire of glycan-specific natural antibodies, obtained by printed glycan array technology, was then associated with the microbial diversity for each animal by metagenome-wide association studies (MWAS). Our data show that the orders clostridiales (most abundant), bacteriodales, lactobacillales, and deferribacterales may be associated with the development of the final repertoire of natural anti-glycan antibodies in GalT-KO mice. The main changes in microbiota diversity (month-2 and month-3) were related to important changes in levels and repertoire of natural anti-glycan antibodies in these mice. Additionally, significant positive and negative associations were found between the gut microbiota and the pattern of specific anti-glycan antibodies. Regarding individual features, the gut microbiota and the corresponding repertoire of natural anti-glycan antibodies showed differences among the examined animals. We also found redundancy in different taxa associated with the development of specific anti-glycan antibodies. Differences in microbial diversity did not, therefore, necessarily influence the overall functional output of the gut microbiome of GalT-KO mice. In summary, the repertoire of natural anti-carbohydrate antibodies may be partially determined by the continuous antigenic stimulation produced by the gut bacterial population of each GalT-KO mouse. Small differences in gut microbiota diversity could determine different repertoire and levels of natural anti-glycan antibodies and consequently might induce different immune responses to pathogens or other potential threats.

Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL).

The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper, we demonstrate a novel, efficient, and effective Multiple Instance Learning (MIL) based computational pipeline to predict patient phenotype from metagenomic data. MIL methods have the advantage that besides predicting the clinical phenotype, we can infer the instance level label or role of microbial sequence reads in the specific disease. Specifically, we use a Bag of Words method, which has been shown to be one of the most effective and efficient MIL methods. This involves assembly of the metagenomic sequence data, clustering of the assembled contigs, extracting features from the contigs, and using an SVM classifier to predict patient labels and identify the most relevant sequence clusters. With the exception of the given labels for the patients, this entire process is de novo (unsupervised). We call our pipeline "CAMIL", which stands for Clustering and Assembly with Multiple Instance Learning. We use multiple state-of-the-art clustering methods for feature extraction, evaluation, and comparison of the performance of our proposed approach for each of these clustering methods. We also present a fast and scalable pre-clustering algorithm as a preprocessing step for our proposed pipeline. Our approach achieves efficiency by partitioning the large number of sequence reads into groups (called canopies) using locality sensitive hashing (LSH). These canopies are then refined by using state-of-the-art sequence clustering algorithms. We use data from a well-known MGWAS study of patients with Type-2 Diabetes and show that our pipeline significantly outperforms the classifier used in that paper, as well as other common MIL methods.

Evaluation of ddRADseq for reduced representation metagenome sequencing.

BackgroundProfiling of microbial communities via metagenomic shotgun sequencing has enabled researches to gain unprecedented insight into microbial community structure and the functional roles of community members. This study describes a method and basic analysis for a metagenomic adaptation of the double digest restriction site associated DNA sequencing (ddRADseq) protocol for reduced representation metagenome profiling.MethodsThis technique takes advantage of the sequence specificity of restriction endonucleases to construct an Illumina-compatible sequencing library containing DNA fragments that are between a pair of restriction sites located within close proximity. This results in a reduced sequencing library with coverage breadth that can be tuned by size selection. We assessed the performance of the metagenomic ddRADseq approach by applying the full method to human stool samples and generating sequence data.ResultsThe ddRADseq data yields a similar estimate of community taxonomic profile as obtained from shotgun metagenome sequencing of the same human stool samples. No obvious bias with respect to genomic G + C content and the estimated relative species abundance was detected.DiscussionAlthough ddRADseq does introduce some bias in taxonomic representation, the bias is likely to be small relative to DNA extraction bias. ddRADseq appears feasible and could have value as a tool for metagenome-wide association studies.

Metagenome-Wide Association Study and Machine Learning Prediction of Bulk Soil Microbiome and Crop Productivity.

Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop genotype, and management practices. One hypothesis is that abiotic or biotic factors in the soils differ between areas resulting in these productivity differences. In this study, bulk soil samples collected from a high and a low productivity area from within six agronomic fields in Illinois were quantified for abiotic and biotic characteristics. Extracted DNA from these bulk soil samples were shotgun sequenced. While logistic regression analyses resulted in no significant association between crop productivity and the 26 soil characteristics, principal coordinate analysis and constrained correspondence analysis showed crop productivity explained a major proportion of the taxa variance in the bulk soil microbiome. Metagenome-wide association studies (MWAS) identified more Bradyrhizodium and Gammaproteobacteria in higher productivity areas and more Actinobacteria, Ascomycota, Planctomycetales, and Streptophyta in lower productivity areas. Machine learning using a random forest method successfully predicted productivity based on the microbiome composition with the best accuracy of 0.79 at the order level. Our study showed that crop productivity differences were associated with bulk soil microbiome composition and highlighted several nitrogen utility-related taxa. We demonstrated the merit of MWAS and machine learning for the first time in a plant-microbiome study.

Further analysis reveals new gut microbiome markers of type 2 diabetes mellitus.

In recent years, metagenome-wide association studies have revealed potential relationships between intestinal microbiomes and the pathogenesis of type 2 diabetes mellitus (T2DM). However, considering the increase in volume of gene catalogues and algorithms, an updated analysis would be expected to confirm previous discoveries and provide new knowledge. We therefore constructed new profiles after mapping the recent catalogue of reference genes in the human gut microbiome to reanalyze samples from T2DM cases and controls in the Chinese population. We identified different compositions between Chinese controls and T2DM patients at the species and genus levels, especially in the case of butyrate-producing bacteria, Haemophilus, and Lactobacillus. An effective metagenomic linkage group random forest model was built to differentiate controls from T2DM cases in different cohorts. Functional markers from the Kyoto Encyclopedia of Genes and Genomes database were identified using new annotations. We also report 16 virulence factor markers and 22 antibiotic resistance markers associated with T2DM.

Metagenome-wide association studies: fine-mining the microbiome.

Metagenome-wide association studies (MWAS) have enabled the high-resolution investigation of associations between the human microbiome and several complex diseases, including type 2 diabetes, obesity, liver cirrhosis, colorectal cancer and rheumatoid arthritis. The associations that can be identified by MWAS are not limited to the identification of taxa that are more or less abundant, as is the case with taxonomic approaches, but additionally include the identification of microbial functions that are enriched or depleted. In this Review, we summarize recent findings from MWAS and discuss how these findings might inform the prevention, diagnosis and treatment of human disease in the future. Furthermore, we highlight the need to better characterize the biology of many of the bacteria that are found in the human microbiota as an essential step in understanding how bacterial strains that have been identified by MWAS are associated with disease.

Type 2 Diabetes Biomarkers of Human Gut Microbiota Selected via Iterative Sure Independent Screening Method.

Type 2 diabetes, which is a complex metabolic disease influenced by genetic and environment, has become a worldwide problem. Previous published results focused on genetic components through genome-wide association studies that just interpret this disease to some extent. Recently, two research groups published metagenome-wide association studies (MGWAS) result that found meta-biomarkers related with type 2 diabetes. However, One key problem of analyzing genomic data is that how to deal with the ultra-high dimensionality of features. From a statistical viewpoint it is challenging to filter true factors in high dimensional data. Various methods and techniques have been proposed on this issue, which can only achieve limited prediction performance and poor interpretability. New statistical procedure with higher performance and clear interpretability is appealing in analyzing high dimensional data. To address this problem, we apply an excellent statistical variable selection procedure called iterative sure independence screening to gene profiles that obtained from metagenome sequencing, and 48/24 meta-markers were selected in Chinese/European cohorts as predictors with 0.97/0.99 accuracy in AUC (area under the curve), which showed a better performance than other model selection methods, respectively. These results demonstrate the power and utility of data mining technologies within the large-scale and ultra-high dimensional genomic-related dataset for diagnostic and predictive markers identifying.

Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer

ObjectiveTo evaluate the potential for diagnosing colorectal cancer (CRC) from faecal metagenomes.DesignWe performed metagenome-wide association studies on faecal samples from 74 patients with CRC and 54 controls from China, and...