Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes

RFW captures species-level metagenomic functions by integrating genome annotation information

Functional profiling of whole-metagenome shotgun sequencing (WMS) enables our understanding of microbe-host interactions. We demonstrate microbial functional information loss by current annotation methods at both the taxon and community levels, particularly at lower read depths. To address information loss, we develop a framework, RFW (reference-based functional profile inference on WMS), that utilizes information from genome functional annotations and taxonomic profiles to infer microbial function abundances from WMS. Furthermore, we provide an algorithm for absolute abundance change quantification between groups as part of the RFW framework. By applying RFW to several datasets related to autism spectrum disorder and colorectal cancer, we show that RFW augments downstream analyses, such as differential microbial function identification and association analysis between microbial function and host phenotype. RFW is open source and freely available at https://github.com/Xingyinliu-Lab/RFW.

Current Trends and Challenges of Microbiome Research in Prostate Cancer

PURPOSE OF REVIEW

The role of the gut microbiome in prostate cancer is an emerging area of research interest. However, no single causative organism has yet been identified. The goal of this paper is to examine the role of the microbiome in prostate cancer and summarize the challenges relating to methodology in specimen collection, sequencing technology, and interpretation of results.

RECENT FINDINGS

Significant heterogeneity still exists in methodology for stool sampling/storage, preservative options, DNA extraction, and sequencing database selection/in silico processing. Debate persists over primer choice in amplicon sequencing as well as optimal methods for data normalization. Statistical methods for longitudinal microbiome analysis continue to undergo refinement. While standardization of methodology may help yield more consistent results for organism identification in prostate cancer, this is a difficult task due to considerable procedural variation at each step in the process. Further reproducibility and methodology research is required.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Genes and genome-resolved metagenomics reveal the microbial functional make up of Amazon peatlands under geochemical gradients

The Pastaza-Marañón Foreland Basin (PMFB) holds the most extensive tropical peatland area in South America. PMFB peatlands store ~7.07 Gt of organic carbon interacting with multiple microbial heterotrophic, methanogenic, and other aerobic/anaerobic respirations. Little is understood about the contribution of distinct microbial community members inhabiting tropical peatlands. Here, we studied the metagenomes of three geochemically distinct peatlands spanning minerotrophic, mixed, and ombrotrophic conditions. Using gene- and genome-centric approaches, we evaluate the functional potential of the underlying microbial communities. Abundance analyses show significant differences in C, N, P, and S acquisition genes. Furthermore, community interactions mediated by toxin-antitoxin and CRISPR-Cas systems were enriched in oligotrophic soils, suggesting that non-metabolic interactions may exert additional controls in low-nutrient environments. Additionally, we reconstructed 519 metagenome-assembled genomes spanning 28 phyla. Our analyses detail key differences across the geochemical gradient in the predicted microbial populations involved in degradation of organic matter, and the cycling of N and S. Notably, we observed differences in the nitric oxide (NO) reduction strategies between sites with high and low N2 O fluxes and found phyla putatively capable of both NO and sulfate reduction. Our findings detail how gene abundances and microbial populations are influenced by geochemical differences in tropical peatlands.

MetaFunc: taxonomic and functional analyses of high throughput sequencing for microbiomes

The identification of functional processes taking place in microbiome communities augment traditional microbiome taxonomic studies, giving a more complete picture of interactions taking place within the community. While there are applications that perform functional annotation on metagenomes or metatranscriptomes, very few of these are able to link taxonomic identity to function or are limited by their input types or databases used. Here we present MetaFunc, a workflow which takes RNA sequences as input reads, and from these (1) identifies species present in the microbiome sample and (2) provides gene ontology annotations associated with the species identified. In addition, MetaFunc allows for host gene analysis, mapping the reads to a host genome, and separating these reads, prior to microbiome analyses. Differential abundance analysis for microbe taxonomies, and differential gene expression analysis and gene set enrichment analysis may then be carried out through the pipeline. A final correlation analysis between microbial species and host genes can also be performed. Finally, MetaFunc builds an R shiny application that allows users to view and interact with the microbiome results. In this paper, we showed how MetaFunc can be applied to metatranscriptomic datasets of colorectal cancer.

Analysis and Interpretation of metagenomics data: an approach

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.

Decoding microbial genomes to understand their functional roles in human complex diseases

Complex diseases such as cardiovascular disease (CVD), obesity, inflammatory bowel disease (IBD), kidney disease, type 2 diabetes (T2D), and cancer have become a major burden to public health and affect more than 20% of the population worldwide. The etiology of complex diseases is not yet clear, but they are traditionally thought to be caused by genetics and environmental factors (e.g., dietary habits), and by their interactions. Besides this, increasing pieces of evidence now highlight that the intestinal microbiota may contribute substantially to the health and disease of the human host via their metabolic molecules. Therefore, decoding the microbial genomes has been an important strategy to shed light on their functional potential. In this review, we summarize the roles of the gut microbiome in complex diseases from its functional perspective. We further introduce artificial tools in decoding microbial genomes to profile their functionalities. Finally, state-of-the-art techniques have been highlighted which may contribute to a mechanistic understanding of the gut microbiome in human complex diseases and promote the development of the gut microbiome-based personalized medicine.

Microbial Functional Diversity Correlates with Species Diversity along a Temperature Gradient

Microbial community diversity is often correlated with physical environmental stresses like acidity, salinity, and temperature. For example, species diversity usually declines with increasing temperature above 20°C. However, few studies have examined whether the genetic functional diversity of community metagenomes varies in a similar way as species diversity along stress gradients. Here, we investigated bacterial communities in thermal spring sediments ranging from 21 to 88°C, representing communities of 330 to 3,800 bacterial and archaeal species based on 16S rRNA gene amplicon analysis. Metagenomes were sequenced, and Pfam abundances were used as a proxy for metagenomic functional diversity. Significant decreases in both species diversity and Pfam diversity were observed with increasing temperatures. The relationship between Pfam diversity and species diversity followed a power function with the steepest slopes in the high-temperature, low-diversity region of the gradient. Species additions to simple thermophilic communities added many new Pfams, while species additions to complex mesophilic communities added relatively fewer new Pfams, indicating that species diversity does not approach saturation as rapidly as Pfam diversity does. Many Pfams appeared to have distinct temperature ceilings of 60 to 80°C. This study suggests that temperature stress limits both taxonomic and functional diversity of microbial communities, but in a quantitatively different manner. Lower functional diversity at higher temperatures is probably due to two factors, including (i) the absence of many enzymes not adapted to thermophilic conditions, and (ii) the fact that high-temperature communities are comprised of fewer species with smaller average genomes and, therefore, contain fewer rare functions. IMPORTANCE Only recently have microbial ecologists begun to assess quantitatively how microbial species diversity correlates with environmental factors like pH, temperature, and salinity. However, still, very few studies have examined how the number of distinct biochemical functions of microbial communities, termed functional diversity, varies with the same environmental factors. Our study examined 18 microbial communities sampled across a wide temperature gradient and found that increasing temperature reduced both species and functional diversity, but in different ways. Initially, functional diversity increased sharply with increasing species diversity but eventually plateaued, following a power function. This pattern has been previously predicted in theoretical models, but our study validates this predicted power function with field metagenomic data. This study also presents a unique overview of the distribution of metabolic functions along a temperature gradient, demonstrating that many functions have temperature "ceilings" above which they are no longer found.

Metagenomics Versus Metatranscriptomics of the Murine Gut Microbiome for Assessing Microbial Metabolism During Inflammation

Shotgun metagenomics studies have improved our understanding of microbial population dynamics and have revealed significant contributions of microbes to gut homeostasis. They also allow in silico inference of the metagenome. While they link the microbiome with metabolic abnormalities associated with disease phenotypes, they do not capture microbial gene expression patterns that occur in response to the multitude of stimuli that constantly ambush the gut environment. Metatranscriptomics closes that gap, but its implementation is more expensive and tedious. We assessed the metabolic perturbations associated with gut inflammation using shotgun metagenomics and metatranscriptomics. Shotgun metagenomics detected changes in abundance of bacterial taxa known to be SCFA producers, which favors gut homeostasis. Bacteria in the phylum Firmicutes were found at decreased abundance, while those in phyla Bacteroidetes and Proteobacteria were found at increased abundance. Surprisingly, inferring the coding capacity of the microbiome from shotgun metagenomics data did not result in any statistically significant difference, suggesting functional redundancy in the microbiome or poor resolution of shotgun metagenomics data to profile bacterial pathways, especially when sequencing is not very deep. Obviously, the ability of metatranscriptomics libraries to detect transcripts expressed at basal (or simply low) levels is also dependent on sequencing depth. Nevertheless, metatranscriptomics informed about contrasting roles of bacteria during inflammation. Functions involved in nutrient transport, immune suppression and regulation of tissue damage were dramatically upregulated, perhaps contributed by homeostasis-promoting bacteria. Functions ostensibly increasing bacteria pathogenesis were also found upregulated, perhaps as a consequence of increased abundance of Proteobacteria. Bacterial protein synthesis appeared downregulated. In summary, shotgun metagenomics was useful to profile bacterial population composition and taxa relative abundance, but did not inform about differential gene content associated with inflammation. Metatranscriptomics was more robust for capturing bacterial metabolism in real time. Although both approaches are complementary, it is often not possible to apply them in parallel. We hope our data will help researchers to decide which approach is more appropriate for the study of different aspects of the microbiome.

Gulf of Mexico blue hole harbors high levels of novel microbial lineages

Exploration of oxygen-depleted marine environments has consistently revealed novel microbial taxa and metabolic capabilities that expand our understanding of microbial evolution and ecology. Marine blue holes are shallow karst formations characterized by low oxygen and high organic matter content. They are logistically challenging to sample, and thus our understanding of their biogeochemistry and microbial ecology is limited. We present a metagenomic and geochemical characterization of Amberjack Hole on the Florida continental shelf (Gulf of Mexico). Dissolved oxygen became depleted at the hole's rim (32 m water depth), remained low but detectable in an intermediate hypoxic zone (40-75 m), and then increased to a secondary peak before falling below detection in the bottom layer (80-110 m), concomitant with increases in nutrients, dissolved iron, and a series of sequentially more reduced sulfur species. Microbial communities in the bottom layer contained heretofore undocumented levels of the recently discovered phylum Woesearchaeota (up to 58% of the community), along with lineages in the bacterial Candidate Phyla Radiation (CPR). Thirty-one high-quality metagenome-assembled genomes (MAGs) showed extensive biochemical capabilities for sulfur and nitrogen cycling, as well as for resisting and respiring arsenic. One uncharacterized gene associated with a CPR lineage differentiated hypoxic from anoxic zone communities. Overall, microbial communities and geochemical profiles were stable across two sampling dates in the spring and fall of 2019. The blue hole habitat is a natural marine laboratory that provides opportunities for sampling taxa with under-characterized but potentially important roles in redox-stratified microbial processes.

Enterococcus faecalis Glucosamine Metabolism Exacerbates Experimental Colitis

BACKGROUND & AIMS

The inflammatory bowel diseases (IBDs), Crohn's disease and ulcerative colitis, are caused in part by aberrant immune responses to resident intestinal bacteria. Certain dietary components, including carbohydrates, are associated with IBDs and alter intestinal bacterial composition. However, the effects of luminal carbohydrates on the composition and colitogenic potential of intestinal bacteria are incompletely understood. We hypothesize that carbohydrate metabolism by resident proinflammatory intestinal bacteria enhances their growth and worsens intestinal inflammation.

METHODS

We colonized germ-free, wild-type, and colitis-susceptible interleukin-10 knockout mice (Il10-/-) with a consortium of resident intestinal bacterial strains and quantified colon inflammation using blinded histologic scoring and spontaneous secretion of IL12/23p40 by colon explants. We measured luminal bacterial composition using real-time 16S polymerase chain reaction, bacterial gene expression using RNA sequencing and real-time polymerase chain reaction, and luminal glucosamine levels using gas chromatography-mass spectrometry.

RESULTS

We show that a consortium of 8 bacterial strains induces severe colitis in Il10-/- mice and up-regulates genes associated with carbohydrate metabolism during colitis. Specifically, Enterococcus faecalis strain OG1RF is proinflammatory and strongly up-regulates OG1RF_11616-11610, an operon that encodes genes of a previously undescribed phosphotransferase system that we show imports glucosamine. Experimental colitis is associated with increased levels of luminal glucosamine and OG1RF_11616 causes worse colitis, not by increasing E faecalis numbers, but rather by mechanisms that require the presence of complex microbiota.

CONCLUSIONS

Further studies of luminal carbohydrate levels and bacterial carbohydrate metabolism during intestinal inflammation will improve our understanding of the pathogenesis of IBDs and may lead to the development of novel therapies for these diseases.

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods

Microbiomes are integral components of diverse ecosystems, and increasingly recognized for their roles in the health of humans, animals, plants, and other hosts. Given their complexity (both in composition and function), the effective study of microbiomes (microbiomics) relies on the development, optimization, and validation of computational methods for analyzing microbial datasets, such as from marker-gene (e.g., 16S rRNA gene) and metagenome data. This review describes best practices for benchmarking and implementing computational methods (and software) for studying microbiomes, with particular focus on unique characteristics of microbiomes and microbiomics data that should be taken into account when designing and testing microbiomics methods.

RBUD: A New Functional Potential Analysis Approach for Whole Microbial Genome Shotgun Sequencing

Whole metagenome shotgun sequencing is a powerful approach to detect the functional potential of microbial communities. Currently, the read-based metagenomics profiling for established database (RBED) method is one of the two kinds of conventional methods for species and functional annotations. However, the databases, which are established based on test samples or specific reference genomes or protein sequences, limit the coverage of global microbial diversity. The other assembly-based metagenomics profiling for unestablished database (ABUD) method has a low utilization rate of reads, resulting in a lot of biological information loss. In this study, we proposed a new method, read-based metagenomics profiling for unestablished database (RBUD), based on Metagenome Database of Global Microorganisms (MDGM), to solve the above problems. To evaluate the accuracy and effectiveness of our method, the intestinal bacterial composition and function analyses were performed in both avian colibacillosis chicken cases and type 2 diabetes mellitus patients. Comparing to the existing methods, RBUD is superior in detecting proteins, percentage of reads mapping and ontological similarity of intestinal microbes. The results of RBUD are in better agreement with the classical functional studies on these two diseases. RBUD also has the advantages of fast analysis speed and is not limited by the sample size.

Xanthohumol microbiome and signature in healthy adults (the XMaS trial): a phase I triple-masked, placebo-controlled clinical trial

BACKGROUND

Natural products may provide a source for the discovery and development of adjunctive pharmacological interventions to modulate the inflammatory pathways contributing to chronic disease. Xanthohumol, a flavonoid from the hops plant (Humulus lupulus), has antioxidant and anti-inflammatory properties and may act as a prebiotic to the intestinal microbiota. Xanthohumol is not currently approved as a drug by the US Food and Drug Administration (FDA), but is available as a dietary supplement and ingredient in medical foods. To formally test the safety of xanthohumol, a phase I clinical trial ("XMaS") was designed and approved under an Investigational New Drug application to the US FDA. The main objective is to examine the clinical safety and subjective tolerability of xanthohumol in healthy adults compared to placebo. Additional aims are to monitor biomarkers related to inflammation, gut permeability, bile acid metabolism, routes, and in vivo products of xanthohumol metabolism, and to evaluate xanthohumol's impact on gut microbial composition.

METHODS

The safety and tolerability of xanthohumol in healthy adults will be evaluated in a triple-masked, randomized, placebo-controlled trial. Participants will be randomized to either 24 mg/day of xanthohumol or placebo for 8 weeks. Blood cell counts, hepatic and renal function tests, electrolytes, and self-reported health-related quality of life measures will be collected every 2 weeks. Participants will be queried for adverse events throughout the trial. Xanthohumol metabolites in blood, urine, and stool will be measured. Biomarkers to be evaluated include plasma tumor necrosis factor-alpha, various interleukins, soluble CD14, lipopolysaccharide-binding protein, fecal calprotectin, and bile acids to assess impact on inflammatory and gut permeability-related mechanisms in vivo. Stool samples will be analyzed to determine effects on the gut microbiome.

DISCUSSION

This phase I clinical trial of xanthohumol will assess safety and tolerability in healthy adults, collect extensive biomarker data for assessment of potential mechanism(s), and provide comparison data necessary for future phase II trials in chronic disease(s). The design and robustness of the planned safety and mechanistic evaluations planned provide a model for drug discovery pursuits from natural products.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03735420 . Registered on November 8, 2018.

An Improved Approach to Identify Bacterial Pathogens to Human in Environmental Metagenome

The identification of bacterial pathogens to humans is critical for environmental microbial risk assessment. However, current methods for identifying pathogens in environmental samples are limited in their ability to detect highly diverse bacterial communities and accurately differentiate pathogens from commensal bacteria. In the present study, we suggest an improved approach using a combination of identification results obtained from multiple databases, including the multilocus sequence typing (MLST) database, virulence factor database (VFDB), and pathosystems resource integration center (PATRIC) databases to resolve current challenges. By integrating the identification results from multiple databases, potential bacterial pathogens in metagenomes were identified and classified into eight different groups. Based on the distribution of genes in each group, we proposed an equation to calculate the metagenomic pathogen identification index (MPII) of each metagenome based on the weighted abundance of identified sequences in each database. We found that the accuracy of pathogen identification was improved by using combinations of multiple databases compared to that of individual databases. When the approach was applied to environmental metagenomes, metagenomes associated with activated sludge were estimated with higher MPII than other environments (i.e., drinking water, ocean water, ocean sediment, and freshwater sediment). The calculated MPII values were statistically distinguishable among different environments (p<0.05). These results demonstrate that the suggested approach allows more for more accurate identification of the pathogens associated with metagenomes.

Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses

Metagenomics and marker gene approaches, coupled with high-throughput sequencing technologies, have revolutionized the field of microbial ecology. Metagenomics is a culture-independent method that allows the identification and characterization of organisms from all kinds of samples. Whole-genome shotgun sequencing analyses the total DNA of a chosen sample to determine the presence of micro-organisms from all domains of life and their genomic content. Importantly, the whole-genome shotgun sequencing approach reveals the genomic diversity present, but can also give insights into the functional potential of the micro-organisms identified. The marker gene approach is based on the sequencing of a specific gene region. It allows one to describe the microbial composition based on the taxonomic groups present in the sample. It is frequently used to analyse the biodiversity of microbial ecosystems. Despite its importance, the analysis of metagenomic sequencing and marker gene data is quite a challenge. Here we review the primary workflows and software used for both approaches and discuss the current challenges in the field.

A brief guide for the measurement and interpretation of microbial functional diversity

The importance of functional diversity for the functioning and behaviour of microbial communities is clear, yet the widespread incorporation of functional diversity measurements into environmental microbiology study designs remains surprisingly limited. This may, at least to some extent, be a consequence of the unique conceptual and methodological challenges to measuring functional diversity in microbial communities. To facilitate the increased incorporation of functional diversity measurements into environmental microbiology study designs, we review here the process and some key caveats for measuring functional diversity and provide specific examples. We highlight three main decision points and provide guidance to making these decisions based on the underlying mechanisms for how functional diversity relates to an ecosystem process or property of interest. We discuss the selection of an appropriate type of functional trait, selection of the specificity at which functional diversity will be measured, and selection of an appropriate metric for estimating functional diversity from quantitative measures of those traits. We further discuss decisions regarding the use of one- or multi-dimensional measures of functional diversity and how advances in the field of trait-based community ecology could be applied or adapted to address questions in environmental microbiology.

Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

Characterization of the bacterial microbiota composition and evolution at different intestinal tract in wild pigs (Sus scrofa ussuricus)

Commensal microorganisms are essential to the normal development and function of many aspects of animal biology, including digestion, nutrient absorption, immunological development, behaviors, and evolution. The specific microbial composition and evolution of the intestinal tracts of wild pigs remain poorly characterized. This study therefore sought to assess the composition, distribution, and evolution of the intestinal microbiome of wild pigs. For these analyses, 16S rRNA V3-V4 regions from five gut sections prepared from each of three wild sows were sequenced to detect the microbiome composition. These analyses revealed the presence of 6,513 operational taxonomic units (OTUs) mostly distributed across 17 phyla and 163 genera in these samples, with Firmicutes and Actinobacteria being the most prevalent phyla of microbes present in cecum and jejunum samples, respectively. Moreover, the abundance of Actinobacteria in wild pigs was higher than that in domestic pigs. At the genus level the Bifidobacterium and Allobaculum species of microbes were most abundant in all tested gut sections, with higher relative abundance in wild pigs relative to domestic pigs, indicating that in the process of pig evolution, the intestinal microbes also evolved, and changes in the intestinal microbial diversity could have been one of the evolutionary forces of pigs. Intestinal microbial functional analyses also revealed the microbes present in the small intestine (duodenum, jejunum, and ileum) and large intestine (cecum and colon) of wild pigs to engage distinct metabolic spatial structures and pathways relative to one another. Overall, these results offer unique insights that would help to advance the current understanding of how the intestinal microbes interact with the host and affect the evolution of pigs.

Carnelian uncovers hidden functional patterns across diverse study populations from whole metagenome sequencing reads

Microbial populations exhibit functional changes in response to different ambient environments. Although whole metagenome sequencing promises enough raw data to study those changes, existing tools are limited in their ability to directly compare microbial metabolic function across samples and studies. We introduce Carnelian, an end-to-end pipeline for metabolic functional profiling uniquely suited to finding functional trends across diverse datasets. Carnelian is able to find shared metabolic pathways, concordant functional dysbioses, and distinguish Enzyme Commission (EC) terms missed by existing methodologies. We demonstrate Carnelian's effectiveness on type 2 diabetes, Crohn's disease, Parkinson's disease, and industrialized and non-industrialized gut microbiome cohorts.

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes

Background

Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX.

Results

Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene.

Conclusions

Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.

Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

Taxonomic and functional assessment using metatranscriptomics reveals the effect of Angus cattle on rumen microbial signatures

A greater understanding of the rumen microbiota and its function may help find new strategies to improve feed efficiency in cattle. This study aimed to investigate whether the cattle breed affects specific ruminal taxonomic microbial groups and functions associated with feed conversion ratio (FCR), using two genetically related Angus breeds as a model. Total RNA was extracted from 24 rumen content samples collected from purebred Black and Red Angus bulls fed the same forage diet and then subjected to metatranscriptomic analysis. Multivariate discriminant analysis (sparse partial least square discriminant analysis (sPLS-DA)) and analysis of composition of microbiomes were conducted to identify microbial signatures characterizing Black and Red Angus cattle. Our analyses revealed relationships among bacterial signatures, host breeds and FCR. Although Black and Red Angus are genetically similar, sPLS-DA detected 25 bacterial species and 10 functions that differentiated the rumen microbial signatures between those two breeds. In Black Angus, we identified bacterial taxa Chitinophaga pinensis, Clostridium stercorarium and microbial functions with large and small subunits ribosomal proteins L16 and S7 exhibiting a higher abundance in the rumen microbiome. In Red Angus, nonetheless, we identified the poorly characterized bacterial taxon Oscillibacter valericigenes with a higher abundance and pathways related to carbohydrate metabolism. Analysis of composition of microbiomes revealed that C. pinensis and C. stercorarium exhibited a higher abundance in Black Angus compared to Red Angus associated with FCR, suggesting that these bacterial species may play a key role in the feed conversion efficiency of forage-fed bulls. This study highlights how the discovery of signatures of bacterial taxa and their functions can be used to harness the full potential of the rumen microbiome in Angus cattle.

Current and Promising Approaches to Identify Horizontal Gene Transfer Events in Metagenomes

High-throughput shotgun metagenomics sequencing has enabled the profiling of myriad natural communities. These data are commonly used to identify gene families and pathways that were potentially gained or lost in an environment and which may be involved in microbial adaptation. Despite the widespread interest in these events, there are no established best practices for identifying gene gain and loss in metagenomics data. Horizontal gene transfer (HGT) represents several mechanisms of gene gain that are especially of interest in clinical microbiology due to the rapid spread of antibiotic resistance genes in natural communities. Several additional mechanisms of gene gain and loss, including gene duplication, gene loss-of-function events, and de novo gene birth are also important to consider in the context of metagenomes but have been less studied. This review is largely focused on detecting HGT in prokaryotic metagenomes, but methods for detecting these other mechanisms are first discussed. For this article to be self-contained, we provide a general background on HGT and the different possible signatures of this process. Lastly, we discuss how improved assembly of genomes from metagenomes would be the most straight-forward approach for improving the inference of gene gain and loss events. Several recent technological advances could help improve metagenome assemblies: long-read sequencing, determining the physical proximity of contigs, optical mapping of short sequences along chromosomes, and single-cell metagenomics. The benefits and limitations of these advances are discussed and open questions in this area are highlighted.

Functional sequencing read annotation for high precision microbiome analysis

The vast majority of microorganisms on Earth reside in often-inseparable environment-specific communities—microbiomes. Meta-genomic/-transcriptomic sequencing could reveal the otherwise inaccessible functionality of microbiomes. However, existing analytical approaches focus on attributing sequencing reads to known genes/genomes, often failing to make maximal use of available data. We created faser (functional annotation of sequencing reads), an algorithm that is optimized to map reads to molecular functions encoded by the read-correspondent genes. The mi-faser microbiome analysis pipeline, combining faser with our manually curated reference database of protein functions, accurately annotates microbiome molecular functionality. mi-faser’s minutes-per-microbiome processing speed is significantly faster than that of other methods, allowing for large scale comparisons. Microbiome function vectors can be compared between different conditions to highlight environment-specific and/or time-dependent changes in functionality. Here, we identified previously unseen oil degradation-specific functions in BP oil-spill data, as well as functional signatures of individual-specific gut microbiome responses to a dietary intervention in children with Prader–Willi syndrome. Our method also revealed variability in Crohn's Disease patient microbiomes and clearly distinguished them from those of related healthy individuals. Our analysis highlighted the microbiome role in CD pathogenicity, demonstrating enrichment of patient microbiomes in functions that promote inflammation and that help bacteria survive it.

Deep-Sourced Fluids From a Convergent Margin Host Distinct Subseafloor Microbial Communities That Change Upon Mud Flow Expulsion

Submarine mud volcanoes (MVs) along continental margins emit mud breccia and globally significant amounts of hydrocarbon-rich fluids from the subsurface, and host distinct chemosynthetic communities of microbes and macrofauna. Venere MV lies at 1,600 m water depth in the Ionian Sea offshore Italy and is located in a forearc basin of the Calabrian accretionary prism. Porewaters of recently extruded mud breccia flowing from its west summit are considerably fresher than seawater (10 PSU), high in Li⁺ and B (up to 300 and 8,000 μM, respectively), and strongly depleted in K⁺ (<1 mM) at depths as shallow as 20 cm below seafloor. These properties document upward transport of fluids sourced from >3 km below seafloor. 16S rRNA gene and metagenomic sequencing were used to characterize microbial community composition and gene content within deep-sourced mud breccia flow deposits as they become exposed to seawater along a downslope transect of Venere MV. Summit samples showed consistency in microbial community composition. However, beta-diversity increased markedly in communities from downslope cores, which were dominated by methyl- and methanotrophic genera of Gammaproteobacteria. Methane, sulfate, and chloride concentrations were minor but significant contributors to variation in community composition. Metagenomic analyses revealed differences in relative abundances of predicted protein categories between Venere MV and other subsurface microbial communities, characterizing MVs as windows into distinct deep biosphere habitats.

What we can see from very small size sample of metagenomic sequences

BACKGROUND

Since the analysis of a large number of metagenomic sequences costs heavy computing resources and takes long time, we examined a selected small part of metagenomic sequences as "sample"s of the entire full sequences, both for a mock community and for 10 different existing metagenomics case studies. A mock community with 10 bacterial strains was prepared, and their mixed genome were sequenced by Hiseq. The hits of BLAST search for reference genome of each strain were counted. Each of 176 different small parts selected from these sequences were also searched by BLAST and their hits were also counted, in order to compare them to the original search results from the full sequences. We also prepared small parts of sequences which were selected from 10 publicly downloadable research data of MG-RAST service, and analyzed these samples with MG-RAST.

RESULTS

Both the BLAST search tests of the mock community and the results from the publicly downloadable researches of MG-RAST show that sampling an extremely small part from sequence data is useful to estimate brief taxonomic information of the original metagenomic sequences. For 9 cases out of 10, the most annotated classes from the MG-RAST analyses of the selected partial sample sequences are the same as the ones from the originals.

CONCLUSIONS

When a researcher wants to estimate brief information of a metagenome's taxonomic distribution with less computing resources and within shorter time, the researcher can analyze a selected small part of metagenomic sequences. With this approach, we can also build a strategy to monitor metagenome samples of wider geographic area, more frequently.

Species-level functional profiling of metagenomes and metatranscriptomes

Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types.

Species-level functional profiling of metagenomes and metatranscriptomes

Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types.

Role of the Gut Microbiome in Vertebrate Evolution

Darwin referred to life as a struggle. Organisms compete for limited resources in nature, and their traits influence the outcome.

Darwin referred to life as a struggle. Organisms compete for limited resources in nature, and their traits influence the outcome. Victory carries great weight as winners survive, reproduce, and progenate subsequent generations. Consequently, organismal traits that influence fitness drive adaptation and their discovery clarifies evolution. Recent research implicates the vertebrate gut microbiome as an agent of fitness, selection, and evolution. Going forward, we must define the functional effects of the gut microbiome to determine how it impacts evolution. Specifically, we must quantify how gut microbiome function diversifies in concert with vertebrate radiation and resolve specific functions that influence natural selection. In so doing, we can discover and potentially capitalize upon the mechanisms by which our gut microbiomes impact our physiology and fitness. Ultimately, we may come to find that while life involves struggle, it also depends upon cooperation.

Experimental design and quantitative analysis of microbial community multiomics

Studies of the microbiome have become increasingly sophisticated, and multiple sequence-based, molecular methods as well as culture-based methods exist for population-scale microbiome profiles. To link the resulting host and microbial data types to human health, several experimental design considerations, data analysis challenges, and statistical epidemiological approaches must be addressed. Here, we survey current best practices for experimental design in microbiome molecular epidemiology, including technologies for generating, analyzing, and integrating microbiome multiomics data. We highlight studies that have identified molecular bioactives that influence human health, and we suggest steps for scaling translational microbiome research to high-throughput target discovery across large populations.

Progressive Colonization of Bacteria and Degradation of Rice Straw in the Rumen by Illumina Sequencing

The aim of this study was to improve the utilization of rice straw as forage in ruminants by investigating the degradation pattern of rice straw in the dairy cow rumen. Ground up rice straw was incubated in situ in the rumens of three Holstein cows over a period of 72 h. The rumen fluid at 0 h and the rice straw at 0.5, 1, 2, 4, 6, 12, 24, 48, and 72 h were collected for analysis of the bacterial community and the degradation of the rice straw. The bacterial community and the carbohydrate-active enzymes in the rumen fluid were analyzed by metagenomics. The diversity of bacteria loosely and tightly attached to the rice straw was investigated by scanning electron microscopy and Miseq sequencing of 16S rRNA genes. The predominant genus in the rumen fluid was Prevotella, followed by Bacteroides, Butyrivibrio, unclassified Desulfobulbaceae, Desulfovibrio, and unclassified Sphingobacteriaceae. The main enzymes were members of the glycosyl hydrolase family, divided into four categories (cellulases, hemicellulases, debranching enzymes, and oligosaccharide-degrading enzymes), with oligosaccharide-degrading enzymes being the most abundant. No significant degradation of rice straw was observed between 0.5 and 6 h, whereas the rice straw was rapidly degraded between 6 and 24 h. The degradation then gradually slowed between 24 and 72 h. A high proportion of unclassified bacteria were attached to the rice straw and that Prevotella, Ruminococcus, and Butyrivibrio were the predominant classified genera in the loosely and tightly attached fractions. The composition of the loosely attached bacterial community remained consistent throughout the incubation, whereas a significant shift in composition was observed in the tightly attached bacterial community after 6 h of incubation. This shift resulted in a significant reduction in numbers of Bacteroidetes and a significant increase in numbers of Firmicutes. In conclusion, the degradation pattern of rice straw in the dairy cow rumen indicates a strong contribution by tightly attached bacteria, especially after 6 h incubation, but most of these bacteria were not taxonomically characterized. Thus, these bacteria should be further identified and subjected to functional analysis to improve the utilization of crop residues in ruminants.

Development of Inflammatory Bowel Disease Is Linked to a Longitudinal Restructuring of the Gut Metagenome in Mice

The gut microbiome is linked to inflammatory bowel disease (IBD) severity and altered in late-stage disease. However, it is unclear how gut microbial communities change over the course of IBD development, especially in regard to function. To investigate microbiome-mediated disease mechanisms and discover early biomarkers of IBD, we conducted a longitudinal metagenomic investigation in an established mouse model of IBD, where damped transforming growth factor β (TGF-β) signaling in T cells leads to peripheral immune activation, weight loss, and severe colitis. IBD development is associated with abnormal gut microbiome temporal dynamics, including damped acquisition of functional diversity and significant differences in abundance trajectories for KEGG modules such as glycosaminoglycan degradation, cellular chemotaxis, and type III and IV secretion systems. Most differences between sick and control mice emerge when mice begin to lose weight and heightened T cell activation is detected in peripheral blood. However, levels of lipooligosaccharide transporter abundance diverge prior to immune activation, indicating that it could be a predisease indicator or microbiome-mediated disease mechanism. Taxonomic structure of the gut microbiome also significantly changes in association with IBD development, and the abundances of particular taxa, including several species of Bacteroides, correlate with immune activation. These discoveries were enabled by our use of generalized linear mixed-effects models to test for differences in longitudinal profiles between healthy and diseased mice while accounting for the distributions of taxon and gene counts in metagenomic data. These findings demonstrate that longitudinal metagenomics is useful for discovering the potential mechanisms through which the gut microbiome becomes altered in IBD. IMPORTANCE IBD patients harbor distinct microbial communities with functional capabilities different from those seen with healthy people. But is this cause or effect? Answering this question requires data on changes in gut microbial communities leading to disease onset. By performing weekly metagenomic sequencing and mixed-effects modeling on an established mouse model of IBD, we identified several functional pathways encoded by the gut microbiome that covary with host immune status. These pathways are novel early biomarkers that may either enable microbes to live inside an inflamed gut or contribute to immune activation in IBD mice. Future work will validate the potential roles of these microbial pathways in host-microbe interactions and human disease. This study was novel in its longitudinal design and focus on microbial pathways, which provided new mechanistic insights into the role of gut microbes in IBD development.

Proteobacteria explain significant functional variability in the human gut microbiome

BACKGROUND

While human gut microbiomes vary significantly in taxonomic composition, biological pathway abundance is surprisingly invariable across hosts. We hypothesized that healthy microbiomes appear functionally redundant due to factors that obscure differences in gene abundance between individuals.

RESULTS

To account for these biases, we developed a powerful test of gene variability called CCoDA, which is applicable to shotgun metagenomes from any environment and can integrate data from multiple studies. Our analysis of healthy human fecal metagenomes from three separate cohorts revealed thousands of genes whose abundance differs significantly and consistently between people, including glycolytic enzymes, lipopolysaccharide biosynthetic genes, and secretion systems. Even housekeeping pathways contain a mix of variable and invariable genes, though most highly conserved genes are significantly invariable. Variable genes tend to be associated with Proteobacteria, as opposed to taxa used to define enterotypes or the dominant phyla Bacteroidetes and Firmicutes.

CONCLUSIONS

These results establish limits on functional redundancy and predict specific genes and taxa that may explain physiological differences between gut microbiomes.

Determining virus-host interactions and glycerol metabolism profiles in geographically diverse solar salterns with metagenomics

Solar salterns are excellent model ecosystems for studying virus-microbial interactions because of their low microbial diversity, environmental stability, and high viral density. By using the power of CRISPR spacers to link viruses to their prokaryotic hosts, we explored virus-host interactions in geographically diverse salterns. Using taxonomic profiling, we identified hosts such as archaeal Haloquadratum, Halorubrum, and Haloarcula and bacterial Salinibacter, and we found that community composition related to not only salinity but also local environmental dynamics. Characterizing glycerol metabolism genes in these metagenomes suggested Halorubrum and Haloquadratum possess most dihydroxyacetone kinase genes while Salinibacter possesses most glycerol-3-phosphate dehydrogenase genes. Using two different methods, we detected fewer CRISPR spacers in Haloquadratum-dominated compared with Halobacteriaceae-dominated saltern metagenomes. After CRISPR detection, spacers were aligned against haloviral genomes to map virus to host. While most alignments for each saltern metagenome linked viruses to Haloquadratum walsbyi, there were also alignments indicating interactions with the low abundance taxa Haloarcula and Haloferax. Further examination of the dinucleotide and trinucleotide usage differences between paired viruses and their hosts confirmed viruses and hosts had similar nucleotide usage signatures. Detection of cas genes in the salterns supported the possibility of CRISPR activity. Taken together, our studies suggest similar virus-host interactions exist in different solar salterns and that the glycerol metabolism gene dihydroxyacetone kinase is associated with Haloquadratum and Halorubrum.

Toward Accurate and Quantitative Comparative Metagenomics

Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized.

HIPPI: highly accurate protein family classification with ensembles of HMMs

BACKGROUND

Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics.

RESULTS

We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy.

CONCLUSION

HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp .

Assessing viral taxonomic composition in benthic marine ecosystems: reliability and efficiency of different bioinformatic tools for viral metagenomic analyses

In benthic deep-sea ecosystems, which represent the largest biome on Earth, viruses have a recognised key ecological role, but their diversity is still largely unknown. Identifying the taxonomic composition of viruses is crucial for understanding virus-host interactions, their role in food web functioning and evolutionary processes. Here, we compared the performance of various bioinformatic tools (BLAST, MG-RAST, NBC, VMGAP, MetaVir, VIROME) for analysing the viral taxonomic composition in simulated viromes and viral metagenomes from different benthic deep-sea ecosystems. The analyses of simulated viromes indicate that all the BLAST tools, followed by MetaVir and VMGAP, are more reliable in the affiliation of viral sequences and strains. When analysing the environmental viromes, tBLASTx, MetaVir, VMGAP and VIROME showed a similar efficiency of sequence annotation; however, MetaVir and tBLASTx identified a higher number of viral strains. These latter tools also identified a wider range of viral families than the others, providing a wider view of viral taxonomic diversity in benthic deep-sea ecosystems. Our findings highlight strengths and weaknesses of available bioinformatic tools for investigating the taxonomic diversity of viruses in benthic ecosystems in order to improve our comprehension of viral diversity in the oceans and its relationships with host diversity and ecosystem functioning.