MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome

Metagenomic analysis of gut microbiome illuminates the mechanisms and evolution of lignocellulose degradation in mangrove herbivorous crabs

BACKGROUND

Sesarmid crabs dominate mangrove habitats as the major primary consumers, which facilitates the trophic link and nutrient recycling in the ecosystem. Therefore, the adaptations and mechanisms of sesarmid crabs to herbivory are not only crucial to terrestrialization and its evolutionary success, but also to the healthy functioning of mangrove ecosystems. Although endogenous cellulase expressions were reported in crabs, it remains unknown if endogenous enzymes alone can complete the whole lignocellulolytic pathway, or if they also depend on the contribution from the intestinal microbiome. We attempt to investigate the role of gut symbiotic microbes of mangrove-feeding sesarmid crabs in plant digestion using a comparative metagenomic approach.

RESULTS

Metagenomics analyses on 43 crab gut samples from 23 species of mangrove crabs with different dietary preferences revealed a wide coverage of 127 CAZy families and nine KOs targeting lignocellulose and their derivatives in all species analyzed, including predominantly carnivorous species, suggesting the crab gut microbiomes have lignocellulolytic capacity regardless of dietary preference. Microbial cellulase, hemicellulase and pectinase genes in herbivorous and detritivorous crabs were differentially more abundant when compared to omnivorous and carnivorous crabs, indicating the importance of gut symbionts in lignocellulose degradation and the enrichment of lignocellulolytic microbes in response to diet with higher lignocellulose content. Herbivorous and detritivorous crabs showed highly similar CAZyme composition despite dissimilarities in taxonomic profiles observed in both groups, suggesting a stronger selection force on gut microbiota by functional capacity than by taxonomy. The gut microbiota in herbivorous sesarmid crabs were also enriched with nitrogen reduction and fixation genes, implying possible roles of gut microbiota in supplementing nitrogen that is deficient in plant diet.

CONCLUSIONS

Endosymbiotic microbes play an important role in lignocellulose degradation in most crab species. Their abundance is strongly correlated with dietary preference, and they are highly enriched in herbivorous sesarmids, thus enhancing their capacity in digesting mangrove leaves. Dietary preference is a stronger driver in determining the microbial CAZyme composition and taxonomic profile in the crab microbiome, resulting in functional redundancy of endosymbiotic microbes. Our results showed that crabs implement a mixed mode of digestion utilizing both endogenous and microbial enzymes in lignocellulose degradation, as observed in most of the more advanced herbivorous invertebrates.

Ecological relevance of flagellar motility in soil bacterial communities

Flagellar motility is a key bacterial trait as it allows bacteria to navigate their immediate surroundings. Not all bacteria are capable of flagellar motility, and the distribution of this trait, its ecological associations, and the life history strategies of flagellated taxa remain poorly characterized. We developed and validated a genome-based approach to infer the potential for flagellar motility across 12 bacterial phyla (26 192 unique genomes). The capacity for flagellar motility was associated with a higher prevalence of genes for carbohydrate metabolism and higher maximum potential growth rates, suggesting that flagellar motility is more prevalent in environments with higher carbon availability. To test this hypothesis, we applied a method to infer the prevalence of flagellar motility in whole bacterial communities from metagenomic data and quantified the prevalence of flagellar motility across four independent field studies that each captured putative gradients in soil carbon availability (148 metagenomes). We observed a positive relationship between the prevalence of bacterial flagellar motility and soil carbon availability in all datasets. Since soil carbon availability is often correlated with other factors that could influence the prevalence of flagellar motility, we validated these observations using metagenomic data from a soil incubation experiment where carbon availability was directly manipulated with glucose amendments. This confirmed that the prevalence of bacterial flagellar motility is consistently associated with soil carbon availability over other potential confounding factors. This work highlights the value of combining predictive genomic and metagenomic approaches to expand our understanding of microbial phenotypic traits and reveal their general environmental associations.

Uncovering Microbiome Adaptations in a Full-Scale Biogas Plant: Insights from MAG-Centric Metagenomics and Metaproteomics

The current focus on renewable energy in global policy highlights the importance of methane production from biomass through anaerobic digestion (AD). To improve biomass digestion while ensuring overall process stability, microbiome-based management strategies become more important. In this study, metagenomes and metaproteomes were used for metagenomically assembled genome (MAG)-centric analyses to investigate a full-scale biogas plant consisting of three differentially operated digesters. Microbial communities were analyzed regarding their taxonomic composition, functional potential, as well as functions expressed on the proteome level. Different abundances of genes and enzymes related to the biogas process could be mostly attributed to different process parameters. Individual MAGs exhibiting different abundances in the digesters were studied in detail, and their roles in the hydrolysis, acidogenesis and acetogenesis steps of anaerobic digestion could be assigned. Methanoculleus thermohydrogenotrophicum was an active hydrogenotrophic methanogen in all three digesters, whereas Methanothermobacter wolfeii was more prevalent at higher process temperatures. Further analysis focused on MAGs, which were abundant in all digesters, indicating their potential to ensure biogas process stability. The most prevalent MAG belonged to the class Limnochordia; this MAG was ubiquitous in all three digesters and exhibited activity in numerous pathways related to different steps of AD.

Multi-omics in Crohn's disease: New insights from inside

Crohn's disease (CD) is an inflammatory bowel disease (IBD) with complex clinical manifestations such as chronic diarrhea, weight loss and hematochezia. Despite the increasing incidence worldwide, cure of CD remains extremely difficult. The rapid development of high-throughput sequencing technology with integrated-omics analyses in recent years has provided a new means for exploring the pathogenesis, mining the biomarkers and designing targeted personalized therapeutics of CD. Host genomics and epigenomics unveil heredity-related mechanisms of susceptible individuals, while microbiome and metabolomics map host-microbe interactions in CD patients. Proteomics shows great potential in searching for promising biomarkers. Nonetheless, single omics technology cannot holistically connect the mechanisms with heterogeneity of pathological behavior in CD. The rise of multi-omics analysis integrates genetic/epigenetic profiles with protein/microbial metabolite functionality, providing new hope for comprehensive and in-depth exploration of CD. Herein, we emphasized the different omics features and applications of CD and discussed the current research and limitations of multi-omics in CD. This review will update and deepen our understanding of CD from integration of broad omics spectra and will provide new evidence for targeted individualized therapeutics.

Data Science and Plant Metabolomics

The study of plant metabolism is one of the most complex tasks, mainly due to the huge amount and structural diversity of metabolites, as well as the fact that they react to changes in the environment and ultimately influence each other. Metabolic profiling is most often carried out using tools that include mass spectrometry (MS), which is one of the most powerful analytical methods. All this means that even when analyzing a single sample, we can obtain thousands of data. Data science has the potential to revolutionize our understanding of plant metabolism. This review demonstrates that machine learning, network analysis, and statistical modeling are some techniques being used to analyze large quantities of complex data that provide insights into plant development, growth, and how they interact with their environment. These findings could be key to improving crop yields, developing new forms of plant biotechnology, and understanding the relationship between plants and microbes. It is also necessary to consider the constraints that come with data science such as quality and availability of data, model complexity, and the need for deep knowledge of the subject in order to achieve reliable outcomes.

The microbiome's fiber degradation profile and its relationship with the host diet

BACKGROUND

The relationship between the gut microbiome and diet has been the focus of numerous recent studies. Such studies aim to characterize the impact of diet on the composition of the microbiome, as well as the microbiome's ability to utilize various compounds in the diet and produce metabolites that may be beneficial for the host. Consumption of dietary fibers (DFs)-polysaccharides that cannot be broken down by the host's endogenous enzymes and are degraded primarily by members of the microbiome-is known to have a profound effect on the microbiome. Yet, a comprehensive characterization of microbiome compositional and functional shifts in response to the consumption of specific DFs is still lacking.

RESULTS

Here, we introduce a computational framework, coupling metagenomic sequencing with careful annotation of polysaccharide degrading enzymes and DF structures, for inferring the metabolic ability of a given microbiome sample to utilize a broad catalog of DFs. We demonstrate that the inferred fiber degradation profile (IFDP) generated by our framework accurately reflects the dietary habits of various hosts across four independent datasets. We further demonstrate that IFDPs are more tightly linked to the host diet than commonly used taxonomic and functional microbiome-based profiles. Finally, applying our framework to a set of ~700 metagenomes that represents large human population cohorts from 9 different countries, we highlight intriguing global patterns linking DF consumption habits with microbiome capacities.

CONCLUSIONS

Combined, our findings serve as a proof-of-concept for the use of DF-specific analysis for providing important complementary information for better understanding the relationship between dietary habits and the gut microbiome.

A Metagenomic Investigation of Spatial and Temporal Changes in Sewage Microbiomes across a University Campus

Wastewater microbial communities are not static and can vary significantly across time and space, but this variation and the factors driving the observed spatiotemporal variation often remain undetermined. We used a shotgun metagenomic approach to investigate changes in wastewater microbial communities across 17 locations in a sewer network, with samples collected from each location over a 3-week period. Fecal material-derived bacteria constituted a relatively small fraction of the taxa found in the collected samples, highlighting the importance of environmental sources to the sewage microbiome. The prokaryotic communities were highly variable in composition depending on the location within the sampling network, and this spatial variation was most strongly associated with location-specific differences in sewage pH. However, we also observed substantial temporal variation in the composition of the prokaryotic communities at individual locations. This temporal variation was asynchronous across sampling locations, emphasizing the importance of independently considering both spatial and temporal variation when assessing the wastewater microbiome. The spatiotemporal patterns in viral community composition closely tracked those of the prokaryotic communities, allowing us to putatively identify the bacterial hosts of some of the dominant viruses in these systems. Finally, we found that antibiotic resistance gene profiles also exhibit a high degree of spatiotemporal variability, with most of these genes unlikely to be derived from fecal bacteria. Together, these results emphasize the dynamic nature of the wastewater microbiome, the challenges associated with studying these systems, and the utility of metagenomic approaches for building a multifaceted understanding of these microbial communities and their functional attributes. IMPORTANCE Sewage systems harbor extensive microbial diversity, including microbes derived from both human and environmental sources. Studies of the sewage microbiome are useful for monitoring public health and the health of our infrastructure, but the sewage microbiome can be highly variable in ways that are often unresolved. We sequenced DNA recovered from wastewater samples collected over a 3-week period at 17 locations in a single sewer system to determine how these communities vary across time and space. Most of the wastewater bacteria, and the antibiotic resistance genes they harbor, were not derived from human feces, but human usage patterns did impact how the amounts and types of bacteria and bacterial genes we found in these systems varied over time. Likewise, the wastewater communities, including both bacteria and their viruses, varied depending on location within the sewage network, highlighting the challenges and opportunities in efforts to monitor and understand the sewage microbiome.

Integrating phylogenetic and functional data in microbiome studies

MOTIVATION

Microbiome functional data are frequently analyzed to identify associations between microbial functions (e.g. genes) and sample groups of interest. However, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed POMS, a package that implements multiple phylogeny-aware frameworks to more robustly identify enriched functions.

RESULTS

The key contribution is an extended balance-tree workflow that incorporates functional and taxonomic information to identify functions that are consistently enriched in sample groups across independent taxonomic lineages. Our package also includes a workflow for running phylogenetic regression. Based on simulated data we demonstrate that these approaches more accurately identify gene families that confer a selective advantage compared with commonly used tools. We also show that POMS in particular can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome.

AVAILABILITY AND IMPLEMENTATION

These workflows are freely available in the POMS R package at https://github.com/gavinmdouglas/POMS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models

Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as genes and proteins. However, most pathway analysis methods neglect the complex nonlinear relationship between biological factors and pathways. In this study, we propose a Deep-learning pathway analysis using Hierarchical structured CoMponent models (DeepHisCoM) that utilize deep learning to consider a nonlinear complex contribution of biological factors to pathways by constructing a multilayered model which accounts for hierarchical biological structure. Through simulation studies, DeepHisCoM was shown to have a higher power in the nonlinear pathway effect and comparable power for the linear pathway effect when compared to the conventional pathway methods. Application to hepatocellular carcinoma (HCC) omics datasets, including metabolomic, transcriptomic and metagenomic datasets, demonstrated that DeepHisCoM successfully identified three well-known pathways that are highly associated with HCC, such as lysine degradation, valine, leucine and isoleucine biosynthesis and phenylalanine, tyrosine and tryptophan. Application to the coronavirus disease-2019 (COVID-19) single-nucleotide polymorphism (SNP) dataset also showed that DeepHisCoM identified four pathways that are highly associated with the severity of COVID-19, such as mitogen-activated protein kinase (MAPK) signaling pathway, gonadotropin-releasing hormone (GnRH) signaling pathway, hypertrophic cardiomyopathy and dilated cardiomyopathy. Codes are available at https://github.com/chanwoo-park-official/DeepHisCoM.

Kernel-based hierarchical structural component models for pathway analysis

MOTIVATION

Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex.

RESULTS

To model complex effects including nonlinear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models nonlinear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies.

AVAILABILITY AND IMPLEMENTATION

Freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Growth promotion and antibiotic induced metabolic shifts in the chicken gut microbiome

Antimicrobial growth promoters (AGP) have played a decisive role in animal agriculture for over half a century. Despite mounting concerns about antimicrobial resistance and demand for antibiotic alternatives, a thorough understanding of how these compounds drive performance is missing. Here we investigate the functional footprint of microbial communities in the cecum of chickens fed four distinct AGP. We find relatively few taxa, metabolic or antimicrobial resistance genes similarly altered across treatments, with those changes often driven by the abundances of core microbiome members. Constraints-based modeling of 25 core bacterial genera associated increased performance with fewer metabolite demands for microbial growth, pointing to altered nitrogen utilization as a potential mechanism of narasin, the AGP with the largest performance increase in our study. Untargeted metabolomics of narasin treated birds aligned with model predictions, suggesting that the core cecum microbiome might be targeted for enhanced performance via its contribution to host-microbiota metabolic crosstalk.

This study compares the functional profiles of the cecal microbiome among chickens fed four different antimicrobial growth promoters. Chickens receiving narasin exhibited the largest performance increase via apparent nitrogen recycling by the core cecal microbiome.

Elevational Constraints on the Composition and Genomic Attributes of Microbial Communities in Antarctic Soils

The inland soils found on the Antarctic continent represent one of the more challenging environments for microbial life on Earth. Nevertheless, Antarctic soils harbor unique bacterial and archaeal (prokaryotic) communities able to cope with extremely cold and dry conditions. These communities are not homogeneous, and the taxonomic composition and functional capabilities (genomic attributes) of these communities across environmental gradients remain largely undetermined. We analyzed the prokaryotic communities in soil samples collected from across the Shackleton Glacier region of Antarctica by coupling quantitative PCR, marker gene amplicon sequencing, and shotgun metagenomic sequencing. We found that elevation was the dominant factor explaining differences in the structures of the soil prokaryotic communities, with the drier and saltier soils found at higher elevations harboring less diverse communities and unique assemblages of cooccurring taxa. The higher-elevation soil communities also had lower maximum potential growth rates (as inferred from metagenome-based estimates of codon usage bias) and an overrepresentation of genes associated with trace gas metabolism. Together, these results highlight the utility of assessing community shifts across pronounced environmental gradients to improve our understanding of the microbial diversity found in Antarctic soils and the strategies used by soil microbes to persist at the limits of habitability.

IMPORTANCE Antarctic soils represent an ideal system to study how environmental properties shape the taxonomic and functional diversity of microbial communities given the relatively low diversity of Antarctic soil microbial communities and the pronounced environmental gradients that occur across soils located in reasonable proximity to one another. Moreover, the challenging environmental conditions typical of most Antarctic soils present an opportunity to investigate the traits that allow soil microbes to persist in some of the most inhospitable habitats on Earth. We used cultivation-independent methods to study the bacterial and archaeal communities found in soil samples collected from across the Shackleton Glacier region of the Transantarctic Mountains. We show that those environmental characteristics associated with elevation have the greatest impact on the structure of these microbial communities, with the colder, drier, and saltier soils found at higher elevations sustaining less diverse communities that were distinct from those in more hospitable soils with respect to their composition, genomic attributes, and overall life-history strategies. Notably, the harsher conditions found in higher-elevation soils likely select for taxa with lower maximum potential growth rates and an increased reliance on trace gas metabolism to support growth.

Microbial drivers of methane emissions from unrestored industrial salt ponds

Wetlands are important carbon (C) sinks, yet many have been destroyed and converted to other uses over the past few centuries, including industrial salt making. A renewed focus on wetland ecosystem services (e.g., flood control, and habitat) has resulted in numerous restoration efforts whose effect on microbial communities is largely unexplored. We investigated the impact of restoration on microbial community composition, metabolic functional potential, and methane flux by analyzing sediment cores from two unrestored former industrial salt ponds, a restored former industrial salt pond, and a reference wetland. We observed elevated methane emissions from unrestored salt ponds compared to the restored and reference wetlands, which was positively correlated with salinity and sulfate across all samples. 16S rRNA gene amplicon and shotgun metagenomic data revealed that the restored salt pond harbored communities more phylogenetically and functionally similar to the reference wetland than to unrestored ponds. Archaeal methanogenesis genes were positively correlated with methane flux, as were genes encoding enzymes for bacterial methylphosphonate degradation, suggesting methane is generated both from bacterial methylphosphonate degradation and archaeal methanogenesis in these sites. These observations demonstrate that restoration effectively converted industrial salt pond microbial communities back to compositions more similar to reference wetlands and lowered salinities, sulfate concentrations, and methane emissions.

Genome-resolved metagenomics provides insights into the functional complexity of microbial mats in Blue Holes, Shark Bay

The present study describes for the first time the community composition and functional potential of the microbial mats found in the supratidal, gypsum-rich, and hypersaline region of Blue Holes, Shark Bay. This was achieved via high throughput metagenomic sequencing of total mat community DNA and complementary analyses using hyperspectral confocal microscopy. Mat communities were dominated by Proteobacteria (29%), followed by Bacteroidetes/Chlorobi Group (11%), and Planctomycetes (10%). These mats were found to also harbor a diverse community of potentially novel microorganisms including members from the DPANN, Asgard archaea, and Candidate Phyla Radiation, with highest diversity found in the lower regions (∼14-20 mm depth) of the mat. In addition to pathways for major metabolic cycles, a range of putative rhodopsins with previously uncharacterized motifs and functions were identified along with heliorhodopsins and putative schizorhodopsins. Critical microbial interactions were also inferred, and from 117 medium-to-high quality metagenome-assembled genomes (MAGs), viral defense mechanisms (CRISPR, BREX, and DISARM), elemental transport, osmoprotection, heavy metal and UV resistance were also detected. These analyses have provided a greater understanding of these distinct mat systems in Shark Bay, including key insights into adaptive responses and proposing that photoheterotrophy may be an important lifestyle in Blue Holes.

Vaccine Hyporesponse Induced by Individual Antibiotic Treatment in Mice and Non-Human Primates Is Diminished upon Recovery of the Gut Microbiome

Emerging evidence demonstrates a connection between microbiome composition and suboptimal response to vaccines (vaccine hyporesponse). Harnessing the interaction between microbes and the immune system could provide novel therapeutic strategies for improving vaccine response. Currently we do not fully understand the mechanisms and dynamics by which the microbiome influences vaccine response. Using both mouse and non-human primate models, we report that short-term oral treatment with a single antibiotic (vancomycin) results in the disruption of the gut microbiome and this correlates with a decrease in systemic levels of antigen-specific IgG upon subsequent parenteral vaccination. We further show that recovery of microbial diversity before vaccination prevents antibiotic-induced vaccine hyporesponse, and that the antigen specific IgG response correlates with the recovery of microbiome diversity. RNA sequencing analysis of small intestine, spleen, whole blood, and secondary lymphoid organs from antibiotic treated mice revealed a dramatic impact on the immune system, and a muted inflammatory signature is correlated with loss of bacteria from Lachnospiraceae, Ruminococcaceae, and Clostridiaceae. These results suggest that microbially modulated immune pathways may be leveraged to promote vaccine response and will inform future vaccine design and development strategies.

Fecal Microbiome Composition Does Not Predict Diet-Induced TMAO Production in Healthy Adults

Background Trimethylamine-N-oxide (TMAO) is a small molecule derived from the metabolism of dietary nutrients by gut microbes and contributes to cardiovascular disease. Plasma TMAO increases following consumption of red meat. This metabolic change is thought to be partly because of the expansion of gut microbes able to use nutrients abundant in red meat. Methods and Results We used data from a randomized crossover study to estimate the degree to which TMAO can be estimated from fecal microbial composition. Healthy participants received a series of 3 diets that differed in protein source (red meat, white meat, and non-meat), and fecal, plasma, and urine samples were collected following 4 weeks of exposure to each diet. TMAO was quantitated in plasma and urine, while shotgun metagenomic sequencing was performed on fecal DNA. While the cai gene cluster was weakly correlated with plasma TMAO (rho=0.17, P=0.0007), elastic net models of TMAO were not improved by abundances of bacterial genes known to contribute to TMAO synthesis. A global analysis of all taxonomic groups, genes, and gene families found no meaningful predictors of TMAO. We postulated that abundances of known genes related to TMAO production do not predict bacterial metabolism, and we measured choline- and carnitine-trimethylamine lyase activity during fecal culture. Trimethylamine lyase genes were only weakly correlated with the activity of the enzymes they encode. Conclusions Fecal microbiome composition does not predict systemic TMAO because, in this case, gene copy number does not predict bacterial metabolic activity. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01427855.

Infants with cystic fibrosis have altered fecal functional capacities with potential clinical and metabolic consequences

Background

Infants with cystic fibrosis (CF) suffer from gastrointestinal (GI) complications, including pancreatic insufficiency and intestinal inflammation, which have been associated with impaired nutrition and growth. Recent evidence identified altered fecal microbiota taxonomic compositions in infants with CF relative to healthy infants that were characterized by differences in the abundances of taxa associated with GI health and nutrition. Furthermore, these taxonomic differences were more pronounced in low length infants with CF, suggesting a potential link to linear growth failure. We hypothesized that these differences would entail shifts in the microbiome’s functional capacities that could contribute to inflammation and nutritional failure in infants with CF.

Results

To test this hypothesis, we compared fecal microbial metagenomic content between healthy infants and infants with CF, supplemented with an analysis of fecal metabolomes in infants with CF. We identified notable differences in CF fecal microbial functional capacities, including metabolic and environmental response functions, compared to healthy infants that intensified during the first year of life. A machine learning-based longitudinal metagenomic age analysis of healthy and CF fecal metagenomic functional profiles further demonstrated that these differences are characterized by a CF-associated delay in the development of these functional capacities. Moreover, we found metagenomic differences in functions related to metabolism among infants with CF that were associated with diet and antibiotic exposure, and identified several taxa as potential drivers of these functional differences. An integrated metagenomic and metabolomic analysis further revealed that abundances of several fecal GI metabolites important for nutrient absorption, including three bile acids, correlated with specific microbes in infants with CF.

Conclusions

Our results highlight several metagenomic and metabolomic factors, including bile acids and other microbial metabolites, that may impact nutrition, growth, and GI health in infants with CF. These factors could serve as promising avenues for novel microbiome-based therapeutics to improve health outcomes in these infants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02305-z.

Divergent Genomic Adaptations in the Microbiomes of Arctic Subzero Sea-Ice and Cryopeg Brines

Subzero hypersaline brines are liquid microbial habitats within otherwise frozen environments, where concentrated dissolved salts prevent freezing. Such extreme conditions presumably require unique microbial adaptations, and possibly altered ecologies, but specific strategies remain largely unknown. Here we examined prokaryotic taxonomic and functional diversity in two seawater-derived subzero hypersaline brines: first-year sea ice, subject to seasonally fluctuating conditions; and ancient cryopeg, under relatively stable conditions geophysically isolated in permafrost. Overall, both taxonomic composition and functional potential were starkly different. Taxonomically, sea-ice brine communities (∼10⁵ cells mL^–1) had greater richness, more diversity and were dominated by bacterial genera, including Polaribacter, Paraglaciecola, Colwellia, and Glaciecola, whereas the more densely inhabited cryopeg brines (∼10⁸ cells mL^–1) lacked these genera and instead were dominated by Marinobacter. Functionally, however, sea ice encoded fewer accessory traits and lower average genomic copy numbers for shared traits, though DNA replication and repair were elevated; in contrast, microbes in cryopeg brines had greater genetic versatility with elevated abundances of accessory traits involved in sensing, responding to environmental cues, transport, mobile elements (transposases and plasmids), toxin-antitoxin systems, and type VI secretion systems. Together these genomic features suggest adaptations and capabilities of sea-ice communities manifesting at the community level through seasonal ecological succession, whereas the denser cryopeg communities appear adapted to intense bacterial competition, leaving fewer genera to dominate with brine-specific adaptations and social interactions that sacrifice some members for the benefit of others. Such cryopeg genomic traits provide insight into how long-term environmental stability may enable life to survive extreme conditions.

mbImpute: an accurate and robust imputation method for microbiome data

A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data-mbImpute-to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances.

Genome-Based Targeted Sequencing as a Reproducible Microbial Community Profiling Assay

Current sequencing-based methods for profiling microbial communities rely on marker gene (e.g., 16S rRNA) or metagenome shotgun sequencing (mWGS) analysis. We present an approach based on a single-primer extension reaction using a highly multiplexed oligonucleotide probe pool. This approach, termed MA-GenTA (microbial abundances from genome tagged analysis), enables quantitative, straightforward, cost-effective microbiome profiling that combines desirable features of both 16S rRNA and mWGS strategies. The use of multiple probes per target genome and rigorous probe design criteria enabled robust determination of relative abundance. To test the utility of the MA-GenTA assay, probes were designed for 830 genome sequences representing bacteria present in mouse stool specimens. Comparison of the MA-GenTA data with mWGS data demonstrated excellent correlation down to 0.01% relative abundance and a similar number of organisms detected per sample. Despite the incompleteness of the reference database, nonmetric multidimensional scaling (NMDS) clustering based on the Bray-Curtis dissimilarity metric of sample groups was consistent between MA-GenTA, mWGS, and 16S rRNA data sets. MA-GenTA represents a potentially useful new method for microbiome community profiling based on reference genomes.

IMPORTANCE New methods for profiling the microbial communities can create new approaches to understanding the composition and function of those communities. In this study, we combined bacterial genome-specific probe design with a highly multiplexed single primer extension reaction as a new method to profile microbial communities, using stool from various mouse strains as a test case. This method, termed MA-GenTA, was benchmarked against 16S rRNA gene sequencing and metagenome sequencing methods and delivered similar relative abundance and clustering data. Since the probes were generated from reference genomes, MA-GenTA was also able to provide functional pathway data for the stool microbiome in the assayed samples. The method is more informative than 16S rRNA analysis while being less costly than metagenome shotgun sequencing.

Syntrophic Hydrocarbon Degradation in a Decommissioned Off-Shore Subsea Oil Storage Structure

Over the last decade, metagenomic studies have revealed the impact of oil production on the microbial ecology of petroleum reservoirs. However, despite their fundamental roles in bioremediation of hydrocarbons, biocorrosion, biofouling and hydrogen sulfide production, oil field and oil production infrastructure microbiomes are poorly explored. Understanding of microbial activities within oil production facilities is therefore crucial for environmental risk mitigation, most notably during decommissioning. The analysis of the planktonic microbial community from the aqueous phase of a subsea oil-storage structure was conducted. This concrete structure was part of the production platform of the Brent oil field (North Sea), which is currently undergoing decommissioning. Quantification and sequencing of microbial 16S rRNA genes, metagenomic analysis and reconstruction of metagenome assembled genomes (MAGs) revealed a unique microbiome, strongly dominated by organisms related to Dethiosulfatibacter and Cloacimonadetes. Consistent with the hydrocarbon content in the aqueous phase of the structure, a strong potential for degradation of low molecular weight aromatic hydrocarbons was apparent in the microbial community. These degradation pathways were associated with taxonomically diverse microorganisms, including the predominant Dethiosulfatibacter and Cloacimonadetes lineages, expanding the list of potential hydrocarbon degraders. Genes associated with direct and indirect interspecies exchanges (multiheme type-C cytochromes, hydrogenases and formate/acetate metabolism) were widespread in the community, suggesting potential syntrophic hydrocarbon degradation processes in the system. Our results illustrate the importance of genomic data for informing decommissioning strategies in marine environments and reveal that hydrocarbon-degrading community composition and metabolisms in man-made marine structures might differ markedly from natural hydrocarbon-rich marine environments.

CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data

Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level. In this study, we introduce CAMAMED as a composition-aware mapping-based metagenomic data analysis pipeline. This pipeline can analyze metagenomic samples at both taxonomic and functional profiling levels. Using this pipeline, metagenome sequences can be mapped to non-redundant gene catalogs and the gene frequency in the samples are obtained. Due to the highly compositional nature of metagenomic data, the cumulative sum-scaling method is used at both taxa and gene levels for compositional data analysis in our pipeline. Additionally, by mapping the genes to the KEGG database, annotations related to each gene can be extracted at different functional levels such as KEGG ortholog groups, enzyme commission numbers and reactions. Furthermore, the pipeline enables the user to identify potential biomarkers in case-control metagenomic samples by investigating functional differences. The source code for this software is available from https://github.com/mhnb/camamed. Also, the ready to use Docker images are available at https://hub.docker.com.

Microbiome analysis and predicted relative metabolomic turnover suggest bacterial heme and selenium metabolism are altered in the gastrointestinal system of zebrafish (Danio rerio) exposed to the organochlorine dieldrin

Dietary exposure to chemicals alters the diversity of microbiome communities and can lead to pathophysiological changes in the gastrointestinal system. The organochlorine pesticide dieldrin is a persistent environmental contaminant that bioaccumulates in fatty tissue of aquatic organisms. The objectives of this study were to determine whether environmentally-relevant doses of dieldrin altered gastrointestinal morphology and the microbiome of zebrafish. Adult zebrafish at ∼4 months of age were fed a measured amount of feed containing either a solvent control or one of two doses of dieldrin (measured at 16, and 163.5 ng/g dry weight) for 4 months. Dieldrin body burden levels in zebrafish after four-month exposure were 0 (control), 11.47 ± 1.13 ng/g (low dose) and 18.32 ± 1.32 ng/g (high dose) wet weight [mean ± std]. Extensive histopathology at the whole organism level revealed that dieldrin exposure did not induce notable tissue pathology, including the gastrointestinal tract. A repeated measure mixed model analysis revealed that, while fish gained weight over time, there were no dieldrin-specific effects on body weight. Fecal content was collected from the gastrointestinal tract of males and 16S rRNA gene sequencing conducted. Dieldrin at a measured feed dose of 16 ng/g reduced the abundance of Firmicutes, a phylum involved in energy resorption. At the level of class, there was a decrease in abundance of Clostridia and Betaproteobacteria, and an increase in Verrucomicrobiae species. We used a computational approach called predicted relative metabolomic turnover (PRMT) to predict how a shift in microbial community composition affects exchange of metabolites. Dieldrin was predicted to affect metabolic turnover of uroporphyrinogen I and coproporphyrinogen I [enzyme]-cysteine, hydrogen selenide, selenite, and methyl-selenic acid in the fish gastrointestinal system. These pathways are related to bacterial heme biosynthesis and selenium metabolism. Our study demonstrates that dietary exposures to dieldrin can alter microbiota composition over 4 months, however the long-term consequences of such impacts are not well understood.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

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.

MetaLAFFA: a flexible, end-to-end, distributed computing-compatible metagenomic functional annotation pipeline

BACKGROUND

Microbial communities have become an important subject of research across multiple disciplines in recent years. These communities are often examined via shotgun metagenomic sequencing, a technology which can offer unique insights into the genomic content of a microbial community. Functional annotation of shotgun metagenomic data has become an increasingly popular method for identifying the aggregate functional capacities encoded by the community's constituent microbes. Currently available metagenomic functional annotation pipelines, however, suffer from several shortcomings, including limited pipeline customization options, lack of standard raw sequence data pre-processing, and insufficient capabilities for integration with distributed computing systems.

RESULTS

Here we introduce MetaLAFFA, a functional annotation pipeline designed to take unfiltered shotgun metagenomic data as input and generate functional profiles. MetaLAFFA is implemented as a Snakemake pipeline, which enables convenient integration with distributed computing clusters, allowing users to take full advantage of available computing resources. Default pipeline settings allow new users to run MetaLAFFA according to common practices while a Python module-based configuration system provides advanced users with a flexible interface for pipeline customization. MetaLAFFA also generates summary statistics for each step in the pipeline so that users can better understand pre-processing and annotation quality.

CONCLUSIONS

MetaLAFFA is a new end-to-end metagenomic functional annotation pipeline with distributed computing compatibility and flexible customization options. MetaLAFFA source code is available at https://github.com/borenstein-lab/MetaLAFFA and can be installed via Conda as described in the accompanying documentation.

Health and disease markers correlate with gut microbiome composition across thousands of people

Variation in the human gut microbiome can reflect host lifestyle and behaviors and influence disease biomarker levels in the blood. Understanding the relationships between gut microbes and host phenotypes are critical for understanding wellness and disease. Here, we examine associations between the gut microbiota and ~150 host phenotypic features across ~3,400 individuals. We identify major axes of taxonomic variance in the gut and a putative diversity maximum along the Firmicutes-to-Bacteroidetes axis. Our analyses reveal both known and unknown associations between microbiome composition and host clinical markers and lifestyle factors, including host-microbe associations that are composition-specific. These results suggest potential opportunities for targeted interventions that alter the composition of the microbiome to improve host health. By uncovering the interrelationships between host diet and lifestyle factors, clinical blood markers, and the human gut microbiome at the population-scale, our results serve as a roadmap for future studies on host-microbe interactions and interventions.

Pathway-Based Integrative Analysis of Metabolome and Microbiome Data from Hepatocellular Carcinoma and Liver Cirrhosis Patients

Aberrations of the human microbiome are associated with diverse liver diseases, including hepatocellular carcinoma (HCC). Even if we can associate specific microbes with particular diseases, it is difficult to know mechanistically how the microbe contributes to the pathophysiology. Here, we sought to reveal the functional potential of the HCC-associated microbiome with the human metabolome which is known to play a role in connecting host phenotype to microbiome function. To utilize both microbiome and metabolomic data sets, we propose an innovative, pathway-based analysis, Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome (HisCoM-MnM), for integrating microbiome and metabolomic data. In particular, we used pathway information to integrate these two omics data sets, thus providing insight into biological interactions between different biological layers, with regard to the host's phenotype. The application of HisCoM-MnM to data sets from 103 and 97 patients with HCC and liver cirrhosis (LC), respectively, showed that this approach could identify HCC-related pathways related to cancer metabolic reprogramming, in addition to the significant metabolome and metagenome that make up those pathways.

Global landscape of phenazine biosynthesis and biodegradation reveals species-specific colonization patterns in agricultural soils and crop microbiomes

Phenazines are natural bacterial antibiotics that can protect crops from disease. However, for most crops it is unknown which producers and specific phenazines are ecologically relevant, and whether phenazine biodegradation can counter their effects. To better understand their ecology, we developed and environmentally-validated a quantitative metagenomic approach to mine for phenazine biosynthesis and biodegradation genes, applying it to >800 soil and plant-associated shotgun-metagenomes. We discover novel producer-crop associations and demonstrate that phenazine biosynthesis is prevalent across habitats and preferentially enriched in rhizospheres, whereas biodegrading bacteria are rare. We validate an association between maize and Dyella japonica, a putative producer abundant in crop microbiomes. D. japonica upregulates phenazine biosynthesis during phosphate limitation and robustly colonizes maize seedling roots. This work provides a global picture of phenazines in natural environments and highlights plant-microbe associations of agricultural potential. Our metagenomic approach may be extended to other metabolites and functional traits in diverse ecosystems.

Life at the Frozen Limit: Microbial Carbon Metabolism Across a Late Pleistocene Permafrost Chronosequence

Permafrost is an extreme habitat yet it hosts microbial populations that remain active over millennia. Using permafrost collected from a Pleistocene chronosequence (19 to 33 ka), we hypothesized that the functional genetic potential of microbial communities in permafrost would reflect microbial strategies to metabolize permafrost soluble organic matter (OM) in situ over geologic time. We also hypothesized that changes in the metagenome across the chronosequence would correlate with shifts in carbon chemistry, permafrost age, and paleoclimate at the time of permafrost formation. We combined high-resolution characterization of water-soluble OM by Fourier-transform ion-cyclotron-resonance mass spectrometry (FT-ICR MS), quantification of organic anions in permafrost water extracts, and metagenomic sequencing to better understand the relationships between the molecular-level composition of potentially bioavailable OM, the microbial community, and permafrost age. Both age and paleoclimate had marked effects on both the molecular composition of dissolved OM and the microbial community. The relative abundance of genes associated with hydrogenotrophic methanogenesis, carbohydrate active enzyme families, nominal oxidation state of carbon (NOSC), and number of identifiable molecular formulae significantly decreased with increasing age. In contrast, genes associated with fermentation of short chain fatty acids (SCFAs), the concentration of SCFAs and ammonium all significantly increased with age. We present a conceptual model of microbial metabolism in permafrost based on fermentation of OM and the buildup of organic acids that helps to explain the unique chemistry of ancient permafrost soils. These findings imply long-term in situ microbial turnover of ancient permafrost OM and that this pooled biolabile OM could prime ancient permafrost soils for a larger and more rapid microbial response to thaw compared to younger permafrost soils.

Evaluation of Microbiome-Host Relationships in the Zebrafish Gastrointestinal System Reveals Adaptive Immunity Is a Target of Bis(2-ethylhexyl) Phthalate (DEHP) Exposure

To improve physical characteristics of plastics such as flexibility and durability, producers enrich materials with phthalates such as di-2-(ethylhexyl) phthalate (DEHP). DEHP is a high production volume chemical associated with metabolic and immune disruption in animals and humans. To reveal mechanisms implicated in phthalate-related disruption in the gastrointestinal system, male and female zebrafish were fed DEHP (3 ppm) daily for two months. At the transcriptome level, DEHP significantly upregulated gene networks in the intestine associated with helper T cells' (Th1, Th2, and Th17) specific pathways. The activation of gene networks associated with adaptive immunity was linked to the suppression of networks for tight junction, gap junctional intercellular communication, and transmembrane transporters, all of which are precursors for impaired gut integrity and performance. On a class level, DEHP exposure increased Bacteroidia and Gammaproteobacteria and decreased Verrucomicrobiae in both the male and female gastrointestinal system. Further, in males there was a relative increase in Fusobacteriia and Betaproteobacteria and a relative decrease in Saccharibacteria. Predictive algorithms revealed that the functional shift in the microbiome community, and the metabolites they produce, act to modulate intestinal adaptive immunity. This finding suggests that the gut microbiota may contribute to the adverse effects of DEHP on the host by altering metabolites sensed by both intestinal and immune Th cells. Our results suggest that the microbiome-gut-immune axis can be modified by DEHP and emphasize the value of multiomics approaches to study microbiome-host interactions following chemical perturbations.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Network-Based Prediction of Novel CRISPR-Associated Genes in Metagenomes

A diversity of clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems provide adaptive immunity to bacteria and archaea through recording "memories" of past viral infections. Recently, many novel CRISPR-associated proteins have been discovered via computational studies, but those studies relied on biased and incomplete databases of assembled genomes. We avoided these biases and applied a network theory approach to search for novel CRISPR-associated genes by leveraging subtle ecological cooccurrence patterns identified from environmental metagenomes. We validated our method using existing annotations and discovered 32 novel CRISPR-associated gene families. These genes span a range of putative functions, with many potentially regulating the response to infection.IMPORTANCE Every branch on the tree of life, including microbial life, faces the threat of viral pathogens. Over the course of billions of years of coevolution, prokaryotes have evolved a great diversity of strategies to defend against viral infections. One of these is the CRISPR adaptive immune system, which allows microbes to "remember" past infections in order to better fight them in the future. There has been much interest among molecular biologists in CRISPR immunity because this system can be repurposed as a tool for precise genome editing. Recently, a number of comparative genomics approaches have been used to detect novel CRISPR-associated genes in databases of genomes with great success, potentially leading to the development of new genome-editing tools. Here, we developed novel methods to search for these distinct classes of genes directly in environmental samples ("metagenomes"), thus capturing a more complete picture of the natural diversity of CRISPR-associated genes.

Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

Microbial community metabolomics, particularly in the human gut, are beginning to provide a new route to identify functions and ecology disrupted in disease. However, these data can be costly and difficult to obtain at scale, while amplicon or shotgun metagenomic sequencing data are readily available for populations of many thousands. Here, we describe a computational approach to predict potentially unobserved metabolites in new microbial communities, given a model trained on paired metabolomes and metagenomes from the environment of interest. Focusing on two independent human gut microbiome datasets, we demonstrate that our framework successfully recovers community metabolic trends for more than 50% of associated metabolites. Similar accuracy is maintained using amplicon profiles of coral-associated, murine gut, and human vaginal microbiomes. We also provide an expected performance score to guide application of the model in new samples. Our results thus demonstrate that this 'predictive metabolomic' approach can aid in experimental design and provide useful insights into the thousands of community profiles for which only metagenomes are currently available.

In-feed bacitracin methylene disalicylate modulates the turkey microbiota and metabolome in a dose-dependent manner

Beginning in 2017, the subtherapeutic use of most antibiotic compounds for growth promotion in food producing animals in the US was prohibited, highlighting the need to discover alternative growth promotants. Identifying the mechanism of action of growth promoting antibiotics may aid in the discovery of antibiotic alternatives. We describe the effects of feeding a subtherapeutic (50 g/ton of feed) and therapeutic (200 g/ton) concentration of bacitracin methylene disalicylate (BMD) to commercial turkeys for 14 weeks, and its effect on turkey intestinal microbial communities and cecal metabolomes. Both BMD concentrations had an immediate and lasting impact on the microbiota structure, and reduced bacterial richness through the end of the study (12 weeks). Metabolomic analysis identified 712 biochemicals, and 69% of metabolites were differentially present in BMD treated turkeys for at least one time point (q < 0.1). Amino acids, carbohydrates, nucleotides, peptides, and lipids were decreased in the turkey ceca early after BMD administration. Long-term metabolome alterations continued even after withdrawal of BMD. The microbial composition, determined by 16S rRNA gene sequencing, was predictive of the metabolome, indicating a connection between the microbiome and metabolome. In-feed BMD may cause bacterial metabolic shifts, leading to beneficial traits that can be targeted to improve animal health and production.

Comparative Metagenomics

Thanks in large part to newer, better, and cheaper DNA sequencing technologies, an enormous number of metagenomic sequence datasets have been and continue to be generated, covering a huge variety of environmental niches, including several different human body sites. Comparing these metagenomes and identifying their commonalities and differences is a challenging task, due not only to the large amounts of data, but also because there are several methodological considerations that need to be taken into account to ensure an appropriate and sound comparison between datasets. In this chapter, we describe current techniques aimed at comparing metagenomes generated by 16S ribosomal RNA and shotgun DNA sequencing, emphasizing methodological issues that arise in these comparative studies. We provide a detailed case study to illustrate some of these techniques using data from the Human Microbiome Project comparing the microbial communities from ten buccal mucosa samples with ten tongue dorsum samples in terms of alpha diversity, beta diversity, and their taxonomic and functional profiles.

Functional shifts in microbial mats recapitulate early Earth metabolic transitions

Phototrophic microbial mats dominated terrestrial ecosystems for billions of years, largely causing, through cyanobacterial oxygenic photosynthesis, but also undergoing, the Great Oxidation Event approximately 2.5 billion years ago. Taking a space-for-time approach based on the universality of core metabolic pathways expressed at ecosystem level, we studied gene content and co-occurrence networks in high-diversity metagenomes from spatially close microbial mats along a steep redox gradient. The observed functional shifts suggest that anoxygenic photosynthesis was present but not predominant under early Precambrian conditions, being accompanied by other autotrophic processes. Our data also suggest that, in contrast to general assumptions, anoxygenic photosynthesis largely expanded in parallel with the subsequent evolution of oxygenic photosynthesis and aerobic respiration. Finally, our observations might represent space-for-time evidence that the Wood-Ljungdahl carbon fixation pathway dominated phototrophic mats in early ecosystems, whereas the Calvin cycle probably evolved from pre-existing variants before becoming the dominant contemporary form of carbon fixation.

Using metagenomics to investigate human and environmental resistomes

Antibiotic resistance is a global health concern declared by the WHO as one of the largest threats to modern healthcare. In recent years, metagenomic DNA sequencing has started to be applied as a tool to study antibiotic resistance in different environments, including the human microbiota. However, a multitude of methods exist for metagenomic data analysis, and not all methods are suitable for the investigation of resistance genes, particularly if the desired outcome is an assessment of risks to human health. In this review, we outline the current state of methods for sequence handling, mapping to databases of resistance genes, statistical analysis and metagenomic assembly. In addition, we provide an overview of important considerations related to the analysis of resistance genes, and recommend some of the currently used tools and methods that are best equipped to inform research and clinical practice related to antibiotic resistance.

Comparison of normalization methods for the analysis of metagenomic gene abundance data

Background

In shotgun metagenomics, microbial communities are studied through direct sequencing of DNA without any prior cultivation. By comparing gene abundances estimated from the generated sequencing reads, functional differences between the communities can be identified. However, gene abundance data is affected by high levels of systematic variability, which can greatly reduce the statistical power and introduce false positives. Normalization, which is the process where systematic variability is identified and removed, is therefore a vital part of the data analysis. A wide range of normalization methods for high-dimensional count data has been proposed but their performance on the analysis of shotgun metagenomic data has not been evaluated.

Results

Here, we present a systematic evaluation of nine normalization methods for gene abundance data. The methods were evaluated through resampling of three comprehensive datasets, creating a realistic setting that preserved the unique characteristics of metagenomic data. Performance was measured in terms of the methods ability to identify differentially abundant genes (DAGs), correctly calculate unbiased p-values and control the false discovery rate (FDR). Our results showed that the choice of normalization method has a large impact on the end results. When the DAGs were asymmetrically present between the experimental conditions, many normalization methods had a reduced true positive rate (TPR) and a high false positive rate (FPR). The methods trimmed mean of M-values (TMM) and relative log expression (RLE) had the overall highest performance and are therefore recommended for the analysis of gene abundance data. For larger sample sizes, CSS also showed satisfactory performance.

Conclusions

This study emphasizes the importance of selecting a suitable normalization methods in the analysis of data from shotgun metagenomics. Our results also demonstrate that improper methods may result in unacceptably high levels of false positives, which in turn may lead to incorrect or obfuscated biological interpretation.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4637-6) contains supplementary material, which is available to authorized users.

The Skin Microbiome of the Neotropical Frog Craugastor fitzingeri: Inferring Potential Bacterial-Host-Pathogen Interactions From Metagenomic Data

Skin symbiotic bacteria on amphibians can play a role in protecting their host against pathogens. Chytridiomycosis, the disease caused by Batrachochytrium dendrobatidis, Bd, has caused dramatic population declines and extinctions of amphibians worldwide. Anti-Bd bacteria from amphibian skin have been cultured, and skin bacterial communities have been described through 16S rRNA gene amplicon sequencing. Here, we present a shotgun metagenomic analysis of skin bacterial communities from a Neotropical frog, Craugastor fitzingeri. We sequenced the metagenome of six frogs from two different sites in Panamá: three frogs from Soberanía (Sob), a Bd-endemic site, and three frogs from Serranía del Sapo (Sapo), a Bd-naïve site. We described the taxonomic composition of skin microbiomes and found that Pseudomonas was a major component of these communities. We also identified that Sob communities were enriched in Actinobacteria while Sapo communities were enriched in Gammaproteobacteria. We described gene abundances within the main functional classes and found genes enriched either in Sapo or Sob. We then focused our study on five functional classes of genes: biosynthesis of secondary metabolites, metabolism of terpenoids and polyketides, membrane transport, cellular communication and antimicrobial drug resistance. These gene classes are potentially involved in bacterial communication, bacterial-host and bacterial-pathogen interactions among other functions. We found that C. fitzingeri metagenomes have a wide array of genes that code for secondary metabolites, including antibiotics and bacterial toxins, which may be involved in bacterial communication, but could also have a defensive role against pathogens. Several genes involved in bacterial communication and bacterial-host interactions, such as biofilm formation and bacterial secretion systems were found. We identified specific genes and pathways enriched at the different sites and determined that gene co-occurrence networks differed between sites. Our results suggest that skin microbiomes are composed of distinct bacterial taxa with a wide range of metabolic capabilities involved in bacterial defense and communication. Differences in taxonomic composition and pathway enrichments suggest that skin microbiomes from different sites have unique functional properties. This study strongly supports the need for shotgun metagenomic analyses to describe the functional capacities of skin microbiomes and to tease apart their role in host defense against pathogens.

Taxa-function robustness in microbial communities

BACKGROUND

The species composition of a microbial community is rarely fixed and often experiences fluctuations of varying degrees and at varying frequencies. These perturbations to a community's taxonomic profile naturally also alter the community's functional profile-the aggregate set of genes encoded by community members-ultimately altering the community's overall functional capacities. The magnitude of such functional changes and the specific shift that will occur in each function, however, are strongly dependent on how genes are distributed across community members' genomes. This gene distribution, in turn, is determined by the taxonomic composition of the community and would markedly differ, for example, between communities composed of species with similar genomic content vs. communities composed of species whose genomes encode relatively distinct gene sets. Combined, these observations suggest that community functional robustness to taxonomic perturbations could vary widely across communities with different compositions, yet, to date, a systematic study of the inherent link between community composition and robustness is lacking.

RESULTS

In this study, we examined how a community's taxonomic composition influences the robustness of that community's functional profile to taxonomic perturbation (here termed taxa-function robustness) across a wide array of environments. Using a novel simulation-based computational model to quantify this taxa-function robustness in host-associated and non-host-associated communities, we find notable differences in robustness between communities inhabiting different body sites, including significantly higher robustness in gut communities compared to vaginal communities that cannot be attributed solely to differences in species richness. We additionally find between-site differences in the robustness of specific functions, some of which are potentially related to site-specific environmental conditions. These taxa-function robustness differences are most strongly associated with differences in overall functional redundancy, though other aspects of gene distribution also influence taxa-function robustness in certain body environments, and are sufficient to cluster communities by environment. Further analysis revealed a correspondence between our robustness estimates and taxonomic and functional shifts observed across human-associated communities.

CONCLUSIONS

Our analysis approach revealed intriguing taxa-function robustness variation across environments and identified features of community and gene distribution that impact robustness. This approach could be further applied for estimating taxa-function robustness in novel communities and for informing the design of synthetic communities with specific robustness requirements.

Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn's disease

BACKGROUND

Crohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples.

RESULTS

We sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response.

CONCLUSIONS

We demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.

Global biogeography of microbial nitrogen-cycling traits in soil

Microorganisms drive much of the Earth's nitrogen (N) cycle, but we still lack a global overview of the abundance and composition of the microorganisms carrying out soil N processes. To address this gap, we characterized the biogeography of microbial N traits, defined as eight N-cycling pathways, using publically available soil metagenomes. The relative frequency of N pathways varied consistently across soils, such that the frequencies of the individual N pathways were positively correlated across the soil samples. Habitat type, soil carbon, and soil N largely explained the total N pathway frequency in a sample. In contrast, we could not identify major drivers of the taxonomic composition of the N functional groups. Further, the dominant genera encoding a pathway were generally similar among habitat types. The soil samples also revealed an unexpectedly high frequency of bacteria carrying the pathways required for dissimilatory nitrate reduction to ammonium, a little-studied N process in soil. Finally, phylogenetic analysis showed that some microbial groups seem to be N-cycling specialists or generalists. For instance, taxa within the Deltaproteobacteria encoded all eight N pathways, whereas those within the Cyanobacteria primarily encoded three pathways. Overall, this trait-based approach provides a baseline for investigating the relationship between microbial diversity and N cycling across global soils.

Space-type radiation induces multimodal responses in the mouse gut microbiome and metabolome

BACKGROUND

Space travel is associated with continuous low dose rate exposure to high linear energy transfer (LET) radiation. Pathophysiological manifestations after low dose radiation exposure are strongly influenced by non-cytocidal radiation effects, including changes in the microbiome and host gene expression. Although the importance of the gut microbiome in the maintenance of human health is well established, little is known about the role of radiation in altering the microbiome during deep-space travel.

RESULTS

Using a mouse model for exposure to high LET radiation, we observed substantial changes in the composition and functional potential of the gut microbiome. These were accompanied by changes in the abundance of multiple metabolites, which were related to the enzymatic activity of the predicted metagenome by means of metabolic network modeling. There was a complex dynamic in microbial and metabolic composition at different radiation doses, suggestive of transient, dose-dependent interactions between microbial ecology and signals from the host's cellular damage repair processes. The observed radiation-induced changes in microbiota diversity and composition were analyzed at the functional level. A constitutive change in activity was found for several pathways dominated by microbiome-specific enzymatic reactions like carbohydrate digestion and absorption and lipopolysaccharide biosynthesis, while the activity in other radiation-responsive pathways like phosphatidylinositol signaling could be linked to dose-dependent changes in the abundance of specific taxa.

CONCLUSIONS

The implication of microbiome-mediated pathophysiology after low dose ionizing radiation may be an unappreciated biologic hazard of space travel and deserves experimental validation. This study provides a conceptual and analytical basis of further investigations to increase our understanding of the chronic effects of space radiation on human health, and points to potential new targets for intervention in adverse radiation effects.

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.

Systematic Characterization and Analysis of the Taxonomic Drivers of Functional Shifts in the Human Microbiome

Comparative analyses of the human microbiome have identified both taxonomic and functional shifts that are associated with numerous diseases. To date, however, microbiome taxonomy and function have mostly been studied independently and the taxonomic drivers of functional imbalances have not been systematically identified. Here, we present FishTaco, an analytical and computational framework that integrates taxonomic and functional comparative analyses to accurately quantify taxon-level contributions to disease-associated functional shifts. Applying FishTaco to several large-scale metagenomic cohorts, we show that shifts in the microbiome's functional capacity can be traced back to specific taxa. Furthermore, the set of taxa driving functional shifts and their contribution levels vary markedly between functions. We additionally find that similar functional imbalances in different diseases are driven by both disease-specific and shared taxa. Such integrated analysis of microbiome ecological and functional dynamics can inform future microbiome-based therapy, pinpointing putative intervention targets for manipulating the microbiome's functional capacity.

Revised computational metagenomic processing uncovers hidden and biologically meaningful functional variation in the human microbiome

BACKGROUND

Recent metagenomic analyses of the human gut microbiome identified striking variability in its taxonomic composition across individuals. Notably, however, these studies often reported marked functional uniformity, with relatively little variation in the microbiome's gene composition or in its overall metabolic capacity.

RESULTS

Here, we address this surprising discrepancy between taxonomic and functional variations and set out to track its origins. Specifically, we demonstrate that the functional uniformity observed in microbiome studies can be attributed, at least partly, to common computational metagenomic processing procedures that mask true functional variation across microbiome samples. We identify several such procedures, including commonly used practices for gene abundance normalization, mapping of gene families to functional pathways, and gene family aggregation. We show that accounting for these factors and using revised metagenomic processing procedures uncovers such hidden functional variation, significantly increasing observed variation in the abundance of functional elements across samples. Importantly, we find that this uncovered variation is biologically meaningful and that it is associated with both host identity and health.

CONCLUSIONS

Accurate characterization of functional variation in the microbiome is essential for comparative metagenomic analyses in health and disease. Our finding that metagenomic processing procedures mask underlying and biologically meaningful functional variation therefore highlights an important challenge such studies may face. Alternative schemes for metagenomic processing that uncover this hidden functional variation can facilitate improved metagenomic analysis and help pinpoint disease- and host-associated shifts in the microbiome's functional capacity.

High-resolution characterization of the human microbiome

The human microbiome plays an important and increasingly recognized role in human health. Studies of the microbiome typically use targeted sequencing of the 16S rRNA gene, whole metagenome shotgun sequencing, or other meta-omic technologies to characterize the microbiome's composition, activity, and dynamics. Processing, analyzing, and interpreting these data involve numerous computational tools that aim to filter, cluster, annotate, and quantify the obtained data and ultimately provide an accurate and interpretable profile of the microbiome's taxonomy, functional capacity, and behavior. These tools, however, are often limited in resolution and accuracy and may fail to capture many biologically and clinically relevant microbiome features, such as strain-level variation or nuanced functional response to perturbation. Over the past few years, extensive efforts have been invested toward addressing these challenges and developing novel computational methods for accurate and high-resolution characterization of microbiome data. These methods aim to quantify strain-level composition and variation, detect and characterize rare microbiome species, link specific genes to individual taxa, and more accurately characterize the functional capacity and dynamics of the microbiome. These methods and the ability to produce detailed and precise microbiome information are clearly essential for informing microbiome-based personalized therapies. In this review, we survey these methods, highlighting the challenges each method sets out to address and briefly describing methodological approaches.

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.