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

Integrative metagenomic analysis reveals distinct gut microbial signatures related to obesity

Obesity is a metabolic disorder closely associated with profound alterations in gut microbial composition. However, the dynamics of species composition and functional changes in the gut microbiome in obesity remain to be comprehensively investigated. In this study, we conducted a meta-analysis of metagenomic sequencing data from both obese and non-obese individuals across multiple cohorts, totaling 1351 fecal metagenomes. Our results demonstrate a significant decrease in both the richness and diversity of the gut bacteriome and virome in obese patients. We identified 38 bacterial species including Eubacterium sp. CAG:274, Ruminococcus gnavus, Eubacterium eligens and Akkermansia muciniphila, and 1 archaeal species, Methanobrevibacter smithii, that were significantly altered in obesity. Additionally, we observed altered abundance of five viral families: Mesyanzhinovviridae, Chaseviridae, Salasmaviridae, Drexlerviridae, and Casjensviridae. Functional analysis of the gut microbiome indicated distinct signatures associated to obesity and identified Ruminococcus gnavus as the primary driver for function enrichment in obesity, and Methanobrevibacter smithii, Akkermansia muciniphila, Ruminococcus bicirculans, and Eubacterium siraeum as functional drivers in the healthy control group. Additionally, our results suggest that antibiotic resistance genes and bacterial virulence factors may influence the development of obesity. Finally, we demonstrated that gut vOTUs achieved a diagnostic accuracy with an optimal area under the curve of 0.766 for distinguishing obesity from healthy controls. Our findings offer comprehensive and generalizable insights into the gut bacteriome and virome features associated with obesity, with the potential to guide the development of microbiome-based diagnostics.

Meta-analysis reveals obesity associated gut microbial alteration patterns and reproducible contributors of functional shift

The majority of cohort-specific studies associating gut microbiota with obesity are often contradictory; thus, the replicability of the signature remains questionable. Moreover, the species that drive obesity-associated functional shifts and their replicability remain unexplored. Thus, we aimed to address these questions by analyzing gut microbial metagenome sequencing data to develop an in-depth understanding of obese host-gut microbiota interactions using 3329 samples (Obese, n = 1494; Control, n = 1835) from 17 different countries, including both 16S rRNA gene and metagenomic sequence data. Fecal metagenomic data from diverse geographical locations were curated, profiled, and pooled using a machine learning-based approach to identify robust global signatures of obesity. Furthermore, gut microbial species and pathways were systematically integrated through the genomic content of the species to identify contributors to obesity-associated functional shifts. The community structure of the obese gut microbiome was evaluated, and a reproducible depletion of diversity was observed in the obese compared to the lean gut. From this, we infer that the loss of diversity in the obese gut is responsible for perturbations in the healthy microbial functional repertoire. We identified 25 highly predictive species and 37 pathway associations as signatures of obesity, which were validated with remarkably high accuracy (AUC, Species: 0.85, and pathway: 0.80) with an independent validation dataset. We observed a reduction in short-chain fatty acid (SCFA) producers (several Alistipes species, Odoribacter splanchnicus, etc.) and depletion of promoters of gut barrier integrity (Akkermansia muciniphila and Bifidobacterium longum) in obese guts. Our analysis underlines SCFAs and purine/pyrimidine biosynthesis, carbohydrate metabolism pathways in control individuals, and amino acid, enzyme cofactor, and peptidoglycan biosynthesis pathway enrichment in obese individuals. We also mapped the contributors to important obesity-associated functional shifts and observed that these are both dataset-specific and shared across the datasets. In summary, a comprehensive analysis of diverse datasets unveils species specifically contributing to functional shifts and consistent gut microbial patterns associated to obesity.

Oral administration of Pinus koraiensis cone essential oil reduces rumen methane emission by altering the rumen microbial composition and functions in Korean native goat (Capra hircus coreanae)

This study aimed to investigate Pinus koraiensis cone essential oil (PEO) as a methane (CH₄) inhibitor and determine its impact on the taxonomic and functional characteristics of the rumen microbiota in goats. A total of 10 growing Korean native goats (Capra hircus coreanae, 29.9 ± 1.58 kg, male) were assigned to different dietary treatments: control (CON; basal diet without additive) and PEO (basal diet +1 g/d of PEO) by a 2 × 2 crossover design. Methane measurements were conducted every 4 consecutive days for 17-20 days using a laser CH₄ detector. Samples of rumen fluid and feces were collected during each experimental period to evaluate the biological effects and dry matter (DM) digestibility after PEO oral administration. The rumen microbiota was analyzed via 16S rRNA gene amplicon sequencing. The PEO oral administration resulted in reduced CH₄ emission (eructation CH₄/body weight^0.75, p = 0.079) without affecting DM intake; however, it lowered the total volatile fatty acids (p = 0.041), molar proportion of propionate (p = 0.075), and ammonia nitrogen (p = 0.087) in the rumen. Blood metabolites (i.e., albumin, alanine transaminase/serum glutamic pyruvate transaminase, creatinine, and triglyceride) were significantly affected (p < 0.05) by PEO oral administration. The absolute fungal abundance (p = 0.009) was reduced by PEO oral administration, whereas ciliate protozoa, total bacteria, and methanogen abundance were not affected. The composition of rumen prokaryotic microbiota was altered by PEO oral administration with lower evenness (p = 0.054) observed for the PEO group than the CON group. Moreover, PICRUSt2 analysis revealed that the metabolic pathways of prokaryotic bacteria, such as pyruvate metabolism, were enriched in the PEO group. We also identified the Rikenellaceae RC9 gut group as the taxa potentially contributing to the enriched KEGG modules for histidine biosynthesis and pyruvate oxidation in the rumen of the PEO group using the FishTaco analysis. The entire co-occurrence networks showed that more nodes and edges were detected in the PEO group. Overall, these findings provide an understanding of how PEO oral administration affects CH₄ emission and rumen prokaryotic microbiota composition and function. This study may help develop potential manipulation strategies to find new essential oils to mitigate enteric CH₄ emissions from ruminants.

High-resolution characterization of short-term temporal variability in the taxonomic and resistome composition of wastewater influent

Wastewater-based epidemiology (WBE) for population-level surveillance of antimicrobial resistance (AMR) is gaining significant traction, but the impact of wastewater sampling methods on results is unclear. In this study, we characterized taxonomic and resistome differences between single-timepoint-grab and 24 h composites of wastewater influent from a large UK-based wastewater treatment work [WWTW (population equivalent: 223 435)]. We autosampled hourly influent grab samples (n=72) over three consecutive weekdays, and prepared additional 24 h composites (n=3) from respective grabs. For taxonomic profiling, metagenomic DNA was extracted from all samples and 16S rRNA gene sequencing was performed. One composite and six grabs from day 1 underwent metagenomic sequencing for metagenomic dissimilarity estimation and resistome profiling. Taxonomic abundances of phyla varied significantly across hourly grab samples but followed a repeating diurnal pattern for all 3 days. Hierarchical clustering grouped grab samples into four time periods dissimilar in both 16S rRNA gene-based profiles and metagenomic distances. 24H-composites resembled mean daily phyla abundances and showed low variability of taxonomic profiles. Of the 122 AMR gene families (AGFs) identified across all day 1 samples, single grab samples identified a median of six (IQR: 5-8) AGFs not seen in the composite. However, 36/36 of these hits were at lateral coverage <0.5 (median: 0.19; interquartile range: 0.16-0.22) and potential false positives. Conversely, the 24H-composite identified three AGFs not seen in any grab with higher lateral coverage (0.82; 0.55-0.84). Additionally, several clinically significant human AGFs (bla _VIM, bla _IMP, bla _KPC) were intermittently or completely missed by grab sampling but captured by the 24 h composite. Wastewater influent undergoes significant taxonomic and resistome changes on short timescales potentially affecting interpretation of results based on sampling strategy. Grab samples are more convenient and potentially capture low-prevalence/transient targets but are less comprehensive and temporally variable. Therefore, we recommend 24H-composite sampling where feasible. Further validation and optimization of WBE methods is vital for its development into a robust AMR surveillance approach.

Temporal, compositional, and functional differences in the microbiome of Bangkok subway air environment

With the growing numbers of the urban population, an increasing number of commuters have relied on subway systems for rapid transportation in daily life. Analyzing the temporal distribution of air microbiomes in subway environments is crucial for the assessment and monitoring of air quality in the subway system, especially with regard to public health. This study employed culture-independent metabarcode sequencing to analyze bacterial diversity and variations in bacterial compositions associated with bioaerosols collected from a subway station in Bangkok over a four-month period. The bacteria obtained were found to consist primarily of Proteobacteria, Firmicutes, and Actinobacteria, with variations at the family, genus, and species levels among samples obtained in different months. The vast majority of these bacteria are most likely derived from outside environments and human body sources. Many of the bacteria found in Bangkok subway station were also identified as "core microorganisms" of subway environments around the world, as suggested by the MetaSUB Consortium. The diversity of bacterial communities was shown to be influenced by several air quality variables, especially ambient temperature and the quantity of particulate matters, which showed positive correlations with several bacterial species such as Acinetobacter lwoffii, Staphylococcus spp., and Moraxella osloensis. In addition, metabolic profiles inferred from metabarcode-derived bacterial diversity showed significant variations across different sampling times and sites and can be used as a starting point to further explore the functional roles of specific groups of bacteria in the subway environment. This study thus introduced the information required for surveillance of microbiological impacts and their contributions to the well-being of subway commuters in Bangkok.

Multi-omics profiles of the intestinal microbiome in irritable bowel syndrome and its bowel habit subtypes

BACKGROUND

Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that is thought to involve alterations in the gut microbiome, but robust microbial signatures have been challenging to identify. As prior studies have primarily focused on composition, we hypothesized that multi-omics assessment of microbial function incorporating both metatranscriptomics and metabolomics would further delineate microbial profiles of IBS and its subtypes.

METHODS

Fecal samples were collected from a racially/ethnically diverse cohort of 495 subjects, including 318 IBS patients and 177 healthy controls, for analysis by 16S rRNA gene sequencing (n = 486), metatranscriptomics (n = 327), and untargeted metabolomics (n = 368). Differentially abundant microbes, predicted genes, transcripts, and metabolites in IBS were identified by multivariate models incorporating age, sex, race/ethnicity, BMI, diet, and HAD-Anxiety. Inter-omic functional relationships were assessed by transcript/gene ratios and microbial metabolic modeling. Differential features were used to construct random forests classifiers.

RESULTS

IBS was associated with global alterations in microbiome composition by 16S rRNA sequencing and metatranscriptomics, and in microbiome function by predicted metagenomics, metatranscriptomics, and metabolomics. After adjusting for age, sex, race/ethnicity, BMI, diet, and anxiety, IBS was associated with differential abundance of bacterial taxa such as Bacteroides dorei; metabolites including increased tyramine and decreased gentisate and hydrocinnamate; and transcripts related to fructooligosaccharide and polyol utilization. IBS further showed transcriptional upregulation of enzymes involved in fructose and glucan metabolism as well as the succinate pathway of carbohydrate fermentation. A multi-omics classifier for IBS had significantly higher accuracy (AUC 0.82) than classifiers using individual datasets. Diarrhea-predominant IBS (IBS-D) demonstrated shifts in the metatranscriptome and metabolome including increased bile acids, polyamines, succinate pathway intermediates (malate, fumarate), and transcripts involved in fructose, mannose, and polyol metabolism compared to constipation-predominant IBS (IBS-C). A classifier incorporating metabolites and gene-normalized transcripts differentiated IBS-D from IBS-C with high accuracy (AUC 0.86).

CONCLUSIONS

IBS is characterized by a multi-omics microbial signature indicating increased capacity to utilize fermentable carbohydrates-consistent with the clinical benefit of diets restricting this energy source-that also includes multiple previously unrecognized metabolites and metabolic pathways. These findings support the need for integrative assessment of microbial function to investigate the microbiome in IBS and identify novel microbiome-related therapeutic targets. Video Abstract.

Efficient computation of contributional diversity metrics from microbiome data with FuncDiv

MOTIVATION

Microbiome datasets with taxa linked to the functions (e.g. genes) they encode are becoming more common as metagenomics sequencing approaches improve. However, these data are challenging to analyze due to their complexity. Summary metrics, such as the alpha and beta diversity of taxa contributing to each function (i.e. contributional diversity), represent one approach to investigate these data, but currently there are no straightforward methods for doing so.

RESULTS

We addressed this gap by developing FuncDiv, which efficiently performs these computations. Contributional diversity metrics can provide novel insights that would be impossible to identify without jointly considering taxa and functions.

AVAILABILITY AND IMPLEMENTATION

FuncDiv is distributed under a GNU Affero General Public License v3.0 and is available at https://github.com/gavinmdouglas/FuncDiv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

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.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Characteristics of Gut Microbiota in Patients with GH-Secreting Pituitary Adenoma

Prior study has demonstrated that gut microbiota at the genus level is significantly altered in patients with growth hormone (GH)-secreting pituitary adenoma (GHPA). Yet, no studies exist describing the state of gut microbiota at species level in GHPA. We performed a study using 16S rRNA amplicon sequencing in a cohort of patients with GH-secreting pituitary adenoma (GHPA, n = 28) and healthy controls (n = 67). Among them, 9 patients and 10 healthy controls were randomly chosen and enrolled in metagenomics shotgun sequencing, generating 280,426,512 reads after aligning to NCBI GenBank DataBase to acquire taxa information at the species level. Weighted UniFrac analysis revealed that microbial diversity was notably decreased in patients with GHPA, consistent with a previous study. With 16S rRNA sequencing, after correction for false-discovery rate (FDR), rank-sum test at the genus level revealed that the relative abundance of Oscillibacter and Enterobacter was remarkably increased in patients and Blautia and Romboutsia genera predominated in the controls, augmented by additional LEfSe (linear discriminant analysis effect size) analysis. As for further comparison at the species level with metagenomics sequencing, rank-sum test together with LEfSe analysis confirmed the enrichment of Alistipes shahii and Odoribacter splanchnicus in the patient group. Notably, LEfSe analysis with metagenomics also demonstrated that Enterobacter sp. DC1 and Enterobacter sp. 940 PEND, derived from Enterobacter, were both significantly enriched in patients. Functional analysis showed that amino acid metabolism pathway was remarkably enriched in GHPA, while carbohydrate metabolism pathway was notably enriched in controls. Further, significant positive correlations were observed between Enterobacter and baseline insulin-like growth factor 1 (IGF-1), indicating that Enterobacter may be strongly associated with GH/IGF-1 axis in GHPA. Our data extend our insight into the GHPA microbiome, which may shed further light on GHPA pathogenesis and facilitate the exploration of novel therapeutic targets based on microbiota manipulation.

IMPORTANCE Dysbiosis of gut microbiota is associated not only with intestinal disorders but also with numerous extraintestinal diseases. Growth hormone-secreting pituitary adenoma (GHPA) is an insidious disease with persistent hypersecretion of GH and IGF-1, causing increased morbidity and mortality. Researches have reported that the GH/IGF-1 axis exerts its own influence on the intestinal microflora. Here, the results showed that compared with healthy controls, GHPA patients not only decreased the alpha diversity of the intestinal flora but also significantly changed their beta diversity. Further, metagenomics shotgun sequencing in the present study exhibited that Enterobacter sp. DC1 and Enterobacter sp. 940 PEND were enriched in patients. Also, we were pleasantly surprised to find that the Enterobacter genus was strongly positively correlated with baseline IGF-1 levels. Collectively, our work provides the first glimpse of the dysbiosis of the gut microbiota at species level, providing a better understanding of the pathophysiological process of GHPA.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

A pilot study of microbial signatures of liver disease in those with HIV mono-infection in Rio de Janeiro, Brazil

OBJECTIVE

The rectal microbiome was examined to assess the relationship between the microbiome and liver disease in HIV-infection.

DESIGN

Eighty-two HIV-1 mono-infected individuals from the PROSPEC-HIV-study (NCT02542020) were grouped into three liver health categories based on results of controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) of transient elastography: normal (n = 30), steatosis (n = 30), or fibrosis (n = 22).

METHODS

Liver steatosis and fibrosis were defined by CAP at least 248 dB/m and LSM at least 8.0 kPa, respectively. 16S rRNA gene and whole genome shotgun metagenomic sequencing were performed on rectal swabs. Bacterial differences were assessed using zero-inflated negative binomial regression and random forests modeling; taxonomic drivers of functional shifts were identified using FishTaco.

RESULTS

Liver health status explained four percentage of the overall variation (r2 = 0.04, P = 0.003) in bacterial composition. Participants with steatosis had depletions of Akkermansia muciniphila and Bacteroides dorei and enrichment of Prevotella copri, Finegoldia magna, and Ruminococcus bromii. Participants with fibrosis had depletions of Bacteroides stercoris and Parabacteroides distasonis and enrichment of Sneathia sanguinegens. In steatosis, functional analysis revealed increases in primary and secondary bile acid synthesis encoded by increased Eubacterium rectale, F. magna, and Faecalibacterium prausnitzii and decreased A. muciniphila, Bacteroides fragilis and B. dorei. Decreased folate biosynthesis was driven by similar changes in microbial composition.

CONCLUSION

HIV mono-infection with steatosis or fibrosis had distinct microbial profiles. Some taxa are similar to those associated with non-alcoholic fatty liver disease in HIV-negative populations. Further studies are needed to define the role of the gut microbiota in the pathogenesis of liver disease in HIV-infected persons.

Lack of Effect of Gluten Challenge on Fecal Microbiome in Patients With Celiac Disease and Non-Celiac Gluten Sensitivity

INTRODUCTION

Celiac disease (CD) may be associated with gut microbial dysbiosis. Whether discrete gluten exposure in subjects with well-controlled disease on a gluten-free diet impacts the gut microbiome is unknown and may have implications for understanding disease activity and symptoms. We conducted a prospective study to evaluate the impact of gluten exposure on the gut microbiome in patients with CD and nonceliac gluten sensitivity (NCGS).

METHODS

Subjects with CD (n = 9) and NCGS (n = 8) previously on a gluten-free diet were administered a 14-day gluten challenge (5 g of gluten per day) and compared with controls (n = 8) on a usual gluten-containing diet. Stool was collected for fecal microbiome analysis using 16S rRNA gene and metagenomic sequencing before, during, and after the gluten challenge. Symptoms were assessed using 2 validated clinical scales.

RESULTS

Among subjects with CD and NCGS, there were no significant fecal microbial changes in response to gluten challenge. Gut microbiome composition differed among controls, subjects with CD, and subjects with NCGS at baseline, and these differences persisted despite gluten exposure. Gastrointestinal and general health symptoms reported by subjects with CD and NCGS were worst in the middle of gluten challenge and lessened by its end, with no consistent associations with gut microbiome composition.

DISCUSSION

Pre-existing fecal microbiome diversity was unaffected by gluten challenge in adult subjects with CD and NCGS. These findings suggest that current microbiome status is unrelated to current disease activity and disease severity.

Metagenomic insights into the microbial communities of inert and oligotrophic outdoor pier surfaces of a coastal city

BACKGROUND

Studies of the microbiomes on surfaces in built environment have largely focused on indoor spaces, while outdoor spaces have received far less attention. Piers are engineered infrastructures commonly found in coastal areas, and due to their unique locations at the interface between terrestrial and aquatic ecosystems, pier surfaces are likely to harbor interesting microbiology. In this study, the microbiomes on the metal and concrete surfaces at nine piers located along the coastline of Hong Kong were investigated by metagenomic sequencing. The roles played by different physical attributes and environmental factors in shaping the taxonomic composition and functional traits of the pier surface microbiomes were determined. Metagenome-assembled genomes were reconstructed and their putative biosynthetic gene clusters were characterized in detail.

RESULTS

Surface material was found to be the strongest factor in structuring the taxonomic and functional compositions of the pier surface microbiomes. Corrosion-related bacteria were significantly enriched on metal surfaces, consistent with the pitting corrosion observed. The differential enrichment of taxa mediating biodegradation suggests differences between the metal and concrete surfaces in terms of specific xenobiotics being potentially degraded. Genome-centric analysis detected the presence of many novel species, with the majority of them belonging to the phylum Proteobacteria. Genomic characterization showed that the potential metabolic functions and secondary biosynthetic capacity were largely correlated with taxonomy, rather than surface attributes and geography.

CONCLUSIONS

Pier surfaces are a rich reservoir of abundant novel bacterial species. Members of the surface microbial communities use different mechanisms to counter the stresses under oligotrophic conditions. A better understanding of the outdoor surface microbiomes located in different environments should enhance the ability to maintain outdoor surfaces of infrastructures. Video Abstract.

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.

Early Life Exposure to Environmental Contaminants (BDE-47, TBBPA, and BPS) Produced Persistent Alterations in Fecal Microbiome in Adult Male Mice

The gut microbiome is a pivotal player in toxicological responses. We investigated the effects of maternal exposure to 3 human health-relevant toxicants (BDE-47, tetrabromobisphenol [TBBPA], and bisphenol S [BPS]) on the composition and metabolite levels (bile acids [BAs] and short-chain fatty acids [SCFAs]) of the gut microbiome in adult pups. CD-1 mouse dams were orally exposed to vehicle (corn oil, 10 ml/kg), BDE-47 (0.2 mg/kg), TBBPA (0.2 mg/kg), or BPS (0.2 mg/kg) once daily from gestational day 8 to the end of lactation (postnatal day 21). 16S rRNA sequencing and targeted metabolomics were performed in feces of 20-week-old adult male pups (n = 14 - 23/group). Host gene expression and BA levels were quantified in liver. BPS had the most prominent effect on the beta-diversity of the fecal microbiome compared with TBPPA and BDE-47 (QIIME). Seventy-three taxa were persistently altered by at least 1 chemical, and 12 taxa were commonly regulated by all chemicals (most of which were from the Clostridia class and were decreased). The most distinct microbial biomarkers were S24-7 for BDE-47, Rikenellaceae for TBBPA, and Lactobacillus for BPS (LefSe). The community-wide contributions to the shift in microbial pathways were predicted using FishTaco. Consistent with FishTaco predictions, BDE-47 persistently increased fecal and hepatic BAs within the 12α hydroxylation pathway, corresponding to an up-regulation with the hepatic BA-synthetic enzyme Cyp7a1. Fecal BAs were also persistently up-regulated by TBBPA and BPS (liquid chromatography-mass spectrometry). TBBPA increased propionic acid and succinate, whereas BPS decreased acetic acid (gas chromatography-mass spectrometry). There was a general trend in the hepatic down-regulation of proinflammatory cytokines and the oxidative stress sensor target gene (Nqo1), and a decrease in G6Pdx (the deficiency of which leads to dyslipidemia). In conclusion, maternal exposure to these toxicants persistently modified the gut-liver axis, which may produce an immune-suppressive and dyslipidemia-prone signature later in life.

Characterization of the public transit air microbiome and resistome reveals geographical specificity

BACKGROUND

The public transit is a built environment with high occupant density across the globe, and identifying factors shaping public transit air microbiomes will help design strategies to minimize the transmission of pathogens. However, the majority of microbiome works dedicated to the public transit air are limited to amplicon sequencing, and our knowledge regarding the functional potentials and the repertoire of resistance genes (i.e. resistome) is limited. Furthermore, current air microbiome investigations on public transit systems are focused on single cities, and a multi-city assessment of the public transit air microbiome will allow a greater understanding of whether and how broad environmental, building, and anthropogenic factors shape the public transit air microbiome in an international scale. Therefore, in this study, the public transit air microbiomes and resistomes of six cities across three continents (Denver, Hong Kong, London, New York City, Oslo, Stockholm) were characterized.

RESULTS

City was the sole factor associated with public transit air microbiome differences, with diverse taxa identified as drivers for geography-associated functional potentials, concomitant with geographical differences in species- and strain-level inferred growth profiles. Related bacterial strains differed among cities in genes encoding resistance, transposase, and other functions. Sourcetracking estimated that human skin, soil, and wastewater were major presumptive resistome sources of public transit air, and adjacent public transit surfaces may also be considered presumptive sources. Large proportions of detected resistance genes were co-located with mobile genetic elements including plasmids. Biosynthetic gene clusters and city-unique coding sequences were found in the metagenome-assembled genomes.

CONCLUSIONS

Overall, geographical specificity transcends multiple aspects of the public transit air microbiome, and future efforts on a global scale are warranted to increase our understanding of factors shaping the microbiome of this unique built environment.

Gut microbiome in Schizophrenia: Altered functional pathways related to immune modulation and atherosclerotic risk

Emerging evidence has linked the gut microbiome changes to schizophrenia. However, there has been limited research into the functional pathways by which the gut microbiota contributes to the phenotype of persons with chronic schizophrenia. We characterized the composition and functional potential of the gut microbiota in 48 individuals with chronic schizophrenia and 48 matched (sequencing plate, age, sex, BMI, and antibiotic use) non-psychiatric comparison subjects (NCs) using 16S rRNA sequencing. Patients with schizophrenia demonstrated significant beta-diversity differences in microbial composition and predicted genetic functional potential compared to NCs. Alpha-diversity of taxa and functional pathways were not different between groups. Random forests analyses revealed that the microbiome predicts differentiation of patients with schizophrenia from NCs using taxa (75% accuracy) and functional profiles (67% accuracy for KEGG orthologs, 70% for MetaCyc pathways). We utilized a new compositionally-aware method incorporating reference frames to identify differentially abundant microbes and pathways, which revealed that Lachnospiraceae is associated with schizophrenia. Functional pathways related to trimethylamine-N-oxide reductase and Kdo₂-lipid A biosynthesis were altered in schizophrenia. These metabolic pathways were associated with inflammatory cytokines and risk for coronary heart disease in schizophrenia. Findings suggest potential mechanisms by which the microbiota may impact the pathophysiology of the disease through modulation of functional pathways related to immune signaling/response and lipid and glucose regulation to be further investigated in future studies.

Emerging computational tools and models for studying gut microbiota composition and function

The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.

Metagenomics reveals impact of geography and acute diarrheal disease on the Central Indian human gut microbiome

BACKGROUND

The Central Indian gut microbiome remains grossly understudied. Herein, we sought to investigate the burden of antimicrobial resistance and diarrheal diseases, particularly Clostridioides difficile, in rural-agricultural and urban populations in Central India, where there is widespread unregulated antibiotic use. We utilized shotgun metagenomics to comprehensively characterize the bacterial and viral fractions of the gut microbiome and their encoded functions in 105 participants.

RESULTS

We observed distinct rural-urban differences in bacterial and viral populations, with geography exhibiting a greater influence than diarrheal status. Clostridioides difficile disease was more commonly observed in urban subjects, and their microbiomes were enriched in metabolic pathways relating to the metabolism of industrial compounds and genes encoding resistance to 3^rd generation cephalosporins and carbapenems. By linking phages present in the microbiome to their bacterial hosts through CRISPR spacers, phage variation could be directly related to shifts in bacterial populations, with the auxiliary metabolic potential of rural-associated phages enriched for carbon and amino acid energy metabolism.

CONCLUSIONS

We report distinct differences in antimicrobial resistance gene profiles, enrichment of metabolic pathways and phage composition between rural and urban populations, as well as a higher burden of Clostridioides difficile disease in the urban population. Our results reveal that geography is the key driver of variation in urban and rural Indian microbiomes, with acute diarrheal disease, including C. difficile disease exerting a lesser impact. Future studies will be required to understand the potential role of dietary, cultural, and genetic factors in contributing to microbiome differences between rural and urban populations.

Microbial signature in IgE-mediated food allergies

BACKGROUND

Multiple studies suggest a key role for gut microbiota in IgE-mediated food allergy (FA) development, but to date, none has studied it in the persistent state.

METHODS

To characterize the gut microbiota composition and short-chain fatty acid (SCFAs) profiles associated with major food allergy groups, we recruited 233 patients with FA including milk (N = 66), sesame (N = 38), peanut (N = 71), and tree nuts (N = 58), and non-allergic controls (N = 58). DNA was isolated from fecal samples, and 16S rRNA gene sequences were analyzed. SCFAs in stool were analyzed from patients with a single allergy (N = 84) and controls (N = 31).

RESULTS

The gut microbiota composition of allergic patients was significantly different compared to age-matched controls both in α-diversity and β-diversity. Distinct microbial signatures were noted for FA to different foods. Prevotella copri (P. copri) was the most overrepresented species in non-allergic controls. SCFAs levels were significantly higher in the non-allergic compared to the FA groups, whereas P. copri significantly correlated with all three SCFAs. We used these microbial differences to distinguish between FA patients and non-allergic healthy controls with an area under the curve of 0.90, and for the classification of FA patients according to their FA types using a supervised learning algorithm. Bacteroides and P. copri were identified as taxa potentially contributing to KEGG acetate-related pathways enriched in non-allergic compared to FA. In addition, overall pathway dissimilarities were found among different FAs.

CONCLUSIONS

Our results demonstrate a link between IgE-mediated FA and the composition and metabolic activity of the gut microbiota.

The intestinal microbiome, weight, and metabolic changes in women treated by adjuvant chemotherapy for breast and gynecological malignancies

BACKGROUND

Adjuvant chemotherapy induces weight gain, glucose intolerance, and hypertension in about a third of women. The mechanisms underlying these events have not been defined. This study assessed the association between the microbiome and weight gain in patients treated with adjuvant chemotherapy for breast and gynecological cancers.

METHODS

Patients were recruited before starting adjuvant therapy. Weight and height were measured before treatment and 4-6 weeks after treatment completion. Weight gain was defined as an increase of 3% or more in body weight. A stool sample was collected before treatment, and 16S rRNA gene sequencing was performed. Data regarding oncological therapy, menopausal status, and antibiotic use was prospectively collected. Patients were excluded if they were treated by antibiotics during the study. Fecal transplant experiments from patients were conducted using Swiss Webster germ-free mice.

RESULTS

Thirty-three patients were recruited; of them, 9 gained 3.5-10.6% of baseline weight. The pretreatment microbiome of women who gained weight following treatment was significantly different in diversity and taxonomy from that of control women. Fecal microbiota transplantation from pretreatment samples of patients that gained weight induced metabolic changes in germ-free mice compared to mice transplanted with pretreatment fecal samples from the control women.

CONCLUSION

The microbiome composition is predictive of weight gain following adjuvant chemotherapy and induces adverse metabolic changes in germ-free mice, suggesting it contributes to adverse metabolic changes seen in patients. Confirmation of these results in a larger patient cohort is warranted.

Deconstructing the Soil Microbiome into Reduced-Complexity Functional Modules

The taxonomic and functional diversity inherent to the soil microbiome complicate assessments of the metabolic potential carried out by the community members. An alternative approach is to break down the soil microbiome into reduced-complexity subsets based on metabolic capacities (functional modules) prior to sequencing and analysis. Here, we demonstrate that this approach successfully identified specific phylogenetic and biochemical traits of the soil microbiome that otherwise remained hidden from a more top-down analysis.

The soil microbiome represents one of the most complex microbial communities on the planet, encompassing thousands of taxa and metabolic pathways, rendering holistic analyses computationally intensive and difficult. Here, we developed an alternative approach in which the complex soil microbiome was broken into components (“functional modules”), based on metabolic capacities, for individual characterization. We hypothesized that reproducible, low-complexity communities that represent functional modules could be obtained through targeted enrichments and that, in combination, they would encompass a large extent of the soil microbiome diversity. Enrichments were performed on a starting soil inoculum with defined media based on specific carbon substrates, antibiotics, alternative electron acceptors under anaerobic conditions, or alternative growing conditions reflective of common field stresses. The resultant communities were evaluated through 16S rRNA amplicon sequencing. Less permissive modules (anaerobic conditions, complex polysaccharides, and certain stresses) resulted in more distinct community profiles with higher richness and more variability between replicates, whereas modules with simple substrates were dominated by fewer species and were more reproducible. Collectively, approximately 27% of unique taxa present in the liquid soil extract control were found across functional modules. Taxa that were underrepresented or undetected in the source soil were also enriched across the modules. Metatranscriptomic analyses were carried out on a subset of the modules to investigate differences in functional gene expression. These results demonstrate that by dissecting the soil microbiome into discrete components it is possible to obtain a more comprehensive view of the soil microbiome and its biochemical potential than would be possible using more holistic analyses.

The bacterial community and metabolome dynamics and their interactions modulate fermentation process of whole crop corn silage prepared with or without inoculants

Multi‐omics approach was adopted to investigate the modulation of bacterial microbiota and metabolome as well as their interactions in whole crop corn ensiling systems by inoculating homofermentative Lactobacillus plantarum or heterofermentative Lactobacillus buchneri. Inoculations of the two different inoculants resulted in substantial differences in microbial community and metabolic composition as well as their dynamics in ensiled corn. Inoculants also altered the correlations of microbiota in different manners, and various keystone species were identified in corn silages with different treatments. Many metabolites with biofunctional activities like bacteriostatic, antioxidant, central nervous system inhibitory and anti‐inflammatory were found in the present silage. A constitutive difference in microbiota dynamics was found for several pathways, which were upregulated by specific taxa in middle stage of fermentation, and widespread associations between metabolites with biofunctions and the species of lactic acid bacteria dominated in silage were observed. Multiple microbial and metabolic structures and dynamics were correlated and affected the fermentation process of the corn ensiling systems. Results of the current study improve our understanding of the complicated biological process underlying silage fermentation and provide a framework to re‐evaluate silages with biofunctions, which may contribute to target‐based regulation methods to produce functional silage for animal production.

Dietary energy sources and levels shift the multi-kingdom microbiota and functions in the rumen of lactating dairy cows

Background

Dietary energy source and level in lactation diets can profoundly affect milk yield and composition. Such dietary effects on lactation performance are underpinned by alteration of the rumen microbiota, of which bacteria, archaea, fungi, and protozoa may vary differently. However, few studies have examined all the four groups of rumen microbes. This study investigated the effect of both the level and source of dietary energy on rumen bacteria, archaea, fungi, and protozoa in the rumen of lactating dairy cows. A 2 × 2 factorial design resulted in four dietary treatments: low and high dietary energy levels (LE: 1.52–1.53; and HE: 1.71–1.72 Mcal/kg dry matter) and two dietary energy sources (GC: finely ground corn; and SFC: steam-flaked corn). We used a replicated 4 × 4 Latin square design using eight primiparous Chinese Holstein cows with each period lasting for 21 d. The rumen microbiota was analyzed using metataxonomics based on kingdom-specific phylogenetic markers [16S rRNA gene for bacteria and archaea, 18S rRNA gene for protozoa, and internally transcribed spacer 1 (ITS1) for fungi] followed with subsequent functional prediction using PICRUSt2.

Results

The GC resulted in a higher prokaryotic (bacterial and archaeal) species richness and Faith’s phylogenetic diversity than SFC. For the eukaryotic (fungi and protozoa) microbiota, the LE diets led to significantly higher values of the above measurements than the HE diets. Among the major classified taxa, 23 genera across all the kingdoms differed in relative abundance between the two dietary energy levels, while only six genera (none being protozoal) were differentially abundant between the two energy sources. Based on prokaryotic amplicon sequence variants (ASVs) from all the samples, overall functional profiles predicted using PICRUSt2 differed significantly between LE and HE but not between the two energy sources. FishTaco analysis identified Ruminococcus and Coprococcus as the taxa potentially contributing to the enriched KEGG pathways for biosynthesis of amino acids and to the metabolisms of pyruvate, glycerophospholipid, and nicotinate and nicotinamide in the rumen of HE-fed cows. The co-occurrence networks were also affected by the dietary treatments, especially the LE and GC diets, resulting in distinct co-occurrence networks. Several microbial genera appeared to be strongly correlated with one or more lactation traits.

Conclusions

Dietary energy level affected the overall rumen multi-kingdom microbiota while little difference was noted between ground corn and steam-flaked corn. Some genera were also affected differently by the four dietary treatments, including genera that had been shown to be correlated with lactation performance or feed efficiency. The co-occurrence patterns among the genera exclusively found for each dietary treatment may suggest possible metabolic interactions specifically affected by the dietary treatment. Some of the major taxa were positively correlated to milk properties and may potentially serve as biomarkers of one or more lactation traits.

Association between Rumen Microbiota and Marbling Score in Korean Native Beef Cattle

Simple Summary

The ruminal microbiome affects various metabolic processes associated with animal development; however, few studies have focused on its correlation with marbling. Results of the present study show differences in ruminal microbiomes among Hanwoo Korean beef cattle, which have low or high marbling scores. By elucidating the effect of the ruminal microbiome on the marbling of Hanwoo, differentially abundant microbial taxa, ruminal taxonomic drivers of lipid metabolism, and the correlation with meat quality indices, the present study provides insights into the potential effects of microbial factors on marbling in beef cattle.

Abstract

This study demonstrated the potential effects of the rumen microbiota on the deposition of intramuscular fat, known as marbling. Previous studies on fatty acid metabolism in beef cattle have mostly focused on biohydrogenating rumen bacteria, whereas those on the overall rumen microbiota—to understand their roles in marbling—have not been systematically performed. The rumen microbiota of 14 Korean beef cattle (Hanwoo), which showed similar carcass characteristics and blood metabolites but different marbling scores, were analyzed by 16S rRNA gene sequencing. The rumen samples were grouped into two extreme marbling score groups of host animals as follows: LMS, marbling score≤ 4 or HMS, marbling score ≥7. Species richness tended to be higher in the HMS group, whereas the overall microbiota differed between LMS and HMS groups. RFP12, Verrucomicrobia, Oscillospira, Porphyromonadaceae, and Paludibacter were differentially abundant in the HMS group, whereas Olsenella was abundant in the LMS group. Some marbling-associated bacterial taxa also contributed to the enrichment of two lipid metabolic pathways including “alpha-linolenic acid metabolism” and “fatty acid biosynthesis” in the HMS microbiome. Taxonomic drivers of fatty acid biosynthesis, particularly in the rumen microbiome of high-marbled meat, could thus be further studied to increase the intramuscular fat content.

Toxicomicrobiomics: The Human Microbiome vs. Pharmaceutical, Dietary, and Environmental Xenobiotics

The harmful impact of xenobiotics on the environment and human health is being more widely recognized; yet, inter- and intraindividual genetic variations among humans modulate the extent of harm, mostly through modulating the outcome of xenobiotic metabolism and detoxification. As the Human Genome Project revealed that host genetic, epigenetic, and regulatory variations could not sufficiently explain the complexity of interindividual variability in xenobiotics metabolism, its sequel, the Human Microbiome Project, is investigating how this variability may be influenced by human-associated microbial communities. Xenobiotic-microbiome relationships are mutual and dynamic. Not only does the human microbiome have a direct metabolizing potential on xenobiotics, but it can also influence the expression of the host metabolizing genes and the activity of host enzymes. On the other hand, xenobiotics may alter the microbiome composition, leading to a state of dysbiosis, which is linked to multiple diseases and adverse health outcomes, including increased toxicity of some xenobiotics. Toxicomicrobiomics studies these mutual influences between the ever-changing microbiome cloud and xenobiotics of various origins, with emphasis on their fate and toxicity, as well the various classes of microbial xenobiotic-modifying enzymes. This review article discusses classic and recent findings in toxicomicrobiomics, with examples of interactions between gut, skin, urogenital, and oral microbiomes with pharmaceutical, food-derived, and environmental xenobiotics. The current state and future prospects of toxicomicrobiomic research are discussed, and the tools and strategies for performing such studies are thoroughly and critically compared.

Computational profiling of the gut-brain axis: microflora dysbiosis insights to neurological disorders

Almost 2500 years after Hippocrates' observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microflora's compositional diversity regulates via its genes (the microbiome) the host's health and provides preliminary insights into disease progression and regulation. The microbiome's involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the host's phenotype but also in neurological investigations, substantiating the aptly named gut-brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome-gut-brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host.

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.

Beyond Taxonomic Analysis of Microbiomes: A Functional Approach for Revisiting Microbiome Changes in Colorectal Cancer

Colorectal cancer (CRC) is one of the most prevalent cancers in the world, especially in developed countries. In different studies, the association between CRC and dysbiosis of gut microbiome has been reported. However, most of these works focus on the taxonomic variation of the microbiome, which presents little, if any, functional insight about the reason behind and/or consequences of microbiome dysbiosis. In this study, we used a previously reported metagenome dataset which is obtained by sequencing 156 microbiome samples of healthy individuals as the control group (Co), as well as microbiome samples of patients with advanced colorectal adenoma (Ad) and colorectal carcinoma (Ca). Features of the microbiome samples have been analyzed at the level of species, as well as four functional levels, i.e., gene, KEGG orthology (KO) group, Enzyme Commission (EC) number, and reaction. It was shown that, at each of these levels, certain features exist which show significant changing trends during cancer progression. In the next step, a list of these features were extracted, which were shown to be able to predict the category of Co, Ad, and Ca samples with an accuracy of >85%. When only one group of features (species, gene, KO group, EC number, reaction) was used, KO-related features were found to be the most successful features for classifying the three categories of samples. Notably, species-related features showed the least success in sample classification. Furthermore, by applying an independent test set, we showed that these performance trends are not limited to our original dataset. We determined the most important classification features at each of the four functional levels. We propose that these features can be considered as biomarkers of CRC progression. Finally, we show that the intra-diversity of each sample at the levels of bacterial species and genes is much more than those of the KO groups, EC numbers, and reactions of that sample. Therefore, we conclude that the microbiome diversity at the species level, or gene level, is not necessarily associated with the diversity at the functional level, which again indicates the importance of KO-, EC-, and reaction-based features in metagenome analysis. The source code of proposed method is freely available from https://www.bioinformatics.org/mamed.

Prebiotics metabolism by gut-isolated probiotics

There are numerous species of bacteria resides in the lumen of human colon. The word 'colon', resembles colony or the colonization of microbiota of which plays an important role in the fermentation of prebiotics. The standpoint of prebiotic nowadays is well reported for attenuating gut dysbiosis in many clinical studies tested on animals and human. However, because of the huge amount of gut microbiome, the attempt to connect the dots between bacterial population and the host are not plainly discernible. Thus, a need to analyse recent research on the pathways of prebiotic metabolism adopted by commonly studied probiotics i.e. Bifidobacteria and Lactobacillus. Several different substrate-dependent gene expressions are induced to break down oligosaccharide molecules shown by those probiotics. The hydrolysis can occur either by membrane bound (extracellular) or cytoplasmic (intracellular) enzyme of the enteric bacteria. Therefore, this review narrates several prebiotic metabolisms occur during gut fermentation, and metabolite production i.e. organic acids conversion.

The microbiome driving anaerobic digestion and microbial analysis

The microbiome residing in anaerobic digesters drives the anaerobic digestion (AD) process to convert various feedstocks to biogas as a renewable source of energy. This microbiome has been investigated in numerous studies in the last century. The early studies used cultivation-based methods and analysis to identify the four guilds (or functional groups) of microorganisms. Molecular biology techniques overcame the limitations of cultivation-based methods and allowed the identification of unculturable microorganisms, revealing the high diversity of microorganisms involved in AD. In the past decade, omics technologies, including metataxonomics, metagenomics, metatranscriptomics, metaproteomics, and metametabolomics, have been or start to be used in comprehensive analysis and studies of biogas-producing microbiomes. In this chapter, we reviewed the utilities and limitations of these analysis methods, techniques, and technologies when they were used in studies of biogas-producing microbiomes, as well as the new information on diversity, composition, metabolism, and syntrophic interactions of biogas-producing microbiomes. We also discussed the current knowledge gaps and the research needed to further improve AD efficiency and stability.

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies

Correlation-based analysis of paired microbiome-metabolome data sets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach and other microbiome-metabolome analysis methods have not been comprehensively evaluated. To address this challenge, we have introduced a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally used a multispecies metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome data sets. We then compared observed taxon-metabolite correlations in these data sets to calculated ground truth taxonomic contribution values. We found that in simulations of both a representative simple 10-species community and complex human gut microbiota, correlation-based analysis poorly identified key contributors, with an extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as by exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.IMPORTANCE Identifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step toward understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation and then examining these contributions in simulated data sets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome data sets can identify true contributions with very low predictive value and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on those of the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

Early exposure to antibiotics in the neonatal intensive care unit alters the taxonomic and functional infant gut microbiome

INTRODUCTION

The infant gut microbiome is thought to play a key role in developing metabolic and immunologic pathways. Antibiotics have been shown to disrupt the human microbiome, but the impact they have on infants during this key window of development remains poorly understood. Through this study, we further characterize the effect antibiotics have on the gut microbiome of infants by looking at metagenomic sequencing data over time.

MATERIALS AND METHODS

Stool samples were collected on infants from a large tertiary care neonatal intensive care unit. After DNA extraction, metagenomics libraries were generated and sequenced. Taxonomic and functional analyses were then performed. Further directed specimen sequencing for fungal species was also performed.

RESULTS

A total of 51 stool samples from 25 infants were analyzed: seven infants were on antibiotics during at least one of their collection time points. Antibiotics given at birth altered the microbiome (PERMANOVA R² = 0.044, p = .002) but later courses did not (R² = 0.023, p = .114). Longitudinal samples collected while off antibiotics were more similar than those collected during a transition on or off antibiotics (mean Bray-Curtis distance 0.29 vs. 0.63, Wilcoxon p = .06). Functional analysis revealed four microbial pathways that were disrupted by antibiotics given at-birth (p < .1, folate synthesis, glycerolipid metabolism, fatty acid biosynthesis, and glycolysis). No functional changes associated with current antibiotic use were identified. In a limited sample set, we saw little evidence of fungal involvement in the overall infant microbiome.

CONCLUSION

Through this study, we have further characterized the role antibiotics have in the development of the infant microbiome. Antibiotics given at birth were associated with alterations in the microbiome and had significant impact on the functional pathways involved in folate synthesis and multiple metabolic pathways. Later courses of antibiotics led to stochastic dysbiosis and a significant decrease in Escherichia coli. Further characterization of the infant mycobiome is still needed.

The Signature Microbiota Drive Rumen Function Shifts in Goat Kids Introduced to Solid Diet Regimes

The feeding regime of early, supplementary solid diet improved rumen development and production in goat kids. However, the signature microbiota responsible for linking dietary regimes to rumen function shifts are still unclear. This work analyzed the rumen microbiome and functions affected by an early solid diet regime using a combination of machine learning algorithms. Volatile fatty acids (i.e., acetate, propionate and butyrate) fermented by microbes were found to increase significantly in the supplementary solid diet groups. Predominant genera were found to alter significantly from unclassified Sphingobacteriaceae (non-supplementary group) to Prevotella (supplementary solid diet groups). Random Forest classification model revealed signature microbiota for solid diet that positively correlated with macronutrient intake, and linearly increased with volatile fatty acid production. Bacteria associated with carbohydrate and protein metabolism were also identified. Utilization of a Fish Taco analysis portrayed a set of intersecting core species contributed to rumen function shifts by the solid diet regime. The core community structures consisted of the specific, signature microbiota and the manipulation of their symbiotic partners are manipulated by extra nutrients from concentrate and/or forage, and then produce more volatile fatty acids to promote rumen development and functions eventually host development. Our study provides mechanisms of the microbiome governed by a solid diet regime early in life, and highlights the signature microbiota involved in animal health and production.

Gut microbiome: An intermediary to neurotoxicity

There is growing recognition that the gut microbiome is an important regulator for neurological functions. This review provides a summary on the role of gut microbiota in various neurological disorders including neurotoxicity induced by environmental stressors such as drugs, environmental contaminants, and dietary factors. We propose that the gut microbiome remotely senses and regulates CNS signaling through the following mechanisms: 1) intestinal bacteria-mediated biotransformation of neurotoxicants that alters the neuro-reactivity of the parent compounds; 2) altered production of neuro-reactive microbial metabolites following exposure to certain environmental stressors; 3) bi-directional communication within the gut-brain axis to alter the intestinal barrier integrity; and 4) regulation of mucosal immune function. Distinct microbial metabolites may enter systemic circulation and epigenetically reprogram the expression of host genes in the CNS, regulating neuroinflammation, cell survival, or cell death. We will also review the current tools for the study of the gut-brain axis and provide some suggestions to move this field forward in the future.

A pollution gradient contributes to the taxonomic, functional, and resistome diversity of microbial communities in marine sediments

BACKGROUND

Coastal marine environments are one of the most productive ecosystems on Earth. However, anthropogenic impacts exert significant pressure on coastal marine biodiversity, contributing to functional shifts in microbial communities and human health risk factors. However, relatively little is known about the impact of eutrophication-human-derived nutrient pollution-on the marine microbial biosphere.

RESULTS

Here, we tested the hypothesis that benthic microbial diversity and function varies along a pollution gradient, with a focus on human pathogens and antibiotic resistance genes. Comprehensive metagenomic analysis including taxonomic investigation, functional detection, and ARG annotation revealed that zinc, lead, total volatile solids, and ammonia nitrogen were correlated with microbial diversity and function. We propose several microbes, including Planctomycetes and sulfate-reducing microbes as candidates to reflect pollution concentration. Annotation of antibiotic resistance genes showed that the highest abundance of efflux pumps was found at the most polluted site, corroborating the relationship between pollution and human health risk factors. This result suggests that sediments at polluted sites harbor microbes with a higher capacity to reduce intracellular levels of antibiotics, heavy metals, or other environmental contaminants.

CONCLUSIONS

Our findings suggest a correlation between pollution and the marine sediment microbiome and provide insight into the role of high-turnover microbial communities as well as potential pathogenic organisms as real-time indicators of water quality, with implications for human health and demonstrate the inner functional shifts contributed by the microcommunities.

Microbial differences between dental plaque and historic dental calculus are related to oral biofilm maturation stage

BACKGROUND

Dental calculus, calcified oral plaque biofilm, contains microbial and host biomolecules that can be used to study historic microbiome communities and host responses. Dental calculus does not typically accumulate as much today as historically, and clinical oral microbiome research studies focus primarily on living dental plaque biofilm. However, plaque and calculus reflect different conditions of the oral biofilm, and the differences in microbial characteristics between the sample types have not yet been systematically explored. Here, we compare the microbial profiles of modern dental plaque, modern dental calculus, and historic dental calculus to establish expected differences between these substrates.

RESULTS

Metagenomic data was generated from modern and historic calculus samples, and dental plaque metagenomic data was downloaded from the Human Microbiome Project. Microbial composition and functional profile were assessed. Metaproteomic data was obtained from a subset of historic calculus samples. Comparisons between microbial, protein, and metabolomic profiles revealed distinct taxonomic and metabolic functional profiles between plaque, modern calculus, and historic calculus, but not between calculus collected from healthy teeth and periodontal disease-affected teeth. Species co-exclusion was related to biofilm environment. Proteomic profiling revealed that healthy tooth samples contain low levels of bacterial virulence proteins and a robust innate immune response. Correlations between proteomic and metabolomic profiles suggest co-preservation of bacterial lipid membranes and membrane-associated proteins.

CONCLUSIONS

Overall, we find that there are systematic microbial differences between plaque and calculus related to biofilm physiology, and recognizing these differences is important for accurate data interpretation in studies comparing dental plaque and calculus.

The gut microbiome and epilepsy

Recently, evidence from both animal studies and human cases has emerged that a dysbiosis in the gut may be associated with certain forms of epilepsy. The ketogenic diet is an alternative treatment of drug-resistant epilepsy, although its precise mechanism of action has been unclear. It has now been shown that the ketogenic diet changes the composition and function of the gut microbiome in epilepsy patients. Studies in mice have demonstrated that the gut microbiota was necessary for the therapeutic effect of the diet and a mechanism of action has been proposed, providing new potential strategies for treatment. Further studies are needed to confirm the clinical relevance of this discovery. Below, we will discuss the scientific evidence of the role of the microbiome in seizure disorders, the impact of the ketogenic diet on the intestinal microbiota as well as the interactions described between commonly used antiepileptic drugs and intestinal microbial communities. We also discuss the potential of modulators of the gut microbiota as possible future anti-seizure therapeutics.

Structural variation in the gut microbiome associates with host health

Differences in the presence of even a few genes between otherwise identical bacterial strains may result in critical phenotypic differences. Here we systematically identify microbial genomic structural variants (SVs) and find them to be prevalent in the human gut microbiome across phyla and to replicate in different cohorts. SVs are enriched for CRISPR-associated and antibiotic-producing functions and depleted from housekeeping genes, suggesting that they have a role in microbial adaptation. We find multiple associations between SVs and host disease risk factors, many of which replicate in an independent cohort. Exploring genes that are clustered in the same SV, we uncover several possible mechanistic links between the microbiome and its host, including a region in Anaerostipes hadrus that encodes a composite inositol catabolism-butyrate biosynthesis pathway, the presence of which is associated with lower host metabolic disease risk. Overall, our results uncover a nascent layer of variability in the microbiome that is associated with microbial adaptation and host health.

The ketogenic diet influences taxonomic and functional composition of the gut microbiota in children with severe epilepsy

The gut microbiota has been linked to various neurological disorders via the gut–brain axis. Diet influences the composition of the gut microbiota. The ketogenic diet (KD) is a high-fat, adequate-protein, low-carbohydrate diet established for treatment of therapy-resistant epilepsy in children. Its efficacy in reducing seizures has been confirmed, but the mechanisms remain elusive. The diet has also shown positive effects in a wide range of other diseases, including Alzheimer’s, depression, autism, cancer, and type 2 diabetes. We collected fecal samples from 12 children with therapy-resistant epilepsy before starting KD and after 3 months on the diet. Parents did not start KD and served as diet controls. Applying shotgun metagenomic DNA sequencing, both taxonomic and functional profiles were established. Here we report that alpha diversity is not changed significantly during the diet, but differences in both taxonomic and functional composition are detected. Relative abundance of bifidobacteria as well as E. rectale and Dialister is significantly diminished during the intervention. An increase in relative abundance of E. coli is observed on KD. Functional analysis revealed changes in 29 SEED subsystems including the reduction of seven pathways involved in carbohydrate metabolism. Decomposition of these shifts indicates that bifidobacteria and Escherichia are important contributors to the observed functional shifts. As relative abundance of health-promoting, fiber-consuming bacteria becomes less abundant during KD, we raise concern about the effects of the diet on the gut microbiota and overall health. Further studies need to investigate whether these changes are necessary for the therapeutic effect of KD.

The ketogenic diet changes both the relative abundance of gut microbiota and their metabolic activities. The diet forces a shift from carbohydrates to ketones as a primary energy source and has demonstrated efficacy in reducing epileptic seizures in children. After animal models implicated gut microbiota in this amelioration, Stefanie Prast-Nielsen, of Sweden’s Karolinska Institutet, and her team sequenced microbiotic DNA of fecal samples from 12 children with epilepsy before and after 3 months on a ketogenic diet. Changes included reductions in the numbers of Bifidobacterium and an increase in Escherichia coli. Carbohydrate metabolism significantly changed after 3 months on the diet. Some reductions raise questions about the diet’s potential impact on gut and overall health. More studies are also needed to discern the mechanistic impact of these changes on seizure activity.

Low nadir CD4+ T-cell counts predict gut dysbiosis in HIV-1 infection

Human immunodeficiency virus (HIV)-1 infection causes severe gut and systemic immune damage, but its effects on the gut microbiome remain unclear. Previous shotgun metagenomic studies in HIV-negative subjects linked low-microbial gene counts (LGC) to gut dysbiosis in diseases featuring intestinal inflammation. Using a similar approach in 156 subjects with different HIV-1 phenotypes, we found a strong, independent, dose-effect association between nadir CD4+ T-cell counts and LGC. As in other diseases involving intestinal inflammation, the gut microbiomes of subjects with LGC were enriched in gram-negative Bacteroides, acetogenic bacteria and Proteobacteria, which are able to metabolize reactive oxygen and nitrogen species; and were depleted in oxygen-sensitive methanogenic archaea and sulfate-reducing bacteria. Interestingly, subjects with LGC also showed increased butyrate levels in direct fecal measurements, consistent with enrichment in Roseburia intestinalis despite reductions in other butyrate producers. The microbiomes of subjects with LGC were also enriched in bacterial virulence factors, as well as in genes associated with beta-lactam, lincosamide, tetracycline, and macrolide resistance. Thus, low nadir CD4+ T-cell counts, rather than HIV-1 serostatus per se, predict the presence of gut dysbiosis in HIV-1 infected subjects. Such dysbiosis does not display obvious HIV-specific features; instead, it shares many similarities with other diseases featuring gut inflammation.

Microbial communities in swine lungs and their association with lung lesions

Under natural farming, environmental pathogenic microorganisms may invade and affect swine lungs, further resulting in lung lesions. However, few studies on swine lung microbiota and their potential relationship with lung lesions were reported. Here, we sampled 20 pigs from a hybrid herd raised under natural conditions; we recorded a lung‐lesion phenotype and investigated lung microbial communities by sequencing the V3‐V4 region of 16S rRNA gene for each individual. We found reduced microbial diversity but more biomass in the severe‐lesion lungs. Methylotenera, Prevotella, Sphingobium and Lactobacillus were the prominent bacteria in the healthy lungs, while Mycoplasma, Ureaplasma, Sphingobium, Haemophilus and Phyllobacterium were the most abundant microbes in the severe‐lesion lungs. Notably, we identified 64 lung‐lesion‐associated OTUs, of which two classified to Mycoplasma were positively associated with lung lesions and 62 showed negative association including thirteen classified to Prevotella and six to Ruminococcus. Cross‐validation analysis showed that lung microbiota explained 23.7% phenotypic variance of lung lesions, suggesting that lung microbiota had large effects on promoting lung healthy. Furthermore, 22 KEGG pathways correlated with lung lesions were predicted. Altogether, our findings improve the knowledge about swine lung microbial communities and give insights into the relationship between lung microbiota and lung lesions.

Intestinal probiotics restore the ecological fitness decline of Bactrocera dorsalis by irradiation

The sterile insect technique (SIT) as an eco-friendly and reliable strategy has been used to control populations of insect pests of agricultural, veterinary and human health importance. Successful applications of SIT rely on the high-level ecological fitness of sterile males. A suitable and stable gut microbiome can contribute to the ecological fitness of insect by influencing their physiology, biochemistry and development processes. Here, we show that a shift in the gut bacterial composition and structure by sterilizing irradiation, characterized by a decrease in the major gut microbiota community Enterobacteriaceae, an expansion of the minor members (e.g., Bacillaceae) and a higher richness and diversity, is tightly linked to radiation-induced ecological fitness (male mating competitiveness, flight capacity, survival rate and life span) decline in Bactrocera dorsalis (Hendel) sterile males. Function prediction of gut microbiota indicated that changes in microbiome taxonomy tend to drive microbiome functional shifts. A higher nutrient consumption of the flourishing minor gut microbiota may cause a decline in nutrients and energy metabolic activity of host and then result in the reduced ecological fitness of irradiated flies. Furthermore, we found that a gut bacterial strain Klebsiella oxytoca (BD177) can restore ecological fitness by improving food intake and increasing haemolymph sugar and amino acid levels of irradiated B. dorsalis flies. Our findings suggest that gut symbiont-based probiotics can be used as agents for reversing radiation-induced ecological fitness decrease.

Re-purposing 16S rRNA gene sequence data from within case paired tumor biopsy and tumor-adjacent biopsy or fecal samples to identify microbial markers for colorectal cancer

Microbes colonizing colorectal cancer (CRC) tumors have the potential to affect disease, and vice-versa. The manner in which they differ from microbes in physically adjacent tissue or stool within the case in terms of both, taxonomy and biological activity remains unclear. In this study, we systematically analyzed previously published 16S rRNA sequence data from CRC patients with matched tumor:tumor-adjacent biopsies (n = 294 pairs, n = 588 biospecimens) and matched tumor biopsy:fecal pairs (n = 42 pairs, n = 84 biospecimens). Procrustes analyses, random effects regression, random forest (RF) modeling, and inferred functional pathway analyses were conducted to assess community similarity and microbial diversity across heterogeneous patient groups and studies. Our results corroborate previously reported association of increased Fusobacterium with tumor biopsies. Parvimonas and Streptococcus abundances were also elevated while Faecalibacterium and Ruminococcaceae abundances decreased in tumors relative to tumor-adjacent biopsies and stool samples from the same case. With the exception of these limited taxa, the majority of findings from individual studies were not confirmed by other 16S rRNA gene-based datasets. RF models comparing tumor and tumor-adjacent specimens yielded an area under curve (AUC) of 64.3%, and models of tumor biopsies versus fecal specimens exhibited an AUC of 82.5%. Although some taxa were shared between fecal and tumor samples, their relative abundances varied substantially. Inferred functional analysis identified potential differences in branched amino acid and lipid metabolism. Microbial markers that reliably occur in tumor tissue can have implications for microbiome based and microbiome targeting therapeutics for CRC.