Vikodak--A Modular Framework for Inferring Functional Potential of Microbial Communities from 16S Metagenomic Datasets

Application of functional metagenomics in the evaluation of microbial community dynamics in the Arabian Sea: Implications of environmental settings

Ocean microbial communities form the base of marine food webs, facilitating energy transfer and nutrient cycling, thereby supporting higher trophic levels. We investigated their composition and functional profiles across depths (surface waters 0, 29, and 63 m and bottom waters 100, 150, and 200 m) in the central-eastern Arabian Sea (CEAS) using next-generation sequencing. It was hypothesized that the composition and functional diversity of these communities would be influenced by depth and environmental parameters. Our research showed that microbial communities vary with depth and are shaped by environmental factors like irradiance, temperature, dissolved oxygen, suspended particulate matter, chlorophyll a, and ammonia concentrations. Cyanobacteria (Prochlorococcus sp) and Mamiellaceae, belonging to picoeukaryotes, exhibited distinct depth-specific distributions up to subsurface chlorophyll maxima (SCM) at 63 m. On the other hand, a community shift in the microbial communities comprising Firmicutes, Bacteroidetes, and Actinobacteria phyla was observed at the deeper water depths. The profiling of functional genes pointed out the expression of carbon fixation by photosynthetic organisms at the surface (0, 29, and 63 m), which shifted to prokaryotic carbon fixation in deeper waters (0, 150, and 200 m). Microcosm experiments (mixing of surface water with water from the SCM) carried out simulating disturbances such as climate change forced mixing (cyclones), revealed shifts in microbial structure and function. It was observed that within 48 h, the carbon fixation activity changed from photosynthetic organisms to prokaryotes and indicated an increase in stress-related biosynthetic pathways such as expression of quorum sensing, biosynthesis of antibiotics, lipopolysaccharides, and secondary metabolites. These findings have implications for predictive modelling of food web dynamics and fisheries management in the context of climate change.

Investigating the Effect of Bacilli and Lactic Acid Bacteria on Water Quality, Growth, Survival, Immune Response, and Intestinal Microbiota of Cultured Litopenaeus vannamei

Shrimp is one of the most important aquaculture industries. Therefore, we determined the effect of nitrifying-probiotic bacteria on water quality, growth, survival, immune response, and intestinal microbiota of Litopenaeus vannamei cultured without water exchange. In vitro, only Bacillus licheniformis used total ammonia nitrogen (TAN), nitrites, and nitrates since nitrogen bubbles were produced. TAN decreased significantly in the treatments with B. licheniformis and Pediococcus pentosaceus and Leuconostoc mesenteroides, but no differences were observed in nitrites. Nitrates were significantly higher in the treatments with bacteria. The final weight was higher only with bacilli and bacilli and LAB treatments. The survival of shrimp in the bacterial treatments increased significantly, and superoxide anion increased significantly only in lactic acid bacteria (LAB) treatment. The activity of phenoloxidase decreased significantly in the treatments with bacteria compared to the control. Shrimp treated with bacilli in the water showed lower species richness. The gut bacterial community after treatments was significantly different from that of the control. Linoleic acid metabolism was positively correlated with final weight and superoxide anion, whereas quorum sensing was correlated with survival. Thus, bacilli and LAB in the water of hyperintensive culture systems act as heterotrophic nitrifers, modulate the intestinal microbiota and immune response, and improve the growth and survival of shrimp. This is the first report on P. pentosaceus and L. mesenteroides identified as nitrifying bacteria.

Composition and functionality of bacterioplankton communities in marine coastal zones adjacent to finfish aquaculture

Finfish aquaculture is a fast-growing primary industry and is increasingly common in coastal ecosystems. Bacterioplankton is ubiquitous in marine environment and respond rapidly to environmental changes. Changes in bacterioplankton community are not well understood in semi-enclosed stratified embayments. This study aims to examine aquaculture effects in the composition and functional profiles of the bacterioplankton community using amplicon sequencing along a distance gradient from two finfish leases in a marine embayment. Results revealed natural stratification in bacterioplankton associated to NOx, conductivity, salinity, temperature and PO₄. Among the differentially abundant bacteria in leases, we found members associated with nutrient enrichment and aquaculture activities. Abundant predicted functions near leases were assigned to organic matter degradation, fermentation, and antibiotic resistance. This study provides a first effort to describe changes in the bacterioplankton community composition and function due to finfish aquaculture in a semi-enclosed and highly stratified embayment with a significant freshwater input.

Understanding the Role of the Microbiome in Cancer Diagnostics and Therapeutics by Creating and Utilizing ML Models

Recent studies have highlighted that gut microbiota can alter colorectal cancer susceptibility and progression due to its impact on colorectal carcinogenesis. This work represents a comprehensive technical approach in modeling and interpreting the drug-resistance mechanisms from clinical data for patients diagnosed with colorectal cancer. To accomplish our aim, we developed a methodology based on evaluating high-performance machine learning models where a Python-based random forest classifier provides the best performance metrics, with an overall accuracy of 91.7%. Our approach identified and interpreted the most significant genera in the cases of resistant groups. Thus far, many studies point out the importance of present genera in the microbiome and intend to treat it separately. The symbiotic bacterial analysis generated different sets of joint feature combinations, providing a combined overview of the model’s predictiveness and uncovering additional data correlations where different genera joint impacts support the therapy-resistant effect. This study points out the different perspectives of treatment since our aggregate analysis gives precise results for the genera that are often found together in a resistant group of patients, meaning that resistance is not due to the presence of one pathogenic genus in the patient microbiome, but rather several bacterial genera that live in symbiosis.

Relationship between nitrifying microorganisms and other microorganisms residing in the maize rhizosphere

The microbial network of rhizosphere is unique as a result of root exudate. Insights into the relationship that exists with the energy metabolic functional groups will help in biofertilizer production. We hypothesize that there exists a relationship between nitrifying microorganisms and other energy metabolic functional microbial groups in the maize rhizosphere across different growth stages. Nucleospin soil DNA extraction kit was used to extract DNA from soil samples collected from maize rhizosphere. The 16S metagenomics sequencing was carried out on Illumina Miseq. The sequence obtained was analyzed on MG-RAST. Nitrospira genera were the most abundant in the nitrifying community. Nitrifying microorganisms were more than each of the studied functional groups except for nitrogen-fixing bacteria. Also, majority of the microorganisms were noticed at the fruiting stage and there was variation in the microbial structure across different growth stages. The result showed that there exists a substantial amount of both negative and positive correlation within the nitrifying microorganisms, and between them and other energy metabolic functional groups. The knowledge obtained from this study will help improve the growth and development of maize through modification of the rhizosphere microbial community structure.

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.

Examining horizontal gene transfer in microbial communities

Bacteria acquire novel DNA through horizontal gene transfer (HGT), a process that enables an organism to rapidly adapt to changing environmental conditions, provides a competitive edge and potentially alters its relationship with its host. Although the HGT process is routinely exploited in laboratories, there is a surprising disconnect between what we know from laboratory experiments and what we know from natural environments, such as the human gut microbiome. Owing to a suite of newly available computational algorithms and experimental approaches, we have a broader understanding of the genes that are being transferred and are starting to understand the ecology of HGT in natural microbial communities. This Review focuses on these new technologies, the questions they can address and their limitations. As these methods are applied more broadly, we are beginning to recognize the full extent of HGT possible within a microbiome and the punctuated dynamics of HGT, specifically in response to external stimuli. Furthermore, we are better characterizing the complex selective pressures on mobile genetic elements and the mechanisms by which they interact with the bacterial host genome.

Beyond taxonomy: Validating functional inference approaches in the context of fish-farm impact assessments

Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have recently been developed which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alternative to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥1,200 m from Norwegian salmon farms, and three metabolic inference approaches (Paprica, Picrust2 and Tax4Fun2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed between approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomic data ranged from 0.52 and 0.60 to 0.76 and 0.79, respectively. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for Paprica and Tax4Fun2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.

An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies

The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been widely applied to study the gut microbiome, including 16S rRNA, 18S rRNA, internal transcribed spacer (ITS) sequencing, shotgun metagenomic sequencing, metatranscriptomic sequencing and viromic sequencing. Various software tools were developed to analyze different sequencing data. In this review, we summarize commonly used computational tools for gut microbiome data analysis, which extended our understanding of the gut microbiome in health and diseases.

Trans-ethnic gut microbiota signatures of type 2 diabetes in Denmark and India

Background

Type 2 diabetes (T2D), a multifactorial disease influenced by host genetics and environmental factors, is the most common endocrine disease. Several studies have shown that the gut microbiota as a close-up environmental mediator influences host physiology including metabolism. The aim of the present study is to examine the compositional and functional potential of the gut microbiota across individuals from Denmark and South India with a focus on T2D. Many earlier studies have investigated the microbiome aspects of T2D, and it has also been anticipated that such microbial associations would be dependent on diet and ethnic origin. However, there has been no large scale trans-ethnic microbiome study earlier in this direction aimed at evaluating any “universal” microbiome signature of T2D.

Methods

16S ribosomal RNA gene amplicon sequencing was performed on stool samples from 279 Danish and 294 Indian study participants. Any differences between the gut microbiota of both populations were explored using diversity measures and negative binomial Wald tests. Study samples were stratified to discover global and country-specific microbial signatures for T2D and treatment with the anti-hyperglycemic drug, metformin. To identify taxonomical and functional signatures of the gut microbiota for T2D and metformin treatment, we used alpha and beta diversity measures and differential abundances analysis, comparing metformin-naive T2D patients, metformin-treated T2D patients, and normoglycemic individuals.

Results

Overall, the gut microbial communities of Danes and Indians are compositionally very different. By analyzing the combined study materials, we identify microbial taxonomic and functional signatures for T2D and metformin treatment. T2D patients have an increased relative abundance of two operational taxonomic units (OTUs) from the Lachnospiraceae family, and a decreased abundance of Subdoligranulum and Butyricicoccus. Studying each population per se, we identified T2D-related microbial changes at the taxonomic level within the Danish population only.

Alpha diversity indices show that there is no significant difference between normoglycemic individuals and metformin-naive T2D patients, whereas microbial richness is significantly decreased in metformin-treated T2D patients compared to metformin-naive T2D patients and normoglycemic individuals. Enrichment of two OTUs from Bacteroides and depletion of Faecalibacterium constitute a trans-ethnic signature of metformin treatment.

Conclusions

We demonstrate major compositional differences of the gut microbiota between Danish and South Indian individuals, some of which may relate to differences in ethnicity, lifestyle, and demography. By comparing metformin-naive T2D patients and normoglycemic individuals, we identify T2D-related microbiota changes in the Danish and Indian study samples. In the present trans-ethnic study, we confirm that metformin changes the taxonomic profile and functional potential of the gut microbiota.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00856-4.

Trans-ethnic gut microbial signatures of prediabetic subjects from India and Denmark

BACKGROUND

Recent studies have indicated an association of gut microbiota and microbial metabolites with type 2 diabetes mellitus (T2D). However, large-scale investigation of the gut microbiota of "prediabetic" (PD) subjects has not been reported. Identifying robust gut microbiome signatures of prediabetes and characterizing early prediabetic stages is important for the understanding of disease development and could be crucial in early diagnosis and prevention.

METHODS

The current study performed amplification and sequencing on the variable regions (V1-V5) of the 16S rRNA genes to profile and compare gut microbiota of prediabetic individuals (N = 262) with normoglycemic individuals (N = 275) from two cohorts in India and Denmark. Similarly, fasting serum inflammatory biomarkers were profiled from the study participants.

RESULTS

After correcting for strong country-specific cohort effect, 16 operational taxonomic units (OTUs) including members from the genera Prevotella9, Phascolarctobacterium, Barnesiella, Flavonifractor, Tyzzerella_4, Bacteroides, Faecalibacterium, and Agathobacter were identified as enriched in normoglycaemic subjects with respect to the subjects with prediabetes using a negative binomial Wald test. We also identified 144 OTUs enriched in the prediabetic subjects, which included members from the genera Megasphaera, Streptococcus, Prevotella9, Alistipes, Mitsuokella, Escherichia/Shigella, Prevotella2, Vibrio, Lactobacillus, Alloprevotella, Rhodococcus, and Klebsiella. Comparative analyses of relative abundance of bacterial taxa revealed that the Streptococcus, Escherichia/Shigella, Prevotella2, Vibrio, and Alloprevotella OTUs exhibited more than fourfold enrichment in the gut microbiota of prediabetic subjects. When considering subjects from the two geographies separately, we were able to identify additional gut microbiome signatures of prediabetes. The study reports a probable association of Megasphaera OTU(s) with impaired glucose tolerance, which is significantly pronounced in Indian subjects. While the overall results confirm a state of proinflammation as early as in prediabetes, the Indian cohort exhibited a characteristic pattern of abundance of inflammatory markers indicating low-grade intestinal inflammation at an overall population level, irrespective of glycemic status.

CONCLUSIONS

The results present trans-ethnic gut microbiome and inflammation signatures associated with prediabetes, in Indian and Danish populations. The identified associations may be explored further as potential early indicators for individuals at risk of dysglycemia.

Bacteria associated with periodontal disease are also increased in health

Background

The objective of this cross-sectional clinical study was to analyze the differences in the microbiome in gingival sulci of adult patients in the presence or absence of chronic periodontitis.

Material and Methods

Patients with or without periodontal disease were included in this cross-sectional study. Subgingival biofilm samples were collected and analyzed by 16S massive pyrosequencing. Functional analyses were also performed.

Results

A total of 15 phyla, 154 genera and 351 species were detected globally. Differences between disease and non-disease samples were observed in all taxonomical levels which suggest functional profile changes in the community. It was found that the main species associated with non-disease samples were reduced in disease but not completely suppressed. Analysis of the functional potential of the biofilms revealed a significantly higher activity related to endocytosis and phosphatidylinositol signaling in the disease group but lower cell adhesion molecules.

Conclusions

Specific differences between health and disease suggest functional profile changes in the community, although bacteria associated with periodontal disease are also increased in health. Transcriptome studies should be conducted to confirm and deepen metabolic dysfunctions.

Key words:Pyrosequencing, 16S rRNA, oral microbiome, periodontitis, functional potential.

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.

Crosstalk Between Female Gonadal Hormones and Vaginal Microbiota Across Various Phases of Women's Gynecological Lifecycle

Functional equilibrium between vaginal microbiota and the host is important for maintaining gynecological and reproductive health. Apart from host genetics, infections, changes in diet, life-style and hygiene status are known to affect this delicate state of equilibrium. More importantly, the gonadal hormones strongly influence the overall structure and function of vaginal microbiota. Several studies have attempted to understand (a) the composition of vaginal microbiota in specific stages of women's reproductive cycle as well as in menopause (b) their association with gonadal hormones, and their potential role in manifestation of specific health conditions (from the perspective of cause/consequence). However, a single study that places, in context, the structural variations of the vaginal microbiome across the entire life-span of women's reproductive cycle and during various stages of menopause is currently lacking. With the objective to obtain a holistic overview of the community dynamics of vaginal micro-environment 'across' various stages of women's reproductive and post-reproductive life-cycle, we have performed a meta-analysis of approximately 1,000 vaginal microbiome samples representing various stages of the reproductive cycle and menopausal states. Objectives of this analysis included (a) understanding temporal changes in vaginal community taxonomic structure and composition as women pass through various reproductive and menopausal stages (b) exploring correlations between the levels of female sex hormones with vaginal microbiome diversity (c) analyzing changes in the pattern of community diversity in cases of dysbiotic conditions such as bacterial vaginosis, and viewing the analyzed changes in the context of a healthy state. Results reveal interesting temporal trends with respect to vaginal microbial community diversity and its pattern of correlation with host physiology. Results indicate significant differences in alpha-diversity and overall vaginal microbial community members in various reproductive and post-reproductive phases. In addition to reinforcing the known influence/role of gonadal hormones in maintaining gynecological health, results indicate how hormonal level perturbations cause/contribute to imbalances in vaginal microbiota. The nature of resulting dysbiotic state and its influence on vaginal health is also analyzed and discussed. Results also suggest that elevated vaginal microbial diversity in pregnancy does not necessarily indicate a state of bacterial infection. The study puts forward a hormone-level driven microbiome diversity hypothesis for explaining temporal patterns in vaginal microbial diversity during various stages of women's reproductive cycle and at menopause.

Bioprospecting potential of microbial communities in solid waste landfills for novel enzymes through metagenomic approach

Landfills are repository for complex microbial diversity responsible for bio-degradation of solid waste. To elucidate this complexity, samples from three different landfill sites of North India (sample V: Bhalswa near Karnal byepass road, New Delhi, India; sample T: Chandigarh, India and sample S3: Una, H.P., India) were analyzed using metagenomic approach. Selected landfill sites had different geographical location, varied in waste composition, size of landfill and climate zone. For comparison, one sample from high altitude (sample J) having less human interference was taken in this study. The aim of this study was to explore microbial diversity of communities responsible for degradation of landfill. Samples were characterized by 16S rRNA gene sequencing. Data from three landfill sites showed abundance of phylum Proteobacteria while less contaminated sample from high altitude showed abundance of phylum Cholroflexi followed by phylum Proteobacteria. The most abundant genus was unknown suggesting that these landfills could be repository for various novel bacterial communities. Sample T was relatively more active in terms of microbial activity. It was relatively abundant in enzymes responsible for dioxin degradation, styrene degradation, steroid degradation, streptomycin biosynthesis, carbapenem biosynthesis, monobactam biosynthesis, furfural degradation pathways while sample J was predicted to be enriched in plant cell wall degrading enzymes. Co-occurrence analysis revealed presence of complex interaction networks between microbial assemblages responsible for bio-degradation of hydrocarbons. The data provides insights about synergetic interactions and functional interplay between bacterial communities in different landfill sites which could be further exploited to develop an effective bioremediation process.

Lifestyle-Induced Microbial Gradients: An Indian Perspective

Introduction: Urbanization is a globally pervasive trend. Although urban settings provide better access to infrastructure and opportunities, urban lifestyles have certain negative consequences on human health. A number of recent studies have found interesting associations between the structure of human gut microbiota and the prevalence of metabolic conditions characterizing urban populations. The present study attempts to expand the footprint of these investigations to an Indian context. The objectives include elucidating specific patterns and gradients based on resident habitat and lifestyles (i.e., tribal and urban) that characterize gut microbial communities.

Methods: Available 16S rRNA sequence datasets corresponding to the gut microbiota of urban and tribal populations from multiple regions of India have been rigorously compared. This analysis was carried out to understand the overall community structure, resident taxa, and their (inferred) functional components as well as their correlations with available meta-information.

Results: The gut microbiota of urban and tribal communities are observed to have characteristically different signatures with respect to diversity as well as taxonomic and functional composition. Primarily, the gut microbiota in tribal communities is found to harbor significantly higher species diversity and richness as compared to that in urban populations. In spite of geographical segregation and diet-related differences, gut microbial diversity was not found to differ significantly between tribal groups. Furthermore, while the taxonomic profiles of different tribal communities cluster together irrespective of their geographic location, enterotype analysis indicates that samples from urban communities form two distinct clusters. Taxonomic analysis of samples in one of these clusters reveals the presence of microbes that are common to both urban and tribal cohorts, indicating a probable transient evolutionary state. Prevotella, previously reported to be the dominant genus resident in Indian gut microbiota, is found to have distinct OTUs and strain-specific oligotypes characterizing resident habitats and diet patterns. Certain interesting associations between microbial abundances and specific metadata have also been observed. Overall, urban lifestyle and diet appear to impact the structure and function of gut microbial communities, and the results of this study provide further evidence of this likely detrimental association.

Conclusion: This study attempts to analyze, in an Indian context, the impact of urbanization on the human gut microbiota. Overall, the analysis elucidates interesting taxonomic and functional signatures characterizing the evolutionary transition in gut microbiota from tribal to urban.

Decoding the microbiome for the development of translational applications: Overview, challenges and pitfalls

Recent studies have highlighted the potential of 'translational' microbiome research in addressing real-world challenges pertaining to human health, nutrition and disease. Additionally, outcomes of microbiome research have also positively impacted various aspects pertaining to agricultural productivity, fuel or energy requirements, and stability/preservation of various ecological habitats. Microbiome data is multi-dimensional with various types of data comprising nucleic and protein sequences, metabolites as well as various metadata related to host and or environment. This poses a major challenge for computational analysis and interpretation of data to reach meaningful, reproducible (and replicable) biological conclusions. In this review, we first describe various aspects of microbiomes that make them an attractive tool/target for developing various translational applications. The challenge of deciphering signatures from an information-rich resource like the microbiome is also discussed. Subsequently, we present three case-studies that exemplify the potential of microbiome- based solutions in solving real-world problems. The final part of the review attempts to familiarize readers with the importance of a robust study design and the diligence required during every stage of analysis for achieving solutions with potential translational value.

Microbial functional diversity: From concepts to applications

Functional diversity is increasingly recognized by microbial ecologists as the essential link between biodiversity patterns and ecosystem functioning, determining the trophic relationships and interactions between microorganisms, their participation in biogeochemical cycles, and their responses to environmental changes. Consequently, its definition and quantification have practical and theoretical implications. In this opinion paper, we present a synthesis on the concept of microbial functional diversity from its definition to its application. Initially, we revisit to the original definition of functional diversity, highlighting two fundamental aspects, the ecological unit under study and the functional traits used to characterize it. Then, we discuss how the particularities of the microbial world disallow the direct application of the concepts and tools developed for macroorganisms. Next, we provide a synthesis of the literature on the types of ecological units and functional traits available in microbial functional ecology. We also provide a list of more than 400 traits covering a wide array of environmentally relevant functions. Lastly, we provide examples of the use of functional diversity in microbial systems based on the different units and traits discussed herein. It is our hope that this paper will stimulate discussions and help the growing field of microbial functional ecology to realize a potential that thus far has only been attained in macrobial ecology.

Can Targeting Non-Contiguous V-Regions With Paired-End Sequencing Improve 16S rRNA-Based Taxonomic Resolution of Microbiomes?: An In Silico Evaluation

Background: Next-generation sequencing (NGS) technologies have enabled probing of microbial diversity in different environmental niches with unprecedented sequencing depth. However, due to read-length limitations of popular NGS technologies, 16S amplicon sequencing-based microbiome studies rely on targeting short stretches of the 16S rRNA gene encompassing a selection of variable (V) regions. In most cases, such a short stretch constitutes a single V-region or a couple of V-regions placed adjacent to each other on the 16S rRNA gene. Given that different V-regions have different resolving ability with respect to various taxonomic groups, selecting the optimal V-region (or a combination thereof) remains a challenge.

Methods: The accuracy of taxonomic profiles generated from sequences encompassing 1) individual V-regions, 2) adjacent V-regions, and 3) pairs of non-contiguous V-regions were assessed and compared. Subsequently, the discriminating capability of different V-regions with respect to different taxonomic lineages was assessed. The possibility of using paired-end sequencing protocols to target combinations of non-adjacent V-regions was finally evaluated with respect to the utility of such an experimental design in providing improved taxonomic resolution.

Results: Extensive validation with simulated microbiome datasets mimicking different environmental and host-associated microbiome samples suggest that targeting certain combinations of non-contiguously placed V-regions might yield better taxonomic classification accuracy compared to conventional 16S amplicon sequencing targets. This work also puts forward a novel in silico combinatorial strategy that enables creation of consensus taxonomic profiles from experiments targeting multiple pair-wise combinations of V-regions to improve accuracy in taxonomic classification.

Conclusion: The study suggests that targeting non-contiguous V-regions with paired-end sequencing can improve 16S rRNA–based taxonomic resolution of microbiomes. Furthermore, employing the novel in silico combinatorial strategy can improve taxonomic classification without any significant additional experimental costs and/or efforts. The empirical observations obtained can potentially serve as a guideline for future 16S microbiome studies, and facilitate researchers in choosing the optimal combination of V-regions for a specific experiment/sampled environment.

Shotgun metagenomic sequencing from Manao-Pee cave, Thailand, reveals insight into the microbial community structure and its metabolic potential

Background

Due to the cave oligotrophic environment, this habitat presents a challenge for microorganisms to colonize and thrive. However, it has been well documented that microorganisms play important roles in cave development. Survival of microbes in this unique habitat likely involves a broad range of adaptive capabilities. Recently, cave microbiomes all over the world are of great scientific interest. However, the majority of investigations focused mostly on small subunit ribosomal RNA (16S rRNA) gene, leaving the ecological role of the microbial community largely unknown. Here, we are particularly interested in exploring the taxonomic composition and metabolic potential of microorganisms in soil from Manao-Pee cave, a subterranean limestone cave in the western part of Thailand, by using high-throughput shotgun metagenomic sequencing.

Results

From taxonomic composition analysis using ribosomal RNA genes (rRNA), the results confirmed that Actinobacteria (51.2%) and Gammaproteobacteria (24.4%) were the dominant bacterial groups in the cave soil community. Metabolic potential analysis, based on six functional modules of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, revealed that functional genes involved in microbial metabolisms are highly represented in this community (40.6%). To better understand how microbes thrive under unfavorable cave condition, we focused on microbial energy metabolism. The results showed that microbial genes involved in oxidative phosphorylation were the most dominant (28.8%) in Manao-Pee cave, and were followed by methane metabolism (20.5%), carbon fixation (16.0%), nitrogen metabolism (14.7%), and sulfur metabolism (6.3%). In addition, microbial genes involved in xenobiotic biodegradation (26 pathways) and in production of secondary metabolites (27 pathways) were also identified.

Conclusion

In addition to providing information on microbial diversity, we also gained insights into microbial adaptations and survival strategies under cave conditions. Based on rRNA genes, the results revealed that bacteria belonging to the Actinobacteria and Gammaproteobacteria were the most abundant in this community. From metabolic potential analysis, energy and nutrient sources that sustain diverse microbial population in this community might be atmospheric gases (methane, carbon dioxide, nitrogen), inorganic sulfur, and xenobiotic compounds. In addition, the presence of biosynthetic pathways of secondary metabolites suggested that they might play important ecological roles in the cave microbiome.

Electronic supplementary material

The online version of this article (10.1186/s12866-019-1521-8) contains supplementary material, which is available to authorized users.

Marker genes as predictors of shared genomic function

Background

Although high-throughput marker gene studies provide valuable insight into the diversity and relative abundance of taxa in microbial communities, they do not provide direct measures of their functional capacity. Recently, scientists have shown a general desire to predict functional profiles of microbial communities based on phylogenetic identification inferred from marker genes, and recent tools have been developed to link the two. However, to date, no large-scale examination has quantified the correlation between the marker gene based taxonomic identity and protein coding gene conservation. Here we utilize 4872 representative prokaryotic genomes from NCBI to investigate the relationship between marker gene identity and shared protein coding gene content.

Results

Even at 99–100% marker gene identity, genomes share on average less than 75% of their protein coding gene content. This occurs regardless of the marker gene(s) used: V4 region of the 16S rRNA, complete 16S rRNA, or single copy orthologs through a multi-locus sequence analysis. An important aspect related to this observation is the intra-organism variation of 16S copies from a single genome. Although the majority of 16S copies were found to have high sequence similarity (> 99%), several genomes contained copies that were highly diverged (< 97% identity).

Conclusions

This is the largest comparison between marker gene similarity and shared protein coding gene content to date. The study highlights the limitations of inferring a microbial community’s functions based on marker gene phylogeny. The data presented expands upon the results of previous studies that examined one or few bacterial species and supports the hypothesis that 16S rRNA and other marker genes cannot be directly used to fully predict the functional potential of a bacterial community.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5641-1) contains supplementary material, which is available to authorized users.

A prospective randomized, double-blind, placebo-controlled, dose-response relationship study to investigate efficacy of fructo-oligosaccharides (FOS) on human gut microflora

Fructo-oligosaccharides (FOS), a prebiotic supplement, is known for its Bifidogenic capabilities. However, aspects such as effect of variable quantities of FOS intake on gut microbiota, and temporal dynamics of gut microbiota (transitioning through basal, dosage, and follow-up phases) has not been studied in detail. This study investigated these aspects through a randomized, double-blind, placebo-controlled, dose-response relationship study. The study involved 80 participants being administered FOS at three dose levels (2.5, 5, and 10 g/day) or placebo (Maltodextrin 10 g/day) during dosage phase. Microbial DNA extracted from fecal samples collected at 9 intervening time-points was sequenced and analysed. Results indicate that FOS consumption increased the relative abundance of OTUs belonging to Bifidobacterium and Lactobacillus. Interestingly, higher FOS dosage appears to promote, in contrast to Maltodextrin, the selective proliferation of OTUs belonging to Lactobacillus. While consumption of prebiotics increased bacterial diversity, withdrawal led to its reduction. Apart from probiotic bacteria, a significant change was also observed in certain butyrate-producing microbes like Faecalibacterium, Ruminococcus and Oscillospira. The positive impact of FOS on butyrate-producing bacteria and FOS-mediated increased bacterial diversity reinforces the role of prebiotics in conferring beneficial functions to the host.

Web-gLV: A Web Based Platform for Lotka-Volterra Based Modeling and Simulation of Microbial Populations

The affordability of high throughput DNA sequencing has allowed us to explore the dynamics of microbial populations in various ecosystems. Mathematical modeling and simulation of such microbiome time series data can help in getting better understanding of bacterial communities. In this paper, we present Web-gLV-a GUI based interactive platform for generalized Lotka-Volterra (gLV) based modeling and simulation of microbial populations. The tool can be used to generate the mathematical models with automatic estimation of parameters and use them to predict future trajectories using numerical simulations. We also demonstrate the utility of our tool on few publicly available datasets. The case studies demonstrate the ease with which the current tool can be used by biologists to model bacterial populations and simulate their dynamics to get biological insights. We expect Web-gLV to be a valuable contribution in the field of ecological modeling and metagenomic systems biology.

iVikodak-A Platform and Standard Workflow for Inferring, Analyzing, Comparing, and Visualizing the Functional Potential of Microbial Communities

Background: The objectives of any metagenomic study typically include identification of resident microbes and their relative proportions (taxonomic analysis), profiling functional diversity (functional analysis), and comparing the identified microbes and functions with available metadata (comparative metagenomics). Given the advantage of cost-effectiveness and convenient data-size, amplicon-based sequencing has remained the technology of choice for exploring phylogenetic diversity of an environment. A recent school of thought, employing the existing genome annotation information for inferring functional capacity of an identified microbiome community, has given a promising alternative to Whole Genome Shotgun sequencing for functional analysis. Although a handful of tools are currently available for function inference, their scope, functionality and utility has essentially remained limited. Need for a comprehensive framework that expands upon the existing scope and enables a standardized workflow for function inference, analysis, and visualization, is therefore felt.

Methods: We present iVikodak, a multi-modular web-platform that hosts a logically inter-connected repertoire of functional inference and analysis tools, coupled with a comprehensive visualization interface. iVikodak is equipped with microbial co-inhabitance pattern driven published algorithms along with multiple updated databases of various curated microbe-function maps. It also features an advanced task management and result sharing system through introduction of personalized and portable dashboards.

Results: In addition to inferring functions from 16S rRNA gene data, iVikodak enables (a) an in-depth analysis of specific functions of interest (b) identification of microbes contributing to various functions (c) microbial interaction patterns through function-driven correlation networks, and (d) simultaneous functional comparison between multiple microbial communities. We have bench-marked iVikodak through multiple case studies and comparisons with existing state of art. We also introduce the concept of a public repository which provides a first of its kind community-driven framework for scientific data analytics, collaboration and sharing in this area of microbiome research.

Conclusion: Developed using modern design and task management practices, iVikodak provides a multi-modular, yet inter-operable, one-stop framework, that intends to simplify the entire approach toward inferred function analysis. It is anticipated to serve as a significant value addition to the existing space of functional metagenomics.

iVikodak web-server may be freely accessed at https://web.rniapps.net/iVikodak/.

Methodological considerations for the identification of choline and carnitine-degrading bacteria in the gut

The bacterial formation of trimethylamine (TMA) has been linked to cardiovascular disease. This review focuses on the methods employed to investigate the identity of the bacteria responsible for the formation of TMA from dietary choline and carnitine in the human gut. Recent studies have revealed the metabolic pathways responsible for bacterial TMA production, primarily the anaerobic glycyl radical-containing, choline-TMA lyase, CutC and the aerobic carnitine monooxygenase, CntA. Identification of these enzymes has enabled bioinformatics approaches to screen both human-associated bacterial isolate genomes and whole gut metagenomes to determine which bacteria are responsible for TMA formation in the human gut. We centre on several key methodological aspects for identifying the TMA-producing bacteria and report how these pathways can be identified in human gut microbiota through bioinformatics analysis of available bacterial genomes and gut metagenomes.

Alterations in the gut bacterial microbiome in fungal Keratitis patients

Dysbiosis in the gut microbiome has been implicated in several diseases including auto-immune diseases, inflammatory diseases, cancers and mental disorders. Keratitis is an inflammatory disease of the eye significantly contributing to corneal blindness in the developing world. It would be worthwhile to investigate the possibility of dysbiosis in the gut microbiome being associated with Keratitis. Here, we have analyzed fungal and bacterial populations in stool samples through high-throughput sequencing of the ITS2 region for fungi and V3-V4 region of 16S rRNA gene for bacteria in healthy controls (HC, n = 31) and patients with fungal keratitis (FK, n = 32). Candida albicans (2 OTUs), Aspergillus (1 OTU) and 3 other denovo-OTUs were enriched in FK samples and an unclassified denovo-OTU was enriched in HC samples. However, the overall abundances of these ‘discriminatory’ OTUs were very low (< 0.001%) and not indicative of significant dysbiosis in the fungal community inhabiting the gut of FK patients. In contrast, the gut bacterial richness and diversity in FK patients was significantly decreased when compared to HC. 52 OTUs were significantly enriched in HC samples whereas only 5 OTUs in FK. The OTUs prominently enriched in HC were identified as Faecalibacterium prausnitzii, Bifidobacterium adolescentis, Lachnospira, Mitsuokella multacida, Bacteroides plebeius, Megasphaera and Lachnospiraceae. In FK samples, 5 OTUs affiliated to Bacteroides fragilis, Dorea, Treponema, Fusobacteriaceae, and Acidimicrobiales were significantly higher in abundance. The functional implications are that Faecalibacterium prausnitzii, an anti-inflammatory bacterium and Megasphaera, Mitsuokella multacida and Lachnospira are butyrate producers, which were enriched in HC patients, whereas Treponema and Bacteroides fragilis, which are pathogenic were abundant in FK patients, playing a potential pro-inflammatory role. Heatmap, PCoA plots and functional profiles further confirm the distinct patterns of gut bacterial composition in FK and HC samples. Our study demonstrates dysbiosis in the gut bacterial microbiomes of FK patients compared to HC. Further, based on inferred functions, it appears that dysbiosis in the gut of FK subjects is strongly associated with the disease phenotype with decrease in abundance of beneficial bacteria and increase in abundance of pro-inflammatory and pathogenic bacteria.

The bacterial community structure and functional profile in the heavy metal contaminated paddy soils, surrounding a nonferrous smelter in South Korea

The pollution of agricultural soils by the heavy metals affects the productivity of the land and has an impact on the quality of the surrounding ecosystems. This study investigated the bacterial community structure in the heavy metal contaminated sites along a smelter and a distantly located paddy field to elucidate the factors that are related to the alterations of the bacterial communities under the conditions of heavy metal pollution. Among the study sites, the bacterial communities in the soil did not show any significant differences in their richness and diversity. The soil bacterial communities at the three study sites were distinct from one another at each site, possessing a distinct set of bacterial phylotypes. Among the study sites, significant changes were observed in the abundances of the bacterial phyla and genera. The variations in the bacterial community structure were mostly related to the general soil properties at the phylum level, while at the finer taxonomic levels, the concentrations of arsenic (As) and lead (Pb) were the significant factors, affecting the community structure. The relative abundances of the genera Desulfatibacillum and Desulfovirga were negatively correlated to the concentrations of As, Pb, and cadmium (Cd) in the soil, while the genus Bacillus was positively correlated to the concentrations of As and Cd. According to the results of the prediction of bacterial community functions, the soil bacterial communities of the heavy metal polluted sites were characterized by the more abundant enzymes involved in DNA replication and repair, translation, transcription, and the nucleotide metabolism pathways, while the amino acid and lipid metabolism, as well as the biodegradation potential of xenobiotics, were reduced. Our results showed that the adaptation of the bacterial communities to the heavy metal contamination was predominantly attributed to the replacement process, while the changes in community richness were linked to the variations in the soil pH values.

S, Shaikh S, P. S, Dubey AK, Mande SS. A snapshot of gut microbiota of an adult urban population from Western region of India

The human gut microbiome contributes to a broad range of biochemical and metabolic functions that directly or indirectly affect human physiology. Several recent studies have indicated that factors like age, geographical location, genetic makeup, and individual health status significantly influence the diversity, stability, and resilience of the gut microbiome. Of the mentioned factors, geographical location (and related dietary/socio-economic context) appears to explain a significant portion of microbiome variation observed in various previously conducted base-line studies on human gut microbiome. Given this context, we have undertaken a microbiome study with the objective of cataloguing the taxonomic diversity of gut microbiomes sampled from an urban cohort from Ahmedabad city in Western India. Computational analysis of microbiome sequence data corresponding to 160 stool samples (collected from 80 healthy individuals at two time-points, 60 days apart) has indicated a Prevotella-dominated microbial community. Given that the typical diet of participants included carbohydrate and fibre-rich components (predominantly whole grains and legume-based preparations), results appear to validate the proposed correlation between diet/geography and microbiome composition. Comparative analysis of obtained gut microbiome profiles with previously published microbiome profiles from US, China, Finland, and Japan additionally reveals a distinct taxonomic and (inferred) functional niche for the sampled microbiomes.

Methane-yielding microbial communities processing lactate-rich substrates: a piece of the anaerobic digestion puzzle

BACKGROUND

Anaerobic digestion, whose final products are methane and carbon dioxide, ensures energy flow and circulation of matter in ecosystems. This naturally occurring process is used for the production of renewable energy from biomass. Lactate, a common product of acidic fermentation, is a key intermediate in anaerobic digestion of biomass in the environment and biogas plants. Effective utilization of lactate has been observed in many experimental approaches used to study anaerobic digestion. Interestingly, anaerobic lactate oxidation and lactate oxidizers as a physiological group in methane-yielding microbial communities have not received enough attention in the context of the acetogenic step of anaerobic digestion. This study focuses on metabolic transformation of lactate during the acetogenic and methanogenic steps of anaerobic digestion in methane-yielding bioreactors.

RESULTS

Methane-yielding microbial communities instead of pure cultures of acetate producers were used to process artificial lactate-rich media to methane and carbon dioxide in up-flow anaerobic sludge blanket reactors. The media imitated the mixture of acidic products found in anaerobic environments/digesters where lactate fermentation dominates in acidogenesis. Effective utilization of lactate and biogas production was observed. 16S rRNA profiling was used to examine the selected methane-yielding communities. Among Archaea present in the bioreactors, the order Methanosarcinales predominated. The acetoclastic pathway of methane formation was further confirmed by analysis of the stable carbon isotope composition of methane and carbon dioxide. The domain Bacteria was represented by Bacteroidetes, Firmicutes, Proteobacteria, Synergistetes, Actinobacteria, Spirochaetes, Tenericutes, Caldithrix, Verrucomicrobia, Thermotogae, Chloroflexi, Nitrospirae, and Cyanobacteria. Available genome sequences of species and/or genera identified in the microbial communities were searched for genes encoding the lactate-oxidizing metabolic machinery homologous to those of Acetobacterium woodii and Desulfovibrio vulgaris. Furthermore, genes for enzymes of the reductive acetyl-CoA pathway were present in the microbial communities.

CONCLUSIONS

The results indicate that lactate is oxidized mainly to acetate during the acetogenic step of AD and this comprises the acetotrophic pathway of methanogenesis. The genes for lactate utilization under anaerobic conditions are widespread in the domain Bacteria. Lactate oxidation to the substrates for methanogens is the most energetically attractive process in comparison to butyrate, propionate, or ethanol oxidation.

Interactions of plant growth-promoting rhizobacteria and soil factors in two leguminous plants

Although the rhizomicrobiome has been extensively studied, little is known about the interactions between soil properties and the assemblage of plant growth-promoting microbes in the rhizosphere. Herein, we analysed the composition and structure of rhizomicrobiomes associated with soybean and alfalfa plants growing in different soil types using deep Illumina 16S rRNA sequencing. Soil pH, P and K significantly affected the composition of the soybean rhizomicrobiome, whereas soil pH and N had a significant effect on the alfalfa rhizomicrobiome. Plant biomass was influenced by plant species, the composition of the rhizomicrobiome, soil pH, N, P and plant growth stage. The beta diversity of the rhizomicrobiome was the second most influential factor on plant growth (biomass). Rhizomicrobes associated with plant biomass were identified and divided into four groups: (1) positively associated with soybean biomass; (2) negatively associated with soybean biomass; (3) positively associated with alfalfa biomass; and (4) negatively associated with alfalfa biomass. Genera assemblages among the four groups differentially responded to soil properties; Group 1 and Group 2 were significantly correlated with soil pH and P, whereas Group 3 and Group 4 were significantly correlated with soil N, K and C. The influence of soil properties on the relative abundance of plant biomass-associated rhizomicrobes differed between soybean and alfalfa. The results suggest the rhizomicrobiome has a pronounced influence on plant growth, and the rhizomicrobiome assemblage and plant growth-associated microbes are differentially structured by soil properties and leguminous plant species.

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.