Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

PhenoMultiOmics: an enzymatic reaction inferred multi-omics network visualization web server

MOTIVATION

Enzymatic reaction play a pivotal role in regulating cellular processes with a high degree of specificity to biological functions. When enzymatic reactions are disrupted by gene, protein, or metabolite dysfunctions in diseases, it becomes crucial to visualize the resulting perturbed enzymatic reaction-induced multi-omics network. Multi-omics network visualization aids in gaining a comprehensive understanding of the functionality and regulatory mechanisms within biological systems.

RESULTS

In this study, we designed PhenoMultiOmics, an enzymatic reaction-based multi-omics web server designed to explore the scope of the multi-omics network across various cancer types. We first curated the PhenoMultiOmics database, which enables the retrieval of cancer-gene-protein-metabolite relationships based on the enzymatic reactions. We then developed the MultiOmics network visualization module to depict the interplay between genes, proteins, and metabolites in response to specific cancer-related enzymatic reactions. The biomarker discovery module facilitates functional analysis through differential omic feature expression and pathway enrichment analysis. PhenoMultiOmics has been applied to analyze the transcriptomics data of gastric cancer and the metabolomics data of lung cancer, providing mechanistic insights into interrupted enzymatic reactions and the associated multi-omics network.

AVAILABILITY AND IMPLEMENTATION

PhenoMultiOmics is freely accessed at https://phenomultiomics.shinyapps.io/cancer/ with a user-friendly and interactive web interface.

Host response to cholestyramine can be mediated by the gut microbiota

Background: The gut microbiota has been implicated as a major factor contributing to metabolic diseases and the response to drugs used for the treatment of such diseases. In this study, we tested the effect of cholestyramine, a bile acid sequestrant that reduces blood cholesterol, on the murine gut microbiota and metabolism. We also explored the hypothesis that some effects of this drug on systemic metabolism can be attributed to alterations in the gut microbiota. Methods: We used a Western diet (WD) for 8 weeks to induce metabolic disease in mice, then treated some mice with cholestyramine added to WD. Metabolic phenotyping, gene expression in liver and ileum, and microbiota 16S rRNA genes were analyzed. Then, transkingdom network analysis was used to find candidate microbes for the cholestyramine effect. Results: We observed that cholestyramine decreased glucose and epididymal fat levels and detected dysregulation of genes known to be regulated by cholestyramine in the liver and ileum. Analysis of gut microbiota showed increased alpha diversity in cholestyramine-treated mice, with fourteen taxa showing restoration of relative abundance to levels resembling those in mice fed a control diet. Using transkingdom network analysis, we inferred two amplicon sequence variants (ASVs), one from the Lachnospiraceae family (ASV49) and the other from the Muribaculaceae family (ASV1), as potential regulators of cholestyramine effects. ASV49 was also negatively linked with glucose levels, further indicating its beneficial role. Conclusion: Our results indicate that the gut microbiota has a role in the beneficial effects of cholestyramine and suggest specific microbes as targets of future investigations.

De Novo Multi-Omics Pathway Analysis Designed for Prior Data Independent Inference of Cell Signaling Pathways

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here, we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data, and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets of relatively low sample sizes. Transcription factor, kinase, subcellular location, and function prediction algorithms were devised for transcriptome, phosphoproteome, and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome, and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.

The Role of Early Life Gut Mycobiome on Child Health

The human gut microbiota is composed of bacteria (microbiota or microbiome), fungi (mycobiome), viruses, and archaea, but most of the research is primarily focused on the bacterial component of this ecosystem. Besides bacteria, fungi have been shown to play a role in host health and physiologic functions. However, studies on mycobiota composition during infancy, the factors that might shape infant gut mycobiota, and implications to child health and development are limited. In this review, we discuss the factors likely shaping gut mycobiota, interkingdom interactions, and associations with child health outcomes and highlight the gaps in our current knowledge of this ecosystem.

Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge

Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C.

Microbial mechanisms to improve immune checkpoint blockade responsiveness

The human microbiota acts as a diverse source of molecular cues that influence the development and homeostasis of the immune system. Beyond endogenous roles in the human holobiont, host-microbial interactions also alter outcomes for immune-related diseases and treatment regimens. Over the past decade, sequencing analyses of cancer patients have revealed correlations between microbiota composition and the efficacy of cancer immunotherapies such as checkpoint inhibitors. However, very little is known about the exact mechanisms that link specific microbiota with patient responses, limiting our ability to exploit these microbial agents for improved oncology care. Here, we summarize current progress towards a molecular understanding of host-microbial interactions in the context of checkpoint inhibitor immunotherapies. By highlighting the successes of a limited number of studies focused on identifying specific, causal molecules, we underscore how the exploration of specific microbial features such as proteins, enzymes, and metabolites may translate into precise and actionable therapies for personalized patient care in the clinic.

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1

Ample evidence indicates that the gut microbiome is a tumor-extrinsic factor associated with antitumor response to anti-programmed cell death protein-1 (PD-1) therapy, but inconsistencies exist between published microbial signatures associated with clinical outcomes. To resolve this, we evaluated a new melanoma cohort, along with four published datasets. Time-to-event analysis showed that baseline microbiota composition was optimally associated with clinical outcome at approximately 1 year after initiation of treatment. Meta-analysis and other bioinformatic analyses of the combined data show that bacteria associated with favorable response are confined within the Actinobacteria phylum and the Lachnospiraceae/Ruminococcaceae families of Firmicutes. Conversely, Gram-negative bacteria were associated with an inflammatory host intestinal gene signature, increased blood neutrophil-to-lymphocyte ratio, and unfavorable outcome. Two microbial signatures, enriched for Lachnospiraceae spp. and Streptococcaceae spp., were associated with favorable and unfavorable clinical response, respectively, and with distinct immune-related adverse effects. Despite between-cohort heterogeneity, optimized all-minus-one supervised learning algorithms trained on batch-corrected microbiome data consistently predicted outcomes to programmed cell death protein-1 therapy in all cohorts. Gut microbial communities (microbiotypes) with nonuniform geographical distribution were associated with favorable and unfavorable outcomes, contributing to discrepancies between cohorts. Our findings shed new light on the complex interaction between the gut microbiome and response to cancer immunotherapy, providing a roadmap for future studies.

The Gut Microbiota in Inflammatory Bowel Disease

Epidemiological surveys indicate that the incidence of inflammatory bowel disease (IBD) is increasing rapidly with the continuous growth of the economy. A large number of studies have investigated the relationship between the genetic factors related to the susceptibility to IBD and the gut microbiota of patients by using high-throughput sequencing. IBD is considered the outcome of the interaction between host and microorganisms, including intestinal microbial factors, abnormal immune response, and a damaged intestinal mucosal barrier. The imbalance of microbial homeostasis leads to the colonization and invasion of opportunistic pathogens in the gut, which increases the risk of the host immune response and promotes the development of IBD. It is critical to identify the specific pathogens related to the pathogenesis of IBD. An in-depth understanding of various pathogenic factors is of great significance for the early detection of IBD. This review highlights the role of gut microbiota in the pathogenesis of IBD and provides a theoretical basis for the personalized approaches that modulate the gut microbiota to treat IBD.

Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System

Human milk is a complex and dynamic biological system that has evolved to optimally nourish and protect human infants. Yet, according to a recent priority-setting review, “our current understanding of human milk composition and its individual components and their functions fails to fully recognize the importance of the chronobiology and systems biology of human milk in the context of milk synthesis, optimal timing and duration of feeding, and period of lactation” (P. Christian et al., Am J Clin Nutr 113:1063–1072, 2021, https://doi.org/10.1093/ajcn/nqab075). We attribute this critical knowledge gap to three major reasons as follows. (i) Studies have typically examined each subsystem of the mother-milk-infant “triad” in isolation and often focus on a single element or component (e.g., maternal lactation physiology or milk microbiome or milk oligosaccharides or infant microbiome or infant gut physiology). This undermines our ability to develop comprehensive representations of the interactions between these elements and study their response to external perturbations. (ii) Multiomics studies are often cross-sectional, presenting a snapshot of milk composition, largely ignoring the temporal variability during lactation. The lack of temporal resolution precludes the characterization and inference of robust interactions between the dynamic subsystems of the triad. (iii) We lack computational methods to represent and decipher the complex ecosystem of the mother-milk-infant triad and its environment. In this review, we advocate for longitudinal multiomics data collection and demonstrate how incorporating knowledge gleaned from microbial community ecology and computational methods developed for microbiome research can serve as an anchor to advance the study of human milk and its many components as a “system within a system.”

Oxidative reactivity across kingdoms in the gut: Host immunity, stressed microbiota and oxidized foods

Reactive oxygen species play a major role in the induction of programmed cell death and numerous diseases. Production of reactive oxygen species is ubiquitous in biological systems such as humans, bacteria, fungi/yeasts, and plants. Although reactive oxygen species are known to cause diseases, little is known about the importance of the combined oxidative stress burden in the gut. Understanding the dynamics and the level of oxidative stress 'reactivity' across kingdoms could help ascertain the combined consequences of free radical accumulation in the gut lumen. Here, we present fundamental similarities of oxidative stress derived from the host immune cells, bacteria, yeasts, plants, and the therein-derived diets, which often accentuate the burden of free radicals by accumulation during storage and cooking conditions. Given the described similarities, oxidative stress could be better understood and minimized by monitoring the levels of oxidative stress in the feces to identify pro-inflammatory factors. However, we illustrate that dietary studies rarely monitor oxidative stress markers in the feces, and therefore our knowledge on fecal oxidative stress monitoring is limited. A more holistic approach to understanding oxidative stress 'reactivity' in the gut could help improve strategies to use diet and microbiota to prevent intestinal diseases.

Microbiota triggers STING-type I IFN-dependent monocyte reprogramming of the tumor microenvironment

The tumor microenvironment (TME) influences cancer progression and therapy response. Therefore, understanding what regulates the TME immune compartment is vital. Here we show that microbiota signals program mononuclear phagocytes in the TME toward immunostimulatory monocytes and dendritic cells (DCs). Single-cell RNA sequencing revealed that absence of microbiota skews the TME toward pro-tumorigenic macrophages. Mechanistically, we show that microbiota-derived stimulator of interferon genes (STING) agonists induce type I interferon (IFN-I) production by intratumoral monocytes to regulate macrophage polarization and natural killer (NK) cell-DC crosstalk. Microbiota modulation with a high-fiber diet triggered the intratumoral IFN-I-NK cell-DC axis and improved the efficacy of immune checkpoint blockade (ICB). We validated our findings in individuals with melanoma treated with ICB and showed that the predicted intratumoral IFN-I and immune compositional differences between responder and non-responder individuals can be transferred by fecal microbiota transplantation. Our study uncovers a mechanistic link between the microbiota and the innate TME that can be harnessed to improve cancer therapies.

The Modulation of Gut Microbiota Composition in the Pathophysiology of Gestational Diabetes Mellitus: A Systematic Review

Simple Summary

Recent studies have placed a great deal of emphasis on the importance of the microbiome, especially the link between the alteration of gut microbiota and multiple associated diseases. Gut microbiota changes in pregnancy have a significant impact on metabolic function and may contribute to gestational diabetes mellitus (GDM). Although GDM carries long-term health risks that affect women, there are also significant short- and severe long-term consequences for the offspring. Regardless, there is a notable lack of research focusing on the impact of prominent microorganisms involved in the development of GDM. A comprehensive review was conducted to gather relevant data on the types of microorganisms that have been associated with GDM. The review found that certain microorganisms impact the onset and progression of GDM during pregnancy. Several bacterial strains associated with GDM are influenced by a diet high in fat and low in fiber. Therefore, integrating the idea of a microbiome-based individualized dietary intervention into gestational diabetes management may be incredibly beneficial.

Abstract

General gut microbial dysbiosis in diabetes mellitus, including gestational diabetes mellitus (GDM), has been reported in a large body of literature. However, evidence investigating the association between specific taxonomic classes and GDM is lacking. Thus, we performed a systematic review of peer-reviewed observational studies and trials conducted among women with GDM within the last ten years using standard methodology. The National Institutes of Health (NIH) quality assessment tools were used to assess the quality of the included studies. Fourteen studies investigating microbial interactions with GDM were found to be relevant and included in this review. The synthesis of literature findings demonstrates that Bacteroidetes, Proteobacteria, Firmicutes, and Actinobacteria phyla, such as Desulfovibrio, Ruminococcaceae, P. distasonis, Enterobacteriaceae, Collinsella, and Prevotella, were positively associated with GDM. In contrast, Bifidobacterium and Faecalibacterium, which produce butyrate, are negatively associated with GDM. These bacteria were associated with inflammation, adiposity, and glucose intolerance in women with GDM. Lack of good diet management demonstrated the alteration of gut microbiota and its impact on GDM glucose homeostasis. The majority of the studies were of good quality. Therefore, there is great potential to incorporate personalized medicine targeting microbiome modulation through dietary intervention in the management of GDM.

Holo-omics for deciphering plant-microbiome interactions

Host-microbiome interactions are recognized for their importance to host health. An improved understanding of the molecular underpinnings of host-microbiome relationships will advance our capacity to accurately predict host fitness and manipulate interaction outcomes. Within the plant microbiome research field, unlocking the functional relationships between plants and their microbial partners is the next step to effectively using the microbiome to improve plant fitness. We propose that strategies that pair host and microbial datasets-referred to here as holo-omics-provide a powerful approach for hypothesis development and advancement in this area. We discuss several experimental design considerations and present a case study to highlight the potential for holo-omics to generate a more holistic perspective of molecular networks within the plant microbiome system. In addition, we discuss the biggest challenges for conducting holo-omics studies; specifically, the lack of vetted analytical frameworks, publicly available tools, and required technical expertise to process and integrate heterogeneous data. Finally, we conclude with a perspective on appropriate use-cases for holo-omics studies, the need for downstream validation, and new experimental techniques that hold promise for the plant microbiome research field. We argue that utilizing a holo-omics approach to characterize host-microbiome interactions can provide important opportunities for broadening system-level understandings and significantly inform microbial approaches to improving host health and fitness. Video abstract.

Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients

Anti-programmed cell death protein 1 (PD-1) therapy provides long-term clinical benefits to patients with advanced melanoma. The composition of the gut microbiota correlates with anti-PD-1 efficacy in preclinical models and cancer patients. To investigate whether resistance to anti-PD-1 can be overcome by changing the gut microbiota, this clinical trial evaluated the safety and efficacy of responder-derived fecal microbiota transplantation (FMT) together with anti-PD-1 in patients with PD-1-refractory melanoma. This combination was well tolerated, provided clinical benefit in 6 of 15 patients, and induced rapid and durable microbiota perturbation. Responders exhibited increased abundance of taxa that were previously shown to be associated with response to anti-PD-1, increased CD8+ T cell activation, and decreased frequency of interleukin-8-expressing myeloid cells. Responders had distinct proteomic and metabolomic signatures, and transkingdom network analyses confirmed that the gut microbiome regulated these changes. Collectively, our findings show that FMT and anti-PD-1 changed the gut microbiome and reprogrammed the tumor microenvironment to overcome resistance to anti-PD-1 in a subset of PD-1 advanced melanoma.

Transkingdom network analysis provides insight into host-microbiome interactions in Atlantic salmon

BACKGROUND

The Atlantic salmon gut constitutes an intriguing system for studying host-microbiota interactions due to the dramatic environmental change salmon experiences during its life cycle. Yet, little is known about the role of interactions in this system and there is a general deficit in computational methods for integrative analysis of omics data from host-microbiota systems.

METHODS

We developed a pipeline to integrate host RNAseq data and microbial 16S rRNA amplicon sequencing data using weighted correlation network analysis. Networks are first inferred from each dataset separately, followed by module detections and finally robust identification of interactions via comparisons of representative module profiles. Through the use of module profiles, this network-based dimensionality reduction approach provides a holistic view into the discovery of potential host-microbiota symbionts.

RESULTS

We analyzed host gene expression from the gut epithelial tissue and microbial abundances from the salmon gut in a long-term feeding trial spanning the fresh-/salt-water transition and including two feeds resembling the fatty acid compositions available in salt- and fresh-water environments, respectively. We identified several host modules with significant correlations to both microbiota modules and variables such as feed, growth and sex. Although the strongest associations largely coincided with the fresh-/salt-water transition, there was a second layer of correlations associating smaller host modules to both variables and microbiota modules. Hence, we identify extensive reprogramming of the gut epithelial transcriptome and large scale coordinated changes in gut microbiota composition associated with water type as well as evidence of host-microbiota interactions linked to feed.

Dietary Indole-3-Carbinol Activates AhR in the Gut, Alters Th17-Microbe Interactions, and Exacerbates Insulitis in NOD Mice

The diet represents one environmental risk factor controlling the progression of type 1 diabetes (T1D) in genetically susceptible individuals. Consequently, understanding which specific nutritional components promote or prevent the development of disease could be used to make dietary recommendations in prediabetic individuals. In the current study, we hypothesized that the immunoregulatory phytochemcial, indole-3-carbinol (I3C) which is found in cruciferous vegetables, will regulate the progression of T1D in nonobese diabetic (NOD) mice. During digestion, I3C is metabolized into ligands for the aryl hydrocarbon receptor (AhR), a transcription factor that when systemically activated prevents T1D. In NOD mice, an I3C-supplemented diet led to strong AhR activation in the small intestine but minimal systemic AhR activity. In the absence of this systemic response, the dietary intervention led to exacerbated insulitis. Consistent with the compartmentalization of AhR activation, dietary I3C did not alter T helper cell differentiation in the spleen or pancreatic draining lymph nodes. Instead, dietary I3C increased the percentage of CD4+RORγt+Foxp3- (Th17 cells) in the lamina propria, intraepithelial layer, and Peyer's patches of the small intestine. The immune modulation in the gut was accompanied by alterations to the intestinal microbiome, with changes in bacterial communities observed within one week of I3C supplementation. A transkingdom network was generated to predict host-microbe interactions that were influenced by dietary I3C. Within the phylum Firmicutes, several genera (Intestinimonas, Ruminiclostridium 9, and unclassified Lachnospiraceae) were negatively regulated by I3C. Using AhR knockout mice, we validated that Intestinimonas is negatively regulated by AhR. I3C-mediated microbial dysbiosis was linked to increases in CD25high Th17 cells. Collectively, these data demonstrate that site of AhR activation and subsequent interactions with the host microbiome are important considerations in developing AhR-targeted interventions for T1D.

Transkingdom interactions between Lactobacilli and hepatic mitochondria attenuate western diet-induced diabetes

Western diet (WD) is one of the major culprits of metabolic disease including type 2 diabetes (T2D) with gut microbiota playing an important role in modulating effects of the diet. Herein, we use a data-driven approach (Transkingdom Network analysis) to model host-microbiome interactions under WD to infer which members of microbiota contribute to the altered host metabolism. Interrogation of this network pointed to taxa with potential beneficial or harmful effects on host's metabolism. We then validate the functional role of the predicted bacteria in regulating metabolism and show that they act via different host pathways. Our gene expression and electron microscopy studies show that two species from Lactobacillus genus act upon mitochondria in the liver leading to the improvement of lipid metabolism. Metabolomics analyses revealed that reduced glutathione may mediate these effects. Our study identifies potential probiotic strains for T2D and provides important insights into mechanisms of their action.

Gut microbiota-derived metabolites as key actors in inflammatory bowel disease

A key role of the gut microbiota in the establishment and maintenance of health, as well as in the pathogenesis of disease, has been identified over the past two decades. One of the primary modes by which the gut microbiota interacts with the host is by means of metabolites, which are small molecules that are produced as intermediate or end products of microbial metabolism. These metabolites can derive from bacterial metabolism of dietary substrates, modification of host molecules, such as bile acids, or directly from bacteria. Signals from microbial metabolites influence immune maturation, immune homeostasis, host energy metabolism and maintenance of mucosal integrity. Alterations in the composition and function of the microbiota have been described in many studies on IBD. Alterations have also been described in the metabolite profiles of patients with IBD. Furthermore, specific classes of metabolites, notably bile acids, short-chain fatty acids and tryptophan metabolites, have been implicated in the pathogenesis of IBD. This Review aims to define the key classes of microbial-derived metabolites that are altered in IBD, describe the pathophysiological basis of these associations and identify future targets for precision therapeutic modulation.

Improvements in Metabolic Syndrome by Xanthohumol Derivatives Are Linked to Altered Gut Microbiota and Bile Acid Metabolism

SCOPE

Two hydrogenated xanthohumol (XN) derivatives, α,β-dihydro-XN (DXN) and tetrahydro-XN (TXN), improved parameters of metabolic syndrome (MetS), a critical risk factor of cardiovascular disease (CVD) and type 2 diabetes, in a diet-induced obese murine model. It is hypothesized that improvements in obesity and MetS are linked to changes in composition of the gut microbiota, bile acid metabolism, intestinal barrier function, and inflammation.

METHODS AND RESULTS

To test this hypothesis, 16S rRNA genes were sequenced and bile acids were measured in fecal samples from C57BL/6J mice fed a high-fat diet (HFD) or HFD containing XN, DXN or TXN. Expression of genes associated with epithelial barrier function, inflammation, and bile acid metabolism were measured in the colon, white adipose tissue (WAT), and liver, respectively. Administration of XN derivatives decreases intestinal microbiota diversity and abundance-specifically Bacteroidetes and Tenericutes-alters bile acid metabolism, and reduces inflammation. In WAT, TXN supplementation decreases pro-inflammatory gene expression by suppressing macrophage infiltration. Transkingdom network analysis connects changes in the microbiota to improvements in MetS in the host.

CONCLUSION

Changes in the gut microbiota and bile acid metabolism may explain, in part, the improvements in obesity and MetS associated with administration of XN and its derivatives.

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

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

Unprecedented bacterial community richness in soybean nodules vary with cultivar and water status

BACKGROUND

Soybean (Glycine max) and other legumes are key crops grown around the world, providing protein and nutrients to a growing population, in a way that is more sustainable than most other cropping systems. Diazotrophs inhabiting root nodules provide soybean with nitrogen required for growth. Despite the knowledge of culturable Bradyrhizobium spp. and how they can differ across cultivars, less is known about the overall bacterial community (bacteriome) diversity within nodules, in situ. This variability could have large functional ramifications for the long-standing scientific dogma related to the plant-bacteriome interaction. Water availability also impacts soybean, in part, as a result of water-deficit sensitive nodule diazotrophs. There is a dearth of information on the effects of cultivar and water status on in situ rhizobia and non-rhizobia populations of nodule microbiomes. Therefore, soybean nodule microbiomes, using 16S rRNA and nifH genes, were sampled from nine cultivars treated with different field water regimes. It was hypothesized that the nodule bacteriome, composition, and function among rhizobia and non-rhizobia would differ in response to cultivar and soil water status.

RESULTS

16S rRNA and nifH showed dominance by Bradyrhizobiaceae, but a large diversity was observed across phylogenetic groups with < 1% and up to 45% relative abundance in cultivars. Other groups primarily included Pseudomonadaceae and Enterobacteriaceae. Thus, nodule bacteriomes were not only dominated by rhizobia, but also described by high variability and partly dependent on cultivar and water status. Consequently, the function of the nodule bacteriomes differed, especially due to cultivar. Amino acid profiling within nodules, for example, described functional changes due to both cultivar and water status.

CONCLUSIONS

Overall, these results reveal previously undescribed richness and functional changes in Bradyrhizobiaceae and non-rhizobia within the soybean nodule microbiome. Though the exact role of these atypical bacteria and relative variations in Bradyrhizobium spp. is not clear, there is potential for exploitation of these novel findings of microbiome diversity and function. This diversity needs consideration as part of bacterial-inclusive breeding of soybean to improve traits, such as yield and seed quality, and environmental resilience.

Host and microbiome multi-omics integration: applications and methodologies

The study of the microbial community-the microbiome-associated with a human host is a maturing research field. It is increasingly clear that the composition of the human's microbiome is associated with various diseases such as gastrointestinal diseases, liver diseases and metabolic diseases. Using high-throughput technologies such as next-generation sequencing and mass spectrometry-based metabolomics, we are able to comprehensively sequence the microbiome-the metagenome-and associate these data with the genomic, epigenomics, transcriptomic and metabolic profile of the host. Our review summarises the application of integrating host omics with microbiome as well as the analytical methods and related tools applied in these studies. In addition, potential future directions are discussed.

Antibiotic-Induced Alterations in Gut Microbiota Are Associated with Changes in Glucose Metabolism in Healthy Mice

The gut microbiome plays an important role in health and disease. Antibiotics are known to alter gut microbiota, yet their effects on glucose tolerance in lean, normoglycemic mice have not been widely investigated. In this study, we aimed to explore mechanisms by which treatment of lean mice with antibiotics (ampicillin, metronidazole, neomycin, vancomycin, or their cocktail) influences the microbiome and glucose metabolism. Specifically, we sought to: (i) study the effects on body weight, fasting glucose, glucose tolerance, and fasting insulin, (ii) examine the changes in expression of key genes of the bile acid and glucose metabolic pathways in the liver and ileum, (iii) identify the shifts in the cecal microbiota, and (iv) infer interactions between gene expression, microbiome, and the metabolic parameters. Treatment with individual or a cocktail of antibiotics reduced fasting glucose but did not affect body weight. Glucose tolerance changed upon treatment with cocktail, ampicillin, or vancomycin as indicated by reduced area under the curve of the glucose tolerance test. Antibiotic treatment changed gene expression in the ileum and liver, and shifted the alpha and beta diversities of gut microbiota. Network analyses revealed associations between Akkermansia muciniphila with fasting glucose and liver farsenoid X receptor (Fxr) in the top ranked host-microbial interactions, suggesting possible mechanisms by which this bacterium can mediate systemic changes in glucose metabolism. We observed Bacteroides uniformis to be positively and negatively correlated with hepatic Fxr and Glucose 6-phosphatase, respectively. Overall, our transkingdom network approach is a useful hypothesis generating strategy that offers insights into mechanisms by which antibiotics can regulate glucose tolerance in non-obese healthy animals. Experimental validation of our predicted microbe-phenotype interactions can help identify mechanisms by which antibiotics affect host phenotypes and gut microbiota.