Transkingdom Network Research Articles

Intestinal microbiome dysbiosis increases Mycobacteria pulmonary colonization in mice by regulating the Nos2-associated pathways

Increasing researches reveal gut microbiota was associated with the development of tuberculosis (TB). How to prevent or reduce Mycobacterium tuberculosis colonization in the lungs is a key measure to prevent TB. However, the data on gut microbiota preventing Mycobacterium colonization in the lungs were scarce. Here, we established the clindamycin-inducing intestinal microbiome dysbiosis and fecal microbial transplantation models in mice to identify gut microbiota’s effect on Mycobacterium’s colonization in the mouse lungs and explore its potential mechanisms. The results showed that clindamycin treatment altered the diversity and composition of the intestinal bacterial and fungal microbiome, weakened the trans-kingdom network interactions between bacteria and fungi, and induced gut microbiome dysbiosis in the mice. Gut microbiota dysbiosis increases intestinal permeability and enhances the susceptibility of Mycobacterium colonization in the lungs of mice. The potential mechanisms were gut microbiota dysbiosis altered the lung transcriptome and increased Nos2 expression through the ‘gut–lung axis’. Nos2 high expression disrupts the intracellular antimicrobial and anti-inflammatory environment by increasing the concentration of nitric oxide, decreasing the levels of reactive oxygen species and Defb1 in the cells, and promoting Mycobacteria colonization in the lungs of mice. The present study raises a potential strategy for reducing the risks of Mycobacteria infections and transmission by regulating the gut microbiome balance.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions.

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .

Combining compositional data sets introduces error in covariance network reconstruction

Abstract Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

Reducing gut microbiome-driven adipose tissue inflammation alleviates metabolic syndrome

BackgroundThe gut microbiota contributes to macrophage-mediated inflammation in adipose tissue with consumption of an obesogenic diet, thus driving the development of metabolic syndrome. There is a need to identify and develop interventions that abrogate this condition. The hops-derived prenylated flavonoid xanthohumol (XN) and its semi-synthetic derivative tetrahydroxanthohumol (TXN) attenuate high-fat diet-induced obesity, hepatosteatosis, and metabolic syndrome in C57Bl/6J mice. This coincides with a decrease in pro-inflammatory gene expression in the gut and adipose tissue, together with alterations in the gut microbiota and bile acid composition.ResultsIn this study, we integrated and interrogated multi-omics data from different organs with fecal 16S rRNA sequences and systemic metabolic phenotypic data using a Transkingdom Network Analysis. By incorporating cell type information from single-cell RNA-seq data, we discovered TXN attenuates macrophage inflammatory processes in adipose tissue. TXN treatment also reduced levels of inflammation-inducing microbes, such as Oscillibacter valericigenes, that lead to adverse metabolic phenotypes. Furthermore, in vitro validation in macrophage cell lines and in vivo mouse supplementation showed addition of O. valericigenes supernatant induced the expression of metabolic macrophage signature genes that are downregulated by TXN in vivo.ConclusionsOur findings establish an important mechanism by which TXN mitigates adverse phenotypic outcomes of diet-induced obesity and metabolic syndrome. TXN primarily reduces the abundance of pro-inflammatory gut microbes that can otherwise promote macrophage-associated inflammation in white adipose tissue.7MrSGPEkxmDTc41cVX6rPoVideo

P922 Gut microbiome and mycobiome in inflammatory bowel disease patients with Clostridium difficile infection

Abstract Background Clostridium difficile infection (CDI) is common in patients with inflammatory bowel disease (IBD) and has been reported as a risk factor for poor outcome. However, gut microbiome and mycobiome of IBD patients with CDI have been barely investigated. This study aimed to assess the gut microbiome and mycobiome in IBD patients with CDI. Methods We collected fecal samples from patients with IBD and concomitant CDI (IBD-CDI group, n=38), patients with IBD and no CDI (IBD-only group, n=91), and healthy subjects (HC, n=40). Patients characteristics including demographic data, disease severity, and medication history were collected. 16S rRNA sequencing, metagenomic sequencing, and untargeted metabolomic analysis were carried out in the samples. Results We found that the bacterial alpha diversity of the IBD-CDI group was lower than the IBD-only group and HC. And the bacterial and fungal beta diversity variations between IBD patients and HC were significant, regardless of CDI status. But the IBD-CDI group did not significantly cluster separately from the IBD-only group. Several bacterial taxa, including Ruminococcus gnavus, Clostridium innocuum, and Veillonella were overrepresented in the IBD-CDI group compared with IBD-only group and HC. Furthermore, IBD patients with CDI were distinguished from the IBD-only group and HC by several fungal taxa, including underrepresentation of Candida albicans, and overrepresentation of Saccharomyces cerevisiae, Eremothecium sinecaudum, Torulaspora delbrueckii, Grosmannia clavigera, and Eremothecium gossypii. The fungal-bacterial transkingdom network was altered in the IBD patients with CDI with a decreased network density. Notably, the relative abundance of Grosmannia clavigera was markedly higher in severe ulcerative colitis patients (defined by Truelove and Witts criteria and Mayo score). Besides, prior antibiotic exposure was associated with decreased biodiversity and increased dysbiosis. Functional differences in IBD patients with CDI include enrichment of branched-chain fatty acid biosynthesis, succinate fermentation to butanoate, peptidoglycan biosynthesis, and tetrahydrofolate biosynthesis. The metabolomic data indicated that the differential metabolites were predominantly involved in amino sugar and nucleotide sugar metabolism, fatty acid metabolism, porphyrin and chlorophyll metabolism, and neomycin, kanamycin and gentamicin biosynthesis. Conclusion IBD patients with CDI had pronounced microbial dysbiosis. Gut micro-ecological changes and interkingdom crosstalk in IBD patients with CDI might provide insight into the pathological process and potential strategies for diagnosis and treatment in this subset of patients.

Abstract LB062: Efficacy of Responder-derived Fecal Microbiota Transplant (R-FMT) and Pembrolizumab in Anti-PD-1 Refractory Patients with Advanced Melanoma

Abstract Background: Monoclonal antibodies (mAb) targeting the programmed cell death protein 1 (PD-1) receptor provide durable long-term benefit in a subset of patients (pts) with advanced melanoma with response rates of 35-42% and 4-year progression-free survival (PFS) rate of 27%. Separately, the composition of the gut microbiota has been shown to correlate with anti-PD-1 efficacy in human cancer pts with melanoma, renal cell cancer and non-small cell lung cancer (NSCLC) although the precise organisms differ considerably across various studies. In preclinical models, responder-derived fecal microbiome and microbiome consortia produce anti-tumor responses. The effect of microbiome modulation in pts with anti-PD-1 refractory melanoma has not been evaluated. Methods: To evaluate whether primary resistance to anti-PD-1 immunotherapy could be overcome by intestinal microbiome modulation, we designed and conducted a phase II study (NCT03341143). We enrolled pts with primary refractory metastatic melanoma with best response of short-term stable disease (≤6 months) or progressive disease (PD) to prior anti-PD-1 based immunotherapy. Pts received single-administration of responder-derived fecal microbiota transplantation (R-FMT) together with pembrolizumab. Candidate donors were pts with advanced melanoma treated with anti-PD-1 immunotherapy with durable partial or complete response (PR, CR). Pembrolizumab was continued till intolerable toxicity or disease progression. Safety and clinical activity (based on RECIST v1.1) were main objectives; while progression-free survival (PFS) was a key secondary endpoint. Results: As of December 1, 2020, 16 pts with primary refractory melanoma were enrolled, of whom 15 were evaluable. LDH was elevated in 14/15 pts; and the median number of prior therapies was 2. Recipient pts were seromatched to receive a single R-FMT from one of eight candidate donors (5 CR; 3 PR; median PFS 58 months, range 43-70). R-FMT was administered via colonoscopy after bowel preparation with no use of antibiotics. Pembrolizumab was administered IV per label. R-FMT/pembrolizumab was well-tolerated, with no unusual toxicity signals. R-FMT induced rapid and durable microbiota perturbation in most pts; while 6 of 15 evaluable pts had evidence of clinical benefit. Response to R-FMT/pembrolizumab was associated with an increased abundance of taxa previously shown to be associated with response to anti-PD-1, increased CD8+ T cell activation, and decreased frequency of IL-8 expressing myeloid cells. Responders had distinct proteomic and metabolomic signatures, and transkingdom network analyses confirmed that the gut microbiome regulated these changes. Conclusions: In pts with anti-PD-1 primary refractory melanoma, R-FMT/pembrolizumab changed the gut microbiome and reprogrammed the tumor microenvironment to overcome resistance to anti-PD-1 immunotherapy. Response was associated with CD8 T cell induction and reduction of IL-8 expressing myeloid cells. Citation Format: Diwakar Davar, Amiran Dzutsev, John A. McCulloch, Richard R. Rodrigues, Joe-Marc Chauvin, Robert M. Morrison, Richelle N. Deblasio, Carmine Menna, Quanquan Ding, Ornella Pagliano, Bochra Zidi, Shuowen Zhang, Jonathan H. Badger, Marie Vetizou, Alicia M. Cole, Miriam R. Fernandes, Stephanie Prescott, Raquel G. Costa, Ascharya K. Balaji, Andrey Morgun, Ivan Vujkovic-Cvijin, Hong Wang, Amir A. Borhani, Marc B. Schwartz, Howard M. Dubner, Scarlett J. Ernst, Amy Rose, Yana G. Najjar, Yasmine Belkaid, John M. Kirkwood, Giorgio Trinchieri, Hassane M. Zarour. Efficacy of Responder-derived Fecal Microbiota Transplant (R-FMT) and Pembrolizumab in Anti-PD-1 Refractory Patients with Advanced Melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB062.

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.

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

BackgroundThe 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. MethodsWe 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. ResultsWe 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.

Fungal and bacterial microbiome dysbiosis and imbalance of trans-kingdom network in asthma

BackgroundFungal and bacterial microbiota play an important role in development of asthma. We aim to characterize airway microbiome (mycobiome, bacteriome) and functional genes in asthmatics and controls.MethodsSputum microbiome of controls, untreated asthma patients and inhaled corticosteroid (ICS) receiving patients was detected using high throughput sequencing. Metagenomic sequencing was used to examine the functional genes of microbiome.Results1. Mycobiome: α diversity was lower in untreated asthma group than that in controls. Mycobiome compositions differed among the three groups. Compared with controls, untreated asthma group has higher abundance of Wallemia, Mortierella and Fusarium. Compared with untreated asthma patients, ICS receiving patients has higher abundance of Fusarium and Mortierella, lower frequency of Wallemia, Alternaria and Aspergillus. 2. Bacteriome: α diversity was lower in untreated asthma group than that in controls. There are some overlaps of bacteriome compositions between controls and untreated asthma patients which were distinct from ICS receiving patients. Untreated asthma group has higher Streptococcus than controls. 3. Potential fungal and bacterial biomarkers of asthma: Trametes, Aspergillus, Streptococcus, Gemella, Neisseria, etc. 4. Correlation network: There are dense and homogenous correlations in controls but a dramatically unbalanced network in untreated asthma and ICS receiving patients, which suggested the existence of disease-specific inter-kingdom and intra-kingdom alterations. 5. Metagenomic analysis: functional pathways were associated with the status of asthma, microbiome and functional genes showed different correlations in different environment.ConclusionWe showed mycobiome and bacteriome dysbiosis in asthma featured by alterations in biodiversity, community composition, inter-kingdom and intra-kingdom network. We also observed several functional genes associated with asthma.

Gut Microbiota and Cancer of the Host: Colliding Interests.

Cancer develops in multicellular organisms from cells that ignore the rules of cooperation and escape the mechanisms of anti-cancer surveillance. Tumorigenesis is jointly encountered by the host and microbiota, a vast collection of microorganisms that live on the external and internal epithelial surfaces of the body. The largest community of human microbiota resides in the gastrointestinal tract where commensal, symbiotic and pathogenic microorganisms interact with the intestinal barrier and gut mucosal lymphoid tissue, creating a tumor microenvironment in which cancer cells thrive or perish. Aberrant composition and function of the gut microbiota (dysbiosis) has been associated with tumorigenesis by inducing inflammation, promoting cell growth and proliferation, weakening immunosurveillance, and altering food and drug metabolism or other biochemical functions of the host. However, recent research has also identified several mechanisms through which gut microbiota support the host in the fight against cancer. These mechanisms include the use of antigenic mimicry, biotransformation of chemotherapeutic agents, and other mechanisms to boost anti-cancer immune responses and improve the efficacy of cancer immunotherapy. Further research in this rapidly advancing field is expected to identify additional microbial metabolites with tumor suppressing properties, map the complex interactions of host-microbe 'transkingdom network' with cancer cells, and elucidate cellular and molecular pathways underlying the impact of specific intestinal microbial configurations on immune checkpoint inhibitor therapy.

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

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. 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. 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.

Transkingdom network reveals bacterial players associated with cervical cancer gene expression program.

Cervical cancer is the fourth most common cancer in women worldwide with human papillomavirus (HPV) being the main cause the disease. Chromosomal amplifications have been identified as a source of upregulation for cervical cancer driver genes but cannot fully explain increased expression of immune genes in invasive carcinoma. Insight into additional factors that may tip the balance from immune tolerance of HPV to the elimination of the virus may lead to better diagnosis markers. We investigated whether microbiota affect molecular pathways in cervical carcinogenesis by performing microbiome analysis via sequencing 16S rRNA in tumor biopsies from 121 patients. While we detected a large number of intra-tumor taxa (289 operational taxonomic units (OTUs)), we focused on the 38 most abundantly represented microbes. To search for microbes and host genes potentially involved in the interaction, we reconstructed a transkingdom network by integrating a previously discovered cervical cancer gene expression network with our bacterial co-abundance network and employed bipartite betweenness centrality. The top ranked microbes were represented by the families Bacillaceae, Halobacteriaceae, and Prevotellaceae. While we could not define the first two families to the species level, Prevotellaceae was assigned to Prevotella bivia. By co-culturing a cervical cancer cell line with P. bivia, we confirmed that three out of the ten top predicted genes in the transkingdom network (lysosomal associated membrane protein 3 (LAMP3), STAT1, TAP1), all regulators of immunological pathways, were upregulated by this microorganism. Therefore, we propose that intra-tumor microbiota may contribute to cervical carcinogenesis through the induction of immune response drivers, including the well-known cancer gene LAMP3.

CVID enteropathy is characterized by exceeding low mucosal IgA levels and interferon-driven inflammation possibly related to the presence of a pathobiont

Common variable immunodeficiency (CVID), the most common symptomatic primary antibody deficiency, is accompanied in some patients by a duodenal inflammation and malabsorption syndrome known as CVID enteropathy (E-CVID).The goal of this study was to investigate the immunological abnormalities in CVID patients that lead to enteropathy as well as the contribution of intestinal microbiota to this process.We found that, in contrast to noE-CVID patients (without enteropathy), E-CVID patients have exceedingly low levels of IgA in duodenal tissues. In addition, using transkingdom network analysis of the duodenal microbiome, we identified Acinetobacter baumannii as a candidate pathobiont in E-CVID. Finally, we found that E-CVID patients exhibit a pronounced activation of immune genes and down-regulation of epithelial lipid metabolism genes. We conclude that in the virtual absence of mucosal IgA, pathobionts such as A. baumannii, may induce inflammation that re-directs intestinal molecular pathways from lipid metabolism to immune processes responsible for enteropathy.

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

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g., mammals and microbes) using diverse types of data.

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.

Investigating a holobiont: Microbiota perturbations and transkingdom networks

The scientific community has recently come to appreciate that, rather than existing as independent organisms, multicellular hosts and their microbiota comprise a complex evolving superorganism or metaorganism, termed a holobiont. This point of view leads to a re-evaluation of our understanding of different physiological processes and diseases. In this paper we focus on experimental and computational approaches which, when combined in one study, allowed us to dissect mechanisms (traditionally named host-microbiota interactions) regulating holobiont physiology. Specifically, we discuss several approaches for microbiota perturbation, such as use of antibiotics and germ-free animals, including advantages and potential caveats of their usage. We briefly review computational approaches to characterize the microbiota and, more importantly, methods to infer specific components of microbiota (such as microbes or their genes) affecting host functions. One such approach called transkingdom network analysis has been recently developed and applied in our study.1 Finally, we also discuss common methods used to validate the computational predictions of host-microbiota interactions using in vitro and in vivo experimental systems.

Uncovering effects of antibiotics on the host and microbiota using transkingdom gene networks

ObjectiveDespite widespread use of antibiotics for the treatment of life-threatening infections and for research on the role of commensal microbiota, our understanding of their effects on the host is still...