In the past decade, an exciting realization has been that diverse liver diseases - ranging from nonalcoholic steatohepatitis, alcoholic steatohepatitis and cirrhosis to hepatocellular carcinoma - fall along a spectrum. Work on the biology of the gut-liver axis has assisted in understanding the basic biology of both alcoholic fatty liver disease and nonalcoholic fatty liver disease (NAFLD). Of immense importance is the advancement in understanding the role of the microbiome, driven by high-throughput DNA sequencing and improved computational techniques that enable the complexity of the microbiome to be interrogated, together with improved experimental designs. Here, we review gut-liver communications in liver disease, exploring the molecular, genetic and microbiome relationships and discussing prospects for exploiting the microbiome to determine liver disease stage and to predict the effects of pharmaceutical, dietary and other interventions at a population and individual level. Although much work remains to be done in understanding the relationship between the microbiome and liver disease, rapid progress towards clinical applications is being made, especially in study designs that complement human intervention studies with mechanistic work in mice that have been humanized in multiple respects, including the genetic, immunological and microbiome characteristics of individual patients. These 'avatar mice' could be especially useful for guiding new microbiome-based or microbiome-informed therapies.

In the original version of Table 1 published online, upward arrows to indicate increased translocation of PAMPs were missing from the row entitled 'Translocation' for both the column on alcoholic liver disease and nonalcoholic fatty liver disease. This error has now been updated in the PDF and HTML version of the article.

The central aims of many host or environmental microbiome studies are to elucidate factors associated with microbial community compositions and to relate microbial features to outcomes. However, these aims are often complicated by difficulties stemming from high-dimensionality, non-normality, sparsity, and the compositional nature of microbiome data sets. A key tool in microbiome analysis is beta diversity, defined by the distances between microbial samples. Many different distance metrics have been proposed, all with varying discriminatory power on data with differing characteristics. Here, we propose a compositional beta diversity metric rooted in a centered log-ratio transformation and matrix completion called robust Aitchison PCA. We demonstrate the benefits of compositional transformations upstream of beta diversity calculations through simulations. Additionally, we demonstrate improved effect size, classification accuracy, and robustness to sequencing depth over the current methods on several decreased sample subsets of real microbiome data sets. Finally, we highlight the ability of this new beta diversity metric to retain the feature loadings linked to sample ordinations revealing salient intercommunity niche feature importance. IMPORTANCE By accounting for the sparse compositional nature of microbiome data sets, robust Aitchison PCA can yield high discriminatory power and salient feature ranking between microbial niches. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/DEICODE; additionally, a QIIME 2 plugin is provided to perform this analysis at https://library.qiime2.org/plugins/deicode/.

Trypanosoma cruzi parasites are the causative agents of Chagas disease. These parasites infect cardiac and gastrointestinal tissues, leading to local inflammation and tissue damage. Digestive Chagas disease is associated with perturbations in food absorption, intestinal traffic and defecation. However, the impact of T. cruzi infection on the gut microbiota and metabolome have yet to be characterized. In this study, we applied mass spectrometry-based metabolomics and 16S rRNA sequencing to profile infection-associated alterations in fecal bacterial composition and fecal metabolome through the acute-stage and into the chronic stage of infection, in a murine model of Chagas disease. We observed joint microbial and chemical perturbations associated with T. cruzi infection. These included alterations in conjugated linoleic acid (CLA) derivatives and in specific members of families Ruminococcaceae and Lachnospiraceae, as well as alterations in secondary bile acids and members of order Clostridiales. These results highlight the importance of multi-'omics' and poly-microbial studies in understanding parasitic diseases in general, and Chagas disease in particular.

Triclosan (TCS), employed as an antiseptic and disinfectant, comes into direct contact with humans through a plethora of consumer products and its rising environmental release. We have demonstrated that TCS promotes liver tumorigenesis in mice, yet the biological and molecular mechanisms by which TCS exerts its toxicity, especially in early stages of liver disease, are largely unexplored. When mice were fed a high-fat diet (HFD), we found that fatty liver and dyslipidemia are prominent early signs of liver abnormality induced by TCS. The presumably protective HFD-induced hepatic expression of the metabolic regulator fibroblast growth factor 21 (FGF21) was blunted by TCS. TCS-altered Fgf21 expression aligned with aberrant expression of genes encoding metabolic enzymes manifested as profound systemic metabolic changes that disturb homeostasis of amino acids, fatty acids, and glucose. Using a type 1 diabetic animal model, TCS potentiates and accelerates the development of steatohepatitis and fibrosis, accompanied by increased levels of hepatic lipid droplets and oxidative stress. Analysis of fecal samples revealed that HFD-fed mice exhibited a reduction in fecal species richness, and that TCS further diminished microbial diversity and shifted the bacterial community toward lower Bacteriodetes and higher Firmicutes, resembling changes in microbiota composition in nonalcoholic steatohepatitis (NASH) patients. Using reverse-genetic approaches, we demonstrate that, along with HFD, TCS induces hepatic steatosis and steatohepatitis jointly regulated by the transcription factor ATF4 and the nuclear receptor PPARα, which participate in the transcriptional regulation of the Fgf21 gene. This study provides evidence linking nutritional imbalance and exposure to TCS with the progression of NASH.

Advances in technical capabilities for reading complex human microbiomes are leading to an explosion of microbiome research, leading in turn to intense interest among clinicians in applying these techniques to their patients. In this review, we discuss the content of the human microbiome, including intersubject and intrasubject variability, considerations of study design including important confounding factors, and different methods in the laboratory and on the computer to read the microbiome and its resulting gene products and metabolites. We highlight several common pitfalls for clinicians, including the expectation that an individuals microbiome will be stable, that diet can induce rapid changes that are large compared with the differences among subjects, that everyone has essentially the same core stool microbiome, and that different laboratory and computational methods will yield essentially the same results. We also highlight the current limitations and future promise of these techniques, with the expectation that an understanding of these considerations will help accelerate the path toward routine clinical application of these techniques developed in research settings.

Microbiome analyses can be challenging because microbial strains are numerous, and often, confounding factors in the data set are also numerous. Many tools reduce, summarize, and visualize these high-dimensional data to provide insight at the community level. However, they lose the detailed information about each taxon and can be misleading (for example, the well-known horseshoe effect in ordination plots). Thus, multiple methods at different levels of resolution are required to capture the full range of microbial patterns. Here we present Calour, a user-friendly data exploration tool for microbiome analyses. Calour provides a study-centric data model to store and manipulate sample-by-feature tables (with features typically being operational taxonomic units) and their associated metadata. It generates an interactive heatmap, allowing visualization of microbial patterns and exploration using microbial knowledge databases. We demonstrate the use of Calour by exploring publicly available data sets, including the gut and skin microbiota of habitat-switched fire salamander larvae, gut microbiota of Trichuris muris-infected mice, skin microbiota of different human body sites, gut microbiota of various ant species, and a metabolome study of mice exposed to intermittent hypoxia and hypercapnia. In these cases, Calour reveals novel patterns and potential contaminants of subgroups of microbes that are otherwise hard to find. Calour is open source under the Berkeley Software Distribution (BSD) license and available from https://github.com/biocore/calour. IMPORTANCE Calour allows us to identify interesting microbial patterns and generate novel biological hypotheses by interactively inspecting microbiome studies and incorporating annotation databases and convenient statistical tools. Calour can be used as a first-step tool for microbiome data exploration.

Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery-driven, tool-driven, and standards-driven research projects. Here we illustrate these issues using several such non-hypothesis-driven projects from our own laboratories, including spatial mapping, the American Gut Project, the Earth Microbiome Project (which is an umbrella project integrating many smaller hypothesis-driven projects), and the knowledgebase-driven tools GNPS and Qiita. We argue that an investment of community resources in infrastructure tasks, and in the controls and standards that underpin them, will greatly enhance the investment in hypothesis-driven research programs.

Recent research highlights the significant impact of the gut microbiota on health and disease. Thalassemia, a hereditary blood disorder, requires regular blood transfusions, leading to an accumulation of iron in the body. Such changes could potentially alter the intestinal microbiota, thereby increasing the susceptibility of thalassemic patients to infection. In this study, we analyzed the fecal microbiota of 70 non-transfusion-dependent (NTDT) β-thalassemia/HbE patients and 30 healthy controls. Our findings indicate that iron chelation intervention had no detectable effect on the microbiome profile of thalassemic patients. However, the cross-sectional analysis revealed that the bacterial diversity and community structure in patients were significantly less diverse and distinct compared to those of healthy subjects. Using reference frames, we were also able to demonstrate that bacterial taxa that are known to produce short chain fatty acids, from the genera Alistipes, Coprococcus, and Oscillospira, and those from the family Ruminococcaceae, were less prevalent in the patients. In contrast, bacterial taxa associated with an unhealthy gut, including the genus Clostridium and those from the families Fusobacteriaceae, Enterobacteriaceae, and Peptostrptococcaceae, were more prevalent in patients and found to be correlated with higher levels of ferritin. Collectively, these changes in the microbiota could be regarded as markers of raised ferritin levels, and therefore, awareness should be exercised as they could interfere, albeit indirectly, with the treatment of the co-morbidities of thalassemia.