Search

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.

In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.

In the past decade, an exciting realization has been that diverse liver diseases, ranging from non-alcoholic steatohepatitis, alcoholic steatohepatitis, and cirrhosis, to hepatocellular carcinoma, are not unrelated but fall along a spectrum. Recent work on the biology of the gut-liver communication axis has assisted in understanding the basic biology of both alcoholic and nonalcoholic fatty liver disease. Of immense importance is the massive advancement in understanding of the role of the microbiome, driven by high-throughput DNA sequencing and improved computational techniques that allow the complexity of the microbiome to be interrogated, together with improved experimental designs. Here, we review the gut-liver communications of these various forms of liver disease, explore the molecular, genetic and microbiome relationships, discuss prospects for exploiting the microbiome to determine the stage of liver disease, and to predict the effects of pharmaceutical, dietary, and other interventions at a population and individual level. We conclude that although much 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” may be especially useful for guiding new microbiome-based or microbiome-informed therapies.