The human gut microbiome is a key contributor to health, and its perturbations are linked to many diseases. Small-molecule xenobiotics such as drugs, chemical pollutants and food additives can alter the microbiota composition and are now recognized as one of the main factors underlying microbiome diversity. Mapping the effects of such compounds on the gut microbiome is challenging because of the complexity of the community, anaerobic growth requirements of individual species and the large number of interactions that need to be quantitatively assessed. High-throughput screening setups offer a promising solution for probing the direct inhibitory effects of hundreds of xenobiotics on tens of anaerobic gut bacteria. When automated, such assays enable the cost-effective investigation of a wide range of compound-microbe combinations. We have developed an experimental setup and protocol that enables testing of up to 5,000 compounds on a target gut species under strict anaerobic conditions within 5 d. In addition, with minor modifications to the protocol, drug effects can be tested on microbial communities either assembled from isolates or obtained from stool samples. Experience in working in an anaerobic chamber, especially in performing delicate work with thick chamber gloves, is required for implementing this protocol. We anticipate that this protocol will accelerate the study of interactions between small molecules and the gut microbiome and provide a deeper understanding of this microbial ecosystem, which is intimately intertwined with human health.

We recently presented a three-pronged association study that integrated human intestinal microbiome data derived from shotgun-based sequencing with untargeted serum metabolome data and measures of host physiology. Metabolome and microbiome data are high dimensional, posing a major challenge for data integration. Here, we present a step-by-step computational protocol that details and discusses the dimensionality-reduction techniques used and methods for subsequent integration and interpretation of such heterogeneous types of data. Dimensionality reduction was achieved through a combination of data normalization approaches, binning of co-abundant genes and metabolites, and integration of prior biological knowledge. The use of prior knowledge to overcome functional redundancy across microbiome species is one central advance of our method over available alternative approaches. Applying this framework, other investigators can integrate various '-omics' readouts with variables of host physiology or any other phenotype of interest (e.g., connecting host and microbiome readouts to disease severity or treatment outcome in a clinical cohort) in a three-pronged association analysis to identify potential mechanistic links to be tested in experimental settings. Although we originally developed the framework for a human metabolome-microbiome study, it is generalizable to other organisms and environmental metagenomes, as well as to studies including other -omics domains such as transcriptomics and proteomics. The provided R code runs in ~1 h on a standard PC.

Lipids organize the activity of the cell's proteome through a complex network of interactions. The assembly of comprehensive atlases embracing all protein-lipid interactions is an important challenge that requires innovative methods. We recently developed a liposome-microarray-based assay (LiMA) that integrates liposomes, microfluidics and fluorescence microscopy and which is capable of measuring protein recruitment to membranes in a quantitative and high-throughput manner. Compared with previous assays that are labor-intensive and difficult to scale up, LiMA improves the protein-lipid interaction assay throughput by at least three orders of magnitude. Here we provide a step-by-step LiMA protocol that includes the following: (i) the serial and generic production of the liposome microarray; (ii) its integration into a microfluidic format; (iii) the measurement of fluorescently labeled protein (either purified proteins or from cell lysate) recruitment to liposomal membranes using high-throughput microscopy; (iv) automated image analysis pipelines to quantify protein-lipid interactions; and (v) data quality analysis. In addition, we discuss the experimental design, including the relevant quality controls. Overall, the protocol-including device preparation, assay and data analysis-takes 6-8 d. This protocol paves the way for protein-lipid interaction screens to be performed on the proteome and lipidome scales.