Marian presented the keynote lecture at the Cold Spring Harbor Laboratory meeting on Systems Biology: Networks in March 2017. Introduction by our collaborator Chad Myers of the University of Minnesota.
Interactions between RNA binding proteins (RBPs) and mRNAs are critical to post-transcriptional gene regulation. Eukaryotic genomes encode thousands of mRNAs and hundreds of RBPs. However, in contrast to interactions between transcription factors (TFs) and DNA, the interactome between RBPs and RNA has been explored for only a small number of proteins and RNAs. This is largely because the focus has been on using ‘protein-centered’ (RBP-to-RNA) interaction mapping methods that identify the RNAs with which an individual RBP interacts. While powerful, these methods cannot as of yet be applied to the entire RBPome. Moreover, it may be desirable for a researcher to identify the repertoire of RBPs that can interact with an mRNA of interest—in a ‘gene-centered’ manner—yet few such techniques are available. Here, we present Protein-RNA Interaction Mapping Assay (PRIMA) with which an RNA ‘bait’ can be tested versus multiple RBP ‘preys’ in a single experiment. PRIMA is a translation-based assay that examines interactions in the yeast cytoplasm, the cellular location of mRNA translation. We show that PRIMA can be used with small RNA elements, as well as with full-length Caenorhabditis elegans 3′ UTRs. PRIMA faithfully recapitulated numerous well-characterized RNA-RBP interactions and also identified novel interactions, some of which were confirmed in vivo. We envision that PRIMA will provide a complementary tool to expand the depth and scale with which the RNA-RBP interactome can be explored.
Tamburino AM, Kaymak E, Shresta S, Holdorf AD, Ryder SP, Walhout AJM (2017) PRIMA: a gene-centered, RNA-to-protein method for mapping RNA-protein interactions. Translation 5, e1295130.
Resident microbes of the human body, particularly the gut microbiota, provide essential functions for the host, and, therefore, have important roles in human health as well as mitigating disease. It is difficult to study the mechanisms by which the microbiota affect human health, especially at a systems-level, due to heterogeneity of human genomes, the complexity and heterogeneity of the gut microbiota, the challenge of growing these bacteria in the laboratory, and the lack of bacterial genetics in most microbiotal species. In the last few years, the interspecies model of the nematode Caenorhabditis elegans and its bacterial diet has proven powerful for studying host–microbiota interactions, as both the animal and its bacterial diet can be subjected to large-scale and high-throughput genetic screening. The high level of homology between many C. elegans and human genes, as well as extensive similarities between human and C. elegans metabolism, indicates that the findings obtained from this interspecies model may be broadly relevant to understanding how the human microbiota affects physiology and disease. In this review, we summarize recent systems studies on how bacteria interact with C. elegans and affect life history traits.
Zhang J, Holdorf AD, Walhout AJ. (2017) C. elegans and its bacterial diet as a model for systems-level understanding of host–microbiota interactions. Curr. Opin. Biotechnol. 46, 74-80.
Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.
Yilmas LS, Walhout AJM (2017) Metabolic network modeling with model organisms. Curr. Opin. Chem. Biol. 36, 32-39.
The December issues of Cold Spring Harbor Protocols features four protocols on how to prepare reagents for yeast one-hybrid analysis.
An important question when studying gene regulation is which transcription factors (TFs) interact with which cis-regulatory elements, such as promoters and enhancers. Addressing this issue in complex multicellular organisms is challenging as several hundreds of TFs and thousands of regulatory elements must be considered in the context of different tissues and physiological conditions. Yeast one-hybrid (Y1H) assays provide a powerful “gene-centered” method to identify the TFs that can bind a DNA sequence of interest. In this introduction, we describe the basic principles of the Y1H assay and its advantages and disadvantages and briefly discuss how it is complementary to “TF-centered” methods that identify protein-DNA interactions for a known protein of interest.
Fuxman Bass JI, Reece-Hoyes JS, Walhout AJ. (2016). Gene-Centered Yeast One-Hybrid Assays. Cold Spring Harb. Prot., 2016(12).
Generating Bait Strains for Yeast One-Hybrid Assays
Performing Yeast One-Hybrid Library Screens
Colony Lift Colorimetric Assay for β-Galactosidase Activity
Zymolyase-Treatment and Polymerase Chain Reaction Amplification from Genomic and Plasmid Templates from Yeast
Transcription factors (TFs) play a central role in controlling spatiotemporal gene expression and the response to environmental cues. A comprehensive understanding of gene regulation requires integrating physical protein–DNA interactions (PDIs) with TF regulatory activity, expression patterns, and phenotypic data. Although great progress has been made in mapping PDIs using chromatin immunoprecipitation, these studies have only characterized ~10% of TFs in any metazoan species. The nematode C. elegans has been widely used to study gene regulation due to its compact genome with short regulatory sequences. Here, we delineated the largest gene‐centered metazoan PDI network to date by examining interactions between 90% of C. elegans TFs and 15% of gene promoters. We used this network as a backbone to predict TF binding sites for 77 TFs, two‐thirds of which are novel, as well as integrate gene expression, protein–protein interaction, and phenotypic data to predict regulatory and biological functions for multiple genes and TFs.
Fuxman Bass, JI, Pons, C, Kozlowski, L, Reece‐Hoyes, JS, Shrestha, S, Holdorf, AD, Mori, A, Myers, CL, Walhout, AJM. (2016). A gene‐centered C. elegans protein–DNA interaction network provides a framework for functional predictions. Mol. Sys. Biol. 12: 884. doi: 10.15252/msb.20167131