The Structure, Function and Evolution of Biological Networks
The Walhout lab wishes to understand biological networks and how these networks adapt to different conditions. We use systems biology approaches to dissect these complex networks. These approaches combine high-quality and large-scale genetic and biochemical data sets and uses computational modeling to integrate the data such that the organizing principles and emergent properties of biological systems are unveiled.
To examine biological networks we mainly use the model organism round worm Caenorhabditis elegans. Worms are highly adaptable, easy to manipulate, and have many analogs in human genetics. Furthermore, there are many genetic tools and worm-specific techniques that are not available for studying higher eukaryotes. Overall, our research involves two broad areas of biology:
1: Gene Regulatory Networks
2: Regulation of Metabolic Networks read more…
- Bacteria differentially affect the C. elegans response to FUDR and camptothecin
- Bacterial metabolism is required for the C. elegans chemotherapeutic response
- Genetic screens with two bacterial species and three drugs to unravel mechanism
- 5-FU and FUDR affect C. elegans through bacterial RNA rather than DNA metabolism
The human microbiota greatly affects physiology and disease; however, the contribution of bacteria to the response to chemotherapeutic drugs remains poorly understood. Caenorhabditis elegans and its bacterial diet provide a powerful system to study host-bacteria interactions. Here, we use this system to study how bacteria affect the C. elegans response to chemotherapeutics. We find that different bacterial species can increase the response to one drug yet decrease the effect of another. We perform genetic screens in two bacterial species using three chemotherapeutic drugs: 5-fluorouracil (5-FU), 5-fluoro-2′-deoxyuridine (FUDR), and camptothecin (CPT). We find numerous bacterial nucleotide metabolism genes that affect drug efficacy in C. elegans. Surprisingly, we find that 5-FU and FUDR act through bacterial ribonucleotide metabolism to elicit their cytotoxic effects in C. elegans rather than by thymineless death or DNA damage. Our study provides a blueprint for characterizing the role of bacteria in the host response to chemotherapeutics.
García-González AP, Ritter AD, Shrestha S, Andersen EC, Yilmaz LS, Walhout AJM. (2017) Bacterial Metabolism Affects the C. elegans Response to Cancer Chemotherapeutics. Cell 169, 431-441.
The bacteria residing in your digestive tract, or your gut microbiota, may play an important role in your ability to respond to chemotherapy drugs, according to a new study by scientists at UMass Medical School. Published in Cell, the study by Marian Walhout, PhD, and colleagues shows that when a common research model, the roundworm Caenorhabditis elegans, was fed a diet of E. coli bacteria, the worms were 100 times more sensitive to the chemotherapy drug floxuridine (FUDR) than worms who were fed different bacteria. FUDR is a commonly used drug to treat colorectal cancer. READ MORE…
UMass Medical School will invest three faculty members into newly endowed chairs and three more to existing endowed chairs, according to a vote by the University of Massachusetts Board of Trustees at its April 12 meeting.
Marian Walhout, PhD, professor of molecular medicine and co-director of the Program in Systems Biology, has been appointed the inaugural recipient of The Maroun Semaan Chair in Biomedical Research. Dr. Walhout is a pioneer among those working to understand how genes are expressed on a system level, and how these complex biological networks adapt to various conditions. Her research, which combines large-scale data sets and uses computational modeling to unravel regulatory networks involved in metabolic and genetic development, has advanced the fundamental understanding of these systems and offers potentially new and innovative pathways to treat human disease. Read more…
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.
Genome-scale metabolic network modeling.
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.