Section: Research

Marian Walhout, Ph.D.

Academic Role: Associate Professor

Faculty Appointment(s) In:
   Program in Gene Function and Expression
   Program in Molecular Medicine

Other Affiliation(s):
   Interdisciplinary Graduate Program

Mapping Transcription Regulatory Circuits in the Nematode C. elegans

Transcription regulation plays a pivotal role in development and disease - many transcription factors (TFs) are deregulated in pathology and resulting in changes in gene expression. The human genome encodes up to 2,500 TFs and for most of these we have no idea what their target genes are and how they function.

The overall goal of our laboratory is to understand the principles that control differential gene expression at a systems level. To do so, we employ high-throughput technologies to identify protein-DNA interactions between transcription factors (TFs) and their target genes and protein-protein interactions between TFs (i.e. homo- and heterodimers). As a model organism, we use the nematode Caenorhabditis elegans.

Differential gene expression

The human genome contains ~25,000 predicted protein-coding genes. Each of these genes is differentially expressed in different cells/tissues/organs and at different times of development (or during pathological conditions). As a result, each cell/tissue/organ in the body expresses a different subset of the total gene collection. The mechanics of transcription have been studied intensely for the past 20 years or so. For this, a handful of "model" TFs have been used.

Systems biology

The goal of molecular systems biology is to understand biological or biochemical processes at a systems level rather than at the level of individual molecules. A biological system can be a cell, tissue, organ or a whole organism. In systems biology, relationships between macromolecules are the focus of study. Complete genome sequences and the use of genome annotation tools have led to the identification of many protein and RNA-coding genes, transcriptional regulatory sequences, etc. To gain insight into the development and function of a biological system, it is important to decipher how these components physically and functionally interact. The research flow in systems biology can be divided into the following steps: 1) systematically gather data that describe the functional relationships between system components; 2) use these data to create a model that describes the system; 3) derive functional predictions from the model; 4) test these predictions experimentally and/or computationally; and 5) refine the model using the results obtained.

Transcription regulatory networks

The presence of large numbers of TF-encoding genes in metazoan genomes, the multiple protein-DNA and protein-protein interactions TFs engage in, together with the concerted action of multiple TFs per gene, suggests that complex gene expression patterns are the result of intricate transcription regulatory networks in which many TFs are functionally connected. Such networks can be represented as graph models in which "nodes" correspond to proteins or genes, and "edges" (i.e. links between nodes) represent functional or physical interactions between those proteins/genes (see figure). Network properties and motifs and topological measures can be used to define how transcription regulatory networks behave and how they are similar to or different from other types of networks. To study differential gene expression at a systems level in our group we aim to: i) identify the genes and TFs expressed or involved in the development, function and/or pathology of a particular system (i.e. cell/tissue/organ); ii) systematically identify protein-DNA interactions between the promoters of these genes and the TFs that contribute to their expression; iii) systematically identify TF-TF interactions of the TFs involved; iv) visualize these protein-protein and protein-DNA interactions in a transcription regulatory network; v) analyze the network to derive hypotheses and principles that help us understand differential gene expression.

Protein-DNA interactions (PDI), protein-protein interactions (PPI) and gene expression data generated by the Walhout laboratory and by other laboratories are collected and made available to the community through EDGEdb (elegans differential gene expression database).

C. elegans as a model system

C. elegans is an excellent system to study the networks that control differential gene expression at a systems level because: 1) it is a relatively simple animal. Its development occurs in a stringently programmed manner and the entire lineage of the 959 somatic cells in hermaphrodites has been described, which allows the unambiguous identification of temporal and spatial gene expression patterns; 2) the animal is transparent, which allows a researcher to follow development, phenotypic aberrations and gene expression patterns in real time using light microscopy; 3) it is a genetically tractable organism and many convenient genetic techniques have been developed that allow the molecular dissection of biological processes. Convenient reverse genetic techniques include the generation of transgenic animals for gene expression studies, and RNA mediated interference (RNAi) for the examination of loss-of-function phenotypes; 4) its complete genome sequence is available and ~19,000 protein-encoding genes have been predicted. Recently, we identified 934 predicted TFs among these protein-coding genes (Reece-Hoyes et al, 2005). 5) C. elegans has proven to be instrumental in understanding human biology because many genes, pathways and biochemical processes are highly conserved. For example, studies of both oncogenic Ras -and apoptotic pathways have been pioneered in C. elegans.

ONGOING PROJECTS

We recently developed a Gateway-compatible yeast one-hybrid (Y1H) system for the high-throughput identification of physical interactions between C. elegans TFs and their target genes (Deplancke et al, 2004). This system is compatible with ORFeome (~13,000 full length open reading frames) and promoterome (~6,500 promoters) resources that are available in our laboratory.

Transcription regulatory networks of the digestive tract - Bart Deplancke

Using high-throughput Y1H assays with a set of 110 digestive tract promoters, we identified hundreds of TF-target gene interactions. These interactions have been modeled into a network model and principles of differential gene expression are emerging. Using other systems, we aim to see the generality and/or specificity of these principles.

Neurons - Vanessa Vermeirssen

We are using the promoters of genes encoding TFs that are expressed in neurons in high-throughput Y1H assays to find upstream regulators. Questions we aim to address include: is the network of neuronal TFs fundamentally different or similar as that of digestive tract genes? Which TFs are found in both systems? How do they function - i.e. do they repress or activate transcription?

Fat storage and metabolism - Efsun Arda

TFs play a major role in fat storage and diabetes. We aim to unravel the networks by which these factors contribute to these processes. We intend to further investigate these networks in vivo using a combination of techniques, including fat assays in worms and RNAi.

MicroRNAs - Natalia Martinez

Together with TFs, microRNA are the main information processing units in metazoan organisms. We aim to identify the upstream regulators of miRNA expression and how these factors function in concert with the miRNAs themselves to regulate gene expression. More general questions include: are miRNA networks different than protein-coding gene networks? Are there special regulators of miRNA expression? Which TFs are targets of miRNAs?

Helix-loop-helix transcription factors - Christian Grove

There are many families of TFs in metazoan organisms, including C. elegans. These families are discriminated by their DNA binding domains. There are 42 predicted helix-loop-helix TFs in the worm. Questions we aim to address include: which bHLH dimers are formed and how does this change the actual number of functional bHLH TFs? Where are these dimers expressed? Based on these expression patterns, we hope to start defining the biological and biochemical function of bHLH TFs.

TF binding sites and network analysis - Inmaculada Barrasa

We aim to find TF binding sites using our Y1H data and published TF binding sites. We intend to validate these binding sites using a variety of experimental approaches. We are deriving hypotheses from the networks described above and are determining general and specific network properties of transcription regulatory networks in metazoan systems. To do so, we use a variety of computational approaches.

Phone: 508-856-4364
E-mail: Marian.Walhout@umassmed.edu
Keywords: Protein-DNA recognition, Systems Biology, Gene Expression, Developmental Biology

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