Genetic Approaches To Understanding Organ Development and Function

National Institute Of Diabetes And Digestive And Kidney Diseases

Investigators

Linked publications & trials

Abstract

Our research on developing computational tools and their use in predicting genetic programs resulted in two publications (PMID: 37132521, 37485422) and our studies on genetic control mechanisms executed by the JAK/STAT signaling pathway resulted in two publications, one on the metabolism of immune cells (PMID: 36427325) and one on immune cells and toxoplasmosis (PMID: 37559725). Hormonal control of genetic programs in the mammary gland and immune cells Our lab continues to investigate how hormones control genetic programs in the mammary gland that lead to a successful pregnancy and lactation. Our most recent studies identified regulatory elements that activate expression of a gene during pregnancy and lactation by more than 1000-fold. In other research, we have investigated the role of cytokines and JAK/STAT pathway in T cell metabolism(PMID: 36427325). When immune cells called lymphocytes become active, they change their metabolism to support their rapid growth and division. Certain proteins known as common gamma chain (c) family cytokines are important for this process, and downstream signaling through a protein called STAT5 is a crucial part of it. Through extensive analyses of genes, transcripts, and metabolites, researchers have discovered that STAT5 plays a central role in controlling energy and amino acid metabolism in a specific type of immune cells called CD4+ T helper cells. STAT5 helps regulate the genes that code for essential enzymes and transporters needed for metabolism. It does this by interacting with other proteins like p300 and affecting the cell's genetic structure. Additionally, STAT5 influences other metabolic regulators, namely the mTOR signaling pathway and the MYC transcription factor, which are also important for cell growth. The research indicates that STAT5 and MYC cooperate in controlling gene activity, which is significant for both normal and cancerous T cells. Overall, these findings provide insights into how T cell metabolism is controlled by cytokines and emphasize the role of the JAK-STAT pathway in cell growth and proliferation. We partnered with intramural and extramural scientists and investigated the contribution of cytokines in the immune response to toxoplasmosis (PMID: 37559725). During infections, the body's immune responses need to be controlled to prevent excessive damage. In a study using genetically modified mice, we found that a signaling protein called STAT1 is crucial for regulating inflammation during a Toxoplasma gondii infection. When STAT1 was absent only in T cells, the mice could eliminate the parasites but ended up succumbing to severe immune-related damage. This damage was characterized by abnormal immune responses, particularly an increase in Th1-type responses with less IL-10 and more IL-13 production. We also found that another signaling protein, STAT3, wasn't necessary for inducing IL-10 or suppressing IL-13 during this infection. We discovered that STAT1 and STAT3 work together at specific genetic locations to regulate IL-10 production and other factors that influence immune responses. This study deepens our understanding of how the body manages T cell responses during infections to prevent immune-related damage and provides insights into the anti-inflammatory role of STAT1, particularly in shaping the characteristics of certain immune responses (PMID: 37559725). Using computational tools to gain insight into genetic circuits In a collaborative study we developed a pipeline that predicts patterns of transcription factor binding to the genome (PMID: 37132521). In living organisms, the expression of genes is controlled by specific regions called cis-regulatory elements (CREs), which include promoters and enhancers. These CREs are bound by molecules called transcription factors (TFs). The way these TFs are expressed and how they interact with CREs determine how genes are turned on or off in different tissues and during development. Combining different sets of genomic data can help us understand how accessible these CREs are, how TFs are active, and thus how genes are regulated. However, working with these mixed datasets is challenging due to technical difficulties. While there are methods to identify differences in TF activity from combined data of chromatin states (like ChIP, ATAC, or DNase sequencing) and RNA sequencing, these methods are not very user-friendly, struggle with handling large amounts of data, and provide limited tools to understand the results visually. We created a tool called TF-Prioritizer that can automatically prioritize specific transcription factors (TFs) based on various types of data and then create interactive web reports. We showed its effectiveness by identifying both already-known TFs and their target genes, as well as new TFs that are active in the mammary glands of lactating mice. We also used a range of datasets from the ENCODE project for two cell lines, K562 and MCF-7. These datasets included different types of chromatin data, like histone modification ChIP sequencing, as well as ATAC and DNase sequencing data. We looked at differences in these datasets based on the type of experiment they came from. TF-Prioritizer (PMID: 37132521) is a computer tool that takes different types of genetic data from experiments and figures out which genes are controlled by specific molecules called transcription factors (TFs). This helps scientists understand how genes are turned on or off in different situations, like in diseases or normal processes, and could even help find new ways to treat illnesses. In a second study we partnered with scientists from Munich and developed a pipeline to identify circular RNAs (circRNAs) that have been hypothesized to contribute to gene control (PMID: 37485422). Circular RNAs (circRNAs) are a type of genetic material that doesnt code for proteins and is linked to diseases. Theyre being explored as possible indicators of diseases and for developing treatments. One of their possible functions is to act like sponges for tiny molecules called microRNAs (miRNAs), which normally stop certain genes from being active. But theres currently no organized way to study how well circRNAs can do this sponging job. We created a computer tool called circRNA-sponging that helps researchers study circular RNAs (circRNAs) and their interaction with microRNAs (miRNAs)21. This tool does several things: it identifies circRNAs from RNA sequencing data, measures their levels compared to their regular counterparts, analyzes how their expression changes, predicts where miRNAs bind to circRNAs, investigates how circRNAs might affect miRNA activity, builds networks of competing RNA molecules, and identifies potential circRNA markers for diseases. We tested this tool using brain tissue data, finding different types of circRNAs based on their miRNA binding characteristics. This tool is the first of its kind to systematically study circRNA-miRNA interactions using raw RNA sequencing and miRNA sequencing data, which helps us better understand the role of circRNAs in gene activity.