New software tool can help identify gene regulators more efficiently



A team of scientists from the University of Illinois at Chicago have developed a software tool that can help researchers identify gene regulators more effectively. The system uses a machine learning algorithm to predict which transcription factors are most likely to be active in individual cells.

Transcription factors are proteins that bind to DNA and control which genes are turned on or off inside a cell. These proteins are relevant to biomedical researchers, because understanding and manipulating these signals in the cell can be an effective way to discover new treatments for certain diseases. However, there are hundreds of transcription factors inside human cells and it can take years of research, often by trial and error, to identify which ones are most active – which ones are expressed, or “on”. – in different types of cells and which could be exploited as drug targets.

One of the challenges in the field is that the same genes can be turned on in a group of cells but turned off in a different group of cells within the same organ. Being able to understand the activity of transcription factors in individual cells would allow researchers to study activity profiles in all major cell types of major organs such as the heart, brain or lungs.. “

Jalees Rehman, Professor in the Department of Medicine and Pharmacology and Regenerative Medicine, College of Medicine, University of Illinois at Chicago

Named BITFAM, for Bayesian Inference Transcription Factor Activity Model, the system developed by UIC works by combining new gene expression profile data collected from single-cell RNA sequencing with existing biological data on the target genes of the factors. transcription. With this information, the system runs numerous computer simulations to find the optimal fit and predict the activity of each transcription factor in the cell.

The UIC researchers, co-led by Rehman and Yang Dai, UIC associate professor in the department of bioengineering at the College of Medicine and the College of Engineering, tested the system in lung, heart and brain cells. Information on the model and the results of their tests are published today in the journal Genome Research.

“Our approach not only identifies significant activities of transcription factors, but also provides valuable information on the regulatory mechanisms of underlying transcription factors,” said Shang Gao, lead author of the study and doctoral student in the department of bioengineering. “For example, if 80% of the targets of a specific transcription factor are activated inside the cell, this tells us that its activity is high. By providing data like this for every transcription factor in the cell. cell, the model can give researchers a good idea which ones to look at first when exploring new drug targets to work on this type of cell. “

Researchers say the new system is publicly available and could be widely applied as users have the flexibility to combine it with additional analytical methods that may be best suited for their studies, such as finding new drug targets. .

“This new approach could be used to develop key biological hypotheses regarding regulatory transcription factors in cells related to a wide range of hypotheses and scientific subjects. It will allow us to better understand the biological functions of cells in many tissues” , said Dai. .

Rehman, whose research focuses on the mechanisms of inflammation in vascular systems, says a relevant application for his lab is to use the new system to focus on transcription factors that lead to disease in cell types. specific.

“For example, we would like to understand if there is transcription factor activity that distinguishes a healthy immune cellular response from an unhealthy response, as in conditions such as COVID-19, heart disease, or disease. Alzheimer’s where there is often an imbalance between healthy and unhealthy immune responses, ”he said.


Journal reference:

Gao, S., et al. (2021) A model of Bayesian inference transcription factor activity for the analysis of unicellular transcriptomes. Genome research.



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