team of scientists at the University of Illinois Chicago has developed a
software tool that can help researchers more efficiently identify the
regulators of genes. The system leverages a machine learning algorithm
to predict which transcription factors are most likely to be active in
factors are proteins that bind to DNA and control what 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 some
illnesses. However, there are hundreds of transcription factors inside human cells and
it can take years of research, often through trial and error, to
identify which are most active—those that are expressed, or "on"—in
different types of cells and that could be leveraged as drug targets.
of the challenges in the field is that the same genes may be turned
'on' in one group of cells but turned 'off' in a different group of
cells within the same organ," said Jalees Rehman, UIC professor in the
department of medicine and the department of pharmacology and
regenerative medicine at the College of Medicine. "Being able to
understand the activity of transcription factors in individual cells would
allow researchers to study activity profiles in all the major cell
types of major organs such as the heart, brain or lungs."
BITFAM, for Bayesian Inference Transcription Factor Activity Model, the
UIC-developed system works by combining new gene expression profile
data gathered from single cell RNA sequencing with existing biological
data on transcription factor target genes. With this information, the
system runs numerous computer-based simulations to find the optimal fit
and predict the activity of each transcription factor in the cell.
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 cells from lung, heart and
brain tissue. Information on the model and the results of their tests
are reported today in the journal Genome Research.
approach not only identifies meaningful transcription factor activities
but also provides valuable insights into underlying transcription
factor regulatory mechanisms," said Shang Gao, first author of the study
and a doctoral student in the department of bioengineering. "For
example, if 80% of a specific transcription factor's targets are turned
on inside the cell, that tells us that its activity is high. By
providing data like this for every transcription factor in the cell, the
model can give researchers a good idea of which ones to look at first
when exploring new drug targets to work on that type of cell."
researchers say that the new system is publicly available and could be
applied widely because users have the flexibility to combine it with
additional analysis methods that may be best suited for their studies,
such as finding new drug targets.
new approach could be used to develop key biological hypotheses
regarding the regulatory transcription factors in cells related to a
broad range of scientific hypotheses and topics. It will allow us to
derive insights into the biological functions of cells from many
tissues," Dai said.
whose research focuses on the mechanisms of inflammation in vascular
systems, says an application relevant to his lab is to use the new
system to focus on the transcription factors that drive diseases in specific cell types.
"For example, we would like to understand if there is transcription factor
activity that distinguished a healthy immune cell response from an
unhealthy one, as in the case of conditions such as COVID-19, heart
disease or Alzheimer's disease where there is often an imbalance between
healthy and unhealthy immune responses," he said.
More information: Shang Gao et al, A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes, Genome Research (2021). DOI: 10.1101/gr.265595.120