This week we profile a recent publication in Cell Reports from the lab of Dr. Nitin Baliga (pictured, left) at ISB with first author Dr. Eliza Peterson (right).
Can you provide a brief overview of your lab’s current research focus?
My lab works on a wide range of complex problems in climate change and environmental sustainability, personalized medicine in cancers, and also in global health and infectious disease, especially tuberculosis. We use a systems approach to study all of these complex problems. In this particular study we used a systems approach that leveraged deep learning techniques to model gene networks responsible for mediating environmental and host adaptation of the tuberculosis pathogen Mycobacterium tuberculosis (Mtb). This network model (called EGRIN2) has provided unprecedented insights into mechanisms by which the pathogen adapts to disparate microenvironments in the host. As such, this model is a gold mine to discover mechanisms by which the pathogen is able to escape antimicrobial treatment to gain resistance.
What is the significance of the findings in this publication?
TB is the biggest infectious disease killer in the world, killing >1.5 million people annually. While TB treatment is very effective, it takes a long time (4 drugs given in varied combinations over six months), and about 5% of patients are not fully cured and may relapse. Some go on to develop drug resistance. Multidrug resistant (MDR) or extremely drug resistant (XDR) TB is even harder to treat, requiring up to 10 drugs given daily for up to 30 months. And even after this long treatment regimen only 56% are cured. The drugs are also very toxic with significant side effects (hepatotoxicity, neurotoxicity, etc.). Over 650K patients die of drug resistant TB annually, and there is a desperate need to find a novel treatment regimen that is more effective against all strains of Mtb.
As I’ve noted above, the TB pathogen is remarkably adept at escaping the host immune system and antimicrobial treatment. One of the key reasons why it is so successful is because it is able to generate heterogeneous populations of cells that are in distinct physiological states depending on the host microenvironment. In fact, the pathogen can dynamically transition from one state to another, which allows it to run and hide, each time it encounters a stressful situation, such as what you might expect during host immune attack or antibiotic treatment. Using the EGRIN2 model, we have discovered how during this adaptation process the pathogen controls cell division, while it remodels its cell wall, using it as an armor against small molecules, such as antimicrobials. Specifically, we have discovered how a transcription factor called MtrA acts in specific host microenvironments to regulate cell division and remodel the Mtb cell wall. When the MtrA network is disrupted it literally breaches the Mtb defense system potentially by making the cell wall porous, which both decreases the viability of the pathogen and makes it incredibly more sensitive to anti-TB drugs.
What are the next steps for this research?
We have mined the EGRIN2 model to discover several vulnerabilities like MtrA in the Mtb gene network. Our next steps are to continue characterizing these mechanistic (molecular and structural) details of these vulnerabilities. We are doing this right now in another Gates Foundation supported effort, where we are working towards finding small molecules that could serve as potential drugs against these mechanisms. Transcription factors, like MtrA , have historically proved to be particularly difficult to drug, but there are modern technologies including DNA-encoded small molecule library screens that could help us overcome some of these legacy challenges. If we are able to do so, then this might represent a novel class of TB drugs, which would substantially expand our arsenal of agents to fight this deadly pathogen.
This research is funded by: NIH (NIAID), Bill and Melinda Gates Foundation
Photo courtesy of ISB
