Tuberculosis research and drug discovery at Michigan State University
The Abramovitch lab is researching how M. tuberculosis (Mtb) senses and adapts to environmental cues encountered by the bacterium during the course of disease. We hypothesize that disrupting the ability of Mtb to adapt to its surroundings may alter the fitness of the bacterium and reduce its virulence.
By identifying compounds and genes that interfere with Mtb environmental adaptation, we may discover new drugs or drug targets that can function to shorten the course of tuberculosis therapy or act against multi-drug resistant tuberculosis.
Chemical Biology and Genetics of Mtb pH-driven Adaptation
Mycobacterium tuberculosis (Mtb) uses environmental pH as a cue to adapt its physiology for survival in macrophages. The phoPR two-component regulatory system functions to sense environmental pH and promote Mtb pathogenesis. We have engineered a pH-inducible biosensor in Mtbthat exhibits inducible fluorescence in response to acidic pH in vitro and in macrophages. We have used this biosensor to screen >280,000 compounds for inhibition of Mtb pH-driven adaptation. This screen discovered that ethoxzolamide (ETZ), an FDA approved carbonic anhydrase (CA) inhibitor, down-regulated PhoPR signaling and inhibited Mtb survival in macrophages and mice (Johnson et al., 2015). We are now defining the links between CA, pH and PhoPR signaling. We have also discovered that PhoPR, specific carbon sources and several newly identified genes regulate Mtb growth rate, persistence and antibiotic tolerance at acidic pH (Baker et al., 2014; Baker and Abramovitch, 2018). In a chemical biology screen for inhibitors of Mtb survival at acidic pH, we found that Mtb is highly sensitive to thiol stress at acidic pH (Coulson et al., 2017). Our ongoing work is focused on characterizing these newly discovered small molecules and genes.
Chemical Biology of Hypoxia-driven Adaptation
Mtb uses hypoxia as a cue to adapts its physiology to survive while trapped inside the granuloma. The dosRST two-component regulatory system functions to sense hypoxia and nitric oxide and promote Mtbpersistence. We have engineered a hypoxia-inducible biosensor in Mtb that exhibits inducible fluorescence in response to hypoxia and nitric oxide in vitro, in macrophages and in mice. We have used this biosensor to screen >600,000 compounds for inhibition of Mtb hypoxia-driven adaptation. This screen successfully identified artemisinin and five other compounds as inhibitors of DosRST signaling, persistence and antibiotic tolerance (Zheng et al., 2017). Efforts are underway to further characterize these compounds and optimize them for studies in vivo.
New Molecules to Inhibit Mtb Pathogenesis and Growth
Our lab has screened over 1 million compounds for inhibition of Mtb growth and we are actively characterizing several new compounds that: 1) Kill Mtb at acidic pH, 2) Kill Mtb at both acidic and neutral pH and 3) selectively inhibit Mtb growth inside macrophages. These newly discovered chemical probes will enable the identification of druggable pathways that are essential for Mtb pathogenesis. Several of these molecules are examples of anti-virulence therapies as described in our recently published review (Johnson et al., 2017).
Research in the Abramovitch lab is funded by the National Institutes of Health, the Bill and Melinda Gates Foundation, the Michigan Initiative for Innovation and Entrepreneurship, the MSU Foundation, the Jean P. Schultz Biomedical Research Fund, and start-up funding from Michigan State University and AgBioResearch.
SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis
This is SPARTA, a turnkey computational workflow application for bacterial reference-based RNA sequencing (RNA-seq). SPARTA also checks for the possibility of batch effects in samples. Batch effects can significantly skew the results of the data analysis, leading to inappropriate experimental conclusions. The innovation of the SPARTA workflow is the combination of several open-source tools for reference-based bacterial RNA-seq in a user-friendly, platform-independent manner.
This step-by-step tutorial (download file) provides a workflow for processing RNA-seq data using the MSU High Performance Computing Cluster (HPCC) and analyzing data using HTSeq and DESeq software packages.
Topics covered include:
1) Managing files on the HPCC; 2) QC and trimming reads using Trimmomatic; 3) Mapping the reads to the genome using Bowtie; 4) Counting mapped reads using HTSeq; and 5) Differential gene expression analysis using DESeq.