Speeding Up Drug Discovery to Fight Tuberculosis

Speeding Up Drug Discovery to Fight Tuberculosis

Seattle researchers created a genetic blueprint of the cunning tuberculosis bacteria, then used it to predict and rank potential drug targets

  • Researchers at the Institute for Systems Biology and Center for Infectious Disease Research have deciphered how the human pathogen Mycobacterium tuberculosis is able to tolerate the recently approved FDA drug
  • The study demonstrated that silencing certain regulatory genes in the bacteria, or pairing with a second drug pretomanid, disrupts a tolerance gene network to improve efficacy of killing by bedaquiline.
  • This systems-approach to rational drug discovery represents significant advance in the fight against tuberculosis, which affects a third of the global population, surpassing HIV/AIDS in the number of deaths worldwide.

By Eliza Peterson
The rise in multi-drug resistant (MDR) and extremely drug resistant (XDR) strains of Mycobacterium tuberculosis (MTB) is becoming a major cause of global health concern for treating tuberculosis, which affects a third of the global population. In fact, the number of worldwide deaths caused by tuberculosis has surpassed HIV/AIDS, and there is greater sense of urgency than ever before to find effective drug cocktails to outsmart MTB.
In a landmark study published on June 6, 2016, in Nature Microbiology, researchers at Institute for Systems Biology and the Center for Infectious Disease Research in Seattle demonstrated a systems biology approach that has the potential to rationally predict combinations of drugs that will disrupt tolerance networks in MTB making it even most susceptible to antibiotic therapy.
“The incredibly large number of possible drug combinations taken together with the difficulty of growing MTB in the laboratory make discovery of effective combination therapy extremely challenging. We hope that our systems-based strategy will accelerate TB drug discovery by helping researchers prioritize combinations that are more likely to be effective,” said Nitin Baliga, of Institute for Systems Biology and the senior author on the paper.
The success of MTB is largely due to its ability to alter gene expression to counteract host defense and anti-tubercular drug treatment. An extended period of tolerance gives MTB a window of opportunity to mutate and evolve longer term resistance.
Previously, researchers in the Baliga lab at ISB and in the Sherman lab at CIDR published a genome-wide regulatory network model that could predict how MTB senses and responds to changes in its environment, including anti-tubercular treatment.
In this study, they used the network model to understand how MTB tolerates killing by the drug bedaquiline, which in 2012 was the first drug in 40 years to be approved by the FDA. They used this network-enabled knowledge to find a second drug (pretomanid) to counteract tolerance against bedaquiline.
They went on to demonstrate that making the tolerance network hyperactive abolished the effectiveness of the combination therapy, confirming the mechanism of combined action of bedaquiline and pretomanid. The success of this systems biology-based method to find drug combinations has the potential to revolutionize and rapidly accelerate efforts towards TB drug discovery.
READ MORE ABOUT ISB’S RESEARCH ON TUBERCULOSIS
Funding was provided by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (U19 AI10676, U19 AI111276 and ISBpilot-10135) and the National Institute of General Medical Sciences of the National Institutes of Health (P50GM076547).

Journal: Nature Microbiology
Title: Network analysis identifies Rv0324 and Rv0880 as regulators of bedaquiline tolerance in Mycobacterium tuberculosis
Authors: Eliza J. R. Peterson, Shuyi Ma, David R. Sherman, Nitin Baliga
Link: nature.com/articles/nmicrobiol201678

miRvestigator Framework

miRvestigator Framework

http://mirvestigator.systemsbiology.net/

The miRvestigator framework is designed to take as input a list of co-expressed genes and will return the most likely miRNA regulating these genes. It does this by searching for an over-represented sequence motif in the 3' untranslated regions (UTRs) of the genes using Weeder and then comparing this to the miRNA seed sequences in miRBase using our custom built miRvestigator hidden Markov model (HMM).

References

Cancer miRNA Regulatory Network

Cancer miRNA Regulatory Network

http://cmrn.systemsbiology.net/

The cancer miRNA regulatory network was constructed by inferring miRNA mediated regulation for 2,240 gene co-expression signatures from 46 cancer transcriptome profiling studies. The cancer miRNA regulatory network can be used to identify clinically relevant miRNAs that are candidates for improved diagnostics, prognostics and therapeutics.

References

EGRIN2 Portal

EGRIN2 Portal

http://egrin2.systemsbiology.net/

EGRIN 2.0 is a systems-level model that delineates the complex relationship between environment, gene regulation, and phenotype in prokaryotes

Why EGRIN 2.0?

A foremost challenge in systems biology is to understand how just a few transcription factors (TFs) in a microbial genome generate a wide array of nuanced responses to varied environmental challenges. EGRIN 2.0 is a new model for the complete gene regulatory network (GRN) of a prokaryote. This model is reverse engineered directly from gene expression data and genomic sequence, and hence the methodology to generate EGRIN 2.0 is applicable to any prokaryotic organism.

References

MeDiChI

MeDiChI

The MeDiChI Model-Based ChIP-chip Deconvolution Algorithm

This is the download and instruction page for the MeDiChI software in support of the Bioinformatics manuscript

Please cite this publication if you utilize this package for your published research.

MeDiChI is method for the automated, model-based deconvolution of protein-DNA binding (Chromatin immunoprecipitation followed by hybridization to a genomic tiling microarray — ChIP-chip) data that discovers DNA binding sites at high resolution (higher resolution than that of the tiling array itself). This enables more stringent analysis of the functional binding (including regulated genes and DNA binding motifs), than would be possible using standard procedures for enrichment detection. The procedure uses a generative model of protein-DNA binding sites, and a linear model of the cumulative effect of those sites on the intensity of microarray probes. It uses constrained linear regression and L1 shrinkage to estimate the parameters of the linear model, which correspond to the high-resolution locations and intensities of the binding peaks. Finally a bootstrap is used to estimate the uncertainties and significance of each binding site.

We have developed a MeDiChI R package (including all functions for analysis and visualization, and all novel data presented in the manuscript).

Source Code

Please visit our Github repository for downloads, source code, installation instructions, and basic usage.

Publications

Inferelator

Inferelator

The Inferelator is an algorithm for infering predictive regulatory networks from gene expression data.

It does so by selecting the regulators (transcription factors or environmental factors) whose levels are most predictive of each gene or bicluster's expression (see cMonkey for more information). Using linear regression, L1 shrinkage and model selection via the LASSO coupled with 10-fold cross validation to strictly enforce parsimony and avoid overfitting, the method fits a multivariate kinetic model of gene expression that includes a sigmoidal activation model via the logistic function and mean decay rate parameter (τ). The model allows for the simultaneously fitting of time-course (τ/Δt > 0) and steady-state (τ/Δt ≈ 0) data, and was chosen from the class of generalized linear models to allow for fast parameter estimation and cross-validation. In addition, we developed a simple way of incorporating a generalized-linear extension of pairwise-logical interactions (AND, OR, XOR) between predictors using the functions min and max (which mimics physical chemistry derivations of logical interactions).

Thus, our generalized-linear dynamical network model cleanly incorporates some details of kinetic models, while maintaining the simplicity, flexibility, and robustness of linear and boolean models.

When integrated with cMonkey, it can also pair potential regulators with their putative cis-elements (DNA binding sites). We used the Inferelator to learn the global regulatory network of H. salinarum NRC-1.

Source Code

Publication

Visualize and explore the Halobacterium regulatory influence network.