Microbial Proteome Profiling and Systems Biology: Applications to Mycobacterium tuberculosis

2015 ◽  
pp. 235-254 ◽  
Author(s):  
Olga T. Schubert ◽  
Ruedi Aebersold
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Proteome Profiling

Social networks to biological networks: systems biology of Mycobacterium tuberculosis

2013 ◽  
Vol 9 (7) ◽  
pp. 1584 ◽  
Author(s):  
Rohit Vashisht ◽  
Anshu Bhardwaj ◽  
OSDD Consortium ◽  
Samir K. Brahmachari
Keyword(s):  
Social Networks ◽  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Biological Networks

scholarly journals Cytosolic Proteome Profiling of Aminoglycosides Resistant Mycobacterium tuberculosis Clinical Isolates Using MALDI-TOF/MS

2016 ◽  
Vol 7 ◽  
Author(s):  
Divakar Sharma ◽  
Manju Lata ◽  
Rananjay Singh ◽  
Nirmala Deo ◽  
Krishnamurthy Venkatesan ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Clinical Isolates ◽  
Maldi Tof Ms ◽  
Proteome Profiling ◽  
Maldi Tof ◽  
Tof Ms

scholarly journals Systems biology of the metabolism of Mycobacterium tuberculosis

2010 ◽  
Vol 38 (5) ◽  
pp. 1286-1289 ◽  
Author(s):  
Dany J.V. Beste ◽  
Johnjoe McFadden
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Experimental Studies ◽  
Public Health Problem ◽  
System Level ◽  
Computational Systems Biology ◽  
A Genome ◽  
Approaches To Study ◽  
Genome Scale ◽  
Computational Systems

Despite decades of research, many aspects of the biology of Mycobacterium tuberculosis remain unclear, and this is reflected in the antiquated tools available to treat and prevent tuberculosis and consequently this disease remains a serious public health problem. Important discoveries linking the metabolism of M. tuberculosis and pathogenesis has renewed interest in this area of research. Previous experimental studies were limited to the analysis of individual genes or enzymes, whereas recent advances in computational systems biology and high-throughput experimental technologies now allows metabolism to be studied on a genome scale. In the present article, we discuss the progress being made in applying system-level approaches to study the metabolism of this important pathogen.

scholarly journals Systems Biology-Based Identification of Mycobacterium tuberculosis Persistence Genes in Mouse Lungs

mBio ◽  
2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Noton K. Dutta ◽  
Nirmalya Bandyopadhyay ◽  
Balaji Veeramani ◽  
Gyanu Lamichhane ◽  
Petros C. Karakousis ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Biological Networks ◽  
Drug Targets ◽  
Experimental Testing ◽  
Data Types ◽  
Term Survival ◽  
Content Type ◽  
Mouse Lungs

ABSTRACTIdentifyingMycobacterium tuberculosispersistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predictM. tuberculosisgenes required for long-term survival in mouse lungs. As the input, we used high-throughputM. tuberculosismutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set ofM. tuberculosisgenes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required forM. tuberculosispersistencein vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidatingM. tuberculosispersistence genes.IMPORTANCEMycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and elucidate mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15% of the ~4,000M. tuberculosisgenes have been tested experimentally. In this paper, as an alternative to brute force experimental screens, we describe computational methods that predict new persistence genes by combining known examples with growing databases of biological networks. Experimental testing demonstrated that these predictions are highly accurate, validating the computational approach and providing new information aboutM. tuberculosispersistence in host tissues. Using the new experimental results as additional input highlights additional genes for testing. Our approach can be extended to other data types and target organisms to characterize host-pathogen interactions relevant to this and other infectious diseases.

scholarly journals Gene expression models based on a reference laboratory strain are bad predictors of Mycobacterium tuberculosis complex transcriptional diversity

2016 ◽  
Author(s):  
Alvaro Chiner-Oms ◽  
Fernando González-Candelas ◽  
Iñaki Comas
Keyword(s):  
Gene Expression ◽  
Mycobacterium Tuberculosis ◽  
Transcription Factors ◽  
Systems Biology ◽  
Computational Models ◽  
Parallel Evolution ◽  
Laboratory Strain ◽  
Reference Laboratory ◽  
Different Genetic Background

ABSTRACTSpecies of the Mycobacterium tuberculosis complex (MTBC) kill more people every year than any other infectious disease. As a consequence of its global distribution and parallel evolution with the human host the bacteria is not genetically homogeneous. The observed genetic heterogeneity has relevance at different phenotypic levels, from gene expression to epidemiological dynamics. However current systems biology datasets have focused in the laboratory reference strain H37Rv. By using large expression datasets testing the role of almost two hundred transcription factors, we have constructed computational models to grab the expression dynamics of Mycobacterium tuberculosis H37Rv genes. However, we have found that many of those transcription factors are deleted or likely dysfunctional across strains of the MTBC. In accordance, we failed to predict expression changes in strains with a different genetic background when compared with experimental data. The results highlight the importance of designing systems biology approaches that take into account the tubercle bacilli, or any other pathogen, genetic diversity if we want to identify universal targets for vaccines, diagnostics and treatments.

scholarly journals The Use of Functional Genomics in Conjunction with Metabolomics forMycobacterium tuberculosisResearch

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Conrad C. Swanepoel ◽  
Du Toit Loots
Keyword(s):  
Infectious Disease ◽  
Drug Resistance ◽  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Functional Genomics ◽  
The Past ◽  
Holistic Understanding ◽  
Transcriptomics Data ◽  
Genomic Studies ◽  
Complex Genome

Tuberculosis (TB), caused byMycobacterium tuberculosis, is a fatal infectious disease, resulting in 1.4 million deaths globally per annum. Over the past three decades, genomic studies have been conducted in an attempt to elucidate the functionality of the genome of the pathogen. However, many aspects of this complex genome remain largely unexplored, as approaches like genomics, proteomics, and transcriptomics have failed to characterize them successfully. In turn, metabolomics, which is relatively new to the “omics” revolution, has shown great potential for investigating biological systems or their modifications. Furthermore, when these data are interpreted in combination with previously acquired genomics, proteomics and transcriptomics data, using what is termed a systems biology approach, a more holistic understanding of these systems can be achieved. In this review we discuss how metabolomics has contributed so far to characterizing TB, with emphasis on the resulting improved elucidation ofM. tuberculosisin terms of (1) metabolism, (2) growth and replication, (3) pathogenicity, and (4) drug resistance, from the perspective of systems biology.

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