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Using a label-free proteomics method to identify differentially abundant proteins in closely related hypo- and hypervirulent clinical Mycobacterium tuberculosis Beijing isolates.

ABSTRACT: Although the genome of the Mycobacterium tuberculosis H37Rv laboratory strain has been available for over 10 years, it is only recently that genomic information from clinical isolates has been used to generate the hypothesis of virulence differences between different strains. In addition, the relationship between strains displaying differing virulence in an epidemiological setting and their behavior in animal models has received little attention. The potential causes for variation in virulence between strains, as determined by differential protein expression, have similarly been a neglected area of investigation. In this study, we used a label-free quantitative proteomics approach to estimate differences in protein abundance between two closely related Beijing genotypes that have been shown to be hyper- and hypovirulent on the basis of both epidemiological and mouse model studies. We were able to identify a total of 1668 proteins from both samples, and protein abundance calculations revealed that 48 proteins were over-represented in the hypovirulent isolate, whereas 53 were over-represented in the hypervirulent. Functional classification of these results shows that molecules of cell wall organization and DNA transcription regulatory proteins may have a critical influence in defining the level of virulence. The reduction in the presence of ESAT-6, other Esx-like proteins, and FbpD (MPT51) in the hypervirulent strain indicates that changes in the repertoire of highly immunogenic proteins can be a defensive process undertaken by the virulent cell. In addition, most of the previously well characterized gene targets related to virulence were found to be similarly expressed in our model. Our data support the use of proteomics as a complementary tool for genomic comparisons to understand the biology of M. tuberculosis virulence.

SUBMITTER: de Souza GA

PROVIDER: S-EPMC2984234 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Using a label-free proteomics method to identify differentially abundant proteins in closely related hypo- and hypervirulent clinical Mycobacterium tuberculosis Beijing isolates.

de Souza Gustavo A GA Fortuin Suereta S Aguilar Diana D Pando Rogelio Hernandez RH McEvoy Christopher R E CR van Helden Paul D PD Koehler Christian J CJ Thiede Bernd B Warren Robin M RM Wiker Harald G HG

Molecular & cellular proteomics : MCP 20100226 11

Although the genome of the Mycobacterium tuberculosis H37Rv laboratory strain has been available for over 10 years, it is only recently that genomic information from clinical isolates has been used to generate the hypothesis of virulence differences between different strains. In addition, the relationship between strains displaying differing virulence in an epidemiological setting and their behavior in animal models has received little attention. The potential causes for variation in virulence b ...[more]

PMID: 20190197

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Project description:IntroductionArsenic is a widely distributed environmental toxicant that can cause multi-tissue pathologies. Proteomic assays allow for the identification of biological processes modulated by arsenic in diverse tissue types.MethodThe altered abundance of proteins from HaCaT human keratinocyte cell line exposed to arsenic was quantified using a label-free LC-MS/MS mass spectrometry workflow. Selected proteomics results were validated using western blot and RT-PCR. A functional annotation analytics strategy that included visual analytical integration of heterogeneous data sets was developed to elucidate functional categories. The annotations integrated were mainly tissue localization, biological process and gene family.ResultThe abundance of 173 proteins was altered in keratinocytes exposed to arsenic; in which 96 proteins had increased abundance while 77 proteins had decreased abundance. These proteins were also classified into 69 Gene Ontology biological process terms. The increased abundance of transferrin receptor protein (TFRC) was validated and also annotated to participate in response to hypoxia. A total of 33 proteins (11 increased abundance and 22 decreased abundance) were associated with 18 metabolic process terms. The Glutamate--cysteine ligase catalytic subunit (GCLC), the only protein annotated with the term sulfur amino acid metabolism process, had increased abundance while succinate dehydrogenase [ubiquinone] iron-sulfur subunit, mitochondrial precursor (SDHB), a tumor suppressor, had decreased abundance.ConclusionA list of 173 differentially abundant proteins in response to arsenic trioxide was grouped using three major functional annotations covering tissue localization, biological process and protein families. A possible explanation for hyperpigmentation pathologies observed in arsenic toxicity is that arsenic exposure leads to increased iron uptake in the normally hypoxic human skin. The proteins mapped to metabolic process terms and differentially abundant are candidates for evaluating metabolic pathways perturbed by arsenicals.

Project description:Background. The Beijing family of Mycobacterium tuberculosis is dominant in countries in East Asia. Genomic polymorphisms are a source of diversity within the M.tuberculosis genome and may account for the variation of virulence among M.tuberculosis isolates. To date there are no studies that have examined the genomic composition of M.tuberculosis isolates from the high TB-burden country, Myanmar. Methodology/Principle findings. Twenty-two M.tuberculosis isolates from Myanmar were screened on whole-genome arrays containing genes from M.tuberculosis H37Rv, M.tuberculosis CDC1551 and M.bovis AF22197. Screening identified 198 deletions or extra regions in the clinical isolates compared to H37Rv. Twenty-two regions differentiated between Beijing and non-Beijing isolates and were verified by PCR on an additional 40 isolates. Six regions (Rv0071-0074 [RD105], Rv1572-1576c [RD149], Rv1585c-1587c[RD149], MT1798-Rv1755c [RD152], Rv1761c [RD152] and Rv0279c) were deleted in Beijing isolates, of which 4 (Rv1572-1576c, Rv1585c-1587c, MT1798-Rv1755c and Rv1761c) were variably deleted among ST42 isolates, indicating a closer relationship between the Beijing and ST42 lineages. The TbD1 region, Mb1582-Mb1583 was deleted in Beijing and ST42 isolates. One M.bovis gene of unknown function, Mb3184c was present in all isolates, except 11 of 13 ST42 isolates. The CDC1551 gene, MT1360 coding for a putative adenylate cyclase, was present in all Beijing and ST42 isolates (except 1). The pks15/1 gene, coding for a putative virulence factor, was intact in all Beijing and non-Beijing isolates, except in ST42 and ST53 isolates. Conclusion. This study describes previously unreported deletions/extra regions in Beijing and non-Beijing M.tuberculosis isolates. The modern and highly frequent ST42 lineage showed a closer relationship to the hypervirulent Beijing lineage than to the ancient non-Beijing lineages. The pks15/1 gene was disrupted only in modern non-Beijing isolates. This is the first report of an in-depth analysis on the genomic diversity of M.tuberculosis isolates from Myanmar. Data is also available from http://bugs.sgul.ac.uk/E-BUGS-66

Project description:While differences in human virulence have been reported across nontyphoidal Salmonella (NTS) serovars and associated subtypes, a rational and scalable approach to identify Salmonella subtypes with differential ability to cause human diseases is not available. Here, we used NTS serovar Saintpaul (S. Saintpaul) as a model to determine if metadata and associated whole-genome sequence (WGS) data in the NCBI Pathogen Detection (PD) database can be used to identify (i) subtypes with differential likelihoods of causing human diseases and (ii) genes and single nucleotide polymorphisms (SNPs) potentially responsible for such differences. S. Saintpaul SNP clusters (n = 211) were assigned different epidemiology types (epi-types) based on statistically significant over- or underrepresentation of human clinical isolates, including human associated (HA; n = 29), non-human associated (NHA; n = 23), and other (n = 159). Comparative genomic analyses identified 384 and 619 genes overrepresented among isolates in 5 HA and 4 NHA SNP clusters most significantly associated with the respective isolation source. These genes included 5 HA-associated virulence genes previously reported to be present on Gifsy-1/Gifsy-2 prophages. Additionally, premature stop codons in 3 and 7 genes were overrepresented among the selected HA and NHA SNP clusters, respectively. Tissue culture experiments with strains representing 4 HA and 3 NHA SNP clusters did not reveal evidence for enhanced invasion or intracellular survival for HA strains. However, the presence of sodCI (encoding a superoxide dismutase), found in 4 HA and 1 NHA SNP clusters, was positively correlated with intracellular survival in macrophage-like cells. Post hoc analyses also suggested a possible difference in intracellular survival among S. Saintpaul lineages. IMPORTANCE Not all Salmonella isolates are equally likely to cause human disease, and Salmonella control strategies may unintentionally focus on serovars and subtypes with high prevalence in source populations but are rarely associated with human clinical illness. We describe a framework leveraging WGS data in the NCBI PD database to identify Salmonella subtypes over- and underrepresented among human clinical cases. While we identified genomic signatures associated with HA/NHA SNP clusters, tissue culture experiments failed to identify consistent phenotypic characteristics indicative of enhanced human virulence of HA strains. Our findings illustrate the challenges of defining hypo- and hypervirulent S. Saintpaul and potential limitations of phenotypic assays when evaluating human virulence, for which in vivo experiments are essential. Identification of sodCI, an HA-associated virulence gene associated with enhanced intracellular survival, however, illustrates the potential of the framework and is consistent with prior work identifying specific genomic features responsible for enhanced or reduced virulence of nontyphoidal Salmonella.

Project description:Rifampicin (RIF) is one of the most important first-line anti-tuberculosis (TB) drugs, and more than 90% of RIF-resistant (RR) Mycobacterium tuberculosis clinical isolates belong to multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. In order to identify specific candidate target proteins as diagnostic markers or drug targets, differential protein expression between drug-sensitive (DS) and drug-resistant (DR) strains remains to be investigated. In the present study, a label-free, quantitative proteomics technique was performed to compare the proteome of DS, RR, MDR, and XDR clinical strains. We found iniC, Rv2141c, folB, and Rv2561 were up-regulated in both RR and MDR strains, while fadE9, espB, espL, esxK, and Rv3175 were down-regulated in the three DR strains when compared to the DS strain. In addition, lprF, mce2R, mce2B, and Rv2627c were specifically expressed in the three DR strains, and 41 proteins were not detected in the DS strain. Functional category showed that these differentially expressed proteins were mainly involved in the cell wall and cell processes. When compared to the RR strain, Rv2272, smtB, lpqB, icd1, and folK were up-regulated, while esxK, PPE19, Rv1534, rpmI, ureA, tpx, mpt64, frr, Rv3678c, esxB, esxA, and espL were down-regulated in both MDR and XDR strains. Additionally, nrp, PPE3, mntH, Rv1188, Rv1473, nadB, PPE36, and sseA were specifically expressed in both MDR and XDR strains, whereas 292 proteins were not identified when compared to the RR strain. When compared between MDR and XDR strains, 52 proteins were up-regulated, while 45 proteins were down-regulated in the XDR strain. 316 proteins were especially expressed in the XDR strain, while 92 proteins were especially detected in the MDR strain. Protein interaction networks further revealed the mechanism of their involvement in virulence and drug resistance. Therefore, these differentially expressed proteins are of great significance for exploring effective control strategies of DR-TB.

Dataset Information

Using a label-free proteomics method to identify differentially abundant proteins in closely related hypo- and hypervirulent clinical Mycobacterium tuberculosis Beijing isolates.

Publications

Using a label-free proteomics method to identify differentially abundant proteins in closely related hypo- and hypervirulent clinical Mycobacterium tuberculosis Beijing isolates.

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