Prospective on Mycobacterium tuberculosis proteomics

Accurate Identification of Closely Related Mycobacterium tuberculosis Complex Species by High Resolution Tandem Mass Spectrometry

Rapid and accurate differentiation of Mycobacterium tuberculosis complex (MTBC) species from other mycobacterium is essential for appropriate therapeutic management, timely intervention for infection control and initiation of appropriate health care measures. However, routine clinical characterization methods for Mycobacterium tuberculosis (Mtb) species remain both, time consuming and labor intensive. In the present study, an innovative liquid Chromatography-Mass Spectrometry method for the identification of clinically most relevant Mycobacterium tuberculosis complex species is tested using a model set of mycobacterium strains. The methodology is based on protein profiling of Mycobacterium tuberculosis complex isolates, which are used as markers of differentiation. To test the resolving power, speed, and accuracy of the method, four ATCC type strains and 37 recent clinical isolates of closely related species were analyzed using this new approach. Using different deconvolution algorithms, we detected hundreds of individual protein masses, with a subpopulation of these functioning as species-specific markers. This assay identified 216, 260, 222, and 201 proteoforms for M. tuberculosis ATCC 27294™, M. microti ATCC 19422™, M. africanum ATCC 25420™, and M. bovis ATCC 19210™ respectively. All clinical strains were identified to the correct species with a mean of 95% accuracy. Our study successfully demonstrates applicability of this novel mass spectrometric approach to identify clinically relevant Mycobacterium tuberculosis complex species that are very closely related and difficult to differentiate with currently existing methods. Here, we present the first proof-of-principle study employing a fast mass spectrometry-based method to identify the clinically most prevalent species within the Mycobacterium tuberculosis species complex.

Interrogating Fractionation and Other Sources of Variability in Shotgun Proteomes Using Quality Metrics

The increasing amount of publicly available proteomics data creates opportunities for data scientists to investigate quality metrics in novel ways. QuaMeter IDFree is used to generate quality metrics from 665 RAW files and 97 WIFF files representing publicly available "shotgun" mass spectrometry datasets. These experiments are selected to represent Mycobacterium tuberculosis lysates, mouse MDSCs, and exosomes derived from human cell lines. Machine learning techniques are demonstrated to detect outliers within experiments and it is shown that quality metrics may be used to distinguish sources of variability among these experiments. In particular, the findings demonstrate that according to nested ANOVA performed on an SDS-PAGE shotgun principal component analysis, runs of fractions from the same gel regions cluster together rather than technical replicates, close temporal proximity, or even biological samples. This indicates that the individual fraction may have had a higher impact on the quality metrics than other factors. In addition, sample type, instrument type, mass analyzer, fragmentation technique, and digestion enzyme are identified as sources of variability. From a quality control perspective, the importance of study design and in particular, the run order, is illustrated in seeking ways to limit the impact of technical variability.

Recent insights into human bronchial proteomics - how are we progressing and what is next?

The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries. Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed. Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.

Identification of functional metabolic biomarkers from lung cancer patient serum using PEP technology

BACKGROUND

Reprogrammed metabolism is a new hallmark of cancer. In many types of cancer, most of the genes in the glycolytic pathway are overexpressed, reflecting an essential shift of metabolism during cancer development. The reprogrammed metabolism contributes to cancer development in multiple ways, from supplying the elevated energy requirement to creating a microenvironment suitable for tumor growth and suppressing the human immune surveillance system.

METHOD

In this study, a functional proteomics top-down approach was used to systematically monitor metabolic enzyme activities in resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP.

RESULTS

We found that the enrichment of low abundance proteins with a bead based product called AlbuVoid™(,) is important to increase the number of observable features and to increase the level of signal achievable from the assay used. From our methods, significant metabolic enzyme activities were detected in both normal and lung cancer patient sera in many fractions after the elution of the 2-D gel separated proteins to the Protein Elution Plate (PEP). Eighteen fractions with the most dramatic metabolic enzyme activity difference between the normal and lung cancer patient sera were submitted for mass spectrometry protein identification. Proteins from the glycolytic metabolic pathway, such as GAPDH along with other proteins not previously annotated to the glycolytic pathway were identified. Further verification with commercially purified GAPDH showed that the addition of purified GAPDH to the metabolic enzyme assay system employed enhanced the enzyme activity, demonstrating that proteins identified from the PEP technology and mass spectrometry could be further verified with biological assay.

CONCLUSION

This study identified several potential functional enzyme biomarkers from lung cancer patient serum, it provides an alternative and complementary approach to sequence annotation for the discovery of biomarkers in human diseases.

Mycobacterial proteomics: analysis of expressed proteomes and post-translational modifications to identify candidate virulence factors

The Mycobacterium tuberculosis bacillus has a number of unique features that make it a particularly effective human pathogen. Although genomic analysis has added to our current understanding of the molecular basis by which M. tuberculosis damages its host, proteomics may be better suited to describe the dynamic interactions between mycobacterial and host systems that underpin this disease. The M. tuberculosis proteome has been investigated using proteomics for over a decade, with increasingly sophisticated mass spectrometry technology and sensitive methods for comparative proteomic profiling. Deeper coverage of the M. tuberculosis proteome has led to the identification of hundreds of putative virulence determinants, as well as an unsurpassed coverage of post-translational modifications. Proteomics is therefore uniquely poised to contribute to our understanding of this pathogen, which may ultimately lead to better management of the disease.

Mycobacterium tuberculosis proteome microarray for global studies of protein function and immunogenicity

Poor understanding of the basic biology of Mycobacterium tuberculosis (MTB), the etiological agent of tuberculosis, hampers development of much-needed drugs, vaccines, and diagnostic tests. Better experimental tools are needed to expedite investigations of this pathogen at the systems level. Here, we present a functional MTB proteome microarray covering most of the proteome and an ORFome library. We demonstrate the broad applicability of the microarray by investigating global protein-protein interactions, small-molecule-protein binding, and serum biomarker discovery, identifying 59 PknG-interacting proteins, 30 bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP) binding proteins, and 14 MTB proteins that together differentiate between tuberculosis (TB) patients with active disease and recovered individuals. Results suggest that the MTB rhamnose pathway is likely regulated by both the serine/threonine kinase PknG and c-di-GMP. This resource has the potential to generate a greater understanding of key biological processes in the pathogenesis of tuberculosis, possibly leading to more effective therapies for the treatment of this ancient disease.

Biofragments: an approach towards predicting protein function using biologically related fragments and its application to Mycobacterium tuberculosis CYP126

We present a novel fragment-based approach that tackles some of the challenges for chemical biology of predicting protein function. The general approach, which we have termed biofragments, comprises two key stages. First, a biologically relevant fragment library (biofragment library) can be designed and constructed from known sets of substrate-like ligands for a protein class of interest. Second, the library can be screened for binding to a novel putative ligand-binding protein from the same or similar class, and the characterization of hits provides insight into the basis of ligand recognition, selectivity, and function at the substrate level. As a proof-of-concept, we applied the biofragments approach to the functionally uncharacterized Mycobacterium tuberculosis (Mtb) cytochrome P450 isoform, CYP126. This led to the development of a tailored CYP biofragment library with notable 3D characteristics and a significantly higher screening hit rate (14%) than standard drug-like fragment libraries screened previously against Mtb CYP121 and 125 (4% and 1%, respectively). Biofragment hits were identified that make both substrate-like type-I and inhibitor-like type-II interactions with CYP126. A chemical-fingerprint-based substrate model was built from the hits and used to search a virtual TB metabolome, which led to the discovery that CYP126 has a strong preference for the recognition of aromatics and substrate-like type-I binding of chlorophenol moieties within the active site near the heme. Future catalytic analyses will be focused on assessing CYP126 for potential substrate oxidative dehalogenation.

The Mtb proteome library: a resource of assays to quantify the complete proteome of Mycobacterium tuberculosis

Research advancing our understanding of Mycobacterium tuberculosis (Mtb) biology and complex host-Mtb interactions requires consistent and precise quantitative measurements of Mtb proteins. We describe the generation and validation of a compendium of assays to quantify 97% of the 4,012 annotated Mtb proteins by the targeted mass spectrometric method selected reaction monitoring (SRM). Furthermore, we estimate the absolute abundance for 55% of all Mtb proteins, revealing a dynamic range within the Mtb proteome of over four orders of magnitude, and identify previously unannotated proteins. As an example of the assay library utility, we monitored the entire Mtb dormancy survival regulon (DosR), which is linked to anaerobic survival and Mtb persistence, and show its dynamic protein-level regulation during hypoxia. In conclusion, we present a publicly available research resource that supports the sensitive, precise, and reproducible quantification of virtually any Mtb protein by a robust and widely accessible mass spectrometric method.

Database resources for the tuberculosis community

Access to online repositories for genomic and associated "-omics" datasets is now an essential part of everyday research activity. It is important therefore that the Tuberculosis community is aware of the databases and tools available to them online, as well as for the database hosts to know what the needs of the research community are. One of the goals of the Tuberculosis Annotation Jamboree, held in Washington DC on March 7th-8th 2012, was therefore to provide an overview of the current status of three key Tuberculosis resources, TubercuList (tuberculist.epfl.ch), TB Database (www.tbdb.org), and Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org). Here we summarize some key updates and upcoming features in TubercuList, and provide an overview of the PATRIC site and its online tools for pathogen RNA-Seq analysis.

MycoProtease-DB: Useful resource for Mycobacterium tuberculosis complex and nontuberculous mycobacterial proteases

MycoProtease-DB is an online MS SQL and CGI-PERL driven relational database that domiciles protease information of Mycobacterium tuberculosis (MTB) complex and Nontuberculous Mycobacteria (NTM), whose complete genome sequence is available. Our effort is to provide comprehensive information on proteases of 5 strains of Mycobacterium tuberculosis (H(37)Rv, H(37)Ra, CDC1551, F11 and KZN 1435), 3 strains of Mycobacterium bovis (AF2122/97, BCG Pasteur 1173P2 and BCG Tokyo 172) and 4 strains of NTM (Mycobacterium avium 104, Mycobacterium smegmatis MC2 155, Mycobacterium avium paratuberculosis K-10 and Nocardia farcinica IFM 10152) at gene, protein and structural level. MycoProtease-DB currently hosts 1324 proteases, which include 906 proteases from MTB complex with 237distinct proteases & 418 from NTM with 404 distinct proteases. Flexible database design and easy expandability & retrieval of information are the main features of MycoProtease-DB. All the data were validated with various online resources and published literatures for reliable serving as comprehensive resources of various Mycobacterial proteases.

AVAILABILITY

The Database is publicly available at http://www.bicjbtdrc-mgims.in/MycoProtease-DB/