Literature DB >> 27093108

Proteogenomics of Candida tropicalis--An Opportunistic Pathogen with Importance for Global Health.

Keshava K Datta1,2, Arun H Patil1,2, Krishna Patel1,3, Gourav Dey1,4, Anil K Madugundu1,5, Santosh Renuse1,3, Jyothi E Kaviyil6, Raja Sekhar1,5, Aryashree Arunima2, Bhavna Daswani7, Inderjeet Kaur8, Jyotirmaya Mohanty9, Ranjana Sinha10, Sangeeta Jaiswal2, S Sivapriya11, Yeshwanth Sonnathi12, Bharat B Chattoo13, Harsha Gowda1,2,14, Raju Ravikumar6, T S Keshava Prasad1,14,15.   

Abstract

The frequency of Candida infections is currently rising, and thus adversely impacting global health. The situation is exacerbated by azole resistance developed by fungal pathogens. Candida tropicalis is an opportunistic pathogen that causes candidiasis, for example, in immune-compromised individuals, cancer patients, and those who undergo organ transplantation. It is a member of the non-albicans group of Candida that are known to be azole-resistant, and is frequently seen in individuals being treated for cancers, HIV-infection, and those who underwent bone marrow transplantation. Although the genome of C. tropicalis was sequenced in 2009, the genome annotation has not been supported by experimental validation. In the present study, we have carried out proteomics profiling of C. tropicalis using high-resolution Fourier transform mass spectrometry. We identified 2743 proteins, thus mapping nearly 44% of the computationally predicted protein-coding genes with peptide level evidence. In addition to identifying 2591 proteins in the cell lysate of this yeast, we also analyzed the proteome of the conditioned media of C. tropicalis culture and identified several unique secreted proteins among a total of 780 proteins. By subjecting the mass spectrometry data derived from cell lysate and conditioned media to proteogenomic analysis, we identified 86 novel genes, 12 novel exons, and corrected 49 computationally-predicted gene models. To our knowledge, this is the first high-throughput proteomics study of C. tropicalis validating predicted protein coding genes and refining the current genome annotation. The findings may prove useful in future global health efforts to fight against Candida infections.

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Year:  2016        PMID: 27093108      PMCID: PMC4840825          DOI: 10.1089/omi.2015.0197

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  20 in total

1.  Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

Authors:  A Krogh; B Larsson; G von Heijne; E L Sonnhammer
Journal:  J Mol Biol       Date:  2001-01-19       Impact factor: 5.469

Review 2.  Current methods of gene prediction, their strengths and weaknesses.

Authors:  Catherine Mathé; Marie-France Sagot; Thomas Schiex; Pierre Rouzé
Journal:  Nucleic Acids Res       Date:  2002-10-01       Impact factor: 16.971

3.  SignalP 4.0: discriminating signal peptides from transmembrane regions.

Authors:  Thomas Nordahl Petersen; Søren Brunak; Gunnar von Heijne; Henrik Nielsen
Journal:  Nat Methods       Date:  2011-09-29       Impact factor: 28.547

4.  Secreted aspartic proteinase from Candida albicans acts as a chemoattractant for peripheral neutrophils.

Authors:  Yuping Ran; Kazuhisa Iwabuchi; Masashi Yamazaki; Ryoji Tsuboi; Hideoki Ogawa
Journal:  J Dermatol Sci       Date:  2013-06-21       Impact factor: 4.563

Review 5.  Proteogenomics.

Authors:  Santosh Renuse; Raghothama Chaerkady; Akhilesh Pandey
Journal:  Proteomics       Date:  2011-01-18       Impact factor: 3.984

Review 6.  Insights into Candida tropicalis nosocomial infections and virulence factors.

Authors:  M Negri; S Silva; M Henriques; R Oliveira
Journal:  Eur J Clin Microbiol Infect Dis       Date:  2011-10-30       Impact factor: 3.267

Review 7.  Candida tropicalis in human disease.

Authors:  Louis Yi Ann Chai; David W Denning; Peter Warn
Journal:  Crit Rev Microbiol       Date:  2010-11       Impact factor: 7.624

8.  Comparison of results of fluconazole and voriconazole disk diffusion testing for Candida spp. with results from a central reference laboratory in the ARTEMIS DISK Global Antifungal Surveillance Program.

Authors:  Michael A Pfaller; Linda Boyken; Richard J Hollis; Jennifer Kroeger; Shawn A Messer; Shailesh Tendolkar; Daniel J Diekema
Journal:  Diagn Microbiol Infect Dis       Date:  2009-09       Impact factor: 2.803

9.  Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration.

Authors:  Helga Thorvaldsdóttir; James T Robinson; Jill P Mesirov
Journal:  Brief Bioinform       Date:  2012-04-19       Impact factor: 11.622

10.  Proteogenomic analysis of pathogenic yeast Cryptococcus neoformans using high resolution mass spectrometry.

Authors:  Lakshmi Dhevi Nagarajha Selvan; Jyothi Embekkat Kaviyil; Raja Sekhar Nirujogi; Babylakshmi Muthusamy; Vinuth N Puttamallesh; Tejaswini Subbannayya; Nazia Syed; Aneesha Radhakrishnan; Dhanashree S Kelkar; Sartaj Ahmad; Sneha M Pinto; Praveen Kumar; Anil K Madugundu; Bipin Nair; Aditi Chatterjee; Akhilesh Pandey; Raju Ravikumar; Harsha Gowda; Thottethodi Subrahmanya Keshava Prasad
Journal:  Clin Proteomics       Date:  2014-02-03       Impact factor: 3.988

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  4 in total

1.  Copper-only superoxide dismutase enzymes and iron starvation stress in Candida fungal pathogens.

Authors:  Sabrina S Schatzman; Ryan L Peterson; Mieraf Teka; Bixi He; Diane E Cabelli; Brendan P Cormack; Valeria C Culotta
Journal:  J Biol Chem       Date:  2019-12-05       Impact factor: 5.157

2.  Temporal Quantitative Proteomics Reveals Proteomic and Phosphoproteomic Alterations Associated with Adaptive Response to Hypoxia in Melanoma Cells.

Authors:  Keshava K Datta; Parthiban Periasamy; Sonali V Mohan; Rebekah Ziegman; Harsha Gowda
Journal:  Cancers (Basel)       Date:  2021-04-30       Impact factor: 6.639

3.  Combination of Proteogenomics with Peptide De Novo Sequencing Identifies New Genes and Hidden Posttranscriptional Modifications.

Authors:  B Blank-Landeshammer; I Teichert; R Märker; M Nowrousian; U Kück; A Sickmann
Journal:  mBio       Date:  2019-10-15       Impact factor: 7.867

4.  Dissecting Plasmodium yoelii Pathobiology: Proteomic Approaches for Decoding Novel Translational and Post-Translational Modifications.

Authors:  Devasahayam Arokia Balaya Rex; Arun H Patil; Prashant Kumar Modi; Mrudula Kinarulla Kandiyil; Sandeep Kasaragod; Sneha M Pinto; Nandita Tanneru; Puran Singh Sijwali; Thottethodi Subrahmanya Keshava Prasad
Journal:  ACS Omega       Date:  2022-03-02
  4 in total

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