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Ducker C, Baines C, Guy J, Euzébio Goulart Santana A, Pickett JA, Oldham NJ. A diterpene synthase from the sandfly Lutzomyia longipalpis produces the pheromone sobralene. Proc Natl Acad Sci U S A 2024; 121:e2322453121. [PMID: 38470919 PMCID: PMC10962984 DOI: 10.1073/pnas.2322453121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 02/06/2024] [Indexed: 03/14/2024] Open
Abstract
The phlebotomine sandfly, Lutzomyia longipalpis, a major vector of the Leishmania parasite, uses terpene pheromones to attract conspecifics for mating. Examination of the L. longipalpis genome revealed a putative terpene synthase (TPS), which-upon heterologous expression in, and purification from, Escherichia coli-yielded a functional enzyme. The TPS, termed LlTPS, converted geranyl diphosphate (GPP) into a mixture of monoterpenes with low efficiency, of which β-ocimene was the major product. (E,E)-farnesyl diphosphate (FPP) principally produced small amounts of (E)-β-farnesene, while (Z,E)- and (Z,Z)-FPP yielded a mixture of bisabolene isomers. None of these mono- and sesquiterpenes are known volatiles of L. longipalpis. Notably, however, when provided with (E,E,E)-geranylgeranyl diphosphate (GGPP), LlTPS gave sobralene as its major product. This diterpene pheromone is released by certain chemotypes of L. longipalpis, in particular those found in the Ceará state of Brazil. Minor diterpene components were also seen as products of the enzyme that matched those seen in a sandfly pheromone extract.
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Affiliation(s)
- Charles Ducker
- School of Chemistry, University of Nottingham, University Park, NottinghamNG7 2RD, United Kingdom
| | - Cameron Baines
- School of Chemistry, University of Nottingham, University Park, NottinghamNG7 2RD, United Kingdom
| | - Jennifer Guy
- School of Chemistry, University of Nottingham, University Park, NottinghamNG7 2RD, United Kingdom
| | | | - John A. Pickett
- School of Chemistry, Cardiff University, CardiffCF10 3AT, United Kingdom
| | - Neil J. Oldham
- School of Chemistry, University of Nottingham, University Park, NottinghamNG7 2RD, United Kingdom
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Bihani S, Gupta A, Mehta S, Rajczewski AT, Johnson J, Borishetty D, Griffin TJ, Srivastava S, Jagtap PD. Metaproteomic Analysis of Nasopharyngeal Swab Samples to Identify Microbial Peptides in COVID-19 Patients. J Proteome Res 2023; 22:2608-2619. [PMID: 37450889 DOI: 10.1021/acs.jproteome.3c00040] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
During the COVID-19 pandemic, impaired immunity and medical interventions resulted in cases of secondary infections. The clinical difficulties and dangers associated with secondary infections in patients necessitate the exploration of their microbiome. Metaproteomics is a powerful approach to study the taxonomic composition and functional status of the microbiome under study. In this study, the mass spectrometry (MS)-based data of nasopharyngeal swab samples from COVID-19 patients was used to investigate the metaproteome. We have established a robust bioinformatics workflow within the Galaxy platform, which includes (a) generation of a tailored database of the common respiratory tract pathogens, (b) database search using multiple search algorithms, and (c) verification of the detected microbial peptides. The microbial peptides detected in this study, belong to several opportunistic pathogens such as Streptococcus pneumoniae, Klebsiella pneumoniae, Rhizopus microsporus, and Syncephalastrum racemosum. Microbial proteins with a role in stress response, gene expression, and DNA repair were found to be upregulated in severe patients compared to negative patients. Using parallel reaction monitoring (PRM), we confirmed some of the microbial peptides in fresh clinical samples. MS-based clinical metaproteomics can serve as a powerful tool for detection and characterization of potential pathogens, which can significantly impact the diagnosis and treatment of patients.
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Affiliation(s)
- Surbhi Bihani
- Department of Bioscience and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
| | - Aryan Gupta
- Department of Chemistry, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 7-129 MCB, 420 Washington Ave SE, Minneapolis, Minnesota 55455, United States
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 7-129 MCB, 420 Washington Ave SE, Minneapolis, Minnesota 55455, United States
| | - James Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Dhanush Borishetty
- Department of Bioscience and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 7-129 MCB, 420 Washington Ave SE, Minneapolis, Minnesota 55455, United States
| | - Sanjeeva Srivastava
- Department of Bioscience and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, 7-129 MCB, 420 Washington Ave SE, Minneapolis, Minnesota 55455, United States
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Mehta S, Bernt M, Chambers M, Fahrner M, Föll MC, Gruening B, Horro C, Johnson JE, Loux V, Rajczewski AT, Schilling O, Vandenbrouck Y, Gustafsson OJR, Thang WCM, Hyde C, Price G, Jagtap PD, Griffin TJ. A Galaxy of informatics resources for MS-based proteomics. Expert Rev Proteomics 2023; 20:251-266. [PMID: 37787106 DOI: 10.1080/14789450.2023.2265062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/06/2023] [Indexed: 10/04/2023]
Abstract
INTRODUCTION Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Matthias Bernt
- Helmholtz Centre for Environmental Research - UFZ, Department Computational Biology, Leipzig, Germany
| | | | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Carlos Horro
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Valentin Loux
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, Jouy-en-Josas, France
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - W C Mike Thang
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Cameron Hyde
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Sippy Downs, University of the Sunshine Coast, Australia
| | - Gareth Price
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
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van der Burgt Y, Wuhrer M. The role of clinical glyco(proteo)mics in precision medicine. Mol Cell Proteomics 2023:100565. [PMID: 37169080 DOI: 10.1016/j.mcpro.2023.100565] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/12/2023] [Accepted: 05/02/2023] [Indexed: 05/13/2023] Open
Abstract
Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.
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Affiliation(s)
- Yuri van der Burgt
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
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Weaver S, DeRosa CM, Schultz SR, Champion MM. PrIntMap-R: An Online Application for Intraprotein Intensity and Peptide Visualization from Bottom-Up Proteomics. J Proteome Res 2023; 22:432-441. [PMID: 36652611 PMCID: PMC9904286 DOI: 10.1021/acs.jproteome.2c00606] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Bottom-up proteomics (BUP) produces rich data, but visualization and analysis are time-consuming and often require programming skills. Many tools analyze these data at the proteome-level, but fewer options exist for individual proteins. Sequence coverage maps are common, but do not proportion peptide intensity. Abundance-based visualization of sequence coverage facilitates detection of protein isoforms, domains, potential truncation sites, peptide "hot-spots", and localization of post-translational modifications (PTMs). Redundant stacked-sequence coverage is an important tool in designing hydrogen-deuterium exchange (HDX) experiments. Visualization tools often lack graphical and tabular-export of processed data which complicates publication of results. Quantitative peptide abundance across amino acid sequences is an essential and missing tool in proteomics toolkits. Here we created PrIntMap-R, an online application that only requires peptide files from a database search and FASTA protein sequences. PrIntMap-R produces a variety of plots for quantitative visualization of coverage; annotation of specific sequences, PTM's, and comparisons of one or many samples overlaid with calculated fold-change or several intensity metrics. We show use-cases including protein phosphorylation, identification of glycosylation, and the optimization of digestion conditions for HDX experiments. PrIntMap-R is freely available, open source, and can run online with no installation, or locally by downloading source code from GitHub.
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Affiliation(s)
- Simon
D. Weaver
- Department
of Chemistry and Biochemistry, University
of Notre Dame, Notre
Dame, Indiana 46556, United States,Integrated
Biomedical Sciences Graduate Program, University
of Notre Dame, Notre
Dame, Indiana 46556, United States
| | - Christine M. DeRosa
- Department
of Chemistry and Biochemistry, University
of Notre Dame, Notre
Dame, Indiana 46556, United States
| | - Sadie R. Schultz
- Department
of Chemistry and Biochemistry, University
of Notre Dame, Notre
Dame, Indiana 46556, United States
| | - Matthew M. Champion
- Department
of Chemistry and Biochemistry, University
of Notre Dame, Notre
Dame, Indiana 46556, United States,Integrated
Biomedical Sciences Graduate Program, University
of Notre Dame, Notre
Dame, Indiana 46556, United States,
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