1
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Holstein T, Muth T. Bioinformatic Workflows for Metaproteomics. Methods Mol Biol 2024; 2820:187-213. [PMID: 38941024 DOI: 10.1007/978-1-0716-3910-8_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
The strong influence of microbiomes on areas such as ecology and human health has become widely recognized in the past years. Accordingly, various techniques for the investigation of the composition and function of microbial community samples have been developed. Metaproteomics, the comprehensive analysis of the proteins from microbial communities, allows for the investigation of not only the taxonomy but also the functional and quantitative composition of microbiome samples. Due to the complexity of the investigated communities, methods developed for single organism proteomics cannot be readily applied to metaproteomic samples. For this purpose, methods specifically tailored to metaproteomics are required. In this work, a detailed overview of current bioinformatic solutions and protocols in metaproteomics is given. After an introduction to the proteomic database search, the metaproteomic post-processing steps are explained in detail. Ten specific bioinformatic software solutions are focused on, covering various steps including database-driven identification and quantification as well as taxonomic and functional assignment.
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Affiliation(s)
- Tanja Holstein
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany
- VIB-UGent Center for Medical Biotechnology, VIB and Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Data Competence Center, Robert Koch Institute, Berlin, Deutschland
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing, Berlin, Germany.
- Data Competence Center, Robert Koch Institute, Berlin, Deutschland.
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2
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Wang T, Li L, Figeys D, Liu YY. Pairing metagenomics and metaproteomics to characterize ecological niches and metabolic essentiality of gut microbiomes. ISME COMMUNICATIONS 2024; 4:ycae063. [PMID: 38808120 PMCID: PMC11131966 DOI: 10.1093/ismeco/ycae063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/15/2024] [Accepted: 04/26/2024] [Indexed: 05/30/2024]
Abstract
The genome of a microorganism encodes its potential functions that can be implemented through expressed proteins. It remains elusive how a protein's selective expression depends on its metabolic essentiality to microbial growth or its ability to claim resources as ecological niches. To reveal a protein's metabolic or ecological role, we developed a computational pipeline, which pairs metagenomics and metaproteomics data to quantify each protein's gene-level and protein-level functional redundancy simultaneously. We first illustrated the idea behind the pipeline using simulated data of a consumer-resource model. We then validated it using real data from human and mouse gut microbiome samples. In particular, we analyzed ABC-type transporters and ribosomal proteins, confirming that the metabolic and ecological roles predicted by our pipeline agree well with prior knowledge. Finally, we performed in vitro cultures of a human gut microbiome sample and investigated how oversupplying various sugars involved in ecological niches influences the community structure and protein abundance. The presented results demonstrate the performance of our pipeline in identifying proteins' metabolic and ecological roles, as well as its potential to help us design nutrient interventions to modulate the human microbiome.
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Affiliation(s)
- Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Leyuan Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- School of Pharmaceutical Sciences and Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H8M5, Canada
| | - Daniel Figeys
- School of Pharmaceutical Sciences and Ottawa Institute of Systems Biology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H8M5, Canada
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Center for Artificial Intelligence and Modeling, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61820, United States
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3
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Mappa C, Alpha-Bazin B, Pible O, Armengaud J. Evaluation of the Limit of Detection of Bacteria by Tandem Mass Spectrometry Proteotyping and Phylopeptidomics. Microorganisms 2023; 11:1170. [PMCID: PMC10223342 DOI: 10.3390/microorganisms11051170] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/20/2023] [Accepted: 04/22/2023] [Indexed: 06/01/2023] Open
Abstract
Shotgun proteomics has proven to be an attractive alternative for identifying a pathogen and characterizing the antimicrobial resistance genes it produces. Because of its performance, proteotyping of microorganisms by tandem mass spectrometry is expected to become an essential tool in modern healthcare. Proteotyping microorganisms that have been isolated from the environment by culturomics is also a cornerstone for the development of new biotechnological applications. Phylopeptidomics is a new strategy that estimates the phylogenetic distances between the organisms present in the sample and calculates the ratio of their shared peptides, thus improving the quantification of their contributions to the biomass. Here, we established the limit of detection of tandem mass spectrometry proteotyping based on MS/MS data recorded for several bacteria. The limit of detection for Salmonella bongori with our experimental set-up is 4 × 104 colony-forming units from a sample volume of 1 mL. This limit of detection is directly related to the amount of protein per cell and therefore depends on the shape and size of the microorganism. We have demonstrated that identification of bacteria by phylopeptidomics is independent of their growth stage and that the limit of detection of the method is not degraded in presence of additional bacteria in the same proportion.
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Affiliation(s)
- Charlotte Mappa
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, 30200 Bagnols-sur-Cèze, France
- Laboratoire Innovations Technologiques pour la Détection et le Diagnostic (Li2D), Université de Montpellier, 30207 Bagnols-sur-Cèze, France
| | - Béatrice Alpha-Bazin
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, 30200 Bagnols-sur-Cèze, France
| | - Olivier Pible
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, 30200 Bagnols-sur-Cèze, France
| | - Jean Armengaud
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, 30200 Bagnols-sur-Cèze, France
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4
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Alves G, Ogurtsov A, Karlsson R, Jaén-Luchoro D, Piñeiro-Iglesias B, Salvà-Serra F, Andersson B, Moore ERB, Yu YK. Identification of Antibiotic Resistance Proteins via MiCId's Augmented Workflow. A Mass Spectrometry-Based Proteomics Approach. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:917-931. [PMID: 35500907 PMCID: PMC9164240 DOI: 10.1021/jasms.1c00347] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 06/01/2023]
Abstract
Fast and accurate identifications of pathogenic bacteria along with their associated antibiotic resistance proteins are of paramount importance for patient treatments and public health. To meet this goal from the mass spectrometry aspect, we have augmented the previously published Microorganism Classification and Identification (MiCId) workflow for this capability. To evaluate the performance of this augmented workflow, we have used MS/MS datafiles from samples of 10 antibiotic resistance bacterial strains belonging to three different species: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The evaluation shows that MiCId's workflow has a sensitivity value around 85% (with a lower bound at about 72%) and a precision greater than 95% in identifying antibiotic resistance proteins. In addition to having high sensitivity and precision, MiCId's workflow is fast and portable, making it a valuable tool for rapid identifications of bacteria as well as detection of their antibiotic resistance proteins. It performs microorganismal identifications, protein identifications, sample biomass estimates, and antibiotic resistance protein identifications in 6-17 min per MS/MS sample using computing resources that are available in most desktop and laptop computers. We have also demonstrated other use of MiCId's workflow. Using MS/MS data sets from samples of two bacterial clonal isolates, one being antibiotic-sensitive while the other being multidrug-resistant, we applied MiCId's workflow to investigate possible mechanisms of antibiotic resistance in these pathogenic bacteria; the results showed that MiCId's conclusions agree with the published study. The new version of MiCId (v.07.01.2021) is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.
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Affiliation(s)
- Gelio Alves
- National
Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
| | - Aleksey Ogurtsov
- National
Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
| | - Roger Karlsson
- Department
of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
- Department
of Clinical Microbiology, Sahlgrenska University
Hospital, 40234 Gothenburg, Sweden
- Center
for Antibiotic Resistance Research (CARe), University of Gothenburg, 40016 Gothenburg, Sweden
- Nanoxis
Consulting AB, 40234 Gothenburg, Sweden
| | - Daniel Jaén-Luchoro
- Department
of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
- Center
for Antibiotic Resistance Research (CARe), University of Gothenburg, 40016 Gothenburg, Sweden
- Culture Collection
University of Gothenburg (CCUG), Sahlgrenska
Academy of the University of Gothenburg, 40234 Gothenburg, Sweden
| | - Beatriz Piñeiro-Iglesias
- Department
of Clinical Microbiology, Sahlgrenska University
Hospital, 40234 Gothenburg, Sweden
- Center
for Antibiotic Resistance Research (CARe), University of Gothenburg, 40016 Gothenburg, Sweden
| | - Francisco Salvà-Serra
- Department
of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
- Department
of Clinical Microbiology, Sahlgrenska University
Hospital, 40234 Gothenburg, Sweden
- Center
for Antibiotic Resistance Research (CARe), University of Gothenburg, 40016 Gothenburg, Sweden
- Culture Collection
University of Gothenburg (CCUG), Sahlgrenska
Academy of the University of Gothenburg, 40234 Gothenburg, Sweden
- Microbiology,
Department of Biology, University of the
Balearic Islands, 07122 Palma de Mallorca, Spain
| | - Björn Andersson
- Bioinformatics
Core Facility at Sahlgrenska Academy, University
of Gothenburg, Box 413, 40530 Gothenburg, Sweden
| | - Edward R. B. Moore
- Department
of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
- Department
of Clinical Microbiology, Sahlgrenska University
Hospital, 40234 Gothenburg, Sweden
- Center
for Antibiotic Resistance Research (CARe), University of Gothenburg, 40016 Gothenburg, Sweden
- Culture Collection
University of Gothenburg (CCUG), Sahlgrenska
Academy of the University of Gothenburg, 40234 Gothenburg, Sweden
| | - Yi-Kuo Yu
- National
Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
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5
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Zhang W, Liu A, Zhang Z, Chen G, Li Q. An adaptive direction-assisted test for microbiome compositional data. Bioinformatics 2022; 38:3493-3500. [PMID: 35640978 PMCID: PMC9890306 DOI: 10.1093/bioinformatics/btac361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 04/11/2022] [Accepted: 05/28/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION Microbial communities have been shown to be associated with many complex diseases, such as cancers and cardiovascular diseases. The identification of differentially abundant taxa is clinically important. It can help understand the pathology of complex diseases, and potentially provide preventive and therapeutic strategies. Appropriate differential analyses for microbiome data are challenging due to its unique data characteristics including compositional constraint, excessive zeros and high dimensionality. Most existing approaches either ignore these data characteristics or only account for the compositional constraint by using log-ratio transformations with zero observations replaced by a pseudocount. However, there is no consensus on how to choose a pseudocount. More importantly, ignoring the characteristic of excessive zeros may result in poorly powered analyses and therefore yield misleading findings. RESULTS We develop a novel microbiome-based direction-assisted test for the detection of overall difference in microbial relative abundances between two health conditions, which simultaneously incorporates the characteristics of relative abundance data. The proposed test (i) divides the taxa into two clusters by the directions of mean differences of relative abundances and then combines them at cluster level, in light of the compositional characteristic; and (ii) contains a burden type test, which collapses multiple taxa into a single one to account for excessive zeros. Moreover, the proposed test is an adaptive procedure, which can accommodate high-dimensional settings and yield high power against various alternative hypotheses. We perform extensive simulation studies across a wide range of scenarios to evaluate the proposed test and show its substantial power gain over some existing tests. The superiority of the proposed approach is further demonstrated with real datasets from two microbiome studies. AVAILABILITY AND IMPLEMENTATION An R package for MiDAT is available at https://github.com/zhangwei0125/MiDAT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wei Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Aiyi Liu
- To whom correspondence should be addressed. or
| | - Zhiwei Zhang
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Qizhai Li
- To whom correspondence should be addressed. or
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6
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Feng S, Sterzenbach R, Guo X. Deep learning for peptide identification from metaproteomics datasets. J Proteomics 2021; 247:104316. [PMID: 34246788 DOI: 10.1016/j.jprot.2021.104316] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 06/02/2021] [Accepted: 06/18/2021] [Indexed: 10/20/2022]
Abstract
Metaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled with liquid chromatography. In this paper, we proposed a deep-learning-based algorithm, named DeepFilter, for improving peptide identifications from a collection of tandem mass spectra. The key advantage of the DeepFilter is that it does not need ad hoc training or fine-tuning as in existing filtering tools. DeepFilter is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/DeepFilter. SIGNIFICANCE: The identification of peptides and proteins from MS data involves the computational procedure of searching MS/MS spectra against a predefined protein sequence database and assigning top-scored peptides to spectra. Existing computational tools are still far from being able to extract all the information out of MS/MS data sets acquired from metaproteome samples. Systematical experiment results demonstrate that the DeepFilter identified up to 12% and 9% more peptide-spectrum-matches and proteins, respectively, compared with existing filtering algorithms, including Percolator, Q-ranker, PeptideProphet, and iProphet, on marine and soil microbial metaproteome samples with false discovery rate at 1%. The taxonomic analysis shows that DeepFilter found up to 7%, 10%, and 14% more species from marine, soil, and human gut samples compared with existing filtering algorithms. Therefore, DeepFilter was believed to generalize properly to new, previously unseen peptide-spectrum-matches and can be readily applied in peptide identification from metaproteomics data.
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Affiliation(s)
- Shichao Feng
- Department of Computer Science and Engineering, University of North Texas, TX, USA
| | - Ryan Sterzenbach
- Department of Biomedical Engineering, University of North Texas, TX, USA
| | - Xuan Guo
- Department of Computer Science and Engineering, University of North Texas, TX, USA.
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7
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Kuhring M, Doellinger J, Nitsche A, Muth T, Renard BY. TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples. J Proteome Res 2020; 19:2501-2510. [PMID: 32362126 DOI: 10.1021/acs.jproteome.9b00714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt.
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Affiliation(s)
- Mathias Kuhring
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.,Core Unit Bioinformatics, Berlin Institute of Health (BIH), 10178 Berlin, Germany.,Berlin Institute of Health Metabolomics Platform, Berlin Institute of Health (BIH), 10178 Berlin, Germany.,Max Delbrück Center (MDC) for Molecular Medicine, 13125 Berlin, Germany
| | - Joerg Doellinger
- Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS 6), Robert Koch Institute, 13353 Berlin, Germany.,Centre for Biological Threats and Special Pathogens, Highly Pathogenic Viruses (ZBS 1), Robert Koch Institute, 13353 Berlin, Germany
| | - Andreas Nitsche
- Centre for Biological Threats and Special Pathogens, Highly Pathogenic Viruses (ZBS 1), Robert Koch Institute, 13353 Berlin, Germany
| | - Thilo Muth
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.,eScience Division (S.3), Federal Institute for Materials Research and Testing, 12489 Berlin, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.,Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, 14482 Potsdam, Germany
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8
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Alves G, Yu YK. Robust Accurate Identification and Biomass Estimates of Microorganisms via Tandem Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:85-102. [PMID: 32881514 PMCID: PMC10501333 DOI: 10.1021/jasms.9b00035] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Rapid and accurate identification of microorganisms and estimation of their biomasses are of extreme importance to public health. Mass spectrometry has become an important technique for these purposes. Previously we published a workflow named Microorganism Classification and Identification (MiCId v.12.26.2017) that was shown to perform no worse than other workflows. This manuscript presents MiCId v.12.13.2018 that, in comparison with the earlier version v.12.26.2017, allows for biomass estimates, provides more accurate microorganism identifications (better controls the number of false positives), and is robust against database size increase. This significant advance is made possible by several new ingredients introduced: first, we apply a modified expectation-maximization method to compute for each taxon considered a prior probability, which can be used for biomass estimate; second, we introduce a new concept called ownership, through which the participation ratio is computed and use it as the number of taxa to be kept within a cluster of closely related taxa; third, based on confidently identified peptides, we calculate for each taxon its degree of independence from the rest of taxa considered to determine whether or not to split this taxon off the cluster. Using 270 data files, each containing a large number of MS/MS spectra, we show that, in comparison with v.12.26.2017, version v.12.13.2018 yields superior retrieval results. We also show that MiCId v.12.13.2018 can estimate species biomass reasonably well. The new MiCId v.12.13.2018, designed to run in Linux environment, is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.
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Affiliation(s)
- Gelio Alves
- National Center for Biotehnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
| | - Yi-Kuo Yu
- National Center for Biotehnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States
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9
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Schiebenhoefer H, Van Den Bossche T, Fuchs S, Renard BY, Muth T, Martens L. Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis. Expert Rev Proteomics 2019; 16:375-390. [PMID: 31002542 DOI: 10.1080/14789450.2019.1609944] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The study of microbial communities based on the combined analysis of genomic and proteomic data - called metaproteogenomics - has gained increased research attention in recent years. This relatively young field aims to elucidate the functional and taxonomic interplay of proteins in microbiomes and its implications on human health and the environment. Areas covered: This article reviews bioinformatics methods and software tools dedicated to the analysis of data from metaproteomics and metaproteogenomics experiments. In particular, it focuses on the creation of tailored protein sequence databases, on the optimal use of database search algorithms including methods of error rate estimation, and finally on taxonomic and functional annotation of peptide and protein identifications. Expert opinion: Recently, various promising strategies and software tools have been proposed for handling typical data analysis issues in metaproteomics. However, severe challenges remain that are highlighted and discussed in this article; these include: (i) robust false-positive assessment of peptide and protein identifications, (ii) complex protein inference against a background of highly redundant data, (iii) taxonomic and functional post-processing of identification data, and finally, (iv) the assessment and provision of metrics and tools for quantitative analysis.
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Affiliation(s)
- Henning Schiebenhoefer
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Tim Van Den Bossche
- b VIB - UGent Center for Medical Biotechnology, VIB , Ghent , Belgium.,c Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences , Ghent University , Ghent , Belgium
| | - Stephan Fuchs
- d FG13 Division of Nosocomial Pathogens and Antibiotic Resistances , Robert Koch Institute , Wernigerode , Germany
| | - Bernhard Y Renard
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Thilo Muth
- a Bioinformatics Unit (MF1), Department for Methods Development and Research Infrastructure , Robert Koch Institute , Berlin , Germany
| | - Lennart Martens
- b VIB - UGent Center for Medical Biotechnology, VIB , Ghent , Belgium.,c Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences , Ghent University , Ghent , Belgium
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10
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Kunath BJ, Minniti G, Skaugen M, Hagen LH, Vaaje-Kolstad G, Eijsink VGH, Pope PB, Arntzen MØ. Metaproteomics: Sample Preparation and Methodological Considerations. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1073:187-215. [DOI: 10.1007/978-3-030-12298-0_8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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11
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Lin H, He QY, Shi L, Sleeman M, Baker MS, Nice EC. Proteomics and the microbiome: pitfalls and potential. Expert Rev Proteomics 2018; 16:501-511. [PMID: 30223687 DOI: 10.1080/14789450.2018.1523724] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Introduction: Human symbiotic microbiota are now known to play important roles in human health and disease. Significant progress in our understanding of the human microbiome has been driven by recent technological advances in the fields of genomics, transcriptomics, and proteomics. As a complementary method to metagenomics, proteomics is enabling detailed protein profiling of the microbiome to decipher its structure and function and to analyze its relationship with the human body. Fecal proteomics is being increasingly applied to discover and validate potential health and disease biomarkers, and Therapeutic Goods Administration (TGA)-approved instrumentation and a range of clinical assays are being developed that will collectively play key roles in advancing personalized medicine. Areas covered: This review will introduce the complexity of the microbiome and its role in health and disease (in particular the gastrointestinal tract or gut microbiome), discuss current genomic and proteomic methods for studying this system, including the discovery of potential biomarkers, and outline the development of clinically accepted protocols leading to personalized medicine. Expert commentary: Recognition of the important role the microbiome plays in both health and disease is driving current research in this key area. A proteogenomics approach will be essential to unravel the biologies underlying this complex network.
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Affiliation(s)
- Huafeng Lin
- a Department of Biotechnology , College of Life Science and Technology, Jinan University , Guangzhou , Guangdong , China.,b Institute of Food Safety and Nutrition Research , Jinan University , Guangzhou , China
| | - Qing-Yu He
- c Institute of Life and Health Engineering, College of Life Science and Technology , Jinan University , Guangzhou , China
| | - Lei Shi
- b Institute of Food Safety and Nutrition Research , Jinan University , Guangzhou , China
| | - Mark Sleeman
- d Biomedicine Discovery Institute , Monash University , Melbourne , Australia
| | - Mark S Baker
- e Department of Biomedical Sciences, Faculty of Medicine and Health Sciences , Macquarie University , Sydney , Australia
| | - Edouard C Nice
- f Department of Biochemistry and Molecular Biology , Monash University , Melbourne , Victoria , Australia
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12
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Alves G, Wang G, Ogurtsov AY, Drake SK, Gucek M, Sacks DB, Yu YK. Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2018; 29:1721-1737. [PMID: 29873019 PMCID: PMC6061032 DOI: 10.1007/s13361-018-1986-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/30/2018] [Accepted: 04/25/2018] [Indexed: 05/30/2023]
Abstract
Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.
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Affiliation(s)
- Gelio Alves
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Guanghui Wang
- Proteomics Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Aleksey Y Ogurtsov
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Steven K Drake
- Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Marjan Gucek
- Proteomics Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - David B Sacks
- Department of Laboratory Medicine, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Yi-Kuo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
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13
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Muth T, Kohrs F, Heyer R, Benndorf D, Rapp E, Reichl U, Martens L, Renard BY. MPA Portable: A Stand-Alone Software Package for Analyzing Metaproteome Samples on the Go. Anal Chem 2017; 90:685-689. [PMID: 29215871 PMCID: PMC5757220 DOI: 10.1021/acs.analchem.7b03544] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
![]()
Metaproteomics,
the mass spectrometry-based analysis of proteins
from multispecies samples faces severe challenges concerning data
analysis and results interpretation. To overcome these shortcomings,
we here introduce the MetaProteomeAnalyzer (MPA) Portable software.
In contrast to the original server-based MPA application, this newly
developed tool no longer requires computational expertise for installation
and is now independent of any relational database system. In addition,
MPA Portable now supports state-of-the-art database search engines
and a convenient command line interface for high-performance data
processing tasks. While search engine results can easily be combined
to increase the protein identification yield, an additional two-step
workflow is implemented to provide sufficient analysis resolution
for further postprocessing steps, such as protein grouping as well
as taxonomic and functional annotation. Our new application has been
developed with a focus on intuitive usability, adherence to data standards,
and adaptation to Web-based workflow platforms. The open source software
package can be found at https://github.com/compomics/meta-proteome-analyzer.
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Affiliation(s)
- Thilo Muth
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute , 13353 Berlin, Germany
| | - Fabian Kohrs
- Bioprocess Engineering, Otto von Guericke University Magdeburg , 39106 Magdeburg, Germany
| | - Robert Heyer
- Bioprocess Engineering, Otto von Guericke University Magdeburg , 39106 Magdeburg, Germany
| | - Dirk Benndorf
- Bioprocess Engineering, Otto von Guericke University Magdeburg , 39106 Magdeburg, Germany.,Max Planck Institute for Dynamics of Complex Technical Systems , Bioprocess Engineering, 39106 Magdeburg, Germany
| | - Erdmann Rapp
- Max Planck Institute for Dynamics of Complex Technical Systems , Bioprocess Engineering, 39106 Magdeburg, Germany
| | - Udo Reichl
- Bioprocess Engineering, Otto von Guericke University Magdeburg , 39106 Magdeburg, Germany.,Max Planck Institute for Dynamics of Complex Technical Systems , Bioprocess Engineering, 39106 Magdeburg, Germany
| | - Lennart Martens
- Department of Biochemistry, Ghent University , 9000 Ghent, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB , 9000 Ghent, Belgium
| | - Bernhard Y Renard
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute , 13353 Berlin, Germany
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14
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Heyer R, Schallert K, Zoun R, Becher B, Saake G, Benndorf D. Challenges and perspectives of metaproteomic data analysis. J Biotechnol 2017; 261:24-36. [PMID: 28663049 DOI: 10.1016/j.jbiotec.2017.06.1201] [Citation(s) in RCA: 117] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 06/20/2017] [Accepted: 06/23/2017] [Indexed: 02/07/2023]
Abstract
In nature microorganisms live in complex microbial communities. Comprehensive taxonomic and functional knowledge about microbial communities supports medical and technical application such as fecal diagnostics as well as operation of biogas plants or waste water treatment plants. Furthermore, microbial communities are crucial for the global carbon and nitrogen cycle in soil and in the ocean. Among the methods available for investigation of microbial communities, metaproteomics can approximate the activity of microorganisms by investigating the protein content of a sample. Although metaproteomics is a very powerful method, issues within the bioinformatic evaluation impede its success. In particular, construction of databases for protein identification, grouping of redundant proteins as well as taxonomic and functional annotation pose big challenges. Furthermore, growing amounts of data within a metaproteomics study require dedicated algorithms and software. This review summarizes recent metaproteomics software and addresses the introduced issues in detail.
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Affiliation(s)
- Robert Heyer
- Otto von Guericke University, Bioprocess Engineering, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Kay Schallert
- Otto von Guericke University, Bioprocess Engineering, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Roman Zoun
- Otto von Guericke University, Institute for Technical and Business Information Systems, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Beatrice Becher
- Otto von Guericke University, Bioprocess Engineering, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Gunter Saake
- Otto von Guericke University, Institute for Technical and Business Information Systems, Universitätsplatz 2, 39106 Magdeburg, Germany.
| | - Dirk Benndorf
- Otto von Guericke University, Bioprocess Engineering, Universitätsplatz 2, 39106 Magdeburg, Germany; Max Planck Institute for Dynamics of Complex Technical Systems, Bioprocess Engineering, Sandtorstraße 1, 39106, Magdeburg, Germany.
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15
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Lee PY, Chin SF, Neoh HM, Jamal R. Metaproteomic analysis of human gut microbiota: where are we heading? J Biomed Sci 2017; 24:36. [PMID: 28606141 PMCID: PMC5469034 DOI: 10.1186/s12929-017-0342-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 06/01/2017] [Indexed: 02/08/2023] Open
Abstract
The human gut is home to complex microbial populations that change dynamically in response to various internal and external stimuli. The gut microbiota provides numerous functional benefits that are crucial for human health but in the setting of a disturbed equilibrium, the microbial community can cause deleterious outcomes such as diseases and cancers. Characterization of the functional activities of human gut microbiota is fundamental to understand their roles in human health and disease. Metaproteomics, which refers to the study of the entire protein collection of the microbial community in a given sample is an emerging area of research that provides informative details concerning functional aspects of the microbiota. In this mini review, we present a summary of the progress of metaproteomic analysis for studying the functional role of gut microbiota. This is followed by an overview of the experimental approaches focusing on fecal specimen for metaproteomics and is concluded by a discussion on the challenges and future directions of metaproteomic research.
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Affiliation(s)
- Pey Yee Lee
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000, Cheras, Kuala Lumpur, Malaysia
| | - Siok-Fong Chin
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000, Cheras, Kuala Lumpur, Malaysia.
| | - Hui-Min Neoh
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000, Cheras, Kuala Lumpur, Malaysia
| | - Rahman Jamal
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000, Cheras, Kuala Lumpur, Malaysia
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16
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Boulund F, Karlsson R, Gonzales-Siles L, Johnning A, Karami N, Al-Bayati O, Åhrén C, Moore ERB, Kristiansson E. Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics. Mol Cell Proteomics 2017; 16:1052-1063. [PMID: 28420677 DOI: 10.1074/mcp.m116.061721] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 03/01/2017] [Indexed: 11/06/2022] Open
Abstract
Methods for rapid and reliable microbial identification are essential in modern healthcare. The ability to detect and correctly identify pathogenic species and their resistance phenotype is necessary for accurate diagnosis and efficient treatment of infectious diseases. Bottom-up tandem mass spectrometry (MS) proteomics enables rapid characterization of large parts of the expressed genes of microorganisms. However, the generated data are highly fragmented, making downstream analyses complex. Here we present TCUP, a new computational method for typing and characterizing bacteria using proteomics data from bottom-up tandem MS. TCUP compares the generated protein sequence data to reference databases and automatically finds peptides suitable for characterization of taxonomic composition and identification of expressed antimicrobial resistance genes. TCUP was evaluated using several clinically relevant bacterial species (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae), using both simulated data generated by in silico peptide digestion and experimental proteomics data generated by liquid chromatography-tandem mass spectrometry (MS/MS). The results showed that TCUP performs correct peptide classifications at rates between 90.3 and 98.5% at the species level. The method was also able to estimate the relative abundances of individual species in mixed cultures. Furthermore, TCUP could identify expressed β-lactamases in an extended spectrum β-lactamase-producing (ESBL) E. coli strain, even when the strain was cultivated in the absence of antibiotics. Finally, TCUP is computationally efficient, easy to integrate in existing bioinformatics workflows, and freely available under an open source license for both Windows and Linux environments.
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Affiliation(s)
- Fredrik Boulund
- From the ‡Division of Applied Mathematics and Statistics, Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-41296 Gothenburg, Sweden.,§Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, SE-41296 Gothenburg, Sweden
| | - Roger Karlsson
- §Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, SE-41296 Gothenburg, Sweden.,¶Nanoxis Consulting AB, SE-40016 Gothenburg, Sweden.,‖Department of Clinical Microbiology, Sahlgrenska University Hospital, SE-41346 Gothenburg, Sweden
| | - Lucia Gonzales-Siles
- §Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, SE-41296 Gothenburg, Sweden.,**Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy of the University of Gothenburg, SE-40234 Gothenburg, Sweden
| | - Anna Johnning
- From the ‡Division of Applied Mathematics and Statistics, Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-41296 Gothenburg, Sweden.,§Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, SE-41296 Gothenburg, Sweden
| | - Nahid Karami
- §Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, SE-41296 Gothenburg, Sweden.,**Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy of the University of Gothenburg, SE-40234 Gothenburg, Sweden
| | - Omar Al-Bayati
- ‖Department of Clinical Microbiology, Sahlgrenska University Hospital, SE-41346 Gothenburg, Sweden
| | - Christina Åhrén
- §Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, SE-41296 Gothenburg, Sweden.,**Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy of the University of Gothenburg, SE-40234 Gothenburg, Sweden
| | - Edward R B Moore
- §Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, SE-41296 Gothenburg, Sweden.,‖Department of Clinical Microbiology, Sahlgrenska University Hospital, SE-41346 Gothenburg, Sweden.,**Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy of the University of Gothenburg, SE-40234 Gothenburg, Sweden
| | - Erik Kristiansson
- From the ‡Division of Applied Mathematics and Statistics, Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-41296 Gothenburg, Sweden; .,§Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, SE-41296 Gothenburg, Sweden
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17
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Noecker C, McNally CP, Eng A, Borenstein E. High-resolution characterization of the human microbiome. Transl Res 2017; 179:7-23. [PMID: 27513210 PMCID: PMC5164958 DOI: 10.1016/j.trsl.2016.07.012] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 07/12/2016] [Accepted: 07/15/2016] [Indexed: 12/29/2022]
Abstract
The human microbiome plays an important and increasingly recognized role in human health. Studies of the microbiome typically use targeted sequencing of the 16S rRNA gene, whole metagenome shotgun sequencing, or other meta-omic technologies to characterize the microbiome's composition, activity, and dynamics. Processing, analyzing, and interpreting these data involve numerous computational tools that aim to filter, cluster, annotate, and quantify the obtained data and ultimately provide an accurate and interpretable profile of the microbiome's taxonomy, functional capacity, and behavior. These tools, however, are often limited in resolution and accuracy and may fail to capture many biologically and clinically relevant microbiome features, such as strain-level variation or nuanced functional response to perturbation. Over the past few years, extensive efforts have been invested toward addressing these challenges and developing novel computational methods for accurate and high-resolution characterization of microbiome data. These methods aim to quantify strain-level composition and variation, detect and characterize rare microbiome species, link specific genes to individual taxa, and more accurately characterize the functional capacity and dynamics of the microbiome. These methods and the ability to produce detailed and precise microbiome information are clearly essential for informing microbiome-based personalized therapies. In this review, we survey these methods, highlighting the challenges each method sets out to address and briefly describing methodological approaches.
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Affiliation(s)
- Cecilia Noecker
- Department of Genome Sciences, University of Washington, Seattle, WA
| | - Colin P McNally
- Department of Genome Sciences, University of Washington, Seattle, WA
| | - Alexander Eng
- Department of Genome Sciences, University of Washington, Seattle, WA
| | - Elhanan Borenstein
- Department of Genome Sciences, University of Washington, Seattle, WA
- Department of Computer Science and Engineering, University of Washington, Seattle, WA
- Santa Fe Institute, Santa Fe, NM
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18
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Muth T, Renard BY, Martens L. Metaproteomic data analysis at a glance: advances in computational microbial community proteomics. Expert Rev Proteomics 2016; 13:757-69. [DOI: 10.1080/14789450.2016.1209418] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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19
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Wilmes P, Heintz-Buschart A, Bond PL. A decade of metaproteomics: where we stand and what the future holds. Proteomics 2015; 15:3409-17. [PMID: 26315987 PMCID: PMC5049639 DOI: 10.1002/pmic.201500183] [Citation(s) in RCA: 117] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 07/06/2015] [Accepted: 08/05/2015] [Indexed: 12/21/2022]
Abstract
We are living through exciting times during which we are able to unravel the “microbial dark matter” in and around us through the application of high‐resolution “meta‐omics”. Metaproteomics offers the ability to resolve the major catalytic units of microbial populations and thereby allows the establishment of genotype‐phenotype linkages from in situ samples. A decade has passed since the term “metaproteomics” was first coined and corresponding analyses were carried out on mixed microbial communities. Since then metaproteomics has yielded many important insights into microbial ecosystem function in the various environmental settings where it has been applied. Although initial progress in analytical capacities and resulting numbers of proteins identified was extremely fast, this trend slowed rapidly. Here, we discuss several representative metaproteomic investigations of activated sludge, acid mine drainage biofilms, freshwater and seawater microbial communities, soil, and human gut microbiota. By using these case studies, we highlight current challenges and possible solutions for metaproteomics to realize its full potential, i.e. to enable conclusive links between microbial community composition, physiology, function, interactions, ecology, and evolution in situ.
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Affiliation(s)
- Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Heintz-Buschart
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Philip L Bond
- Advanced Water Management Centre, University of Queensland, Brisbane, Australia
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20
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Kuhring M, Renard BY. Estimating the computational limits of detection of microbial non-model organisms. Proteomics 2015; 15:3580-4. [DOI: 10.1002/pmic.201400598] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 05/20/2015] [Accepted: 06/26/2015] [Indexed: 11/11/2022]
Affiliation(s)
- Mathias Kuhring
- Research Group Bioinformatics (NG4); Robert Koch Institute; Berlin Germany
| | - Bernhard Y. Renard
- Research Group Bioinformatics (NG4); Robert Koch Institute; Berlin Germany
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21
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Jagtap PD, Blakely A, Murray K, Stewart S, Kooren J, Johnson JE, Rhodus NL, Rudney J, Griffin TJ. Metaproteomic analysis using the Galaxy framework. Proteomics 2015; 15:3553-65. [DOI: 10.1002/pmic.201500074] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 04/25/2015] [Accepted: 06/04/2015] [Indexed: 12/22/2022]
Affiliation(s)
- Pratik D. Jagtap
- Center for Mass Spectrometry and Proteomics; University of Minnesota; Minneapolis MN USA
- Department of Biochemistry; Molecular Biology and Biophysics; University of Minnesota; Minneapolis MN USA
| | | | - Kevin Murray
- Department of Biochemistry; Molecular Biology and Biophysics; University of Minnesota; Minneapolis MN USA
| | | | - Joel Kooren
- Department of Biochemistry; Molecular Biology and Biophysics; University of Minnesota; Minneapolis MN USA
| | | | - Nelson L. Rhodus
- School of Dentistry; University of Minnesota; Minneapolis MN USA
| | - Joel Rudney
- School of Dentistry; University of Minnesota; Minneapolis MN USA
| | - Timothy J. Griffin
- Center for Mass Spectrometry and Proteomics; University of Minnesota; Minneapolis MN USA
- Department of Biochemistry; Molecular Biology and Biophysics; University of Minnesota; Minneapolis MN USA
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22
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Nesvizhskii AI. Proteogenomics: concepts, applications and computational strategies. Nat Methods 2015; 11:1114-25. [PMID: 25357241 DOI: 10.1038/nmeth.3144] [Citation(s) in RCA: 505] [Impact Index Per Article: 56.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 09/22/2014] [Indexed: 12/19/2022]
Abstract
Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to help identify novel peptides (not present in reference protein sequence databases) from mass spectrometry-based proteomic data; in turn, the proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. In recent years, owing to the emergence of new sequencing technologies such as RNA-seq and dramatic improvements in the depth and throughput of mass spectrometry-based proteomics, the pace of proteogenomic research has greatly accelerated. Here I review the current state of proteogenomic methods and applications, including computational strategies for building and using customized protein sequence databases. I also draw attention to the challenge of false positive identifications in proteogenomics and provide guidelines for analyzing the data and reporting the results of proteogenomic studies.
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Affiliation(s)
- Alexey I Nesvizhskii
- 1] Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA. [2] Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
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23
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Muth T, Behne A, Heyer R, Kohrs F, Benndorf D, Hoffmann M, Lehtevä M, Reichl U, Martens L, Rapp E. The MetaProteomeAnalyzer: A Powerful Open-Source Software Suite for Metaproteomics Data Analysis and Interpretation. J Proteome Res 2015; 14:1557-65. [DOI: 10.1021/pr501246w] [Citation(s) in RCA: 124] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Thilo Muth
- Max Planck Institute
for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany
| | - Alexander Behne
- Chair
of Bioprocess Engineering, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
| | - Robert Heyer
- Chair
of Bioprocess Engineering, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
| | - Fabian Kohrs
- Chair
of Bioprocess Engineering, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
| | - Dirk Benndorf
- Chair
of Bioprocess Engineering, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
| | - Marcus Hoffmann
- Max Planck Institute
for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany
| | - Miro Lehtevä
- Department
of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Udo Reichl
- Max Planck Institute
for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany
- Chair
of Bioprocess Engineering, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
| | - Lennart Martens
- Department
of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department
of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Erdmann Rapp
- Max Planck Institute
for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany
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24
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Abram F. Systems-based approaches to unravel multi-species microbial community functioning. Comput Struct Biotechnol J 2014; 13:24-32. [PMID: 25750697 PMCID: PMC4348430 DOI: 10.1016/j.csbj.2014.11.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Revised: 11/25/2014] [Accepted: 11/26/2014] [Indexed: 01/24/2023] Open
Abstract
Some of the most transformative discoveries promising to enable the resolution of this century's grand societal challenges will most likely arise from environmental science and particularly environmental microbiology and biotechnology. Understanding how microbes interact in situ, and how microbial communities respond to environmental changes remains an enormous challenge for science. Systems biology offers a powerful experimental strategy to tackle the exciting task of deciphering microbial interactions. In this framework, entire microbial communities are considered as metaorganisms and each level of biological information (DNA, RNA, proteins and metabolites) is investigated along with in situ environmental characteristics. In this way, systems biology can help unravel the interactions between the different parts of an ecosystem ultimately responsible for its emergent properties. Indeed each level of biological information provides a different level of characterisation of the microbial communities. Metagenomics, metatranscriptomics, metaproteomics, metabolomics and SIP-omics can be employed to investigate collectively microbial community structure, potential, function, activity and interactions. Omics approaches are enabled by high-throughput 21st century technologies and this review will discuss how their implementation has revolutionised our understanding of microbial communities.
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Affiliation(s)
- Florence Abram
- Functional Environmental Microbiology, School of Natural Sciences, National University of Ireland Galway, University Road, Galway, Ireland
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