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metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes*

Open AccessPublished:June 24, 2019DOI:https://doi.org/10.1074/mcp.RA118.001240
      Microbiome research offers promising insights into the impact of microorganisms on biological systems. Metaproteomics, the study of microbial proteins at the community level, integrates genomic, transcriptomic, and proteomic data to determine the taxonomic and functional state of a microbiome. However, standard metaproteomics software is subject to several limitations, commonly supporting only spectral counts, emphasizing exploratory analysis rather than hypothesis testing and rarely offering the ability to analyze the interaction of function and taxonomy - that is, which taxa are responsible for different processes.
      Here we present metaQuantome, a novel, multifaceted software suite that analyzes the state of a microbiome by leveraging complex taxonomic and functional hierarchies to summarize peptide-level quantitative information, emphasizing label-free intensity-based methods. For experiments with multiple experimental conditions, metaQuantome offers differential abundance analysis, principal components analysis, and clustered heat map visualizations, as well as exploratory analysis for a single sample or experimental condition. We benchmark metaQuantome analysis against standard methods, using two previously published datasets: (1) an artificially assembled microbial community dataset (taxonomy benchmarking) and (2) a dataset with a range of recombinant human proteins spiked into an Escherichia coli background (functional benchmarking). Furthermore, we demonstrate the use of metaQuantome on a previously published human oral microbiome dataset.
      In both the taxonomic and functional benchmarking analyses, metaQuantome quantified taxonomic and functional terms more accurately than standard summarization-based methods. We use the oral microbiome dataset to demonstrate metaQuantome's ability to produce publication-quality figures and elucidate biological processes of the oral microbiome. metaQuantome enables advanced investigation of metaproteomic datasets, which should be broadly applicable to microbiome-related research. In the interest of accessible, flexible, and reproducible analysis, metaQuantome is open source and available on the command line and in Galaxy.

      Graphical Abstract

      Microbiome analysis has enabled the understanding of the effect of microorganisms on diverse biological systems (
      • Gilbert J.A.
      • Blaser M.J.
      • Caporaso J.G.
      • Jansson J.K.
      • Lynch S.V.
      • Knight R.
      Current understanding of the human microbiome.
      ,
      • Moran M.A.
      The global ocean microbiome.
      ,
      • Fierer N.
      Embracing the unknown: Disentangling the complexities of the soil microbiome.
      ,
      • Hörmannsperger G.
      • Schaubeck M.
      • Haller D.
      Intestinal microbiota in animal models of inflammatory diseases.
      ). The microbiome can be studied using a variety of methods, including metagenomics (
      • Kuczynski J.
      • Costello E.K.
      • Nemergut D.R.
      • Zaneveld J.
      • Lauber C.L.
      • Knights D.
      • Koren O.
      • Fierer N.
      • Kelley S.T.
      • Ley R.E.
      • Gordon J.I.
      • Knight R.
      Direct sequencing of the human microbiome readily reveals community differences.
      ,
      • Quince C.
      • Walker A.W.
      • Simpson J.T.
      • Loman N.J.
      • Segata N.
      Shotgun metagenomics, from sampling to analysis.
      ,
      • Human Microbiome Project Consortium
      Structure, function and diversity of the healthy human microbiome.
      ), metatranscriptomics (
      • Bashiardes S.
      • Zilberman-Schapira G.
      • Elinav E.
      Use of Metatranscriptomics in microbiome research.
      ), and metaproteomics (
      • Wilmes P.
      • Heintz-Buschart A.
      • Bond P.L.
      A decade of metaproteomics: Where we stand and what the future holds.
      ). Metaproteomics studies detect the presence and abundance of microbial peptides and proteins, offering a more direct understanding of the processes being catalyzed by the microbiome than metatranscriptomics and metagenomics (
      • Wilmes P.
      • Heintz-Buschart A.
      • Bond P.L.
      A decade of metaproteomics: Where we stand and what the future holds.
      ,
      • Verberkmoes N.C.
      • Russell A.L.
      • Shah M.
      • Godzik A.
      • Rosenquist M.
      • Halfvarson J.
      • Lefsrud M.G.
      • Apajalahti J.
      • Tysk C.
      • Hettich R.L.
      • Jansson J.K.
      Shotgun metaproteomics of the human distal gut microbiota.
      ,
      • Xiong W.
      • Giannone R.J.
      • Morowitz M.J.
      • Banfield J.F.
      • Hettich R.L.
      Development of an enhanced metaproteomic approach for deepening the microbiome characterization of the human infant gut.
      ,
      • Heyer R.
      • Schallert K.
      • Zoun R.
      • Becher B.
      • Saake G.
      • Benndorf D.
      Challenges and perspectives of metaproteomic data analysis.
      ,
      • Kolmeder C.A.
      • de Vos W.M.
      Metaproteomics of our microbiome—Developing insight in function and activity in man and model systems.
      ,
      • Heintz-Buschart A.
      • Wilmes P.
      Human gut microbiome: Function matters.
      ,
      • Wilmes P.
      • Bond P.L.
      The application of two-dimensional polyacrylamide gel electrophoresis and downstream analyses to a mixed community of prokaryotic microorganisms.
      ,
      • Zhang X.
      • Figeys D.
      Perspective and guidelines for metaproteomics in microbiome studies.
      ). Furthermore, metaproteomics allows the analysis of both taxonomic abundance and functional state from the same mass spectrometry data.
      Although metaproteomics is an important component of microbiome research and a complement to other 'omics analyses, limitations in current software restrict the range of methods and accuracy of analyses that can be carried out. First, metaproteomics studies have traditionally quantified peptides with spectral counts, based on counting the number of tandem mass (MS/MS)
      The abbreviations used are: MS/MS, tandem mass spectrometry; MS1, precursor mass spectrum; UPS1, UPS2, Universal Proteomics Standards 1 and 2; GO, Gene Ontology; EC, enzyme commission; NCBI, National Center for Biotechnology Information; LCA, lowest common ancestor; MSE, mean squared error; L2FC, logarithm base 2 of the fold change; WS, with sucrose; NS, no sucrose.
      1The abbreviations used are: MS/MS, tandem mass spectrometry; MS1, precursor mass spectrum; UPS1, UPS2, Universal Proteomics Standards 1 and 2; GO, Gene Ontology; EC, enzyme commission; NCBI, National Center for Biotechnology Information; LCA, lowest common ancestor; MSE, mean squared error; L2FC, logarithm base 2 of the fold change; WS, with sucrose; NS, no sucrose.
      spectra assigned to peptides or proteins (
      • Lundgren D.H.
      • Hwang S.-I.
      • Wu L.
      • Han D.K.
      Role of spectral counting in quantitative proteomics.
      ). Accordingly, many available metaproteomics tools only offer amenability to spectral counting-based quantification, including MEGAN (
      • Huson D.H.
      • Beier S.
      • Flade I.
      • Górska A.
      • El-Hadidi M.
      • Mitra S.
      • Ruscheweyh H.-J.
      • Tappu R.
      MEGAN community edition—Interactive exploration and analysis of large-scale microbiome sequencing data.
      ), metaGOmics (
      • Riffle M.
      • May D.H.
      • Timmins-Schiffman E.
      • Mikan M.P.
      • Jaschob D.
      • Noble W.S.
      • Nunn B.L.
      MetaGOmics: A web-based tool for peptide-centric functional and taxonomic analysis of metaproteomics data.
      ), and Unipept (
      • Gurdeep Singh R.
      • Tanca A.
      • Palomba A.
      • Van der Jeugt F.
      • Verschaffelt P.
      • Uzzau S.
      • Martens L.
      • Dawyndt P.
      • Mesuere B.
      Unipept 4.0: Functional analysis of metaproteome data.
      ). However, research has shown that spectral counts offer a less accurate estimate of peptide abundance than the spectral intensity of the precursor peptide (which is typically done by either integrating the MS1 peak or by recording the apex intensity) (
      • Cox J.
      • Hein M.Y.
      • Luber C.A.
      • Paron I.
      • Nagaraj N.
      • Mann M.
      Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.
      ).
      Second, some available bioinformatics tools that intend to support microbiome analysis follow a “gene list” approach and require explicit protein or gene inference, such as DAVID (
      • Huang D.W.
      • Sherman B.T.
      • Tan Q.
      • Kir J.
      • Liu D.
      • Bryant D.
      • Guo Y.
      • Stephens R.
      • Baseler M.W.
      • Lane H.C.
      • Lempicki R.A.
      DAVID bioinformatics resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists.
      ). In metaproteomics, however, it is sometimes difficult to unambiguously assign a parent protein to a detected peptide because proteins between and within species can be highly homologous (
      • 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.
      ). Other tools only support certain types of microbiota in a small number of organisms, such as iMetaLab (
      • Liao B.
      • Ning Z.
      • Cheng K.
      • Zhang X.
      • Li L.
      • Mayne J.
      • Figeys D.
      iMetaLab 1.0: A web platform for metaproteomics data analysis.
      ), which only supports mouse and human gut microbiome analysis.
      Furthermore, metaproteomics tools rarely offer the ability to directly compare many samples or multiple experimental conditions. Some, such as Unipept, focus on detailed exploratory analysis of a single sample. Others, such as metaGOmics, allow comparison between only two samples. However, as metaproteomics is marked by large datasets and many thousands of functional terms and dozens of taxa, it is essential to compare larger numbers of samples to distinguish true effects from random variation. In addition, available metaproteomics tools rarely offer methods to filter out redundant annotations, leading to less informative conclusions from the data.
      Finally, while both the taxonomic origin and functional role of peptides (more specifically, of their parent protein) can be determined, few metaproteomics software tools are able to explore the function-taxonomy interaction, that is, the contribution of different taxa to a given functional process and vice versa.
      In this manuscript, we present a new software suite called metaQuantome, which is composed of several complementary functionalities developed with the intent to fill some of the aforementioned gaps in metaproteomic bioinformatics tools. metaQuantome is free and open source and is available via GitHub, Bioconda (
      • Grüning B.
      • Dale R.
      • Sjödin A.
      • Chapman B.A.
      • Rowe J.
      • Tomkins-Tinch C.H.
      • Valieris R.
      • Köster J.
      • Bioconda Team
      Bioconda: Sustainable and comprehensive software distribution for the life sciences.
      ), and Galaxy (
      • Afgan E.
      • Baker D.
      • Batut B.
      • van den Beek M.
      • Bouvier D.
      • Cech M.
      • Chilton J.
      • Clements D.
      • Coraor N.
      • Grüning B.A.
      • Guerler A.
      • Hillman-Jackson J.
      • Hiltemann S.
      • Jalili V.
      • Rasche H.
      • Soranzo N.
      • Goecks J.
      • Taylor J.
      • Nekrutenko A.
      • Blankenberg D.
      The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.
      ). To our knowledge, metaQuantome is the only software to enable fully quantitative differential abundance analysis of the functional and taxonomic profile of a metaproteome and one of only a few software tools to enable function-taxonomy interaction analysis. metaQuantome is amenable to data quantified using peptide-level MS1 intensity values, as well as data quantified by more traditional spectral counting methods. It also utilizes functional annotation and taxonomic annotation—generated from any software—to carry out a multifaceted analysis of a metaproteomics dataset, without requiring the use of a specific database or explicit protein inference. Importantly, it provides novel and powerful functionality for analyzing function-taxonomy interactions, enabling users to determine microbe-specific contributions to the functional profile or the profile of microbes contributing to a specific functional protein class—and visualize the results from these investigations.
      We evaluate the accuracy of metaQuantome in quantifying abundance measures of taxa and biochemical functions indicated from peptide abundance data, compared with standard summarization-based methods. First, we benchmark taxonomic abundance estimation using a mock microbial community dataset (
      • Kleiner M.
      • Thorson E.
      • Sharp C.E.
      • Dong X.
      • Liu D.
      • Li C.
      • Strous M.
      Assessing species biomass contributions in microbial communities via metaproteomics.
      ). We also benchmark functional abundance estimation with a dataset consisting of the Universal Proteomics Standards 1 and 2 (UPS1 and UPS2, Sigma-Aldrich) spiked into an E. coli background (
      • Cox J.
      • Hein M.Y.
      • Luber C.A.
      • Paron I.
      • Nagaraj N.
      • Mann M.
      Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.
      ). Finally, we demonstrate the analysis and visualization capabilities of the software on a previously published oral microbiome dataset (
      • Rudney J.D.
      • Jagtap P.D.
      • Reilly C.S.
      • Chen R.
      • Markowski T.W.
      • Higgins L.
      • Johnson J.E.
      • Griffin T.J.
      Protein relative abundance patterns associated with sucrose-induced dysbiosis are conserved across taxonomically diverse oral microcosm biofilm models of dental caries.
      ). Our results demonstrate the value of metaQuantome for quantitative analysis of metaproteomics data and advanced exploration of these datasets for microbiome characterization.

      DISCUSSION

      metaQuantome is a novel and multifunctional bioinformatics software suite that leverages quantitative information and functional and taxonomic annotations to describe the multidimensional state of a microbiome. Among the novel features of metaQuantome are: the multifaceted quality control filtering process, which reduces redundancy and spurious annotations; amenability to either label-free MS1-based intensity or spectral counting quantification methods; the support for differential abundance and clustering analysis across multiple experimental conditions; the use of a peptide-centric approach to mitigate the protein inference problem; and the combination of functional and taxonomic information to elucidate their interaction in a microbiome. As we demonstrate, metaQuantome leads to more complete and accurate estimates of functional and taxonomic abundance than more basic summarization methods. It also provides a variety of visualizations of results that should prove valuable to users for biological interpretation and publication. Collectively, these attributes distinguish metaQuantome from other available software for advanced analysis of metaproteomic data.
      An important and unique capability of metaQuantome is its support of function-taxonomy interaction analysis, which allows investigation of how taxa contribute to metabolic pathways, and how the “roles” of the members of a microbial community change due to perturbations of the system. metaQuantome allows investigation of this phenomenon from two directions: the distribution of functional processes for a given taxon and the taxonomic distribution of a certain functional process. As an illustrative example, in the case study, metaQuantome identified a dramatic change in the taxonomic contribution to carbohydrate metabolism: in WS, the Streptococcus genus accounts for a disproportionately higher share of carbohydrate metabolism (82.6% in WS versus 19.7% in NS), while Fusobacteria are responsible for the greatest share of carbohydrate metabolism in NS (66.1%) and hardly any carbohydrate metabolism in WS (1.2%). The identification of such important effects is uniquely facilitated by metaQuantome, through its ability to analyze function and taxonomy at once.
      There are some limitations and challenges that should be noted, which we look forward to addressing in the future. First, in its current version, metaQuantome is only able to work with peptides that can be annotated with functional and taxonomic information and automatically discards peptides of unknown function or organismal source. Peptides and proteins of unknown function and taxonomy are often identified in metaproteomics studies (
      • Heintz-Buschart A.
      • Wilmes P.
      Human gut microbiome: Function matters.
      ). Because the interrogation of peptides and proteins of unknown function and/or taxonomy will be an important part of future metaproteomics studies, we look forward to incorporating the ability to analyze these peptides and proteins via metaQuantome. Second, metaQuantome currently provides static visualizations, which are ideal for publication but less ideal for data exploration. In the future, we anticipate developing an interactive visualization application to allow for easier data exploration, as was recently done for another Galaxy-based tool for proteogenomic data analysis (
      • Sajulga R.
      • Mehta S.
      • Kumar P.
      • Johnson J.E.
      • Guerrero C.R.
      • Ryan M.C.
      • Karchin R.
      • Jagtap P.D.
      • Griffin T.J.
      Bridging the chromosome-centric and biology/disease-driven human proteome projects: Accessible and automated tools for interpreting the biological and pathological impact of protein sequence variants detected via proteogenomics.
      ). Thirdly, we also realize that the outputs generated from metaQuantome are largely dependent on the quality of input datasets. However, as a flexible component of a modular workflow, metaQuantome can always be used with the most cutting-edge quantitation, normalization, functional, and taxonomic assignment tools.
      We also see an opportunity to integrate metaQuantome into existing metaproteomics workflows, including those that have been developed within the Galaxy platform (
      • Blank C.
      • Easterly C.
      • Gruening B.
      • Johnson J.
      • Kolmeder C.A.
      • Kumar P.
      • May D.
      • Mehta S.
      • Mesuere B.
      • Brown Z.
      • Elias J.E.
      • Hervey W.J.
      • McGowan T.
      • Muth T.
      • Nunn B.
      • Rudney J.
      • Tanca A.
      • Griffin T.J.
      • Jagtap P.D.
      Disseminating metaproteomic informatics capabilities and knowledge using the Galaxy-P framework.
      ). Implementation in Galaxy also provides a user interface for the software, in addition to potential for integration with other Galaxy-based tools and workflows. We have designed metaQuantome to take inputs in a standard tabular format, such that it is agnostic to the upstream software used for generating peptide sequence matches from MS/MS data, assigning taxa/function, and quantifying peptides based on label-free methods (MS1-based intensity or spectral counting methods). As such, we envision metaQuantome to fit into a variety of metaproteomic workflows, Galaxy-based or otherwise. It also offers a chance for comparison to, or potentially integration with, other multi-omic workflows for microbiome characterization, such as existing quantitative metatranscriptomics workflows (
      • Batut B.
      • Gravouil K.
      • Defois C.
      • Hiltemann S.
      • Brugère J.-F.
      • Peyretaillade E.
      • Peyret P.
      ASaiM: A Galaxy-based framework to analyze microbiota data.
      ). metaQuantome should offer new possibilities and empower users to perform much deeper and advanced multi-omic studies.
      In the interest of accessibility, we have made metaQuantome available on GitHub (https://github.com/galaxyproteomics/metaquantome), Bioconda, and on Galaxy, and metaQuantome is supported on macOS and Linux environments. All software is freely available and published following the Apache license. An introduction to using metaQuantome on Galaxy, and details on how to install and analyze data via metaQuantome on the command line is provided at https://galaxyproteomics.github.io/metaquantome_mcp_analysis/, as is the full set of analysis scripts for all three datasets discussed here.
      In conclusion, we look forward to the use of metaQuantome in a variety of metaproteomics studies. We have developed the software with an eye toward flexibility and integration with other software tools, and we anticipate further collaborations with others to advance the cause of metaproteomic software development aimed at enabling robust, reproducible, and transparent science. The novel features offered by metaQuantome, combined with usability by bench scientists, should provide a powerful tool to advance our understanding of the role of microbiomes in diverse contexts, from studies related to human health, including clinical applications, to those of environmental and industrial importance.

      Data Availability

      In Supplementary Documents 1 and 2, and a Zenodo repository at http://doi.org/10.5281/zenodo.2652530, we have provided an Excel document containing the peptide reports with accession numbers, the FlashLFQ reports (with MS1 intensity values) and the Unipept outputs (taxonomy and function) for each of the datasets. In Supplementary Document 3, we have included some of the metaQuantome outputs from the oral microbiome case study. The original datasets are available via ProteomeXchange identifiers PXD006118 (mock microbial community), PXD000279 (spiked-in Universal Proteomic Standard), and PXD003151 (oral microbiome case study). The full set of metaQuantome commands for each of the three analyses is available in the GitHub repository associated with this manuscript (https://github.com/galaxyproteomics/metaquantome_mcp_analysis).

      Acknowledgments

      We would like to thank Bjoern Gruening and the Galaxy community for the help in the support during Galaxy implementation. We would also like to thank Brook Nunn (University of Washington, Seattle, Washington), Alessandro Tanca (Porto Conte Ricerche, Italy), Carolin Kolmeder (University of Helsinki, Finland), and Nadia Szeinbaum (Georgia Tech, Atlanta, Georgia) for discussion during the development of metaQuantome. We thank Emma Leith for proofreading the manuscript.

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