Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota—Implementation and Computational Analysis

Aakko, Juhani; Pietilä, Sami; Suomi, Tomi; Mahmoudian, Mehrad; Toivonen, Raine; Kouvonen, Petri; Rokka, Anne; Hänninen, Arno; Elo, Laura L.

doi:10.1021/acs.jproteome.9b00606

Cited by 33 publications

(38 citation statements)

References 22 publications

(42 reference statements)

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“…To demonstrate the feasibility and benefits of our new DIAtools 2.0 DIA-only metaproteomics approach, we applied it to a laboratory assembled microbial mixture containing twelve different bacterial strains (12mix) and to human fecal samples from six healthy donors ( Supplementary Tables 1-2) and compared the performance against the previously introduced DDA-assisted method 6 .…”

Section: Resultsmentioning

confidence: 99%

“…The copyright holder for this this version posted December 22, 2020. ; https://doi.org/10.1101/2020.12.21.423800 doi: bioRxiv preprint tools to analyse single-species proteome data are often not well suited for metaproteomics. While a few tools have been introduced for the analysis of DDA metaproteome samples 16,17 , until our recent work 6 , there have been no tools for DIA mass spectrometry metaproteomics, despite its high potential to improve the reproducibility over the more conventionally used DDA mode. The aim of this study was to address that need.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, we were the first to demonstrate that DIA mass spectrometry can be successfully applied to analyse complex metaproteomic samples by using a spectral library constructed from corresponding DDA data to assist the peptide identification 6 . preprint (which was not certified by peer review) is the author/funder.…”

Section: Introductionmentioning

confidence: 99%

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Metaproteomics boosted up by untargeted data-independent acquisition data analysis framework

Pietilä

Suomi

Elo

2020

Preprint

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Mass spectrometry based metaproteomics is a relatively new field of research that provides the ability to characterize the functionality of microbiota. Recently, we were the first to demonstrate the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the conventionally used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the previous method still required additional DDA data on the samples to assist the DIA analysis. Here, we introduce, for the first time, a DIA metaproteomics approach that does not require any DDA data, but instead replaces a spectral library generated from DDA data with a pseudospectral library generated directly from the metaproteomics DIA samples. We demonstrate that using the new DIA-only approach, we can achieve higher peptide yields than with the DDA-assisted approach, while the amount of required mass spectrometry data is reduced to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as open-source software package DIAtools 2.0, which is freely available from DockerHub.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Metaproteomics boosted up by untargeted data-independent acquisition data analysis framework

Pietilä

Suomi

Elo

2020

Preprint

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“…The mass-spectrometry data were analyzed with Diatools software package as described 19 , 20 (Diatools version 1.0 ( https://github.com/elolab/diatools ) to identify the peptides expressed by fecal microbiota of each donor. The overall protein expression profile was constructed using the integrated gene catalog of the human gut microbiome (IGC) 40 covering over 9 million human gut microbiota proteins.…”

Section: Methodsmentioning

confidence: 99%

“…Although several important aspects of fecal microbiota and gut homeostasis have been addressed by proteome analyses 13 – 17 , CAZy in fecal microbiota have so far been analyzed only on a few occasions on genomic 12 and transcriptomic 18 level. In this study, we studied CAZy in fecal microbiota on whole proteome level by newly designed analysis tools 19 , 20 , and related their CAZy expression to their microbiota composition. We found individual differences between donors, as the expression of various CAZy clustered into several groups of CAZy co-occurring more likely together in same samples.…”

Section: Introductionmentioning

confidence: 99%

A carbohydrate-active enzyme (CAZy) profile links successful metabolic specialization of Prevotella to its abundance in gut microbiota

Aakko

Pietilä

Toivonen

et al. 2020

Sci Rep

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Gut microbiota participates in diverse metabolic and homeostatic functions related to health and well-being. Its composition varies between individuals, and depends on factors related to host and microbial communities, which need to adapt to utilize various nutrients present in gut environment. We profiled fecal microbiota in 63 healthy adult individuals using metaproteomics, and focused on microbial CAZy (carbohydrate-active) enzymes involved in glycan foraging. We identified two distinct CAZy profiles, one with many Bacteroides-derived CAZy in more than one-third of subjects (n = 25), and it associated with high abundance of Bacteroides in most subjects. In a smaller subset of donors (n = 8) with dietary parameters similar to others, microbiota showed intense expression of Prevotella-derived CAZy including exo-beta-(1,4)-xylanase, xylan-1,4-beta-xylosidase, alpha-larabinofuranosidase and several other CAZy belonging to glycosyl hydrolase families involved in digestion of complex plant-derived polysaccharides. This associated invariably with high abundance of Prevotella in gut microbiota, while in subjects with lower abundance of Prevotella, microbiota showed no Prevotella-derived CAZy. Identification of Bacteroides-and Prevotella-derived cAZy in microbiota proteome and their association with differences in microbiota composition are in evidence of individual variation in metabolic specialization of gut microbes affecting their colonizing competence.

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Applying multi‐omics toward tumor microbiome research

Zhang

Kandalai

Zhou

et al. 2023

iMeta

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Rather than a "short-term tenant," the tumor microbiome has been shown to play a vital role as a "permanent resident," affecting carcinogenesis, cancer development, metastasis, and cancer therapies. As the tumor microbiome has great potential to become a target for the early diagnosis and treatment of cancer, recent research on the relevance of the tumor microbiota has attracted a wide range of attention from various scientific fields, resulting in remarkable progress that benefits from the development of interdisciplinary technologies.However, there are still a great variety of challenges in this emerging area, such as the low biomass of intratumoral bacteria and unculturable character of some microbial species. Due to the complexity of tumor microbiome research (e.g., the heterogeneity of tumor microenvironment), new methods with high spatial and temporal resolution are urgently needed. Among these developing methods, multi-omics technologies (combinations of genomics, transcriptomics, proteomics, and metabolomics) are powerful approaches that can facilitate the understanding of the tumor microbiome on different levels of the central dogma. Therefore, multi-omics (especially single-cell omics) will make enormous impacts on the future studies of the interplay between microbes and tumor microenvironment. In this review, we have systematically summarized the advances in multi-omics and their existing and potential applications in tumor microbiome research, thus providing an omics toolbox for investigators to reference in the future.

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Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota—Implementation and Computational Analysis

Cited by 33 publications

References 22 publications

Metaproteomics boosted up by untargeted data-independent acquisition data analysis framework

Metaproteomics boosted up by untargeted data-independent acquisition data analysis framework

A carbohydrate-active enzyme (CAZy) profile links successful metabolic specialization of Prevotella to its abundance in gut microbiota

Applying multi‐omics toward tumor microbiome research

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