Current progress and critical challenges to overcome in the bioinformatics of mass spectrometry-based metaproteomics

Miura, Nobuaki; Okuda, Shujiro

doi:10.1016/j.csbj.2023.01.015

Cited by 21 publications

(6 citation statements)

References 88 publications

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“…The protein inference problem is exacerbated in environmental metaproteomics due to (1) complex and unknown taxonomy, necessitating large databases and a priori information (e.g., taxa inferred from 16S or metagenomic data), (2) frequent cohabitation of prokaryotic and eukaryotic organisms and (3) the presence of conserved protein domains which can exist across phylogenetically distinct organisms. Unfortunately, the large databases required for protein inference provide more opportunities for peptide degeneracy, thus peptide-to-protein and peptide-to-taxa assignments remain significant challenges in metaproteomics studies (Miura & Okuda, 2023).…”

Section: The Protein Inference Problem In Metaproteomicsmentioning

confidence: 99%

“…Since the target‐decoy approach incorporates decoy hits in its estimation of true positives, lower‐scoring true positive hits will be reported as false positives, reducing overall protein identifications. Despite this, an FDR‐based approach using target‐decoy reference databases remains the standard method for protein identification in metaproteomics studies; however, the method is currently being revised (Miura & Okuda, 2023).…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

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Critical steps in an environmental metaproteomics workflow

Nebauer,

Pearson,

Neilan

2024

Environmental Microbiology

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Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non‐expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high‐quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single‐organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

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Section: The Protein Inference Problem In Metaproteomicsmentioning

confidence: 99%

Section: Data Acquisition and Analysismentioning

confidence: 99%

Critical steps in an environmental metaproteomics workflow

Nebauer,

Pearson,

Neilan

2024

Environmental Microbiology

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“…The metaproteomic studies are focused on determining the species diversity in a community, studying various stages of the life cycle of microorganisms, their response to stress, the functional characterization of microbiomes, etc 3 . Being a young field, metaproteomics possess challenges that must be overcome before using for routine research 2,4 . Currently, the research efforts are focused on accuracy, sensitivity, and reproducibility of the analysis, development of bioinformatic solutions, and standardization of workflows 5 .…”

Section: Introductionmentioning

confidence: 99%

Ultrafast metaproteomics for quantitative assessment of strain isolates and microbiomes

Kazakova,

Ivanov,

Kusainova

et al. 2024

Preprint

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Microbial proteomics and metaproteomic studies rely heavily on mass spectrometry. Standard liquid chromatography/tandem mass spectrometry-based methods for protein identification and quantitation exhibit lengthy analysis time. Here, we report on the implementation of the previously developed DirectMS1 approach for metaproteomics studies that provides a unique combination of accurate protein quantitation and rapid analysis time. We validated our method using a series of proteome-wide analyses of bacterial samples including strain isolates, mock microbiomes composed of bacteria spiked at known concentrations and human fecal microbiomes. We demonstrated that the developed two-stage search algorithm identifies the bacterial isolate species with an accuracy of 95%, when no prior information on the sample is available. Microbiome composition was resolved at the genus level with the mean difference between the actual and identified microbiome components of 12%. Pearson coefficient of up to 0.97 was achieved in estimates of strain biomass change in mock microbiome samples. By the example of Rhodococcus biodegradation of n-alkanes, phenols and its derivatives, we showed that our approach effectively characterizes changes in the activity of metabolic pathways of strain isolates. For the benchmarking, the changes in biodegradation activity were also assessed using the standard label-free and TMT DDA approaches.

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“…A metagenomic-sequencing database from the same sample would increase the rate of protein identification. Therefore, in this work, the database is composed of protein amino acid sequences obtained by metagenomic analyses of the samples to be analyzed, which can improve the protein-identification rate [19]. Some studies have shown that multiomics can comprehensively unravel the mechanism of complicated microorganism communities [20,21].…”

Section: Introductionmentioning

confidence: 99%

Metagenomic/Metaproteomic Investigation of the Microbiota in Dongbei Suaicai, a Traditional Fermented Chinese Cabbage

Zhang,

Yan,

et al. 2024

Fermentation

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Dongbei Suaicai (DBSC) has a complicated microbial ecosystem in which the composition and metabolism of microbial communities during the process have not been well explored. Here, combined metagenomic and metaproteomic technology was used to reveal the taxonomic and metabolic profiles of DBSC. The results showed that firmicutes and proteobacteria were the prevalent bacteria in phylum and Pseudomonas, while Weissella, Pediococcus, and Leuconostoc were the prevalent genus. The vital metabolic pathways were involved in glycolysis/gluconeogenesis [path: ko00010], as well as pyruvate metabolism [path: ko00620], fructose and mannose metabolism [path: Ko00051], glycine, and serine and threonine metabolism [path: Ko00260]. Moreover, the key proteins (dps, fliC, tsf, fusA, atpD, metQ, pgi, tpiA, eno, alaS, bglA, tktA, gor, pdhD, aceE, and gnd) in related metabolized pathways were enriched during fermentation. This study will aid in facilitating the understanding of the fermentation mechanisms of DBSC.

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Current progress and critical challenges to overcome in the bioinformatics of mass spectrometry-based metaproteomics

Cited by 21 publications

References 88 publications

Critical steps in an environmental metaproteomics workflow

Critical steps in an environmental metaproteomics workflow

Ultrafast metaproteomics for quantitative assessment of strain isolates and microbiomes

Metagenomic/Metaproteomic Investigation of the Microbiota in Dongbei Suaicai, a Traditional Fermented Chinese Cabbage

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