Function is what counts: how microbial community complexity affects species, proteome and pathway coverage in metaproteomics

Lohmann, Patrick; Schäpe, Stephanie Serena; Haange, Sven-Bastiaan; Oliphant, Kaitlyn; Allen‐Vercoe, Emma; Jehmlich, Nico; Bergen, Martin von

doi:10.1080/14789450.2020.1738931

Cited by 20 publications

(16 citation statements)

References 77 publications

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“…Therefore, the depth of information that can be obtained by metaproteomics is dependent on the taxonomic complexity and the mass spectrometric effort taken to sequence the sample [31, 32, 45]. The dependency of metaproteomic performance on community complexity has been investigated more in detail by Lohmann and co-workers only recently [46].…”

Section: Introductionmentioning

confidence: 99%

Comparative metaproteomics demonstrates different views on the complex granular sludge microbiome

Kleikamp

Grouzdev²,

Schaasberg

et al. 2022

Preprint

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The tremendous progress in sequencing technologies has made 16S amplicon and whole metagenome sequencing routine in microbiome studies. Furthermore, advances in mass spectrometric techniques has expanded conventional proteomics into the field of microbial ecology. Commonly referred to as metaproteomics, this approach measures the gene products (i.e., proteins) to subsequently identify the actively-expressed metabolic pathways and the protein-biomass composition of complete microbial communities. However, more systematic studies on metaproteomic and genomic approaches are urgently needed, to determine the orthogonal character of these approaches. Here we describe a deep, comparative metaproteomic study on the complex aerobic granular sludge microbiome obtained from different wastewater treatment plants. Thereby, we demonstrate the different views that can be obtained on the central nutrient-removing organisms depending on the omic approach and reference sequence databases. Furthermore, we demonstrate a homogenized Genome Taxonomy Database (GTDB) that subsequently enables a more accurate interpretation of data from different omics approaches. Ultimately, our systematic study underscores the importance of metaproteomics in the characterization of complex microbiomes; and the necessity of accurate reference sequence databases to improve the comparison between approaches and accuracy in scientific reporting.

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Section: Introductionmentioning

confidence: 99%

Comparative metaproteomics demonstrates different views on the complex granular sludge microbiome

Kleikamp

Grouzdev²,

Schaasberg

et al. 2022

Preprint

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“…Despite their importance, smORFs that encode these proteins are difficult to identify and as a result they are often overlooked (Su et al, 2013;Storz et al, 2014;Duval and Cossart, 2017). Techniques such as ribosome profiling (Ribo-seq) and proteomic approaches have had some success and provide evidence of transcription and translation of candidate smORFs (Aspden et al, 2014;Miravet-Verde et al, 2019;Weaver et al, 2019;Lohmann et al, 2020). However, these approaches can only detect what is being actively translated; additionally, they are limited by an experimental bottleneck, usually requiring the isolation and culture of an organism of interest.…”

Section: Introductionmentioning

confidence: 99%

Automated Prediction and Annotation of Small Open Reading Frames in Microbial Genomes

Durrant

Bhatt

2021

Cell Host & Microbe

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“…To uncover these active functions within a given sample, the characterization of the protein content is often essential 2 .…”

Section: Introductionmentioning

confidence: 99%

MegaGO: A Fast Yet Powerful Approach to Assess Functional Gene Ontology Similarity across Meta-Omics Data Sets

et al. 2021

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The study of microbiomes has gained in importance over the past few years, and has led to the fields of metagenomics, metatranscriptomics and metaproteomics. While initially focused on the study of biodiversity within these communities the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute functional similarity between two data sets. MegaGO is highly performant: each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and

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Function is what counts: how microbial community complexity affects species, proteome and pathway coverage in metaproteomics

Cited by 20 publications

References 77 publications

Comparative metaproteomics demonstrates different views on the complex granular sludge microbiome

Comparative metaproteomics demonstrates different views on the complex granular sludge microbiome

Automated Prediction and Annotation of Small Open Reading Frames in Microbial Genomes

MegaGO: A Fast Yet Powerful Approach to Assess Functional Gene Ontology Similarity across Meta-Omics Data Sets

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