Proteome adaptations of the organohalide-respiring Desulfitobacterium hafniense strain DCB-2 to various energy metabolisms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36733918", "kind": "science-record", "revision": 4, "content_hash": "sha256:884e5bcdc34f8a8a73acac967d93a2dbb95c6d18ec9c96699fb56536a5ef1395", "created_at": "2026-11-01T19:01:18Z", "updated_at": "2026-11-01T22:51:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Proteome adaptations of the organohalide-respiring Desulfitobacterium hafniense strain DCB-2 to various energy metabolisms.", "doi": "10.3389/fmicb.2023.1058127", "pmid": "36733918", "authors": ["Mathilde Stéphanie Willemin", "Romain Hamelin", "Florence Armand", "Christof Holliger", "Julien Maillard"], "journal": "Frontiers in microbiology", "year": 2023, "abstract": "Introduction: Desulfitobacterium hafniense was isolated for its ability to use organohalogens as terminal electron acceptors via organohalide respiration (OHR). In contrast to obligate OHR bacteria, Desulfitobacterium spp. show a highly versatile energy metabolism with the capacity to use different electron donors and acceptors and to grow fermentatively. Desulfitobacterium genomes display numerous and apparently redundant members of redox enzyme families which confirm their metabolic potential. Nonetheless, the enzymes responsible for many metabolic traits are not yet identified.\nMethods: In the present work, we conducted an extended proteomic study by comparing the proteomes of Desulfitobacterium hafniense strain DCB-2 cultivated in combinations of electron donors and acceptors, triggering five alternative respiratory metabolisms that include OHR, as well as fermentation. Tandem Mass Tag labelling proteomics allowed us to identify and quantify almost 60% of the predicted proteome of strain DCB-2 (2,796 proteins) in all six growth conditions. Raw data are available via ProteomeXchange with identifier PXD030393.\nResults and discussion: This dataset was analyzed in order to highlight the proteins that were significantly up-regulated in one or a subset of growth conditions and to identify possible key players in the different energy metabolisms. The addition of sodium sulfide as reducing agent in the medium - a very widespread practice in the cultivation of strictly anaerobic bacteria - triggered the expression of the dissimilatory sulfite reduction pathway in relatively less favorable conditions such as fermentative growth on pyruvate, respiration with H2 as electron donor and OHR conditions. The presence of H2, CO2 and acetate in the medium induced several metabolic pathways involved in carbon metabolism including the Wood-Ljungdahl pathway and two pathways related to the fermentation of butyrate that rely on electron-bifurcating enzymes. While the predicted fumarate reductase appears to be constitutively expressed, a new lactate dehydrogenase and lactate transporters were identified. Finally, the OHR metabolism with 3-chloro-4-hydroxyphenylacetate as electron acceptor strongly induced proteins encoded in several reductive dehalogenase gene clusters, as well as four new proteins related to corrinoid metabolism. We believe that this extended proteomic database represents a new landmark in understanding the metabolic versatility of Desulfitobacterium spp. and provides a solid basis for addressing future research questions.", "claims": [{"id": "claim-method", "statement": "Differential protein expression analysis was performed using R bioconductor package limma (version 3.34.9, 2018-02-22; Ritchie et al., 2015), followed by the Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36733918", "locator": "sections[10] \"Data analysis\" paragraph 2", "quote": "Differential protein expression analysis was performed using R bioconductor package limma (version 3.34.9, 2018-02-22; Ritchie et al., 2015), followed by the Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2019) partner repository with the dataset identifier PXD030393.1 The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article and in Supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36733918", "locator": "sections[47] \"Data Availability Statement\" paragraphs 1-3", "quote": "The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2019) partner repository with the dataset identifier PXD030393.1 The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article and in Supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Tandem Mass Tag labelling proteomics allowed us to identify and quantify almost 60% of the predicted proteome of strain DCB-2 (2,796 proteins) in all six growth conditions.", "kind": "quantitative", "target": {"value": 60.0, "unit": "per cent", "tolerance": null, "source": "Tandem Mass Tag labelling proteomics allowed us to identify and quantify almost 60% of the predicted proteome of strain DCB-2 (2,796 proteins) in all six growth conditions."}, "evidence": [{"asset": "source:pmid:36733918", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Tandem Mass Tag labelling proteomics allowed us to identify and quantify almost 60% of the predicted proteome of strain DCB-2 (2,796 proteins) in all six growth conditions."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Tandem Mass Tag labelling proteomics allowed us to identify and quantify almost 60% of the predicted proteome of strain DCB-2 (2,796 proteins) in all six growth conditions.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36733918"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36733918", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmicb.2023.1058127", "sha256": "sha256:3d1f212fc095cb36c0ee90b966175649c550ba4740a7da3db4a177c31e78d996", "bytes": 93591, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmicb.2023.1058127", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9888536", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Differential protein expression analysis was performed using R bioconductor package limma (version 3.34.9, 2018-02-22; Ritchie et al., 2015), followed by the Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995)."}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "differential_expression_fit", "expected_value": 60.0, "unit": "per cent", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. 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