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PXD007254

PXD007254 is an original dataset announced via ProteomeXchange.

Dataset Summary
TitleProtein Abundances can Distinguish Between Naturally-occurring and Laboratory Strains of Yersinia pestis, the Causative Agent of Plague
DescriptionThe rapid pace of bacterial evolution enables organisms to adapt to the laboratory environment with repeated passage and thus diverge from naturally-occurring environmental (“wild”) strains. Distinguishing wild and laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to convergent phenotypes, difficulty in detecting certain types of mutations, or perhaps because some adaptive modifications are epigenetic. Monitoring protein abundance, a molecular measure of phenotype, can overcome some of these difficulties. We have assembled a collection of Yersinia pestis proteomics datasets from our own published and unpublished work, and from a proteomics data archive, and demonstrated that protein abundance data can clearly distinguish laboratory-adapted from wild. We developed a lasso logistic regression classifier that uses binary (presence/absence) or quantitative protein abundance measures to predict whether a sample is laboratory-adapted or wild that proved to be ~98% accurate, as judged by replicated 10-fold cross-validation. Protein features selected by the classifier accord well with our previous study of laboratory adaptation in Y. pestis. The input data was derived from a variety of unrelated experiments and contained significant confounding variables. We show that the classifier is robust with respect to these variables. The methodology is able to discover signatures for laboratory facility and culture medium that are largely independent of the signature of laboratory adaptation. Going beyond our previous laboratory evolution study, this work suggests that proteomic differences between laboratory-adapted and wild Y. pestis are general, potentially pointing to a process that could apply to other species as well. Additionally, we show that proteomics datasets (even archived data collected for different purposes) contain the information necessary to distinguish wild and laboratory samples. This work has clear applications in biomarker detection as well as biodefense.
HostingRepositoryPRIDE
AnnounceDate2024-10-22
AnnouncementXMLSubmission_2024-10-22_04:13:13.779.xml
DigitalObjectIdentifier
ReviewLevelPeer-reviewed dataset
DatasetOriginOriginal dataset
RepositorySupportUnsupported dataset by repository
PrimarySubmitterOwen Leiser
SpeciesList scientific name: Yersinia pestis KIM D27; NCBI TaxID: 687916; scientific name: Yersinia pestis CO92; NCBI TaxID: 214092;
ModificationListacetylated residue; iodoacetamide derivatized residue
InstrumentLTQ Orbitrap
Dataset History
RevisionDatetimeStatusChangeLog Entry
02017-08-11 08:05:48ID requested
12017-09-08 00:24:19announced
22024-10-22 04:13:14announced2024-10-22: Updated project metadata.
Publication List
Merkley ED, Sego LH, Lin A, Leiser OP, Kaiser BLD, Adkins JN, Keim PS, Wagner DM, Kreuzer HW, Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis, the causative agent of plague. PLoS One, 12(8):e0183478(2017) [pubmed]
10.1371/journal.pone.0183478;
Keyword List
curator keyword: Biological
submitter keyword: machine learning, evolution, laboratory adaptation,Yersinia pestis
Contact List
Helen Kreuzer
contact affiliationPacific Northwest National Laboratory
contact emailHelen.Kreuzer@pnnl.gov
lab head
Owen Leiser
contact affiliationPacific Northwest National Laboratory
contact emailOwen.Leiser@pnnl.gov
dataset submitter
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Dataset FTP location
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PRIDE project URI
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