Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis, the causative agent of plague
| Autor: | David M. Wagner, Eric D. Merkley, Joshua N. Adkins, Andy Lin, Helen W. Kreuzer, Brooke L. Deatherage Kaiser, Paul Keim, Landon H. Sego, Owen P. Leiser |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Proteomics Research Facilities Proteome Protein Expression lcsh:Medicine Pathology and Laboratory Medicine Genome Biochemistry Database and Informatics Methods Environmental Microbiology Medicine and Health Sciences Data Mining lcsh:Science Multidisciplinary biology Proteomic Databases Organic Compounds Phenotype Adaptation Physiological Yersinia Bacterial Pathogens Chemistry Medical Microbiology Physical Sciences Pathogens Information Technology Research Laboratories Research Article Computer and Information Sciences Yersinia Pestis 030106 microbiology Genomics Computational biology Research and Analysis Methods Microbiology DNA sequencing 03 medical and health sciences Bacterial Proteins Species Specificity Gene Expression and Vector Techniques Humans Molecular Biology Techniques Microbial Pathogens Molecular Biology Biodefense Bacteriological Techniques Plague Molecular Biology Assays and Analysis Techniques Bacteria Ethanol lcsh:R Organic Chemistry Organisms Chemical Compounds Biology and Life Sciences biology.organism_classification 030104 developmental biology Logistic Models Biological Databases Yersinia pestis Alcohols lcsh:Q Protein Abundance Government Laboratories |
| Zdroj: | PLoS ONE PLoS ONE, Vol 12, Iss 8, p e0183478 (2017) |
| ISSN: | 1932-6203 |
| Popis: | The 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 We have assembled a collection of Yersinia pestis proteomics datasets from our own published and unpublished work, and from a proteomics data archive, and demonstrated that protein abundance data can clearly distinguish laboratory-adapted from wild.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. |
| Databáze: | OpenAIRE |
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