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A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry.

ABSTRACT: Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism's proteome-even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by mass spectrometry, however, is a developing field, and the bone proteome, typically consisting of only two proteins, pushes the limits of this technology. Nearly 20% of highly confident spectra from modern human bone samples identify non-human species when searched against a vertebrate database-as would be necessary with a fragment of unknown bone. These non-human peptides are often the result of current limitations in mass spectrometry or algorithm interpretation errors. Consequently, it is difficult to know if a "species-specific" peptide used to identify a sample is actually present in that sample. Here we evaluate the causes of peptide sequence errors and propose an unbiased, probabilistic approach to determine the likelihood that a species is correctly identified from bone without relying on species-specific peptides.

SUBMITTER: Yang H

PROVIDER: S-EPMC8149876 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry.

Yang Heyi H Butler Erin R ER Monier Samantha A SA Teubl Jennifer J Fenyö David D Ueberheide Beatrix B Siegel Donald D

Scientific reports 20210525 1

Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism's proteome-even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by ...[more]

PMID: 34035355

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Project description:Proteomic profiling using mass spectrometry (MS) is one of the most promising methods for the analysis of complex biological samples such as urine, serum and tissue for biomarker discovery. Such experiments are often conducted using MALDI-TOF (matrix-assisted laser desorption/ionisation time-of-flight) and SELDI-TOF (surface-enhanced laser desorption/ionisation time-of-flight) MS. Using such profiling methods it is possible to identify changes in protein expression that differentiate disease states and individual proteins or patterns that may be useful as potential biomarkers. However, the incorporation of quality control (QC) processes that allow the identification of low quality spectra reliably and hence allow the removal of such data before further analysis is often overlooked. In this paper we describe rigorous methods for the assessment of quality of spectral data. These procedures are presented in a user-friendly, web-based program. The data obtained post-QC is then examined using variance components analysis to quantify the amount of variance due to some of the factors in the experimental design.Using data from a SELDI profiling study of serum from patients with different levels of renal function, we show how the algorithms described in this paper may be used to detect systematic variability within and between sample replicates, pooled samples and SELDI chips and spots. Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms. Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.Using the techniques described in this paper it is possible to reliably detect poor quality data within proteomic profiling experiments undertaken by MS. The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

Project description:The lack of early diagnostic maneuvers and effective pharmacotherapy for osteoarthritis (OA) is predominantly attributed to current limited understanding of its pathogenesis. In the present study, the alteration of synovial fluid (SF) proteome in OA were analyzed using SWATH‑mass spectrometry (SWATH‑MS) to further elucidate the pathogenesis of OA. SF samples were collected from 10 OA and 10 rheumatoid arthritis (RA) patients undergoing arthroplasty and 10 patients undergoing arthroscopy for traumatic arthritis (meniscus injury without cartilage lesion). According to the Kellgren‑Lawrence (KL) radiographic grading criteria, all the OA and RA patients were classified as KL grade 4. SWATH‑MS was applied to identify differentially expressed proteins specifically regulated in OA. Differentially expressed proteins identified by SWATH‑MS were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Proteins of interest were quantified by enzyme‑linked immunosorbent assay (ELISA) following identification. With the use of SWATH‑MS, 131 proteins were identified to be differentially expressed in OA, of which 93 corresponded to upregulation and 38 to downregulation. Complement C1r was the most significantly upregulated protein in OA. Twenty‑eight out of the 131 proteins were specifically regulated in OA, of which 17 were upregulated and 11 were downregulated. Dickkopf‑related protein 2 (DKK2) was one of the proteins specifically upregulated in OA. Furthermore, KEGG pathway annotation indicated that differentially expressed proteins in OA were enriched in complement and coagulation cascades. ELISA indicated that OA severity was positively correlated with the levels of complement C1r (r=0.549; P<0.001) and DKK2 (r=0.79; P<0.001) in the SF. The results indicate that complement and coagulation cascades are involved in the pathogenesis of OA. Differentially expressed proteins, including complement C1r and DKK2 may be used as potential biomarkers or drug targets, which may facilitate with intervention of OA.

Dataset Information

A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry.

Publications

A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry.

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