Optimizing Shotgun Proteomics Analysis for a Confident Protein Identification and Quantitation in Orphan Plant Species: The Case of Holm Oak (Quercus ilex)

PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

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Change in the plasma proteome associated with canine cognitive dysfunction syndrome (CCDS) in Thailand

BMC Veterinary Research ◽

10.1186/s12917-021-02744-w ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Sataporn Phochantachinda ◽

Boonrat Chantong ◽

Onrapak Reamtong ◽

Duangthip Chatchaisak

Keyword(s):

Cognitive Dysfunction ◽

Plasma Proteins ◽

Protein Identification ◽

Rating Scale ◽

Neurodegenerative Disorder ◽

Underlying Mechanism ◽

Population Based ◽

Diagnostic Biomarker ◽

Adult Group ◽

Proteomics Analysis

Abstract Background Canine cognitive dysfunction syndrome (CCDS) is a progressive neurodegenerative disorder found in senior dogs. Due to the lack of biological markers, CCDS is commonly underdiagnosed. The aim of this study was to identify potential plasma biomarkers using proteomics techniques and to increase our understanding of the pathogenic mechanism of the disease. Plasma amyloid beta 42 (Aβ42) has been seen to be a controversial biomarker for CCDS. Proteomics analysis was performed for protein identification and quantification. Results Within CCDS, ageing, and adult dogs, 87 proteins were identified specific to Canis spp. in the plasma samples. Of 87 proteins, 48 and 41 proteins were changed in the ageing and adult groups, respectively. Several distinctly expressed plasma proteins identified in CCDS were involved in complement and coagulation cascades and the apolipoprotein metabolism pathway. Plasma Aβ42 levels considerably overlapped within the CCDS and ageing groups. In the adult group, the Aβ42 level was low compared with that in the other groups. Nevertheless, plasma Aβ42 did not show a correlation with the Canine Cognitive Dysfunction Rating scale (CCDR) score in the CCDS group (p = 0.131, R2 = 0.261). Conclusions Our present findings suggest that plasma Aβ42 does not show potential for use as a diagnostic biomarker in CCDS. The nano-LC-MS/MS data revealed that the predictive underlying mechanism of CCDS was the co-occurrence of inflammation-mediated acute phase response proteins and complement and coagulation cascades that partly functioned by apolipoproteins and lipid metabolism. Some of the differentially expressed proteins may serve as potential predictor biomarkers along with Aβ42 in plasma for improved CCDS diagnosis. Further study in larger population-based cohort study is required in validation to define the correlation between protein expression and the pathogenesis of CCDS.

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