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Label-free quantitative (LFQ) proteomic analysis of redox mediated posttranslational modifications (PTM) in the cartilage from young and old osteoarthritis (OA) mice with or without the senolytic treatment

ABSTRACT: Senolytic drugs are designed to selectively clear senescent cells (SnCs) that accumulate with injury or aging. In a mouse model of osteoarthritis (OA), senolysis yields a pro-regenerative response. However, recent research studies showed that the therapeutic benefit is reduced in aged mice. Increased oxidative stress (redox) mediates metabolic dysregulation and accumulation of posttranslational modifications (PTM) on proteins from the cartilage and is one of the major hallmarks of advanced age. This research investigated whether the senolytic treatment differentially affects oxidative load in the joints from young and aged animals. We employed label-free quantitative (LFQ) analysis of changes in the protein expression profiles and associated carbonylated PTMs in the extracted joint proteins. Our proteomic survey focused to a set of PTMs described as advanced glycation-end products (AGEs) or advanced lipooxidation-end products (ALEs), known to accumulate during aging and age-associated diseases. AGEs and ALEs are the result of non-enzymatic reactions of protein with reducing sugars, or with oxidized sugar or lipid degradation products, which may alter and sometimes crosslink the positively charged amino acids lysine and arginine on proteins. Our proteomic dataset supported detection of several AGEs and ALEs, including the adducts 4-ONE (4-oxononenal), 4-ONE+Delta:H(-2)O(-1) (dehydrated 4-oxononenal Michael adduct), carboxyethyl, carboxymethyl, 3-deoxyglucosone derived dihydroxyimidazoline, G-H1 (glyoxal-derived hydroimidazolone), HNE (4-hydroxynonenal), several carbonylated adducts including proline to pyrrolidinone or pyrrolidone Other detected modifications of note include mono-oxidation and di-oxidation products, including tryptophan to kynurenine. We also detected lysine-epsilon-gly-gly (GlyGly), which marks ubiquitylated sites, indicating potential substrates of proteasome-dependent degradation. The grand total PTM change was negative for treated aged OA mice but positive for treated young OA mice, regardless of whether summed across modifications or across proteins. In other words, senolytic treatment reduced oxidation associated PTMs more effectively in aged OA mice than in young OA mice. The LFQ proteomics analysis was complemented with new biophysical computational tools aimed to predict the stability of carbonylated proteins extracted from the joints. The biophysical model predicted divergent proteomic responses between young and aged animals. Altogether, our results showed that senolysis reduces overall oxidative stress in the aged arthritic joints in vivo.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Cartilage

DISEASE(S): Osteoarthritis

SUBMITTER: Cristina Clement

LAB HEAD: Jennifer H. Elisseeff PhD

PROVIDER: PXD031782 | Pride | 2023-09-26

REPOSITORIES: Pride

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			Action	DRS
	20160523_LSantambrogio_S10.mgf	Mgf
	20160523_LSantambrogio_S10.raw	Raw
	20160523_LSantambrogio_S11.mgf	Mgf
	20160523_LSantambrogio_S11.raw	Raw
	20160523_LSantambrogio_S12.mgf	Mgf

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Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10. Synovial tisssue from healthy joints, OA joints, and RA joints.

Dataset Information

Label-free quantitative (LFQ) proteomic analysis of redox mediated posttranslational modifications (PTM) in the cartilage from young and old osteoarthritis (OA) mice with or without the senolytic treatment

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