Circadian time series proteomics reveals daily dynamics in cartilage physiology

Abstract

Objectives: Articular cartilage undergoes cyclical heavy loading and low load recovery during the 24-hour day/night cycle. We investigated the daily changes of protein abundance in mouse femoral head articular cartilage by performing 24-hour timeseries proteomics study.Methods: Tandem mass spectrometry analysis was used to quantify proteins extracted from mouse cartilage. Bioinformatics analysis was performed to quantify rhythmic changes in protein abundance. Primary chondrocytes were isolated and cultured for independent validation of selected rhythmic proteins.Results: 145 rhythmic proteins were detected. Among these were key cartilage molecules including CCN2, MATN1, PAI-1 and PLOD1 & 2. Pathway analysis revealed that proteins related to protein synthesis, cytoskeleton and glucose metabolism exhibited time-of-day dependent peaks in their abundance. Meta-analysis of published proteomics datasets from articular cartilage revealed that numerous rhythmic proteins were dysregulated in osteoarthritis and/or ageing. Conclusions:Our circadian proteomics study revealed that articular cartilage is a much more dynamic tissue than previously thought. Chondrocytes exhibit circadian rhythms not only in gene expression but also in protein abundance. Our results clearly call for the consideration of circadian timing in understanding cartilage biology, osteoarthritis pathogenesis, treatment strategies and biomarker detection.

Drivers of phenotypic variation in cartilage: Circadian clock genes

“…With the emergence of DNA microarrays, mass spectrometry and other advanced molecular biological technologies, more clock genes have been detected in cartilage. More than 600 genes and 145 proteins expressed in cartilage have periodic rhythmicity controlled by clock genes 28‐31 …”

“…More than 600 genes and 145 proteins expressed in cartilage have periodic rhythmicity controlled by clock genes. 28 , 29 , 30 , 31 …”

Two levels of selection of rhythmicity in gene expression: energy saving for rhythmic proteins and noise optimization for rhythmic transcripts

“…Tissues analysed are: adrenal gland, aorta, brain stem, brown adipose, cerebellum, heart, kidney, liver, lung, muscle, and white adipose. Multi-tissues time-serie proteomic data come from: Mauvoisin et al (2014) for the liver, Noya et al (2019) for the forebrain, Chang et al (2020) for the tendon, and Dudek et al (2019) for the cartilage. Finally, single-cell data of thousand of cells in organs for which we had time-series datasets, i.e.…”