Research Article: Discerning the spatio-temporal disease patterns of surgically induced OA mouse models

Date Published: April 11, 2019

Publisher: Public Library of Science

Author(s): Tobias Haase, Vikram Sunkara, Benjamin Kohl, Carola Meier, Patricia Bußmann, Jessica Becker, Michal Jagielski, Max von Kleist, Wolfgang Ertel, Lin Han.

http://doi.org/10.1371/journal.pone.0213734

Abstract

Osteoarthritis (OA) is the most common cause of disability in ageing societies, with no effective therapies available to date. Two preclinical models are widely used to validate novel OA interventions (MCL-MM and DMM). Our aim is to discern disease dynamics in these models to provide a clear timeline in which various pathological changes occur. OA was surgically induced in mice by destabilisation of the medial meniscus. Analysis of OA progression revealed that the intensity and duration of chondrocyte loss and cartilage lesion formation were significantly different in MCL-MM vs DMM. Firstly, apoptosis was seen prior to week two and was narrowly restricted to the weight bearing area. Four weeks post injury the magnitude of apoptosis led to a 40–60% reduction of chondrocytes in the non-calcified zone. Secondly, the progression of cell loss preceded the structural changes of the cartilage spatio-temporally. Lastly, while proteoglycan loss was similar in both models, collagen type II degradation only occurred more prominently in MCL-MM. Dynamics of chondrocyte loss and lesion formation in preclinical models has important implications for validating new therapeutic strategies. Our work could be helpful in assessing the feasibility and expected response of the DMM- and the MCL-MM models to chondrocyte mediated therapies.

Partial Text

Osteoarthritis (OA) is one of the most common degenerative diseases of the musculoskeletal system affecting millions of people with a major loss in life quality. Due to the lack of regenerative capacity of the articular cartilage, OA is a progressive disease leading to increasing functional impairment of the joints. Despite intensive research in this field, the exact pathogenesis of OA is still not completely understood and remains an active area of investigation.

In the present work we used a systematic approach combined with computer based automated analysis to discern the processes involved in OA progression within two widely used OA models. Analysis of various parameters at multiple time points after OA-induction allowed us to gain new insights into disease progression in these models (Fig 8). Our study was performed with a moderate number of animals (N = 54 animals in total, N = 3 animals per time point and surgical model, S1 Data). While this number may not suffice to naively compare adjacent time points, we chose a more suitable analytical method to analyse dynamics and time trends in the obtained data. Utilizing change point analysis, we could first identify critical timepoints where the dynamics of OA markers change in the different models. Subsequent pooling of data with identical dynamics (e.g. increase, decrease or stabilization of a marker) allows not only to conduct appropriate statistical comparison for the purpose of analysing time trends, but also allows to reduce the number of animals to be sacrificed.

 

Source:

http://doi.org/10.1371/journal.pone.0213734

 

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