Autophagy coordinates chondrocyte development and early joint formation in zebrafish

Autophagy is a catabolic process responsible for the removal of waste and damaged cellular components by lysosomal degradation. It plays a key role in fundamental cell processes, including ER stress mitigation, control of cell metabolism, and cell differentiation and proliferation, all of which are essential for cartilage cell (chondrocyte) development and survival, and for the formation of cartilage. Correspondingly, autophagy dysregulation has been implicated in several skeletal disorders such as osteoarthritis and osteoporosis. To test the requirement for autophagy during skeletal development in zebrafish, we generated an atg13 CRISPR knockout zebrafish line. This line showed a complete loss of atg13 expression, and restricted autophagic activity in vivo. In the absence of autophagy, chondrocyte maturation was accelerated, with chondrocytes exhibiting signs of premature hypertrophy. Focussing on the jaw element, autophagy disruption affected joint articulation causing restricted mouth opening. This gross behavioural phenotype corresponded with a failure to thrive, and death in homozygote atg13 nulls within 17 days. Taken together, our results are consistent with autophagy contributing to the timely regulation of chondrocyte maturation and for extracellular matrix formation

Statistical issues related to dietary intake as the response variable in intervention trials.

The focus of this paper is dietary intervention trials. We explore the statistical issues involved when the response variable, intake of a food or nutrient, is based on self-report data that are subject to inherent measurement error. There has been little work on handling error in this context. A particular feature of self-reported dietary intake data is that the error may be differential by intervention group. Measurement error methods require information on the nature of the errors in the self-report data. We assume that there is a calibration sub-study in which unbiased biomarker data are available. We outline methods for handling measurement error in this setting and use theory and simulations to investigate how self-report and biomarker data may be combined to estimate the intervention effect. Methods are illustrated using data from the Trial of Nonpharmacologic Intervention in the Elderly, in which the intervention was a sodium-lowering diet and the response was sodium intake. Simulations are used to investigate the methods under differential error, differing reliability of self-reports relative to biomarkers and different proportions of individuals in the calibration sub-study. When the reliability of self-report measurements is comparable with that of the biomarker, it is advantageous to use the self-report data in addition to the biomarker to estimate the intervention effect. If, however, the reliability of the self-report data is low compared with that in the biomarker, then, there is little to be gained by using the self-report data. Our findings have important implications for the design of dietary intervention trials. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd