Cathepsin K Null Mice Show Reduced Adiposity during the Rapid Accumulation of Fat Stores

Growing evidences indicate that proteases are implicated in adipogenesis and in the onset of obesity. We previously reported that the cysteine protease cathepsin K (ctsk) is overexpressed in the white adipose tissue (WAT) of obese individuals. We herein characterized the WAT and the metabolic phenotype of ctsk deficient animals (ctsk−/−). When the growth rate of ctsk−/− was compared to that of the wild type animals (WT), we could establish a time window (5–8 weeks of age) within which ctsk−/−display significantly lower body weight and WAT size as compared to WT. Such a difference was not observable in older mice. Upon treatment with high fat diet (HFD) for 12 weeks ctsk−/− gained significantly less weight than WT and showed reduced brown adipose tissue, liver mass and a lower percentage of body fat. Plasma triglycerides, cholesterol and leptin were significantly lower in HFD-fed-ctsk−/− as compared to HFD-fed WT animals. Adipocyte lipolysis rates were increased in both young and HFD-fed-ctsk−/−, as compared to WT. Carnitine palmitoyl transferase-1 activity, was higher in mitochondria isolated from the WAT of HFD treated ctsk−/− as compared to WT. Together, these data indicate that ctsk ablation in mice results in reduced body fat content under conditions requiring a rapid accumulation of fat stores. This observation could be partly explained by an increased release and/or utilization of FFA and by an augmented ratio of lipolysis/lipogenesis. These results also demonstrate that under a HFD, ctsk deficiency confers a partial resistance to the development of dyslipidemia

From old organisms to new molecules: integrative biology and therapeutic targets in accelerated human ageing

Understanding the basic biology of human ageing is a key milestone in attempting to ameliorate the deleterious consequences of old age. This is an urgent research priority given the global demographic shift towards an ageing population. Although some molecular pathways that have been proposed to contribute to ageing have been discovered using classical biochemistry and genetics, the complex, polygenic and stochastic nature of ageing is such that the process as a whole is not immediately amenable to biochemical analysis. Thus, attempts have been made to elucidate the causes of monogenic progeroid disorders that recapitulate some, if not all, features of normal ageing in the hope that this may contribute to our understanding of normal human ageing. Two canonical progeroid disorders are Werner’s syndrome and Hutchinson-Gilford progeroid syndrome (also known as progeria). Because such disorders are essentially phenocopies of ageing, rather than ageing itself, advances made in understanding their pathogenesis must always be contextualised within theories proposed to help explain how the normal process operates. One such possible ageing mechanism is described by the cell senescence hypothesis of ageing. Here, we discuss this hypothesis and demonstrate that it provides a plausible explanation for many of the ageing phenotypes seen in Werner’s syndrome and Hutchinson-Gilford progeriod syndrome. The recent exciting advances made in potential therapies for these two syndromes are also reviewed

A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas

Abstract Background Copy Number Alternations (CNAs) is defined as somatic gain or loss of DNA regions. The profiles of CNAs may provide a fingerprint specific to a tumor type or tumor grade. Low-coverage sequencing for reporting CNAs has recently gained interest since successfully translated into clinical applications. Ovarian serous carcinomas can be classified into two largely mutually exclusive grades, low grade and high grade, based on their histologic features. The grade classification based on the genomics may provide valuable clue on how to best manage these patients in clinic. Based on the study of ovarian serous carcinomas, we explore the methodology of combining CNAs reporting from low-coverage sequencing with machine learning techniques to stratify tumor biospecimens of different grades. Results We have developed a data-driven methodology for tumor classification using the profiles of CNAs reported by low-coverage sequencing. The proposed method called Bag-of-Segments is used to summarize fixed-length CNA features predictive of tumor grades. These features are further processed by machine learning techniques to obtain classification models. High accuracy is obtained for classifying ovarian serous carcinoma into high and low grades based on leave-one-out cross-validation experiments. The models that are weakly influenced by the sequence coverage and the purity of the sample can also be built, which would be of higher relevance for clinical applications. The patterns captured by Bag-of-Segments features correlate with current clinical knowledge: low grade ovarian tumors being related to aneuploidy events associated to mitotic errors while high grade ovarian tumors are induced by DNA repair gene malfunction. Conclusions The proposed data-driven method obtains high accuracy with various parametrizations for the ovarian serous carcinoma study, indicating that it has good generalization potential towards other CNA classification problems. This method could be applied to the more difficult task of classifying ovarian serous carcinomas with ambiguous histology or in those with low grade tumor co-existing with high grade tumor. The closer genomic relationship of these tumor samples to low or high grade may provide important clinical value