The causes of impaired skeletal muscle mass and strength during aging are well-studied in healthy populations. Less is known on pathological age-related muscle wasting and weakness termed sarcopenia, which directly impacts physical autonomy and survival. Here, we compare genome-wide transcriptional changes of sarcopenia versus age-matched controls in muscle biopsies from 119 older men from Singapore, Hertfordshire UK and Jamaica. Individuals with sarcopenia reproducibly demonstrate a prominent transcriptional signature of mitochondrial bioenergetic dysfunction in skeletal muscle, with low PGC-1α/ERRα signalling, and downregulation of oxidative phosphorylation and mitochondrial proteostasis genes. These changes translate functionally into fewer mitochondria, reduced mitochondrial respiratory complex expression and activity, and low NAD+ levels through perturbed NAD+ biosynthesis and salvage in sarcopenic muscle. We provide an integrated molecular profile of human sarcopenia across ethnicities, demonstrating a fundamental role of altered mitochondrial metabolism in the pathological loss of skeletal muscle mass and function in older people.

Multiple myeloma is an incurable hematological malignancy that relies on drug combinations for first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure, and there remains a need to identify improved drug combinations. We developed the quadratic phenotypic optimization platform (QPOP) to optimize treatment combinations selected from a candidate pool of 114 approved drugs. QPOP uses quadratic surfaces to model the biological effects of drug combinations to identify effective drug combinations without reference to molecular mechanisms or predetermined drug synergy data. Applying QPOP to bortezomib-resistant multiple myeloma cell lines determined the drug combinations that collectively optimized treatment efficacy. We found that these combinations acted by reversing the DNA methylation and tumor suppressor silencing that often occur after acquired bortezomib resistance in multiple myeloma. Successive application of QPOP on a xenograft mouse model further optimized the dosages of each drug within a given combination while minimizing overall toxicity in vivo, and application of QPOP to ex vivo multiple myeloma patient samples optimized drug combinations in patient-specific contexts.

BackgroundBetter predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database.MethodsIn this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors.ResultsA model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score—climbing stairs were sufficient to predict survival class.ConclusionsUsing combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1–2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.