Matrix produced by diseased cardiac fibroblasts affects early myotube formation and function.

The extracellular matrix (ECM) provides both physical and chemical cues that dictate cell function and contribute to muscle maintenance. Muscle cells require efficient mitochondria to satisfy their high energy demand, however, the role the ECM plays in moderating mitochondrial function is not clear. We hypothesized that the ECM produced by stromal cells with mitochondrial dysfunction (Barth syndrome, BTHS) provides cues that contribute to metabolic dysfunction independent of muscle cell health. To test this, we harnessed the ECM production capabilities of human pluripotent stem-cell-derived cardiac fibroblasts (hPSC-CFs) from healthy and BTHS patients to fabricate cell-derived matrices (CDMs) with controlled topography, though we found that matrix composition from healthy versus diseased cells influenced myotube formation independent of alignment cues. To further investigate the effects of matrix composition, we then examined the influence of healthy- and BTHS-derived CDMs on myotube formation and metabolic function. We found that BTHS CDMs induced lower fusion index, lower ATP production, lower mitochondrial membrane potential, and higher ROS generation than the healthy CDMs. These findings imply that BTHS-derived ECM alone contributes to myocyte dysfunction in otherwise healthy cells. Finally, to investigate potential mechanisms, we defined the composition of CDMs produced by hPSC-CFs from healthy and BTHS patients using mass spectrometry and identified 15 ECM and related proteins that were differentially expressed in the BTHS-CDM compared to healthy CDM. Our results highlight that ECM composition affects skeletal muscle formation and metabolic efficiency in otherwise healthy cells, and our methods to generate patient-specific CDMs are a useful tool to investigate the influence of the ECM on disease progression and to investigate variability among diseased patients. STATEMENT OF SIGNIFICANCE: Muscle function requires both efficient metabolism to generate force and structured extracellular matrix (ECM) to transmit force, and we sought to examine the interactions between metabolism and ECM when metabolic disease is present. We fabricated patient-specific cell derived matrices (CDMs) with controlled topographic features to replicate the composition of healthy and mitochondrial-diseased (Barth syndrome) ECM. We found that disease-derived ECM negatively affects metabolic function of otherwise healthy myoblasts, and we identified several proteins in disease-derived ECM that may be mediating this dysfunction. We anticipate that our patient-specific CDM system could be fabricated with other topographies and cell types to study cell functions and diseases of interest beyond mitochondrial dysfunction and, eventually, be applied toward personalized medicine.

Cloud-based neuro-fuzzy hydro-climatic model for water quality assessment under uncertainty and sensitivity.

River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). However, it is challenging to model the nonlinearity and uncertain behavior of these parameters. When data is deficient and noisy, it creates missing and conflicting parameters within their complex inter-relationships. It is also essential to model how climatic variations and river discharge affect water quality. The present study proposes a cloud-based efficient and resourceful machine learning (ML) modeling framework using an artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and advanced particle swarm optimization (PSO). The framework assesses the sensitivity of five critical water quality parameters namely biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, temperature, and total coliform toward WQI of the River Ganges in India. Monthly datasets of these parameters, river flow, and climate components (rainfall and temperature) for a nine-year (2011-2019) period have been used to build the models. We also propose collecting the data by placing various monitoring sensors in the river and sending the data to the cloud for analysis. This helps in continuous monitoring and analysis. Results indicate that ANN and ANFIS capture the nonlinearity in the relationship among water quality parameters with a root mean square error (RMSE) of 7.5 × 10-7 (0.002%) and 1.02 × 10-5 (0.029%), respectively, while the combined ANN-PSO model gives normalized mean square error (NMSE) of 0.0024. The study demonstrates the role of cloud-based machine learning in developing watershed protection and restoration strategies by analyzing the sensitivity of individual water quality parameters while predicting water quality under changing climate and river discharge.

Benchmarking Protocol Deviations and Their Variation by Major Disease Categories.

BACKGROUND: Little to no data exist quantifying and benchmarking the magnitude of protocol deviation experience. METHODS: Nearly two-dozen companies provided the Tufts Center for the Study of Drug Development (Tufts CSDD) with data on the design and the performance of 187 protocols. RESULTS: The results of this working group study show that phase II and III protocols have a mean total of 75 and 119 protocol deviations, respectively, involving nearly one-third of all patients enrolled in each clinical trial. Oncology clinical trials have the highest relative mean number of protocol deviations affecting more than 40% of patients enrolled in each trial. The number of endpoints, the number of procedures per visit, and the number of countries were modestly positively associated with and predictive of, the incidence of deviations per protocol. A strong positive relationship was shown between the number of investigative sites and the number of protocol deviations. CONCLUSION: The results of this initial study provide useful measures that sponsor companies can use to benchmark their own protocol deviation experience, identify factors most associated with protocol deviations, and determine whether remediation is warranted.

Targeted deubiquitination rescues distinct trafficking-deficient ion channelopathies.

Impaired protein stability or trafficking underlies diverse ion channelopathies and represents an unexploited unifying principle for developing common treatments for otherwise dissimilar diseases. Ubiquitination limits ion channel surface density, but targeting this pathway for the purposes of basic study or therapy is challenging because of its prevalent role in proteostasis. We developed engineered deubiquitinases (enDUBs) that enable selective ubiquitin chain removal from target proteins to rescue the functional expression of disparate mutant ion channels that underlie long QT syndrome (LQT) and cystic fibrosis (CF). In an LQT type 1 (LQT1) cardiomyocyte model, enDUB treatment restored delayed rectifier potassium currents and normalized action potential duration. CF-targeted enDUBs synergistically rescued common (ΔF508) and pharmacotherapy-resistant (N1303K) CF mutations when combined with the US Food and Drug Administation (FDA)-approved drugs Orkambi (lumacaftor/ivacaftor) and Trikafta (elexacaftor/tezacaftor/ivacaftor and ivacaftor). Altogether, targeted deubiquitination via enDUBs provides a powerful protein stabilization method that not only corrects diverse diseases caused by impaired ion channel trafficking, but also introduces a new tool for deconstructing the ubiquitin code in situ.

Development of a High-Throughput Flow Cytometry Assay to Monitor Defective Trafficking and Rescue of Long QT2 Mutant hERG Channels.

Long QT Syndrome (LQTS) is an acquired or inherited disorder characterized by prolonged QT interval, exertion-triggered arrhythmias, and sudden cardiac death. One of the most prevalent hereditary LQTS subtypes, LQT2, results from loss-of-function mutations in the hERG channel, which conducts IKr, the rapid component of the delayed rectifier K+ current, critical for cardiac repolarization. The majority of LQT2 mutations result in Class 2 deficits characterized by impaired maturation and trafficking of hERG channels. Here, we have developed a high-throughput flow cytometric assay to analyze the surface and total expression of wild-type (WT) and mutant hERG channels with single-cell resolution. To test our method, we focused on 16 LQT2 mutations in the hERG Per-Arnt-Sim (PAS) domain that were previously studied via a widely used biochemical approach that compares levels of 135-kDa immature and 155-kDa fully glycosylated hERG protein to infer surface expression. We confirmed that LQT2 mutants expressed in HEK293 cells displayed a decreased surface density compared to WT hERG, and were differentially rescued by low temperature. However, we also uncovered some notable differences from the findings obtained via the biochemical approach. In particular, three mutations (N33T, R56Q, and A57P) with apparent WT-like hERG glycosylation patterns displayed up to 50% decreased surface expression. Furthermore, despite WT-like levels of complex glycosylation, these mutants have impaired forward trafficking, and exhibit varying half-lives at the cell surface. The results highlight utility of the surface labeling/flow cytometry approach to quantitatively assess trafficking deficiencies associated with LQT2 mutations, to discern underlying mechanisms, and to report on interventions that rescue deficits in hERG surface expression.