Mitochondrial fusion and altered beta-oxidation drive muscle wasting in a Drosophila cachexia model.

Cancer cachexia is a tumour-induced wasting syndrome, characterised by extreme loss of skeletal muscle. Defective mitochondria can contribute to muscle wasting; however, the underlying mechanisms remain unclear. Using a Drosophila larval model of cancer cachexia, we observed enlarged and dysfunctional muscle mitochondria. Morphological changes were accompanied by upregulation of beta-oxidation proteins and depletion of muscle glycogen and lipid stores. Muscle lipid stores were also decreased in Colon-26 adenocarcinoma mouse muscle samples, and expression of the beta-oxidation gene CPT1A was negatively associated with muscle quality in cachectic patients. Mechanistically, mitochondrial defects result from reduced muscle insulin signalling, downstream of tumour-secreted insulin growth factor binding protein (IGFBP) homologue ImpL2. Strikingly, muscle-specific inhibition of Forkhead box O (FOXO), mitochondrial fusion, or beta-oxidation in tumour-bearing animals preserved muscle integrity. Finally, dietary supplementation with nicotinamide or lipids, improved muscle health in tumour-bearing animals. Overall, our work demonstrates that muscle FOXO, mitochondria dynamics/beta-oxidation and lipid utilisation are key regulators of muscle wasting in cancer cachexia.

Computational framework to understand the clinical stages of COVID-19 and visualization of time course for various treatment strategies.

Coronavirus disease 2019 is known to be regulated by multiple factors such as delayed immune response, impaired T cell activation, and elevated levels of proinflammatory cytokines. Clinical management of the disease remains challenging due to interplay of various factors as drug candidates may elicit different responses depending on the staging of the disease. In this context, we propose a computational framework which provides insights into the interaction between viral infection and immune response in lung epithelial cells, with an aim of predicting optimal treatment strategies based on infection severity. First, we formulate the model for visualizing the nonlinear dynamics during the disease progression considering the role of T cells, macrophages and proinflammatory cytokines. Here, we show that the model is capable of emulating the dynamic and static data trends of viral load, T cell, macrophage levels, interleukin (IL)-6 and TNF-α levels. Second, we demonstrate the ability of the framework to capture the dynamics corresponding to mild, moderate, severe, and critical condition. Our result shows that, at late phase (>15 days), severity of disease is directly proportional to pro-inflammatory cytokine IL6 and tumor necrosis factor (TNF)-α levels and inversely proportional to the number of T cells. Finally, the simulation framework was used to assess the effect of drug administration time as well as efficacy of single or multiple drugs on patients. The major contribution of the proposed framework is to utilize the infection progression model for clinical management and administration of drugs inhibiting virus replication and cytokine levels as well as immunosuppressant drugs at various stages of the disease.

Analytics Pipeline for Visualization of Single Cell RNA Sequencing Data from Brochoaveolar Fluid in COVID-19 Patients: Assessment of Neuro Fuzzy-C-Means and HDBSCAN.

Since the mutation in SARS-COV2 poses new challenges in designing vaccines, it is imperative to develop advanced tools for visualizing the genetic information. Specially, it remains challenging to address the patient-to-patient variability and identify the signature for severe/critical conditions. In this endeavor we analyze the large-scale RNA-sequencing data collected from broncho-alveolar fluid. In this work, we have used PCA and tSNE for the dimension-reduction. The novelty of the current work is to depict a detailed comparison of k-means, HDBSAN and neuro-fuzzy method in visualization of high-dimension data on gene expression. Clinical Relevance- The subpopulation profiling can be used to study the patient-to patient variability when infected by SARS-COV-2 and its variants. The distribution of cell types can be relevant in designing new drugs that are targeted to control the distribution of epithelial cells T cells and macrophages.

Microfluidic Protein Imaging Platform: Study of Tau Protein Aggregation and Alzheimer's Drug Response.

Tau protein aggregation is identified as one of the key phenomena associated with the onset and progression of Alzheimer's disease. In the present study, we performed on-chip confocal imaging of tau protein aggregation and tau-drug interactions using a spiral-shaped passive micromixing platform. Numerical simulations and experiments were performed in order to validate the performance of the micromixer design. We performed molecular modeling of adenosine triphosphate (ATP)-induced tau aggregation in order to successfully validate the concept of helical tau filament formation. Tau aggregation and native tau restoration were realized using an immunofluorescence antibody assay. The dose-response behavior of an Alzheimer's drug, methylthioninium chloride (MTC), was monitored on-chip for defining the optimum concentration of the drug. The proposed device was tested for reliability and repeatability of on-chip tau imaging. The amount of the tau protein sample used in our experiments was significantly less than the usage for conventional techniques, and the whole protein-drug assay was realized in less than two hours. We identified that intensity-based tau imaging could be used to study Alzheimer's drug response. In addition, it was demonstrated that cell-free, microfluidic tau protein assays could be used as potential on-chip drug evaluation tools for Alzheimer's disease.

Comparison of Calcium Dynamics and Specific Features for G Protein-Coupled Receptor-Targeting Drugs Using Live Cell Imaging and Automated Analysis.

G protein-coupled receptors (GPCRs) are targets for designing a large fraction of the drugs in the pharmaceutical industry. For GPCR-targeting drug screening using cell-based assays, measurement of cytosolic calcium has been widely used to obtain dose-response profiles. However, it remains challenging to obtain drug-specific features due to cell-to-cell heterogeneity in drug-cell responses obtained from live cell imaging. Here, we present a framework combining live cell imaging of a cell population and a feature extraction method for classification of responses of drugs targeting GPCRs CXCR4 and α2AR. We measured the calcium dynamics using confocal microscopy and compared the responses for SDF-1α and norepinephrine. The results clearly show that the clustering patterns of responses for the two GPCRs are significantly different. Additionally, we show that different drugs targeting the same GPCR induce different calcium response signatures. We also implemented principal component analysis and k means for feature extraction and used nondominated (ND) sorting for ranking a group of drugs at various doses. The presented approach can be used to model a cell population as a mixture of subpopulations. It also offers specific advantages, such as higher spatial resolution, classification of responses, and ranking of drugs, potentially providing a platform for high-content drug screening.

Optical control demonstrates switch-like PIP3 dynamics underlying the initiation of immune cell migration.

There is a dearth of approaches to experimentally direct cell migration by continuously varying signal input to a single cell, evoking all possible migratory responses and quantitatively monitoring the cellular and molecular response dynamics. Here we used a visual blue opsin to recruit the endogenous G-protein network that mediates immune cell migration. Specific optical inputs to this optical trigger of signaling helped steer migration in all possible directions with precision. Spectrally selective imaging was used to monitor cell-wide phosphatidylinositol (3,4,5)-triphosphate (PIP3), cytoskeletal, and cellular dynamics. A switch-like PIP3 increase at the cell front and a decrease at the back were identified, underlying the decisive migratory response. Migration was initiated at the rapidly increasing switch stage of PIP3 dynamics. This result explains how a migratory cell filters background fluctuations in the intensity of an extracellular signal but responds by initiating directionally sensitive migration to a persistent signal gradient across the cell. A two-compartment computational model incorporating a localized activator that is antagonistic to a diffusible inhibitor was able to simulate the switch-like PIP3 response. It was also able simulate the slow dissipation of PIP3 on signal termination. The ability to independently apply similar signaling inputs to single cells detected two cell populations with distinct thresholds for migration initiation. Overall the optical approach here can be applied to understand G-protein-coupled receptor network control of other cell behaviors.

Optically triggering spatiotemporally confined GPCR activity in a cell and programming neurite initiation and extension.

G-protein-coupled receptor (GPCR) activity gradients evoke important cell behavior but there is a dearth of methods to induce such asymmetric signaling in a cell. Here we achieved reversible, rapidly switchable patterns of spatiotemporally restricted GPCR activity in a single cell. We recruited properties of nonrhodopsin opsins--rapid deactivation, distinct spectral tuning, and resistance to bleaching--to activate native Gi, Gq, or Gs signaling in selected regions of a cell. Optical inputs were designed to spatiotemporally control levels of second messengers, IP3, phosphatidylinositol (3,4,5)-triphosphate, and cAMP in a cell. Spectrally selective imaging was accomplished to simultaneously monitor optically evoked molecular and cellular response dynamics. We show that localized optical activation of an opsin-based trigger can induce neurite initiation, phosphatidylinositol (3,4,5)-triphosphate increase, and actin remodeling. Serial optical inputs to neurite tips can refashion early neuron differentiation. Methods here can be widely applied to program GPCR-mediated cell behaviors.

A steady state analysis indicates that negative feedback regulation of PTP1B by Akt elicits bistability in insulin-stimulated GLUT4 translocation.

BACKGROUND: The phenomenon of switch-like response to graded input signal is the theme involved in various signaling pathways in living systems. Positive feedback loops or double negative feedback loops embedded with nonlinearity exhibit these switch-like bistable responses. Such feedback regulations exist in insulin signaling pathway as well. METHODS: In the current manuscript, a steady state analysis of the metabolic insulin-signaling pathway is presented. The threshold concentration of insulin required for glucose transporter GLUT4 translocation was studied with variation in system parameters and component concentrations. The dose response curves of GLUT4 translocation at various concentration of insulin obtained by steady state analysis were quantified in-terms of half saturation constant. RESULTS: We show that, insulin-stimulated GLUT4 translocation can operate as a bistable switch, which ensures that GLUT4 settles between two discrete, but mutually exclusive stable steady states. The threshold concentration of insulin required for GLUT4 translocation changes with variation in system parameters and component concentrations, thus providing insights into possible pathological conditions. CONCLUSION: A steady state analysis indicates that negative feedback regulation of phosphatase PTP1B by Akt elicits bistability in insulin-stimulated GLUT4 translocation. The threshold concentration of insulin required for GLUT4 translocation and the corresponding bistable response at different system parameters and component concentrations was compared with reported experimental observations on specific defects in regulation of the system.