ATP potentiates the formation of AChR aggregate in the co-culture of NG108-15 cells with C2C12 myotubes

AbstractThe role of adenosine 5′-triphosphate (ATP) and P2Y1 nucleotide receptor in potentiating agrin-induced acetylcholine receptor (AChR) aggregation is being demonstrated in a co-culture system of NG108-15 cell, a mouse neuroblastoma X rat glioma hybrid cell line that resembles spinal motor neuron, with C2C12 myotube. In the co-cultures, antagonized P2Y1 receptors showed a reduction in NG108-15 cell-induced AChR aggregation. Parallel to this observation, cultured NG108-15 cell secreted ATP into the conditioned medium in a time-dependent manner. Enhancement of ATP release from the cultured NG108-15 cells by overexpression of active mutants of small GTPases increased the aggregation of AChRs in co-culturing with C2C12 myotubes. In addition, ecto-nucleotidase was revealed in the co-culture, which rapidly degraded the applied ATP. These results support the notion that ATP has a role in directing the formation of post-synaptic apparatus in vertebrate neuromuscular junctions

Population-centric risk prediction modeling for gestational diabetes mellitus: A machine learning approach

AimsThe heterogeneity in Gestational Diabetes Mellitus (GDM) risk factors among different populations impose challenges in developing a generic prediction model. This study evaluates the predictive ability of existing UK NICE guidelines for assessing GDM risk in Singaporean women, and used machine learning to develop a non-invasive predictive model.MethodsData from 909 pregnancies in Singapore’s most deeply phenotyped mother-offspring cohort study, Growing Up in Singapore Towards healthy Outcomes (GUSTO), was used for predictive modeling. We used a CatBoost gradient boosting algorithm, and the Shapley feature attribution framework for model building and interpretation of GDM risk attributes.ResultsUK NICE guidelines showed poor predictability in Singaporean women [AUC:0.60 (95% CI 0.51, 0.70)]. The non-invasive predictive model comprising of 4 non-invasive factors: mean arterial blood pressure in first trimester, age, ethnicity and previous history of GDM, greatly outperformed [AUC:0.82 (95% CI 0.71, 0.93)] the UK NICE guidelines.ConclusionsThe UK NICE guidelines may be insufficient to assess GDM risk in Asian women. Our non-invasive predictive model outperforms the current state-of-the-art machine learning models to predict GDM, is easily accessible and can be an effective approach to minimize the economic burden of universal testing &amp; GDM associated healthcare in Asian populations.</p