Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning.

Congenital long QT syndrome (LQTS) is characterized by a prolonged QT-interval on an electrocardiogram (ECG). An abnormal prolongation in the QT-interval increases the risk for fatal arrhythmias. Genetic variants in several different cardiac ion channel genes, including KCNH2, are known to cause LQTS. Here, we evaluated whether structure-based molecular dynamics (MD) simulations and machine learning (ML) could improve the identification of missense variants in LQTS-linked genes. To do this, we investigated KCNH2 missense variants in the Kv11.1 channel protein shown to have wild type (WT) like or class II (trafficking-deficient) phenotypes in vitro. We focused on KCNH2 missense variants that disrupt normal Kv11.1 channel protein trafficking, as it is the most common phenotype for LQTS-associated variants. Specifically, we used computational techniques to correlate structural and dynamic changes in the Kv11.1 channel protein PAS domain (PASD) with Kv11.1 channel protein trafficking phenotypes. These simulations unveiled several molecular features, including the numbers of hydrating waters and hydrogen bonding pairs, as well as folding free energy scores, that are predictive of trafficking. We then used statistical and machine learning (ML) (Decision tree (DT), Random forest (RF), and Support vector machine (SVM)) techniques to classify variants using these simulation-derived features. Together with bioinformatics data, such as sequence conservation and folding energies, we were able to predict with reasonable accuracy (≈75%) which KCNH2 variants do not traffic normally. We conclude that structure-based simulations of KCNH2 variants localized to the Kv11.1 channel PASD led to an improvement in classification accuracy. Therefore, this approach should be considered to complement the classification of variant of unknown significance (VUS) in the Kv11.1 channel PASD.

Organelle Specific Macrophage Engineered Vesicles Differentially Reprogram Macrophage Polarization.

Tumor-associated macrophages (TAMs) represent the majority of the immune cells present in the tumor microenvironment. These macrophages exhibit an anti-inflammatory (M2)-like physiological state and execute immune-suppressive and tumor-supporting properties. With TAMs being plastic, there is a growing interest in reprogramming them toward a pro-inflammatory (M1)-like phenotype that exhibits anti-tumoral properties. Recent studies have demonstrated that both engineered vesicles derived from macrophages and endogenous extracellular vesicles produced by macrophages can be programmed to alter macrophage phenotype. Here it is demonstrated that pro-inflammatory macrophage-engineered subcellular vesicles (MEVs) have differential properties based on their organelle of origin. Endoplasmic reticulum specific MEVs (erMEVs) treated M2 macrophages exhibit enhanced pro-inflammatory cytokine production compared to plasma membrane specific MEVs (pmMEVs) treated M2 macrophages. In addition, under in vitro co-culture conditions, erMEVs elicit superior efficacy in suppressing the viability of cancer cells compared to the same concentration of pmMEVs. Furthermore, erMEVs and pmMEVs maintain differences in their membrane proteins, that regulate the repolarization efficacy of M2 macrophages toward an M1-like phenotype. In addition, The M2 to M1 repolarizing efficacy of MEVs can be altered by changing the activity of the membrane proteins present on erMEVs or pmMEVs.