A simple active fluid model unites cytokinesis, cell crawling, and axonal outgrowth.

Axonal outgrowth, cell crawling, and cytokinesis utilize actomyosin, microtubule-based motors, cytoskeletal dynamics, and substrate adhesions to produce traction forces and bulk cellular motion. While it has long been appreciated that growth cones resemble crawling cells and that the mechanisms that drive cytokinesis help power cell crawling, they are typically viewed as unique processes. To better understand the relationship between these modes of motility, here, we developed a unified active fluid model of cytokinesis, amoeboid migration, mesenchymal migration, neuronal migration, and axonal outgrowth in terms of cytoskeletal flow, adhesions, viscosity, and force generation. Using numerical modeling, we fit subcellular velocity profiles of the motions of cytoskeletal structures and docked organelles from previously published studies to infer underlying patterns of force generation and adhesion. Our results indicate that, during cytokinesis, there is a primary converge zone at the cleavage furrow that drives flow towards it; adhesions are symmetric across the cell, and as a result, cells are stationary. In mesenchymal, amoeboid, and neuronal migration, the site of the converge zone shifts, and differences in adhesion between the front and back of the cell drive crawling. During neuronal migration and axonal outgrowth, the primary convergence zone lies within the growth cone, which drives actin retrograde flow in the P-domain and bulk anterograde flow of the axonal shaft. They differ in that during neuronal migration, the cell body is weakly attached to the substrate and thus moves forward at the same velocity as the axon. In contrast, during axonal outgrowth, the cell body strongly adheres to the substrate and remains stationary, resulting in a decrease in flow velocity away from the growth cone. The simplicity with which cytokinesis, cell crawling, and axonal outgrowth can be modeled by varying coefficients in a simple model suggests a deep connection between them.

Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank.

Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data. We evaluate the performance of Group-LASSO INTERaction-NET (glinternet) and pretrained lasso in disease prediction focusing on diverse ancestries in the UK Biobank. Models were trained on data from White British and other ancestries and validated across a cohort of over 96,000 individuals for 8 diseases. Out of 96 models trained, we report 16 with statistically significant incremental predictive performance in terms of ROC-AUC scores (p-value < 0.05), found for diabetes, arthritis, gall stones, cystitis, asthma and osteoarthritis. For the interaction and pretrained models that outperformed the baseline, the PRS score was the primary driver behind prediction. Our findings indicate that both interaction terms and pre-training can enhance prediction accuracy but for a limited set of diseases and moderate improvements in accuracy.

MMIL: A novel algorithm for disease associated cell type discovery.

Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. To address this, we introduce Mixture Modeling for Multiple Instance Learning (MMIL), an expectation maximization method that enables the training and calibration of cell-level classifiers using patient-level labels. Our approach can be used to train e.g. lasso logistic regression models, gradient boosted trees, and neural networks. When applied to clinically-annotated, primary patient samples in Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL), our method accurately identifies cancer cells, generalizes across tissues and treatment timepoints, and selects biologically relevant features. In addition, MMIL is capable of incorporating cell labels into model training when they are known, providing a powerful framework for leveraging both labeled and unlabeled data simultaneously. Mixture Modeling for MIL offers a novel approach for cell classification, with significant potential to advance disease understanding and management, especially in scenarios with unknown gold-standard labels and high dimensionality.

CAR19 monitoring by peripheral blood immunophenotyping reveals histology-specific expansion and toxicity.

ABSTRACT: Chimeric antigen receptor (CAR) T cells directed against CD19 (CAR19) are a revolutionary treatment for B-cell lymphomas (BCLs). CAR19 cell expansion is necessary for CAR19 function but is also associated with toxicity. To define the impact of CAR19 expansion on patient outcomes, we prospectively followed a cohort of 236 patients treated with CAR19 (brexucabtagene autoleucel or axicabtagene ciloleucel) for mantle cell lymphoma (MCL), follicular lymphoma, and large BCL (LBCL) over the course of 5 years and obtained CAR19 expansion data using peripheral blood immunophenotyping for 188 of these patients. CAR19 expansion was higher in patients with MCL than other lymphoma histologic subtypes. Notably, patients with MCL had increased toxicity and required fourfold higher cumulative steroid doses than patients with LBCL. CAR19 expansion was associated with the development of cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome, and the requirement for granulocyte colony-stimulating factor 14 days after infusion. Younger patients and those with elevated lactate dehydrogenase (LDH) had significantly higher CAR19 expansion. In general, no association between CAR19 expansion and LBCL treatment response was observed. However, when controlling for tumor burden, we found that lower CAR19 expansion in conjunction with low LDH was associated with improved outcomes in LBCL. In sum, this study finds CAR19 expansion principally associates with CAR-related toxicity. Additionally, CAR19 expansion as measured by peripheral blood immunophenotyping may be dispensable to favorable outcomes in LBCL.