Modeling cell-specific dynamics and regulation of the common gamma chain cytokines.

The γ-chain receptor dimerizes with complexes of the cytokines interleukin-2 (IL-2), IL-4, IL-7, IL-9, IL-15, and IL-21 and their corresponding "private" receptors. These cytokines have existing uses and future potential as immune therapies because of their ability to regulate the abundance and function of specific immune cell populations. Here, we build a binding reaction model for the ligand-receptor interactions of common γ-chain cytokines, which includes receptor trafficking dynamics, enabling quantitative predictions of cell-type-specific response to natural and engineered cytokines. We then show that tensor factorization is a powerful tool to visualize changes in the input-output behavior of the family across time, cell types, ligands, and concentrations. These results present a more accurate model of ligand response validated across a panel of immune cell types as well as a general approach for generating interpretable guidelines for manipulation of cell-type-specific targeting of engineered ligands.

Predicting Host Phenotype Based on Gut Microbiome Using a Convolutional Neural Network Approach.

Accurate prediction of the host phenotypes from a microbial sample and identification of the associated microbial markers are important in understanding the impact of the microbiome on the pathogenesis and progression of various diseases within the host. A deep learning tool, PopPhy-CNN, has been developed for the task of predicting host phenotypes using a convolutional neural network (CNN). By representing samples as annotated taxonomic trees and further representing these trees as matrices, PopPhy-CNN utilizes the CNN's innate ability to explore locally similar microbes on the taxonomic tree. Furthermore, PopPhy-CNN can be used to evaluate the importance of each taxon in the prediction of host status. Here, we describe the underlying methodology, architecture, and core utility of PopPhy-CNN. We also demonstrate the use of PopPhy-CNN on a microbial dataset.

c-di-GMP modulates type IV MSHA pilus retraction and surface attachment in Vibrio cholerae.

Biofilm formation by Vibrio cholerae facilitates environmental persistence, and hyperinfectivity within the host. Biofilm formation is regulated by 3',5'-cyclic diguanylate (c-di-GMP) and requires production of the type IV mannose-sensitive hemagglutinin (MSHA) pilus. Here, we show that the MSHA pilus is a dynamic extendable and retractable system, and its activity is directly controlled by c-di-GMP. The interaction between c-di-GMP and the ATPase MshE promotes pilus extension, whereas low levels of c-di-GMP correlate with enhanced retraction. Loss of retraction facilitated by the ATPase PilT increases near-surface roaming motility, and impairs initial surface attachment. However, prolonged retraction upon surface attachment results in reduced MSHA-mediated surface anchoring and increased levels of detachment. Our results indicate that c-di-GMP directly controls MshE activity, thus regulating MSHA pilus extension and retraction dynamics, and modulating V. cholerae surface attachment and colonization.