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Empirical comparison of genomic responses in mouse models and human disease contexts is not sufficient for addressing the challenge of prospective translation from mouse models to human disease contexts.
Brubaker and colleagues address this challenge by developing a semi-supervised machine learning approach that combines supervised modeling of mouse datasets with unsupervised modeling of human disease-context datasets to predict human in vivo differentially expressed genes and enriched pathways.
Semi-supervised training of a feed-forward neural network was the most efficacious model for translating experimentally derived mouse biological associations to the human in vivo disease context. They found that that computational generalization of signaling insights substantially improves upon a direct generalization of mouse experimental insights and argue that such approaches can facilitate the more clinically impactful translation of insights from preclinical studies in model systems to patients.
Fig 1. Dynamics in the stratified epithelium.
Epithelial stratification shapes infection dynamics
Many epithelia are stratified in layers of cells and their infection can result in many pathologies, from rashes to cancer. It is important to understand to what extent the epithelial structure determines infection dynamics and outcomes.
To aid experimental and clinical studies, Murall and colleagues developed a mathematical model that recreates epithelial and infection dynamics. By applying it to a virus, human papillomavirus (HPV), and a bacterium, Chlamydia, they showed that considering stratification improves our general understanding of disease patterns. For instance, the duration of infection can be driven by the rate at which the stem cells of the epithelium divide. Having a general model also allows them to investigate and compare hypotheses. This ecological framework can be modified to study specific pathogens or to estimate parameters from data generated in 3D skin cell culture experiments.
Spatial synchronization codes from coupled rate-phase neurons
Spatial cognition in mammals depends on position-related activity in the hippocampus and entorhinal cortex. Hippocampal place cells and entorhinal grid cells carry distinct maps as rodents move around. The grid cell map is thought to measure angles and distances from previous locations using path integration, a strategy of internally tracking self-motion. However, path integration accumulates errors and must be ‘reset’ by external sensory cues.
Allowing rats to explore an open arena, Monaco and colleagues recorded spiking neurons from areas interconnected with the entorhinal cortex, including subcortical structures and the hippocampus. Many of these subcortical regions help coordinate the hippocampal theta rhythm. Thus, they looked for spatial information in theta-rhythmic spiking and discovered ‘phaser cells’ in the lateral septum, which receives dense hippocampal input.
Phaser cells encoded the rat’s position by shifting spike timing in symmetry with spatial changes in firing rate. They then theorized that symmetric rate-phase coupling allows downstream networks to flexibly learn spatial patterns of synchrony. Using dynamical models and simulations, lastly, they showed that phaser cells may collectively transmit a fast, oscillatory reset signal. Their findings develop a new perspective on the temporal coding of space that may help disentangle competing models of path integration and cross-species differences in navigation.
Source: PLOS EveryONE