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Gou, J.

Publications and source records attributed to Gou, J..

5 recordsLinked to original sources

Study of Principles Governing Epithelial Cell Clustering and Collective Motion In Vitro

In epithelial wounds, physically enlarged cells emerge along injury edges where they interact with regular-size cells during collective migration to repair tissue continuity. Although such large cells are often interpreted as migration leaders, recent observations suggest that regular-size cells can reciprocally influence them and that mixed-size cell clusters engage in distinct rotational motion before merging into confluent sheets. How cell size, polarity and adhesive coupling jointly control distinct collective migration modes remains unclear. Because many features of collective epithelial migration, including large and regular-size cell phenotypes, are conserved between in vivo and in vitro systems, here we developed a multi-scale computational model of interacting epithelial cells in two-dimensional culture with dynamic cell-substrate adhesion, cell-cell interactions, protrusion-based polarity, and contact-induced myosin redistribution as polarity regulator. We also explicitly modeled two functionally distinct cell states, featuring regular and large sizes respectively. Simulations showed that symmetric and asymmetric myosin redistribution at cell-cell junctions can produce stable cell contacts and persistent rotational motions by cell doublets. Intriguingly, in mixed-size cell clusters, straight translation can arise both from leader-like large cell and regular-size cell-mediated motion, whereas rotation emerges when large-cell-generated torque overcomes translation while cell-cell adhesion is maintained. Thus, collective migration mode depends on the balance among myosin-driven torque, regular-size cell-mediated translation, substrate coupling, and cell-cell adhesion. These modeling results suggest that collective epithelial migration can emerge in vitro from reciprocal biomechanical interactions between distinct cell states, rather than from leader-like cell behavior alone, and that such interactions can produce a predator-prey-like pursuit-escape mode of collective migration. Our model also provides a framework for investigating the biochemical and biomechanical regulation of collective cellular migration. It can be readily extended from in vitro to in vivo context and can incorporate the effects of substrate topology and soluble signaling factor-driven chemotaxis. Author summaryWhen epithelial cells repair a wound, they move as coordinated groups rather than as isolated individuals. Cells of different sizes may contribute in distinct but connected ways. We developed a computational model to examine how a large cell and neighboring regular-size cells move together. In the model, cells attach to the underlying surface and one another, form protrusions that set their direction, and redistribute the force-generating protein myosin after contact. We found that group motion depends on a balance among surface attachment, cell-cell adhesion, movement of regular-size cells toward the large cell, and myosin-driven turning of the large cell. Depending on this balance, a mixed-size cluster can travel along a nearly straight path or rotate persistently, exhibiting pursuit-and-escape-like interactions between the large cell and surrounding regular-size cells. Rotation occurs when the large cells turning effect outweighs the translational motion driven by regular-size cells, while cell-cell adhesion keeps the cluster together. Our results show that collective migration can emerge from reciprocal mechanical interactions between cells in different states, not only from a "leader" cell acting alone. The model also provides experimentally testable predictions for how cell size, adhesion, and internal myosin distribution shape cell cluster movement.

cell biology↗

Biological age and brain age in midlife: relationship to multimorbidity and mental health

Multimorbidity, co-occurrence of two or more chronic conditions, is one of the top priorities in global health research and has emerged as the gold standard approach to study disease accumulation. As aging underlies the development of many chronic conditions, surrogate aging biomarkers are not disease-specific and capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Biological age has been examined in recent years as a surrogate biomarker to capture the process of aging. However, relatively few studies have investigated the relationship between biological age and multimorbidity. More research is needed to quantify biological age using a broad range of biological markers and multimorbidity based on a comprehensive set of chronic conditions. Brain age estimated by neuroimaging data and machine learning models is another surrogate aging biomarker predictive of a wide range of health outcomes. Little is known about the relationship between brain age and multimorbidity. To answer these questions, our study investigates whether elevated biological age and accelerated brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States (MIDUS) Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using convolutional neural networks and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity and presents the first effort to test whether sex moderates the relationship between these surrogate aging biomarkers and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of the accumulation of physical and mental health conditions, which may contribute to new strategies to improve the treatment of multimorbidity and detection of at-risk individuals.

neuroscience↗

The Taxus genome provides insights into paclitaxel biosynthesis

The ancient gymnosperm genus Taxus is the exclusive source of the anticancer drug paclitaxel, yet no reference genome sequences are available for comprehensively elucidating the paclitaxel biosynthesis pathway. We have completed a chromosome-level genome of Taxus chinensis var. mairei with a total length of 10.23 Gb. Taxus shared an ancestral whole-genome duplication with the coniferophyte lineage and underwent distinct transposon evolution. We discovered a unique physical and functional grouping of CYP725As (cytochrome P450) in the Taxus genome for paclitaxel biosynthesis. We also identified a gene cluster in the taxadiene biosynthesis, which was mainly formed by gene duplications. This study will facilitate the elucidation of paclitaxel biosynthesis and unleash the biotechnological potential of Taxus. One Sentence SummaryA chromosome-level genome assembly of Taxus chinensis var. mairei uncovers its unique genome evolution process and genetic architectures for the paclitaxel biosynthesis pathway.

genomics↗

Identifying Neural Signatures Mediating Behavioral Symptoms and Psychosis Onset: High-Dimensional Whole Brain Functional Mediation Analysis

Along the pathway from behavioral symptoms to the development of psychotic disorders sits the multivariate mediating brain. The functional organization and structural topography of large-scale neural mediators among patients with brain disorders, however, are not well understood. Here, we design a high-dimensional brain-wide functional mediation framework to investigate brain regions that intermediate between baseline behavioral symptoms and future conversion to full psychosis among individuals at clinical high risk (CHR). Using resting-state functional magnetic resonance imaging (fMRI) data from 263 CHR subjects, we extract an brain atlas and a {beta} brain atlas: the former underlines brain areas associated with prodromal symptoms and the latter highlights brain areas associated with disease onset. In parallel, we identify the P mediators and the N mediators that respectively facilitate or protect against developing brain disorders among subjects with more severe behavioral symptoms and quantify the effect of each neural mediator on disease development. Taken together, the -{beta} atlases and the P-N mediators paint a brain-wide picture of neural markers that are potentially regulating behavioral symptoms and the development of psychotic disorders and highlight a statistical framework that is useful to uncover large-scale intermediating variables in a regulatory biological organization.

bioinformatics↗

A model for autonomous and non-autonomous effects of the Hippo pathway in Drosophila

While significant progress has been made toward understanding morphogen-mediated patterning in development from both the experimental and the theoretical side, the control of size and shape of tissues and organs is poorly understood. Both involve adjustment of the scale of gene expression to the size of the system, but how growth and patterning are coupled to produce scale invariance and how molecular-level information is translated into organ- and organism-level functioning is one of the most difficult problems in biology. The Hippo pathway, which controls cell proliferation and apoptosis in Drosophila and mammalian cells, contains a core kinase mechanism that affects control of the cell cycle and growth. Studies involving over- and under-expression of components in the morphogen and Hippo pathways in Drosophila reveal conditions that lead to over- or undergrowth. Herein we develop a mathematical model that incorporates the current understanding of the Hippo signal transduction network and which can explain qualitatively both the observations on whole-disc manipulations and the results arising from mutant clones. We find that a number of non-intuitive experimental results can be explained by subtle changes in the balances between inputs to the Hippo pathway. Since signal transduction and growth control pathways are highly conserved across species, much of what is learned about Drosophila applies in higher organisms, and may have direct relevance to tumor dynamics in mammalian systems.

biophysics↗