Biology subjects

Grove, K.

Publications and source records attributed to Grove, K..

3 recordsLinked to original sources

bioRxiv2021

Single-cell dissection of obesity-exercise axis in adipose-muscle tissues

Regular physical exercise has long been recognized to reverse the effects of diet-induced obesity, but the molecular mechanisms mediating these multi-tissue beneficial effects remain uncharacterized. Here, we address this challenge by studying the opposing effects of exercise training and high-fat diet at single-cell, deconvolution and tissue-level resolutions across 3 metabolic tissues. We profile scRNA-seq in 204,883 cells, grouped into 53 distinct cell subtypes/states in 22 major cell types, from subcuta-neous and visceral white adipose tissue (WAT), and skeletal muscle (SkM) in mice with diet and exercise training interventions. With a great number of mesenchymal stem cells (MSCs) profiled, we compared depot-specific adipose stem cell (ASC) states, and defined 7 distinct fibro-adipogenic progenitor (FAP) states in SkM including discovering and validating a novel CD140+/CD34+/SCA1-FAP population. Exercise- and obesity-regulated proportion, transcriptional and cell-cell interaction changes were most strongly pronounced in and centered around ASCs, FAPs, macrophages and T-cells. These changes reflected thermogenesis-vs-lipogenesis and hyperplasia-vs-hypertrophy shifts, clustered in pathways including extracellular matrix remodeling and circadian rhythm, and implicated complex single- and multi-tissue communication including training-associated shift of a cytokine from binding to its decoy receptor on ASCs to true receptor on M2 macrophages in vWAT. Overall, our work provides new insights on the metabolic protective effects of exercise training, uncovers a previously-underappreciated role of MSCs in mediating tissue-specific and multi-tissue effects, and serves as a model for multitissue single-cell analyses in physiologically complex and multifactorial traits exemplified by obesity and exercise training.

physiology↗

bioRxiv2021

Metabolic resilience is encoded in genome plasticity

Metabolism plays a central role in evolution, as resource conservation is a selective pressure for fitness and survival. Resource-driven adaptations offer a good model to study evolutionary innovation more broadly. It remains unknown how resource-driven optimization of genome function integrates chromatin architecture with transcriptional phase transitions. Here we show that tuning of genome architecture and heterotypic transcriptional condensates mediate resilience to nutrient limitation. Network genomic integration of phenotypic, structural, and functional relationships reveals that fat tissue promotes organismal adaptations through metabolic acceleration chromatin domains and heterotypic PGC1A condensates. We find evolutionary adaptations in several dimensions; low conservation of amino acid residues within protein disorder regions, nonrandom chromatin location of metabolic acceleration domains, condensate-chromatin stability through cis-regulatory anchoring and encoding of genome plasticity in radial chromatin organization. We show that environmental tuning of these adaptations leads to fasting endurance, through efficient nuclear compartmentalization of lipid metabolic regions, and, locally, human-specific burst kinetics of lipid cycling genes. This process reduces oxidative stress, and fatty-acid mediated cellular acidification, enabling endurance of condensate chromatin conformations. Comparative genomics of genetic and diet perturbations reveal mammalian convergence of phenotype and structural relationships, along with loss of transcriptional control by diet-induced obesity. Further, we find that radial transcriptional organization is encoded in functional divergence of metabolic disease variant-hubs, heterotypic condensate composition, and protein residues sensing metabolic variation. During fuel restriction, these features license the formation of large heterotypic condensates that buffer proton excess, and shift viscoelasticity for condensate endurance. This mechanism maintains physiological pH, reduces pH-resilient inflammatory gene programs, and enables genome plasticity through transcriptionally driven cell-specific chromatin contacts. In vivo manipulation of this circuit promotes fasting-like adaptations with heterotypic nuclear compartments, metabolic and cell-specific homeostasis. In sum, we uncover here a general principle by which transcription uses environmental fluctuations for genome function, and demonstrate how resource conservation optimizes transcriptional self-organization through robust feedback integrators, highlighting obesity as an inhibitor of genome plasticity relevant for many diseases.

systems biology↗

bioRxiv2021

Cellular intelligence: dynamic specialization through non-equilibrium multi-scale compartmentalization

Intelligence is usually associated with the ability to perceive, retain and use information to adapt to changes in ones environment. In this context, systems of living cells can be thought of as intelligent entities. Here, we show that the concepts of non-equilibrium tuning and compartmentalization are sufficient to model manifestations of cellular intelligence such as specialization, division, fusion and communication using the language of operads. We implement our framework as an unsupervised learning algorithm, IO_SCPLOWNTC_SCPLOWCO_SCPLOWYTC_SCPLOW, which we show is able to memorize, organize and abstract reference machine-learning datasets through generative and self-supervised tasks. Overall, our learning framework captures emergent properties programmed in living systems, and provides a powerful new approach for data mining. Structured abstractO_ST_ABSBackgroundC_ST_ABSAlthough intelligence has been given many definitions, we can associate it with the ability to perceive, retain, and use information to adapt to changes in ones environment. In this context, systems of living cells can be thought of as intelligent entities. While one can reasonably describe their adaptive abilities within the realm of homeostatic mechanisms, it is challenging to comprehend the principles governing their metabolic intelligence. In each organism, cells have indeed developed as many ways to adapt as there are cell types, and elucidating the impetus of their evolutionary behaviors could be the key to understanding life processes and likely diseases. AdvancesThe goal of this article is to propose principles for understanding cellular intelligence. Specifically, we show that the concepts of non-equilibrium tuning and compartmentalization are enough to recover cellular adaptive behaviors such as specialization, division, fusion, and communication. Our model has the advantage to encompass all scales of life, from organelles to organisms through systems of organs and cell assemblies. We achieve this flexibility using the language of operads, which provides an elegant framework for reasoning about nested systems and, as an emergent behavior, non-equilibrium compartmentalization. To demonstrate the validity and the practical utility of our model, we implement it in the form of an unsupervised learning algorithm, IO_SCPLOWNTC_SCPLOWCO_SCPLOWYTC_SCPLOW, and apply it to reference machine learning datasets through generative and self-supervised tasks. We find that IO_SCPLOWNTC_SCPLOWCO_SCPLOWYTC_SCPLOWs interpretability, plasticity and accuracy surpass that of a wide range of machine learning algorithms, thus providing a powerful approach for data mining. OutlookOur results indicate that the nested hierarchical language of operads captures the emergent properties of programmed cellular metabolism in the development of living systems, and provide a new biologically-inspired, yet practical and lightweight, computational paradigm for memorizing, organizing and abstracting datasets.

systems biology↗