An alternative mechanism for skeletal muscle dysfunction in long-term post-viral lung disease.

Chronic lung disease is often accompanied by disabling extrapulmonary symptoms, notably skeletal muscle dysfunction and atrophy. Moreover, the severity of respiratory symptoms correlates with decreased muscle mass and in turn lowered physical activity and survival rates. Previous models of muscle atrophy in chronic lung disease often modeled chronic obstructive pulmonary disease (COPD) and relied on cigarette smoke exposure and LPS stimulation, but these conditions independently affect skeletal muscle even without accompanying lung disease. Moreover, there is an emerging and pressing need to understand the extrapulmonary manifestations of long-term post-viral lung disease (PVLD) as found in COVID-19. Here, we examine the development of skeletal muscle dysfunction in the setting of chronic pulmonary disease caused by infection due to the natural pathogen Sendai virus using a mouse model of PVLD. We identify a significant decrease in myofiber size when PVLD is maximal at 49 days after infection. We find no change in the relative types of myofibers, but the greatest decrease in fiber size is localized to fast-twitch-type IIB myofibers based on myosin heavy chain immunostaining. Remarkably, all biomarkers of myocyte protein synthesis and degradation (total RNA, ribosomal abundance, and ubiquitin-proteasome expression) were stable throughout the acute infectious illness and chronic post-viral disease process. Together, the results demonstrate a distinct pattern of skeletal muscle dysfunction in a mouse model of long-term PVLD. The findings thereby provide new insights into prolonged limitations in exercise capacity in patients with chronic lung disease after viral infections and perhaps other types of lung injury.NEW & NOTEWORTHY Our study used a mouse model of post-viral lung disease to study the impact of chronic lung disease on skeletal muscle. The model reveals a decrease in myofiber size that is selective for specific types of myofibers and an alternative mechanism for muscle atrophy that might be independent of the usual markers of protein synthesis and degradation. The findings provide a basis for new therapeutic strategies to correct skeletal muscle dysfunction in chronic respiratory disease.

Dissecting cell identity via network inference and in silico gene perturbation.

Cell identity is governed by the complex regulation of gene expression, represented as gene-regulatory networks1. Here we use gene-regulatory networks inferred from single-cell multi-omics data to perform in silico transcription factor perturbations, simulating the consequent changes in cell identity using only unperturbed wild-type data. We apply this machine-learning-based approach, CellOracle, to well-established paradigms-mouse and human haematopoiesis, and zebrafish embryogenesis-and we correctly model reported changes in phenotype that occur as a result of transcription factor perturbation. Through systematic in silico transcription factor perturbation in the developing zebrafish, we simulate and experimentally validate a previously unreported phenotype that results from the loss of noto, an established notochord regulator. Furthermore, we identify an axial mesoderm regulator, lhx1a. Together, these results show that CellOracle can be used to analyse the regulation of cell identity by transcription factors, and can provide mechanistic insights into development and differentiation.

Gene regulatory network reconfiguration in direct lineage reprogramming.

In direct lineage conversion, transcription factor (TF) overexpression reconfigures gene regulatory networks (GRNs) to reprogram cell identity. We previously developed CellOracle, a computational method to infer GRNs from single-cell transcriptome and epigenome data. Using inferred GRNs, CellOracle simulates gene expression changes in response to TF perturbation, enabling in silico interrogation of network reconfiguration. Here, we combine CellOracle analysis with lineage tracing of fibroblast to induced endoderm progenitor (iEP) conversion, a prototypical direct reprogramming paradigm. By linking early network state to reprogramming outcome, we reveal distinct network configurations underlying successful and failed fate conversion. Via in silico simulation of TF perturbation, we identify new factors to coax cells into successfully converting their identity, uncovering a central role for the AP-1 subunit Fos with the Hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations, providing new mechanistic insights into lineage reprogramming.

An alternative mechanism for skeletal muscle dysfunction in long-term post-viral lung disease.

Chronic lung disease is often accompanied by disabling extrapulmonary symptoms, notably skeletal muscle dysfunction and atrophy. Moreover, the severity of respiratory symptoms correlates with decreased muscle mass and in turn lowered physical activity and survival rates. Previous models of muscle atrophy in chronic lung disease often modeled COPD and relied on cigarette smoke exposure and LPS-stimulation, but these conditions independently affect skeletal muscle even without accompanying lung disease. Moreover, there is an emerging and pressing need to understand the extrapulmonary manifestations of long-term post-viral lung disease (PVLD) as found in Covid-19. Here, we examine the development of skeletal muscle dysfunction in the setting of chronic pulmonary disease using a mouse model of PVLD caused by infection due to the natural pathogen Sendai virus. We identify a significant decrease in myofiber size when PVLD is maximal at 49 d after infection. We find no change in the relative types of myofibers, but the greatest decrease in fiber size is localized to fast-twitch type IIB myofibers based on myosin heavy chain immunostaining. Remarkably, all biomarkers of myocyte protein synthesis and degradation (total RNA, ribosomal abundance, and ubiquitin-proteasome expression) were stable throughout the acute infectious illness and chronic post-viral disease process. Together, the results demonstrate a distinct pattern of skeletal muscle dysfunction in a mouse model of long-term PVLD. The findings thereby provide new insight into prolonged limitations in exercise capacity in patients with chronic lung disease after viral infections and perhaps other types of lung injury.

Basal epithelial stem cells cross an alarmin checkpoint for postviral lung disease.

Epithelial cells are charged with protection at barrier sites, but whether this normally beneficial response might sometimes become dysfunctional still needs definition. Here, we recognized a pattern of imbalance marked by basal epithelial cell growth and differentiation that replaced normal airspaces in a mouse model of progressive postviral lung disease due to the Sendai virus. Single-cell and lineage-tracing technologies identified a distinct subset of basal epithelial stem cells (basal ESCs) that extended into gas-exchange tissue to form long-term bronchiolar-alveolar remodeling regions. Moreover, this cell subset was selectively expanded by crossing a cell-growth and survival checkpoint linked to the nuclear-localized alarmin IL-33 that was independent of IL-33 receptor signaling and instead connected to autocrine chromatin accessibility. This mechanism creates an activated stem-progenitor cell lineage with potential for physiological or pathological function. Thus, conditional loss of Il33 gene function in basal epithelial cells disrupted the homeostasis of the epithelial barrier at skin and gut sites but also markedly attenuated postviral disease in the lung based on the downregulation of remodeling and inflammation. Thus, we define a basal ESC strategy to deploy innate immune machinery that appears to overshoot the primordial goal of self-defense. Our findings reveal new targets to stratify and correct chronic and often deadly postviral disease.

Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues.

Skeletal muscle comprises a family of diverse tissues with highly specialized functions. Many acquired diseases, including HIV and COPD, affect specific muscles while sparing others. Even monogenic muscular dystrophies selectively affect certain muscle groups. These observations suggest that factors intrinsic to muscle tissues influence their resistance to disease. Nevertheless, most studies have not addressed transcriptional diversity among skeletal muscles. Here we use RNAseq to profile mRNA expression in skeletal, smooth, and cardiac muscle tissues from mice and rats. Our data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. We detect mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. We identify candidate genes that may drive tissue specialization, including Smarca4, Vegfa, and Myostatin. By demonstrating the intrinsic diversity of skeletal muscles, these data provide a resource for studying the mechanisms of tissue specialization.

Guidelines for Genome-Scale Analysis of Biological Rhythms.

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding "big data" that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them.