Biology subjects

Hoffmann, C. M.

Publications and source records attributed to Hoffmann, C. M..

3 recordsLinked to original sources

bioRxiv2022

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.

physiology↗

bioRxiv2022

Gene Regulatory Network Reconfiguration in Direct Lineage Reprogramming

In direct lineage reprogramming, transcription factor (TF) overexpression reconfigures Gene Regulatory Networks (GRNs) to convert cell identities between fully differentiated cell types. We previously developed CellOracle, a computational pipeline that integrates single-cell transcriptome and epigenome profiles to infer GRNs. CellOracle leverages these inferred GRNs to simulate gene expression changes in response to TF perturbation, enabling network re-configuration during reprogramming to be interrogated in silico. Here, we integrate CellOracle analysis with lineage tracing of fibroblast to induced endoderm progenitor (iEP) conversion, a prototypical direct lineage reprogramming paradigm. By linking early network state to reprogramming success or failure, we reveal distinct network configurations underlying different reprogramming outcomes. Using these network analyses and in silico simulation of TF perturbation, we identify new factors to coax cells into successfully converting cell 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 at high resolution, providing new mechanistic insights into the regulation and reprogramming of cell identity.

genomics↗

bioRxiv2020

CellOracle: Dissecting cell identity via network inference and in silico gene perturbation

Here, we present CellOracle, a computational tool that integrates single-cell transcriptome and epigenome profiles to infer gene regulatory networks (GRNs), critical regulators of cell identity. Leveraging inferred GRNs, we simulate gene expression changes in response to transcription factor (TF) perturbation, enabling network configurations to be interrogated in silico, facilitating their interpretation. We validate the efficacy of CellOracle to recapitulate known regulatory changes across hematopoiesis, correctly predicting the outcomes of well-characterized TF perturbations. Integrating CellOracle analysis with lineage tracing of direct reprogramming reveals distinct network configurations underlying different reprogramming failure modes. Furthermore, analysis of GRN reconfiguration along successful reprogramming trajectories identifies new factors to enhance target cell yield, uncovering a 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, at high-resolution, promoting new mechanistic insights into the regulation and reprogramming of cell identity.

genomics↗