Biology subjects

Perkins, T. J.

Publications and source records attributed to Perkins, T. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Desktop iDMS: A stand-alone application of intelligent differential mobility spectrometry (iDMS), a neural net-work that predicts optimal separation and compensa-tion voltages for field asymmetric differential ion mobil-ity spectrometry

Summary: Mass spectrometry resolution of glycosphingolipids requires advanced separation methods. The virtual-ly identical structures of glycolipid epimers cannot be discriminated by routine multiple reaction monitoring-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (MRM-LC-ESI-MS/MS). Sep-aration can be achieved using field asymmetric (differential) ion mobility mass spectrometry (FAIMS/DMS). A major bottleneck to MRM-LC-ESI-DMS-MS/MS method development lies in manually optimizing the compound-dependent parameters of separation voltage (SV) and compensation voltage (CoV). To address this challenge, we developed intelligent Differential Mobility Spectrometry (iDMS). iDMS is a supervised deep neural network that evaluates and predicts the optimal ion mobility SV and CoV parameters for any user-requested monoglycosphingolipid from a training set of measured signal intensities composed of 12 lipids (6 stereoisomer pairs). We initially introduced iDMS as a series of python scripts. Here, we share desktop applications of both iDMS and its pre-processing input data assembly modules (DMS Data Extraction Toolkit). Both software assemblies enable users to rapidly deploy a validated machine learning alternative to manual DMS optimization. The applications are user-friendly, operate with-out need for any technical operating environment, make training dataset assembly quick and simple, and return measured, normalized, and predicted ionograms at every user-interrogated SV, evaluation of separability at each SV and CoV combination, and prediction of optimal resolution parameters to accelerate users MRM-LC-ESI-DMS-MS/MS deployment. Availability and implementation: Desktop iDMS and DMS Data Extraction Toolkit are native desktop applications for macOS (Apple Silicon), freely available for download at https://www.neurolipidomics.com/platforms.html under the GNU v3.0 license. The source code is available from GitHub and Zenodo at https://github.com/Neurolipidomics/iDMS, https://zenodo.org/records/22214408, https://github.com/Neurolipidomics/DMSDataExtractionToolkit, and https://zenodo.org/records/22777403. Bench-marking metrics are available at https://www.biorxiv.org/content/10.64898/2026.08.26.747394v1.

bioinformatics↗

bioRxiv2026

Intelligent differential ion mobility spectrometry (iDMS): A deep neural network that predicts optimal space-resolved ion mobility parameters for isomeric monoglycosphingolipids

Simultaneous quantification of monoglycosphingolipid stereoisomers is required to monitor changes in defective enzymatic pathways linked to diseases such as Gaucher Disease, Parkinson's Disease, and Krabbe Disease. Resolution of beta-glucosyl and beta-galactosyl epimers cannot be achieved by standard liquid chromatography, electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS). Separation becomes possible when field asymmetric ion mobility spectrometry (FAIMS), also known as differential mobility mass spectrometry (DMS), is added as an orthogonal separation technique to LC. FAIMS/DMS separates epimeric ion clusters in a high versus low electric field (separation voltage, SV) then redirects the target epimeric ions to the mass spectrometer through the application of a direct current (compensation voltage, CoV). Resolving SVs and CoVs must be manually determined for each lipid. Manual derivation is a labour-intensive process that requires pure synthetic standards, limiting the number of stereoisomers a user can include in an assay. To address this problem, we introduce here intelligent DMS (iDMS). iDMS is an in silico supervised neural network model that learns the ion mobility relationships between SV and CoV and the monoglycosphingolipid structural features of sugar headgroup, N-acyl chain length, and N-acyl degree of unsaturation. iDMS predicts the SV and CoV combinations capable of resolving any stereoisomer pair from a training dataset of composed of measured signal intensities across a range of SVs and CoVs of 12 lipids. This machine learning alternative to manual DMS optimization promises to accelerate the deployment of multiple-reaction-monitoring mode (MRM) RPLC-ESI-DMS-MS/MS assays for the routine and rapid quantification of biologically relevant monoglycosphingolipid stereoisomers.

bioinformatics↗

bioRxiv2026

Histone Modification Metapeaks are Epigenetic Landmarks Predictive of Cell State

Histone modifications are a key component of the epigenetic state of a cell, and they vary widely across different cell and tissue types, conditions, and disease states. Indeed, the majority of the genome is enriched with one histone mark or another across the thousands of cellular conditions that have been studied to date. Here, we use the largest-to-date collection of histone modification ChIP-seq datasets to identify the most important sites of histone modifications genome-wide. Collected and uniformly reprocessed by the International Human Epigenome Consortium, this data includes 5339 datasets enriched at nearly one billion total peaks across 59 different major cell or tissue types and in healthy and disease conditions, for six different histone marks. We propose FindMetapeaks, a new approach to identifying histone mark metapeaks, which are genomic regions with enrichment of a mark across many samples. We show that many of these epigenetic metapeaks are strongly indicative of cell and tissue type, or are associated with other sample characteristics, and highlight key regulatory regions of the genome. However, we also show that many metapeaks contain redundant information, and that parsimonious subsets of metapeaks can be selected by machine learning to predict cell state. Our histone mark metapeak atlas provides a concise set of regions for interpreting the epigenome. Availabilityhttps://github.com/rmbioinfo83/FindMetapeaks/

genomics↗

bioRxiv2025

Metabolic, epigenetic and transcriptomic alterations in postnatal 16p11.2 deficient murine astrocytes

Autism Spectrum Disorders (ASD) are associated with metabolic dysregulation. While astrocytes are integral to cerebral metabolism, their molecular and functional changes in ASD are poorly known. Using early postnatal primary cortical astrocytes from a mouse model of 16p11.2 deletion ASD syndrome (16p11.2df/+ mice), we observed core molecular alterations with sex-specific profiles, suggesting divergent energetic pathways and epigenetic regulation. Targeted metabolomics revealed opposing phenotypes in male versus female 16p11.2df/+ astrocytes, particularly for alpha-ketoglutaric acid. Functionally, 16p11.2df/+astrocytes exhibited elevated phosphorylation in low glucose culture conditions, and reduced glycolysis in high glucose. Epigenetic profiling of male 16p11.2df/+astrocytes revealed differentially hydroxymethylated and methylated regions, with foci on chromosomes 3 and 13. Finally, bulk RNA sequencing in male and female mutant astrocytes indicated differential gene expression with profound sex differences, mostly affecting pathways related to cellular morphology. By establishing 16p11.2df/+ astroglial molecular signatures, this study refines our understanding of glial changes in ASD.

cell biology↗

bioRxiv2025

Spatiotemporal remodeling of chromatin topology architecture and H3K27me3 redistribution underlies vascular pathology in Hutchinson-Gilford progeria syndrome

Hutchinson-Gilford progeria syndrome (HGPS) is a devastating premature aging disorder driven by the accumulation of the lamin A variant, progerin, leading to severe vascular pathology. While epigenetic alterations are implicated, the spatiotemporal reorganization of the higher-order chromatin and its functional impact on vascular smooth muscle cell (VSMC) transcription remain poorly defined. Through an integrated, longitudinal multi-omics approach combining in situ high-throughput chromosome conformation capture (Hi-C) and Cleavage Under Targets and Tagmentation (CUT&Tag) profiling of CTCF, SMC1A, H3K27me3, H3K27ac, and H3K36me3 with transcriptomic analyses from control and HGPS iPSC-derived VSMCs, we reveal that global topologically associating domain (TAD) architecture remains largely intact in HGPS. However, the internal chromatin states of TADs undergo dynamic, passage-specific remodeling, characterized by a progressive accumulation of broad H3K27me3-repressed domains. This is accompanied by a loss of A/B compartment segregation, as confirmed by DNA-FISH, and a pronounced depletion of interchromosomal intermingling, specifically surrounding downregulated genes, suggesting that these interchromosomal contacts are essential for maintaining transcriptional competence in normal VSMCs. Crucially, we uncover widespread rewiring of enhancer-promoter (E-P) loops, which is linked to the dysregulation of genes critical for vascular development, extracellular matrix organization, and atherosclerosis. Our study demonstrates that spatiotemporal redistribution of repressive histone marks and reorganization of E-P interactions within a structurally resilient TAD framework underpin widespread transcriptional dysregulation in HGPS vascular pathogenesis. This uncovers a critical dissociation between higher-order chromatin architecture and histone modification landscape, providing a mechanistic basis for the failure of vascular homeostasis in progeria.

genomics↗

bioRxiv2024

VNC-Dist: A machine learning-based tool for quantification of neuronal positioning in the ventral nerve cord of C. elegans

The C. elegans ventral nerve cord (VNC) provides a simple model for investigating the developmental mechanisms involved in neuronal positioning and organization. The VNC of newly hatched larvae contains a set of 22 motoneurons organized into three distinct classes (DD, DA, and DB) that show consistent positioning and arrangement. This organization arises from the action of multiple convergent genetic pathways, which are poorly understood. To better understand these pathways, accurate and efficient methods for quantifying motoneuron cell body positions within large microscopy datasets are required. Here, we present VNC-Dist (Ventral Nerve Cord Distances), a software toolkit that replaces manual measurements with a faster and more accurate computer-assisted approach, combining machine learning and other tools, to quantify neuron cell body positions in the VNC. The VNC-Dist pipeline integrates several components: manual neuron cell body localization using Fijis multipoint tool, deep learning-based worm segmentation with modified Segment Anything Model (SAM), accurate spline-based measurements of neuronal distances along the VNC, and built-in tools for statistical analysis and graphing. To demonstrate the robustness and versatility of VNC-Dist, we applied it to several genetic mutants known to disrupt neuronal positioning in the VNC. This toolbox will enable batch acquisition and analysis of large datasets across genotypes, thereby advancing investigations into the cellular and molecular mechanisms that govern VNC neuronal positioning and arrangement.

developmental biology↗

bioRxiv2024

Atomic elementary flux modes explain the steady state flow of metabolites in large-scale flux networks

Steady state fluxes are a measure of cellular activity under metabolic homoeostasis, but understanding how individual substrates are metabolized remains a challenge in large-scale networks. Pathway-based approaches such as elementary flux mode (EFM) analysis are limited to small networks due to the combinatorial explosion of pathways and the ambiguity of decomposing fluxes onto EFMs. Here, we present an alternative approach to explain metabolic fluxes in terms of the steady state flow of their atomic constituents. We refer to these pathways as atomic elementary flux modes (AEFMs) and show that computations involving AEFMs are orders of magnitude faster than standard EFMs. Using our approach, we enumerate carbon and nitrogen AEFMs in five genome-scale metabolic models and compute the AEFM decomposition of fluxes estimated in a HepG2 liver cancer cell line. Our results systematically characterize carbon and nitrogen remodelling and, on the HepG2 network, predict glutamine metabolism through a recently discovered non-canonical TCA cycle.

bioinformatics↗

bioRxiv2024

Differential Equation Modeling of Cell Population Dynamics in Skeletal Muscle Regeneration from Single-Cell Transcriptomic Data

Skeletal muscle regeneration is a complex process orchestrated by diverse cell populations within a dynamic niche. In response to muscle damage and intercellular signaling, these cells undergo cell fate and migration decisions including quiescence, activation, proliferation, differentiation, infiltration, apoptosis, and exfiltration. The emergence of single-cell RNA sequencing (scRNA-seq) studies of muscle regeneration offers a significant opportunity to refine models of regeneration and enhance our understanding of cellular interactions. To better understand how crosstalk between cell types governs cell fate decisions and cell population dynamics, we developed a novel non-linear ordinary differential equation model guided by scRNA-seq data. Our model consists of 9 variables and 19 parameters, capturing the dynamics of key myogenic lineage and immune cell types. We calibrated time-series scRNA-seq data to units of cells per cubic millimeter of tissue and fit our models parameters to capture the observed dynamics, validating on an independent time series. The model successfully captures regeneration dynamics, particularly after incorporating a novel type of regulatory interaction between M2 macrophages and satellite cells that has been hypothesized in the literature. Our model lays a foundation for future computational explorations of muscle regeneration, modeling of disease conditions, and in silico testing of therapeutic strategies.

systems biology↗

bioRxiv2022

A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks

Elementary flux modes (EFMs) are minimal, steady state pathways characterizing a flux network. Fundamentally, all steady state fluxes in a network are decomposable into a linear combination of EFMs. While there is typically no unique set of EFM weights that reconstructs these fluxes, several optimization-based methods have been proposed to constrain the solution space by enforcing some notion of parsimony. However, it has long been recognized that optimization-based approaches may fail to uniquely identify EFM weights and return different feasible solutions across objective functions and solvers. Here we show that, for flux networks only involving single molecule transformations, these problems can be avoided by imposing a Markovian constraint on EFM weights. Our Markovian constraint guarantees a unique solution to the flux decomposition problem, and that solution is arguably more biophysically plausible than other solutions. We describe an algorithm for computing Markovian EFM weights via steady state analysis of a certain discrete-time Markov chain, based on the flux network, which we call the cycle-history Markov chain. We demonstrate our method with a differential analysis of EFM activity in a lipid metabolic network comparing healthy and Alzheimers disease patients. Our method is the first to uniquely decompose steady state fluxes into EFM weights for any unimolecular metabolic network.

bioinformatics↗

bioRxiv2022

Cell Type Specific DNA Signatures of Transcription Factor Binding

Transcription factors (TFs) bind to different parts of the genome in different types of cells. These differences may be due to alterations in the DNA-binding preferences of a TF itself, or mechanisms such as chromatin accessibility, steric hindrance, or competitive binding, that result in a DNA "signature" of differential binding. We propose a method called SigTFB (Signatures of TF Binding), based on deep learning, to detect and quantify cell type specificity in a TFs DNA-binding signature. We conduct a wide scale investigation of 194 distinct TFs across various cell types. We demonstrate the existence of cell type specificity in approximately 30% of the TFs. We stratify our analysis by different antibodies for the same TF, to rule out the possibility of certain technical artifacts, yet we find that cell type specificity estimates are largely consistent when the same TF is assayed with different antibodies. To further explain the biology behind a TFs cell type specificity, or lack thereof, we conduct a wide scale motif enrichment analysis of all TFs in question. We show that the presence of alternate motifs correlates with a higher degree of cell type specificity in TFs, such as ATF7, while finding consistent motifs throughout is usually associated with the absence of cell type specificity in a TF, such as CTCF. In particular, we observe that several important TFs show distinct DNA binding signatures in different cancer cell types, which may point to important differences in modes of action. Moreover, we find that motif enrichment sometimes correlates with gene expression in TFs with higher cell type specificity. Our comprehensive investigation provides a basis for further study of the mechanisms behind differences in TF-DNA binding in different cell types.

bioinformatics↗

bioRxiv2022

KDR Signaling in Muscle Stem Cells Promotes Asymmetric Division and Progenitor Generation for Efficient Regeneration

The regulation of muscle stem cell (MuSC) asymmetric division plays an essential role in controlling the growth and repair of skeletal muscle. We discover kinase domain receptor (KDR) as a positive modulator of MuSC asymmetric division using an in-niche high-content screen and confirmed its expression in satellite cells by ddPCR and immunofluorescence. Knockdown of KDR significantly reduces the numbers of asymmetric divisions, whereas ligand stimulation of KDR increases the numbers of asymmetric divisions. KDR signaling is impaired in dystrophin-deficient satellite cells and requires a polarized cell environment established by the dystrophin glycoprotein complex (DGC) to direct asymmetric division. Mice lacking KDR in MuSCs exhibit reduced numbers of satellite cells due to precocious differentiation, and deficits in regeneration consistent with impaired asymmetric division and reduced generation of progenitors. Therefore, our experiments identify KDR signaling as playing an essential role in MuSC function in muscle regeneration. HIGHLIGHTSO_LIKDR and VEGFA are expressed in satellite cells C_LIO_LILigand activated KDR stimulates asymmetric satellite stem cell division C_LIO_LIKDR signaling requires the presence of the DGC C_LIO_LIKDR-deficient satellite cells give rise to reduced numbers of progenitors C_LI eTOC blurbChen et al., performed a chemical screen using a novel screening platform to identify modulators of muscle stem cell asymmetric division. They discovered that KDR signalling requires the presence of the dystrophin associated glycoprotein complex and is an important regulator of muscle stem cell asymmetric division.

molecular biology↗

bioRxiv2021

Transcriptional Reprogramming of Skeletal Muscle Stem Cells by the Niche Environment

Adult stem cells are indispensable for tissue regeneration. Tissue-specific stem cells reside in a specialized location called their niche, where they are in constant cross talk with neighboring niche cells and circulatory signals from their environment. Aging has a detrimental effect on the number and the regenerative function of various stem cells. However, whether the loss of stem cell function is a cause or consequence of their aging niche is unclear. Using skeletal muscle stem cells (MuSCs) as a model, we decouple cell-intrinsic from niche-mediated extrinsic effects of aging on their transcriptome. By combining in vivo MuSC heterochronic transplantation models and computational methods, we show that on a genome-wide scale, age-related altered genes fall into two distinct categories regarding their response to the niche environment. Genes that are inelastic in their response to the niche exhibit altered chromatin accessibility and are associated with differentially methylated regions (DMRs) between young and aged cells. On the other hand, genes that are restorable by niche exposure exhibit altered transcriptome but show no change in chromatin accessibility or DMRs. Taken together, our data suggest that the niche environment plays a decisive role in controlling the transcriptional activity of MuSCs, and exposure to a young niche can reverse approximately half of all age-associated changes that are not epigenetically encoded. The muscle niche therefore serves as an important therapeutic venue to mitigate the negative consequence of aging on tissue regeneration.

genetics↗

bioRxiv2021

BATL: Bayesian annotations for targeted lipidomics

MotivationBioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) data acquired in either selected or multiple reaction monitoring (SRM and MRM) modes. ResultsWe present here Bayesian Annotations for Targeted Lipidomics (BATL), a Gaussian naive Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modelling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine SRM datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified. Availability and implementationBATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Accuracy and Reproducibility of Somatic Point Mutation Calling in Clinical-Type Targeted Sequencing Data

BackgroundTreating cancer depends in part on identifying the mutations driving each patients disease. Many clinical laboratories are adopting high-throughput sequencing for assaying patients tumours, applying targeted panels to formalin-fixed paraffin-embedded tumour tissues to detect clinically-relevant mutations. While there have been some benchmarking and best practices studies of this scenario, much variant-calling work focuses on whole-genome or whole-exome studies, with fresh or fresh-frozen tissue. Thus, definitive guidance on best choices for sequencing platforms, sequencing strategies, and variant calling for clinical variant detection is still being developed. ResultsBecause ground truth for clinical specimens is rarely known, we used the well-characterized Coriell cell lines GM12878 and GM12877 to generate data. We prepared samples to mimic as closely as possible clinical biopsies, including formalin fixation and paraffin embedding. We evaluated two well-known targeted sequencing panels, Illuminas TruSight 170 panel and the Oncomine Focus panel. Sequencing was performed on an Illumina NextSeq500 and an Ion Torrent PGM respectively. We performed multiple biological replicates of each assay, to test reproducibility. Finally, we applied five different public and freely-available somatic single-nucleotide variant (SNV) callers to the data, MuTect2, SAMtools, VarScan2, Pisces and VarDict. Although the TruSight 170 and Oncomine Focus panels cover different amounts of the genome, we did not observe major differences in variant calling success within the regions that each covers. We observed substantial discrepancies between the five variant callers. All had high sensitivity, detecting known SNVs, but highly varying and non-overlapping false positive detections. Harmonizing variant caller parameters or intersecting the results of multiple variant callers reduced disagreements. However, intersecting results from biological replicates was even better at eliminating false positives. ConclusionsReproducibility and accuracy of targeted clinical sequencing results depends less on sequencing platform and panel than on downstream bioinformatics and biological variability. Differences in variant callers default parameters are a greater influence on algorithm disagreement than other differences between the algorithms. Contrary to typical clinical practice, we recommend analyzing replicate samples, as this greatly decreases false positive calls.

bioinformatics↗

bioRxiv2019

Absolute quantification of transcription factors reveals principles of gene regulation in erythropoiesis

Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). Yet, despite years of studies we still do not know the protein copy number of TFs in the nucleus. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first Gene Regulatory Network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that in the nucleus, corepressors are dramatically more abundant than coactivators at the protein, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

systems biology↗

bioRxiv2019

High Resolution Genome Wide Expression Analysis of Single Myofibers Using SMART-Seq

Skeletal muscle is a heterogeneous tissue. Individual myofibers that make up muscle tissue exhibit variation in their metabolic and contractile properties. Although there are biochemical and histological assays to study myofiber heterogeneity, efficient methods to analyze the whole transcriptome of individual myofibers are lacking. We have developed single myofiber RNA-Seq (smfRNA-Seq) to analyze the whole transcriptome of individual myofibers by combining single fiber isolation with Switching Mechanisms at 5 end of RNA Template (SMART) technology. Our method provides high-resolution genome wide expression profiles of single myofibers. Using smfRNA-Seq, we have analyzed the differences in the transcriptome of young and old myofibers to validate the effectiveness of this new method. Using smfRNA-Seq, we performed comparative gene expression analysis between single myofibers from young and old mice. Our data suggests that aging leads to significant changes in the expression of metabolic and structural genes in myofibers. Our data suggests that smfRNA-Seq is a powerful tool to study developmental, disease and age-related dynamics in the composition of skeletal muscle.

molecular biology↗

bioRxiv2019

Bayesian Correlation is a robust similarity measure for single cell RNA-seq data

Assessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq.\n\nRecently, a Bayesian correlation scheme, that assigns low similarity to genes that have low confidence expression estimates, has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering 3 ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression.\n\nWe demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population.\n\nWe conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.

bioinformatics↗

bioRxiv2019

Myf6/MRF4 is a Myogenic Niche Regulator Required for the Maintenance of the Muscle Stem Cell Pool

In metazoans, skeletal muscle evolved to contract and produce force. However, recent experimental evidence suggests that skeletal muscle has also acquired endocrine functions and produces a vast array of myokines. Using ChIP-Seq and gene expression analyses of myogenic factors, we show that Myf6/MRF4 transcriptionally regulates a broad spectrum of myokines and muscle-secreted proteins, including ligands for downstream activation of key signaling pathways such as EGFR, STAT3 and VEGFR. Homozygous deletion of Myf6 causes a significant reduction in the ability of muscle to produce key myokines such as EGF, VEGFA and LIF. Consequently, although Myf6 knockout mice are born with a normal muscle stem cell compartment, they undergo progressive reduction in their stem cell pool during postnatal life. Mechanistically, muscle stem cells from the Myf6 knockout animals show defects in activation of EGFR and STAT3 signaling, upregulate the p38 MAP kinase pathway and spontaneously break from quiescence. Exogenous application of recombinant EGF and LIF rescue the defects in the muscle stem cell pool of Myf6 knockout animals. Finally, skeletal muscles of mice lacking Myf6 have a significantly reduced ability to sustain donor-engrafted muscle stem cells. Taken together, our data uncovers a novel role for Myf6 in regulating the expression of niche factors and myokines to maintain the skeletal muscle stem cell pool in adult mice.

genetics↗