Biology subjects

Cutler, A.

Publications and source records attributed to Cutler, A..

6 recordsLinked to original sources

bioRxiv2026

The Muscle Tissue Environment Limits Muscle Stem Cells in Aged Mice

Frailty arising from loss of muscle function and mass is a significant health concern impacting quality of life and dramatically increasing health care costs as our population ages. Ameliorating frailty derived from reduced muscle function is thus a critical research priority to improve health span. Cell intrinsic defects in muscle stem cells (MuSC), or satellite cells, occur as skeletal muscle ages, reducing the capacity of MuSCs to maintain and repair skeletal muscle and are accompanied by cell nonautonomous changes. Although rejuvenating stem cells in aged tissues or organs has potential to improve muscle aging phenotypes, we found that the extracellular environment in aged mice abrogates rejuvenated muscle stem cell potential. MuSCs from young mice were unable to grow on extracellular matrix derived from aged mice that contains elevated collagen protein levels, establishing a critical role for the environment in contributing to muscle phenotypes in aging. Combining an inducible FGF receptor 1 (FGFR1) to rescue MuSC intrinsic aging defects with a drug to reduce fibrosis partially rescued muscle mass loss in aged mice. We conclude that aging affects tissues, and particularly skeletal muscle tissue, via complex multifactorial processes requiring multifaceted interventions to improve aging phenotypes.

cell biology↗

bioRxiv2023

Gaining Biological Insights through Supervised Data Visualization

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATEs prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

bioinformatics↗

bioRxiv2021

RNA-BINDING PROTEINS DIRECT MYOGENIC CELL FATE DECISIONS

RNA-binding proteins (RBPs) are essential for skeletal muscle regeneration and RBP dysfunction causes muscle degeneration and neuromuscular disease. How ubiquitously expressed RBPs orchestrate complex tissue regeneration and direct cell fate decisions in skeletal muscle remains poorly understood. Single cell RNA-sequencing of regenerating skeletal muscle reveals that RBP expression, including numerous neuromuscular disease-associated RBPs, is temporally regulated in skeletal muscle stem cells and correlates to stages of myogenic differentiation. By combining machine learning with RBP engagement scoring, we discover that the neuromuscular disease associated RBP Hnrnpa2b1 is a differentiation-specifying regulator of myogenesis controlling myogenic cell fate transitions during terminal differentiation. The timing of RBP expression specifies cell fate transitions by providing a layer of post-transcriptional regulation needed to coordinate stem cell fate decisions during complex tissue regeneration.

molecular biology↗

bioRxiv2018

DNA methylation oscillation defines classes of enhancers

Understanding the regulatory landscape of human cells requires the integration of genomic and epigenomic maps, capturing combinatorial levels of cell type-specific and invariant activity states.\n\nHere, we segmented whole-genome bisulfite sequencing-derived methylomes into consecutive blocks of co-methylation (COMETs) to obtain spatial variation patterns of DNA methylation (DNAm oscillations) integrated with histone modifications and promoter-enhancer interactions derived from promoter capture Hi-C (PCHi-C) sequencing of the same purified blood cells.\n\nMapping DNAm oscillations onto regulatory genome annotation revealed that enhancers are enriched for DNAm hyper-oscillations (>30-fold), where multiple machine learning models support DNAm as predictive of enhancer location. Based on this analysis, we report overall predictive power of 99% for DNAm oscillations, 77.3% for DNaseI, 41% for CGIs, 20% for UMRs and 0% for LMRs, demonstrating the power of DNAm oscillations over other methods for enhancer prediction. Methylomes of activated and non-activated CD4+ T cells indicate that DNAm oscillations exist in both states irrespective of activation; hence they can be used to determine the location of latent enhancers.\n\nOur approach advances the identification of tissue-specific regulatory elements and outperforms previous approaches defining enhancer classes based on DNA methylation.

genomics↗

bioRxiv2017

Type 1 diabetes genome-wide association analysis with imputation identifies five new risk regions

Type 1 diabetes genotype datasets have undergone several well powered genome wide analysis studies (GWAS), identifying 57 associated regions at the time of analysis. There are still many regions of smaller effect size or low frequency left to discover, and better exploitation of existing type 1 diabetes cohorts with meta analysis and imputation can precede the acquisition of new or larger cohorts. An existing dataset of 5,913 case and 8,829 control samples was analysed using genome-wide microarrays (Affymetrix GeneChip 500K and Illumina Infinium 550K) with imputation via IMPUTE2 with the 1000 Genomes Project (phase 3) reference panel. Genotyping coverage was doubled in known association regions, and increased by four fold in other regions compared to previous studies. Our analysis resulted in new index variants for 17/57 regions, an expanded set of plausible candidate SNPs for 17 regions, and five novel type 1 diabetes association regions at 1p31.3, 1q24.3, 1q31.2, 2q11.2 and 11q12.2. Candidate genes for the new loci included ITGB3BP, FASLG, RGS1, AFF3 and CD5/CD6. Further prioritisation of causal genes and causal variants will require detailed RNA and protein expression studies, in conjunction with genome annotation studies including analysis of physical promoter-enhancer interactions.

genomics↗

bioRxiv2017

A rare IL2RA haplotype identifies SNP rs61839660 as causal for autoimmunity

IL2RA is associated with multiple autoimmune diseases including type 1 diabetes (T1D). Higher expression of IL2RA mRNA and its protein product CD25 in T lymphocytes is associated with a T1D-protective haplotype. Here we show that a rare variation of this haplotype that loses the protective allele at a single SNP, rs61839660, reduces IL2RA expression and T1D protection, identifying it as the causal factor in disease.

genetics↗