Biology subjects

Banerji, C. R. S.

Publications and source records attributed to Banerji, C. R. S..

7 recordsLinked to original sources

bioRxiv2025

CEBPβ regulates myoblast proliferation and myogenic differentiation during human myogenesis and rescues defective differentiation in FSHD

CCAAT enhancer binding protein {beta} (CEBPB) is a member of the bZIP transcription factor family and is expressed in many cell types. Whilst CEBPB has been shown to regulate the function of murine satellite cells, the resident skeletal muscle stem cell, however little is known of its role in human myogenesis and muscle disease. We report that CEBPB is expressed in proliferating human myoblasts but swiftly down-regulated upon myogenic differentiation. Interestingly, either over-expression or knock-down of CEBPB inhibit the proliferation rate of human myoblasts. Down-regulation of CEBPB upon myogenic differentiation is then necessary for subsequent myogenic differentiation and fusion into multinucleated myotubes. In the muscle disease context, CEBPB protein levels are increased in myoblasts derived from facioscapulohumeral muscular dystrophy (FSHD) patients. Knock-down of CEBPB in FSHD myoblasts improves myogenic differentiation. In summary, we show that CEBPB is a regulator of human myogenesis and altered CEBPB expression may contribute to defective myogenesis in FSHD.

cell biology↗

bioRxiv2025

Normal breast tissue classifiers assess large-scale tissue compartments with high accuracy

Cancer research emphasises early detection, yet quantitative methods for normal tissue analysis remain limited. Digitised haematoxylin and eosin (H&E)-stained slides enable computational histopathology, but artificial intelligence (AI)-based analysis of normal breast tissue (NBT) in whole slide images (WSIs) remains scarce. We curated 70 WSIs of NBTs from multiple sources and cohorts with pathologist-guided manual annotations of epithelium, stroma, and adipocytes (https://github.com/cancerbioinformatics/OASIS). We developed robust convolutional neural network (CNN)-based, patch-level classification models, named NBT-Classifiers, to tessellate and classify NBTs at different scales. Across three external cohorts, NBT-Classifiers trained on 128{square}x{square}128{square}{micro}m and 256{square}x{square}256{square}{micro}m patches achieved AUCs of 0.98-1.00. The model learned independent normal features different from those of precancerous and cancerous epithelium, which were further visualised using two explainable AI techniques. When integrated into an end-to-end preprocessing pipeline, NBT-Classifiers facilitate efficient downstream analysis within peri-lobular regions. NBT-Classifiers provide robust compartment-specific analytical tools and enhance our understanding of NBT appearances, which serve as valuable reference points for identifying premalignant changes and guiding early breast cancer prevention strategies.

pathology↗

bioRxiv2024

Quantifying anti-DUX4 therapy for facioscapulohumeral muscular dystrophy

Facioscapulohumeral muscular dystrophy (FSHD) is an inherited skeletal myopathy with no cure. Expression of the myotoxic transcription factor double homeobox 4 (DUX4) is believed to underlie FSHD pathogenesis and many proposed therapies target DUX4 generation or function. Which of these therapies will be the most effective is unclear. Here, by constructing a Markov-chain-based mathematical model of DUX4-mediated myotoxity in FSHD, we interrogate various anti-DUX4 FSHD therapeutic strategies. We derive an analytical function for myonuclear life expectancy in terms of the parameters of DUX4 expression and function. In a biologically relevant parameter regime, therapeutically decreasing the DUX4 protein diffusion rate is, surprisingly, predicted to be more effective at increasing myonuclear life expectancy than reducing the rate of myonuclear apoptosis caused by the expression of DUX4-target genes. We find that targeting elements of DUX4 transcription/translation, such as mRNA stability via siRNA therapy, has a limited predicted impact on DUX4-meditated toxicity when performed in isolation. However, our model predicts a super-additive effect from combining transcription/translation targeting strategies with approaches that minimise DUX4 diffusion-mediated import into neighbouring myonuclei. Importantly, we provide a computational tool to test and inform therapeutic designs, enabling pre-clinical screening of FSHD treatment approaches.

cell biology↗

bioRxiv2023

Charting cellular differentiation trajectories with Ricci flow

Complex biological processes, such as cellular differentiation, require an intricate rewiring of intra-cellular signalling networks. Previous characterisations of these networks revealed that promiscuity in signalling, quantified by a raised network entropy, underlies a less differentiated and malignant cell state. A theoretical connection between entropy and Ricci curvature has led to applications of discrete curvatures to characterise biological signalling networks at distinct time points during differentiation and malignancy. However, understanding and predicting the dynamics of biological network rewiring remains an open problem. Here we construct a framework to apply discrete Ricci curvature and Ricci flow to the problem of biological network rewiring. By investigating the relationship between network entropy and Forman-Ricci curvature, both theoretically and empirically on single-cell RNA-sequencing data, we demonstrate that the two measures do not always positively correlate, as has been previously suggested, and provide complementary rather than interchangeable information. We next employ discrete normalised Ricci flow, to derive network rewiring trajectories from transcriptomes of stem cells to differentiated cells, which accurately predict true intermediate time points of gene expression time courses. In summary, we present a differential geometry toolkit for investigation of dynamic network rewiring during cellular differentiation and cancer.

bioinformatics↗

bioRxiv2022

An in silico FSHD muscle fibre for modelling DUX4 dynamics and predicting the impact of therapy

Facioscapulohumeral muscular dystrophy (FSHD) is an incurable myopathy linked to over-expression of the myotoxic transcription factor DUX4. Targeting DUX4 is the leading therapeutic approach, however it is only detectable in 0.1-3.8% of FSHD myonuclei. How rare DUX4 drives FSHD and the optimal anti-DUX4 strategy is unclear. We combine stochastic gene expression with compartment models of cell states, building a simulation of DUX4 expression and consequences in FSHD muscle fibres. Investigating iDUX4 myoblasts, scRNAseq and snRNAseq of FSHD muscle we estimate parameters including DUX4 mRNA degradation, transcription and translation rates and DUX4 target gene activation rates. Our model accurately recreates the distribution of DUX4 and target gene positive cells seen in scRNAseq of FSHD myocytes. Importantly we show DUX4 drives significant cell death despite expression in only 0.8% of live cells. Comparing scRNAseq of unfused FSHD myocytes to snRNAseq of fused FSHD myonuclei, we find evidence of DUX4 protein syncytial diffusion and estimate its rate via genetic algorithms. We package our model into freely available tools, to rapidly investigate consequences of anti-DUX4 therapy.

cell biology↗

bioRxiv2022

A circulating biomarker of facioscapulohumeral muscular dystrophy clinical severity, valid in skeletal muscle and blood

Facioscapulohumeral muscular dystrophy (FSHD) is incurable. DUX4 mis-expression is believed to underlie FSHD pathogenesis, alongside PAX7 target gene repression, yet clinical trials lack robust biomarkers of severity. FSHD entails fatty replacement of muscle, accelerated by inflammation, we thus performed RNA-sequencing on both an MRI guided inflamed (TIRM+) and non-inflamed (TIRM-) muscle biopsies from clinically-characterised FSHD patients, alongside peripheral blood mononucleated cells (PBMCs). PAX7 target gene repression in TIRM-muscle associates with severity. DUX4 target gene biomarkers associate with lower limb fat fraction and D4Z4 repeat length, but not severity. PAX7 target gene repression in muscle correlates with levels in matched PBMCs. A refined biomarker computed in PBMCs associates with severity in FSHD patients, and also validates as a classifier of severity in an independent set of 54 FSHD patient blood samples. In summary, we present a minimally-invasive, circulating, transcriptomic biomarker of FSHD clinical severity valid in muscle and blood.

bioinformatics↗

bioRxiv2019

Lymphocytes contribute to DUX4 target genes in FSHD muscle biopsies

Facioscapulohumeral muscular dystrophy (FSHD) is an incurable myopathy linked to overexpression of DUX4. However, DUX4 is difficult to detect in FSHD myoblasts and target gene expression is not a consistent FSHD muscle biopsy biomarker, displaying efficacy only on pathologically inflamed samples. Immune gene misregulation occurs in FSHD muscle biopsies with DUX4 targets enriched for inflammatory processes. However, assessment of the FSHD immune cell transcriptome, and the evaluation of DUX4 and target gene expression has not yet been performed. We show that FSHD lymphoblastoid cell lines (LCLs) display robust DUX4 expression, and express early and late DUX4 targets. Moreover, genes elevated on FSHD LCLs are elevated in FSHD muscle biopsies, correlating with DUX4 target activation and histological inflammation. These genes are importantly unaltered in FSHD myoblasts/myotubes, implying a non-muscle source in biopsies. Our results indicate an immune cell source of DUX4 and target gene expression in FSHD muscle biopsies.

bioinformatics↗