Launay, T. | bioRxiv Science

bioRxiv2026

SIX1-dependent myofiber typology and metabolism controls muscle hypertrophy

The different types of muscle fibres respond in a specific way to hypertrophy or atrophy. The mechanisms underlying these heterogeneous adaptations remain poorly understood. Using single-nucleus RNA sequencing, we propose that fast glycolytic fibres show genetic limitations to hypertrophy induced by mechanical overload. We show that a prior fibre transition, achieved by reducing SIX1 protein expression (hypomorphism), enhances and accelerates overload-induced hypertrophy, bypassing the genetic limitations of fast glycolytic fibres. In contrast and unexpectedly, Six1 knockout in myofibers abolished overload-induced hypertrophy and instead caused atrophy of IIb/IIx fibers, despite the induction of a strong slow oxidative phenotype. In particular, Six1 deletion leads to metabolic defects caused by inhibition of glycolysis, AMPK and mitochondrial biogenesis. Our findings highlight the critical role of SIX1/AMPK/glycolysis-dependent aerobic metabolism in muscle growth and suggest that fibre type transitions, coupled with preserved metabolic function, may optimise hypertrophic responses.

physiology↗

bioRxiv2023

Identification of transcription factor co-binding patterns with non-negative matrix factorization

Transcription factor (TF) binding to DNA is critical to transcription regulation. Although the binding properties of numerous individual TFs are well-documented, a more detailed comprehension of how TFs interact cooperatively with DNA, forming either complex or co-binding to the same region, is required. Indeed, the combinatorial binding of TFs is essential to cell differentiation, development, and response to external stimuli. We present COBIND, a novel method based on non-negative matrix factorization (NMF) to identify TF co-binding patterns automatically. COBIND applies NMF to one-hot encoded regions flanking known TF binding sites (TFBSs) to pinpoint enriched DNA patterns at fixed distances. We applied COBIND to 8,293 TFBS datasets from UniBind for 404 TFs in seven species. The method uncovered already established co-binding patterns (e.g., between POU5F1 and SOX2 or SOX17) and new co-binding configurations not yet reported in the literature and inferred through motif similarity and protein-protein interaction knowledge. Our extensive analyses across species revealed that 84% of the studied TFs share a co-binding motif with other TFs from the same structural family. The co-binding patterns captured by COBIND are likely functionally relevant as they harbor higher evolutionarily conservation than isolated TFBSs. Open chromatin data from matching human cell lines further supported the co-binding predictions. Finally, we used single-molecule footprinting data from mouse embryonic stem cells to confirm that the co-binding events captured by COBIND were likely occurring on the same DNA molecules.

bioinformatics↗