Biology subjects

Derave, W.

Publications and source records attributed to Derave, W..

5 recordsLinked to original sources

bioRxiv2025

Deconvoluting fiber type proportions from human skeletal muscle transcriptomics and proteomics data using FibeRtypeR

Muscle fibers are the dominant, multinucleated cell type in skeletal muscle. In humans, they can be classified as slow (type 1) and fast (type 2) fibers, traditionally based on distinct properties of their contractile machinery. Slow and fast fibers are characterized by shared and specific cellular complexities that are pivotal for their adaptive capacity to exercise or role in metabolic disease progression. In this era of omics research, there is a critical need to accurately infer muscle fiber type proportions from bulk tissue omics datasets, as single-fiber approaches are not feasible in large-scale or retrospective studies. Here we present FibeRtypeR, an easy-to-use web application to accurately estimate fiber type proportions from bulk transcriptomics and proteomics datasets: https://muscleapps.ugent.be. FibeRtypeR exploits transcriptomics and proteomics profiles of 1000 fiber-typed individual human skeletal muscle fibers as a reference dataset and is validated against paired immunohistochemical fiber type determinations in 160 muscle biopsies. We show that FibeRtypeR can be applied to public datasets, illustrating the application potential across a wide range of biological contexts such as aging, disease and exercise training. This new freely accessible computational tool will prove valuable to the skeletal muscle research community. Key pointsO_LIBulk muscle omics datasets lack fiber type specific information C_LIO_LIOur new tool, FibeRtypeR, leverages in-house collected single-fiber profiles allowing for accurate fiber type inference C_LIO_LIFibeRtypeR is methodologically robust across omics technologies and workflows C_LIO_LIWe host FibeRtypeR as an intuitive open-access Shiny app, applicable to new and publicly available transcriptomics and proteomics datasets C_LI

physiology↗

bioRxiv2025

Big Bones Mean Big Muscles: AI Quantifies 71 Individual Muscles Across the Whole Body, Revealing Widespread Links Between Muscle, Bone, and Body Size

Body sizes and shapes vary widely, even among healthy adults, resulting in diverse muscle sizes, strengths, and performance capacities. This study developed a novel AI-driven algorithm to segment and analyze individual muscles and bones from whole-body MRI. Validated via inter- and intra-observer analyses, the algorithm created 3D segmentations of 71 muscles and 13 bones across the upper limbs, trunk, and lower limbs in 48 healthy adults (24 males, 24 females) aged 18-49 years. Muscle volume, asymmetry, length, and fat fraction were quantified. While asymmetry and fat fraction varied across muscles, they did not differ significantly between sexes. Total muscle volume was the strongest predictor of individual muscle volume, followed by bone volume, which correlated with muscle size at whole-body, regional, and individual levels. Muscle-to-body size relationships (e.g., mass, height, BMI) differed between sexes, while bone-to-body size relationships did not. These findings suggest that skeletal size ("frame size") is a key determinant of muscularity. This study provides the most comprehensive in vivo dataset of human skeletal muscle to date, offering a multi-factorial explanation for variation in muscularity and benchmarks for applications ranging from athletic performance to clinical assessments in muscle disorders. SUMMARYUnderstanding how muscle size varies across the body and what influences these differences is key to advancing both health and performance. This study uses cutting-edge AI to analyze individual muscles across the whole body from MRI scans, creating the most detailed dataset of human muscle and bone relationships to date. The findings reveal how skeletal size ("frame size") and body dimensions shape muscularity, providing new insights into why people differ in muscle size and strength. These results have broad implications, from optimizing athletic training to diagnosing and treating muscle disorders. By uncovering the intricate connections between muscles, bones, and body size, this research offers a powerful framework for understanding human movement and variability.

bioengineering↗

bioRxiv2023

Beyond Myosin Heavy Chains: Ribosomal Specialization Drives Human Skeletal Muscle Fiber Heterogeneity

Skeletal muscle is an inherently heterogenous tissue comprised primarily of myofibers, which are historically classified into three distinct fiber types in humans: one "slow" (type 1) and two "fast" (type 2A and type 2X), delineated by the expression of myosin heavy chain isoforms (MYHs). However, heterogeneity between and within traditional fiber types remains underexplored. Indeed, whether MYHs are the main classifiers of skeletal muscle fibers has not been examined in an unbiased manner. Through the development and application of novel transcriptomic and proteomic workflows, applied to 1050 and 1038 single muscle fibers from human vastus lateralis, respectively, we show that MYHs are not the only principal drivers of skeletal muscle fiber heterogeneity. Instead, metabolic, ribosomal, and cell junction proteins are a source of multi-dimensional variation between skeletal muscle fibers. Furthermore, whilst slow and fast fiber clusters can be identified, described by their contractile and metabolic profiles, our data suggests that type 2X fibers are not phenotypically distinct to other fast fibers at an omics level. Moreover, MYH-based classifications do not adequately describe the phenotype of skeletal muscle fibers in one of the most common genetic muscle diseases, nemaline myopathy, with fibers shifting towards a non-oxidative phenotype independently of MYH-based fiber type. We also characterize novel transcriptomic and proteomic features of slow and fast skeletal muscle fibers, including identifying several muscle fiber type-specific polypeptides, termed microproteins, encoded by transcripts annotated as non-coding RNA. Overall, our data indicates that skeletal muscle fiber heterogeneity is multi-dimensional with sources of variation beyond myosin heavy chain isoforms.

physiology↗

bioRxiv2023

Carnosine synthase deficiency aggravates neuroinflammation in multiple sclerosis

Multiple sclerosis (MS) pathology features autoimmune-driven neuroinflammation, demyelination, and failed remyelination. Carnosine is a histidine-containing dipeptide (HCD) with pluripotent homeostatic properties that is able to improve outcomes in an animal MS model (EAE) when supplied exogenously. To uncover if endogenous carnosine is involved in, and protects against, MS-related neuroinflammation, demyelination or remyelination failure, we here studied the HCD-synthesizing enzyme carnosine synthase (CARNS1) in human MS lesions and two preclinical mouse MS models (EAE, cuprizone). We demonstrate that due to its presence in oligodendrocytes, CARNS1 expression is diminished in demyelinated MS lesions and mouse models mimicking demyelination/inflammation, but returns upon remyelination. Carns1-KO mice that are devoid of endogenous HCDs display exaggerated neuroinflammation and clinical symptoms during EAE, which could be partially rescued by exogenous carnosine treatment. Worsening of the disease appears to be driven by a central, not peripheral immune-modulatory, mechanism possibly linked to impaired clearance of the reactive carbonyl acrolein in Carns1-KO mice. In contrast, the presence of CARNS1 and endogenous HCDs does not protect against cuprizone-induced demyelination, and is not required for normal oligodendrocyte precursor cell differentiation and (re)myelin to occur. Exogenously administered carnosine is not effective in blunting demyelination or accelerating remyelination. In conclusion, we show that CARNS1 is diminished in demyelinated MS lesions, which may have detrimental effects on disease progression through weakening the endogenous protection against neuroinflammation.

neuroscience↗

bioRxiv2023

Extensive profiling of histidine-containing dipeptides reveals species-specific distribution and metabolism in mice, rats and humans

Histidine-containing dipeptides (HCDs) are pleiotropic homeostatic molecules linked to inflammatory, metabolic and neurological diseases, as well as exercise performance. Using a sensitive UHPLC-MS/MS approach and an optimized quantification method, we performed a systematic and extensive profiling of HCDs in the mouse, rat, and human body (in n=26, n=25, n=19 tissues, respectively). Our data show that tissue HCD levels are uniquely regulated by carnosine synthase (CARNS1), an enzyme that was preferentially expressed by fast-twitch skeletal muscle fibers and brain oligodendrocytes. Cardiac HCD levels are remarkably low compared to other excitable tissues. Carnosine is unstable in human plasma, but is preferentially transported within red blood cells in humans but not rodents. The low abundant carnosine analog N-acetylcarnosine is the most stable plasma HCD, and is enriched in human skeletal muscles. Here, N-acetylcarnosine is continuously secreted into the circulation, which is further induced by acute exercise in a myokine-like fashion. Collectively, we provide a novel basis to unravel tissue-specific, paracrine, and endocrine roles of HCDs in human health and disease. Significance statementBy extensively profiling the pluripotent histidine-containing dipeptides across three species, we generated many new insights into species- and tissue-specific histidine-containing dipeptide metabolism. For instance, the only stable analog that is specific for the human circulation (N-acetylcarnosine) is continuously released from muscle tissue and is positively regulated by physical exercise. The great number of analyses and experiments involving humans establishes great translational value of the findings. These new data open exciting opportunities to study histidine-containing dipeptide metabolism, including paracrine and/or endocrine signaling of these dipeptides, possibly contributing to the potent health-promoting exercise effects.

physiology↗