The modification in question is isonicotinylation, abbreviated Kinic, a recently discovered post-translational mark in which an isonicotinyl group is attached to proteins. It was first described in liver cancer, where it modifies histones to boost oncogenic protein expression and activate the PI3K–AKT signaling pathway. Subsequent work showed that Kinic can also modify non-histone proteins, driving epithelial–mesenchymal transition and invasion in liver cells, and an independent artificial intelligence study suggested that Kinic signatures could support risk stratification and drug design in hepatocellular carcinoma. What remained unclear was whether this modification plays any role in the immune system—and, by extension, in immune-mediated diseases such as DMD.

Mast cells, meanwhile, have long been suspected participants in muscular dystrophy. These inflammatory cells are recruited to damaged muscle, where they can promote progressive myofiber necrosis and sustain chronic inflammation. Earlier studies in dystrophin-deficient humans, dogs, and mice, including double-mutant mdx mice with exaggerated mast cell activity, correlated mast cell burden with disease severity. But whether the balance of mast cell activity could serve as a measurable indicator for DMD patients had never been systematically tested. The research team, led by investigators from hospitals in Zhejiang and Jiangsu, China, set out to close both gaps at once by asking how Kinic-related biology and mast cell behavior intersect in dystrophic muscle.

Methodologically, the study is a tour de force of modern bioinformatics. The researchers drew on four publicly available bulk gene expression datasets from the Gene Expression Omnibus—GSE38417, GSE109178, GSE6011, and GSE32720—covering a total of 98 muscle samples from DMD patients and healthy controls. After normalizing the data and correcting for batch effects using the sva and limma packages, they applied gene set variation analysis to quantify Kinic pathway activity, the CIBERSORT algorithm to estimate immune cell infiltration, and weighted gene co-expression network analysis to identify gene modules correlated with each feature. In GSE38417, the black module correlated most strongly with Kinic activity (R = 0.49) and negatively with DMD pathology (R = −0.86); in GSE109178, the green module tracked mast cell signatures (R = −0.63) and disease status (R = 0.91). Intersecting these modules yielded nine shared genes, which the team designated KM-related differentially expressed genes—KM standing for the combined Kinic and mast cell axis.

Functional enrichment revealed that these nine genes were principally involved in maintaining intracellular homeostasis, a finding that hints at how a protein-modification program and an immune cell population might converge on a shared cellular vulnerability. Using consensus clustering on the GSE6011 training cohort, the researchers then split DMD patients into two molecular subgroups, C1 and C2, based on the expression patterns of these KM-related genes. The subgroups differed in molecular function, gene expression, and immune infiltration profiles, suggesting that the KM axis captures biologically meaningful variation among patients rather than statistical noise.

The heart of the diagnostic effort lay in an ensemble machine learning pipeline. The team systematically combined twelve machine learning algorithms with multiple feature selection and preprocessing strategies, generating 132 candidate models, each trained on GSE6011 with ten-fold cross-validation and then tested on the independent validation set GSE32720. The winning combination—random forest paired with stepwise generalized linear modeling, abbreviated RF + Stepglm—achieved favorable discrimination in both cohorts, as assessed by receiver operating characteristic and precision–recall analyses, and its accuracy was further confirmed through nomogram and calibration plots. To interpret which genes drove the model’s decisions, the researchers applied SHAP analysis, a game-theoretic approach for quantifying each feature’s contribution to predictions. The verdict was unambiguous: USP18, a gene encoding a ubiquitin-specific protease, emerged as the dominant contributor.

USP18’s credentials made the finding compelling. The enzyme is known to regulate ubiquitination and has been implicated in muscular disorders, including as an ISG15 activator promoting inflammation in dermatomyositis muscle with perifascicular atrophy and in juvenile inflammatory myopathies. In the new study, USP18 expression was consistently upregulated in DMD samples across all four bulk datasets, and single-gene pathway analysis in GSE6011 tied it to inflammatory signaling. Crucially, the team also found that high USP18 expression was negatively correlated with mast cell activation, suggesting a regulatory relationship between the two. To validate these patterns experimentally, the researchers engineered human mast cell lines using CRISPR–Cas9: one line (DMDI) carried a deletion of exon 52 of the DMD gene, and another (DMDII) carried a stop codon introduced within exon 52. Quantitative PCR and Western blotting confirmed the USP18 expression patterns observed computationally, anchoring the bioinformatic predictions in wet-lab measurements.

To place USP18 in its cellular context, the team turned to single-cell transcriptomics. They analyzed a DMD mouse muscle dataset (GSE213925) comprising 382,392 cells, which resolved into 24 clusters and 10 cell types after quality control and dimensionality reduction. Mast cells represented a relatively small fraction of this landscape, but AUCELL scoring showed that Kinic activity was comparatively enriched within them, and Usp18 was distributed in mast cells specifically. Using the scTenifoldKnk package, the researchers performed an in silico knockout: they constructed a gene regulatory network from the single-cell data via tensor decomposition, virtually set Usp18 expression to zero, and recomputed the network to identify genes whose regulatory connections changed significantly. The perturbed targets were enriched for molecules involved in maintaining muscular function and linked to muscular disease. Pseudotime trajectory analysis with monocle2 further revealed five differentiation trajectories along which Usp18 expression correlated with mast cell maturation, and cell–cell communication and metabolic profiling rounded out the atlas.

The therapeutic arm of the study was equally computational. Using the DrugReflector platform—a multi-omics drug repositioning database integrating transcriptomic, proteomic, and protein–protein interaction data—the team scored compounds against the GSE6011 signature using a Drug–Target Association Score that weighs gene expression similarity, chemical structure similarity, known drug–target interactions, and network topology. The deep learning pipeline flagged BRD-K10482608 as the top candidate. In parallel, network pharmacology based on the molecular perturbations caused by virtual USP18 knockout identified finafloxacin, an antibiotic, as a USP18-targeted agent. Molecular docking using AlphaFold2-predicted protein structures, AutoDock Vina simulations, and visualization in PyMOL and MOE showed favorable binding affinity between finafloxacin and USP18, with defined hydrogen bond and hydrophobic interactions at the binding interface. Together, the two compounds represent computationally derived starting points for DMD drug development.

The authors are careful to frame their conclusions within clear limitations. The Kinic signal was inferred from a transcriptomic gene signature rather than measured directly by mass spectrometry or antibody-based detection, so the findings should be read as signature-based associations rather than direct biochemical proof. The in vitro work quantified USP18 expression but did not dissect how Kinic modification of USP18 might function mechanistically. The single-cell cohort comprised only two DMD mouse samples, which may not capture the full heterogeneity of mast cell states across the disease spectrum, and the bulk datasets, despite batch correction, may retain unmodeled technical and biological variability. The drug candidates, identified entirely in silico, will require rigorous preclinical testing to confirm efficacy, dosing, and muscle tissue penetration. Still, the study offers something DMD research has lacked: a unified, data-driven framework linking a novel protein modification, a specific immune cell type, and a druggable target, with a validated diagnostic model that could support molecular stratification of patients. If future multi-center trials bear out the model’s predictive power and the therapeutic candidates’ promise, the convergence of artificial intelligence, multi-omics, and targeted validation demonstrated here could reshape how this relentless disease is diagnosed and treated.

Subject of Research: Isonicotinylation and mast cell-associated gene signatures in Duchenne muscular dystrophy identified through ensemble machine learning and in vitro validation

Article Title: Deciphering isonicotinylation and mast cell-associated pathogenic patterns in Duchenne muscular dystrophy: evidence from ensemble learning-driven multi-omics and in vitro validation

Article References: Yu, S., Xu, D., Hua, Y., Mo, H., & Liu, Y. (2026). Deciphering isonicotinylation and mast cell-associated pathogenic patterns in Duchenne muscular dystrophy: evidence from ensemble learning-driven multi-omics and in vitro validation. Frontiers in Molecular Biosciences, 13, Article 1853169. https://doi.org/10.3389/fmolb.2026.1853169

Image Credits: AI Generated

DOI: 10.3389/fmolb.2026.1853169

Keywords: Duchenne muscular dystrophy, isonicotinylation, mast cells, USP18, machine learning, multi-omics, single-cell RNA sequencing, drug repositioning, post-translational modification, biomarkers, CRISPR, inflammation

The research, led by Melissa Bowerman of Keele University’s School of Medicine together with colleagues at the University of Hertfordshire and the Wolfson Centre for Inherited Neuromuscular Disease, builds on earlier work from the same laboratory that combined bioinformatics with drug repositioning strategies to identify compounds capable of correcting metabolic defects in SMA. Rather than starting from scratch with novel molecules, the team deliberately focused on drugs that are already approved for human use, an approach that could shorten the path from bench to bedside considerably. The three candidates that emerged—pioglitazone, a thiazolidinedione used to treat type 2 diabetes; melatonin, a hormone that regulates circadian rhythms; and insulin, the classic metabolic hormone—each target a different facet of cellular energy handling.

The experimental system at the heart of the study is the Smn2B/- mouse, a well-established model of SMA that carries a deletion of the mouse Smn gene alongside a human SMN2 transgene. This model recapitulates many features of the human disease, including motor dysfunction, muscle atrophy, shortened lifespan and systemic metabolic abnormalities. The researchers treated these mice with each of the three compounds and assessed a battery of behavioural, molecular and histological endpoints. Alongside the mouse work, they used the nematode worm Caenorhabditis elegans as a complementary in vivo platform, allowing rapid evaluation of the compounds’ effects in a second organism with conserved metabolic pathways.

The results, the team reports, were encouraging across the board, albeit to different degrees for each compound. All three drugs improved various pathological features in the SMA mice and worms, including survival, body weight, motor function, muscle size, the health of the spinal cord and the accumulation of lipids in the liver. Hepatic steatosis—the abnormal build-up of fat in liver tissue—is one of the metabolic pathologies increasingly recognised in SMA patients and models, and its improvement following treatment suggests that the compounds were acting on genuine disease-relevant biology rather than producing non-specific effects. Improvements in muscle size and spinal cord health point to benefits that extend beyond the liver into tissues central to neuromuscular function.

Of the three interventions, melatonin produced the most significant effect on survival, the endpoint that carries the greatest weight in preclinical SMA research. This finding is particularly striking because melatonin is not typically thought of as a metabolic drug; its best-known role is as a circadian signal produced by the pineal gland. Yet the study found that melatonin’s activity in SMA mice extended across several tissues, influencing molecular effectors involved in circadian rhythm regulation, glucose metabolism, mitochondrial biogenesis and the browning of white adipose tissue. Browning—the conversion of energy-storing white fat into energy-burning beige or brown fat—is a process with profound implications for systemic energy balance, and its disruption has been implicated in the metabolic dysfunction seen in SMA.

The involvement of mitochondria is another thread worth unpacking. Mitochondria are the power plants of the cell, and their biogenesis—the generation of new mitochondrial material—is governed by molecular pathways that respond to metabolic stress. In SMA, mitochondrial defects have been observed in multiple tissues, and the ability of melatonin to influence mitochondrial biogenesis suggests a mechanism by which the hormone could restore cellular energy capacity in vulnerable tissues such as muscle and liver. Similarly, its effects on glucose metabolism echo the insulin resistance and dyslipidaemia documented in SMA patients, abnormalities that current disease-modifying therapies do not adequately address.

That last point is crucial to understanding why the study matters. The last several years have seen a remarkable transformation in SMA care, with the arrival of nusinersen, an antisense oligonucleotide that modifies SMN2 splicing; onasemnogene abeparvovec, a gene therapy that delivers a functional copy of the SMN1 gene; and risdiplam, an oral SMN2 splicing modifier. These therapies have delivered dramatic gains in survival and motor function, particularly when treatment begins early. Yet they share a common limitation: they are designed to raise SMN levels, primarily benefiting the neuromuscular system, and they leave the peripheral and metabolic pathologies of the disease largely untouched. Patients treated with these drugs still exhibit metabolic abnormalities, and the peripheral tissues—skeletal muscle, liver, pancreas and adipose tissue—remain sites of ongoing dysfunction. The new study argues that this therapeutic gap is precisely where metabolism-targeted interventions could make a difference.

The concept of SMA as a metabolic disease is grounded in the biology of SMN itself. Although the protein is best known for its role in the assembly of spliceosomal ribonucleoproteins, its loss produces detectable abnormalities in tissues far removed from the motor neuron. Insulin resistance, hepatic steatosis, dyslipidaemia and circadian disruption have all been documented in SMA, and these abnormalities are not merely consequences of immobility; they appear to arise from cell-autonomous metabolic defects. Reframing SMA pathology from a neurocentric to a metabolic perspective, as the authors put it, reveals a disease landscape in which energy handling goes wrong in parallel with, and possibly contributes to, neuromuscular degeneration. On this view, correcting metabolic dysfunction is not an afterthought but a legitimate therapeutic goal in its own right.

The translational appeal of the approach lies in the fact that all three compounds are already commercially approved. Pioglitazone is prescribed worldwide for type 2 diabetes and has a well-characterised safety profile, although its use is associated with weight gain and, in some patients, concerns about fluid retention and fracture risk. Melatonin is available over the counter in many countries and is generally well tolerated. Insulin, of course, is a mainstay of diabetes management, though its use in non-diabetic patients would require careful dosing and monitoring. The authors are careful to note that the study was conducted in mouse and worm models, and that the doses, timing and combinations used in animals may not translate directly to humans. Clinical trials would be needed to establish whether these interventions, alone or in combination with existing SMN-restoring therapies, can deliver measurable benefits in patients. Nevertheless, the fact that the compounds are approved means that safety data already exist, potentially accelerating the design of early-phase studies.

The broader significance of the work may lie in the shift it represents in how the field thinks about combination therapy for SMA. Just as oncology moved from single-agent treatments to rational multi-drug regimens that attack different vulnerabilities simultaneously, neuromuscular medicine may be heading toward pairing SMN-restoring therapies with second-generation treatments aimed at specific non-neurological pathologies. The findings position metabolism, as the authors conclude, at the forefront of targets for SMA treatments and provide a strong rationale for exploring metabolism-targeted second-generation therapies to complement currently approved disease-modifying treatments. For a disease that was once considered untreatable, and which now has effective but incomplete therapies, the prospect of adding metabolic correction to the therapeutic arsenal represents a meaningful step forward—one that could matter most for the patients whose peripheral symptoms persist despite the most advanced neuromuscular interventions available today.

Subject of Research: Testing commercially approved metabolism-targeting drugs in a mouse model of spinal muscular atrophy

Article Title: Treating Smn2B/− spinal muscular atrophy (SMA) mice with commercially approved metabolism-targeting interventions leads to improved disease phenotypes

Article References: Çetin, Ö., McCallion, E., Hoolachan, J. M., Sutton, E. R., Cook, J., Al-Mozani, Y., Dhoowooah, A., Varughese, M. S., Velandia Dorta, L. F., Koomson, J., Rashid, S., Pacheco-Torres, P., Dimitriadi, M., & Bowerman, M. (2026). Treating Smn2B/− spinal muscular atrophy (SMA) mice with commercially approved metabolism-targeting interventions leads to improved disease phenotypes. Cellular and Molecular Life Sciences. https://doi.org/10.1007/s00018-026-06461-1

Image Credits: AI Generated

DOI: 10.1007/s00018-026-06461-1

Keywords: spinal muscular atrophy, metabolism, melatonin, pioglitazone, insulin, Smn2B/- mice, drug repositioning, mitochondrial biogenesis, hepatic steatosis, circadian rhythm, adipose tissue browning, combination therapy