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BackgroundMigraine is a complex neurovascular disorder with a strong genetic component. There are rare monogenic forms of migraine, as well as more common polygenic forms; research into the genes involved in both types has provided insights into the many contributing genetic factors. This review summarises advances that have been made in the knowledge and understanding of the genes and genetic variations implicated in migraine etiology.FindingsMigraine is characterised into two main types, migraine without aura (MO) and migraine with aura (MA). Hemiplegic migraine is a rare monogenic MA subtype caused by mutations in three main genes - CACNA1A, ATP1A2 and SCN1A - which encode ion channel and transport proteins. Functional studies in cellular and animal models show that, in general, mutations result in impaired glutamatergic neurotransmission and cortical hyperexcitability, which make the brain more susceptible to cortical spreading depression, a phenomenon thought to coincide with aura symptoms. Variants in other genes encoding ion channels and solute carriers, or with roles in regulating neurotransmitters at neuronal synapses, or in vascular function, can also cause monogenic migraine, hemiplegic migraine and related disorders with overlapping symptoms. Next-generation sequencing will accelerate the finding of new potentially causal variants and genes, with high-throughput bioinformatics analysis methods and functional analysis pipelines important in prioritising, confirming and understanding the mechanisms of disease-causing variants.With respect to common migraine forms, large genome-wide association studies (GWAS) have greatly expanded our knowledge of the genes involved, emphasizing the role of both neuronal and vascular pathways. Dissecting the genetic architecture of migraine leads to greater understanding of what underpins relationships between subtypes and comorbid disorders, and may have utility in diagnosis or tailoring treatments. Further work is required to identify causal polymorphisms and the mechanism of their effect, and studies of gene expression and epigenetic factors will help bridge the genetics with migraine pathophysiology.ConclusionsThe complexity of migraine disorders is mirrored by their genetic complexity. A comprehensive knowledge of the genetic factors underpinning migraine will lead to improved understanding of molecular mechanisms and pathogenesis, to enable better diagnosis and treatments for migraine sufferers.

The suitability of human mesenchymal stem cells (hMSCs) in regenerative medicine relies on retention of their proliferative expansion potential in conjunction with the ability to differentiate toward multiple lineages. Successful utilisation of these cells in clinical applications linked to tissue regeneration requires consideration of biomarker expression, time in culture and donor age, as well as their ability to differentiate towards mesenchymal (bone, cartilage, fat) or non-mesenchymal (e.g., neural) lineages. To identify potential therapeutic suitability we examined hMSCs after extended expansion including morphological changes, potency (stemness) and multilineage potential. Commercially available hMSC populations were expanded in vitro for > 20 passages, equating to > 60 days and > 50 population doublings. Distinct growth phases (A-C) were observed during serial passaging and cells were characterised for stemness and lineage markers at representative stages (Phase A: P+5, approximately 13 days in culture; Phase B: P+7, approximately 20 days in culture; and Phase C: P+13, approximately 43 days in culture). Cell surface markers, stem cell markers and lineage-specific markers were characterised by FACS, ICC and Q-PCR revealing MSCs maintained their multilineage potential, including neural lineages throughout expansion. Co-expression of multiple lineage markers along with continued CD45 expression in MSCs did not affect completion of osteogenic and adipogenic specification or the formation of neurospheres. Improved standardised isolation and characterisation of MSCs may facilitate the identification of biomarkers to improve therapeutic efficacy to ensure increased reproducibility and routine production of MSCs for therapeutic applications including neural repair.

Polygenic profiling has been proposed for elite endurance performance, using an additive model determining the proportion of optimal alleles in endurance athletes. To investigate this model's utility for elite triathletes, we genotyped seven polymorphisms previously associated with an endurance polygenic profile (ACE Ins/Del, ACTN3 Arg577Ter, AMPD1 Gln12Ter, CKMM 1170bp/985+185bp, HFE His63Asp, GDF8 Lys153Arg and PPARGC1A Gly482Ser) in a cohort of 196 elite athletes who participated in the 2008 Kona Ironman championship triathlon. Mean performance time (PT) was not significantly different in individual marker analysis. Age, sex, and continent of origin had a significant influence on PT and were adjusted for. Only the AMPD1 endurance-optimal Gln allele was found to be significantly associated with an improvement in PT (model p = 5.79 x 10-17, AMPD1 genotype p = 0.01). Individual genotypes were combined into a total genotype score (TGS); TGS distribution ranged from 28.6 to 92.9, concordant with prior studies in endurance athletes (mean±SD: 60.75±12.95). TGS distribution was shifted toward higher TGS in the top 10% of athletes, though the mean TGS was not significantly different (p = 0.164) and not significantly associated with PT even when adjusted for age, sex, and origin. Receiver operating characteristic curve analysis determined that TGS alone could not significantly predict athlete finishing time with discriminating sensitivity and specificity for three outcomes (less than median PT, less than mean PT, or in the top 10%), though models with the age, sex, continent of origin, and either TGS or AMPD1 genotype could. These results suggest three things: that more sophisticated genetic models may be necessary to accurately predict athlete finishing time in endurance events; that non-genetic factors such as training are hugely influential and should be included in genetic analyses to prevent confounding; and that large collaborations may be necessary to obtain sufficient sample sizes for powerful and complex analyses of endurance performance.

Chronic kidney disease (CKD) is a multifactorial condition driven by diverse etiologies that lead to a gradual loss of kidney function. Although genome-wide association studies (GWAS) have identified numerous genetic loci linked to CKD, a large portion of its genetic basis remains unexplained. This knowledge gap may partly arise from the reliance on single biomarkers, such as estimated glomerular filtration rate (eGFR), to assess kidney function. To address this limitation, we developed and applied a novel multi-phenotype approach, combinatorial Principal Component Analysis (cPCA), to better understand the complex genetic architecture of CKD. Using UK Biobank dataset (n = 337,112), we analyzed 21 CKD-related phenotypes, generating over 2 million composite phenotypes (CPs) through cPCA. Nearly 50,000 of these CPs demonstrated significantly higher classification power for clinical CKD compared to individual biomarkers. The top-ranked CP—a combination of albumin, cystatin C, eGFR, gamma-glutamyltransferase, HbA1c, low-density lipoprotein, and microalbuminuria, achieved an AUC of 0.878 (95% CI: 0.873–0.882), significantly outperforming eGFR alone (AUC: 0.830, 95% CI: 0.825–0.835). Genetic association analysis of the ~ 50,000 high-performing CPs identified all major eGFR-associated loci, except for the SH2B3 locus rs3184504, a loss-of-function variant, which was uniquely identified in CPs (p = 3.1 × 10 -56 ) but not in eGFR within the same sample size. In addition, SH2B3 locus showed strong evidence of colocalization with eGFR, supporting its role in kidney function. These results highlight the power of the multi-phenotype cPCA approach in understanding the genetic basis of CKD, with potential applications to other complex diseases.

Cognitive decline associated with healthy ageing and pathological conditions is driven by multifactorial processes, including oxidative stress, mitochondrial dysfunction and chronic neuroinflammation. Alzheimer’s Disease (AD), a progressive neurodegenerative disorder affecting cognition and behaviour, is the leading cause of dementia worldwide. Current pharmacological interventions provide modest and transient benefits, targeting limited molecular pathways with safety and cost concerns, underscoring the need for safe, accessible and multi-targeted strategies. This review explores new avenues of therapy with a focus on bioactive compounds derived from brown, red and green seaweeds and their potential to modulate key mechanisms underlying AD. Preclinical and emerging clinical studies demonstrate that phlorotannins, fucoidans, fucoxanthin, lutein, zeaxanthin, ulvan, and astaxanthin exert antioxidant, anti-inflammatory, cholinergic-modulating and neuroprotective effects. Supplementation with seaweed-derived bioactive compounds has been shown to exert molecular and cellular effects that lead to reduced amyloid burden, preservation of synaptic integrity, and enhanced cognitive performance. Collectively, seaweed-derived compounds represent promising candidates for multi-target therapeutic strategies in cognitive decline prevention in the context of AD and healthy brain ageing.

Background Ageing is associated with DNA methylation changes in all human tissues, and epigenetic markers can estimate chronological age based on DNA methylation patterns across tissues. However, the construction of the original pan‐tissue epigenetic clock did not include skeletal muscle samples and hence exhibited a strong deviation between DNA methylation and chronological age in this tissue. Methods To address this, we developed a more accurate, muscle‐specific epigenetic clock based on the genome‐wide DNA methylation data of 682 skeletal muscle samples from 12 independent datasets (18–89 years old, 22% women, 99% Caucasian), all generated with Illumina HumanMethylation (HM) arrays (HM27, HM450, or HMEPIC). We also took advantage of the large number of samples to conduct an epigenome‐wide association study of age‐associated DNA methylation patterns in skeletal muscle. Results The newly developed clock uses 200 cytosine‐phosphate–guanine dinucleotides to estimate chronological age in skeletal muscle, 16 of which are in common with the 353 cytosine‐phosphate–guanine dinucleotides of the pan‐tissue clock. The muscle clock outperformed the pan‐tissue clock, with a median error of only 4.6 years across datasets (vs. 13.1 years for the pan‐tissue clock, P < 0.0001) and an average correlation of ρ = 0.62 between actual and predicted age across datasets (vs. ρ = 0.51 for the pan‐tissue clock). Lastly, we identified 180 differentially methylated regions with age in skeletal muscle at a false discovery rate < 0.005. However, gene set enrichment analysis did not reveal any enrichment for gene ontologies. Conclusions We have developed a muscle‐specific epigenetic clock that predicts age with better accuracy than the pan‐tissue clock. We implemented the muscle clock in an r package called Muscle Epigenetic Age Test available on Bioconductor to estimate epigenetic age in skeletal muscle samples. This clock may prove valuable in assessing the impact of environmental factors, such as exercise and diet, on muscle‐specific biological ageing processes.

Ischemic stroke, a leading cause of death and disability worldwide, has been increasingly linked to ceRNA networks, which regulate neuronal damage and recovery. Despite growing interest, a comprehensive bibliometric analysis of ceRNA's role in stroke remains limited. This study examines the research landscape, key trends, and future directions using Bibliometrix R package, VOSviewer, and CiteSpace. Bibliometrix (Biblioshiny) was used to analyze research growth, author productivity, and global collaboration. VOSviewer facilitated network visualization in co-occurrence, co-citation, and bibliographic coupling analyses, while CiteSpace identified emerging trends and key contributors through citation burst analysis and thematic clustering. Our analysis revealed a rapid surge in ceRNA-related ischemic stroke research from 2018 to 2024, with China leading in research output and global collaborations. Co-citation analysis identified three major thematic clusters: circRNAs in autophagy, lncRNAs within the ceRNA hypothesis, and the complexity of ceRNA networks in middle cerebral artery occlusion. Bibliographic coupling analysis highlighted five key research domains: lncRNA- and circRNA-mediated ceRNA networks, neurovascular injury, epigenetic regulation, and immune pathogenesis, highlighting their pivotal role in stroke mechanisms and therapeutic strategies. Molecular Medicine Reports ranked as the most influential journal, while Fudan University led institutional contributions. Thematic mapping identified inflammation and biomarkers as emerging research frontiers, suggesting potential novel therapeutic targets. This study provides a comprehensive analysis of ceRNA research in ischemic stroke, highlighting key trends, emerging frontiers, and therapeutic potential. The increasing focus on lncRNA- and circRNA-mediated networks, inflammation, and biomarkers reflects a shift toward precision medicine and innovative therapeutic interventions. These findings establish a foundation for future molecular diagnostics and targeted therapies, bridging the gap between research and clinical practice.

Markus Schürks and colleagues report a genome-wide association study for common migraine. They identify three new susceptibility loci at PRDM16 , TRPM8 and LRP1 . Migraine is a common, heterogeneous and heritable neurological disorder. Its pathophysiology is incompletely understood, and its genetic influences at the population level are unknown. In a population-based genome-wide analysis including 5,122 migraineurs and 18,108 non-migraineurs, rs2651899 (1p36.32, PRDM16 ), rs10166942 (2q37.1, TRPM8 ) and rs11172113 (12q13.3, LRP1 ) were among the top seven associations ( P < 5 × 10 −6 ) with migraine. These SNPs were significant in a meta-analysis among three replication cohorts and met genome-wide significance in a meta-analysis combining the discovery and replication cohorts (rs2651899, odds ratio (OR) = 1.11, P = 3.8 × 10 −9 ; rs10166942, OR = 0.85, P = 5.5 × 10 −12 ; and rs11172113, OR = 0.90, P = 4.3 × 10 −9 ). The associations at rs2651899 and rs10166942 were specific for migraine compared with non-migraine headache. None of the three SNP associations was preferential for migraine with aura or without aura, nor were any associations specific for migraine features. TRPM8 has been the focus of neuropathic pain models, whereas LRP1 modulates neuronal glutamate signaling, plausibly linking both genes to migraine pathophysiology.

Unspecialized, self-renewing stem cells have extraordinary application to regenerative medicine due to their multilineage differentiation potential. Stem cell therapies through replenishing damaged or lost cells in the injured area is an attractive treatment of brain trauma and neurodegenerative neurological disorders. Several stem cell types have neurogenic potential including neural stem cells (NSCs), embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and mesenchymal stem cells (MSCs). Currently, effective use of these cells is limited by our lack of understanding and ability to direct lineage commitment and differentiation of neural lineages. Heparan sulfate proteoglycans (HSPGs) are ubiquitous proteins within the stem cell microenvironment or niche and are found localized on the cell surface and in the extracellular matrix (ECM), where they interact with numerous signaling molecules. The glycosaminoglycan (GAG) chains carried by HSPGs are heterogeneous carbohydrates comprised of repeating disaccharides with specific sulfation patterns that govern ligand interactions to numerous factors including the fibroblast growth factors (FGFs) and wingless-type MMTV integration site family (Wnts). As such, HSPGs are plausible targets for guiding and controlling neural stem cell lineage fate. In this review, we provide an overview of HSPG family members syndecans and glypicans, and perlecan and their role in neurogenesis. We summarize the structural changes and subsequent functional implications of heparan sulfate as cells undergo neural lineage differentiation as well as outline the role of HSPG core protein expression throughout mammalian neural development and their function as cell receptors and co-receptors. Finally, we highlight suitable biomimetic approaches for exploiting the role of HSPGs in mammalian neurogenesis to control and tailor cell differentiation into specific lineages. An improved ability to control stem cell specific neural lineage fate and produce abundant cells of lineage specificity will further advance stem cell therapy for the development of improved repair of neurological disorders. We propose a deeper understanding of HSPG-mediated neurogenesis can potentially provide novel therapeutic targets of neurogenesis.

The gene SMART (genes and the Skeletal Muscle Adaptive Response to Training) Study aims to identify genetic variants that predict the response to both a single session of High-Intensity Interval Exercise (HIIE) and to four weeks of High-Intensity Interval Training (HIIT). While the training and testing centre is located at Victoria University, Melbourne, three other centres have been launched at Bond University, Queensland University of Technology, Australia, and the University of Brighton, UK. Currently 39 participants have already completed the study and the overall aim is to recruit 200 moderately-trained, healthy Caucasians participants (all males 18–45 y, BMI < 30). Participants will undergo exercise testing and exercise training by an identical exercise program. Dietary habits will be assessed by questionnaire and dietitian consultation. Activity history is assessed by questionnaire and current activity level is assessed by an activity monitor. Skeletal muscle biopsies and blood samples will be collected before, immediately after and 3 h post HIIE, with the fourth resting biopsy and blood sample taken after four weeks of supervised HIIT (3 training sessions per week). Each session consists of eight to fourteen 2-min intervals performed at the pre-training lactate threshold (LT) power plus 40 to 70% of the difference between pre-training lactate threshold (LT) and peak aerobic power (W peak ). A number of muscle and blood analyses will be performed, including (but not limited to) genotyping, mitochondrial respiration, transcriptomics, protein expression analyses, and enzyme activity. The participants serve as their own controls. Even though the gene SMART study is tightly controlled, our preliminary findings still indicate considerable individual variability in both performance (in-vivo) and muscle (in-situ) adaptations to similar training. More participants are required to allow us to better investigate potential underlying genetic and molecular mechanisms responsible for this individual variability.