BackgroundSepsis is a major contributor to high morbidity and mortality, often leading to coagulation disorders (CD) in affected individuals. Baicalein, a natural compound with well-established anti-inflammatory properties, shows promise as a potential treatment for sepsis. However, its molecular mechanisms in sepsis-associated CD remain poorly understood. This study investigated the therapeutic effects of baicalein in sepsis and identified candidate genes involved in its mechanism of action.MethodsTranscriptomic data, baicalein-related targets from public databases, and CD-related genes from the literature were analyzed to identify potential candidate genes. Machine learning algorithms and expression validation techniques were employed to screen initial candidate genes from the candidates. A nomogram was then constructed based on these candidate genes. Functional enrichment and immune infiltration analyses were conducted to explore the underlying mechanisms, while molecular docking was used to assess interactions between baicalein and the candidate genes. Gene expression was further validated by reverse transcription-quantitative PCR (RT-qPCR).ResultsSeven initial candidate genes were identified. Machine learning and expression validation confirmed MMP9, ARG1, and FYN as the final candidate genes involved in sepsis. A highly accurate nomogram, constructed using these candidate genes, demonstrated strong predictive value for sepsis diagnosis. Functional enrichment analysis revealed their pivotal roles in sepsis pathogenesis, while immune infiltration analysis indicated immune dysregulation in sepsis. Additionally, molecular docking revealed strong binding interactions between baicalein and proteins encoded by these candidate genes, supporting further investigation of its therapeutic potential in sepsis. However, these in silico findings are preliminary and require validation through in vitro and in vivo experiments to confirm biological activity. RT-qPCR further validated differential expression of these genes in patients with sepsis compared to healthy controls, confirming the results.ConclusionThis study identified MMP9, ARG1, and FYN as candidate genes in sepsis involved in immune regulation. Additionally, molecular docking revealed strong binding interactions between baicalein and the proteins encoded by these candidate genes, supporting further investigation of its therapeutic potential in sepsis.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40246-025-00818-6.

BackgroundLimb-girdle muscular dystrophies (LGMDs) constitute a genetically diverse group of disorders characterized by progressive proximal muscle weakness and atrophy. Despite advances in genetic diagnostics, numerous cases remain unresolved due to extensive genetic heterogeneity, emphasizing the necessity for continued identification of novel pathogenic variants.ResultsUsing whole-exome sequencing (WES) in a Saudi family affected by autosomal recessive LGMD, we identified a novel homozygous frameshift mutation (c.891delT; p.Ala298ArgfsTer64) in the TRIM72 (MG53) gene, which we propose as a strong candidate gene for LGMD. Segregation analysis via Sanger sequencing confirmed that the variant co-segregated precisely with the disease phenotype and was absent in ethnically matched control cohorts. TRIM72 encodes a muscle-specific E3 ubiquitin ligase involved in sarcolemmal membrane repair, critical for maintaining muscle cell integrity. Functional parallels between TRIM72 and the LGMD-associated TRIM32, alongside corroborating evidence from animal models and cellular studies, support the candidacy of TRIM72 in LGMD pathogenesis.ConclusionOur findings identify TRIM72 as a novel candidate gene implicated in autosomal recessive LGMD, expanding the genetic spectrum of this heterogeneous disease. This discovery underscores the critical roles of TRIM family proteins in muscle pathology and reinforces the value of advanced genetic sequencing methodologies in diagnosing unresolved muscular dystrophy cases.

The human sideroflexin (SFXN) gene family, also classified as solute carrier family 56 (SLC56), encodes a group of five mitochondrial transmembrane proteins (SFXN1–SFXN5) involved in key aspects of mitochondrial metabolism, cellular homeostasis, and development. SFXNs are highly conserved across eukaryotic species, with evolutionary the origin traced back to the earliest metazoans. Functionally, each of the five family members exhibits distinct functional specialization. Particularly, SFXN1 and SFXN3 facilitate mitochondrial serine transport, supporting one-carbon metabolism. SFXN2 and SFXN4 are implicated in mitochondrial iron regulation, heme biosynthesis, and iron–sulfur cluster assembly. SFXN5, predominantly expressed in the brain, is proposed to regulate citrate metabolism and immune cell functions. Mutations or dysregulation of SFXN genes have been linked to certain human diseases, including congenital sideroblastic anemia, oxidative phosphorylation disorders, neurodegenerative conditions, and cancers. Structurally, SFXNs share conserved transmembrane domains and key motifs critical for substrate transport, mitochondrial iron homeostasis, and overall mitochondrial function. The evolutionary trajectory of the SFXN family—from amino acid transport to functionally specialized roles in higher organisms—highlights their biological and clinical significance. Comparative studies across model organisms reveal both conserved and divergent functions, emphasizing their importance in health and disease. A comprehensive understanding of the SFXN family not only advances fundamental mitochondrial research but also opens avenues for novel therapeutic interventions.

IntroductionEarly diagnosis of breast cancer leads to higher long-term survival rates. The development of a germline genetic test, or polygenic risk score, to identify women at high risk of breast cancer holds the potential to reduce cancer deaths. However, current tests based on SNPs do not perform much better than predictions based on family history and perform significantly worse in populations with non-European ancestry. We have developed an alternative method to characterize a genome, called chromosomal-scale length variation, which can be applied to polygenic risk scores.ObjectiveThe objective of this paper is to characterize a breast cancer genetic risk score based on chromosomal-scale length variation using the NIH All of Us dataset in different self-identified racial groups when trained on different populations.MethodsWe used the NIH All of Us dataset to compile a dataset with 4,533 women who have been diagnosed with breast cancer (including 440 who self-identified as Black) and 44,518 women who have not. We acquired, through All of Us, genetic information for each of these women. We computed a set of 88 values for each woman in the dataset, representing the chromosomal-scale length variation parameters. These numbers are average log R ratios for four different segments from each of the 22 autosomes. We used machine learning algorithms to find a model that best differentiates the women with breast cancer from the women without breast cancer based on the set of 88 numbers that characterize each woman’s germline genome.ResultsThe best model had an AUC of 0.70 (95% CI, 0.67–0.73) in the All of Us population. Women who scored in the top quintile by this model were nine times more likely to have breast cancer when compared to women who scored in the lowest quintile.ConclusionIn conclusion, we found that this method of computing genetic risk scores for breast cancer is a substantial improvement over SNP-based polygenic risk scores. In addition, we compared models trained on populations of only White women and only Black women. We found that the models trained only on White women performed better than models trained only on Black women when tested on only White women. We did not see a significant difference between the two models when tested on only Black women.

BackgroundNewborn genomic screening offers the potential for early detection and management of genetic disorders. Understanding parental perspectives is essential before integrating genomic testing into standard newborn screening.MethodsThis was a descriptive cross-sectional study surveyed 568 parents in Abu Dhabi, United Arab Emirates (UAE). An online self-administered validated and piloted questionnaire was used to gather information on demographic characteristic and perspectives regarding newborn genomic screening. Data were analysed using R version 4.4.3.ResultsMost parents (78.2%) supported integrating genomics into newborn screening programs, with 63.5% stating it requires distinct management from standard screening. Females preferred geneticists (38.2% vs. 32.5%, p < 0.001) and hospitals (45.1% vs. 39.2%, p < 0.001) for discussions, with 74.2% emphasizing explicit consent compared to 68.5% of males (p < 0.002). Treatability (82.7%), age of symptom onset (74.1%), and severity (72.2%) were key decision-making factors. Additionally, 66.7% preferred genomic testing to be covered by insurance, and 82.2% supported storing genomic data for future use.ConclusionParents participated in the study strongly support genomic newborn screening. Gender-based differences emphasize the need for tailored communication and culturally sensitive strategies to inform policy development and implementation of newborn genomic screening program in the UAE and similar contexts.

BackgroundThe timing of menarche and menopause significantly affects women’s health, with influences on cancer, cardiovascular disease, obesity, type 2 diabetes, and psychosocial problems. In addition, observational studies have reported that ages at menarche (AAM) and natural menopause (ANM) are correlated with obesity. To understand the genetic bases of these reproductive traits, we conducted a genome-wide association study (GWAS) of AAM and ANM in the Korean population. We also investigated the genetic correlation and causal relationship to explore the shared genetic architecture between reproductive traits and obesity in women.ResultsOur GWA analyses of 45,608 and 21,599 adult women identified two and six genome-wide significant associations (P-value < 5 × 10− 8) for AAM and ANM, respectively. Although most of the loci that we detected have been reported in previous studies, we have newly linked the JHY locus containing the SNP rs11605693 to AAM. Leveraging the GWAS results, we tested the shared genetic basis underlying AAM and ANM, which appear to be closely related to female hormone activity. Linkage disequilibrium score regression (LDSC) analysis did not identify a significant genetic correlation between the two traits. Our LDSC analyses indicated that AAM was inversely correlated with two obesity traits, body mass index (BMI) and waist circumference (WC). However, Mendelian randomization (MR) analyses did not provide evidence of a causal relationship between AAM and obesity traits.ConclusionsOverall, our study provides insights into the genetic architecture of women’s reproductive traits and the shared genetic basis between AAM and obesity. Our MR analyses suggest that the genetic correlation between AAM and obesity traits results from the direct effects of genetic variants on both traits rather than a causal relationship between them.

BackgroundPrevious studies have examined the cellular and molecular interactions between chronic kidney disease (CKD) and PANoptosis, yet the genetic underpinnings remain unclear.MaterialsData at the summary level regarding the methylation, gene expression, and protein levels associated with PANoptosis were obtained from quantitative trait locus (QTL) studies. Genome-wide association study (GWAS) summary statistics for CKD were derived from a GWAS study, supplemented by a replication dataset from the FinnGen database. Genetic variants proximal to or within genes involved in PANoptosis, which showed robust associations with CKD, were utilized as instrumental variables. These variants were the subjected to SMR analysis to explore their causal relationship. The associations among QTLs were systematically analyzed. Additionally, a colocalization analysis was conducted to ascertain whether the signals identified corresponded to a shared genetic basis.ResultsSMR and colocalization analysis revealed 28 methylation sites and 5 genes associated with CKD.Notably, cg01304814 (PRKAR2A) and cg09177106, cg15114474 (CCND1) were inversely associated with CKD risk. Integrating mQTL and eQTL data, we identified four genes (CCND1, GUCY2D, HGF, MADD) causally associated with CKD, with a positive correlation between HGF gene expression and protein levels.ConclusionOur results provide evidence for the PANoptosis-related genes in the pathogenesis of CKD. Notably, PRKAR2A, HGF, CCND1 and MADD, emerged as potential mediators in the pathogenesis of CKD.Trial registrationNot applicable.

Understanding the interplay between genetic predisposition and environmental and lifestyle exposures is essential for advancing precision medicine and public health. The exposome, defined as the sum of all environmental exposures an individual encounters throughout their lifetime, complements genomic data by elucidating how external and internal exposure factors influence health outcomes. This treatise highlights the emerging discipline of translational exposomics that integrates exposomics and genomics, offering a comprehensive approach to decipher the complex relationships between environmental and lifestyle exposures, genetic variability, and disease phenotypes. We highlight cutting-edge methodologies, including multi-omics technologies, exposome-wide association studies (EWAS), physiology-based biokinetic modeling, and advanced bioinformatics approaches. These tools enable precise characterization of both the external and the internal exposome, facilitating the identification of biomarkers, exposure-response relationships, and disease prediction and mechanisms. We also consider the importance of addressing socio-economic, demographic, and gender disparities in environmental health research. We emphasize how exposome data can contextualize genomic variation and enhance causal inference, especially in studies of vulnerable populations and complex diseases. By showcasing concrete examples and proposing integrative platforms for translational exposomics, this work underscores the critical need to bridge genomics and exposomics to enable precision prevention, risk stratification, and public health decision-making. This integrative approach offers a new paradigm for understanding health and disease beyond genetics alone.

BackgroundThe protective effects of higher educational attainment (EA) and intelligence on COVID-19 outcomes are not yet understood with regard to their dependency on income. The objective of our study was to examine the overall as well as independent effects of the three psychosocial factors on the susceptibility to and severity of COVID-19. To accomplish this, we utilized genetic correlation, Mendelian randomization (MR), and multivariable MR (MVMR) analyses to evaluate genetic associations between EA, intelligence, household income, and three specific COVID-19 outcomes: SARS-CoV-2 infection, hospitalized COVID-19, and critical COVID-19.ResultsThe genetic correlation analysis revealed that COVID-19 outcomes were negatively correlated with the three psychosocial factors (rg: -0.19‒-0.36). The MR analysis indicated that genetic liability to EA, intelligence, and income exerted overall protective effects against SARS-CoV-2 infection (OR: 0.86‒0.92), hospitalized COVID-19 (OR: 0.70‒0.80), and critical COVID-19 (OR: 0.65‒0.85). MVMR analysis revealed that elevated levels of EA conferred independent protective effects against SARS-CoV-2 infection (OR: 0.85), hospitalization due to COVID-19 (OR: 0.79), and critical COVID-19 (OR: 0.63). Furthermore, intelligence exhibited a negative association with the risk of SARS-CoV-2 infection (OR: 0.91), whereas a higher income was linked to an elevated risk of SARS-CoV-2 infection (OR: 1.13).ConclusionsOur findings indicated that EA could significantly reduce the risk and severity of COVID-19, regardless of intelligence and income. However, the impact of intelligence or income on COVID-19 severity was not supported by our research.