Tania Dottorini

Antimicrobial resistance (AMR) is rising globally, yet factors shaping its genomic evolution remain poorly understood. We investigated how socioeconomic, climate, and health factors are associated with AMR spread in multi-drug resistant (MDR) Salmonella enterica across China’s food chain, mapped hotspots and forecasted genomic trait prevalence to 2050. We analysed 2,536 MDR genomes from 15 provinces over 16 years, covering patients, food and non-food workers, meat, livestock, and environmental sources. A machine learning framework integrated genomic surveillance with risk assessment to identify high-risk AMR traits (clinically relevant, MDR, mobile). Regression models linked trait prevalence to socioeconomic, climate, health, and antimicrobial consumption data, informing projections to 2050. Eighty-four high-risk traits, including carbapenem, colistin, and β-lactam resistance, are projected to rise, with antibiotic use, population growth, healthcare spending, and urbanisation. Projections were trait-specific: bla _CTX-M-64 and bla _TEM-1 may increase up to 20%, while bla _CTX-M-65 and bla _CTX-M-14 remain below 5%. A mechanistic shift in fluoroquinolone resistance is projected: mutation-based resistance ( gyrA 87) declines while plasmid-mediated genes ( qnrB17 , qnrS1 ) rise. Food workers emerged as a potential reservoir, showing higher trait prevalence than food sources, uniquely carrying last-resort antibiotic-resistance genes ( bla _TEM-30 , bla _NDM-5 , bla _CTX-M-64 ). Integrating genomic with socioeconomic surveillance could shift AMR interventions from reactive to preventive.

Gout is a prevalent and painful form of inflammatory arthritis associated with hyperuricemia, which leads to monosodium urate crystal deposition in joints and surrounding tissues, triggering acute inflammatory responses. This disease is also closely linked to serious comorbidities, including cardiovascular diseases, chronic kidney diseases, diabetes, and increased mortality risk, significantly impacting global health. In this study, we conducted a comprehensive genome-wide association study (GWAS) based on the UK Biobank pain questionnaire 2019, comprising 10 474 gout cases and 140 068 controls, identifying 13 loci associated with gout. These findings were further explored in the FinnGen cohort, with 10 loci being replicated significantly. Sex-stratified analyses revealed notable differences, with 16 loci identified in males and two loci identified in females, reflecting both shared and sex -stratified genetic influences on gout susceptibility. In addition, genetic correlation analyses demonstrated strong associations between gout and traits related to urate levels, specific medication use, and metabolic functions. Transcriptome-wide association studies highlighted several genes, such as SLC16A9 and ASAH2B, which showed significant expression patterns across various tissues, implicating metabolic and immune pathways in gout. Phenome-wide association studies of significant single nucleotide polymorphisms revealed links to metabolic, immunological, and skeletal traits, underscoring the multi-faceted nature of gout. These results contribute valuable insights into the genetic architecture and biological mechanisms underlying gout, suggesting potential avenues for tailored interventions.

Introduction: Neck and shoulder pain are prevalent musculoskeletal disorders that significantly affect the quality of life for a substantial portion of the global population. Studies have shown that women are more susceptible than men. Objective: This study aims to discover genetic variants associated with neck or shoulder pain through a genome-wide association study (GWAS), using data from 430,193 participants in the UK Biobank. Methods: A genome-wide association study was performed adjusting for age, sex, BMI, and 8 population principal components. Significant and independent genetic variants were replicated by FinnGen. Results: The primary GWAS revealed 5 significant genetic loci (including 2 novel) associated with neck or shoulder pain, with the most significant single nucleotide polymorphism (SNP) being rs9889282 ( P = 2.63 × 10 ⁻¹² ) near CA10 on chromosome 17. Two novel significant associations were detected on chromosomes 18 and 14, with the top SNPs being rs4608411 ( P = 8.20 × 10 ⁻⁹ ) near TCF4 and rs370565192 ( P = 3.80 × 10 ⁻⁸ ) in DCAF5 , respectively. Our secondary GWAS identified a single novel genetic locus in SLC24A3 among males and 2 genetic loci (including one novel near LINC02770 ) among females. In the replication stage, the SLC39A8 locus was weakly supported by the FinnGen cohort. The tissue expression analysis revealed a significant association between brain tissues and neck or shoulder pain. Conclusion: In summary, this study has identified novel genetic variants for neck or shoulder pain. Sex-stratified GWAS also suggested that sex played a role in the occurrence of the phenotype.

Antimicrobial resistance (AMR) is projected to increase globally, yet the genomic traits and social determinants of health driving its trajectory are poorly understood. Here, we present the first global-scale AMR forecasting analysis, integrating machine learning, Monte Carlo simulations and forecasting modelling to identify clinically relevant AMR traits projected to increase, and the key determinants driving their increase over the next 25 years. We analysed 45,645 bacterial genomes (including ESKAPE and World Health Organisation high/critical priority pathogens), 298,288 resistance profiles, spanning 84 antibiotics across 27 antimicrobial classes and 1112 determinants of health across 125 countries. We identified 78 clinically relevant AMR feature-pathogen pairs projected to rise globally by 2050. Among these, 94.9% were multidrug resistant (MDR), 89.7% globally widespread, 80.8% persisting over time, and 48.7% multi-host. Our analysis reveals mortality indicators (68.79%) are the strongest predictors, followed by socioeconomic factors (15.29%), and antibiotic consumption (4.46%). However, at the molecular level, socioeconomic disparities are the primary drivers of AMR, strongly associated to the fourteen most critical features (aadA, aadA2, APH(3')-Ia, APH(3'')-Ib, APH(6)-Id, ermB, mphA, qacEdelta1, rep_cluster_1118, sul1, sul2, TEM-34, tet(A), and tet(M)). These features emerged as primary health threats, clinically relevant, MDR, human-enriched, inter-species spreaders, plasmid-associated, and present across up to 10 hosts in at least four World Bank regions. By understanding the interplay of biological and social factors in shaping AMR trends to 2050, we provide a roadmap for targeted AMR mitigation.

Gout is a prevalent and painful form of inflammatory arthritis associated with hyperuricemia, which leads to monosodium urate crystal deposition in joints and surrounding tissues, triggering acute inflammatory responses. This disease is also closely linked to serious comorbidities, including cardiovascular diseases, chronic kidney diseases, diabetes, and increased mortality risk, significantly impacting global health. In this study, we conducted a comprehensive genome-wide association study (GWAS) based on the UK Biobank pain questionnaire 2019, comprising 10,474 gout cases and 140,068 controls, identifying 13 loci associated with gout, including four novel loci. These findings were further explored in the FinnGen cohort, with 12 loci being replicated significantly. Sex-stratified analyses revealed notable differences, with 16 loci identified in males and two loci identified in females, reflecting both shared and sex-specific genetic influences on gout susceptibility. In addition, genetic correlation analyses demonstrated strong associations between gout and traits related to urate levels, specific medication use, and metabolic functions. Transcriptome-wide association studies highlighted several genes, such as SLC16A9 and ASAH2B , which showed significant expression patterns across various tissues, implicating metabolic and immune pathways in gout. Phenome-wide association studies of significant SNPs revealed links to metabolic, immunological, and skeletal traits, underscoring the multi-faceted nature of gout. These results contribute valuable insights into the genetic architecture and biological mechanisms underlying gout, suggesting potential avenues for tailored interventions.

In Bangladesh, Vibrio cholerae lineages are undergoing genomic evolution, with increased virulence and spreading ability. However, our understanding of the genomic determinants influencing lineage transmission and disease severity remains incomplete. Here, we developed a computational framework using machine-learning, genome scale metabolic modelling (GSSM) and 3D structural analysis, to identify V. cholerae genomic traits linked to lineage transmission and disease severity. We analysed in-patients isolates from six Bangladeshi regions (2015-2021), and uncovered accessory genes and core SNPs unique to the most recent dominant lineage, with virulence, motility and bacteriophage resistance functions. We also found a strong correlation between V. cholerae genomic traits and disease severity, with some traits overlapping those driving lineage transmission. GSMM and 3D structure analysis unveiled a complex interplay between transcription regulation, protein interaction and stability, and metabolic networks, associated to lifestyle adaptation, intestinal colonization, acid tolerance and symptom severity. Our findings support advancing therapeutics and targeted interventions to mitigate cholera spread.

Knee pain is a widespread musculoskeletal condition affecting millions globally, with significant socio-economic implications. This study endeavors to identify genetic variants associated with knee pain through a comprehensive genome-wide association study (GWAS) using data from 441,757 individuals in the UK Biobank. The primary GWAS identified ten significant loci, including eight novel loci, with the most significant single nucleotide polymorphism (SNP) being rs143384 near the GDF5 gene on chromosome 20 ( p = 4.68 x 10 ^-19 ). In the replication study, seven loci (rs143384, rs919642, rs55760279, rs56076919, rs3892354, rs687878, rs368636424) were found to be significant in the FinnGen cohort. Further, sex-specific analyses revealed distinct genetic associations, identifying three loci (rs143384 with p = 1.70x10 ^-15 , rs56076919 with p = 1.60x10 ^-9 , rs919642 with p = 1.45x10 ^-8 ) in females and four loci ( rs2899611 with p = 2.77 x 10 ^-11 , rs891720 with p = 5.55 x 10 ^-11 , rs2742313 with p = 4.19 x 10 ^-9 , rs2019689 with p = 6.51 x 10 ^-9 ) in males. The phenome-wide association analysis and Mendelian randomization analysis revealed significant links between several phenotypes and knee pain such as leg pain on walking. These findings enhance our understanding of the genetic factors of knee pain, offering potential pathways for therapeutic interventions and personalized medical strategies.

Purpose: Diabetic retinopathy (DR), a complication affecting the eyes, is associated with diabetes. This study aims to identify genetic variants associated with DR in patients with type 1 diabetes in the UK Biobank cohort (n = 1,004). Methods: A genome-wide association study (GWAS) was conducted to identify significant genetic variants of DR in type 1 diabetes. The findings are set to undergo validation during the replication and meta-analysis stages by using six cohorts: African American, European, FinnGen, GoSHARE, GoDARTS and Caucasian Australians. Results: In a locus, top single nucleotide polymorphism (SNP) rs184619214 in CCDC7 reached a GWAS significance level ( p = 6.38 x 10 ⁻⁹ ) and rs79853754 in ITGB1 ( p = 3.24 x 10 ⁻⁸ ), with both genes being adjacent to each other. The SNP-based heritability was estimated to be 31.09%. Rs184619214 was replicated and reached statistical significance ( p < 5.0 x 10 ⁻⁸ ) in the meta-analysis stage. Pathway analysis revealed that ITGB1 is involved in the generation of biomolecules that impact the progression of DR. PheWAS analysis revealed that osteoarthritis (OA) of the hip was significantly associated with most of the SNPs of the locus. Mendelian Randomization further confirmed an association between OA and DR. Conclusions: Our study has identified a novel genomic risk locus associated with DR in type 1 diabetes, located in the intergenic region between the CCDC7 and ITGB1 genes, providing insights for DR researchers.