Pathogen genomics has increasingly been integrated into infectious disease surveillance, outbreak detection, and response globally. However, formal evaluation of pathogen genomic surveillance systems has been a major gap. In Australia, the AusTrakka platform was established and deployed nationally to address barriers to genomic data sharing across jurisdictions, enhance interoperability and usability, and improve governance of public health genomic data. Here we present our evaluation of AusTrakka and examine how its utilisation and impact shifted throughout the COVID-19 pandemic. We utilised the US Centers for Disease Control (CDC) Updated Guidelines for Evaluating Public Health Surveillance Systems to guide assessment of the AusTrakka platform. The evaluation used a mixed-methods approach consisting of a quantitative analysis of AusTrakka utilisation data throughout the COVID-19 pandemic and a qualitative component comprised key informant interviews and analysis of investigation reports produced by the AusTrakka National Analysis Team. Quantitative and qualitative data were collected concurrently between June 2020 and October 2022. Semi-structured individual and group interviews were held with key informants (n = 63) representing all jurisdictions across Australia and New Zealand. These included individuals representing public health laboratories and health departments, infectious disease physicians, genomic epidemiologists, and bioinformaticians. AusTrakka users reported that the platform had a very high degree of usefulness as a centralised platform to enable sharing sequence data across jurisdictions, facilitate multijurisdictional outbreak investigations, and clarify transmission chains. Acceptability was a key system that contributed to the usefulness of the platform, enhanced through collective design of data governance frameworks. Integration of epidemiological data with the pathogen genomic data was an ongoing challenge in data completeness. Robust evaluation of pathogen genomics surveillance systems is critical to identify contextual and system elements that impact the capacity of these systems to accomplish their objectives. Our findings demonstrate the importance of strong stakeholder engagement in developing data governance mechanisms for pathogen genomics in ultimately ensuring the capacity of surveillance systems to detect outbreaks and support public health utility, and reinforce the value of a nationally developed, purpose-built approach in Australia.
Genomic medicine increasingly depends on patients' willingness to share genomic and medical data. While data sharing supports advances in personalised care, it also raises ethical and social concerns related to privacy, trust and participation. Understanding these factors requires attention to patients' health literacy and their capacity to interpret and act upon genomic information. A systematic review was conducted according to PRISMA guidelines to identify empirical studies published between 2015 and 2025 that explored patients' understanding of genomic information and their willingness to share data. Searches were performed in PubMed, Web of Science and Scopus. Eligible studies included qualitative, quantitative and mixed-methods designs. Findings were synthesised thematically and Nutbeam's model of health literacy was used in the discussion to interpret the results. Fifteen studies met the inclusion criteria. Participants demonstrated basic understanding of genetic terms but limited knowledge of data infrastructures and governance. Trust was a central factor influencing willingness to share data, often compensating for limited genomic literacy. Moral and altruistic motives encouraged engagement, whereas financial considerations played a minor, context-dependent role. Data sharing in genomic medicine relies on more than factual knowledge. Strengthening health literacy through transparent, dialogue-based, and participatory approaches can promote informed, autonomous, and ethically responsible participation in genomic research.
Genomics analyses often rely on command-line tools executed via remote servers, imposing usability barriers for non-technical users and raising privacy concerns. WebAssembly (WASM) enables native-code execution directly in web browsers, eliminating installations and data transfers. We introduce BioChef, a client-side genomic workflow platform that uses WASM. BioChef compiles a genomics toolkit into browser-executable modules and exposes them through a drag-and-drop GUI designed to be intuitive. The system provides real-time validation, flexible input methods (form-based and JSON), intermediate step inspections, and reproducible workflows exportable as bash scripts or configuration files. Performance benchmarks across major browsers (Chromium, Gecko, WebKit) demonstrate rapid initialization (LCP 0.583 s), responsive interactivity (INP 30.5 ms), minimal layout shifts (CLS 0.01), and acceptable overhead (average 181.5 ms initial WASM module load). Although browser execution introduced performance penalties (∼130× slower than native), BioChef workflows still significantly outperformed traditional web services such as Galaxy by avoiding network delays and server-side queueing (11.3× faster in a standard pipeline benchmark). BioChef shows how WebAssembly on the client side can democratize genomic data processing, ensuring privacy, reproducibility and ease of use without external dependencies. To our knowledge, this is the first fully client-side, graphical genomic workflow environment powered by WASM.
Mobilized colistin resistance (mcr) gene has emerged as a major driver of colistin resistance. Therefore, this study aimed to determine the distribution of mcr-variants and mcr-carrying genomes deposited in the NCBI database by sample collection periods and across continents, countries, genera, species, and ecosystems. In this database mining study, the keyword "mcr" was used to identify all mcr-carrying genomes deposited in the NCBI Pathogen Detection database until June 07, 2025, 12h15 GMT. A purely descriptive approach was used in this study, and percentages were calculated by dividing the number of an event by the total number of events (percentage = n/Nx100). Of the 2422739 whole genomes registered in the NCBI database, 18785 (0.78%) carried complete mcr variant sequences. Seventy-seven mcr subvariants were detected, including mostly mcr-1.1 (9431/18785; 50%), and mcr-9.1 (5971/18785; 32%). Mcr-9.1 was the most frequently detected subvariant in several genera, including Serratia spp. (17/17; 100%), Cronobacter spp. (155/160; 97%), and Pluralibacter spp. (19/20; 95%), whereas mcr-1.1 was the most commonly detected subvariant in Escherichia and Shigella spp. (8235/9678; 85%). Regarding geographical distribution, mcr-1.1 was the most observed subvariant in Asia (6759/9033; 75%) and Europe (1886/4680; 40%), whereas mcr-9.1 was the most identified in America (2982/4017; 74%) and Oceania (546/771; 71%). In Africa, mcr-10.1 (52/160; 33%), and mcr-1.1 (50/160; 31%) were the most frequent subvariants. Mcr-carrying genomes deposited in the NCBI database were distributed across ecosystems, including humans (n = 8185), animals (n = 4521), the environment (n = 468), and food (n = 48). The sample collection years for mcr-carrying bacteria ranged from 1953 to 2025, and the distribution of mcr-carrying genomes was as follows: 1953-1990 (n = 49), 1991-1999 (n = 47), 2000-2009 (n = 704), 2010-2019 (n = 12810), and 2020-2025 (n = 4297). Another key finding was that 705 of the 18785 mcr-carrying genomes deposited in the NCBI database (3.8%) harbored multiple mcr genes, including 693 and 12 genomes co-carrying two and three mcr genes, respectively. Mcr-carrying bacteria represent a significant One Health concern because of their major role in colistin resistance and potential for global dissemination. Key actions, such as global surveillance, One Health monitoring, and appropriate stewardship, should be taken to preserve the efficacy of colistin for decades.
This systematic review synthesizes applications of machine learning (ML) for multi-omics data integration in crop improvement, evaluating its dual potential to enhance predictive accuracy for selection (breeding utility) and to generate interpretable biological insights (mechanistic discovery). Following a systematic review of 76 eligible studies, we synthesized patterns in methodological adoption. The integration of environmental data (envirotyping) with multiple omics layers for genotype-by-environment (G×E) prediction was identified as a major emerging frontier, though currently addressed in fewer than 20% of studies. Tree-based models such as random forest and XGBoost were the most prevalent, favored for their interpretability and robustness with small to medium-sized datasets. In contrast, deep learning approaches, while reporting high performance, were primarily applied to larger datasets and constrained by higher computational costs. Emerging hybrid models show promise, but their efficacy is highly architecture- dependent. The most consistent accuracy gains (10–15%) were observed for feature-engineering hybrids (autoencoders compressing multi-omics data followed by XGBoost). Stacking ensembles showed more variable performance (5–12% gains), while integrated hybrids like convolutional neural network-long short-term memory (CNN-LSTM) delivered high accuracy for specific data structures. A recurring trend indicated that genomics with transcriptomics frequently boosted prediction for stress-related traits, while genomics with metabolomics excelled for quality traits. Tri-omics integration enhanced prediction for complex yield traits, though with marginal gains (< 5%) and substantial computational cost increases. Comparatively, ML-based approaches often outperformed classical genomic selection (GS) for low-heritability traits, while GS remained competitive for high-heritability traits. Deep learning models showed particular strength in handling population structure, reducing prediction errors by up to 20% in diverse panels. Critical gaps were identified: an overwhelming focus on point estimates of accuracy, (with fewer than 10% of studies reporting calibrated uncertainty metrics: and a relative scarcity of intrinsically interpretable model architectures that incorporates biological constraints as a core design principle. ML- driven multi-omics integration holds transformative potential but requires strategic implementation tailored to specific breeding objectives, trait architecture, and resource availability. Collective efforts to standardize data protocols, enhance model interpretability, and democratize computational tools are critical. Realizing equitable potential requires strategies to develop user-friendly platforms that extend advances to under-resourced crops. Transfer learning from data-rich species and federated data-sharing models are concrete avenues for promoting equitable innovations and enhancing global agricultural resilience.
Cholangiocarcinoma (CCA) is a molecularly heterogeneous malignancy of the biliary tract with rising global incidence and distinct geographic variations in oncogenic drivers. Despite India’s significant cancer burden, genomic data on CCA in Indian patients remain sparse, limiting the development of targeted therapies. This retrospective, multi-institutional study analyzed molecular data from 220 patients with cholangiocarcinoma (CCA), including 147 cases of cholangiocarcinoma not otherwise specified (CCA-unclassified), 63 intrahepatic cholangiocarcinomas (ICA), and 10 distal extrahepatic cholangiocarcinomas (dCCA). Samples were evaluated across three Indian institutions using multiple validated next-generation sequencing (NGS) platforms. The resulting genomic profiles were subsequently compared with international datasets available through cBioPortal. To minimize platform bias, mutation frequency comparisons were restricted to 42 DNA and 13 RNA genes present across all platforms. Statistical significance was assessed using Fisher’s exact test with false discovery rate (FDR) correction for multiple testing. Among 220 CCA cases, 147 (66.8%) were anatomically unclassified, 63 (28.6%) intrahepatic (ICA), and 10 (4.5%) distal extrahepatic (dCCA), with pronounced male predominance overall (M: F 1.7:1) and in ICA cases (2.2:1). In the unclassified cohort, the most frequent mutations were TP53 (35.6%), KRAS (19.4%), IDH1(10.2% ) and PIK3CA (9.4%). Compared to international cBioPortal data, the Indian cohort showed significantly elevated TP53 (35.8% vs. 24.2%; P = 0.0001), KRAS (19.4% vs. 13.0%; P = 0.009), and PIK3CA (9.4% vs. 4.1%; P = 0.001) mutations, but lower BAP1 frequencies (5.0% vs. 13.2%; P = 0.007). IDH1 mutations were similar in both cohorts (10.2% vs. 11.1%; p = 0.74). Anatomically defined cases (N = 73) showed concordant patterns. Notably, a distinctive spectrum of non-fusion FGFR2 alterations (8 alterations: 3 pathogenic mutations, 3 copy number amplifications, 1 VUS) was identified, distinct from the global pattern of FGFR2 fusions. Tumor mutation burden was predominantly low (14/15 cases), and microsatellite instability-high was rare (1/29, 3.4%). This study reveals region-specific genomic alterations in Indian CCA, including elevated TP53/KRAS mutations and a distinctive non-fusion FGFR2 alteration pattern, distinct from global CCA registries. These ethnicity-stratified findings underscore the importance of population-specific genomic profiling for precision oncology and warrant prospective studies correlating these alterations with treatment response in Indian patients.
In China, breast cancer occurs at a much younger age and has a higher recurrence and mortality rate. However, with changes in lifestyle, there has been a trend towards an older age of breast cancer incidence in Chinese women. There is a paucity of large-scale next-generation sequencing cohorts for the analysis of genomic characterization in these populations and the identification of potential therapeutic targets. To address this gap, we performed prospective targeted sequencing of tumor and blood samples from Chinese patients and collected detailed clinical information. We then categorized patients into two groups based on age (< 40 years, n = 637; ≥ 40 years, n = 3442) and proceeded to provide comprehensive descriptions of somatic and germline mutations in both groups. The somatic mutation analysis revealed that PIK3CA, FOXA1, and TBX3 mutations were more prevalent in elderly patients. By leveraging the aforementioned mutational characteristics, we employed our institution's FUTURE-SUPER clinical trial, an umbrella study targeting metastatic breast cancer, to confirm the potential benefits of PI3K-AKT-mTOR pathway inhibitors among elderly patients with breast cancer. Furthermore, TP53 and ERBB2 were more likely to be co-mutated in young women. Patients with TP53 and ERBB2 co-mutation tend to have a poorer prognosis, but through investigation of the SPARK cohort, patients carrying the TP53 and ERBB2 co-mutation are more likely to benefit from immune checkpoint inhibitor combination with tyrosine kinase inhibitor therapy. In our study, we observed a higher frequency of mutations in the DNA homology-dependent recombination pathway in young patients with breast cancer, which was associated with an elevated Ki67 index. Additionally, we confirmed a significant prevalence of germline breast cancer susceptibility gene 1 (gBRCA1) mutations in young patients, whereas germline checkpoint kinase 2 (gCHEK2) mutations are more common in elderly patients. Our study, which makes use of the largest Chinese breast cancer sequencing cohort, sought to characterize the age-related genomic profile of breast cancer patients and identify novel therapeutic opportunities for individuals with breast cancer.
Freshwater snails of the genus Biomphalaria include several species of major public health importance as intermediate hosts of schistosomiasis and represent a key group for evolutionary and taxonomic research within Hygrophila. However, complete mitochondrial genomes are available for only a limited number of Biomphalaria species, restricting comparative assessments of mitogenome evolution and the ability of mitogenomic data to resolve contentious relationships within Planorbidae and across Hygrophila. Here, we generate and analyse six complete mitochondrial genomes from four Neotropical Biomphalaria species collected in Argentina, including the first mitogenomes for B. peregrina and B. orbignyi, and evaluate genomic architecture, molecular evolutionary patterns, and phylogenetic relationships using multiple mitogenome-based approaches. All six mitogenomes contained the standard complement of 37 mitochondrial genes and exhibited a fully conserved gene order and strand orientation across Biomphalaria. Genomes were highly compact, with numerous gene overlaps. An unusually long non-coding region (210–242 bp) was detected exclusively in B. peregrina, contributing to its slightly larger mitogenome size. Intraspecific variation in the secondary structure of tRNACys, including arm truncation, was also observed within B. peregrina. All genomes showed negative AT-skew and positive GC-skew, indicating conserved strand asymmetry across Hygrophila. Protein-coding genes displayed strong codon-usage bias and were predominantly shaped by purifying selection, with atp8 identified as the most variable and least constrained gene. Phylogenetic analyses recovered two closely related topologies across datasets, supported monophyly of Bulinidae and Lymnaeidae, and consistently placed bulinids within planorbids, rendering Planorbidae non-monophyletic. Amphipepleinae was monophyletic, whereas Lymnaeinae was paraphyletic in a pattern sensitive to taxon sampling. Within Biomphalaria, B. peregrina was consistently resolved as the earliest-diverging lineage among sampled species. These six newly generated mitogenomes substantially expand genomic resources for Neotropical Biomphalaria and reveal strong conservation of mitochondrial genome organisation alongside pronounced heterogeneity in gene-specific evolutionary dynamics. Complete mitochondrial genomes enhance phylogenetic resolution within Hygrophila and provide independent evidence relevant to ongoing debates in planorbid systematics, including the affinity between Bulininae and Planorbinae. Nevertheless, broader taxon sampling and the integration of nuclear genomic data will be essential to fully resolve subfamilial relationships and species boundaries within this medically important group. The online version contains supplementary material available at 10.1186/s12862-026-02520-0.
In a context of increasing efforts towards the establishment of a Regional Health Data Hub for the African Region, the 2024 West African Policy Dialogue brought together researchers and policymakers from seven West African countries in a two-day meeting in Aburi, Ghana. This report provides a high-level summary of the discussions at the meeting. The forum emphasized that the use of poor, incomplete, or inaccurate data will have negative consequences, regardless of the sophistication of the analytic tools used. New technologies have emerged that can support the generation and effective use of data. Yet, governments in West-Africa struggle to maximize the benefits of these technologies, including genomic surveillance, real-time data generation, and supranational data integration and exchange. Policies are needed that support and regulate new technologies and contribute to greater capabilities for better data.
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Escherichia coli (E. coli) is a leading cause of bloodstream infections (BSIs) globally, with multidrug-resistant (MDR) strains complicating treatment outcomes. In South Africa, genomic data on such isolates remain scarce. To elucidate the genomic landscape of E. coli isolates from bloodstream infections (BSIs) collected over a one-year period in the uMgungundlovu District, South Africa, with a focus on the resistance and virulence genes, mobile genetic elements (MGEs), genetic synteny, sequence types (STs), and phylogenomic context. Twenty-five non-duplicate E. coli isolates were recovered from blood cultures and subjected to antimicrobial susceptibility testing. All twelve MDR isolates underwent whole-genome sequencing and bioinformatic analysis. The MDR isolates comprised six distinct STs, notably high-risk international lineages ST131 (33.3%) and ST69 (25.0%), spanning five phylogroups, predominantly B2 and D. Eight O: H serotypes were identified, with O25:H4 and O45:H16 being most frequent. CH-typing revealed dominant CH types CH40-30 (ST131) and CH35-27 (ST69) and fimH alleles such as fimH30 and fimH27. The isolates encoded β-lactamases, including blaCTX-M-15, blaTEM-1B and blaOXA-1, frequently co-located with MGEs. Notably, blaCTX-M-15 was chromosomally integrated within a Tn3–ISEcp1 transposon unit, while blaTEM-1B and blaOXA-1 were associated with diverse plasmid-associated syntenic architectures flanked by IS1, IS91, or integron-associated regions. Other antibiotic resistance genes were detected, conferring resistance to aminoglycosides (aph(3’)-Ia, aac(3)-IIa, aac(3)-IIe, aadA1, aadA2), sulfonamides (sul1, sul2) and trimethoprim (dfrA variants). A diverse array of virulence genes was identified, associated with adhesion, iron acquisition, serum resistance, and toxin production. Phylogenomic clustering revealed close relationships between local ST131/ST69 isolates and counterparts across Africa. The study identifies diverse MDR E. coli clones circulating in bloodstream infections, notably high-risk lineages ST131, ST69 and ST10, with complex resistance and virulence gene arsenals facilitated by MGEs. These insights reinforce the imperative for genomic surveillance to guide infection control and antibiotic stewardship in high-burden settings. The online version contains supplementary material available at 10.1186/s12920-026-02349-y.
The Ixworth chicken is a British dual-purpose breed and mostly maintained by small-scale farmers. Due to legislation regarding the ban on male chick culling in European countries, such as in Germany, renewed interest has arisen in rearing dual-purpose chickens that provide both meat and eggs from the same genetic line. This dataset was generated within the scope of projects aiming to evaluate the viability of dual-purpose breeds for sustainable and welfare-oriented poultry production. One of the objectives was to characterize the genetic potential of the Ixworth chicken as a model for breeding programs that combine conservation and practical use in ecological farming systems. Liver samples from 49 male Ixworth chickens were collected after scheduled slaughter at the Campus Frankenforst of the Faculty of Agricultural, Nutritional and Engineering Sciences of the University of Bonn, Germany. Genomic DNA was extracted and subjected to whole-genome resequencing using the Illumina NovaSeq 6000 platform. The dataset provides high-resolution genomic information on a rare breed with a pure dual-purpose background. This resource represents the first public sequencing dataset of the Ixworth chicken and thus offers a valuable foundation for future studies on genetic diversity, conservation genomics, and breeding strategies for sustainable poultry production.
Transcriptome sequencing data offer a valuable resource for inferring genetic variants, yet their application in forensic individual identification and kinship analysis remains insufficiently explored. This study analyzed an open-access transcriptome sequencing dataset comprising 731 individuals from five continental populations. We obtained a total of 5,863,540 transcript SNPs (tSNPs) across these individuals. By comparing these with SNP genotypes obtained from whole-genome sequencing data, we observed that transcriptome-derived genotypes exhibited high reliability, achieving up to 99% concordance with paired genomic SNP genotypes. Based on this, we meticulously selected 735 polymorphic tSNPs characterized by high heterozygosity (Het ≥ 0.4) and low frequency variation across different populations (Fst < 0.06). The global mean match probability of these tSNPs was calculated to be 10^-305, rendering them a promising candidate set for individual identification. Validation in an independent population demonstrated strong detection stability for this locus set, with an average detection rate of 98.34%. Forensic genetic parameters were highly consistent with those of the original screening set, confirming its robust population portability. Furthermore, we evaluated the system power of 735 tSNPs in identifying various kinship relationships through the application of the likelihood ratio method. The findings indicated that, at cutoff thresholds of t1 = 4 and t2 = -4, these tSNPs could effectively distinguish parent-offspring, full sibling, and second-degree kinship pairs from unrelated individual pairs, with system powers of 1, 1, and 0.9863, respectively. This suggests that transcriptome data holds significant potential for forensic individual identification and kinship analysis. In addition, due to its ability to concurrently detect gene expression levels and nucleotide sequences, transcriptome profiling could be employed for diverse forensic genetic applications, including the simultaneous identification of body fluids and donors. Through the analysis of five forensically relevant body fluids/tissues, we ascertained 196 stably detectable core tSNPs. By integrating these core loci with tSNPs located on body fluid-specific RNA markers, we characterized a set of loci with the potential for both body fluid and individual identification. Additionally, tSNPs obtained from transcriptome data show promise for phenotypic prediction, biogeographical ancestry inference, and forensic genetic genealogy. In conclusion, our study provides essential evidence supporting the utility of transcriptomics in forensic genetics, thereby establishing a foundation for the increased integration of RNA-based evidence in future forensic methodologies. The online version contains supplementary material available at 10.1186/s12864-026-12782-z.
Protozoa are key members of the rumen microbiome playing significant roles in nutrient cycling and methane production, yet are understudied. As rumen metataxonomic studies increasingly incorporate protozoal primers, the lack of curated dedicated reference databases limits accurate classification. This dataset was developed to address that gap and support future protozoa-focused rumen microbial analyses. The curated dataset comprises 228 rumen ciliate protozoal 18S rRNA gene sequences sourced from publicly available datasets. Sequences were processed to remove redundancy and standardise naming. The final database spans 23 families, 53 genera, and 100 species, and is suitable for use in metataxonomic pipelines, including QIIME2. It provides a valuable resource for researchers aiming to improve taxonomic resolution of protozoal communities in rumen environments.
In livestock, understanding the genetic basis of adaptation to the environment is essential for enhancing resilience to climate change and sustaining productivity in diverse environments. Indigenous Ethiopian cattle represent an ideal model for such studies, as they have evolved across a wide range of environments from the cool, oxygen-limited highlands to the hot, pathogen-rich lowlands. These environmental gradients imposed intense selective pressures, shaping their genomic landscape. In this study, we performed the first comprehensive analysis of X-linked adaptive signatures in Ethiopian indigenous cattle using whole-genome sequencing data. Population structure analysis revealed clear genetic differentiations between Abigar and Barka cattle, while the remaining populations showed substantial shared ancestry and admixtures. Pairwise fixation index ([Formula: see text] estimates, runs of homozygosity (ROH) patterns, and linkage disequilibrium (LD) decay further supported historical isolation and stronger selection pressure in Barka, contrasting with the greater diversity and faster LD decay in Gojjam Highland cattle. Complementary selection signature detections ([Formula: see text], XP-EHH, and[Formula: see text]) revealed population-specific and shared genomic regions under selection on the X chromosome. Notably, signals associated with high-altitude adaptation were detected near the RBM3, RPS4X, and TSC22D3 loci. Additional signals were observed in genes related to thermoregulation and oxidative stress response (EDA, SUV39H1, and HDAC8), as well as immune regulation (IRAK1, BDA20, and IL1RAPL1), suggesting adaptation to hot and pathogen-rich environments. Functional enrichment analysis highlighted genes involved in extracellular matrix organization and immune signaling pathways, underscoring their roles in environmental adaptation. This study provides the first genome-wide evidence of X-linked adaptive divergence in the Ethiopian cattle. The findings highlight the contribution of the X chromosome to heat tolerance, hypoxia adaptation, and immune resilience, offering valuable genomic insights for breeding programs aimed at enhancing productivity and climate adaptability in tropical cattle.
Genetic nephrology has emerged as a distinct subspecialty within nephrology with growing evidence supporting its clinical utility and cost-effectiveness across diagnostic, prognostic, and management pathways. Yet significant implementation gaps such as undefined clinic models and outcomes, limited use of implementation science frameworks, ad hoc strategy development, and a narrow focus on diagnostic yield and clinical utility hinder its routine adoption and integration. This research programme aims to systematically design, implement, and evaluate a multidisciplinary kidney genetics clinic model, using a theory-informed and stakeholder-engaged implementation science approach, to translate genomic advances into improved service, patient-reported, and implementation outcomes, and to inform sustainable and scalable kidney genomics care policy. Using a interconnected four-part, mixed-methods, multi-frameworks design underpinned by implementation science theory, the research programme will (1) review global kidney genetics clinic models, outcomes, determinants of successful implementation, and implementation strategies; (2) assess local stakeholder perceptions, priorities and contextual determinants using Consolidated Framework for Implementation Research (CFIR)-guided qualitative inquiry; (3) co-design and refine tailored implementation strategies via Consolidated Framework for Implementation Research-Expert Recommendations for Implementing Change (CFIR-ERIC) mapping and hybrid participatory methods; and (4) evaluate the clinic’s service, patients, and implementation outcomes in a prospective effectiveness-implementation hybrid type 2 quasi-experimental interrupted time-series study design. Outcomes assessed include (1) service outcomes (diagnostic yield, clinical utility, and timeliness of care); (2) patient-reported outcomes (personal utility, patient-reported outcome measures); and (3) Proctor-defined implementation outcomes (acceptability, appropriateness, readiness, feasibility, and fidelity, and penetration). We will measure outcomes longitudinally across pre-implementation, implementation, and post-implementation phases, and analyse data using segmented regression analysis to assess changes in outcomes over time. The Implementation Research Logic Model (IRLM) will guide evaluation and adaptation throughout. This programme is designed to address critical implementation gaps between genomic evidence and clinical practice. Through four sequential studies, the programme will generate standardised kidney genetics clinic models and care pathways, evidence-informed implementation determinants, and context-tailored, co-designed implementation strategies. Taken together, this programme will establish a policy-relevant genetic nephrology care model with improved service, patient-reported, and implementation outcomes for the benefit of patients and families living with genetic kidney disease.
Understanding how populations diverge is one of the most compelling questions in evolutionary biology but our grasp on the genomic mechanisms underpinning divergence is limited to a handful of species. Indeed, we know even less about divergence in the pelagic zone, where barriers to gene flow are seemingly absent. The holopelagic ctenophore Mnemiopsis leidyi is the most widely used ctenophore in experimental biology and has become an important model system in studies ranging from developmental biology to neurobiology. In addition, its relatively small and tractable genome provides a powerful foundation for genomic and evolutionary analyses. However, we still lack a clear understanding of species boundaries, population structure, and the evolutionary forces shaping divergence within Mnemiopsis, limiting both evolutionary and ecological interpretations. To expand our general understanding of divergence across novel environments as well as resolve a long-standing taxonomic debate, we generated the most comprehensive genomic study to date of the holopelagic ctenophore Mnemiopsis across a large expanse of its native range. By leveraging multiple analytical approaches and generating two near-chromosome level genomes, we identify two distinct species of Mnemiopsis with high levels of genome-wide divergence along the US Atlantic coast, which correspond to M. leidyi and M. gardeni. Our demographic analyses suggest that M. leidyi and M. gardeni began to diverge during the mid-to-late Pleistocene climate transitions and were later shaped by post-glacial oceanographic changes. We highlight substantial genomic rearrangements and copy number variation between species, as well as uncover key genes under selection that are likely important for environmental adaptation. Together, these findings provide compelling evidence that the ctenophore currently recognized as M. leidyi represents more than one species. Recognizing cryptic species boundaries is critical for future study designs, environmental monitoring, and developing targeted management strategies. Altogether, we connect microevolutionary processes with macroevolutionary patterns and provide new insights into how ocean dynamics drive speciation and adaptation in pelagic ecosystems.
While GWAS (genome-wide association studies) have identified over 1000 obesity-associated loci, their functional impact on gene expression remains unclear. Moreover, many studies have not fully captured the genetic architecture of obesity in high-risk populations or considered the complexity of adiposity beyond traditional measures. To address these gaps, this study explores the genetic and transcriptomic pathways of obesity using diverse obesity phenotypes in a high-risk population. We analyzed genomic and whole-blood transcriptomic data from the CCHC (Cameron County Hispanic Cohort), performing GWAS on 13 obesity-related traits. Differential expression analysis was conducted for genes near GWAS-identified single nucleotide polymorphisms (P<5×10-6) followed by expression quantitative trait loci mapping and GWAS-expression quantitative trait loci colocalization. GWAS identified 486 trait associations, including 6 genome-wide significant (P<5×10-8) loci, with 3 novel signals linked to abdominal subcutaneous adipose tissue, body fat percentage, and waist circumference. Among 3024 genes near these loci, 60 showed differential expression. Further expression quantitative trait loci analysis suggested 2 single nucleotide polymorphism-gene-trait relationships: rs543314376-MAPK11, associated with subcutaneous adipose tissue volume in females, and rs963018484-PER1, linked to body mass index in females. Both genes play key roles in obesity-related pathways, including inflammation and circadian rhythm regulation. This integrative genomic-transcriptomic analysis uncovers 2 novel candidate genes for obesity and underscores the critical need for involving all populations and comprehensive adiposity measures in obesity research. By expanding beyond body mass index in a Hispanic/Latino population, we move closer to a deeper and more inclusive understanding of obesity's genetic architecture.
Klebsiella pneumoniae is a major opportunistic pathogen and a recognized contributor to the global burden of antimicrobial resistance (AMR). Although traditionally associated with healthcare settings, it is increasingly detected in environmental compartments influenced by anthropogenic activities. Aquatic ecosystems may act as reservoirs and dissemination hubs for multidrug-resistant (MDR) lineages and resistance genes. However, genomic data on environmental K. pneumoniae in Morocco remain scarce. This pilot study aimed to characterize a targeted subset of MDR K. pneumoniae isolates recovered from Moroccan rivers using whole-genome sequencing (WGS), with a focus on resistance determinants, virulence-associated loci, plasmid content, and phylogenetic relationships. The main findings revealed marked genetic heterogeneity among the environmental K. pneumoniae isolates. Of the 44 isolates recovered from six Moroccan rivers, 11 MDR isolates were selected for WGS and belonged to nine distinct sequence types, including the high-risk clones ST147 and ST307. Resistance to extended-spectrum β-lactams was mainly associated with ESBL genes, particularly blaCTX-M-15, whereas carbapenem resistance was linked to blaOXA-48 and blaNDM-14 in a subset of isolates. The genomes also carried multiple determinants associated with resistance to other major antimicrobial classes. In addition, the isolates showed diverse plasmid backgrounds, heterogeneous virulence-associated loci, and substantial capsular diversity. Phylogenetic and pangenome analyses further indicated high intraspecific variability and extensive genome plasticity across the collection. This pilot study provides the first whole-genome-based characterization of river-derived MDR K. pneumoniae in Morocco. It demonstrates that aquatic ecosystems harbor genetically diverse lineages carrying clinically relevant resistance and virulence determinants. These findings underscore the imperative of integrating environmental surveillance into national AMR monitoring strategies within a One Health framework.