Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach.

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

HMGA1 regulates trabectedin sensitivity in advanced soft-tissue sarcoma (STS): A Spanish Group for Research on Sarcomas (GEIS) study.

HMGA1 is a structural epigenetic chromatin factor that has been associated with tumor progression and drug resistance. Here, we reported the prognostic/predictive value of HMGA1 for trabectedin in advanced soft-tissue sarcoma (STS) and the effect of inhibiting HMGA1 or the mTOR downstream pathway in trabectedin activity. The prognostic/predictive value of HMGA1 expression was assessed in a cohort of 301 STS patients at mRNA (n = 133) and protein level (n = 272), by HTG EdgeSeq transcriptomics and immunohistochemistry, respectively. The effect of HMGA1 silencing on trabectedin activity and gene expression profiling was measured in leiomyosarcoma cells. The effect of combining mTOR inhibitors with trabectedin was assessed on cell viability in vitro studies, whereas in vivo studies tested the activity of this combination. HMGA1 mRNA and protein expression were significantly associated with worse progression-free survival of trabectedin and worse overall survival in STS. HMGA1 silencing sensitized leiomyosarcoma cells for trabectedin treatment, reducing the spheroid area and increasing cell death. The downregulation of HGMA1 significantly decreased the enrichment of some specific gene sets, including the PI3K/AKT/mTOR pathway. The inhibition of mTOR, sensitized leiomyosarcoma cultures for trabectedin treatment, increasing cell death. In in vivo studies, the combination of rapamycin with trabectedin downregulated HMGA1 expression and stabilized tumor growth of 3-methylcholantrene-induced sarcoma-like models. HMGA1 is an adverse prognostic factor for trabectedin treatment in advanced STS. HMGA1 silencing increases trabectedin efficacy, in part by modulating the mTOR signaling pathway. Trabectedin plus mTOR inhibitors are active in preclinical models of sarcoma, downregulating HMGA1 expression levels and stabilizing tumor growth.

Shared germline genomic variants in two patients with double primary gastrointestinal stromal tumours (GISTs).

BACKGROUND: Gastrointestinal stromal tumours (GISTs) are prevalent mesenchymal tumours of the gastrointestinal tract, commonly exhibiting structural variations in KIT and PDGFRA genes. While the mutational profiling of somatic tumours is well described, the genes behind the susceptibility to develop GIST are not yet fully discovered. This study explores the genomic landscape of two primary GIST cases, aiming to identify shared germline pathogenic variants and shed light on potential key players in tumourigenesis. METHODS: Two patients with distinct genotypically and phenotypically GISTs underwent germline whole genome sequencing. CNV and single nucleotide variant (SNV) analyses were performed. RESULTS: Both patients harbouring low-risk GISTs with different mutations (PDGFRA and KIT) shared homozygous germline pathogenic deletions in both CFHR1 and CFHR3 genes. CNV analysis revealed additional shared pathogenic deletions in other genes such as SLC25A24. No particular pathogenic SNV shared by both patients was detected. CONCLUSION: Our study provides new insights into germline variants that can be associated with the development of GISTs, namely, CFHR1 and CFHR3 deep deletions. Further functional validation is warranted to elucidate the precise contributions of identified germline mutations in GIST development.

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.

BACKGROUND: Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. METHODS: By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. RESULTS: A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. CONCLUSIONS: The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

drexml: A command line tool and Python package for drug repurposing.

We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.

Molecular and phylogenetic characterization of the monkeypox outbreak in the South of Spain.

Background and Aim: Until the May 2022 Monkeypox (MPXV) outbreak, which spread rapidly to many non-endemic countries, the virus was considered a viral zoonosis limited to some African countries. The Andalusian circuit of genomic surveillance was rapidly applied to characterize the MPXV outbreak in the South of Spain. Methods: Whole genome sequencing was used to obtain the genomic profiles of samples collected across the south of Spain, representative of all the provinces of Andalusia. Phylogenetic analysis was used to study the relationship of the isolates and the available sequences of the 2022 outbreak. Results: Whole genome sequencing of a total of 160 MPXV viruses from the different provinces that reported cases were obtained. Interestingly, we report the sequences of MPXV viruses obtained from two patients who died. While one of the isolates bore no noteworthy mutations that explain a potential heightened virulence, in another patient the second consecutive genome sequence, performed after the administration of tecovirimat, uncovered a mutation within the A0A7H0DN30 gene, known to be a prime target for tecovirimat in its Vaccinia counterpart. In general, a low number of mutations were observed in the sequences reported, which were very similar to the reference of the 2022 outbreak (OX044336), as expected from a DNA virus. The samples likely correspond to several introductions of the circulating MPXV viruses from the last outbreak. The virus sequenced from one of the two patients that died presented a mutation in a gene that bears potential connections to drug resistance. This mutation was absent in the initial sequencing before treatment.

Genotypic characterization and antimicrobial susceptibility of human Campylobacter jejuni isolates in Southern Spain.

Campylobacter jejuni is the main cause of bacterial gastroenteritis and a public health problem worldwide. Little information is available on the genotypic characteristics of human C. jejuni in Spain. This study is based on an analysis of the resistome, virulome, and phylogenetic relationship, antibiogram prediction, and antimicrobial susceptibility of 114 human isolates of C. jejuni from a tertiary hospital in southern Spain from October 2020 to June 2023. The isolates were sequenced using Illumina technology, and a bioinformatic analysis was subsequently performed. The susceptibility of C. jejuni isolates to ciprofloxacin, tetracycline, and erythromycin was also tested. The resistance rates for each antibiotic were 90.3% for ciprofloxacin, 66.7% for tetracycline, and 0.88% for erythromycin. The fluoroquinolone resistance rate obtained is well above the European average (69.1%). CC-21 (n = 23), ST-572 (n = 13), and ST-6532 (n = 13) were the most prevalent clonal complexes (CCs) and sequence types (STs). In the virulome, the cadF, ciaB, and cdtABC genes were detected in all the isolates. A prevalence of 20.1% was obtained for the genes wlaN and cstIII, which are related to the pathogenesis of Guillain-Barré syndrome (GBS). The prevalence of the main antimicrobial resistance markers detected were CmeABC (92.1%), RE-cmeABC (7.9%), the T86I substitution in gyrA (88.9%), blaOXA-61 (72.6%), tet(O) (65.8%), and ant (6)-Ia (17.1%). High antibiogram prediction rates (>97%) were obtained, except for in the case of the erythromycin-resistant phenotype. This study contributes significantly to the knowledge of C. jejuni genomics for the prevention, treatment, and control of infections caused by this pathogen.IMPORTANCEDespite being the pathogen with the greatest number of gastroenteritis cases worldwide, Campylobacter jejuni remains a poorly studied microorganism. A sustained increase in fluoroquinolone resistance in human isolates is a problem when treating Campylobacter infections. The development of whole genome sequencing (WGS) techniques has allowed us to better understand the genotypic characteristics of this pathogen and relate them to antibiotic resistance phenotypes. These techniques complement the data obtained from the phenotypic analysis of C. jejuni isolates. The zoonotic transmission of C. jejuni through the consumption of contaminated poultry supports approaching the study of this pathogen through "One Health" approach. In addition, due to the limited information on the genomic characteristics of C. jejuni in Spain, this study provides important data and allows us to compare the results with those obtained in other countries.

Characterization of the Common Genetic Variation in the Spanish Population of Navarre.

Large-scale genomic studies have significantly increased our knowledge of genetic variability across populations. Regional genetic profiling is essential for distinguishing common benign variants from disease-causing ones. To this end, we conducted a comprehensive characterization of exonic variants in the population of Navarre (Spain), utilizing whole genome sequencing data from 358 unrelated individuals of Spanish origin. Our analysis revealed 61,410 biallelic single nucleotide variants (SNV) within the Navarrese cohort, with 35% classified as common (MAF > 1%). By comparing allele frequency data from 1000 Genome Project (excluding the Iberian cohort of Spain, IBS), Genome Aggregation Database, and a Spanish cohort (including IBS individuals and data from Medical Genome Project), we identified 1069 SNVs common in Navarre but rare (MAF ≤ 1%) in all other populations. We further corroborated this observation with a second regional cohort of 239 unrelated exomes, which confirmed 676 of the 1069 SNVs as common in Navarre. In conclusion, this study highlights the importance of population-specific characterization of genetic variation to improve allele frequency filtering in sequencing data analysis to identify disease-causing variants.

The integrated genomic surveillance system of Andalusia (SIEGA) provides a One Health regional resource connected with the clinic.

The One Health approach, recognizing the interconnectedness of human, animal, and environmental health, has gained significance amid emerging zoonotic diseases and antibiotic resistance concerns. This paper aims to demonstrate the utility of a collaborative tool, the SIEGA, for monitoring infectious diseases across domains, fostering a comprehensive understanding of disease dynamics and risk factors, highlighting the pivotal role of One Health surveillance systems. Raw whole-genome sequencing is processed through different species-specific open software that additionally reports the presence of genes associated to anti-microbial resistances and virulence. The SIEGA application is a Laboratory Information Management System, that allows customizing reports, detect transmission chains, and promptly alert on alarming genetic similarities. The SIEGA initiative has successfully accumulated a comprehensive collection of more than 1900 bacterial genomes, including Salmonella enterica, Listeria monocytogenes, Campylobacter jejuni, Escherichia coli, Yersinia enterocolitica and Legionella pneumophila, showcasing its potential in monitoring pathogen transmission, resistance patterns, and virulence factors. SIEGA enables customizable reports and prompt detection of transmission chains, highlighting its contribution to enhancing vigilance and response capabilities. Here we show the potential of genomics in One Health surveillance when supported by an appropriate bioinformatic tool. By facilitating precise disease control strategies and antimicrobial resistance management, SIEGA enhances global health security and reduces the burden of infectious diseases. The integration of health data from humans, animals, and the environment, coupled with advanced genomics, underscores the importance of a holistic One Health approach in mitigating health threats.