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Abstract Extensive genetic studies have elucidated cardiomyocyte differentiation and associated gene networks using single-cell RNA-seq, yet the intricate transcriptional mechanisms governing cardiac conduction system (CCS) development and working cardiomyocyte differentiation remain largely unexplored. Here we show that mice deleted for Dhx36 (encoding the Dhx36 helicase) in the embryonic or neonatal heart develop overt dilated cardiomyopathy, surface ECG alterations related to cardiac impulse propagation, and (in the embryonic heart) a lack of a ventricular conduction system (VCS). Heart snRNA-seq and snATAC-seq reveal the role of Dhx36 in CCS development and in the differentiation of working cardiomyocytes. Dhx36 deficiency directly influences cardiomyocyte gene networks by disrupting the resolution of promoter G-quadruplexes in key cardiac genes, impacting cardiomyocyte differentiation and CCS morphogenesis, and ultimately leading to dilated cardiomyopathy and atrioventricular block. These findings further identify crucial genes and pathways that regulate the development and function of the VCS/Purkinje fiber (PF) network.

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.

Abstract 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.

Abstract The short-lived nature and heterogeneity of Natural Killer (NK) cells limit the development of NK cell-based therapies, despite their proven safety and efficacy against cancer. Here, we describe the biological basis, detailed phenotype and function of long-lived anti-tumour human NK cells (CD56highCD16+), obtained without cell sorting or feeder cells, after priming of peripheral blood cells with Bacillus Calmette-Guérin (BCG). Further, we demonstrate that survival doses of a cytokine combination, excluding IL18, administered just weekly to BCG-primed NK cells avoids innate lymphocyte exhaustion and leads to specific long-term proliferation of innate cells that exert potent cytotoxic function against a broad range of solid tumours, mainly through NKG2D. Strikingly, a NKG2C+CD57-FcεRIγ+ NK cell population expands after BCG and cytokine stimulation, independently of HCMV serology. This strategy was exploited to rescue anti-tumour NK cells even from the suppressor environment of cancer patients’ bone marrow, demonstrating that BCG confers durable anti-tumour features to NK cells.

ABSTRACT 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.

Abstract The relation of antipsychotics with severe Coronavirus Disease 19 (COVID-19) outcomes is a matter of debate since the beginning of the pandemic. To date, controversial results have been published on this issue. We aimed to prove whether antipsychotics might exert adverse or protective effects against fatal outcomes derived from COVID-19. A population-based retrospective cohort study (January 2020 to November 2020) comprising inpatients (15,968 patients) who were at least 18 years old and had a laboratory-confirmed COVID-19 infection. Two sub-cohorts were delineated, comprising a total of 2536 inpatients: individuals who either had no prescription medication or were prescribed an antipsychotic within the 15 days preceding hospitalization. We conducted survival and odds ratio analyses to assess the association between antipsychotic use and mortality, reporting both unadjusted and covariate-adjusted results. We computed the average treatment effects, using the untreated group as the reference, and the average treatment effect on the treated, focusing solely on the antipsychotic-treated population. Among the eight antipsychotics found to be in use, only aripiprazole showed a significant decrease in the risk of death from COVID-19 [adjusted odds ratio (OR) = 0.86; 95% CI, 0.79–0.93, multiple-testing adjusted p-value

Abstract 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.

Background/aims: Changes in gene expression profiles among individuals with inflammatory bowel diseases (IBDs) could potentially influence the responsiveness to anti-TNF treatment. The aim of this study was to identify genes that could serve as predictors of early response to anti-TNF therapies in pediatric IBD patients prior to the initiation of treatment. Methods: We conducted a prospective, longitudinal, and multicenter study, enrolling 24 pediatric IBD patients aged less than 18 years who were initiating treatment with either infliximab or adalimumab. RNA-seq from blood samples was analyzed using the DESeq2 library by comparing responders and non-responders to anti-TNF drugs. Results: Bioinformatic analyses unveiled 102 differentially expressed genes, with 99 genes exhibiting higher expression in responders compared to non-responders prior to the initiation of anti-TNF therapy. Functional enrichment analyses highlighted defense response to Gram-negative bacteria (FDR = 2.3 ×10–7) as the most significant biological processes, and hemoglobin binding (FDR = 0.002), as the most significant molecular function. Gene Set Enrichment Analysis (GSEA) revealed notable enrichment in transcriptional misregulation in cancer (FDR = 0.016). Notably, 13 genes (CEACAM8, CEACAM6, CILP2, COL17A1, OLFM4, INHBA, LCN2, LTF, MMP8, DEFA4, PRTN3, AZU1, and ELANE) were selected for validation, and a consistent trend of increased expression in responders prior to drug administration was observed for most of these genes, with findings for 4 of them being statistically significant (CEACAM8, LCN2, LTF2, and PRTN3). Conclusions: We identified 102 differentially expressed genes involved in the response to anti-TNF drugs in children with IBDs and validated CEACAM8, LCN2, LTF2, and PRTN3. Genes participating in defense response to Gram-negative bacterium, serine-type endopeptidase activity, and transcriptional misregulation in cancer are good candidates for anticipating the response to anti-TNF drugs in children with IBDs.

Abstract 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.

Abstract Purpose Despite the extensive vaccination campaigns in many countries, COVID-19 is still a major worldwide health problem because of its associated morbidity and mortality. Therefore, finding efficient treatments as fast as possible is a pressing need. Drug repurposing constitutes a convenient alternative when the need for new drugs in an unexpected medical scenario is urgent, as is the case with COVID-19. Methods Using data from a central registry of electronic health records (the Andalusian Population Health Database), the effect of prior consumption of drugs for other indications previous to the hospitalization with respect to patient outcomes, including survival and lymphocyte progression, was studied on a retrospective cohort of 15,968 individuals, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020. Results Covariate-adjusted hazard ratios and analysis of lymphocyte progression curves support a significant association between consumption of 21 different drugs and better patient survival. Contrarily, one drug, furosemide, displayed a significant increase in patient mortality. Conclusions In this study we have taken advantage of the availability of a regional clinical database to study the effect of drugs, which patients were taking for other indications, on their survival. The large size of the database allowed us to control covariates effectively.