The excessive hospital mortality associated with acute respiratory distress syndrome (ARDS) in adults mandates an urgent need for developing new therapies and tools for the early risk assessment of these patients. ARDS is a heterogeneous syndrome with multiple different pathogenetic processes contributing differently in different patients depending on clinical as well as genetic factors. Identifying genetic-based biomarkers holds the promise for establishing effective predictive and prognostic stratification methods and for targeting new therapies to improve ARDS outcomes. Here we provide an updated review of the available evidence supporting the presence of genetic factors that are predictive of ARDS development and of fatal outcomes in adult critically ill patients and that have been identified by applying different genomic and genetic approaches. We also introduce other incipient genomics approximations, such as admixture mapping, metagenomics and genome sequencing, among others, that will allow to boost this knowledge and likely reveal new genetic predictors of ARDS susceptibility and prognosis among critically ill patients.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with limited treatment options and poor survival. The airway microbiome plays a critical role in IPF, and there is emerging interest in the potential involvement of the gut microbiome through the gut-lung axis. Previous studies have reported genetic associations with IPF, and there is increasing evidence that microbiome composition also has a genetic basis. Previous studies have identified genetic signals involved in lung defence and cell proliferation to IPF risk and progression, but their effects on microbiome composition remain unclear. To uncover potential causal mechanisms linking the microbiome and lung fibrosis, we explored previously reported genetic association signals and their relationship with gut microbiome composition and IPF risk and outcomes. This revealed shared signals between gut microbiome variation and IPF that warrant further investigation. Our findings emphasise the value of further research leveraging genetic associations to improve our understanding of causal disease mechanisms and identify novel therapeutic opportunities.

The Regional Student Group of Spain is recognized by the Student Council and the International Society for Computational Biology. The objective of these institutions is to connect and share useful information among all professionals and students working in the field of bioinformatics and computational biology. In this article, we intend to present and publicize RSG-Spain, the Spanish ISCB regional student group, by relating its recent history of a growing community of students and young professionals and how it helps their development in the field. RSG-Spain, since its creation, has been involved in the organization of events with the aim of gathering bioinformaticians in the country and promoting research and collaboration in the field. Here the VIII Student Symposium held online in 2021 is presented, analyzing the output of the event and showing the main challenges found in its organization.

Background: Early detection of sepsis is essential for its successful management. Although genome-wide association studies (GWAS) have shown potential in identifying sepsis-related genetic variants, they often involve heterogeneous patient groups and use single-locus analysis methods. Here, we aim to identify new sepsis susceptibility loci in post-surgical patients using an explainable artificial intelligence (XAI) approach applied to GWAS data. Methods: GWAS was performed in 750 post-operative patients with sepsis and 3,500 population controls. We applied a novel XAI-based methodology to GWAS-derived single nucleotide polymorphisms (SNPs) to predict sepsis and prioritize new genetic variants associated with post-operative sepsis susceptibility. We also assessed functional and enrichment effects using empirical data from integrated software tools and datasets, with the top-ranked variants and associated genes. Results: Our XAI-GWAS approach showed a notable performance in predicting post-surgical sepsis and prioritized SNPs (such as rs17653532, rs1575081785, and rs74707084) with higher contribution to post-operative sepsis prediction. It also facilitated the discovery of post-operative sepsis risk loci with important functional implications related to gene expression regulation, DNA replication, cyclic nucleotide signaling, cell proliferation, and cardiac dysfunction. Conclusion: The combination of GWAS and XAI prioritized loci associated with post-operative sepsis susceptibility. The determination of key genes, such as PRIM2 , SYNPR , and RBSN , through pre-operative blood tests could enhance risk stratification, enable early detection of post-operative sepsis, and guide targeted interventions to improve patient outcomes. Further research with additional and ethnically diverse cohorts comprising sepsis and non-sepsis patients undergoing major surgery is needed to validate these exploratory findings.