The COVID-19 pandemic has had a devastating impact worldwide and has been a great challenge for the scientific community. Vaccines against SARS-CoV-2 are now efficiently lessening COVID-19 mortality, although finding a cure for this infection is still a priority. An unbalanced immune response and the uncontrolled release of proinflammatory cytokines are features of COVID-19 pathophysiology and contribute to disease progression and worsening. Histone deacetylases (HDACs) have gained interest in immunology, as they regulate the innate and adaptative immune response at different levels. Inhibitors of these enzymes have already proven therapeutic potential in cancer and are currently being investigated for the treatment of autoimmune diseases. We thus tested the effects of different HDAC inhibitors, with a focus on a selective HDAC6 inhibitor, on immune and epithelial cells in in vitro models that mimic cells activation after viral infection. Our data indicate that HDAC inhibitors reduce cytokines release by airway epithelial cells, monocytes and macrophages. This anti-inflammatory effect occurs together with the reduction of monocytes activation and T cell exhaustion and with an increase of T cell differentiation towards a T central memory phenotype. Moreover, HDAC inhibitors hinder IFN-I expression and downstream effects in both airway epithelial cells and immune cells, thus potentially counteracting the negative effects promoted in critical COVID-19 patients by the late or persistent IFN-I pathway activation. All these data suggest that an epigenetic therapeutic approach based on HDAC inhibitors represents a promising pharmacological treatment for severe COVID-19 patients.

The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process often fails to deliver a clear classification, resulting in either variants of unknown significance (VUSs) or variants with conflicting interpretation of pathogenicity (CIP); these represent a major clinical problem because they do not provide useful information for decision-making, causing a large fraction of genetically determined disease to remain undertreated. We developed a machine learning (random forest)-based tool, RENOVO, that classifies variants as pathogenic or benign on the basis of publicly available information and provides a pathogenicity likelihood score (PLS). Using the same feature classes recommended by guidelines, we trained RENOVO on established pathogenic/benign variants in ClinVar (training set accuracy ¼ 99%) and tested its performance on variants whose interpretation has changed over time (test set accuracy ¼ 95%). We further validated the algorithm on additional datasets including unreported variants validated either through expert consensus (ENIGMA) or laboratory-based functional techniques (on BRCA1/2 and SCN5A). On all datasets, REN-OVO outperformed existing automated interpretation tools. On the basis of the above validation metrics, we assigned a defined PLS to all existing ClinVar VUSs, proposing a reclassification for 67% with >90% estimated precision. RENOVO provides a validated tool to reduce the fraction of uninterpreted or misinterpreted variants, tackling an area of unmet need in modern clinical genetics.

Background Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of “on-demand” Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server’s initial configuration and deployment. Results “Laniakea@ReCaS”, the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform. Conclusions During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers’ work.

Obesity is associated with a higher risk of developing many cancer types including acute promyelocytic leukaemia (APL), a subset of acute myeloid leukemias (AML) characterized by expression of the PML-RARα oncogene. The molecular mechanisms linking obesity and APL development are not known. To model clinical observations, we established a mouse model of diet-induced obesity using transgenic mice constitutively expressing PML-RARΑ α in the hematopoietic system (PML-RARα KI mice) fed either standard (SD) or high-fat (HFD) diets. HFD-fed PML-RARα KI mice developed leukaemia with reduced latency and increased penetrance, as compared to SD-fed mice. HFD leads to accumulation of DNA damage in hematopoietic stem cells (HSCs), but, surprisingly, this was not associated with mutational load gain, as shown by whole genome/exome sequencing of pre-leukemic and leukemic cells. Importantly, very few of the observed mutations were predicted to act as cancer drivers, suggesting the relevance of nongenetic mechanisms. HFD led to an expansion of hematopoietic progenitor cells with a concomitant reduction in long-term hematopoietic stem cells, and in the presence of PML-RARα this was also accompanied by an enhancement of in vitro and in vivo self-renewal. Interestingly, Linoleic Acid (LA), abundant in HFD, recapitulates the effect of HFD on the self-renewal of PML-RARα HPCs by activating the peroxisome proliferator-activated receptor delta (PPARδ), a central regulator of fatty acid metabolism involved in the promotion of cancer progression. Our findings have implications for dietary or pharmacological interventions aimed at counteracting the cancer-promoting effect of obesity.