A new procedure to analyze RNA non-branching structures

RNA structure prediction and structural motifs analysis are challenging tasks in the investigation of RNA function. We propose a novel procedure to detect structural motifs shared between two RNAs (a reference and a target). In particular, we developed two core modules: (i) nbRSSP_extractor, to assign a unique structure to the reference RNA encoded by a set of non-branching structures; (ii) SSD_finder, to detect structural motifs that the target RNA shares with the reference, by means of a new score function that rewards the relative distance of the target non-branching structures compared to the reference ones. We integrated these algorithms with already existing software to reach a coherent pipeline able to perform the following two main tasks: prediction of RNA structures (integration of RNALfold and nbRSSP_extractor) and search for chains of matches (integration of Structator and SSD_finder)

Intensified high yield production of GMMA based vaccines for high burden neglected disease

Vaccines are among the most important public health interventions currently available. The global demand for vaccines is growing due to global population growth, ongoing global immunization campaigns and the increase of antibiotic-resistant bacteria. Vaccines are of high importance in Low- and Middle-Income Countries (LMIC) where millions of deaths occur annually due to vaccines-preventable diseases. Global coverage would need at least 200 million doses annually of a two-dose vaccine to cover the at-risk birth cohort, and considerably more for catch up vaccination during a roll out phase. Especially for vaccines against diseases in LMIC, there is a major added constraint: the vaccine production cost per unit and manufacturing facilities investments must be minimized to ensure the vaccines will be both viable and sustainable. For diseases caused by Gram-negative bacteria, the vaccine platform based on the GMMA technology is promising. GMMA are highly immunogenic outer membrane exosomes produced from genetically engineered bacteria modified for high yield hyper-blebbing that can be purified with minimal downstream processing. Simple robust scalable processes make GMMA ideal for cost-conscious vaccines. Please click Download on the upper right corner to see the full abstract

Molecular engineering of Ghfp, the gonococcal orthologue of neisseria meningitidis factor H binding protein

Knowledge of the sequences and structures of proteins produced by microbial pathogens is continuously increasing. Besides offering the possibility of unraveling the mechanisms of pathogenesis at the molecular level, structural information provides new tools for vaccine development, such as the opportunity to improve viral and bacterial vaccine candidates by rational design. Structure-based rational design of antigens can optimize the epitope repertoire in terms of accessibility, stability, and variability. In the present study, we used epitope mapping information on the well-characterized antigen of Neisseria meningitidis factor H binding protein (fHbp) to engineer its gonococcal homologue, Ghfp. Meningococcal fHbp is typically classified in three distinct antigenic variants. We introduced epitopes of fHbp variant 1 onto the surface of Ghfp, which is naturally able to protect against meningococcal strains expressing fHbp of variants 2 and 3. Heterologous epitopes were successfully transplanted, as engineered Ghfp induced functional antibodies against all three fHbp variants. These results confirm that structural vaccinology represents a successful strategy for modulating immune responses, and it is a powerful tool for investigating the extension and localization of immunodominant epitopes

AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources

MONSTER v1.1: a tool to extract and search for RNA non-branching structures

Background: Detection of RNA structure similarities is still one of the major computational problems in the discovery of RNA functions. A case in point is the study of the new appreciated long non-coding RNAs (lncRNAs), emerging as new players involved in many cellular processes and molecular interactions. Among several mechanisms of action, some lncRNAs show specific substructures that are likely to be instrumental for their functioning. For instance, it has been reported in literature that some lncRNAs have a guiding or scaffolding role by binding chromatin-modifying protein complexes. Thus, a functionally characterized lncRNA (reference) can be used to infer the function of others that are functionally unknown (target), based on shared structural motifs. Methods: In our previous work we presented a tool, MONSTER v1.0, able to identify structural motifs shared between two full-length RNAs. Our procedure is mainly composed of two ad-hoc developed algorithms: nbRSSP_extractor for characterizing the folding of an RNA sequence by means of a sequence-structure descriptor (i.e., an array of non-overlapping substructures located on the RNA sequence and coded by dot-bracket notation); and SSD_finder, to enable an effective search engine for groups of matches (i.e., chains) common to the reference and target RNA based on a dynamic programming approach with a new score function. Here, we present an updated version of the previous one (MONSTER v1.1) accounting for the peculiar feature of lncRNAs that are not expected to have a unique fold, but appear to fluctuate among a large number of equally-stable folds. In particular, we improved our SSD_finder algorithm in order to take into account all the alternative equally-stable structures. Results: We present an application of MONSTER v1.1 on lincRNAs, which are a specific class of lncRNAs located in genomic regions which do not overlap protein-coding genes. In particular, we provide reliable predictions of the shared chains between HOTAIR, ANRIL and COLDAIR. The latter are lincRNAs which interact with the same protein complexes of the Polycomb group and hence they are expected to share structural motifs. Software availability: the software package is provided as additional file 1 ("archive_updated.zip")