Genetic investigations, boosted by modern sequencing techniques, allow dissecting the genetic component of different phenotypic traits. These efforts result in the compilation of lists of genes related to diseases and show that an increasing number of diseases is associated with multiple genes. Investigating functional relations among genes associated with the same disease contributes to highlighting molecular mechanisms of the pathogenesis. We present eDGAR, a database collecting and organizing the data on gene/disease associations as derived from OMIM, Humsavar and ClinVar. For each disease-associated gene, eDGAR collects information on its annotation. Specifically, for lists of genes, eDGAR provides information on: i) interactions retrieved from PDB, BIOGRID and STRING; ii) co-occurrence in stable and functional structural complexes; iii) shared Gene Ontology annotations; iv) shared KEGG and REACTOME pathways; v) enriched functional annotations computed with NET-GE; vi) regulatory interactions derived from TRRUST; vii) localization on chromosomes and/or co-localisation in neighboring loci. The present release of eDGAR includes 2672 diseases, related to 3658 different genes, for a total number of 5729 gene-disease associations. 71% of the genes are linked to 621 multigenic diseases and eDGAR highlights their common GO terms, KEGG/REACTOME pathways, physical and regulatory interactions. eDGAR includes a network based enrichment method for detecting statistically significant functional terms associated to groups of genes. eDGAR offers a resource to analyze disease-gene associations. In multigenic diseases genes can share physical interactions and/or co-occurrence in the same functional processes. eDGAR is freely available at: edgar.biocomp.unibo.it

Poultry farms are hotspots for the development and spread of antibiotic resistance genes (ARGs), due to high stocking densities and extensive use of antibiotics, posing a threat of spread and contagion to workers and the external environment. Here, we applied shotgun metagenome sequencing to characterize the gut microbiome and resistome of poultry, workers and their households - also including microbiomes from the internal and external farm environment - in three different farms in Italy during a complete rearing cycle. Our results highlighted a relevant overlap among the microbiomes of poultry, workers, and their families (gut and skin), with clinically relevant ARGs and associated mobile elements shared in both poultry and human samples. On a finer scale, the reconstruction of species-level genome bins (SGBs) allowed us to delineate the dynamics of microorganism and ARGs dispersion from farm systems. We found the associations with worker microbiomes representing the main route of ARGs dispersion from poultry to human populations. Collectively, our findings clearly demonstrate the urgent need to implement more effective procedures to counteract ARGs dispersion from poultry food systems and the relevance of metagenomics-based metacommunity approaches to monitor the ARGs dispersion process for the safety of the working environment on farms.

The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase α‐N‐acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population‐scale analysis of disease epidemiology and rare variant association analysis.

Modern sequencing technologies provide an unprecedented amount of data of single-nucleotide variations occurring in coding regions and leading to changes in the expressed protein sequences. A significant fraction of these single-residue variations is linked to disease onset and collected in public databases. In recent years, many scientific studies have been focusing on the dissection of salient features of disease-related variations from different perspectives. In this work, we complement previous analyses by updating a dataset of disease-related variations occurring in proteins with 3D structure. Within this dataset, we describe functional and structural features that can be of interest for characterizing disease-related variations, including major chemico-physical properties, the strength of association to disease of variation types, their effect on protein stability, their location on the protein structure, and their distribution in Pfam structural/functional protein models. Our results support previous findings obtained in different data sets and introduce Pfam models as possible fingerprints of patterns of disease related single-nucleotide variations.

Body lesions in pigs are a common welfare concern, particularly during the weaning period. These lesions can lead to pain, infection, and impaired mobility, resulting in reduced growth performance and increased mortality. Moreover, weaning stress can affect gut microbiota, immune response and increase the oxidative stress of piglets during this transition period. It has been hypothesised that social stress and body lesions could contribute to affect the gut microbiota, physiological and immune response of piglets. The study aims to evaluate the impact of the body lesions due to social stress on microbial profile, immune response, and oxidative status of weaned piglets. Lesion score (LS) on skin, tail, ear, neck, middle trunk, and hind quarters was measured 1 week (28 days of age, T1) and 7 weeks postweaning (T2) on 45 tail-docked pigs according to the method suggested from the Walfer Quality® (2009) on a scale from 0 to 2. Based on the LS, at T1, piglets were classified as High LS (n = 16), when LS was >1 in at least two of the areas considered, or Low LS (n = 29). At T2, based on the same scoring system and to the LS observed at T1, piglets were divided into four groups: High to Low LS (H-L, n = 11), High to High LS (H-H, n = 5), Low to Low LS (L-L, n = 21) and Low to High LS (L-H, n = 8). Blood and faecal samples were collected at T1 and T2. At T1, pigs with a high LS had a lower biological antioxidant potential compared with the L group (P < 0.02). At T2, the L-H group had a lower Reactive Oxygen Metabolites concentration compared with the H-H group (P = 0.03) while the L-L group had a lower concentration of Immunoglobulin A compared with H-H and L-H groups (P = 0.02 and P = 0.04, respectively). At T1, piglets with high LS had a different microbiota compared to piglets with low LS (R² = 0.04, P < 0.01). Low LS pigs were characterised by a higher abundance of Firmicutes, Blautia, Eubacterium coprostanoligenes, Faecalibacterium, Megasphaera, Subdoligranulum (P.adj < 0.05), while pigs with high LS were characterised by higher abundance of Bacteroidota, Rikenellaceae RC9, Prevotellaceae UCG-003, uncultured-Lachnospiraceae and uncultured-Oscillospiraceae (P.adj < 0.05). At T2, the H-H group were characterised by Oscillospirales-UCG-010, H-L by Agatobachter and L-L by Alloprevotella (P.adj < 0.05). Overall, this study provides valuable insights into the relationship between body lesions, oxidative stress, and gut microbiota in weaned pigs.

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.