1,721,055 research outputs found
One Health and Cattle Genetic Resources: Mining More than 500 Cattle Genomes to Identify Variants in Candidate Genes Potentially Affecting Coronavirus Infections
Full text linkEpidemiological and biological characteristics of coronaviruses and their ability to cross species barriers are a matter of increasing concerns for these zoonotic agents. To prevent their spread, One Health approaches should be designed to include the host (animal) genome variability as a potential risk factor that might confer genetic resistance or susceptibility to coronavirus infections. At present, there is no example that considers cattle genetic resources for this purpose. In this study, we investigated the variability of six genes (ACE2, ANPEP, CEACAM1 and DPP4 encoding for host receptors of coronaviruses; FURIN and TMPRSS2 encoding for host proteases involved in coronavirus infection) by mining whole genome sequencing datasets from more than 500 cattle of 34 Bos taurus breeds and three related species. We identified a total of 180 protein variants (44 already known from the ARS-UCD1.2 reference genome). Some of them determine altered protein functions or the virus–host interaction and the related virus entry processes. The results obtained in this study constitute a first step towards the definition of a One Health strategy that includes cattle genetic resources as reservoirs of host gene variability useful to design conservation and selection programs to increase resistance to coronavirus diseases
Genome-wide association analyses for coat colour patterns in the autochthonous Nero Siciliano pig breed
Full text linkNero Siciliano (or Sicilian Black) is an Italian autochthonous pig breed reared in the Sicily island, mainly under
extensive management systems. Nero Siciliano pigs are black (with black skin and black hair), but animals with
white face or partially white face ("suino facciolo") can be registered to the breed herd book. Sometimes, other
white patterns on extreme portions of legs could appear in this population. This study took advantage from the
rare occurrence of pigs with white patterns in the Nero Siciliano population to carry out a genome-wide association
study and comparative genome-wide Fixation index (FST) analysis to identify genomic regions that could
affect coat colour variability (solid black vs white patterns over black) in this autochthonous pig breed. Analyses
have been conducted on 66 Nero Siciliano pigs: 30 completely black and 36 black with white patterns. All
samples have been genotyped for the KIT gene duplication and MC1R mutations, two genes well known to affect
coat colours in pigs. Only pigs that did not carry any duplication of the KIT gene and were homozygous for the
ED2 black dominant MC1R gene allele (n = 26 completely black and n. 22 with white patterns) were genotyped
with the Illumina PorcineSNP60 BeadChip. The genome-wide analyzes identified on chromosome 2 a significant
marker (rs81329493) associated with the coat colour white patterns in this breed. The homologous chromosome
region in felids contains the gene responsible for the blotched tabby and striped coat colour patterns. Further
studies, including a larger number of pigs, are needed to confirm this result and identify the causative mutation
(s) affecting this coat colour diversity, which might be used to design a conservation programme in this breed
aiming to maintain phenotypic homogeneity (i.e. solid black) that is typically associated with Nero Siciliano
pigs. This study demonstrated how genetic diversity segregating in an autochthonous genetic resource can be
explored to understand the genetic mechanisms affecting phenotypic traits in a livestock species
A genotyping by sequencing approach can disclose Apis mellifera population genomic information contained in honey environmental DNA
Full text linkAwareness has been raised over the last years on the genetic integrity of autochthonous honey bee subspecies. Genomic tools available in Apis mellifera can make it possible to measure this information by targeting individual honey bee DNA. Honey contains DNA traces from all organisms that contributed or were involved in its production steps, including the honey bees of the colony. In this study, we designed and tested a genotyping by sequencing (GBS) assay to analyse single nucleotide polymorphisms (SNPs) of A. mellifera nuclear genome using environmental DNA extracted from honey. A total of 121 SNPs (97 SNPs informative for honey bee subspecies identification and 24 SNPs associated with relevant traits of the colonies) were used in the assay to genotype honey DNA, which derives from thousands of honey bees. Results were integrated with information derived from previous studies and whole genome resequencing datasets. This GBS method is highly reliable in estimating honey bee SNP allele frequencies of the whole colony from which the honey derived. This assay can be used to identify the honey bee subspecies of the colony that produced the honey and, in turn, to authenticate the entomological origin of the honey
NET-GE: a web-server for NETwork-based human gene enrichment
Full text linkGene enrichment is a requisite for the interpretation of biological complexity related to specific molecular pathways and biological processes. Furthermore, when interpreting NGS data and human variations, including those related to pathologies, gene enrichment allows the inclusion of other genes that in the human interactome space may also play important key roles in the emergency of the phenotype. Here, we describe NET-GE, a web server for associating biological processes and pathways to sets of human proteins involved in the same phenotype RESULTS: NET-GE is based on protein-protein interaction networks, following the notion that for a set of proteins, the context of their specific interactions can better define their function and the processes they can be related to in the biological complexity of the cell. Our method is suited to extract statistically validated enriched terms from Gene Ontology, KEGG and REACTOME annotation databases. Furthermore, NET-GE is effective even when the number of input proteins is small
The eDGAR database of Disease-Gene Associations with annotated Relationships among genes
No full texteDGAR is 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.i
Entomological signatures in honey: an environmental DNA metabarcoding approach can disclose information on plant-sucking insects in agricultural and forest landscapes
Full text linkHoneydew produced from the excretion of plant-sucking insects (order Hemiptera) is a carbohydrate-rich material that is foraged by honey bees to integrate their diets. In this study, we used DNA extracted from honey as a source of environmental DNA to disclose its entomological signature determined by honeydew producing Hemiptera that was recovered not only from honeydew honey but also from blossom honey. We designed PCR primers that amplified a fragment of mitochondrial cytochrome c oxidase subunit 1(COI) gene of Hemiptera species using DNA isolated from unifloral, polyfloral and honeydew honeys. Ion Torrent next generation sequencing metabarcoding data analysis assigned Hemiptera species using a customized bioinformatic pipeline. The forest honeydew honeys reported the presence of high abundance of Cinara pectinatae DNA, confirming their silver fir forest origin. In all other honeys, most of the sequenced reads were from the planthopper Metcalfa pruinosa for which it was possible to evaluate the frequency of different mitotypes. Aphids of other species were identified from honeys of different geographical and botanical origins. This unique entomological signature derived by environmental DNA contained in honey opens new applications for honey authentication and to disclose and monitor the ecology of plant-sucking insects in agricultural and forest landscapes
Comparative targeted metabolomic profiles of porcine plasma and serum
Full text linkMetabolomics has been used to characterise many biological matrices and obtain detailed pictures of biological systems based on many metabolites. Plasma and serum are two blood-derived biofluids commonly used to assess and monitor the organismal metabolism and obtain information on the physiological and health conditions of an animal. Plasma is the supernatant that is separated from the cellular components after centrifugation of the blood that is first added with an anticoagulant. Serum is obtained after centrifugation of the blood that has been coagulated. The choice of one or the other biofluid for metabolomic analyses is related to specific analytical needs and technical issues, to problems derived by the collection and preparation steps, in particular when specimens are sampled from animals involved in field studies. Thus far, most of the metabolomic studies that compared plasma and serum have been carried out in humans and very little is known on the pigs. In this study, we used a targeted metabolomic platform that can detect about 180 metabolites of five biochemical classes to compare plasma and serum profiles of samples collected from 24 pigs. To also obtain a cross-species comparative metabolomic analysis, information for human plasma and serum derived from the same platform was retrieved from previous studies. Statistical analyses included univariate and multivariate approaches aimed at identifying stable and/or differentially abundant metabolites between the two porcine biofluids. A total of 154 (∼83%) metabolites passed the initial quality control, indicating a good repeatability of the analytical platform in pigs. Discarded metabolites included aspartate and biogenic amines that were already reported to be unstable in human studies. More than 80% of the metabolites had similar profiles in both porcine biofluids (average correlation was 0.75). Concentrations were usually higher in serum than in plasma, in agreement with what was already reported in humans. The univariate analysis identified 44 metabolites that had statistically different concentrations between porcine plasma and serum, of which 28 metabolites were also confirmed by the multivariate analysis. The obtained picture described similarities and differences between these two biofluids in pigs and the related human-pig comparisons. The obtained information can be useful for the choice of one or the other matrix for the implementation of metabolomic studies in this livestock species. The results can also provide useful hints to valuing the pig as animal model, in particular when metabolite-derived physiological states are relevant
Genomic diversity and signatures of selection in meat and fancy rabbit breeds based on high-density marker data
Full text linkBackground: Domestication of the rabbit (Oryctolagus cuniculus) has led to a multi-purpose species that includes many breeds and lines with a broad phenotypic diversity, mainly for external traits (e.g. coat colours and patterns, fur structure, and morphometric traits) that are valued by fancy rabbit breeders. As a consequence of this human-driven selection, distinct signatures are expected to be present in the rabbit genome, defined as signatures of selection or selective sweeps. Here, we investigated the genome of three Italian commercial meat rabbit breeds (Italian Silver, Italian Spotted and Italian White) and 12 fancy rabbit breeds (Belgian Hare, Burgundy Fawn, Champagne d’Argent, Checkered Giant, Coloured Dwarf, Dwarf Lop, Ermine, Giant Grey, Giant White, Rex, Rhinelander and Thuringian) by using high-density single nucleotide polymorphism data. Signatures of selection were identified based on the fixation index (FST) statistic with different approaches, including single-breed and group-based methods, the latter comparing breeds that are grouped based on external traits (different coat colours and body sizes) and types (i.e. meat vs. fancy breeds). Results: We identified 309 genomic regions that contained signatures of selection and that included genes that are known to affect coat colour (ASIP, MC1R and TYR), coat structure (LIPH), and body size (LCORL/NCAPG, COL11A1 and HOXD) in rabbits and that characterize the investigated breeds. Their identification proves the suitability of the applied methodologies for capturing recent selection events. Other regions included novel candidate genes that might contribute to the phenotypic variation among the analyzed breeds, including genes for pigmentation-related traits (EDNRA, EDNRB, MITF and OCA2) and body size, with a strong candidate for dwarfism in rabbit (COL2A1). Conclusions: We report a genome-wide view of genetic loci that underlie the main phenotypic differences in the analyzed rabbit breeds, which can be useful to understand the shift from the domestication process to the development of breeds in O. cuniculus. These results enhance our knowledge about the major genetic loci involved in rabbit external traits and add novel information to understand the complexity of the genetic architecture underlying body size in mammals
NET-GE: a web-server for linking protein variations to biological processes and pathways
No full textIntroduction
High-Throughput Sequencing technologies allow a fast discovery of genetic variations characterizing specific human phenotypes (such us diseases) and in the perspective of personalized medicine each individual phenotype needs annotations for reconciling such variations with common biological processes and pathways. To this purpose, we developed NET-GE [1] a NETwork-based Gene Enrichment tool for associating biological processes and pathways to sets of human proteins involved in the same phenotype. NET-GE is available at http://net-ge.biocomp.unibo.it/enrich
Methods
NET-GE implements a standard and a network based enrichment. The network based enrichment relies on the human protein interactome available in the STRING database (http://string-db.org/). For each set of annotations (Gene Ontology, KEGG and Reactome pathways), proteins sharing the same annotation term are collected in a seed set and than mapped in STRING. Each originated subgraph is followed by a procedure aimed to reduce the resulting network and the protein set to be analyzed is mapped on the sub-networks and tested for enrichment by applying a Fisher's exact test. NET-GE web server runs on a web2py engine and it is publicly accessible.
Results
Given a set of human proteins (Uniprot accession, http://www.uniprot.org/), NET-GE perform a standard and network-based enrichment. Options are possible for Gene Ontology terms (http://geneontology.org/), KEGG (http://www.genome.jp/kegg/) or Reactome (http://www.reactome.org/). When tested on an OMIM-derived (http://www.ncbi.nlm.nih.gov/omim) benchmark, our method is able to detect functional associations not detectable by standard enrichment.
Conclusions
NET-GE is useful for highlighting new hypotheses on the molecular mechanisms underlying a given human phenotype. Furthermore, with our procedure, it is possible to explore new genes/proteins in the subgraph-enriched-network for helping the prioritization of genetic variant discovery.
References
[1] Di Lena, P., Martelli, P.L., Fariselli, P., Casadio, R. (2015) NET-GE: a novel NETwork-based Gene Enrichment for detecting biological processes associated to Mendelian diseases. BMC Genomics 16 Suppl 8, S6
Are machine learning based methods suited to address complex biological problems? Lessons from CAGI-5 challenges
No full textIn silico approaches are routinely adopted to predict the effects of genetic variants and their relation to diseases. The critical assessment of genome interpretation (CAGI) has established a common framework for the assessment of available predictors of variant effects on specific problems and our group has been an active participant of CAGI since its first edition. In this paper, we summarize our experience and lessons learned from the last edition of the experiment (CAGI-5). In particular, we analyze prediction performances of our tools on five CAGI-5 selected challenges grouped into three different categories: prediction of variant effects on protein stability, prediction of variant pathogenicity, and prediction of complex functional effects. For each challenge, we analyze in detail the performance of our tools, highlighting their potentialities and drawbacks. The aim is to better define the application boundaries of each tool
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