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Background Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. Results In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. Conclusions Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to treatment with drugs. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. High prediction accuracies reported in the literature likely result from significant overlaps among training, validation, and testing sets, making many predictors inapplicable to new data. To address these issues, we developed GraphGR, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, GraphGR integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and genedisease associations, into a unified graph structure. In order to construct diverse and information-rich cancer-specific networks, we devised a novel graph reduction protocol based on not only the topological information, but also the biological knowledge. The performance of GraphGR, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches on the same data. Finally, several new predictions are validated against the biomedical literature demonstrating that GraphGR generalizes well to unseen data, i.e. it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. GraphGR is freely available to the academic community at https://github.com/pulimeng/GraphGR.

Motivation Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. Results We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures. Availability and implementation BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet. Supplementary information Supplementary data are available at Bioinformatics online.

Mastitis is one of the costliest diseases affecting the world's dairy industry. The important contribution of complement Component 5 (C5) to phagocytosis, which plays a major role in the defence of the bovine mammary gland against infection, makes this component of innate immunity a potential contributor in defending udder against mastitis. The objectives of this study were to sequence and analyse the whole coding region of the C5 gene in Egyptian buffalo and cattle, to detect any nucleotide variations (polymorphisms) and to investigate their associations with milk somatic cell score (SCS) as an indicator of mastitis in dairy animals. We sequenced a buffalo C5 cDNA fragment of 5336 bp (KP221293) and a cattle C5 cDNA fragment of 5303 bp (KP221294), which included the whole coding region and 3-UTR. Buffalo and cattle C5 cDNA shared sequence identity of 99%. The predicted complement C5 proteins consist of 1677 amino acid residues in both animals, one amino acid less than in humans and three amino acids more than in mouse C5 protein. Comparing cDNA sequences of different animals revealed nine novel SNPs in buffalo and seven SNPs in cattle, with two of them being novel. The association analysis revealed that five SNPs in buffalo are highly associated with SCS; indicating the contribution of complement C5 variants in buffalo mastitis resistance. No significant associations were detected between C5 variants and SCS in cattle. This is the first report about C5 variants in buffalo and its association with SCS.

Reproductive efficiency is one of the most critical factors affecting the productivity and profitability of the sheep industry. Therefore, the goal of this study is to identify candidate genes and/or loci that could be used for improving prolificacy in Egyptian sheep breeds. We selected four genes (GDF9, BMP15, BMP6 and BMPR1B), because of their previously reported contributions to sheep prolificacy and ovulation rate, to explore the effects of their variants on Egyptian sheep breeds as well as the effect of their expression level on follicular maturation. Sequence analysis of the four genes in Rahmani rams revealed seven SNPs, three of them are novel. The detected SNPs were genotyped in both Rahmani and Ossimi breeds. Association analysis showed that two SNPs located in GDF9 and BMPR1B are in association with litter size. Analysis of the mRNA transcripts in different ovarian cells and tissues showed that GDF9 and BMP15 transcripts are expressed exclusively by oocytes, however, their levels are significantly reduced in poor qualities oocytes, which underlines their importance in oocyte ovulation. The most interesting findings are the expression of BMPR1B mRNA in oocytes, granulosa, cumulus cells and corpora lutea along with BMP6 mRNA. This observation suggests that BMP6 would be the most likely ligand regulating follicular maturation and ovulation rate through BMPR1B. Also the presence of their transcripts in corpora lutea suggests the role these two genes in controlling luteolysis. However further studies are required to confirm these suggestions. To the best of our knowledge, this is the first report about the expression of BMP6 and BMPR1B in sheep cumulus cells and corpora lutea.

Background: In Egypt, the importance of goats comes from its potential source of meat and milk production. Thus, goat productivity improvement acts as a global strategy to provide Egyptians by important source of protein feeding. Molecular techniques have been employed to analyze growth hormone (GH) gene that promotes goat muscles, bone formation, regulating fat content and other important traits in goat. Our objective was to document and find out an accurate genetic marker sites, single nucleotide polymorphisms (SNPs) in exon-3 region of GH gene in some Egyptian goat breeds. Methods: Fifteen blood samples were collected from three Egyptian goat breeds (Baladi, Barki and Zaraibi), DNA was isolated and primer was used to amplify GH gene region corresponding to exon-3 region. Finally, sequencing methodology was applied for PCR products regarding the three goat breeds. Results: The results showed four nucleotide substitutions; The first SNP (CaT) was located at nucleotide position 106, the second SNP( GaA) was located at nucleotide position118 and the third SNP (GaA) was located at nucleotide position 128 .Finally, the fourth SNP (TaC) was located at nucleotide position 136. It was found that the SNP (GaA) at nucleotide position 128 caused an amino acid change from Glycine to Serine in the protein sequence of GH gene. Conclusion: Our findings demonstrated several genetic variations across exon-3 region of GH gene which can be used as marker in selection of goats with high valued traits.

With the aim of characterizing the genetic background of Egyptian buffalo and identifying genomic regions and potential causative mutations associated with milk yield, we performed a Genome-Wide Association Study (GWAS) in Egyptian buffalo using Axiom Buffalo Genotyping Array 90 K. This study was carried out with 250 buffalo cows using 89,069 daily milk records. After quality control, a total of 42,269 single nucleotide polymorphisms ( SNPs) remained for further analysis. Genome-wide analysis was performed in the way of SNP-by-SNP, through regressing the observations of an average daily milk yield deviations on SNP alleles. Several genomic regions were detected with suggestive signals of association on chromosomes BTA1, BTA5, BTA6, and BTA27. The most significant SNP (Affx-79526274) was located on chromosome BTA27. The convincingly associated SNPs were located within or close to several candidate genes. A GO analysis ranked immune response at the top of all biological process associated with those genes. This is the first GWAS in Egyptian buffalo. Although a small sample size was used in this study, several suggestive genomic loci associated with daily milk production were detected. Further work is required on a larger sample size with fine mapping of identified QTL to detect potential candidate regions.

Mastitis is an infectious disease of the mammary gland that leads to reduced milk production and change in milk composition. Complement component C3 plays a major role as a central molecule of the complement cascade involving in killing of microorganisms, either directly or in cooperation with phagocytic cells. C3 cDNA were isolated, from Egyptian buffalo and cattle, sequenced and characterized. The C3 cDNA sequences of buffalo and cattle consist of 5025 and 5019 bp, respectively. Buffalo and cattle C3 cDNAs share 99% of sequence identity with each other. The 4986 bp open reading frame in buffalo encodes a putative protein of 1661 amino acids—as in cattle—and includes all the functional domains. Further, analysis of the C3 cDNA sequences detected six novel single-nucleotide polymorphisms (SNPs) in buffalo and three novel SNPs in cattle. The association analysis of the detected SNPs with milk somatic cell score as an indicator of mastitis revealed that the most significant association in buffalo was found in the C >A substitution (ss: 1752816097) in exon 27, whereas in cattle it was in the C >T substitution (ss: 1752816085) in exon 12. Our findings provide preliminary information about the contribution of C3 polymorphisms to mastitis resistance in buffalo and cattle.

Goat productivity improvement in developing countries is essential since this animal provides an important source of milk, meat, wool, and skin. One gene with important physiological functions in animals is growth hormone (GH). This study was undertaken to characterize and investigate polymorphic sites in the GH gene in Egyptian goat breeds. Additionally, the effects of the detected mutations on the GH protein's structure and function were predicted using different bioinformatics tools. To achieve these goals, the complete GH gene (promoter, UTR, exons, and introns) was sequenced from 15 blood samples collected from three Egyptian goat breeds (Zaraibi, Barki, and Baladi). The sequencing resulted in a 2500 bp fragment (KU935713, KU976149, KX032517). A protein sequence of 217 amino acids was predicted to include six main domains. Comparisons of the GH sequences in different individuals revealed 15 SNPs distributed over the three breeds. Six SNPs were novel and three were non-synonymous. Using a combination of prediction tools, the nsSNPs were predicted to have damaging effects on the 3D structure of GH. The results of this work can be used in future studies to associate the SNPs discovered in the GH gene with economically important traits in goat, and thus can be used as markers to improve Egyptian goat breeds.