scholarly journals An integrative systems biology approach to identify the molecular basis of sperm quality in swine

2020 ◽  
Author(s):  
Marta Godia ◽  
Antonio Reverter ◽  
Rayner Gonzalez-Prendes ◽  
Yuliaxis Ramayo-Caldas ◽  
Anna Castello ◽  
...  
Keyword(s):  
Systems Biology ◽  
Candidate Genes ◽  
Molecular Basis ◽  
Sperm Quality ◽  
Interaction Network ◽  
Gene Interaction ◽  
Phenotypic Variance ◽  
Rna Seq ◽  
Gene Interaction Network ◽  
Sperm Traits

Abstract Background:Genetic pressure in animal breeding is sparking the interest to select for elite boars with higher sperm quality to maximize ejaculate doses and fertility rates. However, the molecular basis of sperm quality remains largely unexplored. In this study, we sought to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analyzing 25 sperm-related phenotypes using a systems biology approach that integrates GWAS and RNA-seq.Results:By GWAS, we identified 12 QTL regions associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2, KATNAL2, SLC14A2 or ABCA1. By RNA-seq, we detected 6,128 significant correlations between sperm traits and gene RNA abundances. We built a gene interaction network with the GWAS and the RNA-seq data. To build a robust gene interaction network, only the pair-wise interactions present in both the genetic co-association and the RNA co-abundance network were kept. Moreover, we also included to the Final Network both the genes which RNA abundances correlated with more than 4 semen traits as well as the miRNAs interacting with the genes on the network. The Final Network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. We finally designed a panel of 73 SNPs provided from the GWAS, eGWAS and the Final Network, that explains between 5 to 36% of the phenotypic variance of the sperm traits.Conclusions:By means of a systems biology approach, we identified potential key genes affecting sperm quality. Furthermore, we propose a SNP panel that might explain a substantial part of the genetic variance for semen quality in swine and may thus be of interest for the pig breeding sector.

scholarly journals A systems biology framework integrating GWAS and RNA-seq to shed light on the molecular basis of sperm quality in swine

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Marta Gòdia ◽  
Antonio Reverter ◽  
Rayner González-Prendes ◽  
Yuliaxis Ramayo-Caldas ◽  
Anna Castelló ◽  
...  
Keyword(s):  
Systems Biology ◽  
Candidate Genes ◽  
Molecular Basis ◽  
Sperm Quality ◽  
Interaction Network ◽  
Gene Interaction ◽  
Phenotypic Variance ◽  
Rna Seq ◽  
Gene Interaction Network ◽  
Sperm Traits

Abstract Background Genetic pressure in animal breeding is sparking the interest of breeders for selecting elite boars with higher sperm quality to optimize ejaculate doses and fertility rates. However, the molecular basis of sperm quality is not yet fully understood. Our aim was to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analysing 25 sperm-related phenotypes and integrating genome-wide association studies (GWAS) and RNA-seq under a systems biology framework. Results By GWAS, we identified 12 quantitative trait loci (QTL) associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2, KATNAL2, SLC14A2 and ABCA1. By RNA-seq, we identified a wide repertoire of mRNAs (e.g. PRM1, OAZ3, DNAJB8, TPPP2 and TNP1) and miRNAs (e.g. ssc-miR-30d, ssc-miR-34c, ssc-miR-30c-5p, ssc-miR-191, members of the let-7 family and ssc-miR-425-5p) with functions related to sperm biology. We detected 6128 significant correlations (P-value ≤ 0.05) between sperm traits and mRNA abundances. By expression (e)GWAS, we identified three trans-expression QTL involving the genes IQCJ, ACTR2 and HARS. Using the GWAS and RNA-seq data, we built a gene interaction network. We considered that the genes and interactions that were present in both the GWAS and RNA-seq networks had a higher probability of being actually involved in sperm quality and used them to build a robust gene interaction network. In addition, in the final network we included genes with RNA abundances correlated with more than four semen traits and miRNAs interacting with the genes on the network. The final network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. Finally, we designed a panel of 73 SNPs based on the GWAS, eGWAS and final network data, that explains between 5% (for sperm cell concentration) and 36% (for percentage of neck abnormalities) of the phenotypic variance of the sperm traits. Conclusions By applying a systems biology approach, we identified genes that potentially affect sperm quality and constructed a SNP panel that explains a substantial part of the phenotypic variance for semen quality in our study and that should be tested in other swine populations to evaluate its relevance for the pig breeding sector.

scholarly journals Gene network analyses unveil possible molecular basis underlying drug-induced glaucoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ruo-Fan Ding ◽  
Qian Yu ◽  
Ke Liu ◽  
Juan Du ◽  
Hua-Jun Yin ◽  
...  
Keyword(s):  
Early Onset ◽  
Molecular Basis ◽  
Gene Network ◽  
Adverse Drug Events ◽  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network ◽  
Drug Induced ◽  
Network Analyses ◽  
Potential Biomarker

Abstract Background Drug-induced glaucoma (DIG) is a kind of serious adverse drug reaction that can cause irreversible blindness. Up-to-date, the molecular mechanism of DIG largely remains unclear yet due to the medical complexity of glaucoma onset. Methods In this study, we conducted data mining of tremendous historical adverse drug events and genome-wide drug-regulated gene signatures to identify glaucoma-associated drugs. Upon these drugs, we carried out serial network analyses, including the weighted gene co-expression network analysis (WGCNA), to illustrate the gene interaction network underlying DIG. Furthermore, we applied pathogenic risk assessment to discover potential biomarker genes for DIG. Results As the results, we discovered 13 highly glaucoma-associated drugs, a glaucoma-related gene network, and 55 glaucoma-susceptible genes. These genes likely played central roles in triggering DIGs via an integrative mechanism of phototransduction dysfunction, intracellular calcium homeostasis disruption, and retinal ganglion cell death. Further pathogenic risk analysis manifested that a panel of nine genes, particularly OTOF gene, could serve as potential biomarkers for early-onset DIG prognosis. Conclusions This study elucidates the possible molecular basis underlying DIGs systematically for the first time. It also provides prognosis clues for early-onset glaucoma and thus assists in designing better therapeutic regimens.

scholarly journals Rheumatoid arthritis-associated gene-gene interaction network for rheumatoid arthritis candidate genes

2009 ◽  
Vol 3 (Suppl 7) ◽  
pp. S75 ◽  
Author(s):  
Chien-Hsun Huang ◽  
Lei Cong ◽  
Jun Xie ◽  
Bo Qiao ◽  
Shaw-Hwa Lo ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Candidate Genes ◽  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network

scholarly journals Transcriptomic Analysis Reveals Candidate Genes Responsive to Sclerotinia scleroterum and Cloning of the Ss-Inducible Chitinase Genes in Morus laevigata

2020 ◽  
Vol 21 (21) ◽  
pp. 8358
Author(s):  
Huanhuan Jiang ◽  
Xiaoyun Jin ◽  
Xiaofeng Shi ◽  
Yufei Xue ◽  
Jiayi Jiang ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Molecular Basis ◽  
Expression Patterns ◽  
Interaction Network ◽  
Sclerotinia Stem Rot ◽  
Response Characteristics ◽  
Kegg Pathways ◽  
Cdna Sequences ◽  
Chitinase Genes ◽  
Morus Laevigata

Sclerotinia sclerotiorum (Ss) is a devastating fungal pathogen that causes Sclerotinia stem rot in rapeseed (Brassica napus), and is also detrimental to mulberry and many other crops. A wild mulberry germplasm, Morus laevigata, showed high resistance to Ss, but the molecular basis for the resistance is largely unknown. Here, the transcriptome response characteristics of M. laevigata to Ss infection were revealed by RNA-seq. A total of 833 differentially expressed genes (DEGs) were detected after the Ss inoculation in the leaf of M. laevigata. After the GO terms and KEGG pathways enrichment analyses, 42 resistance-related genes were selected as core candidates from the upregulated DEGs. Their expression patterns were detected in the roots, stems, leaves, flowers, and fruits of M. laevigata. Most of them (30/42) were specifically or mainly expressed in flowers, which was consistent with the fact that Ss mainly infects plants through floral organs, and indicated that Ss-resistance genes could be induced by pathogen inoculation on ectopic organs. After the Ss inoculation, these candidate genes were also induced in the two susceptible varieties of mulberry, but the responses of most of them were much slower with lower extents. Based on the expression patterns and functional annotation of the 42 candidate genes, we cloned the full-length gDNA and cDNA sequences of the Ss-inducible chitinase gene set (MlChi family). Phylogenetic tree construction, protein interaction network prediction, and gene expression analysis revealed their special roles in response to Ss infection. In prokaryotic expression, their protein products were all in the form of an inclusion body. Our results will help in the understanding of the molecular basis of Ss-resistance in M. laevigata, and the isolated MlChi genes are candidates for the improvement in plant Ss-resistance via biotechnology.

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

Detection of a Major QTL and Development of KASP Markers for Seed Weight by Combining QTL-seq, QTL-mapping and RNA-seq in Peanut

2021 ◽  
Author(s):  
Zhihui Wang ◽  
Liying Yan ◽  
Yuning Chen ◽  
Xin Wang ◽  
Dongxin Huai ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Candidate Genes ◽  
Seed Development ◽  
Seed Weight ◽  
Molecular Mechanisms ◽  
Large Contribution ◽  
Phenotypic Variance ◽  
Rna Seq ◽  
Late Stages ◽  
Kasp Markers

Abstract Seed weight is a major target of peanut breeding as an important component of seed yield. However, relatively little is known about QTLs and candidate genes associated with seed weight in peanut. In this study, three major QTLs on chromosomes A05, B02 and B06 were determined by applying NGS-based QTL-seq approach for a RIL population. These three QTL regions have been successfully narrowed down through newly developed SNP and SSR markers based on traditional QTL mapping. Among these three QTL regions, qSWB06.3 exhibited stable expression with large contribution to phenotypic variance across all environments. Furthermore, RNA-seq were applied for early, middle and late stages of seed development, and differentially expression genes (DEGs) were identified in ubiquitin-proteasome pathway, serine/threonine protein pathway and signal transduction of hormones and transcription factors. Notably, DEGs at early stage were majorly related to regulating cell division, whereas DEGs at middle and late stages were mainly associated with cell expansion during seed development. Through integrating SNP variation, gene expression and functional annotation, candidate genes related to seed weight in qSWB06.3 were predicted and distinct expression pattern of those genes were exhibited using qRT-PCR. In addition, KASP-markers in qSWB06.3 were successfully validated in diverse peanut varieties and the alleles of parent Zhonghua16 in qSWB06.3 was associated with high seed weight. This suggested that qSWB06.3 was reliable and the markers in qSWB06.3 could be deployed in marker-assisted breeding to enhance seed weight. This study provided insights into the understanding of genetic and molecular mechanisms of seed weight in peanut.

Sign in / Sign up

Export Citation Format

Share Document