agReg-SNPdb: A Database of Regulatory SNPs for Agricultural Animal Species

Omics-based construction of regulatory variants can be applied to help decipher pig liver-related traits

Genetic variants can influence complex traits by altering gene expression through changes to regulatory elements. However, the genetic variants that affect the activity of regulatory elements in pigs are largely unknown, and the extent to which these variants influence gene expression and contribute to the understanding of complex phenotypes remains unclear. Here, we annotate 90,991 high-quality regulatory elements using acetylation of histone H3 on lysine 27 (H3K27ac) ChIP-seq of 292 pig livers. Combined with genome resequencing and RNA-seq data, we identify 28,425 H3K27ac quantitative trait loci (acQTLs) and 12,250 expression quantitative trait loci (eQTLs). Through the allelic imbalance analysis, we validate two causative acQTL variants in independent datasets. We observe substantial sharing of genetic controls between gene expression and H3K27ac, particularly within promoters. We infer that 46% of H3K27ac exhibit a concomitant rather than causative relationship with gene expression. By integrating GWAS, eQTLs, acQTLs, and transcription factor binding prediction, we further demonstrate their application, through metabolites dulcitol, phosphatidylcholine (PC) (16:0/16:0) and published phenotypes, in identifying likely causal variants and genes, and discovering sub-threshold GWAS loci. We provide insight into the relationship between regulatory elements and gene expression, and the genetic foundation for dissecting the molecular mechanism of phenotypes.

Transcriptional regulatory mechanism of NR2F2 and ZNF423 in avian preadipocyte differentiation

In the poultry industry, excessive abdominal fat deposition is not conducive to meat quality. Therefore, selection for optimal fat content levels in poultry has become a major breeding goal. We previously constructed NR2F2 overexpression (NR2F2OE) and knockout (NR2F2Δ/Δ/83-125aa) cell lines using Piggybac and CRISPR/Cas9 techniques, and confirmed that the transcription factor NR2F2 can significantly inhibit the differentiation of avian preadipocytes. In this study, we identified a downstream gene ZNF423 regulated by NR2F2, which is also involved in regulating avian fat deposition. First, we performed transcriptome analysis of the NR2F2-edited lines, which has been proven to be an inhibitor of avian fat deposition in our previous studies. Our findings revealed that NR2F2 affects a series of candidate regulators related to adipogenesis. Among these, we focused on ZNF423, which was significantly down-regulated in the NR2F2OE cell line and up-regulated in the NR2F2Δ/Δ/83-125aa cell line. Next, dual luciferase reporter assay results showed that the DNA-binding domain (DBDΔ72-143aa) of transcription factor NR2F2 may negatively affect the expression of downstream target gene ZNF423 by binding to its distal promoter region (-2356 to -2346). Moreover, we constructed a function analytical model and found that overexpression of ZNF423 significantly facilitated the differentiation of adipocytes in immortalized chicken preadipocytes (ICP1). Consistent with these findings, global transcriptome analysis of the ZNF423-overexpressed cell line (ZNF423OE) further demonstrated that the process of adipogenesis was significantly enriched. These results indicate that ZNF423 is a positive regulator of avian adipocyte differentiation. Overexpression of ZNF423 in the NR2F2OE cell line compensated for the inhibition of fat deposition phenotype, further suggesting that ZNF423 is a downstream target gene of NR2F2. These findings uncover a novel function of ZNF423 in avian adipocyte differentiation and analyzed the transcriptional regulation by its upstream transcription factor NR2F2. Additionally, we identified a list of functional candidate genes, providing important insights for further research on the mechanism of avian fat deposition.

A novel candidate gene CLN8 regulates fat deposition in avian

BACKGROUND

The fat deposition has a crucial role in animal meat flavor, and fat deposition-related traits are vital for breeding in the commercial duck industry. Avian fat-related traits are typical complex phenotypes, which need a large amount of data to analyze the genetic loci.

RESULTS

In this study, we performed a new phenotypic analysis of fat traits and genotyped whole-genome variations for 1,246 ducks, and combed with previous GWAS data to reach 1,880 ducks for following analysis. The carcass composition traits, subcutaneous fat weight (SFW), subcutaneous fat percentage (SFP), abdominal fat weight (AFW), abdominal fat percentage (AFP) and the body weight of day 42 (BW42) for each duck were collected. We identified a set of new loci that affect the traits related to fat deposition in avian. Among these loci, ceroid-lipofuscinosis, neuronal 8 (CLN8) is a novel candidate gene controlling fat deposition. We investigated its novel function and regulation in avian adipogenesis. Five significant SNPs (the most significant SNP, P-value = 21.37E-12) and a single haplotype were detected in the upstream of CLN8 for subcutaneous fat percentage. Subsequently, luciferase assay demonstrated that 5 linked SNPs in the upstream of the CLN8 gene significantly decreased the transcriptional activity of CLN8. Further, ATAC-seq analysis showed that transcription factor binding sites were identified in a region close to the haplotype. A set of luciferase reporter gene vectors that contained different deletion fragments of the CLN8 promoter were constructed, and the core promoter area of CLN8 was finally identified in the -1,884/-1,207 bp region of the 5' flanking sequences, which contains adipogenesis-related transcription factors binding sites. Moreover, the over-expression of CLN8 can remarkably facilitate adipocyte differentiation in ICPs. Consistent with these, the global transcriptome profiling and functional analysis of the over-expressed CLN8 in the cell line further revealed that the lipid biosynthetic process during the adipogenesis was significantly enriched.

CONCLUSIONS

Our results demonstrated that CLN8 is a positive regulator of avian adipocyte differentiation. These findings identify a novel function of CLN8 in adipocyte differentiation, which provides important clues for the further study of the mechanism of avian fat deposition.

Deciphering the Molecular Mechanism Underlying African Animal Trypanosomiasis by Means of the 1000 Bull Genomes Project Genomic Dataset

Simple Summary

Climate change is increasing the risk of spreading vector-borne diseases such as African Animal Trypanosomiasis (AAT), which is causing major economic losses, especially in sub-Saharan African countries. Mainly considering this disease, we have investigated transcriptomic and genomic data from two cattle breeds, namely Boran and N‘Dama, where the former is known for its susceptibility and the latter one for its tolerance to the AAT. Despite the rich literature on this disease, there is still a need to investigate underlying genetic mechanisms to decipher the complex interplay of regulatory SNPs (rSNPs), their corresponding gene expression profiles and the downstream effectors associated with the AAT disease. The findings of this study complement our previous results, which mainly involve the upstream events, including transcription factors (TFs) and their co-operations as well as master regulators. Moreover, our investigation of significant rSNPs and effectors found in the liver, spleen and lymph node tissues of both cattle breeds could enhance the understanding of distinct mechanisms leading to either resistance or susceptibility of cattle breeds.

Abstract

African Animal Trypanosomiasis (AAT) is a neglected tropical disease and spreads by the vector tsetse fly, which carries the infectious Trypanosoma sp. in their saliva. Particularly, this parasitic disease affects the health of livestock, thereby imposing economic constraints on farmers, costing billions of dollars every year, especially in sub-Saharan African countries. Mainly considering the AAT disease as a multistage progression process, we previously performed upstream analysis to identify transcription factors (TFs), their co-operations, over-represented pathways and master regulators. However, downstream analysis, including effectors, corresponding gene expression profiles and their association with the regulatory SNPs (rSNPs), has not yet been established. Therefore, in this study, we aim to investigate the complex interplay of rSNPs, corresponding gene expression and downstream effectors with regard to the AAT disease progression based on two cattle breeds: trypanosusceptible Boran and trypanotolerant N’Dama. Our findings provide mechanistic insights into the effectors involved in the regulation of several signal transduction pathways, thereby differentiating the molecular mechanism with regard to the immune responses of the cattle breeds. The effectors and their associated genes (especially MAPKAPK5, CSK, DOK2, RAC1 and DNMT1) could be promising drug candidates as they orchestrate various downstream regulatory cascades in both cattle breeds.

Deciphering Pleiotropic Signatures of Regulatory SNPs in Zea mays L. Using Multi-Omics Data and Machine Learning Algorithms

Maize is one of the most widely grown cereals in the world. However, to address the challenges in maize breeding arising from climatic anomalies, there is a need for developing novel strategies to harness the power of multi-omics technologies. In this regard, pleiotropy is an important genetic phenomenon that can be utilized to simultaneously enhance multiple agronomic phenotypes in maize. In addition to pleiotropy, another aspect is the consideration of the regulatory SNPs (rSNPs) that are likely to have causal effects in phenotypic development. By incorporating both aspects in our study, we performed a systematic analysis based on multi-omics data to reveal the novel pleiotropic signatures of rSNPs in a global maize population. For this purpose, we first applied Random Forests and then Markov clustering algorithms to decipher the pleiotropic signatures of rSNPs, based on which hierarchical network models are constructed to elucidate the complex interplay among transcription factors, rSNPs, and phenotypes. The results obtained in our study could help to understand the genetic programs orchestrating multiple phenotypes and thus could provide novel breeding targets for the simultaneous improvement of several agronomic traits.

agReg-SNPdb-Plants: A Database of Regulatory SNPs for Agricultural Plant Species

Simple Summary

In breeding research, the investigation of regulatory SNPs (rSNPs) is becoming increasingly important due to their potential causal role for specific functional traits. Especially for crop species, there is still a lack of systematic analyses to detect rSNPs and their predicted effects on the binding of transcription factors. In this study, we present agReg-SNPdb-Plants, a database storing genome-wide collections of regulatory SNPs for agricultural plant species which can be queried via a web interface.

Abstract

Single nucleotide polymorphisms (SNPs) that are located in the promoter regions of genes and affect the binding of transcription factors (TFs) are called regulatory SNPs (rSNPs). Their identification can be highly valuable for the interpretation of genome-wide association studies (GWAS), since rSNPs can reveal the biologically causative variant and decipher the regulatory mechanisms behind a phenotype. In our previous work, we presented agReg-SNPdb, a database of regulatory SNPs for agriculturally important animal species. To complement this previous work, in this study we present the extension agReg-SNPdb-Plants storing rSNPs and their predicted effects on TF-binding for 13 agriculturally important plant species and subspecies (Brassica napus, Helianthus annuus, Hordeum vulgare, Oryza glaberrima, Oryza glumipatula, Oryza sativa Indica, Oryza sativa Japonica, Solanum lycopersicum, Sorghum bicolor, Triticum aestivum, Triticum turgidum, Vitis vinifera, and Zea mays). agReg-SNPdb-Plants can be queried via a web interface that allows users to search for SNP IDs, chromosomal regions, or genes. For a comprehensive interpretation of GWAS results or larger SNP-sets, it is possible to download the whole list of SNPs and their impact on transcription factor binding sites (TFBSs) from the website chromosome-wise.