In Silico Identification of the Complex Interplay between Regulatory SNPs, Transcription Factors, and Their Related Genes in Brassica napus L. Using Multi-Omics Data

Genome-wide chromatin accessibility analysis unveils open chromatin convergent evolution during polyploidization in cotton

Allopolyploidization, resulting in divergent genomes in the same cell, is believed to trigger a “genome shock”, leading to broad genetic and epigenetic changes. However, little is understood about chromatin and gene-expression dynamics as underlying driving forces during allopolyploidization. Here, we examined the genome-wide DNase I-hypersensitive site (DHS) and its variations in domesticated allotetraploid cotton ( Gossypium hirsutum and Gossypium barbadense , AADD) and its extant AA ( Gossypium arboreum ) and DD ( Gossypium raimondii ) progenitors. We observed distinct DHS distributions between G. arboreum and G. raimondii . In contrast, the DHSs of the two subgenomes of G. hirsutum and G. barbadense showed a convergent distribution. This convergent distribution of DHS was also present in the wild allotetraploids Gossypium darwinii and G. hirsutum var. yucatanense , but absent from a resynthesized hybrid of G. arboreum and G. raimondii , suggesting that it may be a common feature in polyploids, and not a consequence of domestication after polyploidization. We revealed that putative cis -regulatory elements (CREs) derived from polyploidization-related DHSs were dominated by several families, including Dof, ERF48, and BPC1. Strikingly, 56.6% of polyploidization-related DHSs were derived from transposable elements (TEs). Moreover, we observed positive correlations between DHS accessibility and the histone marks H3K4me3, H3K27me3, H3K36me3, H3K27ac, and H3K9ac, indicating that coordinated interplay among histone modifications, TEs, and CREs drives the DHS landscape dynamics under polyploidization. Collectively, these findings advance our understanding of the regulatory architecture in plants and underscore the complexity of regulome evolution during polyploidization.

Integrative System Biology Analysis of Transcriptomic Responses to Drought Stress in Soybean (Glycine max L.)

Drought is a major abiotic stressor that causes yield losses and limits the growing area for most crops. Soybeans are an important legume crop that is sensitive to water-deficit conditions and suffers heavy yield losses from drought stress. To improve drought-tolerant soybean cultivars through breeding, it is necessary to understand the mechanisms of drought tolerance in soybeans. In this study, we applied several transcriptome datasets obtained from soybean plants under drought stress in comparison to those grown under normal conditions to identify novel drought-responsive genes and their underlying molecular mechanisms. We found 2168 significant up/downregulated differentially expressed genes (DEGs) and 8 core modules using gene co-expression analysis to predict their biological roles in drought tolerance. Gene Ontology and KEGG analyses revealed key biological processes and metabolic pathways involved in drought tolerance, such as photosynthesis, glyceraldehyde-3-phosphate dehydrogenase and cytokinin dehydrogenase activity, and regulation of systemic acquired resistance. Genome-wide analysis of plants’ cis-acting regulatory elements (CREs) and transcription factors (TFs) was performed for all of the identified DEG promoters in soybeans. Furthermore, the PPI network analysis revealed significant hub genes and the main transcription factors regulating the expression of drought-responsive genes in each module. Among the four modules associated with responses to drought stress, the results indicated that GLYMA_04G209700, GLYMA_02G204700, GLYMA_06G030500, GLYMA_01G215400, and GLYMA_09G225400 have high degrees of interconnection and, thus, could be considered as potential candidates for improving drought tolerance in soybeans. Taken together, these findings could lead to a better understanding of the mechanisms underlying drought responses in soybeans, which may useful for engineering drought tolerance in plants.

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.

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

Transcription factors (TFs) govern transcriptional gene regulation by specifically binding to short DNA motifs, known as transcription factor binding sites (TFBSs), in regulatory regions, such as promoters. Today, it is well known that single nucleotide polymorphisms (SNPs) in TFBSs can dramatically affect the level of gene expression, since they can cause a change in the binding affinity of TFs. Such SNPs, referred to as regulatory SNPs (rSNPs), have gained attention in the life sciences due to their causality for specific traits or diseases. In this study, we present agReg-SNPdb, a database comprising rSNP data of seven agricultural and domestic animal species: cattle, pig, chicken, sheep, horse, goat, and dog. To identify the rSNPs, we constructed a bioinformatics pipeline and identified a total of 10,623,512 rSNPs, which are located within TFBSs and affect the binding affinity of putative TFs. Altogether, we implemented the first systematic analysis of SNPs in promoter regions and their impact on the binding affinity of TFs for livestock and made it usable via a web interface.

In Silico Prediction of Transcription Factor Collaborations Underlying Phenotypic Sexual Dimorphism in Zebrafish (Danio rerio)

The transcriptional regulation of gene expression in higher organisms is essential for different cellular and biological processes. These processes are controlled by transcription factors and their combinatorial interplay, which are crucial for complex genetic programs and transcriptional machinery. The regulation of sex-biased gene expression plays a major role in phenotypic sexual dimorphism in many species, causing dimorphic gene expression patterns between two different sexes. The role of transcription factor (TF) in gene regulatory mechanisms so far has not been studied for sex determination and sex-associated colour patterning in zebrafish with respect to phenotypic sexual dimorphism. To address this open biological issue, we applied bioinformatics approaches for identifying the predicted TF pairs based on their binding sites for sex and colour genes in zebrafish. In this study, we identified 25 (e.g., STAT6-GATA4; JUN-GATA4; SOX9-JUN) and 14 (e.g., IRF-STAT6; SOX9-JUN; STAT6-GATA4) potentially cooperating TFs based on their binding patterns in promoter regions for sex determination and colour pattern genes in zebrafish, respectively. The comparison between identified TFs for sex and colour genes revealed several predicted TF pairs (e.g., STAT6-GATA4; JUN-SOX9) are common for both phenotypes, which may play a pivotal role in phenotypic sexual dimorphism in zebrafish.

Unravelling the Complex Interplay of Transcription Factors Orchestrating Seed Oil Content in Brassica napus L

Transcription factors (TFs) and their complex interplay are essential for directing specific genetic programs, such as responses to environmental stresses, tissue development, or cell differentiation by regulating gene expression. Knowledge regarding TF-TF cooperations could be promising in gaining insight into the developmental switches between the cultivars of Brassica napus L., namely Zhongshuang11 (ZS11), a double-low accession with high-oil- content, and Zhongyou821 (ZY821), a double-high accession with low-oil-content. In this regard, we analysed a time series RNA-seq data set of seed tissue from both of the cultivars by mainly focusing on the monotonically expressed genes (MEGs). The consideration of the MEGs enables the capturing of multi-stage progression processes that are orchestrated by the cooperative TFs and, thus, facilitates the understanding of the molecular mechanisms determining seed oil content. Our findings show that TF families, such as NAC, MYB, DOF, GATA, and HD-ZIP are highly involved in the seed developmental process. Particularly, their preferential partner choices as well as changes in their gene expression profiles seem to be strongly associated with the differentiation of the oil content between the two cultivars. These findings are essential in enhancing our understanding of the genetic programs in both cultivars and developing novel hypotheses for further experimental studies.