TDTHub, a web server tool for the analysis of transcription factor binding sites in plants

Advances in Protein Kinase Regulation of Stress Responses in Fruits and Vegetables

Fruits and vegetables (F&Vs) are essential in daily life and industrial production. These perishable produces are vulnerable to various biotic and abiotic stresses during their growth, postharvest storage, and handling. As the fruit detaches from the plant, these stresses become more intense. This unique biological process involves substantial changes in a variety of cellular metabolisms. To counter these stresses, plants have evolved complex physiological defense mechanisms, including regulating cellular activities through reversible phosphorylation of proteins. Protein kinases, key components of reversible protein phosphorylation, facilitate the transfer of the γ-phosphate group from adenosine triphosphate (ATP) to specific amino acid residues on substrates. This phosphorylation alters proteins' structure, function, and interactions, thereby playing a crucial role in regulating cellular activity. Recent studies have identified various protein kinases in F&Vs, underscoring their significant roles in plant growth, development, and stress responses. This article reviews the various types of protein kinases found in F&Vs, emphasizing their roles and regulatory mechanisms in managing stress responses. This research sheds light on the involvement of protein kinases in metabolic regulation, offering key insights to advance the quality characteristics of F&Vs.

The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes

BACKGROUND

The precise prediction of transcription factor binding sites (TFBSs) is pivotal for unraveling the gene regulatory networks underlying biological processes. While numerous tools have emerged for in silico TFBS prediction in recent years, the evolving landscape of computational biology necessitates thorough assessments of tool performance to ensure accuracy and reliability. Only a limited number of studies have been conducted to evaluate the performance of TFBS prediction tools comprehensively. Thus, the present study focused on assessing twelve widely used TFBS prediction tools and four de novo motif discovery tools using a benchmark dataset comprising real, generic, Markov, and negative sequences. TFBSs of Arabidopsis thaliana and Homo sapiens genomes downloaded from the JASPAR database were implanted in these sequences and the performance of tools was evaluated using several statistical parameters at different overlap percentages between the lengths of known and predicted binding sites.

RESULTS

Overall, the Multiple Cluster Alignment and Search Tool (MCAST) emerged as the best TFBS prediction tool, followed by Find Individual Motif Occurrences (FIMO) and MOtif Occurrence Detection Suite (MOODS). In addition, MotEvo and Dinucleotide Weight Tensor Toolbox (DWT-toolbox) demonstrated the highest sensitivity in identifying TFBSs at 90% and 80% overlap. Further, MCAST and DWT-toolbox managed to demonstrate the highest sensitivity across all three data types real, generic, and Markov. Among the de novo motif discovery tools, the Multiple Em for Motif Elicitation (MEME) emerged as the best performer. An analysis of the promoter regions of genes involved in the anthocyanin biosynthesis pathway in plants and the pentose phosphate pathway in humans, using the three best-performing tools, revealed considerable variation among the top 20 motifs identified by these tools.

CONCLUSION

The findings of this study lay a robust groundwork for selecting optimal TFBS prediction tools for future research. Given the variability observed in tool performance, employing multiple tools for identifying TFBSs in a set of sequences is highly recommended. In addition, further studies are recommended to develop an integrated toolbox that incorporates TFBS prediction or motif discovery tools, aiming to streamline result precision and accuracy.

GLABRA2 transcription factor integrates arsenic tolerance with epidermal cell fate determination

Arsenic poses a global threat to living organisms, compromising crop security and yield. Limited understanding of the transcriptional network integrating arsenic-tolerance mechanisms with plant developmental responses hinders the development of strategies against this toxic metalloid. Here, we conducted a high-throughput yeast one-hybrid assay using as baits the promoter region from the arsenic-inducible genes ARQ1 and ASK18 from Arabidopsis thaliana, coupled with a transcriptomic analysis, to uncover novel transcriptional regulators of the arsenic response. We identified the GLABRA2 (GL2) transcription factor as a novel regulator of arsenic tolerance, revealing a wider regulatory role beyond its established function as a repressor of root hair formation. Furthermore, we found that ANTHOCYANINLESS2 (ANL2), a GL2 subfamily member, acts redundantly with this transcription factor in the regulation of arsenic signaling. Both transcription factors act as repressors of arsenic response. gl2 and anl2 mutants exhibit enhanced tolerance and reduced arsenic accumulation. Transcriptional analysis in the gl2 mutant unveils potential regulators of arsenic tolerance. These findings highlight GL2 and ANL2 as novel integrators of the arsenic response with developmental outcomes, offering insights for developing safer crops with reduced arsenic content and increased tolerance to this hazardous metalloid.

Maize mutant screens: from classical methods to new CRISPR-based approaches

Mutations play a pivotal role in shaping the trajectory and outcomes of a species evolution and domestication. Maize (Zea mays) has been a major staple crop and model for genetic research for more than 100 yr. With the arrival of site-directed mutagenesis and genome editing (GE) driven by the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), maize mutational research is once again in the spotlight. If we combine the powerful physiological and genetic characteristics of maize with the already available and ever increasing toolbox of CRISPR-Cas, prospects for its future trait engineering are very promising. This review aimed to give an overview of the progression and learnings of maize screening studies analyzing forward genetics, natural variation and reverse genetics to focus on recent GE approaches. We will highlight how each strategy and resource has contributed to our understanding of maize natural and induced trait variability and how this information could be used to design the next generation of mutational screenings.

The landscape of transcription factor promoter activity during vegetative development in Marchantia

Transcription factors (TFs) are essential for the regulation of gene expression and cell fate determination. Characterizing the transcriptional activity of TF genes in space and time is a critical step toward understanding complex biological systems. The vegetative gametophyte meristems of bryophytes share some characteristics with the shoot apical meristems of flowering plants. However, the identity and expression profiles of TFs associated with gametophyte organization are largely unknown. With only ∼450 putative TF genes, Marchantia (Marchantia polymorpha) is an outstanding model system for plant systems biology. We have generated a near-complete collection of promoter elements derived from Marchantia TF genes. We experimentally tested reporter fusions for all the TF promoters in the collection and systematically analyzed expression patterns in Marchantia gemmae. This allowed us to build a map of expression domains in early vegetative development and identify a set of TF-derived promoters that are active in the stem-cell zone. The cell markers provide additional tools and insight into the dynamic regulation of the gametophytic meristem and its evolution. In addition, we provide an online database of expression patterns for all promoters in the collection. We expect that these promoter elements will be useful for cell-type-specific expression, synthetic biology applications, and functional genomics.

TSPTFBS 2.0: trans-species prediction of transcription factor binding sites and identification of their core motifs in plants

Introduction

An emerging approach using promoter tiling deletion via genome editing is beginning to become popular in plants. Identifying the precise positions of core motifs within plant gene promoter is of great demand but they are still largely unknown. We previously developed TSPTFBS of 265 Arabidopsis transcription factor binding sites (TFBSs) prediction models, which now cannot meet the above demand of identifying the core motif.

Methods

Here, we additionally introduced 104 maize and 20 rice TFBS datasets and utilized DenseNet for model construction on a large-scale dataset of a total of 389 plant TFs. More importantly, we combined three biological interpretability methods including DeepLIFT, in-silico tiling deletion, and in-silico mutagenesis to identify the potential core motifs of any given genomic region.

Results

For the results, DenseNet not only has achieved greater predictability than baseline methods such as LS-GKM and MEME for above 389 TFs from Arabidopsis, maize and rice, but also has greater performance on trans-species prediction of a total of 15 TFs from other six plant species. A motif analysis based on TF-MoDISco and global importance analysis (GIA) further provide the biological implication of the core motif identified by three interpretability methods. Finally, we developed a pipeline of TSPTFBS 2.0, which integrates 389 DenseNet-based models of TF binding and the above three interpretability methods.

Discussion

TSPTFBS 2.0 was implemented as a user-friendly web-server (http://www.hzau-hulab.com/TSPTFBS/), which can support important references for editing targets of any given plant promoters and it has great potentials to provide reliable editing target of genetic screen experiments in plants.

A translatome-transcriptome multi-omics gene regulatory network reveals the complicated functional landscape of maize

BACKGROUND

Maize (Zea mays L.) is one of the most important crops worldwide. Although sophisticated maize gene regulatory networks (GRNs) have been constructed for functional genomics and phenotypic dissection, a multi-omics GRN connecting the translatome and transcriptome is lacking, hampering our understanding and exploration of the maize regulatome.

RESULTS

We collect spatio-temporal translatome and transcriptome data and systematically explore the landscape of gene transcription and translation across 33 tissues or developmental stages of maize. Using this comprehensive transcriptome and translatome atlas, we construct a multi-omics GRN integrating mRNAs and translated mRNAs, demonstrating that translatome-related GRNs outperform GRNs solely using transcriptomic data and inter-omics GRNs outperform intra-omics GRNs in most cases. With the aid of the multi-omics GRN, we reconcile some known regulatory networks. We identify a novel transcription factor, ZmGRF6, which is associated with growth. Furthermore, we characterize a function related to drought response for the classic transcription factor ZmMYB31.

CONCLUSIONS

Our findings provide insights into spatio-temporal changes across maize development at both the transcriptome and translatome levels. Multi-omics GRNs represent a useful resource for dissection of the regulatory mechanisms underlying phenotypic variation.

Genome-wide identification of BBX gene family and their expression patterns under salt stress in soybean

BACKGROUND

BBX genes are key players in the regulation of various developmental processes and stress responses, which have been identified and functionally characterized in many plant species. However, our understanding of BBX family was greatly limited in soybean.

RESULTS

In this study, 59 BBX genes were identified and characterized in soybean, which can be phylogenetically classified into 5 groups. GmBBXs showed diverse gene structures and motif compositions among the groups and similar within each group. Noticeably, synteny analysis suggested that segmental duplication contributed to the expansion of GmBBX family. Moreover, our RNA-Seq data indicated that 59 GmBBXs showed different transcript profiling under salt stress, and qRT-PCR analysis confirmed their expression patterns. Among them, 22 GmBBXs were transcriptionally altered with more than two-fold changes by salt stress, supporting that GmBBXs play important roles in soybean tolerance to salt stress. Additionally, Computational assay suggested that GmBBXs might potentially interact with GmGI3, GmTOE1b, GmCOP1, GmCHI and GmCRY, while eight types of transcription factors showed potentials to bind the promoter regions of GmBBX genes.

CONCLUSIONS

Fifty-nine BBX genes were identified and characterized in soybean, and their expression patterns under salt stress and computational assays suggested their functional roles in response to salt stress. These findings will contribute to future research in regard to functions and regulatory mechanisms of soybean BBX genes in response to salt stress.