TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction

Genome-Wide Identification and Characterization of Lignin Synthesis Genes in Maize

Lignin is a crucial substance in the formation of the secondary cell wall in plants. It is widely distributed in various plant tissues and plays a significant role in various biological processes. However, the number of copies, characteristics, and expression patterns of genes involved in lignin biosynthesis in maize are not fully understood. In this study, bioinformatic analysis and gene expression analysis were used to discover the lignin synthetic genes, and two representative maize inbred lines were used for stem strength phenotypic analysis and gene identification. Finally, 10 gene families harboring 117 related genes involved in the lignin synthesis pathway were retrieved in the maize genome. These genes have a high number of copies and are typically clustered on chromosomes. By examining the lignin content of stems and the expression patterns of stem-specific genes in two representative maize inbred lines, we identified three potential stem lodging resistance genes and their interactions with transcription factors. This study provides a foundation for further research on the regulation of lignin biosynthesis and maize lodging resistance genes.

MYC2 regulates stomatal density and water use efficiency via targeting EPF2/EPFL4/EPFL9 in poplar

Although polyploid plants have lower stomatal density than their diploid counterparts, the molecular mechanisms underlying this difference remain elusive. Here, we constructed a network based on the triploid poplar transcriptome data and triple-gene mutual interaction algorithm and found that PpnMYC2 was related to stomatal development-related genes PpnEPF2, PpnEPFL4, and PpnEPFL9. The interactions between PpnMYC2 and PagJAZs were experimentally validated. PpnMYC2-overexpressing poplar and Arabidopsis thaliana had reduced stomatal density. Poplar overexpressing PpnMYC2 had higher water use efficiency and drought resistance. RNA-sequencing data of poplars overexpressing PpnMYC2 showed that PpnMYC2 promotes the expression of stomatal density inhibitors PagEPF2 and PagEPFL4 and inhibits the expression of the stomatal density-positive regulator PagEPFL9. Yeast one-hybrid system, electrophoretic mobility shift assay, ChIP-qPCR, and dual-luciferase assay were employed to substantiate that PpnMYC2 directly regulated PagEPF2, PagEPFL4, and PagEPFL9. PpnMYC2, PpnEPF2, and PpnEPFL4 were significantly upregulated, whereas PpnEPFL9 was downregulated during stomatal formation in triploid poplar. Our results are of great significance for revealing the regulation mechanism of plant stomatal occurrence and polyploid stomatal density, as well as reducing stomatal density and improving plant water use efficiency by overexpressing MYC2.

Regulation of regeneration in Arabidopsis thaliana

We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. Searching for TF-binding sites in the promoters of the co-expressed and co-regulated (CCGs) genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each subnetwork. Finally, six potential candidate TFs-WOX9A, LEC2, PGA37, WIP5, PEI1, and AIL1 from subnetwork 1-were identified, and their roles in somatic embryogenesis (GO:0010262) and regeneration (GO:0031099) were discussed, so were the TFs in Subnetwork 12 and 17 associated with regeneration. The TFs identified were also assessed using the CIS-BP database and Expression Atlas. Our analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to regeneration. The tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-023-00121-9.

TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization

Four statistical selection methods for inferring transcription factor (TF)-target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) or least absolute shrinkage and selection operator (Lasso) penalty. Two methods were also developed for inferring pathway gene regulatory networks (GRNs) by combining Huber or MSE loss function with a network (Net)-based penalty. To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter selection processes, resulting in an equally effective but much faster algorithm than the commonly used convex optimization solver. The synthetic data generated in a general setting was used to test four TF-TG identification methods, ENET-based methods performed better than Lasso-based methods. Synthetic data generated from two network settings was used to test Huber-Net and MSE-Net, which outperformed all other methods. The TF-TG identification methods were also tested with SND1 and gl3 overexpression transcriptomic data, Huber-ENET and MSE-ENET outperformed all other methods when genome-wide predictions were performed. The TF-TG identification methods fill the gap of lacking a method for genome-wide TG prediction of a TF, and potential for validating ChIP/DAP-seq results, while the two Net-based methods are instrumental for predicting pathway GRNs.

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

Machine learning bridges omics sciences and plant breeding

Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.

Construction of a Hierarchical Gene Regulatory Network to Reveal the Drought Tolerance Mechanism of Shanxin Poplar

Drought stress is a common adverse environment that plants encounter, and many drought-tolerant genes have been characterized. The gene regulatory network (GRN) is important in revealing the drought tolerance mechanism. Here, to investigate the regulatory mechanism of Shanxin poplar (Populus davidiana × P. bolleana) responding to drought stress, a three-layered GRN was built, and the regulatory relationship between genes in the GRN were predicted from expression correlation using a partial correlation coefficient-based algorithm. The GRN contains 1869 regulatory relationships, and includes 11 and 19 transcription factors (TFs) in the first and second layers, respectively, and 158 structural genes in the bottom layers involved in eight enriched biological processes. ChIP-PCR and qRT-PCR based on transient transformation were performed to validate the reliability of the GRN. About 88.0% of predicted interactions between the first and second layers, and 82.0% of predicted interactions between the second and third layers were correct, suggesting that the GRN is reliable. Six TFs were randomly selected from the top layer for characterizing their function in drought, and all of these TFs can confer drought tolerance. The important biological processes related to drought tolerance were identified, including "response to jasmonic acid", "response to oxidative stress", and "response to osmotic stress". In this GRN, PdbERF3 is predicted to play an important role in drought tolerance. Our data revealed the key regulators, TF-DNA interactions, and the main biological processes involved in adaption of drought stress in Shanxin poplar.

Transcriptional regulation of proanthocyanidin biosynthesis pathway genes and transcription factors in Indigofera stachyodes Lindl. roots

BACKGROUND

Proanthocyanidins (PAs) have always been considered as important medicinal value component. In order to gain insights into the PA biosynthesis regulatory network in I. stachyodes roots, we analyzed the transcriptome of the I. stachyodes in Leaf, Stem, RootI (one-year-old root), and RootII (two-year-old root).

RESULTS

In this study, a total of 110,779 non-redundant unigenes were obtained, of which 63,863 could be functionally annotated. Simultaneously, 75 structural genes that regulate PA biosynthesis were identified, of these 6 structural genes (IsF3'H1, IsANR2, IsLAR2, IsUGT72L1-3, IsMATE2, IsMATE3) may play an important role in the synthesis of PAs in I. stachyodes roots. Furthermore, co-expression network analysis revealed that 34 IsMYBs, 18 IsbHLHs, 15 IsWRKYs, 9 IsMADSs, and 3 IsWIPs hub TFs are potential regulators for PA accumulation. Among them, IsMYB24 and IsMYB79 may be closely involved in the PA biosynthesis in I. stachyodes roots.

CONCLUSIONS

The biosynthesis of PAs in I. stachyodes roots is mainly produced by the subsequent pathway of cyanidin. Our work provides new insights into the molecular pathways underlying PA accumulation and enhances our global understanding of transcriptome dynamics throughout different tissues.

Hierarchical transcription factor and regulatory network for drought response in Betula platyphylla

Although many genes and biological processes involved in abiotic stress response have been identified, how they are regulated remains largely unclear. Here, to study the regulatory mechanism of birch (Betula platyphylla) responding to drought induced by polyethylene glycol (PEG) 6000 (20%, w/v), a partial correlation coefficient-based algorithm for constructing gene regulatory network (GRN) was proposed, and a three-layer hierarchical GRN was constructed, including 68 transcription factors (TFs), and 252 structural genes. Totally, 1448 predicted regulatory relationships are included, and most of them are novel. The reliability of GRN was verified by ChIP-PCR and qRT-PCR based on transient transformation. About 55% of genes in the bottom layer of GRN could confer drought tolerance. We selected the two TFs, BpMADS11 and BpNAC090, from the up layer and characterized their function in drought tolerance. Overexpression of BpMADS11 and BpNAC090 both reduces electrolyte leakage, ROS and MDA contents, displaying increased drought tolerance than wild-type birch. According to this GRN, the important biological processes involved in drought were identified, including "signaling hormone pathways", "water transport", "regulation of stomatal movement" and "response to oxidative stress". This work indicated that BpERF017, BpAGL61 and BpNAC090 are the key upstream regulators in birch drought tolerance. Our data clearly revealed the upstream regulators and TF-DNA interaction regulate different biological processes to adapt drought stress.

TSMiner: a novel framework for generating time-specific gene regulatory networks from time-series expression profiles

Time-series gene expression profiles are the primary source of information on complicated biological processes; however, capturing dynamic regulatory events from such data is challenging. Herein, we present a novel analytic tool, time-series miner (TSMiner), that can construct time-specific regulatory networks from time-series expression profiles using two groups of genes: (i) genes encoding transcription factors (TFs) that are activated or repressed at a specific time and (ii) genes associated with biological pathways showing significant mutual interactions with these TFs. Compared with existing methods, TSMiner demonstrated superior sensitivity and accuracy. Additionally, the application of TSMiner to a time-course RNA-seq dataset associated with mouse liver regeneration (LR) identified 389 transcriptional activators and 49 transcriptional repressors that were either activated or repressed across the LR process. TSMiner also predicted 109 and 47 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly interacting with the transcriptional activators and repressors, respectively. These findings revealed the temporal dynamics of multiple critical LR-related biological processes, including cell proliferation, metabolism and the immune response. The series of evaluations and experiments demonstrated that TSMiner provides highly reliable predictions and increases the understanding of rapidly accumulating time-series omics data.

An Arabidopsis expression predictor enables inference of transcriptional regulators for gene modules

The regulation of gene expression by transcription factors (TFs) has been studied for a long time, but no model that can accurately predict transcriptome profiles based on TF activities currently exists. Here, we developed a computational approach, named EXPLICIT (Expression Prediction via Log-linear Combination of Transcription Factors), to construct a universal predictor for Arabidopsis to predict the expression of 29 182 non-TF genes using 1678 TFs. When applied to RNA-Seq samples from diverse tissues, EXPLICIT generated accurate predicted transcriptomes correlating well with actual expression, with an average correlation coefficient of 0.986. After recapitulating the quantitative relationships between TFs and their target genes, EXPLICIT enabled downstream inference of TF regulators for genes and gene modules functioning in diverse plant pathways, including those involved in suberin, flavonoid, glucosinolate metabolism, lateral root, xylem, secondary cell wall development or endoplasmic reticulum stress response. Our approach showed a better ability to recover the correct TF regulators when compared with existing plant tools, and provides an innovative way to study transcriptional regulation.

Identification of biological pathway and process regulators using sparse partial least squares and triple-gene mutual interaction

Identification of biological process- and pathway-specific regulators is essential for advancing our understanding of regulation and formation of various phenotypic and complex traits. In this study, we applied two methods, triple-gene mutual interaction (TGMI) and Sparse Partial Least Squares (SPLS), to identify the regulators of multiple metabolic pathways in Arabidopsis thaliana and Populus trichocarpa using high-throughput gene expression data. We analyzed four pathways: (1) lignin biosynthesis pathway in A. thaliana and P. trichocarpa; (2) flavanones, flavonol and anthocyannin biosynthesis in A. thaliana; (3) light reaction pathway and Calvin cycle in A. thaliana. (4) light reaction pathway alone in A. thaliana. The efficiencies of two methods were evaluated by examining the positive known regulators captured, the receiver operating characteristic (ROC) curves and the area under ROC curves (AUROC). Our results showed that TGMI is in general more efficient than SPLS in identifying true pathway regulators and ranks them to the top of candidate regulatory gene lists, but the two methods are to some degree complementary because they could identify some different pathway regulators. This study identified many regulators that potentially regulate the above pathways in plants and are valuable for genetic engineering of these pathways.

Transcriptional and Post-transcriptional Regulation of Lignin Biosynthesis Pathway Genes in Populus

Lignin is a heterogeneous polymer of aromatic subunits derived from phenylalanine. It is polymerized in intimate proximity to the polysaccharide components in plant cell walls and provides additional rigidity and compressive strength for plants. Understanding the regulatory mechanisms of lignin biosynthesis is important for genetic modification of the plant cell wall for agricultural and industrial applications. Over the past 10 years the transcriptional regulatory model of lignin biosynthesis has been established in plants. However, the role of post-transcriptional regulation is still largely unknown. Increasing evidence suggests that lignin biosynthesis pathway genes are also regulated by alternative splicing, microRNA, and long non-coding RNA. In this review, we briefly summarize recent progress on the transcriptional regulation, then we focus on reviewing progress on the post-transcriptional regulation of lignin biosynthesis pathway genes in the woody model plant Populus.

Computational prediction of gene regulatory networks in plant growth and development

Plants integrate a wide range of cellular, developmental, and environmental signals to regulate complex patterns of gene expression. Recent advances in genomic technologies enable differential gene expression analysis at a systems level, allowing for improved inference of the network of regulatory interactions between genes. These gene regulatory networks, or GRNs, are used to visualize the causal regulatory relationships between regulators and their downstream target genes. Accordingly, these GRNs can represent spatial, temporal, and/or environmental regulations and can identify functional genes. This review summarizes recent computational approaches applied to different types of gene expression data to infer GRNs in the context of plant growth and development. Three stages of GRN inference are described: first, data collection and analysis based on the dataset type; second, network inference application based on data availability and proposed hypotheses; and third, validation based on in silico, in vivo, and in planta methods. In addition, this review relates data collection strategies to biological questions, organizes inference algorithms based on statistical methods and data types, discusses experimental design considerations, and provides guidelines for GRN inference with an emphasis on the benefits of integrative approaches, especially when a priori information is limited. Finally, this review concludes that computational frameworks integrating large-scale heterogeneous datasets are needed for a more accurate (e.g. fewer false interactions), detailed (e.g. discrimination between direct versus indirect interactions), and comprehensive (e.g. genetic regulation under various conditions and spatial locations) inference of GRNs.