A deep learning approach to automate whole-genome prediction of diverse epigenomic modifications in plants

PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants

N 6-methyladenosine (m6A), which is the mostly prevalent modification in eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism processes. Accurate prediction of m6A modification is important for understanding its molecular mechanisms in different biological contexts. However, most existing models have limited range of application and are species-centric. Here we present PEA-m6A, a unified, modularized and parameterized framework that can streamline m6A-Seq data analysis for predicting m6A-modified regions in plant genomes. The PEA-m6A framework builds ensemble learning-based m6A prediction models with statistic-based and deep learning-driven features, achieving superior performance with an improvement of 6.7% to 23.3% in the area under precision-recall curve compared with state-of-the-art regional-scale m6A predictor WeakRM in 12 plant species. Especially, PEA-m6A is capable of leveraging knowledge from pretrained models via transfer learning, representing an innovation in that it can improve prediction accuracy of m6A modifications under small-sample training tasks. PEA-m6A also has a strong capability for generalization, making it suitable for application in within- and cross-species m6A prediction. Overall, this study presents a promising m6A prediction tool, PEA-m6A, with outstanding performance in terms of its accuracy, flexibility, transferability, and generalization ability. PEA-m6A has been packaged using Galaxy and Docker technologies for ease of use and is publicly available at https://github.com/cma2015/PEA-m6A.

Identification of Novel Regulators of Leaf Senescence Using a Deep Learning Model

Deep learning has emerged as a powerful tool for investigating intricate biological processes in plants by harnessing the potential of large-scale data. Gene regulation is a complex process that transcription factors (TFs), cooperating with their target genes, participate in through various aspects of biological processes. Despite its significance, the study of gene regulation has primarily focused on a limited number of notable instances, leaving numerous aspects and interactions yet to be explored comprehensively. Here, we developed DEGRN (Deep learning on Expression for Gene Regulatory Network), an innovative deep learning model designed to decipher gene interactions by leveraging high-dimensional expression data obtained from bulk RNA-Seq and scRNA-Seq data in the model plant Arabidopsis. DEGRN exhibited a compared level of predictive power when applied to various datasets. Through the utilization of DEGRN, we successfully identified an extensive set of 3,053,363 high-quality interactions, encompassing 1430 TFs and 13,739 non-TF genes. Notably, DEGRN's predictive capabilities allowed us to uncover novel regulators involved in a range of complex biological processes, including development, metabolism, and stress responses. Using leaf senescence as an example, we revealed a complex network underpinning this process composed of diverse TF families, including bHLH, ERF, and MYB. We also identified a novel TF, named MAF5, whose expression showed a strong linear regression relation during the progression of senescence. The mutant maf5 showed early leaf decay compared to the wild type, indicating a potential role in the regulation of leaf senescence. This hypothesis was further supported by the expression patterns observed across four stages of leaf development, as well as transcriptomics analysis. Overall, the comprehensive coverage provided by DEGRN expands our understanding of gene regulatory networks and paves the way for further investigations into their functional implications.

Epigenetic variation in maize agronomical traits for breeding and trait improvement

Epigenetics-mediated breeding (Epibreeding) involves engineering crop traits and stress responses through the targeted manipulation of key epigenetic features to enhance agricultural productivity. While conventional breeding methods raise concerns about reduced genetic diversity, epibreeding propels crop improvement through epigenetic variations that regulate gene expression, ultimately impacting crop yield. Epigenetic regulation in crops encompasses various modes, including histone modification, DNA modification, RNA modification, non-coding RNA, and chromatin remodeling. This review summarizes the epigenetic mechanisms underlying major agronomic traits in maize and identifies candidate epigenetic landmarks in the maize breeding process. We propose a valuable strategy for improving maize yield through epibreeding, combining CRISPR/Cas-based epigenome editing technology and Synthetic Epigenetics (SynEpi). Finally, we discuss the challenges and opportunities associated with maize trait improvement through epibreeding.

MECE: a method for enhancing the catalytic efficiency of glycoside hydrolase based on deep neural networks and molecular evolution

The demand for high efficiency glycoside hydrolases (GHs) is on the rise due to their various industrial applications. However, improving the catalytic efficiency of an enzyme remains a challenge. This investigation showcases the capability of a deep neural network and method for enhancing the catalytic efficiency (MECE) platform to predict mutations that improve catalytic activity in GHs. The MECE platform includes DeepGH, a deep learning model that is able to identify GH families and functional residues. This model was developed utilizing 119 GH family protein sequences obtained from the Carbohydrate-Active enZYmes (CAZy) database. After undergoing ten-fold cross-validation, the DeepGH models exhibited a predictive accuracy of 96.73%. The utilization of gradient-weighted class activation mapping (Grad-CAM) was used to aid us in comprehending the classification features, which in turn facilitated the creation of enzyme mutants. As a result, the MECE platform was validated with the development of CHIS1754-MUT7, a mutant that boasts seven amino acid substitutions. The kcat/Km of CHIS1754-MUT7 was found to be 23.53 times greater than that of the wild type CHIS1754. Due to its high computational efficiency and low experimental cost, this method offers significant advantages and presents a novel approach for the intelligent design of enzyme catalytic efficiency. As a result, it holds great promise for a wide range of applications.

MethSemble-6mA: an ensemble-based 6mA prediction server and its application on promoter region of LBD gene family in Poaceae

The Lateral Organ Boundaries Domain (LBD) containing genes are a set of plant-specific transcription factors and are crucial for controlling both organ development and defense mechanisms as well as anthocyanin synthesis and nitrogen metabolism. It is imperative to understand how methylation regulates gene expression, through predicting methylation sites of their promoters particularly in major crop species. In this study, we developed a user-friendly prediction server for accurate prediction of 6mA sites by incorporating a robust feature set, viz., Binary Encoding of Mono-nucleotide DNA. Our model,MethSemble-6mA, outperformed other state-of-the-art tools in terms of accuracy (93.12%). Furthermore, we investigated the pattern of probable 6mA sites at the upstream promoter regions of the LBD-containing genes in Triticum aestivum and its allied species using the developed tool. On average, each selected species had four 6mA sites, and it was found that with speciation and due course of evolution in wheat, the frequency of methylation have reduced, and a few sites remain conserved. This obviously cues gene birth and gene expression alteration through methylation over time in a species and reflects functional conservation throughout evolution. Since DNA methylation is a vital event in almost all plant developmental processes (e.g., genomic imprinting and gametogenesis) along with other life processes, our findings on epigenetic regulation of LBD-containing genes have dynamic implications in basic and applied research. Additionally, MethSemble-6mA (http://cabgrid.res.in:5799/) will serve as a useful resource for a plant breeders who are interested to pursue epigenetic-based crop improvement research.

Regulation of plant epigenetic memory in response to cold and heat stress: towards climate resilient agriculture

Plants have evolved to adapt and grow in hot and cold climatic conditions. Some also adapt to daily and seasonal temperature changes. Epigenetic modifications play an important role in regulating plant tolerance under such conditions. DNA methylation and post-translational modifications of histone proteins influence gene expression during plant developmental stages and under stress conditions, including cold and heat stress. While short-term modifications are common, some modifications may persist and result in stress memory that can be inherited by subsequent generations. Understanding the mechanisms of epigenomes responding to stress and the factors that trigger stress memory is crucial for developing climate-resilient agriculture, but such an integrated view is currently limited. This review focuses on the plant epigenetic stress memory during cold and heat stress. It also discusses the potential of machine learning to modify stress memory through epigenetics to develop climate-resilient crops.

On the prediction of non-CG DNA methylation using machine learning

DNA methylation can be detected and measured using sequencing instruments after sodium bisulfite conversion, but experiments can be expensive for large eukaryotic genomes. Sequencing nonuniformity and mapping biases can leave parts of the genome with low or no coverage, thus hampering the ability of obtaining DNA methylation levels for all cytosines. To address these limitations, several computational methods have been proposed that can predict DNA methylation from the DNA sequence around the cytosine or from the methylation level of nearby cytosines. However, most of these methods are entirely focused on CG methylation in humans and other mammals. In this work, we study, for the first time, the problem of predicting cytosine methylation for CG, CHG and CHH contexts on six plant species, either from the DNA primary sequence around the cytosine or from the methylation levels of neighboring cytosines. In this framework, we also study the cross-species prediction problem and the cross-context prediction problem (within the same species). Finally, we show that providing gene and repeat annotations allows existing classifiers to significantly improve their prediction accuracy. We introduce a new classifier called AMPS (annotation-based methylation prediction from sequence) that takes advantage of genomic annotations to achieve higher accuracy.

Epigenetic stress memory: A new approach to study cold and heat stress responses in plants

Understanding plant stress memory under extreme temperatures such as cold and heat could contribute to plant development. Plants employ different types of stress memories, such as somatic, intergenerational and transgenerational, regulated by epigenetic changes such as DNA and histone modifications and microRNAs (miRNA), playing a key role in gene regulation from early development to maturity. In most cases, cold and heat stresses result in short-term epigenetic modifications that can return to baseline modification levels after stress cessation. Nevertheless, some of the modifications may be stable and passed on as stress memory, potentially allowing them to be inherited across generations, whereas some of the modifications are reactivated during sexual reproduction or embryogenesis. Several stress-related genes are involved in stress memory inheritance by turning on and off transcription profiles and epigenetic changes. Vernalization is the best example of somatic stress memory. Changes in the chromatin structure of the Flowering Locus C (FLC) gene, a MADS-box transcription factor (TF), maintain cold stress memory during mitosis. FLC expression suppresses flowering at high levels during winter; and during vernalization, B3 TFs, cold memory cis-acting element and polycomb repressive complex 1 and 2 (PRC1 and 2) silence FLC activation. In contrast, the repression of SQUAMOSA promoter-binding protein-like (SPL) TF and the activation of Heat Shock TF (HSFA2) are required for heat stress memory. However, it is still unclear how stress memory is inherited by offspring, and the integrated view of the regulatory mechanisms of stress memory and mitotic and meiotic heritable changes in plants is still scarce. Thus, in this review, we focus on the epigenetic regulation of stress memory and discuss the application of new technologies in developing epigenetic modifications to improve stress memory.

Plant synthetic epigenomic engineering for crop improvement

Efforts have been directed to redesign crops with increased yield, stress adaptability, and nutritional value through synthetic biology-the application of engineering principles to biology. A recent expansion in our understanding of how epigenetic mechanisms regulate plant development and stress responses has unveiled a new set of resources that can be harnessed to develop improved crops, thus heralding the promise of "synthetic epigenetics." In this review, we summarize the latest advances in epigenetic regulation and highlight how innovative sequencing techniques, epigenetic editing, and deep learning-driven predictive tools can rapidly extend these insights. We also proposed the future directions of synthetic epigenetics for the development of engineered smart crops that can actively monitor and respond to internal and external cues throughout their life cycles.

Epigenetic variation: A major player in facilitating plant fitness under changing environmental conditions

Recent research in plant epigenetics has increased our understanding of how epigenetic variability can contribute to adaptive phenotypic plasticity in natural populations. Studies show that environmental changes induce epigenetic switches either independently or in complementation with the genetic variation. Although most of the induced epigenetic variability gets reset between generations and is short-lived, some variation becomes transgenerational and results in heritable phenotypic traits. The short-term epigenetic responses provide the first tier of transient plasticity required for local adaptations while transgenerational epigenetic changes contribute to stress memory and help the plants respond better to recurring or long-term stresses. These transgenerational epigenetic variations translate into an additional tier of diversity which results in stable epialleles. In recent years, studies have been conducted on epigenetic variation in natural populations related to various biological processes, ecological factors, communities, and habitats. With the advent of advanced NGS-based technologies, epigenetic studies targeting plants in diverse environments have increased manifold to enhance our understanding of epigenetic responses to environmental stimuli in facilitating plant fitness. Taking all points together in a frame, the present review is a compilation of present-day knowledge and understanding of the role of epigenetics and its fitness benefits in diverse ecological systems in natural populations.

Deciphering the molecular basis of tissue-specific gene expression in plants: Can synthetic biology help?

Gene expression differences between distinct cell types are orchestrated by specific sets of transcription factors and epigenetic regulators acting upon the genome. In plants, the mechanisms underlying tissue-specific gene activity remain largely unexplored. Although transcriptional and epigenetic profiling of individual organs, tissues, and more recently, of single cells can easily detect the molecular signatures of different biological samples, how these unique cell identities are established at the mechanistic level is only beginning to be decoded. Computational methods, including machine learning, used in combination with experimental approaches, enable the identification and validation of candidate cis-regulatory elements driving cell-specific expression. Synthetic biology shows great promise not only as a means of testing candidate DNA motifs but also for establishing the general rules of nature driving promoter architecture and for the rational design of genetic circuits in research and agriculture to confer tissue-specific expression to genes or molecular pathways of interest.

Distribution Pattern of N6-Methyladenine DNA Modification in the Seashore Paspalum (Paspalum vaginatum) Genome

N6-methyladenine (6mA) DNA modification has been detected in several eukaryotic organisms, in some of them, it plays important role in the regulation process of stress-resistance response. However, the genome-wide distribution patterns and potential functions of 6mA DNA modification in halophyte Seashore paspalum (Paspalum vaginatum) remain largely unknown. Here, we examined the 6mA landscape in the P. vaginatum genome by adopting single molecule real-time sequencing technology and found that 6mA modification sites were broadly distributed across the P. vaginatum genome. We demonstrated distinct 6mA methylation levels and 6mA distribution patterns in different types of transcription genes, which hinted at different epigenetic rules. Furthermore, the moderate 6mA density genes in P. vaginatum functionally correlated with stress resistance, which also maintained a higher transcriptional level. On the other hand, a specific 6mA distribution pattern in the gene body and near TSS was observed in gene groups with higher RNA expression, which maybe implied some kind of regularity between 6mA site distribution and the protein coding genes transcription was possible. Our study provides new insights into the association between 6mA methylation and gene expression, which may also contribute to key agronomic traits in P. vaginatum.

DNA N6-Methyladenine Modification in Eukaryotic Genome

DNA methylation is treated as an important epigenetic mark in various biological activities. In the past, a large number of articles focused on 5 mC while lacking attention to N6-methyladenine (6 mA). The presence of 6 mA modification was previously discovered only in prokaryotes. Recently, with the development of detection technologies, 6 mA has been found in several eukaryotes, including protozoans, metazoans, plants, and fungi. The importance of 6 mA in prokaryotes and single-celled eukaryotes has been widely accepted. However, due to the incredibly low density of 6 mA and restrictions on detection technologies, the prevalence of 6 mA and its role in biological processes in eukaryotic organisms are highly debated. In this review, we first summarize the advantages and disadvantages of 6 mA detection methods. Then, we conclude existing reports on the prevalence of 6 mA in eukaryotic organisms. Next, we highlight possible methyltransferases, demethylases, and the recognition proteins of 6 mA. In addition, we summarize the functions of 6 mA in eukaryotes. Last but not least, we summarize our point of view and put forward the problems that need further research.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block

DNA N6-methyladenine (6mA) is produced by the N6 position of the adenine being methylated, which occurs at the molecular level, and is involved in numerous vital biological processes in the rice genome. Given the shortcomings of biological experiments, researchers have developed many computational methods to predict 6mA sites and achieved good performance. However, the existing methods do not consider the occurrence mechanism of 6mA to extract features from the molecular structure. In this paper, a novel deep learning method is proposed by devising DNA molecular graph feature and residual block structure for 6mA sites prediction in rice, named MGF6mARice. Firstly, the DNA sequence is changed into a simplified molecular input line entry system (SMILES) format, which reflects chemical molecular structure. Secondly, for the molecular structure data, we construct the DNA molecular graph feature based on the principle of graph convolutional network. Then, the residual block is designed to extract higher level, distinguishable features from molecular graph features. Finally, the prediction module is used to obtain the result of whether it is a 6mA site. By means of 10-fold cross-validation, MGF6mARice outperforms the state-of-the-art approaches. Multiple experiments have shown that the molecular graph feature and residual block can promote the performance of MGF6mARice in 6mA prediction. To the best of our knowledge, it is the first time to derive a feature of DNA sequence by considering the chemical molecular structure. We hope that MGF6mARice will be helpful for researchers to analyze 6mA sites in rice.

Epigenome and Epitranscriptome: Potential Resources for Crop Improvement

Crop breeding faces the challenge of increasing food demand, especially under climatic changes. Conventional breeding has relied on genetic diversity by combining alleles to obtain desired traits. In recent years, research on epigenetics and epitranscriptomics has shown that epigenetic and epitranscriptomic diversity provides additional sources for crop breeding and harnessing epigenetic and epitranscriptomic regulation through biotechnologies has great potential for crop improvement. Here, we review epigenome and epitranscriptome variations during plant development and in response to environmental stress as well as the available sources for epiallele formation. We also discuss the possible strategies for applying epialleles and epitranscriptome engineering in crop breeding.