Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning

Arabidopsis as a model for translational research

Arabidopsis thaliana is currently the most-studied plant species on earth, with an unprecedented number of genetic, genomic, and molecular resources having been generated in this plant model. In the era of translating foundational discoveries to crops and beyond, we aimed to highlight the utility and challenges of using Arabidopsis as a reference for applied plant biology research, agricultural innovation, biotechnology, and medicine. We hope that this review will inspire the next generation of plant biologists to continue leveraging Arabidopsis as a robust and convenient experimental system to address fundamental and applied questions in biology. We aim to encourage laboratory and field scientists alike to take advantage of the vast Arabidopsis datasets, annotations, germplasm, constructs, methods, and molecular and computational tools in our pursuit to advance understanding of plant biology and help feed the world's growing population. We envision that the power of Arabidopsis-inspired biotechnologies and foundational discoveries will continue to fuel the development of resilient, high-yielding, nutritious plants for the betterment of plant and animal health and greater environmental sustainability.

Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds

BACKGROUND

The fatty acid content represents a crucial quality trait in Brassica napus or rapeseed. Improvements in fatty acid composition markedly enhance the quality of rapeseed oil.

RESULTS

Here, we perform a genome-wide association study (GWAS) to identify quantitative trait locus (QTLs) associated with fatty acid content. We identify a total of seven stable QTLs and find two loci, qFA.A08 and qFA.A09.1, subjected to strong selection pressure. By transcriptome-wide association analysis (TWAS), we characterize 3295 genes that are significantly correlated with the composition of at least one fatty acid. To elucidate the genetic underpinnings governing fatty acid composition, we then employ a combination of GWAS, TWAS, and dynamic transcriptomic analysis during seed development, along with the POCKET algorithm. We predict six candidate genes that are associated with fatty acid composition. Experimental validation reveals that four genes (BnaA09.PYRD, BnaA08.PSK1, BnaA08.SWI3, and BnaC02.LTP15) positively modulate oleic acid content while negatively impact erucic acid content. Comparative analysis of transcriptome profiles suggests that BnaA09.PYRD may influence fatty acid composition by regulating energy metabolism during seed development.

CONCLUSIONS

This study establishes a genetic framework for a better understanding of plant oil biosynthesis in addition to providing theoretical foundation and valuable genetic resources for enhancing fatty acid composition in rapeseed breeding.

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing

MAIN CONCLUSION

Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

Identification, characterization, and design of plant genome sequences using deep learning

Due to its excellent performance in processing large amounts of data and capturing complex non-linear relationships, deep learning has been widely applied in many fields of plant biology. Here we first review the application of deep learning in analyzing genome sequences to predict gene expression, chromatin interactions, and epigenetic features (open chromatin, transcription factor binding sites, and methylation sites) in plants. Then, current motif mining and functional component design and synthesis based on generative adversarial networks, large models, and attention mechanisms are elaborated in detail. The progress of protein structure and function prediction, genomic prediction, and large model applications based on deep learning is also discussed. Finally, this work provides prospects for the future development of deep learning in plants with regard to multiple omics data, algorithm optimization, large language models, sequence design, and intelligent breeding.

Identification of lineage-specific cis-trans regulatory networks related to kiwifruit ripening initiation

Previous research on the ripening process of many fruit crop varieties typically involved analyses of the conserved genetic factors among species. However, even for seemingly identical ripening processes, the associated gene expression networks often evolved independently, as reflected by the diversity in the interactions between transcription factors (TFs) and the targeted cis-regulatory elements (CREs). In this study, explainable deep learning (DL) frameworks were used to predict expression patterns on the basis of CREs in promoter sequences. We initially screened potential lineage-specific CRE-TF interactions influencing the kiwifruit ripening process, which is triggered by ethylene, similar to the corresponding processes in other climacteric fruit crops. Some novel regulatory relationships affecting ethylene-induced fruit ripening were identified. Specifically, ABI5-like bZIP, G2-like, and MYB81-like TFs were revealed as trans-factors modulating the expression of representative ethylene signaling/biosynthesis-related genes (e.g., ACS1, ERT2, and ERF143). Transient reporter assays and DNA affinity purification sequencing (DAP-Seq) analyses validated these CRE-TF interactions and their regulatory relationships. A comparative analysis with co-expression networking suggested that this DL-based screening can identify regulatory networks independently of co-expression patterns. Our results highlight the utility of an explainable DL approach for identifying novel CRE-TF interactions. These  imply that fruit crop species may have evolved lineage-specific fruit ripening-related cis-trans regulatory networks.

Unlocking epigenetic breeding potential in tomato and potato

Tomato (Solanum lycopersicum) and potato (Solanum tuberosum), two integral crops within the nightshade family, are crucial sources of nutrients and serve as staple foods worldwide. Molecular genetic studies have significantly advanced our understanding of their domestication, evolution, and the establishment of key agronomic traits. Recent studies have revealed that epigenetic modifications act as "molecular switches", crucially regulating phenotypic variations essential for traits such as fruit ripening in tomatoes and tuberization in potatoes. This review summarizes the latest findings on the regulatory mechanisms of epigenetic modifications in these crops and discusses the integration of biotechnology and epigenomics to enhance breeding strategies. By highlighting the role of epigenetic control in augmenting crop yield and adaptation, we underscores its potential to address the challenges posed by a growing global population as well as changing climate.

The PATROL1 function in roots contributes to the increase in shoot biomass

MAIN CONCLUSION

PATOL1 contributes to increasing biomass not only by effective stomatal movement but also by root meristematic activity. PATROL1 (PROTON ATPase TRANSLOCATION CONTROL 1), a protein with a MUN domain, is involved in the intercellular trafficking of AHA1 H+-ATPase to the plasma membrane in guard cells. This allows for larger stomatal opening and more efficient photosynthesis, leading to increased biomass. Although PATROL1 is expressed not only in stomata but also in other tissues of the shoot and root, the role in other tissues than stomata has not been determined yet. Here, we investigated PATROL1 functions in roots using a loss-of-function mutant and an overexpressor. Cytological observations revealed that root meristematic size was significantly smaller in the mutant resulting in the short primary root. Grafting experiments showed that the shoot biomass of the mutant scion was increased when it grafted onto wild-type or overexpressor rootstocks. Conversely, grafting of the overexpressor scion shoot enhanced the growth of the mutant rootstock. The leaf temperatures of the grafted plants were consistent with those of their respective genotypes, indicating cell-autonomous behavior of stomatal movement and independent roles of PATROL1 in plant growth. Moreover, plasma membrane localization of AHA1 was not altered in root epidermal cells in the patrol1 mutant implying existence of a different mode of PATROL1 action in roots. Thus PATROL1 plays a role in root meristem and contributes to increase shoot biomass.

Deep learning with a small dataset predicts chromatin remodelling contribution to winter dormancy of apple axillary buds

Epigenetic changes serve as a cellular memory for cumulative cold recognition in both herbaceous and tree species, including bud dormancy. However, most studies have discussed predicted chromatin structure with respect to histone marks. In the present study, we investigated the structural dynamics of bona fide chromatin to determine how plants recognize prolonged chilling during the initial stage of bud dormancy. The vegetative axillary buds of the 'Fuji' apple, which shows typical low temperature-dependent, but not photoperiod, dormancy induction, were used for the chromatin structure and transcriptional change analyses. The results were integrated using a deep-learning model and interpreted using statistical models, including Bayesian estimation. Although our model was constructed using a small dataset of two time points, chromatin remodelling due to random changes was excluded. The involvement of most nucleosome structural changes in transcriptional changes and the pivotal contribution of cold-driven circadian rhythm-dependent pathways regulated by the mobility of cis-regulatory elements were predicted. These findings may help to develop potential genetic targets for breeding species with less bud dormancy to overcome the effects of short winters during global warming. Our artificial intelligence concept can improve epigenetic analysis using a small dataset, especially in non-model plants with immature genome databases.

Integrating machine learning and genome editing for crop improvement

Genome editing is a promising technique that has been broadly utilized for basic gene function studies and trait improvements. Simultaneously, the exponential growth of computational power and big data now promote the application of machine learning for biological research. In this regard, machine learning shows great potential in the refinement of genome editing systems and crop improvement. Here, we review the advances of machine learning to genome editing optimization, with emphasis placed on editing efficiency and specificity enhancement. Additionally, we demonstrate how machine learning bridges genome editing and crop breeding, by accurate key site detection and guide RNA design. Finally, we discuss the current challenges and prospects of these two techniques in crop improvement. By integrating advanced genome editing techniques with machine learning, progress in crop breeding will be further accelerated in the future.

Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data

BACKGROUND

Plant specialized (or secondary) metabolites (PSM), also known as phytochemicals, natural products, or plant constituents, play essential roles in interactions between plants and environment. Although many research efforts have focused on discovering novel metabolites and their biosynthetic genes, the resolution of metabolic pathways and identified biosynthetic genes was limited by rudimentary analysis approaches and enormous number of candidate genes.

RESULTS

Here we integrated state-of-the-art automated machine learning (ML) frame AutoGluon-Tabular and multi-omics data from Arabidopsis to predict genes encoding enzymes involved in biosynthesis of plant specialized metabolite (PSM), focusing on the three main PSM categories: terpenoids, alkaloids, and phenolics. We found that the related features of genomics and proteomics were the top two crucial categories of features contributing to the model performance. Using only these key features, we built a new model in Arabidopsis, which performed better than models built with more features including those related with transcriptomics and epigenomics. Finally, the built models were validated in maize and tomato, and models tested for maize and trained with data from two other species exhibited either equivalent or superior performance to intraspecies predictions.

CONCLUSIONS

Our external validation results in grape and poppy on the one hand implied the applicability of our model to the other species, and on the other hand showed enormous potential to improve the prediction of enzymes synthesizing PSM with the inclusion of valid data from a wider range of species.

Deep learning the cis-regulatory code for gene expression in selected model plants

Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.

Functional genomics of Brassica napus: Progresses, challenges, and perspectives

Brassica napus, commonly known as rapeseed or canola, is a major oil crop contributing over 13% to the stable supply of edible vegetable oil worldwide. Identification and understanding the gene functions in the B. napus genome is crucial for genomic breeding. A group of genes controlling agronomic traits have been successfully cloned through functional genomics studies in B. napus. In this review, we present an overview of the progress made in the functional genomics of B. napus, including the availability of germplasm resources, omics databases and cloned functional genes. Based on the current progress, we also highlight the main challenges and perspectives in this field. The advances in the functional genomics of B. napus contribute to a better understanding of the genetic basis underlying the complex agronomic traits in B. napus and will expedite the breeding of high quality, high resistance and high yield in B. napus varieties.

Sea-ATI unravels novel vocabularies of plant active cistrome

The cistrome consists of all cis-acting regulatory elements recognized by transcription factors (TFs). However, only a portion of the cistrome is active for TF binding in a specific tissue. Resolving the active cistrome in plants remains challenging. In this study, we report the assay sequential extraction assisted-active TF identification (sea-ATI), a low-input method that profiles the DNA sequences recognized by TFs in a target tissue. We applied sea-ATI to seven plant tissues to survey their active cistrome and generated 41 motif models, including 15 new models that represent previously unidentified cis-regulatory vocabularies. ATAC-seq and RNA-seq analyses confirmed the functionality of the cis-elements from the new models, in that they are actively bound in vivo, located near the transcription start site, and influence chromatin accessibility and transcription. Furthermore, comparing dimeric WRKY CREs between sea-ATI and DAP-seq libraries revealed that thermodynamics and genetic drifts cooperatively shaped their evolution. Notably, sea-ATI can identify not only positive but also negative regulatory cis-elements, thereby providing unique insights into the functional non-coding genome of plants.

Gene regulatory network inference in soybean upon infection by Phytophthora sojae

Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae.

Analysis of Heat Shock Proteins Based on Amino Acids for the Tomato Genome

This research aimed to investigate heat shock proteins in the tomato genome through the analysis of amino acids. The highest length among sequences was found in seq19 with 3534 base pairs. This seq19 was reported and contained a family of proteins known as HsfA that have a domain of transcriptional activation for tolerance to heat and other abiotic stresses. The values of the codon adaptation index (CAI) ranged from 0.80 in Seq19 to 0.65 in Seq10, based on the mRNA of heat shock proteins for tomatoes. Asparagine (AAT, AAC), aspartic acid (GAT, GAC), phenylalanine (TTT, TTC), and tyrosine (TAT, TAC) have relative synonymous codon usage (RSCU) values bigger than 0.5. In modified relative codon bias (MRCBS), the high gene expressions of the amino acids under heat stress were histidine, tryptophan, asparagine, aspartic acid, lysine, phenylalanine, isoleucine, cysteine, and threonine. RSCU values that were less than 0.5 were considered rare codons that affected the rate of translation, and thus selection could be effective by reducing the frequency of expressed genes under heat stress. The normal distribution of RSCU shows about 68% of the values drawn from the standard normal distribution were within 0.22 and -0.22 standard deviations that tend to cluster around the mean. The most critical component based on principal component analysis (PCA) was the RSCU. These findings would help plant breeders in the development of growth habits for tomatoes during breeding programs.