Characteristics of Plant Essential Genes Allow for within- and between-Species Prediction of Lethal Mutant Phenotypes

A Hot-Swappable Genetic Switch: Building an inducible and trackable functional assay for the essential gene MEDIATOR 21

Essential genes, estimated at approximately 20% of the Arabidopsis genome, are broadly expressed and required for reproductive success. They are difficult to study, as interfering with their function leads to premature death. Transcription is one of the essential functions of life, and the multi-protein Mediator complex coordinates the regulation of gene expression at nearly every eukaryotic promoter. In this study, we focused on a core Mediator component called MEDIATOR21 (MED21), which is required for activation of transcription. Our previous work has also shown a role for MED21 in repression of gene expression through its interaction with a corepressor protein. Here, we sought to differentiate the role MED21 plays in activation versus repression using the model plant Arabidopsis. As mutations in MED21 lead to embryo lethal phenotypes, we constructed a set of synthetic switches using PhiC31 serine integrases to create an "on-to-off" inducible loss of function MED21 in a non-essential tissue. Our technology, which we call Integrase Erasers, made it possible for med21 mutant plants to survive into adulthood by ablating protein expression selectively in lateral root primordia, allowing quantification and characterization of med21 mutant phenotypes in a post-embryonic context. In addition, we engineered chemical induction of the Integrase Eraser to ablate MED21 expression in whole seedlings at a user-specified timepoint. Finally, we extended this technology to build a hot swappable Integrase Isoform Switch where expression of the integrase toggled cells from expressing wildtype MED21 to expressing MED21 sequence variants. Our analysis of the entire set of new integrase-based tools demonstrates that this is a highly efficient and robust approach to the study of essential genes.

Dosage sensitivity shapes balanced expression and gene longevity of homoeologs after whole-genome duplications in angiosperms

Following whole-genome duplication (WGD), duplicate gene pairs (homoeologs) can evolve varying degrees of expression divergence. However, the determinants influencing these relative expression level differences (RFPKM) between homoeologs remain elusive. In this study, we analyzed the RFPKM between homoeologs in 3 angiosperms, Nymphaea colorata, Nelumbo nucifera, and Acorus tatarinowii, all having undergone a single WGD since the origin of angiosperms. Our results show significant positive correlations in RFPKM of homoeologs among tissues within the same species, and among orthologs across these 3 species, indicating convergent expression balance/bias between homoeologous gene copies following independent WGDs. We linked RFPKM between homoeologs to gene attributes associated with dosage-balance constraints, such as protein-protein interactions, lethal-phenotype scores in Arabidopsis (Arabidopsis thaliana) orthologs, domain numbers, and expression breadth. Notably, homoeologs with lower RFPKM often had more interactions and higher lethal-phenotype scores, indicating selective pressures favoring balanced expression. Also, homoeologs with lower RFPKM were more likely to be retained after WGDs in angiosperms. Within Nelumbo, greater RFPKM between homoeologs correlated with increased cis- and trans-regulatory differentiation between species, highlighting the ongoing escalation of gene expression divergence. We further found that expression degeneration in 1 copy of homoeologs is inclined toward nonfunctionalization. Our research highlights the importance of balanced expression, shaped by dosage-balance constraints, in the evolutionary retention of homoeologs in plants.

OsIPK1 frameshift mutations disturb phosphorus homeostasis and impair starch synthesis during grain filling in rice

Inositol 1,3,4,5,6-pentakisphosphate 2-kinase (IPK1) catalyzes the final step in phytic acid (InsP6) synthesis. In this study, the effects of OsIPK1 mutations on InsP6 synthesis, grain filling and their underlying mechanisms were investigated. Seven gRNAs were designed to disrupt the OsIPK1 gene via CRISPR/CAS9 system. Only 4 of them generated 29 individual insertion or deletion T0 plants, in which nine biallelic or heterozygous genotypes were identified. Segregation analysis revealed that OsIPK1 frameshift mutants are homozygous lethality. The biallelic and heterozygous frameshift mutants exhibited significant reduction in yield-related traits, particularly in the seed-setting rate and yield per plant. Despite a notable decline in pollen viability, the male and female gametes had comparable transmission rates to their progenies in the mutants. A significant number of the filling-aborted (FA) grains was observed in mature grains of these heterozygous frameshift mutants. These grains exhibited a nearly complete blockage of InsP6 synthesis, resulting in a pronounced increase in Pi content. In contrast, a slight decline in InsP6 content was observed in the plump grains. During the filling stage, owing to the excessive accumulation of Pi, starch synthesis was significantly impaired, and the endosperm development-specific gene expression was nearly abolished. Consistently, the activity of whereas AGPase, a key enzyme in starch synthesis, was significantly decreased and Pi transporter gene expression was upregulated in the FA grains. Taken together, these results demonstrate that OsIPK1 frameshift mutations result in excessive Pi accumulation, decreased starch synthesis, and ultimately leading to lower yields in rice.

H3K4me1 recruits DNA repair proteins in plants

DNA repair proteins can be recruited by their histone reader domains to specific epigenomic features, with consequences on intragenomic mutation rate variation. Here, we investigated H3K4me1-associated hypomutation in plants. We first examined 2 proteins which, in plants, contain Tudor histone reader domains: PRECOCIOUS DISSOCIATION OF SISTERS 5 (PDS5C), involved in homology-directed repair, and MUTS HOMOLOG 6 (MSH6), a mismatch repair protein. The MSH6 Tudor domain of Arabidopsis (Arabidopsis thaliana) binds to H3K4me1 as previously demonstrated for PDS5C, which localizes to H3K4me1-rich gene bodies and essential genes. Mutations revealed by ultradeep sequencing of wild-type and msh6 knockout lines in Arabidopsis show that functional MSH6 is critical for the reduced rate of single-base substitution (SBS) mutations in gene bodies and H3K4me1-rich regions. We explored the breadth of these mechanisms among plants by examining a large rice (Oryza sativa) mutation data set. H3K4me1-associated hypomutation is conserved in rice as are the H3K4me1-binding residues of MSH6 and PDS5C Tudor domains. Recruitment of DNA repair proteins by H3K4me1 in plants reveals convergent, but distinct, epigenome-recruited DNA repair mechanisms from those well described in humans. The emergent model of H3K4me1-recruited repair in plants is consistent with evolutionary theory regarding mutation modifier systems and offers mechanistic insight into intragenomic mutation rate variation in plants.

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.

Genomic view of heavy-ion-induced deletions associated with distribution of essential genes in Arabidopsis thaliana

Heavy-ion beam, a type of ionizing radiation, has been applied to plant breeding as a powerful mutagen and is a promising tool to induce large deletions and chromosomal rearrangements. The effectiveness of heavy-ion irradiation can be explained by linear energy transfer (LET; keV µm-1). Heavy-ion beams with different LET values induce different types and sizes of mutations. It has been suggested that deletion size increases with increasing LET value, and complex chromosomal rearrangements are induced in higher LET radiations. In this study, we mapped heavy-ion beam-induced deletions detected in Arabidopsis mutants to its genome. We revealed that deletion sizes were similar between different LETs (100 to 290 keV μm-1), that their upper limit was affected by the distribution of essential genes, and that the detected chromosomal rearrangements avoid disrupting the essential genes. We also focused on tandemly arrayed genes (TAGs), where two or more homologous genes are adjacent to one another in the genome. Our results suggested that 100 keV µm-1 of LET is enough to disrupt TAGs and that the distribution of essential genes strongly affects the heritability of mutations overlapping them. Our results provide a genomic view of large deletion inductions in the Arabidopsis genome.

Evolutionary and synteny analysis of HIS1, BADH2, GBSS1, and GBSS2 in rice: insights for effective introgression breeding strategies

The key genes BADH2, GBSS1, GBSS2, and HIS1 regulate the fragrance, starch synthesis, and herbicide resistance in rice. Although the molecular functions of four genes have been investigated in the Oryza sativa species, little is known regarding their evolutionary history in the Oryza genus. Here, we studied the evolution of four focal genes in 10 Oryza species using phylogenetic and syntenic approaches. The HIS1 family underwent several times of tandem duplication events in the Oryza species, resulting in copy number variation ranging from 2 to 7. At most one copy of BADH2, GBSS1, and GBSS2 orthologs were identified in each Oryza species, and gene loss events of BADH2 and GBSS2 were identified in three Oryza species. Gene transfer analysis proposed that the functional roles of GBSS1 and GBSS2 were developed in the Asian and African regions, respectively, and most allelic variations of BADH2 in japonica rice emerged after the divergence between the Asian and African rice groups. These results provide clues to determine the origin and evolution of the key genes in rice breeding as well as valuable information for molecular breeders and scientists to develop efficient strategies to simultaneously improve grain quality and yield potential in rice.

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

Identification and Expression Analysis of the Nucleotidyl Transferase Protein (NTP) Family in Soybean (Glycine max) under Various Abiotic Stresses

Nucleotidyl transferases (NTPs) are common transferases in eukaryotes and play a crucial role in nucleotide modifications at the 3' end of RNA. In plants, NTPs can regulate RNA stability by influencing 3' end modifications, which in turn affect plant growth, development, stress responses, and disease resistance. Although the functions of NTP family members have been extensively studied in Arabidopsis, rice, and maize, there is limited knowledge about NTP genes in soybeans. In this study, we identified 16 members of the NTP family in soybeans, including two subfamilies (G1 and G2) with distinct secondary structures, conserved motifs, and domain distributions at the protein level. Evolutionary analysis of genes in the NTP family across multiple species and gene collinearity analysis revealed a relatively conserved evolutionary pattern. Analysis of the tertiary structure of the proteins showed that NTPs have three conserved aspartic acids that bind together to form a possible active site. Tissue-specific expression analysis indicated that some NTP genes exhibit tissue-specific expression, likely due to their specific functions. Stress expression analysis showed significant differences in the expression levels of NTP genes under high salt, drought, and cold stress. Additionally, RNA-seq analysis of soybean plants subjected to salt and drought stress further confirmed the association of soybean NTP genes with abiotic stress responses. Subcellular localization experiments revealed that GmNTP2 and GmNTP14, which likely have similar functions to HESO1 and URT1, are located in the nucleus. These research findings provide a foundation for further investigations into the functions of NTP family genes in soybeans.

Essential genes identification model based on sequence feature map and graph convolutional neural network

BACKGROUND

Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes.

RESULTS

In this study, we introduce GCNN-SFM, a computational model for identifying essential genes in organisms, based on graph convolutional neural networks (GCNN). GCNN-SFM integrates a graph convolutional layer, a convolutional layer, and a fully connected layer to model and extract features from gene sequences of essential genes. Initially, the gene sequence is transformed into a feature map using coding techniques. Subsequently, a multi-layer GCN is employed to perform graph convolution operations, effectively capturing both local and global features of the gene sequence. Further feature extraction is performed, followed by integrating convolution and fully-connected layers to generate prediction results for essential genes. The gradient descent algorithm is utilized to iteratively update the cross-entropy loss function, thereby enhancing the accuracy of the prediction results. Meanwhile, model parameters are tuned to determine the optimal parameter combination that yields the best prediction performance during training.

CONCLUSIONS

Experimental evaluation demonstrates that GCNN-SFM surpasses various advanced essential gene prediction models and achieves an average accuracy of 94.53%. This study presents a novel and effective approach for identifying essential genes, which has significant implications for biology and genomics research.

Meta-analysis of dispensable essential genes and their interactions with bypass suppressors

Genes have been historically classified as essential or non-essential based on their requirement for viability. However, genomic mutations can sometimes bypass the requirement for an essential gene, challenging the binary classification of gene essentiality. Such dispensable essential genes represent a valuable model for understanding the incomplete penetrance of loss-of-function mutations often observed in natural populations. Here, we compiled data from multiple studies on essential gene dispensability in Saccharomyces cerevisiae to comprehensively characterize these genes. In analyses spanning different evolutionary timescales, dispensable essential genes exhibited distinct phylogenetic properties compared with other essential and non-essential genes. Integration of interactions with suppressor genes that can bypass the gene essentiality revealed the high functional modularity of the bypass suppression network. Furthermore, dispensable essential and bypass suppressor gene pairs reflected simultaneous changes in the mutational landscape of S. cerevisiae strains. Importantly, species in which dispensable essential genes were non-essential tended to carry bypass suppressor mutations in their genomes. Overall, our study offers a comprehensive view of dispensable essential genes and illustrates how their interactions with bypass suppressors reflect evolutionary outcomes.

Maize Feature Store: A centralized resource to manage and analyze curated maize multi-omics features for machine learning applications

The big-data analysis of complex data associated with maize genomes accelerates genetic research and improves agronomic traits. As a result, efforts have increased to integrate diverse datasets and extract meaning from these measurements. Machine learning models are a powerful tool for gaining knowledge from large and complex datasets. However, these models must be trained on high-quality features to succeed. Currently, there are no solutions to host maize multi-omics datasets with end-to-end solutions for evaluating and linking features to target gene annotations. Our work presents the Maize Feature Store (MFS), a versatile application that combines features built on complex data to facilitate exploration, modeling and analysis. Feature stores allow researchers to rapidly deploy machine learning applications by managing and providing access to frequently used features. We populated the MFS for the maize reference genome with over 14 000 gene-based features based on published genomic, transcriptomic, epigenomic, variomic and proteomics datasets. Using the MFS, we created an accurate pan-genome classification model with an AUC-ROC score of 0.87. The MFS is publicly available through the maize genetics and genomics database. Database URL  https://mfs.maizegdb.org/.

Signaling and Resistosome Formation in Plant Innate Immunity to Viruses: Is There a Common Mechanism of Antiviral Resistance Conserved across Kingdoms?

Virus-specific proteins, including coat proteins, movement proteins, replication proteins, and suppressors of RNA interference are capable of triggering the hypersensitive response (HR), which is a type of cell death in plants. The main cell death signaling pathway involves direct interaction of HR-inducing proteins with nucleotide-binding leucine-rich repeats (NLR) proteins encoded by plant resistance genes. Singleton NLR proteins act as both sensor and helper. In other cases, NLR proteins form an activation network leading to their oligomerization and formation of membrane-associated resistosomes, similar to metazoan inflammasomes and apoptosomes. In resistosomes, coiled-coil domains of NLR proteins form Ca2+ channels, while toll-like/interleukin-1 receptor-type (TIR) domains form oligomers that display NAD+ glycohydrolase (NADase) activity. This review is intended to highlight the current knowledge on plant innate antiviral defense signaling pathways in an attempt to define common features of antiviral resistance across the kingdoms of life.

Callose synthesis at the center point of plant development-An evolutionary insight

Polar callose deposition into the extracellular matrix is tightly controlled in time and space. Its presence in the cell wall modifies the properties of the surrounding area, which is fundamental for the correct execution of numerous processes such as cell division, male gametophyte development, intercellular transport, or responses to biotic and abiotic stresses. Previous studies have been invaluable in characterizing specific callose synthases (CalSs) during individual cellular processes. However, the complex view of the relationships between a particular CalS and a specific process is still lacking. Here we review the recent proceedings on the role of callose and individual CalSs in cell wall remodelling from an evolutionary perspective and with a particular focus on cytokinesis. We provide a robust phylogenetic analysis of CalS across the plant kingdom, which implies a 3-subfamily distribution of CalS. We also discuss the possible linkage between the evolution of CalSs and their function in specific cell types and processes.

An atypical NLR protein modulates the NRC immune receptor network in Nicotiana benthamiana

The NRC immune receptor network has evolved in asterid plants from a pair of linked genes into a genetically dispersed and phylogenetically structured network of sensor and helper NLR (nucleotide-binding domain and leucine-rich repeat-containing) proteins. In some species, such as the model plant Nicotiana benthamiana and other Solanaceae, the NRC (NLR-REQUIRED FOR CELL DEATH) network forms up to half of the NLRome, and NRCs are scattered throughout the genome in gene clusters of varying complexities. Here, we describe NRCX, an atypical member of the NRC family that lacks canonical features of these NLR helper proteins, such as a functional N-terminal MADA motif and the capacity to trigger autoimmunity. In contrast to other NRCs, systemic gene silencing of NRCX in N. benthamiana markedly impairs plant growth resulting in a dwarf phenotype. Remarkably, dwarfism of NRCX silenced plants is partially dependent on NRCX paralogs NRC2 and NRC3, but not NRC4. Despite its negative impact on plant growth when silenced systemically, spot gene silencing of NRCX in mature N. benthamiana leaves doesn't result in visible cell death phenotypes. However, alteration of NRCX expression modulates the hypersensitive response mediated by NRC2 and NRC3 in a manner consistent with a negative role for NRCX in the NRC network. We conclude that NRCX is an atypical member of the NRC network that has evolved to contribute to the homeostasis of this genetically unlinked NLR network.

Feature importance network reveals novel functional relationships between biological features in Arabidopsis thaliana

Understanding how the different cellular components are working together to form a living cell requires multidisciplinary approaches combining molecular and computational biology. Machine learning shows great potential in life sciences, as it can find novel relationships between biological features. Here, we constructed a dataset of 11,801 gene features for 31,522 Arabidopsis thaliana genes and developed a machine learning workflow to identify linked features. The detected linked features are visualised as a Feature Important Network (FIN), which can be mined to reveal a variety of novel biological insights pertaining to gene function. We demonstrate how FIN can be used to generate novel insights into gene function. To make this network easily accessible to the scientific community, we present the FINder database, available at finder.plant.tools.

Bacterial genome reductions: Tools, applications, and challenges

Bacterial cells are widely used to produce value-added products due to their versatility, ease of manipulation, and the abundance of genome engineering tools. However, the efficiency of producing these desired biomolecules is often hindered by the cells’ own metabolism, genetic instability, and the toxicity of the product. To overcome these challenges, genome reductions have been performed, making strains with the potential of serving as chassis for downstream applications. Here we review the current technologies that enable the design and construction of such reduced-genome bacteria as well as the challenges that limit their assembly and applicability. While genomic reductions have shown improvement of many cellular characteristics, a major challenge still exists in constructing these cells efficiently and rapidly. Computational tools have been created in attempts at minimizing the time needed to design these organisms, but gaps still exist in modelling these reductions in silico. Genomic reductions are a promising avenue for improving the production of value-added products, constructing chassis cells, and for uncovering cellular function but are currently limited by their time-consuming construction methods. With improvements to and the creation of novel genome editing tools and in silico models, these approaches could be combined to expedite this process and create more streamlined and efficient cell factories.

Targeting TOR and SnRK1 Genes in Rice with CRISPR/Cas9

Genome targeting with CRISPR/Cas9 is a popular method for introducing mutations and creating knock-out effects. However, limited information is currently available on the mutagenesis of essential genes. This study investigated the efficiency of CRISPR/Cas9 in targeting rice essential genes: the singleton TARGET OF RAPAMYCIN (OsTOR) and the three paralogs of the Sucrose non-fermenting-1 (SNF1)-related kinase 1 (OsSnRK1α), OsSnRK1αA, OsSnRK1αB and OsSnRK1αC. Strong activity of constitutively expressed CRISPR/Cas9 was effective in creating mutations in OsTOR and OsSnRK1α genes, but inducible CRISPR/Cas9 failed to generate detectable mutations. The rate of OsTOR mutagenesis was relatively lower and only the kinase domain of OsTOR could be targeted, while mutations in the HEAT region were unrecoverable. OsSnRK1α paralogs could be targeted at higher rates; however, sterility or early senescence was observed in >50% of the primary mutants. Additionally, OsSnRK1αB and OsSnRK1αC, which bear high sequence homologies, could be targeted simultaneously to generate double-mutants. Further, although limited types of mutations were found in the surviving mutants, the recovered lines displayed loss-of-function or knockdown tor or snrk1 phenotypes. Overall, our data show that mutations in these essential genes can be created by CRISPR/Cas9 to facilitate investigations on their roles in plant development and environmental response in rice.

Interplay between gene expression and gene architecture as a consequence of gene and genome duplications: evidence from metabolic genes of Arabidopsis thaliana

Gene and genome duplications have been widespread during the evolution of flowering plant which resulted in the increment of biological complexity as well as creation of plasticity of a genome helping the species to adapt to changing environments. Duplicated genes with higher evolutionary rates can act as a mechanism of generating novel functions in secondary metabolism. In this study, we explored duplication as a potential factor governing the expression heterogeneity and gene architecture of Primary Metabolic Genes (PMGs) and Secondary Metabolic Genes (SMGs) of Arabidopsis thaliana. It is remarkable that different types of duplication processes controlled gene expression and tissue specificity differently in PMGs and SMGs. A complex relationship exists between gene architecture and expression patterns of primary and secondary metabolic genes. Our study reflects, expression heterogeneity and gene structure variation of primary and secondary metabolism in Arabidopsis thaliana are partly results of duplication events of different origins. Our study suggests that duplication has differential effect on PMGs and SMGs regarding expression pattern by controlling gene structure, epigenetic modifications, multifunctionality and subcellular compartmentalization. This study provides an insight into the evolution of metabolism in plants in the light of gene and genome scale duplication.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01188-2.

Recent advancements in CRISPR/Cas technology for accelerated crop improvement

Precise genome engineering approaches could be perceived as a second paradigm for targeted trait improvement in crop plants, with the potential to overcome the constraints imposed by conventional CRISPR/Cas technology. The likelihood of reduced agricultural production due to highly turbulent climatic conditions increases as the global population expands. The second paradigm of stress-resilient crops with enhanced tolerance and increased productivity against various stresses is paramount to support global production and consumption equilibrium. Although traditional breeding approaches have substantially increased crop production and yield, effective strategies are anticipated to restore crop productivity even further in meeting the world's increasing food demands. CRISPR/Cas, which originated in prokaryotes, has surfaced as a coveted genome editing tool in recent decades, reshaping plant molecular biology in unprecedented ways and paving the way for engineering stress-tolerant crops. CRISPR/Cas is distinguished by its efficiency, high target specificity, and modularity, enables precise genetic modification of crop plants, allowing for the creation of allelic variations in the germplasm and the development of novel and more productive agricultural practices. Additionally, a slew of advanced biotechnologies premised on the CRISPR/Cas methodologies have augmented fundamental research and plant synthetic biology toolkits. Here, we describe gene editing tools, including CRISPR/Cas and its imitative tools, such as base and prime editing, multiplex genome editing, chromosome engineering followed by their implications in crop genetic improvement. Further, we comprehensively discuss the latest developments of CRISPR/Cas technology including CRISPR-mediated gene drive, tissue-specific genome editing, dCas9 mediated epigenetic modification and programmed self-elimination of transgenes in plants. Finally, we highlight the applicability and scope of advanced CRISPR-based techniques in crop genetic improvement.

State-of-the-Art in CRISPR Technology and Engineering Drought, Salinity, and Thermo-tolerant crop plants

Our review has described principles and functional importance of CRISPR-Cas9 with emphasis on the recent advancements, such as CRISPR-Cpf1, base editing (BE), prime editing (PE), epigenome editing, tissue-specific (CRISPR-TSKO), and inducible genome editing and their potential applications in generating stress-tolerant plants. Improved agricultural practices and enhanced food crop production using innovative crop breeding technology is essential for increasing access to nutritious foods across the planet. The crop plants play a pivotal role in energy and nutrient supply to humans. The abiotic stress factors, such as drought, heat, and salinity cause a substantial yield loss in crop plants and threaten food security. The most sustainable and eco-friendly way to overcome these challenges are the breeding of crop cultivars with improved tolerance against abiotic stress factors. The conventional plant breeding methods have been highly successful in developing abiotic stress-tolerant crop varieties, but usually cumbersome and time-consuming. Alternatively, the CRISPR/Cas genome editing has emerged as a revolutionary tool for making efficient and precise genetic manipulations in plant genomes. Here, we provide a comprehensive review of the CRISPR/Cas genome editing (GE) technology with an emphasis on recent advances in the plant genome editing, including base editing (BE), prime editing (PE), epigenome editing, tissue-specific (CRISPR-TSKO), and inducible genome editing (CRISPR-IGE), which can be used for obtaining cultivars with enhanced tolerance to various abiotic stress factors. We also describe tissue culture-free, DNA-free GE technology, and some of the CRISPR-based tools that can be modified for their use in crop plants.

Conditional and tissue-specific approaches to dissect essential mechanisms in plant development

Reverse genetics approaches are routinely used to investigate gene function. However, mutations, especially in critical genes, can lead to pleiotropic effects as severe as lethality, thus limiting functional studies in specific contexts. Approaches that allow for modifications of genes or gene products in a specific spatial or temporal setting can overcome these limitations. The advent of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technologies has not only revolutionized targeted genome modification in plants but also enabled new possibilities for inducible and tissue-specific manipulation of gene functions at the DNA and RNA levels. In addition, novel approaches for the direct manipulation of target proteins have been introduced in plant systems. Here, we review the current development in tissue-specific and conditional manipulation approaches at the DNA, RNA, and protein levels.

Mutation bias reflects natural selection in Arabidopsis thaliana

Since the first half of the twentieth century, evolutionary theory has been dominated by the idea that mutations occur randomly with respect to their consequences1. Here we test this assumption with large surveys of de novo mutations in the plant Arabidopsis thaliana. In contrast to expectations, we find that mutations occur less often in functionally constrained regions of the genome-mutation frequency is reduced by half inside gene bodies and by two-thirds in essential genes. With independent genomic mutation datasets, including from the largest Arabidopsis mutation accumulation experiment conducted to date, we demonstrate that epigenomic and physical features explain over 90% of variance in the genome-wide pattern of mutation bias surrounding genes. Observed mutation frequencies around genes in turn accurately predict patterns of genetic polymorphisms in natural Arabidopsis accessions (r = 0.96). That mutation bias is the primary force behind patterns of sequence evolution around genes in natural accessions is supported by analyses of allele frequencies. Finally, we find that genes subject to stronger purifying selection have a lower mutation rate. We conclude that epigenome-associated mutation bias2 reduces the occurrence of deleterious mutations in Arabidopsis, challenging the prevailing paradigm that mutation is a directionless force in evolution.

Contrasting gene-level signatures of selection with reproductive fitness

Selection leaves signatures in the DNA sequence of genes, with many test statistics devised to detect its action. While these statistics are frequently used to support hypotheses about the adaptive significance of particular genes, the effect these genes have on reproductive fitness is rarely quantified experimentally. Consequently, it is unclear how gene-level signatures of selection are associated with empirical estimates of gene effect on fitness. Eukaryotic datasets that permit this comparison are very limited. Using the model plant Arabidopsis thaliana, for which these resources are available, we calculated seven gene-level substitution and polymorphism-based statistics commonly used to infer selection (dN/dS, NI, DOS, Tajima's D, Fu and Li's D*, Fay and Wu's H, and Zeng's E) and, using knockout lines, compared these to gene-level estimates of effect on fitness. We found that consistent with expectations, essential genes were more likely to be classified as negatively selected. By contrast, using 379 Arabidopsis genes for which data was available, we found no evidence that genes predicted to be positively selected had a significantly different effect on fitness than genes evolving more neutrally. We discuss these results in the context of the analytic challenges posed by Arabidopsis, one of the only systems in which this study could be conducted, and advocate for examination in additional systems. These results are relevant to the evaluation of genome-wide studies across species where experimental fitness data is unavailable, as well as highlighting an increasing need for the latter.

Adaptation Strategies to Improve the Resistance of Oilseed Crops to Heat Stress Under a Changing Climate: An Overview

Temperature is one of the decisive environmental factors that is projected to increase by 1. 5°C over the next two decades due to climate change that may affect various agronomic characteristics, such as biomass production, phenology and physiology, and yield-contributing traits in oilseed crops. Oilseed crops such as soybean, sunflower, canola, peanut, cottonseed, coconut, palm oil, sesame, safflower, olive etc., are widely grown. Specific importance is the vulnerability of oil synthesis in these crops against the rise in climatic temperature, threatening the stability of yield and quality. The natural defense system in these crops cannot withstand the harmful impacts of heat stress, thus causing a considerable loss in seed and oil yield. Therefore, a proper understanding of underlying mechanisms of genotype-environment interactions that could affect oil synthesis pathways is a prime requirement in developing stable cultivars. Heat stress tolerance is a complex quantitative trait controlled by many genes and is challenging to study and characterize. However, heat tolerance studies to date have pointed to several sophisticated mechanisms to deal with the stress of high temperatures, including hormonal signaling pathways for sensing heat stimuli and acquiring tolerance to heat stress, maintaining membrane integrity, production of heat shock proteins (HSPs), removal of reactive oxygen species (ROS), assembly of antioxidants, accumulation of compatible solutes, modified gene expression to enable changes, intelligent agricultural technologies, and several other agronomic techniques for thriving and surviving. Manipulation of multiple genes responsible for thermo-tolerance and exploring their high expressions greatly impacts their potential application using CRISPR/Cas genome editing and OMICS technology. This review highlights the latest outcomes on the response and tolerance to heat stress at the cellular, organelle, and whole plant levels describing numerous approaches applied to enhance thermos-tolerance in oilseed crops. We are attempting to critically analyze the scattered existing approaches to temperature tolerance used in oilseeds as a whole, work toward extending studies into the field, and provide researchers and related parties with useful information to streamline their breeding programs so that they can seek new avenues and develop guidelines that will greatly enhance ongoing efforts to establish heat stress tolerance in oilseeds.

Identifying essential genes across eukaryotes by machine learning

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

Optimising the use of gene expression data to predict plant metabolic pathway memberships

Plant metabolites from diverse pathways are important for plant survival, human nutrition and medicine. The pathway memberships of most plant enzyme genes are unknown. While co-expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts. Utilising > 600 tomato (Solanum lycopersicum) expression data combinations, three strategies for predicting memberships in 85 pathways were explored. Optimal predictions for different pathways require distinct data combinations indicative of pathway functions. Naive prediction (i.e. identifying pathways with the most similarly expressed genes) is error prone. In 52 pathways, unsupervised learning performed better than supervised approaches, possibly due to limited training data availability. Using gene-to-pathway expression similarities led to prediction models that outperformed those based simply on expression levels. Using 36 experimental validated genes, the pathway-best model prediction accuracy is 58.3%, significantly better compared with that for predicting annotated genes without experimental evidence (37.0%) or random guess (1.2%), demonstrating the importance of data quality. Our study highlights the need to extensively explore expression-based features and prediction strategies to maximise the accuracy of metabolic pathway membership assignment. The prediction framework outlined here can be applied to other species and serves as a baseline model for future comparisons.

Chromosome number is key to longevity of polyploid lineages

Polyploidy is ubiquitous and often recursive in plant lineages, most frequently resulting in extinction but occasionally associated with great evolutionary success. However, instead of chromosome numbers exponentially increasing due to recurrent polyploidy, most angiosperm species have fewer than 14 chromosome pairs. Following genome duplication, diploidisation can render one copy of essential genes nonfunctional without fitness cost. In isolated subpopulations, alternate (homoeologous) gene copies can be lost, creating incompatibilities that reduce fitness of hybrids between subpopulations, constraining exchange of favourable genetic changes and reducing species fitness. When multiple sets of incompatible genes are genetically linked, their deleterious effects are not independent. The effective number of independently acting sets of incompatible loci in hybrids is limited by chromosome number and recombination. Therefore, species with many chromosomes are subject to a higher fitness penalty during diploidisation. Karyotypic changes, especially fusions, that reduce gene flow are normally fitness disadvantages, but during the diploidisation process, can increase fitness by reducing mixing of differentially diploidised alleles. Fitness penalties caused by diploidisation favour accelerated karyotypic change, with each change increasing barriers to gene flow, contributing to speciation. Lower chromosome numbers and increased chromosome fusions confer advantages to surviving the diploidisation process following polyploid formation, by independent mechanisms.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

Lateral Root Initiation and the Analysis of Gene Function Using Genome Editing with CRISPR in Arabidopsis

Lateral root initiation is a post-embryonic process that requires the specification of a subset of pericycle cells adjacent to the xylem pole in the primary root into lateral root founder cells. The first visible event of lateral root initiation in Arabidopsis is the simultaneous migration of nuclei in neighbouring founder cells. Coinciding cell cycle activation is essential for founder cells in the pericycle to undergo formative divisions, resulting in the development of a lateral root primordium (LRP). The plant signalling molecule, auxin, is a major regulator of lateral root development; the understanding of the molecular mechanisms controlling lateral root initiation has progressed tremendously by the use of the Arabidopsis model and a continual improvement of molecular methodologies. Here, we provide an overview of the visible events, cell cycle regulators, and auxin signalling cascades related to the initiation of a new LRP. Furthermore, we highlight the potential of genome editing technology to analyse gene function in lateral root initiation, which provides an excellent model to answer fundamental developmental questions such as coordinated cell division, growth axis establishment as well as the specification of cell fate and cell polarity.

Visualizing protein-protein interactions in plants by rapamycin-dependent delocalization

Identifying protein-protein interactions (PPIs) is crucial for understanding biological processes. Many PPI tools are available, yet only some function within the context of a plant cell. Narrowing down even further, only a few tools allow complex multi-protein interactions to be visualized. Here, we present a conditional in vivo PPI tool for plant research that meets these criteria. Knocksideways in plants (KSP) is based on the ability of rapamycin to alter the localization of a bait protein and its interactors via the heterodimerization of FKBP and FRB domains. KSP is inherently free from many limitations of other PPI systems. This in vivo tool does not require spatial proximity of the bait and prey fluorophores and it is compatible with a broad range of fluorophores. KSP is also a conditional tool and therefore the visualization of the proteins in the absence of rapamycin acts as an internal control. We used KSP to confirm previously identified interactions in Nicotiana benthamiana leaf epidermal cells. Furthermore, the scripts that we generated allow the interactions to be quantified at high throughput. Finally, we demonstrate that KSP can easily be used to visualize complex multi-protein interactions. KSP is therefore a versatile tool with unique characteristics and applications that complements other plant PPI methods.

A semi-dominant NLR allele causes whole-seedling necrosis in wheat

Programmed cell death (PCD) and apoptosis have key functions in development and disease resistance in diverse organisms; however, the induction of necrosis remains poorly understood. Here, we identified a semi-dominant mutant allele that causes the necrotic death of the entire seedling (DES) of wheat (Triticum aestivum L.) in the absence of any pathogen or external stimulus. Positional cloning of the lethal allele mDES1 revealed that this premature death via necrosis was caused by a point mutation from Asp to Asn at amino acid 441 in a nucleotide-binding leucine-rich repeat protein containing nucleotide-binding domain and leucine-rich repeats. The overexpression of mDES1 triggered necrosis and PCD in transgenic plants. However, transgenic wheat harboring truncated wild-type DES1 proteins produced through gene editing that exhibited no significant developmental defects. The point mutation in mDES1 did not cause changes in this protein in the oligomeric state, but mDES1 failed to interact with replication protein A leading to abnormal mitotic cell division. DES1 is an ortholog of Sr35, which recognizes a Puccinia graminis f. sp. tritici stem rust disease effector in wheat, but mDES1 gained function as a direct inducer of plant death. These findings shed light on the intersection of necrosis, apoptosis, and autoimmunity in plants.

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification

Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments. A few models have addressed this problem, but performance is still not satisfactory because of high dimensional features and the use of traditional machine learning algorithms. Thus, there is a need to create a novel model to improve the predictive performance of this problem from DNA sequence features. This study took advantage of a natural language processing (NLP) model in learning biological sequences by treating them as natural language words. To learn the NLP features, a supervised learning model was consequentially employed by an ensemble deep neural network. Our proposed method could identify essential genes with sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC) values of 60.2%, 84.6%, 76.3%, 0.449, and 0.814, respectively. The overall performance outperformed the single models without ensemble, as well as the state-of-the-art predictors on the same benchmark dataset. This indicated the effectiveness of the proposed method in determining essential genes, in particular, and other sequencing problems, in general.

Less Is More, Natural Loss-of-Function Mutation Is a Strategy for Adaptation

Gene gain and loss are crucial factors that shape the evolutionary success of diverse organisms. In the past two decades, more attention has been paid to the significance of gene gain through gene duplication or de novo genes. However, gene loss through natural loss-of-function (LoF) mutations, which is prevalent in the genomes of diverse organisms, has been largely ignored. With the development of sequencing techniques, many genomes have been sequenced across diverse species and can be used to study the evolutionary patterns of gene loss. In this review, we summarize recent advances in research on various aspects of LoF mutations, including their identification, evolutionary dynamics in natural populations, and functional effects. In particular, we discuss how LoF mutations can provide insights into the minimum gene set (or the essential gene set) of an organism. Furthermore, we emphasize their potential impact on adaptation. At the genome level, although most LoF mutations are neutral or deleterious, at least some of them are under positive selection and may contribute to biodiversity and adaptation. Overall, we highlight the importance of natural LoF mutations as a robust framework for understanding biological questions in general.

Genes and gene models, an important distinction

Genome sequencing has fundamentally changed how plant biologists think about genes. All or nearly all genes can ultimately be associated with a gene model. However, many gene models appear to play little or no role in the traits of an organism. A range of structural, molecular, population and evolutionary features all show a separation between genes with known phenotypes and the overall set of annotated gene models. These different features could be combined to develop models to distinguish the genes that determine the traits of plants from the subset gene other annotated gene models which are unlikely to play a role in doing so. Efforts to identify the subset of annotated gene models likely involved in specifying the characteristics of plants would help aid a wide range of researchers.

Applications of CRISPR-Cas in agriculture and plant biotechnology

The prokaryote-derived CRISPR-Cas genome editing technology has altered plant molecular biology beyond all expectations. Characterized by robustness and high target specificity and programmability, CRISPR-Cas allows precise genetic manipulation of crop species, which provides the opportunity to create germplasms with beneficial traits and to develop novel, more sustainable agricultural systems. Furthermore, the numerous emerging biotechnologies based on CRISPR-Cas platforms have expanded the toolbox of fundamental research and plant synthetic biology. In this Review, we first briefly describe gene editing by CRISPR-Cas, focusing on the newest, precise gene editing technologies such as base editing and prime editing. We then discuss the most important applications of CRISPR-Cas in increasing plant yield, quality, disease resistance and herbicide resistance, breeding and accelerated domestication. We also highlight the most recent breakthroughs in CRISPR-Cas-related plant biotechnologies, including CRISPR-Cas reagent delivery, gene regulation, multiplexed gene editing and mutagenesis and directed evolution technologies. Finally, we discuss prospective applications of this game-changing technology.

Tomato fruit as a model for tissue-specific gene silencing in crop plants

Use of CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR-associated 9)-mediated genome editing has proliferated for use in numerous plant species to modify gene function and expression, usually in the context of either transient or stably inherited genetic alternations. While extremely useful in many applications, modification of some loci yields outcomes detrimental to further experimental evaluation or viability of the target organism. Expression of Cas9 under a promoter conferring gene knockouts in a tissue-specific subset of genomes has been demonstrated in insect and animal models, and recently in Arabidopsis. We developed an in planta GFP (green fluorescent protein) assay system to demonstrate fruit-specific gene editing in tomato using a phosphoenolpyruvate carboxylase 2 gene promoter. We then targeted a SET-domain containing polycomb protein, SlEZ2, previously shown to yield pleiotropic phenotypes when targeted via 35S-driven RNA interference and we were able to characterize fruit phenotypes absent additional developmental perturbations. Tissue-specific gene editing will have applications in assessing function of essential genes otherwise difficult to study via germline modifications and will provide routes to edited genomes in tissues that could not otherwise be recovered when their germline modification perturbs their normal development.

Distinct Expression and Methylation Patterns for Genes with Different Fates following a Single Whole-Genome Duplication in Flowering Plants

For most sequenced flowering plants, multiple whole-genome duplications (WGDs) are found. Duplicated genes following WGD often have different fates that can quickly disappear again, be retained for long(er) periods, or subsequently undergo small-scale duplications. However, how different expression, epigenetic regulation, and functional constraints are associated with these different gene fates following a WGD still requires further investigation due to successive WGDs in angiosperms complicating the gene trajectories. In this study, we investigate lotus (Nelumbo nucifera), an angiosperm with a single WGD during the K-pg boundary. Based on improved intraspecific-synteny identification by a chromosome-level assembly, transcriptome, and bisulfite sequencing, we explore not only the fundamental distinctions in genomic features, expression, and methylation patterns of genes with different fates after a WGD but also the factors that shape post-WGD expression divergence and expression bias between duplicates. We found that after a WGD genes that returned to single copies show the highest levels and breadth of expression, gene body methylation, and intron numbers, whereas the long-retained duplicates exhibit the highest degrees of protein-protein interactions and protein lengths and the lowest methylation in gene flanking regions. For those long-retained duplicate pairs, the degree of expression divergence correlates with their sequence divergence, degree in protein-protein interactions, and expression level, whereas their biases in expression level reflecting subgenome dominance are associated with the bias of subgenome fractionation. Overall, our study on the paleopolyploid nature of lotus highlights the impact of different functional constraints on gene fate and duplicate divergence following a single WGD in plant.

Machine learning: A powerful tool for gene function prediction in plants

Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule-based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning-based approaches in plants.

Genome-wide identification of EMBRYO-DEFECTIVE (EMB) genes required for growth and development in Arabidopsis

With the emergence of high-throughput methods in plant biology, the importance of long-term projects characterized by incremental advances involving multiple laboratories can sometimes be overlooked. Here, I highlight my 40-year effort to isolate and characterize the most common class of mutants encountered in Arabidopsis (Arabidopsis thaliana): those defective in embryo development. I present an updated dataset of 510 EMBRYO-DEFECTIVE (EMB) genes identified throughout the Arabidopsis community; include important details on 2200 emb mutants and 241 pigment-defective embryo (pde) mutants analyzed in my laboratory; provide curated datasets with key features and publication links for each EMB gene identified; revisit past estimates of 500-1000 total EMB genes in Arabidopsis; document 83 double mutant combinations reported to disrupt embryo development; emphasize the importance of following established nomenclature guidelines and acknowledging allele history in research publications; and consider how best to extend community-based curation and screening efforts to approach saturation for this diverse class of mutants in the future. Continued advances in identifying EMB genes and characterizing their loss-of-function mutant alleles are needed to understand genotype-to-phenotype relationships in Arabidopsis on a broad scale, and to document the contributions of large numbers of essential genes to plant growth and development.

Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification

Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene data makes it possible for researchers to train a machine-learning classifier by using some features of the known human essential genes and to use the classifier to predict new human essential genes. Previous studies have found that the essentiality of genes closely relates to their properties in the protein–protein interaction (PPI) network. In this work, we propose a novel supervised method to predict human essential genes by network embedding the PPI network. Our approach implements a bias random walk on the network to get the node network context. Then, the node pairs are input into an artificial neural network to learn their representation vectors that maximally preserves network structure and the properties of the nodes in the network. Finally, the features are put into an SVM classifier to predict human essential genes. The prediction results on two human PPI networks show that our method achieves better performance than those that refer to either genes’ sequence information or genes’ centrality properties in the network as input features. Moreover, it also outperforms the methods that represent the PPI network by other previous approaches.

Pangenomics Comes of Age: From Bacteria to Plant and Animal Applications

The pangenome refers to a collection of genomic sequence found in the entire species or population rather than in a single individual; the sequence can be core, present in all individuals, or accessory (variable or dispensable), found in a subset of individuals only. While pangenomic studies were first undertaken in bacterial species, developments in genome sequencing and assembly approaches have allowed construction of pangenomes for eukaryotic organisms, fungi, plants, and animals, including two large-scale human pangenome projects. Analysis of the these pangenomes revealed key differences, most likely stemming from divergent evolutionary histories, but also surprising similarities.

CRISPR-TSKO: A Technique for Efficient Mutagenesis in Specific Cell Types, Tissues, or Organs in Arabidopsis

Detailed functional analyses of many fundamentally important plant genes via conventional loss-of-function approaches are impeded by the severe pleiotropic phenotypes resulting from these losses. In particular, mutations in genes that are required for basic cellular functions and/or reproduction often interfere with the generation of homozygous mutant plants, precluding further functional studies. To overcome this limitation, we devised a clustered regularly interspaced short palindromic repeats (CRISPR)-based tissue-specific knockout system, CRISPR-TSKO, enabling the generation of somatic mutations in particular plant cell types, tissues, and organs. In Arabidopsis (Arabidopsis thaliana), CRISPR-TSKO mutations in essential genes caused well-defined, localized phenotypes in the root cap, stomatal lineage, or entire lateral roots. The modular cloning system developed in this study allows for the efficient selection, identification, and functional analysis of mutant lines directly in the first transgenic generation. The efficacy of CRISPR-TSKO opens avenues for discovering and analyzing gene functions in the spatial and temporal contexts of plant life while avoiding the pleiotropic effects of system-wide losses of gene function.

CRISPR-TSKO: A Technique for Efficient Mutagenesis in Specific Cell Types, Tissues, or Organs in Arabidopsis

Detailed functional analyses of many fundamentally important plant genes via conventional loss-of-function approaches are impeded by the severe pleiotropic phenotypes resulting from these losses. In particular, mutations in genes that are required for basic cellular functions and/or reproduction often interfere with the generation of homozygous mutant plants, precluding further functional studies. To overcome this limitation, we devised a clustered regularly interspaced short palindromic repeats (CRISPR)-based tissue-specific knockout system, CRISPR-TSKO, enabling the generation of somatic mutations in particular plant cell types, tissues, and organs. In Arabidopsis (Arabidopsis thaliana), CRISPR-TSKO mutations in essential genes caused well-defined, localized phenotypes in the root cap, stomatal lineage, or entire lateral roots. The modular cloning system developed in this study allows for the efficient selection, identification, and functional analysis of mutant lines directly in the first transgenic generation. The efficacy of CRISPR-TSKO opens avenues for discovering and analyzing gene functions in the spatial and temporal contexts of plant life while avoiding the pleiotropic effects of system-wide losses of gene function.

QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice

Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene controlling the trait. With the availability of a rich assortment of genomic and functional genomic data, it is possible to develop a computational method to facilitate faster identification of causal genes. We developed QTG-Finder, a machine learning based algorithm to prioritize causal genes by ranking genes within a quantitative trait locus (QTL). Two predictive models were trained separately based on known causal genes in Arabidopsis and rice. An independent validation analysis showed that the models could recall about 64% of Arabidopsis and 79% of rice causal genes when the top 20% ranked genes were considered. The top 20% ranked genes can range from 10 to 100 genes, depending on the size of a QTL. The models can prioritize different types of traits though at different efficiency. We also identified several important features of causal genes including paralog copy number, being a transporter, being a transcription factor, and containing SNPs that cause premature stop codon. This work lays the foundation for systematically understanding characteristics of causal genes and establishes a pipeline to predict causal genes based on public data.

Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae

Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Among 43,301 ITRs across the four species, 34,460 (80%) are species-specific. ITRs found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established in Oryza sativa (rice) that could accurately distinguish between phenotype genes and pseudogenes (area under curve-receiver operating characteristic = 0.94). Based on the models, 584 (8%) and 4391 (61%) rice ITRs are classified as likely functional and nonfunctional with high confidence, respectively. ITRs with conserved expression and ancient retained duplicates, features that were not part of the model, are frequently classified as likely-functional, suggesting these characteristics could serve as pragmatic rules of thumb for identifying candidate sequences likely to be under selection. This study also provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems.