GC content of plant genes is linked to past gene duplications

The frequency of G and C nucleotides in genomes varies from species to species, and sometimes even between different genes in the same genome. The monocot grasses have a bimodal distribution of genic GC content absent in dicots. We categorized plant genes from 5 dicots and 4 monocot grasses by synteny to related species and determined that syntenic genes have significantly higher GC content than non-syntenic genes at their 5`-end in the third position within codons for all 9 species. Lower GC content is correlated with gene duplication, as lack of synteny to distantly related genomes is associated with past interspersed gene duplications. Two mutation types can account for biased GC content, mutation of methylated C to T and gene conversion from A to G. Gene conversion involves non-reciprocal exchanges between homologous alleles and is not detectable when the alleles are identical or heterozygous for presence-absence variation, both likely situations for genes duplicated to new loci. Gene duplication can cause production of siRNA which can induce targeted methylation, elevating mC→T mutations. Recently duplicated plant genes are more frequently methylated and less likely to undergo gene conversion, each of these factors synergistically creating a mutational environment favoring AT nucleotides. The syntenic genes with high GC content in the grasses compose a subset that have undergone few duplications, or for which duplicate copies were purged by selection. We propose a “biased gene duplication / biased mutation” (BDBM) model that may explain the origin and trajectory of the observed link between duplication and genic GC bias. The BDBM model is supported by empirical data based on joint analyses of 9 angiosperm species with their genes categorized by duplication status, GC content, methylation levels and functional classes.

Multiple horizontal acquisitions of plant genes in the whitefly Bemisia tabaci

The extent to which horizontal gene transfer (HGT) has shaped eukaryote evolution remains an open question. Two recent studies reported four plant-like genes acquired through two HGT events by the whitefly Bemisia tabaci, a major agricultural pest (Lapadula et al. 2020; Xia et al. 2021). Here, we performed a systematic search for plant-to-insect HGT in B. tabaci and uncovered a total of 50 plant-like genes deriving from at least 24 independent HGT events. Most of these genes are present in three cryptic B. tabaci species, show high level of amino-acid identity to plant genes (mean = 64%), are phylogenetically nested within plant sequences, and are expressed and evolve under purifying selection. The predicted functions of these genes suggest that most of them are involved in plant-insect interactions. Thus, substantial plant-to-insect HGT may have facilitated the evolution of B. tabaci towards adaptation to a large host spectrum. Our study shows that eukaryote-to-eukaryote HGT may be relatively common in some lineages and it provides new candidate genes that may be targeted to improve current control strategies against whiteflies.

Bridging functional annotation gaps in non-model plant genes with AlphaFold, DeepFRI and small molecule docking

Background: Functional annotation assigns descriptive biological meaning to genetic sequences. Limited availability of manually curated or experimentally validated plant genes from a diverse range of taxa poses a significant challenge for functional annotation in non-model organisms. Accurate computational approaches are required. We argue that recent breakthroughs in deep learning have the potential to not only narrow the functional annotation gap between non-model and model plant organisms, but also annotate and reveal novel functions even for genes with no homologs in public databases. Results: Deep learning models were applied to functionally annotate a set of previously published differentially expressed genes. Predicted protein structures and functional annotations were generated using the AlphaFold protein structure and DeepFRI protein language inference models respectively. The resulting structures and functional annotations were validated using small molecule docking experiments. DeepFRI and AlphaFold models not only correctly annotated differentially expressed genes, but also revealed detailed mechanisms involving protein-protein interactions. Conclusions: Deep learning models are capable of inferring novel functions and achieving high accuracy in functional annotation. Their increased use in plant research will result in major improvements in annotations for non-model plants that are underrepresented in genome databases. We illustrate how integrating protein structure prediction, functional residue prediction, and small molecule docking can infer plausible protein-protein interactions and yield additional mechanistic insights. This approach will aid in the selection of candidate genes for further study from differential expression studies that generate large gene lists.

IDENTIFYING PLANT GENES SHAPING MICROBIOTA COMPOSITION IN THE BARLEY RHIZOSPHERE

A prerequisite to exploiting soil microbes for sustainable crop production is the identification of the plant genes shaping microbiota composition in the rhizosphere, the interface between roots and soil. Here we used metagenomics information as an external quantitative phenotype to map the host genetic determinants of the rhizosphere microbiota in wild and domesticated genotypes of barley, the fourth most cultivated cereal globally. We identified a small number of loci with a major effect on the composition of rhizosphere communities. One of those, designated the QRMC-3HS locus, emerged as a major determinant of microbiota composition. We then subjected soil-grown sibling lines harbouring contrasting alleles at QRMC-3HS and hosting contrasting microbiotas to comparative root RNA-seq profiling. This allowed us to identify three primary candidate genes, including a Nucleotide-Binding-Leucine-Rich-Repeat (NLR) gene in a region of structural variation of the barley genome. Our results provide novel insights into the footprint of crop improvement on the plants capacity of shaping rhizosphere microbes.

Epigenetic features improve TALE target prediction

Background The yield of many crop plants can be substantially reduced by plant-pathogenic Xanthomonas bacteria. The infection strategy of many Xanthomonas strains is based on transcription activator-like effectors (TALEs), which are secreted into the host cells and act as transcriptional activators of plant genes that are beneficial for the bacteria.The modular DNA binding domain of TALEs contains tandem repeats, each comprising two hyper-variable amino acids. These repeat-variable diresidues (RVDs) bind to their target box and determine the specificity of a TALE.All available tools for the prediction of TALE targets within the host plant suffer from many false positives. In this paper we propose a strategy to improve prediction accuracy by considering the epigenetic state of the host plant genome in the region of the target box. Results To this end, we extend our previously published tool PrediTALE by considering two epigenetic features: (i) chromatin accessibility of potentially bound regions and (ii) DNA methylation of cytosines within target boxes. Here, we determine the epigenetic features from publicly available DNase-seq, ATAC-seq, and WGBS data in rice.We benchmark the utility of both epigenetic features separately and in combination, deriving ground-truth from RNA-seq data of infections studies in rice. We find an improvement for each individual epigenetic feature, but especially the combination of both.Having established an advantage in TALE target predicting considering epigenetic features, we use these data for promoterome and genome-wide scans by our new tool EpiTALE, leading to several novel putative virulence targets. Conclusions Our results suggest that it would be worthwhile to collect condition-specific chromatin accessibility data and methylation information when studying putative virulence targets of Xanthomonas TALEs.

Regulatory Network of Cotton Genes in Response to Salt, Drought and Wilt Diseases (Verticillium and Fusarium): Progress and Perspective

In environmental conditions, crop plants are extremely affected by multiple abiotic stresses including salinity, drought, heat, and cold, as well as several biotic stresses such as pests and pathogens. However, salinity, drought, and wilt diseases (e.g., Fusarium and Verticillium) are considered the most destructive environmental stresses to cotton plants. These cause severe growth interruption and yield loss of cotton. Since cotton crops are central contributors to total worldwide fiber production, and also important for oilseed crops, it is essential to improve stress tolerant cultivars to secure future sustainable crop production under adverse environments. Plants have evolved complex mechanisms to respond and acclimate to adverse stress conditions at both physiological and molecular levels. Recent progresses in molecular genetics have delivered new insights into the regulatory network system of plant genes, which generally includes defense of cell membranes and proteins, signaling cascades and transcriptional control, and ion uptake and transport and their relevant biochemical pathways and signal factors. In this review, we mainly summarize recent progress concerning several resistance-related genes of cotton plants in response to abiotic (salt and drought) and biotic (Fusarium and Verticillium wilt) stresses and classify them according to their molecular functions to better understand the genetic network. Moreover, this review proposes that studies of stress related genes will advance the security of cotton yield and production under a changing climate and that these genes should be incorporated in the development of cotton tolerant to salt, drought, and fungal wilt diseases (Verticillium and Fusarium).

Impairment of Tomato WAT1 Enhances Resistance to Vascular Wilt Fungi Despite Severe Growth Defects

Verticillium dahliae is a particularly notorious vascular wilt pathogen of tomato and poses a reoccurring challenge to crop protection as limited qualitative resistance is available. Therefore, alternative approaches for crop protection are pursued. One such strategy is the impairment of disease susceptibility (S) genes, which are plant genes targeted by pathogens to promote disease development. In Arabidopsis and cotton, the Walls Are Thin 1 (WAT1) gene has shown to be a S gene for V. dahliae. In this study, we identified the tomato WAT1 homolog Solyc04g080940 (SlWAT1). Transient and stable silencing of SlWAT1, based on virus-induced gene silencing (VIGS) and RNAi, respectively, did not consistently lead to reduced V. dahliae susceptibility in tomato. However, CRISPR-Cas9 tomato mutant lines carrying targeted deletions in SlWAT1 showed significantly enhanced resistance to V. dahliae, and furthermore also to Verticillium albo-atrum and Fusarium oxysporum f. sp. lycopersici (Fol). Thus, disabling the tomato WAT1 gene resulted in broad-spectrum resistance to various vascular pathogens in tomato. Unfortunately these tomato CRISPR mutant lines suffered from severe growth defects. In order to overcome the pleiotropic effect caused by the impairment of the tomato WAT1 gene, future efforts should be devoted to identifying tomato SlWAT1 mutant alleles that do not negatively impact tomato growth and development.

Cotton CC-NBS-LRR Gene GbCNL130 Confers Resistance to Verticillium Wilt Across Different Species

Verticillium wilt (VW) is a destructive disease in cotton caused by Verticillium dahliae and has a significant impact on yield and quality. In the absence of safe and effective chemical control, VW is difficult to manage. Thus, at present, developing resistant varieties is the most economical and effective method of controlling Verticillium wilt of cotton. The CC-NBS-LRR (CNL) gene family is an important class of plant genes involved in disease resistance. This study identified 141 GbCNLs in Gossypium barbadense genome, with 37.5% (53 genes) GbCNLs enriched in 12 gene clusters (GC01–GC12) based on gene distribution in the chromosomes. Especially, seven GbCNLs from two largest clusters (GC11 and GC12) were significantly upregulated in the resistant cultivar (Hai No. 7124) and the susceptible (Giza No. 57). Virus-induced gene silencing of GbCNL130 in G. barbadense, one typical gene in the gene cluster 12 (GC12), significantly altered the response to VW, compromising plant resistance to V. dahliae. In contrast, GbCNL130 overexpression significantly increased the resistance to VW in the wild-type Arabidopsis thaliana. Based on our research findings presented here, we conclude that GbCNL130 promotes resistance to VW by activating the salicylic acid (SA)-dependent defense response pathway resulting in strong accumulation of reactive oxygen species and upregulation of pathogenesis-related (PR) genes. In conclusion, our study resulted in the discovery of a new CNL resistance gene in cotton, GbCNL130, that confers resistance to VW across different hosts.

Synthetic directed evolution in plants: unlocking trait engineering and improvement

Genetic variation accelerates adaptation and resilience and enables the survival of species in their changing environment. Increasing the genetic diversity of crop species is essential to improve their yield and enhance food security. Synthetic directed evolution (SDE) employs localized sequence diversification (LSD) of gene sequence and selection pressure to evolve gene variants with better fitness, improved properties and desired phenotypes. Recently, CRISPR–Cas-dependent and -independent technologies have been applied for LSD to mediate synthetic evolution in diverse species, including plants. SDE holds excellent promise to discover, accelerate and expand the range of traits of the value in crop species. Here, we highlight the efficient SDE approaches for the LSD of plant genes, selection strategies and critical traits for targeted improvement. We discuss the potential of emerging technologies, including CRISPR–Cas base editing, retron editing, EvolvR and prime editing, to establish efficient SDE in plants. Moreover, we cover CRISPR–Cas-independent technologies, including T7 polymerase editor for continuous evolution. We highlight the key challenges and potential solutions of applying SDE technologies to improve the plant traits of the value.