Transcriptional chronology reveals conserved genes involved in pennate diatom sexual reproduction.

Sexual reproduction is a major driver of adaptation and speciation in eukaryotes. In diatoms, siliceous microalgae with a unique cell size reduction-restitution life cycle and among the world's most prolific primary producers, sex also acts as the main mechanism for cell size restoration through the formation of an expanding auxospore. However, the molecular regulators of the different stages of sexual reproduction and size restoration are poorly explored. Here, we combined RNA sequencing with the assembly of a 55 Mbp reference genome for Cylindrotheca closterium to identify patterns of gene expression during different stages of sexual reproduction. These were compared with a corresponding transcriptomic time series of Seminavis robusta to assess the degree of expression conservation. Integrative orthology analysis revealed 138 one-to-one orthologues that are upregulated during sex in both species, among which 56 genes consistently upregulated during cell pairing and gametogenesis, and 11 genes induced when auxospores are present. Several early, sex-specific transcription factors and B-type cyclins were also upregulated during sex in other pennate and centric diatoms, pointing towards a conserved core regulatory machinery for meiosis and gametogenesis across diatoms. Furthermore, we find molecular evidence that the pheromone-induced cell cycle arrest is short-lived in benthic diatoms, which may be linked to their active mode of mate finding through gliding. Finally, we exploit the temporal resolution of our comparative analysis to report the first marker genes for auxospore identity called AAE1-3 ("Auxospore-Associated Expression"). Altogether, we introduce a multi-species model of the transcriptional dynamics during size restoration in diatoms and highlight conserved gene expression dynamics during different stages of sexual reproduction.

MINI-AC: inference of plant gene regulatory networks using bulk or single-cell accessible chromatin profiles.

Gene regulatory networks (GRNs) represent the interactions between transcription factors (TF) and their target genes. Plant GRNs control transcriptional programs involved in growth, development, and stress responses, ultimately affecting diverse agricultural traits. While recent developments in accessible chromatin (AC) profiling technologies make it possible to identify context-specific regulatory DNA, learning the underlying GRNs remains a major challenge. We developed MINI-AC (Motif-Informed Network Inference based on Accessible Chromatin), a method that combines AC data from bulk or single-cell experiments with TF binding site (TFBS) information to learn GRNs in plants. We benchmarked MINI-AC using bulk AC datasets from different Arabidopsis thaliana tissues and showed that it outperforms other methods to identify correct TFBS. In maize, a crop with a complex genome and abundant distal AC regions, MINI-AC successfully inferred leaf GRNs with experimentally confirmed, both proximal and distal, TF-target gene interactions. Furthermore, we showed that both AC regions and footprints are valid alternatives to infer AC-based GRNs with MINI-AC. Finally, we combined MINI-AC predictions from bulk and single-cell AC datasets to identify general and cell-type specific maize leaf regulators. Focusing on C4 metabolism, we identified diverse regulatory interactions in specialized cell types for this photosynthetic pathway. MINI-AC represents a powerful tool for inferring accurate AC-derived GRNs in plants and identifying known and novel candidate regulators, improving our understanding of gene regulation in plants.

Characterization of Gene Regulatory Networks in Plants Using New Methods and Data Types.

A major question in plant biology is to understand how plant growth, development, and environmental responses are controlled and coordinated by the activities of regulatory factors. Gene regulatory network (GRN) analyses require integrated approaches that combine experimental approaches with computational analyses. A wide range of experimental approaches and tools are now available, such as targeted perturbation of gene activities, quantitative and cell-type specific measurements of dynamic gene activities, and systematic analysis of the molecular 'hard-wiring' of the systems. At the computational level, different tools and databases are available to study regulatory sequences, including intuitive visualizations to explore data-driven gene regulatory networks in different plant species. Furthermore, advanced data integration approaches have recently been developed to efficiently leverage complementary regulatory data types and learn context-specific networks.

Prediction of Transcription Factor Regulators and Gene Regulatory Networks in Tomato Using Binding Site Information.

Gene regulatory networks (GRNs) represent the regulatory links between transcription factors (TF) and their target genes. In plants, they are essential to understand transcriptional programs that control important agricultural traits such as yield or (a)biotic stress response. Although several high- and low-throughput experimental methods have been developed to map GRNs in plants, these are sometimes expensive, come with laborious protocols, and are not always optimized for tomato, one of the most important horticultural crops worldwide. In this chapter, we present a computational method that covers two protocols: one protocol to map gene identifiers between two different tomato genome assemblies, and another protocol to predict putative regulators and delineate GRNs given a set of functionally related or coregulated genes by exploiting publicly available TF-binding information. As an example, we applied the motif enrichment protocol on tomato using upregulated genes in response to jasmonate, as well as upregulated and downregulated genes in plants with genotypes OENAM1 and nam1, respectively. We found that our protocol accurately infers the expected TFs as top enriched regulators and identifies GRNs functionally enriched in biological processes related with the experimental context under study.

Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice.

Plants have evolved various mechanisms to adapt to adverse environmental stresses, such as the modulation of gene expression. Expression of stress-responsive genes is controlled by specific regulators, including transcription factors (TFs), that bind to sequence-specific binding sites, representing key components of cis-regulatory elements and regulatory networks. Our understanding of the underlying regulatory code remains, however, incomplete. Recent studies have shown that, by training machine learning (ML) algorithms on genomic sequence features, it is possible to predict which genes will transcriptionally respond to a specific stress. By identifying the most important features for gene expression prediction, these trained ML models allow, in theory, to further elucidate the regulatory code underlying the transcriptional response to abiotic stress. Here, we trained random forest ML models to predict gene expression in rice (Oryza sativa) in response to heat or drought stress. Apart from thoroughly assessing model performance and robustness across various input training data, the importance of promoter and gene body sequence features to train ML models was evaluated. The use of enriched promoter oligomers, complementing known TF binding sites, allowed us to gain novel insights in DNA motifs contributing to the stress regulatory code. By comparing genomic feature importance scores for drought and heat stress over time, general and stress-specific genomic features contributing to the performance of the learned models and their temporal variation were identified. This study provides a solid foundation to build and interpret ML models accurately predicting transcriptional responses and enables novel insights in biological sequence features that are important for abiotic stress responses.

Diatom adhesive trail proteins acquired by horizontal gene transfer from bacteria serve as primers for marine biofilm formation.

Biofilm-forming benthic diatoms are key primary producers in coastal habitats, where they frequently dominate sunlit intertidal substrata. The development of gliding motility in raphid diatoms was a key molecular adaptation that contributed to their evolutionary success. However, the structure-function correlation between diatom adhesives utilized for gliding and their relationship to the extracellular matrix that constitutes the diatom biofilm is unknown. Here, we have used proteomics, immunolocalization, comparative genomics, phylogenetics and structural homology analysis to investigate the evolutionary history and function of diatom adhesive proteins. Our study identified eight proteins from the adhesive trails of Craspedostauros australis, of which four form a new protein family called Trailins that contain an enigmatic Choice-of-Anchor A (CAA) domain, which was acquired through horizontal gene transfer from bacteria. Notably, the CAA-domain shares a striking structural similarity with one of the most widespread domains found in ice-binding proteins (IPR021884). Our work offers new insights into the molecular basis for diatom biofilm formation, shedding light on the function and evolution of diatom adhesive proteins. This discovery suggests that there is a transition in the composition of biomolecules required for initial surface colonization and those utilized for 3D biofilm matrix formation.

Distinctive and complementary roles of E2F transcription factors during plant replication stress responses.

Survival of living organisms is fully dependent on their maintenance of genome integrity, being permanently threatened by replication stress in proliferating cells. Although the plant DNA damage response (DDR) regulator SOG1 has been demonstrated to cope with replication defects, accumulating evidence points to other pathways functioning independent of SOG1. Here, we report the roles of the Arabidopsis E2FA and EF2B transcription factors, two well-characterized regulators of DNA replication, in plant response to replication stress. Through a combination of reverse genetics and chromatin immunoprecipitation approaches, we show that E2FA and E2FB share many target genes with SOG1, providing evidence for their involvement in the DDR. Analysis of double- and triple-mutant combinations revealed that E2FB, rather than E2FA, plays the most prominent role in sustaining plant growth in the presence of replication defects, either operating antagonistically or synergistically with SOG1. Conversely, SOG1 aids in overcoming the replication defects of E2FA/E2FB-deficient plants. Collectively, our data reveal a complex transcriptional network controlling the replication stress response in which E2Fs and SOG1 act as key regulatory factors.

Transcription factors KANADI 1, MYB DOMAIN PROTEIN 44, and PHYTOCHROME INTERACTING FACTOR 4 regulate long intergenic noncoding RNAs expressed in Arabidopsis roots.

Thousands of long intergenic noncoding RNAs (lincRNAs) have been identified in plant genomes. While some lincRNAs have been characterized as important regulators in different biological processes, little is known about the transcriptional regulation for most plant lincRNAs. Through the integration of 8 annotation resources, we defined 6,599 high-confidence lincRNA loci in Arabidopsis (Arabidopsis thaliana). For lincRNAs belonging to different evolutionary age categories, we identified major differences in sequence and chromatin features, as well as in the level of conservation and purifying selection acting during evolution. Spatiotemporal gene expression profiles combined with transcription factor (TF) chromatin immunoprecipitation (ChIP) data were used to construct a TF-lincRNA regulatory network containing 2,659 lincRNAs and 15,686 interactions. We found that properties characterizing lincRNA expression, conservation, and regulation differ between plants and animals. Experimental validation confirmed the role of 3 TFs, KANADI 1, MYB DOMAIN PROTEIN 44, and PHYTOCHROME INTERACTING FACTOR 4, as key regulators controlling root-specific lincRNA expression, demonstrating the predictive power of our network. Furthermore, we identified 58 lincRNAs, regulated by these TFs, showing strong root cell type-specific expression or chromatin accessibility, which are linked with genome-wide association studies genetic associations related to root system development and growth. The multilevel genome-wide characterization covering chromatin state information, promoter conservation, and chromatin immunoprecipitation-based TF binding, for all detectable lincRNAs across 769 expression samples, permits rapidly defining the biological context and relevance of Arabidopsis lincRNAs through regulatory networks.

A redundant transcription factor network steers spatiotemporal Arabidopsis triterpene synthesis.

Plant specialized metabolites modulate developmental and ecological functions and comprise many therapeutic and other high-value compounds. However, the mechanisms determining their cell-specific expression remain unknown. Here we describe the transcriptional regulatory network that underlies cell-specific biosynthesis of triterpenes in Arabidopsis thaliana root tips. Expression of thalianol and marneral biosynthesis pathway genes depends on the phytohormone jasmonate and is limited to outer tissues. We show that this is promoted by the activity of redundant bHLH-type transcription factors from two distinct clades and coactivated by homeodomain factors. Conversely, the DOF-type transcription factor DAG1 and other regulators prevent expression of the triterpene pathway genes in inner tissues. We thus show how precise expression of triterpene biosynthesis genes is determined by a robust network of transactivators, coactivators and counteracting repressors.

Dicer-like 3a mediates intergenerational resistance against root-knot nematodes in rice via hormone responses.

In a continuously changing and challenging environment, passing down the memory of encountered stress factors to offspring could provide an evolutionary advantage. In this study, we demonstrate the existence of "intergenerational acquired resistance" in the progeny of rice (Oryza sativa) plants attacked by the belowground parasitic nematode Meloidogyne graminicola. Transcriptome analyses revealed that genes involved in defense pathways are generally downregulated in progeny of nematode-infected plants under uninfected conditions but show a stronger induction upon nematode infection. This phenomenon was termed "spring loading" and depends on initial downregulation by the 24-nucleotide (nt) siRNA biogenesis gene dicer-like 3a (dcl3a) involved in the RNA-directed DNA methylation pathway. Knockdown of dcl3a led to increased nematode susceptibility and abolished intergenerational acquired resistance, as well as jasmonic acid/ethylene spring loading in the offspring of infected plants. The importance of ethylene signaling in intergenerational resistance was confirmed by experiments on a knockdown line of ethylene insensitive 2 (ein2b), which lacks intergenerational acquired resistance. Taken together, these data indicate a role for DCL3a in regulating plant defense pathways during both within-generation and intergenerational resistance against nematodes in rice.

Rhizogenic Agrobacterium protein RolB interacts with the TOPLESS repressor proteins to reprogram plant immunity and development.

Rhizogenic Agrobacterium strains comprise biotrophic pathogens that cause hairy root disease (HRD) on hydroponically grown Solanaceae and Cucurbitaceae crops, besides being widely explored agents for the creation of hairy root cultures for the sustainable production of plant-specialized metabolites. Hairy root formation is mediated through the expression of genes encoded on the T-DNA of the root-inducing (Ri) plasmid, of which several, including root oncogenic locus B (rolB), play a major role in hairy root development. Despite decades of research, the exact molecular function of the proteins encoded by the rol genes remains enigmatic. Here, by means of TurboID-mediated proximity labeling in tomato (Solanum lycopersicum) hairy roots, we identified the repressor proteins TOPLESS (TPL) and Novel Interactor of JAZ (NINJA) as direct interactors of RolB. Although these interactions allow RolB to act as a transcriptional repressor, our data hint at another in planta function of the RolB oncoprotein. Hence, by a series of plant bioassays, transcriptomic and DNA-binding site enrichment analyses, we conclude that RolB can mitigate the TPL functioning so that it leads to a specific and partial reprogramming of phytohormone signaling, immunity, growth, and developmental processes. Our data support a model in which RolB manipulates host transcription, at least in part, through interaction with TPL, to facilitate hairy root development. Thereby, we provide important mechanistic insights into this renowned oncoprotein in HRD.

Plant lineage-specific PIKMIN1 drives APC/CCCS52A2 E3-ligase activity-dependent cell division.

The anaphase-promoting complex/cyclosome (APC/C) marks key cell cycle proteins for proteasomal breakdown, thereby ensuring unidirectional progression through the cell cycle. Its target recognition is temporally regulated by activating subunits, one of which is called CELL CYCLE SWITCH 52 A2 (CCS52A2). We sought to expand the knowledge on the APC/C by using the severe growth phenotypes of CCS52A2-deficient Arabidopsis (Arabidopsis thaliana) plants as a readout in a suppressor mutagenesis screen, resulting in the identification of the previously undescribed gene called PIKMIN1 (PKN1). PKN1 deficiency rescues the disorganized root stem cell phenotype of the ccs52a2-1 mutant, whereas an excess of PKN1 inhibits the growth of ccs52a2-1 plants, indicating the need for control of PKN1 abundance for proper development. Accordingly, the lack of PKN1 in a wild-type background negatively impacts cell division, while its systemic overexpression promotes proliferation. PKN1 shows a cell cycle phase-dependent accumulation pattern, localizing to microtubular structures, including the preprophase band, the mitotic spindle, and the phragmoplast. PKN1 is conserved throughout the plant kingdom, with its function in cell division being evolutionarily conserved in the liverwort Marchantia polymorpha. Our data thus demonstrate that PKN1 represents a novel, plant-specific protein with a role in cell division that is likely proteolytically controlled by the CCS52A2-activated APC/C.

Charting plant gene functions in the multi-omics and single-cell era.

Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.

MINI-EX: Integrative inference of single-cell gene regulatory networks in plants.

Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factors (TFs) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest. We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data, and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, leaf development in Arabidopsis, and ear development in maize, enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.

The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization.

The OMICAS alliance is part of the Colombian government's Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program's kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS' R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.

Identification of growth regulators using cross-species network analysis in plants.

With the need to increase plant productivity, one of the challenges plant scientists are facing is to identify genes that play a role in beneficial plant traits. Moreover, even when such genes are found, it is generally not trivial to transfer this knowledge about gene function across species to identify functional orthologs. Here, we focused on the leaf to study plant growth. First, we built leaf growth transcriptional networks in Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and aspen (Populus tremula). Next, known growth regulators, here defined as genes that when mutated or ectopically expressed alter plant growth, together with cross-species conserved networks, were used as guides to predict novel Arabidopsis growth regulators. Using an in-depth literature screening, 34 out of 100 top predicted growth regulators were confirmed to affect leaf phenotype when mutated or overexpressed and thus represent novel potential growth regulators. Globally, these growth regulators were involved in cell cycle, plant defense responses, gibberellin, auxin, and brassinosteroid signaling. Phenotypic characterization of loss-of-function lines confirmed two predicted growth regulators to be involved in leaf growth (NPF6.4 and LATE MERISTEM IDENTITY2). In conclusion, the presented network approach offers an integrative cross-species strategy to identify genes involved in plant growth and development.

Chromosome-scale assembly and annotation of the perennial ryegrass genome.

BACKGROUND: The availability of chromosome-scale genome assemblies is fundamentally important to advance genetics and breeding in crops, as well as for evolutionary and comparative genomics. The improvement of long-read sequencing technologies and the advent of optical mapping and chromosome conformation capture technologies in the last few years, significantly promoted the development of chromosome-scale genome assemblies of model plants and crop species. In grasses, chromosome-scale genome assemblies recently became available for cultivated and wild species of the Triticeae subfamily. Development of state-of-the-art genomic resources in species of the Poeae subfamily, which includes important crops like fescues and ryegrasses, is lagging behind the progress in the cereal species. RESULTS: Here, we report a new chromosome-scale genome sequence assembly for perennial ryegrass, obtained by combining PacBio long-read sequencing, Illumina short-read polishing, BioNano optical mapping and Hi-C scaffolding. More than 90% of the total genome size of perennial ryegrass (approximately 2.55 Gb) is covered by seven pseudo-chromosomes that show high levels of collinearity to the orthologous chromosomes of Triticeae species. The transposon fraction of perennial ryegrass was found to be relatively low, approximately 35% of the total genome content, which is less than half of the genome repeat content of cultivated cereal species. We predicted 54,629 high-confidence gene models, 10,287 long non-coding RNAs and a total of 8,393 short non-coding RNAs in the perennial ryegrass genome. CONCLUSIONS: The new reference genome sequence and annotation presented here are valuable resources for comparative genomic studies in grasses, as well as for breeding applications and will expedite the development of productive varieties in perennial ryegrass and related species.

Spatiotemporal expression profile of novel and known small RNAs throughout rice plant development focussing on seed tissues.

BACKGROUND: Small RNAs (sRNAs) regulate numerous plant processes directly related to yield, such as disease resistance and plant growth. To exploit this yield-regulating potential of sRNAs, the sRNA profile of one of the world's most important staple crops - rice - was investigated throughout plant development using next-generation sequencing. RESULTS: Root and leaves were investigated at both the vegetative and generative phase, and early-life sRNA expression was characterized in the embryo and endosperm. This led to the identification of 49,505 novel sRNAs and 5581 tRNA-derived sRNAs (tsRNAs). In all tissues, 24 nt small interfering RNAs (siRNAs) were highly expressed and associated with euchromatic, but not heterochromatic transposable elements. Twenty-one nt siRNAs deriving from genic regions in the endosperm were exceptionally highly expressed, mimicking previously reported expression levels of 24 nt siRNAs in younger endosperm samples. In rice embryos, sRNA content was highly diverse while tsRNAs were underrepresented, possibly due to snoRNA activity. Publicly available mRNA expression and DNA methylation profiles were used to identify putative siRNA targets in embryo and endosperm. These include multiple genes related to the plant hormones gibberellic acid and ethylene, and to seed phytoalexin and iron content. CONCLUSIONS: This work introduces multiple sRNAs as potential regulators of rice yield and quality, identifying them as possible targets for the continuous search to optimize rice production.

Corrigendum: The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization.

[This corrects the article DOI: 10.3389/fpls.2022.992663.].