Inferring and analyzing gene regulatory networks from multi-factorial expression data: a complete and interactive suite

The white lupin CCR1 receptor-like kinase controls systemic Autoregulation of Cluster Root and Nodule Development

Root development is tightly regulated in plants to optimize nutrient acquisition and interactions with soil microorganisms. In legumes, the Autoregulation of Nodulation (AoN) pathway systemically controls the proliferation of root nodules, which are energy-intensive organs. Mutations affecting the AoN pathway result in a hypernodulation phenotype accompanied by altered root development. However, it remains unclear whether this modification of root development is also systemic and coordinated with nodulation. In this study, we report the identification of the constitutive cluster root 1 (ccr1) mutant in white lupin (Lupinus albus), which exhibits constitutive production of an excessive number of cluster roots. We demonstrate that CCR1 is an ortholog of HAR1/SUNN/NARK leucin-rich repeat-receptor like kinases (LRR-RLKs), which are key regulators of the AoN pathway. Furthermore, we show that CCR1 negatively regulates both nodule and cluster root development. Interspecific grafting experiments between white and narrow-leaved lupin (Lupinus angustifolius), a species incapable of producing cluster roots, show that ccr1 shoots can induce the formation of cluster-like roots in narrow-leaved lupin rootstocks. This highlights the conservation of a CCR1-dependent signaling cascade. Transcriptome analyses reveal that CCR1 targets the conserved NIN/LBD16-NFYA regulatory module, which connects nodule and lateral root development through a shared inhibitory systemic pathway. We propose that this pathway represents a broader developmental control mechanism of root organogenesis, termed Autoregulation of Cluster Root and Nodule Development (AoDev).

Genetic control of the leaf ionome in pearl millet and correlation with root and agromorphological traits

Pearl millet (Pennisetum glaucum) thrives in arid and nutrient-poor environments, establishing its role as a crucial cereal crop for food security in sub-Saharan Africa. Despite its remarkable adaptability, its yields remain below genetic potential, primarily due to limited water and nutrient availability. In this study, we conducted ionomic profiling and genome-wide association studies (GWAS) in field conditions across two growing seasons to unravel the genetic basis of nutrient acquisition in pearl millet. Soil ion content analyses revealed significant differences in nutrient distribution between field sites, while certain ions, such as phosphorus (P) and zinc (Zn), consistently displayed stratified accumulation patterns across years, suggesting stable depth-dependent trends. Evaluation of a genetically diverse panel of inbred lines revealed substantial variation in leaf ion concentrations, with high heritability estimates. Correlations between leaf ion content and root anatomical or agromorphological traits highlighted the intricate interplay between genetic and environmental factors shaping leaf ion accumulation. These analyses also uncovered potential trade-offs in nutrient acquisition strategies. GWAS identified genomic regions associated with leaf ion concentrations, and the integration of genetic and gene expression data facilitated the identification of candidate genes implicated in ion transport and homeostasis. Our findings provide valuable insights into the genetic regulation of nutrient acquisition in pearl millet, offering potential targets for breeding nutrient-efficient and climate-resilient varieties. This study underscores the importance of integrating genetic, physiological, and root architectural traits to enhance agricultural productivity and sustainability in resource-constrained environments.

Rice JASMONIC ACID OXIDASES control resting jasmonate metabolism to promote growth and repress basal immune responses

Catabolic conversions within the jasmonate pathway have substantial consequences on phytohormone signaling output. In dicots, the jasmonic acid oxidase (JAO) catabolic route leads to jasmonic acid (JA) hydroxylation, which limits its conjugation into bioactive jasmonoyl-isoleucine (JA-Ile). Here, we functionally characterized the JAO pathway in rice (Oryza sativa) and demonstrated its key function in promoting growth and attenuating JA responses in vegetative tissues. The rice genome encodes 4 JAO-related homologs, 3 of which generate hydroxy-JA in vitro and rescue the high-defense phenotype of the Arabidopsis jao2-2 mutant. By generating and analyzing a series of single to quadruple rice jao mutants, we showed additive effects of cumulative JAO depletion on JA metabolism, basal defense levels, growth inhibition, fitness, and global metabolic reprogramming. The growth of JAO-deficient lines was substantially repressed at the juvenile stage, while the impact was milder in later vegetative development, during which plants opposed enhanced resistance to virulent and avirulent strains of Magnaporthe oryzae, the causal agent of fungal blast disease. Moreover, jao mutants exhibited slightly reduced fertility and impaired seed filling. Our findings identify the JAO pathway as an integral component of basal JA/JA-Ile homeostasis and an important determinant of the growth-defense tradeoff in rice. The regulatory function of this pathway is conserved in monocots, opening possibilities for selectively modulating basal JA responses in major cereal crops to optimize agronomic traits.

The role of DNA content in shaping chromatin architecture and gene expression

Whole-genome duplication is an evolutionary force that drives speciation in all living kingdoms and is notably prevalent in plants. The evolutionary history of plants involved at least two genomic duplications that significantly expanded the plant morphology and physiology spectrum. Many important crops are polyploids, showing valuable features relative to morphological and stress response traits. After genome duplication, diploidization processes facilitate genomic adjustments to restore disomic inheritance. However, little is known about the chromatin changes triggered by nuclear DNA content alterations. Here, we report that synthetically induced genome duplication leads to chromatinization and significant changes in gene expression, resulting in a transcriptional landscape resembling a natural tetraploid. Interestingly, synthetic diploidization elicits only minor alterations in transcriptional activity and chromatin accessibility compared to the more pronounced effects of tetraploidization. We identified epigenetic factors, including specific histone variants, that showed increased expression following genome duplication and decreased expression after genome reduction. These changes may play a key role in the epigenetic mechanisms underlying the phenotypic complexity after tetraploidization in plants. Our findings shed light on the mechanisms that modulate chromatin accessibility remodeling and gene transcription regulation underlying plant genome adaptation in response to changes in genome size.

A network-enabled pipeline for gene discovery and validation in non-model plant species

Identifying key regulators of important genes in non-model crop species is challenging due to limited multi-omics resources. To address this, we introduce the network-enabled gene discovery pipeline NEEDLE, a user-friendly tool that systematically generates coexpression gene network modules, measures gene connectivity, and establishes network hierarchy to pinpoint key transcriptional regulators from dynamic transcriptome datasets. After validating its accuracy with two independent datasets, we applied NEEDLE to identify transcription factors (TFs) regulating the expression of cellulose synthase-like F6 (CSLF6), a crucial cell wall biosynthetic gene, in Brachypodium and sorghum. Our analyses uncover regulators of CSLF6 and also shed light on the evolutionary conservation or divergence of gene regulatory elements among grass species. These results highlight NEEDLE's capability to provide biologically relevant TF predictions and demonstrate its value for non-model plant species with dynamic transcriptome datasets.

Identification of a drought stress response module in tomato plants commonly induced by fungal endophytes that confer increased drought tolerance

Global climate change exacerbates abiotic stresses, as drought, heat, and salt stresses are anticipated to increase significantly in the coming years. Plants coexist with a diverse range of microorganisms. Multiple inter-organismic relationships are known to confer benefits to plants, including growth promotion and enhanced tolerance to abiotic stresses. In this study, we investigated the mutualistic interactions between three fungal endophytes originally isolated from distinct arid environments and an agronomically relevant crop, Solanum lycopersicum. We demonstrated a significant increase in shoot biomass under drought conditions in co-cultivation with Penicillium chrysogenum isolated from Antarctica, Penicillium minioluteum isolated from the Atacama Desert, Chile, and Serendipita indica isolated from the Thar Desert, India. To elucidate plant gene modules commonly induced by the different endophytes that could explain the observed drought tolerance effect in tomato, a comprehensive transcriptomics analysis was conducted. This analysis led to the identification of a shared gene module in the fungus-infected tomato plants. Within this module, gene network analysis enabled us to identify genes related to abscisic acid (ABA) signaling, ABA transport, auxin signaling, ion homeostasis, proline biosynthesis, and jasmonic acid signaling, providing insights into the molecular basis of drought tolerance commonly mediated by fungal endophytes. Our findings highlight a conserved response in the mutualistic interactions between endophytic fungi isolated from unrelated environments and tomato roots, resulting in improved shoot biomass production under drought stress.

Genetic and transcriptomic analysis of the Bradyrhizobium T3SS-triggered nodulation in the legume Aeschynomene evenia

Some Bradyrhizobium strains nodulate certain Aeschynomene species independently of Nod factors, but thanks to their type III secretion system (T3SS). While different T3 effectors triggering nodulation (ErnA and Sup3) have been identified, the plant signalling pathways they activate remain unknown. Here, we explored the intraspecies variability in T3SS-triggered nodulation within Aeschynomene evenia and investigated transcriptomic responses that occur during this symbiosis. Furthermore, Bradyrhizobium strains having different effector sets were tested on A. evenia mutants altered in various symbiotic signalling genes. We identified the A. evenia accession N21/PI 225551 as appropriate for deciphering the T3SS-dependent process. Comparative transcriptomic analysis of A. evenia N21 roots inoculated with ORS3257 strain and its ∆ernA mutant revealed genes differentially expressed, including some involved in plant defences and auxin signalling. In the other A. evenia accession N76, all tested strains nodulated the AeCRK mutant but not the AeNIN and AeNSP2 mutants, indicating a differential requirement of these genes for T3SS-dependent nodulation. Furthermore, the effects of AePOLLUX, AeCCaMK and AeCYCLOPS mutations differed between the strains. Notably, ORS86 nodulated these three mutant lines and required for this both ErnA and Sup3. Taken together, these results shed light on how the T3SS-dependent nodulation process is achieved in legumes.

The Arabidopsis splicing factor PORCUPINE/SmE1 orchestrates temperature-dependent root development via auxin homeostasis maintenance

Appropriate abiotic stress response is pivotal for plant survival and makes use of multiple signaling molecules and phytohormones to achieve specific and fast molecular adjustments. A multitude of studies has highlighted the role of alternative splicing in response to abiotic stress, including temperature, emphasizing the role of transcriptional regulation for stress response. Here we investigated the role of the core-splicing factor PORCUPINE (PCP) on temperature-dependent root development. We used marker lines and transcriptomic analyses to study the expression profiles of meristematic regulators and mitotic markers, and chemical treatments, as well as root hormone profiling to assess the effect of auxin signaling. The loss of PCP significantly alters RAM architecture in a temperature-dependent manner. Our results indicate that PCP modulates the expression of central meristematic regulators and is required to maintain appropriate levels of auxin in the RAM. We conclude that alternative pre-mRNA splicing is sensitive to moderate temperature fluctuations and contributes to root meristem maintenance, possibly through the regulation of phytohormone homeostasis and meristematic activity.

k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm

Gene networks allow researchers to understand the underlying mechanisms between diseases and genes while reducing the need for wet lab experiments. Numerous gene network inference (GNI) algorithms have been presented in the literature to infer accurate gene networks. We proposed a hybrid GNI algorithm, k-Strong Inference Algorithm (ksia), to infer more reliable and robust gene networks from omics datasets. To increase reliability, ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) scores to determine mutual information scores between molecules to increase diversity of relation predictions. To infer a more robust gene network, ksia applies three different elimination steps to remove redundant and spurious relations between genes. The performance of ksia was evaluated on microbe microarrays database in the overlap analysis with other GNI algorithms, namely ARACNE, C3NET, CLR, and MRNET. Ksia inferred less number of relations due to its strict elimination steps. However, ksia generally performed better on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) gene expression datasets due to F- measure and precision values. The integration of association estimator scores and three elimination stages slightly increases the performance of ksia based gene networks. Users can access ksia R package and user manual of package via https://github.com/ozgurcingiz/ksia .

Comparative transcriptomic analyses of diploid and tetraploid citrus reveal how ploidy level influences salt stress tolerance

Introduction

Citrus is an important fruit crop for human health. The sensitivity of citrus trees to a wide range of abiotic stresses is a major challenge for their overall growth and productivity. Among these abiotic stresses, salinity results in a significant loss of global citrus yield. In order to find straightforward and sustainable solutions for the future and to ensure citrus productivity, it is of paramount importance to decipher the mechanisms responsible for salinity stress tolerance. Thisstudy aimed to investigate how ploidy levels influence salt stress tolerance in citrus by comparing the transcriptomic responses of diploid and tetraploid genotypes. In a previous article we investigated the physiological and biochemical response of four genotypes with different ploidy levels: diploid trifoliate orange (Poncirus trifoliata [L.] Raf.) (PO2x) and Cleopatra mandarin (Citrus reshni Hort. Ex Tan.) (CL2x) and their respective tetraploids (PO4x, CL4x).

Methods

In this study, we useda multifactorial gene selection and gene clustering approach to finely dissect the influence of ploidy level on the salt stress response of each genotype. Following transcriptome sequencing, differentially expressed genes (DEGs) were identified in response to salt stress in leaves and roots of the different citrus genotypes.

Result and discussion

Gene expression profiles and functional characterization of genes involved in the response to salt stress, as a function of ploidy level and the interaction between stress response and ploidy level, have enabled us to highlight the mechanisms involved in the varieties tested. Saltstress induced overexpression of carbohydrate biosynthesis and cell wall remodelling- related genes specifically in CL4x Ploidy level enhanced oxidative stress response in PO and ion management capacity in both genotypes. Results further highlighted that under stress conditions, only the CL4x genotype up- regulated genes involved in sugar biosynthesis, transport management, cell wall remodelling, hormone signalling, enzyme regulation and antioxidant metabolism. These findings provide crucial insights that could inform breeding strategies for developing salt-tolerant citrus varieties.

Transcriptomic and metabolomic reveal OsCOI2 as the jasmonate-receptor master switch in rice root

Jasmonate is an essential phytohormone involved in plant development and stress responses. Its perception occurs through the CORONATINE INSENSITIVE (COI) nuclear receptor allowing to target the Jasmonate-ZIM domain (JAZ) repressors for degradation by the 26S proteasome. Consequently, repressed transcription factors are released and expression of jasmonate responsive genes is induced. In rice, three OsCOI genes have been identified, OsCOI1a and the closely related OsCOI1b homolog, and OsCOI2. While the roles of OsCOI1a and OsCOI1b in plant defense and leaf senescence are well-established, the significance of OsCOI2 in plant development and jasmonate signaling has only emerged recently. To unravel the role of OsCOI2 in regulating jasmonate signaling, we examined the transcriptomic and metabolomic responses of jasmonate-treated rice lines mutated in both the OsCOI1a and OsCOI1b genes or OsCOI2. RNA-seq data highlight OsCOI2 as the primary driver of the extensive transcriptional reprogramming observed after a jasmonate challenge in rice roots. A series of transcription factors exhibiting an OsCOI2-dependent expression were identified, including those involved in root development or stress responses. OsCOI2-dependent expression was also observed for genes involved in specific processes or pathways such as cell-growth and secondary metabolite biosynthesis (phenylpropanoids and diterpene phytoalexins). Although functional redundancy exists between OsCOI1a/b and OsCOI2 in regulating some genes, oscoi2 plants generally exhibit a weaker response compared to oscoi1ab plants. Metabolic data revealed a shift from the primary metabolism to the secondary metabolism primarily governed by OsCOI2. Additionally, differential accumulation of oryzalexins was also observed in oscoi1ab and oscoi2 lines. These findings underscore the pivotal role of OsCOI2 in jasmonate signaling and suggest its involvement in the control of the growth-defense trade-off in rice.

The joint toxicity effect of glyphosate and cadmium in a concentration-dependent manner on nematode Caenorhabditis elegans

The co-occurrence of glyphosate (GPS), a commonly used organophosphorus herbicide, and cadmium (Cd), a neurotoxic metal, in agricultural environments prompts concerns about their combined toxic effects on ecosystems. This study explores the combined effects of GPS and Cd on the model organism Caenorhabditis elegans (C. elegans), to understand their cumulative effects in organismal living environments. We investigated the interaction between GPS and Cd over 24 hours using a comprehensive approach that included a variety of toxicity endpoints as well as the novel Automated Recognition and Statistics Tool (NCLE) for body bend measurement. Our data show a concentration-dependent interplay in which antagonistic effects at lower concentrations reduce phenotypic damage while synergistic effects emerge at higher concentrations, particularly at GPS's LC50. Transcriptome analysis under antagonistic conditions revealed significant downregulation of Cd toxicity-related genes and identified Y22D7AL.16, which has a C2H2-type zinc finger domain, as a novel gene involved in metal stress response, implying an alternative Cd-resilience mechanism. The expression profile of this gene shows that it plays a larger role in both development and metal stress adaption. These findings highlight the complexities of compound pollutant interactions, emphasizing the importance of including such dynamics in environmental risk assessments and control techniques.

Probing plant signal processing optogenetically by two channelrhodopsins

Early plant responses to different stress situations often encompass cytosolic Ca2+ increases, plasma membrane depolarization and the generation of reactive oxygen species1-3. However, the mechanisms by which these signalling elements are translated into defined physiological outcomes are poorly understood. Here, to study the basis for encoding of specificity in plant signal processing, we used light-gated ion channels (channelrhodopsins). We developed a genetically engineered channelrhodopsin variant called XXM 2.0 with high Ca2+ conductance that enabled triggering cytosolic Ca2+ elevations in planta. Plant responses to light-induced Ca2+ influx through XXM 2.0 were studied side by side with effects caused by an anion efflux through the light-gated anion channelrhodopsin ACR1 2.04. Although both tools triggered membrane depolarizations, their activation led to distinct plant stress responses: XXM 2.0-induced Ca2+ signals stimulated production of reactive oxygen species and defence mechanisms; ACR1 2.0-mediated anion efflux triggered drought stress responses. Our findings imply that discrete Ca2+ signals and anion efflux serve as triggers for specific metabolic and transcriptional reprogramming enabling plants to adapt to particular stress situations. Our optogenetics approach unveiled that within plant leaves, distinct physiological responses are triggered by specific ion fluxes, which are accompanied by similar electrical signals.

Vitis rotundifolia Genes Introgressed with RUN1 and RPV1: Poor Recombination and Impact on V. vinifera Berry Transcriptome

Thanks to several Vitis vinifera backcrosses with an initial V. vinifera L. × V. rotundifolia (previously Muscadinia rotundifolia) interspecific cross, the MrRUN1/MrRPV1 locus (resistance to downy and powdery mildews) was introgressed in genotypes phenotypically close to V. vinifera varieties. To check the consequences of introgressing parts of the V. rotundifolia genome on gene expression during fruit development, we conducted a comparative RNA-seq study on single berries from different V. vinifera cultivars and V. vinifera × V. rotundifolia hybrids, including 'G5' and two derivative microvine lines, 'MV102' (resistant) and 'MV32' (susceptible) segregating for the MrRUN1/RPV1 locus. RNA-Seq profiles were analyzed on a comprehensive set of single berries from the end of the herbaceous plateau to the ripe stage. Pair-end reads were aligned both on V. vinifera PN40024.V4 reference genome, V. rotundifolia cv 'Trayshed' and cv 'Carlos', and to the few resistance genes from the original V. rotundifolia cv '52' parent available at NCBI. Weighted Gene Co-expression Network Analysis (WGCNA) led to classifying the differentially expressed genes into 15 modules either preferentially correlated with resistance or berry phenology and composition. Resistance positively correlated transcripts predominantly mapped on the 4-5 Mb distal region of V. rotundifolia chromosome 12 beginning with the MrRUN1/MrRPV1 locus, while the negatively correlated ones mapped on the orthologous V. vinifera region, showing this large extremity of LG12 remained recalcitrant to internal recombination during the successive backcrosses. Some constitutively expressed V. rotundifolia genes were also observed at lower densities outside this region. Genes overexpressed in developing berries from resistant accessions, either introgressed from V. rotundifolia or triggered by these in the vinifera genome, spanned various functional groups, encompassing calcium signal transduction, hormone signaling, transcription factors, plant-pathogen-associated interactions, disease resistance proteins, ROS and phenylpropanoid biosynthesis. This transcriptomic insight provides a foundation for understanding the disease resistance inherent in these hybrid cultivars and suggests a constitutive expression of NIR NBS LRR triggering calcium signaling. Moreover, these results illustrate the magnitude of transcriptomic changes caused by the introgressed V. rotundifolia background in backcrossed hybrids, on a large number of functions largely exceeding the ones constitutively expressed in single resistant gene transformants.

Optimizing data integration improves gene regulatory network inference in Arabidopsis thaliana

MOTIVATIONS

Gene regulatory networks (GRNs) are traditionally inferred from gene expression profiles monitoring a specific condition or treatment. In the last decade, integrative strategies have successfully emerged to guide GRN inference from gene expression with complementary prior data. However, datasets used as prior information and validation gold standards are often related and limited to a subset of genes. This lack of complete and independent evaluation calls for new criteria to robustly estimate the optimal intensity of prior data integration in the inference process.

RESULTS

We address this issue for two regression-based GRN inference models, a weighted random forest (weigthedRF) and a generalized linear model estimated under a weighted LASSO penalty with stability selection (weightedLASSO). These approaches are applied to data from the root response to nitrate induction in Arabidopsis thaliana. For each gene, we measure how the integration of transcription factor binding motifs influences model prediction. We propose a new approach, DIOgene, that uses model prediction error and a simulated null hypothesis in order to optimize data integration strength in a hypothesis-driven, gene-specific manner. This integration scheme reveals a strong diversity of optimal integration intensities between genes, and offers good performance in minimizing prediction error as well as retrieving experimental interactions. Experimental results show that DIOgene compares favorably against state-of-the-art approaches and allows to recover master regulators of nitrate induction.

AVAILABILITY AND IMPLEMENTATION

The R code and notebooks demonstrating the use of the proposed approaches are available in the repository https://github.com/OceaneCsn/integrative_GRN_N_induction.

Natural genetic variation underlying the negative effect of elevated CO2 on ionome composition in Arabidopsis thaliana

The elevation of atmospheric CO2 leads to a decline in plant mineral content, which might pose a significant threat to food security in coming decades. Although few genes have been identified for the negative effect of elevated CO2 on plant mineral composition, several studies suggest the existence of genetic factors. Here, we performed a large-scale study to explore genetic diversity of plant ionome responses to elevated CO2, using six hundred Arabidopsis thaliana accessions, representing geographical distributions ranging from worldwide to regional and local environments. We show that growth under elevated CO2 leads to a global decrease of ionome content, whatever the geographic distribution of the population. We observed a high range of genetic diversity, ranging from the most negative effect to resilience or even to a benefit in response to elevated CO2. Using genome-wide association mapping, we identified a large set of genes associated with this response, and we demonstrated that the function of one of these genes is involved in the negative effect of elevated CO2 on plant mineral composition. This resource will contribute to understand the mechanisms underlying the effect of elevated CO2 on plant mineral nutrition, and could help towards the development of crops adapted to a high-CO2 world.

Hybrid whale algorithm with evolutionary strategies and filtering for high-dimensional optimization: Application to microarray cancer data

The standard whale algorithm is prone to suboptimal results and inefficiencies in high-dimensional search spaces. Therefore, examining the whale optimization algorithm components is critical. The computer-generated initial populations often exhibit an uneven distribution in the solution space, leading to low diversity. We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. We conduct simulation experiments and compare the proposed algorithm with the original WOA on thirteen benchmark test functions. Simulation experiments on unimodal or multimodal benchmarks verified the better performance of the proposed RESHWOA, such as accuracy, minimum mean, and low standard deviation rate. Furthermore, we performed two data reduction techniques, Bhattacharya distance and signal-to-noise ratio. Support Vector Machine (SVM) excels in dealing with high-dimensional datasets and numerical features. When users optimize the parameters, they can significantly improve the SVM's performance, even though it already works well with its default settings. We applied RESHWOA and WOA methods on six microarray cancer datasets to optimize the SVM parameters. The exhaustive examination and detailed results demonstrate that the new structure has addressed WOA's main shortcomings. We conclude that the proposed RESHWOA performed significantly better than the WOA.

Integrative systems biology of wheat susceptibility to Fusarium graminearum uncovers a conserved gene regulatory network and identifies master regulators targeted by fungal core effectors

BACKGROUND

Plant diseases are driven by an intricate set of defense mechanisms counterbalanced by the expression of host susceptibility factors promoted through the action of pathogen effectors. In spite of their central role in the establishment of the pathology, the primary components of plant susceptibility are still poorly understood and challenging to trace especially in plant-fungal interactions such as in Fusarium head blight (FHB) of bread wheat. Designing a system-level transcriptomics approach, we leveraged the analysis of wheat responses from a susceptible cultivar facing Fusarium graminearum strains of different aggressiveness and examined their constancy in four other wheat cultivars also developing FHB.

RESULTS

In this study, we describe unexpected differential expression of a conserved set of transcription factors and an original subset of master regulators were evidenced using a regulation network approach. The dual-integration with the expression data of pathogen effector genes combined with database mining, demonstrated robust connections with the plant molecular regulators and identified relevant candidate genes involved in plant susceptibility, mostly able to suppress plant defense mechanisms. Furthermore, taking advantage of wheat cultivars of contrasting susceptibility levels, a refined list of 142 conserved susceptibility gene candidates was proposed to be necessary host's determinants for the establishment of a compatible interaction.

CONCLUSIONS

Our findings emphasized major FHB determinants potentially controlling a set of conserved responses associated with susceptibility in bread wheat. They provide new clues for improving FHB control in wheat and also could conceivably leverage further original researches dealing with a broader spectrum of plant pathogens.

Inhibition of OCT4 binding at the MYCN locus induces neuroblastoma cell death accompanied by downregulation of transcripts with high-open reading frame dominance

Amplification of MYCN is observed in high-risk neuroblastomas (NBs) and is associated with a poor prognosis. MYCN expression is directly regulated by multiple transcription factors, including OCT4, MYCN, CTCF, and p53 in NB. Our previous study showed that inhibition of p53 binding at the MYCN locus induces NB cell death. However, it remains unclear whether inhibition of alternative transcription factor induces NB cell death. In this study, we revealed that the inhibition of OCT4 binding at the MYCN locus, a critical site for the human-specific OCT4-MYCN positive feedback loop, induces caspase-2-mediated cell death in MYCN-amplified NB. We used the CRISPR/deactivated Cas9 (dCas9) technology to specifically inhibit transcription factors from binding to the MYCN locus in the MYCN-amplified NB cell lines CHP134 and IMR32. In both cell lines, the inhibition of OCT4 binding at the MYCN locus reduced MYCN expression, thereby suppressing MYCN-target genes. After inhibition of OCT4 binding, differentially downregulated transcripts were associated with high-open reading frame (ORF) dominance score, which is associated with the translation efficiency of transcripts. These transcripts were enriched in splicing factors, including MYCN-target genes such as HNRNPA1 and PTBP1. Furthermore, transcripts with a high-ORF dominance score were significantly associated with genes whose high expression is associated with a poor prognosis in NB. Because the ORF dominance score correlates with the translation efficiency of transcripts, our findings suggest that MYCN maintains the expression of transcripts with high translation efficiency, contributing to a poor prognosis in NB. In conclusion, the inhibition of OCT4 binding at the MYCN locus resulted in reduced MYCN activity, which in turn led to the downregulation of high-ORF dominance transcripts and subsequently induced caspase-2-mediated cell death in MYCN-amplified NB cells. Therefore, disruption of the OCT4 binding at the MYCN locus may serve as an effective therapeutic strategy for MYCN-amplified NB.

Transcriptomic analysis of developing sorghum grains to detect genes related to cell wall biosynthesis and remodelling

OBJECTIVE

Sorghum (Sorghum bicolor (L.) Moench) is the fifth most important grain produced in the world. Interest for cultivating sorghum is increasing all over the world in the context of climate change, due to its low input and water requirements. Like other cultivated cereals, sorghum has significant nutritional value thanks to its protein, carbohydrate and dietary fiber content, these latter mainly consisting of cell wall polysaccharides. This work describes for the first time a transcriptomic analysis dedicated to identify the genes involved in the biosynthesis and remodelling of cell walls both in the endosperm and outer layers of sorghum grain during its development. Further analysis of these transcriptomic data will improve our understanding of cell wall assembly, which is a key component of grain quality.

DATA DESCRIPTION

This research delineates the steps of our analysis, starting with the cultivation conditions and the grain harvest at different stages of development, followed by the laser microdissection applied to separate the endosperm from the outer layers. It also describes the procedures implemented to generate RNA libraries and to obtain a normalized and filtered table of transcript counts, and finally determine the number of putative cell wall-related genes already listed in literature.

Early transcriptomic responses of rice leaves to herbivory by Spodoptera frugiperda

During herbivory, chewing insects deposit complex oral secretions (OS) onto the plant wound. Understanding how plants respond to the different cues of herbivory remains an active area of research. In this study, we used an herbivory-mimick experiment to investigate the early transcriptional response of rice plants leaves to wounding, OS, and OS microbiota from Spodoptera frugiperda larvae. Wounding induced a massive early response associated to hormones such as jasmonates. This response switched drastically upon OS treatment indicating the activation of OS specific pathways. When comparing native and dysbiotic OS treatments, we observed few gene regulation. This suggests that in addition to wounding the early response in rice is mainly driven by the insect compounds of the OS rather than microbial. However, microbiota affected genes encoding key phytohormone synthesis enzymes, suggesting an additional modulation of plant response by OS microbiota.

Asterics: a simple tool for the ExploRation and Integration of omiCS data

BACKGROUND

The rapid development of omics acquisition techniques has induced the production of a large volume of heterogeneous and multi-level omics datasets, which require specific and sometimes complex analyses to obtain relevant biological information. Here, we present ASTERICS (version 2.5), a publicly available web interface for the analyses of omics datasets.

RESULTS

ASTERICS is designed to make both standard and complex exploratory and integration analysis workflows easily available to biologists and to provide high quality interactive plots. Special care has been taken to provide a comprehensive documentation of the implemented analyses and to guide users toward sound analysis choices regarding some specific omics data. Data and analyses are organized in a comprehensive graphical workflow within ASTERICS workspace to facilitate the understanding of successive data editions and analyses leading to a given result.

CONCLUSION

ASTERICS provides an easy to use platform for omics data exploration and integration. The modular organization of its open source code makes it easy to incorporate new workflows and analyses by external contributors. ASTERICS is available at https://asterics.miat.inrae.fr and can also be deployed using provided docker images.

AI-powered therapeutic target discovery

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.

Transcriptomic Responses of Salvia hispanica to the Infestation of Red Spider Mites (Tetranychus neocaledonicus)

Salvia hispanica (chia) is a highly nutritious food source and has gained popularity due to its high omega-3 fatty acid content. Red spider mites are a serious problem in the production of S. hispanica. However, no study has been conducted to analyze the defensive response to the infestation of red spider mites in S. hispanica. To elucidate the molecular mechanisms of the defensive response of S. hispanica to red spider mites, we performed a transcriptomic analysis of S. hispanica when infested by red spider mites. In the comparative assessment of leaf transcriptomes, a total of 1743 differentially expressed genes (DEGs) were identified between control and mite-infested S. hispanica. From these, 1208 (69%) transcripts were upregulated and 535 (31%) were downregulated. The DEGs included transcription factors, defense hormones, and secondary metabolites that were either suppressed or activated in response to spider mite herbivory. Gene Ontology (GO) enrichment analysis revealed that plant secondary metabolites, such as glucosinolates, and signaling pathways, including the jasmonic acid signaling pathway, may play an important role in the defense against red spider mites. This study provides novel insights into the defense response of S. hispanica to insect herbivory and could be a resource for the improvement of pest resistance in the chia.

Jasmonate signaling controls negative and positive effectors of salt stress tolerance in rice

Plant responses to salt exposure involve large reconfigurations of hormonal pathways that orchestrate physiological changes towards tolerance. Jasmonate (JA) hormones are essential to withstand biotic and abiotic assaults, but their roles in salt tolerance remain unclear. Here we describe the dynamics of JA metabolism and signaling in root and leaf tissue of rice, a plant species that is highly exposed and sensitive to salt. Roots activate the JA pathway in an early pulse, while the second leaf displays a biphasic JA response with peaks at 1 h and 3 d post-exposure. Based on higher salt tolerance of a rice JA-deficient mutant (aoc), we examined, through kinetic transcriptome and physiological analysis, the salt-triggered processes that are under JA control. Profound genotype-differential features emerged that could underlie the observed phenotypes. Abscisic acid (ABA) content and ABA-dependent water deprivation responses were impaired in aoc shoots. Moreover, aoc accumulated more Na+ in roots, and less in leaves, with reduced ion translocation correlating with root derepression of the HAK4 Na+ transporter gene. Distinct reactive oxygen species scavengers were also stronger in aoc leaves, along with reduced senescence and chlorophyll catabolism markers. Collectively, our results identify contrasted contributions of JA signaling to different sectors of the salt stress response in rice.

DYSCALCULIA, a Venus flytrap mutant without the ability to count action potentials

The Venus flytrap Dionaea muscipula estimates prey nutrient content by counting trigger hair contacts initiating action potentials (APs) and calcium waves traveling all over the trap.^1,2,3 A first AP is associated with a subcritical rise in cytosolic calcium concentration, but when the second AP arrives in time, calcium levels pass the threshold required for fast trap closure. Consequently, memory function and decision-making are timed via a calcium clock.^3,4 For higher numbers of APs elicited by the struggling prey, the Ca²⁺ clock connects to the networks governed by the touch hormone jasmonic acid (JA), which initiates slow, hermetic trap sealing and mining of the animal food stock.⁵ Two distinct phases of trap closure can be distinguished within Dionaea's hunting cycle: (1) very fast trap snapping requiring two APs and crossing of a critical cytosolic Ca²⁺ level and (2) JA-dependent slow trap sealing and prey processing induced by more than five APs. The Dionaea mutant DYSC is still able to fire touch-induced APs but does not snap close its traps and fails to enter the hunting cycle after prolonged mechanostimulation. Transcriptomic analyses revealed that upon trigger hair touch/AP stimulation, activation of calcium signaling is largely suppressed in DYSC traps. The observation that external JA application restored hunting cycle progression together with the DYSC phenotype and its transcriptional landscape indicates that DYSC cannot properly read, count, and decode touch/AP-induced calcium signals that are key in prey capture and processing.

A gene regulatory network in Arabidopsis roots reveals features and regulators of the plant response to elevated CO2

The elevation of CO₂ in the atmosphere increases plant biomass but decreases their mineral content. The genetic and molecular bases of these effects remain mostly unknown, in particular in the root system, which is responsible for plant nutrient uptake. To gain knowledge about the effect of elevated CO₂ on plant growth and physiology, and to identify its regulatory in the roots, we analyzed genome expression in Arabidopsis roots through a combinatorial design with contrasted levels of CO₂ , nitrate, and iron. We demonstrated that elevated CO₂ has a modest effect on root genome expression under nutrient sufficiency, but by contrast leads to massive expression changes under nitrate or iron deficiencies. We demonstrated that elevated CO₂ negatively targets nitrate and iron starvation modules at the transcriptional level, associated with a reduction in high-affinity nitrate uptake. Finally, we inferred a gene regulatory network governing the root response to elevated CO₂ . This network allowed us to identify candidate transcription factors including MYB15, WOX11, and EDF3 which we experimentally validated for their role in the stimulation of growth by elevated CO₂ . Our approach identified key features and regulators of the plant response to elevated CO₂ , with the objective of developing crops resilient to climate change.

The banana genome hub: a community database for genomics in the Musaceae

The Banana Genome Hub provides centralized access for genome assemblies, annotations, and the extensive related omics resources available for bananas and banana relatives. A series of tools and unique interfaces are implemented to harness the potential of genomics in bananas, leveraging the power of comparative analysis, while recognizing the differences between datasets. Besides effective genomic tools like BLAST and the JBrowse genome browser, additional interfaces enable advanced gene search and gene family analyses including multiple alignments and phylogenies. A synteny viewer enables the comparison of genome structures between chromosome-scale assemblies. Interfaces for differential expression analyses, metabolic pathways and GO enrichment were also added. A catalogue of variants spanning the banana diversity is made available for exploration, filtering, and export to a wide variety of software. Furthermore, we implemented new ways to graphically explore gene presence-absence in pangenomes as well as genome ancestry mosaics for cultivated bananas. Besides, to guide the community in future sequencing efforts, we provide recommendations for nomenclature of locus tags and a curated list of public genomic resources (assemblies, resequencing, high density genotyping) and upcoming resources-planned, ongoing or not yet public. The Banana Genome Hub aims at supporting the banana scientific community for basic, translational, and applied research and can be accessed at https://banana-genome-hub.southgreen.fr.

Angiogenesis goes computational - The future way forward to discover new angiogenic targets?

Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.

Loss of Polycomb proteins CLF and LHP1 leads to excessive RNA degradation in Arabidopsis

Polycomb-group (PcG) proteins are major chromatin complexes that regulate gene expression, mainly described as repressors keeping genes in a transcriptionally silent state during development. Recent studies have nonetheless suggested that PcG proteins might have additional functions, including targeting active genes or acting independently of gene expression regulation. However, the reasons for the implication of PcG proteins and their associated chromatin marks on active genes are still largely unknown. Here, we report that combining mutations for CURLY LEAF (CLF) and LIKE HETEROCHROMATIN PROTEIN1 (LHP1), two Arabidopsis PcG proteins, results in deregulation of expression of active genes that are targeted by PcG proteins or enriched in associated chromatin marks. We show that this deregulation is associated with accumulation of small RNAs corresponding to massive degradation of active gene transcripts. We demonstrate that transcriptionally active genes and especially those targeted by PcG proteins are prone to RNA degradation, even though deregulation of RNA degradation following the loss of function of PcG proteins is not likely to be mediated by a PcG protein-mediated chromatin environment. Therefore, we conclude that PcG protein function is essential to maintain an accurate level of RNA degradation to ensure accurate gene expression.

Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge

Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale.

Dynamic Development of White Lupin Rootlets Along a Cluster Root

White lupin produces cluster roots in response to phosphorus deficiency. Along the cluster root, numerous short rootlets successively appear, creating a spatial and temporal gradient of developmental stages that constitutes a powerful biological model to study the dynamics of the structural and functional evolution of these organs. The present study proposes a fine histochemical, transcriptomic and functional analysis of the rootlet development from its emergence to its final length. Between these two stages, the tissue structures of the rootlets were observed, the course of transcript expressions for the genes differentially expressed was monitored and some physiological events linked to Pi nutrition were followed. A switch between (i) a growing phase, in which a normal apical meristem is present and (ii) a specialized phase for nutrition, in which the rootlet is completely differentiated, was highlighted. In the final stage of its determinate growth, the rootlet is an organ with a very active metabolism, especially for the solubilization and absorption of several nutrients. This work discusses how the transition between a growing to a determinate state in response to nutritional stresses is found in other species and underlines the fundamental dilemma of roots between soil exploration and soil exploitation.