Epitranscriptomic modulations optimize crop traits via messenger RNA modifications.

Spatial and temporal control of gene expression in Drosophila is essential in elucidating gene function. Spatial control is facilitated by the UAS/GAL4 system, and this can be coupled with additional adaptations for precise temporal control and fine tuning of gene expression levels. Here we directly compare the level of pan-neuronal transgene expression governed by nSyb-GAL4 and elav-GAL4, as well as mushroom body-specific expression alongside OK107-GAL4. We also compare the temporal modulation of gene expression in neurons with the auxin-inducible gene expression system (AGES) and temporal and regional gene expression targeting (TARGET) systems.

Are carotenoids the true colors of crop improvement?

Over the past decade, extensive research has advanced our understanding of plants' responses to abiotic stress, such as extreme temperatures, water scarcity, and metal toxicity. These stresses arise from human activities and urbanization that accelerate global warming and ultimately reduce crop yields. The use of agrochemicals further intensifies these stresses. In response, plants undergo a cascade of physiological changes, including the generation of reactive oxygen species (ROS), stomatal opening and closing, changes in cytosolic calcium ion concentrations, activation of potassium channels, metabolite production, and stress-responsive gene expression. To investigate these changes at molecular, cellular and morphological levels in plants, genomics, transcriptomics, epigenomics, and metabolomics approaches have been employed. Advances in plant genomics have identified key genes related to stress, quantitative trait loci (QTLs) and regulatory networks involved in stress perception, signal transduction, and adaptive responses. Epigenomic studies have revealed the dynamic regulation of gene expression through modifications such as DNA methylation, histone modifications, and small RNAs, which play crucial roles in fine-tuning stress-responsive gene expression and memory. Metabolomic profiling, validated by techniques such as liquid chromatography–mass spectrometry (LC–MS/MS), has identified metabolic pathways and molecular signatures associated with stress acclimation and resilience. We propose an integrative multi-omics approach, combined with CRISPR-based genome editing and high-throughput phenotyping, to accelerate the discovery of stress-resilient traits. This comprehensive strategy provides a powerful framework to unravel complex stress response networks and identify potential targets for genetic improvement of stress tolerance in crops. For example, CRISPR enables precise editing of stress-responsive genes, while phenotyping platforms allow rapid screening of multi-trait responses under field conditions. Ultimately, the synergistic application of plant genomics, epigenomics, and metabolomics is driving forward our understanding of plant stress biology. This integrated knowledge opens new avenues for sustainable agriculture in the face of climate change and environmental challenges.

Functional Divergence between Subgenomes and Gene Pairs after Whole Genome Duplications

The inexorable exposure of plants to the combinations of abiotic stresses has affected the worldwide food supply. The crop improvement against these abiotic stresses has been captivating approach to increase the yield and enhance the stress tolerance. By using traditional and modern breeding methods, the characters that confer tolerance to these stresses were accomplished. No doubt genetic engineering and molecular breeding have helped in comprehending the intricate nature of stress response. Understanding of abiotic stress-involved cellular pathways provides vital information on such responses. On the other hand, genomic research for crop improvement has raised new assessments in breeding new varieties against abiotic stresses. Interpretation of responses of the crop plants under stress is of great significance by studying the main role of crops in food and biofuel production. This review presents genomic-based approaches revealing the complex networks controlling the mechanisms of abiotic stress tolerance, and the possible modes of assimilating information attained by genomic-based approaches due to the advancement in isolation and functional analysis of genes controlling the yield and abiotic stress tolerance are discussed.

Plants respond to survive under water-deficit conditions via a series of physiological, cellular, and molecular processes culminating in stress tolerance. Many drought-inducible genes with various functions have been identified by molecular and genomic analyses in Arabidopsis, rice, and other plants, including a number of transcription factors that regulate stress-inducible gene expression. The products of stress-inducible genes function both in the initial stress response and in establishing plant stress tolerance. In this short review, recent progress resulting from analysis of gene expression during the drought-stress response in plants as well as in elucidating the functions of genes implicated in the stress response and/or stress tolerance are summarized. A description is also provided of how various genes involved in stress tolerance were applied in genetic engineering of dehydration stress tolerance in transgenic Arabidopsis plants.

Gene and genome duplications are key drivers of plant genome evolution, expanding genetic repertoires and facilitating functional innovation. However, genes originating from different duplication mechanisms undergo different outcomes, especially at the expression level. Previous studies have demonstrated that segmental or whole-genome duplications generate duplicates with similar and somewhat redundant expression profiles across multiple tissues, while other duplicates display increased divergence, ultimately leading to functional innovations. However, little is known about how duplicates diverge in expression across cell types in a single tissue. Here, we used high-resolution spatial transcriptomic data from five species (Arabidopsis thaliana, Glycine max, Phalaenopsis aphrodite, Zea mays,andHordeum vulgare) to investigate the evolution of gene expression following gene duplications. We found that genes originating from segmental or whole-genome duplications display increased expression levels, expression breadths, spatial variability, and number of coexpression partners. Duplication mechanisms that preserve cis-regulatory landscapes typically generate paralogs with more preserved expression profiles, but such differences by duplication mode disappear over time. Expression divergence also depends on gene functions, with dosage-sensitive gene families displaying highly preserved expression profiles, while families involved in more specialized processes (e.g., flowering and phytohormone biosynthesis) display increased divergence. Paralogs originating from large-scale (including whole-genome) duplications display redundant and/or overlapping expression profiles, indicating functional redundancy and/or subfunctionalization, while small-scale duplicates diverge asymmetrically, indicating neofunctionalization. Collectively, our findings provide new insights into the tempo and mode of gene expression evolution, helping understand how gene and genome duplications shape cell identities.

B lymphocytes (B cells) are a type of white blood cell that play an essential role in the adaptive immune response. They are derived from pluripotent hematopoietic stem cells and undergo several developmental stages in the bone marrow and secondary lymphoid organs to become effector cells. B cells can act as antigen-presenting cells, secrete cytokines, generate immunological memory as memory B cells, and produce and secrete high-affinity antibodies as plasma B cells.B-cell development occurs in discontinuous steps within specific organs and niche environments, progressing through checkpoints controlled by the relative levels of numerous transcription factors, cytokines, and surface receptors. These complex interactions of distinct developmental programs operate through balanced control mechanisms rather than simple "on/off" signals.Over the past two decades, much has been learned about short non-coding RNA (ncRNA) molecules that play a critical role in fine-tuning gene expression by targeting specific messenger RNAs (mRNAs) for degradation or translational repression. In the intricate orchestration of B-cell development, ncRNAs contribute to the delicate balance between proliferation, differentiation, and apoptosis by influencing key checkpoints in the maturation process.Therefore, in this chapter, I will review the role of different classes of small ncRNAs, including microRNAs, glycoRNAs, tRNA-derived fragments, and ribosomal RNA-derived fragments, in modulating gene expression at the post-transcriptional level and their contribution to the intricate regulatory network that controls B-cell maturation.

Climate change is leading to significant biotic and abiotic stresses, which is alarming for the future of sustainable crop production and improvement. Global agriculture faces mounting challenges. Understanding epigenetic mechanisms can help improve crops. Exploring plant epigenetic mechanisms can enhance crop resilience, productivity, and stress tolerance, ensuring food security for the growing global populations. Epigenetic mechanisms often involve heritable modifications in organisms without any direct changes in the Deoxyribonucleic Acid (DNA). Epigenetics is essential for making plants adaptive to different stressed conditions. Abiotic and biotic stresses are major limiting factors for plant growth, development, and survival. Epigenetic mechanisms, such as DNA methylation, histone tail modifications, and Ribonucleic Acid (RNA)-directed pathways, regulate gene expression and allow plants to develop memory against these stresses. This memory is crucial for plants to respond effectively to uncertain stresses in the future. These epigenetic modifications are heritable to the next generations. Researchers have shown that plants use epigenetic memory to adapt to a rapidly changing environment, particularly harsh biotic and abiotic challenges, enabling plants to enhance their ability to adapt to diverse environments and withstand abrupt changes in their surroundings. A thorough understanding of these mechanisms can help us develop safer and more efficient methods of crop improvement to make crops more resilient and create a food-secure future for generations. The mechanisms of RNA-directed pathways, DNA methylation, and histone tail modifications are critical in modulating plant responses to diverse stressors, as they directly take part in the expression and suppression of targeted genes.

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

Polyploidization is a common occurrence in the evolutionary history of flowering plants, significantly contributing to their adaptability and diversity. However, the molecular mechanisms behind these adaptive advantages are not well understood. Through comprehensive phenotyping of diploid and tetraploid clones from Citrus and Poncirus genera, we discovered that genome doubling significantly enhances salt stress resilience. Epigenetic and transcriptomic analyses revealed that increased ethylene production in the roots of tetraploid plants was associated with hypomethylation and enhanced chromatin accessibility of the ACO1 gene. This increased ethylene production activates the transcription of reactive oxygen species scavenging genes and stress-related hormone biosynthesis genes. Consequently, tetraploid plants exhibited superior root functionality under salt stress, maintaining improved cytosolic K+/Na+ homeostasis. To genetically validate the link between salt stress resilience and ACO1 expression, we generated overexpression and knockout lines, confirming the central role of ACO1 expression regulation following genome doubling in salt stress resilience. Our work elucidates the molecular mechanisms underlying the role of genome doubling in stress resilience. We also highlight the importance of chromatin dynamics in fine-tuning ethylene gene expression and activating salt stress resilience pathways, offering valuable insights into plant adaptation and crop genome evolution.

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Lung squamous cell carcinoma (LUSC) is a type of lung cancer with a dismal prognosis that lacks adequate therapies and actionable targets. This disease is characterized by a sequence of low- and high-grade preinvasive stages with increasing probability of malignant progression. Increasing our knowledge about the biology of these premalignant lesions (PMLs) is necessary to design new methods of early detection and prevention, and to identify the molecular processes that are key for malignant progression. To facilitate this research, we have designed XTABLE (Exploring Transcriptomes of Bronchial Lesions), an open-source application that integrates the most extensive transcriptomic databases of PMLs published so far. With this tool, users can stratify samples using multiple parameters and interrogate PML biology in multiple manners, such as two- and multiple-group comparisons, interrogation of genes of interests, and transcriptional signatures. Using XTABLE, we have carried out a comparative study of the potential role of chromosomal instability scores as biomarkers of PML progression and mapped the onset of the most relevant LUSC pathways to the sequence of LUSC developmental stages. XTABLE will critically facilitate new research for the identification of early detection biomarkers and acquire a better understanding of the LUSC precancerous stages. Editor's evaluation The authors have created a resource tool that is valuable in assessing precancerous lesions in the lung, which may serve as a tool for investigators working in this area, and as an example for additional similar resources. The accessibility of the tool is a concern but does not diminish the quality of the product. https://doi.org/10.7554/eLife.77507.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Lung squamous cell carcinoma (LUSC) is a type of non-small cell lung cancer that accounts for 20–30% of all lung cancer cases (Chen et al., 2014; Sung et al., 2021; Bray et al., 2021). Despite being the second most frequent type of lung cancer (Torre et al., 2016), our knowledge regarding the biology of this disease as well as the therapeutic modalities to treat it remain far behind the most frequent type of lung cancer, lung adenocarcinoma (LUAD) (Hirsch et al., 2016; Khuder, 2001). LUAD genetics is dominated by mutations (that are often druggable) that activate the RTK/RAS pathway, including EGFR and KRAS mutations (The Cancer Genome Atlas Research Network, 2012; Jamal-Hanjani et al., 2017). However, the genetic landscape of LUSC is more complex, with multiple pathways altered in subsets of patients and a lack of actionable mutations (Campbell et al., 2016; The Cancer Genome Atlas Research Network, 2012; Kim et al., 2014), precluding the development of new therapies. Hence, the only pharmacological therapies available to treat LUSC patients are immune checkpoint inhibitor monotherapy or in combination with chemotherapy (Mok et al., 2019; Paz-Ares et al., 2018; Weinberg and Gadgeel, 2019). Furthermore, the National Lung Cancer Matrix Trial and The Lung Master Protocol, the largest personalized medicine trials in lung cancer, have not shown clear therapeutic benefits with targeted agents in LUSC (; Middleton et al., 2020; Redman et al., 2020). LUSC also has a worse prognosis than LUAD independent of stage at detection, with a 5-year overall survival (OS) of 6.2% for patients diagnosed with distant metastasis (9.5% for LUAD). However, patients diagnosed with localized disease are eligible for curative surgery and the 5-year OS is 50% (National Cancer Institute and DCCPS, 2018). Therefore, early detection is currently the most valuable tool to prevent deaths by LUSC as evidenced by several ongoing programmes of lung cancer early detection (Crosbie et al., 2019a; Crosbie et al., 2019b; Aberle et al., 2011). These initiatives make use of low-dose CT scans in high-risk populations, but in spite of the frequent detection of localized lung cancer eligible for resection with curative intent, 40% of early diagnosed patients die within 5 years. LUSC progresses through a series of premalignant stages characterized by alterations of the normal bronchial epithelium (Figure 1; Ishizumi et al., 2010; Kadara et al., 2016; Thakrar et al., 2017; Pennycuick et al., 2020). These endobronchial premalignant lesions (PMLs) are classified as low-grade (squamous metaplasia, mild and moderate dysplasia) and high-grade (severe dysplasia, and carcinomas in situ [CIS]) (Figure 1). However, not all PMLs progress to LUSC. Although obvious differences exist between the multiple studies published on the topic, high-grade PMLs have a higher risk of progression than low grade, and high levels of chromosomal instability (CIN) are also predictive of progressive PMLs (Thakrar et al., 2017; van Boerdonk et al., 2014; Teixeira et al., 2019; Merrick et al., 2016). This potential role of CIN as biomarker of PMLs progression has been observed in low- and high-grade PMLs (van Boerdonk et al., 2014; Teixeira et al., 2019). These reports showed that high levels of copy number variations was the best predictor of progressive PMLs (van Boerdonk et al., 2014; Teixeira et al., 2019) and that immune response is the most likely cause of regression as these lesions contained higher levels of immune infiltration (Pennycuick et al., 2020; Beane et al., 2019). These results are supported by transcriptomic analysis of PMLs that indicate immune evasion in the transition to invasiveness (Mascaux et al., 2019). Figure 1 Download asset Open asset Developmental stages of lung squamous cell carcinoma (LUSC) premalignant lesions (PMLs) with representative histological images for each stage (haematoxilyn-eosin) and a summary of the four studies included in XTABLE (Exploring Transcriptomes of Bronchial Lesions). PMLs are typically classified as normal epithelium (including hyperplasia), low-grade and high-grade. Two studies (Mascaux et al., 2019, and Beane et al., 2019) carried out gene expression analysis of multiple developmental stages, whereas Merrick et al., 2018, and Teixeira et al., 2019, focused on dysplasias (blue boxes) and carcinomas in situ (CIS) (pink boxes), respectively. The most relevant findings of each article are summarized in the figure. Error bars=50 µm. The detection of PMLs cannot be carried out by routine patient imaging techniques such as CT or PET scans as the morphological change that they cause in the airway does not result in radiological contrast. Alternatively, ablation of high-grade PMLs detected by autofluorescence bronchoscopy using minimally invasive endoscopic procedures in high-risk populations is an innovative and interesting strategy to prevent LUSC (Guibert et al., 2016). Nonetheless autofluorescence bronchoscopy is an expensive and complex technique of limited use in large screening programmes. Therefore, simpler, more cost effective, and scalable methods of high-grade PML detection are needed to prevent deaths by LUSC. Improving the detection of PMLs requires a better understanding of their biology and the validation of adequate biomarkers of progressive lesions that can be translated into new technologies for large screening initiatives. Cell surface proteins, metabolites, nasal-based biomarkers, blood and sputum/bronchoalveolar lavage biomarkers are examples of biomarkers that can be used to improve and/or complement current diagnostic techniques (such as CT and PET scans) or develop new ones. A user-friendly application to interrogate gene expression from studies focusing on precancerous LUSC stages will enhance the advances in PML biology. However, such application does not exist. Recently, scientific interest in the biology of preinvasive LUSC stages has motivated the publication of several articles characterizing PML transcriptomes from various perspectives (Figure 1). Two reports published by Beane et al., 2019, and Mascaux et al., 2019, showed stage-by-stage gene expression analyses of PMLs. These studies provided the most detailed transcriptomic characterization of all PML stages so far and identified changes in the immune microenvironment associated with invasive transformation. Additionally, longitudinal studies by Beane et al., 2019, Merrick et al., 2018, and Teixeira et al., 2019, contained samples with known progression potential. Beane et al., 2019, identified molecular subtypes with specific biological traits whereas articles published by Merrick et al., 2018, and Teixeira et al., 2019 focused on gene expression of specific preinvasive stages (dysplasias and CIS, respectively) with the objective of identifying predictors of PML progression and the biomolecular processes involved (Figure 1). These studies provide a valuable source of gene expression data to identify candidate biomarkers for the detection of high-risk PMLs and/or early stage LUSC as well as to investigate the biology of premalignant LUSC progression. Transparent and straightforward accessibility to transcriptomic databases is a key requirement for the open science philosophy. Applications that integrate multiple databases focusing on the same biological and clinical problem, such as camcAPP (Dunning et al., 2017), allow cross-comparisons between independent studies, strengthen the robustness of results obtained, and allow the selection of high-confidence data. In this report, we provide an overall description of XTABLE (Exploring Transcriptomes of Bronchial Lesions) and provide examples of its functions. XTABLE is a new open-source application that will enable scientists to interrogate currently four LUSC PML transcriptomic datasets in a versatile manner that can be adapted to the needs of each researcher. Specifically, LUSC prevention and diagnosis are the main areas that can benefit the most from XTABLE, but its versatility and multiple functions lend themselves to the exploration of a variety of research questions. Without XTABLE, researchers would have to put together all the packages, data processing steps themselves for each analysis they wished to run. In this report, we provide an overall explanation of all the functions of XTABLE as well as a detailed description of the most important analysis modules, including two-group comparisons, gene-of-interest analyses, and interrogation of transcriptional signatures. Additionally, we explored the use of CIN-related signatures as a biomarker of progressive PMLs and mapped the onset of the most important LUSC pathways to its developmental stages. Results Description of the studies included in XTABLE Four datasets originating from four independent PML transcriptomic studies have been included in the XTABLE application (Figure 1, Table 1). Two datasets, GSE109743 (Beane et al., 2019) and GSE33479 (Mascaux et al., 2019), provide gene expression data of the developmental LUSC stages (with and without progression status information, respectively), whereas the remaining studies, GSE114489 (Merrick et al., 2018) and GSE108124 (Teixeira et al., 2019), focus on analysing specific PML stages (dysplasias and CIS, respectively) that have been followed up to establish their progressive, persistent, or regressive potential. Table 1 Description of the four cohorts included in XTABLE (Exploring Transcriptomes of Bronchial Lesions). GEO accessionPMIDStagesProgression status knownNumber of samplesSample typeTranscriptomeGSE3347931243362MultipleNo122Whole biopsiesMicroarrayGSE10974331015447Multiple*Yes448Discovery cohort (197 Bx, 91 Br)Validation cohort (111Bx, 49 Br)Whole biopsies and brushingsRNAseqGSE11448929997230Dysplasias normalYes63Whole biopsiesMicroarrayGSE10812430664780CISYes33MicrodissectedMicroarray Bx: biopsies; Br: brushings. * This cohort includes neither carcinomas in situ (CIS) nor invasive carcinomas. Dataset GSE33479 (Mascaux et al., 2019) comprises expression microarray analyses of 122 endobronchial biopsies of unknown progression status. Samples were obtained following autofluorescence bronchoscopy and as no enrichment or microdissection of the biopsy epithelium was carried out, variable levels of stromal component are present in samples. Although this results in dilution of epithelial signals, it has the advantage of providing information about the microenvironment as well as insights into the infiltrated immune cells. Dataset GSE109743 (Beane et al., 2019) consists of a transcriptomic analysis of whole PML biopsies with no purification of the epithelial compartment as well as bronchial brushings obtained from adjacent normal regions of the bronchial mucosa. Unlike GSE33479, GSE109743 used RNAseq and more importantly, includes the progression status for some lesions established by serial biopsies. Samples were classified as ‘normal-stable’ when they changed between normal, hyperplasia, and metaplasia, ‘regressive’ when they regress from dysplasia to a less severe dysplastic grade, or from dysplasia to normal/hyperplasia/metaplasia. Remaining samples were considered persistent/progressive. One advantage of this dataset is the large number of samples divided into a discovery and a validation cohort (Figure 1, Table 1). However, the representation of each PML stage is not homogeneous and CIS samples are not included. Datasets GSE114489 (Merrick et al., 2018) and GSE108124 (Teixeira et al., 2019) constitute a different type of study. Both make use of sequential biopsies to classify lesions according to their progression potential, but they differ in the stages included, the classification of progression status and sample processing. In GSE114489, the authors collected 63 baseline bronchial biopsies (with corresponding follow-up biopsies) and classified samples in four groups: 23 persistent dysplasias (dysplastic lesions with the same or higher severity scores in follow-up biopsies), 15 regressive dysplasias (dysplasias progressing to lower severity scores), 9 progressive non-dysplasias (biopsies with normal or hyperplastic morphologies that progress to more severe morphologies), and 16 stable non-dysplasias (normal or hyperplastic pathology that remains stable in follow-up biopsies). Microarray analysis of gene expression was performed on whole biopsies. The study published by Teixeira et al., 2019 (GSE108124) also makes use of follow-up biopsies to classify the progression potential of PMLs, but unlike GSE114489, GSE108124 focuses on CIS and progression is defined as the transition to invasive carcinomas in follow-up biopsies. RNA for gene expression microarray analysis was extracted from microdissected FFPE samples to enrich the epithelial component. XTABLE access, interface, and functions XTABLE download can be carried out from https://gitlab.com/cruk-mi/xtable (copy archived at Roberts, 2022) following the instructions in the Materials and methods section and Video 1. XTABLE has been designed using the shiny app interface (see Materials and methods section) and its functions have been divided into 11 interrelated tabs that contain a specific function to interrogate each dataset separately (Figure 2A). Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Step-by-step instructions to install XTABLE (Exploring Transcriptomes of Bronchial Lesions) using RStudio. Figure 2 with 3 supplements see all Download asset Open asset Overall organization of XTABLE (Exploring Transcriptomes of Bronchial Lesions) functions and use of the DEG function. (A) Organization of all the functions in the XTABLE interface. The functions are interrelated and completing certain analyses requires the use of several functions. For instance, the GSEA and PA functions operate with gene lists obtained with the DEG function. (B) Workflow to obtain differentially expressed genes between two groups using the DEG function. The example shows groups of samples arranged by developmental stage to compare low-grade and high-grade premalignant lesions (PMLs) in the GSE33479 cohort. (C and D) Workflow to obtain differentially expressed genes between two groups using the DEG function. The two groups have been arranged by progression status using in the GSE114489 and GSE108124 cohorts, respectively. Introduction Detailed description of the four studies, analyses performed in each tab, output formats, links to the four articles used in the application, bioinformatic packages used in the different functions and additional references. Dataset tab Explanation of the differences in methodology and progression status definitions between the four studies. Load tab In this tab, the user selects the database (GSE114489, GSE108124, GSE109743, or GSE33479) for subsequent interrogation. Due to specific differences between the four studies, only one dataset can be interrogated at a time. Since dataset GSE109743 contains a discovery and a validation cohort, as well as biopsies and brushings, preselection of the cohort and type of sample has to be carried out (Figure 2—figure supplement 1). CIN-score tab CIN has been identified as a good predictor of PML progression. Except for Teixeira et al., 2019, the other three studies do not provide genomic analyses that provide an estimate of the level of chromosomal alterations in the samples. Several gene expression signatures that correlate with CIN (CIN-scores) have been described in the literature (Teixeira et al., 2019; Carter et al., 2006). This CIN-score function returns a list of several CIN-scores (CIN70, CIN25, and CIN5, depending on the number of genes included in the signature) for all samples included in the study and a graph depicting CIN-scores in different sample types classified according to the sample classification defined in the study. A line marking a selected CIN-score threshold can be added for visualization purpose and to help determine appropriate thresholds for additional CIN-related analyses (Figure 2—figure supplement 2). DEG tab Function to identify differentially expressed genes in comparisons of two groups of samples determined by the user. This function allows the selection of p-value and fold-change cutoffs for the analysis. Additionally, AUC is calculated for each gene to provide further confidence to the differentially expressed gene results and additional gene IDs are added at this stage that have been sourced from two separate datasets to maximize identification and discrepancies manually reviewed to improve downstream enrichment and pathway analyses. GSEA and PA In these two tabs, the user can carry out gene set and pathway enrichment analyses using multiple tools (goseq/ideal, fgsea/MSigDB, enrichR, gage/gageData, kegga/pathview, ReactomePA, Progeny, and Dorothea). This function operates with the list of differentially expressed genes obtained with the DEG function. Signature This function returns the gene expression values of a user-defined gene list. Lists can be manually entered or uploaded from a .csv file. Deconvolution Estimation of immune and stromal component in samples from gene expression data using the ESTIMATE tool. ROC This function returns receiver operating characteristic (ROC) curves in a user-defined comparison of two groups of samples stratified by the expression of a gene or by CIN-scores. Heatmap Returns a gene expression heatmap for genes selected by variance, user-defined gene signatures, and differentially expressed genes in the DEG tab. PCA Principal component analysis (PCA) of all the samples included in each study. Several sample characteristics can be highlighted in the PCA plot including CIN signatures, progression status, and PML analysis of expression of genes selected by the user. Several for sample are expression analysis The discovery of candidate biomarkers for the detection of PMLs at high risk of malignant progression and the interrogation of PML biology on the comparison of gene expression between lesions with known progressive or regressive potential. The four databases included in XTABLE contain different types of information which users can interrogate these To facilitate the interrogation of the four transcriptomic databases in a manner that allows the versatile of we have designed a in XTABLE DEG (Figure that returns the differentially expressed genes in two user-defined groups of samples stratified by PML stage and by known progression status GSE114489, and or by CIN-score In XTABLE, we have included three et al., and (Teixeira et al., 2019). and have been in the with the the number of genes for interrogation. is from the used by Teixeira et al., 2019, and is to a good with progression in CIS groups by stage using DEG allows the comparison of two PML stages or the of stages into two For instance, to compare the expression between low-grade and high-grade PMLs, we can two one including metaplasia, low and moderate dysplasia and one including severe dysplasia and CIS using cohort GSE33479 (Figure selection of the cohort and up the groups (Figure the application returns a list of differentially expressed genes with associated information (Figure including AUC by ROC analysis. ROC curves associated with each gene in a two-group for biomarker can be using the ROC tab (Figure 2—figure supplement of progressive and regressive samples can also be carried out by the in studies that provide this information (Figure and To carry out two-group comparison by CIN signatures, the CIN-score tab a visualization of the three CIN-scores for all samples to the selection of a CIN-score threshold for sample (Figure A .csv the CIN-scores of all samples can be The selected CIN threshold can be used in the DEG tab to stratify and low samples and a list of differentially expressed genes (Figure Figure 3 Download asset Open asset expression analysis between two groups of samples classified according to a chromosomal instability The CIN-score function allows the visualization of CIN-scores for all samples in a study. A CIN-score threshold selected by the user can be on the graph The CIN-score threshold selected by the user can be used in the DEG tab to the two-group the developmental stages of LUSC as described in Mascaux et al., 2019 CIN25, and can be used in the DEG tab. 1, and 2 and 3 and and the and largest within The list of differentially expressed genes obtained in the DEG tab can be used in the GSEA and PA tabs to carry out gene set enrichment and pathway analyses. The GSEA tab allows the user to the gene set enrichment tool fgsea/MSigDB, enrichR, and p-value and the gene to (Figure For instance, using the function to one of the three and molecular to for analysis (Figure with the tool, the user can the gene set of interest (Figure The PA tab operates in a similar manner using four pathway analysis tools ReactomePA, Progeny, and (Figure supplement 1). Figure with 1 supplement see all Download asset Open asset set enrichment analyses in a list differentially expressed genes using the GSEA tab. (A) set enrichment analysis using the tool of a list of differentially expressed genes obtained in the DEG tab. One of the three main can be selected for analysis at a time. selection of a XTABLE (Exploring Transcriptomes of Bronchial Lesions) returns a list of with associated (B) set enrichment analysis using the tool. This tool allows the selection of included in and returns a list of signatures with associated The example shows the selection of the in the analysis and user-defined transcriptional signatures can investigate their gene or of genes of interest in the To facilitate this type of analyses, we have included the and Signature functions in In the tab, the user can the expression of one gene of This tab is divided in three The tool returns a list of the multiple to the same gene expression values for the gene of interest in all samples (Figure The function returns the differentially expressed gene analysis results including fold-change and values for a gene in all groups of samples with a (Figure and on the study. The function to the function but allows of up to four groups of samples and the for analysis can be determined by the user. The example in Figure shows the of expression in four groups of samples that normal, low-grade PMLs, high-grade PMLs, and invasive carcinomas (Figure The and are the result with the comparison with the (normal normal and as the (Figure Figure 5 Download asset Open asset XTABLE (Exploring Transcriptomes of Bronchial Lesions) functions to analyses on genes and user defined gene signatures (A) The function the the expression values for a gene of interest in all samples. (B) The tool the expression of a gene of interest in groups of samples with a In the the function the expression of in all stages with the normal in The tool the of samples in up to four groups and comparison with a determined by the user. The example shows the analysis of expression in four groups of samples from the GSE33479 cohort and invasive The is set as for analysis. of the use of the Heatmap tab to interrogate to the expression of gene in premalignant lesions can be defined by the user in the and are shown using the stage classification and entered manually or from a .csv file. Alternatively, the heatmap can be from a list of differentially expressed genes from the DEG tab or a selected number of genes by The three can be selected in the In the example the heatmap shows all microarray associated to each gene calculated using To facilitate the interrogation of biological processes PML XTABLE also allows the interrogation of transcriptomic signatures using multiple functions. The Signature tab returns a list of expression values and a graph with the scores of expression for a gene set determined by the user in all samples of the selected study. The Heatmap tab returns a heatmap that the expression values in all samples in a selected study. The gene set shown in the heatmap can be selected using three from the With the the user can manually or a list of The example in Figure shows a heatmap from the GSE33479 dataset with transcriptional of an important LUSC The selects the list of genes differentially expressed in the DEG tab. the selects genes using a user-defined number of genes with the of the between CIN signatures and progression potential Two studies have highlighted the potential role of CIN as predictor of low-grade (van Boerdonk et al., and high-grade (Teixeira et al., 2019) PMLs progression in LUSC. XTABLE can be used to

Allopolyploidization, which entails interspecific hybridization and whole genome duplication (WGD), is associated with emergent genetic and epigenetic instabilities that are thought to contribute to adaptation and evolution. One frequent genomic consequence of nascent allopolyploidization is homoeologous exchange (HE), which arises from compromised meiotic fidelity and generates genetically and phenotypically variable progenies. Here, we used a genetically tractable synthetic rice segmental allotetraploid system to interrogate genome-wide DNA methylation and gene expression responses and outcomes to the separate and combined effects of hybridization, WGD and HEs. Progenies of the tetraploid rice were genomically diverse due to genome-wide HEs that affected all chromosomes, yet they exhibited overall methylome stability. Nonetheless, regional variation of cytosine methylation states was widespread in the tetraploids. Transcriptome profiling revealed genome-wide alteration of gene expression, which at least in part associates with changes in DNA methylation. Intriguingly, changes of DNA methylation and gene expression could be decoupled from hybridity and sustained and amplified by HEs. Our results suggest that HEs, a prominent genetic consequence of nascent allopolyploidy, can exacerbate, diversify and perpetuate the effects of allopolyploidization on epigenetic and gene expression variation, and hence may contribute to allopolyploid evolution.

Sesame (Sesamum indicum) is a globally cultivated oilseed crop known for its nutraceutical and pharmaceutical significance. Its rich content of antioxidant lignans, mono- and polyunsaturated fatty acids, vitamins, minerals, carbohydrates, and proteins contributes to its importance. To enhance understanding of sesame's genetic potential for crop improvement and utilization, transcriptome data from two sesame species, Sesamum indicum and Sesamum mulayanum, at two developmental stages (10 and 30 days after pollination, DAP) were analyzed using the Galaxy platform to identify differentially expressed genes. The results showed that 170 genes were up-regulated, and 46 genes were down-regulated. Gene ontology analysis revealed that up-regulated genes were involved in diverse molecular functions and biological processes related to defense response to nematode, systemic acquired resistance, abscisic acid response, and detoxification, among others. Similarly, pathway analysis revealed that the up-regulated genes were involved in pathways related to plant defense, secondary metabolite synthesis, fatty acid synthesis, and phenylalanine, tyrosine and tryptophan biosynthesis. A network analysis was also predicted for describing the interaction of secondary metabolites and stress tolerance genes. The results of the present study provide new insights into the genetic and genomic understanding of sesame, thereby helping in future crop improvement.

With more than 30,000 species, ray-finned fish represent approximately half of vertebrates. The evolution of ray-finned fish was impacted by several whole genome duplication (WGD) events including a teleost-specific WGD event (TGD) that occurred at the root of the teleost lineage about 350 million years ago (Mya) and more recent WGD events in salmonids, carps, suckers and others. In plants and animals, WGD events are associated with adaptive radiations and evolutionary innovations. WGD-spurred innovation may be especially relevant in the case of teleost fish, which colonized a wide diversity of habitats on earth, including many extreme environments. Fish biodiversity, the use of fish models for human medicine and ecological studies, and the importance of fish in human nutrition, fuel an important need for the characterization of gene expression repertoires and corresponding evolutionary histories of ray-finned fish genes. To this aim, we performed transcriptome analyses and developed the PhyloFish database to provide (i) de novo assembled gene repertoires in 23 different ray-finned fish species including two holosteans (i.e. a group that diverged from teleosts before TGD) and 21 teleosts (including six salmonids), and (ii) gene expression levels in ten different tissues and organs (and embryos for many) in the same species. This resource was generated using a common deep RNA sequencing protocol to obtain the most exhaustive gene repertoire possible in each species that allows between-species comparisons to study the evolution of gene expression in different lineages. The PhyloFish database described here can be accessed and searched using RNAbrowse, a simple and efficient solution to give access to RNA-seq de novo assembled transcripts.