Cis-regulatory elements (CREs) are critical for modulating gene expression and phenotypic diversity in maize. While genome-wide association study (GWAS) hits and expression quantitative trait loci (eQTLs) are often enriched in CREs, their molecular mechanisms remain poorly understood. Characterizing CREs within accessible chromatin regions (ACRs) offers a powerful approach to link noncoding variants to chromatin structure alterations and phenotypic variation. Here, we generated ATAC-seq profiles from seedling leaves of 214 maize inbred lines, identifying 82 174 consensus ACRs. Notably, 39.55% of these ACRs exhibited significant population-wide chromatin accessibility variation. By mapping chromatin accessibility quantitative trait loci (caQTLs), we discovered 27 004 loci, including 1398 predicted to disrupt transcription factor (TF)-binding sites. Integration with multi-omics data revealed 7405 caACR-target gene pairs and linked 56 caACRs to GWAS signals for 51 agronomic traits, with significant enrichment in flowering-related pathways. Functional candidates such as ZmZIM30 - putatively regulated by caACRs - emerged as key regulators of flowering time. At the fad7 locus associated with linolenic acid content, allelic variants overlapping a caQTL showed differential chromatin accessibility. Our study provides a high-resolution cis-elements of maize leaves, deciphers the genetic basis of chromatin accessibility variation, and bridges noncoding caQTLs to molecular mechanisms underlying GWAS hits.

doi: 10.1111/tpj.70437 PubMed: 40827700 Google Scholar

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Secondary metabolites (SMs) are crucial for plant adaptation and human health, yet the regulatory mechanisms underlying their biosynthesis remain incompletely understood. Cornus wilsoniana, a woody oil crop and medicinal plant, is renowned for its rich flavonoid content, yet the genetic and metabolic basis of its secondary metabolism remains largely unexplored. Here, we integrate transcriptomic and metabolomic analyses to dissect the regulatory landscape governing flavonoid biosynthesis during flower bud development. Comparative analyses between high-yield and low-yield genotypes across distinct developmental stages reveal a coordinated reprogramming of flavonoid pathways, with significant shifts in gene expression and metabolite accumulation. WGCNA links critical biosynthetic genes to co-expression modules enriched in hormone signaling, redox homeostasis, and light perception, underscoring the interplay between developmental and environmental cues in shaping metabolic fluxes. Furthermore, comparative genomics reveals 17 candidate genes forming a complete anthocyanin and proanthocyanidin biosynthetic network. Our findings illuminate the molecular framework underlying flavonoid metabolism in C. wilsoniana, providing a foundation for metabolic engineering and genetic improvement strategies aimed at enhancing bioactive compound production for medicinal and agricultural applications.

doi: 10.48130/mpb-0025-0015 Google Scholar

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Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into plant cellular diversity by enabling high-resolution analyses of gene expression at the single-cell level. However, the complexity of scRNA-seq data, including challenges in batch integration, cell type annotation, and gene regulatory network (GRN) inference, demands advanced computational approaches. To address these challenges, we developed scPlantLLM, a Transformer model trained on millions of plant single-cell data points. Using a sequential pretraining strategy incorporating masked language modeling and cell type annotation tasks, scPlantLLM generates robust and interpretable single-cell data embeddings. When applied to Arabidopsis thaliana datasets, scPlantLLM excels in clustering, cell type annotation, and batch integration, achieving an accuracy of up to 0.91 in zero-shot learning scenarios. Furthermore, the model demonstrates an ability to identify biologically meaningful GRNs and subtle cellular subtypes, showcasing its potential to advance plant biology research. Compared to traditional methods, scPlantLLM outperforms in key metrics such as adjusted rand index (ARI), normalized mutual information (NMI) and silhouette score (SIL), highlighting its superior clustering accuracy and biological relevance. scPlantLLM represents a foundational model for exploring plant single-cell expression atlases, offering unprecedented capabilities to resolve cellular heterogeneity and regulatory dynamics across diverse plant systems. The code used in this study is available at https://github.com/compbioNJU/scPlantLLM.

doi: 10.1093/gpbjnl/qzaf024 PubMed: 40094454 Google Scholar

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Awareness of estrogen's effects on health is broadening rapidly. The effects of long-term high levels of estrogen on the body involve multiple organs. Here, we used both single-cell chromatin accessibility and RNA sequencing data to analyze the potential effect of estrogen on major organs. The integrated cell map enabled in-depth dissection and comparison of molecular dynamics, cell-type compositions, and cellular heterogeneity across multiple tissues and organs under estrogen stimulation. We also inferred pseudotime cell trajectories and cell-cell communications to uncover key molecular signatures underlying their cellular processes in major organs in response to estrogen. For example, estrogen could induce the differentiation of IFIT3 + neutrophils into S100A9 + neutrophils involved in the function of endosome-to-lysosome transport and the multivesicular body sorting pathway in liver tissues. Furthermore, through integration with human genome-wide association study data, we further identified a subset of risk genes during disease development that were induced by estrogen, such as AKT1 (related to endometrial cancer), CCND1 (related to breast cancer), HSPH1 (related to colorectal cancer), and COVID-19 and asthma-related risk genes. Our work uncovers the impact of estrogen on the major organs, constitutes a useful resource, and reveals the contribution and mechanism of estrogen to related diseases.

doi: 10.1093/lifemedi/lnae012 PubMed: 39872660 Google Scholar

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An increasing number of non-coding RNAs (ncRNAs) are found to have roles in gene expression and cellular regulations. However, there are still a large number of ncRNAs whose functions remain to be studied. Despite decades of research, the field continues to evolve, with each newly identified ncRNA undergoing processes such as biogenesis, identification, and functional annotation. Bioinformatics methodologies, alongside traditional biochemical experimental methods, have played an important role in advancing ncRNA research across various stages. Presently, over 50 types of ncRNAs have been characterized, each exhibiting diverse functions. However, there remains a need for standardization and integration of these ncRNAs within a unified framework. In response to this gap, this review traces the historical trajectory of ncRNA research and proposes a unified notation system. Additionally, we comprehensively elucidate the ncRNA interactome, detailing its associations with DNAs, RNAs, proteins, complexes, and chromatin. A web portal named ncRNA Hub ( https://bis.zju.edu.cn/nchub/ ) is also constructed to provide detailed notations of ncRNAs and share a collection of bioinformatics resources. This review aims to provide a broader perspective and standardized paradigm for advancing ncRNA research.

doi: 10.1007/s10142-024-01494-w PubMed: 39557706 Google Scholar

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Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.

doi: 10.1126/sciadv.adp4942 PubMed: 39331720 Google Scholar

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Chromatin accessibility sequencing has been widely used for uncovering genetic regulatory mechanisms and inferring gene regulatory networks. However, effectively integrating large-scale chromatin accessibility datasets has posed a significant challenge. This is due to the lack of a comprehensive end-to-end solution, as many existing tools primarily emphasize data preprocessing and overlook downstream analyses. To bridge this gap, we have introduced cisDynet, a holistic solution that combines streamlined data preprocessing using Snakemake and R functions with advanced downstream analysis capabilities. cisDynet excels in conventional data analyses, encompassing peak statistics, peak annotation, differential analysis, motif enrichment analysis, and more. Additionally, it allows to perform sophisticated data exploration, such as tissue-specific peak identification, time course data modeling, integration of RNA-seq data to establish peak-to-gene associations, constructing regulatory networks, and conducting enrichment analysis of genome-wide association study (GWAS) variants. As a proof of concept, we applied cisDynet to reanalyze comprehensive ATAC-seq datasets across various tissues from the Encyclopedia of DNA Elements (ENCODE) project. The analysis successfully delineated tissue-specific open chromatin regions (OCRs), established connections between OCRs and target genes, and effectively linked these discoveries with 1861 GWAS variants. Furthermore, cisDynet was instrumental in dissecting the time course open chromatin data of mouse embryonic development, revealing the dynamic behavior of OCRs over developmental stages and identifying key transcription factors governing differentiation trajectories. In summary, cisDynet offers researchers a user-friendly solution that minimizes the need for extensive coding, ensures the reproducibility of results, and greatly simplifies the exploration of epigenomic data.

doi: 10.1002/imt2.152 PubMed: 38868212 Google Scholar

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Spike architecture influences both grain weight and grain number per spike, which are the two major components of grain yield in bread wheat (Triticum aestivum L.). However, the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits. Here, we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat. We identified 170 loci that are responsible for variations in spike length, spikelet number per spike, and grain number per spike through genome-wide association study and meta-QTL analyses. We constructed gene regulatory networks for young inflorescences at the double ridge stage and the floret primordium stage, in which the spikelet meristem and the floret meristem are predominant, respectively, by integrating transcriptome, histone modification, chromatin accessibility, eQTL, and protein-protein interactome data. From these networks, we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits. The functions of TaZF-B1, VRT-B2, and TaSPL15-A/D in establishment of wheat spike architecture were verified. This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.

doi: 10.1016/j.xplc.2024.100879 PubMed: 38486454 Google Scholar

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Cornus wilsoniana W. is a woody oil plant with high oil content and strong hypolipidemic effects, making it a valuable species for medicinal, landscaping, and ecological purposes in China. To advance genetic research on this species, we employed PacBio together with Hi-C data to create a draft genome assembly for C. wilsoniana. Based on an 11-chromosome anchored chromosome-level assembly, the estimated genome size was determined to be 843.51 Mb. The N50 contig size and N50 scaffold size were calculated to be 4.49 and 78.00 Mb, respectively. Furthermore, 30 474 protein-coding genes were annotated. Comparative genomics analysis revealed that C. wilsoniana diverged from its closest species ~12.46 million years ago (Mya). Furthermore, the divergence between Cornaceae and Nyssaceae occurred >62.22 Mya. We also found evidence of whole-genome duplication events and whole-genome triplication γ, occurring at ~44.90 and 115.86 Mya. We further inferred the origins of chromosomes, which sheds light on the complex evolutionary history of the karyotype of C. wilsoniana. Through transcriptional and metabolic analysis, we identified two FAD2 homologous genes that may play a crucial role in controlling the oleic to linoleic acid ratio. We further investigated the correlation between metabolites and genes and identified 33 MADS-TF homologous genes that may affect flower morphology in C. wilsoniana. Overall, this study lays the groundwork for future research aimed at identifying the genetic basis of crucial traits in C. wilsoniana.

doi: 10.1093/hr/uhad196 PubMed: 38023476 Google Scholar

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Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled the comprehensive profiling of gene expression patterns at the single-cell level, offering unprecedented insights into cellular diversity and heterogeneity within plant tissues. In this study, we present a systematic approach to construct a plant single-cell database, scPlantDB, which is publicly available at https://biobigdata.nju.edu.cn/scplantdb. We integrated single-cell transcriptomic profiles from 67 high-quality datasets across 17 plant species, comprising approximately 2.5 million cells. The data underwent rigorous collection, manual curation, strict quality control and standardized processing from public databases. scPlantDB offers interactive visualization of gene expression at the single-cell level, facilitating the exploration of both single-dataset and multiple-dataset analyses. It enables systematic comparison and functional annotation of markers across diverse cell types and species while providing tools to identify and compare cell types based on these markers. In summary, scPlantDB serves as a comprehensive database for investigating cell types and markers within plant cell atlases. It is a valuable resource for the plant research community.

doi: 10.1093/nar/gkad706 PubMed: 37638765 Google Scholar

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Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.

doi: 10.1016/j.xplc.2023.100631 PubMed: 37254480 Google Scholar

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BACKGROUND: Homoeologs are defined as homologous genes resulting from allopolyploidy. Bread wheat, Triticum aestivum, is an allohexaploid species with many homoeologs. Homoeolog expression bias, referring to the relative contribution of homoeologs to the transcriptome, is critical for determining the traits that influence wheat growth and development. Asymmetric transcription of homoeologs has been so far investigated in a tissue or organ-specific manner, which could be misleading due to a mixture of cell types. RESULTS: Here, we perform single nuclei RNA sequencing and ATAC sequencing of wheat root to study the asymmetric gene transcription, reconstruct cell differentiation trajectories and cell-type-specific gene regulatory networks. We identify 22 cell types. We then reconstruct cell differentiation trajectories that suggest different origins between epidermis/cortex and endodermis, distinguishing bread wheat from Arabidopsis. We show that the ratio of asymmetrically transcribed triads varies greatly when analyzing at the single-cell level. Hub transcription factors determining cell type identity are also identified. In particular, we demonstrate that TaSPL14 participates in vasculature development by regulating the expression of BAM1. Combining single-cell transcription and chromatin accessibility data, we construct the pseudo-time regulatory network driving root hair differentiation. We find MYB3R4, REF6, HDG1, and GATAs as key regulators in this process. CONCLUSIONS: Our findings reveal the transcriptional landscape of root organization and asymmetric gene transcription at single-cell resolution in polyploid wheat.

doi: 10.1186/s13059-023-02908-x PubMed: 37016448 Google Scholar

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Genomic studies have demonstrated a high frequency of genetic alterations in components of the SWI/SNF complex including the core subunit SMARCA4. However, the mechanisms of tumorigenesis driven by SMARCA4 mutations, particularly in colorectal cancer (CRC), remain largely unknown. In this study, we identified a specific, hotspot mutation in SMARCA4 (c. 3721C>T) which results in a conversion from arginine to tryptophan at residue 1157 (R1157W) in human CRC tissues associated with higher-grade tumors and controls CRC progression. Mechanistically, we found that the SMARCA4R1157W mutation facilitated its recruitment to PRMT1-mediated H4R3me2a (asymmetric dimethylation of Arg 3 in histone H4) and enhanced the ATPase activity of SWI/SNF complex to remodel chromatin in CRC cells. We further showed that the SMARCA4R1157W mutant reinforced the transcriptional expression of EGFR and TNS4 to promote the proliferation of CRC cells and patient-derived tumor organoids. Importantly, we demonstrated that SMARCA4R1157W CRC cells and mutant cell-derived xenografts were more sensitive to the combined inhibition of PRMT1 and SMARCA4 which act synergistically to suppress cell proliferation. Together, our findings show that SMARCA4-R1157W is a critical activating mutation, which accelerates CRC progression through facilitating chromatin recruitment and remodeling. Our results suggest a potential precision therapeutic strategy for the treatment of CRC patients carrying the SMARCA4R1157W mutation.

doi: 10.1038/s41698-023-00367-y PubMed: 36922568 Google Scholar

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Gene transcription is largely regulated by cis-regulatory elements. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is an emerging technology that can accurately map cis-regulatory elements in animals and plants. However, the presence of cell walls and chloroplasts in plants hinders the extraction of high-quality nuclei, thereby affects the quality of ATAC-seq data. Meanwhile, it is tricky to perform ATAC-seq with different tissue types, especially for those with limited size and amount. Moreover, with rapid growth of ATAC-seq datasets from plants, powerful and easy-to-use data analysis pipelines for ATAC-seq, especially for wheat is lacking. Here, we provided an all-in-one solution for mapping open chromatin in wheat including both experimental and data analysis procedure. We efficiently obtained nuclei with less cell debris from various wheat tissues. High-quality ATAC-seq data from young spike and ovary, which are hard to harvest were generated. We determined that the saturation sequencing depth of wheat ATAC-seq is about 16 Gb. Particularly, we developed a powerful and easy-to-use online pipeline to analyze the wheat ATAC-seq data and this pipeline can be easily extended to other plant species. The method developed here will facilitate plant regulatory genome study not only for wheat but also for other plant species.

doi: 10.3389/fpls.2022.1074873 PubMed: 36466281 Google Scholar

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Non-human primates are attractive laboratory animal models that accurately reflect both developmental and pathological features of humans. Here we present a compendium of cell types across multiple organs in cynomolgus monkeys (Macaca fascicularis) using both single-cell chromatin accessibility and RNA sequencing data. The integrated cell map enables in-depth dissection and comparison of molecular dynamics, cell-type compositions and cellular heterogeneity across multiple tissues and organs. Using single-cell transcriptomic data, we infer pseudotime cell trajectories and cell-cell communications to uncover key molecular signatures underlying their cellular processes. Furthermore, we identify various cell-specific cis-regulatory elements and construct organ-specific gene regulatory networks at the single-cell level. Finally, we perform comparative analyses of single-cell landscapes among mouse, monkey and human. We show that cynomolgus monkey has strikingly higher degree of similarities in terms of immune-associated gene expression patterns and cellular communications to human than mouse. Taken together, our study provides a valuable resource for non-human primate cell biology.

doi: 10.1038/s41467-022-31770-x PubMed: 35831300 Google Scholar

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Hibiscus hamabo is a semi-mangrove species with strong tolerance to salt and waterlogging stress. However, the molecular basis and mechanisms that underlie this strong adaptability to harsh environments remain poorly understood. Here, we assembled a high-quality, chromosome-level genome of this semi-mangrove plant and analyzed its transcriptome under different stress treatments to reveal regulatory responses and mechanisms. Our analyses suggested that H. hamabo has undergone two recent successive polyploidy events, a whole-genome duplication followed by a whole-genome triplication, resulting in an unusually large gene number (107 309 genes). Comparison of the H. hamabo genome with that of its close relative Hibiscus cannabinus, which has not experienced a recent WGT, indicated that genes associated with high stress resistance have been preferentially preserved in the H. hamabo genome, suggesting an underlying association between polyploidy and stronger stress resistance. Transcriptomic data indicated that genes in the roots and leaves responded differently to stress. In roots, genes that regulate ion channels involved in biosynthetic and metabolic processes responded quickly to adjust the ion concentration and provide metabolic products to protect root cells, whereas no such rapid response was observed from genes in leaves. Using co-expression networks, potential stress resistance genes were identified for use in future functional investigations. The genome sequence, along with several transcriptome datasets, provide insights into genome evolution and the mechanism of salt and waterlogging tolerance in H. hamabo, suggesting the importance of polyploidization for environmental adaptation.

doi: 10.1093/hr/uhac067 PubMed: 35480957 Google Scholar

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Colorectal cancer (CRC), a malignant tumor worldwide consists of microsatellite instability (MSI) and stable (MSS) phenotypes. Although SHP2 is a hopeful target for cancer therapy, its relationship with innate immunosuppression remains elusive. To address that, single-cell RNA sequencing was performed to explore the role of SHP2 in all cell types of tumor microenvironment (TME) from murine MC38 xenografts. Intratumoral cells were found to be functionally heterogeneous and responded significantly to SHP099, a SHP2 allosteric inhibitor. The malignant evolution of tumor cells was remarkably arrested by SHP099. Mechanistically, STING-TBK1-IRF3-mediated type I interferon signaling was highly activated by SHP099 in infiltrated myeloid cells. Notably, CRC patients with MSS phenotype exhibited greater macrophage infiltration and more potent SHP2 phosphorylation in CD68+ macrophages than MSI-high phenotypes, suggesting the potential role of macrophagic SHP2 in TME. Collectively, our data reveals a mechanism of innate immunosuppression mediated by SHP2, suggesting that SHP2 is a promising target for colon cancer immunotherapy.

doi: 10.1016/j.apsb.2021.08.006 PubMed: 35127377 Google Scholar

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With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of biological molecules underlying disease etiology. Herein, we summarized two main categories of biological networks: evidence-based and statistically inferred. These different types of molecular networks complement each other at both bulk and single-cell levels. We also review three main strategies to incorporate quantitative genetics results with multi-omics data by network analysis: (a) network propagation, (b) functional module-based methods, (c) comparative/dynamic networks. These strategies not only aid in elucidating molecular mechanisms of complex diseases but can guide the search for therapeutic targets.

doi: 10.1016/j.cbpa.2021.102101 PubMed: 34861483 Google Scholar

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Nucleotide-binding leucine-rich-repeat (NLR) genes comprise the largest family of plant disease-resistance genes. Angiosperm NLR genes are phylogenetically divided into the TNL, CNL, and RNL subclasses. NLR copy numbers and subclass composition vary tremendously across angiosperm genomes. However, the evolutionary associations between genomic NLR content and ecological adaptation, or between NLR content and signal transduction components, are poorly characterized because of limited genome availability. In this study, we established an angiosperm NLR atlas (ANNA, https://biobigdata.nju.edu.cn/ANNA/) that includes NLR genes from over 300 angiosperm genomes. Using ANNA, we revealed that NLR copy numbers differ up to 66-fold among closely related species owing to rapid gene loss and gain. Interestingly, NLR contraction was associated with adaptations to aquatic, parasitic, and carnivorous lifestyles. The convergent NLR reduction in aquatic plants resembles the lack of NLR expansion during the long-term evolution of green algae before the colonization of land. A co-evolutionary pattern between NLR subclasses and plant immune pathway components was also identified, suggesting that immune pathway deficiencies may drive TNL loss. Finally, we identified a conserved TNL lineage that may function independently of the EDS1-SAG101-NRG1 module. Collectively, these findings provide new insights into the evolution of NLR genes in the context of ecological adaptation and genome content variation.

doi: 10.1016/j.molp.2021.08.001 PubMed: 34364002 Google Scholar

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doi: 10.1007/s11427-020-1717-9 PubMed: 32394245 Google Scholar

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With ongoing climate change, drought events are becoming more frequent and will affect biomass formation when occurring during pre-flowering stages. We explored growth over time under such a drought scenario, via non-invasive imaging and revealed the underlying key genetic factors in spring barley. By comparing with well-watered conditions investigated in an earlier study and including information on timing, QTL could be classified as constitutive, drought or recovery-adaptive. Drought-adaptive QTL were found in the vicinity of genes involved in dehydration tolerance such as dehydrins (Dhn4, Dhn7, Dhn8, and Dhn9) and aquaporins (e.g. HvPIP1;5, HvPIP2;7, and HvTIP2;1). The influence of phenology on biomass formation increased under drought. Accordingly, the main QTL during recovery was the region of HvPPD-H1. The most important constitutive QTL for late biomass was located in the vicinity of HvDIM, while the main locus for seedling biomass was the HvWAXY region. The disappearance of QTL marked the genetic architecture of tiller number. The most important constitutive QTL was located on 6HS in the region of 1-FEH. Stage and tolerance specific QTL might provide opportunities for genetic manipulation to stabilize biomass and tiller number under drought conditions and thereby also grain yield.

doi: 10.3389/fpls.2019.01307 PubMed: 31708943 Google Scholar

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Enhancers are critical for developmental stage-specific gene expression, but their dynamic regulation in plants remains poorly understood. Here we compare genome-wide localization of H3K27ac, chromatin accessibility and transcriptomic changes during flower development in Arabidopsis. H3K27ac prevalently marks promoter-proximal regions, suggesting that H3K27ac is not a hallmark for enhancers in Arabidopsis. We provide computational and experimental evidence to confirm that distal DNase І hypersensitive sites are predictive of enhancers. The predicted enhancers are highly stage-specific across flower development, significantly associated with SNPs for flowering-related phenotypes, and conserved across crucifer species. Through the integration of genome-wide transcription factor (TF) binding datasets, we find that floral master regulators and stage-specific TFs are largely enriched at developmentally dynamic enhancers. Finally, we show that enhancer clusters and intronic enhancers significantly associate with stage-specific gene regulation by floral master TFs. Our study provides insights into the functional flexibility of enhancers during plant development, as well as hints to annotate plant enhancers.

doi: 10.1038/s41467-019-09513-2 PubMed: 30979870 Google Scholar

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The wave of high-throughput technologies in genomics and phenomics are enabling data to be generated on an unprecedented scale and at a reasonable cost. Exploring the large-scale data sets generated by these technologies to derive biological insights requires efficient bioinformatic tools. Here we introduce an interactive, open-source web application (HTPmod) for high-throughput biological data modeling and visualization. HTPmod is implemented with the Shiny framework by integrating the computational power and professional visualization of R and including various machine-learning approaches. We demonstrate that HTPmod can be used for modeling and visualizing large-scale, high-dimensional data sets (such as multiple omics data) under a broad context. By reinvestigating example data sets from recent studies, we find not only that HTPmod can reproduce results from the original studies in a straightforward fashion and within a reasonable time, but also that novel insights may be gained from fast reinvestigation of existing data by HTPmod.

doi: 10.1038/s42003-018-0091-x PubMed: 30271970 Google Scholar

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Targeted changes in chromatin state at thousands of genes are central to eukaryotic development. RELATIVE OF EARLY FLOWERING 6 (REF6) is a Jumonji-type histone demethylase that counteracts Polycomb repressive complex 2 (PRC2)-mediated gene silencing in plants and was reported to select its binding sites in a direct, sequence-specific manner1-3. Here we show that REF6 and its two close paralogues determine spatial 'boundaries' of the repressive histone H3K27me3 mark in the genome and control the tissue-specific release from PRC2-mediated gene repression. Targeted mutagenesis revealed that these histone demethylases display pleiotropic, redundant functions in plant development, several of which depend on trans factor-mediated recruitment. Thus, Jumonji-type histone demethylases restrict repressive chromatin domains and contribute to tissue-specific gene activation via complementary targeting mechanisms.

doi: 10.1038/s41477-018-0219-5 PubMed: 30104650 Google Scholar

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Flowering time is an important factor affecting grain yield in wheat. In this study, we divided reproductive spike development into eight sub-phases. These sub-phases have the potential to be delicately manipulated to increase grain yield. We measured 36 traits with regard to sub-phase durations, determined three grain yield-related traits in eight field environments and mapped 15 696 single nucleotide polymorphism (SNP, based on 90k Infinium chip and 35k Affymetrix chip) markers in 210 wheat genotypes. Phenotypic and genetic associations between grain yield traits and sub-phase durations showed significant consistency (Mantel test; r = 0.5377, P < 0.001). The shared quantitative trait loci (QTLs) revealed by the genome-wide association study suggested a close association between grain yield and sub-phase duration, which may be attributed to effects on spikelet initiation/spikelet number (double ridge to terminal spikelet stage, DR-TS) and assimilate accumulation (green anther to anthesis stage, GA-AN). Moreover, we observed that the photoperiod-sensitivity allele at the Ppd-D1 locus on chromosome 2D markedly extended all sub-phase durations, which may contribute to its positive effects on grain yield traits. The dwarfing allele at the Rht-D1 (chromosome 4D) locus altered the sub-phase duration and displayed positive effects on grain yield traits. Data for 30 selected genotypes (from among the original 210 genotypes) in the field displayed a close association with that from the greenhouse. Most importantly, this study demonstrated specific connections to grain yield in narrower time windows (i.e. the eight sub-phases), rather than the entire stem elongation phase as a whole.

doi: 10.1111/tpj.13998 PubMed: 29906301 Google Scholar

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Background: Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results: In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions: We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species.

doi: 10.1093/gigascience/giy001 PubMed: 29346559 Google Scholar

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Increasing grain yield is still the main target of wheat breeding; yet today's wheat plants utilize less than half of their yield potential. Owing to the difficulty of determining grain yield potential in a large population, few genetic factors regulating floret fertility (i.e. the difference between grain yield potential and grain number) have been reported to date. In this study, we conducted a genome-wide association study (GWAS) by quantifying 54 traits (16 floret fertility traits and 38 traits for assimilate partitioning and spike morphology) in 210 European winter wheat accessions. The results of this GWAS experiment suggested potential associations between floret fertility, assimilate partitioning and spike morphology revealed by shared quantitative trait loci (QTLs). Several candidate genes involved in carbohydrate metabolism, phytohormones or floral development colocalized with such QTLs, thereby providing potential targets for selection. Based on our GWAS results we propose a genetic network underlying floret fertility and related traits, nominating determinants for improved yield performance.

doi: 10.1111/nph.14342 PubMed: 27918076 Google Scholar

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BACKGROUND: The efficiency of multiplex editing in plants by the RNA-guided Cas9 system is limited by efficient introduction of its components into the genome and by their activity. The possibility of introducing large fragment deletions by RNA-guided Cas9 tool provides the potential to study the function of any DNA region of interest in its 'endogenous' environment. RESULTS: Here, an RNA-guided Cas9 system was optimized to enable efficient multiplex editing in Arabidopsis thaliana. We demonstrate the flexibility of our system for knockout of multiple genes, and to generate heritable large-fragment deletions in the genome. As a proof of concept, the function of part of the second intron of the flower development gene AGAMOUS in Arabidopsis was studied by generating a Cas9-free mutant plant line in which part of this intron was removed from the genome. Further analysis revealed that deletion of this intron fragment results 40 % decrease of AGAMOUS gene expression without changing the splicing of the gene which indicates that this regulatory region functions as an activator of AGAMOUS gene expression. CONCLUSIONS: Our modified RNA-guided Cas9 system offers a versatile tool for the functional dissection of coding and non-coding DNA sequences in plants.

doi: 10.1186/s13007-016-0125-7 PubMed: 27118985 Google Scholar

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Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. To solve these global challenges, novel approaches are required to identify quantitative phenotypes and to explain the genetic basis of agriculturally important traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments. Recently, plant phenomics has offered and integrated a suite of new technologies, and we are on a path to improve the description of complex plant phenotypes. High-throughput phenotyping platforms have also been developed that capture phenotype data from plants in a non-destructive manner. In this review, we discuss recent developments of high-throughput plant phenotyping infrastructure including imaging techniques and corresponding principles for phenotype data analysis.

doi: 10.3389/fpls.2015.00619 PubMed: 26322060 Google Scholar

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Natural antisense transcripts (NATs) are endogenous transcripts that can form double-stranded RNA structures. Many protein-coding genes (PCs) and non-protein-coding genes (NPCs) tend to form cis-NATs and trans-NATs, respectively. In this work, we identified 4,080 cis-NATs and 2,491 trans-NATs genome-widely in Arabidopsis. Of these, 5,385 NAT-siRNAs were detected from the small RNA sequencing data. NAT-siRNAs are typically 21nt, and are processed by Dicer-like 1 (DCL1)/DCL2 and RDR6 and function in epigenetically activated situations, or 24nt, suggesting these are processed by DCL3 and RDR2 and function in environment stress. NAT-siRNAs are significantly derived from PC/PC pairs of trans-NATs and NPC/NPC pairs of cis-NATs. Furthermore, NAT pair genes typically have similar pattern of epigenetic status. Cis-NATs tend to be marked by euchromatic modifications, whereas trans-NATs tend to be marked by heterochromatic modifications.

doi: 10.1093/dnares/dsv008 PubMed: 25922535 Google Scholar

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Phenotyping large numbers of genotypes still represents the rate-limiting step in many plant genetic experiments and in breeding. To address this issue, novel automated phenotyping technologies have been developed. We investigated for a core set of barley cultivars if high-throughput image analysis can help to dissect vegetative biomass accumulation in response to two different watering regimes under semi-controlled greenhouse conditions. We found that experiments, treatments, genotypes and genotype by environment interaction (G × E) can be characterized at any time point by certain digital traits. Biomass accumulation under control and stress conditions was highly heritable. Growth model-derived maximum vegetative biomass (K max), inflection point (I) and regrowth rate (k) were identified as promising candidate traits for genome-wide association studies. Drought stress symptoms can be visualized, dissected and modelled. Especially the highly heritable regrowth rate, which had the biggest influence on biomass accumulation in stress treatment, seems promising for future studies to improve drought tolerance in different crop species. A proof of concept study revealed potential correlations between digital traits obtained from pot experiments under greenhouse conditions and agronomic traits from field experiments. Overall, non-invasive, imaging-based phenotyping platforms under greenhouse conditions offer excellent possibilities for trait discovery, trait development and industrial applications.

doi: 10.1111/pce.12516 PubMed: 25689277 Google Scholar

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BACKGROUND: In plants, microRNAs (miRNAs) regulate gene expression mainly at the post-transcriptional level. Previous studies have demonstrated that miRNA-mediated gene silencing pathways play vital roles in plant development. Here, we used a high-throughput sequencing approach to characterize the miRNAs and their targeted transcripts in the leaf, flower and fruit of sweet orange. RESULTS: A total of 183 known miRNAs and 38 novel miRNAs were identified. An in-house script was used to identify all potential secondary siRNAs derived from miRNA-targeted transcripts using sRNA and degradome sequencing data. Genome mapping revealed that these miRNAs were evenly distributed across the genome with several small clusters, and 69 pre-miRNAs were co-localized with simple sequence repeats (SSRs). Noticeably, the loop size of pre-miR396c was influenced by the repeat number of CUU unit. The expression pattern of miRNAs among different tissues and developmental stages were further investigated by both qRT-PCR and RNA gel blotting. Interestingly, Csi-miR164 was highly expressed in fruit ripening stage, and was validated to target a NAC transcription factor. This study depicts a global picture of miRNAs and their target genes in the genome of sweet orange, and focused on the comparison among leaf, flower and fruit tissues. CONCLUSIONS: This study provides a global view of miRNAs and their target genes in different tissue of sweet orange, and focused on the identification of miRNA involved in the regulation of fruit ripening. The results of this study lay a foundation for unraveling key regulators of orange fruit development and ripening on post-transcriptional level.

doi: 10.1186/1471-2164-15-695 PubMed: 25142253 Google Scholar

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High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays 'Fernandez') plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.

doi: 10.1104/pp.113.233932 PubMed: 24760818 Google Scholar

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Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II-associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We observe that MIRs display widespread communication in those transcriptionally active communities. Moreover, miRNA-target interactions are significantly enriched among communities with functional homogeneity while depleted from the same community from which they originated, suggesting MIRs coordinating function-related pathways at posttranscriptional level. Further investigation demonstrates the existence of spatial MIR-MIR chromatin interacting networks. We show that groups of spatially coordinated MIRs are frequently from the same family and involved in the same disease category. The spatial interaction network possesses both common and cell-specific subnetwork modules that result from the spatial organization of chromatin within different cell types. Together, our study unveils an entirely unexplored layer of MIR regulation throughout the human genome that links the spatial coordination of MIRs to their co-expression and function.

doi: 10.1093/nar/gkt1294 PubMed: 24357409 Google Scholar

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Transcriptome analysis of early-developing maize (Zea mays) seed was conducted using Illumina sequencing. We mapped 11,074,508 and 11,495,788 paired-end reads from endosperm and embryo, respectively, at 9 d after pollination to define gene structure and alternative splicing events as well as transcriptional regulators of gene expression to quantify transcript abundance in both embryo and endosperm. We identified a large number of novel transcribed regions that did not fall within maize annotated regions, and many of the novel transcribed regions were tissue-specifically expressed. We found that 50.7% (8,556 of 16,878) of multiexonic genes were alternatively spliced, and some transcript isoforms were specifically expressed either in endosperm or in embryo. In addition, a total of 46 trans-splicing events, with nine intrachromosomal events and 37 interchromosomal events, were found in our data set. Many metabolic activities were specifically assigned to endosperm and embryo, such as starch biosynthesis in endosperm and lipid biosynthesis in embryo. Finally, a number of transcription factors and imprinting genes were found to be specifically expressed in embryo or endosperm. This data set will aid in understanding how embryo/endosperm development in maize is differentially regulated.

doi: 10.1104/pp.113.214874 PubMed: 23478895 Google Scholar

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Pseudorabies virus (PRV) belongs to Alphaherpesvirinae subfamily that causes huge economic loss in pig industry worldwide. It has been recently demonstrated that many herpesviruses encode microRNAs (miRNAs), which play crucial roles in viral life cycle. However, the knowledge about PRV-encoded miRNAs is still limited. Here, we report a comprehensive analysis of both viral and host miRNA expression profiles in PRV-infected porcine epithelial cell line (PK-15). Deep sequencing data showed that the ∼4.6 kb intron of the large latency transcript (LLT) functions as a primary microRNA precursor (pri-miRNA) that encodes a cluster of 11 distinct miRNAs in the PRV genome, and 209 known and 39 novel porcine miRNAs were detected. Viral miRNAs were further confirmed by stem-loop RT-PCR and northern blot analysis. Intriguingly, all of these viral miRNAs exhibited terminal heterogeneity both at the 5' and 3' ends. Seven miRNA genes produced mature miRNAs from both arms and two of the viral miRNA genes showed partially overlapped in their precursor regions. Unexpectedly, a terminal loop-derived small RNA with high abundance and one special miRNA offset RNA (moRNA) were processed from a same viral miRNA precursor. The polymorphisms of viral miRNAs shed light on the complexity of host miRNA-processing machinery and viral miRNA-regulatory mechanism. The swine genes and PRV genes were collected for target prediction of the viral miRNAs, revealing a complex network formed by both host and viral genes. GO enrichment analysis of host target genes suggests that PRV miRNAs are involved in complex cellular pathways including cell death, immune system process, metabolic pathway, indicating that these miRNAs play significant roles in virus-cells interaction of PRV and its hosts. Collectively, these data suggest that PRV infected epithelial cell line generates a diverse set of host miRNAs and a special cluster of viral miRNAs, which might facilitate PRV replication in cells.

doi: 10.1371/journal.pone.0030988 PubMed: 22292087 Google Scholar

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SUMMARY: MyBioNet is a web-based application for biological network analysis, which provides user-friendly web interfaces to visualize, edit and merge biological networks. In addition, MyBioNet integrated KEGG metabolic network data from 1366 organisms and allows users to search and navigate interesting networks. AVAILABILITY AND IMPLEMENTATION: All KEGG metabolic network data are organized and stored in the MySQL database. MyBioNet is implemented in Flex/Actionscript and PHP languages and deployed on an Apache web server. MyBioNet is accessible through all the Flash-embedded browsers at http://bis.zju.edu.cn/mybionet/. CONTACT: mchen@zju.edu.cn.

doi: 10.1093/bioinformatics/btr557 PubMed: 21984760 Google Scholar

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Rice (Oryza sativa) feeds over half of the global population. A web-based integrated platform for rice microarray annotation and data analysis in various biological contexts is presented, which provides a convenient query for comprehensive annotation compared with similar databases. Coupled with existing rice microarray data, it provides online analysis methods from the perspective of bioinformatics. This comprehensive bioinformatics analysis platform is composed of five modules, including data retrieval, microarray annotation, sequence analysis, results visualization and data analysis. The BioChip module facilitates the retrieval of microarray data information via identifiers of "Probe Set ID", "Locus ID" and "Analysis Name". The BioAnno module is used to annotate the gene or probe set based on the gene function, the domain information, the KEGG biochemical and regulatory pathways and the potential microRNA which regulates the genes. The BioSeq module lists all of the related sequence information by a microarray probe set. The BioView module provides various visual results for the microarray data. The BioAnaly module is used to analyze the rice microarray's data set.

doi: 10.1007/s11427-010-4101-6 PubMed: 21181349 Google Scholar

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The currently developed next-generation sequencing (NGS) technology has significantly enhanced our capacity of small RNA (sRNA) exploration. Several ambitious sRNA projects have been established based on the technical support of NGS. Thanks to the high-throughput feature of NGS, huge amounts of sRNA sequencing data have been generated. However, much more research efforts are needed to further exploit these valuable data. In this study, we carried out functional analyses of sRNAs from 26 angiosperms by utilizing public sRNA NGS data. We proposed that the endogenous sRNAs largely represented by the 24-nt ones had a potential role in transposable element (TE) control in both Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa), based on the comparison of sRNA locus densities within the TEs to the non-TE genes. Functional analysis was performed for the predicted targets of the conserved sRNAs that were classified into eudicot-specific, monocot-specific, and angiosperm-conserved ones. Moreover, several miRNA families were found to be highly conserved, and miR396 was suggested to be the most conserved miRNA family between the eudicots and the monocots, indicating its essential role in angiosperm development. At last, we demonstrated that it was a great challenge for researchers to fully exploit these huge sRNA NGS datasets and numerous sRNA species remained to be uncovered and functionally characterized.

doi: 10.1016/j.compbiolchem.2010.10.001 PubMed: 21030312 Google Scholar

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The approximately 21 nucleotide microRNAs (miRNAs) are one type of well-defined small RNA species, and they play critical roles in various biological processes in organisms. In plants, most miRNAs exert repressive regulation on their targets through cleavage, and a number of miRNA-target pairs have been validated either by modified 5' RACE (rapid amplification of cDNA ends), or by newly developed high-throughput strategies. All these data have greatly advanced our understanding of the regulatory roles of plant miRNAs. On the other hand, deep insights into miRNA precursor processing, and miRNA- or miRNA*-mediated self-regulation of their host precursors could be gained from high-throughput degradome sequencing data, based on the general framework of miRNA generation in plants. Here, the focus is on the recent research progress on this issue, and several interesting points were raised.

doi: 10.1093/jxb/erq209 PubMed: 20643809 Google Scholar

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High-throughput sequencing (HTS) has opened up a new era for small RNA (sRNA) exploration. Using HTS data for a global survey of sRNAs in 26 angiosperms, elevated GC contents were detected in the monocots, whereas the 5(')-terminal compositions were quite uniform among the angiosperms. Chromosome-wide distribution patterns of sRNAs were investigated by using scrolling-window analysis. We performed de novo natural antisense transcript (NAT) prediction, and found that the overlapping regions of trans-NATs, but not cis-NATs, were hotspots for sRNA generation. One cis-NAT generates phased natural antisense short interfering RNAs (nat-siRNAs) specifically from flowers in Arabidopsis, while one in rice produces phased nat-siRNAs from grains, suggesting their organ-specific regulatory roles.

doi: 10.1093/bioinformatics/btq150 PubMed: 20378553 Google Scholar

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Auxin, known as the central hormone, plays essential roles in plant growth and development. In auxin signaling pathways, the tiny RNA molecules, i.e., microRNAs (miRNAs), show their strong potential in modulating the auxin signal transduction. Recently, we isolated a novel auxin resistant rice mutant osaxr (Oryza sativa auxin resistant) that exhibited plethoric root defects. Microarray experiments were carried out to investigate the expression patterns of both the miRNAs and the protein-coding genes in osaxr. A number of miRNAs showed reduced auxin sensitivity in osaxr compared with the wild type (WT), which may contribute to the auxin-resistant phenotype of the mutant. Auxin response elements (AuxREs) were demonstrated to be more frequently present in the promoters of auxin-related miRNAs. In our previous report, a comparative analysis of miRNA and protein-coding gene expression datasets uncovered a number of reciprocally expressed miRNA-target pairs. A feedback circuit between miRNA and auxin response factor (ARF) was then proposed. Here, we will discuss in-depth some points raised in the previous report, in particular, the organ-specific expression patterns of miR164, the feedback regulatory model between miR167 and certain ARFs, and the potential signal interactions between auxin and nutrition or stress that are mediated by miRNAs in rice roots.

doi: 10.4161/psb.5.3.10549 PubMed: 20023405 Google Scholar

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Single-cell omics technologies have revolutionized the study of long non-coding RNAs (lncRNAs), offering unprecedented resolution in elucidating their expression dynamics, cell-type specificity, and associated gene regulatory networks (GRNs). Concurrently, the integration of artificial intelligence (AI) methodologies has significantly advanced our understanding of lncRNA functions and its implications in disease pathogenesis. This chapter discusses the progress in single-cell omics data analysis, emphasizing its pivotal role in unraveling the molecular mechanisms underlying cellular heterogeneity and the associated regulatory networks involving lncRNAs. Additionally, we provide a summary of single-cell omics resources and AI models for constructing single-cell gene regulatory networks (scGRNs). Finally, we explore the challenges and prospects of exploring scGRNs in the context of lncRNA biology.

doi: 10.1007/978-1-0716-4290-0_11 PubMed: 39702712 Google Scholar

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Key transcription factors (TFs) controlling the morphogenesis of flowers and leaves have been identified in the model plant Arabidopsis thaliana. Recent genome-wide approaches based on chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) enable systematic identification of genome-wide TF binding sites (TFBSs) of these regulators. Here, we describe a computational pipeline for analyzing ChIP-seq data to identify TFBSs and to characterize gene regulatory networks (GRNs) with applications to the regulatory studies of flower development. In particular, we provide step-by-step instructions on how to download, analyze, visualize, and integrate genome-wide data in order to construct GRNs for beginners of bioinformatics. The practical guide presented here is ready to apply to other similar ChIP-seq datasets to characterize GRNs of interest.

doi: 10.1007/978-1-4939-7125-1_16 PubMed: 28623590 Google Scholar

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Genomics and phenomics are two fundamentally important branches of biological sciences, and they stand at both ends of the multiple “omics” families. A central goal of current biology is to establish complete functional links between the genome and phenome, the so-called genotype–phenotype map. Recent advances in high-throughput and high-dimensional genotyping and phenotyping technologies enable us to uncover the casual networks inside the “black box” that lies between genotypes and phenotypes using the principles of genome-wide association studies (GWAS). Application of GWAS and analogous methodologies and incorporation of multiple omics data begin to unravel the contribution of genetic variation to phenotypic diversity. Integrating “omics” data at broad levels by using the systems-biology approach is paramount to further bridging the gaps between genomics and phenomics and eventually making accurate predictions of phenotypes based on genetic contribution.

doi: 10.1007/978-3-642-41281-3_11 Google Scholar

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