Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity.

Understanding the organizational logic of neural circuits requires deciphering the biological basis of neuronal diversity and identity, but there is no consensus on how neuron types should be defined. We analyzed single-cell transcriptomes of a set of anatomically and physiologically characterized cortical GABAergic neurons and conducted a computational genomic screen for transcriptional profiles that distinguish them from one another. We discovered that cardinal GABAergic neuron types are delineated by a transcriptional architecture that encodes their synaptic communication patterns. This architecture comprises 6 categories of ∼40 gene families, including cell-adhesion molecules, transmitter-modulator receptors, ion channels, signaling proteins, neuropeptides and vesicular release components, and transcription factors. Combinatorial expression of select members across families shapes a multi-layered molecular scaffold along the cell membrane that may customize synaptic connectivity patterns and input-output signaling properties. This molecular genetic framework of neuronal identity integrates cell phenotypes along multiple axes and provides a foundation for discovering and classifying neuron types.

EpMYB2 positively regulates chicoric acid biosynthesis by activating both primary and specialized metabolic genes in purple coneflower.

Chicoric acid is the major active ingredient of the world-popular medicinal plant purple coneflower (Echinacea purpurea (L.) Menoch). It is recognized as the quality index of commercial hot-selling Echinacea products. While the biosynthetic pathway of chicoric acid in purple coneflower has been elucidated recently, its regulatory network remains elusive. Through co-expression and phylogenetic analysis, we found EpMYB2, a typical R2R3-type MYB transcription factor (TF) responsive to methyl jasmonate (MeJA) simulation, is a positive regulator of chicoric acid biosynthesis. In addition to directly regulating chicoric acid biosynthetic genes, EpMYB2 positively regulates genes of the upstream shikimate pathway. We also found that EpMYC2 could activate the expression of EpMYB2 by binding to its G-box site, and the EpMYC2-EpMYB2 module is involved in the MeJA-induced chicoric acid biosynthesis. Overall, we identified an MYB TF that positively regulates the biosynthesis of chicoric acid by activating both primary and specialized metabolic genes. EpMYB2 links the gap between the JA signaling pathway and chicoric acid biosynthesis. This work opens a new direction toward engineering purple coneflower with higher medicinal qualities.

Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer.

Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.

Screening lncRNAs essential for cardiomyocyte proliferation by integrative profiling of lncRNAs and mRNAs associated with heart development.

BACKGROUND: The proliferation potential of mammalian cardiomyocytes declines markedly shortly after birth. Both long non-coding RNAs (lncRNAs) and mRNAs demonstrate altered expression patterns during cardiac development. However, the role of lncRNAs in the cell cycle arrest of cardiomyocytes remains inadequately understood. METHOD: The expression pattern of lncRNAs and mRNAs was analyzed in mouse hearts exhibiting varying regenerative potentials on postnatal days (P) 1, 7, and 28. Weighted correlation network analysis (WGCNA) was employed to elucidate the co-expression relationship between lncRNAs and mRNAs. Protein-protein interaction (PPI) network was built using the STRING database, and hub lncRNAs were identified by CytoHubba. Molecular Complex Detection (MCODE) was used to screen core modules of the PPI network in Cytoscape. Upstream lncRNAs and miRNAs which may regulate mRNAs were predicted using miRTarBase and AnnoLnc2, respectively. Myocardial infarction (MI) was induced by ligation of the left anterior descending coronary artery. RESULTS: Compared with the P1 heart, 618 mRNAs and 414 lncRNAs displayed. transcriptional changes in the P7 heart, while 2358 mRNAs and 1290 lncRNAs showed from P7 to P28. Gene Ontology (GO) analysis revealed that module 1 in the both comparisons was enriched in the mitotic cell cycle process. 2810408I11Rik and 2010110K18Rik were identified as hub lncRNAs and their effects on the proliferation of cardiomyocytes were verified in vitro. Additionally, four lncRNA-miRNA-mRNA regulatory axes were predicted to explain the mechanism by which 2810408I11Rik and 2010110K18Rik regulate cardiomyocyte proliferation. Notably, the overexpression of 2810408I11Rik enhances cardiomyocyte proliferation and heart regeneration in the adult heart following MI. CONCLUSION: This study systematically analyzed the landscape of lncRNAs and mRNAs at P1, P7, and P28. These findings may enhance our understanding of the framework for heart development and could have significant implications for heart regeneration.

Exploring the role of transcriptional and post-transcriptional processes in mRNA co-expression.

Co-expression of two or more genes at the single-cell level is usually associated with functional co-regulation. While mRNA co-expression-measured as the correlation in mRNA levels-can be influenced by both transcriptional and post-transcriptional events, transcriptional regulation is typically considered dominant. We review and connect the literature describing transcriptional and post-transcriptional regulation of co-expression. To enhance our understanding, we integrate four datasets spanning single-cell gene expression data, single-cell promoter activity data and individual transcript half-lives. Confirming expectations, we find that positive co-expression necessitates promoter coordination and similar mRNA half-lives. Surprisingly, negative co-expression is favored by differences in mRNA half-lives, contrary to initial predictions from stochastic simulations. Notably, this association manifests specifically within clusters of genes. We further observe a striking compensation between promoter coordination and mRNA half-lives, which additional stochastic simulations suggest might give rise to the observed co-expression patterns. These findings raise intriguing questions about the functional advantages conferred by this compensation between distal kinetic steps.

Dual RNA-seq reveals distinct families of co-regulated and structurally conserved effectors in Botrytis cinerea infection of Arabidopsis thaliana.

BACKGROUND: Botrytis cinerea is a broad-host-range pathogen causing gray mold disease and significant yield losses of numerous crops. However, the mechanisms underlying its rapid invasion and efficient killing of plant cells remain unclear. RESULTS: In this study, we elucidated the dynamics of B. cinerea infection in Arabidopsis thaliana by live cell imaging and dual RNA sequencing. We found extensive transcriptional reprogramming events in both the pathogen and the host, which involved metabolic pathways, signaling cascades, and transcriptional regulation. For the pathogen, we identified 591 candidate effector proteins (CEPs) and comprehensively analyzed their co-expression, sequence similarity, and structural conservation. The results revealed temporal co-regulation patterns of these CEPs, indicating coordinated deployment of effectors during B. cinerea infection. Through functional screening of 48 selected CEPs in Nicotiana benthamiana, we identified 11 cell death-inducing proteins (CDIPs) in B. cinerea. CONCLUSIONS: The findings provide important insights into the transcriptional dynamics and effector biology driving B. cinerea pathogenesis. The rapid infection of this pathogen involves the temporal co-regulation of CEPs and the prominent role of CDIPs in host cell death. This work highlights significant changes in gene expression associated with gray mold disease, underscoring the importance of a diverse repertoire of effectors crucial for successful infection.

Integrated gene co-expression network analysis and experimental validation revealed potential targets of human urine extract CDA-II in treating chronic myeloid leukemia.

BACKGROUND: Cell differentiation agent II (CDA-II) exhibits potent anti-proliferative and apoptosis-inducing properties against a variety of cancer cells. However, its mechanism of action in chronic myeloid leukemia (CML) remains unclear. METHODS: Cell counting Kit 8 (CCK-8) and flow cytometry were used to investigate the effects of CDA-II on the biological characteristics of K562 cells. Gene (mRNA and lncRNA) expression profiles were analyzed by bioinformatics to screen differentially expressed genes and to perform enrichment analysis. The Pearson correlation coefficients of lncRNAs and mRNAs were calculated using gene expression values, and a lncRNA/mRNA co-expression network was constructed. The MCODE and cytoHubba plugins were used to analyze the co-expression network. RESULTS: The Results, derived from CCK-8 and flow cytometry, indicated that CDA-II exerts dual effects on K562 cells: it inhibits their proliferation and induces apoptosis. From bioinformatics analysis, we identified 316 mRNAs and 32 lncRNAs. These mRNAs were predominantly related to the meiotic cell cycle, DNA methylation, transporter complex and peptidase regulator activity, complement and coagulation cascades, protein digestion and absorption, and cell adhesion molecule signaling pathways. The co-expression network comprised of 163 lncRNA/mRNA interaction pairs. Notably, our analysis results implicated clustered histone gene families and five lncRNAs in the biological effects of CDA-II on K562 cells. CONCLUSION: This study highlights the hub gene and lncRNA/mRNA co-expression network as crucial elements in the context of CDA-II treatment of CML. This insight not only enriches our understanding of CDA-II's mechanism of action but also might provide valuable clues for subsequent experimental studies of CDA-II, and potentially contribute to the discovery of new therapeutic targets for CML.

Brain lncRNA-mRNA co-expression regulatory networks and alcohol use disorder.

Prolonged alcohol consumption can disturb the expression of both coding and noncoding genes in the brain. These dysregulated genes may co-express in modules and interact within networks, consequently influencing the susceptibility to developing alcohol use disorder (AUD). In the present study, we performed an RNA-seq analysis of the expression of both long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in 192 postmortem tissue samples collected from eight brain regions (amygdala, caudate nucleus, cerebellum, hippocampus, nucleus accumbens, prefrontal cortex, putamen, and ventral tegmental area) of 12 AUD and 12 control subjects of European ancestry. Applying the limma-voom method, we detected a total of 57 lncRNAs and 51 mRNAs exhibiting significant differential expression (Padj < 0.05 and fold-change ≥2) across at least one of the eight brain regions investigated. Machine learning analysis further confirmed the potential of these top genes in predicting AUD. Through Weighted Gene Co-expression Network Analysis (WGCNA), we identified distinct lncRNA-mRNA co-expression modules associated with AUD in each of the eight brain regions. Additionally, lncRNA-mRNA co-expression networks were constructed for each brain region using Cytoscape to reveal gene regulatory interactions implicated in AUD. Hub genes within these networks were found to be enriched in several key KEGG pathways, including Axon Guidance, MAPK Signaling, p53 Signaling, Adherens Junction, and Neurodegeneration. Our results underscore the significance of networks involving AUD-associated lncRNAs and mRNAs in modulating neuroplasticity in response to alcohol exposure. Further elucidating these molecular mechanisms holds promise for the development of targeted therapeutic interventions for AUD.

SGCP: a spectral self-learning method for clustering genes in co-expression networks.

BACKGROUND: A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules. RESULTS: We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines. CONCLUSION: Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Leveraging gene correlations in single cell transcriptomic data.

BACKGROUND: Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). RESULTS: We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. CONCLUSIONS: New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

Identification of SNHG11 as a Therapeutic Target in Pulmonary Hypertension.

Pulmonary hypertension (PH) is a life-threatening condition characterized by pulmonary vascular remodeling and endothelial dysfunction. Current therapies primarily target vasoactive imbalances but often fail to address adverse vascular remodeling. Long non-coding RNA (lncRNA), which are key regulators of various cellular processes, remain underexplored in the context of PH. To investigate the role of lncRNA in PH, we performed a comprehensive analysis using Weighted Gene Co-expression Network Analysis (WGCNA) on the GSE113439 dataset, comprising human lung tissue samples from different PH subtypes. Our analysis identified the lncRNA SNHG11 as consistently downregulated in PH. Functional assays in human pulmonary artery endothelial cells (HPAECs) demonstrated that SNHG11 plays a critical role in modulating inflammation, cell proliferation, apoptosis, and the JAK/STAT and MAPK signaling pathways. Mechanistically, SNHG11 influences the stability of PRPF8, a crucial mRNA spliceosome component, thereby affecting multiple cellular functions beyond splicing. In vivo experiments using a hypoxic rat model showed that knockdown of SNHG11 alleviates PH development and improves right ventricular function. These findings highlight SNHG11 as a key regulator in PH pathogenesis and suggest it as a potential therapeutic target.

Contribution of FOS in neutrophils to venous thromboembolism via miR-144 based on bioinformatic prediction and validation.

The Finkel-Biskis-Jinkins Osteosarcoma (c-Fos; encoded by FOS) plays an important role in several cardiovascular diseases, including atherosclerosis and stroke. However, the relationship between FOS and venous thromboembolism (VTE) remains unknown. We identified differentially expressed genes in Gene Expression Omnibus dataset, GSE48000, comprising VTE patients and healthy individuals, and analysed them using CIBERSORT and weighted co-expression network analysis (WGCNA). FOS and CD46 expressions were significantly downregulated (FOS p = 2.26E-05, CD64 p = 8.83E-05) and strongly linked to neutrophil activity in VTE. We used GSE19151 and performed PCR to confirm that FOS and CD46 had diagnostic potential for VTE; however, only FOS showed differential expression by PCR and ELISA in whole blood samples. Moreover, we found that hsa-miR-144 which regulates FOS expression was significantly upregulated in VTE. Furthermore, FOS expression was significantly downregulated in neutrophils of VTE patients (p = 0.03). RNA sequencing performed on whole blood samples of VTE patients showed that FOS exerted its effects in VTE via the leptin-mediated adipokine signalling pathway. Our results suggest that FOS and related genes or proteins can outperform traditional clinical markers and may be used as diagnostic biomarkers for VTE.

Core cellular and tissue-specific mechanisms enable desiccation tolerance in Craterostigma.

Resurrection plants can survive prolonged life without water (anhydrobiosis) in regions with seasonal drying. This desiccation tolerance requires the coordination of numerous cellular processes across space and time, and individual plant tissues face unique constraints related to their function. Here, we analyzed the complex, octoploid genome of the model resurrection plant Craterostigma (C. plantagineum), and surveyed spatial and temporal expression dynamics to identify genetic elements underlying desiccation tolerance. Homeologous genes within the Craterostigma genome have divergent expression profiles, suggesting the subgenomes contribute differently to desiccation tolerance traits. The Craterostigma genome contains almost 200 tandemly duplicated early light-induced proteins, a hallmark trait of desiccation tolerance, with massive upregulation under water deficit. We identified a core network of desiccation-responsive genes across all tissues, but observed almost entirely unique expression dynamics in each tissue during recovery. Roots and leaves have differential responses related to light and photoprotection, autophagy and nutrient transport, reflecting their divergent functions. Our findings highlight a universal set of likely ancestral desiccation tolerance mechanisms to protect cellular macromolecules under anhydrobiosis, with secondary adaptations related to tissue function.

Time of day and genotype sensitivity adjust molecular responses to temperature stress in sorghum.

Sorghum is one of the four major C4 crops that are considered to be tolerant to environmental extremes. Sorghum shows distinct growth responses to temperature stress depending on the sensitivity of the genetic background. About half of the transcripts in sorghum exhibit diurnal rhythmic expressions emphasizing significant coordination with the environment. However, an understanding of how molecular dynamics contribute to genotype-specific stress responses in the context of the time of day is not known. We examined whether temperature stress and the time of day impact the gene expression dynamics in thermo-sensitive and thermo-tolerant sorghum genotypes. We found that time of day is highly influencing the temperature stress responses, which can be explained by the rhythmic expression of most thermo-responsive genes. This effect is more pronounced in thermo-tolerant genotypes, suggesting a stronger regulation of gene expression by the time of day and/or by the circadian clock. Genotypic differences were mostly observed on average gene expression levels, which may be responsible for contrasting sensitivities to temperature stress in tolerant versus susceptible sorghum varieties. We also identified groups of genes altered by temperature stress in a time-of-day and genotype-specific manner. These include transcriptional regulators and several members of the Ca2+ -binding EF-hand protein family. We hypothesize that expression variation of these genes between genotypes along with time-of-day independent regulation may contribute to genotype-specific fine-tuning of thermo-responsive pathways. These findings offer a new opportunity to selectively target specific genes in efforts to develop climate-resilient crops based on their time-of-day and genotype variation responses to temperature stress.

Organ-enriched gene expression during floral morphogenesis in wild barley.

Floral morphology varies considerably between dicots and monocots. The ABCDE model explaining how floral organ development is controlled was formulated using core eudicots and applied to grass crops. Barley (Hordeum. vulgare) has unique floral morphogenesis. Wild barley (H. vulgare ssp. spontaneum), which is the immediate ancestor of cultivated barley (H. vulgare ssp. vulgare), contains a rich reservoir of genetic diversity. However, the wild barley genes involved in floral organ development are still relatively uncharacterized. In this study, we generated an organ-specific transcriptome atlas for wild barley floral organs. Genome-wide transcription profiles indicated that 22 838 protein-coding genes were expressed in at least one organ. These genes were grouped into seven clusters according to the similarities in their expression patterns. Moreover, 5619 genes exhibited organ-enriched expression, 677 of which were members of 47 transcription factor families. Gene ontology analyses suggested that the functions of the genes with organ-enriched expression influence the biological processes in floral organs. The co-expression regulatory network showed that the expression of 690 genes targeted by MADS-box proteins was highly positively correlated with the expression of ABCDE model genes during floral morphogenesis. Furthermore, the expression of 138 genes was specific to the wild barley OUH602 genome and not the Morex genome; most of these genes were highly expressed in the glume, awn, lemma, and palea. This study revealed the global gene expression patterns underlying floral morphogenesis in wild barley. On the basis of the study findings, a molecular mechanism controlling floral morphology in barley was proposed.

Ectomycorrhizal symbiosis prepares its host locally and systemically for abiotic cue signaling.

Tree growth and survival are dependent on their ability to perceive signals, integrate them, and trigger timely and fitted molecular and growth responses. While ectomycorrhizal symbiosis is a predominant tree-microbe interaction in forest ecosystems, little is known about how and to what extent it helps trees cope with environmental changes. We hypothesized that the presence of Laccaria bicolor influences abiotic cue perception by Populus trichocarpa and the ensuing signaling cascade. We submitted ectomycorrhizal or non-ectomycorrhizal P. trichocarpa cuttings to short-term cessation of watering or ozone fumigation to focus on signaling networks before the onset of any physiological damage. Poplar gene expression, metabolite levels, and hormone levels were measured in several organs (roots, leaves, mycorrhizas) and integrated into networks. We discriminated the signal responses modified or maintained by ectomycorrhization. Ectomycorrhizas buffered hormonal changes in response to short-term environmental variations systemically prepared the root system for further fungal colonization and alleviated part of the root abscisic acid (ABA) signaling. The presence of ectomycorrhizas in the roots also modified the leaf multi-omics landscape and ozone responses, most likely through rewiring of the molecular drivers of photosynthesis and the calcium signaling pathway. In conclusion, P. trichocarpa-L. bicolor symbiosis results in a systemic remodeling of the host's signaling networks in response to abiotic changes. In addition, ectomycorrhizal, hormonal, metabolic, and transcriptomic blueprints are maintained in response to abiotic cues, suggesting that ectomycorrhizas are less responsive than non-mycorrhizal roots to abiotic challenges.

Gene expression and expression quantitative trait loci analyses uncover natural variations underlying the improvement of important agronomic traits during modern maize breeding.

Maize (Zea mays L.) is a major staple crop worldwide, and during modern maize breeding, cultivars with increased tolerance to high-density planting and higher yield per plant have contributed significantly to the increased yield per unit land area. Systematically identifying key agronomic traits and their associated genomic changes during modern maize breeding remains a significant challenge because of the complexity of genetic regulation and the interactions of the various agronomic traits, with most of them being controlled by numerous small-effect quantitative trait loci (QTLs). Here, we performed phenotypic and gene expression analyses for a set of 137 elite inbred lines of maize from different breeding eras in China. We found four yield-related traits are significantly improved during modern maize breeding. Through gene-clustering analyses, we identified four groups of expressed genes with distinct trends of expression pattern change across the historical breeding eras. In combination with weighted gene co-expression network analysis, we identified several candidate genes regulating various plant architecture- and yield-related agronomic traits, such as ZmARF16, ZmARF34, ZmTCP40, ZmPIN7, ZmPYL10, ZmJMJ10, ZmARF1, ZmSWEET15b, ZmGLN6 and Zm00001d019150. Further, by combining expression quantitative trait loci (eQTLs) analyses, correlation coefficient analyses and population genetics, we identified a set of candidate genes that might have been under selection and contributed to the genetic improvement of various agronomic traits during modern maize breeding, including a number of known key regulators of plant architecture, flowering time and yield-related traits, such as ZmPIF3.3, ZAG1, ZFL2 and ZmBES1. Lastly, we validated the functional variations in GL15, ZmPHYB2 and ZmPYL10 that influence kernel row number, flowering time, plant height and ear height, respectively. Our results demonstrates the effectiveness of our combined approaches for uncovering key candidate regulatory genes and functional variation underlying the improvement of important agronomic traits during modern maize breeding, and provide a valuable genetic resource for the molecular breeding of maize cultivars with tolerance for high-density planting.

An across breed, diet and tissue analysis reveals the transcription factor NR1H3 as a key mediator of residual feed intake in beef cattle.

BACKGROUND: Provision of feed is a major determinant of overall profitability in beef production systems, accounting for up to 75% of the variable costs. Thus, improving cattle feed efficiency, by way of determining the underlying genomic control and subsequently selecting for feed efficient cattle, provides a method through which feed input costs may be reduced. The objective of this study was to undertake gene co-expression network analysis using RNA-Sequence data generated from Longissimus dorsi and liver tissue samples collected from steers of two contrasting breeds (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI), across two consecutive distinct dietary phases (zero-grazed grass and high-concentrate). Categories including differentially expressed genes (DEGs) based on the contrasts of RFI phenotype, breed and dietary source, as well as key transcription factors and proteins secreted in plasma were utilised as nodes of the gene co-expression network. RESULTS: Of the 2,929 DEGs within the network analysis, 1,604 were reported to have statistically significant correlations (≥ 0.80), resulting in a total of 43,876 significant connections between genes. Pathway analysis of clusters of co-expressed genes revealed enrichment of processes related to lipid metabolism (fatty acid biosynthesis, fatty acid ß-oxidation, cholesterol biosynthesis), immune function, (complement cascade, coagulation system, acute phase response signalling), and energy production (oxidative phosphorylation, mitochondrial L-carnitine shuttle pathway) based on genes related to RFI, breed and dietary source contrasts. CONCLUSIONS: Although similar biological processes were evident across the three factors examined, no one gene node was evident across RFI, breed and diet contrasts in both liver and muscle tissues. However within the liver tissue, the IRX4, NR1H3, HOXA13 and ZNF648 gene nodes, which all encode transcription factors displayed significant connections across the RFI, diet and breed comparisons, indicating a role for these transcription factors towards the RFI phenotype irrespective of diet and breed. Moreover, the NR1H3 gene encodes a protein secreted into plasma from the hepatocytes of the liver, highlighting the potential for this gene to be explored as a robust biomarker for the RFI trait in beef cattle.

Understanding the molecular regulation of flavonoid 3'-hydroxylase in anthocyanin synthesis: insights from purple qingke.

BACKGROUND: The Flavonoid 3'-hydroxylase gene(F3'H) is an important structural gene in the anthocyanin synthesis pathway of plants, which has been proven to be involved in the color formation of organs such as leaves, flowers, and fruits in many plants. However, the mechanism and function in barley are still unclear. RESULTS: In order to explore the molecular mechanism of the grain color formation of purple qingke, we used the cultivated qingke variety Nierumzha (purple grain) and the selected qingke variety Kunlun 10 (white grain) to conduct transcriptomic sequencing at the early milk, late milk and soft dough stage. Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct weighted gene co-expression network related to grain color formation, and three key modules (brown, yellow, and turquoise modules) related to purple grain of qingke were selected. F3'H (HORVU1Hr1G094880) was selected from the hub gene of the module for the yeast library, yeast two-hybrid (Y2H), subcellular localization and other studies. It was found that in purple qingke, HvnF3'H mainly distributed in the cytoplasm and cell membrane and interacted with several stress proteins such as methyltransferase protein and zinc finger protein. CONCLUSIONS: The results of this study provide reference for the regulation mechanism of anthocyanin-related genes in purple grain qingke.