Polymorphism of Transferrin Gene Impacts the Mediating Effects of Psychotic Symptoms on the Relationship between Oxidative Stress and Cognition in Patients with Chronic Schizophrenia

A series of studies indicated that iron distribution that partly derives from transferrin-bound iron in the peripheral nervous system in the brain may act in processes such as myelination and brain development. However, the relationship between schizophrenia, its psychotic symptoms, and the transferrin (TF) gene has not been systematically explored. Our study aimed to investigate how a particular polymorphism of the transferrin gene, rs3811655, affects the superoxide dismutase (SOD), malondialdehyde (MDA), psychotic symptoms, cognition, or the mediation model between antioxidant enzymes and cognition via symptoms. A total of 564 patients with chronic schizophrenia and 468 healthy control subjects were recruited. The psychotic symptoms and cognition were assessed by the Positive and Negative Syndrome Scale (PANSS) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), respectively. Furthermore, the serum SOD, MDA activity, and transferrin gene polymorphism were measured in patients. Our results demonstrated that patients with the G allele possessed more severe negative symptoms, worse cognitive performance with respect to attention, and higher serum Mn-SOD activity. Additionally, the rs3811655 polymorphism may act as a moderator in the association between Cu/Zn-SOD activity and cognition, as well as psychotic symptoms in patients suffering from schizophrenia. According to this study, the single nucleotide polymorphism (SNP) rs3811655 polymorphism may fail to contribute to the susceptibility of schizophrenia in an individual but is involved in the iron-induced oxidative stress disturbance and cognitive impairment in schizophrenia. This deepens our understanding of the critical role of iron-induced oxidative stress that might underlie the pathophysiology of schizophrenia.

The P2Y2 Nucleotide Receptor Mediates Monocyte Tissue Factor Expression and Endotoxemia Death in Mice

Recently we reported that in human coronary artery endothelial cells, activation of the P2Y2 receptor (P2Y2R) induces up-regulation of tissue factor (TF), a vital initiator of the coagulation cascade. However, others have shown that monocyte TF is more critical than endothelial TF in provoking a pro-thrombotic state. Thus, we aimed to study whether monocytes express the P2Y2R, its role in controlling TF expression, and its relevance in vivo. RT-PCR and receptor activity assays revealed that among the eight P2Y nucleotide receptors, the P2Y2 subtype was selectively and functionally expressed in human monocytic THP-1 cells and primary monocytes. Stimulation of the cells by ATP or UTP dramatically increased TF protein expression, which was abolished by AR-C118925, a selective P2Y2R antagonist, or by siRNA silencing the P2Y2R. In addition, UTP or ATP treatment induced a rapid accumulation of TF mRNA preceded with an increased TF pre-mRNA, indicating enhanced TF gene transcription. In addition, stimulation of the monocyte P2Y2R significantly activated ERK1/2, JNK, p38, and Akt, along with their downstream transcription factors including c-Jun, c-Fos, and ATF-2, whereas blocking these pathways respectively, all significantly suppressed P2Y2R-mediated TF expression. Furthermore, we found that LPS triggered ATP release and TF expression, the latter of which was suppressed by apyrase or P2Y2R blockage. Importantly, P2Y2R-null mice were more resistant than wild-type mice in response to a lethal dose of LPS, accompanied by much less TF expression in bone marrow cells. These findings demonstrate for the first time that the P2Y2R mediates TF expression in human monocytes through mechanisms involving ERK1/2, JNK, p38, and AKT, and that P2Y2R deletion protects the mice from endotoxemia-induced TF expression and death, highlighting monocyte P2Y2R may be a new drug target for the prevention and/or treatment of relevant thrombotic disease.

TaLBD1, a LOB Transcription Factor Gene In T. Aestivum, Improves Plant N Starvation Adaptation Via Modulating N Acquisition-Associated Processes

Members of transcription factor (TF) families contribute largely to plant N starvation tolerance by regulating downstream stress defensive genes. In this study, we characterized TaLBD1, a Lateral Organ Boundary (LOB) TF gene in T. aestivum, in regulating plant low-N stress adaptation. TaLBD1 harbors the conserved domains specified by plant LOB proteins, targeting onto nucleus after endoplasmic reticulum (ER) assortment. The TaLBD1 transcripts were response sensitively to N starvation (NS) signaling, showing to be gradually upregulated in aerial and root tissues over a 27-h NS condition. The N. tabacum lines overexpressing TaLBD1 improved phenotype, root system architecture (RSA) establishment, biomass, and N contents of plants under NS treatment. The nitrate transporter gene NtNRT2.4 and PIN-FORMED gene NtPIN6 significantly upregulated in expression in NS-challenged lines; knockdown expression of NtNRT2.4 decreased N uptake and that of NtPIN6 alleviated RSA establishment relative to WT. These results validate the function of NRT and PIN genes in regulating plant N uptake and RSA behavior. RNA-seq analyses revealed that a quantity of genes modify expression in N-deprived lines overexpressing TaLBD1, which enriched into functional groups of signal transduction, transcription, protein biosynthesis, primary or secondary metabolism, and stress defensiveness. These findings suggested that the TaLBD1-improved NS adaptation attributes largely to its role in transcriptionally regulating NRT and PIN genes as well as in modulating those functional in various biological processes. TaLBD1 is a crucial regulator in plant N starvation tolerance and valuable target for molecular breeding high N use efficiency (NUE) crop cultivars.

GRaNIE and GRaNPA: Inference and evaluation of enhancer-mediated gene regulatory networks applied to study macrophages

Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases.Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases.Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R-packages, for building and evaluating enhancer-mediated gene regulatory networks (eGRNs) called GRaNIE (Gene Regulatory Network Inference including Enhancers - https://git.embl.de/grp-zaugg/GRaNIE) and GRaNPA (Gene Regulatory Network Performance Analysis - https://git.embl.de/grp-zaugg/GRaNPA), respectively. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection, and their involvement in common genetic diseases including autoimmune diseases.

GT Factor ZmGT-3b Is Associated With Regulation of Photosynthesis and Defense Response to Fusarium graminearum Infection in Maize Seedling

It is of critical importance for plants to correctly and efficiently allocate their resources between growth and defense to optimize fitness. Transcription factors (TFs) play crucial roles in the regulation of plant growth and defense response. Trihelix TFs display multifaceted functions in plant growth, development, and responses to various biotic and abiotic stresses. In our previous investigation of maize stalk rot disease resistance mechanism, we found a trihelix TF gene, ZmGT-3b, which is primed for its response to Fusarium graminearum challenge by implementing a rapid and significant reduction of its expression to suppress seedling growth and enhance disease resistance. The disease resistance to F. graminearum was consistently increased and drought tolerance was improved, while seedling growth was suppressed and photosynthesis activity was significantly reduced in the ZmGT-3b knockdown seedlings. Thus, the seedlings finally led to show a kind of growth–defense trade-off phenotype. Moreover, photosynthesis-related genes were specifically downregulated, especially ZmHY5, which encodes a conserved central regulator of seedling development and light responses; ZmGT-3b was confirmed to be a novel interacting partner of ZmHY5 in yeast and in planta. Constitutive defense responses were synchronically activated in the ZmGT-3b knockdown seedlings as many defense-related genes were significantly upregulated, and the contents of major cell wall components, such as lignin, were increased in the ZmGT-3b knockdown seedlings. These suggest that ZmGT-3b is involved in the coordination of the metabolism during growth–defense trade-off by optimizing the temporal and spatial expression of photosynthesis- and defense-related genes.

R2R3-MYB Transcription Factor PlMYB108 Confers Drought Tolerance in Herbaceous Peony (Paeonia lactiflora Pall.)

The MYB transcription factor (TF) is crucial for plant growth, development, and response to abiotic stress, but it is rarely reported in the herbaceous peony (Paeonia lactiflora Pall.). Here, an MYB TF gene was isolated, and based on our prior mRNA data from P. lactiflora samples, it was treated with drought stress (DS). Its complete cDNA structure was 1314 bp, which encoded 291 amino acids (aa). Furthermore, using sequence alignment analysis, we demonstrated that PlMYB108 was an R2R3-MYB TF. We also revealed that PlMYB108 was primarily localized in the nucleus. Its levels rose during DS, and it was positively correlated with drought tolerance (DT) in P. lactiflora. In addition, when PlMYB108 was overexpressed in tobacco plants, the flavonoid content, antioxidant enzyme activities, and photosynthesis were markedly elevated. Hence, the transgenic plants had stronger DT with a higher leaf water content and lower H2O2 accumulation compared to the wild-type (WT) plants. Based on these results, PlMYB108 is a vital gene that serves to increase flavonoid accumulation, reactive oxygen species (ROS), scavenging capacity, and photosynthesis to confer DT. The results would provide a genetic resource for molecular breeding to enhance plant DT.

Bioinformatics Approach to Identify Common Gene Signatures of Patients With Coronavirus 2019 and Lung Adenocarcinoma

Coronavirus disease 2019 (COVID-19) continues as a global pandemic. Patients with lung cancer infected with COVID-19 may develop severe disease or die. Treating such patients severely burdens overwhelmed healthcare systems. Here we identified potential pathological mechanisms shared between patients with COVID-19 and lung adenocarcinoma (LUAD). Co-expressed, differentially expressed genes (DEGs) in patients with COVID-19 and LUAD were identified and used to construct a protein-protein interaction (PPI) network and to perform enrichment analysis. We used the NetworkAnalyst platform to establish a co-regulatory of the co-expressed DEGs, and we used Spearman’s correlation to evaluate the significance of associations of hub genes with immune infiltration and immune checkpoints. Analysis of three datasets identified 112 shared DEGs, which were used to construct a protein-PPI network. Subsequent enrichment analysis revealed co-expressed genes related to biological process (BP), molecular function (MF), cellular component (CC) as well as to pathways, specific organs, cells and diseases. Ten co-expressed hub genes were employed to construct a gene-miRNA, transcription factor (TF)-gene and TF-miRNA network. Hub genes were significantly associated with immune infiltration and immune checkpoints. Finally methylation level of hub genes in LUAD was obtained via UALCAN database. The present multi-dimensional study reveals commonality in specific gene expression by patients with COVID-19 and LUAD. These findings provide insights into developing strategies for optimising the management and treatment of patients with LUAD with COVID-19.

Regulatory association of long noncoding RNAs and chromatin accessibility facilitates erythroid differentiation

Erythroid differentiation is a dynamic process regulated by multiple factors, while the interaction between long non-coding RNAs and chromatin accessibility and its influence on erythroid differentiation remains unclear. To elucidate this interaction, we employed hematopoietic stem cells, multipotent progenitor cells, common myeloid progenitor cells, megakaryocyte-erythroid progenitor cells, and erythroblasts from human cord blood as an erythroid differentiation model to explore the coordinated regulatory functions of lncRNAs and chromatin accessibility by integrating RNA-Seq and ATAC-Seq data. We revealed that the integrated network of chromatin accessibility and lncRNAs exhibits stage-specific changes throughout the erythroid differentiation process, and that the changes at the EB stage of maturation are dramatic. We identified a subset of stage-specific lncRNAs and transcription factors (TFs) that associate with chromatin accessibility during erythroid differentiation, in which lncRNAs are key regulators of terminal erythroid differentiation via a lncRNA-TF-gene network. LncRNA PCED1B-AS1 was revealed to regulate terminal erythroid differentiation by coordinating GATA1 dynamically binding to the chromatin and interacting with cytoskeleton network during erythroid differentiation. DANCR, another lncRNA that is highly expressed at the MEP stage, was verified to promote erythroid differentiation by compromising megakaryocyte differentiation and coordinating with chromatin accessibility and TFs, such as RUNX1. Overall, our results identified the associated network of lncRNAs and chromatin accessibility in erythropoiesis and provide novel insights into erythroid differentiation and abundant resources for further study.

KLF11 Protects against Venous Thrombosis via Suppressing Tissue Factor Expression

Krüppel-like factors (KLFs) play essential roles in multiple biological functions, including maintaining vascular homeostasis. KLF11, a causative gene for maturity-onset diabetes of the young type 7, inhibits endothelial activation and protects against stroke. However, the role of KLF11 in venous thrombosis remains to be explored. Utilizing stasis-induced murine deep vein thrombosis (DVT) model and cultured endothelial cells (ECs), we identified an increase of KLF11 expression under prothrombotic conditions both in vivo and in vitro. The expression change of thrombosis-related genes was determined by utilizing gain- and loss-of-function approaches to alter KLF11 expression in ECs. Among these genes, KLF11 significantly downregulated tumor necrosis factor-α (TNF-α)-induced tissue factor (TF) gene transcription. Using reporter gene assay, chromatin immunoprecipitation assay, and co-immunoprecipitation, we revealed that KLF11 could reduce TNF-α-induced binding of early growth response 1 (EGR1) to TF gene promoter in ECs. In addition, we demonstrated that conventional Klf11 knockout mice were more susceptible to developing stasis-induced DVT. These results suggest that under prothrombotic conditions, KLF11 downregulates TF gene transcription via inhibition of EGR1 in ECs. In conclusion, KLF11 protects against venous thrombosis, constituting a potential molecular target for treating thrombosis.

Regulus, a transcriptional regulatory networks inference tool based on Semantic Web technologies

Motivation: Transcriptional regulation -a major field of investigation in life science- is performed by binding of specialized proteins called transcription factors (TF) to DNA in specific, context-dependent regulatory regions, leading to either activation or inhibition of gene expression. Relations between TF, regions and genes can be described as regulatory networks, which are basically knowledge graphs containing the relationships between the different entities. Current methods of transcriptional regulatory networks inference rarely use information about TF binding or regulatory regions, often require a large number of samples and most of time do not indicate if the TF-gene relation is an activation or an inhibition. The resulting networks may then contain inconsistent relations and the methods are not applicable for common experimental or clinical settings, where the number of samples is limited. Therefore, based on our previous experience of formalizing the Regulatory Circuits data-sets with Semantic Web Technologies, we decided to create a new tool for transcriptional networks inference, that could solve these issues. Results: Our tool, Regulus, provides candidate signed TF-gene relations computed from gene expressions, regulatory region activities and TF binding sites data, together with the genomic location of all entities. After creating expressions and activities patterns, data are integrated into a RDF endpoint. A dedicated SPARQL query retrieves all potential TF-region relations for a given gene expression pattern. These ternary TF-region-gene pattern relations are then filtered and signed using a logical consistency check translated from biological knowledge. Regulus compares favorably to its closest network inference method, provides signs which are consistent with public databases and, when applied to real biological data, identifies both known and potential new regulators. We also provide several means to more stringently filter the output regulators. Altogether, we propose a new tool devoted to transcriptional network inference in settings where samples are scarce and cell populations may be closely related.