Gene Regulation Networks Research Articles

Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods.

Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics methods. The oocyte aging datasets GSE155179 and GSE158802 were obtained from the GEO database and analyzed as the training set. The GSE164371 dataset was then defined as the validation set. Differentially expressed genes were analyzed via the limma package and weighted gene coexpression network analysis, and intersected with cellular senescence-associated genes from the Cell Senescence database. The hub genes were identified via three machine learning algorithms, namely, support vector machine recursive feature elimination, random forest, and least absolute shrinkage and selection operator logistic, which were also confirmed via the validation set. Finally, a microRNA-mRNA‒transcription factor regulatory network and single-gene gene set enrichment analysis were performed to clarify the pathogenesis of oocyte aging. A competing endogenous RNA network of GSE155179 and GSE158802 with 124 mRNAs, 31 long noncoding RNAs, and 31 miRNAs was constructed. Two modules with 814 genes were considered the key modules of oocyte aging. PDIK1L, SIRT1, and MCU were subsequently identified as hub genes; on the basis of these hub genes, a regulatory network of oocyte aging with 8 miRNAs, 3 mRNAs, and 227 TFs was ultimately constructed. This study contributes to a deeper understanding of oocyte aging and may aid in the development of therapeutic approaches to improve reproductive outcomes in older women.

Abstract B026: Identification of small RNAs that function as multi-target therapeutics and as tools for novel target discovery in prostate cancer

Abstract Introduction: A major contributor to prostate cancer (PCa) mortality is the development of resistance to single agents targeting androgen receptor (AR) signaling. We have developed an unbiased shRNA-based negative selection screen to identify small RNAs (shRNAs and siRNAs) that inhibit PCa cell growth and survival (toxic sRNAs). Computational analyses of the most toxic sRNAs identified revealed that each can simultaneously target multiple known AR-coregulatory mRNAs. AR-coregulators modulate AR transcriptional response and are essential to AR signaling. We have proposed that the identified toxic sRNAs inhibit androgen signaling through seed-mediated targeting of androgen regulatory gene networks, resulting in inhibition of cell growth and viability. The goal of the current studies was to begin to analyze the broad therapeutic potential of these toxic sRNAs for PCa. We focused on analyzing i) their potential to thwart cell growth and/or viability when delivered to preclinical models of PCa, and ii) the targetome of the toxic sRNAs as a mean to uncover novel AR-coregulators that represent a targetable axis within androgen signaling. Methods: i) cationic lipid nanoparticles (LNPs), that efficiently deliver the toxic siRNAs to PCa cell lines, were used to deliver toxic siRNAs to organoids grown from PCa patient derived xenografts (PDX) models. 3D image analysis using the Ominer® software package was used to determine organoid count and size, the numbers of nuclei per organoid, and the fraction of dead cells to discriminate between cytotoxic and cytostatic effects. ii) to identify novel AR-coregulators, toxic siRNA direct targets were identified by sequencing of transcripts captured by biotinylated toxic siRNAs. Directly bound targets, were subjected to pathway enrichment and network analysis followed by identification of hub genes (Cytoscape). The effect of individually silencing potential AR-coregulators on the expression of androgen responsive genes was analyzed using Nanostring. Results and Conclusions: i) The effect of the toxic siRNAs on growth and viability of the PCa PDX-derived organoids will be presented and discussed as compelling preliminary data for therapeutic testing in more complex pre-clinical models (PCa PDXs). ii) Based on bioinformatic and downstream analysis of the direct targets of two toxic siRNAs, we selected 12 potentially novel AR- coregulators, with varied known functions including chromatin modifiers, transcriptional regulators, etc. Silencing of several of these potential AR-coregulators significantly affect expression of numerous AR responsive genes. Future experiments will focus on analyzing their interaction and functional association with the AR (i.e. effect of AR binding to chromatin). In conclusion, we have developed an assay that allowed the identification of toxic sRNAs for PCa cells and demonstrated their potential as therapeutics and as a tool for discovery of potential novel targets for PCa. Citation Format: Joshua M Corbin, Hamilton Young, Stefan Wilhelm, Adam S Asch, Chao Xu, Maria J Ruiz Echevarria. Identification of small RNAs that function as multi-target therapeutics and as tools for novel target discovery in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr B026.

Deciphering the transcriptional regulatory networks of FOX genes in nitrite-induced spleen injury in largemouth bass

Deciphering the transcriptional regulatory networks of FOX genes in nitrite-induced spleen injury in largemouth bass

Deciphering Immune-related Gene Signatures in Diabetic Retinopathy: Insights from In silico Analysis and In vitro Experiment.

Diabetes retinopathy (DR) is one of the most common microvascular consequences of diabetes, and the economic burden is increasing. Our aim is to decipher the relevant mechanisms of immune-related gene features in DR and explore biomarkers targeting DR. Provide a basis for the treatment and prevention of DR. The immune infiltration enrichment score of DR patients was evaluated from the single- cell RNA sequencing dataset, and the samples were divided into low immune subgroups and high immune subgroups based on this result. Through weighted gene correlation network analysis, differentially expressed genes (DEGs) between two subgroups were identified and crossed with genes with the strongest immune association, resulting in significant key genes. Then divide the DR individuals into two immune related differentially expressed gene (IDEG) clusters, A and B. Submit cross DEGs between two clusters through Gene Set Enrichment Analysis (GSEA) to further explore their functions. A protein-protein interaction (PPI) network of IDEG was established to further identify central genes associated with DR. Use the discovered central genes to predict the regulatory network involved in the pathogenesis of DR. Then, the role of the identified hub gene in the pathogenesis of DR was further studied through in vitro experiments. We found that the immune scores of DR and control groups were different, and 27 IDEGs were found in the DR subgroup. Compared with cluster A, the proportion of cytotoxic lymphocytes, B lineage, monocyte lineage, and fibroblasts in DR patients in cluster B is significantly enriched. GSEA indicates that these genes are associated with T cell activation, regulation of immune response processes, lymphocyte-mediated immunity, TNF signaling pathway, and other signaling pathways. The PPI network subsequently identified 10 hub genes in DR, including SIGLEC10, RGS10, PENK, FGD2, LILRA6, CIITA, EGR2, SIGLEC7, LILRB1, and CD300LB. The upstream regulatory network and lncRNA miRNA mRNA ceRNA network of these hub genes were ultimately constructed. The discovery and identification of these genes will provide biomarkers for targeted prediction and treatment of DR. By integrating bioinformatics analysis and in vitro experiments, we have identified a set of central genes, indicating that these genes can serve as potential biomarkers for DR, which may be promising targets for future DR immunotherapy interventions.

The effect of Polyethylene Terephthalate (PET) Microplastic stress on the composition and gene regulatory network of amino acid in Capsicum annuum

Polyethylene Terephthalate (PET) is a widely used plastic in daily life. The extensive accumulation of PET microplastics (PET-MPs) in the environment adversely affects plant growth in multiple ways. However, the impact of PET-MPs exposure on the plant metabolism and the underlying molecular mechanisms are largely unexplored. To address this gap, we employed metabolomics and transcriptomics combination analyses to investigate the effects of PET-MPs exposure, varying in particle size and concentration, on the amino acid content and composition in pepper, as well as the underlying genes regulatory network. A total of 282 amino acids and their derivatives were identified, including 8 essential amino acids. Significant changes in differentially accumulated amino acids (DAAs) and differentially expressed genes (DEGs) were observed across different treatments, indicating that PET-MPs exposure affects amino acid metabolism in peppers, with these effects closely related to the size and concentration of PET-MPs. Ten DAAs with significant variable importance were identified through OPLS-DA. Weighted gene co-expression network analysis (WGCNA) revealed that the red module was significantly correlated with most of the DAAs indicators, highlighting the essential roles of HMSI, BCAT, and 12 transcription factor (TF) genes in regulating amino acid synthesis under PET-MPs exposure. Furthermore, correlation and redundancy analysis (RDA) identified three candidate genes, HSMI, PROC, and FHM, involved in amino acid biosynthesis pathways. This study enhances our understanding of MPs pollution and provides novel insights into the impact of MPs on crop growth and nutrition.

Transcriptome and metabolome analyses reveal that GA3ox regulates the dwarf trait in mango (Mangifera indica L.)

BackgroundMango is a tropical fruit with high economic value. The selection of suitable dwarf mango varieties is an important aspect of mango breeding. However, the mechanisms that regulate mango dwarfing remain unclear.ResultsIn this study, we compared the transcriptomes and metabolomes of mango varieties Guiqi (a dwarfed variety) and Jinhuang (an arborized variety). A total of 4,954 differentially expressed genes and 317 differentially abundant metabolites were identified between the two varieties, revealing the molecular mechanism of the gibberellin 3β-hydroxylase gene GA3ox in regulating dwarfing traits in mangoes using joint transcriptome and metabolome analyses. The results showed that differentially expressed genes were enriched in the diterpenoid biosynthesis pathway and that differentially abundant metabolites were annotated to their upstream pathway, the terpenoid backbone biosynthesis. A gene regulation network based on these two pathways was constructed, indicating the upregulation of the GA3ox gene and the accumulation of gibberellin in dwarfed mangoes. We then transferred the GA3ox gene to tobacco plants following the application of gibberellin, and the morphology and height of the transgenic tobacco plants largely recovered the phenotype.ConclusionsThese results demonstrated that GA3ox plays a role in the regulation of dwarf traits. Our study provides an important theoretical basis for studying the regulatory mechanisms underlying mango dwarfism to facilitate mango breeding.

An integrative network-based approach to identify driving gene communities in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is an etiologically complex disease characterized by acute exacerbations and stable phases. We aimed to identify biological functions modulated in specific COPD conditions, using whole blood samples collected in the AERIS clinical study (NCT01360398). Considered conditions were exacerbation onset, severity of airway obstruction, and presence of respiratory pathogens in sputum samples. With an integrative multi-network gene community detection (MNGCD) approach, we analyzed expression profiles to identify communities of correlated genes. The approach combined different layers of gene interactions for each explored condition/subset of samples: gene expression similarity, protein-protein interactions, transcription factors, and microRNAs validated regulons. Heme metabolism, interferon-alpha, and interferon-gamma pathways were modulated in patients at both exacerbation and stable-state visits, but with the involvement of distinct sets of genes. An important gene community was enriched with G2M checkpoint, E2F targets, and mitotic spindle pathways during exacerbation. Targets of TAL1 regulator and hsa−let−7b − 5p microRNA were modulated with increasing severity of airway obstruction. Bacterial infections with Moraxella catarrhalis and, particularly, Haemophilus influenzae triggered a specific cellular and inflammatory response in acute exacerbations, indicating an active reaction of the host to infections. In conclusion, COPD is a complex multifactorial disease that requires in-depth investigations of its causes and features during its evolution and whole blood transcriptome profiling can contribute to capturing some relevant regulatory mechanisms associated with this disease. In this work, we explored multi-network modeling that integrated diverse layers of regulatory gene networks and enhanced our comprehension of the biological functions implicated in the COPD pathogenesis.

Regulating the formation of Müller glia-derived progenitor cells in the retina.

We summarize recent findings in different animal models regarding the different cell-signaling pathways and gene networks that influence the reprogramming of Müller glia into proliferating, neurogenic progenitor cells in the retina. Not surprisingly, most of the cell-signaling pathways that guide the proliferation and differentiation of embryonic retinal progenitors also influence the ability of Müller glia to become proliferating Müller glia-derived progenitor cells (MGPCs). Further, the neuronal differentiation of MGPC progeny is potently inhibited by networks of neurogenesis-suppressing genes in chick and mouse models but occurs freely in zebrafish. There are important differences between the model systems, particularly pro-inflammatory signals that are active in mature Müller glia in damaged rodent and chick retinas, but less so in fish retinas. These pro-inflammatory signals are required to initiate the process of reprogramming, but if sustained suppress the potential of Müller glia to become neurogenic MGPCs. Further, there are important differences in how activated Müller glia up- or downregulate pro-glial transcription factors in the different model systems. We review recent findings regarding regulatory cell signaling and gene networks that influence the activation of Müller glia and the transition of these glia into proliferating progenitor cells with neurogenic potential in fish, chick, and mouse model systems.

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

BackgroundCardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI.MethodsThe aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity.ResultsA total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI.ConclusionsAnalysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

BackgroundEndometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.MethodsNext-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.ResultsA total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.ConclusionsThis investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.

CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, limitations in safely delivering high quantities of CRISPR machinery demand careful target gene selection to achieve reliable therapeutic effects. Informed target gene selection requires a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) and thus their impact on cell phenotype. Effective decoding of these complex networks has been achieved using machine learning models, but current techniques are limited to single cell types and focus mainly on transcription factors, limiting their applicability to CRISPR strategies. To address this, we present CRISPR-GEM, a multilayer perceptron (MLP) based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types, respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually, and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts toward a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.

Genome-wide analysis of the MADS-box gene family in mango and ectopic expression of MiMADS77 in Arabidopsis results in early flowering

Mango (Mangifera indica L.) is an important tropical fruit, and timely flowering and fruit setting are very important for mango production. The MADS-box gene family is involved in the regulation of flower induction, floral organ specification, and fruit development in plants. The identification and analysis of the MADS-box gene family can lay a foundation for the study of the molecular mechanism of flowering and fruit development in mango. In this study, 119 MiMADS-box genes were identified on the basis of genome and transcriptome data. Phylogenetic analysis revealed that these genes can be divided into two classes. Forty-one type I proteins were further divided into three subfamilies, and seventy-eight type II proteins were further classified into eleven subfamilies. Several pairs of alternative splicing genes were found, especially in the SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) subfamily. The MiMADS-box genes were distributed on 18 out of the 20 mango chromosomes. Cis-element analysis revealed many light-, stress-, and hormone-responsive elements in the promoter regions of the mango MiMADS-box genes. Expression pattern analysis revealed that these genes were differentially expressed in multiple tissues in mango. The highly expressed MiMADS77 was subsequently transformed into Arabidopsis, resulting in significant early flowering and abnormal floral organs. Yeast two-hybrid (Y2H) assays revealed that MiMADS77 interacts with several MiMADS-box proteins. In addition, we constructed a preliminary flowering regulatory network of MADS-box genes in mango on the basis of related studies. These results suggest that MiMADS77 genes may be involved in flowering regulation of mango.

Establishment of prognosis model of hepatocellular carcinoma based on prognosis related gene analysis and study on gene regulation mechanism of model

ObjectiveTo analyze the expression and mechanism of prognosis related differentially expressed genes (DEGs) in hepatocellular carcinoma (HCC), and establish a prognosis risk model of prognosis related DEGs. MethodsTranscriptome data and clinical information of 374 HCC samples were downloaded from TCGA data. Kaplan-Meier (KM) survival analysis screened prognostic genes in DEGs. Protein-protein interaction (PPI) network was constructed based on prognostic genes and key genes were screened. Cox regression was used to analyze the key genes and construct the prognostic risk model, calculate the risk score of each patient, divide the patients into low-risk group and high-risk group according to the median risk value, use KM analysis method for survival analysis and draw the survival curve, and use receiver operating characteristic (ROC) curve to evaluate the prognostic risk model, The relationship between risk score and clinicopathological features of HCC patients was analyzed. GEPIA and the human protein atlas (HPA) databases were used for expression verification of model genes. The mirDIP database is used to analyze the regulatory network of model genes. GSCAlite platform is used to analyze the mechanism and drug sensitivity of model genes. ResultsA total of 1987 DEGs were extracted from the transcriptome data of HCC and normal samples, of which 258 were related to the prognosis of HCC (P < 0.01). Six key genes (CDK1, CCNA2, BUB1, CDC20, CCNB1 and TOP2A) were screened from the PPI network based on prognostic related genes, and the prognostic risk model was established. Survival analysis showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.01). The AUC values of 1, 3 and 5 years in the prognostic risk model were 0.716, 0.678 and 0.633. Multivariate Cox regression analysis showed that patient age and patient risk score were independent risk factors for the prognosis of HCC. The model gene is highly expressed in HCC and can promote apoptosis, cell cycle and EMT pathway. In addition, the high expression of model gene produced drug resistance to trametinib, selumetinib and RDEA119 (refametinib). ConclusionThe prognostic risk model based on six prognostic related DEGs is an independent prognostic factor of HCC, which can effectively predict the survival and prognosis of HCC patients.

The spatial and cellular portrait of transposable element expression during gastric cancer.

Gastric Cancer (GC) is a lethal malignancy, with urgent need for the discovery of novel biomarkers for its early detection. I previously showed that Transposable Elements (TEs) become activated in early GC (EGC), suggesting a role in gene expression. Here, I follow-up on that evidence using single-cell data from gastritis to EGC, and show that TEs are expressed and follow the disease progression, with 2,430 of them being cell populations markers. Pseudotemporal trajectory modeling revealed 111 TEs associated with the origination of cancer cells. Analysis of spatial data from GC also confirms TE expression, with 204 TEs being spatially enriched in the tumor regions and the tumor microenvironment, hinting at a role of TEs in tumorigenesis. Finally, a network of TE-mediated gene regulation was modeled, indicating that ~ 2,000 genes could be modulated by TEs, with ~ 500 of them already implicated in cancer. These results suggest that TEs might play a functional role in GC progression, and highlights them as potential biomarker for its early detection.

Profiling the lncRNA–miRNA–mRNA interaction network in the cold-resistant exercise period of grape (Vitis amurensis Rupr.)

BackgroundGrape is a plant that is sensitive to low temperature and vulnerable to low-temperature damage. However, little is known about the roles of lncRNAs, miRNAs and mRNAs in regulating the hypothermia response mechanism in Vitis amurensis Rupr.MethodsIn this study, the expression and regulatory network of low-temperature response genes were studied in the phloem of grape under different low-temperature stress.ResultsHere, we performed analyses related to RNA-seq and miRNA-seq on grape phloem tissues from five periods of cold resistance campaigns. Three RNAs (lncRNAs, miRNAs and mRNAs) obtained by KEGG and GO analyses were used to identify starch and sucrose metabolism associated with cold resistance, and specific changes in BP, CC, and MF were identified in four comparisons. Venn diagrams, thermograms and pathway maps were used to analyze the differentially expressed genes (DEGs), and their specific gene expression during the cold exercise were obtained. The six DEGs finally selected were used for qRT-PCR to verify the RNA-seq data. In addition, we found that the regulatory networks of miRNAs and lncRNAs correspond to the six DEGs. This study will contribute to further experimental studies to elucidate the cold resistance mechanism of Vitis amurensis Rupr.ConclusionsThe low-temperature response genes of grape are mainly enriched in the starch and sucrose metabolism, and they are regulated by miRNAs and lncRNAs. The conclusions will provide basic information for further understanding of the cold resistance mechanism of grape in the future.Graphical

Physiological and Transcriptome Analyses Reveal the Effects of Fertilization on the Yield of Winter Wheat and on the Photosynthetic Performance of Leaves during the Flowering Period.

Fertilization significantly affects the growth and development of wheat. However, the precise mechanisms underlying gene regulation during flowering in response to fertilization deficiency remain elusive. In this study, fertilization (F) and non-fertilization (CK) ) treatments were set up to reveal examine the effect of fertilization on the photosynthetic capacity of winter wheat during the flowering period through physiological, biochemical, and transcriptome analyses. Upon analyzing analysing their yield, leaf photosynthetic system exchange parameters during flowering, antioxidant enzyme activity, and endogenous hormone parameters, we found that the F treatment resulted in higher net photosynthetic rates during flowering periods than the CK treatment. The superoxide dismutase (SOD) (83.92%), peroxidase (POD) (150.75%), and catalase (CAT) (22.74%) activities of leaves in treated with F during the flowering period were notably elevated compared to those of CK-treated leaves. Abscisic acid (ABA) (1.86%) and gibberellin acid (GA3) (33.69%) levels were reduced, whereas Auxin auxin (IAA) (98.27%) content was increasedwas increased under F treatment compared to those the results under the CK treatment. The chlorophyll a (32.53%), chlorophyll b (56%), total chlorophyll (37.96%), and carotenoid contents (29.80%) under F treatment were also increased compared to CK., exceeded exceeding those obtained under the CK treatment. Furthermore, transcriptional differences between the F and CK conditions were analyzed, and key genes were screened and validated by using q-PCR. Transcriptome analysis identified 2281 differentially expressed genes (DEGs), with enriched pathways related to photosynthesis and light harvesting. DEGs were subjected to cluster simulation, which revealed that 53 DEGS, both up- and down-regulated, responded to the F treatment. qRT-PCR-based validation confirmed the differential expression of genes associated with carbohydrate transport and metabolism, lipid transport, and signal transduction. This study revealed distinctive transcriptional patterns and crucial gene regulation networks in wheat during flowering under fertilization, providing transcriptomic guidance for the precise regulation of wheat breeding.

Identifying the regulatory network of microRNAs and mRNAs to clarify molecular mechanisms in stroke by bioinformatics analysis.

Stroke is one of the leading causes of disability and mortality worldwide. To identify the regulatory network of microRNAs (miRNAs) and mRNAs to clarify molecular mechanisms in stroke. Four miRNA datasets and two mRNA datasets of stroke were downloaded from the GEO database. R-Studio was utilized to analyze differentially expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) in the blood of stroke and control patients. FunRich software was utilized to conduct GO and biological pathway analysis on DEmiRNAs, and to search for transcription factors (TFs) of DEmiRNAs. Subsequently, we used miRDB, miRTarBase, and TargetScan to identify DEmiRNAs target genes and intersected with DEmRNAs to find common target genes. The miRNA-mRNA regulatory network of common target genes was constructed by using the Cytoscape. The biological and functional roles of target genes in the regulatory network were predicted using GO and KEGG pathway analyses. 464 DEmiRNAs and 329 DEmRNAs were screened. The top ten TFs (SP1, SP4, EGR1, TCF3, NKX6-1, ZFP161, RREB1, MEF2A, NFIC, POU2F1) were visualized. 16747 target genes of DEmiRNAs were predicted. Target genes were intersected with DEmRNAs, 107 common target genes and 162 DEmiRNAs regulating these common genes were obtained, and then a regulatory network was constructed. Target genes of the regulatory network were primarily enriched in VEGF signaling pathway, lipid and atherosclerosis, T cell receptor signaling pathway. This study found that VEGF signaling pathway, lipid and atherosclerosis, T cell receptor signaling pathway are implicated in the biological process of stroke by constructing the regulatory network of miRNAs-mRNAs, which may have guide significance for the pathogenesis and treatment of stroke.

Single-Cell WGCNA Combined with Transcriptome Sequencing to Study the Molecular Mechanisms of Inflammation-Related Ferroptosis in Myocardial Ischemia-Reperfusion Injury.

Myocardial ischemia-reperfusion injury (MIRI) is characterized by inflammation and ferroptosis, but the precise mechanisms remain unknown. This study used single-cell transcriptomics technology to investigate the changes in various cell subtypes during MIRI and the regulatory network of ferroptosis-related genes and immune infiltration. Datasets GSE146285, GSE83472, GSE61592, and GSE160516 were obtained from Gene Expression Omnibus. Each cell subtype in the tissue samples was documented. The Seurat package was used for data preprocessing, standardization, and clustering. Cellphonedb was used to investigate the ligand-receptor interactions between cells. The hdWGCNA analysis was used to create a gene co-expression network. GSVA and GSEA were combined to perform functional enrichment and pathway analysis on the gene set. Furthermore, characteristic genes of the disease were identified using Lasso regression and SVM algorithms. Immune cell infiltration analysis was also performed. MIRI rat models were created, and samples were taken for RT-qPCR and Western blot validation. The proportion of MIRI samples in the C2, C6, and C11 subtypes was significantly higher than that of control samples. Three genes associated with ferroptosis (CD44, Cfl1, and Zfp36) were identified as MIRI core genes. The expression of these core genes was significantly correlated with mast cells and monocyte immune infiltrating cells. The experimental validation confirmed the upregulation of Cd44 and Zfp36 expression levels in MIRI, consistent with current study trends. This study used single-cell transcriptomics technology to investigate the molecular mechanisms underpinning MIRI. Numerous important cell subtypes, gene regulatory networks, and disease-associated immune infiltration were also discovered. These findings provide new information and potential therapeutic targets for MIRI diagnosis and treatment.

EWS-WT1 fusion isoforms establish oncogenic programs and therapeutic vulnerabilities in desmoplastic small round cell tumors

EWS fusion oncoproteins underlie several human malignancies including Desmoplastic Small Round Cell Tumor (DSRCT), an aggressive cancer driven by EWS-WT1 fusion proteins. Here we combine chromatin occupancy and 3D profiles to identify EWS-WT1-dependent gene regulation networks and target genes. We show that EWS-WT1 is a powerful chromatin activator controlling an oncogenic gene expression program that characterizes primary tumors. Similar to wild type WT1, EWS-WT1 has two isoforms that differ in their DNA binding domain and we find that they have distinct DNA binding profiles and are both required to generate viable tumors that resemble primary DSRCT. Finally, we identify candidate EWS-WT1 target genes with potential therapeutic implications, including CCND1, whose inhibition by the clinically-approved drug Palbociclib leads to marked tumor burden decrease in DSRCT PDXs in vivo. Taken together, our studies identify gene regulation programs and therapeutic vulnerabilities in DSRCT and provide a mechanistic understanding of the complex oncogenic activity of EWS-WT1.

Lactylation signature identifies liver fibrosis phenotypes and traces fibrotic progression to hepatocellular carcinoma.

Precise staging and classification of liver fibrosis are crucial for the hierarchy management of patients. The roles of lactylation are newly found in the progression of liver fibrosis. This study is committed to investigating the signature genes with histone lactylation and their connection with immune infiltration among liver fibrosis with different phenotypes. Firstly, a total of 629 upregulated and 261 downregulated genes were screened out of 3 datasets of patients with liver fibrosis from the GEO database and functional analysis confirmed that these differentially expressed genes (DEGs) participated profoundly in fibrosis-related processes. After intersecting with previously reported lactylation-related genes, 12 DEGs related to histone lactylation were found and narrowed down to 6 core genes using R algorithms, namely S100A6, HMGN4, IFI16, LDHB, S100A4, and VIM. The core DEGs were incorporated into the Least absolute shrinkage and selection operator (LASSO) model to test their power to distinguish the fibrotic stage. Advanced fibrosis presented a pattern of immune infiltration different from mild fibrosis, and the core DEGs were significantly correlated with immunocytes. Gene set and enrichment analysis (GSEA) results revealed that core DEGs were closely linked to immune response and chemokine signaling. Samples were classified into 3 clusters using the LASSO model, followed by gene set variation analysis (GSVA), which indicated that liver fibrosis can be divided into status featuring lipid metabolism reprogramming, immunity immersing, and intermediate of both. The regulatory networks of the core genes shared several transcription factors, and certain core DEGs also presented dysregulation in other liver fibrosis and idiopathic pulmonary fibrosis (IPF) cohorts, indicating that lactylation may exert comparable functions in various fibrotic pathology. Lastly, core DEGs also exhibited upregulation in HCC. Lactylation extensively participates in the pathological progression and immune infiltration of fibrosis. Lactylation and related immune infiltration could be a worthy focus for the investigation of HCC developed from liver fibrosis.