Identification Of Regulatory Networks Research Articles

Identification of potential hub genes and regulatory networks of smoking-related endothelial dysfunction in atherosclerosis using bioinformatics analysis.

Endothelial dysfunction, the earliest stage of atherosclerosis, can be caused by smoking, but its molecular mechanism requires further investigation. This study aimed to use bioinformatics analysis to identify potential mechanisms involved in smoking-related atherosclerotic endothelial dysfunction. The transcriptome data used for this bioinformatics analysis were obtained from the Gene Expression Omnibus (GEO) database. The GSE137578 and GSE141136 datasets were used to identify common differentially expressed genes (co-DEGs) in endothelial cells treated with oxidized low-density lipoprotein (ox-LDL) and tobacco. The co-DEGs were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) databases. Additionally, a protein-protein interaction (PPI) network was constructed to visualize their interactions and screen for hub genes. GSE120521 dataset was used to verify the expression of hub genes in unstable plaques. The miRNA expression profile GSE137580 and online databases (starBase 2.0, TargetScan 8.0 and DGIdb v4.2.0) were used to predict the related non-coding RNAs and drugs. A total of 232 co-DEGs were identified, including 113 up-regulated genes and 119 down-regulated genes. These DEGs were primarily enriched in detrimental autophagy, cell death, transcription factors, and cytokines, and were implicated in ferroptosis, abnormal lipid metabolism, inflammation, and oxidative stress pathways. Ten hub genes were screened from the constructed PPI network, including up-regulated genes such as FOS, HMOX1, SQSTM1, PTGS2, ATF3, DDIT3, and down-regulated genes MCM4, KIF15, UHRF1, and CCL2. Importantly, HMOX1 was further up-regulated in unstable plaques (p= 0.034). Finally, a regulatory network involving lncRNA/circRNA-miRNA-hub genes and drug-hub genes was established. Atherosclerotic endothelial dysfunction is associated with smoking-induced injury. Through bioinformatics analysis, we identified potential mechanisms and provided potential therapeutic targets.

Transcriptional repression of the oncofetal LIN28B gene by the transcription factor SOX6

The identification of regulatory networks contributing to fetal/adult gene expression switches is a major challenge in developmental biology and key to understand the aberrant proliferation of cancer cells, which often reactivate fetal oncogenes. One key example is represented by the developmental gene LIN28B, whose aberrant reactivation in adult tissues promotes tumor initiation and progression. Despite the prominent role of LIN28B in development and cancer, the mechanisms of its transcriptional regulation are largely unknown. Here, by using quantitative RT-PCR and single cell RNA sequencing data, we show that in erythropoiesis the expression of the transcription factor SOX6 matched a sharp decline of LIN28B mRNA during human embryo/fetal to adult globin switching. SOX6 overexpression repressed LIN28B not only in a panel of fetal-like erythroid cells (K562, HEL and HUDEP1; ≈92% p < 0.0001, 54% p = 0.0009 and ≈60% p < 0.0001 reduction, respectively), but also in hepatoblastoma HepG2 and neuroblastoma SH-SY5H cells (≈99% p < 0.0001 and ≈59% p < 0.0001 reduction, respectively). SOX6-mediated repression caused downregulation of the LIN28B/Let-7 targets, including MYC and IGF2BP1, and rapidly blocks cell proliferation. Mechanistically, Lin28B repression is accompanied by SOX6 physical binding within its locus, suggesting a direct mechanism of LIN28B downregulation that might contribute to the fetal/adult erythropoietic transition and restrict cancer proliferation.

Abstract LB289: Single-cell CRISPR screens at scale to understand T cell biology

Abstract CRISPR screens have become the primary discovery engine in modern biology and are widely used to uncover novel targets in immuno-oncology. However, these genetic screens are usually coupled to rather simplistic read-outs such as cell fitness. In contrast, CROP-Seq (or “CRISPR Droplet” Sequencing) screens combine CRISPR perturbation with single-cell transcriptomics, enabling high-content phenotyping at single-cell resolution. Briefly, cells are perturbed with a pooled sgRNA library and transcriptomic profiles of each cell are recorded using conventional single-cell RNA sequencing platforms. Currently, the scale of these screens is limited to the perturbation of a couple of hundred genes, possibly up to 1.000 genes, because of increased costs for single-cell library preparation and next generation sequencing (NGS). At Myllia, we have built a platform allowing high-content CROP-Seq screens in both CRISPR-ready cancer cell lines as well as primary immune cells, e.g., human T cells. Using CRISPR KO, CRISPR interference or CRISPR activation workflows, we can help accelerate the identification of target genes and regulatory networks involved in immuno-oncology. Primary T cells are of great interest in the immunotherapy community as they are key players in autoimmune and inflammatory disease. Engineered T-lymphocytes such as CAR-T cells are currently developed as novel cellular medicines, yet many T cell-intrinsic features involved in CAR-T potency remain elusive. To enable the discovery of novel targets, we have performed multiple customized CROP-Seq CRISPR KO screens in primary human T cells investigating T cell stemness and effector phenotypes as well as CD4+ T helper cell differentiation towards Th1, Th2, or Th17 subsets. Here, we present screens for regulators of functional T cell plasticity and Th2 commitment in which we aimed to identify genes determining critical T cell fate decisions, potentially linking gene (dys)function to either autoimmune/inflammatory disease or functional persistence of cytotoxic T cells for cancer immunotherapy. Overall, the advanced CROP-Seq platform combining large-scale perturbations and targeted sequencing (TA) read-outs will catalyse a paradigm shift for CROP-Seq enabling high-throughput functional genomic screens that support the validation of drug targets in autoimmunity, inflammation and immuno-oncology. Citation Format: Nicole Untermoser, Johanna Irnstorfer, Anatoly Vasilyev, Nikola Vinko, Sumit Pawar, Anke Loregger, Adam Krejci, Henrik Schmidt, Tilmann Bürckstümmer. Single-cell CRISPR screens at scale to understand T cell biology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB289.

Identification of microRNA-mRNA Regulatory Networks with Therapeutic Values in Alzheimer's Disease by Bioinformatics Analysis.

Alzheimer's disease (AD) is the most prevalent neurological disorder worldwide, affecting approximately 24 million individuals. Despite more than a century of research on AD, its pathophysiology is still not fully understood. Recently, genetic studies of AD have focused on analyzing the general expression profile by employing high-throughput genomic techniques such as microarrays. Current research has leveraged bioinformatics advancements in genetic science to build upon previous efforts. Data from the GSE118553 dataset used in this investigation, and the analyses carried out using programs such as Limma and BioBase. Differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRs) associated with AD identified in the studied areas of the brain. Target genes of the DEmiRs identified using the MultiMiR package. Gene ontology (GO) completed using the Enrichr website, and the protein-protein interaction (PPI) network for these genes drawn using STRING and Cytoscape software. The findings introduced DEGs including CTNNB1, PAK2, MAP2K1, PNPLA6, IGF1R, FOXL2, DKK3, LAMA4, PABPN1, and GDPD5, and DEmiRs linked to AD (miR-106A, miR-1826, miR-1253, miR-10B, miR-18B, miR-101-2, miR-761, miR-199A1, miR-379 and miR-668), (miR-720, miR-218-2, miR-25, miR-602, miR-1226, miR-548K, miR-H1, miR-410, miR-548F2, miR-181A2), (miR-1470, miR-651, miR-544, miR-1826, miR-195, miR-610, miR-599, miR-323, miR-587 and miR-340), and (miR-1282, miR-1914, miR-642, miR-1323, miR-373, miR-323, miR-1322, miR-612, miR-606 and miR-758) in cerebellum, frontal cortex, temporal cortex, and entorhinal cortex, respectively. The majority of the genes and miRNAs identified by our findings may be employed as biomarkers for prediction, diagnosis, or therapy response monitoring.

The Impact of Innovative Research Methods for Enhancing Agricultural Plants for Sustainable Development in The Future

In the pursuit of sustainable agricultural practices, the role of innovative research methods in enhancing agricultural plants is paramount. This abstract delineates the profound impact of such methodologies on sustainable development in the foreseeable future. Cutting-edge techniques such as genomic sequencing, molecular markers, and genome editing technologies revolutionize plant breeding by identifying and manipulating genes responsible for desirable traits, including disease resistance and drought tolerance. Omics technologies, encompassing transcriptomics, proteomics, and metabolomics, provide comprehensive insights into gene expression patterns and metabolic pathways, thereby facilitating targeted breeding strategies aimed at enhancing plant resilience and productivity. High-throughput phenotyping platforms enable rapid and accurate characterization of plant traits, empowering breeders to select superior varieties with enhanced performance under diverse environmental conditions. Concurrently, bioinformatics tools and data analytics play a pivotal role in deciphering complex genomic datasets, aiding in the identification of key genes and regulatory networks governing important agronomic traits. Biotechnology and synthetic biology approaches offer novel avenues for genetic manipulation, allowing for the design and engineering of plants with optimized traits, such as improved nutritional content and stress tolerance. Additionally, the integration of climate modeling with systems biology provides valuable insights into the interaction between genotype and environment, facilitating the development of climate-resilient crop varieties tailored to specific agroecosystems. Through the synergistic application of these innovative research methods, agricultural scientists and breeders can accelerate the pace of crop improvement while simultaneously promoting sustainability by fostering resilience, resource efficiency, and environmental stewardship. This transformative paradigm in agricultural research promises to address the complex challenges of food security, climate change, and environmental degradation, thereby laying the foundation for a more sustainable and resilient agricultural future.

Leveraging chromatin state transitions for the identification of regulatory networks orchestrating heart regeneration.

The limited regenerative capacity of the human heart contributes to high morbidity and mortality worldwide. In contrast, zebrafish exhibit robust regenerative capacity, providing a powerful model for studying how to overcome intrinsic epigenetic barriers maintaining cardiac homeostasis and initiate regeneration. Here, we present a comprehensive analysis of the histone modifications H3K4me1, H3K4me3, H3K27me3and H3K27ac during various stages of zebrafish heart regeneration. We found a vast gain of repressive chromatin marks one day after myocardial injury, followed by the acquisition of active chromatin characteristics on day four and a transition to a repressive state on day 14, and identified distinct transcription factor ensembles associated with these events. The rapid transcriptional response involves the engagement of super-enhancers at genes implicated in extracellular matrix reorganization and TOR signaling, while H3K4me3 breadth highly correlates with transcriptional activity and dynamic changes at genes involved in proteolysis, cell cycle activity, and cell differentiation. Using loss- and gain-of-function approaches, we identified transcription factors in cardiomyocytes and endothelial cells influencing cardiomyocyte dedifferentiation or proliferation. Finally, we detected significant evolutionary conservation between regulatory regions that drive zebrafish and neonatal mouse heart regeneration, suggesting that reactivating transcriptional and epigenetic networks converging on these regulatory elements might unlock the regenerative potential of adult human hearts.

Identification of immune-related regulatory networks and diagnostic biomarkers in thyroid eye disease.

Thyroid eye disease (TED) is an orbit-associated autoimmune inflammatory disorder intricately linked to immune dysregulation. Complete pathogenesis of TED remains elusive. This work aimed to mine pathogenesis of TED from immunological perspective and identify diagnostic genes. Gene expression microarray data for TED patients were downloaded from Gene Expression Omnibus, immune-related genes (IRGs) were from ImmPort database, and TED-related transcription factors (TFs) were from Cirtrome Cancer database. Differential analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Regulatory networks of TFs and IRGs were constructed with Cytoscape. Diagnostic biomarkers in TED were identified through LASSO. Immune cell infiltration analysis was performed using CIBERSORT. Twenty-three immune-related DEmRNAs were revealed and were primarily enriched in humoral immune response, positive regulation of inflammatory response, IL-17, and TNF pathways. Co-expression regulatory network included four TFs and 16 immune-related DEmRNAs. Seven diagnostic genes were identified, with Area Under the Curve (AUC) of 0.993 for training set and AUC value of 0.836 for validation set. TED patients exhibited elevated infiltration levels by macrophages M2, mast cells, and CD8 T cells among 22 immune cell types, whereas macrophages M2 and mast cells resting were significantly lower than normal group. The seven feature genes had high diagnostic value for TED patients. Our work explored regulatory network and diagnostic biomarkers, laying theoretical basis for TED diagnosis and treatment.

Identification of Senescence-Related Biomarkers and Regulatory Networks in Intracerebral Hemorrhage.

Intracerebral hemorrhage (ICH) is a severe neurological disorder with substantial societal implications. Cellular senescence plays a critical role in ICH pathogenesis. This study aims to identify senescence-related biomarkers in ICH for diagnostic and therapeutic purposes. Raw data from GSE24265 in Gene Expression Omnibus was downloaded. Senescence-related genes were acquired from CellAge. Differential gene analysis was done between patients with ICH and controls. The intersection of ICH differentially expressed genes and senescence-related genes for senescence-related ICH genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. Protein-protein interaction network was constructed through the Search Tool for the Retrieval of Interacting Genes. Single sample gene set enrichment analysis was done for immune cell infiltration and function evaluation in control and ICH groups. miRWalk2.0 database was used for microRNA predictions targeting ICH biomarkers. Transcriptional regulatory relationships unraveled by sentence-based text mining database was employed to predict transcription factors regulating identified biomarkers. Thirteen senescence-related ICH genes were identified. They were primarily enriched in the positive regulation of angiogenesis and the Advanced Glycation End Product -Receptor for AGE signaling pathway in diabetic complications. Validation in the GSE149317 data set and receiver operating characteristic analysis highlighted Caveolin 1, C-X-C Motif Chemokine Ligand 1, ETS proto-oncogene 1, transcription factor, and Serpin Family E Member 1 as potential ICH biomarkers. Single sample gene set enrichment analysis revealed increased Type 2 T helper cell 2_cells, Treg cells, and immune functions like Antigen-presenting cells_co_stimulation in patients with ICH. Fourteen microRNA, including has-miR-6728-3p, were predicted to regulate these biomarkers. transcription factors such as PPARG, RARA, HMGA1, and NFKB1 were identified as potential regulators of the ICH biomarkers. Caveolin 1, C-X-C Motif Chemokine Ligand 1, ETS proto-oncogene 1, transcription factor, and Serpin Family E Member 1 may serve as valuable biomarkers in ICH. Targeting these genes could contribute to ICH prevention and treatment.

Identification of pivotal genes and regulatory networks associated with atherosclerotic carotid artery stenosis based on comprehensive bioinformatics analysis and machine learning.

Bioinformatics methods were applied to investigate the pivotal genes and regulatory networks associated with atherosclerotic carotid artery stenosis (ACAS) and provide new insights for the treatment of this disease. The study utilized five ACAS datasets (GSE100927, GSE11782, GESE28829, GSE41571, and GSE43292) downloaded from the NCBI GEO database. The first four datasets were combined as the training set (n = 99), while GSE43292 (n = 64) was used as the validation set. Difference analysis and functional enrichment analysis were then performed on the training set. The pathogenic targets of ACAS were screened by protein-protein interaction networks and MCODE analyses, combined with three machine learning algorithms. The results were next verified by analysis of inter-group differences and ROC curve analysis. Next, immune-related function and immune cell correlation analyses were performed, and plaques of human ACAS were applied to verify the results via immunohistochemistry (IH) and immunofluorescence (IF). Finally, the competing endogenous RNAs (ceRNA) and transcription factors (TFs) regulatory networks of the characterized genes were constructed. A total of 177 differentially expressed genes were identified, including 67 genes downregulated and 110 genes upregulated. Gene set enrichment analysis revealed that five pathways were active in the experimental group, including xenograft rejection, autoimmune thyroid disease, graft-versus-host disease, leishmaniasis infection, and lysosomes. Four key genes were identified, with C3AR1 being upregulated and FBLN5, PPP1R12A, and TPM1 being downregulated. The analysis of inter-group differences demonstrated that the four characterized genes were differentially expressed in both the control and experimental groups. The ROC analysis showed that they had high AUC values in both the training and validation sets. Therefore, a predictive ACAS patient nomogram model based on the screened genes was established. Correlation analysis revealed a positive correlation between C3AR1 expression and neutrophils, which was further validated in IH and IF. One or multiple lncRNAs may compete with the characterized genes for binding miRNAs. Additionally, each characterized gene interacts with multiple TFs. Four pivotal genes were screened, and relevant ceRNA and TFs were predicted. These molecules may exert a crucial role in ACAS and serve as potential biomarkers and therapeutic targets.

Identification of miRNA-mRNA Regulatory Networks Associated with Diabetic Retinopathy using Bioinformatics Analysis.

Diabetic retinopathy (DR) is a major complication of diabetes and a leading cause of visual loss. This study aimed to explore biomarkers for DR that may provide additional reference to DR pathogenesis and development. The differentially expressed genes (DEGs) between the DR and control samples in the GSE53257 dataset were identified. Logistics analyses were performed to identify DR-associated miRNAs and genes, and correlation analysis was performed to determine the correlation between them in GSE160306. A total of 114 DEGs in DR were identified in GSE53257. Three genes, including ATP5A1 (down), DAUFV2 (down), and OXA1L (down), were differentially expressed between DR and control samples in GSE160306. Univariate logistics analysis identified that ATP5A1 (OR=0.007, p = 1.40E-02), NDUFV2 (OR = 0.003, p = 6.40E-03), and OXA1L (OR = 0.093, p = 3.08E-02) were DR-associated genes. ATP5A1 and OXA1L were regulated by multiple miRNAs, of which hsa-let- 7b-5p (OR = 26.071, p = 4.40E-03) and hsa-miR-31-5p (OR = 4.188, p = 5.09E-02) were related to DR. ATP5A1 and OXA1L were closely correlated with each other in DR. The hsa-miR-31-5p-ATP5A1 and hsa-let-7b-5p-OXA1L axes might play novel and important roles in the pathogenesis and development of DR.

Identification of key genes and regulatory networks involved in the Comorbidity of atrial fibrillation and chronic obstructive pulmonary disease

BackgroundThe underlying molecular processes of atrial fibrillation (AF) and chronic obstructive pulmonary disease (COPD) are frequently linked to increased morbidity and mortality when they co-occur. However, their underlying molecular mechanisms are questioned due to their incomplete analysis. ObjectiveThis study aimed to identify common differentially expressed genes (DEGs) in AF and COPD patients and investigate their potential biological functions and pathways. We hope to complement and update previous research through clearer figure presentation and different bioinformatic analysis methods with different datasets. MethodsWe used statistical analysis to identify DEGs in the expression profiles of AF and COPD patients using datasets from the Gene Expression Omnibus database. To ascertain whether the common DEGs were functionally enriched, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were used. In addition, we generated protein‒protein interaction networks and identified significant hub genes. Furthermore, the hub genes were used to analyze transcription factor (TF)-gene interactions and TF-miRNA coregulatory networks, and their expression levels were validated in additional datasets. ResultsWe identified a total of 15 DEGs that were upregulated, whereas 36 were downregulated in AF and COPD patients. The DEGs were commonly expressed in both AF and COPD patients, with functional enrichment analysis revealing their involvement in metabolic processes and neuron-to-neuron synapses. We identified significant hub genes, including TGM2, ITPR1, CHL1, ALDOC, RPS3, FBLN2, NDUFS2, ITGA5, CTNNB1, RBP1, CLSTN2, FABP5, EPHA4, LDHA, and HNRNPL, and analyzed their coexpression and biological functions. TF-gene interaction and TF-miRNA coregulatory network analyses revealed the regulatory relationships of the hub genes. Additional datasets were analyzed to validate hub gene expression, and ALDOC, HNRNPL, and NDUFS2 displayed similar processes in AF and COPD patients. ConclusionsIn our study, we demonstrate that metabolic processes and neuron-to-neuron synaptic connections may contribute to the cooccurrence of AF and COPD. The identified hub genes and regulatory networks may act as potential biomarkers and therapeutic targets for these diseases.

Identification of milk-related genes and regulatory networks in Bactrian camel either supplemented or under grazing.

Using gene co-expression networks to understand dynamic characterizations in lactating animals becomes a common method. However, there are rarely reporters focusing on milk traits in Bactrian camel by high-throughput sequencing. We used RNA-seq to generate the camel transcriptome from the blood of 16 lactating Alxa Bactrian camel in different feeding groups. In total, we obtained 1185 milk-related genes correlated with milk yield, milk protein, milk fat, and milk lactose across the WGCNA analysis. Moreover, 364 milk-related genes were differentially expressed between supplementation and grazing feeding groups. The differential expression-camel milk-related genes CMRGs (DE-CMRGs) in supplement direct an intensive gene co-expression network to improve milk performance in lactating camels. This study provides a non-invasive method to identify the camel milk-related genes in camel blood for four primary milk traits and valuable theoretical basis and research ideas for the study of the milk performance regulation mechanism of camelid animals.

Insights into morphological and physio-biochemical adaptive responses in mungbean (Vigna radiata L.) under heat stress.

Mungbean (Vigna radiata L. Wilczek) is an important food legume crop which contributes significantly to nutritional and food security of South and Southeast Asia. The crop thrives in hot and humid weather conditions, with an optimal temperature range of 28°-35°C, and is mainly cultivated under rainfed environments. However, the rising global temperature has posed a serious threat to mungbean cultivation. Optimal temperature is a vital factor in cellular processes, and every crop species has evolved with its specific temperature tolerance ability. Moreover, variation within a crop species is inevitable, given the diverse environmental conditions under which it has evolved. For instance, various mungbean germplasm can grow and produce seeds in extreme ambient temperatures as low as 20°C or as high as 45°C. This range of variation in mungbean germplasm for heat tolerance plays a crucial role in developing heat tolerant and high yielding mungbean cultivars. However, heat tolerance is a complex mechanism which is extensively discussed in this manuscript; and at the same time individual genotypes have evolved with various ways of heat stress tolerance. Therefore, to enhance understanding towards such variability in mungbean germplasm, we studied morphological, anatomical, physiological, and biochemical traits which are responsive to heat stress in plants with more relevance to mungbean. Understanding heat stress tolerance attributing traits will help in identification of corresponding regulatory networks and associated genes, which will further help in devising suitable strategies to enhance heat tolerance in mungbean. The major pathways responsible for heat stress tolerance in plants are also discussed.

Abstract 6544: Identification of epigenetic regulatory networks associated with the basal-like breast cancer subtype

Abstract Purpose: Basal-like breast cancer (BLBC) is a biologically aggressive and molecular heterogeneous breast cancer subtype that confers to the patient´s poor prognosis. The crosstalk of epigenetic regulators, such as methylation events and microRNA (miRNA) deregulation, drives breast cancer progression and modulates aggressive tumor phenotypes. However, few studies have specifically characterized the epigenetic regulatory networks of genome-wide DNA methylation-based modifications, miRNAs and transcription factors (TF) expression in BLBC. Therefore, the main objective of this study was to identify and correlate the DNA methylation profiles and the transcriptional expression of miRNAs and TFs of the BLBC subtype. Methods: To achieve our goal, a comprehensive computational pipeline using the R language was used. First, the transcriptional expression profiles and DNA methylation data of BLBC and non-tumor samples were downloaded from The Cancer Genome Atlas (TCGA). Then, the ELMER package was used to determine the differentially methylated distal probes between these two groups, identifying gene-probes pairs that can be regulated by un/methylated regions that affect the function of TFs. The different expressed (DE) miRNAs were identified using the DESeq2 package. To determine the regulatory pairs (gene, miRNA and TF interactions) the multiMiR, miRWalk, ENCODE, and hTFtarget were used and the regulatory networks were identified by the FANMOD algorithm. Finally, pathway enrichment analysis (PEA) was conducted using the Reactome database and microT-CDS (Diana Tools mirPath v.3). Results: A total of 147 and 199 pairs of gene-probes were identified in hypermethylation and hypomethylation analyses, respectively. Fifty genes and 59 regulatory TFs whose expression was associated with DNA methylation were selected, including members of the E2F (E2Fs) and Krüppel-like factor (KLFs) family. Eleven DE miRNAs, out of the 51 identified, were selected based on their interaction with genes and TFs. These pairs of miRNAs, TF, and genes generated three types of feed-forward loops (FFLs): coherent FFLs, mediated by miRNAs; incoherent FFLs, mediated by TFs; and compost FFLs, mediated by TF and miRNAs. Finally, the PEA analysis showed 51 pathways in which miRNAs, genes, or TFs may interact, including pathways related to DNA repair, cell cycle, pathways in cancer, as well as the defective binding of RB1 mutants to E2F1, and the ErbB signaling pathway. Conclusion: Our findings demonstrated the epigenetic regulation of methylation patterns and miRNA expression and their impact on the expression of TFs that may exert critical roles in the BLBC. The functional analysis of these regulatory networks in well-established BLBC models, targeting the signaling pathways identified, can determine whether they modulate the BLBC phenotype and can be used as potential clinical targets. Citation Format: Larissa Miyuki Okano, Alexandre Luiz Azevedo, Tamyres Mingorance Carvalho, Fernanda Brandão Berti, Luciane Regina Cavalli. Identification of epigenetic regulatory networks associated with the basal-like breast cancer subtype. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6544.

Identification of lncRNA and mRNA regulatory networks associated with gastric cancer progression.

Gastric cancer is a tumor type characterized by lymph node metastasis and the invasion of local tissues. There is thus a critical need to clarify the molecular mechanisms governing gastric cancer onset and progression to guide the treatment of this disease. Long non-coding RNAs and mRNA expression profiles associated with early and local advanced gastric cancer were examined through microarray analyses, with GO and KEGG analyses being employed as a means of exploring the functional roles of those long non-coding RNAs and mRNAs that were differentially expressed in gastric cancer. In total, 1005 and 1831 lncRNAs and mRNAs, respectively, were found to be differentially expressed between early and local advanced gastric cancer. GO and KEGG analyses revealed several pathways and processes that were dysregulated, including the RNA transport, ECM-receptor interaction, and mRNA splicing pathways. In co-expression networks, E2F1, E2F4, and STAT2 were identified as key transcriptional regulators of these processes. Moreover, thrombospondin-2 was confirmed as being expressed at high levels in more advanced gastric cancer by both the GEO and TCGA databases. RNA-sequencing analyses of SGC-790 cells transfected to express thrombospondin-2 further revealed this gene to enhance NF-kB and TNF pathway signaling activity. These results offer insight into gastric cancer-related regulatory networks and suggest thrombospondin-2 to be an important oncogene that drives the progression of this deadly cancer type.

Regulatory networks driving expression of genes critical for glioblastoma are controlled by the transcription factor c-Jun and the pre-existing epigenetic modifications

BackgroundGlioblastoma (GBM, WHO grade IV) is an aggressive, primary brain tumor. Despite extensive tumor resection followed by radio- and chemotherapy, life expectancy of GBM patients did not improve over decades. Several studies reported transcription deregulation in GBMs, but regulatory mechanisms driving overexpression of GBM-specific genes remain largely unknown. Transcription in open chromatin regions is directed by transcription factors (TFs) that bind to specific motifs, recruit co-activators/repressors and the transcriptional machinery. Identification of GBM-related TFs-gene regulatory networks may reveal new and targetable mechanisms of gliomagenesis.ResultsWe predicted TFs-regulated networks in GBMs in silico and intersected them with putative TF binding sites identified in the accessible chromatin in human glioma cells and GBM patient samples. The Cancer Genome Atlas and Glioma Atlas datasets (DNA methylation, H3K27 acetylation, transcriptomic profiles) were explored to elucidate TFs-gene regulatory networks and effects of the epigenetic background. In contrast to the majority of tumors, c-Jun expression was higher in GBMs than in normal brain and c-Jun binding sites were found in multiple genes overexpressed in GBMs, including VIM, FOSL2 or UPP1. Binding of c-Jun to the VIM gene promoter was stronger in GBM-derived cells than in cells derived from benign glioma as evidenced by gel shift and supershift assays. Regulatory regions of the majority of c-Jun targets have distinct DNA methylation patterns in GBMs as compared to benign gliomas, suggesting the contribution of DNA methylation to the c-Jun-dependent gene expression.ConclusionsGBM-specific TFs-gene networks identified in GBMs differ from regulatory pathways attributed to benign brain tumors and imply a decisive role of c-Jun in controlling genes that drive glioma growth and invasion as well as a modulatory role of DNA methylation.

Identification of key events and regulatory networks in the formation process of primordial germ cell based on proteomics.

Currently, studies have analyzed the formation mechanism of primordial germ cell (PGC) at the transcriptional level, but few at the protein level, which made the mechanism study of PGC formation not systematic. Here, we screened differential expression proteins (DEPs) regulated PGC formation by label-free proteomics with a novel sampling strategy of embryonic stem cellsand PGC. Analysis of DEPs showed that multiple key events were involved, such as the transition from glycolysis to oxidative phosphorylation, activation of autophagy, low DNA methylation ensured the normal formation of PGC, beyond that, protein ubiquitination also played an important role in PGC formation. Importantly, the progression of such events was attributed to the inconsistency between transcription and translation. Interestingly, MAPK, PPAR, Wnt, and JAK signaling pathways not only interact with each other but also interact with different events to participate in the formation of PGC, which formed the PGC regulatory network. According to the regulatory network, the efficiency of PGC formation in induction system can be significantly improved. In conclusion, our results indicate that chicken PGC formation is a complex process involving multiple events and signals, which provide technical support for the specific application in PGC research.

Identification of CeRNA Regulatory Networks in Atrial Fibrillation Using Nanodelivery.

The initiation and maintenance of AF is a complex biological process that is the ultimate manifestation of many cardiovascular diseases. And the pathogenesis of atrial fibrillation (AF) is unclear. Therefore, this study aimed to find the potential competing endogenous RNAs (ceRNAs) network and molecular dysregulation mechanism associated with AF. GSE135445, GSE2240, and GSE68475 were obtained from the Gene Expression Omnibus (GEO). Differential analysis was utilized to identify the differentially expressed mRNAs, miRNAs, and lncRNAs between AF and sinus rhythms (SR). AF-associated mRNAs and nanomaterials were screened and their biological functions and KEGG signaling pathways were identified. Nanomaterials for targeted delivery are uniquely capable of localizing the delivery of therapeutics and diagnostics to diseased tissues. The target mRNAs and target lncRNAs of differentially expressed miRNAs were identified using TargetScan and LncBase databases. Finally, we constructed the ceRNAs network and its potential molecular regulatory mechanism. We obtained 643 AF-associated mRNAs. They were significantly involved in focal adhesion and the PI3K-Akt signaling pathway. Among the 16 differentially expressed miRNAs identified, 31 differentially expressed target mRNAs, as well as 5 differentially expressed target lncRNAs were identified. Among them, we obtained 2 ceRNAs networks (hsa-miR-125a-5p and hsa-let-7a-3p). The aberrant expression of network target genes in AF mainly activated the HIF-1 signaling pathway. We speculated that the interaction pairs of miR-125a-5p and let-7a-3p with target mRNAs and target lncRNAs may be involved in AF. Our findings have a positive influence on investigating the pathogenesis of AF and identifying potential therapeutic targets.

PGRNIG: novel parallel gene regulatory network identification algorithm based on GPU.

Molecular biology has revealed that complex life phenomena can be treated as the result of many gene interactions. Investigating these interactions and understanding the intrinsic mechanisms of biological systems using gene expression data have attracted a lot of attention. As a typical gene regulatory network (GRN) inference method, the S-system has been utilized to deal with small-scale network identification. However, it is extremely difficult to optimize it to infer medium-to-large networks. This paper proposes a novel parallel swarm intelligent algorithm, PGRNIG, to optimize the parameters of the S-system. We employed the clone selection strategy to improve the whale optimization algorithm (CWOA). To enhance the time efficiency of CWOA optimization, we utilized a parallel CWOA (PCWOA) based on the compute unified device architecture (CUDA) platform. Decomposition strategy and L1 regularization were utilized to reduce the search space and complexity of GRN inference. We applied the PGRNIG algorithm on three synthetic datasets and two real time-series expression datasets of the species of Escherichia coli and Saccharomyces cerevisiae. Experimental results show that PGRNIG could infer the gene regulatory network more accurately than other state-of-the-art methods with a convincing computational speed-up. Our findings show that CWOA and PCWOA have faster convergence performances than WOA.

Emerging roles of protein phosphorylation in plant iron homeostasis.

Remarkable progress has been made in dissecting the molecular mechanisms involved in iron (Fe) homeostasis in plants, especially the identification of key transporter and transcriptional regulatory networks. But how the protein activity of these master players is regulated by Fe status remains underexplored. Recent studies show that major players toggle switch their properties by protein phosphorylation under different Fe conditions and consequently control the signaling cascade and metabolic adjustment. Moreover, Fe deficiency causes changes of multiple kinases and phosphatases. Here, we discuss how these findings highlight the emergence of the protein phosphorylation-dependent regulation for rapid and precise responses to Fe status to attain Fe homeostasis. Further studies will be needed to fully understand the regulation of these intricate networks.