Target Genes Of Transcription Factors Research Articles

Gene Regulatory Network Analysis of Decidual Stromal Cells and Natural Killer Cells.

Human reproductive success relies on the proper differentiation of the uterine endometrium to facilitate implantation, formation of the placenta, and pregnancy. This process involves two critical types of decidual uterine cells: endometrial/decidual stromal cells (dS) and uterine/decidual natural killer (dNK) cells. To better understand the transcription factors governing the in vivo functions of these cells, we analyzed single-cell transcriptomics data from first-trimester terminations of pregnancy, and for the first time conducted gene regulatory network analysis of dS and dNK cell subpopulations. Our analysis revealed stromal cell populations that corresponded to previously described in vitro decidualized cells and senescent decidual cells. We discovered new decidualization driving transcription factors of stromal cells for early pregnancy, including DDIT3 and BRF2, which regulate oxidative stress protection. For dNK cells, we identified transcription factors involved in the immunotolerant (dNK1) subpopulation, including IRX3 and RELB, which repress the NFKB pathway. In contrast, for the less immunotolerant (dNK3) population we predicted TBX21 (T-bet) and IRF2-mediated upregulation of the interferon pathway. To determine the clinical relevance of our findings, we tested the overrepresentation of the predicted transcription factors target genes among cell type-specific regulated genes from pregnancy disorders, such as recurrent pregnancy loss and preeclampsia. We observed that the predicted decidualized stromal and dNK1-specific transcription factor target genes were enriched with the genes downregulated in pregnancy disorders, whereas the predicted dNK3-specific targets were enriched with genes upregulated in pregnancy disorders. Our findings emphasize the importance of stress tolerance pathways in stromal cell decidualization and immunotolerance promoting regulators in dNK differentiation.

An evolutionary view of the function of CC-type glutaredoxins in plant development and adaptation to the environment.

Land plants have to face an oxidizing, heterogeneous, and fast changing environment. Redox-dependent post-translational modifications emerge as a critical component of plant responses to stresses. Among the thiol oxidoreductase superfamily, class III CC-type glutaredoxins (called ROXYs) are land plant specific, and their evolutionary history is highly dynamic. Angiosperms encode many isoforms, classified into five subgroups (Aα, Aβ, Bα, Bβ, Bγ) that probably evolved from five common ancestral ROXYs, with higher evolutionary dynamics in the Bγ subgroup compared with the other subgroups. ROXYs can modulate the transcriptional activity of TGA transcription factor target genes, although their biochemical function is still debated. ROXYs participate in the control of proper plant development and reproduction, and are mainly negative regulators of plant responses to biotic and abiotic stresses. This suggests that most ROXYs could play essential and conserved functions in resetting redox-dependent changes in transcriptional activity upon stress signaling to ensure the responsiveness of the system and/or avoid exaggerated responses that could lead to major defects in plant growth and reproduction. In Arabidopsis Bγ members acquired important functions in responses to nitrogen availability and endogenous status, but the rapid and independent evolution of this subclass might suggest that this function results from neofunctionalization, specifically observed in core eudicots.

Study of hub nodes of transcription factor-target gene regulatory network and immune mechanism for type 2 diabetes based on chip analysis of GEO database

Identification of novel therapeutic targets for type 2 diabetes is a key area of contemporary research. In this study, we screened differentially expressed genes in type 2 diabetes through the GEO database and sought to identify the key virulence factors for type 2 diabetes through a transcription factor regulatory network. Our findings may help identify new therapeutic targets for type 2 diabetes. Data pertaining to the humoral (whole blood) gene expression profile of diabetic patients were obtained from the NCBI’s GEO Datasets database and gene sets with differential expression were identified. Subsequently, the TRED transcriptional regulatory element database was integrated to build a gene regulatory network for type 2 diabetes. Functional analysis (GO-Analysis) and Pathway-analysis of differentially expressed genes were performed using the DAVID database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, gene-disease correlation analysis was performed using the DAVID online annotation tool. A total of 236 pathogenic genes, four transcription factors related to the pathogenic genes, and 261 corresponding target genes were identified. A transcription factor-target gene regulatory network for type 2 diabetes was constructed. Most of the key factors of the transcription factor-target gene regulatory network for type 2 diabetes were found closely related to the immune metabolic system and the functions of cell proliferation and transformation.

PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection.

Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for understanding cellular processes and analyzing disease molecular mechanisms. Computational approaches can be challenging to employ when attempting to predict potential interactions between TFs and target genes. In this paper, we present a novel graph model (PPRTGI) for detecting TF-target gene interactions using DNA sequence features. Feature representations of TFs and target genes are extracted from sequence embeddings and biological associations. Then, by combining the aggregated node feature with graph structure, PPRTGI uses a graph neural network with personalized PageRank to learn interaction patterns. Finally, a bilinear decoder is applied to predict interaction scores between TF and target gene nodes. We designed experiments on six datasets from different species. The experimental results show that PPRTGI is effective in regulatory interaction inference, with our proposed model achieving an area under receiver operating characteristic score of 93.87% and an area under precision-recall curves score of 88.79% on the human dataset. This paper proposes a new method for predicting TF-target gene interactions, which provides new insights into modeling molecular networks and can thus be used to gain a better understanding of complex biological systems.

TFTG: A comprehensive database for human transcription factors and their targets

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

Single-Cell Nuclear Sequencing Atlas Revealed the Induction of Parkinson's Disease by RELN+ Neuron 3 and the Gene Regulatory Network of MSRA.

To determine the cell types that promoted the progression of Parkinson's disease (PD) using the substantia nigra in the brain tissues derived from patients with PD and normal controls. PD is an incurable neurodegenerative disease that threatens the physical activity of the aging population, and the complex molecular mechanisms remain be comprehensively elucidated. To describe potential disease-promoting cell types in PD and to provide a theoretical basis. Single-cell nuclear sequencing data of nine PD samples and control samples from Gene Expression Omnibus (GEO) were included, and heterogeneous cell subpopulations in the substantia nigra were identified by annotation analysis. Potential pathogenic cell subpopulations of PD were determined based on the expression data of marker genes. Cell differentiation trajectories and communication networks were generated by Pseudotime trajectory analysis and cell communication analysis. Furthermore, single-- cell regulatory network inference and clustering (SCENIC) analysis was conducted to determine the regulatory network of transcription factor-target genes in PD. Among the nine cell subpopulations classified, RELN+neuron 3 showed reduced abundance and dopamine secretion capacity in PD and was therefore considered as a promoter of PD pathogenesis and progression. The regulatory network of MSRA action was involved in the developmental process of cells in the central nervous system, indicating that MSRA and its targets might serve as potential therapeutic targets for PD. RELN+neuron 3 had two directions of differentiation, specifically, branch 1 exhibited a high apoptotic profile and branch 2 exhibited a high cell death profile. In addition, the intensity of EPHA and EPHB signaling was attenuated between RELN+neuron 3 and other cell subpopulations. To conclude, this study identified a subpopulation of RELN+neuron 3 cells with markedly reduced abundance in the brain substantia nigra in PD. The MSRA-involved gene regulatory networks was considered as a novel therapeutic network for PD.

Single-cell analysis reveals ADGRL4+ renal tubule cells as a highly aggressive cell type in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous cancer that poses great challenge to clinical treatment and prognostic prediction. Characterizing the cellular landscape of ccRCC in a single-cell dimension can help better understand the tumor heterogeneity and molecular mechanisms of ccRCC. This study analyzed single-cell profiles in ccRCC samples and para-tumor samples from Gene Expression Omnibus and identified a highly heterogeneous subcluster of renal tubule cells. Single-cell regulatory network inference and clustering analyses and cell communication analysis were performed to develop transcription factor-target gene regulatory networks and cell–cell interactions. Additionally, the distribution and prognostic risk of renal tubule cells from spatial transcriptome data (GSM6415706) and The Cancer Genome Atlas-Kidney Clear Cell Carcinoma data were analyzed. A total of 10 cell types were identified in ccRCC and para-tumor samples. The ccRCC renal tubule cells showed a high expression of the oncogene nicotinamide N-methyltransferase and a significantly high degree of tumor heterogeneity. We further identified 6 cell subclusters with specific expression of BEX2, PTHLH, SFRP2, KLRB1, ADGRL4, and HGF from the ccRCC renal tubule cells. ADGRL4+ renal tubule cells had highly metastatic and angiogenesis-inducing characteristics, with more ADGRL4+ renal tubule cells indicating a worse survival. ADGRL4+ renal tubule cells regulated the metastasis of other renal tubule cells through metastasis-related receptor-ligand communication. We also found that ADGRL4+ renal tubule cells clustered around the glomeruli but the rest of the renal tubule cell subclusters rarely localized in ccRCC tissues. ETS1 and ELK3 -dominant GRNs were remarkably activated in ADGRL4+ renal tubule cells, functionally, knockdown of ELK3 in A498 significantly disturbedaffected the cell migration and invasion. ADGRL4+ renal tubule cells, which were highly metastatic and invasive, might be an essential cell subcluster for ccRCC, and ADGRL4 could be used a novel therapeutic target.

KLF17 is an important regulatory component of the transcriptomic response of Atlantic salmon macrophages to Piscirickettsia salmonis infection.

Piscirickettsia salmonis is the most important health problem facing Chilean Aquaculture. Previous reports suggest that P. salmonis can survive in salmonid macrophages by interfering with the host immune response. However, the relevant aspects of the molecular pathogenesis of P. salmonis have been poorly characterized. In this work, we evaluated the transcriptomic changes in macrophage-like cell line SHK-1 infected with P. salmonis at 24- and 48-hours post-infection (hpi) and generated network models of the macrophage response to the infection using co-expression analysis and regulatory transcription factor-target gene information. Transcriptomic analysis showed that 635 genes were differentially expressed after 24- and/or 48-hpi. The pattern of expression of these genes was analyzed by weighted co-expression network analysis (WGCNA), which classified genes into 4 modules of expression, comprising early responses to the bacterium. Induced genes included genes involved in metabolism and cell differentiation, intracellular transportation, and cytoskeleton reorganization, while repressed genes included genes involved in extracellular matrix organization and RNA metabolism. To understand how these expression changes are orchestrated and to pinpoint relevant transcription factors (TFs) controlling the response, we established a curated database of TF-target gene regulatory interactions in Salmo salar, SalSaDB. Using this resource, together with co-expression module data, we generated infection context-specific networks that were analyzed to determine highly connected TF nodes. We found that the most connected TF of the 24- and 48-hpi response networks is KLF17, an ortholog of the KLF4 TF involved in the polarization of macrophages to an M2-phenotype in mammals. Interestingly, while KLF17 is induced by P. salmonis infection, other TFs, such as NOTCH3 and NFATC1, whose orthologs in mammals are related to M1-like macrophages, are repressed. In sum, our results suggest the induction of early regulatory events associated with an M2-like phenotype of macrophages that drives effectors related to the lysosome, RNA metabolism, cytoskeleton organization, and extracellular matrix remodeling. Moreover, the M1-like response seems delayed in generating an effective response, suggesting a polarization towards M2-like macrophages that allows the survival of P. salmonis. This work also contributes to SalSaDB, a curated database of TF-target gene interactions that is freely available for the Atlantic salmon community.

Unsupervised Machine Learning Identify Cis‐Regulatory Network Involved in Neurodegeneration

AbstractBackgroundCis‐Regulatory Modules (CRMs) are crucial to ensure the precise spatiotemporal gene expression, but we still lack a complete catalog of mammalian CRMs.MethodsWe developed the Mammalian Regulatory Module Detector (MrMOD), that accurately predicts a comprehensive set of high‐resolution CRMs in mouse and human genomes including enhancers, promoters, silencers, locus control regions, and microRNA regulatory modules not limited to any tissue, cell type, developmental stage, or stimuli. We validated our predictions using 19 orthogonal experimental data sets, including thousands of experimental defined CRMs and millions of putative regulatory elements from multiple databases. Next, we used an unsupervised machine learning method to annotate CRM functions. We scanned the mouse genome for transcription factor binding sites (TFBS) using position weight matrices of known transcription factors (TF) from the CIS‐BP database and obtained TFBS abundance in each CRM for every TF. Unsupervised clustering was performed using Seurat on CRMs from chromosome 17. Each CRM cluster represents a set of CRMs with similar TFBS compositions. Cluster marker genes represent TFs whose binding sites are enriched in the CRM cluster. Genes associated with the CRMs in the cluster represent the putative target genes. Functional enrichment analyses of the TFs and CRM associated genes were performed using ClusterProfiler and disgenet2r.ResultsSeurat identified a total of 43 clusters. In particular, CRM cluster #17 is linked to neurodegeneration based on five supporting evidence: 1) TF marker genes of cluster #17 include TFs known to be involved in neurodegeneration such as Spi1, Yy1, Nr4a2, Nr4a3, and Tfeb. 2) TF marker genes of cluster #17 were significantly enriched for Alzheimer’s Disease, Parkinson’s Disease, Multiple Sclerosis, among others. 3) Genes associated with cluster #17 CRMs were significantly enriched for neurodegeneration: Multiple Sclerosis and neurodegenerative disorders. Gene Ontology Cellular Components terms significantly enriched for synapse, presynapse, axon, and synaptic membrane. 4) Genes associated with cluster #17 CRMs significantly overlap the known TF target genes in the ChIP‐Atlas database defined by human ChIP‐seq experiments. 5) Top TF marker genes of cluster #17 known to activate transcription in neurons cooperatively.ConclusionsWe developed a novel approach to elucidate mechanisms involved in neurodegeneration.

Transcription factor-target gene regulatory network analysis in human lung adenocarcinoma.

Transcription factors (TFs) play a crucial role in the occurrence and progression of lung adenocarcinoma (LUAD), and targeting TFs is an important direction for treating LUAD. However, targeting a single TF often fails to achieve satisfactory therapeutic outcomes. Furthermore, the regulatory TF-target gene networks involved in the development of LUAD is complex and not yet fully understood. In this study, we performed RNA sequencing (RNA-seq) to analyze the transcriptome profile of human LUAD tissues and matched adjacent nontumor tissues. We selected the differentially expressed TFs, performed enrichment analysis and survival curve analysis, and predicted the regulatory networks of the top differential TFs with their target genes. Finally, alternative splicing analyses were also performed. We found that TFs GRHL3, SIX1, SIX2, SPDEF, and ETV4 were upregulated, while TAL1, EPAS1, SOX17, NR4A1, and EGR3 were significantly downregulated in LUAD tissues compared to normal tissues. We propose a potential GRHL3-CDH15-Wnt-β-catenin pro-oncogenic signaling axis and a potential TAL1-ADAMTS1-vascular antioncogenic signaling axis. In addition, we found that alternative splicing of intron retention (IR), approximate IR (XIR), multi-IR (MIR), approximate MIR (XMIR), and approximate alternative exon ends (XAE) showed abnormally increased frequencies in LUAD tissues. These findings revealed a novel TF-target gene regulatory axis related to tumorigenesis and provided potential therapeutic targets and mechanisms for LUAD.

MICA: a multi-omics method to predict gene regulatory networks in early human embryos.

Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.

MethMotif.Org 2024: a database integrating context-specific transcription factor-binding motifs with DNA methylation patterns.

MethMotif (https://methmotif.org) is a publicly available database that provides a comprehensive repository of transcription factor (TF)-binding profiles, enriched with DNA methylation patterns. In this release, we have enhanced the platform, expanding our initial collection to over 700 position weight matrices (PWM), all of which include DNA methylation profiles. One of the key advancements in this release is the segregation of TF-binding motifs based on their cofactors and DNA methylation status. We have previously demonstrated that gene ontology (GO) enriched terms associated with TF target genes may differ based on their association with alternative cofactors and DNA methylation status. MethMotif provides precomputed GO annotations for each human TF of interest, as well as for TF-co-TF complexes, enabling a comprehensive analysis of TF functions in the context of their co-factors. Additionally, MethMotif has been updated to encompass data for two new species, Mus musculus and Arabidopsis thaliana, widening its applicability to a broader community. MethMotif stands out as the first and only TF-binding motifs database to incorporate context-specific PWM coupled with epigenetic information, thereby enlightening context-specific TF functions. This enhancement allows the community to explore and gain deeper insights into the regulatory mechanisms governing transcriptional processes.

Single-cell RNA sequencing of a new transgenic t(8;21) preleukemia mouse model reveals regulatory networks promoting leukemic transformation.

T(8;21)(q22;q22), which generates the AML1-ETO fusion oncoprotein, is a common chromosomal abnormality in acute myeloid leukemia (AML) patients. Despite having favorable prognosis, 40% of patients will relapse, highlighting the need for innovative models and application of the newest technologies to study t(8;21) leukemogenesis. Currently, available AML1-ETO mouse models have limited utility for studying the pre-leukemic stage because AML1-ETO produces mild hematopoietic phenotypes and no leukemic transformation. Conversely, overexpression of a truncated variant, AML1-ETO9a (AE9a), promotes fully penetrant leukemia and is too potent for studying pre-leukemic changes. To overcome these limitations, we devised a germline-transmitted Rosa26 locus AE9a knock-in mouse model that moderately overexpressed AE9a and developed leukemia with long latency and low penetrance. We observed pre-leukemic alterations in AE9a mice, including skewing of progenitors towards granulocyte/monocyte lineages and replating of stem and progenitor cells. Next, we performed single-cell RNA sequencing to identify specific cell populations that contribute to these pre-leukemic phenotypes. We discovered a subset of common myeloid progenitors that have heightened granulocyte/monocyte bias in AE9a mice. We also observed dysregulation of key hematopoietic transcription factor target gene networks, blocking cellular differentiation. Finally, we identified Sox4 activation as a potential contributor to stem cell self-renewal during the pre-leukemic stage.

Role of neutrophil extracellular trap and immune infiltration in atherosclerotic plaque instability: Novel insight from bioinformatics analysis and machine learning.

The instability of atherosclerotic plaques increases the risk of acute coronary syndrome. Neutrophil extracellular traps (NETs), mesh-like complexes consisting of extracellular DNA adorned with various protein substances, have been recently discovered to play an essential role in atherosclerotic plaque formation and development. This study aimed to investigate novel diagnostic biomarkers that can identify unstable plaques for early distinction and prevention of plaque erosion or disruption. Differential expression analysis was used to identify the differentially expressed NET-related genes, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. We filtered the characteristic genes using machine learning and estimated diagnostic efficacy using receiver operating characteristic curves. Immune infiltration was detected using single-sample gene set enrichment analysis and the biological signaling pathways involved in characteristic genes utilizing gene set enrichment analysis were explored. Finally, miRNAs- and transcription factors-target genes networks were established. We identified 8 differentially expressed NET-related genes primarily involved in immune-related pathways. Four were identified as capable of distinguishing unstable plaques. More immune cells infiltrated unstable plaques than stable plaques, and these cells were predominantly positively related to characteristic genes. These 4 diagnostic genes are involved in immune responses and the modulation of smooth muscle contractility. Several miRNAs and transcription factors were predicted as upstream regulatory factors, providing further information on the identification and prevention of atherosclerotic plaques rupture. We identified several promising NET-related genes (AQP9, C5AR1, FPR3, and SIGLEC9) and immune cell subsets that may identify unstable atherosclerotic plaques at an early stage and prevent various complications of plaque disruption.

The Neurod1/4-Ntrk3-Src pathway regulates gonadotrope cell adhesion and motility

Pituitary gonadotrope cells are essential for the endocrine regulation of reproduction in vertebrates. These cells emerge early during embryogenesis, colonize the pituitary glands and organize in tridimensional networks, which are believed to be crucial to ensure proper regulation of fertility. However, the molecular mechanisms regulating the organization of gonadotrope cell population during embryogenesis remain poorly understood. In this work, we characterized the target genes of NEUROD1 and NEUROD4 transcription factors in the immature gonadotrope αT3-1 cell model by in silico functional genomic analyses. We demonstrated that NEUROD1/4 regulate genes belonging to the focal adhesion pathway. Using CRISPR/Cas9 knock-out approaches, we established a double NEUROD1/4 knock-out αT3-1 cell model and demonstrated that NEUROD1/4 regulate cell adhesion and cell motility. We then characterized, by immuno-fluorescence, focal adhesion number and signaling in the context of NEUROD1/4 insufficiency. We demonstrated that NEUROD1/4 knock-out leads to an increase in the number of focal adhesions associated with signaling abnormalities implicating the c-Src kinase. We further showed that the neurotrophin tyrosine kinase receptor 3 NTRK3, a target of NEUROD1/4, interacts physically with c-Src. Furthermore, using motility rescue experiments and time-lapse video microscopy, we demonstrated that NTRK3 is a major regulator of gonadotrope cell motility. Finally, using a Ntrk3 knock-out mouse model, we showed that NTRK3 regulates gonadotrope cells positioning in the developing pituitary, in vivo. Altogether our study demonstrates that the Neurod1/4-Ntrk3-cSrc pathway is a major actor of gonadotrope cell mobility, and thus provides new insights in the regulation of gonadotrope cell organization within the pituitary gland.

Meta-Analysis of Heat-Stressed Transcriptomes Using the Public Gene Expression Database from Human and Mouse Samples.

Climate change has significantly increased the frequency of our exposure to heat, adversely affecting human health and industries. Heat stress is an environmental stress defined as the exposure of organisms and cells to abnormally high temperatures. To comprehensively explain the mechanisms underlying an organism's response to heat stress, it is essential to investigate and analyze genes that have been under-represented or less well-known in previous studies. In this study, we analyzed heat stress-responsive genes using a meta-analysis of numerous gene expression datasets from the public database. We obtained 322 human and 242 mouse pairs as the heat exposure and control data. The meta-analysis of these data identified 76 upregulated and 37 downregulated genes common to both humans and mice. We performed enrichment, protein-protein interaction network, and transcription factor target gene analyses for these genes. Furthermore, we conducted an integrated analysis of these genes using publicly available chromatin immunoprecipitation sequencing (ChIP-seq) data for HSF1, HSF2, and PPARGC1A (PGC-1α) as well as gene2pubmed data from the existing literature. The results identified previously overlooked genes, such as ABHD3, ZFAND2A, and USPL1, as commonly upregulated genes. Further functional analysis of these genes can contribute to coping with climate change and potentially lead to technological advancements.

Long Noncoding RNA TALAM1 Is a Transcriptional Target of the RUNX2 Transcription Factor in Lung Adenocarcinoma.

Lung cancer is the leading cause of cancer death worldwide. It has been reported that genetic and epigenetic factors play a crucial role in the onset and evolution of lung cancer. Previous reports have shown that essential transcription factors in embryonic development contribute to this pathology. Runt-related transcription factor (RUNX) proteins belong to a family of master regulators of embryonic developmental programs. Specifically, RUNX2 is the master transcription factor (TF) of osteoblastic differentiation, and it can be involved in pathological conditions such as prostate, thyroid, and lung cancer by regulating apoptosis and mesenchymal-epithelial transition processes. In this paper, we identified TALAM1 (Metastasis Associated Lung Adenocarcinoma Transcript 1) as a genetic target of the RUNX2 TF in lung cancer and then performed functional validation of the main findings. We performed ChIP-seq analysis of tumor samples from a patient diagnosed with lung adenocarcinoma to evaluate the target genes of the RUNX2 TF. In addition, we performed shRNA-mediated knockdown of RUNX2 in this lung adenocarcinoma cell line to confirm the regulatory role of RUNX2 in TALAM1 expression. We observed RUNX2 overexpression in cell lines and primary cultured lung cancer cells. Interestingly, we found that lncRNA TALAM1 was a target of RUNX2 and that RUNX2 exerted a negative regulatory effect on TALAM1 transcription.

Rice WRKY13 TF protein binds to motifs in the promoter region to regulate downstream disease resistance-related genes.

In plants, pathogen resistance is brought about by the binding of certain transcription factor (TF) proteins to the cis-elements of certain target genes. These cis-elements are present upstream in the motif of the promoters of each gene. This ensures the binding of a specific TF to a specific promoter, therefore regulating the expression of that gene. Therefore, the study of each promoter sequence of all the rice genes would help identify the target genes of a specific TF. Rice 1kb upstream promoter sequences of 55,986 annotated genes were analyzed using the Perl program algorithm to detect WRKY13 binding motifs (bm). The resulting genes were grouped using Gene Ontology and gene set enrichment analysis. A gene with more than 4 TF bm in their promoter was selected. Ten genes reported to have a role in rice disease resistance were selected for further analysis. Cis-acting regulatory element analysis was carried out to find the cis-elements and confirm the presence of the corresponding motifs in the promoter sequences of these genes. The 3D structure of WRKY13 TF and the corresponding ten genes were built, and the interacting residues were determined. The binding capacity of WRKY13 to the promoter of these selected genes was analyzed using docking studies. WRKY13 was considered for docking analysis based on the prior reports of autoregulation. Molecular dynamic simulations provided more details regarding the interactions. Expression data revealed the expression of the genes that helped provide the mechanism of interaction. Further co-expression network helped to characterize the interaction of these selected disease resistance-related genes with the WRKY13 TF protein. This study suggests downstream target genes that are regulated by the WRKY13 TF. The molecular mechanism involving the gene network regulated by WRKY13 TF in disease resistance against rice fungal pathogens is explored.

Promoter hypermethylation and comprehensive regulation of ncRNA lead to the down-regulation of ZNF880, providing a new insight for the therapeutics and research of colorectal cancer

The human genome encodes more than 350 kinds of Krüppel-associated box (KRAB) domain-containing zinc-finger proteins (KZFPs), KRAB-type ZNF transcription factor family (KZNF) plays a vital role in gene regulatory networks. The KZNF family members include a large number of highly homologous genes, gene subtypes and pseudogenes, and their expression has a high degree of tissue specificity and precision. Due to the high complexity of its regulatory network, the KZNF gene family has not been researched in sufficient, and the role of its members in the occurrence of cancer is mostly unexplored. In this study, ZNF880 was significantly associated with overall survival (OS) and disease-free survival (DFS) in colorectal carcinoma (CRC) patients. Low ZNF880 expression resulted in shorter OS and DFS. Combined with Colon adenocarcinoma (COAD) and Rectum adenocarcinoma (READ) data collection in the TCGA database, we found that ZNF880 was significantly down-regulated in CRC. Further analysis of the sequence variation of ZNF880 in CRC showed that ZNF880 accumulated a large number of SNV in the C2H2 domain and KRAB domain, while promoter region of ZNF880 also showed high methylation in COAD and READ. Combined with the Cbioportal and TIMER databases, the expression of mutant ZNF880 was significantly lower in COAD compared to the wild type. Simultaneously, the lncRNA-miRNA-ZNF880 ceRNA regulatory network was constructed through co-expression and miRNAs target gene prediction, demonstrating the precision of the ZNF880 regulatory network. In addition, the decreased expression of ZNF880 caused the significant immune infiltration decreases of CD8 + cells in COAD. In contrast, the immune infiltration of CD4 + cells and macrophages in COAD is positively correlated with ZNF880. Finally, through protein–protein interaction (PPI) network analysis and transcription factor target gene prediction, we screened out the genes most likely to be related to the function of ZNF880. CENPK, IFNGR2, REC8 and ZBTB17 were identified as the most closely functioning genes with ZNF880, which may indicate that ZNF880 has important links with the formation of cell centromere, tumor immunity, cell cycle and other pathways closely related to the occurrence of CRC. These studies show that the down-regulation of ZNF880 gene is closely related to CRC, and the targeted change of the expression of its regulatory molecules (miRNA and lncRNA) may be a new perspective for CRC treatment.

Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques.

Hypertrophic scar (HS) is a chronic inflammatory skin disease characterized by excessive deposition of extracellular matrix, but the exact mechanisms related to its formation remain unclear, making it difficult to treat. This study aimed to investigate the potential role of cuproptosis in the information of HS. To this end, we used single-cell sequencing and bulk transcriptome data, and screened for cuproptosis-related genes (CRGs) using differential gene analysis and machine learning algorithms (random forest and support vector machine). Through this process, we identified a group of genes, including ATP7A, ULK1, and MTF1, as novel therapeutic targets for HS. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to confirm the mRNA expression of ATP7A, ULK1, and MTF1 in both HS and normal skin (NS) tissues. We also constructed a diagnostic model for HS and analyzed the immune infiltration characteristics. Additionally, we used the expression profiles of CRGs to perform subgroup analysis of HS. We focused mainly on fibroblasts in the transcriptional profile at single-cell resolution. By calculating the cuproptosis activity of each fibroblast, we found that cuproptosis activity of normal skin fibroblasts increased, providing further insights into the pathogenesis of HS. We also analyzed the cell communication network and transcription factor regulatory network activity, and found the existence of a fibroblast-centered communication regulation network in HS, where cuproptosis activity in fibroblasts affects intercellular communication. Using transcription factor regulatory activity network analysis, we obtained highly active transcription factors, and correlation analysis with CRGs suggested that CRGs may serve as potential target genes for transcription factors. Overall, our study provides new insights into the pathophysiological mechanisms of HS, which may inspire new ideas for the diagnosis and treatment.