Transcription Factor Enrichment Analysis Research Articles

Identification of potential therapeutic targets from bioinformatics analysis of necroptosis and immune infiltration in acute myocardial infarction

IntroductionAcute myocardial infarction (AMI) is a serious, deadly disease with a high incidence. However, it remains unclear how necroptosis affects the pathophysiology of AMI. Using bioinformatic analyses, this study investigated necroptosis in AMI.MethodsWe obtained the GSE66360 dataset related to AMI by the GEO database. Venn diagrams were used to identify necroptosis-related differential genes (NRDEGs). The genes with differential expression in AMI were analyzed using gene set enrichment analysis, and a PPI network was established. A transcription factor prediction and enrichment analysis were conducted for the NRDEGs, and the relationships between AMI, NRDEGs, and immune cells were determined. Finally, in the additional dataset, NRDEG expression levels, immune infiltration, and ROC curve analysis were confirmed, and gene expression levels were further verified experimentally.ResultsGSEA revealed that necroptosis pathways were significantly enriched in AMI. We identified 10 NRDEGs, including TNF, TLR4, FTH1 and so on. Enrichment analysis indicated that the NOD-like receptor and NF-kappa B signaling pathways were significantly enriched. Four NRDEGs, FTH1, IFNGR1, STAT3, and TLR4, were identified; however, additional datasets and further experimental validation are required to confirm their roles. In addition, we determined that a high abundance of macrophages and neutrophils prompted AMI development.ConclusionsIn this study, four potential genes that affect the development of AMI through necroptosis (FTH1, IFNGR1, STAT3, and TLR4) were identified. In addition, we found that a high abundance of macrophages and neutrophils affected AMI. This helps determine the pathological mechanism of necroptosis and immune cells that influence AMI and provides a novel strategy for targeted therapy.

Postprandial cardiac hypertrophy is sustained by mechanics, epigenetic, and metabolic reprogramming in pythons

Constricting pythons, known for their ability to consume infrequent, massive meals, exhibit rapid and reversible cardiac hypertrophy following feeding. Our primary goal was to investigate how python hearts achieve this adaptive response after feeding. Isolated myofibrils increased force after feeding without changes in sarcomere ultrastructure and without increasing energy cost. Ca2+ transients were prolonged after feeding with no changes in myofibril Ca2+ sensitivity. Feeding reduced titin-based tension, resulting in decreased cardiac tissue stiffness. Feeding also reduced the activity of sirtuins, a metabolically linked class of histone deacetylases, and increased chromatin accessibility. Transcription factor enrichment analysis on transposase-accessible chromatin with sequencing revealed the prominent role of transcription factors Yin Yang1 and NRF1 in postfeeding cardiac adaptation. Gene expression also changed with the enrichment of translation and metabolism. Finally, metabolomics analysis and adenosine triphosphate production demonstrated that cardiac adaptation after feeding not only increased energy demand but also energy production. These findings have broad implications for our understanding of cardiac adaptation across species and hold promise for the development of innovative approaches to address cardiovascular diseases.

Cellular zinc status alters chromatin accessibility and binding of p53 to DNA.

Zn2+ is an essential metal required by approximately 850 human transcription factors. How these proteins acquire their essential Zn2+ cofactor and whether they are sensitive to changes in the labile Zn2+ pool in cells remain open questions. Using ATAC-seq to profile regions of accessible chromatin coupled with transcription factor enrichment analysis, we examined how increases and decreases in the labile zinc pool affect chromatin accessibility and transcription factor enrichment. We found 685 transcription factor motifs were differentially enriched, corresponding to 507 unique transcription factors. The pattern of perturbation and the types of transcription factors were notably different at promoters versus intergenic regions, with zinc-finger transcription factors strongly enriched in intergenic regions in elevated Zn2+ To test whether ATAC-seq and transcription factor enrichment analysis predictions correlate with changes in transcription factor binding, we used ChIP-qPCR to profile six p53 binding sites. We found that for five of the six targets, p53 binding correlates with the local accessibility determined by ATAC-seq. These results demonstrate that changes in labile zinc alter chromatin accessibility and transcription factor binding to DNA.

ScBlood: A comprehensive single-cell accessible chromatin database of blood cells

The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood (https://bio.liclab.net/scBlood/), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.

ATRT-09. MULTI-OMICS SEQUENCING OF ATYPICAL TERATOID RHABDOID TUMORS UNVEILS A SHARED GROUND-STATE POPULATION PROMOTING SUBGROUP-SPECIFIC DIFFERENTIATION TRAJECTORIES

Abstract BACKGROUND Atypical teratoid rhabdoid tumors (ATRTs) remain a significant clinical challenge in pediatric neurooncology. Despite the simplicity of the genomic profile, mostly characterized by the functional loss of SMARCB1 and in rare cases SMARCA4, ATRTs are molecularly and epigenetically diverse, with three main molecular subgroups identified: ATRT-TYR, ATRT-SHH and ATRT-MYC. METHODS To address the lack of effective mechanism-of-action based treatments and comprehend the biology of these subgroups, we conducted 10X single-nucleus transcriptome (n = 12) and multiome (transcriptome + ATAC) (n = 7) sequencing on tumor samples (ATRT-TYR = 6, ATRT-SHH = 9, ATRT-MYC = 4). Validation datasets included 10X single-cell transcriptomics (n = 1), 10X single-nucleus transcriptomics of cell lines (n = 5), and single-cell (n = 3) and single-nucleus (n = 9) SMART-seq transcriptomics. Separation of malignant cells from tumor microenvironment (TME) cells was achieved by comparison of expression profiles against literature-based marker genesets of normal cell types. Tumor cell populations were characterized based on enrichment in fetal cell types and recurrent tumor programs across cancer types, coupled with non-negative matrix factorization (NMF) and differential expression analyses. RESULTS ATRT-TYR exhibited choroid plexus-like and cilia-like cell populations, ATRT-SHH displayed radial glia-like, OPC-like and NPC-like cell populations and ATRT-MYC mainly demonstrated a mesenchymal-like cell population. Each subgroup represented unique differentiation trajectories. However, we identified a population of intermediate neuronal precursor-like cells (IPC-like) shared across all subgroups, expressing markers of pluripotent stem cells and cell cycle. RNA + ATAC transcription factor (TF) enrichment analysis for IPC-like cells revealed TFs linked to each subgroup’s differentiation trajectory, suggesting IPC-like cells as a putative ground-state tumor cell population driving differentiation of each subgroup. Functional validation of these findings in ATRT organoid models is currently ongoing. CONCLUSIONS This study elucidates defined ATRT subgroup-specific differentiation trajectories that may provide guidance for the development of subgroup-based therapies.

TNBC response to paclitaxel phenocopies interferon response which reveals cell cycle-associated resistance mechanisms.

Paclitaxel is a standard of care neoadjuvant therapy for patients with triple negative breast cancer (TNBC); however, it shows limited benefit for locally advanced or metastatic disease. Here we used a coordinated experimental-computational approach to explore the influence of paclitaxel on the cellular and molecular responses of TNBC cells. We found that escalating doses of paclitaxel resulted in multinucleation, promotion of senescence, and initiation of DNA damage induced apoptosis. Single-cell RNA sequencing (scRNA-seq) of TNBC cells after paclitaxel treatment revealed upregulation of innate immune programs canonically associated with interferon response and downregulation of cell cycle progression programs. Systematic exploration of transcriptional responses to paclitaxel and cancer-associated microenvironmental factors revealed common gene programs induced by paclitaxel, IFNB, and IFNG. Transcription factor (TF) enrichment analysis identified 13 TFs that were both enriched based on activity of downstream targets and also significantly upregulated after paclitaxel treatment. Functional assessment with siRNA knockdown confirmed that the TFs FOSL1, NFE2L2 and ELF3 mediate cellular proliferation and also regulate nuclear structure. We further explored the influence of these TFs on paclitaxel-induced cell cycle behavior via live cell imaging, which revealed altered progression rates through G1, S/G2 and M phases. We found that ELF3 knockdown synergized with paclitaxel treatment to lock cells in a G1 state and prevent cell cycle progression. Analysis of publicly available breast cancer patient data showed that high ELF3 expression was associated with poor prognosis and enrichment programs associated with cell cycle progression. Together these analyses disentangle the diverse aspects of paclitaxel response and identify ELF3 upregulation as a putative biomarker of paclitaxel resistance in TNBC.

Altered extracellular matrix–related pathways accelerate the transition from normal to prefibroid myometrium in Black women

BackgroundBlack women experience a disproportionate impact of uterine fibroids compared to White women, including earlier diagnosis, higher frequency, and more severe symptoms. The etiology underlying this racial disparity remains elusive. ObjectiveThe aim of this study was to evaluate the molecular differences in normal myometrium (fibroid-free uteri) and at-risk myometrium (fibroid-containing uteri) tissues in Black and White women. Study designWe conducted whole-genome RNA-seq on normal and at-risk myometrium tissues obtained from both self-identified Black and White women (not Hispanic or Latino) to determine global gene expression profiles and to conduct enriched pathway analyses (n=3 per group). We initially assessed the differences within the same type of tissue (normal or at-risk myometrium) between races. Subsequently, we analyzed the transcriptome of normal myometrium compared to at-risk myometrium in each race and determined the differences between them. We validated our findings through real-time PCR (sample size range=5-12), western blot (sample size range=5-6), and immunohistochemistry techniques (sample size range=9-16). ResultsThe transcriptomic analysis revealed distinct profiles between Black and White women in normal and at-risk myometrium tissues. Interestingly, genes and pathways related to extracellular matrix and mechanosensing were more enriched in normal myometrium from Black than White women. Transcription factor enrichment analysis detected greater activity of the serum response transcription factor positional motif in normal myometrium from Black compared to White women. Furthermore, we observed increased expression levels of myocardin-related transcription factor-serum response factor and the serum response factor in the same comparison. In addition, we noted increased expression of both mRNA and protein levels of vinculin, a target gene of the serum response factor, in normal myometrium tissues from Black women as compared to White women. Importantly, the transcriptomic profile of normal to at-risk myometrium conversion differs between Black and White women. Specifically, we observed that extracellular matrix-related pathways are involved in the transition from normal to at-risk myometrium and that these processes are exacerbated in Black women. We found increased levels of Tenascin C, type I collagen alpha 1 chain, fibronectin and, phospho-p38 MAPK (Thr180/Tyr182, active) protein levels in at-risk over normal myometrium tissues from Black women, whereas such differences were not observed in samples from White women. ConclusionsThese findings indicate that the racial disparities in uterine fibroids may be attributed to heightened production of extracellular matrix in the myometrium in Black women, even before the tumors appear. Future research is needed to understand early life determinants of the observed racial differences.

Integrative Single-Cell Analysis of Cardiomyopathy Identifies Differences in Cell Stemness and Transcriptional Regulatory Networks among Fibroblast Subpopulations.

Cardiomyopathy encompasses a broad spectrum of diseases affecting myocardial tissue, characterized clinically by abnormalities in cardiac structure, heart failure, and/or arrhythmias. Clinically heterogeneous, major types include dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RM), ischemic cardiomyopathy (ICM), among which DCM is more prevalent, while ICM exhibits higher incidence and mortality rates. Myocardial injury during cardiomyopathy progression may lead to myocardial fibrosis. Failure to intervene early and inhibit the process of myocardial fibrosis may culminate in heart failure. Cardiac fibroblasts constitute crucial cellular components determining the extent and quality of myocardial fibrosis, with various subpopulations exerting diverse roles in cardiomyopathy progression. Despite this, understanding of the cellular plasticity and transcriptional regulatory networks of cardiac fibroblasts in cardiomyopathy remains limited. Therefore, in this study, we conducted comprehensive single-cell analysis of cardiac fibroblasts in cardiomyopathy to explore differences in cellular plasticity and transcriptional regulatory networks among fibroblast subpopulations, with the aim of providing as many useful references as possible for the diagnosis, prognosis, and treatment of cardiomyopathy. Cells with mitochondrial gene expression comprising >20% of total expressed genes were excluded. Differential expression genes (DEGs) and stemness genes within cardiac fibroblast subpopulations were subjected to Gene Ontology (GO) analysis of biological processes (BP) and AUCell analysis. Monocle software was employed to analyze the pseudo-temporal trajectory of cardiac fibroblasts in cardiomyopathy. Additionally, the Python package SCENIC was utilized to assess enrichment of transcription factors and activity of regulators within cardiac fibroblast subpopulations in cardiomyopathy. Following batch effect correction, 179,927 cells were clustered into 32 clusters, designated as T_NK cells, endothelial cells, myeloid cells, fibroblasts, pericytes, SMCs, CMs, proliferating cells, EndoCs, and EPCs. Among them, 8148 fibroblasts were further subdivided into 4 subpopulations, namely C0 THBS4+ Fibroblasts, C1 LINC01133+ Fibroblasts, C2 FGF7+ Fibroblasts, and C3 AGT + Fibroblasts. Results from GO_BP and AUCell analyses suggest that C3 AGT + Fibroblasts may be associated with immune response activation, protein transport, and myocardial contractile function, correlating with disease progression in cardiomyopathy. Transcription factor enrichment analysis indicates that FOS is the most significant TF in C3 AGT + Fibroblasts, also associated with the M1 module, possibly implicated in protein hydrolysis, intracellular DNA replication, and cell proliferation. Moreover, correlation analysis of transcriptional regulatory activity between fibroblast subpopulations reveals a more pronounced heterogeneity within C3 AGT + Fibroblasts in cardiomyopathy. C3 AGT + Fibroblasts exhibit increased sensitivity towards adverse outcomes in cardiomyopathy, such as myocardial fibrosis and impaired cardiac contractile function, compared to other cardiac fibroblast subpopulations. The differential cellular plasticity and transcriptional regulatory activity between C3 AGT + Fibroblasts and other subgroups offer new perspectives for targeting fibroblast subpopulation activity to treat cardiomyopathy. Additionally, stemness genes EPAS1 and MYC, along with the regulator FOS, may play roles in modulating the biological processes of cardiac fibroblasts in cardiomyopathy.

Abstract PO2-01-08: Differential HOXB13 gene expression and promoter methylation analysis in breast cancer

Abstract Background: The Breast Cancer Index (BCI) is a gene expression-based signature comprising two functional biomarker panels: the Molecular Grade Index (MGI) and BCI (H/I), which is a ratio of the HOXB13 and IL17BR gene expression. Several studies have shown that BCI (H/I) is a predictive biomarker for extended adjuvant endocrine therapy benefit in hormone receptor-positive (HR+) early-stage breast cancer. Molecular mechanisms underlying endocrine responsiveness remain to be fully explored. The objective of this analysis is to evaluate the correlation between gene expression and methylation in breast cancer patients displaying different HOXB13 mRNA expression levels from The Cancer Genome Atlas (TCGA) project. Methods: Data from methylation microarrays and mRNA sequencing were examined in combination with clinical metadata from 1,095 TCGA breast cancer patients. After removing outliers, HR+ samples were ordered based on their HOXB13 expression levels and the top 10% of samples (HOXB13-high; n=95) and bottom 10% (HOXB13-low; n=95) were selected for further analysis. Differentially expressed genes (DEGs) were determined by comparing all protein-coding genes between the HOXB13-high and HOXB13-low sets, based on an absolute log-fold change above 2 and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Among the subset of samples with matching methylation data (n=60 for the HOXB13-high group and n=48 for the HOXB13-low group), hypo-methylated and hyper-methylated differentially methylated probes (DMPs) were identified based on at least 10% change of β (i.e., average promoter methylation) and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Motif and transcription factor (TF) enrichment analyses were identified at a minimum incidence of 10 and a lower boundary of 1.5 for the 95% confidence interval of the odds ratio. Results: Our analysis identified a total of 613 DEGs between the HOXB13-high and HOXB13-low groups. Gene ontology analysis of DEGs revealed a statistically significant enrichment of genes associated with PTK6 regulated cell cycle, oncogene-induced cellular senescence, and regulation of T cell activation. Analysis of protein-protein interactions of differentially expressed genes revealed differences in key biological processes including changes in cell cycle regulation, p53 pathway, and IL-17 signaling. A total of 5,295 hyper-methylated and 580 hypo-methylated DMPs were discovered when comparing HOXB13-high and HOXB13-low samples. Changes in global methylation patterns enabled classification of breast cancer samples with high or low HOXB13 expression with an accuracy of 0.90. Reconstruction of gene regulatory networks from DNA methylation and transcriptome profiles elucidated significant pairs of DMPs and differentially expressed genes. Specifically, a total of 197 differentially expressed genes were attributed to hyper-methylated DMPs, including genes involved in response to estrogen, activation of HOX genes during differentiation, and drug-mediated inhibition of CDK4/6 activity. Additionally, 8 differentially expressed genes were attributed to hypo-methylated DMPs and were associated with proteasomal protein catabolic processes. Lastly, motif analysis of transcriptional factors for the hyper-methylated DMPs revealed enrichment of binding motifs attributed to FOXA1, ESR1, XBR1, and FOXP1. Conclusion: A comprehensive and comparative analysis of mRNA expression levels and methylation patterns between TCGA breast cancer samples with high and low expression of HOXB13 revealed key signaling pathways and biological processes that may provide insights on the molecular mechanism of HOXB13 in the regulation of response to endocrine therapy in HR+ breast cancer. Citation Format: Natalia Siuliukina, Yi Zhang, Kai Treuner, Mark Pegram. Differential HOXB13 gene expression and promoter methylation analysis in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-08.

Intestinal carbapenem-resistant Klebsiella pneumoniae undergoes complex transcriptional reprogramming following immune activation

ABSTRACT Carbapenem-resistant Klebsiella pneumoniae (CR-Kp) is a significant threat to public health worldwide. The primary reservoir for CR-Kp is the intestinal tract. There, the bacterium is usually present at low density but can bloom following antibiotic treatment, mostly in hospital settings. The impact of disturbances in the intestinal environment on the fitness, survival, expansion, and drug susceptibility of this pathogen is not well-understood, yet it may be relevant to devise strategies to tackle CR-Kp colonization and infection. Here, we adopted an in vivo model to examine the transcriptional adaptation of a CR-Kp clinical isolate to immune activation in the intestine. We report that as early as 6 hours following host treatment with anti-CD3 antibody, CR-Kp underwent rapid transcriptional changes including downregulation of genes involved in sugar utilization and amino acid biosynthesis and upregulation of genes involved in amino acid uptake and catabolism, antibiotic resistance, and stress response. In agreement with these findings, treatment increased the concentration of oxidative species and amino acids in the mouse intestine. Genes encoding for proteins containing the domain of unknown function (DUF) 1471 were strongly upregulated, however their deletion did not impair CR-Kp fitness in vivo upon immune activation. Transcription factor enrichment analysis identified the global regulator cAMP-Receptor Protein, CRP, as a potential orchestrator of the observed transcriptional signature. In keeping with the recognized role of CRP in regulating utilization of alternative carbon sources, crp deletion in CR-Kp resulted in strongly impaired gut colonization, although this effect was not amplified by immune activation. Thus, following intestinal colonization, which occurs in a CRP-dependent manner, CR-Kp can rapidly respond to immune cues by implementing a well-defined and complex transcriptional program whose direct relevance toward bacterial fitness warrants further investigation. Additional analyses utilizing this model may identify key factors to tackle CR-Kp colonization of the intestine.

Rummagene: massive mining of gene sets from supporting materials of biomedical research publications

Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com.

Abstract 7346: Optimizing pan-cancer driver pathway analysis from patient transcriptomics and phosphoproteomics

Abstract Alterations in cell signaling pathways drive cancer progression. Such changes can be detected by transcriptomics and proteomics profiling of tumor samples. Although proteomics profiling captures these alterations more directly, RNA-seq transcriptomics remains the most widely available and cost-effective source of data for elucidating tumor-specific cell signaling mechanisms. The eXpression2Kinases (X2K) pipeline is a computational workflow that starts with differentially expressed mRNAs, the identified differentially expressed genes are then used as input for transcription factor enrichment analysis. Then, the top-ranked transcription factors are connected via additional protein-protein interactions from several databases. Finally, the top-ranked transcription factors and the proteins that directly interact and connect these factors are subjected to kinase enrichment analysis. Such analysis identifies protein kinases most likely responsible for the observed changes in gene expression. The X2K pipeline can be applied to analyze tumor-specific transcriptomics to infer upstream regulatory transcription factors, protein intermediates, and kinases that are likely the drivers of cancer progression. While the X2K pipeline infers regulatory pathways from transcriptomics alone, phosphoproteomics can be used to independently validate the inferred kinases, and to calibrate the parameters of the X2K pipeline. Furthermore, this multiomics approach predicts cell signaling pathways with multiple layers of evidence by linking changes observed in the phosphoproteome to changes observed in the transcriptome. Here, we used the transcriptomics and phosphoproteomics profiles from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) pan-cancer dataset, including 1,020 samples across 10 different cancer types, to optimize the parameters of the X2K pipeline and predict pan-cancer as well as tumor-specific signaling pathways. While inferred pathways were distinct between different tumor types, we observed global enrichment of receptor tyrosine kinases and cell cycle kinases. These pathways were subsequently tested for association with patient survival to identify pathways that may be effective therapeutic targets, for example, PTK2 signaling was identified for many lung cancer tumors (LUAD and LSCC) as well as some breast, head & neck squamous. Altogether, the X2K pipeline presents a rational approach to better identify driver regulatory mechanisms from patient-specific profiling of tumors with both transcriptomics and phosphoproteomics. Citation Format: Eden Z. Deng, Giacomo B. Marino, Daniel J. Clarke, Weiping Ma, Pei Wang, Avi Ma'ayan. Optimizing pan-cancer driver pathway analysis from patient transcriptomics and phosphoproteomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7346.

Cuproptosis/OXPHOS tendency prediction of prognosis and immune microenvironment of esophageal squamous cell carcinoma: Bioinformatics analysis and experimental validation

BackgroundCuproptosis is a newly discovered cell death mechanism that relies on mitochondrial respiration, for which oxidative phosphorylation (OXPHOS) is an essential part. However, the detailed mechanisms of cuproptosis associated with OXPHOS in esophageal squamous cell carcinoma (ESCC) and how this correlation affects prognosis still remains unclear. MethodsscRNA-seq data of ESCC were downloaded from SRA (Sequence Read Archive) database. “AUCell” algorithm was used to grouping epithelial cells according to cuproptosis and OXPHOS score. Cell-cell communication, Pseudo-time Trajectory and transcription factor enrichment analysis were repectively conducted by “CellChat”, “monocle3” package and “pySCENIC” algorithm. Univariate and LASSO cox regression analysis were used to construct the prognostic cuproptosis-OXPHOS signature. Finally, CCK-8 assay and DCFH-DA staining assay were respectively validated the sensitive and ROS production of elesclomol. ResultsscRNA-seq data were analyzed to identify 10 core cell types. According to the median scores for cuproptosis and OXPHOS, malignant epithelial cells were divided into double high, double low, and mixed groups. The double high group distributed at the end of the pseudo-time trajectory and harbored HMGA1(+) as specific transcriptional regulons. Knockdown of HMGA1 partly reversed the inhibition of cell viability visualized by CCK-8 assay, while reactive oxygen species (ROS) production by elesclomol was enhanced after HMGA1 silencing. Furthermore, the immunosuppressive signal was significantly increased in the double high group detected by ‘CellChat’ in single-cell data and ‘ssGSEA’ in bulk data followed by ‘CIBERSORTx’ algorithm. Finally, a new cuproptosis-OXPHOS prognostic signature (CNN2, ATP6V0E1, PSMD6, CCDC25, IGFBP2, MT1E, and RPS4Y1) was constructed for the prediction of the prognosis, and a high-risk group corresponding to a more sensitive tendency to erlotinib, dasatinib, and bosutinib treatment was identified. ConclusionsOur study revealed the relationship between OXPHOS and tendency of cuproptosis in ESCC, and malignant cells with this characteristic exerted immunosuppressive signals and indicated poor prognosis. Furthermore, we constructed the regulatory network in high cuproptosis-OXPHOS ESCC and identified HMGA1 as a potential regulator molecule of cuproptosis mediated by elesclomol.

Systematic identification of genotype-dependent enhancer variants in eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a rare atopic disorder associated with esophageal dysfunction, including difficulty swallowing, food impaction, and inflammation, that develops in a small subset of people with food allergies. Genome-wide association studies (GWASs) have identified 9 independent EoE risk loci reaching genome-wide significance (p<5×10-8) and 27 additional loci of suggestive significance (5×10-8<p<1×10-5). In the current study, we perform linkage disequilibrium (LD) expansion of these loci to nominate a set of 531 variants that are potentially causal. To systematically interrogate the gene regulatory activity of these variants, we designed a massively parallel reporter assay (MPRA) containing the alleles of each variant within their genomic sequence context cloned into a GFP reporter library. Analysis of reporter gene expression in TE-7, HaCaT, and Jurkat cells revealed cell-type-specific gene regulation. We identify 32 allelic enhancer variants, representing 6 genome-wide significant EoE loci and 7 suggestive EoE loci, that regulate reporter gene expression in a genotype-dependent manner in at least one cellular context. By annotating these variants with expression quantitative trait loci (eQTL) and chromatin looping data in related tissues and cell types, we identify putative target genes affected by genetic variation in individuals with EoE. Transcription factor enrichment analyses reveal possible roles for cell-type-specific regulators, including GATA3. Our approach reduces the large set of EoE-associated variants to a set of 32 with allelic regulatory activity, providing functional insights into the effects of genetic variation in this disease.

Malignant Transformation of Normal Oral Tissue to Dysplasia and Early Oral Squamous Cell Carcinoma: An In Silico Transcriptomics Approach

Background: Oral squamous cell carcinoma (OSCC) is a prevalent and aggressive form of head and neck cancer, often diagnosed at advanced stages. Elucidating the molecular mechanisms involved in the malignant transformation from normal oral tissue to oral preinvasive lesions (OPL) and primary OSCC could facilitate early diagnosis and improve therapeutic strategies.Methods: Differentially expressed genes (DEGs) were identified from the GSE30784 dataset by comparing normal oral tissue, oral dysplasia, and primary OSCC samples. Cross‐validation was performed using an independent RNA‐seq dataset, GSE186775. Protein–protein interaction (PPI) network analysis, gene ontology annotation, and pathway enrichment analysis were conducted on the common DEGs. Hub genes were identified, and their prognostic significance was evaluated using survival analysis. Transcription factor (TF) enrichment analysis, cross‐validation, and immunohistochemistry analyses were also performed.Results: A total of 226 proteins and 677 interactions were identified in the PPI network, with 34 hub genes, including FN1, SERPINE1, PLAUR, THBS1, and ITGA6. Pathways such as “Formation of the cornified envelope,” “Keratinization,” and “Developmental biology” were enriched. Overexpression of SERPINE1, PLAUR, THBS1, and ITGA6 correlated with poor prognosis, while upregulation of CALML5 and SPINK5 was associated with favorable outcomes. NFIB emerged as the most significant TF‐regulating hub genes. Immunohistochemistry validated ITGA6 overexpression in primary OSCC. Cross‐validation using the RNA‐seq dataset supported the involvement of critical genes in the malignant transformation process.Conclusion: This study identified vital genes, pathways, and prognostic markers involved in the malignant transformation from normal oral tissue to OPL and primary OSCC, providing insights for early diagnosis and targeted therapy development. Cross‐validation with an independent RNA‐seq dataset and immunohistochemistry reinforced the findings, supporting the robustness of the identified molecular signatures.

Human and mouse neutrophils share core transcriptional programs in both homeostatic and inflamed contexts

Neutrophils are frequently studied in mouse models, but the extent to which findings translate to humans remains poorly defined. In an integrative analysis of 11 mouse and 13 human datasets, we find a strong correlation of neutrophil gene expression across species. In inflammation, neutrophils display substantial transcriptional diversity but share a core inflammation program. This program includes genes encoding IL-1 family members, CD14, IL-4R, CD69, and PD-L1. Chromatin accessibility of core inflammation genes increases in blood compared to bone marrow and further in tissue. Transcription factor enrichment analysis implicates members of the NF-κB family and AP-1 complex as important drivers, and HoxB8 neutrophils with JunB knockout show a reduced expression of core inflammation genes in resting and activated cells. In independent single-cell validation data, neutrophil activation by type I or type II interferon, G-CSF, and E. coli leads to upregulation in core inflammation genes. In COVID-19 patients, higher expression of core inflammation genes in neutrophils is associated with more severe disease. In vitro treatment with GM-CSF, LPS, and type II interferon induces surface protein upregulation of core inflammation members. Together, we demonstrate transcriptional conservation in neutrophils in homeostasis and identify a core inflammation program shared across heterogeneous inflammatory conditions.

ScGRN: a comprehensive single-cell gene regulatory network platform of human and mouse.

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237051 cell type-specific GRNs (62 999692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizingand downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

Chlamydial YAP activation in host endocervical epithelial cells mediates pro-fibrotic paracrine stimulation of fibroblasts.

Chronic or repeated infection of the female upper genital tract by C. trachomatis can lead to severe fibrotic sequelae, including tubal factor infertility and ectopic pregnancy. However, the molecular mechanisms underlying this effect are unclear. In this report, we define a transcriptional program specific to C. trachomatis infection of the upper genital tract, identifying tissue-specific induction of host YAP-a pro-fibrotic transcriptional cofactor-as a potential driver of infection-mediated fibrotic gene expression. Furthermore, we show that infected endocervical epithelial cells stimulate collagen production by fibroblasts and implicate chlamydial induction of YAP in this effect. Our results define a mechanism by which infection mediates tissue-level fibrotic pathology via paracrine signaling and identify YAP as a potential therapeutic target for the prevention of Chlamydia-associated scarring of the female genital tract.

Identification of miRNAs in extracellular vesicles as potential diagnostic markers for pediatric epilepsy and drug-resistant epilepsy via bioinformatics analysis.

Pediatric epilepsy (PE) is a common neurological disease. However, many challenges regarding the clinical diagnosis and treatment of PE and drug-resistant epilepsy (DRE) remain unsettled. Our study aimed to identify potential miRNA biomarkers in children with epilepsy and drug-resistant epilepsy by scrutinizing differential miRNA expression profiles. In this study, miRNA expression profiles in plasma extracellular vesicles (EV) of normal controls, children with drug-effective epilepsy (DEE), and children with DRE were obtained. In addition, differential analysis, transcription factor (TF) enrichment analysis, Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and target gene prediction were used to identify specifically expressed miRNAs and their potential mechanisms of action. Potential diagnostic markers for DRE were identified using machine learning algorithms, and their diagnostic efficiency was assessed by the receiver operating characteristic curve (ROC). The hsa-miR-1307-3p, hsa-miR-196a-5p, hsa-miR-199a-3p, and hsa-miR-21-5p were identified as diagnostic markers for PE, with values of area under curve (AUC) 0.780, 0.840, 0.832, and 0.816, respectively. In addition, the logistic regression model incorporating these four miRNAs had an AUC value of 0.940, and its target gene enrichment analysis highlighted that these miRNAs were primarily enriched in the PI3K-Akt, MAPK signaling pathways, and cell cycle. Furthermore, hsa-miR-99a-5p, hsa-miR-532-5p, hsa-miR-181d-5p, and hsa-miR-181a-5p showed good performance in differentiating children with DRE from those with DEE, with AUC values of 0.737 (0.534-0.940), 0.737 (0.523-0.952), 0.788 (0.592-0.985), and 0.788 (0.603-0.974), respectively. This study characterized the expression profile of miRNAs in plasma EVs of children with epilepsy and identified miRNAs that can be used for the diagnosis of DRE.

Innovative breakthroughs facilitated by single-cell multi-omics: manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells.

Melanoma is typically regarded as the most dangerous form of skin cancer. Although surgical removal of in situ lesions can be used to effectively treat metastatic disease, this condition is still difficult to cure. Melanoma cells are removed in great part due to the action of natural killer (NK) and T cells on the immune system. Still, not much is known about how the activity of NK cell-related pathways changes in melanoma tissue. Thus, we performed a single-cell multi-omics analysis on human melanoma cells in this study to explore the modulation of NK cell activity. Cells in which mitochondrial genes comprised > 20% of the total number of expressed genes were removed. Gene ontology (GO), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and AUCcell analysis of differentially expressed genes (DEGs) in melanoma subtypes were performed. The CellChat package was used to predict cell-cell contact between NK cell and melanoma cell subtypes. Monocle program analyzed the pseudotime trajectories of melanoma cells. In addition, CytoTRACE was used to determine the recommended time order of melanoma cells. InferCNV was utilized to calculate the CNV level of melanoma cell subtypes. Python package pySCENIC was used to assess the enrichment of transcription factors and the activity of regulons in melanoma cell subtypes. Furthermore, the cell function experiment was used to confirm the function of TBX21 in both A375 and WM-115 melanoma cell lines. Following batch effect correction, 26,161 cells were separated into 28 clusters and designated as melanoma cells, neural cells, fibroblasts, endothelial cells, NK cells, CD4+ T cells, CD8+ T cells, B cells, plasma cells, monocytes and macrophages, and dendritic cells. A total of 10137 melanoma cells were further grouped into seven subtypes, i.e., C0 Melanoma BIRC7, C1 Melanoma CDH19, C2 Melanoma EDNRB, C3 Melanoma BIRC5, C4 Melanoma CORO1A, C5 Melanoma MAGEA4, and C6 Melanoma GJB2. The results of AUCell, GSEA, and GSVA suggested that C4 Melanoma CORO1A may be more sensitive to NK and T cells through positive regulation of NK and T cell-mediated immunity, while other subtypes of melanoma may be more resistant to NK cells. This suggests that the intratumor heterogeneity (ITH) of melanoma-induced activity and the difference in NK cell-mediated cytotoxicity may have caused NK cell defects. Transcription factor enrichment analysis indicated that TBX21 was the most important TF in C4 Melanoma CORO1A and was also associated with M1 modules. In vitro experiments further showed that TBX21 knockdown dramatically decreases melanoma cells' proliferation, invasion, and migration. The differences in NK and T cell-mediated immunity and cytotoxicity between C4 Melanoma CORO1A and other melanoma cell subtypes may offer a new perspective on the ITH of melanoma-induced metastatic activity. In addition, the protective factors of skin melanoma, STAT1, IRF1, and FLI1, may modulate melanoma cell responses to NK or T cells.