Predicted Transcription Factor Research Articles

CARD9 protein SUMOylation regulates HOXB5 nuclear translocation and Parkin-mediated mitophagy in myocardial I/R injury.

Myocardial injury induced by ischemia-reperfusion (I/R) remains a difficult clinical problem. However, the exact mechanisms underlying I/R-induced have yet to be clarified. CARD9 is an important cytoplasmic-binding protein. In this study, an immunocoprecipitation assay showed that SUMOylation of the CARD9 protein promoted the binding of CARD9 to HOXB5, but hindered the O-GlcNAc glycosylation of HOXB5, a predicted transcription factor of Parkin and a key factor in mitophagy. O-GlcNAc glycosylation is an important signal for translocation of proteins from the cytoplasm to the nucleus. CARD9 protein SUMOylation is regulated by PIAS3, which is related to I/R-induced myocardial injury. Therefore, we propose that knockdown of PIAS3 inhibits SUMOylation of the CARD9 protein, facilitates the dissociation of CARD9 and HOXB5, which increases the O-GlcNAc-mediated glycosylation of HOXB5, while the resulting HOXB5 nuclear translocation promotes Parkin-induced mitophagy and alleviates myocardial I/R injury.

High expression of PLA2G2A in fibroblasts plays a crucial role in the early progression of carotid atherosclerosis

BackgroundIn mouse models of atherosclerosis, knockout of the PLA2G2A gene has been shown to reduce the volume of atherosclerotic plaques. Clinical trials have demonstrated the potential of using the sPLA2 inhibitor Varespladib in combination with statins to reduce lipid levels. However, this approach has not yielded the expected results in reducing the risk of cardiovascular events. Therefore, it is necessary to further investigate the mechanisms of PLA2G2A.MethodsSingle-cell transcriptome data from two sets of carotid plaques, combined with clinical patient information. were used to describe the expression characteristics of PLA2G2A in carotid plaques at different stages. In order to explore the mechanisms of PLA2G2A, we conducted enrichment analysis, cell–cell communication analysis and single-cell regulatory network inference and clustering analyses. We validated the above findings at the cellular level.ResultsOur findings indicate that PLA2G2A is primarily expressed in vascular fibroblasts and shows significant cell interactions with macrophages in the early-stage, especially in complement and inflammation-related pathways. We also found that serum sPLA2 levels have stronger diagnostic value in patients with mild carotid artery stenosis. Subsequent comparisons of single-cell transcriptomic data from early and late-stage carotid artery plaques corroborated these findings and predicted transcription factors that might regulate the progression of early carotid atherosclerosis (CA) and the expression of PLA2G2A.ConclusionsOur study discovered and validated that PLA2G2A is highly expressed by vascular fibroblasts and promotes plaque progression through the activation of macrophage complement and coagulation cascade pathways in the early-stage of CA.

Transcriptomic atlas of the morphologic development of the fungal pathogen Coccidioides reveals key phase-enriched transcripts.

Coccidioides spp . are highly understudied but significant dimorphic fungal pathogens that can infect both immunocompetent and immunocompromised people. In the environment, they grow as multicellular filaments (hyphae) that produce vegetative spores called arthroconidia. Upon inhalation by mammals, arthroconidia undergo a process called spherulation. They enlarge and undergo numerous nuclear divisions to form a spherical structure, and then internally segment until the spherule is filled with multiple cells called endospores. Mature spherules rupture and release endospores, each of which can form another spherule, in a process thought to facilitate dissemination. Spherulation is unique to Coccidioides and its molecular determinants remain largely unknown. Here, we report the first high-density transcriptomic analyses of Coccidioides development, defining morphology-dependent transcripts and those whose expression is regulated by Ryp1, a major regulator required for spherulation and virulence. Of approximately 9000 predicted transcripts, we discovered 273 transcripts with consistent spherule-associated expression, 82 of which are RYP1 -dependent, a set likely to be critical for Coccidioides virulence. ChIP-Seq revealed 2 distinct regulons of Ryp1, one shared between hyphae and spherules and the other unique to spherules. Spherulation regulation was elaborate, with the majority of 227 predicted transcription factors in Coccidioides displaying spherule-enriched expression. We identified provocative targets, including 20 transcripts whose expression is endospore-enriched and 14 putative secreted effectors whose expression is spherule-enriched, of which 6 are secreted proteases. To highlight the utility of these data, we selected a cluster of RYP1 -dependent, arthroconidia-associated transcripts and found that they play a role in arthroconidia cell wall biology, demonstrating the power of this resource in illuminating Coccidioides biology and virulence.

12246 The Stage-Dependent Transcriptome And Methylome Reveal Significance Of The Neonatal Period In Postnatal Metabolic Maturation Of The Mouse Liver

Abstract Disclosure: S. Yano: None. N. Sato: None. H. Araki: None. F. Miura: None. T. Ito: None. Y. Ogawa: None. The mammalian liver serves as an extramedullary hematopoietic organ during the fetus and matures into a metabolic organ after birth, adapting to changes in the nutritional environment. DNA demethylation upregulates fatty acid β-oxidation (FAO) genes in the postnatal mouse liver to produce energy from breast milk lipids. However, the detailed interactions between DNA methylation (DNAme) and gene expression through the perinatal stages remain poorly understood. Here, we perform an integrative analysis of the transcriptome and methylome of the mouse liver at the fetal, neonatal, and postweaning stages. We found a drastic loss of DNAme at the neonatal stage in specific regions associated with metabolic pathways such as FAO and gluconeogenesis in concordance with the activation of metabolic processes. Moreover, in silico analysis predicted transcription factors including HNF4α, PPARα, and NR1H2 as key epigenetic regulators within the neonatal period. We also observed an inactivation of erythrocyte development with a concordant gain of DNAme. This analysis extends the epigenetic landscape of postnatal hepatic maturation, suggesting the importance of the nutritional environment during the neonatal period for epigenetic regulation. Presentation: 6/3/2024

Identification of novel hub genes and immune infiltration in atopic dermatitis using integrated bioinformatics analysis

The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein–protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine–cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD.

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.

Identification and validation of diagnostic biomarkers and immune infiltration in dilated cardiomyopathies with heart failure and construction of diagnostic model

Dilated cardiomyopathy (DCM) is characterized by immune cell infiltration and can readily progress to heart failure (HF). In the study, differential expression analysis, enrichment analysis, and protein–protein interaction (PPI) network analysis were performed on DCM with HF-related datasets. The CytoHubba was used to identify hub genes. Diagnostic biomarkers were obtained by validating their expression and diagnostic value in another external dataset, and a diagnostic model was constructed. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to predict immune cell infiltration in cardiac samples. The associations between diagnostic biomarkers and immune cells were investigated. The NetworkAnalyst and miRDB databases were used to predict transcription factors and microRNAs, followed by establishing regulatory networks. The DSigDB database was used to predict drug candidates. Subsequently, a mouse model of DCM with HF was used to validate the expression levels of these genes. The present study revealed that differentially expressed genes were enriched in the extracellular matrix organization, cardiac muscle hypertrophy, and other immune-related biological processes. OMD and THBS4 were finally identified, and the nomogram has satisfactory prediction and strong calibration ability. In addition, the two diagnostic biomarkers exhibited significant associations with multiple immune infiltrating cells. Finally, two TFs, 65 microRNAs, and 10 drug candidates were obtained. In animal experiments, two diagnostic biomarkers showed expression trends consistent with the results of bioinformatic analysis. OMD and THBS4 have been identified as hub immune-related diagnostic biomarkers for DCM with HF. Our research provides novel insights into the diagnosis and treatment of the disease.

The impact of site-specific DNA methylation in KCNJ11 promoter on type 2 diabetes

AimsThis study explores the correlation between site-specific methylation levels of the KCNJ11 promoter and type 2 diabetes mellitus (T2DM), analyzing potential molecular mechanisms. MethodsThirty patients newly diagnosed with T2DM and 30 healthy controls were selected to determine the CpG methylation levels in the promoter region of the KCNJ11 gene using the bisulfite assay. The online software JASPAR was used to predict transcription factors binding to differentially methylated sites. Key transcription factors were further validated through quantitative PCR (q-PCR) and chromatin immunoprecipitation followed by PCR (ChIP-PCR). ResultsMethylation at multiple CpG sites within the KCNJ11 gene promoter was generally reduced in newly diagnosed T2DM patients compared with healthy individuals. The methylation status of CpG-471, a site crucial for the binding of the transcription factor TCF12, emerged as potentially influential in T2DM pathogenesis. This reduction in methylation at CpG-471 may enhance TCF12 binding, thereby altering KCNJ11 expression. ConclusionHypomethylation of specific CpG sites in the promoter region of the KCNJ11 gene in patients with incipient T2DM potentially contributes to the disease's pathogenesis. This hypomethylation may influence TCF12 binding, with potential regulatory effects on KCNJ11 expression and pancreatic beta-cell function, though further studies are needed to confirm the exact mechanisms involved.

Bioinformatics analysis identifies WNK1 gene as a potential biomarker for cholangiocarcinoma diagnosis and immune infiltration

BackgroundCholangiocarcinoma (CHOL) is a malignant epithelial carcinoma of the digestive system with poor prognosis and high mortality. WNK lysine deficient protein kinase 1 (WNK1) is known to be associated with tumorigenesis in various cancers. However, the relationship between WNK1 and CHOL development, as well as the potential mechanisms involved, remains poorly understood. MethodsMicroarray datasets of CHOL (GSE22633 and GSE32879) were retrieved from the Gene Expression Omnibus (GEO) database. Functional enrichment and immunoinfiltration analyses were performed for genes co-expressed with WNK1. GraphPad Prism 9 was utilized for statistical data analysis and the construction of receiver operating characteristic (ROC) curves. The impact of WNK1 on the CHOL tumor microenvironment was analyzed using Tumor Immune Estimation Resource (TIMER), Venn diagrams, STRING, and TISIDB database for information on WNK1-related chemokines and chemokine receptors. Protein-protein interaction (PPI) networks were used to predict transcription factors and microRNAs interacting with WNK1 and the associated hub genes. ResultsDifferential expression of WNK1 was observed between CHOL and normal samples, suggesting its diagnostic value. Functional analysis showed that WNK1 and its associated genes were primarily enriched in pathways such as leukocyte transendothelial migration and chemokine signaling. Neutrophils were the only type of infiltrating immune cells associated with WNK1 in the CHOL tumor microenvironment (TME). VEGFA and ALB were identified as hub genes, and X-C motif chemokine receptor 1 (XCR1) and C-X-C motif chemokine ligand 5 (CXCL5) were identified as core chemokines and chemokine receptors related to WNK1 and neutrophil infiltration in CHOL. ConclusionsBased on network analysis and the summary of previous studies, it was proposed that CHOL tumor cells secrete CXCL5, leading to neutrophil recruitment to the tumor microenvironment. Vascular endothelial growth factor A (VEGFA) released by the infiltrating neutrophils is suggested to promote overexpression of WNK1 by tumor cells, activating the VEGFA downstream pathway to promote angiogenesis and tumor progression.

In Silico Analysis of the Effect of LINC00525 Knockdown on mRNA Profile in A549 Lung Adenocarcinoma Cells

Background: High expression of lncRNA LINC00525 is associated with the progressive development of various cancers, including lung adenocarcinoma (LUAD). LncRNAs play regulatory roles in transcription and gene expression. Objectives: This study aimed to investigate, for the first time, the effect of LINC00525 knockdown on the transcriptome and signaling pathways of A549 LUAD cells. Methods: Differentially expressed genes (DEGs) were identified after LINC00525 silencing in A549 cells using the gene expression profile GSE171460. R packages were employed to identify enriched pathways. Hub genes were identified using a PPI network based on the STRING database. The RNAInter and TRRUST databases were used to predict transcription factors (TFs) that interact with LINC00525 and regulate DEGs. Results: LINC00525 knockdown significantly altered the expression of approximately 3,093 genes. The most significantly enriched pathways were the cell cycle and cytokine signaling pathways. All identified hub genes were upregulated, many of which were related to the cytokine signaling pathway, including STAT1, IL6, ISG15, IRF1, IRF7, IFIH1, and IFIT3. According to the GEPIA database, decreased LINC00525 expression showed a positive correlation with increased expression of the hub genes IL6, DDX58, IFIH1, OAS1, OAS2, and IFIT3, supporting the findings of this study. Based on the transcriptional regulatory network, some TFs of DEGs and hub genes may interact with LINC00525. Conclusions: These findings support the positive association between LINC00525 overexpression and tumor growth. LINC00525 may serve as a potential therapeutic target for LUAD. Further research is needed to explore other functional mechanisms of LINC00525.

Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial TFs-DNA interactions remains an intricate challenge. A deeper understanding of DNA secondary structures could greatly enhance our knowledge of how TFs recognize and interact with DNA, thereby elucidating their biological function. In this study, we employed machine learning algorithms to predict transcription factor binding sites (TFBS) and classify them as directed-repeat (DR) or inverted-repeat (IR). To accomplish this, we divided the set of TFBS nucleotide sequences by size, ranging from 8 to 20 base pairs, and converted them into thermodynamic data known as DNA duplex stability (DDS). Our results demonstrate that the Random Forest algorithm accurately predicts TFBS with an average accuracy of over 82% and effectively distinguishes between IR and DR with an accuracy of 89%. Interestingly, upon converting the base pairs of several TFBS-IR into DDS values, we observed a symmetric profile typical of the palindromic structure associated with these architectures. This study presents a novel TFBS prediction model based on a DDS characteristic that may indicate how respective proteins interact with base pairs, thus providing insights into molecular mechanisms underlying bacterial TFs-DNA interaction.

MLSNet: a deep learning model for predicting transcription factor binding sites.

Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be enhanced. In this study, we propose MLSNet, a novel deep learning architecture designed specifically to predict TFBSs. MLSNet innovatively integrates multisize convolutional fusion with long short-term memory (LSTM) networks to effectively capture DNA-sparse higher-order sequence features. Further, MLSNet incorporates super token attention and Bi-LSTM to systematically extract and integrate higher-order DNA shape features. Experimental results on 165 ChIP-seq (chromatin immunoprecipitation followed by sequencing) datasets indicate that MLSNet consistently outperforms several state-of-the-art algorithms in the prediction of TFBSs. Specifically, MLSNet reports average metrics: 0.8306 for ACC, 0.8992 for AUROC, and 0.9035 for AUPRC, surpassing the second-best methods by 1.82%, 1.68%, and 1.54%, respectively. This research delineates the effectiveness of combining multi-size convolutional layers with LSTM and DNA shape-based features in enhancing predictive accuracy. Moreover, this study comprehensively assesses the variability in model performance across different cell lines and transcription factors. The source code of MLSNet is available at https://github.com/minghaidea/MLSNet.

Biochanin A suppresses Klf6-mediated Smad3 transcription to attenuate renal fibrosis in UUO mice

BackgroundRenal fibrosis is a hallmark of chronic kidney disease (CKD). Smad3 serves as the principal transcription factor mediating the pro-fibrosis effects of TGF-β signaling in renal fibrosis. Biochanin A (BCA), a natural isoflavone, has been shown to attenuate renal fibrosis by inhibiting TGF-β signaling but the detailed mechanisms remain unresolved. This study aimed to elucidate the specific mechanisms by which BCA modulates TGF-β signaling. MethodsRenal fibrosis models were established both in vitro, using TGF-β1-stimulated mouse renal tubular TCMK1 cells, and in vivo, employing mice with unilateral ureter obstruction (UUO). RNA-seq was conducted to identify BCA-regulated genes. The AnimalTFDB4.0 database was utilized to predict transcription factors with potential binding to Smad3 promoter. The activities of TGF-β signaling and the cloned mouse Smad3 promoter were assessed using luciferase reporter assays. Plasmid transfection was performed using polyethylenimine in TCMK1 cells or ultrasound microbubbles in UUO kidneys. Gene expression was analyzed by RT-PCR, western blot, and immunohistochemistry assays. ResultsBCA significantly inhibited TGF-β signaling activity and suppressed TGF-β1-induced fibrotic gene expression in TCMK1 cells. RNA-seq and in silico analyses identified Smad3 as the key gene downregulated by BCA, while leaving Smad2 unaffected. This selective transcriptional suppression of Smad3 by BCA was validated by luciferase reporter assays using the cloned Smad3 promoter. Furthermore, transcription factor binding prediction identified that Klf6, a transcription factor downregulated by BCA, has binding potential to the Smad3 promoter and promotes Smad3 transcription. Klf6 expression was induced in TGF-β1-stimulated TCMK1 cells and UUO kidneys, but this induction was abolished upon BCA treatment. Importantly, Klf6 overexpression restored Smad3 expression and counteracted the anti-fibrosis effects of BCA in both TGF-β1-stimulated TCMK1 cells and UUO kidneys. ConclusionTGF-β-responsive Klf6 transcriptionally transactivates Smad3 expression. BCA exerts anti-renal fibrosis effects by inhibiting the Klf6-Smad3 signaling axis, underscoring its therapeutic potential in the treatment of CKD.

Glyphosate and a glyphosate-based herbicide dysregulate the epigenetic landscape of Homeobox A10 (Hoxa10) gene during the endometrial receptivity in Wistar rats.

We observed that gestational plus lactational exposure to glyphosate (Gly), as active ingredient, or a glyphosate-based herbicide (GBH) lead to preimplantation losses in F1 female Wistar rats. Here, we investigated whether GBH and/or Gly exposure could impair Hoxa10 gene transcription by inducing epigenetic changes during the receptive stage in rats, as a possible herbicide mechanism implicated in implantation failures. F0 dams were treated with Gly or a GBH through a food dose of 2mg Gly/kg bw/day from gestational day (GD) 9 up to lactational day 21. F1 female rats were bred, and uterine tissues were analyzed on GD5 (preimplantation period). Transcripts levels of Hoxa10, DNA methyltransferases (Dnmt1, Dnmt3a and Dnmt3b), histone deacetylases (Hdac-1 and Hdac-3) and histone methyltransferase (EZH2) were assessed by quantitative polymerase chain reaction (qPCR). Four CpG islands containing sites targeted by BstUI methylation-sensitive restriction enzyme and predicted transcription factors (TFs) were identified in Hoxa10 gene. qPCR-based methods were used to evaluate DNA methylation and histone post-translational modifications (hPTMs) in four regulatory regions (RRs) along the gene by performing methylation-sensitive restriction enzymes and chromatin immunoprecipitation assays, respectively. GBH and Gly downregulated Hoxa10 mRNA. GBH and Gly increased DNA methylation levels and Gly also induced higher levels than GBH in all the RRs analyzed. Both GBH and Gly enriched histone H3 and H4 acetylation in most of the RRs. While GBH caused higher H3 acetylation, Gly caused higher H4 acetylation in all RRs. Finally, GBH and Gly enhanced histone H3 lysine 27 trimethylation (H3K27me3) marker at 3 out of 4 RRs studied which was correlated with increased EZH2 levels. In conclusion, exposure to GBH and Gly during both gestational plus lactational phases induces epigenetic modifications in regulatory regions of uterine Hoxa10 gene. We show for the first time that Gly and a GBH cause comparable gene expression and epigenetic changes. Our results might contribute to delineate the mechanisms involved in the implantation failures previously reported. Finally, we propose that epigenetic information might be a valuable tool for risk assessment in the near future, although more research is needed to establish a cause-effect relationship.

Telomere-to-telomere, gap-free assembly of the Rosa rugosa reference genome

Rosa, a genus esteemed worldwide for its ornamental plants, has encountered barriers in functional genomic studies and further genetic enhancement due to incomplete sequences and floating regions in previously sequenced genomes. Our groundbreaking study introduced a meticulously assembled, continuous, and fully bridged reference genome for Rosa rugosa, constructed through a sophisticated combination of PacBio High-Fidelity, ONT ultra-long reads, and Hi-C data. This robust assembly spanned 444.55 Mb and encompassed 34 109 protein-coding genes. We have uniquely assembled each chromosome into single, gap-free structures, successfully identifying all 14 telomeres and seven centromeres, a feat not achieved previously. The centromeric regions were distinguished by tandem repeats, primarily composed of centromere-specific 159-bp monomers, and a significant enrichment of ATHILA/Gypsy long terminal repeat retrotransposons in proximal regions. Our research highlighted recent tandem duplications as instrumental in bolstering R. rugosa's stress tolerance, environmental adaptability, and enhanced anthocyanin synthesis. Furthermore, our study ventured into uncharted territory by predicting transcription factors potentially regulating anthocyanin biosynthesis through the employment of gene co-expression networks, providing new avenues for research. This comprehensive reference genome not only serves as a cornerstone for in-depth exploration of genomic architecture and functionalities in R. rugosa but also acts as a catalyst for innovative breeding strategies and genetic refinement within the genus.

Predicting transcription factor binding sites by a multi-modal representation learning method based on cross-attention network

The prediction of transcription factor binding sites (TFBS) plays a crucial role in studying cellular functions and understanding transcriptional regulatory processes. With the development of chromatin immunoprecipitation sequencing (ChIP-seq) technology, an increasing number of computer-aided TFBS prediction models have emerged. However, how to integrate multi-modal information of DNA and obtain efficient features to improve prediction accuracy remains a major challenge. Here, we propose MultiTF, a multi-modal representation learning method based on a cross-attention network for predicting transcription factor binding sites. Among TFBS prediction methods, we are the first to use graph neural networks and cross-attention networks for representation learning. MultiTF uses dna2vec to extract global contextual features of DNA sequences, DNAshapeR to extract shape features, and the CDPfold model and graph attention network for learning and representation of DNA structural features. Finally, with the help of our cross-attention module, we successfully combine sequence, structural, and shape features to achieve interactive fusion. When comparing MultiTF to other state-of-the-art methods using 165 ENCODE ChIP-seq datasets, we find that MultiTF exhibits average ACC, ROC-AUC, and PR-AUC values of 0.911, 0.978, and 0.982, respectively. The results show that MultiTF achieves unprecedented prediction accuracy compared to previous TFBS prediction models. In addition, our visual analysis of structural features provides interpretability for the prediction results.

Ligify: Automated Genome Mining for Ligand-Inducible Transcription Factors.

Prokaryotic transcription factors can be repurposed into biosensors for the ligand-inducible control of gene expression, but the landscape of chemical ligands for which biosensors exist is extremely limited. To expand this landscape, we developed Ligify, a web application that leverages information in enzyme reaction databases to predict transcription factors that may be responsive to user-defined chemicals. Candidate transcription factors are then incorporated into automatically generated plasmid sequences that are designed to express GFP in response to the target chemical. Our benchmarking analyses demonstrated that Ligify correctly predicted 31/100 previously validated biosensors and highlighted strategies for further improvement. We then used Ligify to build a panel of genetic circuits that could induce a 47-fold, 5-fold, 9-fold, and 27-fold change in fluorescence in response to D-ribose, L-sorbose, isoeugenol, and 4-vinylphenol, respectively. Ligify should enhance the ability of researchers to quickly develop biosensors for an expanded range of chemicals and is publicly available at https://ligify.groov.bio.

Knockout of Hsp70 genes significantly affects locomotion speed and gene expression in leg skeletal muscles of Drosophila melanogaster.

The functions of the heat shock protein 70 (Hsp70) genes were studied using a line of Drosophila melanogaster with a knockout of 6 of these genes out of 13. Namely, the effect of knockout of Hsp70 genes on negative geotaxis climbing (locomotor) speed and the ability to adapt to climbing training (0.5-1.5 h/day, 7 days/wk, 19 days) were examined. Seven- and 23-day-old Hsp70- flies demonstrated a comparable reduction (twofold) in locomotor speed and widespread changes in leg skeletal muscle transcriptome (RNA sequencing) compared with w1118 flies. To identify the functions of genes related to decreased locomotor speed, the overlapped differentially expressed genes at both time points were analyzed: the upregulated genes encoded extracellular proteins, regulators of drug metabolism, and the antioxidant response, whereas downregulated genes encoded regulators of carbohydrate metabolism and transmembrane proteins. In addition, in Hsp70- flies, activation of transcription factors related to disruption of the fibril structure and heat shock response (Hsf) was predicted, using the position weight matrix approach. In control flies, adaptation to chronic exercise training was associated mainly with gene response to a single exercise bout, whereas the predicted transcription factors were related to stress/immune (Hsf, NF-κB, etc.) and early gene response. In contrast, Hsp70- flies demonstrated no adaptation to training as well as a significantly impaired gene response to a single exercise bout. In conclusion, the knockout of Hsp70 genes not only reduced physical performance but also disrupted adaptation to chronic physical training, which is associated with changes in the leg skeletal muscle transcriptome and impaired gene response to a single exercise bout.NEW & NOTEWORTHY Knockout of six heat shock protein 70 (Hsp70) genes in Drosophila melanogaster reduced locomotion (climbing) speed that is associated with genotype-specific differences in leg skeletal muscle gene expression. Disrupted adaptation of Hsp70- flies to chronic exercise training is associated with impaired gene response to a single exercise bout.

The Co-pathogenic Target Gene CNTN1 Involved in Coronary Artery Disease and Pulmonary Arterial Hypertension Has Potential for Diagnosis of Coronary Artery Disease.

We aimed to find a gene for coronary artery disease (CAD) early diagnosis by detecting co-pathogenic target gene involved in CAD and pulmonary arterial hypertension (PAH).
 
 Methods: Datasets were obtained from the Gene Expression Omnibus (GEO) database, including GSE113079, GSE113439, and GSE12288, to investigate gene expression patterns in cardiovascular diseases. Weighted Gene Co-expression Network Analysis (WGCNA) was performed to identify gene modules associated with clinical traits. Differential gene expression analysis and functional enrichment analysis were carried out. Protein-protein interaction (PPI) networks were constructed. JASPAR database and FIMO tool were utilized to predict transcription factor (TF) binding sites.
 
 Results: Fifteen key genes were identified in CAD and PAH, with CNTN1 being prioritized for further investigation due to its high connectivity degree. Upstream regulation analysis identified potential TFs (DRGX, HOXD3, and RAX) and 7 miRNAs targeting CNTN1. The expression profile of CNTN1 was significantly upregulated in CAD samples, and ROC analysis indicated potential diagnostic value for CAD. CMap database analysis predicted potential targeted drugs for CAD.
 
 Conclusion: CNTN1 was detected as a co-pathogenetic gene for CAD and PAH. It is highly expressed in CAD patients and has potential value for CAD diagnosis. CNTN1 is potentially regulated by 3 TFs and 7 miRNAs.

BACH2 regulates diversification of regulatory and proinflammatory chromatin states in TH17 cells.

Interleukin-17 (IL-17)-producing helper T (TH17) cells are heterogenous and consist of nonpathogenic TH17 (npTH17) cells that contribute to tissue homeostasis and pathogenic TH17 (pTH17) cells that mediate tissue inflammation. Here, we characterize regulatory pathways underlying TH17 heterogeneity and discover substantial differences in the chromatin landscape of npTH17 and pTH17 cells both in vitro and in vivo. Compared to other CD4+ T cell subsets, npTH17 cells share accessible chromatin configurations with regulatory T cells, whereas pTH17 cells exhibit features of both npTH17 cells and type 1 helper T (TH1) cells. Integrating single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq), we infer self-reinforcing and mutually exclusive regulatory networks controlling different cell states and predicted transcription factors regulating TH17 cell pathogenicity. We validate that BACH2 promotes immunomodulatory npTH17 programs and restrains proinflammatory TH1-like programs in TH17 cells in vitro and in vivo. Furthermore, human genetics implicate BACH2 in multiple sclerosis. Overall, our work identifies regulators of TH17 heterogeneity as potential targets to mitigate autoimmunity.