Predicted Transcription Factor Research Articles

Enhancing Transcription Factor Prediction through Multi-Task Learning (Student Abstract)

Transcription factors (TFs) play a fundamental role in gene regulation by selectively binding to specific DNA sequences. Understanding the nature and behavior of these TFs is essential for insights into gene regulation dynamics. In this study, we introduce a robust multi-task learning framework specifically tailored to harness both TF-specific annotations and TF-related domain annotations, thereby enhancing the accuracy of TF predictions. Notably, we incorporate cutting-edge language models that have recently garnered attention for their outstanding performance across various fields, particularly in biological computations like protein sequence modeling. Comparative experimental analysis with existing models, DeepTFactor and TFpredict, reveals that our multi-task learning framework achieves an accuracy exceeding 92% across four evaluation metrics on the TF prediction task, surpassing both competitors. Our work marks a significant leap in the domain of TF prediction, enriching our comprehension of gene regulatory mechanisms and paving the way for the discovery of novel regulatory motifs.

Functional analysis of a first hindlimb positioning enhancer via Gdf11 expression.

During the early development of tetrapods, including humans, the embryonic body elongates caudally once the anterior-posterior axis is established. During this process, region-specific vertebral morphogenesis occurs, with the determination of limb positioning along the anterior-posterior axis. We previously reported that Gdf11 functions as an anatomical integration system that determines the positioning of hindlimbs and sacral vertebrae where Gdf11 is expressed. However, the molecular mechanisms underlying induction of Gdf11 expression remain unclear. In this study, we searched for non-coding regions near the Gdf11 locus that were conserved across species to elucidate the regulatory mechanisms of Gdf11 expression. We identified an enhancer of the Gdf11 gene in intron 1 and named it highly conserved region (HCR). In HCR knockout mice, the expression level of endogenous Gdf11 was decreased, and the position of the sacral-hindlimb unit was shifted posteriorly. We also searched for factors upstream of Gdf11 based on the predicted transcription factor binding sites within the HCR. We found that inhibition of FGF signaling increased endogenous Gdf11 expression, suggesting that FGF signaling negatively regulates Gdf11 expression. However, FGF signaling does not regulate HCR activity. Our results suggest that there are species-specific Gdf11 enhancers other than HCR and that FGF signaling regulates Gdf11 expression independent of HCR.

Prediction of drought regulating gene (s) in barley (Hordeum vulgare L.) using express sequence tags (ESTs) through in silico approaches

Climate change is a major current issue in the world, affecting the availability and stability of food. It leads to the most damaging abiotic stress for the ecosystem and the one that results in the greatest output loss is drought. Barley was once thought to be an abiotic stress-tolerant plant but the production of barley dropped significantly due to drought-stress. In this study, a computational method was used to predict the genes in barley that are associated with drought stress. The expressed sequence tags (ESTs) were investigated to identify the gene expression profiling in barley during drought stress. Various software, tools, and servers were used to predict the genes using ESTs, which were responsive to drought stress. The TFcat and blast2GO tools were used to predict transcription factors and enzymes respectively. Phytozome and plantCARE databases were used to validate newly predicted novel candidate genes using promoter studies. By using the Ensemble plant genome database, new genes related to drought stress were discovered. There were only 37 locations in the barley genome that had candidate genes. According to the promoter analysis, twenty nine genes had more than ten cis acting controlling elements that was related to drought stress in various plants. The promoter study showed that 29 out of 37 genes contained more than 10 cis-acting regulatory elements involved in drought stress in different plants. These findings will be helpful to identify the novel drought stress tolerant gene in barley that may play an important role in developing new varieties in near future.

Protein regulator of cytokinesis 1 accentuates cholangiocarcinoma progression via mTORC1-mediated glycolysis.

This study aimed to investigate the expression of protein regulator of cytokinesis 1 (PRC1) in cholangiocarcinoma (CHOL) and elucidate its potential impact as well as the underlying mechanisms governing the progression of CHOL. In this study, we used CHOL cells (HUCCT1, RBE, and CCLP1) and conducted a series of experiments, including qRT-PCR, cell counting kit-8 assays, EdU assays, flow cytometry, wound healing assays, Transwell assays, western blotting, double luciferase assays, and ELISA. Subsequently, a mouse model was established using cancer cell injections. Haematoxylin-eosin staining, along with Ki67 and TUNEL assays, were employed to assess tissue histopathology, cell proliferation, and apoptosis. Our findings revealed significantly elevated PRC1 expression in CHOL. According to bioinformatics analysis, it was found that the increased PRC1 level is correlated with the high tumour grades, metastases, and unfavourable prognoses. Notably, PRC1 knockdown inhibited cell viability, proliferation, migration, and invasion while promoting apoptosis in CHOL cells. Analysing TCGA-CHOL data and utilising transcription factor prediction tools (hTFtarget and HumanTFDB), we identified that genes positively correlated with PRC1 in TCGA-CHOL intersect with predicted transcription factors, revealing the activation of PRC1 by forkhead box protein M1 (FOXM1). Moreover, PRC1 was found to exert regulatory control over glycolysis and the mammalian target of rapamycin complex 1 (mTORC1) pathway in the context of CHOL based on KEGG and GSEA analysis. Collectively, these results underscore the pivotal role of PRC1 in CHOL progression, wherein it modulates glycolysis and the mTORC1 pathway under the regulatory influence of FOXM1.

Implications of IFNγ SNP rs2069705 in primary Sjögren's syndrome: transcriptional activation and B cell infiltration.

Primary Sjögren's syndrome (pSS) is characterized by its autoimmune nature. This study investigates the role of the IFNγ SNP rs2069705 in modulating the susceptibility to pSS. Differential expression of IFNγ and BAFF was analyzed using the GEO database's mRNA microarray GSE84844. Genotyping of the IFNγ SNP rs2069705 was conducted via the dbSNP website. The JASPAR tool was used for predicting transcription factor bindings. Techniques such as dual-luciferase reporter assays, Chromatin immunoprecipitation, and analysis of a pSS mouse model were applied to study gene and protein interactions. A notable increase in the mutation frequency of IFNγ SNP rs2069705 was observed in MNCs from the exocrine glands of pSS mouse models. Bioinformatics analysis revealed elevated levels of IFNγ and BAFF in pSS samples. The model exhibited an increase in both CD20+ B cells and cells expressing IFNγ and BAFF. Knocking down IFNγ resulted in lowered BAFF expression and less lymphocyte infiltration, with BAFF overexpression reversing this suppression. Activation of the Janus kinase (JAK)/STAT1 pathway was found to enhance transcription in the BAFF promoter region, highlighting IFNγ's involvement in pSS. In addition, rs2069705 was shown to boost IFNγ transcription by promoting interaction between its promoter and STAT4. SNP rs2069705 in the IFNγ gene emerges as a pivotal element in pSS susceptibility, primarily by augmenting IFNγ transcription, activating the JAK/STAT1 pathway, and leading to B-lymphocyte infiltration in the exocrine glands.NEW & NOTEWORTHY The research employed a combination of bioinformatics analysis, genotyping, and experimental models, providing a multifaceted approach to understanding the complex interactions in pSS. We have uncovered that the rs2069705 SNP significantly affects the transcription of IFNγ, leading to altered immune responses and B-lymphocyte activity in pSS.

Complex regulatory networks influence pluripotent cell state transitions in human iPSCs

Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discover 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which are highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlie the coordinated expression of genes in the GNMs. Epigenetic analyses reveal that regulatory networks underlying self-renewal and pluripotency are more complex than previously realized. Genetic analyses identify thousands of regulatory variants that overlapped predicted transcription factor binding sites and are associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network are significantly enriched for regulatory variants with large effects, suggesting that they play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work bins tens of thousands of regulatory elements in hiPSCs into discrete regulatory networks, shows that pluripotency and self-renewal processes have a surprising level of regulatory complexity, and suggests that genetic factors may contribute to cell state transitions in human iPSC lines.

Identification of genes and key pathways underlying the pathophysiological association between sarcopenia and chronic obstructive pulmonary disease

PurposeChronic obstructive pulmonary disease (COPD) patients are likely to develop sarcopenia, while the exact mechanism underlying the association between sarcopenia and COPD is still not clear. This cohort study aims to explore the genes, signaling pathways, and transcription factors (TFs) that are related to the molecular pathogenesis of sarcopenia and COPD. MethodsAccording to the strict inclusion criteria, two gene sets (GSE8479 for sarcopenia and GSE76925 for COPD) were obtained from the Gene Expression Omnibus (GEO) platform. Overlapping differentially expressed genes (DEGs) in sarcopenia and COPD were detected, and comprehensive bioinformatics analysis was conducted, including functional annotation, enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), construction of a protein–protein interaction (PPI) network, co-expression analysis, identification and validation of hub genes, and TFs prediction and verification. ResultsIn total, 118 downregulated and 92 upregulated common DEGs were detected. Functional analysis revealed that potential pathogenesis involves oxidoreductase activity and ferroptosis. Thirty hub genes were detected, and ATP metabolic process and oxidative phosphorylation were identified to be closely related to the hub genes. Validation analysis revealed that SAA1, C3, and ACSS2 were significantly upregulated, whereas ATF4, PPARGC1A, and MCTS1 were markedly downregulated in both sarcopenia and COPD. In addition, six TFs (NFKB1, RELA, IRF7, SP1, MYC, and JUN) were identified to regulate the expression of these genes, and SAA1 was found to be coregulated by NFKB1 and RELA. ConclusionThis study uncovers potential common mechanisms of COPD complicated by sarcopenia. The hub gene SAA1 and the NF-κB signaling pathway could be involved, and oxidative phosphorylation and ferroptosis might be important contributors to this comorbidity.

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

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

SinCMat: A single-cell-based method for predicting functional maturation transcription factors

A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.

Harnessing regulatory networks in Actinobacteria for natural product discovery.

This review provides insights into advances in experimental and computational methods aimed at predicting transcription factor binding sites and their applications to guide natural product discovery.

Deciphering Collagen Phenotype Dynamics Regulators: Insights from In-Silico Analysis.

Collagen (Col) types I and III are integral components in wound healing and tissue regeneration, influencing tissue development, homeostasis, and related pathologies. Col I and Col III expression changes during different stages of wound healing and understanding the regulation of collagen phenotype determination is crucial for unraveling the complexities of these processes. Transcription factors and microRNAs, directly and indirectly, play a critical role in regulating collagen expression, however, a comprehensive understanding of the factors regulating Col I and III phenotypes remains elusive. This critically analyzed published reports with focuses on various factors regulating the expression of Col I and Col III at the transcriptional and translational levels. We performed bioinformatics analysis with an input of proinflammatory mediators, growth factors, elastases, and matrix metalloproteinases and predicted transcription factors and microRNAs involved in the regulation of collagen expression. Network analysis revealed an interaction between genes, transcription factors, and microRNAs and provided a holistic view of the regulatory landscape governing collagen expression and unveils intricate interconnections. This analysis lays a founda-tional framework for guiding future research and therapeutic interventions to promote extracellular matrix remodeling, wound healing, and tissue regeneration after an injury by modulating collagen expression. In essence, this scientific groundwork offers a comprehensive exploration of the regulatory dynamics in collagen synthesis, serving as a valuable resource for advancing both basic research and clinical interventions in tissue repair.

Plasma proteome profiling reveals molecular mechanisms underlying the effects of daily consumption of 'Bahia' and 'Cara Cara' orange juices.

Orange juice is an important food source of bioactive compounds, mainly the flavanones hesperidin and narirutin. This study aimed to investigate the underlying molecular mechanisms of action of orange juice's health properties by analyzing changes in the plasma proteome of healthy Brazilian volunteers after consuming juices made from 'Bahia' (BOJ-source of flavanones) and 'Cara Cara' (CCOJ-source of flavanones and carotenoids) oranges cultivated in Brazil. We used an untargeted proteomic approach, with a particular emphasis on the juices' effects on blood coagulant activity. We identified 247 differentially expressed proteins, of which 170 significantly increased or decreased after BOJ consumption and 145 after CCOJ. These proteins are involved in 105 processes that can significantly regulate cell adhesion, cell signaling, cell metabolism, inflammation, or others. Bioinformatic analysis evidenced proteins with major cellular regulatory capacity (e.g., FN1 and GAPDH) and predicted transcription factors (TFs) (e.g., SP1 and CEBPA) and miRNAs (e.g., miR-1-3p and miR-615-3p) that could be involved in the regulation of differentially expressed proteins. In-silico docking analyses between flavanone metabolites and TFs evidenced the higher binding capacity of narirutin phase II metabolites with akt1 and p38, interactions that suggest how the expression of genes of differentially expressed proteins were activated or inhibited. Moreover, the study shed light on proteins of coagulation cascade that presented expression modulated by both juices, proposing the modulation of blood coagulant activity as a potential benefit of OJ (mainly CCOJ) consumption. Taken together, this study revealed that BOJ and CCOJ consumption affected plasma proteome in healthy individuals, suggesting potential molecular targets and mechanisms of OJ bioactive compounds in humans.

A hybrid approach for predicting transcription factors.

Transcription factors are essential DNA-binding proteins that regulate the transcription rate of several genes and control the expression of genes inside a cell. The prediction of transcription factors with high precision is important for understanding biological processes such as cell differentiation, intracellular signaling, and cell-cycle control. In this study, we developed a hybrid method that combines alignment-based and alignment-free methods for predicting transcription factors with higher accuracy. All models have been trained, tested, and evaluated on a large dataset that contains 19,406 transcription factors and 523,560 non-transcription factor protein sequences. To avoid biases in evaluation, the datasets were divided into training and validation/independent datasets, where 80% of the data was used for training, and the remaining 20% was used for external validation. In the case of alignment-free methods, models were developed using machine learning techniques and the composition-based features of a protein. Our best alignment-free model obtained an AUC of 0.97 on an independent dataset. In the case of the alignment-based method, we used BLAST at different cut-offs to predict the transcription factors. Although the alignment-based method demonstrated excellent performance, it was unable to cover all transcription factors due to instances of no hits. To combine the strengths of both methods, we developed a hybrid method that combines alignment-free and alignment-based methods. In the hybrid method, we added the scores of the alignment-free and alignment-based methods and achieved a maximum AUC of 0.99 on the independent dataset. The method proposed in this study performs better than existing methods. We incorporated the best models in the webserver/Python Package Index/standalone package of "TransFacPred" (https://webs.iiitd.edu.in/raghava/transfacpred).

Transcription Factor Binding Site Prediction Using CnNet Approach.

Controlling the gene expression is the most important development in a living organism, which makes it easier to find different kinds of diseases and their causes. It's very difficult to know what factors control the gene expression. Transcription Factor (TF) is a protein that plays an important role in gene expression. Discovering the transcription factor has immense biological significance, however, it is challenging to develop novel techniques and evaluation for regulatory developments in biological structures. In this research, we mainly focus on 'sequence specificities' that can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for predicting transcription factor binding. Specifically, Multiple Expression motifs for Motif Elicitation (MEME) technique with Convolution Neural Network (CNN) named as CnNet, has been used for discovering the 'sequence specificities' of DNA gene sequences dataset. This process involves two steps: a) discovering the motifs that are capable of identifying useful TF binding site by using MEME technique, and b) computing a score indicating the likelihood of a given sequence being a useful binding site by using CNN technique. The proposed CnNet approach predicts the TF binding score with much better accuracy compared to existing approaches. The source code and datasets used in this work are available at https://github.com/masoodbai/CnNet-Approach-for-TFBS.git.

TeaTFactor: a prediction tool for tea plant transcription factors based on BERT.

A transcription factor (TF) is a sequence-specific DNA-binding protein, which plays key roles in cell-fate decision by regulating gene expression. Predicting TFs is key for tea plant research community, as they regulate gene expression, influencing plant growth, development, and stress responses. It is a challenging task through wet lab experimental validation, due to their rarity, as well as the high cost and time requirements. As a result, computational methods are increasingly popular to be chosen. The pre-training strategy has been applied to many tasks in natural language processing (NLP) and has achieved impressive performance. In this paper, we present a novel recognition algorithm named TeaTFactor that utilizes pre-training for the model training of TFs prediction. The model is built upon the BERT architecture, initially pre-trained using protein data from UniProt. Subsequently, the model was fine-tuned using the collected TFs data of tea plants. We evaluated four different word segmentation methods and the existing state-of-the-art prediction tools. According to the comprehensive experimental results and a case study, our model is superior to existing models and achieves the goal of accurate identification. In addition, we have developed a web server at http://teatfactor.tlds.cc, which we believe will facilitate future studies on tea transcription factors and advance the field of crop synthetic biology.

Ascertainment Bias in the Genomic Test of Positive Selection on Regulatory Sequences.

Evolution of gene expression mediated by cis-regulatory changes is thought to be an important contributor to organismal adaptation, but identifying adaptive cis-regulatory changes is challenging due to the difficulty in knowing the expectation under no positive selection. A new approach for detecting positive selection on transcription factor binding sites (TFBSs) was recently developed, thanks to the application of machine learning in predicting transcription factor (TF) binding affinities of DNA sequences. Given a TFBS sequence from a focal species and the corresponding inferred ancestral sequence that differs from the former at n sites, one can predict the TF-binding affinities of many n-step mutational neighbors of the ancestral sequence and obtain a null distribution of the derived binding affinity, which allows testing whether the binding affinity of the real derived sequence deviates significantly from the null distribution. Applying this test genomically to all experimentally identified binding sites of 3 TFs in humans, a recent study reported positive selection for elevated binding affinities of TFBSs. Here, we show that this genomic test suffers from an ascertainment bias because, even in the absence of positive selection for strengthened binding, the binding affinities of known human TFBSs are more likely to have increased than decreased in evolution. We demonstrate by computer simulation that this bias inflates the false positive rate of the selection test. We propose several methods to mitigate the ascertainment bias and show that almost all previously reported positive selection signals disappear when these methods are applied.

ScANANSE gene regulatory network and motif analysis of single-cell clusters

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation.

BTG2 Super-Enhancer Mutations Disrupt TFAP4 Binding and Deregulate BTG2 Expression in Diffuse Large B-Cell Lymphoma

Diffuse Large B-cell Lymphoma (DLBCL), the most frequent lymphoid malignancy, remains incurable in ~40% of patients. Coding-genome sequencing efforts identified several genes/pathways altered in this disease, as well as genetic subgroups of prognostic and therapeutic significance. However, the large non-coding portion of the genome remained largely unexplored. We recently identified a pervasive hypermutation mechanism targeting active super-enhancers (SEs) in >90% of DLBCLs and leading to dysregulation of multiple genes, including well-known lymphoma oncogenes (Bal et al., Nature 2022). This pervasive phenomenon exhibits hallmarks of AID activity, and we provided evidence of its oncogenic relevance by demonstrating that mutational hotspots in the BCL6, BCL2 and CXCR4 SEs impair the binding of specific transcriptional repressors, preventing their negative regulation and creating oncogenic dependencies in DLBCL cells. Here we aimed to define the pathogenic role of non-coding mutations targeting the intragenic SE (iSE) of the BTG2 gene, the second most commonly mutated in DLBCL (39% of samples analyzed, including 36/93 primary cases and 7/29 cell lines). BTG2 encodes a member of the B-cell translocation gene (BTG)/ Transducer of ErbB2.1(TOB) family involved in transcriptional co-activation and modulation of mRNA abundance. BTG2 is also targeted by somatic missense mutations in 6-11% of DLBCL samples, suggesting a major role in the pathogenesis of this disease, which has not been explored. In order to identify functionally relevant BTG2 SE mutations, we first screened an extended panel of 286 DLBCL samples for the presence of mutational clusters targeting predicted transcription factor binding motifs. We identified a 20bp sequence stretch that was significantly hypermutated in these tumors, as compared to other lymphoma types or to non-lymphoid tumors (n=64/286, 22%; vs 0/17 Burkitt Lymphomas; 18/284 Follicular Lymphomas; 1/240 Chronic Lymphocytic Leukemias, and 0/1596 other tumors; p<0.0001, Fisher's exact test). In particular, mutations of the C at position +904 to G or T were exclusively detected in 11% DLBCL samples (32/286, p<0.0001, Fisher's exact test). To define the consequences of BTG2 C904 mutations, we then investigated the impact of reverting the mutation to the wild type nucleotide in 2 mutant DLBCL cell lines, using the CRISPR-Cas9 technology. Correction of the mutation led to a significant counter selection of the corrected clones (~85% reduction in a clone recovery assay, compared to control; p<0.001, Fisher's exact test) and to reduced BTG2 mRNA expression (p<0.01, one-way ANOVA), suggesting oncogenic addiction. In silico motif prediction and in vitro DNA-binding assays using mutant vs wild type oligonucleotides in two different DLBCL cell lines, followed by mass spectrometry, identified the Transcription Factor AP4 (TFAP4) as a protein specifically bound to the wild type, but not to the mutant sequences. TFAP4 is an important regulator of B-cell proliferation and cell fate decisions, which can function as a transcriptional activator or repressor in germinal center B-cells and acts downstream of/in parallel with c-MYC. We confirmed that the C904G and C904T SE mutation impaired TFAP4 binding in isogenic DLBCL cell lines by ChIP-qPCR and led to increased BTG2 expression (p<0.001, one-way ANOVA). Moreover, loss of TFAP4 in CRISPR-Cas9 edited DLBCL cells led to higher BTG2 expression levels, documenting TFAP4 as a direct negative regulator of BTG2 expression (p<0.001, one-way ANOVA). Consistently, single cell analysis of normal GC B cells revealed an inverse correlation between the expression pattern of TFAP4 and BTG2 in cells primed to undergo plasma cell differentiation vs cells committed to dark zone re-entry, suggesting that these two genes may function as a molecular switch in the cell fate decision of GC B cells. The programs modulated by BTG2 and TFAP4 in the GC, and disrupted in tumors carrying deregulated BTG2 alleles, will be presented, as defined by RNA-seq analysis of 3 isogenic GC-derived cell lines followed by interrogation of over 100 clinically annotated primary DLBCL biopsies. Together, these data identify TFAP4 as a novel regulator of BTG2 and reveal a previously unappreciated mechanism by which mutations in the BTG2 SE disrupt this circuit and contribute to lymphomagenesis through deregulating BTG2 expression.

Emergence of multiple fluconazole-resistant Candida parapsilosis sensu stricto clones with persistence and transmission in China.

We explored the epidemiological and molecular characteristics of Candida parapsilosis sensu stricto isolates in China, and their mechanisms of azole resistance. Azole susceptibilities of 2318 non-duplicate isolates were determined using CLSI broth microdilution. Isolates were genotyped by a microsatellite typing method. Molecular resistance mechanisms were also studied and functionally validated by CRISPR/Cas9-based genetic alterations. Fluconazole resistance occurred in 2.4% (n = 56) of isolates, and these isolates showed a higher frequency of distribution in ICU inpatients compared with susceptible isolates (48.2%, n = 27/56 versus 27.8%, 613/2208; P = 0.019). Microsatellite-genotyping analysis yielded 29 genotypes among 56 fluconazole-resistant isolates, of which 10 genotypes, including 37 isolates, belonged to clusters, persisting and transmitting in Chinese hospitals for 1-29 months. Clusters harbouring Erg11Y132F (5/10; 50%) were predominant in China. Among these, the second most dominant cluster MT07, including seven isolates, characteristically harbouring Erg11Y132F and Mrr1Q625K, lent its carriage to being one of the strongest associations with cross-resistance and high MICs of fluconazole (>256 mg/L) and voriconazole (2-8 mg/L), causing transmission across two hospitals. Among mutations tested, Mrr1Q625K led to the highest-level increase of fluconazole MIC (32-fold), while mutations located within or near the predicted transcription factor domain of Tac1 (D440Y, T492M and L518F) conferred cross-resistance to azoles. This study is the first Chinese report of persistence and transmissions of multiple fluconazole-resistant C. parapsilosis sensu stricto clones harbouring Erg11Y132F, and the first demonstration of the mutations Erg11G307A, Mrr1Q625K, Tac1L263S, Tac1D440Y and Tac1T492M as conferring resistance to azoles.

Identification the immune related marker genes and transcription-factor network in ruptured cerebral aneurysms using bioinformatics analysis and machine-learning strategies

BackgroundAs a high level of mortality and morbidity disease, ruptured cerebral aneurysms (RCA) is the most common cause of intracranial hemorrhage. MethodsThe expression profiles of GSE13353, GSE26969 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between RCA and unruptured cerebral aneurysms (URCA). The Least Absolute Shrinkage Selection Operator (LASSO) and the SVM-RFE (SVM-RFE) analysis were used to screen the potential diagnostic genes for RCA, which were further tested in the validation cohort (GSE54083). The TRRUST database was used to predict transcription factors (TF) and the JASPAR database was used to validate the marker genes and STAT1 binding sites. Finally, the CMAP database was used to predict RCA-associated drugs for clinical transformation. ResultsA total of 120 DEGs were identified. The functional enrichment analysis revealed that NF-kappa B signaling pathway, tuberculosis, rheumatoid arthritis were enriched in the RCA group. NANS, NTRK3, OLFML2A were identified as diagnostic biomarkers of RCA. Transcription of all three marker genes is regulated by the predicted transcription factor STAT1, the CMAP database was eventually used to screen 10 most promising drugs for the treatment of RCA. ConclusionNANS, NTRK3, OLFML2A may become new candidate biomarkers of RCA, moreover, STAT1 plays an important role in the development of RCA. The mentioned immune cells may play a key role in the development and progression of RCA. MEK and MAP kinase inhibitors are the most likely drug mechanisms for RCA therapy.