Gene Regulatory Processes Research Articles

ERNIE-ac4C: A novel deep learning model for effectively predicting N4-acetylcytidine sites.

RNA modifications are known to play a critical role in gene regulation and cellular processes. Specifically, N4-acetylcytidine (ac4C) modification has emerged as a significant marker involved in mRNA translation efficiency, stability, and various diseases. Accurate identification of ac4C modification sites is essential for unraveling its functional implications. However, currently available experimental methods suffer from drawbacks such as lengthy detection times, complexity, and high costs, resulting in low efficiency and accuracy in prediction. Although several bioinformatics methods have been proposed and have advanced the prediction of ac4C modification sites, there is still ample room for improvement. In this research, we propose a novel deep learning model, ERNIE-ac4C, which combines the ERNIE-RNA language model and a two-dimensional Convolutional Neural Network (CNN). ERNIE-ac4C utilizes the fusion of sequence features and attention map features to predict ac4C modification sites. ERNIE-ac4C surpasses other state-of-the-art deep learning methods, demonstrating superior accuracy and effectiveness. The availability of the code on GitHub (https://github.com/lrlbcxdd/ERNIEac4C.git) and our openness to feedback from the research community contribute to the model's accessibility and its potential for further advancements. Our study provides valuable insights into ac4C research and enhances our understanding of the functional consequences of RNA modifications.

Benchmarking recent computational tools for DNA-binding protein identification.

Identification of DNA-binding proteins (DBPs) is a crucial task in genome annotation, as it aids in understanding gene regulation, DNA replication, transcriptional control, and various cellular processes. In this paper, we conduct an unbiased benchmarking of 11 state-of-the-art computational tools as well as traditional tools such as ScanProsite, BLAST, and HMMER for identifying DBPs. We highlight the data leakage issue in conventional datasets leading to inflated performance. We introduce new evaluation datasets to support further development. Through a comprehensive evaluation pipeline, we identify potential limitations in models, feature extraction techniques, and training methods, and recommend solutions regarding these issues. We show that combining the predictions of the two best computational tools with BLAST-based prediction significantly enhances DBP identification capability. We provide this consensus method as user-friendly software. The datasets and software are available at https://github.com/Rafeed-bot/DNA_BP_Benchmarking.

Deciphering the role of NcRNAs in Pancreatic Cancer immune evasion and drug resistance: a new perspective for targeted therapy.

Pancreatic cancer (PC) is a very aggressive digestive system tumor, known for its high mortality rate, low cure rate, low survival rate and poor prognosis. In particular, pancreatic ductal adenocarcinoma (PADC), which accounts for more than 90% of PC cases, has an overall 5-year survival rate of only 5%, which is an extremely critical situation. Early detection and effective treatment of PC is extremely difficult, which leads many patients to despair. In the current medical context, targeted therapy, as an important strategy for cancer treatment, is expected. However, the problems of immune escape and drug resistance in PC have become two major obstacles that are difficult to be overcome by targeted therapy. How to break through these two difficulties has become a key issue to be solved in the field of PC therapy. In recent years, non-coding RNAs (ncRNAs) have continued to heat up in the field of cancer research. NcRNAs play a pivotal role in gene regulation, cell differentiation, development, and disease processes, and their important roles in the genesis, development, and therapeutic response of PC have been gradually revealed. More importantly, ncRNAs have many advantages as therapeutic targets, such as high specificity and low side effects, making them a new favorite in the field of PC therapy. Therefore, the aim of this paper is to provide new ideas and methods for the targeted therapy of PC by reviewing the mechanism of action of four major ncRNAs (circRNAs, lncRNAs, miRNAs, siRNAs) in both immune escape and drug resistance of PC. It is expected that an effective way to overcome immune escape and drug resistance can be found through in-depth study of ncRNA, bringing a ray of hope to PC patients.

The Role of Long Intergenic Noncoding RNA in Fetal Development.

The role of long intergenic noncoding RNAs (lincRNAs) in fetal development has emerged as a significant area of study, challenging the traditional protein-centric view of gene expression. While messenger RNAs (mRNAs) have long been recognized for their role in encoding proteins, recent advances have illuminated the critical functions of lincRNAs in various biological processes. Initially identified through high-throughput sequencing technologies, lincRNAs are transcribed from intergenic regions between protein-coding genes and exhibit unique regulatory functions. Unlike mRNAs, lincRNAs are involved in complex interactions with chromatin and chromatin-modifying complexes, influencing gene expression and chromatin structure. LincRNAs are pivotal in regulating tissue-specific development and embryogenesis. For example, they are crucial for proper cardiac, neural, and reproductive system development, with specific lincRNAs being associated with organogenesis and differentiation processes. Their roles in embryonic development include regulating transcription factors and modulating chromatin states, which are essential for maintaining developmental programs and cellular identity. Studies using RNA sequencing and genetic knockout models have highlighted the importance of lincRNAs in processes such as cell differentiation, tissue patterning, and organ development. Despite their functional significance, the comprehensive annotation and understanding of lincRNAs remain limited. Ongoing research aims to elucidate their mechanisms of action and potential applications in disease diagnostics and therapeutics. This review summarizes current knowledge on the functional roles of lincRNAs in fetal development, emphasizing their contributions to tissue-specific gene regulation and developmental processes.

Insights into the Mode and Mechanism of Interactions Between RNA and RNA-Binding Proteins.

Both RNA and protein play important roles in the process of gene expression and regulation, and it has been widely discussed that the interactions between RNA and protein affect gene transcription, translation efficiency, and post-translational modification. As an important class of proteins, RNA-binding proteins bind to RNA and affect gene expression in various ways. Here, we review the structural and functional properties of RNA-binding proteins and illustrate the specific modes of interactions between RNA and RNA-binding proteins and describe the involvement of some representative RNA-binding protein families in this network of action. Furthermore, we also explore the association that exists between RNA-binding proteins and the onset of diseases, as well as their potential in terms of serving as a therapeutic tool for the treatment of diseases. The in-depth exploration of the interactions between RNA and RNA-binding proteins reveals the dynamic process of gene expression and regulation, as well as offering valuable insights to advance the progress in the dissection of disease mechanisms and research and discovery of drugs, which promote the development of molecular biology.

GRNMOPT: Inference of gene regulatory networks based on a multi-objective optimization approach

Background and objectiveThe reconstruction of gene regulatory networks (GRNs) stands as a vital approach in deciphering complex biological processes. The application of nonlinear ordinary differential equations (ODEs) models has demonstrated considerable efficacy in predicting GRNs. Notably, the decay rate and time delay are pivotal in authentic gene regulation, yet their systematic determination in ODEs models remains underexplored. The development of a comprehensive optimization framework for the effective estimation of these key parameters is essential for accurate GRN inference. MethodThis study introduces GRNMOPT, an innovative methodology for inferring GRNs from time-series and steady-state data. GRNMOPT employs a combined use of decay rate and time delay in constructing ODEs models to authentically represent gene regulatory processes. It incorporates a multi-objective optimization approach, optimizing decay rate and time delay concurrently to derive Pareto optimal sets for these factors, thereby maximizing accuracy metrics such as AUROC (Area Under the Receiver Operating Characteristic curve) and AUPR (Area Under the Precision-Recall curve). Additionally, the use of XGBoost for calculating feature importance aids in identifying potential regulatory gene links. ResultsComprehensive experimental evaluations on two simulated datasets from DREAM4 and three real gene expression datasets (Yeast, In vivo Reverse-engineering and Modeling Assessment [IRMA], and Escherichia coli [E. coli]) reveal that GRNMOPT performs commendably across varying network scales. Furthermore, cross-validation experiments substantiate the robustness of GRNMOPT. ConclusionWe propose a novel approach called GRNMOPT to infer GRNs based on a multi-objective optimization framework, which effectively improves inference accuracy and provides a powerful tool for GRNs inference.

A fast and adaptive detection framework for genome-wide chromatin loop mapping from Hi-C data.

Chromatin loop identification plays an important role in molecular biology and 3D genomics research, as it constitutes a fundamental process in transcription and gene regulation. Such precise chromatin structures can be identified across genome-wide interaction matrices via Hi-C data analysis, which is essential for unraveling the intricacies of transcriptional regulation. Given the increasing number of genome-wide contact maps, derived from both in situ Hi-C and single-cell Hi-C experiments, there is a pressing need for efficient and resilient algorithms capable of processing data from diverse experiments rapidly and adaptively. Here, we propose YOLOOP, a novel detection-based framework that is different from the conventional paradigm. YOLOOP stands out for its speed, surpassing the performance of previous state-of-the-art (SOTA) chromatin loop detection methods. It achieves a 30-fold acceleration compared with classification-based methods, up to 20-fold acceleration compared with the SOTA kernel-based framework, and a fivefold acceleration compared with statistical algorithms. Furthermore, the proposed framework is capable of generalizing across various cell types, multiresolution Hi-C maps, and diverse experimental protocols. Compared with the existing paradigms, YOLOOP shows up to a 10% increase in recall and a 15% increase in F1-score, particularly noteworthy in the GM12878 cell line. YOLOOP also offers fast adaptability with straightforward fine-tuning, making it readily applicable to extremely sparse single-cell Hi-C contact maps. It maintains its exceptional speed, completing genome-wide detection at a 10 kb resolution for a single-cell contact map within 1 min and for a 900-cell-superimposed contact map within 3 min, enabling fast analysis of large-scale single-cell data.

Evolutionary Innovations in Conserved Regulatory Elements Associate With Developmental Genes in Mammals.

Transcriptional enhancers orchestrate cell type- and time point-specific gene expression programs. Genetic variation within enhancer sequences is an important contributor to phenotypic variation including evolutionary adaptations and human disease. Certain genes and pathways may be more prone to regulatory evolution than others, with different patterns across diverse organisms, but whether such patterns exist has not been investigated at a sufficient scale. To address this question, we identified signatures of accelerated sequence evolution in conserved enhancer elements throughout the mammalian phylogeny at an unprecedented scale. While different genes and pathways were enriched for regulatory evolution in different parts of the tree, we found a striking overall pattern of pleiotropic genes involved in gene regulatory and developmental processes being enriched for accelerated enhancer evolution. These genes were connected to more enhancers than other genes, which was the basis for having an increased amount of sequence acceleration over all their enhancers combined. We provide evidence that sequence acceleration is associated with turnover of regulatory function. Detailed study of one acceleration event in an enhancer of HES1 revealed that sequence evolution led to a new activity domain in the developing limb that emerged concurrently with the evolution of digit reduction in hoofed mammals. Our results provide evidence that enhancer evolution has been a frequent contributor to regulatory innovation at conserved developmental signaling genes in mammals.

Maternal Plasma miRNAs as Early Biomarkers of Moderate-to-Late-Preterm Birth.

Globally, preterm birth (PTB) is a primary cause of mortality and morbidity in infants, with PTB rates increasing worldwide over the last two decades. Biomarkers for accurate early prediction of PTB before the clinical event do not currently exist. Given their roles in the development and progression of many disease states, there has been increasing interest in the utility of microRNAs (miRNAs) as early biomarkers for pregnancy-related disorders, including PTB. The present study was designed to examine potential differences in miRNA abundances in maternal plasma from mothers with infants born following a moderate to late (28-36 weeks' gestation, n = 54) spontaneous PTB (SPTB) compared to mothers with matched term infants (n = 54). Maternal plasma collected at 15 weeks' gestation were utilised from the Auckland and Adelaide cohorts from the Screening for Pregnancy Endpoints (SCOPE) study. miRNAs in plasma were quantified using the NanoString nCounter expression panel (800 miRNAs). The top four most abundant miRNAs were significantly decreased in the plasma of mothers in the SPTB group with results consistent across both cohorts and pathway analysis was undertaken to examine the biological processes linked to the dysregulated miRNAs. The top candidate miRNAs (miRs-451a, -223-3p, let-7a-5p, and -126-3p) were linked to gene pathways associated with inflammation, apoptosis, and mitochondrial biogenesis. Moreover, miRNAs were consistently less abundant in the plasma of mothers of preterm infants across both sites, suggesting potential global dysregulation in miRNA biogenesis. This was supported by a significant downregulation in expression of key genes that are involved in miRNA biogenesis (DROSHA, DICER, and AGO2) across both sites in the SPTB group. In summary, the present study has identified miRNAs in maternal plasma that may provide predictive utility as early biomarkers for the risk of later SPTB. Importantly, these observations were conserved across two independent cohorts. Further, our data provide evidence for a persistent decrease in miRNA abundance in mothers who later experienced an SPTB, which is likely to have widespread consequences for gene regulation and epigenetic processes.

Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes.

A major goal in evolutionary biology and biomedicine is to understand the complex interactions between genetic variants, the epigenome, and gene expression. However, the causal relationships between these factors remain poorly understood. mSTARR-seq, a methylation-sensitive massively parallel reporter assay, is capable of identifying methylation-dependent regulatory activity at many thousands of genomic regions simultaneously, and allows for the testing of causal relationships between DNA methylation and gene expression on a region-by-region basis. Here, we developed a multiplexed mSTARR-seq protocol to assay naturally occurring human genetic variation from 25 individuals sampled from 10 localities in Europe and Africa. We identified 6,957 regulatory elements in either the unmethylated or methylated state, and this set was enriched for enhancer and promoter annotations, as expected. The expression of 58% of these regulatory elements was modulated by methylation, which was generally associated with decreased RNA expression. Within our set of regulatory elements, we used allele-specific expression analyses to identify 8,020 sites with genetic effects on gene regulation; further, we found that 42.3% of these genetic effects varied between methylated and unmethylated states. Sites exhibiting methylation-dependent genetic effects were enriched for GWAS and EWAS annotations, implicating them in human disease. Compared to datasets that assay DNA from a single European individual, our multiplexed assay uncovers dramatically more genetic effects and methylation-dependent genetic effects, highlighting the importance of including diverse individuals in assays which aim to understand gene regulatory processes.

A convenient single-cell newly synthesized transcriptome assay reveals gene expression dynamics during early-stage T-cell activation.

Sequencing newly synthesized transcriptome alongside regular transcriptome in single cells enables the study of gene expression temporal dynamics during rapid chromatin and gene regulation processes. However, existing assays to profile single-cell newly synthesized transcriptome require in-house technical expertise to achieve high cellular throughput, limiting their widespread application. Here, we developed NOTE-seq, a method that simultaneously profiles regular and newly synthesized transcriptomes in single cells. NOTE-seq integrates 4-thiouridine labeling of newly synthesized RNA, thiol-alkylation-based chemical conversion, and a streamlined workflow on the 10X Genomics platform, offering high cellular throughput that is accessible and convenient for regular biology laboratories without specialized single-cell expertise. Using NOTE-seq, we characterized the temporal dynamics of gene expression during early-stage T-cell activation in Jurkat and naïve T cells, identified transcription factors and regulons, and discovered Fli-1 as a master transcription factor for gene regulation upon T-cell stimulation. Interestingly, chemotherapeutic topoisomerase inhibitor affects Fli-1 level in T cells, indicating potential complications for the immune system.

Identification of overlapping molecular mechanisms in tuberculosis and sarcoidosis: A bioinformatics approach

BackgroundTuberculosis (TB) and sarcoidosis are chronic granulomatous diseases sharing similar symptoms, immune responses, and radiological characteristics. Transcriptome analysis offers insights into gene expression, regulation, and cellular processes, facilitating the understanding of shared molecular mechanisms. MethodsMicroarray datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) were analysed to identify differentially expressed genes (DEGs) in TB and sarcoidosis compared to controls. DEGs were identified using the GEO2R tool, and subsequent functional enrichment analysis was conducted using EnrichR. Protein-protein interaction (PPI) networks, as well as gene-miRNA and transcription factor-DEG interaction networks, were constructed. In addition, pathway analysis and molecular docking of target proteins were conducted to further elucidate the biological mechanisms involved in both diseases. ResultsFifteen genes, including ANKRD22, BATF2, DHRS9, EPSTI1, ETV7, FCGR1A, FCGR1B, GBP1, GBP5, SERPING1, NELL2, CCR7, PASK, LRRN3, and SLC16A10, were commonly altered in TB and sarcoidosis as compared to controls. Gene network analysis revealed 48.89% co-expression and 26.10% physical interaction between these overlapping genes. PPI networks showed a total of 15 nodes and 28 edges present between the connected proteins (PPI enrichment p-value:<1.0e−16). MiRNAs and transcription factors that exhibited the highest interaction with DEGs included hsa-miR-26a-5p, hsa-miR-16-5p, hsa-miR-335-5p, and EPAS1, HIF1A, KLF2, respectively. Pathway analysis indicated enrichment of IFN gamma signaling in both diseases. Molecular docking revealed weighted scores of −884.4, −851.9, and − 637.1 between three key proteins (PASK-GBP1, PASK-GBP5, and GBP1-GBP5). ConclusionThe shared dysregulated genes in TB and sarcoidosis demonstrate notable co-expression and physical interaction, constituting a PPI network enriched in the IFN-gamma signaling pathway.

Gene regulatory networks reveal sex difference in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively.MethodsSample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data.ResultsWe found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database.ConclusionsThese findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.Graphical

Sex-biased Regulation of Extracellular Matrix Genes in COPD.

Compared to men, women often develop COPD at an earlier age with worse respiratory symptoms despite lower smoking exposure. However, most preventive, and therapeutic strategies ignore biological sex differences in COPD. Our goal was to better understand sex-specific gene regulatory processes in lung tissue and the molecular basis for sex differences in COPD onset and severity. We analyzed lung tissue gene expression and DNA methylation data from 747 individuals in the Lung Tissue Research Consortium (LTRC), and 85 individuals in an independent dataset. We identified sex differences in COPD-associated gene regulation using gene regulatory networks. We used linear regression to test for sex-biased associations of methylation with lung function, emphysema, smoking, and age. Analyzing gene regulatory networks in the control group, we identified that genes involved in the extracellular matrix (ECM) have higher transcriptional factor targeting in females than in males. However, this pattern is reversed in COPD, with males showing stronger regulatory targeting of ECM-related genes than females. Smoking exposure, age, lung function, and emphysema were all associated with sex-specific differential methylation of ECM-related genes. We identified sex-based gene regulatory patterns of ECM-related genes associated with lung function and emphysema. Multiple factors including epigenetics, smoking, aging, and cell heterogeneity influence sex-specific gene regulation in COPD. Our findings underscore the importance of considering sex as a key factor in disease susceptibility and severity.

Downregulated expression of exosomal long non-coding RNA UCA1 in plasma of patients with non-small cell lung cance

Long non-coding RNAs (lncRNAs), mainly found in the nucleus of cells, are involved in different gene expression and regulation processes. Growing evidence has shown that abnormalities in the expression levels of lncRNAs play an important role in tumour metastasis, invasion, and proliferation, especially in non-small cell lung cancer (NSCLC). LncRNAs were also found to be upregulated or downregulated in exosomes secreted by cancer cells, thus they have the potential to be used as a non-invasive cancer biomarker. In this study, the expression of exosomal lncRNA UCA1 was determined on 29 plasma samples of patients with NSCLC and 29 controls who did not have NSCLC or related lung diseases by quantitative PCR using SYBR Green. Then, statistical analyses were used to evaluate the association between the expression of lncRNA UCA1 and some pathological features of NSCLC. The results showed that lncRNA UCA1 was downregulated in the exosomes of NSCLC patients compared with the control group (p=0.050), especially in the early stage (stage I + II). However, there was no association between the expression of lncRNA UCA1 and some pathological features of NSCLC, such as age, sex, smoking, alcohol consumption, tumour size, disease stage, lymph nodes, or tumour metastasis. This study provided the initial data on the expression of exosomal lncRNA UCA1 in the plasma of Vietnamese patients with NSCLC and further studies on the larger samples are needed to be continued.

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.

Context-dependent antisense transcription from a neighboring gene interferes with the expression of mNeonGreen as a functional in vivo fluorescent reporter in the chloroplast of Chlamydomonas reinhardtii.

Advancing chloroplast genetic engineering in Chlamydomonas reinhardtii remains challenging, decades after its first successful transformation. This study introduces the development of a chloroplast-optimized mNeonGreen fluorescent reporter, enabling in vivo observation through a sixfold increase in fluorescence via context-aware construct engineering. Our research highlights the influence of transcriptional readthrough and antisense mRNA pairing on post-transcriptional regulation, pointing to novel strategies foroptimizing heterologous gene expression. We further demonstrate the applicability of these insights using an accessible experimentation system using glass-bead transformation and reestablishment of photosynthesis using psbH mutants, focusing on the mitigation of transcriptional readthrough effects. By characterizing heterologous expression using regulatory elements such as PrrnS, 5'atpA, and 3' rbcL in a sense-transcriptional context, we further documented up to twofold improvement in fluorescence levels. Our findings contribute new tools for molecular biology research in the chloroplast and evidence fundamental gene regulation processes that could enable the development of more effective chloroplast engineering strategies. This work not only paves the way for more efficient genetic engineering of chloroplasts but also deepens our understanding of the regulatory mechanisms at play.

Role of diffusion and reaction of the constituents in spreading of histone modification marks.

Cells switch genes ON or OFF by altering the state of chromatin via histone modifications at specific regulatory locations along the chromatin polymer. These gene regulation processes are carried out by a network of reactions in which the histone marks spread to neighboring regions with the help of enzymes. In the literature, this spreading has been studied as a purely kinetic, non-diffusive process considering the interactions between neighboring nucleosomes. In this work, we go beyond this framework and study the spreading of modifications using a reaction-diffusion (RD) model accounting for the diffusion of the constituents. We quantitatively segregate the modification profiles generated from kinetic and RD models. The diffusion and degradation of enzymes set a natural length scale for limiting the domain size of modification spreading, and the resulting enzyme limitation is inherent in our model. We also demonstrate the emergence of confined modification domains without the explicit requirement of a nucleation site. We explore polymer compaction effects on spreading and show that single-cell domains may differ from averaged profiles. We find that the modification profiles from our model are comparable with existing H3K9me3 data of S. pombe.

Development and disease-specific regulation of RNA splicing in cardiovascular system.

Alternative splicing is a complex gene regulatory process that distinguishes itself from canonical splicing by rearranging the introns and exons of an immature pre-mRNA transcript. This process plays a vital role in enhancing transcriptomic and proteomic diversity from the genome. Alternative splicing has emerged as a pivotal mechanism governing complex biological processes during both heart development and the development of cardiovascular diseases. Multiple alternative splicing factors are involved in a synergistic or antagonistic manner in the regulation of important genes in relevant physiological processes. Notably, circular RNAs have only recently garnered attention for their tissue-specific expression patterns and regulatory functions. This resurgence of interest has prompted a reevaluation of the topic. Here, we provide an overview of our current understanding of alternative splicing mechanisms and the regulatory roles of alternative splicing factors in cardiovascular development and pathological process of different cardiovascular diseases, including cardiomyopathy, myocardial infarction, heart failure and atherosclerosis.

Emergence of the ZNF675 Gene During Primate Evolution-Influenced Human Neurodevelopment Through Changing HES1 Autoregulation.

In this study, we investigated recurrent copy number variations (CNVs) in the 19p12 locus, which are associated with neurodevelopmental disorders. The two genes in this locus, ZNF675 and ZNF681, arose via gene duplication in primates, and their presence in several pathological CNVs in the human population suggests that either or both of these genes are required for normal human brain development. ZNF675 and ZNF681 are members of the Krüppel-associated box zinc finger (KZNF) protein family, a class of transcriptional repressors important for epigenetic silencing of specific genomic regions. About 170 primate-specific KZNFs are present in the human genome. Although KZNFs are primarily associated with repressing retrotransposon-derived DNA, evidence is emerging that they can be co-opted for other gene regulatory processes. We show that genetic deletion of ZNF675 causes developmental defects in cortical organoids, and our data suggest that part of the observed neurodevelopmental phenotype is mediated by a gene regulatory role of ZNF675 on the promoter of the neurodevelopmental gene Hes family BHLH transcription factor 1 (HES1). We also find evidence for the recently evolved regulation of genes involved in neurological disorders, microcephalin 1 and sestrin 3. We show that ZNF675 interferes with HES1 auto-inhibition, a process essential for the maintenance of neural progenitors. As a striking example of how some KZNFs have integrated into preexisting gene expression networks, these findings suggest the emergence of ZNF675 has caused a change in the balance of HES1 autoregulation. The association of ZNF675 CNV with human developmental disorders and ZNF675-mediated regulation of neurodevelopmental genes suggests that it evolved into an important factor for human brain development.