Gene Expression Regulatory Elements Research Articles

Promoter of Cassava MeAHL31 Responds to Diverse Abiotic Stresses and Hormone Signals in Transgenic Arabidopsis

The AT-hook motif nuclear-localized (AHL) family is pivotal for the abiotic stress response in plants. However, the function of the cassava AHL genes has not been elucidated. Promoters, as important regulatory elements of gene expression, play a crucial role in stress resistance. In this study, the promoter of the cassava MeAHL31 gene was cloned. The MeAHL31 protein was localized to the cytoplasm and the nucleus. qRT-PCR analysis revealed that the MeAHL31 gene was expressed in almost all tissues tested, and the expression in tuber roots was 321.3 times higher than that in petioles. Promoter analysis showed that the MeAHL31 promoter contains drought, methyl jasmonate (MeJA), abscisic acid (ABA), and gibberellin (GA) cis-acting elements. Expression analysis indicated that the MeAHL31 gene is dramatically affected by treatments with salt, drought, MeJA, ABA, and GA3. Histochemical staining in the proMeAHL31-GUS transgenic Arabidopsis corroborated that the GUS staining was found in most tissues and organs, excluding seeds. Beta-glucuronidase (GUS) activity assays showed that the activities in the proMeAHL31-GUS transgenic Arabidopsis were enhanced by different concentrations of NaCl, mannitol (for simulating drought), and MeJA treatments. The integrated findings suggest that the MeAHL31 promoter responds to the abiotic stresses of salt and drought, and its activity is regulated by the MeJA hormone signal.

GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells.

The organization of mammalian genomes features a complex, multiscale three-dimensional (3D) architecture, whose functional significance remains elusive because of limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we introduce genome architecture and gene expression by sequencing (GAGE-seq), a scalable, robust single-cell co-assay measuring 3D genome structure and transcriptome simultaneously within the same cell. Applied to mouse brain cortex and human bone marrow CD34+ cells, GAGE-seq characterized the intricate relationships between 3D genome and gene expression, showing that multiscale 3D genome features inform cell-type-specific gene expression and link regulatory elements to target genes. Integration with spatial transcriptomic data revealed in situ 3D genome variations in mouse cortex. Observations in human hematopoiesis unveiled discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level. GAGE-seq provides a powerful, cost-effective approach for exploring genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

Synthetic Biology Tools for Engineering Aspergillus oryzae.

For more than a thousand years, Aspergillus oryzae has been used in traditional culinary industries, including for food fermentation, brewing, and flavoring. In recent years, A. oryzae has been extensively used in deciphering the pathways of natural product synthesis and value-added compound bioproduction. Moreover, it is increasingly being used in modern biotechnology industries, such as for the production of enzymes and recombinant proteins. The investigation of A. oryzae has been significantly accelerated through the successive application of a diverse array of synthetic biology techniques and methodologies. In this review, the advancements in biological tools for the synthesis of A. oryzae, including DNA assembly technologies, gene expression regulatory elements, and genome editing systems, are discussed. Additionally, the challenges associated with the heterologous expression of A. oryzae are addressed.

In silico comparative transcriptome analysis of Papaver somniferum cultivars

Papaver somniferum is a medicinal plant of the Papaveraceae family that has traditionally been used for diet or its therapeutic value for thousands of years. Mainly, morphine and noscapine alkaloids exhibit anti-analgesic and anti-cancer effects. However, gene expression patterns and regulatory elements, such as transcription factors between different tissues, still need to be detected. In this study, comparative in silico transcriptome analyses were conducted to examine the tissue-specificity of the benzylisoquinoline alkaloids (BIAs) biosynthetic genes and transcription factors (TFs) between morphine and noscapine cultivars. Analysis showed that BIA biosynthetic genes are expressed in a different pattern between two varieties. Results showed that some members of plant-specific secondary metabolites related to TF families, such as MYB, MADS-box, bHLH, NAC, and WRKY, are differentially expressed between tissues and varieties.

Enhancer-mediated FOXO3 expression promotes MSC adipogenic differentiation by activating autophagy

BackgroundMesenchymal stem cells (MSCs) are pluripotent stem cells capable of differentiating into osteocytes, adipocytes and chondrocytes. However, in osteoporosis, the balance of differentiation is tipped toward adipogenesis and the key mechanism is controversial. Researches have shown that, as upstream regulatory elements of gene expression, enhancers ar involved in the expression of identity genes. In this study, we identified enhancers-mediated gene FOXO3 promoting MSC adipogenic differentiation by activating autophagy. MethodsWe integrated data of RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq) and ATAC-sequencing (ATAC-seq) to find the identity gene FOXO3. The expression of FOXO3 protein, adipogenic transcription factors and the substrate of autophagy were measured by western blotting. The Oil Red O (ORO) staining was used to visualize the adipogenesis of MSCs. Immunohistochemistry was used to visualize the FOXO3 expression in adipocytes in bone marrow. Immunofluorescence was used to detect the expression of PPARγ and LC3B. ResultsDuring adipogenesis, enhancers redistribute to genes associated with adipogenic differentiation, among which we identified the pivotal identity gene FOXO3. FOXO3 could promote the expression of the adipogenic transcription factors PPARγ, CEBPα, and CEBPβ during adipogenic differentiation, while PPARγ, CEBPα, and CEBPβ could in turn bind to FOXO3 and continue to promote FOXO3 expression to form a positive feedback loop. Consistently elevated FOXO3 expression promotes autophagy by activating the PI3K-AKT pathway which mediates adipogenic differentiation. ConclusionsPivotal identity gene FOXO3 promotes autophagy by activating PI3K-AKT pathway, which provokes adipogenic differentiation of MSCs. Enhancer-regulated adipogenic identity gene FOXO3 could be an attractive treatment for osteoporosis.

Single-cell chromatin accessibility and transcriptome atlas of mouse embryos

Cis-regulatory elements regulate gene expression and lineage specification. However, the potential regulation of cis-elements on mammalian embryogenesis remains largely unexplored. To address this question, we perform single-cell assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq in embryonic day 7.5 (E7.5) and E13.5 mouse embryos. We construct the chromatin accessibility landscapes with cell spatial information in E7.5 embryos, showing the spatial patterns of cis-elements and the spatial distribution of potentially functional transcription factors (TFs). We further show that many germ-layer-specific cis-elements and TFs in E7.5 embryos are maintained in the cell types derived from the corresponding germ layers at later stages, suggesting that these cis-elements and TFs are important during cell differentiation. We also find a potential progenitor for Sertoli and granulosa cells in gonads. Interestingly, both Sertoli and granulosa cells exist in male gonads and female gonads during gonad development. Collectively, we provide a valuable resource to understand organogenesis in mammals.

Identification of clade-wide putative cis-regulatory elements from conserved non-coding sequences in Cucurbitaceae genomes.

Cis-regulatory elements regulate gene expression and play an essential role in the development and physiology of organisms. Many conserved non-coding sequences (CNSs) function as cis-regulatory elements. They control the development of various lineages. However, predicting clade-wide cis-regulatory elements across several closely related species remains challenging. Based on the relationship between CNSs and cis-regulatory elements, we present a computational approach that predicts the clade-wide putative cis-regulatory elements in 12 Cucurbitaceae genomes. Using 12-way whole-genome alignment, we first obtained 632 112 CNSs in Cucurbitaceae. Next, we identified 16 552 Cucurbitaceae-wide cis-regulatory elements based on collinearity among all 12 Cucurbitaceae plants. Furthermore, we predicted 3271 potential regulatory pairs in the cucumber genome, of which 98 were verified using integrative RNA sequencing and ChIP sequencing datasets from samples collected during various fruit development stages. The CNSs, Cucurbitaceae-wide cis-regulatory elements, and their target genes are accessible at http://cmb.bnu.edu.cn/cisRCNEs_cucurbit/. These elements are valuable resources for functionally annotating CNSs and their regulatory roles in Cucurbitaceae genomes.

Genome-Wide Investigation of Apyrase (APY) Genes in Peanut (Arachis hypogaea L.) and Functional Characterization of a Pod-Abundant Expression Promoter AhAPY2-1p

Peanut (Arachis hypogaea L.) is an important food and feed crop worldwide and is affected by various biotic and abiotic stresses. The cellular ATP levels decrease significantly during stress as ATP molecules move to extracellular spaces, resulting in increased ROS production and cell apoptosis. Apyrases (APYs) are the nucleoside phosphatase (NPTs) superfamily members and play an important role in regulating cellular ATP levels under stress. We identified 17 APY homologs in A. hypogaea (AhAPYs), and their phylogenetic relationships, conserved motifs, putative miRNAs targeting different AhAPYs, cis-regulatory elements, etc., were studied in detail. The transcriptome expression data were used to observe the expression patterns in different tissues and under stress conditions. We found that the AhAPY2-1 gene showed abundant expression in the pericarp. As the pericarp is a key defense organ against environmental stress and promoters are the key elements regulating gene expression, we functionally characterized the AhAPY2-1 promoter for its possible use in future breeding programs. The functional characterization of AhAPY2-1P in transgenic Arabidopsis plants showed that it effectively regulated GUS gene expression in the pericarp. GUS expression was also detected in flowers of transgenic Arabidopsis plants. Overall, these results strongly suggest that APYs are an important future research subject for peanut and other crops, and AhPAY2-1P can be used to drive the resistance-related genes in a pericarp-specific manner to enhance the defensive abilities of the pericarp.

New long-non coding RNAs related to fat deposition based on pig model

Abstract Obesity has been a problem in the last decades since the development of certain technologies has led to a faster pace of life, resulting in nutritional changes. Domestic pigs are an excellent animal model in recognition of adiposity-related processes, corresponding to the size of individual organs, the distribution of body fat in the organism, and similar metabolism. The present study applied next-generation sequencing to identify adipose tissue (AT) transcriptomic signals related to increased fat content by identifying differentially expressed genes (DEGs), including long non-coding RNAs in Złotnicka White pigs (n=16). Moreover, besides commonly used functional analysis, we applied the Freiburg RNA tool to predict DE lncRNA targets based on calculation hybridisation energy. And in addition, DE lncRNAs were recognized based on information available in databases. The obtained results show that close to 230 gene expressions were found to be dependent on fat content, including 8 lncRNAs. The most interesting was that among identified DE lncRNAs was transcript corresponding to human MALAT1, which was previously considered in the obesity-related context. Moreover, it was determined that in ENSSSCG00000048394, ENSSSCG00000047210, ENSSSCG00000047442 and ENSSSCG00000041577 lncRNAs are contained repeat insertion domains of LncRNAs (RIDLs) considered as important gene expression regulatory elements, and ENSSSCG00000041577 seems to be the host for mir1247(NR_031649.1). The analysis of energy hybridisation between DE lncRNAs and DEGs using the Freiburg IntaRNAv2 tool, including isoforms expressed in AT, showed that ENSSSCG00000047210 lncRNA interacted with the highest number of DEGs and ENSSSCG00000047210 expression was only correlated with positive fat-related DEGs. The functional analysis showed that down-regulated DEGs involved in ECM proteoglycan pathways could be under the control of both positive and negative fat-related lncRNAs. The present study, using pigs as an animal model, expands current knowledge of possible gene expression regulation by lncRNAs in fat tissue and indicates for MALAT1 role in the fat deposition determination, whose function is still often questioned or doubtful.

Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30 to 80% depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures (word reading, nonword reading, spelling, phoneme awareness, and nonword repetition) in samples of 13,633 to 33,959 participants aged 5 to 26 y. We identified genome-wide significant association with word reading (rs11208009, P = 1.098 × 10-8) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP heritability, accounting for 13 to 26% of trait variability. Genomic structural equation modeling revealed a shared genetic factor explaining most of the variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence, and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis with neuroimaging traits identified an association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to the processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide avenues for deciphering the biological underpinnings of uniquely human traits.

The snoGloBe interaction predictor reveals a broad spectrum of C/D snoRNA RNA targets.

Box C/D small nucleolar RNAs (snoRNAs) are a conserved class of RNA known for their role in guiding ribosomal RNA 2′-O-ribose methylation. Recently, C/D snoRNAs were also implicated in regulating the expression of non-ribosomal genes through different modes of binding. Large scale RNA–RNA interaction datasets detect many snoRNAs binding messenger RNA, but are limited by specific experimental conditions. To enable a more comprehensive study of C/D snoRNA interactions, we created snoGloBe, a human C/D snoRNA interaction predictor based on a gradient boosting classifier. SnoGloBe considers the target type, position and sequence of the interactions, enabling it to outperform existing predictors. Interestingly, for specific snoRNAs, snoGloBe identifies strong enrichment of interactions near gene expression regulatory elements including splice sites. Abundance and splicing of predicted targets were altered upon the knockdown of their associated snoRNA. Strikingly, the predicted snoRNA interactions often overlap with the binding sites of functionally related RNA binding proteins, reinforcing their role in gene expression regulation. SnoGloBe is also an excellent tool for discovering viral RNA targets, as shown by its capacity to identify snoRNAs targeting the heavily methylated SARS-CoV-2 RNA. Overall, snoGloBe is capable of identifying experimentally validated binding sites and predicting novel sites with shared regulatory function.

Comprehensive characterization of posttranscriptional impairment-related 3′-UTR mutations in 2413 whole genomes of cancer patients

The 3′ untranslated region (3′-UTR) is the vital element regulating gene expression, but most studies have focused on variations in RNA-binding proteins (RBPs), miRNAs, alternative polyadenylation (APA) and RNA modifications. To explore the posttranscriptional function of 3′-UTR somatic mutations in tumorigenesis, we collected whole-genome data from 2413 patients across 18 cancer types. Our updated algorithm, PIVar, revealed 25,216 3′-UTR posttranscriptional impairment-related SNVs (3′-UTR piSNVs) spanning 2930 genes; 24 related RBPs were significantly enriched. The somatic 3′-UTR piSNV ratio was markedly increased across all 18 cancer types, which was associated with worse survival for four cancer types. Several cancer-related genes appeared to facilitate tumorigenesis at the protein and posttranscriptional regulation levels, whereas some 3′-UTR piSNV-affected genes functioned mainly via posttranscriptional mechanisms. Moreover, we assessed immune cell and checkpoint characteristics between the high/low 3′-UTR piSNV ratio groups and predicted 80 compounds associated with the 3′-UTR piSNV-affected gene expression signature. In summary, our study revealed the prevalence and clinical relevance of 3′-UTR piSNVs in cancers, and also demonstrates that in addition to affecting miRNAs, 3′-UTR piSNVs perturb RBPs binding, APA and m6A RNA modification, which emphasized the importance of considering 3′-UTR piSNVs in cancer biology.

Transcriptomic analysis reveals myometrial topologically associated domains linked to the onset of human term labour.

Changes in cell phenotype are thought to occur through the expression of groups of co-regulated genes within topologically associated domains (TADs). In this paper, we allocate genes expressed within the myometrium of the human uterus during the onset of term labour into TADs. Transformation of the myometrial cells of the uterus into a contractile phenotype during term human labour is the result of a complex interaction of different epigenomic and genomic layers. Recent work suggests that the transcription factor (TF) RELA lies at the top of this regulatory network. Using deep RNA sequencing (RNAseq) analysis of myometrial samples (n = 16) obtained at term from women undergoing caesarean section prior to or after the onset of labour, we have identified evidence for how other gene expression regulatory elements interact with TFs in the labour phenotype transition. Gene set enrichment analysis of our RNAseq data identified three modules of enriched genes (M1, M2 and M3), which in gene ontology studies are linked to matrix degradation, smooth muscle and immune gene signatures, respectively. These genes were predominantly located within chromosomal TADs suggesting co-regulation of expression. Our transcriptomic analysis also identified significant differences in the expression of long non-coding RNAs (lncRNA), microRNAs (miRNA) and TFs that were predicted to target genes within the TADs. Additionally, network analysis revealed 15 new lncRNA (MCM3AP-AS1, TUG1, MIR29B2CHG, HCG18, LINC00963, KCNQ1OT1, NEAT1, HELLPAR, SNHG16, NUTM2B-AS1, MALAT1, PSMA3-AS1, GABPB1-AS1, NORAD and NKILA) and 4 miRNA (mir-145, mir-223, mir-let-7a and mir-132) as top gene hubs with three TFs (NFKB1, RELA and ESR1) as master regulators. Together, these factors are likely to be involved in co-regulatory networks driving a myometrial transformation to generate an estrogen-sensitive phenotype. We conclude that lncRNA and miRNA targeting the estrogen receptor 1 and nuclear factor kappa B pathways play a key role in the initiation of human labour. For the first time, we perform an integrative analysis to present a multi-level genomic signature made of mRNA, non-coding RNA and TFs in the myometrium for spontaneous term labour.

Genome-scale analysis of genetic regulatory elements in Streptomyces avermitilis MA-4680 using transcript boundary information

BackgroundThe gram-positive bacterium, Streptomyces avermitilis, holds industrial importance as the producer of avermectin, a widely used anthelmintic agent, and a heterologous expression host of secondary metabolite-biosynthetic gene clusters. Despite its industrial importance, S. avermitilis’ genome organization and regulation of gene expression remain poorly understood. In this study, four different types of Next-Generation Sequencing techniques, including dRNA-Seq, Term-Seq, RNA-Seq and ribosome profiling, were applied to S. avermitilis to determine transcription units of S. avermitilis at a genome-wide level and elucidate regulatory elements for transcriptional and translational control of individual transcription units.ResultBy applying dRNA-Seq and Term-Seq to S. avermitilis MA-4680, a total of 2361 transcription start sites and 2017 transcript 3′-end positions were identified, respectively, leading to determination of 1601 transcription units encoded in S. avermitilis’ genome. Cataloguing the transcription units and integrated analysis of multiple high-throughput data types revealed the presence of diverse regulatory elements for gene expression, such as promoters, 5′-UTRs, terminators, 3′-UTRs and riboswitches. The conserved promoter motifs were identified from 2361 transcription start sites as 5′-TANNNT and 5′-BTGACN for the − 10 and − 35 elements, respectively. The − 35 element and spacer lengths between − 10 and − 35 elements were critical for transcriptional regulation of functionally distinct genes, suggesting the involvement of unique sigma factors. In addition, regulatory sequences recognized by antibiotic regulatory proteins were identified from the transcription start site information. Analysis of the 3′-end of RNA transcript revealed that stem structure formation is a major determinant for transcription termination of most transcription units.ConclusionsThe transcription unit architecture elucidated from the transcripts’ boundary information provides insights for unique genetic regulatory mechanisms of S. avermitilis. Our findings will elevate S. avermitilis’ potential as a production host for a diverse set of secondary metabolites.

The Emerging Landscape of Long Non-Coding RNAs in Wilms Tumor.

Long noncoding RNAs (LncRNAs) are transcripts of nucleic acid sequences with a length of more than 200 bp, which have only partial coding capabilities. Recent studies have shown that lncRNAs located in the nucleus or cytoplasm can be used as gene expression regulatory elements due to their important regulatory effects in a variety of biological processes. Wilms tumor (WT) is a common abdominal tumor in children whose pathogenesis remains unclear. In recent years, many specifically expressed lncRNAs have been found in WT, which affect the occurrence and development of WT. At the same time, lncRNAs may have the capacity to become novel biomarkers for the diagnosis and prognosis of WT. This article reviews related research progress on the relationship between lncRNAs and WT, to provide a new direction for clinical diagnosis and treatment of WT.

Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization

Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulatory control elements (i.e., promoters and ribosome binding sites). Finally, there is also the need to tune expression of multiple genes along a heterologous pathway to relieve constraints from rate-limiting step and help reduce metabolic burden on cells from unnecessary over-expression of high activity enzymes. While the aforementioned tasks could be performed through combinatorial experiments, such an approach incurs significant cost, time and effort, which is a handicap that can be relieved by application of modern machine learning tools. Such tools could attempt to predict high activity enzymes from sequence, but they are currently most usefully applied in classifying strong promoters from weaker ones as well as combinatorial tuning of expression of multiple genes. This perspective reviews the application of machine learning tools to aid metabolic pathway optimization through identifying challenges in metabolic engineering that could be overcome with the help of machine learning tools.

Cap analysis of gene expression (CAGE) and noncoding regulatory elements.

Cap analysis of gene expression (CAGE) was developed to detect the 5' end of RNA. Trapping of the RNA 5'-cap structure enables the enrichment and selective sequencing of complete transcripts. Upscaled high-throughput versions of CAGE have enabled the genome-wide identification of transcription start sites, including transcriptionally active promoters and enhancers. CAGE sequencing can be exploited to draw comprehensive maps of active genomic regulatory elements in a cell type- and activation-specific manner. The cells of the immune system are among the best candidates to be analyzed in humans, since they are easily accessible. In this review, we discuss how CAGE data are instrumental for integrative analyses with quantitative trait loci and omics data, and their usefulness in the mechanistic interpretation of the effects of genetic variations over the entire human genome. Integrating CAGE data with the currently available omics information will contribute to better understanding of the genome-wide association study variants that lie outside of annotated genes, deepening our knowledge on human diseases, and enabling the targeted design of more specific therapeutic interventions.

MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity.

Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus's estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.

Single nucleotide variations in ZBTB46 are associated with post-thrombolytic parenchymal haematoma.

Haemorrhagic transformation is a complication of recombinant tissue-plasminogen activator treatment. The most severe form, parenchymal haematoma, can result in neurological deterioration, disability, and death. Our objective was to identify single nucleotide variations associated with a risk of parenchymal haematoma following thrombolytic therapy in patients with acute ischaemic stroke. A fixed-effect genome-wide meta-analysis was performed combining two-stage genome-wide association studies (n = 1904). The discovery stage (three cohorts) comprised 1324 ischaemic stroke individuals, 5.4% of whom had a parenchymal haematoma. Genetic variants yielding a P-value < 0.05 1 × 10-5 were analysed in the validation stage (six cohorts), formed by 580 ischaemic stroke patients with 12.1% haemorrhagic events. All participants received recombinant tissue-plasminogen activator; cases were parenchymal haematoma type 1 or 2 as defined by the European Cooperative Acute Stroke Study (ECASS) criteria. Genome-wide significant findings (P < 5 × 10-8) were characterized by in silico functional annotation, gene expression, and DNA regulatory elements. We analysed 7 989 272 single nucleotide polymorphisms and identified a genome-wide association locus on chromosome 20 in the discovery cohort; functional annotation indicated that the ZBTB46 gene was driving the association for chromosome 20. The top single nucleotide polymorphism was rs76484331 in the ZBTB46 gene [P = 2.49 × 10-8; odds ratio (OR): 11.21; 95% confidence interval (CI): 4.82-26.55]. In the replication cohort (n = 580), the rs76484331 polymorphism was associated with parenchymal haematoma (P = 0.01), and the overall association after meta-analysis increased (P = 1.61 × 10-8; OR: 5.84; 95% CI: 3.16-10.76). ZBTB46 codes the zinc finger and BTB domain-containing protein 46 that acts as a transcription factor. In silico studies indicated that ZBTB46 is expressed in brain tissue by neurons and endothelial cells. Moreover, rs76484331 interacts with the promoter sites located at 20q13. In conclusion, we identified single nucleotide variants in the ZBTB46 gene associated with a higher risk of parenchymal haematoma following recombinant tissue-plasminogen activator treatment.

Fantastic databases and where to find them: Web applications for researchers in a rush

Public databases are essential to the development of multi-omics resources. The amount of data created by biological technologies needs a systematic and organized form of storage, that can quickly be accessed, and managed. This is the objective of a biological database. Here, we present an overview of human databases with web applications. The databases and tools allow the search of biological sequences, genes and genomes, gene expression patterns, epigenetic variation, protein-protein interactions, variant frequency, regulatory elements, and comparative analysis between human and model organisms. Our goal is to provide an opportunity for exploring large datasets and analyzing the data for users with little or no programming skills. Public user-friendly web-based databases facilitate data mining and the search for information applicable to healthcare professionals. Besides, biological databases are essential to improve biomedical search sensitivity and efficiency and merge multiple datasets needed to share data and build global initiatives for the diagnosis, prognosis, and discovery of new treatments for genetic diseases. To show the databases at work, we present a a case study using ACE2 as example of a gene to be investigated. The analysis and the complete list of databases is available in the following website <https://kur1sutaru.github.io/fantastic_databases_and_where_to_find_them/>.