Millions Of Base Pairs Research Articles

The Complex Landscape of Structural Divergence Between the Drosophila pseudoobscura and D. persimilis Genomes.

Structural genomic variants are key drivers of phenotypic evolution. They can span hundreds to millions of base pairs and can thus affect large numbers of genetic elements. Although structural variation is quite common within and between species, its characterization depends upon the quality of genome assemblies and the proportion of repetitive elements. Using new high-quality genome assemblies, we report a complex and previously hidden landscape of structural divergence between the genomes of Drosophila persimilis and D. pseudoobscura, two classic species in speciation research, and study the relationships among structural variants, transposable elements, and gene expression divergence. The new assemblies confirm the already known fixed inversion differences between these species. Consistent with previous studies showing higher levels of nucleotide divergence between fixed inversions relative to collinear regions of the genome, we also find a significant overrepresentation of INDELs inside the inversions. We find that transposable elements accumulate in regions with low levels of recombination, and spatial correlation analyses reveal a strong association between transposable elements and structural variants. We also report a strong association between differentially expressed (DE) genes and structural variants and an overrepresentation of DE genes inside the fixed chromosomal inversions that separate this species pair. Interestingly, species-specific structural variants are overrepresented in DE genes involved in neural development, spermatogenesis, and oocyte-to-embryo transition. Overall, our results highlight the association of transposable elements with structural variants and their importance in driving evolutionary divergence.

Analyzing The Metagenomics of Bacterial Diversity in The Backwater Mangrove Regions of Pichavaram and Parangipettai, Tamil Nadu, India, Reveals Insights into Sediment Microbial Communities

This metagenomic study delves into the microbial diversity and functional potential of sediment samples from the Pichavaram and Parangipettai mangrove ecosystems. The dataset, initially comprising millions of base pairs and sequences, undergoes meticulous quality control, revealing refined characteristics post-artificial duplicate read identification. Taxonomic analyses unveil a rich microbial landscape, dominated by bacteria, with intriguing presence of Eukaryota, Archaea, and unclassified sequences. The intricate community structures, highlighted through phylogenetic distributions, showcase the diversity and unexpected taxa, providing a nuanced understanding of microbial dynamics. Functional gene exploration uncovers key enzymes driving biosynthesis of secondary metabolites and metabolic pathways, shedding light on the microbial community's ecological roles and biotechnological potential. This comprehensive metagenomic analysis serves as a foundation for further ecological assessments, taxonomic refinements, and investigations into the adaptive strategies of these microbial communities in mangrove sediments.

Advances in sequencing-based studies of microDNA and ecDNA: Databases, identification methods, and integration with single-cell analysis

Extrachromosomal circular DNA (eccDNA) is a class of circular DNA molecules that originate from genomic DNA but are separate from chromosomes. They are common in various organisms, with sizes ranging from a few hundred to millions of base pairs. A special type of large extrachromosomal DNA (ecDNA) is prevalent in cancer cells. Research on ecDNA has significantly contributed to our comprehension of cancer development, progression, evolution, and drug resistance. The use of next-generation (NGS) and third-generation sequencing (TGS) techniques to identify eccDNAs throughout the genome has become a trend in current research. Here, we briefly review current advances in the biological mechanisms and applications of two distinct types of eccDNAs: microDNA and ecDNA. In addition to presenting available identification tools based on sequencing data, we summarize the most recent efforts to integrate ecDNA with single-cell analysis and put forth suggestions to promote the process.

3D chromatin structure in human macrophages and iPSC‐derived microglia identifies putative Alzheimer’s Disease risk genes

AbstractBackgroundAlzheimer’s disease (AD) genome‐wide association studies (GWAS) identified 75 disease‐associated loci. The vast majority of risk variants are located in non‐coding genomic regions, suggesting a regulatory function (i.e. enhancer activity). Enhancers can regulate distal genes via DNA loops, bringing elements millions of base pairs away into close physical proximity in three‐dimensional (3D) space. Mapping 3D chromatin contact frequencies using Hi‐C can reveal enhancer‐promoter connections and assign disease‐associated variants to putative causal genes. Because enhancer and 3D chromatin structures are cell‐type specific, it is critical to map these features in the relevant cell type and biological context. Genetic risk factors for AD are located within microglia and macrophages chromatin‐accessible regions. The aim of this work was to map 3D chromatin structures in human microglia and macrophages and intersect them with AD GWAS to identify novel genes regulated by non‐coding AD risk variants.MethodWe performed deeply sequenced Hi‐C, RNA‐seq, ATAC‐seq, and Cut&Run targeting histone H3K27 acetylation to map regulatory networks in THP‐1 macrophages and human iPSC‐derived microglia‐like cells (IMGLs). Next, we performed differential analysis to identify cell‐type specific differences in 3D chromatin structure, enhancer activity, and gene expression. Finally, we intersected these regulatory networks with AD GWAS to identify variants that may disrupt these networks and reveal novel AD risk genes for further study.ResultWe identified 19,608 chromatin loops, 766 of which were specific to either cell type. These loops connected 14,413 enhancers (8,023 of which were cell‐type specific) to 7,548 genes (2,280 of which differed between cell types). Cell‐type specific regulatory features correlated with cell‐type specific gene expression. For example, a microglia‐specific loop connected a distal regulatory loci to the promoter of PER1, which was also upregulated in microglia and has a known role in circadian rhythm regulation, underscoring microglia’s known circadian‐regulated behavior.ConclusionWe identified cell‐type specific regulatory networks in human macrophages and microglia. While these networks exhibited similarities, we identified thousands of enhancer‐promoter connections that differed. Intersecting with AD GWAS enables us to identify cell‐type specific genes regulated by non‐coding AD risk variants and reveal novel regulatory connections that would be otherwise unknown.

3D genome organization links non-coding disease-associated variants to genes.

Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.

How do emerging long-read sequencing technologies function in transforming the plant pathology research landscape?

Long-read sequencing technologies are revolutionizing the sequencing and analysis of plant and pathogen genomes and transcriptomes, as well as contributing to emerging areas of interest in plant-pathogen interactions, disease management techniques, and the introduction of new plant varieties or cultivars. Long-read sequencing (LRS) technologies are progressively being implemented to study plants and pathogens of agricultural importance, which have substantial economic effects. The variability and complexity of the genome and transcriptome affect plant growth, development and pathogen responses. Overcoming the limitations of second-generation sequencing, LRS technology has significantly increased the length of a single contiguous read from a few hundred to millions of base pairs. Because of the longer read lengths, new analysis methods and tools have been developed for plant and pathogen genomics and transcriptomics. LRS technologies enable faster, more efficient, and high-throughput ultralong reads, allowing direct sequencing of genomes that would be impossible or difficult to investigate using short-read sequencing approaches. These benefits include genome assembly in repetitive areas, creating more comprehensive and exact genome determinations, assembling full-length transcripts, and detecting DNA and RNA alterations. Furthermore, these technologies allow for the identification of transcriptome diversity, significant structural variation analysis, and direct epigenetic mark detection in plant and pathogen genomic regions. LRS in plant pathology is found efficient for identifying and characterization of effectors in plants as well as known and unknown plant pathogens. In this review, we investigate how these technologies are transforming the landscape of determination and characterization of plant and pathogen genomes and transcriptomes efficiently and accurately. Moreover, we highlight potential areas of interest offered by LRS technologies for future study into plant-pathogen interactions, disease control strategies, and the development of new plant varieties or cultivars.

An optimal variant to gene distance window derived from an empirical definition of cis and trans protein QTLs

BackgroundA genome-wide association study (GWAS) correlates variation in the genotype with variation in the phenotype across a cohort, but the causal gene mediating that impact is often unclear. When the phenotype is protein abundance, a reasonable hypothesis is that the gene encoding that protein is the causal gene. However, as variants impacting protein levels can occur thousands or even millions of base pairs from the gene encoding the protein, it is unclear at what distance this simple hypothesis breaks down.ResultsBy making the simple assumption that cis-pQTLs should be distance dependent while trans-pQTLs are distance independent, we arrive at a simple and empirical distance cutoff separating cis- and trans-pQTLs. Analyzing a recent large-scale pQTL study (Pietzner in Science 374:eabj1541, 2021) we arrive at an estimated distance cutoff of 944 kilobasepairs (95% confidence interval: 767–1,161) separating the cis and trans regimes.ConclusionsWe demonstrate that this simple model can be applied to other molecular GWAS traits. Since much of biology is built on molecular traits like protein, transcript and metabolite abundance, we posit that the mathematical models for cis and trans distance distributions derived here will also apply to more complex phenotypes and traits.

Epigenomic and enhancer dysregulation in uterine leiomyomas.

Uterine leiomyomas, also known as uterine fibroids or myomas, are the most common benign gynecological tumors and are found in women of reproductive and postmenopausal age. There is an exceptionally high prevalence of this tumor in women by the age of 50 years. Black women are particularly affected, with an increased incidence, earlier age of onset, larger and faster growing fibroids and greater severity of symptoms as compared to White women. Although advances in identifying genetic and environmental factors to delineate these fibroids have already been made, only recently has the role of epigenomics in the pathogenesis of this disease been considered. Over recent years, studies have identified multiple epigenomic aberrations that may contribute to leiomyoma development and growth. This review will focus on the most recent discoveries in three categories of epigenomic changes found in uterine fibroids, namely aberrant DNA methylation, histone tail modifications and histone variant exchange, and their translation into altered target gene architecture and transcriptional outcome. The findings demonstrating how the altered 3D shape of the enhancer can regulate gene expression from millions of base pairs away will be discussed. Additionally, translational implications of these discoveries and potential roadblocks in leiomyoma treatment will be addressed. A comprehensive PubMed search was performed to identify published articles containing keywords relevant to the focus of the review, such as: uterine leiomyoma, uterine fibroids, epigenetic alterations, epigenomics, stem cells, chromatin modifications, extracellular matrix [ECM] organization, DNA methylation, enhancer, histone post-translational modifications and dysregulated gene expression. Articles until September 2021 were explored and evaluated to identify relevant updates in the field. Most of the articles focused on in the discussion were published between 2015 and 2021, although some key discoveries made before 2015 were included for background information and foundational purposes. We apologize to the authors whose work was not included because of space restrictions or inadvertent omission. Chemical alterations to the DNA structure and of nucleosomal histones, without changing the underlying DNA sequence, have now been implicated in the phenotypic manifestation of uterine leiomyomas. Genome-wide DNA methylation analysis has revealed subsets of either suppressed or overexpressed genes accompanied by aberrant promoter methylation. Furthermore, differential promoter access resulting from altered 3D chromatin structure and histone modifications plays a role in regulating transcription of key genes thought to be involved in leiomyoma etiology. The dysregulated genes function in tumor suppression, apoptosis, angiogenesis, ECM formation, a variety of cancer-related signaling pathways and stem cell differentiation. Aberrant DNA methylation or histone modification is also observed in altering enhancer architecture, which leads to changes in enhancer-promoter contact strength, producing novel explanations for the overexpression of high mobility group AT-hook 2 and gene dysregulation found in mediator complex subunit 12 mutant fibroids. While many molecular mechanisms and epigenomic features have been investigated, the basis for the racial disparity observed among those in the Black population remains unclear. A comprehensive understanding of the exact pathogenesis of uterine leiomyoma is lacking and requires attention as it can provide clues for prevention and viable non-surgical treatment. These findings will widen our knowledge of the role epigenomics plays in the mechanisms related to uterine leiomyoma development and highlight novel approaches for the prevention and identification of epigenome targets for long-term non-invasive treatment options of this significantly common disease.

Simulating structural transitions during protein-DNA recognition

DNA-binding proteins specifically bind certain DNA sequences after scanning through millions of base pairs. During the nonspecific exploration the interactions between protein and DNA constantly form and break on the timescale of microseconds. Specific interactions at the target sites, on the other hand, are stable for seconds. The speed accuracy trade-off leads to the frequent target site missing observed in the case of our model system, the transcription factor LacI. Here, we study the conformational landscape of the dimeric LacI and a number of operator sequences during nonspecific and specific interaction to investigate how the transcription factor optimises fast search (<4 min) and strong specific binding (<10−9 M).

Coarse-grained molecular dynamics simulations of base-pair mismatch recognition protein MutS sliding along DNA

DNA mismatches are frequently generated by various intrinsic and extrinsic factors including DNA replication errors, oxygen species, ultraviolet, and ionizing radiation. These mismatches should be corrected by the mismatches repair (MMR) pathway to maintain genome integrity. In the Escherichia coli (E. coli) MMR pathway, MutS searches and recognizes a base-pair mismatch from millions of base-pairs. Once recognized, ADP bound to MutS is exchanged with ATP, which induces a conformational change in MutS. Previous single-molecule fluorescence microscopy studies have suggested that ADP-bound MutS temporarily slides along double-stranded DNA in a rotation-coupled manner to search a base-pair mismatch and so does ATP-bound MutS in a rotation-uncoupled manner. However, the detailed structural dynamics of the sliding remains unclear. In this study, we performed coarse-grained molecular dynamics simulations of the E. coli MutS bound on DNA in three different conformations: ADP-bound (MutSADP), ATP-bound open clamp (), and ATP-bound closed clamp () conformations. In the simulations, we observed conformation-dependent diffusion of MutS along DNA. MutSADP and diffused along DNA in a rotation-coupled manner with rare and frequent groove-crossing events, respectively. In the groove-crossing events, MutS overcame an edge of a groove and temporarily diffused in a rotation-uncoupled manner. It was also indicated that mismatch searches by is inefficient in terms of mismatch checking even though it diffuses along DNA and reaches unchecked regions more rapidly than MutSADP.

Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data

BackgroundDetection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005–0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection.ResultHere, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs (< 2 Mbp) were detected. There was also significant variability in their ability to identify CNVs in the sex chromosomes. Overall, BIC-seq2 was found to be the best method in terms of statistical performance. However, its significant drawback was by far the slowest runtime among the methods (> 3 h) compared with FREEC (~ 3 min), which we considered the second-best method.ConclusionsOur comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.

ClipSV: improving structural variation detection by read extension, spliced alignment and tree-based decision rules.

Structural variation (SV), which consists of genomic variation from 50 to millions of base pairs, confers considerable impacts on human diseases, complex traits and evolution. Accurately detecting SV is a fundamental step to characterize the features of individual genomes. Currently, several methods have been proposed to detect SVs using the next-generation sequencing (NGS) platform. However, due to the short length of sequencing reads and the complexity of SV content, the SV-detecting tools are still limited by low sensitivity, especially for insertion detection. In this study, we developed a novel tool, ClipSV, to improve SV discovery. ClipSV utilizes a read extension and spliced alignment approach to overcoming the limitation of read length. By reconstructing long sequences from SV-associated short reads, ClipSV discovers deletions and short insertions from the long sequence alignments. To comprehensively characterize insertions, ClipSV implements tree-based decision rules that can efficiently utilize SV-containing reads. Based on the evaluations of both simulated and real sequencing data, ClipSV exhibited an overall better performance compared to currently popular tools, especially for insertion detection. As NGS platform represents the mainstream sequencing capacity for routine genomic applications, we anticipate ClipSV will serve as an important tool for SV characterization in future genomic studies.

Prenatal Diagnosis of Fetuses With Increased Nuchal Translucency by Genome Sequencing Analysis.

Background: Increased nuchal translucency (NT) is an important biomarker associated with increased risk of fetal structural anomalies. It is known to be contributed by a wide range of genetic etiologies from single-nucleotide variants to those affecting millions of base pairs. Currently, prenatal diagnosis is routinely performed by karyotyping and chromosomal microarray analysis (CMA); however, both of them have limited resolution. The diversity of the genetic etiologies warrants an integrated assay such as genome sequencing (GS) for comprehensive detection of genomic variants. Herein, we aim to evaluate the feasibility of applying GS in prenatal diagnosis for the fetuses with increased NT. Methods: We retrospectively applied GS (> 30-fold) for fetuses with increased NT (≥3.5 mm) who underwent routine prenatal diagnosis. Detection of single-nucleotide variants, copy number variants, and structural rearrangements was performed simultaneously, and the results were integrated for interpretation in accordance with the guidelines of the American College of Medical Genetics and Genomics. Pathogenic or likely pathogenic (P/LP) variants were selected for validation and parental confirmation, when available. Results: Overall, 50 fetuses were enrolled, including 34 cases with isolated increased NT and 16 cases with other fetal structural malformations. Routine CMA and karyotyping reported eight P/LP CNVs, yielding a diagnostic rate of 16.0% (8/50). In comparison, GS provided a twofold increase in diagnostic yield (32.0%, 16/50), including one mosaic turner syndrome, eight cases with microdeletions/microduplications, and seven cases with P/LP point mutations. Moreover, GS identified two cryptic insertions and two inversions. Follow-up study further demonstrated the potential pathogenicity of an apparently balanced insertion that disrupted an OMIM autosomal dominant disease-causing gene at the insertion site. Conclusions: Our study demonstrates that applying GS in fetuses with increased NT can comprehensively detect and delineate the various genomic variants that are causative to the diseases. Importantly, prenatal diagnosis by GS doubled the diagnostic yield compared with routine protocols. Given a comparable turnaround time and less DNA required, our study provides strong evidence to facilitate GS in prenatal diagnosis, particularly in fetuses with increased NT.

The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions

Here, we introduce the 3D Genome Browser, http://3dgenome.org, which allows users to conveniently explore both their own and over 300 publicly available chromatin interaction data of different types. We design a new binary data format for Hi-C data that reduces the file size by at least a magnitude and allows users to visualize chromatin interactions over millions of base pairs within seconds. Our browser provides multiple methods linking distal cis-regulatory elements with their potential target genes. Users can seamlessly integrate thousands of other omics data to gain a comprehensive view of both regulatory landscape and 3D genome structure.

Artificial intelligence used in genome analysis studies

Abstract Next Generation Sequencing (NGS) or deep sequencing technology enables parallel reading of multiple individual DNA fragments, thereby enabling the identification of millions of base pairs in several hours. Recent research has clearly shown that machine learning technologies can efficiently analyse large sets of genomic data and help to identify novel gene functions and regulation regions. A deep artificial neural network consists of a group of artificial neurons that mimic the properties of living neurons. These mathematical models, termed Artificial Neural Networks (ANN), can be used to solve artificial intelligence engineering problems in several different technological fields (e.g., biology, genomics, proteomics, and metabolomics). In practical terms, neural networks are non-linear statistical structures that are organized as modelling tools and are used to simulate complex genomic relationships between inputs and outputs. To date, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN) have been demonstrated to be the best tools for improving performance in problem solving tasks within the genomic field.

Light in the darkness: New perspective on lanternfish relationships and classification using genomic and morphological data

Massive parallel sequencing allows scientists to gather DNA sequences composed of millions of base pairs that can be combined into large datasets and analyzed to infer organismal relationships at a genome-wide scale in non-model organisms. Although the use of these large datasets is becoming more widespread, little to no work has been done in estimating phylogenetic relationships using UCEs in deep-sea fishes. Among deep-sea animals, the 257 species of lanternfishes (Myctophiformes) are among the most important open-ocean lineages, representing half of all mesopelagic vertebrate biomass. With this relative abundance, they are key members of the midwater food web where they feed on smaller invertebrates and fishes in addition to being a primary prey item for other open-ocean animals. Understanding the evolution and relationships of midwater organisms generally, and this dominant group of fishes in particular, is necessary for understanding and preserving the underexplored deep-sea ecosystem. Despite substantial congruence in the evolutionary relationships among deep-sea lanternfishes at higher classification levels in previous studies, the relationships among tribes, genera, and species within Myctophidae often conflict across phylogenetic studies or lack resolution and support. Herein we provide the first genome-scale phylogenetic analysis of lanternfishes, and we integrate these data from across the nuclear genome with additional protein-coding gene sequences and morphological data to further test evolutionary relationships among lanternfishes. Our phylogenetic hypotheses of relationships among lanternfishes are entirely congruent across a diversity of analyses that vary in methods, taxonomic sampling, and data analyzed. Within the Myctophiformes, the Neoscopelidae is inferred to be monophyletic and sister to a monophyletic Myctophidae. The current classification of lanternfishes is incongruent with our phylogenetic tree, so we recommend revisions that retain much of the traditional tribal structure and recognize five subfamilies instead of the traditional two subfamilies. The revised monophyletic taxonomy of myctophids includes the elevation of three former lampanyctine tribes to subfamilies. A restricted Lampanyctinae was recovered sister to Notolychninae. These two clades together were recovered as the sister group to the Gymnoscopelinae. Combined, these three subfamilies were recovered as the sister group to a clade composed of a monophyletic Diaphinae sister to the traditional Myctophinae. Our results corroborate recent multilocus molecular studies that infer a polyphyletic Myctophum in Myctophinae, and a para- or polyphyletic Lampanyctus and Nannobrachium within Lampanyctinae. We resurrect Dasyscopelus and Ctenoscopelus for the independent clades traditionally classified as species of Myctophum, and we place Nannobrachium into the synonymy of Lampanyctus.

Chromosomal Microarray Testing For Children With Unexplained Neurodevelopmental Disorders

(Abstracted from JAMA 2017;317(24):2545–2546) Chromosomal microarray analysis (CMA) has emerged as a robust means to detect chromosomal abnormalities including unbalanced chromosomal rearrangements, as well as smaller chromosomal abnormalities than those detectable by karyotype. Chromosomal microarray analysis has replaced conventional G-banded karyotyping due to its greater sensitivity and resolution; G-banding effectively identifies imbalances of DNA greater than 5 to 10 megabases (millions of base pairs), whereas CMA can detect genome-wide imbalances of more than 400 kilobases (thousands of base pairs).

Phandango: an interactive viewer for bacterial population genomics.

SummaryFully exploiting the wealth of data in current bacterial population genomics datasets requires synthesizing and integrating different types of analysis across millions of base pairs in hundreds or thousands of isolates. Current approaches often use static representations of phylogenetic, epidemiological, statistical and evolutionary analysis results that are difficult to relate to one another. Phandango is an interactive application running in a web browser allowing fast exploration of large-scale population genomics datasets combining the output from multiple genomic analysis methods in an intuitive and interactive manner.Availability and implementationPhandango is a web application freely available for use at www.phandango.net and includes a diverse collection of datasets as examples. Source code together with a detailed wiki page is available on GitHub at https://github.com/jameshadfield/phandango.

Abstract P160: A Genome-spanning Map of Chromatin Structure Near the Renin Proximal Promoter

Chromatin conformation capture technologies (3C, 4C-seq) allow mapping of the three dimensional spatial structure of the genome. Physical proximity mapping, which captures these 3 dimensional interactions, fills an important gap in our understanding of enhancer-gene relationship by linking possible regulatory regions to specific genes that may be distant from the enhancer by thousands or millions of base pairs or even located on different chromosomes. The goal of the current study was to expand our understanding of renin regulation by identifying regions containing potential regulatory elements of renin more than a few kilobases from the renin proximal promoter, using 4C-seq technology, in isolated SS/JrHrdMcwi cardiac microvascular endothelial cells. Three replicates of ten million cells were fixed, digested with EcoRI, ligated, and then decrosslinked. Secondary digestion with NlaIII followed by a second ligation captured an unknown sequence surrounded by the sequence of the renin promoter. A sequencing library was prepared using primers specific to the renin promoter to identify the captured sequences. Sequence reads were mapped to rat genome (Rn 5) divided into fragments cut by EcoRI. Reads were verified to contain the renin proximal promoter sequence and a uniquely mapped captured sequence. The read counts were binarized and Z-scores were calculated based on a windowed average reads relative to background, and Z-scores corresponding to FDR &lt; 0.01 were considered significant. We found 62 loci spanning the genome in contact with the renin proximal promoter. Clusters of interactions were found on Chr13 at 39.6-40.5Mpb and at 53.9-56.3Mbp where the Renin gene is located. Additional contacting loci were found on all chromosomes except X. Quantitative PCR in newly isolated endothelial cells processed as 3C samples (fixed, digested, ligated, decrosslinked) were used to test 9 of the interactions, of which 8 were validated. These loci are enriched with genes whose expression is correlated with renin (53 of 268 genes, p = 7x10 -10 ) in humans, rats and mice. The present study generated a genome-wide map of segments physically interacting with the renin proximal promoter producing the first such map for a gene that is essential for cardiovascular physiology.

Splicing and transcription touch base: co-transcriptional spliceosome assembly and function

Several macromolecular machines collaborate to produce eukaryotic messenger RNA. RNA polymerase II (Pol II) translocates along genes that are up to millions of base pairs in length and generates a flexible RNA copy of the DNA template. This nascent RNA harbours introns that are removed by the spliceosome, which is a megadalton ribonucleoprotein complex that positions the distant ends of the intron into its catalytic centre. Emerging evidence that the catalytic spliceosome is physically close to Pol II in vivo implies that transcription and splicing occur on similar timescales and that the transcription and splicing machineries may be spatially constrained. In this Review, we discuss aspects of spliceosome assembly, transcription elongation and other co-transcriptional events that allow the temporal coordination of co-transcriptional splicing.