Abstract 18584: Decoding the Regulatory LncRNAs in Neonatal Heart During Perinatal Circulatory Transition

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Marlin Touma ◽  
Ashley Cass ◽  
Xuedong Kang ◽  
Yan Zhao ◽  
Reshma Biniwale ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Regulatory Function ◽  
Newborn Mouse ◽  
Protein Coding ◽  
Congenital Diseases ◽  
Gene Pairs ◽  
Neonatal Heart

Background: Fetal to neonatal transition of heart is an elaborate process, during which, neonatal cardiomyocytes undergo functional maturation and terminal exit from the cell cycle. However, transcriptome programming in neonatal cardiac chambers during perinatal stages is understudied. In particular, the changes in long non-coding RNAs (lncRNAs) in neonatal heart have not been explored. Objective: To achieve transcriptome-wide analysis of lncRNAs in neonatal left ventricle (LV) and right ventricle (RV) during maturation stages using deep RNA-Sequencing Methods: Deep RNA-sequencing was performed on male newborn mouse (C57 BL) LV and RV at 3 time points of perinatal circulatory transition: P0, P3 and P7. Reads were mapped to mouse genome (mm10). The lncRNAs annotated in NONCODE database were identified. Differentially expressed lncRNAs were defined as those with coefficient of variation ≥0.2, at a false discovery rate ≤0.05, and expressed at ≥3 RPKM in at least one sample. Correlated lncRNAs/ gene pairs were identified using Pearson’s (r2≥0.8, P≤0.05). A subset of LncRNAs/gene expression was validated using qRT-PCR. Results: Out of the 70, 983 observed unique lncRNAs, approximately 7000 were identified exhibiting significant variation during maturation windows with highly spatial-temporal dependent expression patterns, including approximately 5000 known and 2000 novel lncRNAs. Notably, 20% of these lncRNAs were located within 50 KB of a protein coding gene. Out of a total of 2400 lncRNAs/gene pairs, 10 % exhibited significantly concordant (lncRNA/gene) expression patterns. These correlated genes were significantly enriched in metabolism, cell cycle and contractility functional ontology. Interestingly, some of these lncRNAs exhibited concordance with their neighboring gene in human tissues with congenital heart defects, suggesting conserved, potentially significant, regulatory function. Conclusions: Transcriptome programming during neonatal heart maturation involves global changes in lncRNAs. Their expression concordance with neighboring protein coding genes implicates potential important regulatory role of lncRNAs in neonatal heart chamber specification and congenital diseases.

scholarly journals Analysis of single-cell RNA-sequencing data to identify quiescent and proliferating neural cell populations in Glioblastoma

2021 ◽  
Author(s):  
Rajeev Vikram ◽  
Wen□Cheng Chou ◽  
Pei-Ei Wu ◽  
Wei-Ting Chen ◽  
Chen-Yang Shen
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
High Grade ◽  
Sequencing Data ◽  
Quiescent Cells ◽  
Single Cell Rna Sequencing ◽  
Tumorigenic Cells

ABSTRACTBackgroundDiffuse Glioblastoma (GBM) has high mortality and remains one of the most challenging type of cancer to treat. Identifying and characterizing the cells populations driving tumor growth and therapy resistance has been particularly difficult owing to marked inter and intra tumoral heterogeneity observed in these tumors. These tumorigenic populations contain long lived cells associated with latency, immune evasion and metastasis.MethodsHere, we analyzed the single-cell RNA-sequencing data of high grade glioblastomas from four different studies using integrated analysis of gene expression patterns, cell cycle stages and copy number variation to identify gene expression signatures associated with quiescent and cycling neuronal tumorigenic cells.ResultsThe results show that while cycling and quiescent cells are present in GBM of all age groups, they exist in a much larger proportion in pediatric glioblastomas. These cells show similarities in their expression patterns of a number of pluripotency and proliferation related genes. Upon unbiased clustering, these cells explicitly clustered on their cell cycle stage. Quiescent cells in both the groups specifically overexpressed a number of genes for ribosomal protein, while the cycling cells were enriched in the expression of high-mobility group and heterogeneous nuclear ribonucleoprotein group genes. A number of well-known markers of quiescence and proliferation in neurogenesis showed preferential expression in the quiescent and cycling populations identified in our analysis. Through our analysis, we identify ribosomal proteins as key constituents of quiescence in glioblastoma stem cells.ConclusionsThis study identifies gene signatures common to adult and pediatric glioblastoma quiescent and cycling stem cell niches. Further research elucidating their role in controlling quiescence and proliferation in tumorigenic cells in high grade glioblastoma will open avenues in more effective treatment strategies for glioblastoma patients.

scholarly journals Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

scholarly journals Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽  
2021 ◽  
Vol 24 (4) ◽  
pp. 102357
Author(s):  
Brenda Morsey ◽  
Meng Niu ◽  
Shetty Ravi Dyavar ◽  
Courtney V. Fletcher ◽  
Benjamin G. Lamberty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Related Gene ◽  
Single Cell Rna Sequencing ◽  
Disease Related Gene

Abstract W P73: Feasibility and Preliminary Results of Whole Blood RNA-Sequencing Analysis in Patients With Intracranial Aneurysms

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Blake Haas ◽  
Nestor R Gonzalez ◽  
Elina Nikkola ◽  
Mark Connolly ◽  
William Hsu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Rna Sequencing ◽  
Whole Blood ◽  
Intracranial Aneurysms ◽  
Small Sample Size ◽  
Expression Patterns ◽  
Small Sample ◽  
Cellular Respiration ◽  
Sequencing Analysis

Introduction: Intracranial aneurysms (IA) growth and rupture have been associated with chronic remodeling of the arterial wall. However, the pathobiology of this process remains poorly understood. The objective of the present study was to evaluate the feasibility of analyzing gene expression patterns in peripheral blood of patients with ruptured and unruptured saccular IAs. Materials and Methods: We analyzed human whole blood transcriptomes by performing paired-end, 100 bp RNA-sequencing (RNAseq) using the Illumina platform. We used STAR to align reads to the genome, HTSeq to count reads, and DESeq to normalize counts across samples. Self-reported patient information was used to correct expression values for ancestry, age, and sex. We utilized weighted gene co-expression network analysis (WGCNA) to identify gene expression network modules associated with IA size and rupture. The DAVID tool was employed to search for Gene Ontology enrichment in relevant modules. Results: Samples from 12 patients (9 females, age 57.6 +/-12) with IAs were analyzed. Four had ruptured aneurysms. RNA isolation and application of the methodology described above was successful in all samples. Although the small sample size prevents us from drawing definite conclusions, we observed promising novel co-expression networks for IAs: WCGNA analysis showed down-regulation of two transcript modules associated with ruptured IA status (r=-0.78, p=0.008 and r=-0.77, p=0.009), and up-regulation of two modules associated with aneurysm size (r=0.86, p=0.002 and r=0.9, p=4e-04), respectively. DAVID analyses showed that genes upregulated in an IA size-associated module were enriched with genes involved in cellular respiration and translation, while genes involved in transcription were down-regulated in a module associated with ruptured IAs. Conclusions: Whole blood RNAseq analysis is a feasible tool to capture transcriptome dynamics and achieve a better understanding of the pathophysiology of IAs. Further longitudinal studies of patients with IAs using network analysis are justified.

FC 011KIDNEYNETWORK: USING KIDNEY DERIVED GENE EXPRESSION DATA TO PREDICT AND PRIORITIZE NOVEL GENES INVOLVED IN KIDNEY DISEASE

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Candidate Gene ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Genetic Diagnosis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Exome Sequencing Data

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

scholarly journals Mapping-based all-RNA-information sequencing analysis (ARIseq) pipeline simultaneously revealed taxonomic composition, gene expression, and their correlation in an acidic stream ecosystem

2017 ◽  
Author(s):  
Arisa Tsuboi ◽  
Misao Itoga ◽  
Yuichi Hongoh ◽  
Shigeharu Moriya
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Taxonomic Composition ◽  
Rrna Genes ◽  
Reference Database ◽  
Similar Distribution ◽  
Sequencing Analysis ◽  
Protein Coding ◽  
Microbial Ecosystems ◽  
Sulfur Oxidizing

AbstractWe developed a new pipeline for simultaneous analyses of both rRNA profile as a taxonomic marker and mRNA profile as a functional marker, to understand microbial ecosystems in natural environments. Our pipeline, named All-RNA-Information sequencing (ARIseq), comprises a high-throughput sequencing of reverse transcribed total RNA and several widely used computational tools, and generates quantitatively reliable information on both community structures and gene expression patterns, which were verified by quantitative PCR analyses in this study. Particularly, correlation network analysis in the pipeline can reveal microbial taxa and expressed genes that share patterns of dynamics among different time and/or geographical points. The pipeline is primarily mapping-based, using a public database for small subunit rRNA genes and obtained contigs as the reference database for protein-coding genes. We applied this pipeline to biofilm samples, as examples, collected from an acidic spring water stream in the Chyatsubomi-goke Park in Gunma prefecture, Japan. Our analyses revealed the predominance of iron and sulfur-oxidizing bacteria and Pinnularia diatoms, and also indicated that the distributions of the iron-sulfur-oxidizing bacterial consortium and the Pinnularia diatoms largely overlapped but showed distinct patterns. In addition, our analyses showed that the iron-oxidizing bacterial genus Acidithiobacillus and co-occurring Acidiphilium shared similar distribution pattern whereas another iron-oxidizing genus Leptospirillum exhibited a distinct pattern. Our pipeline enables researchers to more easily capture the outline of microbial ecosystems based on the taxonomic composition, protein-coding gene expression, and their correlations.

scholarly journals An integrative multi-omics approach in Sjögren’s Syndrome identifies novel genetic drivers with regulatory function and disease-specificity

2020 ◽  
Author(s):  
María Teruel ◽  
Guillermo Barturen ◽  
Manuel Martínez-Bueno ◽  
Miguel Barroso ◽  
Olivia Castelli ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Sjögren’S Syndrome ◽  
Genetic Variants ◽  
Sjögren's Syndrome ◽  
Expression Patterns ◽  
Sjogren’S Syndrome ◽  
Sjogren's Syndrome ◽  
Regulatory Function ◽  
Sjögren‘S Syndrome

ABSTRACTPrimary Sjögren’s syndrome (SS) is a systemic autoimmune disease characterized by lymphocytic infiltration and damage of exocrine salivary and lacrimal glands. The etiology of SS is complex with environmental triggers and genetic factors involved. By conducting an integrated multi-omics study we identified vast coordinated hypomethylation and overexpression effects, that also exhibit increased variability, in many already known IFN-regulated genes. We report a novel epigenetic signature characterized by increased DNA methylation levels in a large number of novel genes enriched in pathways such as collagen metabolism and extracellular matrix organization. We identified new genetic variants associated with SS that mediate their risk by altering DNA methylation or gene expression patterns, as well as disease-interacting genetic variants that exhibit regulatory function only in the SS population. Our study sheds new light on the interaction between genetics, DNA methylation, gene expression and SS, and contributes to elucidate the genetic architecture of gene regulation in an autoimmune population.

Abstract 17402: Single Nuclei RNA-sequencing of Human Hypertrophic Cardiomyopathy Myectomy Samples Reveals Common Novel Mechanisms of Pathogenesis and Potential Therapeutic Targets Regardless of Genotype

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Amy Larson ◽  
Hassan Rastegar ◽  
Gordon S Huggins ◽  
Ethan J Rowin ◽  
Martin S Maron ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hypertrophic Cardiomyopathy ◽  
Rna Sequencing ◽  
Alternative Treatment ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Treatment Strategies ◽  
Ventricular Outflow Tract ◽  
Left Ventricular ◽  
Surgical Myectomy

Introduction: Hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease, often resulting in left ventricular outflow tract obstruction, relieved by surgical myectomy. Current treatments are largely palliative and do not target the root causes. Understanding the molecular drivers of the disease could lead to alternative treatment strategies through identification of novel therapeutic targets. Methods: We performed single nuclei RNA-sequencing (snRNA-seq) on thousands of nuclei from 9 patient myectomy samples and septal tissue from 4 unused donor hearts selected randomly without regard to genotype to identify the cell populations and determine the gene expression patterns in individual cells. Each sample was processed individually using Seurat v3 for quality control and normalization. Next, all 13 samples were integrated into a combined dataset for clustering and differential gene expression analysis to identify markers specific to each cluster and to assign cell identities. Results: Our results revealed several clusters of cardiomyocytes with differences in sarcomeric and metabolic gene expression. Several fibroblast populations were also observed. Numerous genes were differentially expressed between the HCM and normal samples. For example, RARRES1 expression was observed in many of the fibroblast populations in the normal samples, but was absent in the HCM samples. RARRES1 is involved in regulating fatty acid metabolism and autophagy, both of which are altered in HCM. Additionally, expression of PLA2G2A was absent in the HCM samples but was present in almost every cell type in the normal controls. PLA2G2A is involved in suppression of RTK mediated hypertrophic signaling, impacts lipid signaling, and has tumor suppressor properties. Thus, both RARRES1 and PLA2G2A may represent novel targets in HCM. Conclusions: This approach reveals novel potential therapeutic targets within common final HCM pathological pathways independent of genotype that have the potential to guide development of alternative treatment strategies. Further analysis of larger datasets using this approach will likely identify even more common pathway targets and identify additional common mechanisms in the pathogenesis of obstructive HCM.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

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