scholarly journals Coding RNA Sequencing of Equine Endometrium during Maternal Recognition of Pregnancy

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 749 ◽  
Author(s):  
Kristin M. Klohonatz ◽  
Stephen J. Coleman ◽  
Alma D. Islas-Trejo ◽  
Juan F. Medrano ◽  
Ann M. Hess ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sample Preparation ◽  
Rna Sequencing ◽  
Splice Variants ◽  
Expression Patterns ◽  
Prostaglandin F2α ◽  
Endometrial Biopsy ◽  
Pregnancy Status ◽  
Maternal Recognition Of Pregnancy ◽  
Uterine Lavage

Equine maternal recognition of pregnancy (MRP) is a process whose signal remains unknown. During MRP the conceptus and endometrium communicate to attenuate prostaglandin F2α (PGF) secretion, sparing the corpus luteum and maintaining progesterone production. Recognition of a mobile conceptus by the endometrium is critical by days 14–16 post-ovulation (PO), when endometrium produces PGF, initiating luteolysis. The objective of this study was to evaluate endometrial gene expression changes based upon pregnancy status via RNA sequencing. This experiment utilized a cross-over design with each mare serving as both a pregnant and non-mated control on days nine, 11, and 13 PO (n = 3/status/day). Mares were randomly assigned to collection day and pregnancy confirmed by terminal uterine lavage at the time of endometrial biopsy. Total RNA was isolated and libraries prepared using Illumina TruSeq RNA sample preparation kit. Reads were mapped and annotated using HISAT2 and Stringtie. Expression values were evaluated with DESEQ2 (P ≤ 0.05 indicated significance). On day nine, 11, and 13 there were 1435, 1435 and 916 significant transcripts, respectively. Multiple genes with splice variants had different expression patterns within the same day. These are the first data to evaluate the endometrial transcriptome during MRP on days nine, 11, and 13.

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.

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.

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 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.

RNA-sequencing and bioinformatic analysis to pre-assess sensitivity to targeted therapeutics in recurrent glioblastoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13533-e13533
Author(s):  
Ella L Kim ◽  
Anton Buzdin ◽  
Maxim Sorokin ◽  
Elena Poddubskaya ◽  
Artem Poddubskiy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Cell Cultures ◽  
Radiation Treatment ◽  
Clinical Stage ◽  
Expression Patterns ◽  
Recurrent Glioblastoma ◽  
Targeted Therapeutics ◽  
Biopsy Material ◽  
Tissue Samples

e13533 Background: This study developed molecular guided tools for individualized selection of chemotherapeutics for recurrent glioblastoma (rGB). A consortium involving clinical neurooncologists, molecular biologists and bioinformaticians identified gene expression patterns in rGB and quantitatively analyzed pathways involved in response to FDA approved oncodrugs. Methods: From2016 to 2018 biopsies from GB were collected using a multisampling approach. Biopsy material was used to isolate glioma stem-like cells and examined by RNA-sequencing. RNA-seq results were subjected to differential expression (DE) analysis and Oncobox analysis – a bioinformatic tool for quantitative pathway activation analysis. Results for newly diagnosed (nGB) and rGB (tissue samples and cell cultures) were compared. Oncobox analysis was further used to examine differential activation of pathways involved in response to existing chemotherapeutics. Results: 128 tissue samples and 28 cell cultures from a total of 44 GBs including 23 nGB, 19 rGB and 2 second-recurrent GBs were analyzed. 14 patient-matched pairs of nGB and rGB were obtained. DE analysis of nGB and rGB, showed a distinct “signature” associated with rGB. Oncobox analysis found down regulation of pathways related to cell cycle and DNA repair and upregulation of immune response pathways in rGB vs corresponding nGB. Specifically, pathways targeted by temozolomide, which is the first line chemotherapy for GB, were found down regulated in rGB. Among the top pathways upregulated in rGB were the pathways targeted by durvalumab and pomalidomide currently under investigation in phase II or III trials for GB. Conclusions: Specific pathway analysis revealed regional and clinical stage-associated differences in the transcriptional landscapes of nGB and rGB. Our results support a concept of treatment-induced resistance to cytotoxic therapeutics and indicate that temozolomide and radiation treatment have important impacts on gene expression changes associated with GB recurrence. Systematic molecular profiling of rGB is a promising avenue towards predicting sensitivity to targeted therapeutics in rGBs on an individual basis.

scholarly journals Evaluating Alteration of Blood Transcriptome Affected by Storage Conditions Using RNA Sequencing

2020 ◽  
Author(s):  
Eun Jung Koh ◽  
So Yeon Yu ◽  
Seung Jun Kim ◽  
Eun-Il Lee ◽  
Seung Yong Hwang
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Whole Blood ◽  
Expression Patterns ◽  
Rna Degradation ◽  
Biological Research ◽  
P Value ◽  
Sample Handling ◽  
Rna Quality

Abstract BackgroundWhole blood is one of the most widely utilized human samples in biological research and is useful for analysing the mechanisms of diverse bio-molecular phenomena. However, owing to its fluidic properties, whole blood is relatively unstable in the frozen state compared to other biopsy samples. Because RNA is structurally unstable, sample damage can severely affect RNA quality, thereby reducing its usability. This study aimed to assess the quality of RNA prepared from blood stored at different temperatures and times prior to freezing, as well as the effect of freezer storage time. ResultsThe quality of the RNA derived from different blood samples was assessed by determining the RNA integrity number and RNA sequencing to identify genes (|fold-change (FC)| > 1.5, p-value < 0.05, false discovery rate (FDR) < 0.05) that were differentially expressed between the differently prepared RNA samples. We found that improper sample handling critically influenced both RNA quality and gene expression patterns. In particular, storing blood at room temperature over 12 h before freezing led to RNA degradation. Differential gene expression analysis revealed that expression of the CXCR1 gene was substantially reduced when using impaired RNA. ConclusionsThis study emphasizes the importance of proper sample management for obtaining reliable downstream application outcomes and suggests the CXCR1 gene as a candidate screening marker for RNA damage caused by improper sample handling.

scholarly journals High Glucose Causes Distinct Expression Patterns of Primary Human Skin Cells by RNA Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Shan Zhang ◽  
Zunxiang Ke ◽  
Chao Yang ◽  
Peng Zhou ◽  
Huanzong Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Human Skin ◽  
High Glucose ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Skin Cells ◽  
Skin Cell ◽  
Glucose Treatment

Diabetes-related skin problems represent the most common long-term complications in diabetes mellitus patients. These complications, which include diabetic dermopathy, diabetic blisters, necrobiosis lipoidica diabeticorum, and eruptive xanthomatosis, may dramatically impair patients’ quality of life and cause long-lasting disability. However, the cellular and molecular mechanisms linking diabetes-related hyperglycemia and skin complications are still incompletely understood. To assess the role of the various skin-cell types in hyperglycemia-induced skin disorders, we performed RNA sequencing-based transcriptome analysis, measuring gene expression patterns in biological replicates in normal- and high glucose-stimulated skin cells. Three primary human skin-cell types were examined, i.e., epidermal keratinocytes, dermal fibroblasts, and dermal microvascular endothelial cells. For each separate cell type, we identified gene expression. Comparing gene abundances and expression levels revealed that transcription profiles exhibit distinct patterns in the three skin-cell types exposed to normal (i.e., physiological) glucose treatment and high (i.e., supraphysiological) glucose treatment. The obtained data indicate that high glucose induced differential gene expression and distinct activity patterns in signaling pathways in each skin-cell type. We are adding these data to the public database in the hope that they will facilitate future studies to develop novel targeted interventions for diabetic skin complications.

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