scholarly journals Landscape of Overlapping Gene Expression in the Equine Placenta

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 503 ◽  
Author(s):  
Dini ◽  
Norris ◽  
Ali ◽  
Loux ◽  
Carossino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Overlapping Genes ◽  
Sequencing Data ◽  
Chromosome 11 ◽  
Antisense Gene ◽  
Overlapping Transcription ◽  
Regulatory Effects ◽  
Antisense Regulation ◽  
First Time

Increasing evidence suggests that overlapping genes are much more common in eukaryotic genomes than previously thought. These different-strand overlapping genes are potential sense–antisense (SAS) pairs, which might have regulatory effects on each other. In the present study, we identified the SAS loci in the equine genome using previously generated stranded, paired-end RNA sequencing data from the equine chorioallantois. We identified a total of 1261 overlapping loci. The ratio of the number of overlapping regions to chromosomal length was numerically higher on chromosome 11 followed by chromosomes 13 and 12. These results show that overlapping transcription is distributed throughout the equine genome, but that distributions differ for each chromosome. Next, we evaluated the expression patterns of SAS pairs during the course of gestation. The sense and antisense genes showed an overall positive correlation between the sense and antisense pairs. We further provide a list of SAS pairs with both positive and negative correlation in their expression patterns throughout gestation. This study characterizes the landscape of sense and antisense gene expression in the placenta for the first time and provides a resource that will enable researchers to elucidate the mechanisms of sense/antisense regulation during pregnancy.

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 1450-1545 Young Investigator Awards & Presentations Basic/Translational Exploring cellular subpopulations in glioblastoma and matched organoids using single-cell RNA-seq 52

2018 ◽  
Vol 45 (S3) ◽  
pp. S13-S14
Author(s):  
VG LeBlanc ◽  
D Trinh ◽  
M Hughes ◽  
I Luthra ◽  
D Livingstone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Tissue Sample ◽  
Expression Patterns ◽  
Model Systems ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Original Tumour ◽  
Current Therapies ◽  
Intratumoural Heterogeneity

Glioblastomas (GBMs) account for nearly half of all primary malignant brain tumours, and current therapies are often only marginally effective. Our understanding of the underlying biology of these tumours and the development of new therapies have been complicated in part by widespread inter- and intratumoural heterogeneity. To characterize this heterogeneity, we performed regional subsampling of primary glioblastomas and derived organoids from these tissue samples. We then performed single-cell RNA-sequencing (scRNA-seq) on these primary regional subsamples and 1-3 matched organoids per sample. We have profiled samples from six tumour sets to date and have obtained sequencing data for 21,234 primary tissue cells and 14,742 organoid cells. While the most apparent differences in gene expression appear to be between individual tumours, we were also able to identify similar cellular subpopulations across tissue samples and across organoids. Importantly, organoids derived from the same tissue sample appeared to be composed of similar cellular subpopulations and were highly comparable to each other, indicating that replicate organoids faithfully represent the original tumour tissue. Overall, our scRNA-seq approach will help evaluate the utility of tumour-derived organoids as model systems for GBM and will aid in identifying cellular subpopulations defined by gene expression patterns, both in primary GBM regional subsamples and their associated organoids. These analyses will allow for the characterization of clonal or subclonal populations that are likely to respond to different therapeutic approaches and may also uncover novel therapeutic targets previously unrevealed through bulk analyses.

scholarly journals Raw sequence to target gene prediction: An integrated inference pipeline for ChIP-seq and RNA-seq datasets

2017 ◽  
Author(s):  
Nisar Wani ◽  
Khalid Raza
Keyword(s):  
Gene Expression ◽  
Target Gene ◽  
Target Genes ◽  
Gene Prediction ◽  
Expression Patterns ◽  
Dna Binding Proteins ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cellular Processes ◽  
Regulatory Effects

AbstractGene expression patterns determine the manner whereby organisms regulate various cellular processes and therefore their organ functions.These patterns do not emerge on their own, but as a result of diverse regulatory factors such as, DNA binding proteins known as transcription factors (TF), chromatin structure and various other environmental factors. TFs play a pivotal role in gene regulation by binding to different locations on the genome and influencing the expression of their target genes. Therefore, predicting target genes and their regulation becomes an important task for understanding mechanisms that control cellular processes governing both healthy and diseased cells.In this paper, we propose an integrated inference pipeline for predicting target genes and their regulatory effects for a specific TF using next-generation data analysis tools.

scholarly journals P13.10 Glioblastoma within the subventricular zone associates with increased mesenchymal transition: an intratumoral gene expression analysis

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii64-iii64
Author(s):  
S Berendsen ◽  
D Dalemans ◽  
K Draaisma ◽  
P A Robe ◽  
T J Snijders
Keyword(s):  
Gene Expression ◽  
Subventricular Zone ◽  
Epithelial Mesenchymal Transition ◽  
Expression Patterns ◽  
Sequencing Data ◽  
Gene Set Enrichment ◽  
En Bloc ◽  
Tissue Samples ◽  
Mesenchymal Transition ◽  
Gene Set

Abstract BACKGROUND Involvement of the subventricular zone (SVZ) in GBM is associated with poor prognosis and suggested to associate with specific tumor-biological characteristics. The SVZ microenvironment can influence gene expression and migration in GBM cells in preclinical models. We aimed to investigate whether the SVZ microenvironment has any influence on intratumoral gene expression patterns in GBM patients. MATERIAL AND METHODS The publicly available Ivy GBM database contains clinical, radiological and whole exome sequencing data from multiple regions from en bloc resected GBMs. SVZ involvement of the various tissue samples was evaluated on MRI scans. In the tumors that contacted the SVZ, we performed gene expression analyses and gene set enrichment analyses to compare gene (set) expression in tumor regions within the SVZ to tumor regions outside the SVZ, within the same tumors. We also compared these samples to GBMs that made no contact with the SVZ. RESULTS Within GBMs that contacted the SVZ, tissue samples within the SVZ showed enrichment of gene sets involved in (epithelial-)mesenchymal transition, NF-κB and STAT3 signaling, angiogenesis and hypoxia, compared to the samples outside of the SVZ region from the same tumors (p<0.05, FDR<0.25). Comparison of GBM samples within the SVZ region to samples from tumors that did not contact the SVZ yielded similar results. In contrast, we observed no difference in gene set enrichment when comparing the samples outside of the SVZ from SVZ-contacting GBMs with samples from GBMs that did not contact the SVZ at all. CONCLUSION GBM samples in the SVZ region associate with increased (epithelial-)mesenchymal transition and angiogenesis/hypoxia signaling, possibly mediated by the SVZ microenvironment.

Whole Exome Sequencing of Type 1 and Type 2 Enteropathy-Associated T Cell Lymphoma Reveals Genetic Basis of Eatl Oncogenesis

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 575-575
Author(s):  
Sarah Lynn Ondrejka ◽  
Andrea B. Moffitt ◽  
Eric Tse ◽  
Eric D. Hsi ◽  
John R. Goodlad ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Somatic Mutations ◽  
Cell Lymphoma ◽  
Expression Patterns ◽  
T Cell Lymphoma ◽  
Sequencing Data ◽  
Whole Exome

Abstract Introduction Enteropathy-associated T cell lymphoma (EATL) is an intestinal tumor of the intraepithelial T lymphocytes, with a median survival time of less than 1 year. It is a rare disease in general and has two main subtypes described. Type 1 EATL is a complication in patients with celiac disease, a chronic gluten-sensitive enteropathy. Type 2 EATL, characterized by smaller monomorphic lymphocytes, typically occurs sporadically in patients without celiac disease. Very little is known about the genetic mutations and gene expression signatures that define this disease, or the extent to which the two types of EATL are genetically distinct. It has been suggested that the two types of EATLs should be reclassified as separate diseases in future WHO categories. Methods In this study, we performed whole exome sequencing to 100-fold depth of 41 EATL tumors including 23 type 1 cases and 18 type 2 cases. Both alpha-beta (65%) and gamma-delta (35%) T cell receptor rearrangements were seen among these cases. Paired normal DNA was sequenced in most (N=30) cases. We defined somatic mutations, copy number alterations, and HLA genotypes in these cases from sequencing data. Additionally, we generated RNA sequencing data on the same EATL tumors. Corresponding clinical and outcome data was collected on the same cohort. Results We found that both type 1 and type 2 EATLs had overlapping patterns of mutations and similar overall survival. The most commonly mutated genes were chromatin modifier genes (34%) including ATRX and ARID1B. We also identified recurrent somatic mutations in signal transduction genes, including JAK1 and BCL9L. TP53 mutations were also recurrent (12%). Copy number amplifications in 9q, 1q, and 8q occurred most frequently and were present in both subtypes. We further compared the mutational profiles to peripheral T cell lymphoma, angioimmunoblastic T cell lymphoma, cutaneous T cell lymphoma, natural killer/T cell lymphoma, diffuse large B cell lymphoma, and Burkitt lymphoma. These comparisons identify EATL as a genetically distinct disease with a very different pattern of mutations. RNAseq identified the gene expression patterns that are unique to EATL and also identified gene expression signatures that distinguish the two types of EATL. The DQ2 or DQ8 HLA genotype is present in the majority of type 1 cases (73%) while occurring infrequently in type 2 cases (27%). Conclusions Our study defines the genetic landscape of enteropathy associated T cell lymphoma and highlights the genetic and clinical overlap between the two types. While the two types have differences in mutations and gene expression patterns, they have more in common with each other compared to other lymphoma types. Our data may inform future decisions regarding the potential separation of the two EATL types as distinct entities. Disclosures No relevant conflicts of interest to declare.

scholarly journals Transcriptomic analysis reveals distinct resistant response by physcion and chrysophanol against cucumber powdery mildew

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1991 ◽  
Author(s):  
Yanping Li ◽  
Shilin Tian ◽  
Xiaojun Yang ◽  
Xin Wang ◽  
Yuhai Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Disease Resistance ◽  
Powdery Mildew ◽  
Differentially Expressed Genes ◽  
Expression Patterns ◽  
Defense Responses ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcriptional Change

Physcion and chrysophanol induce defense responses against powdery mildew in cucumbers. The combination of these two compounds has synergistic interaction against the disease. We performed RNA-seq on cucumber leaf samples treated with physcion and chrysophanol alone and with their combination. We generated 17.6 Gb of high-quality sequencing data (∼2 Gb per sample) and catalogued the expressions profiles of 12,293 annotated cucumber genes in each sample. We identified numerous differentially expressed genes that exhibited distinct expression patterns among the three treatments. The gene expression patterns of the Chr and Phy treatments were more similar to each other than to the Phy × Chr treatment. The Phy × Chr treatment induced the highest number of differentially expressed genes. This dramatic transcriptional change after Phy × Chr treatment leaves reflects that physcion combined with chrysophanol treatment was most closely associated with induction of disease resistance. The analysis showed that the combination treatment caused expression changes of numerous defense-related genes. These genes have known or potential roles in structural, chemical and signaling defense responses and were enriched in functional gene categories potentially responsible for cucumber resistance. These results clearly demonstrated that disease resistance in cucumber leaves was significantly influenced by the combined physcion and chrysophanol treatment. Thus, physcion and chrysophanol are appealing candidates for further investigation of the gene expression and associated regulatory mechanisms related to the defense response.

scholarly journals Co-expression networks reveal the tissue-specific regulation of transcription and splicing

2016 ◽  
Author(s):  
Ashis Saha ◽  
Yungil Kim ◽  
Ariel D. H. Gewirtz ◽  
Brian Jo ◽  
Chuan Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Rna Binding ◽  
Expression Patterns ◽  
Regulation Of Transcription ◽  
Sequencing Data ◽  
Tissue Specific ◽  
Human Transcriptome ◽  
Network Analyses ◽  
Specific Regulation

AbstractGene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

scholarly journals A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

2021 ◽  
Author(s):  
Manuel Neumann ◽  
Xiaocai Xu ◽  
Cezary Smaczniak ◽  
Julia Schumacher ◽  
Wenhao Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Floral Meristem ◽  
Cellular Heterogeneity ◽  
Sequencing Data ◽  
Gene Expression Atlas ◽  
Spatial Reconstruction ◽  
Expression Atlas ◽  
3D Gene

Identity and functions of plant cells are influenced by their precise cellular location within the plant body. Cellular heterogeneity in growth and differentiation trajectories results in organ patterning. Therefore, assessing this heterogeneity at molecular scale is a major question in developmental biology. Single-cell transcriptomics (scRNA-seq) allows to characterize and quantify gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during the scRNA-seq procedure. To recover the original location of cells is essential to link gene activity with cellular function and morphology. Here, we reconstruct genome-wide gene expression patterns of individual cells in a floral meristem by combining single-nuclei RNA-seq with 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the data are used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com

scholarly journals Female-male differences in COVID vaccine adverse events have precedence in seasonal flu shots: a potential link to sex-associated baseline gene expression patterns

2021 ◽  
Author(s):  
AJ Venkatakrishnan ◽  
Praveen Kumar-M ◽  
Eli Silvert ◽  
Enrique Garcia-Rivera ◽  
Mariola Szenk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adverse Events ◽  
Expression Patterns ◽  
Epidemiological Data ◽  
Cell Types ◽  
The United States ◽  
Clinical Findings ◽  
Clinical Consequence ◽  
Sequencing Data ◽  
Mechanistic Basis

Nearly 150 million doses of FDA-authorized COVID vaccines have been administered in the United States. Sex-based differences of adverse events remain poorly understood, mandating the need for real-world investigation from Electronic Health Records (EHRs) and broader epidemiological data sets. Based on an augmented curation of EHR clinical notes of 31,064 COVID-vaccinated individuals (19,321 females and 11,743 males) in the Mayo Clinic, we find that nausea and vomiting were documented significantly more frequently in females than males after both vaccine doses (nausea: RRDose 1 = 1.67, pDose 1 <0.001, RRDose 2 = 2.2, pDose 1 < 0.001; vomiting: RRDose 1 = 1.58, pDose 1 < 0.001, RRDose 2 = 1.88, pDose 1 = 3.4x10-2). Conversely, fever, fatigue, and lymphadenopathy were more common in males after the first dose vaccination (fever RR = 0.62; p = 8.65x10-3; fatigue RR = 0.86, p = 2.89x10-2; lymphadenopathy RR = 0.61, p = 3.45x10-3). Analysis of the Vaccine Adverse Events Reporting System (VAERS) database further confirms that nausea comprises a larger fraction of total reports among females than males (RR: 1.58; p<0.001), while fever comprises a larger fraction of total reports among males than females (RR: 0.84; p<0.001). Importantly, increased reporting of nausea and fever among females and males, respectively, is also observed for prior influenza vaccines in the VAERS database, establishing that these differences are not unique to the recently developed COVID-19 vaccines. Investigating the mechanistic basis underlying these clinical findings, an analysis of bulk RNA-sequencing data from 12,158 human blood samples (8626 female, 3532 male) reveals 85 genes that are not only significantly different in their gene expression between females and males at baseline, but also have established literature-based associations to COVID-19 as well as the vaccine-related adverse events of clinical consequence. The NLRP3 inflammasome and the NR3C1 glucocorticoid receptor emerge as particularly promising baseline links to sex-associated vaccine adverse events, warranting targeted investigation of these signaling pathways and associated cell types. From a public health standpoint, our clinical findings shall aid in educating patients on the sex-associated risks they should expect for COVID-19 vaccines and also promote better clinical management of vaccine-associated adverse events.

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