scholarly journals Identification of Distinct Heterogenic Subtypes and Molecular Signatures Associated with African Ancestry in Triple Negative Breast Cancer Using Quantified Genetic Ancestry Models in Admixed Race Populations

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1220
Author(s):  
Melissa Davis ◽  
Rachel Martini ◽  
Lisa Newman ◽  
Olivier Elemento ◽  
Jason White ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Triple Negative ◽  
African Ancestry ◽  
Genetic Ancestry ◽  
Breast Cancers ◽  
Sequencing Data ◽  
Genome Wide ◽  
Ethnic Variations ◽  
Racial Ethnic

Triple negative breast cancers (TNBCs) are molecularly heterogeneous, and the link between their aggressiveness with African ancestry is not established. We investigated primary TNBCs for gene expression among self-reported race (SRR) groups of African American (AA, n = 42) and European American (EA, n = 33) women. RNA sequencing data were analyzed to measure changes in genome-wide expression, and we utilized logistic regressions to identify ancestry-associated gene expression signatures. Using SNVs identified from our RNA sequencing data, global ancestry was estimated. We identified 156 African ancestry-associated genes and found that, compared to SRR, quantitative genetic analysis was a more robust method to identify racial/ethnic-specific genes that were differentially expressed. A subset of African ancestry-specific genes that were upregulated in TNBCs of our AA patients were validated in TCGA data. In AA patients, there was a higher incidence of basal-like two tumors and altered TP53, NFB1, and AKT pathways. The distinct distribution of TNBC subtypes and altered oncologic pathways show that the ethnic variations in TNBCs are driven by shared genetic ancestry. Thus, to appreciate the molecular diversity of TNBCs, tumors from patients of various ancestral origins should be evaluated.

scholarly journals Identification of Distinct Heterogenic Subtypes and Molecular Signatures Associated With African Ancestry in Triple Negative Breast Cancer Using Quantified Genetic Ancestry Models in Admixed Race Populations

2020 ◽  
Author(s):  
Melissa Davis ◽  
Rachel Martini ◽  
Lisa Newman ◽  
Olivier Elemento ◽  
Jason White ◽  
...  
Keyword(s):  
Gene Expression ◽  
Triple Negative ◽  
African Ancestry ◽  
Variant Calling ◽  
Genetic Ancestry ◽  
The Cancer Genome Atlas ◽  
Breast Cancers ◽  
Sequencing Data ◽  
Cancer Genome Atlas ◽  
Ethnic Variations

Triple negative breast cancers (TNBCs) are molecularly heterogeneous, and the link between their aggressiveness with African ancestry is not established. We investigated primary TNBCs for gene expression among self-reported race (SRR) groups of African American (AA, n=42) and European American (EA, n=33) women. Using The Cancer Genome Atlas (TCGA) approaches, we analyzed RNA sequencing data to measure changes in genome-wide expression and used logistic regressions to identify ancestry-associated gene expression signatures. To determine global ancestry, GATK best practices were followed for variant calling, and used the 1000 Genomes Project as reference data. We identified >150 African ancestry-associated genes and found that, compared to SRR, quantitative genetic analysis was a more robust method to identify racial/ethnic-specific genes that were differentially expressed. A subset of African ancestry-specific genes that were upregulated in TNBCs of our AA patients were validated in TCGA data. In AA patients, there was a higher incidence of basal-like 2 tumors and altered TP53, NFB1, and AKT pathways. The distinct distribution of TNBC subtypes and altered oncologic pathways show that the ethnic variations in TNBCs are driven by shared genetic ancestry. Thus, to appreciate the molecular diversity of TNBCs, tumors from patients of various ancestral origins should be evaluated.

scholarly journals Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue

Circulation ◽  
2020 ◽  
Vol 142 (14) ◽  
pp. 1374-1388
Author(s):  
Yanming Li ◽  
Pingping Ren ◽  
Ashley Dawson ◽  
Hernan G. Vasquez ◽  
Waleed Ageedi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Aortic Wall ◽  
Genome Wide Association ◽  
Aortic Tissue ◽  
Sequencing Data ◽  
Genome Wide ◽  
Single Cell Rna Sequencing ◽  
Differential Gene

Background: Ascending thoracic aortic aneurysm (ATAA) is caused by the progressive weakening and dilatation of the aortic wall and can lead to aortic dissection, rupture, and other life-threatening complications. To improve our understanding of ATAA pathogenesis, we aimed to comprehensively characterize the cellular composition of the ascending aortic wall and to identify molecular alterations in each cell population of human ATAA tissues. Methods: We performed single-cell RNA sequencing analysis of ascending aortic tissues from 11 study participants, including 8 patients with ATAA (4 women and 4 men) and 3 control subjects (2 women and 1 man). Cells extracted from aortic tissue were analyzed and categorized with single-cell RNA sequencing data to perform cluster identification. ATAA-related changes were then examined by comparing the proportions of each cell type and the gene expression profiles between ATAA and control tissues. We also examined which genes may be critical for ATAA by performing the integrative analysis of our single-cell RNA sequencing data with publicly available data from genome-wide association studies. Results: We identified 11 major cell types in human ascending aortic tissue; the high-resolution reclustering of these cells further divided them into 40 subtypes. Multiple subtypes were observed for smooth muscle cells, macrophages, and T lymphocytes, suggesting that these cells have multiple functional populations in the aortic wall. In general, ATAA tissues had fewer nonimmune cells and more immune cells, especially T lymphocytes, than control tissues did. Differential gene expression data suggested the presence of extensive mitochondrial dysfunction in ATAA tissues. In addition, integrative analysis of our single-cell RNA sequencing data with public genome-wide association study data and promoter capture Hi-C data suggested that the erythroblast transformation-specific related gene( ERG ) exerts an important role in maintaining normal aortic wall function. Conclusions: Our study provides a comprehensive evaluation of the cellular composition of the ascending aortic wall and reveals how the gene expression landscape is altered in human ATAA tissue. The information from this study makes important contributions to our understanding of ATAA formation and progression.

scholarly journals Gene expression in African Americans and Latinos reveals ancestry-specific patterns of genetic architecture

2021 ◽  
Author(s):  
Angel C.Y. Mak ◽  
Linda Kachuri ◽  
Donglei Hu ◽  
Celeste Eng ◽  
Scott Huntsman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Prediction Models ◽  
African Ancestry ◽  
Diverse Populations ◽  
Sequencing Data ◽  
Blood Gene Expression ◽  
Genome Wide ◽  
Genomic Studies

We explored the role of genetic ancestry in shaping the genetic architecture of whole blood gene expression using whole genome and RNA sequencing data from 2,733 African American and Hispanic/Latino children. We find that heritability of gene expression significantly increases with greater proportion of genome-wide African ancestry and decreases with higher levels of Indigenous American ancestry. Fine-mapping of expression quantitative trait loci (eQTLs) in individuals with predominantly African or Indigenous American ancestry revealed ancestry-specific eQTLs in over 30% of heritable genes. We leveraged our data to train genetically derived transcriptome prediction models, which identified significantly more associated genes when applied to 28 traits from a multi-ancestry population. Our findings underscore the importance of increasing representation from ancestrally diverse populations in genomic studies to enable new discoveries and ensure their equitable translation.

scholarly journals A primer for generating and using transcriptome data and gene sets

Development ◽  
2020 ◽  
Vol 147 (24) ◽  
pp. dev193854
Author(s):  
Chad Cockrum ◽  
Kiyomi R. Kaneshiro ◽  
Andreas Rechtsteiner ◽  
Tomoko M. Tabuchi ◽  
Susan Strome
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cells ◽  
Transcript Abundance ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Sequencing Data ◽  
Gene Sets ◽  
Genome Wide

ABSTRACTTranscriptomic approaches have provided a growing set of powerful tools with which to study genome-wide patterns of gene expression. Rapidly evolving technologies enable analysis of transcript abundance data from particular tissues and even single cells. This Primer discusses methods that can be used to collect and profile RNAs from specific tissues or cells, process and analyze high-throughput RNA-sequencing data, and define sets of genes that accurately represent a category, such as tissue-enriched or tissue-specific gene expression.

scholarly journals Receptor-driven invasion profiles in diffuse intrinsic pontine glioma (DIPG)

2021 ◽  
Author(s):  
Anju Karki ◽  
Noah E Berlow ◽  
Jin-Ah Kim ◽  
Esther Hulleman ◽  
Qianqian Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Factor ◽  
Protein Expression ◽  
Cell Viability ◽  
Rna Sequencing ◽  
Cell Invasion ◽  
Growth Factor Receptor ◽  
Diffuse Intrinsic Pontine Glioma ◽  
Sequencing Data ◽  
Pontine Glioma

Abstract Background Diffuse intrinsic pontine glioma (DIPG) is a devastating pediatric cancer with unmet clinical need. DIPG is invasive in nature, where tumor cells interweave into the fiber nerve tracts of the pons making the tumor unresectable. Accordingly, novel approaches in combating the disease is of utmost importance and receptor-driven cell invasion in the context of DIPG is under-researched area. Here we investigated the impact on cell invasion mediated by PLEXINB1, PLEXINB2, platelet growth factor receptor (PDGFR)α, PDGFRβ, epithelial growth factor receptor (EGFR), activin receptor 1 (ACVR1), chemokine receptor 4 (CXCR4) and NOTCH1. Methods We used previously published RNA-sequencing data to measure gene expression of selected receptors in DIPG tumor tissue versus matched normal tissue controls (n=18). We assessed protein expression of the corresponding genes using DIPG cell culture models. Then, we performed cell viability and cell invasion assays of DIPG cells stimulated with chemoattractants/ligands. Results RNA-sequencing data showed increased gene expression of receptor genes such as PLEXINB2, PDGFRα, EGFR, ACVR1, CXCR4 and NOTCH1 in DIPG tumors compared to the control tissues. Representative DIPG cell lines demonstrated correspondingly increased protein expression levels of these genes. Cell viability assays showed minimal effects of growth factors/chemokines on tumor cell growth in most instances. Recombinant SEMA4C, SEM4D, PDGF-AA, PDGF-BB, ACVA, CXCL12 and DLL4 ligand stimulation altered invasion in DIPG cells. Conclusions We show that no single growth factor-ligand pair universally induces DIPG cell invasion. However, our results reveal a potential to create a composite of cytokines or anti-cytokines to modulate DIPG cell invasion.

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 LSTrAP-Crowd: Prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data

2020 ◽  
Author(s):  
Benedict Hew ◽  
Qiao Wen Tan ◽  
William Goh ◽  
Jonathan Wei Xiong Ng ◽  
Kenny Koh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Rna Sequencing ◽  
Gene Expression Data ◽  
Large Scale ◽  
Bacterial Resistance ◽  
Expression Data ◽  
Sequencing Data ◽  
Novel Proteins ◽  
Novel Antibiotics

AbstractBacterial resistance to antibiotics is a growing problem that is projected to cause more deaths than cancer in 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the bacterial ribosomes, proteins that are involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. The data can be used to identify other vulnerabilities or bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowdsourced.

scholarly journals A Review of the Hereditary Component of Triple Negative Breast Cancer: High- and Moderate-Penetrance Breast Cancer Genes, Low-Penetrance Loci, and the Role of Nontraditional Genetic Elements

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Darrell L. Ellsworth ◽  
Clesson E. Turner ◽  
Rachel E. Ellsworth
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Large Scale ◽  
Triple Negative ◽  
Association Studies ◽  
African Ancestry ◽  
Genome Wide Association Studies ◽  
Genetic Elements ◽  
Genome Wide ◽  
Increased Risk

Triple negative breast cancer (TNBC), representing 10-15% of breast tumors diagnosed each year, is a clinically defined subtype of breast cancer associated with poor prognosis. The higher incidence of TNBC in certain populations such as young women and/or women of African ancestry and a unique pathological phenotype shared between TNBC and BRCA1-deficient tumors suggest that TNBC may be inherited through germline mutations. In this article, we describe genes and genetic elements, beyond BRCA1 and BRCA2, which have been associated with increased risk of TNBC. Multigene panel testing has identified high- and moderate-penetrance cancer predisposition genes associated with increased risk for TNBC. Development of large-scale genome-wide SNP assays coupled with genome-wide association studies (GWAS) has led to the discovery of low-penetrance TNBC-associated loci. Next-generation sequencing has identified variants in noncoding RNAs, viral integration sites, and genes in underexplored regions of the human genome that may contribute to the genetic underpinnings of TNBC. Advances in our understanding of the genetics of TNBC are driving improvements in risk assessment and patient management.

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