scholarly journals Effects of tamoxifen inducible MerCreMer on gene expression in cardiac myocytes in mice

Author(s):  
Leila Rouhi ◽  
Siyang Fan ◽  
Sirisha M. Cheedipudi ◽  
Melis Olcum ◽  
Hyun-Hwan Jeong ◽  
...  

The Cre-LoxP technology, including the tamoxifen (TAM) inducible MerCreMer (MCM), is increasingly used to delineate gene function, understand the disease mechanisms, and test therapeutic interventions. We set to determine the effects of TAM-MCM on cardiac myocyte transcriptome. Expression of the MCM was induced specifically in cardiac myocytes upon injection of TAM to myosin heavy chain 6-MCM (Myh6-Mcm) mice for 5 consecutive days. Cardiac function, myocardial histology, and gene expression (RNA-sequencing) were analyzed 2 weeks after TAM injection. A total of 346 protein coding genes (168 up- and 178 down-regulated) were differentially expressed. Transcript levels of 85 genes, analyzed by a reverse transcription-polymerase chain reaction in independent samples, correlated with changes in the RNA-sequencing data. The differentially expressed genes were modestly enriched for genes involved in the interferon response and the tumor protein 53 (TP53) pathways. The changes in gene expression were relatively small and mostly transient and had no discernible effects on cardiac function, myocardial fibrosis, and apoptosis or induction of double-stranded DNA breaks. Thus, TAM-inducible activation of MCM alters cardiac myocytes gene expression, provoking modest and transient interferon and DNA damage responses without exerting other discernible phenotypic effects. Thus, the effects of TAM-MCM on gene expression should be considered in discerning the bona fide changes that result from the targeting of the gene of interest.

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 654-654
Author(s):  
Raphael M Byrne ◽  
Rebecca Ruhl ◽  
Christian Lanciault ◽  
Sudarshan Anand ◽  
Abhinav Nellore ◽  
...  

654 Background: Colorectal cancer (CRC) in young patients is increasing in incidence and is associated with worse outcomes than CRC in older patients. While distinct molecular subtypes of CRC have been recently characterized, it is unclear whether there are molecular differences between the tumors of young and old patients. We sought to identify differences in gene expression of CRC between these two groups. Our discovery analysis identified a gene signature of several differentially expressed RNAs, from which we validated PEG10. The PEG10 gene on chromosome 7q21.3 has been implicated in liver, gallbladder, thyroid, and blood cancers, and is thought to play a role in cancer cell survival and regulation of apoptosis. In hepatocellular carcinoma, increased PEG10 expression has been associated with younger patient age. Methods: RNA sequencing data was obtained from The Cancer Genome Atlas (TCGA) and analyzed for differences in gene expression between patients ≤ 45 years old and those ≥ 65 years old. The identified differentially expressed genes were then validated with qPCR using human CRC tissue from patients ≤ 45 years old and those ≥ 65 years old. Results: RNA sequencing data from patients ≤ 45 years old (n = 29) and patients ≥ 65 years old (n = 299) identified seven genes with increased expression in younger patients: ZNF334 (log2 [fold change] = 2.30), DSC3 (1.78), PEG10 (1.67), CACNA1I (1.54), PKIA (1.33), MAP9 (1.27), and EPHX3 (1.17) (p < 0.07). Validation with qPCR for PEG10 was most promising, and was performed on both young (n = 10, mean age = 39) and old patient samples ( n= 8, mean age = 72). Two cancers (20%) in the young group received radiation treatment and five (50%) received chemotherapy. One cancer (12.5%) in the old group received radiation and two (25%) received chemotherapy. PEG10 had increased expression in the young group with log2 [fold change] = 3.16 (p < 0.02). Conclusions: We have identified a potentially unique gene expression signature for CRC in young patients, which includes PEG10. Functional analysis of PEG10 and other genes is underway using in vitro cell culture, archived human tumor tissue, and mouse tumor models.


2021 ◽  
Vol 271 ◽  
pp. 03058
Author(s):  
Yihan Tong

Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product. With the development of techniques, many methods to analyze the differentially expressed (DE) genes have emerged, especially the downstream analysis approaches, such as limma, DESeq2, and edgeR. However, it is unclear whether using different methods leads to different results. This article has compared the results gained from DESeq2 and limma when conducting downstream analysis for RNA sequencing data. Evidently, the number of genes they found is different from each other. DESeq2 found more genes than limma. But more than 90% of the genes detected by the two methods are overlapped, which means both methods are reliable. If precise results are needed, limma has a better ability to find the accurate DE genes. In the end, we analyzed the reason of the difference and summarized when it is better to use limma than DESeq2.


Author(s):  
Anju Karki ◽  
Noah E Berlow ◽  
Jin-Ah Kim ◽  
Esther Hulleman ◽  
Qianqian Liu ◽  
...  

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.


2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  

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.


2007 ◽  
Vol 29 (2) ◽  
pp. 118-127 ◽  
Author(s):  
Angela Clerk ◽  
Timothy J. Kemp ◽  
Georgia Zoumpoulidou ◽  
Peter H. Sugden

High levels of oxidative stress promote cardiac myocyte death, though lower levels are potentially cytoprotective/anabolic. We examined the changes in gene expression in rat neonatal cardiac myocytes exposed to apoptotic (0.2 mM) or nontoxic (0.04 mM) concentrations of H2O2 (2, 4, or 24 h) using Affymetrix microarrays. Using U34B arrays, we identified a ubiquitously expressed, novel H2O2-responsive gene [putative peroxide-inducible transcript 1 (Perit1)], which generates two alternatively spliced transcripts. Using 230 2.0 arrays, H2O2 (0.04 mM) promoted significant changes in expression of only 32 genes, all of which were seen with 0.2 mM H2O2. We failed to detect any increase in the rate of protein synthesis in cardiac myocytes exposed to <0.1 mM H2O2, further suggesting that global, low concentrations of H2O2 are not anabolic in this system. H2O2 (0.2 mM) promoted significant ( P < 0.05, >1.75-fold) changes in expression of 649 mRNAs and 187 RNAs corresponding to no established gene. Of the mRNAs, 114 encoded transcriptional regulators including Krüppel-like factors (Klfs). Quantitative PCR independently verified the changes in Klf expression. Thus, H2O2-induced cardiac myocyte apoptosis is associated with dynamic changes in gene expression. The expression of these genes and their protein products potentially influences the progression of the apoptotic response.


2022 ◽  
Vol 12 ◽  
Author(s):  
Beatrice E. Gee ◽  
Andrea Pearson ◽  
Iris Buchanan-Perry ◽  
Roger P. Simon ◽  
David R. Archer ◽  
...  

Whole transcriptome RNA-sequencing was performed to quantify RNA expression changes in whole blood samples collected from steady state sickle cell anemia (SCA) and control subjects. Pediatric SCA and control subjects were recruited from Atlanta (GA)—based hospital(s) systems and consented for RNA sequencing. RNA sequencing was performed on an Ion Torrent S5 sequencer, using the Ion Total RNA-seq v2 protocol. Data were aligned to the hg19 reference genome and analyzed in the Partek Genomics studio package (v7.0). 223 genes were differentially expressed between SCA and controls (± 1.5 fold change FDR p &lt; 0.001) and 441 genes show differential transcript expression (± 1.5 fold FDR p &lt; 0.001). Differentially expressed RNA are enriched for hemoglobin associated genes and ubiquitin-proteasome pathway genes. Further analysis shows higher gamma globin gene expression in SCA (33-fold HBG1 and 49-fold HBG2, both FDR p &lt; 0.05), which did not correlate with hemoglobin F protein levels. eQTL analysis identified SNPs in novel non-coding RNA RYR2 gene as having a potential regulatory role in HBG1 and HBG2 expression levels. Gene expression correlation identified JHDM1D-AS1(KDM7A-DT), a non-coding RNA associated with angiogenesis, enhanced GATA1 and decreased JAK-STAT signaling to correlate with HBG1 and HBG2 mRNA levels. These data suggest novel regulatory mechanisms for fetal hemoglobin regulation, which may offer innovative therapeutic approaches for SCA.


2020 ◽  
Author(s):  
Benedict Hew ◽  
Qiao Wen Tan ◽  
William Goh ◽  
Jonathan Wei Xiong Ng ◽  
Kenny Koh ◽  
...  

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.


Circulation ◽  
2020 ◽  
Vol 142 (14) ◽  
pp. 1374-1388
Author(s):  
Yanming Li ◽  
Pingping Ren ◽  
Ashley Dawson ◽  
Hernan G. Vasquez ◽  
Waleed Ageedi ◽  
...  

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.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 374-374 ◽  
Author(s):  
Chase Miller ◽  
Jennifer Yesil ◽  
Mary Derome ◽  
Andrea Donnelly ◽  
Jean Marrian ◽  
...  

Abstract Fluorescent in situ hybridization (FISH) is commonly used in the multiple myeloma field to subtype and risk-stratify patients. There are many benefits to FISH based assays, which are widely used around the world and represent true single cell assays. However, there are significant discrepancies in the specific assays, utilization of reflex testing strategies, and enumeration requirements between clinical centers. By comparison next-generation sequencing tests can be designed to simultaneously detect the copy number abnormalities and translocations detected by clinical FISH along with gene mutations that cannot be detected by FISH. As part of the MMRF CoMMpass Study we have compared the results attained using clinical FISH assays compared to sequencing based FISH (Seq-FISH) results. Clinical FISH reports from a random subset of 339 CoMMpass patients were extraction by a single individual based on the ISCN result lines of each report. To validate the accuracy of the central data extraction, two independent cross validations of 10% of the cohort were performed, after which our data entry error rate is expected to be less than 0.348%. The Seq-FISH results were extracted from the whole genome sequencing data available from each patient using a rapid and fully automated informatics process and the results were cross-validated using the matching exome sequencing data for copy number abnormalities and by RNA sequencing data for dysregulated immunoglobulin translocation target genes. There were 230 patients with clinical FISH and Seq-FISH results. In this cohort, 151 translocations were identified by Seq-FISH. This includes translocations to MYC, CCND2, MAFA, and those involving IgK and IgL, which are not tested by clinical FISH. After filtering non-tested translocations there are 118 translocations identified by Seq-FISH. Only 97 of these translocations had a clinical FISH assay performed with 89 (91.75%) of these being detected by clinical FISH, yet spiked target gene expression was observed in all 89 cases by RNA sequencing. Conversely, 93 translocations were called by clinical FISH, of these 89 were called by Seq-FISH(95.7%). Of the 4 translocations only called by clinical FISH, 3 were t(4;14) and 1 was a t(11;14). In two of these t(4;14) cases we did observe spiked target gene expression by RNA sequencing, suggesting these are false negatives by Seq-FISH. However, the remaining two events appear to be false positive clinical FISH results. The t(4;14) event was only observed in 1/200 cells and a co-occuring t(11;14) was also called, which was confirmed by Seq-FISH and spiked gene expression. Similarly, the one t(11;14) was observed in 3/56 cells but a del13q14 was seen in 47/50 cells, unfortunately RNA sequencing data is not available to cross-validate in this case. Plasma cell enrichment or identification is commonly used to prepare myeloma samples for FISH because even in myeloma, the total plasma cell percentage can be low (median 8.3% in the MMRF CoMMpass Baseline Cohort). Therefore, performing FISH on a sample without performing purification or plasma cell identification will indiscriminately assay non-plasma cells and limit the efficacy of the assay. We looked at the two most common translocations in myeloma, t(4;14) and t(11;14), to test the effect of enrichment on sensitivity. Sensitivity was higher for both sets of translocations in the enriched cohort. There was 1 false negative in the enriched population, yielding sensitivities of 100% (32/32) and 95%(19/20) for CCND1 and WHSC1 respectively. For those reports that did not indicate enrichment was performed the observed sensitivities were 86.36% (19/22) and 92.86% (13/14). Seq-FISH identified almost all of the translocations called by clinical FISH and simultaneously; it identified 30 translocations missed by clinical FISH. The translocations that were not reported by clinical FISH can be attributed to a mixture of the correct assay not being performed and the translocation being missed even though the assay was performed. We believe that Seq-FISH is a viable alternative to clinical FISH, with similar specificity and greater sensitivity. It is important to note that a single Seq-FISH assay is sufficient to investigate all translocations, while each translocation must be investigated separately with clinical FISH. As such, Seq-FISH obviates the concern that a translocation would be missed because the correct assay was not performed. Disclosures McBride: Instat: Employment.


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