scholarly journals Detection of CCND1 Overexpression By RNA-Seq from Tna Samples As a Surrogate for t(11:14) Translocation Traditionally Measured By FISH in Multiple Myeloma Patients for Improved Patient Care

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2961-2961
Author(s):  
Abhisek Ghosal ◽  
Francys Alarcon ◽  
Samuel Koo ◽  
Soo Jin Kang ◽  
Archana Ramesh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Housekeeping Gene ◽  
Lower Sensitivity ◽  
Cancer Type ◽  
Rna Seq ◽  
Gold Standard Method ◽  
Normal Volunteers ◽  
Qrt Pcr ◽  
Rna Levels

Abstract Background: Multiple myeloma (MM) is a blood cancer type affecting plasma cell in bone marrow. MM is heterogenous in nature but t(11;14)(q13;q32) translocation is a common prognostic marker among MM patients. One of the most frequent oncogenic drivers involved in this chromosomal rearrangement is CCND1 (Cyclin D1) gene translocation downstream to the immunoglobulin heavy chain (IGH), which results on overexpression of CCND1, thus promoting abnormal cell proliferation. Oncogenic CCND1 RNA levels can result from translocations such as t(11;14), gene amplifications, increased transcription rates and/or RNA stability. Indeed, CCND1 RNA overexpression has a favorable prognostic value for patients treated with high doses of chemotherapies but important challenges remain in accurate detection of CCND1 RNA levels. Currently, FISH is the gold standard method for detecting t(11:14) translocations at the DNA level. However, it cannot detect CCND1 overexpression. Therefore, a method that can detect CCND1 overexpression levels, as well as in frame transcripts has clinical implications. In the current study we leveraged in-use NeoGenomics Heme TNA single tube NGS assay to enable the detection of CCND1 RNA overexpression as a complementary test to FISH testing. Methods: We performed RNA sequencing from 32 healthy donors and on fixed cell pellets from 94 CD138-enriched BM samples from MM patients and from using the amplicon based (Qiagen, inc) NGS assay. We developed pipeline for gene expression by TPM count (transcript per million) for CCND1, and further normalized to the "housekeeping" gene GUSB. We validated the normalization to GUSB by comparing to normalization using the geometric mean of four housekeeping genes (GUSB, PGD, RPL5 and RPL19) showing a high correlation (R 2>0.95). A commercially available qRT-PCR assay was used as orthogonal method to further confirm the linearity of the quantitative gene expression signal in NGS. The analytical cutoff was determined from normalized TPM calculation from 32 healthy volunteers following CLSI guideline (CLSI_EP17-A2) and further updated from MM-PCE samples with t(11:14) translocations from a CLIS-validated FISH assay . Results: From 94 CD138-enriched BM samples, 26 had t(11:14) translocations, or CCND1 gains as detected by FISH, 15 samples were confirmed negative by FISH and 32 normal volunteers with no suspected disease. Also, we determined the analytical cutoff for CCND1 overexpression based on the CLSI guidelines to be 2.37 times the expression level of GUSB ("housekeeping" gene) using normal volunteers (n=32) (sensitivity 86% and specificity 77%). We found specificity to be low, so further evaluated the threshold using a ROC curve analysis with multiple tests. Using Fischer's exact test, we found CCND1 expression 3.27 times the GUSB expression to yield higher specificity of 86.5 % and sensitivity for 78.9%. Further, we used 26 FISH positive and 32 normal samples to build a new model and determined the cutoff for CCND1 overexpression to be 4.15 times GUSB expression, which resulted lower sensitivity but higher specificity (75% sensitivity and 100% specificity). When we evaluated 15 FISH negative samples with this cutoff we observed CCND1 was not overexpressed in six samples, but 9 samples did have some degree of overexpression. Overexpression was confirmed by qRT-PCR. Two CCND1 high- and low- expressing normal samples (MM-PCE 27 and 48) were further evaluated using alternative extraction methods to test the dependencies on extractions and the data showed concordant to each other for overexpression. Interestingly, 1 sample (MM-PCE-27) showed very high overexpression without t(11:14) translocation event (~100 fold over expressed). Cytogenetic studies were discordant with FISH as well for this sample, showing abnormalities related to chr7q,13q,12p but no indication of any chr11 related event. Conclusions: In this study we evaluated our existing NGS assay for CCND1 overexpression using TNA as a surrogate for traditional FISH, while demonstrating the accuracy of the RNA quantitation by NGS using qRT-PCR. We developed an RNA-seq based CCND1 expression assay that could be used to complement traditional FISH testing especially if there is limited specimen. The confirmation of overexpression in FISH negative samples may suggest new ways to improve MM patients risk stratification and treatment. Disclosures Ghosal: NeoGenomics Laboratories: Current Employment. Alarcon: NeoGenomics Laboratories: Current Employment. Koo: Neo Genomics Laboratories: Current Employment. Kang: Neo Genomics Laboratories: Current Employment. Ramesh: Neo Genomics Laboratories: Current Employment. Gyuris: Neo Genomics Laboratories: Current Employment. Jung: NeoGenomics Laboratories, Inc.: Current Employment. Thomas: NeoGenomics Laboratories, Inc.: Current Employment. Fabunan: NeoGenomics Laboratories, Inc.: Current Employment. Magnan: NeoGenomics Laboratories, Inc.: Current Employment. Nam: NeoGenomics Laboratories, Inc.: Current Employment. Petersen: Neo Genomics Laboratories: Current Employment. Lopez-Diaz: NeoGenomics Laboratories, Inc.: Current Employment. Yamahata: Neo Genomics Laboratories: Current Employment. Bender: NeoGenomics Laboratories, Inc.: Current Employment. Agersborg: NeoGenomics Laboratories, Inc.: Current Employment. Ye: Neo Genomics Laboratories: Current Employment. Funari: NeoGenomics Laboratories, Inc.: Current Employment.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

Application of qRT-PCR and RNA-Seq analysis for the identification of housekeeping genes useful for normalization of gene expression values during Striga hermonthica development

2012 ◽  
Vol 40 (4) ◽  
pp. 3395-3407 ◽  
Author(s):  
M. Fernández-Aparicio ◽  
K. Huang ◽  
E. K. Wafula ◽  
L. A. Honaas ◽  
N. J. Wickett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Housekeeping Genes ◽  
Striga Hermonthica ◽  
Rna Seq ◽  
Qrt Pcr

scholarly journals Identification and validation of miRNA reference genes in poplar under pathogen stress

2021 ◽  
Author(s):  
Lichun Zhang ◽  
Xiaoqian Yang ◽  
Yiyi Yin ◽  
Jinxing Wang ◽  
Yanwei Wang
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Small Rna ◽  
Reference Genes ◽  
Expression Profiles ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Internal Standards ◽  
Qrt Pcr ◽  
Canker Pathogen

Abstract Quantitative real time polymerase chain reaction (qRT-PCR) is a common method to analyze gene expression. Due to differences in RNA quantity, quality, and reverse transcription efficiency between qRT-PCR samples, reference genes are used as internal standards to normalize gene expression. However, few universal genes especially miRNAs have been identified as reference so far. Therefore, it is essential to identify reference genes that can be used across various experimental conditions, stress treatments, or tissues. In this study, 14 microRNAs (miRNAs) and 5.8S rRNA were assessed for expression stability in poplar trees infected with canker pathogen. Using three reference gene analysis programs, we found that miR156g and miR156a exhibited stable expression throughout the infection process. miR156g and miR156a were then tested as internal standards to measure the expression of miR1447 and miR171c, and the results were compared to small RNA sequencing (RNA-seq) data. We found that when miR156a was used as the reference gene, the expression of miR1447 and miR171c were consistent with the small RNA-seq expression profiles. Therefore, miR156a was the most stable miRNAs examined in this study, and could be used as a reference gene in poplar under canker pathogen stress, which should enable comprehensive comparisons of miRNAs expression and avoid the bias caused by different lenth between detected miRNAs and traditional referece genes. The present study has expanded the miRNA reference genes available for gene expression studies in trees under biotic stress.

Loss of the Histone Demethylase UTX Contributes to Multiple Myeloma and Sensitizes Cells to EZH2 Inhibitors

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 611-611 ◽  
Author(s):  
Teresa Ezponda ◽  
Relja Popovic ◽  
Yupeng Zheng ◽  
Behnam Nabet ◽  
Christine Will ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cell Lines ◽  
Genetic Alterations ◽  
Histone Demethylase ◽  
Mass Spectrometry Analysis ◽  
Rna Seq ◽  
Wild Type ◽  
Altered Expression ◽  
E Cadherin

Abstract Genetic alterations of epigenetic regulators have become a recurrent theme in hematological malignancies. In particular, aberrations that alter the levels or distribution of methylation of lysine 27 on histone H3 (H3K27me) have emerged as a common feature of a wide variety of cancers, including multiple myeloma (MM). The histone demethylase UTX/KDM6A activates gene expression by removing the H3K27me3 repressive histone mark, counteracting the activity of EZH2, the enzyme that places this modification. UTX somatic inactivating mutations and deletions are found in up to 10% of MM cases; nevertheless, the epigenetic impact of UTX loss in MM and the mechanisms by which it contributes to this disease remain to be elucidated. To ascertain the biological impact of UTX loss, we used a recently identified isogenic cell line pair: ARP-1 (UTX wild-type) and ARD (UTX null). UTX-null ARD cells were engineered to express UTX in a doxycycline-inducible manner. UTX add-back slowed the proliferation rate of ARD cells, without affecting their viability. Soft agar assays demonstrated that UTX-null ARD cells have increased clonogenicity compared to UTX-wild-type ARP-1 cells. Re-expression of UTX partially reversed this effect, decreasing the number and size of colonies formed. ARD cells also showed increased adhesion to Hs-5 bone marrow stromal cells and to fibronectin than ARP-1 cells, an ability associated with cell survival and drug resistance. UTX add-back decreased the adhesive properties of ARD cells demonstrating this effect is dependent on UTX loss. Mass spectrometry analysis of the add-back system and a panel of UTX wild-type and mutant MM cell lines showed that global levels of H3K27me are not altered after UTX loss or upon its add-back. Therefore, UTX depletion may alter H3K27me at specific loci, and control the expression of a limited number of genes. To identify the genes and pathways that are altered upon UTX loss, we performed RNA-sequencing (RNA-seq) on the paired MM cell lines and the add-back system. This analysis revealed approximately 5,000 genes differentially expressed between ARP-1 and ARD cells. Re-expression of UTX in the UTX-null ARD cells reversed the expression of approximately 1,400 genes, most of them being upregulated upon reintroduction of UTX. Gene ontology analysis of genes responsive to UTX manipulation identified pathways such as JAK-STAT, cadherin, integrin and Wnt pathways. Many of these pathways are related to cell adhesion properties, correlating with the effects observed in vitro. Some examples of the genes which expression was restored upon UTX add-back are E-cadherin, whose loss has been associated with MM progression; and PTPN6, a negative regulator of the JAK-STAT pathway. Chromatin immunoprecipitation (ChIP) experiments at UTX target genes revealed a decrease in H3K27me3 and a concomitant increase in H3K4me3 upon UTX add-back, correlating with the observed changes in gene expression. As loss of UTX leads to a failure in the removal of H3K27me3, we hypothesized that UTX-null cells may be more dependent on EZH2 to maintain high H3K27me3 levels at specific loci. Treatment of the paired cell lines with the EZH2 inhibitor GSK343 for 7 days significantly decreased the viability of UTX-null ARD cells, but had no effect on the UTX wild-type ARP-1 cells. This effect was not exclusive to these cell lines, as treatment of a panel of UTX wild-type and mutant MM cells corroborated the increased sensitivity in UTX-mutant cells. RNA-seq of ARD cells treated with GSK343 for 7 days identified approximately 2,000 genes with altered expression in response to this drug, most of them being upregulated upon EZH2 inhibition. These genes partially overlapped with the genes that were responsive to UTX add-back, including E-cadherin, suggesting that treatment with EZH2 inhibitors is somewhat similar to UTX add-back. Collectively, this work demonstrates that loss of UTX alters the epigenetic landscape of MM cells, leading to altered expression of a specific set of genes, ultimately benefiting cells through increased proliferation, clonogenicity and adhesion. Moreover, inhibition of EZH2 partially reverses aberrations promoted by UTX loss and may represent a rationale therapy for the treatment of this type of MM. Disclosures No relevant conflicts of interest to declare.

NGS-based targeted RNA sequencing for expression analysis of patients with triple-negative breast cancer using a modulized, 96-gene biomarker panel.

2012 ◽  
Vol 30 (30_suppl) ◽  
pp. 56-56
Author(s):  
Byung-In Lee ◽  
Kahuku Oades ◽  
Lien Vo ◽  
Jerry Lee ◽  
Mark Landers ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Sequence Analysis ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Triple Negative ◽  
Dynamic Range ◽  
Rna Seq ◽  
Single Tube ◽  
Qrt Pcr

56 Background: Gene expression profiling has been shown to be effective in analyzing postoperative tumor samples in various cancers. However, in analyzing small specimens such as core biopsies, the limited amount of available material makes multi-gene analyses difficult or impossible. Microarray-based analyses also provide limited dynamic range. We describe the development of targeted RNA-sequencing methodology which combines the power of a universal RNA amplification with NGS for an ultra-deep expression analysis of multiple target genes, enabling <100 ng of sample input for multi-gene analysis in a single tube format. Methods: The gene expression patterns of triple-negative breast cancer FFPE samples were analyzed using a 96-gene breast cancer biomarker panel across three different platforms: Affymetrix Human Gene ST 1.0 microarrays, a pre-developed OncoScore qRT-PCR panel, and targeted RNA-seq. For targeted RNA-seq analysis, the 96-gene panel was amplified using a universal, single-tube “XP-PCR” amplification strategy followed by sequence analysis using the Ion-Torrent Personal Genome Machine. Results: Targeted RNA-seq provided the most sensitivity in terms of detection rates with <100 ng FFPE RNA input and provides unlimited dynamic range with increased sequencing depth. Expression ratio compression issues typically associated with a high number of pre-amplification cycles in standard multiplex-primed methods were not observed here. Low expressing genes, undetectable by qRT-PCR analysis from 1,000 ng input FFPE RNA, were detected and eligible for expression analysis with a significant number of sequencing reads. Alternative transcription/splicing analysis is also possible from sequence analysis of the target transcripts using targeted RNA-seq. Conclusions: By combining universally primed pre-amplification and NGS in multi-gene expression analysis, targeted RNA-seq provides the most sensitive gene expression analysis methodology.

scholarly journals Dysregulation of Splicing in Multiple Myeloma: The Splicing Factor SRSF1 Supports MM Cell Proliferation Via Splicing Control

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4500-4500
Author(s):  
Mariateresa Fulciniti ◽  
Michael A Lopez ◽  
Anil Aktas Samur ◽  
Eugenio Morelli ◽  
Hervé Avet-Loiseau ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Alternative Splicing ◽  
Board Of Directors ◽  
Research Funding ◽  
Splicing Factor ◽  
Splicing Factors ◽  
Rna Seq ◽  
Advisory Committees ◽  
Disease Biology

Abstract Gene expression profile has provided interesting insights into the disease biology, helped develop new risk stratification, and identify novel druggable targets in multiple myeloma (MM). However, there is significant impact of alternative pre-mRNA splicing (AS) as one of the key transcriptome modifier. These spliced variants increases the transcriptomic complexity and its misregulation affect disease behavior impacting therapeutic consideration in various disease processes including cancer. Our large well annotated deep RNA sequencing data from purified MM cells data from 420 newly-diagnosed patients treated homogeneously have identified 1534 genes with one or more splicing events observed in at least 10% or more patients. Median alternative splicing event per patient was 595 (range 223 - 2735). These observed global alternative splicing events in MM involves aberrant splicing of critical growth and survival genes affects the disease biology as well as overall survival. Moreover, the decrease of cell viability observed in a large panel of MM cell lines after inhibition of splicing at the pre-mRNA complex and stalling at the A complex confirmed that MM cells are exquisitely sensitive to pharmacological inhibition of splicing. Based on these data, we further focused on understanding the molecular mechanisms driving aberrant alternative splicing in MM. An increasing body of evidence indicates that altered expression of regulatory splicing factors (SF) can have oncogenic properties by impacting AS of cancer-associated genes. We used our large RNA-seq dataset to create a genome wide global alterations map of SF and identified several splicing factors significantly dysregulated in MM compared to normal plasma cells with impact on clinical outcome. The splicing factor Serine and Arginine Rich Splicing Factor 1 (SRSF1), regulating initiation of spliceosome assembly, was selected for further evaluation, as its impact on clinical outcome was confirmed in two additional independent myeloma datasets. In gain-of (GOF) studies enforced expression of SRSF1 in MM cells significantly increased proliferation, especially in the presence of bone marrow stromal cells; and conversely, in loss-of function (LOF) studies, downregulation of SRSF1, using stable or doxy-inducible shRNA systems significantly inhibited MM cell proliferation and survival over time. We utilized SRSF1 mutants to dissect the mechanisms involved in the SRSF1-mediated MM growth induction, and observed that the growth promoting effect of SRSF1 in MM cells was mainly due to its splicing activity. We next investigated the impact of SRSF1 on allelic isoforms of specific gene targets by RNA-seq in LOF and confirmed in GOF studies. Splicing profiles showed widespread changes in AS induced by SRSF1 knock down. The most recurrent splicing events were skipped exon (SE) and alternative first (AF) exon splicing as compared to control cells. SE splice events were primarily upregulated and AF splice events were evenly upregulated and downregulated. Genes in which splicing events in these categories occurred mostly did not show significant difference in overall gene expression level when compared to control, following SRSF1 depletion. When analyzing cellular functions of SRSF1-regulated splicing events, we found that SRSF1 knock down affects genes in the RNA processing pathway as well as genes involved in cancer-related functions such as mTOR and MYC-related pathways. Splicing analysis was corroborated with immunoprecipitation (IP) followed by mass spectrometry (MS) analysis of T7-tagged SRSF1 MM cells. We have observed increased levels of SRSF phosphorylation, which regulates it's subcellular localization and activity, in MM cell lines and primary patient MM cells compared to normal donor PBMCs. Moreover, we evaluated the chemical compound TG003, an inhibitor of Cdc2-like kinase (CLK) 1 and 4 that regulate splicing by fine-tuning the phosphorylation of SR proteins. Treatment with TG003 decreased SRSF1 phosphorylation preventing the spliceosome assembly and inducing a dose dependent inhibition of MM cell viability. In conclusions, here we provide mechanistic insights into myeloma-related splicing dysregulation and establish SRSF1 as a tumor promoting gene with therapeutic potential. Disclosures Avet-Loiseau: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Munshi:OncoPep: Other: Board of director.

scholarly journals Selecting optimal reference genes for breast cancer research

2020 ◽  
Author(s):  
Qiang Song ◽  
Man Huang ◽  
Guicheng Wu ◽  
Lu Dou ◽  
Wenjin Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Research ◽  
Cell Lines ◽  
Breast Tumor ◽  
Reference Genes ◽  
Breast Cancer Research ◽  
Rna Seq ◽  
Qrt Pcr ◽  
Cancer Tissues

Abstract Background Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) is the most sensitive technique for evaluating gene expression levels. Choosing appropriate reference genes (RGs) is critical for normalizing and evaluating changes in the expression of target genes. However, uniform and reliable RGs for breast cancer research have not been identified, limiting the value of target gene expression studies. Here, we provide a novel approach for mining RGs by using the RNA-seq dataset to identify reliable and accurate RGs that can be applied to different types of breast cancer tissues and cell lines. Methods First, we compiled the transcriptome profiling data from the TCGA database involving 1217 samples to identify novel RGs and then ten genes (SF1, TARDBP, THRAP3, QRICH1, TRA2B, SRSF3, YY1, DNAJC8, RNF10, and RHOA) with relatively stable expression levels were chosen as novel candidate RGs. Additionally, six conventional RGs (ACTB, TUBA1A, RPL13A, B2M, GAPDH, and GUSB) were also selected. To determine and validate the optimal RGs we performed qRT-PCR experiments on 87 samples from 5 types of surgically excised breast tumor specimens including HR+HER2-, HR+HER2+, HR-HER2-, HR-HER2+, breast cancer after neoadjuvant chemotherapy (NAC) and their matched para-carcinoma tissues, furthermore, we also included a benign breast tumor sample. Six biological replicates were included for each tissue. Moreover, we assessed 7 breast cancer cell lines (MCF-10A, MCF-7, T-47D, MDA-MB-231, MDA-MB-468, as well as MDA-MB-231 with either CNR2 knockdown or overexpression; 3 biological replicates for each line). Five statistical algorithms (geNorm, NormFinder, ΔCt method, BestKeeper, and ComprFinder) were used to assess the stability of expression of each RG across all breast cancer tissues and cell lines. Results Our results show that RG combinations SF1+TRA2B+THRAP3 and THRAP3+RHOA+QRICH1 showed stable expression in breast cancer tissues and cell lines, respectively, and that these two combinations displayed good interchangeability. Therefore, we propose that the above two combinations are optimal triplet RGs for breast cancer research. Conclusions In summary, we identified novel and reliable RG combinations for breast cancer research based on a public RNA-seq dataset which lays a solid foundation for accurate normalization of qRT-PCR results across different breast cancer tissues and cells.

scholarly journals Selection of suitable reference genes for qRT-PCR studies during SE initial dedifferentiation in cotton of different SE capability

2018 ◽  
Author(s):  
Cao Ai Ping ◽  
Shao Dong Nan ◽  
Cui Bai Ming ◽  
Zheng Yin Ying ◽  
Sun jie
Keyword(s):  
Gene Expression ◽  
Reference Genes ◽  
Gene Expression Level ◽  
Expression Level ◽  
Rna Seq ◽  
Qrt Pcr ◽  
Cotton Species ◽  
Wide Range ◽  
The Stability ◽  
Suitable Reference

Analysis of gene expression level by RNA sequencing (RNA-seq ) has a wide range of biological purposes in various species. Real-time fluorescent quantitative PCR (qRT-PCR) evaluated gene expression levels and validated transcriptomic, which will depend on the stably expressed reference genes for normalization of the gene expression level under specific situations. In this study, 15 candidate genes were selected from transcriptome datasets during somatic embryogenesis (SE) initial dedifferentiation in Gossypium hirsutum L. of different SE capability. To evaluate the stability of those genes, geNorm, NormFinder and BestKeeper were used. The results revealed that ENDO4 and 18srRNA could be as appropriate reference genes under all conditions. The stability and reliability of the reference genes were further tested through comparison of qRT-PCR results and RNA-seq data, as well as evaluation of the expression profiles of auxin-responsive protein (AUX22) and ethylene-responsive transcription factor (ERF17). In summary, the results of our study indicate the most suitable reference genes for qRT-PCR during three induction stages in four cotton species.

Gene Expression Profiling in Multiple Myeloma Cells Treated with the Novel Anti-Myeloma Agents Zoledronate and Fluvastatin.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 5078-5078
Author(s):  
Timothy J. Molloy ◽  
Baulch-Brown Cindy ◽  
Yi-Mo Deng ◽  
Andrew Spencer ◽  
David F. Ma
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Real Time ◽  
Real Time Pcr ◽  
Mevalonate Pathway ◽  
Annexin V ◽  
Quantitative Real Time Pcr ◽  
Myeloma Cells ◽  
Qrt Pcr

Abstract We have shown in vitro that multiple myeloma (MM) cells can be destroyed by treating them with the mevalonate pathway inhibitors zoledronate and fluvastatin. While the efficacy of these compounds singly and combination have been demonstrated, their exact modes of action remain largely unknown. The present study aimed to use microarray and quantitative real-time PCR (QRT-PCR) techniques to analyse gene expression in treated myeloma cells to identify novel genes and pathways involved in the anti-myeloma action of these compounds. The human MM cell line NCI-H929 was treated with zoledronate and fluvastatin singly and in combination, and RNA was extracted and used to interrogate oligonucleotide microarrays consisting of 19,000 features representing known and unknown genes. Quantitative real-time PCR was subsequently used to confirm the expression of several genes of interest. Flow cytometry with Annexin V FITC staining was used to detect apoptosis. It was observed that genes related to apoptosis (caspases and p53-related genes), cell cycle control (cyclins), GTPase signalling (Rabs), and growth and proliferation (growth factors) were particularly affected by zoledronate and fluvastatin, and some of these genetic effects were synergistic when a combination of zoledronate and fluvastatin was used. QRT-PCR confirmed the effects on the caspase- and p53-related apoptotic pathways, and these effects were correlated with increased apoptosis in the myeloma cells. The mevalonate pathway inhibitors fluvastatin and zoledronate are highly efficient at killing MM cells, and their effects appear to be synergistic. Our microarray and QT-PCR analyses demonstrated that the expression of specific groups of genes important to the survival and proliferation of myeloma cells are affected by these compounds. p53 and caspase-dependent pathways appear to be the key apoptotic cascades stimulated. Insights into the mechanisms of these novel therapeutics are important as they might help to define their roles in the treatment of multiple myeloma.

Gene Expression Profiling Of Multiple Myeloma Samples Using RNA Sequencing Identifies Frequent Rearrangements Of FCHSD2

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 534-534
Author(s):  
Venkata D Yellapantula ◽  
Christopher Murray ◽  
Winnie Liang ◽  
Daniel Auclair ◽  
Joan Levy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Genome Sequencing ◽  
Expression Profiling ◽  
Fusion Transcript ◽  
Rna Seq ◽  
Gene Index

Abstract The Multiple Myeloma Research Consortium (MMRC) has characterized over 300 hundred patient samples using a variety of platforms as part of the Multiple Myeloma Genomics Initiative (MMGI). Part of this large study includes a subset of 84 patients that were screened for somatic mutations using whole genome sequencing (WGS) or whole exome sequencing (WES) in combination with mRNA sequencing. This represents one of the first cohorts of myeloma patients with matched genome and transcriptome sequencing results. Given the historic value of microarray based gene expression profiling (GEP), this cohort provides the unique opportunity to compare gene expression measurements from the two platforms as Affymetrix U133Plus2.0 based GEP was performed on 42 of these samples. As part of the MMGI study, the Broad Institute has completed the genome sequencing, using WGS and WES, for 213 patients. A frequently mutated list of 9 genes including NRAS, KRAS, TP53, PNRC1, MAGED1, FAM46C, DIS3, CCND1 and ALOX12B were identified initially. Given the potential for RNAseq data to be used to define gene expression levels and to identify mutations in expressed genes we tested the feasibility of mutation calling on RNAseq alone. We independently called mutations on the entire transcriptome of the 84 patients and used a filtering method to eliminate likely germline variants in the absence of a matched normal control. We looked for point mutation concordance between, the calls identified by RNA-Seq alone and the previously reported variants through exome sequencing in the 9 frequently mutated genes. Out of the 66 SNV’s identified by these criteria using WGS or WES sequencing, 55(84%) were detected using RNA-Seq. Of the remaining 11 loci, 7(10%) were not detectably expressed and in 4(6%) cases the mutation was not detectable even though there was ample coverage. It is unclear if the last 6% represent false positives in the genome calls or the preferential expression of the wild-type allele. To interrogate the utility of RNAseq based GEP in myeloma we independently recapitulated many of common GEP measurements. First we independently used the 84 samples to define cutoffs for the implementation of the TC classification method. We compared our independent assignment of the 42 samples with matched gene expression array data, to their existing microarray assignments. This resulted in 40/42 (95%) samples being classified 40(95%) into identical TC classes. The two discordant samples MMRC0312 and MMRC0387 classified as TC class “none” by expression arrays were classified as other classes by RNAseq. MMRC0312 exhibited high CCND3 expression using RNA-Seq and was assigned to ‘6p21’ class. MMRC0387 exhibited elevated CCND1 expression and was classified as ‘D1’ using RNAseq. For the indexes we showed a strong correlation for the proliferation index (R2=0.971) and the NFKB index (R2=0.961) but only a moderate correlation for the 70-gene index (R2=0.761). The decreased correlation in the 70-gene index is clearly due to the large number of probesets used, which are associated with genes that are clearly not expressed by RNA-seq. One additional advantage of RNAseq over microarray based gene expression measurements is the potential to detect fusion transcripts. We have applied fusion transcript detection to this cohort of patients and 69 human myeloma cell lines, which were also screened by RNAseq and WES as part of the MMGI study. The most common fusion transcript detected is the @IGH-MMSET fusion characteristic of t(4;14). The next most common fusion we identified appears to be a promoter replacement event were the highly expressed gene, FCHSD2, is fused to multiple partners including known myeloma related genes, MMSET and MYC, and previously unreported genes in myeloma, CARNS1 and NCF2. Additional structural rearrangements involving FCHSD2 are also predicted based on the high frequency of copy number abnormalities encompassing the 5′ region of this gene as detected by comparative genomic hybridization in the MMGI study. This study should provide the basis for the migration of myeloma based gene expression profiling from microarrays to RNA sequencing based approaches. In the future RNA sequencing has the potential to provide novel classification schemes that leverage the multitude of measurements that can be made from this single assay. Disclosures: Levy: MMRC: Employment.

Sign in / Sign up

Export Citation Format

Share Document