scholarly journals Characterization of kinase gene expression and splicing profile in prostate cancer with RNA-Seq data

2016 ◽  
Author(s):  
Huijuan Feng ◽  
Tingting Li ◽  
Xuegong Zhang
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Alternative Splicing ◽  
Differential Expression ◽  
Functional Enrichment ◽  
Cancer Development ◽  
Rna Seq ◽  
Differential Splicing ◽  
Kinase Gene ◽  
Isoform Switching

AbstractBackgroundAlternative splicing is a ubiquitous post-transcriptional process in most eukaryotic genes. Aberrant splicing isoforms and abnormal isoform ratios can contribute to cancer development. Kinase genes are key regulators of various cellular processes. Many kinases are found to be oncogenic and have been intensively investigated in the study of cancer and drugs. RNA-Seq provides a powerful technology for genome-wide study of alternative splicing in cancer besides the conventional gene expression profiling. But this potential has not been fully demonstrated yet.MethodsHere we characterized the transcriptome profile of prostate cancer using RNA-Seq data from viewpoints of both differential expression and differential splicing, with an emphasis on kinase genes and their splicing variations. We built up a pipeline to conduct differential expression and differential splicing analysis. Further functional enrichment analysis was performed to explore functional interpretation of the genes. With focus on kinase genes, we performed kinase domain analysis to identify the functionally important candidate kinase gene in prostate cancer. We further calculated the expression level of isoforms to explore the function of isoform switching of kinase genes in prostate cancer.ResultsWe identified distinct gene groups from differential expression and splicing analysis, which suggested that alternative splicing adds another level to gene expression regulation. Enriched GO terms of differentially expressed and spliced kinase genes were found to play different roles in regulation of cellular metabolism. Function analysis on differentially spliced kinase genes showed that differentially spliced exons of these genes are significantly enriched in protein kinase domains. Among them, we found that gene CDK5 has isoform switching between prostate cancer and benign tissues, which may affect cancer development by changing androgen receptor (AR) phosphorylation. The observation was validated in another RNA-Seq dataset of prostate cancer cell lines.ConclusionsOur work characterized the expression and splicing profile of kinase genes in prostate cancer and proposed a hypothetical model on isoform switching of CDK5 and AR phosphorylation in prostate cancer. These findings bring new understanding to the role of alternatively spliced kinases in prostate cancer and demonstrate the use of RNA-Seq data in studying alternative splicing in cancer.

scholarly journals Alternative Splicing Is a Frequent Event and Impacts Clinical Outcome in Myeloma: A Large RNA-Seq Data Analysis of Newly-Diagnosed Myeloma Patients

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 638-638 ◽  
Author(s):  
Naim Rashid ◽  
Stephane Minvielle ◽  
Florence Magrangeas ◽  
Mehmet Kemal Samur ◽  
Alice Clynen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Exon Skipping ◽  
P Value ◽  
Newly Diagnosed ◽  
Rna Seq ◽  
Exon Level ◽  
Total Gene Expression ◽  
Isoform Switching ◽  
Alternative Splicing Events

Abstract Alternative splicing is an important post-translational change that alters gene function. Misregulation of alternative splicing has been implicated in number of disease processes including cancer. Here we have analyzed alternative splicing in myeloma using high throughput RNA-seq. Our analytic pipeline for RNA-seq data used in this investigation not only provides information on expression levels for genes, but also provides information on the expression of known splice variants of genes (isoforms), and can identify novel exon level events across individuals (i.e. exon skipping events). We conducted a study of 328 newly-diagnosed patients with multiple myeloma treated homogeneously with novel agent combination containting lenalidomide, bortezomib and dexamethsone with or without high-dose melphalan followed by lenalidomide maintenance in the IFM/DFCI study. RNA isolated from purified CD138+ MM cells collected at the time of diagnosis and from 18 normal donor plasma cells were processed by RNA-seq (100 million paired end reads on Illumina HiSeq) and analyzed using a custom computational and statistical pipeline. Following read alignment to hg19, we utilized RSEM to quantify both gene-level and isoform-level expression of known ENSEMBL transcripts. We then implemented a novel testing approach based on compositional regression to discover genes that show significant isoform switching between the 328 MM samples and 18 Normal Plasma Cell (NPC) samples from healthy donors. Using various programs and their modifications, we also identified novel alternative splicing events, such as exon skipping and mutually exclusive exon usage, among others. Patient data for MM characteristics, cytogenetic and FISH as well as clinical survival outcomes were also analyzed and correlated with genomic data. We observed over 600 genes showing significant changes in relative isoform abundances (isoform switching) between MM and normal samples. A number of previously characterized genes including MYCL1 (adj. p = 0.0014) and CCND3 (adj. p = 0.0013), and MAP kinase-related genes (MAP3K8, MAPKAPK2, MAPKAPK3, MAP4K4) exhibited significant isoform switching compared to normal, in addition to some not well characterized genes. Genes showing the greatest magnitude of isoform switching include MEFV (adj. p = 2.7 x 10-5), showing a two fold change in the relative major isoform abundance compared to normal, and has been previously shown to have a role in lymphoid neoplasms. We applied hierarchical clustering to the isoforms showing significant changes in isoform-switching and identified 4 distinct clusters, which are currently being investigated for correlation with clinical subtypes of MM. Exon level analyses of alternative splicing events, such as exon skipping, are currently underway. Clinical data including MM characteristics, cytogenetics, FISH and survival outcomes was available for a subset of 265 patients. We found that 109 genes showed significant isoform switching between t(4;14) and non-t(4;14) patients, such as CD44 (adj. p =1.8 x 10-6) and WHSC1 (adj. p =5.1 x 10-28). Comparing del17p (28 in total) and non del17p patients, we found no significant splicing changes after multiple testing adjustment. Of these genes, only a subset (40%) were shown to be differentially expressed in terms of total gene expression, suggesting the importance of examining alternative splicing events in addition to total gene expression. With respect to treatment response, we compared the expression of gene isoforms between patients achieving complete response (CR) versus others and identified 38 isoforms associated with response to treatment (adj. p value < 0.05), with SEPT9, SLC2A5, and UBX6 having the strongest associations (adj. p-value < 3 x 10-4). Using a univariate cox regression model, 4 spliced isoforms relating to 3 genes were identified as having significant correlation with event-free survival (EFS) (FDR-adjusted cox p value < 0.05). We are in the process of now integrating the gene expression data with altered splicing data to develop an integrated survival model. In summary, this study highlights the significant frequency, biological and clinical importance of alternative splicing in MM and points to the need for evaluation of not only the expression level of genes but also post-translational modifications. The genes identified here are important targets for therapy as well as possible immune modulation. Disclosures Moreau: Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees.

scholarly journals Hotspot Mutations in SF3B1 Result in Increased Alternative Splicing in Multiple Myeloma and Activation of Key Cellular Pathways

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4454-4454
Author(s):  
Michael A Bauer ◽  
Cody Ashby ◽  
Christopher Wardell ◽  
Maria Ortiz ◽  
Erin Flynt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
P Value ◽  
Rna Seq ◽  
Splice Sites ◽  
Differential Splicing ◽  
Employment Equity ◽  
Differential Gene ◽  
Equity Ownership ◽  
Hotspot Mutations

Abstract Introduction: Mutations in the components of the spliceosome have been shown to occur at relatively high frequency in many cancers such as chronic lymphocytic leukemia, myelodysplastic syndromes and breast cancer. One component in particular, encoded by SF3B1, has hotspot missense mutations that result in a significant increase in alternatively spliced transcripts. RNA splicing in Multiple Myeloma (MM) has not been investigated and in particular the extent of mutations in SF3B1 and its effects on the transcriptome. Methods: Using the MMRF CoMMpass dataset (N=1273) of newly diagnosed MM patients, samples with whole exome sequencing (WES) were analyzed for mutations using Strelka and Mutect, and samples with SF3B1 mutations identified. A range of approaches were used to explore the effect of the SF3B1 mutations on the transcriptome and to determine possible downstream effects. Using RNA-seq with matched WES samples (n=615), the splice junction usage of SF3B1 mutants was compared against non-mutated samples which were matched for key MM molecular sub-types. The RNA-seq data was analyzed using a pipeline that included STAR and Salmon, aligning to human reference genome hg38, gene and transcript differential expression analysis tools DESeq2 and StringTie/Ballgown, differential splicing exon usage tools JunctionSeq/QoRTs, DEXSeq, and SUPPA and for Gene Set Enrichment Analysis (GSEA) the R package FGSEA was used. Results: From the WES data 1.7% (22/1273) of samples had mutations in SF3B1 of which 5 had mutations in the hotpot codons of K666 and K700. Differential isoform analysis of the 22 SF3B1 mutant samples compared to non-mutated samples did not identify any transcripts. However, when the analysis was restricted to the 5 samples with hotspot mutations differential gene expression identified 146 genes that were significantly differentially expressed at an adjusted p-value <0.05. Additionally, many genes that did not show an overall gene expression change between the control and the SF3B1 hotspot mutants did at transcriptional level where we observed isoform switching which included the protein coding genes BCL2L1, SNUR, ACKR3 and CRLF2. Results of differential gene analysis between the control and SF3B1 mutants were used in GSEA and significant normalized enrichment scores (NES) identifying increased protein secretion (p-value =0.009, NES= 1.9) and unfolded protein response (UPR) (p-value = 0.02, NES = 1.52) pathways. Conversely GSEA identified decreased apoptosis (p-value = 0.008, NES = -1.76), KRAS signaling (p-value = 0.008, NES = -1.92), TNFA signaling via NF-κB (p-value = 0.008, NES= 2.12) pathways in SF3B1 mutant samples. Investigation of splicing loci revealed that novel splice loci were significantly more abundant in the SF3B1 mutants versus control samples. Differential splicing analysis detected 474 genes to be significantly differentially spliced and of those 311 were not found to be differentially expressed at the gene level, indicating that alternative splicing is as important alternative mechanism to gene expression differences. 59 novel splice sites were identified, as well as 152 known splice sites and 218 exon significant differential usage with a p-value of < 0.05. The genes with most significant levels of alternative splicing and found by more than one approach were DYNLL1, TMEM14C, CRNDE, BRD4 and BCL2L1, several of which are also seen in other cancers with mutated SF3B1. Conclusions: Hotspot mutations in SF3B1 result in alternative splicing of genes as well as the introduction of novel splice sites. The confirmation that SF3B1 hotspot mutations in MM increases alternative splicing as well as the identification of the genes undergoing alternative splicing may present novel therapeutic targets. Gene expression analysis of these samples identifies key deregulated pathways, perhaps in response to alternative splicing, including the UPR and protein secretion pathways. These analyses indicate that disruption of these pathways are potential avenues of therapeutic intervention in patients with SF3B1 mutations. Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene Corporation: Employment, Equity Ownership. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria.

scholarly journals Transcriptome analysis provides a blueprint of coral egg and sperm functions

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9739
Author(s):  
Julia Van Etten ◽  
Alexander Shumaker ◽  
Tali Mass ◽  
Hollie M. Putnam ◽  
Debashish Bhattacharya
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Coral Species ◽  
Functional Enrichment ◽  
Ion Binding ◽  
Rna Seq ◽  
Missing Information ◽  
Broadcast Spawning ◽  
Gamete Recognition ◽  
Sperm Functions

Background Reproductive biology and the evolutionary constraints acting on dispersal stages are poorly understood in many stony coral species. A key piece of missing information is egg and sperm gene expression. This is critical for broadcast spawning corals, such as our model, the Hawaiian species Montipora capitata, because eggs and sperm are exposed to environmental stressors during dispersal. Furthermore, parental effects such as transcriptome investment may provide a means for cross- or trans-generational plasticity and be apparent in egg and sperm transcriptome data. Methods Here, we analyzed M. capitata egg and sperm transcriptomic data to address three questions: (1) Which pathways and functions are actively transcribed in these gametes? (2) How does sperm and egg gene expression differ from adult tissues? (3) Does gene expression differ between these gametes? Results We show that egg and sperm display surprisingly similar levels of gene expression and overlapping functional enrichment patterns. These results may reflect similar environmental constraints faced by these motile gametes. We find significant differences in differential expression of egg vs. adult and sperm vs. adult RNA-seq data, in contrast to very few examples of differential expression when comparing egg vs. sperm transcriptomes. Lastly, using gene ontology and KEGG orthology data we show that both egg and sperm have markedly repressed transcription and translation machinery compared to the adult, suggesting a dependence on parental transcripts. We speculate that cell motility and calcium ion binding genes may be involved in gamete to gamete recognition in the water column and thus, fertilization.

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.

scholarly journals Giant Island Mice Exhibit Widespread Gene Expression Changes in Key Metabolic Organs

2020 ◽  
Vol 12 (8) ◽  
pp. 1277-1301
Author(s):  
Mark J Nolte ◽  
Peicheng Jing ◽  
Colin N Dewey ◽  
Bret A Payseur
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Rapid Evolution ◽  
Regulatory Elements ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
House Mice ◽  
Nutrient Metabolism ◽  
Island Populations ◽  
Gough Island

Abstract Island populations repeatedly evolve extreme body sizes, but the genomic basis of this pattern remains largely unknown. To understand how organisms on islands evolve gigantism, we compared genome-wide patterns of gene expression in Gough Island mice, the largest wild house mice in the world, and mainland mice from the WSB/EiJ wild-derived inbred strain. We used RNA-seq to quantify differential gene expression in three key metabolic organs: gonadal adipose depot, hypothalamus, and liver. Between 4,000 and 8,800 genes were significantly differentially expressed across the evaluated organs, representing between 20% and 50% of detected transcripts, with 20% or more of differentially expressed transcripts in each organ exhibiting expression fold changes of at least 2×. A minimum of 73 candidate genes for extreme size evolution, including Irs1 and Lrp1, were identified by considering differential expression jointly with other data sets: 1) genomic positions of published quantitative trait loci for body weight and growth rate, 2) whole-genome sequencing of 16 wild-caught Gough Island mice that revealed fixed single-nucleotide differences between the strains, and 3) publicly available tissue-specific regulatory elements. Additionally, patterns of differential expression across three time points in the liver revealed that Arid5b potentially regulates hundreds of genes. Functional enrichment analyses pointed to cell cycling, mitochondrial function, signaling pathways, inflammatory response, and nutrient metabolism as potential causes of weight accumulation in Gough Island mice. Collectively, our results indicate that extensive gene regulatory evolution in metabolic organs accompanied the rapid evolution of gigantism during the short time house mice have inhabited Gough Island.

scholarly journals Differential gene expression during early development in recently evolved and sympatric Arctic charr morphs

2017 ◽  
Author(s):  
Jóhannes Guðbrandsson ◽  
Sigríður Rut Franzdóttir ◽  
Bjarni Kristófer Kristjánsson ◽  
Ehsan Pashay Ahi ◽  
Valerie Helene Maier ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Early Development ◽  
Explanatory Variable ◽  
Arctic Charr ◽  
Transcriptome Assembly ◽  
Developmental Time ◽  
The Body ◽  
Functional Enrichment ◽  
Population Divergence

Phenotypic differences between closely related taxa or populations can arise through genetic variation or be environmentally induced, in both cases leading to altered transcription of genes during the structural and functional development of the body. Comparative developmental studies of closely related species or variable populations of the same species can help to elucidate the molecular mechanisms related to population divergence and speciation. Studies of Arctic charr (Salvelinus alpinus) and related salmonids have revealed considerable phenotypic variation among populations and in Arctic charr many cases of extensive variation within lakes (resource polymorphism) have been recorded. One example is the four Arctic charr morphs in the ~10.000 year old Lake Thingvallavatn, which differ in numerous morphological and life history traits. We set out to investigate the molecular and developmental roots of this polymorphism by studying gene expression in embryos of three of the morphs reared in a common garden set-up. We performed RNA-sequencing, de-novo transcriptome assembly and compared gene expression among morphs during a timeframe in early development. Expectedly, developmental time was the predominant explanatory variable. As the data were affected by RNA-degradation, an estimate of 3’-bias was the second most common explanatory variable. Morph, both as a independent variable and as interaction with developmental time, affected the expression of numerous transcripts. The majority of transcripts with significant morph effects separated the limnetic and the benthic morphs. However, gene ontology analyses did not reveal clear functional enrichment of transcripts between groups. Verification via qPCR confirmed differential expression of several genes between the morphs, including regulatory genes such as Arid4a and Tsn. The data are consistent with a scenario where genetic divergence has contributed to differential expression of multiple genes and systems during early development of these sympatric Arctic charr morphs.

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 Transcriptomic Evaluation of CD34+ Marrow Cells from Myelodysplastic Syndrome (MDS) Patients

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1894-1894
Author(s):  
Hogune Im ◽  
Varsha Rao ◽  
Kunju Joshi Sridhar ◽  
Rui Chen ◽  
George Mias ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differential Expression ◽  
High Throughput ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Reference Sequence ◽  
Rna Seq ◽  
Cd34 Cells ◽  
Marrow Cells

Abstract Background: Prior studies using microarray platforms have shown alterations of gene expression profiles (GEPs) in MDS CD34+ marrow cells related to clinical outcomes (Sridhar et al, Blood 2009, Pellagatti et al, JCO 2013). Given the increased sensitivity and accuracy of high-throughput RNA sequencing (RNA-Seq) (Mortazavi et al, Nat Meth 2008, Soon et al, Mol Syst Bio 2012) for detecting and quantifying mRNA transcripts, we applied this methodology for evaluating differential gene expression between MDS and normal CD34+ marrow cells. Methods:RNA was isolated from magnetic bead affinity-enriched CD34+ (>90%) marrow aspirate cells (Miltenyi Biotec, Auburn, CA) and amplified using the Smarter Kit (Clontech, Mt View, CA). The amplified product (ds DNA) was fragmented to a size distribution of ~200-300bp using the E220 Focused Ultrasonicator (Covaris Inc, Woburn, MA). End repair, adapter ligation and PCR amplification were performed using the NEBNext Ultra RNA library prep kit for Illumina (New England Biolabs, Ipswich, MA). The indexed cDNA libraries were sequenced (paired end, 100bp) on an Illumina HiSeq2000 platform with median read counts of 69 million. The sequences were aligned to Human Reference sequence hg19 using DNAnexus mapper with gene detection focused on known annotated genes. The differential expression was analyzed using edgeR. DAVID and Ingenuity IPA programs were used for pathway analyses. Gene Set Enrichment Analysis (GSEA) was used to identify biologic processes in our dataset present across phenotypes. Results: Correlations of RNA-Seq data from unamplified to amplified transcripts demonstrated high fidelity of transcripts obtained (Pearson and Spearman R2 = 0.80). After filtering samples for adequate read counts, 12,323 genes were evaluated. Differential expression analysis yielded 719 differentially expressed genes (DEGs) in MDS (n=30) vs normal (n=21) with FDR <.05. Among the DEGs, 548 and 171 were over- and under-expressed ≥2 fold in MDS vs Normal, respectively: 20% of the overexpressed genes were present in >50% of the patients. Hierarchical cluster analysis using these DEGs confirmed clear separation of MDS patients from normals, with 2 differential expression clusters—one region overexpressed and one underexpressed. A distinctive trend toward clustering of the patients was seen which related to their IPSS categories and marrow blast %. In functional pathway analysis of the 2 distinctive gene clusters which distinguished MDS from normal, the underexpressed MDS DEGs demonstrated enrichment of inflammatory cytokines, oxidative stress and interleukin signaling pathways, plus mitochondrial calcium transport; whereas the MDS overexpressed DEG cluster showed enrichment of adherens junction/cytokeletal remodeling, cell cycle control of chromosome replication and DNA damage response pathways. Using GSEA analysis, significantly increased numbers of genes in MDS vs normal, common to those in gene sets present within curated public databases, were involved with TP53 targets and mTOR signaling pathways. Conclusions: Our study demonstrated that RNA-Seq methodology, a high-throughput and more comprehensive technique than most gene expression microarrays, was capable of showing significant and distinctive differences in gene expression between MDS and normal marrow CD34+ cells. Specific clustering of the DEGs was demonstrated to distinguish patient subsets associated with their major clinical features. Further, the stringently identified DEGs shown to be engaged in functional pathways and biologic processes highly relevant for MDS were extant within the patients’ CD34+ cells. These transcriptomic data provide information complementary to exomic mutational findings contributing to improved understanding of biologic mechanisms underlying MDS. Disclosures No relevant conflicts of interest to declare.

scholarly journals Differential gene expression during early development in recently evolved and sympatric Arctic charr morphs

2017 ◽  
Author(s):  
Jóhannes Guðbrandsson ◽  
Sigríður Rut Franzdóttir ◽  
Bjarni Kristófer Kristjánsson ◽  
Ehsan Pashay Ahi ◽  
Valerie Helene Maier ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Early Development ◽  
Explanatory Variable ◽  
Arctic Charr ◽  
Transcriptome Assembly ◽  
Developmental Time ◽  
The Body ◽  
Functional Enrichment ◽  
Population Divergence

Phenotypic differences between closely related taxa or populations can arise through genetic variation or be environmentally induced, in both cases leading to altered transcription of genes during the structural and functional development of the body. Comparative developmental studies of closely related species or variable populations of the same species can help to elucidate the molecular mechanisms related to population divergence and speciation. Studies of Arctic charr (Salvelinus alpinus) and related salmonids have revealed considerable phenotypic variation among populations and in Arctic charr many cases of extensive variation within lakes (resource polymorphism) have been recorded. One example is the four Arctic charr morphs in the ~10.000 year old Lake Thingvallavatn, which differ in numerous morphological and life history traits. We set out to investigate the molecular and developmental roots of this polymorphism by studying gene expression in embryos of three of the morphs reared in a common garden set-up. We performed RNA-sequencing, de-novo transcriptome assembly and compared gene expression among morphs during a timeframe in early development. Expectedly, developmental time was the predominant explanatory variable. As the data were affected by RNA-degradation, an estimate of 3’-bias was the second most common explanatory variable. Morph, both as a independent variable and as interaction with developmental time, affected the expression of numerous transcripts. The majority of transcripts with significant morph effects separated the limnetic and the benthic morphs. However, gene ontology analyses did not reveal clear functional enrichment of transcripts between groups. Verification via qPCR confirmed differential expression of several genes between the morphs, including regulatory genes such as Arid4a and Tsn. The data are consistent with a scenario where genetic divergence has contributed to differential expression of multiple genes and systems during early development of these sympatric Arctic charr morphs.

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