scholarly journals Mapping eQTLs With RNA-Seq Reveals Novel SLE Susceptibility Genes, Non-Coding RNAs, and Alternative-Splicing Events That Are Concealed Using Microarrays

2016 ◽  
Author(s):  
Christopher A. Odhams ◽  
Andrea Cortini ◽  
Lingyan Chen ◽  
Amy L. Roberts ◽  
Ana Vinuela ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lupus Erythematosus ◽  
Molecular Mechanisms ◽  
Complex Disease ◽  
Splice Junction ◽  
Susceptibility Genes ◽  
Eqtl Analysis ◽  
Rna Seq ◽  
Control Of Gene Expression ◽  
Non Coding Rnas

AbstractStudies attempting to functionally interpret complex-disease susceptibility loci by GWAS and eQTL integration have predominantly employed microarrays to quantify gene-expression. RNA-Seq has the potential to discover a more comprehensive set of eQTLs and illuminate the underlying molecular consequence. We examine the functional outcome of 39 variants associated with Systemic Lupus Erythematosus (SLE) through integration of GWAS and eQTL data from the TwinsUK microarray and RNA-Seq cohort in lymphoblastoid cell lines. We use conditional analysis and a Bayesian colocalisation method to provide evidence of a shared causal-variant, then compare the ability of each quantification type to detect disease relevant eQTLs and eGenes. We discovered a greater frequency of candidate-causal eQTLs using RNA-Seq, and identified novel SLE susceptibility genes that were concealed using microarrays (e.g. NADSYN1, SKP1, and TCF7). Many of these eQTLs were found to influence the expression of several genes, suggesting risk haplotypes may harbour multiple functional effects. We pinpointed eQTLs modulating expression of four non-coding RNAs; three of which were replicated in whole-blood. Novel SLE associated splicing events were identified in the T-reg restricted transcription factor, IKZF2, the autophagy-related gene WDFY4, and the redox coenzyme NADSYN1, through asQTL mapping using the Geuvadis cohort. We have significantly increased our understanding of the genetic control of gene-expression in SLE by maximising the leverage of RNA-Seq and performing integrative GWAS-eQTL analysis against gene, exon, and splice-junction quantifications. In doing so, we have identified novel SLE candidate genes and specific molecular mechanisms that will serve as the basis for targeted follow-up studies.

Abstract 286: Long Noncoding RNAs Are Differentially Expressed in Heart Failure

2012 ◽  
Vol 111 (suppl_1) ◽  
Author(s):  
Emma L Robinson ◽  
Syed Haider ◽  
Hillary Hei ◽  
Richard T Lee ◽  
Roger S Foo
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Significant Proportion ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Control Of Gene Expression ◽  
Failing Heart ◽  
Novel Transcripts ◽  
Non Coding Rnas

Heart failure comprises of clinically distinct inciting causes but a consistent pattern of change in myocardial gene expression supports the hypothesis that unifying biochemical mechanisms underlie disease progression. The recent RNA-seq revolution has enabled whole transcriptome profiling, using deep-sequencing technologies. Up to 70% of the genome is now known to be transcribed into RNA, a significant proportion of which is long non-coding RNAs (lncRNAs), defined as polyribonucleotides of ≥200 nucleotides. This project aims to discover whether the myocardium expression of lncRNAs changes in the failing heart. Paired end RNA-seq from a 300-400bp library of ‘stretched’ mouse myocyte total RNA was carried out to generate 76-mer sequence reads. Mechanically stretching myocytes with equibiaxial stretch apparatus mimics pathological hypertrophy in the heart. Transcripts were assembled and aligned to reference genome mm9 (UCSC), abundance determined and differential expression of novel transcripts and alternative splice variants were compared with that of control (non-stretched) mouse myocytes. Five novel transcripts have been identified in our RNA-seq that are differentially expressed in stretched myocytes compared with non-stretched. These are regions of the genome that are currently unannotated and potentially are transcribed into non-coding RNAs. Roles of known lncRNAs include control of gene expression, either by direct interaction with complementary regions of the genome or association with chromatin remodelling complexes which act on the epigenome.Changes in expression of genes which contribute to the deterioration of the failing heart could be due to the actions of these novel lncRNAs, immediately suggesting a target for new pharmaceuticals. Changes in the expression of these novel transcripts will be validated in a larger sample size of stretched myocytes vs non-stretched myocytes as well as in the hearts of transverse aortic constriction (TAC) mice vs Sham (surgical procedure without the aortic banding). In vivo investigations will then be carried out, using siLNA antisense technology to silence novel lncRNAs in mice.

Epigenetics

2019 ◽  
pp. 56-70
Author(s):  
Edward Hookway ◽  
Nicholas Athanasou ◽  
Udo Oppermann
Keyword(s):  
Gene Expression ◽  
Histone Deacetylases ◽  
Molecular Mechanisms ◽  
Current Knowledge ◽  
Tumour Suppressor Genes ◽  
Epigenetic Mechanisms ◽  
Therapeutic Approaches ◽  
Control Of Gene Expression ◽  
Non Coding Rnas ◽  
Epigenetic Processes

Epigenetics is a term that refers to a collection of diverse mechanisms that are important in both the control of gene expression and the transmission of this information during cell division. Epigenetic processes are deranged in many cancers, leading to a combination of inappropriate silencing of tumour suppressor genes and overexpression of oncogenes. In this chapter, the molecular mechanisms that underpin the major epigenetic processes of DNA methylation, histone modification, and non-coding RNAs will be described in both their normal physiological roles and in the context of cancer. The challenge of understanding the complexity of the interactions between different epigenetic mechanisms and the limitations of our current knowledge will be highlighted. Therapeutic approaches towards targeting deranged epigenetic processes will also be described, such as the use of small molecule inhibitors of histone deacetylases.

scholarly journals Comparative RNA-Seq Analyses of Solenopsis japonica (Hymenoptera: Formicidae) Reveal Gene in Response to Cold Stress

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1610
Author(s):  
Mohammad Vatanparast ◽  
Youngjin Park
Keyword(s):  
Gene Expression ◽  
Cold Stress ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Predatory Behavior ◽  
P Value ◽  
Molecular Response ◽  
Rna Seq ◽  
Protein Database ◽  
Brood Chamber

Solenopsis japonica, as a fire ant species, shows some predatory behavior towards earthworms and woodlice, and preys on the larvae of other ant species by tunneling into a neighboring colony’s brood chamber. This study focused on the molecular response process and gene expression profiles of S. japonica to low (9 °C)-temperature stress in comparison with normal temperature (25 °C) conditions. A total of 89,657 unigenes (the clustered non-redundant transcripts that are filtered from the longest assembled contigs) were obtained, of which 32,782 were annotated in the NR (nonredundant protein) database with gene ontology (GO) terms, gene descriptions, and metabolic pathways. The results were 81 GO subgroups and 18 EggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) keywords. Differentially expressed genes (DEGs) with log2fold change (FC) > 1 and log2FC < −1 with p-value ≤ 0.05 were screened for cold stress temperature. We found 215 unigenes up-regulated and 115 unigenes down-regulated. Comparing transcriptome profiles for differential gene expression resulted in various DE proteins and genes, including fatty acid synthases and lipid metabolism, which have previously been reported to be involved in cold resistance. We verified the RNA-seq data by qPCR on 20 up- and down-regulated DEGs. These findings facilitate the basis for the future understanding of the adaptation mechanisms of S. japonica and the molecular mechanisms underlying the response to low temperatures.

scholarly journals Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

2019 ◽  
Author(s):  
William A Figgett ◽  
Katherine Monaghan ◽  
Milica Ng ◽  
Monther Alhamdoosh ◽  
Eugene Maraskovsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Clinical Trials ◽  
Lupus Erythematosus ◽  
Whole Blood ◽  
Rna Seq ◽  
Systemic Lupus ◽  
Disease Heterogeneity ◽  
Healthy Donors

ABSTRACTObjectiveSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.MethodsWe applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.ResultsExamination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.ConclusionGiven that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

scholarly journals BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Paulo Rapazote-Flores ◽  
Micha Bayer ◽  
Linda Milne ◽  
Claus-Dieter Mayer ◽  
John Fuller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Splice Junction ◽  
Rna Seq ◽  
Rt Pcr ◽  
High Quality ◽  
Transcript Quantification ◽  
Reference Transcript ◽  
Alternatively Spliced ◽  
Time Required ◽  
Comprehensive Reference

Abstract Background The time required to analyse RNA-seq data varies considerably, due to discrete steps for computational assembly, quantification of gene expression and splicing analysis. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants. Results A high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts – BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al. Nature; 544: 427–433, 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al. Plant Physiol; 156: 20–28, 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5′ and 3′ UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2791 differentially alternatively spliced genes and 2768 transcripts with differential transcript usage. Conclusion A high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.

scholarly journals Transcriptome Analysis Reveals the Molecular Mechanisms Underlying Adenosine Biosynthesis in Anamorph Strain of Caterpillar Fungus

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Shan Lin ◽  
Zhicheng Zou ◽  
Cuibing Zhou ◽  
Hancheng Zhang ◽  
Zhiming Cai
Keyword(s):  
Gene Expression ◽  
Transcriptome Analysis ◽  
Molecular Mechanisms ◽  
Purine Metabolism ◽  
Expression Data ◽  
Rna Seq ◽  
Metabolism Pathway ◽  
Caterpillar Fungus ◽  
Regulated Gene Expression ◽  
Late Stages

Caterpillar fungus is a well-known fungal Chinese medicine. To reveal molecular changes during early and late stages of adenosine biosynthesis, transcriptome analysis was performed with the anamorph strain of caterpillar fungus. A total of 2,764 differentially expressed genes (DEGs) were identified (p≤0.05, |log2 Ratio| ≥ 1), of which 1,737 were up-regulated and 1,027 were down-regulated. Gene expression profiling on 4–10 d revealed a distinct shift in expression of the purine metabolism pathway. Differential expression of 17 selected DEGs which involved in purine metabolism (map00230) were validated by qPCR, and the expression trends were consistent with the RNA-Seq results. Subsequently, the predicted adenosine biosynthesis pathway combined with qPCR and gene expression data of RNA-Seq indicated that the increased adenosine accumulation is a result of down-regulation of ndk, ADK, and APRT genes combined with up-regulation of AK gene. This study will be valuable for understanding the molecular mechanisms of the adenosine biosynthesis in caterpillar fungus.

scholarly journals Transcriptomic Analysis of mRNA-lncRNA-miRNA Interactions in Hepatocellular Carcinoma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xia Tang ◽  
Delong Feng ◽  
Min Li ◽  
Jinxue Zhou ◽  
Xiaoyuan Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Rna Seq ◽  
Pairwise Interactions ◽  
Micro Rnas ◽  
Non Coding Rnas ◽  
First Time

Abstract Fully elucidating the molecular mechanisms of non-coding RNAs (ncRNAs), including micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs), underlying hepatocarcinogenesis is challenging. We characterized the expression profiles of ncRNAs and constructed a regulatory mRNA-lncRNA-miRNA (MLMI) network based on transcriptome sequencing (RNA-seq) of hepatocellular carcinoma (HCC, n = 9) patients. Of the identified miRNAs (n = 203) and lncRNAs (n = 1,090), we found 16 significantly differentially expressed (DE) miRNAs and three DE lncRNAs. The DE RNAs were highly enriched in 21 functional pathways implicated in HCC (p < 0.05), including p53, MAPK, and NAFLD signaling. Potential pairwise interactions between DE ncRNAs and mRNAs were fully characterized using in silico prediction and experimentally-validated evidence. We for the first time constructed a MLMI network of reciprocal interactions for 16 miRNAs, three lncRNAs, and 253 mRNAs in HCC. The predominant role of MEG3 in the MLMI network was validated by its overexpression in vitro that the expression levels of a proportion of MEG3-targeted miRNAs and mRNAs was changed significantly. Our results suggested that the comprehensive MLMI network synergistically modulated carcinogenesis, and the crosstalk of the network provides a new avenue to accurately describe the molecular mechanisms of hepatocarcinogenesis.

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