Differential Gene Expression in the Siphonophore Nanomia bijuga (Cnidaria) Assessed with Multiple Next-Generation Sequencing Workflows

Abstract Background: DNA methylation plays important functions in gene expression regulation that is involved in individual development and various diseases. DNA methylation has been well studied in human and model organisms, but only limited data exist in companion animals like dog. Results: Using methylation-sensitive restriction enzyme-based next generation sequencing (Canine DREAM), we obtained canine DNA methylation maps from 16 somatic tissues. In total, we evaluated 130,861 CpG sites. The majority of CpG sites were either highly methylated (>70%, 52.5%-64.6% of all CpG sites analyzed) or unmethylated (<30%, 22.5%-28.0% of all CpG sites analyzed) which are methylation patterns similar to other species. The overall methylation status of CpG sites across the 32 methylomes were remarkably similar. However, the tissue types were clearly defined by principle component analysis and hierarchical clustering analysis with DNA methylome. We found 6416 CpG sites located closely at promoter region of genes and inverse correlation between DNA methylation and gene expression of these genes. Conclusions: Our study provides basic dataset for DNA methylation profiles in dogs.

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243 GENE EXPRESSION PROFILING BY NEXT-GENERATION SEQUENCING OF cDNA SAMPLES FROM INNER CELL MASS AND TROPHECTODERM OF A SINGLE-DAY 12 PORCINE EMBRYO

Reproduction Fertility and Development ◽

10.1071/rdv22n1ab243 ◽

2010 ◽

Vol 22 (1) ◽

pp. 279

Author(s):

S. C. Isom ◽

R. S. Prather

Keyword(s):

Gene Expression ◽

Next Generation Sequencing ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Inner Cell Mass ◽

Cell Mass ◽

Next Generation ◽

Sequencing Technologies ◽

Cdna Fragments ◽

Generation Sequencing

Traditional microarray approaches to gene expression profiling often require RNA or cDNA amplification when working with extremely small or valuable tissue samples.This process is generally viewed as being undesirable because there is potential for bias to be introduced during amplification. Very recently, the so-called next-generation sequencing technologies were adapted for use in global gene expression profiling. Herein we report our efforts to apply these sequencing technologies to assess relative transcript abundances in pre-implantation-stage porcine embryos, without additional nucleic acid amplification before sequencing. As a proof-of-principle experiment, we have isolated total RNA from the embryonic disc (inner cell mass; ICM) and a small piece of trophectoderm (TE) from a Day 12 in vivo-produced embryo, which were estimated to be composed of 500 to 1000 cells each. The RNA was reverse transcribed using oligo-dT priming followed by second-strand cDNA synthesis. The double-stranded cDNA was then randomly sheared by sonication, and 10 ng of double-stranded cDNA fragments was used for sample preparation before sequencing. Prepared cDNA fragments (at 7 picomolar concentrations) were submitted for sequencing using the Illumina/Solexa platform as recommended. The millions of short (36 bp) reads generated by Illumina sequencing for each sample were then aligned to the swine UniGene database from NCBI, allowing for zero or one mismatches. Relative transcript abundances between cell types were profiled by considering the read counts for a given UniGene member as a percentage of the total number of reads generated for each cell type. It was demonstrated that approximately 11 000 and 9000 UniGene members were represented by a normalized average of 5 or more short reads per lane (0.001% of the total) in the ICM and TE samples, respectively. As expected, pluripotency factors, chromatin remodeling components, and cell-cell communication molecules were overrepresented in the ICM sample as compared with the TE sample. Conversely, epithelial determinants, ion transporters, and components of the steroid biosynthesis pathways were more abundant in the TE sample than in the ICM sample. Relative abundances of representative transcripts in these samples were verified by quantitative RT-PCR. In conclusion, we demonstrate the utility of next-generation sequencing technologies for gene expression profiling using even minute tissue samples and show that such analyses are possible even in species without a sequenced genome.

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Promiscuous Cryptic Rearrangements of the MYC Locus Cis-Dysregulate MYC Expression and Are Present in the Majority of Patients with Hyperdiploid Myeloma

Blood ◽

10.1182/blood.v120.21.724.724 ◽

2012 ◽

Vol 120 (21) ◽

pp. 724-724

Author(s):

P Leif Bergsagel ◽

Maurizio Affer ◽

Oleg K Glebov ◽

Wei-Dong D Chen ◽

Jonathan J Keats ◽

...

Keyword(s):

Gene Expression ◽

Next Generation Sequencing ◽

Copy Number ◽

Genetic Mutation ◽

Monoallelic Expression ◽

Next Generation ◽

Activating Mutations ◽

Reference Collection ◽

In Cis ◽

Generation Sequencing

Abstract Abstract 724 Background: Chromosome content identifies two pathogenic pathways, each occurring in about half of patients with MGUS and multiple myeloma (MM). Hyperdiploid MM (HRD) has 48–75 chromosomes with multiple trisomies selectively involving chromosomes 3, 5, 7, 9, 11, 15, 19 and 21; only 10% of these HRD tumors have primary IgH translocations and no frequent focal genetic mutations have been identified. In contrast primary IgH translocations are identified in over 70% of non-hyperdiploid MM (NHRD). Rearrangements of MYC have been detected by FISH in only 16% of untreated MM, but over 90% of MM cell lines, identifying a late role for MYC in the progression of MM. The introduction of a MYC transgene into a mouse strain predisposed to MGUS results in mice that uniformly develop MM, suggesting a distinct early role of MYC in the progression of MGUS to MM. We report here that rearrangements in a 4Mb region surrounding MYC are present in 70% of HRD, representing the most frequent focal genetic mutation in this genetic subtype of MM. Results: We analyzed the MMRC reference collection of gene expression (Affymetrix Hu133Plus2) and copy number (Agilent 244k CGH) data and performed FISH to identify MYC rearrangements with IgH or IgL loci in 218 patients with untreated and relapsed MM. We found MYC rearrangements in 48% of MM (identified only by FISH in 5%, only by aCGH in 33%, and by both FISH and aCGH in 10%), including 43% of untreated, and 51% of relapsed MM. Using a hyperdiploid index calculated from the median copy number of the chromosomes involved in trisomies we determined that rearrangements of MYC were present in 70% of the top third, 35% of the middle third, and 25% of the bottom third. Using the paired gene expression data we found that the expression of MYC was approximately two-fold higher in the samples with rearrangements compared to those without rearrangements (p<0.001) and about three-fold higher in MM tumors without rearrangements compared to MGUS (p<0001). Using paired RNA and DNA from the MMRC reference collection we determined in 22 informative patients that MYC rearrangements are associated with monoallelic expression of MYC (p<0.01), consistent with cis-dysregulation of MYC. Analysis of the various changes on aCGH, and fine mapping of the genetic architecture of the rearrangements using next generation sequencing identifies a promiscuous array of rearrangements that often result in the introduction of an enhancer within the MYC locus, resulting in its cis-dysregulation. Since they cannot be comprehensively identified by either CGH or FISH alone, more sensitive techniques, such as next generation sequencing approaches, will be required to comprehensively identify all MYC rearrangements in MM. Conclusions: Rearrangements of MYC are the most frequent focal genetic mutation in untreated MM and are particularly prevalent in hyperdiploid MM. While only one third involve an immunoglobulin locus, they all result in cis-dysregulated expression of MYC, and may be one mechanism responsible for the progression of MGUS to MM. Tumors lacking MYC rearrangements bi-allelically over-express MYC by a trans mechanism including potentially inactivating mutations of BLIMP1/PRDM1, or activating mutations of IRF4. We propose two largely non-overlapping pathogenic pathways in MM: HRD associated with frequent MYC rearrangements, and NHRD associated with frequent primary IgH translocations. The prevalence of MYC rearrangements increases with tumor progression, identifying a role for MYC both early and late in tumorigenesis. As therapies that have been reported to target MYC (e.g., IMiDs®, bortezomib, bromodomain inhibitors) are used in the clinic, it will be important to associate their effect with the presence or absence of MYC rearrangements. Disclosures: Bergsagel: Constellation Pharmaceuticals: Consultancy. Keats:Tgen: Employment.

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Molecular profiling of multiple myeloma: from gene expression analysis to next-generation sequencing

Expert Opinion on Biological Therapy ◽

10.1517/14712598.2013.793305 ◽

2013 ◽

Vol 13 (sup1) ◽

pp. S55-S68 ◽

Cited By ~ 16

Author(s):

Luca Agnelli ◽

Pierfrancesco Tassone ◽

Antonino Neri

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Next Generation Sequencing ◽

Expression Analysis ◽

Gene Expression Analysis ◽

Molecular Profiling ◽

Next Generation ◽

Generation Sequencing

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Genetic approaches to study plant responses to environmental stresses

10.31221/osf.io/cxbdw ◽

2018 ◽

Author(s):

Khaled Moustafa ◽

Joanna M. Cross

Keyword(s):

Gene Expression ◽

Next Generation Sequencing ◽

Systems Biology ◽

Massively Parallel Sequencing ◽

Environmental Stresses ◽

Plant Responses ◽

Rna Seq ◽

Next Generation ◽

Rnase Protection ◽

Generation Sequencing

The assessment of gene expression levels is an important step toward elucidating gene functions temporally and spatially. Decades ago, typical studies were focusing on a few genes individually, whereas now researchers are able to examine whole genomes at once. The upgrade of throughput levels aided the introduction of systems biology approaches whereby cell functional networks can be scrutinized in their entireties to unravel potential functional interacting components. The birth of systems biology goes hand-in-hand with huge technological advancements and enables a fairly rapid detection of all transcripts in studied biological samples. Even so, earlier technologies that were restricted to probing single genes or a subset of genes still have their place in research laboratories. The objective here is to highlight key approaches used in gene expression analysis in plant responses to environmental stresses, or, more generally, any other condition of interest. Northern blots, RNase protection assays, and qPCR are described for their targeted detection of one or a few transcripts at a once. Differential display and serial analysis of gene expression represent non-targeted methods to evaluate expression changes of a significant number of gene transcripts. Finally, microarrays and RNA-seq (next-generation sequencing) contribute to the ultimate goal of identifying and quantifying all transcripts in a cell under conditions or stages of study. Recent examples of applications as well as principles, advantages, and drawbacks of each method are contrasted. We also suggest replacing the term "Next-Generation Sequencing (NGS)" with another less confusing synonym such as "RNA-seq", "high throughput sequencing", or "massively parallel sequencing" to avoid confusion with any future sequencing technologies.

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Identification of potential genes in upper tract urothelial carcinoma using next-generation sequencing with bioinformatics and in vitro analyses

PeerJ ◽

10.7717/peerj.11343 ◽

2021 ◽

Vol 9 ◽

pp. e11343

Author(s):

Hsiang-Ying Lee ◽

Ching-Chia Li ◽

Wei-Ming Li ◽

Ya-Ling Hsu ◽

Hsin-Chih Yeh ◽

...

Keyword(s):

Gene Expression ◽

Next Generation Sequencing ◽

Candidate Genes ◽

Venn Diagram ◽

Next Generation ◽

Upper Tract ◽

Urothelial Carcinomas ◽

Gene And Protein Expression ◽

Generation Sequencing

Background We aimed to identify prognostic biomarkers of upper tract urothelial carcinomas (UTUCs), including microRNAs (miRNAs) and genes which account for only 5% to 10% of all urothelial carcinomas (UCs). In Taiwan, this figure is markedly higher, where it can reach up to 30% of UC cases. Materials and Methods Using next-generation sequencing (NGS), we analyzed two pairs of renal pelvis tumors and adjacent normal urothelial tissues to screen miRNAs and messenger RNAs. By combining bioinformatics analysis from miRmap, Gene Expression Omnibus (GEO), and Oncomine and Ingenuity® Pathway Analysis databases, we identified candidate genes. To search for upstream miRNAs with exact target binding sites, we used miRmap, TargetScan, and miRDB to enforce evidence. Then, we clarified gene and protein expression through an in vitro study using western blot analysis and quantitative real-time reverse transcriptase-PCR. Results Interactions between selected target genes obtained using the NGS and miRmap methods were assessed through a Venn diagram analysis. Six potential genes, namely, PDE5A, RECK, ZEB2, NCALD, PLCXD3 and CYBRD1 showed significant differences. Further analysis of gene expression from the GEO dataset indicated lower expression of PDE5A, RECK, ZEB2, and CYBRD1 in bladder cancer tissue than in normal bladder mucosa, which indicated that PDE5A, RECK, ZEB2, and CYBRD1 may act as tumor suppressors in UTUC. In addition, we compared the expression of these genes in various UC cell lines (RT4, BFTC905, J82, T24, UMUC3, 5637, BFTC 909, UMUC14) and found decreased expression of PDE5A in muscle-invasive UC cells compared with the RT4 cell line. Furthermore, by using paired UTUC and normal tissues from 20 patients, lower PDE5A expression was also demonstrated in tumor specimens. Conclusions Our findings suggest these candidate genes may play some roles in UTUC progression. We propose that these markers may be potential targets clarified by in vitro and in vivo experiments. PDE5A also potentially presents tumor suppressor genes, as identified by comparing the expression between normal and tumor specimens.

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Wx: a neural network-based feature selection algorithm for next-generation sequencing data

10.1101/221911 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sungsoo Park ◽

Bonggun Shin ◽

Yoonjung Choi ◽

Kilsoo Kang ◽

Keunsoo Kang

Keyword(s):

Neural Network ◽

Gene Expression ◽

Next Generation Sequencing ◽

Supplementary Information ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Selection Algorithm ◽

Sequencing Data ◽

Optimal Set ◽

Generation Sequencing

AbstractMotivationNext-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. For example, an NGS-based transcriptomic assay called RNA-seq can be used to estimate the abundance of approximately 190,000 transcripts together. As the cost of next-generation sequencing sharply declines, researchers in many fields have been conducting research using NGS. The amount of information produced by NGS has made it difficult for researchers to choose the optimal set of target genes (or genomic loci).ResultsWe have sought to resolve this issue by developing a neural network-based feature (gene) selection algorithm called Wx. The Wx algorithm ranks genes based on the discriminative index (DI) score that represents the classification power for distinguishing given groups. With a gene list ranked by DI score, researchers can institutively select the optimal set of genes from the highest-ranking ones. We applied the Wx algorithm to a TCGA pan-cancer gene-expression cohort to identify an optimal set of gene-expression biomarker (universal gene-expression biomarkers) candidates that can distinguish cancer samples from normal samples for 12 different types of cancer. The 14 gene-expression biomarker candidates identified by Wx were comparable to or outperformed previously reported universal gene expression biomarkers, highlighting the usefulness of the Wx algorithm for next-generation sequencing data. Thus, we anticipate that the Wx algorithm can complement current state-of-the-art analytical applications for the identification of biomarker candidates as an alternative method.Availabilityhttps://github.com/deargen/[email protected] informationSupplementary data are available at online.

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