Study on the differences of gene expression between pear and apple wild cultivation materials based on RNA-seq technique

Abstract Background Pears and apples are both perennial deciduous trees of the Rosaceae family, and both are important economic fruit trees worldwide. The emergence of many varieties in the market has been mostly domesticated from wild to cultivated and regulated by the differential expression of genes. However, the molecular process and pathways underlying this phenomenon remain unclear. Four typical wild and cultivar pear and apple trees at three developmental stages were used in our study to investigate the molecular process at the transcriptome level. Result Physiological observations indicated the obvious differences of size, weight, sugar acid content and peel color in wild and cultivar fruit among each developmental stage. Using next-generation sequencing based RNA-seq expression profiling technology, we produced a transcriptome in procession of a large fraction of annotated pear and apple genes, and provided a molecular basis underlying the phenomenon of wild and cultivar fruit tree differences. 5921 and 5744 differential expression genes were identified in pear and apple at three developmental stages respectively. We performed temporal and spatial differential gene expression profiling in developing fruits. Several key pathways such as signal transduction, photosynthesis, translation and many metabolisms were identified as involved in the differentiation of wild and cultivar fruits. Conclusion In this study, we reported on the next-generation sequencing study of the temporal and spatial mRNA expression profiling of pear and apple fruit trees. Also, we demonstrated that the integrated analysis of pear and apple transcriptome, which strongly revealed the consistent process of domestication in Rosaceae fruit trees. The results will be great influence to the improvement of cultivar species and the utilization of wild resources.

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RNA-Seq Atlas—a reference database for gene expression profiling in normal tissue by next-generation sequencing

Bioinformatics ◽

10.1093/bioinformatics/bts084 ◽

2012 ◽

Vol 28 (8) ◽

pp. 1184-1185 ◽

Cited By ~ 125

Author(s):

Markus Krupp ◽

Jens U. Marquardt ◽

Ugur Sahin ◽

Peter R. Galle ◽

John Castle ◽

...

Keyword(s):

Gene Expression ◽

Next Generation Sequencing ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Normal Tissue ◽

Reference Database ◽

Rna Seq ◽

Next Generation ◽

Generation Sequencing

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Computational Methods for Gene Expression Profiling Using Next-Generation Sequencing (RNA-Seq)

Handbook of RNA Biochemistry ◽

10.1002/9783527647064.ch36 ◽

2014 ◽

pp. 821-832

Author(s):

John C. Castle

Keyword(s):

Gene Expression ◽

Next Generation Sequencing ◽

Gene Expression Profiling ◽

Computational Methods ◽

Expression Profiling ◽

Rna Seq ◽

Next Generation ◽

Generation Sequencing

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GENE EXPRESSION PROFILING DURING GONADAL DIFFERENTIATION IN RAINBOW TROUT (ONCORHYNCHUS MYKISS) USING A NEXT GENERATION SEQUENCING (NGS) APPROACH

Indian Journal of Science and Technology ◽

10.17485/ijst/2011/v4is.38 ◽

2011 ◽

Vol 4 (s1) ◽

pp. 73-73

Author(s):

Ayaka Yano

Keyword(s):

Gene Expression ◽

Rainbow Trout ◽

Next Generation Sequencing ◽

Oncorhynchus Mykiss ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Gonadal Differentiation ◽

Rainbow Trout Oncorhynchus Mykiss ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

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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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Gene Expression Profiling of Sorted Peripheral Blood Cells Using Microarray and Next Generation Sequencing Reveals Distinct Molecular Signatures in the Polymorphonuclear and Mononuclear Cells of Patients with Polycythemia Vera and Primary Myelofibrosis

Blood ◽

10.1182/blood.v126.23.5201.5201 ◽

2015 ◽

Vol 126 (23) ◽

pp. 5201-5201

Author(s):

Chieh Lee Wong ◽

Baoshan Ma ◽

Gareth Gerrard ◽

Martyna Adamowicz-Brice ◽

Zainul Abidin Norziha ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Gene Expression Profiling ◽

Differential Expression ◽

Differentially Expressed Genes ◽

Expression Profiling ◽

Differentially Expressed ◽

Cell Type ◽

Cell Type Specific ◽

Generation Sequencing

Abstract Background The past decade has witnessed a significant progress in the understanding of the molecular pathogenesis of myeloproliferative neoplasms (MPN). A large number of genes have now been implicated in the pathogenesis of MPN but their relative importance, the mechanisms by which they cause different cell types to predominate and their implications for prognosis remain unknown. We hypothesized that there are other genes which may contribute to the pathogenesis of the different disease subtypes detectable only by cell-type specific analysis. Aim The aim of this study was to perform gene expression profiling on different cell types from patients with MPN in order to identify novel variants and driver mutations, to elucidate the pathogenesis and to identify predictors of survival in patients with MPN in a multiracial country. Methods We performed gene expression profiling on normal controls (NC) and patients with MPN from 3 different races (Malay, Chinese and Indian) in Malaysia who were diagnosed with essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF) according to the 2008 WHO diagnostic criteria for MPN. Two cohorts of patients, the patient and validation cohorts, from 3 tertiary-level hospitals were recruited prospectively over 3 years and informed consents were obtained. Peripheral blood samples were taken and sorted into polymorphonuclear cells (PMNs), mononuclear cells (MNCs) and T cells. RNA was extracted from each cell population. Gene expression profiling was performed using the Illumina HumanHT-12 Expression Beadchip for microarray and the Illumina Nextera XT DNA Sample Preparation Kit for next generation sequencing on the patient and validation cohorts respectively. Results Twenty-eight patients (10 ET, 11 PV and 7 PMF) and 11 NC were recruited into the patient cohort. Twelve patients (4 ET, 4 PV and 4 PMF) and 4 NC were recruited into the validation cohort. Gene expression levels for each cell type in each disease were compared with NC. In the patient cohort, the number of differentially expressed genes in ET, PV and PMF was 0, 141 and 15 respectively for PMNs (p < 0.05 after multiple testing correction) and 5, 170 and 562 respectively for MNCs (p < 0.05). No differentially expressed genes were identified for T cells in any of the three disease groups. RNA-seq analysis of samples from the validation cohort was used to corroborate these findings. After combination, we were able to confirm differential expression of 0, 14 and 7 genes in ET, PV and PMF respectively for PMNs (p < 0.05) and 51 genes in only PMF for MNCs (p < 0.05). The validated differentially expressed genes for PMNs and MNCs were mutually exclusive except for one gene. The differentially expressed genes in PV and PMF for PMNs were involved in cellular processes and metabolic pathways whereas the differentially expressed genes for PMF in MNCs were involved in regulation of cytoskeleton, focal adhesion and cell signaling pathways. Conclusion This is the first study to use microarray and next generation sequencing techniques to compare cell type-specific expression of genes between different subtypes of MPN. The lack of differential expression in T cells validates the techniques used and indicates that they are not part of the neoplastic clone. Differential expression of genes for MNCs was seen only in PMF which may be related to their more severe phenotype. Interestingly, there were fewer differentially expressed genes in PMF compared to PV for PMNs. The lack of differential expression in ET may either reflect the relatively milder phenotype of the disease or that differential expression is limited to megakaryocytes-platelets which were not studied. The lists of mutually exclusive cell type-specific differentially expressed genes for PMNs and MNCs provide further insight into the pathogenesis of MPN and into the differences between its different forms. The identified genes also indicate further routes for investigation of pathogenesis and possible disease-specific targets for therapy. Disclosures Aitman: Illumina: Honoraria.

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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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Genetic approaches to study plant responses to environmental stresses

10.31226/osf.io/689my ◽

2018 ◽

Author(s):

Khaled Moustafa

Keyword(s):

Gene Expression ◽

Next Generation Sequencing ◽

Systems Biology ◽

Massively Parallel Sequencing ◽

Environmental Stresses ◽

Plant Responses ◽

Rna Seq ◽

Next Generation ◽

Rnase Protection ◽

Generation Sequencing

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The Next Generation Sequencing Techniques and Application in Drug Discovery and Development

Advances in Medical Technologies and Clinical Practice - Computer Applications in Drug Discovery and Development ◽

10.4018/978-1-5225-7326-5.ch011 ◽

2019 ◽

pp. 240-259

Author(s):

Afzal Hussain

Keyword(s):

Next Generation Sequencing ◽

Rna Sequencing ◽

Expression Profiling ◽

Massively Parallel Sequencing ◽

Life Sciences ◽

Rna Seq ◽

Next Generation ◽

Parallel Sequencing ◽

Short Period ◽

Generation Sequencing

Next-generation sequencing or massively parallel sequencing describe DNA sequencing, RNA sequencing, or methylation sequencing, which shows its great impact on the life sciences. The recent advances of these parallel sequencing for the generation of huge amounts of data in a very short period of time as well as reducing the computing cost for the same. It plays a major role in the gene expression profiling, chromosome counting, finding out the epigenetic changes, and enabling the future of personalized medicine. Here the authors describe the NGS technologies and its application as well as applying different tools such as TopHat, Bowtie, Cufflinks, Cuffmerge, Cuffdiff for analyzing the high throughput RNA sequencing (RNA-Seq) data.

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