Analysis and Differential Expression of Primo Genes Using RNA-Seq and qRT-PCR Experiments

Background:: In Traditional Chinese Medicine (TCM), the heads and tails of Angelica sinensis (Oliv.) Diels (AS) is used in treating different diseases due to their different pharmaceutical efficacies. The underline mechanisms, however, have not been fully explored. Objective:: Novel mechanisms responsible for the discrepant activities between AS heads and tails were explored by a combined strategy of transcriptomes and metabolomics. Method:: Six pairs of the heads and tails of AS roots were collected in Min County, China. Total RNA and metabolites, which were used for RNA-seq and untargeted metabolomics analysis, were respectively isolated from each AS sample (0.1 g) by Trizol and methanol reagent. Subsequently, differentially expressed genes (DEGs) and discrepant pharmaceutical metabolites were identified for comparing AS heads and tails. Key DEGs and metabolites were quantified by qRT-PCR and targeted metabolomics experiment. Results:: Comprehensive analysis of transcriptomes and metabolomics results suggested that five KEGG pathways with significant differences included 57 DEGs. Especially, fourteen DEGs and six key metabolites were relation to the metabolic regulation of Phenylpropanoid biosynthesis (PB) pathway. Results of qRT-PCR and targeted metabolomics indicated that higher levels of expression of crucial genes in PB pathway, such as PAL, CAD, COMT and peroxidase in the tail of AS were positively correlated with levels of ferulic acid-related metabolites. The average content of ferulic acid in tails (569.58162.39 nmol/g) was higher than those in the heads (168.73  67.30 nmol/g) (P˂0.01); Caffeic acid in tails (3.82  0.88 nmol/g) vs heads (1.37  0.41 nmol/g) (P˂0.01), and Cinnamic acid in tails (0.24  0.09 nmol/g) vs heads (0.14  0.02 nmol/g) (P˂0.05). Conclusion:: Our work demonstrated that overexpressed genes and accumulated metabolites derived from PB pathway might be responsible for the discrepant pharmaceutical efficacies between AS heads and tails.

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Best practices on the differential expression analysis of multi-species RNA-seq

Genome Biology ◽

10.1186/s13059-021-02337-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Matthew Chung ◽

Vincent M. Bruno ◽

David A. Rasko ◽

Christina A. Cuomo ◽

José F. Muñoz ◽

...

Keyword(s):

Best Practices ◽

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Single Species ◽

Rna Seq ◽

Species Analysis ◽

Differential Gene ◽

Multiple Species ◽

Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

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

Human Genomics ◽

10.1186/s40246-021-00308-5 ◽

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.

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Transcriptome Analysis of Responses to Dengue Virus 2 Infection in Aedes albopictus (Skuse) C6/36 Cells

Viruses ◽

10.3390/v13020343 ◽

2021 ◽

Vol 13 (2) ◽

pp. 343

Author(s):

Manjin Li ◽

Dan Xing ◽

Duo Su ◽

Di Wang ◽

Heting Gao ◽

...

Keyword(s):

Dengue Virus ◽

Aedes Albopictus ◽

Bioinformatics Analysis ◽

Interaction Mechanism ◽

Transcriptional Analysis ◽

Functional Verification ◽

Mosquito Vector ◽

Rna Seq ◽

Sequencing Data ◽

Qrt Pcr

Dengue virus (DENV), a member of the Flavivirus genus of the Flaviviridae family, can cause dengue fever (DF) and more serious diseases and thus imposes a heavy burden worldwide. As the main vector of DENV, mosquitoes are a serious hazard. After infection, they induce a complex host–pathogen interaction mechanism. Our goal is to further study the interaction mechanism of viruses in homologous, sensitive, and repeatable C6/36 cell vectors. Transcriptome sequencing (RNA-Seq) technology was applied to the host transcript profiles of C6/36 cells infected with DENV2. Then, bioinformatics analysis was used to identify significant differentially expressed genes and the associated biological processes. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to verify the sequencing data. A total of 1239 DEGs were found by transcriptional analysis of Aedes albopictus C6/36 cells that were infected and uninfected with dengue virus, among which 1133 were upregulated and 106 were downregulated. Further bioinformatics analysis showed that the upregulated DEGs were significantly enriched in signaling pathways such as the MAPK, Hippo, FoxO, Wnt, mTOR, and Notch; metabolic pathways and cellular physiological processes such as autophagy, endocytosis, and apoptosis. Downregulated DEGs were mainly enriched in DNA replication, pyrimidine metabolism, and repair pathways, including BER, NER, and MMR. The qRT-PCR results showed that the concordance between the RNA-Seq and RT-qPCR data was very high (92.3%). The results of this study provide more information about DENV2 infection of C6/36 cells at the transcriptome level, laying a foundation for further research on mosquito vector–virus interactions. These data provide candidate antiviral genes that can be used for further functional verification in the future.

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How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

RNA ◽

10.1261/rna.053959.115 ◽

2016 ◽

Vol 22 (6) ◽

pp. 839-851 ◽

Cited By ~ 301

Author(s):

Nicholas J. Schurch ◽

Pietá Schofield ◽

Marek Gierliński ◽

Christian Cole ◽

Alexander Sherstnev ◽

...

Keyword(s):

Differential Expression ◽

Rna Seq

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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

Molecular Biology Reports ◽

10.1007/s11033-012-2417-y ◽

2012 ◽

Vol 40 (4) ◽

pp. 3395-3407 ◽

Cited By ~ 22

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

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The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab028 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Xueyi Dong ◽

Luyi Tian ◽

Quentin Gouil ◽

Hasaru Kariyawasam ◽

Shian Su ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Transcriptomic Analysis ◽

Statistical Testing ◽

Rna Seq ◽

Sequencing Data ◽

Short Read ◽

Sequencing Platform ◽

Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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