scholarly journals Transcriptome and Small RNA Combined Sequencing Analysis of Cold Tolerance in Non-heading Chinese Cabbage

2021 ◽  
Vol 12 ◽  
Author(s):  
Jin Wang ◽  
Qinxue Zhang ◽  
Xiong You ◽  
Xilin Hou
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Small Rna ◽  
Target Genes ◽  
Differentially Expressed ◽  
Sequencing Analysis ◽  
Pak Choi ◽  
Expression Levels ◽  
Heading Chinese Cabbage ◽  
Gene Expression Levels

BackgroundNon-heading Chinese cabbage (Brassica rapa ssp. chinensis) is an important leaf vegetable grown worldwide. However, there has currently been not enough transcriptome and small RNA combined sequencing analysis of cold tolerance, which hinders further functional genomics research.ResultsIn this study, 63.43 Gb of clean data was obtained from the transcriptome analysis. The clean data of each sample reached 6.99 Gb, and the basic percentage of Q30 was 93.68% and above. The clean reads of each sample were sequence aligned with the designated reference genome (Brassica rapa, IVFCAASv1), and the efficiency of the alignment varied from 81.54 to 87.24%. According to the comparison results, 1,860 new genes were discovered in Pak-choi, of which 1,613 were functionally annotated. Among them, 13 common differentially expressed genes were detected in all materials, including seven upregulated and six downregulated. At the same time, we used quantitative real-time PCR to confirm the changes of these gene expression levels. In addition, we sequenced miRNA of the same material. Our findings revealed a total of 34,182,333 small RNA reads, 88,604,604 kinds of small RNAs, among which the most common size was 24 nt. In all materials, the number of common differential miRNAs is eight. According to the corresponding relationship between miRNA and its target genes, we carried out Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis on the set of target genes on each group of differentially expressed miRNAs. Through the analysis, it is found that the distributions of candidate target genes in different materials are different. We not only used transcriptome sequencing and small RNA sequencing but also used experiments to prove the expression levels of differentially expressed genes that were obtained by sequencing. Sequencing combined with experiments proved the mechanism of some differential gene expression levels after low-temperature treatment.ConclusionIn all, this study provides a resource for genetic and genomic research under abiotic stress in Pak-choi.

scholarly journals Transcriptome and small RNA combined sequencing analysis of cold tolerance response in non-heading Chinese cabbage

2020 ◽  
Author(s):  
Jin Wang ◽  
Qinxue Zhang ◽  
Xiong You ◽  
Xilin Hou
Keyword(s):  
Gene Expression ◽  
Cold Tolerance ◽  
Brassica Rapa ◽  
Chinese Cabbage ◽  
Small Rna ◽  
Target Genes ◽  
Differentially Expressed ◽  
Sequencing Analysis ◽  
Heading Chinese Cabbage ◽  
Gene Expression Levels

Abstract Background: Non-heading Chinese cabbage ( Brassica rapa ssp. chinensis ), as an important leaf vegetable grown worldwide. However, there is currently no enough transcriptome and small RNA combined sequencing analysis of cold tolerance, which hinders further functional genomics research.Results: In this study, 63.43 Gb of clean data was obtained from the transcriptome analysis. The clean data of each sample reached 6.99 Gb, and the basic percentage of Q30 was 93.68% and above. The clean reads of each sample were sequence aligned with the designated reference genome ( Brassica rapa, IVFCAASv1 ), and the efficiency of the alignment varied from 81.54% to 87.24%. According to the comparison results, 1,860 new genes were discovered, of which 1,613 were functionally annotated. Among them, 13 common differentially expressed genes were detected in all materials, including 7 up-regulated and 6 down-regulated. At the same time, we used quantitative real-time PCR to confirm the changes of these gene expression levels. In addition, we sequenced miRNA of the same material. Our findings revealed a total of 34,182,333 small RNA reads, 88,604,604 kinds of small RNA, among which the most common size was 24 nt. In all materials, the number of common differential miRNAs is 8. According to the corresponding relationship between miRNA and its target genes, we carried out Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis on the set of target genes that each group of differentially expressed miRNAs. Through the analysis, it is found that the distribution of candidate target genes in different materials is different. We not only use transcriptome sequencing and small RNA sequencing, but also use experiment to prove that the expression level of differentially expressed genes are obtained by sequencing. Sequencing combined with experiments proved the mechanism of some differential gene expression levels after low temperature treatment.Conclusions: In all, this study provides a resource for genetic and genomic research under abiotic stress in Pak-choi.

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 HIV-Associated Neurocognitive Disorder (HAND) Biomarker Identification: Significance Analysis of Microarrays and Two Persuasive Approaches with Random Forest

2020 ◽  
Author(s):  
Hansapani Rodrigo ◽  
Bryan Martinez ◽  
Roberto De La Garza ◽  
Upal Roy
Keyword(s):  
Gene Expression ◽  
White Matter ◽  
Basal Ganglia ◽  
Frontal Cortex ◽  
Differentially Expressed ◽  
Control Group ◽  
Expression Levels ◽  
Significance Analysis ◽  
The Subject ◽  
Gene Expression Levels

Abstract Background: HIV Associated Neurological Disorders (HAND) is relatively common among people with HIV-1 infection, even those taking combined antiretroviral treatment (cART). Genome-wide screening of transcription regulation in brain tissue helps in identifying substantial abnormalities present in patients’ gene transcripts and to discover possible biomarkers for HAND. This study explores the possibility of identifying differentially expressed (DE) genes, which can serve as potential biomarkers to detect HAND. In this study, we have investigated the gene expression levels of three subject groups with different impairment levels of HAND along with a control group in three distinct brain sectors: white matter, frontal cortex, and basal ganglia. Methods: Linear models with weighted least squares along with Benjamini-Hochberg multiple corrections were used to identify DE genes in each brain region. Genes with an adjusted p-value of less than 0.01 were identified as differentially expressed. Principal component analyses (PCA) were performed to detect any groupings among the subject groups. Significance Analysis of Microarrays (SAM) and random forests (RF) methods with two distinct approaches were used to identify DE genes. Results: A total of 710 genes in basal ganglia, 794 genes in the frontal cortex, and 1481 genes in white matter were screened. The highest proportion of DE genes was observed within the two brain regions, frontal neocortex, and basal ganglia. PCA analyses do not exhibit clear groupings among four subject groups. SAM and RF models reveal the genes, CIRBP, RBM3, GPNMB, ISG15, IFIT6, IFI6, and IFIT3, to have DE genes in the frontal cortex or basal ganglia among the subject groups. The gene, GADD45A, a protein-coding gene whose transcript levels tend to increase with stressful growth arrest conditions, was consistently ranked among the top genes by both RF models within the frontal cortex. Conclusions: Our study contributes to a comprehensive understanding of the gene expression levels of the subject with different severity levels of HAND. Several genes that appear to play critical roles in the inflammatory response have been found, and they have an excellent potential to be used as biomarkers to detect HAND under further investigations.

scholarly journals D-GPM: A Deep Learning Method for Gene Promoter Methylation Inference

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 807 ◽  
Author(s):  
Pan ◽  
Liu ◽  
Wen ◽  
Liu ◽  
Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Promoter Methylation ◽  
Methylation Level ◽  
Target Genes ◽  
Gene Promoter ◽  
Support Vector ◽  
Whole Genome ◽  
Expression Levels ◽  
Gene Expression Levels

Whole-genome bisulfite sequencing generates a comprehensive profiling of the gene methylation levels, but is limited by a high cost. Recent studies have partitioned the genes into landmark genes and target genes and suggested that the landmark gene expression levels capture adequate information to reconstruct the target gene expression levels. This inspired us to propose that the methylation level of the promoters in landmark genes might be adequate to reconstruct the promoter methylation level of target genes, which would eventually reduce the cost of promoter methylation profiling. Here, we propose a deep learning model called Deep-Gene Promoter Methylation (D-GPM) to predict the whole-genome promoter methylation level based on the promoter methylation profile of the landmark genes from The Cancer Genome Atlas (TCGA). D-GPM-15%-7000 × 5, the optimal architecture of D-GPM, acquires the least overall mean absolute error (MAE) and the highest overall Pearson correlation coefficient (PCC), with values of 0.0329 and 0.8186, respectively, when testing data. Additionally, the D-GPM outperforms the regression tree (RT), linear regression (LR), and the support vector machine (SVM) in 95.66%, 92.65%, and 85.49% of the target genes by virtue of its relatively lower MAE and in 98.25%, 91.00%, and 81.56% of the target genes based on its relatively higher PCC, respectively. More importantly, the D-GPM predominates in predicting 79.86% and 78.34% of the target genes according to the model distribution of the least MAE and the highest PCC, respectively.

Identification of reference genes for gene expression studies during seed germination and seedling establishment in Ricinus communis L.

2014 ◽  
Vol 24 (4) ◽  
pp. 341-352 ◽  
Author(s):  
Paulo R. Ribeiro ◽  
Bas J. W. Dekkers ◽  
Luzimar G. Fernandez ◽  
Renato D. de Castro ◽  
Wilco Ligterink ◽  
...  
Keyword(s):  
Gene Expression ◽  
Seed Germination ◽  
Reference Genes ◽  
Target Genes ◽  
Ricinus Communis ◽  
Quantitative Polymerase Chain Reaction ◽  
Optimal Combination ◽  
Expression Levels ◽  
Gene Expression Studies ◽  
Gene Expression Levels

AbstractReverse transcription-quantitative polymerase chain reaction (RT-qPCR) is an important technology to analyse gene expression levels during plant development or in response to different treatments. An important requirement to measure gene expression levels accurately is a properly validated set of reference genes. In this context, we analysed the potential use of 17 candidate reference genes across a diverse set of samples, including several tissues, different stages and environmental conditions, encompassing seed germination and seedling growth in Ricinus communis L. These genes were tested by RT-qPCR and ranked according to the stability of their expression using two different approaches: GeNorm and NormFinder. GeNorm and Normfinder indicated that ACT, POB and PP2AA1 comprise the optimal combination for normalization of gene expression data in inter-tissue (heterogeneous sample panel) studies. We also describe the optimal combination of reference genes for a subset of root, endosperm and cotyledon samples. In general, the most stable genes suggested by GeNorm are very consistent with those indicated by NormFinder, which highlights the strength of the selection of reference genes in our study. We also validated the selected reference genes by normalizing the expression levels of three target genes involved in energy metabolism with the reference genes suggested by GeNorm and NormFinder. The approach used in this study to identify stably expressed genes, and thus potential reference genes, was applied successfully for R. communis and it provides important guidelines for RT-qPCR studies in seeds and seedlings for other species (especially in those cases where extensive microarray data are not available).

scholarly journals Effectiveness of orally-delivered double-stranded RNA on gene silencing in the stinkbug Plautia stali

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245081
Author(s):  
Yudai Nishide ◽  
Daisuke Kageyama ◽  
Yoshiaki Tanaka ◽  
Kakeru Yokoi ◽  
Akiya Jouraku ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Insect Pest ◽  
Crop Protection ◽  
High Sensitivity ◽  
Double Stranded Rna ◽  
Expression Levels ◽  
Rnai Efficiency ◽  
Plautia Stali ◽  
Gene Expression Levels

Development of a reliable method for RNA interference (RNAi) by orally-delivered double-stranded RNA (dsRNA) is potentially promising for crop protection. Considering that RNAi efficiency considerably varies among different insect species, it is important to seek for the practical conditions under which dsRNA-mediated RNAi effectively works against each pest insect. Here we investigated RNAi efficiency in the brown-winged green stinkbug Plautia stali, which is notorious for infesting various fruits and crop plants. Microinjection of dsRNA into P. stali revealed high RNAi efficiency–injection of only 30 ng dsRNA into last-instar nymphs was sufficient to knockdown target genes as manifested by their phenotypes, and injection of 300 ng dsRNA suppressed the gene expression levels by 80% to 99.9%. Knockdown experiments by dsRNA injection showed that multicopper oxidase 2 (MCO2), vacuolar ATPase (vATPase), inhibitor of apoptosis (IAP), and vacuolar-sorting protein Snf7 are essential for survival of P. stali, as has been demonstrated in other insects. By contrast, P. stali exhibited very low RNAi efficiency when dsRNA was orally administered. When 1000 ng/μL of dsRNA solution was orally provided to first-instar nymphs, no obvious phenotypes were observed. Consistent with this, RT-qPCR showed that the gene expression levels were not affected. A higher concentration of dsRNA (5000 ng/μL) induced mortality in some cohorts, and the gene expression levels were reduced to nearly 50%. Simultaneous oral administration of dsRNA against potential RNAi blocker genes did not improve the RNAi efficiency of the target genes. In conclusion, P. stali shows high sensitivity to RNAi with injected dsRNA but, unlike the allied pest stinkbugs Halyomorpha halys and Nezara viridula, very low sensitivity to RNAi with orally-delivered dsRNA, which highlights the varied sensitivity to RNAi across different species and limits the applicability of the molecular tool for controlling this specific insect pest.

scholarly journals Transcriptomic Analysis Suggests Genes Expressed Stage-Independently and Stage–Dependently Modulating the Wing Dimorphism of the Brown Planthopper

Genes ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 19 ◽  
Author(s):  
Chao Zhang ◽  
Xiang-Dong Liu
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Brown Planthopper ◽  
Adherens Junction ◽  
Sugar Metabolism ◽  
Differentially Expressed ◽  
Wing Dimorphism ◽  
Expression Levels ◽  
Three Stages ◽  
Gene Expression Levels

Wing dimorphism is considered as an adaptive trait of insects. Brown planthoppers (BPHs) Nilaparvata lugens, a serious pest of rice, are either macropterous or brachypterous. Genetic and environmental factors are both likely to control wing morph determination in BPHs, but the hereditary law and genes network are still unknown. Here, we investigated changes in gene expression levels between macropterous and brachypterous BPHs by creating artificially bred morphotype lines. The nearly pure-bred strains of macropterous and brachypterous BPHs were established, and their transcriptomes and gene expression levels were compared. Over ten-thousand differentially expressed genes (DEGs) between macropterous and brachypterous strains were found in the egg, nymph, and adult stages, and the three stages shared 6523 DEGs. The regulation of actin cytoskeleton, focal adhesion, tight junction, and adherens junction pathways were consistently enriched with DEGs across the three stages, whereas insulin signaling pathway, metabolic pathways, vascular smooth muscle contraction, platelet activation, oxytocin signaling pathway, sugar metabolism, and glycolysis/gluconeogenesis were significantly enriched by DEGs in a specific stage. Gene expression trend profiles across three stages were different between the two strains. Eggs, nymphs, and adults from the macropterous strain were distinguishable from the brachypterous based on gene expression levels, and genes that were related to wing morphs were differentially expressed between wing strains or strain × stage. A proposed mode based on genes and environments to modulate the wing dimorphism of BPHs was provided.

scholarly journals Gene Expression and Photophysiological Changes in Pocillopora acuta Coral Holobiont Following Heat Stress and Recovery

2020 ◽  
Vol 8 (8) ◽  
pp. 1227
Author(s):  
Rosa Celia Poquita-Du ◽  
Yi Le Goh ◽  
Danwei Huang ◽  
Loke Ming Chou ◽  
Peter A. Todd
Keyword(s):  
Gene Expression ◽  
Heat Stress ◽  
Target Genes ◽  
Differential Expression Analysis ◽  
Heat Exposure ◽  
Small Heat Shock Protein ◽  
Expression Levels ◽  
Chl A ◽  
Coral Holobiont ◽  
Gene Expression Levels

The ability of corals to withstand changes in their surroundings is a critical survival mechanism for coping with environmental stress. While many studies have examined responses of the coral holobiont to stressful conditions, its capacity to reverse responses and recover when the stressor is removed is not well-understood. In this study, we investigated among-colony responses of Pocillopora acuta from two sites with differing distance to the mainland (Kusu (closer to the mainland) and Raffles Lighthouse (further from the mainland)) to heat stress through differential expression analysis of target genes and quantification of photophysiological metrics. We then examined how these attributes were regulated after the stressor was removed to assess the recovery potential of P. acuta. The fragments that were subjected to heat stress (2 °C above ambient levels) generally exhibited significant reduction in their endosymbiont densities, but the extent of recovery following stress removal varied depending on natal site and colony. There were minimal changes in chl a concentration and maximum quantum yield (Fv/Fm, the proportion of variable fluorescence (Fv) to maximum fluorescence (Fm)) in heat-stressed corals, suggesting that the algal endosymbionts’ Photosystem II was not severely compromised. Significant changes in gene expression levels of selected genes of interest (GOI) were observed following heat exposure and stress removal among sites and colonies, including Actin, calcium/calmodulin-dependent protein kinase type IV (Camk4), kinesin-like protein (KIF9), and small heat shock protein 16.1 (Hsp16.1). The most responsive GOIs were Actin, a major component of the cytoskeleton, and the adaptive immune-related Camk4 which both showed significant reduction following heat exposure and subsequent upregulation during the recovery phase. Our findings clearly demonstrate specific responses of P. acuta in both photophysiological attributes and gene expression levels, suggesting differential capacity of P. acuta corals to tolerate heat stress depending on the colony, so that certain colonies may be more resilient than others.

scholarly journals Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells

2017 ◽  
Author(s):  
John D. Blischak ◽  
Ludovic Tailleux ◽  
Marsha Myrthil ◽  
Cécile Charlois ◽  
Emmanuel Bergot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dendritic Cells ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Healthy Individuals ◽  
Data Set ◽  
Expression Levels ◽  
Infected Cells ◽  
Latent Tb ◽  
Gene Expression Levels

ABSTRACTTuberculosis (TB) is a deadly infectious disease, which kills millions of people every year. The causative pathogen, Mycobac-terium tuberculosis (MTB), is estimated to have infected up to a third of the world’s population; however, only approximately 10% of infected healthy individuals progress to active TB. Despite evidence for heritability, it is not currently possible to predict who may develop TB. To explore approaches to classify susceptibility to TB, we infected with MTB dendritic cells (DCs) from putatively resistant individuals diagnosed with latent TB, and from susceptible individuals that had recovered from active TB. We measured gene expression levels in infected and non-infected cells and found hundreds of differentially expressed genes between susceptible and resistant individuals in the non-infected cells. We further found that genetic polymorphisms nearby the differentially expressed genes between susceptible and resistant individuals are more likely to be associated with TB susceptibility in published GWAS data. Lastly, we trained a classifier based on the gene expression levels in the non-infected cells, and demonstrated decent performance on our data and an independent data set. Overall, our promising results from this small study suggest that training a classifier on a larger cohort may enable us to accurately predict TB susceptibility.

scholarly journals D-GPM: a deep learning method for gene promoter methylation inference

2018 ◽  
Author(s):  
Xingxin Pan ◽  
Biao Liu ◽  
Xingzhao Wen ◽  
Yulu Liu ◽  
Xiuqing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Promoter Methylation ◽  
Methylation Level ◽  
Target Genes ◽  
Gene Promoter ◽  
Support Vector ◽  
Whole Genome ◽  
Expression Levels ◽  
Gene Expression Levels

AbstractBackgroundGene promoter methylation plays a critical role in a wide range of biological processes, such as transcriptional expression, gene imprinting, X chromosome inactivation,etc. Whole-genome bisulfite sequencing generates a comprehensive profiling of the gene methylation levels but is limited by a high cost. Recent studies have partitioned the genes into landmark genes and target genes and suggested that the landmark gene expression levels capture adequate information to reconstruct the target gene expression levels. Moreover, the methylation level of the promoter is usually negatively correlated with its corresponding gene expression. This result inspired us to propose that the methylation level of the promoters might be adequate to reconstruct the promoter methylation level of target genes, which would eventually reduce the cost of promoter methylation profiling.ResultsHere, we developed a deep learning model (D-GPM) to predict the whole-genome promoter methylation level based on the methylation profile of the landmark genes. We benchmarked D-GPM against three machine learning methods, namely, linear regression (LR), regression tree (RT) and support vector machine (SVM), based on two criteria: the mean absolute deviation (MAE) and the Pearson correlation coefficient (PCC). After profiling the methylation beta value (MBV) dataset from the TCGA, with respect to MAE and PCC, we found that D-GPM outperforms LR by 9.59% and 4.34%, RT by 27.58% and 22.96% and SVM by 6.14% and 3.07% on average, respectively. For the number of better-predicted genes, D-GPM outperforms LR in 92.65% and 91.00%, RT in 95.66% and 98.25% and SVM in 85.49% and 81.56% of the target genes.ConclusionsD-GPM acquires the least overall MAE and the highest overall PCC on MBV-te compared to LR, RT, and SVM. For a genewise comparative analysis, D-GPM outperforms LR, RT, and SVM in an overwhelming majority of the target genes, with respect to the MAE and PCC. Most importantly, D-GPM predominates among the other models in predicting a majority of the target genes according to the model distribution of the least MAE and the highest PCC for the target genes.

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