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 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 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 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.

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation

2020 ◽  
Vol 36 (10) ◽  
pp. 3131-3138
Author(s):  
Ke Jin ◽  
Le Ou-Yang ◽  
Xing-Ming Zhao ◽  
Hong Yan ◽  
Xiao-Fei Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Supplementary Information ◽  
Expression Levels ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis ◽  
Gene Expression Levels

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Effects of methionine supplementation on the expression of oxidative stress-related genes in acute heat stress-exposed broilers

2015 ◽  
Vol 113 (4) ◽  
pp. 549-559 ◽  
Author(s):  
Ana Paula Del Vesco ◽  
Eliane Gasparino ◽  
Daiane de Oliveira Grieser ◽  
Vittor Zancanela ◽  
Maria Amélia Menck Soares ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heat Stress ◽  
Thermal Comfort ◽  
Expression Level ◽  
Glutathione Synthetase ◽  
Environment Interaction ◽  
Expression Levels ◽  
Methionine Supplementation ◽  
Stress Related Genes ◽  
Gene Expression Levels

The aim of the present study was to evaluate the effects of heat stress (HS) and methionine supplementation on the markers of stress and on the gene expression levels of uncoupling proteins (UCP), betaine–homocysteine methyltransferase (BHMT), cystathionine β-synthase (CBS), glutathione synthetase (GSS) and glutathione peroxidase 7 (GPx7). Broilers from 1 to 21 d and from 22 to 42 d of age were divided into three treatment groups related to methionine supplementation: without methionine supplementation (MD); recommended level of methionine supplementation (DL1); excess methionine supplementation (DL2). The broilers were either kept at a comfortable thermal temperature or exposed to HS (38°C for 24 h). During the starter period, we observed the effects of the interaction between diet and environment on the gene expression levels of UCP, BHMT and GSS. Higher gene expression levels of UCP and BHMT were observed in broilers that were maintained at thermal comfort conditions and received the MD diet. HS broilers fed the DL1 and DL2 diets had the highest expression level of GSS. The expression levels of the CBS and GPx7 genes were influenced by both the environment and methionine supplementation. During the grower period, the gene expression levels of BHMT, CBS, GSS and GPx7 were affected by the diet × environment interaction. A higher expression level of BHMT was observed in broilers maintained at thermal comfort conditions and on the MD diet. HS induced higher expression levels of CBS, GSS and GPx7 in broilers that received the DL1 and DL2 diets. The present results suggest that under HS conditions, methionine supplementation could mitigate the effects of stress, since methionine contributed to the increased expression levels of genes related to antioxidant activity.

scholarly journals DE NOVO SEQUENCING AND ANALYSIS OF THE RANA CHENSINENSIS TRANSCRIPTOME TO DISCOVER PUTATIVE GENES ASSOCIATED WITH POLYUNSATURATED FATTY ACIDS

2020 ◽  
Author(s):  
Jingmeng Sun ◽  
Zhuoming Wang ◽  
Weiyu Zhang
Keyword(s):  
Gene Expression ◽  
Fatty Acids ◽  
Polyunsaturated Fatty Acids ◽  
Unsaturated Fatty Acids ◽  
De Novo ◽  
Differential Expression Analysis ◽  
Expression Levels ◽  
Rana Chensinensis ◽  
Oviductus Ranae ◽  
Gene Expression Levels

ABSTRACTRana chensinensis (R. chensinensis) is an important wild animal found in China, and a precious animal in Chinese herbal medicine. R. chensinensis is rich in polyunsaturated fatty acids (PUFAS). However, information regarding the genes of R. chensinensis related to the synthesis of PUFAs is limited. To identify these genes, we performed Illumina sequencing of R. chensinensis RNA from the skin and Oviductus Ranae. The Illumina Hiseq 2000 platform was used for sequencing, and the I-Sanger cloud platform was used for transcriptome de novo sequencing and information analysis to generate a database. Through the database generated by the transcriptome and the pathway map, we found the pathway for the biosynthesis of R. chensinensis PUFAs. The Pearson coefficient method was used to analyze the correlation of gene expression levels between samples, and the similarity of gene expression in different tissues and the characteristics in their respective tissues were found. Twelve differentially expressed genes of PUFA in skin and Oviductus Ranae were screened by gene differential expression analysis. The 12 unigenes expression levels of qRT-PCR were used to verify the results of gene expression levels consistent with transcriptome analysis. Based on the sequencing, key genes involved in biosynthesis of unsaturated fatty acids were isolated, which established a biotechnological platform for further research on R. chensinensis.

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