Dataset Information

Third-Generation Sequencing Reveals LncRNA-Regulated HSP Genes in the Populus x canadensis Moench Heat Stress Response.

ABSTRACT: Long non-coding RNAs (lncRNAs) regulate plant responses to abiotic stresses. However, the short reads produced by second-generation sequencing technology make it difficult to accurately explore full-length transcripts, limiting the study of lncRNAs. In this study, we used third-generation long-read sequencing technology with the PacBio Sequel and Illumina platform to explore the role of lncRNAs in the heat stress response of Populus x canadensis Moench trees. We using 382,034,416 short reads to correct 4,297,179 long reads by resulted in 66,657 full-length transcripts, representing 33,840 genes. Then, 753 putative lncRNAs were identified, including 658 sense lncRNAs (87.38%), 41 long intervening/intergenic non-coding RNAs (lincRNAs) (5.44%), 12 antisense lncRNAs (1.59%), and 42 sense intronic lncRNAs (5.58%). Using the criteria | log2FC| ? 1 and q-value < 0.05, 3,493 genes and 78 lncRNAs were differentially expressed under the heat treatment. Furthermore, 923 genes were detected as targets of 43 differently expressed lncRNAs by cis regulation. Functional annotation demonstrated that these target genes were related to unfolded protein binding, response to stress, protein folding, and response to stimulus. Lastly, we identified a lncRNA-gene interaction network consisting of four lncRNAs and six genes [Heat Shock Protein 82 (HSP82), HSP83, Disease Resistance Protein 27 (DRL27), DnaJ family protein (DNJH), and two other predicted protein-coding genes], which showed that lncRNAs could regulate HSP family genes in response to heat stress in Populus. Therefore, our third-generation sequencing has improved the description of the P. canadensis transcriptome. The potential lncRNAs and HSP family genes identified here present a genetic resource to improve our understanding of the heat-adaptation mechanisms of trees.

SUBMITTER: Xu J

PROVIDER: S-EPMC7221187 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Third-Generation Sequencing Reveals LncRNA-Regulated HSP Genes in the <i>Populus x canadensis</i> Moench Heat Stress Response.

Xu Jiahong J Fang Meng M Li Zhihao Z Zhang Maoning M Liu Xiaoyu X Peng Yuanyuan Y Wan Yinglang Y Chen Jinhui J

Frontiers in genetics 20200507

Long non-coding RNAs (lncRNAs) regulate plant responses to abiotic stresses. However, the short reads produced by second-generation sequencing technology make it difficult to accurately explore full-length transcripts, limiting the study of lncRNAs. In this study, we used third-generation long-read sequencing technology with the PacBio Sequel and Illumina platform to explore the role of lncRNAs in the heat stress response of <i>Populus x canadensis</i> Moench trees. We using 382,034,416 short re ...[more]

PMID: 32457788

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Project description:BackgroundAs air temperatures increase globally, more and more plants are exposed to heat-stress conditions. Although many studies have explored regulation networks in plants with the aim of improving their heat-stress tolerance, only few have revealed them in trees. Here, individuals of Populus qiongdaoensis seedlings, which grows naturally in tropical areas, exposed to heat at 40 °C and the non-coding regulation networks were explored using the PacBio RSII and the Illumina sequencing platform.ResultsIn total, we obtained 88,161 full-length transcripts representing 39,343 genes using 5,498,988 long reads and 350,026,252 clean reads, and also 216 microRNAs (miRNAs) via 95,794,107 reads. We then identified 928 putative long non-coding RNAs (lncRNAs), consisting of 828 sense lncRNAs (89.22%), 34 long intergenic non-coding RNAs (3.66%), 16 antisense (1.72%), and 50 sense intronic lncRNAs (5.39%). Under the dual criteria of |log2fold-change| ≥ 1 and P-value < 0.05, 1690 genes, 25 lncRNAs, and 15 miRNAs were found differentially expressed under the heat stress treatment. Furthermore, 563 and 595 mRNAs were detected as target genes of 14 differently expressed miRNAs and 26 differentially expressed lncRNAs. Functional annotation analysis of these target genes demonstrated they were related to cell membrane stability, plant hormone signal transduction, antioxidation, and aldarate metabolism. Lastly, we uncovered a key interaction network of lncRNAs, miRNAs and mRNAs that consisted of miR1444d, miR482a.1, miR530a, lncHSP18.2, HSP18.1, and HSP18.2. Expression level analysis showed that miRNAs in the network were up-regulated, while mRNAs and lncRNA were down-regulated, and also found that lncHSP18.2 may cis-regulate HSP18.2.ConclusionsFunctional enrichment analysis of target genes of miRNAs and lncRNAs indicated that miRNAs and lncRNAs play an important role in the response to heat stress P. qiongdaoensis. Lastly, by investigating the miRNA-lncRNA-mRNA network of this species, we revealed that miRNAs may negatively regulate both lncRNAs and mRNAs in tree responses to heat stress, and found that lncHSP18.2 may cis-regulate HSP18.2.

Project description:BackgroundThird-generation sequencing offers some advantages over next-generation sequencing predecessors, but with the caveat of harboring a much higher error rate. Clustering-related sequences is an essential task in modern biology. To accurately cluster sequences rich in errors, error type and frequency need to be accounted for. Levenshtein distance is a well-established mathematical algorithm for measuring the edit distance between words and can specifically weight insertions, deletions and substitutions. However, there are drawbacks to using Levenshtein distance in a biological context and hence has rarely been used for this purpose. We present novel modifications to the Levenshtein distance algorithm to optimize it for clustering error-rich biological sequencing data.ResultsWe successfully introduced a bidirectional frameshift allowance with end-user determined accommodation caps combined with weighted error discrimination. Furthermore, our modifications dramatically improved the computational speed of Levenstein distance. For simulated ONT MinION and PacBio Sequel datasets, the average clustering sensitivity for 3GOLD was 41.45% (S.D. 10.39) higher than Sequence-Levenstein distance, 52.14% (S.D. 9.43) higher than Levenshtein distance, 55.93% (S.D. 8.67) higher than Starcode, 42.68% (S.D. 8.09) higher than CD-HIT-EST and 61.49% (S.D. 7.81) higher than DNACLUST. For biological ONT MinION data, 3GOLD clustering sensitivity was 27.99% higher than Sequence-Levenstein distance, 52.76% higher than Levenshtein distance, 56.39% higher than Starcode, 48% higher than CD-HIT-EST and 70.4% higher than DNACLUST.ConclusionOur modifications to Levenshtein distance have improved its speed and accuracy compared to the classic Levenshtein distance, Sequence-Levenshtein distance and other commonly used clustering approaches on simulated and biological third-generation sequenced datasets. Our clustering approach is appropriate for datasets of unknown cluster centroids, such as those generated with unique molecular identifiers as well as known centroids such as barcoded datasets. A strength of our approach is high accuracy in resolving small clusters and mitigating the number of singletons.

Dataset Information

Third-Generation Sequencing Reveals LncRNA-Regulated HSP Genes in the Populus x canadensis Moench Heat Stress Response.

Publications

Third-Generation Sequencing Reveals LncRNA-Regulated HSP Genes in the <i>Populus x canadensis</i> Moench Heat Stress Response.

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