Comparative analysis of the ovarian transcriptome reveals novel insights into fertility differences in Large White sows

Hu, Huiyan; Jia, Qiong; Zhou, Bo; Zhang, Jing; Li, Zhiqiang; Liu, Zhongwu

doi:10.1007/s13258-020-00926-8

“…which was calculated using the SPSS19.0 software package (IBM Corp, Armonk, NY, USA). The high (H; TNB > 15.73) and low (L; TNB < 11.11) fertility groups of sows were determined according to our previous study [60]. Eight sows with similarly high or low parity from each group were chosen for the study (n = 8 for the H group and n = 8 for the L group).…”

Section: Discussionmentioning

confidence: 99%

“…Total RNA was extracted from each ovarian sample, and then puri ed with RNeasy Mini Kit (Qiagen, Valencia, CA, USA). Total RNA from each sample was quanti ed and quali ed as previously described [60]. The same sample was used for both sequencing and RT-qRCR analysis.…”

Section: Library Preparation and Solexa Sequencingmentioning

confidence: 99%

“…Clean data from each library were obtained and then mapped to the Sscrofa10.2 reference genome that was downloaded from the Ensembl Genomes (http://www.ensembl.org/). The mapped reads from each individual animal were assembled as previously described [60]. Rsem (V1.2.6) [63] was used to calculate gene expression levels using the FPKM (Fragments per Kilo bases per Million reads) method for both the lncRNAs and coding genes in each sample.…”

Section: Analysis Of Rna-seq Datamentioning

confidence: 99%

“…Rsem (V1.2.6) [63] was used to calculate gene expression levels using the FPKM (Fragments per Kilo bases per Million reads) method for both the lncRNAs and coding genes in each sample. Additionally, the method of differential gene expression analysis was described in detail in ref [60]. Transcripts of genes showing P values <0.05 and with a |log 2 (fold change)| ≥ 1 were identi ed as differentially expressed.…”

Section: Analysis Of Rna-seq Datamentioning

confidence: 99%

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Integrated Analysis of lncRNA, miRNA and mRNA Reveals Novel Insights into the Fertility Regulation of Large White Sows

Hu

¹

,

Jia²,

Xi

³

et al. 2020

Preprint

Self Cite

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Background: Improving sow fertility is extremely important as it can lead to increased reproductive efficiency and thus profitability for swine producers. There are considerable differences in fertility rates among individual animals, but the underlying molecular mechanisms remain unclear. In this study, by using different types of RNA libraries, we investigated the complete transcriptome of ovarian tissue during the luteal (L) and follicular (F) phases of the estrous cycle in Large White pigs with high (H) and low (L) fecundity, and performed a comprehensive analysis of long noncoding RNAs (lncRNAs), mRNAs and micro RNAs (miRNAs) from 16 samples by combining RNA sequencing (RNA-seq) with bioinformatics.Results: In total, 24,447 lncRNAs, 27,370 mRNAs, and 216 known miRNAs were identified in ovarian tissues. The genomic features of lncRNAs, such as length distribution and number of exons, were further analyzed. We selected a threshold of P <0.05 and |log2 (fold change)| ≥ 1 to obtain the differentially expressed lncRNAs, miRNAs and mRNAs by pairwise comparison (LH vs. LL, FH vs. FL). Bioinformatics analysis of these differentially expressed RNAs revealed multiple significantly enriched pathways (P <0.05) that were closely involved in the reproductive process, such as ovarian steroidogenesis, lysosome, steroid biosynthesis, and the estrogen and GnRH signaling pathways. Moreover, bioinformatics screening of differentially expressed miRNAs that share common miRNA response elements (MREs) with lncRNAs and their downstream mRNA targets were performed. Finally, we constructed lncRNA–miRNA–mRNA regulation networks. The key genes in these networks were verified by Reverse Transcription Real-time Quantitative PCR (RT-qRCR), which were consistent with the results from RNA-Seq data.Conclusions: These results provide further insights into the fertility of pigs andcan contribute to further experimental investigation of the functions of these genes.

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“…Clean data from each library were obtained and then mapped to the Sscrofa10.2 reference genome that was downloaded from the Ensembl Genomes (ftp://ftp.ensembl.org/pub/release-86/fasta/sus_scrofa/dna/Sus_scrofa.Sscrofa10.2.dna.toplevel.fa.gz). The mapped reads from each individual animal were assembled as previously described [60]. Rsem (V1.2.6) [63] was used to calculate gene expression levels using the FPKM (Fragments per Kilo bases per Million reads) method for both the lncRNAs and coding genes in each sample.…”

Section: Analysis Of Rna-seq Datamentioning

confidence: 99%

Integrated Analysis of lncRNA, miRNA and mRNA Reveals Novel Insights into the Fertility Regulation of Large White Sows

Hu

¹

,

Jia²,

Xi

³

et al. 2020

Preprint

Self Cite

0

View full text Add to dashboard Cite

Background: Improving sow fertility is extremely important as it can lead to increased reproductive efficiency and thus profitability for swine producers. There are considerable differences in fertility rates among individual animals, but the underlying molecular mechanisms remain unclear. In this study, by using different types of RNA libraries, we investigated the complete transcriptome of ovarian tissue during the luteal (L) and follicular (F) phases of the estrous cycle in Large White pigs with high (H) and low (L) fecundity, and performed a comprehensive analysis of long noncoding RNAs (lncRNAs), mRNAs and micro RNAs (miRNAs) from 16 samples by combining RNA sequencing (RNA-seq) with bioinformatics.Results: In total, 24,447 lncRNAs, 27,370 mRNAs, and 216 known miRNAs were identified in ovarian tissues. The genomic features of lncRNAs, such as length distribution and number of exons, were further analyzed. We selected a threshold of P <0.05 and |log2 (fold change)| ≥ 1 to obtain the differentially expressed lncRNAs, miRNAs and mRNAs by pairwise comparison (LH vs. LL, FH vs. FL). Bioinformatics analysis of these differentially expressed RNAs revealed multiple significantly enriched pathways (P <0.05) that were closely involved in the reproductive process, such as ovarian steroidogenesis, lysosome, steroid biosynthesis, and the estrogen and GnRH signaling pathways. Moreover, bioinformatics screening of differentially expressed miRNAs that share common miRNA response elements (MREs) with lncRNAs and their downstream mRNA targets were performed. Finally, we constructed lncRNA–miRNA–mRNA regulation networks. The key genes in these networks were verified by Reverse Transcription Real-time Quantitative PCR (RT-qRCR), which were consistent with the results from RNA-Seq data.Conclusions: These results provide further insights into the fertility of pigs andcan contribute to further experimental investigation of the functions of these genes.

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Impairment of caprine oocyte maturation in vitro and alteration of granulosa cells functions by widely used fungicide mancozeb

Dinisri

¹

,

Kodikara

²

,

Prasadani

³

et al. 2021

Trop Anim Health Prod

4

0

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Comparative analysis of the ovarian transcriptome reveals novel insights into fertility differences in Large White sows

Cited by 8 publications

References 37 publications

Integrated Analysis of lncRNA, miRNA and mRNA Reveals Novel Insights into the Fertility Regulation of Large White Sows

Integrated Analysis of lncRNA, miRNA and mRNA Reveals Novel Insights into the Fertility Regulation of Large White Sows

Integrated Analysis of lncRNA, miRNA and mRNA Reveals Novel Insights into the Fertility Regulation of Large White Sows

Impairment of caprine oocyte maturation in vitro and alteration of granulosa cells functions by widely used fungicide mancozeb

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