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Diet-induced hyperglycemia regulates the hepatic miRNAome in glucose-intolerant rainbow trout, Oncorhynchus mykiss

ABSTRACT: Purpose: Next-generation sequencing (NGS) of small RNAs allows transcriptome level analysis of the miRNAome. The goals of this study are to identify differentially regulated hepatic miRNAs in response to a diet with high (>20%) or low (0%) carbohydrate content following a short term fast using small RNA next generation sequencing. Specific quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods were used to validate optimal high-throughput data analysis, and in silico prediction to identify bona fide target pathways regulated by differentially regulated miRNAs following these nutritional stimuli. Method: Small RNA sequencing was performed using four randomly selected samples from each treatment group were used for sequencing. The quality of total RNA was confirmed by RNA Integrity Numbers greater than 9.0 using an Agilent Technologies 2100 Bioanalyzer. Nucleotide fractions (15-50) of small RNA were isolated from the total RNA using polyacrylamide gel electrophoresis and were ligated to a 30 adapter followed by a 50 adapter (Illumina, San Diego, CA, USA). The small RNA ligated to the adaptors was reverse transcribed to cDNA, PCR amplified and gel purified. The gel-extracted cDNA was used for library preparation, which was further used for cluster generation on Illumina's Cluster Station before sequencing using Illumina GAIIx. Raw sequence data was obtained from image data using Illumina's Sequencing Control Studio software version 2.8 (SCS v2.8) following real-time sequencing image analysis and base-calling by Illumina's Real-Time Analysis version 1.8.70 (RTA v1.8.70). The ACGT101-miR v4.2 pipeline (LC Sciences) was used to analyze sequenced data. Sequences with low Q scores, reads mapped to mRNA, RFam, Repbase and piRNA database were deleted and unique families were generated from identical sequences. These filtered unique sequences were then mapped to fish pre-miRNA and miRNA using miRBase or to the rainbow trout genome (Berthelot et al., 2014) to identify conserved miRNA following ACGT-101 User's Manual. Unique or novel miRNAs were identified after the BLAST performed against fish miRNAs from miRbase database (release 21) and published miRNAs from rainbow trout (Mennigen et al., 2016). The miRNAs that do not match any sequences in the specified databases and have the propensity of forming hairpin structure with the extended sequences at the mapped positions were classified as unique miRNAs. These unique miRNAs were further classified into different groups depending on the mappable reads to selected miRNAs in miRbase. Differentially expressed miRNAs were identified using the statistical software R (Version 3.2.2) package DESeq2 for one-way ANOVA comparison of all three treatment groups. Real-time RT–PCR validation was performed using SYBR Green assays Results: Refeeding differentially regulates hepatic miRNA abundance in a diet dependent manner: In trout refed with a low (0%) CHO diet, refeeding resulted in the 23 differntiaally regulated miRNA, of which 17 miRNAs were upregulated and 6 miRNAs were downregulated. In trout refed with a high (>20%) CHO diet, refeeding resulted in 49 differentially regulated miRNA, of which 20 miRNAs were upregulated and 29 miRNA were downregulated. Irrespecitve of the diet, 5 miRNAs were commonly regulated by refeeding alone, 4 miRNAs were upregulated and 1 was downregulated. When comparing hepatic miRNA profiles in trout following refeeding, there were 30 miRNAs that were differntially regulated after refeeding with different diets, 16 miRNAs were upregulated with the high CHO diet while 14 miRNAs were downregulated with the high CHO diet. Conclusions: Our study represents the first detailed analysis of the rainbow trout miRNAome in response to nutritional stimuli. Specifically, we here identify common hepatic miRNAs differentially regulated by refeeding following a short-term fast (2d), as well as miRNAs differentially regulated in the postprandial state depending on the dieatary stimulus (no carbohydrates compared to >20% dietary carbohydrates)

ORGANISM(S): Oncorhynchus mykiss

PROVIDER: GSE112814 | GEO | 2021/04/06

REPOSITORIES: GEO

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Project description:Purpose: Next-generation sequencing (NGS) of small RNAs allows transcriptome level analysis of the miRNAome. The goals of this study are to identify differentially regulated hepatic miRNAs in response to a dominant or subordinate social status using small RNA next generation sequencing. Specific quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods were used to validate optimal high-throughput data analysis, and in silico prediction to identify bona fide target pathways regulated by differentially regulated miRNAs following these social interactions. Method: Small RNA sequencing was performed using four randomly selected samples from each treatment group were used for sequencing. The quality of total RNA was confirmed by RNA Integrity Numbers greater than 9.0 using an Agilent Technologies 2100 Bioanalyzer. Nucleotide fractions (15-50) of small RNA were isolated from the total RNA using polyacrylamide gel electrophoresis and were ligated to a 30 adapter followed by a 50 adapter (Illumina, San Diego, CA, USA). The small RNA ligated to the adaptors was reverse transcribed to cDNA, PCR amplified and gel purified. The gel-extracted cDNA was used for library preparation, which was further used for cluster generation on Illumina's Cluster Station before sequencing using Illumina GAIIx. Raw sequence data was obtained from image data using Illumina's Sequencing Control Studio software version 2.8 (SCS v2.8) following real-time sequencing image analysis and base-calling by Illumina's Real-Time Analysis version 1.8.70 (RTA v1.8.70). The ACGT101-miR v4.2 pipeline (LC Sciences) was used to analyze sequenced data. Sequences with low Q scores, reads mapped to mRNA, RFam, Repbase and piRNA database were deleted and unique families were generated from identical sequences. These filtered unique sequences were then mapped to fish pre-miRNA and miRNA using miRBase or to the rainbow trout genome (Berthelot et al., 2014) to identify conserved miRNA following ACGT-101 User's Manual. Unique or novel miRNAs were identified after the BLAST performed against fish miRNAs from miRbase database (release 21) and published miRNAs from rainbow trout (Mennigen et al., 2016). The miRNAs that do not match any sequences in the specified databases and have the propensity of forming hairpin structure with the extended sequences at the mapped positions were classified as unique miRNAs. These unique miRNAs were further classified into different groups depending on the mappable reads to selected miRNAs in miRbase. Differentially expressed miRNAs were identified using the statistical software R (Version 3.2.2) package DESeq2 for one-way ANOVA comparison of all three treatment groups. Real-time RT–PCR validation was performed using SYBR Green assays Results: 24 miRNAs are differentially regulated by social status. 9 miRNAs are higher expressed in dominant trout. 15 miRNAs are higher expressed in subordinate trout. Conclusions: Our study represents the first detailed analysis of the rainbow trout miRNAome in response to nutritional stimuli. Specifically, we here identify common hepatic miRNAs differentially regulated by refeeding following a short-term fast (2d), as well as miRNAs differentially regulated in the postprandial state depending on the dieatary stimulus (no carbohydrates compared to >20% dietary carbohydrates)

Project description:Purpose: Next-generation sequencing (NGS) of small RNAs allows transcriptome level analysis of the miRNAome. The goals of this study are to identify differentially regulated hepatic miRNAs in response to a short term fast using small RNA next generation sequencing. Specific quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods were used to validate optimal high-throughput data analysis, and in silico prediction to identify bona fide target pathways regulated by differentially regulated miRNAs following this fasting stressor. Method: Small RNA sequencing was performed using four randomly selected samples from each treatment group were used for sequencing. The quality of total RNA was confirmed by RNA Integrity Numbers greater than 9.0 using an Agilent Technologies 2100 Bioanalyzer. Nucleotide fractions (15-50) of small RNA were isolated from the total RNA using polyacrylamide gel electrophoresis and were ligated to a 30 adapter followed by a 50 adapter (Illumina, San Diego, CA, USA). The small RNA ligated to the adaptors was reverse transcribed to cDNA, PCR amplified and gel purified. The gel-extracted cDNA was used for library preparation, which was further used for cluster generation on Illumina's Cluster Station before sequencing using Illumina GAIIx. Raw sequence data was obtained from image data using Illumina's Sequencing Control Studio software version 2.8 (SCS v2.8) following real-time sequencing image analysis and base-calling by Illumina's Real-Time Analysis version 1.8.70 (RTA v1.8.70). The ACGT101-miR v4.2 pipeline (LC Sciences) was used to analyze sequenced data. Sequences with low Q scores, reads mapped to mRNA, RFam, Repbase and piRNA database were deleted and unique families were generated from identical sequences. These filtered unique sequences were then mapped to fish pre-miRNA and miRNA using miRBase or to the rainbow trout genome (Berthelot et al., 2014) to identify conserved miRNA following ACGT-101 User's Manual. Unique or novel miRNAs were identified after the BLAST performed against fish miRNAs from miRbase database (release 21) and published miRNAs from rainbow trout (Mennigen et al., 2016). The miRNAs that do not match any sequences in the specified databases and have the propensity of forming hairpin structure with the extended sequences at the mapped positions were classified as unique miRNAs. These unique miRNAs were further classified into different groups depending on the mappable reads to selected miRNAs in miRbase. Differentially expressed miRNAs were identified using the statistical software R (Version 3.2.2) package DESeq2 for one-way ANOVA comparison of all three treatment groups. Real-time RT–PCR validation was performed using SYBR Green assays Results: 11 miRNA differentially regulated by fasting. 4 miRNAs are downregulated with fasting. 7 miRNAs are upregulated with fasting. Conclusions: Our study represents the first detailed analysis of the rainbow trout miRNAome in response to nutritional stimuli. Specifically, we here identify common hepatic miRNAs differentially regulated by refeeding following a short-term fast (2d), as well as miRNAs differentially regulated in the postprandial state depending on the dieatary stimulus (no carbohydrates compared to >20% dietary carbohydrates)

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Diet-induced hyperglycemia regulates the hepatic miRNAome in glucose-intolerant rainbow trout, Oncorhynchus mykiss

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