Dataset Information

Social status regulates the hepatic miRNAome in rainbow trout: Implications for posttranscriptional regulation of metabolic pathways.

ABSTRACT: Juvenile rainbow trout develop social hierarchies when held in dyads, and the development of socially subordinate (SS) and social dominance (SD) phenotypes in this context has been linked to specific changes in the hepatic energy metabolism of all major macronutrients. Following our recently reported finding that transcript abundance of drosha, a key component of the microRNA (miRNA) biogenesis pathway, is increased in paired juvenile rainbow trout irrespective of social status compared to socially isolated (SI) controls, we here determined global changes of the hepatic miRNA pathway genes in detail at the transcript and protein level. Both SD and SS rainbow trout exhibited increased Ago2 protein abundance compared to SI rainbow trout, suggesting that hepatic miRNA function is increased in rainbow trout maintained in dyads. Given the well-described differences in hepatic intermediary metabolism between SD and SS rainbow trout, and the important role of miRNAs in the posttranscriptional regulation of metabolic pathways, we also identified changes in hepatic miRNA abundance between SS and SD rainbow trout using small RNA next generation sequencing. We identified a total of 24 differentially regulated miRNAs, with 15 miRNAs that exhibited increased expression, and 9 miRNAs that exhibited decreased expression in the liver of SS trout compared to SD trout. To identify potential miRNA-dependent posttranscriptional regulatory pathways important for social status-dependent regulation of hepatic metabolism in rainbow trout, we used an in silico miRNA target prediction and pathway enrichment approach. We identified enrichment for pathways related to metabolism of carbohydrates, lipids and proteins in addition to organelle-specific processes involved in energy metabolism, especially mitochondrial fusion and fission. Select predicted miRNA-mRNA target pairs within these categories were quantitatively analyzed by real-time RT-PCR to validate candidates for future studies that will probe the functional metabolic roles of specific hepatic miRNAs in the development of SD and SS metabolic phenotypes.

SUBMITTER: Kostyniuk DJ

PROVIDER: S-EPMC6563994 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Social status regulates the hepatic miRNAome in rainbow trout: Implications for posttranscriptional regulation of metabolic pathways.

Kostyniuk Daniel J DJ Zhang Dapeng D Martyniuk Christopher J CJ Gilmour Kathleen M KM Mennigen Jan A JA

PloS one 20190613 6

Juvenile rainbow trout develop social hierarchies when held in dyads, and the development of socially subordinate (SS) and social dominance (SD) phenotypes in this context has been linked to specific changes in the hepatic energy metabolism of all major macronutrients. Following our recently reported finding that transcript abundance of drosha, a key component of the microRNA (miRNA) biogenesis pathway, is increased in paired juvenile rainbow trout irrespective of social status compared to socia ...[more]

PMID: 31194802

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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:Carnivorous rainbow trout exhibit prolonged postprandial hyperglycemia when fed a diet exceeding 20% carbohydrate content. This poor capacity to utilize carbohydrates has led to rainbow trout being classified as "glucose-intolerant" (GI). The metabolic phenotype has spurred research to identify the underlying cellular and molecular mechanisms of glucose intolerance, largely because carbohydrate-rich diets provide economic and ecological advantages over traditionally used fish meal, considered unsustainable for rainbow trout aquaculture operations. Evidence points to a contribution of hepatic intermediary carbohydrate and lipid metabolism, as well as upstream insulin signaling. Recently, microRNAs (miRNAs), small noncoding RNAs acting as negative posttranscriptional regulators affecting target mRNA stability and translation, have emerged as critical regulators of hepatic control of glucose-homeostasis in mammals, revealing that dysregulated hepatic miRNAs might play a role in organismal hyperglycemia in metabolic disease. To determine whether hepatic regulatory miRNA networks may contribute to GI in rainbow trout, we induced prolonged postprandial hyperglycemia in rainbow trout by using a carbohydrate-rich diet and profiled genome-wide hepatic miRNAs in hyperglycemic rainbow trout compared with fasted trout and trout fed a diet devoid of carbohydrates. Using small RNA next-generation sequencing and real-time RT-PCR validation, we identified differentially regulated hepatic miRNAs between these groups and used an in silico approach to predict bona fide mRNA targets and enriched pathways. Diet-induced hyperglycemia resulted in differential regulation of hepatic miRNAs compared with fasted fish. Some of the identified miRNAs, such as miRNA-27b-3p and miRNA-200a-3p, are known to be responsive to hyperglycemia in the liver of hyperglycemic glucose-tolerant fish and mammals, suggesting an evolutionary conserved regulation. Using Gene Ontology term-based enrichment analysis, we identify intermediate carbohydrate and lipid metabolism and insulin signaling as potential targets of posttranscriptional regulation by hyperglycemia-regulated miRNAs and provide correlative expression analysis of specific predicted miRNA-target pairs. This study identifies hepatic miRNAs in rainbow trout that exhibit differential postprandial expression in response to diets with different carbohydrate content and predicts posttranscriptionally regulated target mRNAs enriched for pathways involved in glucoregulation. Together, these results provide a framework for testable hypotheses of functional involvement of specific hepatic miRNAs in GI in rainbow trout.

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)

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 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)

Dataset Information

Social status regulates the hepatic miRNAome in rainbow trout: Implications for posttranscriptional regulation of metabolic pathways.

Publications

Social status regulates the hepatic miRNAome in rainbow trout: Implications for posttranscriptional regulation of metabolic pathways.

Similar Datasets

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