Dataset Information

Expression profiling of preadipocyte microRNAs by deep sequencing on chicken lines divergently selected for abdominal fatness.

ABSTRACT: Through posttranscriptional gene regulation, microRNA (miRNA) is linked to a wide variety of biological processes, including adipogenesis and lipid metabolism. Although miRNAs in mammalian adipogenesis have been worked on extensively, their study in chicken adipogenesis is still very limited. To find miRNAs potentially important for chicken preadipocyte development, we compared the preadipocyte miRNA expression profiles in two broiler lines divergently selected for abdominal fat content, by sequencing two small RNA libraries constructed for primary preadipocytes isolated from abdominal adipose tissues. After bioinformatics analyses, from chicken miRNAs deposited in miRBase 20.0, we identified 225 miRNAs to be expressed in preadipocytes, 185 in the lean line and 200 in the fat line (derived from 208 and 203 miRNA precursors, respectively), which corresponds to 114 miRNA families. The let-7 family miRNAs were the most abundant. Furthermore, we validated the sequencing results of 15 known miRNAs by qRT-PCR, and confirmed that the expression levels of most miRNAs correlated well with those of Solexa sequencing. A total of 33 miRNAs was significantly differentially expressed between the two chicken lines (P<0.05). Gene ontology analysis revealed that they could target genes enriched in the regulation of gene transcription and chromatin function, response to insulin stimulation, and IGF-1 signaling pathways, which could have important roles in preadipocyte development. Therefore, a valuable information and resource of miRNAs on chicken adipogenesis were provided in this study. Future functional investigations on these miRNAs could help explore related genes and molecular networks fundamental to preadipocyte development.

SUBMITTER: Wang W

PROVIDER: S-EPMC4326283 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Expression profiling of preadipocyte microRNAs by deep sequencing on chicken lines divergently selected for abdominal fatness.

Wang Weishi W Du Zhi-Qiang ZQ Cheng Bohan B Wang Yuxiang Y Yao Jing J Li Yumao Y Cao Zhiping Z Luan Peng P Wang Ning N Li Hui H

PloS one 20150212 2

Through posttranscriptional gene regulation, microRNA (miRNA) is linked to a wide variety of biological processes, including adipogenesis and lipid metabolism. Although miRNAs in mammalian adipogenesis have been worked on extensively, their study in chicken adipogenesis is still very limited. To find miRNAs potentially important for chicken preadipocyte development, we compared the preadipocyte miRNA expression profiles in two broiler lines divergently selected for abdominal fat content, by sequ ...[more]

PMID: 25675096

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Project description:BackgroundExcessive deposition of abdominal fat poses serious problems in broilers owing to rapid growth. Recently, the evolution of the existing knowledge on long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) have established their indispensable roles in multiple physiological metabolic processes, including adipogenesis and fat deposition. However, not much has been explored on their profiles in the abdominal fat tissues of broilers to date. In the study, we aimed to characterize the vital candidates of lncRNAs and circRNAs and their underlying regulations for abdominal fat deposition in broilers.ResultsThe present study sequenced the lncRNAs and circRNAs expression profiles in the abdominal fat tissues isolated from 7-week-old broilers, who were divergently selected for their fatness. It identified a total of 3359 lncRNAs and 176 circRNAs, demonstrating differential expressed (DE) 30 lncRNAs and 17 circRNAs between the fat- and lean-line broilers (|log2FC| ≥ 1, P < 0.05). Subsequently, the 20 cis-targets and 48 trans-targets of the candidate DE lncRNAs were identified for depositing abdominal fat by adjacent gene analysis and co-expression analysis, respectively. In addition, the functional enrichment analysis showed the DE lncRNAs targets and DE circRNAs host genes to be mainly involved in the cellular processes, amino/fatty acid metabolism, and immune inflammation-related pathways and GO terms. Finally, the vital 16 DE lncRNAs located in cytoplasm and specifically expressed in fat/lean line and their targets were used to construct the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) regulatory network, comprising 7 DE lncRNAs, 28 miRNAs, 11 DE mRNAs. Notably, three lncRNAs including XR_001468036.2, XR_003077610.1 and XR_001466431.2 with the most connected degrees might play hub regulatory roles in abdominal fat deposition of broilers.ConclusionsThis study characterized the whole expression difference of lncRNAs and circRNAs between the two lines broilers with divergently ability of abdominal fat. The vital candidate DE lncRNAs/circRNAs and ceRNA regulations were identified related to the deposition of abdominal fat in chicken. These results might further improve our understanding of regulating the non-coding RNAs in obesity.

Project description:The methylation status of pivotal genes involved in fat deposition in chickens has been extensively studied. However, the whole-genome DNA methylation profiles of broiler abdominal adipose tissue remain poorly understood. Using whole-genome bisulfite sequencing, we generated DNA methylation profiles of chicken abdominal adipose tissue from Northeast Agricultural University broiler lines divergently selected for abdominal fat content. We aimed to explore whether DNA methylation was associated with abdominal fat deposition in broilers. The whole-genome DNA methylation profiles of fat- and lean-line broilers abdominal adipose tissue were constructed. The DNA methylation levels of functional genomic regions in the fat broiler were higher than those in the lean broiler, especially in the 3' untranslated regions (UTRs) and exons in the non-CG contexts. Additionally, we identified 29,631 differentially methylated regions and, subsequently, annotated 6,484 and 2,016 differentially methylated genes (DMGs) in the gene body and promoter regions between the two lines, respectively. Functional annotation showed that the DMGs in promoter regions were significantly enriched mainly in the triglyceride catabolic process, lipid metabolism-related pathways, and extracellular matrix signal pathways. When the DMG in promoter regions and differentially expressed genes were integrated, we identified 30 genes with DNA methylation levels that negatively correlated with their messenger RNA (mRNA) expression, of which CMSS1 reached significant levels (false discovery rate < 0.05). These 30 genes were mainly involved in fatty acid metabolism, peroxisome-proliferator-activated receptor signaling, Wnt signaling pathways, transmembrane transport, RNA degradation, and glycosaminoglycan degradation. Comparing the DNA methylation profiles between fat- and lean-line broilers demonstrated that DNA methylation is involved in regulating broiler abdominal fat deposition. Our study offers a basis for further exploring the underlying mechanisms of abdominal adipose deposition in broilers.

Project description:Chickens divergently selected for either high abdominal fat content (fat genotype) or low abdominal fat content (lean genotype) at SRA-INRA, France were used to profile abdominal adipose gene expression during juvenile development (1 to 11 weeks of age) and to identify differentially expressed genes associated with genotype and age. The fat line (FL) and lean line (LL) chickens are different in various phenotypic and metabolic measurements, including abdominal fatness, plasma glycemia and T3. The FL and LL chickens represent unique models for characterizing biomedical and agricultural traits. The Del-Mar 14K Chicken Integrated Systems microarrays were used for a transcriptional scan in abdominal adipose during juvenile development using a balanced block hybridization design. Fluorescence intensities were normalized within array (without background subtraction), and between array (aquantile method) in LIMMA package R [Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, Vol. 3, No. 1, Article 3] producing M-values (log-2 expression ratios) and A-values (average log-2 expression values). Normalized values were analyzed using a two factor ANOVA model. A total of 3,669 unique differentially expressed functional genes were identified (FDR<0.05). Genes were determined to have a significant effect of age (3,222), genotype (344), or an age by genotype interaction (254). The differentially expressed genes include metabolic enzymes, acute phase proteins, growth factors, coagulation factors, immune factors and transcription factors involved in various pathways. Several of the functional genes are also identified as positional candidate genes within QTLs in an F2 population established from an intercross between the FL and LL lines. Keywords: Divergently selected chickens, fatness, transcriptional profiling, differentially expressed genes

Project description:BACKGROUND: The chicken (Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. Copy number variations (CNVs) are a form of genomic structural variation widely distributed in the genome. CNV analysis has recently gained greater attention and momentum, as the identification of CNVs can contribute to a better understanding of traits important to both humans and other animals. To detect chicken CNVs, we genotyped 475 animals derived from two broiler chicken lines divergently selected for abdominal fat content using chicken 60 K SNP array, which is a high-throughput method widely used in chicken genomics studies. RESULTS: Using PennCNV algorithm, we detected 438 and 291 CNVs in the lean and fat lines, respectively, corresponding to 271 and 188 CNV regions (CNVRs), which were obtained by merging overlapping CNVs. Out of these CNVRs, 99% were confirmed also by the CNVPartition program. These CNVRs covered 40.26 and 30.60 Mb of the chicken genome in the lean and fat lines, respectively. Moreover, CNVRs included 176 loss, 68 gain and 27 both (i.e. loss and gain within the same region) events in the lean line, and 143 loss, 25 gain and 20 both events in the fat line. Ten CNVRs were chosen for the validation experiment using qPCR method, and all of them were confirmed in at least one qPCR assay. We found a total of 886 genes located within these CNVRs, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed they could play various roles in a number of biological processes. Integrating the results of CNVRs, known quantitative trait loci (QTL) and selective sweeps for abdominal fat content suggested that some genes (including SLC9A3, GNAL, SPOCK3, ANXA10, HELIOS, MYLK, CCDC14, SPAG9, SOX5, VSNL1, SMC6, GEN1, MSGN1 and ZPAX) may be important for abdominal fat deposition in the chicken. CONCLUSIONS: Our study provided a genome-wide CNVR map of the chicken genome, thereby contributing to our understanding of genomic structural variations and their potential roles in abdominal fat content in the chicken.

Project description:Chickens divergently selected for either high abdominal fat content (fat genotype) or low abdominal fat content (lean genotype) at SRA-INRA, France were used to profile abdominal adipose gene expression during juvenile development (1 to 11 weeks of age) and to identify differentially expressed genes associated with genotype and age. The fat line (FL) and lean line (LL) chickens are different in various phenotypic and metabolic measurements, including abdominal fatness, plasma glycemia and T3. The FL and LL chickens represent unique models for characterizing biomedical and agricultural traits. The Del-Mar 14K Chicken Integrated Systems microarrays were used for a transcriptional scan in abdominal adipose during juvenile development using a balanced block hybridization design. Fluorescence intensities were normalized within array (without background subtraction), and between array (aquantile method) in LIMMA package R [Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, Vol. 3, No. 1, Article 3] producing M-values (log-2 expression ratios) and A-values (average log-2 expression values). Normalized values were analyzed using a two factor ANOVA model. A total of 1,020 differentially expressed functional genes were identified (FDR<0.05). Genes were determined to have a significant effect of age (422), genotype (344), or an age by genotype interaction (254). The differentially expressed genes include metabolic enzymes, acute phase proteins, growth factors, coagulation factors, immune factors and transcription factors involved in various pathways. Several of the functional genes are also identified as positional candidate genes within QTLs in an F2 population established from an intercross between the FL and LL lines. Keywords: Divergently selected chickens, fatness, transcriptional profiling, differentially expressed genes A balanced block design was used for microarray hybridizations, where half of the birds of each genotype and age were labeled with Alexa Flour 647 (red) and the other half with Alexa Flour 555 (green). Four biological replicates were used for each genotype (FL or LL) at six different ages (1, 3, 5, 7, 9 and 11 wk).

Project description:Background: The chicken (Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. Copy number variations (CNVs) are a form of genomic structural variation widely distributed in the genome. CNV analysis has recently gained greater attention and momentum, as the identification of CNVs can contribute to a better understanding of traits important to both humans and other animals. To detect chicken CNVs, we genotyped 475 animals derived from two broiler chicken lines divergently selected for abdominal fat content using chicken 60K SNP array, which is a high-throughput method widely used in chicken genomics studies. Results: Using PennCNV algorithm, we detected 438 and 291 CNVs in the lean and fat lines, respectively, corresponding to 271 and 188 CNV regions (CNVRs), which were obtained by merging overlapping CNVs. Out of these CNVRs, 99% were confirmed also by the CNVPartition program. These CNVRs covered 40.26 and 30.60 Mb of the chicken genome in the lean and fat lines, respectively. Moreover, CNVRs included 176 loss, 68 gain and 27 both (i.e. loss and gain within the same region) events in the lean line, and 143 loss, 25 gain and 20 both events in the fat line. Ten CNVRs were chosen for the validation experiment using qPCR method, and all of them were confirmed in at least one qPCR assay. We found a total of 886 genes located within these CNVRs, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed they could play various roles in a number of biological processes. Integrating the results of CNVRs, known quantitative trait loci (QTL) and selective sweeps for abdominal fat content suggested that some genes (including SLC9A3, GNAL, SPOCK3, ANXA10, HELIOS, MYLK, CCDC14, SPAG9, SOX5, VSNL1, SMC6, GEN1, MSGN1 and ZPAX) may be important for abdominal fat deposition in the chicken. Conclusions: Our study provided a genome-wide CNVR map of the chicken genome, thereby contributing to our understanding of genomic structural variations and their potential roles in abdominal fat content in the chicken.

Dataset Information

Expression profiling of preadipocyte microRNAs by deep sequencing on chicken lines divergently selected for abdominal fatness.

Publications

Expression profiling of preadipocyte microRNAs by deep sequencing on chicken lines divergently selected for abdominal fatness.

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