Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1061375 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27471339_1.fastq.gz | Fastqsanger.gz | |||
| SRR27471339_2.fastq.gz | Fastqsanger.gz | |||
| SRR27471340_1.fastq.gz | Fastqsanger.gz | |||
| SRR27471340_2.fastq.gz | Fastqsanger.gz | |||
| SRR27471341_1.fastq.gz | Fastqsanger.gz |
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Project description:Purpose: With the advent of Next-generation sequencing (NGS), several novel genes/proteins and cellular pathways in wide variety of tissues has been discovered. The aim of this study are to perform uterine transcriptome profiling (RNA-seq) to determine differently expressed genes in laying and non-laying hens and to further validate the expression of candidate genes using real-time quantitative reverse transcription polymerase chain reaction (qRT–PCR) in laying, non-laying and molting hens. Methods: Uterine mRNA profiles of 35-60 weeks-old laying and non-laying hens, three each, were generated with NextSeq 500 sequencer in single-end mode with a read length of 1x76 bp. Raw sequencing reads were cleaned and trimmmed with Prinseq tool and good reads were aligned against the chicken reference gemone (Galgal 5.0) in Array Studio. Differential gene expression analysis was performed by the DESeq2 algorithm as implemented in Array Studio. The genes with at least two-fold change (FC) and Benjamini and Hochberg q-value < 0.05 were called differentially expressed. Results: Using an optimized data analysis workflow, we mapped about 32 million reads from layers and 28 million reads from non-layers to the chicken genome. A total of 19,152 gene transcripts were annotated from Ensembl alignment which represents 50.24% of the chicken genome assembly. Differential gene expression analysis showed 616 were differentially expressed between layer and non-layer hens. 229 DEGs were significantly up-regulated and 286 were significantly down-regulated in the laying hens when compared to the non-laying hens. Twelve candiate genes, linked to calcium remodeling, were identified by gene function analysis and validated using qPCR. MEPE, CALCB, OTOP2, STC2 and ATP2C2 were confirmed to be highly expressed in laying hens as compared to molting and non-laying hens. RNA-seq and qPCR data for relative gene expression were highly correlated (R2 =0.99). Conclusions: Our study reports the expression of four novel genes that are speculated to transport calcium ions across the uterine epithellium for eggshell mineralization. These genes can be used as quantitative basis of selecting hens with an improved eggshell quality.
2018-12-06 | GSE114103 | GEO
Project description:Purpose: With the advent of Next-generation sequencing (NGS), several novel genes/proteins and cellular pathways in wide varitey of tissues has discovered. The aim of this study are to perform transcriptome profiling (RNA-seq) of magnum to determine differently expressed genes in laying and non-laying hens and to further validate the expression of candidate genes using real-time quantitative reverse transcription polymerase chain reaction (qRT–PCR) in laying, non-laying and molting hens. Methods: Magnum mRNA profiles of 35-60 weeks-old laying and non-laying hens, three each, were generated with NextSeq 500 sequencer in single-end mode with a read length of 1x76 bp. Raw sequencing reads were cleaned and trimmmed with Prinseq tool and good reads were aligned against the chicken reference gemone (Galgal 5.0) in Array Studio. Differential gene expression analysis was performed by the DESeq2 algorithm as implemented in Array Studio. The genes with at least three-fold change (FC) and Benjamini and Hochberg q-value < 0.05 were called differentially expressed. Results: Using an optimized data analysis workflow, we mapped about 30.5 million reads from layers and 33.4 million reads from non-layers to the chicken genome. A total of 19,152 gene transcripts were annotated from Ensembl alignment which represents 50.24% of the chicken genome assembly. Differential gene expression analysis showed 540 were differentially expressed between layer and non-layer hens. 152 DEGs were significantly up-regulated and 388 were significantly down-regulated in the laying hens when compared to the non-laying hens. Conclusions: Our study reports the expression of several pre-discovered and many novel genes that may be involved in the transport of precurosor molecules for biosynthesis and secretion of the egg-white proteins in the magnum. These genes can be used as quantitative basis of selecting hens with an improved egg quality.
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Project description:In this study, RNA-Seq technology was adopted to investigate the differences in expression profiles of the hepatic lipid metabolism-related genes and the associated pathways between juvenile and laying hens. RNA-Seq analysis was carried out to estimate total RNA harvested from the liver of juvenile hens (n = 3) and laying hens (n = 3). Compared with juvenile hens, 2574 differentially expressed (DE) genes (1487 down and 1087 up) with P ⤠0.05 were obtained, and 955 of these genes were significantly DE (SDE) at a false discovery rate (FDR) of 0.05 and fold-change ⥠2 in laying hens. There were 198 SDE novel genes (107 down-regulated and 91 up-regulated) (FDR ⤠0.05) that were obtained from the transcriptome, and most of them were highly expressed. Moreover, 332 SDE isoforms were identified. Gene Ontology (GO) enrichment and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis showed that SDE genes were significantly associated with steroid biosynthesis, PPAR signaling pathway, biosynthesis of unsaturated fatty acids, glycerophospholipid metabolism, three amino acid pathways, and pyruvate metabolism (P ⤠0.05). The top significantly enriched GO terms included lipid biosynthesis, cholesterol and sterol metabolic, and oxidation reduction suggesting the principal lipogenesis in the liver of laying hens. This study suggests that the major changes at the level of transcriptome in laying hen liver are closely related to fat metabolism. Some highly differentially expressed uncharacterized novel genes and alternative splicing isoforms detected might also take part in lipid metabolism, though it needs investigation. Therefore, this study provides valuable information of mRNA of chicken liver, and deeper functional investigations on the mRNAs could help explore or provide new insights into molecular networks of lipid metabolism in chicken liver. The liver expression profile of juvenile hens and laying hens were generated by RNA-seq.
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