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Integrative Analysis of Exosomal miR-452 and miR-4713 Downregulating NPY1R for the Prevention of Childhood Obesity. DISEASE MARKERS 2022; 2022:2843353. [PMID: 35401881 PMCID: PMC8986441 DOI: 10.1155/2022/2843353] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 12/25/2022]
Abstract
Neuropeptides are associated with childhood obesity and exploring their regulatory mechanisms may reveal new insights for novel treatments. Childhood obesity data were downloaded from the GEO database and were used to screen for differentially expressed neuropeptides in patients with obesity. NPY1R expression was significantly upregulated in children with obesity compared to children without obesity (p < 0.05). The GEO database was used to filter differentially expressed miRNAs in patients with obesity. And hsa-mir-4713 and hsa-mir-452 were found significantly downregulated in adipose tissue. The GEO, TRRUST, and TFacts databases were used to screen all transcription factors for differentially expressed genes (DEGs). The potential regulatory networks between the differentially expressed miRNAs, TFs, and neuropeptides were mapped. In the constructed NPY1R regulatory network, the transcription factors TCF4, HEY1, and GATA3 are significantly associated with NPY1R. TCF4 and HEY1 were positively correlated with NPY1R, while GATA3 was negatively correlated with NPY1R. In the clinical peripheral blood samples, NPY1R, TCF4, and HEY1 were significantly more expressed in the obesity and the obesity with fracture group compared to the control group, while there was no statistically significant difference between the obesity group and the obesity with fracture group in terms of expression. The expression of GATA3, miR-452, and miR-4713 was also significantly lower in the obesity and the obesity with fracture groups when compared to the NC group. Therefore, NPY1R, TCF4, HEY1, GATA3, miR-452, and miR-4713 may be risk factors for fracture in obese children. The potential NPY1R regulatory function was exerted by two pathways: positive regulation caused by TCF4 and HEY1 acting on miR-4713 and negative regulation via GATA3 acting on miR-452. Potential NPY1R-related targets for the treatment of childhood obesity were provided in this study.
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Te Pas MFW, Borg R, Buddiger NJH, Wood BJ, Rebel JMJ, van Krimpen MM, Calus MPL, Park JE, Schokker D. Regulating appetite in broilers for improving body and muscle development - A review. J Anim Physiol Anim Nutr (Berl) 2020; 104:1819-1834. [PMID: 32592266 PMCID: PMC7754290 DOI: 10.1111/jpn.13407] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 05/27/2020] [Accepted: 05/28/2020] [Indexed: 12/12/2022]
Abstract
Appetite is the desire for feed and water and the voluntary intake of feed and is an important regulator of livestock productivity and animal health. Economic traits such as growth rate and muscle development (meat deposition) in broilers are directly correlated to appetite. Factors that may influence appetite include environmental factors, such as stress and temperature variation, and animal‐specific factors, such as learning period, eating capacity and preferences. Feed preferences have been reported to be determined in early life, and this period is important in broilers due to their fast growth and relatively short growth trajectories. This may be of importance when contemplating the use of more circular and sustainable feeds and the optimization of appetite for these feeds. The objective of this review was to review the biological mechanisms underlying appetite using data from human, animal and bird models and to consider the option for modulating appetite particularly as it relates to broiler chickens.
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Affiliation(s)
- Marinus F W Te Pas
- Wageningen University and Research, Wageningen Livestock Research, Wageningen, The Netherlands
| | | | | | - Benjamin J Wood
- Hendrix Genetics North America Office, Kitchener, ON, Canada
| | - Johanna M J Rebel
- Wageningen University and Research, Wageningen Livestock Research, Wageningen, The Netherlands
| | - Marinus M van Krimpen
- Wageningen University and Research, Wageningen Livestock Research, Wageningen, The Netherlands
| | - Mario P L Calus
- Wageningen University and Research, Wageningen Livestock Research, Wageningen, The Netherlands
| | - Jong-Eun Park
- Animal Genomics & Bioinformatics Division, National Institute of Animal Science, Rural Development Administration, Jeonju, Korea
| | - Dirkjan Schokker
- Wageningen University and Research, Wageningen Livestock Research, Wageningen, The Netherlands
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Time course analysis of large-scale gene expression in incised muscle using correspondence analysis. PLoS One 2020; 15:e0230737. [PMID: 32210454 PMCID: PMC7094855 DOI: 10.1371/journal.pone.0230737] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 03/06/2020] [Indexed: 11/23/2022] Open
Abstract
Studying the time course of gene expression in injured skeletal muscle would help to estimate the timing of injuries. In this study, we investigated large-scale gene expression in incision-injured mouse skeletal muscle by DNA microarray using correspondence analysis (CA). Biceps femoris muscle samples were collected 6, 12, and 24 hours after injury, and RNA was extracted and prepared for microarray analysis. On a 2-dimensional plot by CA, the genes (row score coordinate) located farther from each time series (column score coordinate) had more upregulation at particular times. Each gene was situated in 6 subdivided triangular areas according to the magnitude of the relationship of the fold change (FC) value at each time point compared to the control. In each area, genes for which the ratios of two particular FC values were close to 1 were distributed along the two border lines. There was a tendency for genes whose FC values were almost equal to be distributed near the intersection of these 6 areas. Therefore, the gene marker candidates for estimation of the timing of injuries were detectable according to the location on the CA plot. Moreover, gene sets created by a specific gene and its surrounding genes were composed of genes that showed similar or identical fluctuation patterns to the specific gene. In various analyses on these sets, significant gene ontology term and pathway activity may reflect changes in specific genes. In conclusion, analyses of gene sets based on CA plots is effective for investigation of the time-dependent fluctuation in gene expression after injury.
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Dou T, Zhao S, Rong H, Gu D, Li Q, Huang Y, Xu Z, Chu X, Tao L, Liu L, Ge C, Te Pas MFW, Jia J. Biological mechanisms discriminating growth rate and adult body weight phenotypes in two Chinese indigenous chicken breeds. BMC Genomics 2017. [PMID: 28633640 PMCID: PMC5477733 DOI: 10.1186/s12864-017-3845-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Background Intensive selection has resulted in increased growth rates and muscularity in broiler chickens, in addition to adverse effects, including delayed organ development, sudden death syndrome, and altered metabolic rates. The biological mechanisms underlying selection responses remain largely unknown. Non-artificially-selected indigenous Chinese chicken breeds display a wide variety of phenotypes, including differential growth rate, body weight, and muscularity. The Wuding chicken breed is a fast growing large chicken breed, and the Daweishan mini chicken breed is a slow growing small chicken breed. Together they form an ideal model system to study the biological mechanisms underlying broiler chicken selection responses in a natural system. The objective of this study was to study the biological mechanisms underlying differential phenotypes between the two breeds in muscle and liver tissues, and relate these to the growth rate and body development phenotypes of the two breeds. Results The muscle tissue in the Wuding breed showed higher expression of muscle development genes than muscle tissue in the Daweishan chicken breed. This expression was accompanied by higher expression of acute inflammatory response genes in Wuding chicken than in Daweishan chicken. The muscle tissue of the Daweishan mini chicken breed showed higher expression of genes involved in several metabolic mechanisms including endoplasmic reticulum, protein and lipid metabolism, energy metabolism, as well as specific immune traits than in the Wuding chicken. The liver tissue showed fewer differences between the two breeds. Genes displaying higher expression in the Wuding breed than in the Daweishan breed were not associated with a specific gene network or biological mechanism. Genes highly expressed in the Daweishan mini chicken breed compared to the Wuding breed were enriched for protein metabolism, ABC receptors, signal transduction, and IL6-related mechanisms. Conclusions We conclude that faster growth rates and larger body size are related to increased expression of genes involved in muscle development and immune response in muscle, while slower growth rates and smaller body size are related to increased general cellular metabolism. The liver of the Daweishan breed displayed increased expression of metabolic genes. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3845-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tengfei Dou
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Sumei Zhao
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Hua Rong
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Dahai Gu
- Department of Food Science, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Qihua Li
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Ying Huang
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Zhiqiang Xu
- Department of Food Science, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Xiaohui Chu
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Linli Tao
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Lixian Liu
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Changrong Ge
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China
| | - Marinus F W Te Pas
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Building 107, Radix, Droevendaalsesteeg 1, P.O. Box 338, 6708 PB, 6700 AH, Wageningen, The Netherlands. .,Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China. .,Dali University, Dali, People's Republic of China.
| | - Junjing Jia
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Yunnan Agricultural University, Kunming, 650201, Yunnan Province, People's Republic of China.
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Te Pas MFW, Hulsegge I, Schokker D, Smits MA, Fife M, Zoorob R, Endale ML, Rebel JMJ. Meta-analysis of chicken--salmonella infection experiments. BMC Genomics 2012; 13:146. [PMID: 22531008 PMCID: PMC3411418 DOI: 10.1186/1471-2164-13-146] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2011] [Accepted: 04/24/2012] [Indexed: 11/10/2022] Open
Abstract
Background Chicken meat and eggs can be a source of human zoonotic pathogens, especially Salmonella species. These food items contain a potential hazard for humans. Chickens lines differ in susceptibility for Salmonella and can harbor Salmonella pathogens without showing clinical signs of illness. Many investigations including genomic studies have examined the mechanisms how chickens react to infection. Apart from the innate immune response, many physiological mechanisms and pathways are reported to be involved in the chicken host response to Salmonella infection. The objective of this study was to perform a meta-analysis of diverse experiments to identify general and host specific mechanisms to the Salmonella challenge. Results Diverse chicken lines differing in susceptibility to Salmonella infection were challenged with different Salmonella serovars at several time points. Various tissues were sampled at different time points post-infection, and resulting host transcriptional differences investigated using different microarray platforms. The meta-analysis was performed with the R-package metaMA to create lists of differentially regulated genes. These gene lists showed many similarities for different chicken breeds and tissues, and also for different Salmonella serovars measured at different times post infection. Functional biological analysis of these differentially expressed gene lists revealed several common mechanisms for the chicken host response to Salmonella infection. The meta-analysis-specific genes (i.e. genes found differentially expressed only in the meta-analysis) confirmed and expanded the biological functional mechanisms. Conclusions The meta-analysis combination of heterogeneous expression profiling data provided useful insights into the common metabolic pathways and functions of different chicken lines infected with different Salmonella serovars.
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Affiliation(s)
- Marinus F W Te Pas
- Animal Breeding and Genetics Centre (ABGC), Wageningen UR Livestock Research, Animal Sciences Group, Wageningen University and Research Centre, P.O. Box 65, 8200 AB Lelystad, The Netherlands.
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