Dataset Information

Comparative metabolomics reveals serum metabolites changes in goats during different developmental stages

ABSTRACT:

Goats can provide meat, milk and skins for humans and are livestock with high economic benefits. However, despite their economic significance, the comprehensive analysis of goats' serum metabolic profile and its intricate alterations throughout their developmental journey remains conspicuously absent. To investigate the stage-specificity and dynamic change characteristics of metabolites during the growth and development of goats, this study compared the alterations in serum hormone levels and serum biochemical markers across different developmental stages of female goats (1, 60, 120, 180 days old; n=5). Additionally, a serum untargeted LC-MS metabolomics analysis was conducted. A total of 504 DAMs were identified with age. The results indicated that PE, PC, Lyso-PE, Lyso-PC and FAFHA may play important roles in lipid metabolism in goats after birth. Weighted gene co-expression network analysis (WGCNA) identified two metabolite modules (Turquoise and Yellow) and key metabolites within these modules that were significantly associated with phenotypic features. L-carnitine may be a metabolite related to muscle development in goats. The findings of this study demonstrate notable variations in serum metabolites across distinct developmental phases in goats. Lipids and organic acids play important roles in different developmental stages of goats.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, Liquid Chromatography MS - positive - reverse phase

PROVIDER: MTBLS8944 | MetaboLights | 2024-06-04

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 187

Publications

Comparative metabolomics reveals serum metabolites changes in goats during different developmental stages.

Li Qing Q Chao Tianle T Wang Yanyan Y Xuan Rong R Guo Yanfei Y He Peipei P Zhang Lu L Wang Jianmin J

Scientific reports 20240327 1

PMID: 38538719

Similar Datasets

Project description:Background: Mycobacterium avium subsp. paratuberculosis (M. paratuberculosis) is the causative agent of Johne’s disease, a chronic enteric infection of ruminants. Infection occurs within the first few months of life but remains subclinical for an average of 2-5 years. Current diagnostics to detect early subclinical infections lack diagnostic sensitivity, which hinders disease control resulting in significant economic losses to the dairy industry worldwide. The pathophysiology of early infection with M. paratuberculosis is still not well understood and represents a key hurdle towards the development of better diagnostics. Methods: The present study employed a large-scale RNA-Sequencing technology to better understand early stages of M. paratuberculosis infection and immunization. Specifically, gene expression profiles of peripheral blood mononuclear cells (PBMCs) from infected and vaccinated goats were compared to controls. Results: The transcriptome of the control goats revealed a large number of genes (N = 226, 1018, 1714, 1133) that were differentially expressed compared to the M. paratuberculosis-infected goats, goats vaccinated with live attenuated or inactivated vaccines, and goats both vaccinated and infected. Bioinformatics evaluation of the differentially expressed genes indicated the regulation of a large number of genes with immunity and inflammatory functions including IL-18BP, IFN-γ, IL-17A, NOS2, LIPG and IL-22. Interestingly, a large number of goat genes (N=667) were regulated whether live or inactivated vaccine were used. Some of the regulated genes (e.g. IL-17A, IFN-g) continued its unique transcriptional profile up to 12 months post-challenge. Conclusions: Overall, transcriptome analysis of infected and/or immunized goats identified potential targets for developing early diagnostics for Johne’s disease and a potential approach to differentiate infected from vaccinated animals. A similar approach could be used to analyze later stages of Johne’s disease or other chronic infections.

Project description:Background: Platelets may be pivotal mediators of the thrombo-haemorrhagic complications of preeclampsia (PE), linking inflammation and thrombosis with endothelial and vascular dysfunction. While gestational hypertension (GH) falls within the spectrum of hypertensive complications of pregnancy and is a risk factor for preeclampsia, it is unclear what biomarkers distinguish PE from GH. Aim: To identify specific plasma and platelet thrombo-inflammatory biomarkers indicative of preeclampsia and distinguish PE from GH. Methods: We performed multiplex immunoassays, assessed platelet and plasma proteomics and metabolomics data of PE patients, and compared with non-pregnant (NP), healthy pregnant (PC) and GH participants. Results: We report plasma proteins upregulated, enriched plasma metabolites and proteins distinctly overexpressed in platelets of PE and GH compared to NP and PC. Whilst procoagulation in PC may be fibrinogen driven, Inter-Alpha-Trypsin Inhibitors ITIH2 and ITIH3 were enriched in hypertensive complications of pregnancy (PE and GH), and fibronectin and S100A8/9 may be major procoagulant agonists in PE but not GH. In addition, platelet leucine-rich repeat-containing protein 27 and 42 (LRRC27/42) subunits of volume-regulated VRAC anion channels were markedly overexpressed in preeclampsia and may contribute to the heightened glucose sensitivity and the pro-thrombotic tendency of this disorder. Additionally, our multiplex immunoassays confirmed previous reports of increases in preeclampsia plasma cytokines, including SDF-1α, which can directly activate platelets; but also, i-309 and CTACK cytokines, whose effects on platelets we explored using STRING analysis. Conclusion: We identified biomarkers that may be monitored for preeclampsia onset and progression, and distinguish PE from GH. Also, through protein-protein interactions analysis, we generated a new hypothesis for platelets’ contribution to the thrombo-inflammatory states of preeclampsia.

			Action	DRS
	HF3_1574486_CN_QC1.mzML	Mzml
	HF3_1574486_CN_QC2.mzML	Mzml
	HF3_1574486_CN_QC3.mzML	Mzml
	HF3_1574486_CN_QC4.mzML	Mzml
	HF3_1574486_CN_QC5.mzML	Mzml