Hepatopancreas Proteomic Analysis Reveals Key Proteins and Pathways in Regulatory of Ovary Maturation of Macrobrachium nipponense .
Sufei JiangHui QiaoHongtuo FuZemao GuPublished in: Animals : an open access journal from MDPI (2023)
A TMT-based (Tandem Mass Tag) liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics approach was employed to explore differentially expressed proteins (DEPs) and KEGG pathways in hepatopancreas of 5 ovary stages. In total, 17,999 peptides were detected, among which 3395 proteins were identified. Further analysis revealed 26, 24, 37, and 308 DEPs in HE-I versus HE-II, HE-II versus HE-Ⅲ, HE-Ⅲ versus HE-Ⅳ, and HE-Ⅳ versus HE-Ⅴ, respectively (HE-I, HE-II, HE-III, HE-IV, and HE-V means hepatopancreas sampled from ovary stage I to V.). Gene ontology (GO) analysis indicated that DEPs were significantly enriched in "catalytic activity", "metabolic process", and "cell" of 4 comparison groups in turn. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results showed that in hepatopancreas, as the ovaries developed to maturation, carbohydrate metabolism, lipid metabolism, amino acid metabolism, and lysosome played important roles in turn. The mRNA expression of 15 selected DEPs were consistent with proteome results by qPCR analysis. Further mRNA expression investigation results suggested 4 proteins (fatty acid-binding protein, NPC intracellular cholesterol transporter 1, Serine hydroxymethyltransferase, and Crustapin) were involved in ovary maturation. These results enhance the understanding of the regulatory role of hepatopancreas in M. nipponense ovary maturation and provide new insights for understanding the crustacean regulation mechanisms.
Keyphrases
- liquid chromatography tandem mass spectrometry
- fatty acid
- amino acid
- binding protein
- genome wide
- fluorescent probe
- single cell
- dna methylation
- gene expression
- transcription factor
- ms ms
- simultaneous determination
- copy number
- bone marrow
- mesenchymal stem cells
- liquid chromatography
- quantum dots
- single molecule
- data analysis
- bioinformatics analysis