Evaluation of the Immune Response of Patulin by Proteomics.
Feng WangLukai MaQin WangBruce D HammockGengsheng XiaoRuijing LiuPublished in: Biosensors (2024)
Patulin, an emerging mycotoxin with high toxicity, poses great risks to public health. Considering the poor antibody production in patulin immunization, this study focuses on the four-dimensional data-independent acquisition (4D-DIA) quantitative proteomics to reveal the immune response of patulin in rabbits. The rabbit immunization was performed with the complete developed antigens of patulin, followed by the identification of the immune serum. A total of 554 differential proteins, including 292 up-regulated proteins and 262 down-regulated proteins, were screened; the differential proteins were annotated; and functional enrichment analysis was performed. The differential proteins were associated with the pathways of metabolism, gene information processing, environmental information processing, cellular processes, and organismal systems. The functional enrichment analysis indicated that the immunization procedures mostly resulted in the regulation of biochemical metabolic and signal transduction pathways, including the biosynthesis of amino acid (glycine, serine, and threonine), ascorbate, and aldarate metabolism; fatty acid degradation; and antigen processing and presentation. The 14 key proteins with high connectivity included G1U9T1, B6V9S9, G1SCN8, G1TMS5, G1U9U0, A0A0G2JH20, G1SR03, A0A5F9DAT4, G1SSA2, G1SZ14, G1T670, P30947, P29694, and A0A5F9C804, which were obtained by the analysis of protein-protein interaction networks. This study could provide potential directions for protein interaction and antibody production for food hazards in animal immunization.
Keyphrases
- immune response
- public health
- protein protein
- amino acid
- mass spectrometry
- fatty acid
- human health
- transcription factor
- small molecule
- genome wide
- healthcare
- risk assessment
- functional connectivity
- toll like receptor
- inflammatory response
- multiple sclerosis
- dna methylation
- artificial intelligence
- big data
- machine learning
- binding protein
- deep learning