Multi-omics Approaches To Study Ketosis In Perinatal Dairy Cows

Ketosis is one of the most serious metabolic disorder in perinatal dairy cows.It is mainly caused by excessive body fat mobilization when energy intake is not equal to energy expenditure,which leads to excessive ketone bodies accumulation and body malfunction.Ketosis has been found to affect production status of affected animals and lead to a high risk in developing other diseases,which has caused huge economic loss to the dairy industry.At present,most studies are focued on nutrition strategies and feeding management,but limited studies have inverestgated the molecular networks associated with the incidence and progression of ketosis at the transcriptional,proteomic,and metabolomic levels.In particular,lacking of stidies focus on mutiple omics profiling of ketosis at the same time.In this study,an integrated trancriptomics,proteomics and metabolomics approach was performed on 38 blood samples collected from cows affected with clinical ketosis and healthy controls at both prepartum and postpartum.We tried to explore the metabolic changes and regulatory mechanisms in the development and occurrence of ketosis in perinatal dairy cows,and aimed at uncovering the important regulatory factors and regulatory networks related to ketosis.The main results were as following:(1)A total of 97 metabolites were obtained among all the 38 serum samples by using LC-MS based metabolomic analysis,and multivariate analysis revealed a clear separation among the samples of each group as well as distinct cluster together within each group.The differentially accumulated metabolites corrected to ketosis were found to be significantly enriched in anmino acid metabolism,Pyruvate metabolism,and TCA cycle.(2)We found that the changes in expression of 20 shared metabolites were consistent with the clinical determination of ketosis,these metabolites might play important roles in the disease.In addition,two metabolites of 4-Hydroxy-6-Methylpyran-2-one and cinnamoylglycine were highly expressed in ketotic cows,which might be used as potential biomarkers for ketosis diagnosis and/or identification.(3)In total,540 proteins were identified among 20 serum samples by using Data-Independent Acquisition(DIA)LC-MS based proteomic analysis,and the protein expression profiles of sera from different groups were significantly different.A total of 65 different abundant proteins corrected to ketosis were found to be significantly enriched in biological pathways,such as protein binding,enzyme regulator activity,enzyme inhibitor activity,biological regulation,regulation of biological quality,regulation of molecular function,multicellular organismal process,and response to stimulus.And the KEGG results showed these proteins corrected to ketosis were enriched in Vitamin digestion and absorption,Phagosome,and ECM-receptor interaction pathways.(4)A total of 626 G raw data was produced among the above 38 blood samples,which ultimately produced 581.22 G clean data after quality control and accouting for 93%of raw data.From which,a total of 194 genes closely related to ketosis were found to be enriched in ion homeostasis,erythrocyte homeostasis,stress response,amino acid metabolism,energy metabolism and disease related pathways.(5)Weighted gene co-expression network analysis identified 21 modules.The MEgreenyellow module included 197 genes was found to be most corrected to ketosis.In MEgreenyellow module,the PLEK,ATP6AP1,NDRG1,LONRF3 and TNFRSF1 A genes were found with highest connectivity,whcich may play important roles in cow ketosis.(6)A total of 784 long intergenic non-coding RNA(linc RNAs)were indentified among all the 38 samples,incuding 489 linc RNAs in the bovine NONCODE database and295 novel linc RNAs.Furthermore,the bovine linc RNAs were shorter in length,fewer exons per transcript,and expressed lower than protein-coding RNAs.In addition,functional enrichment analysis of the targets of those linc RNAs corrected to ketosis showed that these genes were significantly enriched in cellular function,positive regulation of protein kinase B signaling,ion binding,biological stress response,triglyceride catabolic process biological pathways.And the KEGG enrichment analysis showed that these genes significantly enriched in cell adhesion molecules,MAPK,PI3K-Akt,NF-kappa B,metabolic pathways,and disease-related pathways,etc.(7)Pearson correlation coefficients were calculated for proteome and metabolome data integration,and which revealed 993 significant correlations.Further,there were 78 strong correlations(|R|>0.7)between 25 proteins and 38 metabolites.These strong correlated proteins and metabolites were significantly enriched in 25 pathways,such as transport of small molecules,metabolism,metabolism of proteins,vitamin digestion and absorption,post-translational protein modification,amino acid metabolism and regulation of gene expression,etc.(8)Pearson correlation coefficients were calculated for transcriptome and metabolome data integration,and which revealed 47 strong correlations(|R|>0.7)between21 differentially expressed genes and 17 differentially accumulated metabolites.These strong correlated genes and metabolites were significantly enriched in several pathways,such as metabolism of amino acids and derivatives,fatty acid beta oxidation,gamma-glutamyl cycle,trans-sulfuration pathway,Glutathione metabolism,and fatty acid degradation,etc.In summary,by using LC-MS metabolomics analysis,DIA proteomics analysis,and RNA-seq,we studied the metabolite and protein expression profiles of sera,as well as gene expression profiles of blood.Bioinformatics analysis futher clarified the relationships of these profiles with ketosis.These results will provide reference for further exploring the molecular mechanisms of ketosis.