Systems physiology and nutrition in dairy cattle: applications of omics and bioinformatics to better understand the hepatic metabolomics and transcriptomics adaptations in transition dairy cows

Bioinformatics;Data integration;Gas chromatography (GC-MS);Ketosis;Liquid chromatography (LC-MS);Mass spectrometry;Metabolomics;Methionine supplementation;Microarray;Network analysis;Ruminant nutrition;Systems biology;Transcriptomics

Shahzad, Khuram

关键词: Bioinformatics;    Data integration;    Gas chromatography (GC-MS);    Ketosis;    Liquid chromatography (LC-MS);    Mass spectrometry;    Metabolomics;    Methionine supplementation;    Microarray;    Network analysis;    Ruminant nutrition;    Systems biology;    Transcriptomics;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/97554/SHAHZAD-DISSERTATION-2017.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Application of systems concepts to better understand physiological and metabolic changes in dairy cows during the transition into lactation could enhance our understanding about the role of nutrients in helping to meet the animal’s requirements for optimal production and health. Four different analyses focused on the liver were conducted, and dealt with a metabolic disorder or thermal stress. The first three analyses dealt with supplementation of methionine to prevent clinical ketosis development in high-genetic merit dairy cows. Four groups of cows were formed retrospectively based on clinical health evaluated at 1 week postpartum: cows that remained healthy (OVE), cows that developed ketosis (K), and healthy cows supplemented with one of two commercial methionine products [Smartamine M (SM), and MetaSmart (MS)]. The liver tissue samples (n = 6/group) were harvested at -10 d before calving, and were used for metabolomics (GC-MS, LC-MS; Metabolon Inc.) and transcriptomics (44K-whole-transcriptome microarray; Agilent) analyses. Therefore, the main goals of the analyses were to 1) uncover metabolome and transcriptome patterns in the prepartum liver that were unique to those cows that became ketotic postpartum, and to 2) uncover unique patterns affected by supplemental methionine. The data were analyzed using the MIXED procedure of SAS. The metabolomics analysis (p ≤ 0.10) resulted in 13, 16, 26, 36, 13 and 43 biochemical compounds out of 313 identified for the comparisons K vs. OVE, SM vs. OVE, MS vs. OVE, SM vs. MS, K vs. SM and K vs. MS, respectively. The transcriptomics analysis (p ≤ 0.05 and fold change (FC) ≥ |1.5|) resulted in 3,065, 710, 786, 601, 1,021 and 771 number of differentially expressed genes (DEG) for the respective comparisons. The functional analysis of the data was performed using dynamic impact approach (DIA). The network reconstruction and data integration was performed with Ingenuity Pathway Analysis (IPA). In the first analysis of K vs. OVE, the results indicated inhibition of several carbohydrate- and lipid-related metabolic pathways, while activation of ‘Selenoamino acid metabolism’, ‘Ribosome’, and ‘Replication and repair’ was predominant. In the second analysis of SM vs. OVE, ‘Nitrogen metabolism’, ‘Glycosaminoglycan biocynthesis-chondroitin sulfate’, ‘Synthesis and degradation of ketone bodies’ and ‘Selenoamino acid metabolism’ were induced while the ‘Cyanoamino acid metabolism’, ‘Taurine and hypotaruine metabolism’ and ‘Inositol phosphate metabolism’ were inhibited. The analysis of MS vs. OVE revealed activation of ‘Riboflavin metabolism’, ‘Bile secretion’ and ‘Vitamin digestion and absorption’, while inhibition of ‘Base excision repair’, ‘Cyanoamino acid metabolism’, and ‘One carbon pool’. The analysis of SM vs. MS indicated activation of ‘Intestinal immune network for IgA production’, ‘Antigen processing and presentation’, and ‘Riboflavin metabolism’, while the inhibition ‘Glycosaminoglycan degradation’, ‘Other glycan degradation’ and ‘Bile secretion’. In the third analysis of K vs. SM, among the top 10 affected pathways, most were inhibited. Examples include ‘Cynoamino acid metabolism’, ‘Fructose and Mannose metabolism’, ‘Erb signaling’ and ‘Pentose phosphate pathway’. In contrast, the analysis of K vs. MS revealed an induction of ‘Nitrogen metabolism’ among the top 10 pathways, while pathways such as ‘Riboflavin metabolism’, ‘Pentose phosphate pathway’ and other carbohydrate and glycan biosynthesis related pathways were inhibited. The fourth analysis dealt with the effect of thermal stress on the liver transcriptome as it is related to health and productivity. During this study, we used gene network analysis on transcriptome data to uncover transcription regulators and their target genes in the liver tissue harvested at -30, +3, and +35 d relative to parturition during spring (SP, n = 6) and summer (SU, n = 6). Statistical analysis (FDR ≤ 0.10) of data from SU vs. SP revealed a total of 618, 1,030 and 894 DEG at -30, +3 and +35 d, respectively. IPA was used for gene network reconstructions. A total of 6, 7 and 7 transcription regulators were identified at -30, +3 and +35 d, respectively during SU vs. SP. The evaluation of these results suggests that calving during SU vs. SP is associated with the molecular phenotypes of the liver.