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Genetic Regulation of Liver Metabolites and Transcripts Linking to Biochemical-Clinical Parameters
(2019)
Given the central metabolic role of the liver, hepatic metabolites and transcripts reflect the organismal physiological state. Biochemical-clinical plasma biomarkers, hepatic metabolites, transcripts, and single nucleotide polymorphism (SNP) genotypes of some 300 pigs were integrated by weighted correlation networks and genome-wide association analyses. Network-based approaches of transcriptomic and metabolomics data revealed linked of transcripts and metabolites of the pentose phosphate pathway (PPP). This finding was evidenced by using a NADP/NADPH assay and HDAC4 and G6PD transcript quantification with the latter coding for first limiting enzyme of this pathway and by RNAi knockdown experiments of HDAC4. Other transcripts including ARG2 and SLC22A7 showed link to amino acids and biomarkers. The amino acid metabolites were linked with transcripts of immune or acute phase response signaling, whereas the carbohydrate metabolites were highly enrich in cholesterol biosynthesis transcripts. Genome-wide association analyses revealed 180 metabolic quantitative trait loci (mQTL) (p < 10-4). Trans-4-hydroxy-L-proline (p = 6 × 10-9), being strongly correlated with plasma creatinine (CREA), showed strongest association with SNPs on chromosome 6 that had pleiotropic effects on PRODH2 expression as revealed by multivariate analysis. Consideration of shared marker association with biomarkers, metabolites, and transcripts revealed 144 SNPs associated with 44 metabolites and 69 transcripts that are correlated with each other, representing 176 mQTL and expression quantitative trait loci (eQTL). This is the first work to report genetic variants associated with liver metabolite and transcript levels as well as blood biochemical-clinical parameters in a healthy porcine model. The identified associations provide links between variation at the genome, transcriptome, and metabolome level molecules with clinically relevant phenotypes. This approach has the potential to detect novel biomarkers displaying individual variation and promoting predictive biology in medicine and animal breeding.
Introduction
Neurofilament light (NfL) can be detected in blood of healthy individuals and at elevated levels in those with different neurological diseases. We investigated if the choice of biological matrix can affect results when using NfL as biomarker in epidemiological studies.
Method
We obtained paired serum and EDTA-plasma samples of 299 individuals aged 37–67 years (BiDirect study) and serum samples of 373 individuals aged 65–83 years (MEMO study). In BiDirect, Passing–Bablok analyses were performed to assess proportional and systematic differences between biological matrices. Associations between serum or EDTA-plasma NfL and renal function (serum creatinine, serum cystatin C, glomerular filtration rate, and kidney disease) were investigated using linear or logistic regression, respectively. All regression coefficients were estimated (1) per one ng/L increase and (2) per one standard deviation increase (standardization using z-scores). In MEMO, regression coefficients were estimated (1) per one ng/L increase of serum or calculated EDTA-plasma NfL and (2) per one standard deviation increase providing a comparison to the results from BiDirect.
Results
We found proportional and systematic differences between paired NfL measurements in BiDirect, i.e., serum NfL [ng/L] = −0.33 [ng/L] + 1.11 × EDTA-plasma NfL [ng/L]. Linear regression coefficients for the associations between NfL and renal function did not vary between the different NfL measurements. In MEMO, one standard deviation increase in serum NfL was associated with greater changes in the outcomes than in BiDirect.
Conclusion
Although there are differences between serum and EDTA-plasma NfL, results can be used interchangeably if standardized values are used.