Association of serum s-adenosylmethionine, s-adenosylhomocysteine, and their ratio with the risk of dementia and death in a community

SAH is a major metabolic sensor mediating worsening metabolic crosstalk in metabolic syndrome

In this study, we observed worsening metabolic crosstalk in mouse models with concomitant metabolic disorders such as hyperhomocysteinemia (HHcy), hyperlipidemia, and hyperglycemia and in human coronary artery disease by analyzing metabolic profiles. We found that HHcy worsening is most sensitive to other metabolic disorders. To identify metabolic genes and metabolites responsible for the worsening metabolic crosstalk, we examined mRNA levels of 324 metabolic genes in Hcy, glucose-related and lipid metabolic systems. We examined Hcy-metabolites (Hcy, SAH and SAM) by LS-ESI-MS/MS in 6 organs (heart, liver, brain, lung, spleen, and kidney) from C57BL/6J mice. Through linear regression analysis of Hcy-metabolites and metabolic gene mRNA levels, we discovered that SAH-responsive genes were responsible for most metabolic changes and all metabolic crosstalk mediated by Serine, Taurine, and G3P. SAH-responsive genes worsen glucose metabolism and cause upper glycolysis activation and lower glycolysis suppression, indicative of the accumulation of glucose/glycogen and G3P, Serine synthesis inhibition, and ATP depletion. Insufficient Serine due to negative correlation of PHGDH with SAH concentration may inhibit the folate cycle and transsulfurarion pathway and consequential reduced antioxidant power, including glutathione, taurine, NADPH, and NAD+. Additionally, we identified SAH-activated pathological TG loop as the consequence of increased fatty acid (FA) uptake, FA β-oxidation and Ac-CoA production along with lysosomal damage. We concluded that HHcy is most responsive to other metabolic changes in concomitant metabolic disorders and mediates worsening metabolic crosstalk mainly via SAH-responsive genes, that organ-specific Hcy metabolism determines organ-specific worsening metabolic reprogramming, and that SAH, acetyl-CoA, Serine and Taurine are critical metabolites mediating worsening metabolic crosstalk, redox disturbance, hypomethylation and hyperacetylation linking worsening metabolic reprogramming in metabolic syndrome.

Effect of Lead Exposure and Lifestyle Factors on Methylation Index Markers Among Pb-Exposed Workers

S-Adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) and the ratio of SAM and SAH in Pb-exposed workers need to be assessed. In this study, we investigated the effects of Pb exposure on SAM, SAH, and methylation index (MI) in Pb-exposed workers with contemplation of lifestyle factors. Blood lead levels (BLLs), SAM, SAH, MI, and lifestyle factors were assessed in 338 male Pb-exposed workers. BLLs are estimated by ICP-OES method. SAM and SAH levels in serum were determined by ELISA method. The MI was calculated using SAM and SAH individual values. The lifestyle factors were collected using standard questionnaire. Levels of SAM and MI were significantly decreased with increased age, experience > 5 years, habits of tobacco chewing, smoking, alcohol consumption, and BLLs 10-30, 30-50, and > 50 µg/dL. Levels of SAH were significantly increased with increased age, habits of tobacco chewing and smoking, and BLLs 10-30, 30-50, and > 50 µg/dL. The association between BLLs and methylation index markers (SAM and MI) was reported as negative and significant. The association between BLLs and SAH was noted positive and significant. The influence of BLLs and lifestyle factors on SAM was noted at 12%, SAH at 35%, and MI at 27%, respectively. The highest percentage of influence was noted in SAH, followed by MI and SAM. In the workers exposed to Pb, lifestyle factors resulted in decreased SAM and MI and increased SAH levels. Adaptation of healthy lifestyle factors, personal hygiene practices, and use of PPE were suggested to minimize the reduction of methylation index markers.

Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.

Positive Regulation of S-Adenosylmethionine on Chondrocytic Differentiation via Stimulation of Polyamine Production and the Gene Expression of Chondrogenic Differentiation Factors

S-adenosylmethionine (SAM) is considered to be a useful therapeutic agent for degenerative cartilage diseases, although its mechanism is not clear. We previously found that polyamines stimulate the expression of differentiated phenotype of chondrocytes. We also found that the cellular communication network factor 2 (CCN2) played a huge role in the proliferation and differentiation of chondrocytes. Therefore, we hypothesized that polyamines and CCN2 could be involved in the chondroprotective action of SAM. In this study, we initially found that exogenous SAM enhanced proteoglycan production but not cell proliferation in human chondrocyte-like cell line-2/8 (HCS-2/8) cells. Moreover, SAM enhanced gene expression of cartilage-specific matrix (aggrecan and type II collagen), Sry-Box transcription factor 9 (SOX9), CCN2, and chondroitin sulfate biosynthetic enzymes. The blockade of the methionine adenosyltransferase 2A (MAT2A) enzyme catalyzing intracellular SAM biosynthesis restrained the effect of SAM on chondrocytes. The polyamine level in chondrocytes was higher in SAM-treated culture than control culture. Additionally, Alcian blue staining and RT-qPCR indicated that the effects of SAM on the production and gene expression of aggrecan were reduced by the inhibition of polyamine synthesis. These results suggest that the stimulation of polyamine synthesis and gene expression of chondrogenic differentiation factors, such as CCN2, account for the mechanism underlying the action of SAM on chondrocytes.

ReACT (redox-activated chemical tagging) chemistry enables direct derivatization and fluorescence detection of S-adenosyl-L-homocysteine (SAH)

S-Adenosyl-L-homocysteine (SAH) is a universal byproduct and product inhibitor of the methyltransferase-catalyzed methylation reaction. Here based on ReACT (redox-activated chemical tagging) chemistry, direct derivatization and fluorescence measurement of SAH were achieved with features such as mild reaction conditions and simple operation.

Alzheimer's Disease Protein Relevance Analysis Using Human and Mouse Model Proteomics Data

The principles governing genotype-phenotype relationships are still emerging(1-3), and detailed translational as well as transcriptomic information is required to understand complex phenotypes, such as the pathogenesis of Alzheimer's disease. For this reason, the proteomics of Alzheimer disease (AD) continues to be studied extensively. Although comparisons between data obtained from humans and mouse models have been reported, approaches that specifically address the between-species statistical comparisons are understudied. Our study investigated the performance of two statistical methods for identification of proteins and biological pathways associated with Alzheimer's disease for cross-species comparisons, taking specific data analysis challenges into account, including collinearity, dimensionality reduction and cross-species protein matching. We used a human dataset from a well-characterized cohort followed for over 22 years with proteomic data available. For the mouse model, we generated proteomic data from whole brains of CVN-AD and matching control mouse models. We used these analyses to determine the reliability of a mouse model to forecast significant proteomic-based pathological changes in the brain that may mimic pathology in human Alzheimer's disease. Compared with LASSO regression, partial least squares discriminant analysis provided better statistical performance for the proteomics analysis. The major biological finding of the study was that extracellular matrix proteins and integrin-related pathways were dysregulated in both the human and mouse data. This approach may help inform the development of mouse models that are more relevant to the study of human late-onset Alzheimer's disease.