Regulation of homocysteine metabolism and methylation in human and mouse tissues

Abstract

Hyperhomocysteinemia is an independent risk factor for cardiovascular disease. Homocysteine (Hcy) metabolism involves multiple enzymes; however, tissue Hcy metabolism and its relevance to methylation remain unknown. Here, we established gene expression profiles of 8 Hcy metabolic and 12 methylation enzymes in 20 human and 19 mouse tissues through bioinformatic analysis using expression sequence tag clone counts in tissue cDNA libraries. We analyzed correlations between gene expression, Hcy, S-adenosylhomocysteine (SAH), and S-adenosylmethionine (SAM) levels, and SAM/SAH ratios in mouse tissues. Hcy metabolic and methylation enzymes were classified into two types. The expression of Type 1 enzymes positively correlated with tissue Hcy and SAH levels. These include cystathionine beta-synthase, cystathionine-gamma-lyase, paraxonase 1, 5,10-methylenetetrahydrofolate reductase, betaine:homocysteine methyltransferase, methionine adenosyltransferase, phosphatidylethanolamine N-methyltransferases and glycine N-methyltransferase. Type 2 enzyme expressions correlate with neither tissue Hcy nor SAH levels. These include SAH hydrolase, methionyl-tRNA synthase, 5-methyltetrahydrofolate:Hcy methyltransferase, S-adenosylmethionine decarboxylase, DNA methyltransferase 1/3a, isoprenylcysteine carboxyl methyltransferases, and histone-lysine N-methyltransferase. SAH is the only Hcy metabolite significantly correlated with Hcy levels and methylation enzyme expression. We established equations expressing combined effects of methylation enzymes on tissue SAH, SAM, and SAM/SAH ratios. Our study is the first to provide panoramic tissue gene expression profiles and mathematical models of tissue methylation regulation.

A nonpyrrolysine member of the widely distributed trimethylamine methyltransferase family is a glycine betaine methyltransferase

“…The route of catabolic bacterial degradation of GB differs markedly depending on the presence or absence of oxygen. Aerobically, various oxidases or dehydrogenases can oxidatively demethylate GB to dimethylglycine (DMG), and then to sarcosine (monomethylglycine) (11)(12)(13)(14)(15)(16). Anaerobically, catabolic pathways containing GB reductase cleave GB to acetate and trimethylamine (TMA) as excreted products.…”

Database Mining: Defining the Pathogenesis of Inflammatory and Immunological Diseases

“…In order to generate valid confidence intervals, sample sizes have to be estimated with statistical tools of sample size determination (Rosner, 2000) as we reported ; 5) Verifiable methods: Experimental methods are available to verify the data generated by the database mining ; and 6) A new working model/hypothesis: Through database mining, a new knowledge gap will be identified, and a new hypothesis will be proposed to test fewer, much more-focused genes in further experiments. The following sections will illustrate these principles in our own publications (Chen et al, 2010;Jan et al, 2010;Ng et al, 2004;Virtue, 2011;Yang et al, 2006a;Yang et al, 2006b;Yin et al, 2009). In our invited review, we pointed out that the identification and molecular characterization of self-antigens expressed by human malignancies, that are capable of elicitation of anti-tumor immune responses in patients, have been an active field in tumor immunology .…”

“…NCBI-PubMed search and Google Scholar search; 2) experimentally screening cDNA libraries and various arrays (nucleic acid arrays, antibody arrays, protein arrays and metabolic arrays) (King et al, 2005;Loza et al, 2007;Pandey et al, 2004;Warner & Dieckgraefe, 2002); and 3) mining experimental databases (Chen et al, 2010;Jan et al, 2010;Ng et al, 2004;Yang et al, 2006a;Yang et al, 2006b;Yin et al, 2009). The screening analysis of microarray data often requires bioinformatic methods, algorithms, and expertise.…”

“…These progresses suggest that a data mining approach has gradually been accepted as mainstream practice in analyzing experimental data and generating new hypotheses for various projects (Salimi et al, 2010). Our lab has successfully pioneered major advances in database mining in the fields of adaptive immune reactions, innate immune responses, and inflammation (Chen et al, 2010;Jan et al, 2010;Ng et al, 2004;Virtue, 2011;Yang et al, 2006a;Yang et al, 2006b;Yin et al, 2009). In this chapter, we will summarize the general approaches, principles, and databases used and new working models proposed in our database mining research.…”

Homocysteine and Polyamines: Metabolic Relationship and Its Clinical Significance in Folate Inhibitors Chemotherapy

“…Гомоцистеин, в свою очередь, является ингибитором метилтранс-фераз, поэтому соотношение SAM/SAH является важной детерминантой объема внутриклеточного трансметилирования [36][37][38].…”