Formimidoyltransferase Cyclodeaminase

Introduction

Formimidoyltransferase cyclodeaminase is an enzyme crucial for central metabolism, specifically involved in the breakdown of the amino acid histidine and the recycling of one-carbon units via the folate pathway. This bifunctional enzyme plays a vital role in converting formiminoglutamate (FIGLU), an intermediate from histidine catabolism, into a usable form of folate, 5,10-methenyltetrahydrofolate. Its activities are essential for maintaining cellular metabolic balance and supporting fundamental biological processes.

Biological Basis

The enzyme exhibits two distinct enzymatic activities: formimidoyltransferase and cyclodeaminase. The formimidoyltransferase activity first catalyzes the transfer of a formimino group from FIGLU to tetrahydrofolate (THF), producing 5-formiminotetrahydrofolate. Subsequently, the cyclodeaminase activity facilitates the intramolecular cyclization and deamination of 5-formiminotetrahydrofolate to yield 5,10-methenyltetrahydrofolate. This product is a key one-carbon donor, indispensable for various biosynthetic reactions, including the synthesis of purines and pyrimidines (components of DNA and RNA), and the methylation cycle, which is critical for gene regulation and neurotransmitter synthesis.

Clinical Relevance

The proper functioning of formimidoyltransferase cyclodeaminase is directly linked to folate status. A deficiency in either the enzyme’s activity or in folate itself can lead to the accumulation of FIGLU, a condition known as formiminoglutamic aciduria (FIGLUuria). This accumulation can serve as a sensitive indicator of folate deficiency, even before other clinical signs manifest. Chronic folate deficiency, often detectable through elevated FIGLU levels, is associated with a range of health issues, including megaloblastic anemia, neurological impairments, and increased risk for certain chronic diseases, highlighting the enzyme’s importance in maintaining overall health.

Social Importance

Understanding the role of formimidoyltransferase cyclodeaminase holds significant social importance, particularly in the fields of nutrition and public health. Folate is a critical nutrient, especially during periods of rapid cell division like pregnancy, where adequate intake is crucial for preventing neural tube defects. By elucidating the mechanisms of enzymes like formimidoyltransferase cyclodeaminase, researchers can gain deeper insights into the complex interplay between diet, metabolism, and disease. This knowledge can inform public health strategies, such as food fortification programs and dietary guidelines, aimed at preventing nutritional deficiencies and promoting better health outcomes across populations.

Limitations

Methodological and Statistical Constraints

The methodologies employed in these studies present several statistical and design-related limitations. Many analyses were conducted in a sex-pooled manner, which inherently risks overlooking genetic associations that may be sex-specific, thus potentially missing important biological insights into trait regulation. ^[1] Furthermore, the reliance on subsets of available SNPs, such as those from older HapMap builds, means that genomic coverage may be incomplete, leading to a potential failure to detect all relevant genetic variants or to comprehensively study candidate genes. ^[1] The choice to focus predominantly on multivariable models, while robust, may also have inadvertently obscured significant bivariate associations between individual genetic markers and phenotypes. ^[2]

A critical limitation across several studies is the issue of statistical rigor and replicability. Some reported p-values were not adjusted for the extensive multiple comparisons inherent in genome-wide association studies, increasing the likelihood of false positive findings unless validated through stringent correction. ^[3] The need for independent replication is frequently acknowledged, with some findings explicitly stated as requiring validation in diverse cohorts to confirm their authenticity and avoid spurious associations. ^[2] Challenges in replication can arise from differences in linkage disequilibrium patterns across populations, the presence of multiple causal variants within a gene, or variations in study power and design, which can lead to non-replication at the single SNP level even if a genetic locus is genuinely associated with a trait. ^[4] Additionally, effect sizes estimated from only a subset of discovery samples, such as stage 2 cohorts, may be subject to inflation, potentially overstating the true genetic contribution. ^[5]

Generalizability and Phenotype Characterization

A significant limitation impacting the broader applicability of these findings stems from the demographic characteristics of the study populations. Several cohorts are explicitly described as being of predominantly white European or Caucasian ancestry, with individuals not clustering with these groups often excluded from analyses. ^[2] This lack of ethnic diversity means that the observed genetic associations may not be directly generalizable to other ethnic or ancestral groups, limiting the universal applicability of the findings and potentially missing population-specific genetic effects. ^[2]

Concerns regarding phenotype measurement and characterization also warrant consideration. For instance, in studies evaluating kidney function, cystatin C was utilized as a continuous trait without applying established transforming equations for glomerular filtration rate (GFR), primarily due to concerns about the derivation of these equations in smaller, selected samples or using different measurement methodologies. ^[2]While this approach avoids certain biases, it introduces the possibility that cystatin C levels may reflect cardiovascular disease risk independently of kidney function, complicating the interpretation of genetic associations. Similarly, thyroid function was assessed using TSH as a proxy, lacking more comprehensive measures such as free thyroxine or detailed assessments of thyroid disease, which could limit the precision of genetic associations with thyroid-related traits.^[2]

Unaccounted Factors and Broader Interpretive Challenges

Despite rigorous genetic analyses, several factors remain unaddressed or pose broader interpretive challenges. While some studies adjusted for a range of covariates including age, sex, and various health conditions, the potential influence of unmeasured environmental factors or complex gene-environment interactions on the observed associations may not have been fully captured. ^[6] Moreover, even for traits where significant genetic contributions are identified, a substantial proportion of phenotypic variance often remains unexplained, highlighting the ongoing challenge of “missing heritability” and indicating that many causal variants or their interactions are yet to be discovered. ^[3] The sheer volume of associations generated by genome-wide studies also presents a challenge in prioritizing and functionally validating the most impactful genetic signals, underscoring the need for further research beyond initial statistical associations. ^[7]

Finally, the independence and interpretation of some findings could be influenced by external factors. Acknowledgment of sponsorship by pharmaceutical companies and the employment of several authors by such entities in some research introduces potential conflicts of interest, which, while disclosed, warrant consideration when evaluating the research objectives and reported outcomes. ^[8] These broader challenges indicate that while current research provides valuable insights, a complete understanding of the genetic architecture of complex traits requires further comprehensive investigation into environmental interactions, the identification of remaining genetic contributors, and transparent reporting of all potentially influencing factors.

Variants

FTCD(formimidoyltransferase cyclodeaminase) is a crucial enzyme in the folate-dependent one-carbon metabolism pathway, playing a vital role in converting formiminoglutamate (FIGLU) to glutamate and ammonia. This process is essential for the proper recycling of tetrahydrofolate, a key coenzyme in various metabolic reactions. Variants inFTCD, such as rs61735836 , rs59142618 , and rs149667449 , can influence the efficiency of this metabolic step. Impaired FTCD activity can lead to the accumulation of FIGLU, which is often used as a biomarker for folate deficiency, highlighting the gene’s importance in maintaining healthy folate status. ^[2] Consequently, these genetic variations may impact overall metabolic health and potentially contribute to conditions associated with altered folate metabolism, affecting a broad range of biological processes. ^[7]

Several other variants are known to influence broader metabolic health, particularly lipid processing. The rs3747207 variant in the PNPLA3(patatin-like phospholipase domain-containing protein 3) gene is a well-established genetic factor associated with increased liver fat content and progression of non-alcoholic fatty liver disease (NAFLD). This variant, also known as I148M, reduces the enzyme’s ability to hydrolyze triglycerides, leading to their accumulation in hepatocytes. Similarly, thers112875651 variant, associated with TRIB1AL (Tribbles pseudokinase 1, alias, referring to the functional TRIB1 gene), plays a significant role in lipid metabolism. Variants in TRIB1 are known to influence plasma levels of triglycerides and LDL cholesterol by affecting the stability of key transcription factors involved in lipid synthesis and catabolism. ^[9] These genetic influences on lipid and liver health underscore the interconnectedness of metabolic pathways, where dysregulation can indirectly impact nutrient processing, including the folate-dependent reactions facilitated by FTCD. ^[10]

Other variants impact fundamental cellular processes that, while not directly linked to FTCD activity, are critical for overall physiological function. The rs112223870 variant in the PCNT (pericentrin) gene affects a protein crucial for centrosome formation and cell division, with implications for growth and development. Mutations in PCNT are known to cause various forms of primordial dwarfism, highlighting its essential role in cellular architecture. ^[11] Likewise, the rs2839115 variant in COL6A2(Collagen Type VI Alpha 2 Chain) influences a component of collagen type VI, a vital structural protein in connective tissues throughout the body; alterations can lead to conditions like Bethlem myopathy, affecting muscle and tissue integrity. Thers739846 variant in SUGP1 (SURP and G-patch domain-containing protein 1) impacts pre-mRNA splicing, a fundamental step in gene expression, which can broadly affect protein production and cellular function. ^[7] Finally, the rs10883451 variant in ERLIN1 (ER lipid raft associated 1) is involved in endoplasmic reticulum (ER) function and protein quality control, pathways essential for maintaining cellular homeostasis. Disruptions in these foundational cellular mechanisms can indirectly affect metabolic efficiency and overall cellular health.

Variants impacting immune response and nutrient homeostasis also contribute to a comprehensive understanding of metabolic health. The rs7041363 variant in the AKNA gene, which encodes a transcription factor involved in B-cell differentiation and lymphocyte activation, can influence immune system function. A robust immune system is essential for maintaining overall health and responding to various stressors that can impact metabolic balance. ^[6] Furthermore, the rs16919942 variant in HEPHL1(Hephaestin-like 1) is associated with a ferroxidase protein, suggesting a role in iron metabolism and transport. Iron is a critical micronutrient involved in numerous enzymatic reactions and cellular processes, including energy production. Variations affecting iron homeostasis can have widespread effects on metabolic pathways and overall physiological well-being, indirectly interacting with other metabolic processes such as those involving formimidoyltransferase cyclodeaminase.^[12]

Key Variants

RS ID	Gene	Related Traits
rs61735836 rs59142618 rs149667449	FTCD	formimidoyltransferase-cyclodeaminase measurement
rs3747207	PNPLA3	platelet count serum alanine aminotransferase amount aspartate aminotransferase measurement triglyceride measurement non-alcoholic fatty liver disease
rs112223870	PCNT	formimidoyltransferase-cyclodeaminase measurement
rs2839115	COL6A2	formimidoyltransferase-cyclodeaminase measurement
rs7041363	AKNA	apolipoprotein A 1 measurement serum alanine aminotransferase amount high density lipoprotein cholesterol measurement formimidoyltransferase-cyclodeaminase measurement glutathione S-transferase A1 measurement
rs739846	SUGP1	aspartate aminotransferase measurement serum alanine aminotransferase amount liver fibrosis measurement triglyceride measurement, blood VLDL cholesterol amount free cholesterol measurement, blood VLDL cholesterol amount
rs16919942	HEPHL1	formimidoyltransferase-cyclodeaminase measurement
rs10883451	ERLIN1	triglyceride measurement alcohol consumption quality level of fucose mutarotase in blood level of beta-ureidopropionase in blood fructose-1,6-bisphosphatase 1 measurement
rs112875651	TRIB1AL	low density lipoprotein cholesterol measurement total cholesterol measurement reticulocyte count diastolic blood pressure systolic blood pressure

Biological Background

Enzymatic Functions and Metabolic Regulation

Enzymes are fundamental biomolecules that catalyze a vast array of metabolic processes within cells and throughout the body. For instance, hexokinase (HK1) plays a critical role in glycolysis, a central pathway for energy production, particularly noted for its red blood cell-specific isozyme. ^[13] Other enzymes, such as carboxypeptidase N, are crucial regulators of inflammation ^[8]while phosphodiesterase 5 influences cGMP signaling in vascular smooth muscle cells.^[14] These enzymatic activities are tightly regulated, often through genetic mechanisms and interactions with other key biomolecules, to maintain cellular homeostasis and enable complex cellular functions like signal transduction.

Genetic Influence on Cellular Processes

Genetic mechanisms profoundly impact cellular functions by dictating gene expression patterns and protein activity. Variations in genes, such as single nucleotide polymorphisms (SNPs), can alter the efficiency or specificity of enzymes, as observed withFADS1 affecting fatty acid desaturase reactions ^[15] or ABO alleles encoding glycosyltransferase enzymes with different activities. ^[13] Furthermore, regulatory elements and processes like alternative splicing, exemplified by HMGCR and APOB genes, can lead to diverse protein isoforms or alter protein abundance, thereby modulating various molecular pathways and cellular functions. ^[16]These genetic differences contribute to individual variations in metabolic traits and disease susceptibility.

Lipid Metabolism and Homeostasis

Lipid metabolism is a complex network of molecular pathways involving numerous enzymes and structural components crucial for energy storage, membrane integrity, and signaling. For example, the FADS1 gene cluster is associated with the fatty acid composition of phospholipids, influencing the balance of polyunsaturated fatty acids like arachidonyl-CoA. ^[15] Disruptions in this balance, such as reduced efficiency of the delta-5 desaturase reaction, can lead to altered concentrations of various glycerophospholipids and sphingomyelins, impacting overall lipid homeostasis. ^[15] Similarly, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) is a key enzyme in the mevalonate pathway, which is essential for cholesterol biosynthesis, and its regulation is critical for lipid management. ^[16]

Systemic Health and Pathophysiological Links

Disruptions in metabolic processes and enzymatic functions can have widespread systemic consequences, contributing to various pathophysiological conditions. Liver enzymes, such as gamma-glutamyltransferase, are not only indicators of liver health but have also been associated with the risk of developing diabetes and cardiovascular disease.^[8] Genetic variants in genes related to metabolic pathways, including FTO, MC4R, and LEPR, are linked to traits like adiposity, insulin resistance, and metabolic syndrome, highlighting the intricate connection between genetic makeup and systemic health.^[8] These insights underscore how molecular and cellular dysfunctions can manifest as broader homeostatic disruptions and contribute to the development of complex diseases affecting multiple organ systems.

Pathways and Mechanisms

Metabolic Regulation and Energy Homeostasis

The intricate balance of metabolic pathways is central to cellular function, encompassing processes from energy production to the synthesis and breakdown of complex molecules. Lipid metabolism is a prominent area, with mechanisms like the mevalonate pathway playing a critical role in cholesterol biosynthesis, regulated by enzymes such as HMGCR. Genetic variations, including common single nucleotide polymorphisms (SNPs) inHMGCR, can influence alternative splicing and impact LDL-cholesterol levels, highlighting precise regulatory steps in lipid homeostasis. ^[16]Beyond cholesterol, plasma triglyceride levels are influenced by genes likeMLXIPL, and the broader lipid landscape involves regulatory proteins such as ANGPTL3 and ANGPTL4, which modulate lipid metabolism, and SREBP-2 which links isoprenoid and adenosylcobalamin metabolism. ^[17]

Glucose metabolism is equally vital, controlled by enzymes like glucokinase, whose activity can be functionally analyzed through gene mutations affecting its regulatory mechanisms.^[18] Hexokinase 1 (HK1), an enzyme critical for the first step of glycolysis, is particularly important in red blood cell energy metabolism and has been associated with glycated hemoglobin levels.^[13]Additionally, the transport of specific metabolites, such as urate, is mediated by transporters likeSLC2A9, influencing serum urate concentrations and excretion, often exhibiting sex-specific effects.^[19] Fatty acid metabolism also contributes significantly, with gene clusters like FADS1-FADS2 influencing the composition of polyunsaturated fatty acids in phospholipids, and enzymes like medium-chain acyl-CoA dehydrogenase (ACADM) being crucial for fatty acid oxidation. ^[20]

Cellular Signaling and Regulatory Cascades

Cellular responses to external stimuli are orchestrated through complex signaling pathways involving receptor activation and subsequent intracellular cascades. The mitogen-activated protein kinase (MAPK) pathway, for instance, is a fundamental signaling cascade involved in various cellular processes, with its activation affected by factors such as age and acute exercise in human skeletal muscle.^[21] Another critical signaling axis involves the cyclic GMP (cGMP) pathway, which can be antagonized by angiotensin II through its ability to increase the expression of phosphodiesterase 5A (PDE5A) in vascular smooth muscle cells, thereby modulating cellular responses.^[22] Receptor dynamics also play a role in regulating angiogenesis, where isoforms of NTAK/neuregulin-2 exhibit inhibitory activity. ^[23]Furthermore, the cystic fibrosis transmembrane conductance regulator (CFTR) functions as a chloride channel, and its expression and activity are crucial in human endothelia and smooth muscle cells, influencing mechanical properties and cAMP-dependent chloride transport.^[24]

Gene Expression and Post-Translational Control

Regulation at the genetic and protein levels ensures cellular adaptability and proper function. Gene regulation mechanisms, such as those governing the expression of C-reactive protein (CRP), involve transcription factors like HNF-1 binding at distinct sites to synergistically trans-activate the promoter. ^[25]Beyond transcriptional control, post-translational modifications are crucial for fine-tuning protein activity and stability. Allosteric control, a type of protein modification, is exemplified in the regulation of glucokinase activity, where functional analysis of gene mutations reveals its importance.^[18] Additionally, alternative splicing, as seen with HMGCR exon13, can impact protein function and contribute to phenotypic variation, such as LDL-cholesterol levels. ^[16] The assembly and sorting of proteins, particularly mitochondrial beta-barrel proteins, are dependent on dedicated machinery involving components like Sam50, highlighting the complex regulatory steps in cellular compartmentalization and function. ^[26]

Systems-Level Integration and Disease Pathogenesis

Biological systems operate through highly integrated networks where various pathways crosstalk and exhibit emergent properties critical for overall physiological balance. Pathway crosstalk is evident in the antagonistic relationship between angiotensin II and cGMP signaling, illustrating how different molecular signals interact to modulate cellular outcomes. ^[22] Such intricate network interactions are often implicated in the pathogenesis of common diseases. Dysregulation of metabolic pathways contributes significantly to conditions like type 2 diabetes and the metabolic syndrome, involving genes such as LEPR, HNF1A, IL6R, and GCKR, which also associate with plasma C-reactive protein levels, a marker of inflammation. ^[27]Obesity, influenced by genes likeFTO and calpain-10, affects metabolic traits including BMI and insulin sensitivity. ^[28] Compensatory mechanisms and therapeutic targets often arise from understanding these complex interdependencies; for instance, the role of carboxypeptidase N as a pleiotropic regulator of inflammation suggests its potential as a target for inflammatory conditions. ^[29]Similarly, understanding the genetic architecture of gene expression in organs like the human liver provides insights into systemic metabolic regulation and disease susceptibility.^[30]

Clinical Relevance of formimidoyltransferase cyclodeaminase

Prognostic and Diagnostic Utility in Metabolic and Cardiovascular Health

Levels of liver enzymes, including those measured in routine panels, serve as significant biomarkers with both prognostic and diagnostic utility in assessing metabolic and cardiovascular health. Elevated levels of certain enzymatic markers, such as gamma-glutamyl transferase (GGT), have been consistently associated with an increased risk of metabolic syndrome, cardiovascular disease, and overall mortality.^[31]These enzymatic profiles can aid in the early identification of individuals at risk for these complex conditions, offering insights into disease progression and long-term implications. The predictive value extends to specific cardiovascular events, with studies demonstrating that serumGGTcan predict non-fatal myocardial infarction and fatal coronary heart disease.^[8]

Associations with Comorbidities and Disease Progression

The clinical relevance of liver enzymes extends to their strong associations with various comorbidities and their role in tracking disease progression. Elevated levels of liver enzymes, includingGGT, AST, and ALT, are linked to an increased risk of developing type 2 diabetes mellitus and cardiovascular disease, highlighting overlapping phenotypes and systemic implications beyond hepatic health.^[8] Research indicates a substantial genetic influence on biochemical liver function tests, suggesting that genetic predispositions can impact these enzymatic levels and their associated health risks. ^[8] Furthermore, the genetic covariation between serum GGTactivity and cardiovascular risk factors underscores the interconnectedness of liver function with broader physiological systems.^[8]

Risk Stratification and Personalized Medicine Approaches

Liver enzyme levels hold considerable value in risk stratification and can inform personalized medicine strategies and prevention efforts. By identifying individuals with elevated enzymatic markers, clinicians can implement targeted interventions and monitoring strategies to mitigate the risk of adverse outcomes. ^[8]The assessment of liver enzymes, often adjusted for factors such as age, sex, body mass index, alcohol intake, and existing conditions like diabetes or hypertension, provides a comprehensive view for risk assessment.^[7]This allows for more personalized approaches to patient care, including lifestyle modifications or pharmacological interventions, aimed at reducing the burden of metabolic and cardiovascular diseases.

References

[1] Yang, Q., et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 55.

[2] Hwang, S. J., et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 50.

[3] Benyamin, B., et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”Am J Hum Genet, vol. 84, no. 1, 2009, pp. 60-65.

[4] Sabatti, C., et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, vol. 40, no. 11, 2008, pp. 1363-1368.

[5] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161-169.

[6] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, e1000072.

[7] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 51.

[8] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, vol. 83, no. 5, 2008, pp. 568-578.

[9] Kathiresan, Sekar, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nature Genetics, vol. 38, no. 12, Dec. 2006, pp. 1504–1516.

[10] Saxena, R., et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.”Science, vol. 316, no. 5829, 2007, pp. 1331-1336.

[11] Wilk, J. B., et al. “Framingham Heart Study genome-wide association: results for pulmonary function measures.” BMC Medical Genetics, vol. 8, suppl. 1, 2007, p. S8.

[12] Wallace, Chris. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”American Journal of Human Genetics, vol. 82, no. 1, Jan. 2008, pp. 139–149.

[13] Pare, G., et al. “Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women.” PLoS Genet, vol. 4, no. 7, 2008, e1000118.

[14] Vasan, R.S., et al. “Genome-Wide Association of Echocardiographic Dimensions, Brachial Artery Endothelial Function and Treadmill Exercise Responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, no. 1, 2007, pp. 58.

[15] Gieger, C., et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, vol. 4, no. 11, 2008, e1000282.

[16] Burkhardt, R., et al. “Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13.” Arterioscler Thromb Vasc Biol, vol. 28, no. 10, 2008, pp. 1824-1830.

[17] Kooner, J.S., et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet, vol. 40, no. 2, 2008, pp. 149-151.

[18] Garcia-Herrero, C.M., et al. “Functional analysis of human glucokinase gene mutations causing MODY2: exploring the regulatory mechanisms of glucokinase activity.”Diabetologia, vol. 50, no. 2, 2007, pp. 325-333.

[19] Doring, A., et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, vol. 40, no. 4, 2008, pp. 430-436.

[20] Schaeffer, L., et al. “Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids.” Hum Mol Genet, vol. 15, no. 10, 2006, pp. 1745-1756.

[21] Bickel, C., et al. “Activated protein kinase (MAPK) pathway activation: effects of age and acute exercise on human skeletal muscle.”The Journal of Physiology, vol. 547, no. 3, 2003, pp. 977-987.

[22] Kim, D., et al. “Angiotensin II increases phosphodiesterase 5A expression in vascular smooth muscle cells: a mechanism by which angiotensin II antagonizes cGMP signaling.”J Mol Cell Cardiol, vol. 38, no. 1, 2005, pp. 175-184.

[23] Nakano, N., et al. “The N-terminal region of NTAK/neuregulin-2 isoforms has an inhibitory activity on angiogenesis.” J Biol Chem, vol. 279, no. 12, 2004, pp. 11465-11470.

[24] Robert, R., et al. “Disruption of CFTR chloride channel alters mechanical properties and cAMP-dependent Cl-transport of mouse aortic smooth muscle cells.”J Physiol (Lond), vol. 568, no. 2, 2005, pp. 483-495.

[25] Toniatti, C., et al. “Synergistic trans-activation of the human C-reactive protein promoter by transcription factor HNF-1 binding at two distinct sites.”EMBO J, vol. 9, no. 13, 1990, pp. 4467-4475.

[26] Kozjak, V., et al. “An essential role of Sam50 in the protein sorting and assembly machinery of the mitochondrial outer membrane.” J Biol Chem, vol. 278, no. 49, 2003, pp. 48520-48523.

[27] Ridker, P.M., et al. “Loci related to metabolic-syndrome pathways including LEPR, HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women’s Genome Health Study.”Am J Hum Genet, vol. 82, no. 5, 2008, pp. 1182-1190.

[28] Frayling, T.M., et al. “A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity.”Science, vol. 316, no. 5826, 2007, pp. 889-894.

[29] Matthews, K.W., et al. “Carboxypeptidase N: A pleiotropic regulator of inflammation.” Mol Immunol, vol. 40, no. 12, 2004, pp. 785-793.

[30] Schadt, E.E., et al. “Mapping the genetic architecture of gene expression in human liver.” PLoS Biol, vol. 6, no. 5, 2008, e107.

[31] Lee, D. S., et al. “Gamma glutamyl transferase and metabolic syndrome, cardiovascular disease, and mortality risk: the Framingham Heart Study.”Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 27, no. 1, 2007, pp. 127–133.