Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts: Gene/Protein Disease Symptom Drug Enzyme Compound   Target Concepts: Gene/Protein Disease Symptom Drug Enzyme Compound

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Query: EC:3.4.15.1 (ACE)
18,300 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Studies on the role that genetic variation may play in a complex human disease can be empowered by an assessment of both disease risk in case-control or family models and of quantitative traits that reflect elements of disease etiology. An excellent example of this can be found for the epsilon4 allele of APOE in relation to Alzheimer's disease (AD) for which association with both risk and age-at-onset (AAO) is evident. Following a recent demonstration that variants of the gene encoding angiotensin I converting enzyme ( ACE) contribute to AD risk, we have explored the potential influence of ACE upon AAO in AD. A total of 2861 individuals from three European populations, including six independent AD samples, have been examined in this study. Three single nucleotide polymorphisms (SNPs) previously demonstrated to have maximum effects upon ACE plasma levels and that span the ACE locus were genotyped in these materials. A strong effect upon AAO was observed for marker rs4343 in exon 17 ( P<0.0001), but evidence was also obtained indicating a possible independent effect of marker rs4291 ( P=0.0095) located in the ACE promoter. Effects were consistent with data from previous studies suggesting association with AD in case-control models, whereby alleles demonstrated to confer risk to disease also appear to reduce AAO. Equivalent effects were evident regardless of APOE epsilon4 carrier status and in both males and females. These results provide an important complement to existing AD risk data, confirming that ACE harbors sequence variants that contribute to aspects of AD pathology.
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PMID:Common variants of ACE contribute to variable age-at-onset of Alzheimer's disease. 1498 5

In ethnic Russians, MHC (HLA) was shown to be the major locus determining the predisposition to type 1 diabetes mellitus (T1DM). To map the regions linked to T1DM, families with concordant or discordant sib pairs were selected from the Russian population of Moscow. With these families, linkage to T1DM was demonstrated for CTLA4 (IDDM12, 2q32.1-q33), which codes for a T-cell surface antigen, and PDCD2 (IDDM8, 6q25-q27), which is homologous to the mouse programmed cell death activator gene. With polymorphic microsatellites, regions 3q21-q25 (IDDM9) and 10p12.2 (IDDM10) were also linked to T1DM. Case/control and family studies of the polymorphic markers from region 11p13 revealed a new T1DM-associated locus in the vicinity of the catalase gene (CAT); linkage to this locus was not reported earlier for other populations. Diabetic polyneuropathy (DPN) proved to be associated with single-nucleotide polymorphisms Ala(-9)Val (SOD2), Arg213Gly (SOD3), and T(-262)C (CAT) and with a polymorphic microsatellite of the NOS2 promoter. Hence oxidative stress, which results from hyperglycemia, increased mitochondrial production of superoxide radicals, and insufficient activities of antioxidative enzymes, was assumed to play an important part in DPN development in T1DM. Diabetic nephropathy (DN) showed no association with the antioxidative enzyme genes. However, the association was observed for the insertion/deletion (I/D) polymorphism of ACE and the ecNOS34a/4b polymorphism of NOS3, two genes involved in controlling vascular tonicity, and for the I/D polymorphism of APOB and the epsilon 2/epsilon 3/epsilon 4 polymorphism of APOE, two genes involved in lipid transport. In addition, polymorphic microsatellites of chromosome 3q21-q25 proved to be closely associated with DN. The tightest association was established for D3S1550, carriers of allele 12 or genotype 12/14 having high risk of DN (OR = 4.85 and 6.25, respectively). Region 3q21-q25 was assumed to contain a major gene determining DN development, while the other DN-associated genes mostly affect the progression of DN.
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PMID:[Genomics of type I diabetes mellitus and its late complications]. 1504 45

Alzheimer's disease (AD) and dementia with vascular component (DVC) are the most prevalent forms of dementia. Both clinical entities share many similarities, but they differ in major phenotypic and genotypic profiles as revealed by structural and functional genomics studies. Comparative phenotypic studies have identified significant differences in 25% of more than 100 parametric variables, including anthropometry, cardiovascular function, aortic atherosclerosis, brain atrophy, blood pressure, blood biochemistry, hematology, thyroid function, folate and vitamin B12 levels, brain hemodynamics and lymphocyte markers. The phenotypic profile of patients with DVC differs from that of AD patients in the following: anthropometric values (weight, height); cardiovascular function (ECG, heart rate); blood pressure; lipid metabolism (HDL-CHO, TGs); uric acid metabolism; peripheral calcium homeostasis; liver function (GOT, GPT, GGT); alkaline phosphatase; lactate dehydrogenase; red and white blood cells; regional brain atrophy (left temporal region, inter-hippocampal distance); and left anterior blood flow velocity. Functional genomics studies incorporating APOE-related changes in biological markers extended the difference between AD and DVC up to 57%. Brain perfusion studies show a severe brain hypoperfusion in dementia associated with enlarged age-dependent arterial perfusion times. Structural genomics studies with AD-related genes, including APP, MAPT, APOE, PS1, PS2, A2M, ACE, AGT, cFOS and PRNP genes, demonstrate different genetic profiles in AD and DVC, with an absolute genetic variation rate ranging from 30% to 80%, depending upon genes and genetic clusters. Single gene analysis identifies relative genetic variations ranging from 0% to 5%. The relative polymorphic variation in genetic clusters integrated by two, three or four genes associated with AD ranges from 1% to 3%. The main phenotypic differences between AD and DVC are genotype-dependent, especially in AD, probably indicating that different genomic factors are determinant for the expression of dementia symptoms which might be accelerated or induced by environmental and/or cerebrovascular factors.
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PMID:Phenotypic profiles and functional genomics in Alzheimer's disease and in dementia with a vascular component. 1526 64

Constitutive genomics are probably determinant for the onset of dementia in conjunction with cerebrovascular and environmental factors. Furthermore, pharmacogenomic studies predict that the therapeutic response in Alzheimer's disease (AD) is genotype-specific, and that the expression of genes involved in the regulation of drug metabolism can influence efficacy and safety issues in pharmacotherapy. AD and dementia with a vascular component (DVC = VD + MXD) are the most prevalent forms of dementia. These clinical entities share many similarities, but they differ in major phenotypic and genotypic profiles, as revealed by structural and functional genomics studies. Comparative phenotypic studies have identified significant differences in 25% of more than 100 parametric variables, including anthropometry, cardiovascular function, aortic atherosclerosis, brain atrophy, blood pressure, blood biochemistry, hematology, thyroid function, folic acid and vitamin B(12) levels, brain hemodynamics and lymphocyte markers. The phenotypic profile of patients with DVC differs from that of AD patients in the following: (a) anthropometric values, (b) cardiovascular function, (c) blood pressure, (d) lipid metabolism, (e) uric acid levels, (f) peripheral calcium levels, (g) liver function (GOT, GPT, GGT), (h) alkaline phosphatase, (i) lactate dehydrogenase, (j) red and white blood cells, (k) regional brain atrophy (left temporal region, inter-hippocampal distance) and (l) brain blood flow velocity. Functional genomics studies incorporating APOE-related changes in biological markers extended the difference between AD and DVC up to 57%. Structural genomics studies with AD-related genes, including APP, MAPT, APOE, PS1, PS2, A2M, ACE, AGT, cFOS and PRNP genes, demonstrate different genetic profiles in AD and DVC, with an absolute genetic variation rate ranging from 30 to 80%, depending upon genes and genetic clusters. Single gene analysis identifies relative genetic variations ranging from 0 to 5%. The relative polymorphic variation in genetic clusters integrated by 2, 3 or 4 genes associated with AD ranges from 1 to 3%. The main phenotypic differences between AD and DVC are genotype-dependent, especially in AD, probably indicating that different genomic factors are essential for the expression of dementia symptoms that might be accelerated or induced by environmental and/or cerebrovascular factors.
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PMID:Genomics and phenotypic profiles in dementia: implications for pharmacological treatment. 1534 38

There are a total of 17 families of drugs that are used for treating the heterogeneous group of cardiovascular diseases. We propose a comprehensive pharmacogenomic approach in the field of cardiovascular therapy that considers the five following sources of variability: the genetics of pharmacokinetics, the genetics of pharmacodynamics (drug targets), genetics linked to a defined pathology and its corresponding drug therapies, the genetics of physiologic regulation, and environmental-genetic interactions. Examples of the genetics of pharmacokinetics are presented for phase I (cytochromes P450) and phase II (conjugating enzymes) drug-metabolizing enzymes and for phase III drug transporters. The example used to explain the genetics of pharmacodynamics is glycoprotein IIIa and the response to antiplatelet effects of aspirin. Genetics linked to a defined pathology and its corresponding drug therapies is exemplified by ADRB1, ACE, CETP and APOE and drug response in metabolic syndrome. The examples of cytochrome P450s, APOE and ADRB2 in relation to ethnicity, age and gender are presented to describe genetics of physiologic regulation. Finally, environmental-genetic interactions are exemplified by CYP7A1 and the effects of diet on plasma lipid levels, and by APOE and the effects of smoking in cardiovascular disease. We illustrate this five-tiered approach using examples of cardiovascular drugs in relation to genetic polymorphism.
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PMID:Pharmacogenomics and drug response in cardiovascular disorders. 1546 3

For the best understanding of aging, we must consider a genetic pool in which genes with negative effects (deleterious genes that shorten the life span) interact with genes with positive effects (helpful genes that promote longevity) in a constant epistatic relationship that results in a modulation of the final expression under particular environmental influences. Examples of deleterious genes affecting aging (predisposition to early-life pathology and disease) are those that confer risk for developing vascular disease in the heart, brain, or peripheral vessels (APOE, ACE, MTFHR, and mutation at factor II and factor V genes), a gene associated with sporadic late-onset Alzheimer's disease (APOE E4), a polymorphism (COLIA1 Sp1) associated with an increased fracture risk, and several genetic polymorphisms involved in hormonal metabolism that affect adverse reactions to estrogen replacement in postmenopausal women. In summary, the process of aging can be regarded as a multifactorial trait that results from an interaction between stochastic events and sets of epistatic alleles that have pleiotropic age-dependent effects. Lacking those alleles that predispose to disease and having the longevity-enabling genes (those beneficial genetic variants that confer disease resistance) are probably both important to such a remarkable survival advantage.
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PMID:Genetic contribution to aging: deleterious and helpful genes define life expectancy. 1639 87

Alzheimer's disease is a genetically complex disorder associated with multiple genetic defects, either mutational or of susceptibility. Although potentially associated with an accelerated stochastically driven aging process, Alzheimer's disease is an independent clinical entity in which the aging process exerts a deleterious effect on brain activity in conjunction with polymodal genetic factors and other pathological conditions (i.e., age-related cerebrovascular deterioration) and environmental factors (i.e., nutrition). Alzheimer's disease genetics does not explain in full the etiopathogenesis of this disease. Therefore, it is likely that environmental factors and/or epigenetic phenomena also contribute to Alzheimer's disease pathology and phenotypic expression of dementia. The genomics of Alzheimer's disease is still in its infancy, but this field is aiding the understanding of novel aspects of this disease, including genetic epidemiology, multifactorial risk factors, pathogenic mechanisms associated with genetic networks and genetically regulated metabolic cascades. Alzheimer's disease genomics is also helping to develop new strategies in pharmacogenomic research and prevention. Functional genomics, proteomics, pharmacogenomics, high-throughput methods, combinatorial chemistry and modern bioinformatics will greatly contribute to accelerate drug development for Alzheimer's disease and other complex disorders. The multifactorial genetic dysfunction in dementia includes mutational loci (APP, PS1, PS2, TAU) and diverse susceptibility loci (APOE, alpha2M, alphaACT, LRP1, IL1 alpha, TNF, ACE, BACE, BCHE, CST3, MTHFR, GSK3 beta, NOS3 and many other genes) distributed across the human genome, probably converging in a common pathogenic mechanism that leads to premature neuronal death, in which mitochondrial DNA mutations may contribute to increased genetic variability and heterogeneity. In Alzheimer's disease, multiple pathogenic events, including genetic factors, accumulation of aberrant or misfolded proteins, protofibril formation, ubiquitin-proteasome system dysfunction, excitotoxic reactions, oxidative and nitrosative stress, mitochondrial injury, synaptic failure, altered metal homeostasis, dysfunction of axonal and dendritic transport, and chaperone misoperation may converge in pathogenic pathways leading to premature death and neurodegeneration. Some of these mechanisms are common to several neurodegenerative disorders, which differ depending upon the gene(s) affected and the involvement of specific genetic networks, together with epigenetic factors and environmental events. Many genes potentially associated with Alzheimer's disease in some studies cannot be confirmed as candidate genes in replication studies, indicating that methodological problems and genomic complexity are leading to erroneous conclusions. A different approach to Alzheimer's disease functional genomics is to integrate individual genetic information in polygenic genotypes (haplotype-like model) and to investigate genotype-phenotype correlations and genotype-related pharmacogenomic behaviors. The application of functional genomics to Alzheimer's disease can be a suitable strategy for molecular diagnosis and for understanding pathophysiological mechanisms associated with Alzheimer's disease-related neurodegeneration. Furthermore, the pharmacogenomics of Alzheimer's disease may contribute in the future to optimize drug development and therapeutics, increasing efficacy and safety, and reducing side-effects and unnecessary costs.
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PMID:Molecular genetics of Alzheimer's disease and aging. 1647 Feb 48

Information on gene variants and blood levels (APOE, BCHE-K, TF-C2, HFE-D, HFE-Y, ACE I/D, AR1; homocysteine, folate and vitamin B(12)) is available for participants in the Oxford Project to Investigate Memory and Ageing (OPTIMA) cohort (n=575). This information identified four risk sets for Alzheimer's disease (AD) using grade of membership analysis (GoM). Graded membership scores that relate individuals to each set are automatically generated. Sets I and III had low intrinsic risk. Set II had high intrinsic risk associated with multiple gene variants, e.g., APOE44/34. Set IV also had high intrinsic risk demonstrating low folate and B(12) levels. Membership in the high intrinsic risk sets was summed, coded as either close versus not close (>or=0.80 versus <0.80) and input into logistic models to predict relative risk: close resemblance multiplied risk 80-fold for possible AD before age 65 and 55-fold for probable or definite AD at ages 65-74. These findings implicate both biochemical and genetic factors in the risk for AD and further support dietary supplementation with folate and vitamin B(12) as a potential means to delay the onset of AD and/or its rate of progression.
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PMID:Susceptibility groups for Alzheimer's disease (OPTIMA cohort): integration of gene variants and biochemical factors. 1711 17

The past decade has witnessed hundreds of reports declaring or refuting genetic association with putative Alzheimer disease susceptibility genes. This wealth of information has become increasingly difficult to follow, much less interpret. We have created a publicly available, continuously updated database that comprehensively catalogs all genetic association studies in the field of Alzheimer disease (http://www.alzgene.org). We performed systematic meta-analyses for each polymorphism with available genotype data in at least three case-control samples. In addition to identifying the epsilon4 allele of APOE and related effects, we pinpointed over a dozen potential Alzheimer disease susceptibility genes (ACE, CHRNB2, CST3, ESR1, GAPDHS, IDE, MTHFR, NCSTN, PRNP, PSEN1, TF, TFAM and TNF) with statistically significant allelic summary odds ratios (ranging from 1.11-1.38 for risk alleles and 0.92-0.67 for protective alleles). Our database provides a powerful tool for deciphering the genetics of Alzheimer disease, and it serves as a potential model for tracking the most viable gene candidates in other genetically complex diseases.
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PMID:Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. 1719 85

Several genes have been implicated as influencing the outcome following traumatic brain injury (TBI). Currently the most extensively studied gene has been APOE. APOE can influence overall and rehabilitation outcome, coma recovery, risk of posttraumatic seizures, as well as cognitive and behavioral functions following TBI. Pathologically, APOE is associated with increased amyloid deposition, amyloid angiopathy, larger intracranial hematomas and more severe contusional injury. The proposed mechanism by which APOE affects the clinicopathological consequences of TBI is multifactorial and includes amyloid deposition, disruption of cytoskeletal stability, cholinergic dysfunction, oxidative stress, neuroprotection and central nervous system plasticity in response to injury. Other putative genes have been less extensively studied and require replication of the clinical findings. The COMT and DRD2 genes may influence dopamine dependent cognitive processes such as executive/frontal lobe functions. Inflammation which is a prominent component in the pathophysiological cascade initiated by TBI, is in part is mediated by the interleukin genes, while apoptosis that occurs as a consequence of TBI may be modulated by polymorphisms of the p53 gene. The ACE gene may affect TBI outcome via mechanisms of cerebral blood flow and/or autoregulation and the CACNA1A gene may exert an influence via the calcium channel and its effect on delayed cerebral edema. Although several potential genes that may influence outcome following TBI have been identified, future investigations are needed to validate these genetic studies and identify new genes that might influence outcome following TBI.
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PMID:Genetic influences on outcome following traumatic brain injury. 1734 13


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