N-Acetylneuraminate
N-acetylneuraminate, commonly known as sialic acid, is a critical nine-carbon sugar acid found extensively in biological systems. It serves as a terminal sugar residue on the oligosaccharide chains of glycoconjugates, such as glycoproteins and glycolipids, which are ubiquitous on cell surfaces and in secreted molecules. These molecules are fundamental to numerous physiological processes, acting as key recognition determinants in intercellular communication and host-pathogen interactions.
Biological Basis
Section titled “Biological Basis”Sialic acids are integral components of the glycome, the complete set of sugars in an organism. Their diverse structures and linkages contribute significantly to the functional complexity of these sugar modifications. They play vital roles in the development of the nervous system, modulate immune responses, and are involved in cell adhesion and signaling pathways. Genetic variations that influence the synthesis, modification, or transport of sialic acids can therefore impact these fundamental biological processes. Genome-wide association studies (GWAS) have identified common genetic variations that influence a wide array of biochemical parameters and metabolite profiles in human serum, demonstrating how genetic factors can broadly affect metabolic pathways, including those involving complex sugars like n-acetylneuraminate.[1]
Clinical Relevance
Section titled “Clinical Relevance”Dysregulation of sialic acid metabolism or alterations in their expression patterns are implicated in a variety of human health conditions, including chronic inflammatory diseases, various forms of cancer, and neurological disorders. Research has shown that common genetic variations can influence biomarkers associated with cardiovascular disease[2], [3]and other metabolic traits. [4]While specific links to n-acetylneuraminate are not detailed in the provided studies, the broader understanding that genetic loci impact metabolic pathways suggests potential clinical relevance for variations affecting this critical biomolecule. For example, specific genetic loci have been identified that influence lipid concentrations and the risk of coronary artery disease[5]. [6]
Social Importance
Section titled “Social Importance”Understanding the genetic underpinnings of n-acetylneuraminate metabolism and its associated pathways holds significant social importance. Insights derived from genetic studies can pave the way for advancements in personalized medicine, facilitating earlier disease detection, the development of targeted therapeutic interventions, and potentially preventative strategies. Identifying individuals at genetic risk for conditions influenced by complex biomolecules contributes to public health by informing clinical practice and guiding the development of novel diagnostic tools. For instance, studies have explored genetic associations with uric acid concentrations, a key metabolic marker, revealing significant genetic loci[7], [8]. [9] This highlights the broader impact of genetic research on understanding and managing human health.
Limitations
Section titled “Limitations”Methodological and Replication Challenges
Section titled “Methodological and Replication Challenges”The power to detect modest genetic effects in genome-wide association studies (GWAS) can be limited by sample size and the extensive multiple testing corrections required, potentially leading to false negatives or an underestimation of true genetic influences.[10] Current GWAS designs are particularly underpowered to detect associations with infrequent variants, meaning that some associations, especially those with large effect sizes found in specific populations, may be challenging to replicate in other cohorts or may represent incidental findings. [4] This limitation underscores the need for larger and more diverse studies to comprehensively map the genetic architecture of complex traits.
Replication of findings is critical for validating genetic associations, but challenges exist in defining and achieving precise replication. While some studies successfully replicate specific single nucleotide polymorphisms (SNPs) with consistent effect directions and magnitudes, others observe associations within the same gene region but with different SNPs, which can be due to multiple causal variants or differing linkage disequilibrium patterns across populations.[4] Furthermore, the use of only a subset of all available SNPs in HapMap for genotyping in some GWAS may result in incomplete coverage of genetic variation, potentially missing some causal genes or failing to comprehensively study a candidate gene. [11]
Population Specificity and Generalizability
Section titled “Population Specificity and Generalizability”Many genetic association studies are conducted primarily in populations of European ancestry, which can limit the generalizability of findings to other ethnic groups. [12] Rare variants, for instance, may exhibit substantial frequency variations across populations, especially in founder populations that have experienced genetic bottlenecks and extensive genetic drift, leading to associations that are difficult to replicate elsewhere. [4] While some studies employ methods like principal component analysis and genomic control to account for population stratification, the reliance on HapMap reference panels, often CEU (Caucasian) samples, for SNP imputation can introduce imprecision in estimating allele frequencies and linkage disequilibrium patterns in non-European or genetically distinct populations. [4]
Phenotype measurement also presents limitations, as traits may be influenced by various factors or require specific ascertainment methods. While some studies average trait measurements across multiple examinations to enhance precision, the inherent variability and potential for measurement error can still impact the observed genetic associations. [10] The choice of reference panels for imputation, such as HapMap build35 or release 22, can also affect the accuracy of imputed genotypes, with reported error rates ranging from 1.46% to 2.14% per allele, which may influence the confidence in associations involving imputed SNPs. [13]
Environmental Interactions and Unidentified Causal Variants
Section titled “Environmental Interactions and Unidentified Causal Variants”Genetic variants do not operate in isolation; their influence on phenotypes can be modulated by environmental factors, leading to context-specific associations. [10] Many studies, however, do not comprehensively investigate gene-environment interactions, which means that significant environmental confounders or modifying effects may remain undetected, limiting a complete understanding of genetic contributions to complex traits. [10] Disentangling these complex interactions is crucial for fully explaining phenotypic variation and identifying the true underlying biological pathways.
A significant challenge in genetic research is that the precise causal variant often remains unidentified, even when strong associations with specific SNPs or gene regions are observed. [4] Many associated SNPs may be in linkage disequilibrium with an unknown causal variant, or multiple causal variants may exist within the same gene, contributing to allelic heterogeneity. [4] Consequently, while statistical associations highlight genomic regions of interest, further functional validation and fine-mapping are essential to pinpoint the exact genetic and molecular mechanisms driving the observed trait variations, and to fully account for the “missing heritability” not explained by identified variants. [2]
Variants
Section titled “Variants”Genetic variations play a crucial role in influencing a wide array of biological processes, from cellular structure and signaling to complex metabolic pathways. These variants, often single nucleotide polymorphisms (SNPs), can subtly alter gene activity, protein function, or expression levels, thereby impacting an individual’s phenotype and susceptibility to various conditions. The interactions of these genes and their variants are particularly relevant to the intricate metabolism of N-acetylneuraminate, a vital sialic acid involved in cell surface recognition, signaling, and host-pathogen interactions.
Several genes with diverse cellular functions contribute to the complex interplay that can indirectly affect N-acetylneuraminate-related pathways. For instance, LAMC1 (Laminin subunit gamma-1) encodes a key component of laminins, essential extracellular matrix proteins that form basement membranes, critical for cell adhesion and tissue organization. Variants like rs116448311 and rs58423714 could influence the structural integrity and signaling capacity of these membranes, potentially impacting the presentation or function of N-acetylneuraminate-containing glycoconjugates. ARHGEF3(Rho Guanine Nucleotide Exchange Factor 3) regulates Rho GTPases, which are central to cytoskeletal dynamics and cell migration, processes that underpin cellular responses and metabolic regulation. Variantsrs1354034 and rs13063588 might modulate this signaling, affecting cellular transport and metabolism. Furthermore, DHX38 (DExH-box helicase 38) is involved in pre-mRNA splicing, a fundamental aspect of gene expression, meaning its variant rs9302635 could broadly influence protein production, including enzymes or transporters relevant to metabolism. Similarly, SNHG16 (Small Nucleolar RNA Host Gene 16), a long non-coding RNA, can regulate gene expression, and its variant rs2109101 may affect widespread cellular processes. Genetic studies have identified numerous loci that influence diverse biomarker traits, underscoring the broad impact of genetic variation on biological systems. [2] These genes, through their roles in structural integrity and gene regulation, can indirectly modulate cellular environments where N-acetylneuraminate plays a part in cell surface interactions or signaling. [14]
Other genes are more directly involved in lipid metabolism and transport, pathways that are intrinsically linked to overall cellular health and the synthesis of complex biomolecules. OSBPL10 (Oxysterol Binding Protein Like 10) participates in lipid transport, particularly involving cholesterol and phospholipids, which are integral to cell membranes. The variant rs558755774 could affect its function, influencing membrane composition and indirectly impacting the availability of precursors for N-acetylneuraminate-containing glycolipids. ELOVL6 (Elongation Of Very Long Chain Fatty Acids Like 6) is an enzyme crucial for the elongation of fatty acids, essential for lipid synthesis. Its variant rs76338299 might alter fatty acid profiles, which can influence the availability of precursors for complex lipid and carbohydrate structures, potentially impacting the regulation of N-acetylneuraminate-containing molecules.[1] Additionally, SLC45A4 (Solute Carrier Family 45 Member 4) encodes a transporter protein, vital for moving specific molecules across cell membranes, influencing nutrient uptake and metabolic homeostasis. The variant rs753778 could affect its transport efficiency, influencing the intracellular concentrations of substrates or products relevant to various metabolic pathways, including those involving N-acetylneuraminate. [15] Genetic variations in lipid-related genes are known to significantly influence lipid concentrations and risk of metabolic diseases, highlighting their broad systemic impact. [5]
A particularly relevant gene is NPL (N-acetylneuraminate lyase), which directly participates in the metabolism of N-acetylneuraminate (sialic acid) by catalyzing its reversible cleavage. Variants such as rs78799057 , rs73065363 , and rs146355388 could directly impact the enzymatic activity of NPL, thereby altering the cellular levels of free N-acetylneuraminate and its precursors. These changes can have significant implications for cellular recognition, signaling, and host-pathogen interactions, as N-acetylneuraminate is a critical component of cell surface glycoconjugates. The study of glycan structures and metabolism, as facilitated by resources like GlyGen, emphasizes the importance of these pathways. [8] Another gene, FUT2 (Fucosyltransferase 2), is involved in glycosylation pathways, specifically in synthesizing the H antigen. The variant rs492602 can influence an individual’s secretor status and the composition of the gut microbiota. Changes in fucose metabolism can indirectly impact other glycan structures, including those incorporating N-acetylneuraminate, by altering substrate availability or enzymatic competition.[16] Finally, ASAP2 (Arf-GAP With SH3 Domain, Ankyrin Repeat And PH Domain 2) is involved in membrane trafficking and cytoskeletal dynamics. Its variant rs1510796 could modulate cellular organization and transport, influencing the localization and turnover of N-acetylneuraminate-containing molecules on the cell surface.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs116448311 rs58423714 | LAMC1 | N-acetylneuraminate measurement |
| rs78799057 rs73065363 rs146355388 | NPL | N-acetylneuraminate measurement |
| rs1354034 rs13063588 | ARHGEF3 | platelet count platelet crit reticulocyte count platelet volume lymphocyte count |
| rs2109101 | SNHG16 | N-acetylneuraminate measurement |
| rs492602 | FUT2 | total cholesterol measurement vitamin B12 measurement tissue factor measurement protein measurement low density lipoprotein cholesterol measurement |
| rs558755774 | OSBPL10 | N-acetylneuraminate measurement |
| rs9302635 | DHX38 | urate measurement blood protein amount phospholipids:totallipids ratio, high density lipoprotein cholesterol measurement triglycerides:total lipids ratio, blood VLDL cholesterol amount ferritin measurement |
| rs76338299 | ELOVL6 | N-acetylneuraminate measurement |
| rs753778 | SLC45A4 | reticulocyte count N-acetylneuraminate measurement |
| rs1510796 | ASAP2 | N-acetylneuraminate measurement |
Biological Background
Section titled “Biological Background”There is no information about N-acetylneuraminate in the provided context. Therefore, this section cannot be written.
References
Section titled “References”[1] Gieger C. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.” PLoS Genet. 2008 Nov 28;4(11):e1000282.
[2] Benjamin EJ. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet. 2007 Oct 23;8 Suppl 1:S11.
[3] Wallace, C. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, vol. 82, no. 1, 2008, pp. 109-119.
[4] Sabatti, C., et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, vol. 41, no. 1, 2009, pp. 35-46.
[5] Willer CJ. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.” Nat Genet. 2008 Feb;40(2):161-9.
[6] Aulchenko, Y. S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nat Genet, vol. 41, no. 1, 2009, pp. 47-55.
[7] Dehghan, A., et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”Lancet, vol. 372, no. 9654, 2008, pp. 1823-1831.
[8] Doring A. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.” Nat Genet. 2008 Apr;40(4):430-6.
[9] Li, S., et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, vol. 3, no. 11, 2007, e194.
[10] Vasan, Ramachandran S., et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, no. 1, 2007, p. 64.
[11] Yang, Qiong, et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, no. 1, 2007, p. 55.
[12] Melzer, David, et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genetics, vol. 4, no. 5, 2008, e1000072.
[13] Yuan, Xin, et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” American Journal of Human Genetics, vol. 83, no. 4, 2008, pp. 520-528.
[14] Wilk JB. “Framingham Heart Study genome-wide association: results for pulmonary function measures.” BMC Med Genet. 2007 Oct 23;8 Suppl 1:S8.
[15] McArdle PF. “Association of a common nonsynonymous variant in GLUT9 with serum uric acid levels in old order amish.” Arthritis Rheum. 2008 Dec;58(12):3994-4001.
[16] Kathiresan S. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.” Nat Genet. 2009 May 14;41(2):185-91.