Threonine
Threonine is an alpha-amino acid that plays a vital role in human biology. It is one of the 20 common amino acids found in proteins and is categorized as an essential amino acid, meaning the human body cannot synthesize it and must obtain it through dietary intake. Its discovery in 1935 made it the last of the proteinogenic amino acids to be identified.
Biological Basis
Section titled “Biological Basis”As a fundamental building block, threonine is crucial for protein synthesis and maintaining the structural integrity of various proteins. Its unique side chain, which includes a hydroxyl group, makes it a polar, uncharged amino acid. This characteristic allows threonine to participate in hydrogen bonding, a critical interaction for proper protein folding and the catalytic activity of enzymes. Beyond its role in protein structure, threonine is metabolically active, serving as a precursor for other important amino acids like glycine and serine, which are involved in various metabolic pathways. It also contributes to the metabolism of fats and sugars. Furthermore, threonine is a key component of mucin proteins, which form a protective layer in the gastrointestinal tract, and is involved in the synthesis of antibodies, thereby supporting immune system function.
Clinical Relevance
Section titled “Clinical Relevance”The diverse functions of threonine underscore its clinical importance. Adequate dietary intake is essential for maintaining overall health, as deficiencies can negatively impact protein synthesis, immune response, and the integrity of the digestive system. It is particularly important for the health of the gut lining. Conditions that affect protein metabolism or nutrient absorption may necessitate careful consideration of threonine levels. Ongoing research explores its potential therapeutic applications, including its role in supporting liver function, partly due to its conversion to glycine, which is involved in detoxification processes.
Social Importance
Section titled “Social Importance”The classification of threonine as an essential amino acid highlights the significance of a balanced and nutritious diet. It is naturally abundant in a variety of protein-rich foods, including meats, fish, dairy products, eggs, and legumes. For individuals following specific dietary patterns, such as vegetarian or vegan diets, it is important to consume a diverse range of plant-based foods or consider supplementation to ensure sufficient intake of all essential amino acids, including threonine. Its contribution to immune health and digestive well-being also emphasizes its broader impact on public health and nutrition.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genetic association studies, particularly genome-wide association studies (GWAS) like those used to investigate various biomarkers and phenotypes, face inherent statistical challenges that can impact the reliability and interpretation of findings for any studied trait. A significant limitation found in some cohorts is the moderate sample size, which can lead to insufficient statistical power to detect genetic effects of modest size, increasing the likelihood of false negative findings.[1]Conversely, the extensive multiple testing inherent in GWAS, where thousands to millions of single nucleotide polymorphisms (SNPs) are tested, raises the potential for false positive associations, making it difficult to definitively distinguish true genetic signals from random chance.[1] The chosen significance thresholds in these studies, while pragmatic, are still subject to the study’s power and the a priori probability of a true association, further complicating interpretation.[2] Additionally, the genotyping platforms used, such as the Affymetrix 100K GeneChip, only assay a subset of all genetic variations, potentially missing important genes or variants due to incomplete genomic coverage.[3] This limited coverage can also hinder comprehensive study of candidate genes and restrict the ability to replicate previously reported associations that may not be present on the array.[3] Furthermore, analytical choices, such as performing only sex-pooled analyses, may obscure sex-specific genetic effects that influence phenotypes differently in males and females.[3] while focusing solely on multivariable models might overlook important bivariate associations.[4] Even with sophisticated methods to account for relatedness and population stratification, these remain critical considerations, as ignoring them can inflate false-positive rates and mislead P values.[5] and imputation of missing genotypes, while necessary, introduces a small but measurable error rate.[5]
Generalizability and Phenotype Characterization
Section titled “Generalizability and Phenotype Characterization”The generalizability of findings from genetic studies, including those on complex traits, is often constrained by the characteristics of the study populations and the methods used for phenotype assessment. Many cohorts, such as the Framingham Heart Study, are predominantly composed of individuals of white European descent and specific age ranges, limiting the direct applicability of findings to younger populations or individuals of other ethnic or racial backgrounds.[1] This lack of diversity means that genetic associations identified may not be universally relevant, necessitating replication in more varied cohorts for validation.[1] Cohort recruitment strategies, such as DNA collection at later examination cycles, can also introduce survival bias, further impacting the representativeness of the sample.[1] Phenotype measurement itself presents several challenges, including the use of proxy measures when direct assessments are unavailable, which may not fully capture the biological trait of interest, or may reflect other confounding conditions.[4]For instance, using cystatin C as a kidney function marker may also reflect cardiovascular disease risk beyond kidney function, complicating specific interpretations.[4] Averaging phenotype measurements over extended periods, sometimes spanning decades, can introduce misclassification due to changes in diagnostic equipment and may mask age-dependent genetic effects, as the underlying genetic and environmental influences on a trait can evolve over a wide age range.[6]
Environmental Context and Unexplored Interactions
Section titled “Environmental Context and Unexplored Interactions”Genetic associations, regardless of the specific trait, do not exist in isolation, and the influence of environmental factors and gene-environment interactions represents a significant area of unexplored complexity. Studies often acknowledge that genetic variants can influence phenotypes in a context-specific manner, with environmental factors modulating their effects.[6] However, comprehensive investigations into these gene-environment interactions are frequently not undertaken, leaving a critical gap in understanding how, for instance, dietary factors might modify the impact of specific genes, such as ACE or AGTR2.[6]Differences in “key factors” between study cohorts, which could include varying environmental exposures or lifestyle choices, are also plausible explanations for the frequent lack of replication of genetic associations.[1] While some studies carefully exclude individuals on specific medications, like lipid-lowering therapies, to avoid confounding.[7] this highlights the pervasive influence of environmental and treatment-related factors that, if not accounted for, can obscure or distort true genetic signals. The cumulative effect of these unmeasured or unaddressed environmental and interactive factors contributes to the “missing heritability” and represents a substantial knowledge gap in fully elucidating the genetic architecture of complex traits.
Variants
Section titled “Variants”Genetic variations play a crucial role in influencing metabolic pathways, including those involving essential amino acids like threonine. Threonine is vital for protein synthesis, immune function, and various metabolic processes, with its catabolism contributing to glucose and energy production. Understanding how specific single nucleotide polymorphisms (SNPs) impact genes involved in metabolic regulation, amino acid transport, or cellular processes can shed light on individual differences in threonine metabolism and related health outcomes.
The GCKRgene, encoding Glucokinase Regulatory Protein, is a key player in glucose homeostasis, primarily by regulating the activity of glucokinase in the liver. Variants withinGCKR, such as rs1260326 , are known to influence hepatic glucose phosphorylation, thereby affecting blood glucose and triglyceride concentrations. TheGCKRgene has been identified in genome-wide association studies as significantly associated with biomarkers of cardiovascular disease and type 2 diabetes, highlighting its broad metabolic impact.[2]These alterations in glucose metabolism can indirectly influence the overall metabolic environment, potentially affecting the demand for and utilization of amino acids like threonine, which is involved in both protein synthesis and energy pathways.
Other variants impact genes central to amino acid and nitrogen metabolism.CPS1(Carbamoyl Phosphate Synthetase 1) is a rate-limiting enzyme in the urea cycle, essential for ammonia detoxification. Variants likers715 and rs1047891 can affect CPS1activity, potentially leading to altered ammonia levels and impacting the broader amino acid pool. Similarly,GLS2(Glutaminase 2) converts glutamine to glutamate and ammonia, playing roles in renal ammoniagenesis and cellular energy production. The variantrs2657879 , which is also associated with SPRYD4, could influence GLS2function, thereby modulating nitrogen balance. Such genetic variations in central amino acid metabolic enzymes highlight the intricate network that can influence threonine availability and its metabolic fate.[8]Further genetic influences extend to protein turnover, cellular signaling, and amino acid availability. TheELOC (Elongin C) gene, a component of the Elongin complex, is critical for regulating transcription elongation and protein degradation. Variants such as rs72661853 and rs147183463 may alter ELOC function, impacting cellular protein dynamics. MEIOB (Meiosis-Specific Nuclear Structural Protein) is essential for meiotic recombination, while ASPG(Asparaginase) is involved in asparagine metabolism. Variants likeMEIOB rs1742425 and ASPG rs1744297 can influence these specific biological processes, with potential downstream effects on overall cellular health and the availability of amino acids including threonine. Genome-wide association studies frequently identify such variants that contribute to complex biological traits.[9] Variants in non-coding regions and transporter genes also contribute to metabolic regulation. SLC38A4-AS1 is a long non-coding RNA that may regulate SLC38A4, a sodium-coupled neutral amino acid transporter. Variants likers2465216 and rs2711697 could influence the expression or function of this transporter, affecting amino acid uptake. Similarly, the intergenic regionLINC02448 - RNU4-20P and the TERLR1 - SLC6A19 region contain non-coding RNAs and the SLC6A19gene, which encodes a key neutral amino acid transporter in the kidney and intestine. Variantrs11133665 in this region could impact amino acid absorption and reabsorption, directly affecting systemic threonine levels. EvenMIP (Major Intrinsic Protein, or Aquaporin 0), a water channel, with its variant rs2933243 , can indirectly affect cellular hydration and metabolic efficiency. These diverse genetic variations underscore the multifaceted regulatory mechanisms influencing threonine metabolism.[1]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs1260326 | GCKR | urate measurement total blood protein measurement serum albumin amount coronary artery calcification lipid measurement |
| rs72661853 rs147183463 | ELOC | threonine measurement alkaline phosphatase measurement |
| rs2657879 | SPRYD4, GLS2 | metabolite measurement serum metabolite level glucose measurement urate measurement glutamine measurement, amino acid measurement |
| rs2465216 rs2711697 | SLC38A4-AS1 | threonine measurement |
| rs715 rs1047891 | CPS1 | circulating fibrinogen levels plasma betaine measurement eosinophil percentage of leukocytes platelet crit macular telangiectasia type 2 |
| rs1742425 | MEIOB | threonine measurement |
| rs1744297 | ASPG | asparagine measurement, amino acid measurement strand of hair color serine measurement, amino acid measurement threonine measurement tryptophan measurement, amino acid measurement |
| rs74095612 | LINC02448 - RNU4-20P | threonine measurement |
| rs11133665 | TERLR1 - SLC6A19 | urinary metabolite measurement kynurenine measurement N-acetyl-1-methylhistidine measurement methionine sulfone measurement Methionine sulfoxide measurement |
| rs2933243 | MIP | threonine measurement metabolite measurement histidine measurement serum metabolite level serum albumin amount |
References
Section titled “References”[1] Benjamin EJ. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, 2007.
[2] Wallace C. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, 2008.
[3] Yang Q. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, 2007.
[4] Hwang SJ. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, 2007.
[5] Willer CJ et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, 2008.
[6] Vasan RS. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, 2007.
[7] Kathiresan S et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nat Genet, 2008.
[8] Doring, Angela, et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nature Genetics, vol. 40, no. 4, 2008, pp. 430-36.
[9] Wilk, J. B., et al. “Framingham Heart Study genome-wide association: results for pulmonary function measures.” BMC Medical Genetics, vol. 8, no. Suppl 1, 2007, p. S8.