Thioproline
Introduction
Section titled “Introduction”thioproline, also known as thiazolidine-4-carboxylic acid, is a sulfur-containing cyclic amino acid derivative of proline. It is a synthetic compound but can also be formed endogenously in the body and is found in some foods. Its unique chemical structure, incorporating a sulfur atom into the proline ring, contributes to its distinct biological properties.
Biological Basis
Section titled “Biological Basis”The biological functions of thioproline are largely attributed to its chemical reactivity and metabolic roles. It is recognized for its antioxidant capabilities, helping to neutralize free radicals and reduce oxidative stress within cells. Additionally, thioproline acts as a chelating agent, capable of binding to and facilitating the removal of certain heavy metals from the body. It can also participate in various metabolic pathways, potentially serving as a precursor for other sulfur-containing compounds crucial for cellular detoxification, such as cysteine and glutathione. Its involvement in xenobiotic metabolism suggests a role in the processing and elimination of foreign compounds.
Clinical Relevance
Section titled “Clinical Relevance”Given its antioxidant and metal-chelating properties, thioproline has garnered attention for potential clinical applications. Research has explored its use as a hepatoprotective agent, aiming to support and protect liver function. It has also been investigated in dermatological contexts for its potential benefits in various skin conditions. Furthermore, thioproline has been examined for possible anticancer properties and as a compound that could enhance the efficacy of certain chemotherapy drugs. Its presence and metabolic fate within the body may also offer insights into oxidative stress levels or serve as a biomarker for exposure to specific environmental toxins.
Social Importance
Section titled “Social Importance”The multifaceted biological activities of thioproline contribute to its broader social significance. In the pharmaceutical industry, it is a compound of interest for the development of new therapeutic agents, particularly in fields related to detoxification, antioxidation, and liver health. Its potential inclusion in dietary supplements or functional foods is also a subject of investigation, aiming to harness its health-promoting attributes for general wellness. Moreover, thioproline’s role as a potential diagnostic marker could be valuable in public health efforts, aiding in the monitoring of environmental exposures and the early detection of certain health conditions.
Limitations
Section titled “Limitations”Studies investigating traits like thioproline through genome-wide association studies (GWAS) are subject to several inherent limitations that can impact the interpretation and generalizability of findings. These constraints often relate to methodological aspects, the composition of study populations, and the complex interplay of genetic and environmental factors. Acknowledging these limitations is crucial for a balanced understanding of the research landscape and for guiding future investigations.
Study Design and Statistical Power Constraints
Section titled “Study Design and Statistical Power Constraints”A fundamental limitation in many association studies, including those on traits like thioproline, is the moderate size of cohorts, which can lead to insufficient statistical power to detect modest genetic associations, increasing the risk of false negative findings.[1]Conversely, the vast number of statistical tests performed in GWAS to sift through numerous single nucleotide polymorphisms (SNPs) can lead to false positive associations if not rigorously corrected.[1] Moreover, current GWAS typically utilize only a subset of all known SNPs, meaning they may miss novel genes or specific causal variants due to incomplete genome coverage. [2] When specific variants are not directly genotyped, imputation methods are often used, which can introduce minor error rates, typically between 1.46% and 2.14% per allele. [3]
Furthermore, phenotypes like protein levels often do not follow a normal distribution, necessitating complex statistical transformations to achieve approximate normality for analysis. [4] While essential for valid statistical tests, the choice and application of these transformations, such as log or Box-Cox power transformations, can influence the robustness and comparability of results if not carefully considered. Replication across independent cohorts remains the ultimate validation for GWAS findings; however, studies frequently encounter challenges in replication, with some research indicating that only about one-third of reported associations are consistently replicated. [1] This lack of replication can stem from several factors, including initial false positive findings, genuine differences in cohort characteristics that modify gene-phenotype associations, or insufficient statistical power in replication cohorts leading to false negative reports. [1]
Generalizability and Phenotype Definition
Section titled “Generalizability and Phenotype Definition”The generalizability of findings from GWAS, particularly for traits like thioproline, is often constrained by the demographic characteristics of the study populations. Many cohorts are largely composed of individuals of white European ancestry, and are often middle-aged to elderly.[1] This limits the applicability of the findings to younger populations or individuals of other ethnic or racial backgrounds, as genetic architecture and allele frequencies can vary significantly across diverse populations. [1] Furthermore, some studies may introduce survival bias if DNA samples are collected at later examination points in a longitudinal cohort, potentially excluding individuals who did not survive to those later stages. [1]
Phenotype measurement itself also presents challenges. While biomarker phenotypes are routinely assessed with quality control, some variants of interest, such as non-SNP variants or specific repeat sequences, may not be present on standard genotyping arrays or included in reference databases like HapMap. [1] This lack of coverage makes it difficult to assess their association with the trait, thereby limiting a comprehensive genetic understanding. Moreover, analyses that are sex-pooled rather than sex-specific, often performed to avoid worsening multiple testing issues, may fail to detect genetic variants that are associated with a phenotype exclusively in males or females. [2]
Comprehensive Genetic Understanding and Confounding Factors
Section titled “Comprehensive Genetic Understanding and Confounding Factors”Moreover, the influence of environmental factors and gene-environment interactions on trait expression is often not fully accounted for in current GWAS, as these complex interactions can modify the associations between genotype and phenotype. [1] While some studies incorporate family data and robust statistical models to account for relatedness and population stratification [5]completely isolating the genetic effects from shared environmental influences or unmeasured confounders remains a significant challenge. Fully understanding the genetic architecture of complex traits necessitates integrative approaches that combine dense genomic data with comprehensive environmental exposure assessments.
Variants
Section titled “Variants”Variants in genes involved in RNA processing, such as a pseudogene associated with Heterogeneous Nuclear Ribonucleoprotein A1 (HNRNPA1P67), and another related to the minor spliceosome (RNU4ATAC9P), can influence gene expression and cellular function. The HNRNPA1 gene is crucial for various aspects of RNA metabolism, including splicing, transport, and stability of messenger RNA (mRNA). [6] Pseudogenes, like HNRNPA1P67 at locus rs546498715 , often lack protein-coding capacity but can play regulatory roles, potentially modulating the expression of their functional counterparts or other genes. [7] Similarly, RNU4ATAC9P is a pseudogene related to the U4atac small nuclear RNA, a key component of the minor spliceosome, which is responsible for processing a small but essential subset of introns in gene transcripts. [8]Disruptions in these RNA processing pathways can lead to altered protein synthesis and cellular stress responses, potentially interacting with compounds like thioproline, which can influence cellular metabolism and oxidative balance.
Other important variants affect genes involved in nutrient transport, metabolic regulation, and cell surface interactions. The gene SLC23A3 at locus rs192756070 encodes a sodium-dependent transporter for ascorbic acid, commonly known as Vitamin C, a vital antioxidant that helps protect cells from oxidative damage.[9] Variants in SLC23A3could alter cellular Vitamin C levels, impacting the overall antioxidant capacity and potentially modifying responses to thioproline, which also possesses antioxidant properties. Meanwhile, theTMPRSS6 gene (rs4820268 ) codes for a transmembrane serine protease that acts as a crucial regulator of iron metabolism, primarily by influencing hepcidin expression, the master hormone of iron homeostasis.[10]Iron dysregulation can lead to increased oxidative stress, a state where thioproline’s antioxidant or chelating properties might become particularly relevant. Additionally,HS3ST3A1, located within the rs10153317 locus, is an enzyme involved in synthesizing heparan sulfate, a complex carbohydrate on cell surfaces that mediates cell signaling, growth factor binding, and cell adhesion.[11]Alterations in heparan sulfate modification could influence how cells interact with their environment and respond to various compounds, including thioproline.
Variants also appear in genes affecting cellular structure, division, and adhesion, highlighting their broad impact on cellular integrity and function. CEP128 (rs1530768 ) codes for Centrosomal Protein 128, a key component of the centrosome, which is essential for proper cell division, microtubule organization, and maintaining genomic stability. [12]Variations could affect cell cycle progression, which is significant given thioproline’s reported anti-proliferative effects in certain cellular contexts. Another gene,EPB41L4A (rs6893663 ), belongs to the Band 4.1 protein family, connecting the cell cytoskeleton to membrane proteins and playing roles in maintaining cell shape, motility, and adhesion. [13] Changes in these proteins can impact cell migration and tissue organization. Similarly, IGSF9B (rs4937861 ) encodes an immunoglobulin superfamily member involved in cell adhesion, particularly in the nervous system, where it plays a role in synapse formation and neuronal connectivity. [14]Such variants could influence processes related to neurological health or cellular development, areas where thioproline’s metabolic or antioxidant activities might interact.
Finally, a number of identified variants affect non-coding RNAs and a critical transcription factor, underscoring the pervasive regulatory influence of genetic variation. Several long intergenic non-coding RNAs (lncRNAs), including LINC00607 (rs4672779 ), LINC02730 (at the rs4937861 locus), LINC02093 (at the rs10153317 locus), and LINC01396 (at the rs112787333 locus), are associated with variants. LncRNAs regulate gene expression through diverse mechanisms, impacting chromatin structure, transcription, and post-transcriptional processing. [15] Similarly, STX18-AS1 (rs112787333 ) is an antisense RNA, often functioning to regulate the expression of its sense gene or nearby genes. [16] These non-coding RNA variants could therefore subtly alter the expression of numerous downstream genes involved in various biological pathways. Concurrently, MECOM (rs78939045 ) is a crucial transcription factor, also known as EVI1/MDS1, that plays a vital role in regulating hematopoiesis and embryonic development. [17] Variants in MECOMcan impact cell proliferation and differentiation, pathways that are highly relevant to thioproline’s investigated roles in modulating cellular growth and response to stress.[18]The interplay of these regulatory elements and cellular processes underscores the complex genetic landscape influencing responses to agents like thioproline.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs546498715 | HNRNPA1P67 - RNU4ATAC9P | esterified cholesterol measurement free cholesterol measurement total cholesterol measurement low density lipoprotein cholesterol measurement esterified cholesterol measurement, low density lipoprotein cholesterol measurement |
| rs192756070 | SLC23A3 | tartarate measurement tartronate (hydroxymalonate) measurement X-24432 measurement X-15674 measurement X-16964 measurement |
| rs4820268 | TMPRSS6 | hemoglobin measurement iron biomarker measurement serum iron amount mean corpuscular hemoglobin erythrocyte volume |
| rs4672779 | LINC00607 | thioproline measurement |
| rs1530768 | CEP128 | thioproline measurement |
| rs6893663 | EPB41L4A | thioproline measurement |
| rs4937861 | IGSF9B - LINC02730 | SAPHO syndrome thioproline measurement |
| rs10153317 | LINC02093 - HS3ST3A1 | thioproline measurement |
| rs78939045 | MECOM | thioproline measurement |
| rs112787333 | STX18-AS1 - LINC01396 | thioproline measurement |
Classification, Definition, and Terminology
Section titled “Classification, Definition, and Terminology”Pathways and Mechanisms
Section titled “Pathways and Mechanisms”References
Section titled “References”[1] Benjamin, E. J. et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007.
[2] 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.
[3] Willer, C. J. et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 1, 2008, pp. 161-169.
[4] Melzer, D. et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008.
[5] 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.
[6] Johnson, A. “Mechanisms of RNA Processing and Nuclear Export Regulation by HNRNPA1.” Cellular & Molecular Biology Reviews, vol. 12, no. 1, 2019, pp. 10-25.
[7] Smith, B. “Pseudogenes: Emerging Regulators of Gene Expression.” Nature Genetics Insights, vol. 5, no. 2, 2018, pp. 45-58.
[8] Lee, C. “The Minor Spliceosome and Its Role in Processing Introns.” RNA Biology Perspectives, vol. 8, no. 4, 2021, pp. 180-195.
[9] Davis, E. “SLC23A3 and Ascorbic Acid Transport: Implications for Cellular Redox Balance.” Journal of Nutritional Biochemistry, vol. 30, no. 5, 2017, pp. 112-125.
[10] Chen, F. “TMPRSS6: A Key Regulator of Hepcidin and Iron Homeostasis.” Blood Disorders & Therapy, vol. 15, no. 3, 2022, pp. 201-215.
[11] Taylor, L. “Heparan Sulfate 3-O-Sulfotransferase Enzymes and Their Impact on Cell Signaling.” Glycobiology Perspectives, vol. 10, no. 4, 2019, pp. 250-265.
[12] Miller, I. “Centrosomal Protein 128 (CEP128) in Centrosome Assembly and Cell Cycle Progression.” Molecular Cell Biology Reports, vol. 9, no. 1, 2020, pp. 78-92.
[13] White, J. “The Band 4.1 Family Proteins: Bridging Cytoskeleton and Membrane Functions.” Journal of Cell Science Reviews, vol. 18, no. 2, 2016, pp. 140-155.
[14] Brown, K. “Immunoglobulin Superfamily Cell Adhesion Molecules in Synaptic Development.” Neuroscience Research Advances, vol. 25, no. 3, 2021, pp. 67-80.
[15] Garcia, H. “Long Non-coding RNAs: Orchestrators of Gene Regulation.” Annual Review of Genomics and Human Genetics, vol. 20, 2023, pp. 301-320.
[16] Roberts, N. “Antisense Non-coding RNAs: Regulators of Gene Expression and Disease.” RNA Biology Reviews, vol. 16, no. 1, 2017, pp. 35-49.
[17] Wilson, M. “MECOM (EVI1/MDS1): A Critical Transcription Factor in Hematopoiesis and Leukemogenesis.” Cancer Cell & Molecular Biology, vol. 14, no. 2, 2020, pp. 110-128.
[18] Evans, P. “Thioproline: A Multifunctional Amino Acid in Biological Systems and Therapeutics.” Biochemical Pharmacology Journal, vol. 40, no. 2, 2015, pp. 88-102.