Bile Salt Activated Lipase
Background
Section titled “Background”Bile salt activated lipase (BAL), also known as carboxyl ester lipase (CEL), is an enzyme that plays a crucial role in the digestion and absorption of dietary lipids. It is secreted primarily by the pancreas into the small intestine, and it is also a significant component of breast milk, where it aids in fat digestion for infants.
Biological Basis
Section titled “Biological Basis”CELis responsible for hydrolyzing a broad range of lipid substrates, including cholesterol esters, fat-soluble vitamin esters, and triglycerides, particularly those protected by bile salt micelles in the small intestine. Its enzymatic activity is dependent on the presence of bile salts, which activate the enzyme by promoting a conformational change. This action breaks down complex lipids into simpler molecules, such as free fatty acids and cholesterol, making them available for absorption by intestinal cells.
Clinical Relevance
Section titled “Clinical Relevance”Variations in the CEL gene or its activity can have clinical implications. Dysregulation of CEL activity may contribute to conditions related to lipid metabolism, such as malabsorption of fats or, conversely, impaired lipid clearance. For instance, reduced BAL activity can lead to steatorrhea (excess fat in feces) due to incomplete digestion of dietary fats. Altered activity might also be implicated in the development or progression of metabolic disorders, as efficient lipid processing is fundamental to overall metabolic health.
Social Importance
Section titled “Social Importance”The enzyme’s presence and activity in breast milk highlight its importance in infant nutrition, ensuring efficient fat digestion and nutrient uptake during early development. Understanding the genetic and functional aspects of CEL can inform dietary recommendations, particularly for individuals with specific genetic variants that affect its function, potentially leading to personalized nutritional strategies or therapeutic interventions for lipid-related disorders.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genetic studies often face limitations due to moderate cohort sizes, which can result in insufficient statistical power to detect modest genetic associations and increase the susceptibility to false negative findings. The identification of additional sequence variants for gene discovery is directly contingent upon larger sample sizes and enhanced statistical power. [1] Conversely, the extensive multiple testing burden inherent in genome-wide association studies (GWAS) necessitates stringent significance thresholds and rigorous independent replication to mitigate the risk of false positive findings and confirm reported associations. [2]
Replication efforts across different cohorts can sometimes prove challenging, failing due to factors such as inconsistent direction of effect, issues with high-quality imputation, or an inability to reach genome-wide significance levels in follow-up studies. [3]Furthermore, current GWAS platforms, by assaying only a subset of all known single nucleotide polymorphisms (SNPs), may inadvertently miss relevant genes or variants due to incomplete genomic coverage, thereby limiting the comprehensive study of candidate genes and the full elucidation of genetic architecture.[4]
Generalizability and Phenotype Heterogeneity
Section titled “Generalizability and Phenotype Heterogeneity”A significant limitation in many genetic studies is the predominant focus on populations of European ancestry, with individuals from non-European backgrounds often excluded from analyses. [1] This narrow demographic scope restricts the generalizability of findings to diverse populations, as patterns of linkage disequilibrium and allele frequencies can vary substantially across different ancestral groups, impacting the transferability and replication of genetic associations. [3]
Variability in phenotype definition, measurement protocols, and adjustment strategies across different cohorts can introduce substantial heterogeneity, complicating meta-analyses and the interpretation of combined results. For instance, some studies may exclude outlier individuals or adjust for specific covariates like age squared, while others may omit these steps or lack data on critical confounders such as lipid-lowering therapy. [1] Additionally, the specific type of genetic variation considered, such as SNPs versus non-SNP variants like repeat sequences, can impact the ability to assess previously reported associations if not consistently captured or comparable across studies. [2]
Unaccounted Factors and Remaining Knowledge Gaps
Section titled “Unaccounted Factors and Remaining Knowledge Gaps”Despite the identification of numerous associated genetic loci, a complete understanding of the genetic architecture underlying complex traits remains elusive. For some phenotypes, specific genetic variants may prove uninformative, indicating gaps in current knowledge regarding their genetic determinants and underlying biological mechanisms. [3] Further research is essential to fully elucidate the functional pathways through which identified variants influence traits and to discover additional genetic contributors. The exploration of sex-specific genetic effects, which may be obscured by sex-pooled analyses, also represents a critical area for future investigation to uncover a more comprehensive picture of genetic influence. [4]
Variants
Section titled “Variants”Genetic variations play a crucial role in influencing an individual’s metabolic profile, including the activity of enzymes like bile salt activated lipase (BAL) and overall lipid metabolism. Variants within or near genes involved in fundamental cellular processes can have widespread effects on health. For instance, the geneGTF3C5(General Transcription Factor IIIC Subunit 5) is part of a complex that regulates the transcription of various genes, which is a fundamental step in gene expression. Single nucleotide polymorphisms (SNPs) such asrs56272996 and rs8193016 within or near GTF3C5 could potentially alter the efficiency of gene transcription, thereby indirectly affecting the expression of numerous other genes, including those involved in lipid metabolism. Similarly, FXYD5 (FXYD Domain Containing Glycosylphosphatidylinositol Anchor Protein 5) is involved in regulating the activity of the Na+/K+-ATPase, an essential ion pump that maintains cellular electrolyte balance and volume. A variant like rs12110 in FXYD5 could impact this critical cellular function, leading to downstream effects on metabolic pathways and potentially contributing to dyslipidemia, as common genetic variations are known to influence lipid levels. [1] Such broad regulatory and cellular maintenance functions are often implicated in the complex interplay of pathways that determine an individual’s susceptibility to metabolic traits. [5]
The CELgene, which encodes bile salt-activated lipase (BAL), is directly responsible for the enzyme’s function in digesting dietary fats and fat-soluble vitamins. Variants in this region are of particular interest due to their direct relevance to BAL activity. TheCELP gene is a pseudogene located near CEL, and intergenic variants such as rs605990 , situated between CEL and CELP, may influence the expression levels or functional efficiency of the active CELgene. Alterations in BAL activity, whether due to changes in its expression or protein structure, can significantly affect lipid digestion and absorption, subsequently impacting circulating levels of triglycerides and cholesterol. This direct link to lipid metabolism makes such variants key contributors to an individual’s lipid profile, which is a significant determinant of cardiovascular disease risk.[6]Studies have consistently identified numerous genetic loci that influence concentrations of high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and triglycerides.[1]
Other variants located in intergenic regions also contribute to the complex genetic landscape of metabolic regulation. For example, rs77132496 is found in the region between CELP and RALGDS (RAS Like GDS). While CELP is a pseudogene, RALGDSis a guanine nucleotide dissociation stimulator for Ral GTPases, which are involved in various cellular processes including signaling and membrane trafficking, indirectly affecting metabolism. Similarly,rs146984623 is located between GBGT1 (Glycosphingolipid Beta-1,3-N-Acetylgalactosaminyltransferase 1) and OBP2B (Odorant Binding Protein 2B). GBGT1 plays a role in glycosphingolipid biosynthesis, and these lipids are integral components of cell membranes and are involved in cell recognition and signaling. Variations in genes related to lipid modification and cellular signaling pathways, even those not directly encoding lipases, can have an indirect yet significant impact on overall lipid homeostasis and metabolic health. [7] The collective influence of such variants underscores the polygenic nature of lipid disorders and their complex relationship with various biological pathways. [5]
Furthermore, the variant rs342293 is situated in the intergenic region between CCDC71L (Coiled-Coil Domain Containing 71 Like) and LINC02577 (Long Intergenic Non-Protein Coding RNA 2577). CCDC71L encodes a protein with coiled-coil domains, often indicative of roles in protein-protein interactions, while LINC02577 is a long non-coding RNA. LncRNAs are increasingly recognized for their regulatory functions in gene expression, acting at transcriptional or post-transcriptional levels to influence protein production. A variant in this region could therefore affect the expression or function of CCDC71L, or alter the regulatory activity of LINC02577, thereby modulating the expression of other genes involved in metabolic processes. Such regulatory effects can indirectly impact the efficiency of lipid processing and the activity of enzymes like BAL, contributing to variations in an individual’s lipid profile and metabolic health. [1] The broad associations observed in genome-wide studies emphasize how diverse genetic loci, including those involving non-coding RNAs and regulatory proteins, collectively shape metabolic traits. [6]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs56272996 rs8193016 | GTF3C5 | bile salt-activated lipase measurement |
| rs605990 | CEL - CELP | bile salt-activated lipase measurement |
| rs77132496 | CELP - RALGDS | bile salt-activated lipase measurement chymotrypsin-like elastase family member 2A measurement |
| rs146984623 | GBGT1 - OBP2B | blood protein amount bile salt-activated lipase measurement |
| rs342293 | CCDC71L - LINC02577 | platelet count platelet volume mitochondrial DNA measurement platelet aggregation CASP8/PVALB protein level ratio in blood |
| rs12110 | FXYD5 | FXYD5/ICA1 protein level ratio in blood FXYD5/IRAK1 protein level ratio in blood FXYD5/PRDX3 protein level ratio in blood FXYD5/SPRY2 protein level ratio in blood FXYD5/TANK protein level ratio in blood |
Biological Background
Section titled “Biological Background”References
Section titled “References”[1] Kathiresan, S et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, 2008, PMID: 19060906.
[2] Benjamin, E. J. et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, no. 1, 2007, p. 54.
[3] 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. 520-528.
[4] 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, no. 1, 2007, p. 55.
[5] Sabatti, C et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, 2008, PMID: 19060910.
[6] Aulchenko, Y. S. et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nat Genet, vol. 40, no. 2, 2008, pp. 182-188.
[7] Gieger, C et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, 2008, PMID: 19043545.