The Multifaceted Role of AASDH: From Metabolic Regulation to Cancer and Cardiomyopathies

Cellular and Molecular Functions of AASDH

ATP binding and lysine metabolism. Lysine metabolism represents a fundamental biochemical pathway involved in cellular stress responses, with diverse roles across metabolic and regulatory processes. Lysine is catabolized through the saccharopine pathway, which plays a role in abiotic and biotic stress responses (2). When a stressful event occurs in tissues or cells, AASDH, the third enzyme in the saccharopine pathway, is significantly up-regulated. This indicates that AASDH is strongly related to osmotic stress response (3). Saccharopyropine dehydrogenase catalyzes the initial stages of lysine catabolism, although the enzyme’s name is derived from the reverse reaction. Condensing lysine with α-ketoglutarate to produce a Schiff base is one such process. Saccharopine is then formed by reducing the Schiff base. In this phase, the Schiff base contains the keto group of α-ketoglutarate and the side-chain amino group of lysine. The α-ketoglutarate moiety separates in a later enzymatic step, removing the amino group and resulting in the production of glutamic acid. At this stage, the lysine catabolite α-amino adipic acid-d-semialdehyde is produced. Two molecules of CO₂ and one molecule of acetoacetic-CoA are the end-products of this pathway (4).

Acid thiol ligand activity. Investigation of global changes in bacterial protein expression has revealed the up-regulation of aldehyde dehydrogenases after exposure to environmental and chemical stressors, which are critical components of general stress response pathways in bacteria and oxidative stress responses. For example, the exposure of Escherichia coli to low levels of the oxidant hydrogen peroxide (H₂O₂) renders bacteria less vulnerable to the toxic effects of normally lethal H₂O₂ concentrations. This presumably occurs because of the induction of cytoprotective mechanisms. H₂O₂-exposed E. coli were also protected from normally toxic doses of several reactive aldehydes, including formaldehyde, glutaraldehyde, glyoxal, methyl glyoxal, and chloroacetaldehyde (5). Furthermore, the up-regulation of AASDH in plants and bacterial cells under stress conditions supports its role in stress response mechanisms. These observations support the notion that aldehyde metabolism is an important component of the bacterial cytoprotective response to oxidative stress, and thus the same applies for cancer cells (6). Furthermore, its role in pyridoxine-dependent seizures provides information on the brain abnormalities associated with this condition, as well as methods for prenatal detection, mainly through analysis of urine metabolites (6).

A type of epileptic encephalopathy, known as pyridoxine-dependent epilepsy (PDE), is identified by the clinical or electroencephalogram (EEG) response to pyridoxine. ALDH7A1 encodes the enzyme α-AASDH, which is essential for pyridoxine metabolism (7). Human antiquitin functions as an α-AASDH dehydrogenase in the pipecolic acid pathway of lysine catabolism. Mutations in the ALDH7A1 gene, which encodes an antiquitin, prevent the antiquitin from acting as a D1-piperideine-6-carboxylate (P6C)–(a-AASDH) in children with PDE. Pyridoxal 5½-phosphate (PLP) is rendered inactive by P6C accumulation through the formation of a Knoevenagel condensation product (8). Nevertheless, pyridoxine-dependent epilepsy, such as hyperprolinemic type I, can have other causes (9). Pyridoxine is used in pharmacological amounts to treat secondary PLP insufficiency; as a result, pyridoxine is essential for managing PDE. Approximately 75% of all patients have intellectual or developmental delay (IDD), which is observed despite early diagnosis and adequate seizure control (10). Given the relatively poor outcomes, testing for PDE-ALDH7A1 is recommended in individuals with seizures of unknown etiology, infants and children with partially responsive seizures, and those under one year of age without identifiable brain malformations (11). Elevated plasma or urine α-AASDH/Δ1-P6C and elevated pipecolic acid levels have been found in all patients with PDE-ALDH7A1; however, it should be noted that individuals with peroxisomal diseases may also have high levels (12). This highlights that α-AASDH/Δ1-P6C is the preferred biomarker for this condition. As a result, measuring urine a-AASDH offers a straightforward method for verifying the diagnosis of PDS, whereas analyzing the ALDH7A1 gene permits a prenatal diagnosis.

Possible Role of AASDH in Malignancies

CRC is a major contributor to cancer-related illnesses and deaths worldwide, representing approximately 7% of new cancer cases and 11% of cancer fatalities (13). In 2019, there were 2,166,169 new CRC diagnoses and 1,085,798 deaths attributed to CRC. The global rise in CRC incidence and mortality indicates significant shifts in its epidemiology (14). Approximately 15% of CRCs exhibit Microsatellite instability (MSI), with 3% being inherited and the remaining 12% being sporadic. This instability results from defects in the DNA mismatch repair (MMR) system, which is crucial for correcting errors in newly synthesized DNA (13).

AASDH encodes an enzyme thought to activate β-alanine, and it is located on chromosome 4, a region that shows copy number loss in early onset CRC. In addition to well-known MSI target genes, AASDH is frequently affected by clonal insertions and deletions (indels) in tumors (in 78 out of 93 tumors). Deletion of two thymine bases in a three-thymine microsatellite has been observed in this gene in familial CRC of unknown origin. MSI of CRCs have also shown resistance to the chemotherapy drug, 5-fluorouracil (5-FU), the main metabolite of which is α-fluoro-β-alanine. Inactivation of AASDH might impact the processing of this metabolite (13).

Recent studies have suggested a possible association between AASDH and MSI cancers (13), Although these findings are promising, further studies are essential to validate the pathological role of this gene in MSI-CRC. A deeper understanding of this gene could open new avenues for treatment strategies, potentially improving the management of patients with MSI-CRC.

Physiologically, AASDH provides protection against oxidative and hyperosmotic stress (15) and pathologically, AASDH up-regulation has been observed in patients presenting with various disorders, ranging from epileptic encephalopathy (15) to cardiomyopathies and malignancies (15-17). Investigating the pathogenesis of the involvement of the AASDH gene in various pathologies is required to elucidate the mechanisms by which the gene expression levels, and subsequent levels of the enzyme alter the progression of the pathologies, which will allow further investigation of the potential of the gene and the enzyme to be utilized as a biomarker for prognosis as well as a therapeutic target.

Despite CRC, it has been found that, AASDH plays a multifaceted role in other malignancies including hepatocellular carcinoma and lung adenocarcinoma (15, 16). The role of this gene is explained by its involvement in lipid metabolism, which is essential for the survival and proliferation of malignant cells, as tumor cells revert to metabolizing lipids to support their rapid proliferation (15).

In HCC, the expression of the AASDH gene is up-regulated and is significantly linked to tumor progression, advanced disease stages, and poorer overall and disease-free survival (15). Therefore, in addition to the potential of AASDH gene being used as a prognostic biomarker, its contribution to the tumor immune microenvironment as a regulator of lipid metabolism may serve as a possible therapeutic target. It negatively correlates with immune cell infiltration, which suggests that it plays a role in immune cell modulation, and might serve as an indicator of clinical outcomes (15).

Studies on lung adenocarcinoma (LUAD) have also revealed an association with the AASDH gene through a different mechanism, as the gene undergoes back-splicing, forming a circular RNA molecule termed circ-AASDH, which is considered oncogenic as it plays a significant role in tumor progression (16). Circ-AASDH was significantly up-regulated in tumor cells and tissues, and patients who exhibited higher expression levels were found to have larger tumor sizes, poorer prognosis, and more advanced clinical stages (16). This correlation was mechanistically explained by circ-AASDH acting as a sponge for miR-140-3p, which relieves its inhibitory effect on the expression of the gene responsible for the oncogenic transcription factor E2F7, leading to tumor progression (16).

The involvement of AASDH in metabolic pathways suggests a possible role in tumor biology, which warrants further investigation for potential therapeutic implications. The metabolic support that tumors depend on may be disrupted by inhibiting AASDH or its downstream signaling pathways, which may increase the efficacy of current treatments. Moreover, the correlation between AASDH expression and tumor stage and prognosis across various cancer types makes it a potentially useful biomarker. In cases of HCC and LUAD, monitoring AASDH levels may help stratify patients for individualized treatment regimens.

Role of AASDH in Cardiomyopathy

Cardiomyopathy is a disease of the heart muscle that affects its ability to effectively pump blood. There are three main types of cardiomyopathies, including hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and restrictive cardiomyopathy (RCM) (18).

Al-Hassanan et al. (2020) identified AASDH as a novel candidate associated with childhood-onset cardiomyopathy (19). In a study involving a consanguineous population, researchers found that homozygous variants of the AASDH gene were present in patients with cardiomyopathy. This finding suggests that AASDH plays a role in the development of cardiomyopathy, particularly in populations with high rates of consanguinity (19). Additionally, it highlights the importance of adopting a categorized population-sensitive genetic approach to unravel the genetic causes of childhood-onset cardiomyopathy. Researchers have used a combination of targeted genetic tests and whole-exome sequencing to identify genetic variants associated with the condition. The high prevalence of homozygous variants in the AASDH gene and other novel candidates underscores the need for further research to understand the mechanisms by which these variants contribute to cardiomyopathy (19).

Moreover, Kong et al. (2023) provided evidence that AASDH may be a potential biomarker for ischemic cardiomyopathy-induced heart failure through a Weighted Gene Co-expression Network Analysis (WGCNA), emphasizing its possible role in pathways related to mitochondrial damage and lipid metabolism disorders (20).

Therefore, identification of the AASDH gene as a potential contributor to cardiomyopathy opens new avenues for research and genetic testing. Understanding the role of AASDH in cardiomyopathy could lead to improved diagnosis and treatment options for patients with this condition. As genetic research continues to advance, it is crucial to explore the complex interactions between genes and their effects on heart health.

In Silico Analysis

AASDH PPI network, GSEA, and ceRNA network. In silico analysis was performed to 1) construct the protein-protein interaction (PPI) network of AASDH, 2) conduct gene set enrichment analysis (GSEA) to identify associated biological pathways, and 3) build a competing endogenous RNA (ceRNA) regulatory network. The ceRNA network was specifically generated to provide insights into the regulatory circuit of AASDH in post-transcriptional regulation as well as the potential for function in cellular pathways. The AASDH PPI network, consisting of 11 nodes and 38 degrees, was constructed using string database (21) and visualized using the Cytoscape software (22) (version 3.9.1; Figure 1A). To evaluate the functional annotation and pathway enrichment of AASDH and its interacting genes, we performed a gene enrichment analysis utilizing the Enrichr database (23). With adjusted p-value <0.05, 69 biological processes (BP), 19 molecular functions (MF), and nine KEGG pathways were identified. Fatty acid biosynthesis, long-chain fatty acid-CoA ligase activity (GO:0004467), and fatty acid biosynthesis were the top significant BP, MF, and KEGG pathways, respectively, which indicate its crucial role in lipid metabolism.

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Figure 1.

(A) AASDH PPI network. (B) Identified miRNAs targeting AASDH based on mirDB, MirTarBase, and TargetScan databases. Four miRNAs were common between three databases. (C) AASDH ceRNA network.

To enhance the reliability of predicted miRNA–mRNA interactions, we adopted a consensus-based approach by integrating results from the three databases: miRDB, miRTarBase (27), and TargetScan (28), following established multi-database integration methods (24, 25). A total of 71, 261, and 115 miRNAs targeting AASDH were identified using the three databases, respectively.

By intersectional analysis, 13 miRNAs (hsa-miR-98-3p, hsa-let-7a-3p, hsa-miR-607, hsa-miR-95-5p, hsa-miR-605-5p, hsa-let-7b-3p, hsa-let-7f-1-3p, hsa-miR-4715-3p, hsa-miR-3064-3p, hsa-miR-3688-3p, hsa-miR-4436b-5p, hsa-miR-3671, and hsa-miR-936) were found common between the three databases (Figure 1B). Additionally, 302 long non coding RNAs (lncRNAs) were found sponging the identified miRNAs using DIANA-LncBase v3 database (29). Among the lncRNAs, MALAT1 had the most interactions (5 interactions) with the identified miRNAs. Finally, a competing endogenous RNA (ceRNA) network was constructed including AASDH, 13 miRNAs targeting AASDH, and 16 lncRNAs, which sponge ≥3 identified miRNAs (Figure 1C).

Beta-Alanine-activating enzyme and its isoforms. The beta-alanine-activating enzyme, encoded by the AASDH gene, is a human protein with a sequence length of 1,098 amino acids (the canonical isoform). It functions as an ATP-dependent enzyme, catalyzing the covalent attachment of beta-alanine to its phosphopantetheine group, forming a thioester bond and transferring it to an unidentified acceptor molecule. This enzyme is hypothesized to be involved in post-translational protein modification or post-transcriptional RNA modification. Functionally, it exhibits acid-thiol ligase activity and ATP binding and plays a role in amino acid activation for nonribosomal peptide biosynthesis, beta-alanine metabolism, and fatty acid metabolism.

Several ATP-binding sites are crucial for the enzyme’s function, specifically located at residues 198-206, 428, 442, and 527. Additionally, multiple natural variants have been identified, with significant single nucleotide polymorphisms (SNPs) at positions 61, 93, 368, 747, 774, 865, and 1030, based on the data from the Uniport (30). The enzyme undergoes post-translational modifications, including sumoylation at lysine residues 544 and 745, phosphorylation at serine residues 589, 649, and 724, and pantetheine 4′-phosphorylation at serine 589.

Structurally, AASDH belongs to the ATP-dependent AMP-binding enzyme family, with a carrier domain spanning residues 553-630. This protein shares sequence similarities with other AMP-binding enzymes and falls under conserved domain families, including AMP-binding domains and phosphopantetheine-binding domains, which are critical for its catalytic activity.

To further explore the structural and functional diversity of AASDH, we computationally modeled its known isoforms using AlphaFold (31, 32). These isoforms vary considerably in length and composition (Figure 2). The canonical isoform (Q4L235), referred to as the beta-alanine-activating enzyme, consists of 1,098 amino acids. The second isoform (E9PH98) has a sequence length of 86 amino acids, and the third isoform (D6RJA2) has a slightly longer sequence length of 120 amino acids. The fourth isoform (R4GNB1) has a significantly longer sequence length of 945 amino acids. The variation in sequence length across these isoforms suggests alternative splicing or functional domain differences, leading to potential differences in enzymatic activity, substrate specificity, or intracellular localization.

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Figure 2.

Computationally mapped isoforms and structural features of the beta-alanine-activating enzyme (AASDH). (A) Isoform Q4L235, (B) isoform D6RJA2, (C) isoform R4GNB1, and (D) isoform E9PH98. The figure illustrates the per-residue confidence score (pLDDT) used by AlphaFold to predict the reliability of structural models. The confidence score ranges from 0 to 100, with different color codes representing varying levels of confidence: Dark blue (pLDDT >90): Very high confidence, indicating well-structured and reliable regions. Light blue (90> pLDDT >70): High confidence, signifying mostly well-structured regions with some flexibility. Yellow (70> pLDDT >50): Low confidence, suggesting potentially flexible or unstructured regions. Orange (pLDDT <50): Very low confidence, typically indicating intrinsically disordered regions or regions that may not adopt a stable structure in isolation.