ASRGL1 Human

What is human ASRGL1 and what is its primary function?

Human asparaginase and isoaspartyl peptidase 1 (ASRGL1) is a member of the plant-type L-asparaginase subfamily that functions as both an L-asparaginase and beta-aspartyl peptidase enzyme. It catalyzes the hydrolysis of L-asparagine to L-aspartate and ammonia. ASRGL1 may be involved in the formation of L-aspartate, which can operate as an excitatory neurotransmitter in certain brain regions . Unlike bacterial L-asparaginases, human ASRGL1 lacks glutaminase activity and requires an activation step through autoprocessing to become catalytically active .

The enzyme is expressed in various human tissues, with particularly high expression noted in testicular tissues. Interestingly, only processed ASRGL1 has been observed in human sperm extract (appearing as 25 and 17 kDa bands on western blots), suggesting tissue-specific regulation of its activation .

How does human ASRGL1 differ structurally and functionally from bacterial L-asparaginases?

Several key differences distinguish human ASRGL1 from bacterial L-asparaginases:

What is known about ASRGL1 expression patterns in human tissues and disease states?

ASRGL1 expression varies across human tissues, with notable expression in testicular tissues where it appears in a fully processed form. In pathological contexts, ASRGL1 has emerged as a biomarker in several disease states:

• Endometrial Cancer: Low ASRGL1 expression intensity correlates with poor disease-specific survival in a dose-dependent manner. Reduced expression is associated with stage II-IV disease, high-grade tumors, lymphovascular space invasion, and deep myometrial invasion .

• Molecular Associations: Low ASRGL1 expression is most prevalent in p53 abnormal carcinomas and is associated with positive L1CAM expression and negative estrogen and progesterone receptor expression .

• Retinal Degeneration: Variants in ASRGL1 have been reported in retinitis pigmentosa patients, suggesting a role in retinal health .

After adjustment for stage and uterine factors in endometrial cancer, strong ASRGL1 staining intensity is associated with a lower risk for cancer-related deaths (hazard ratio 0.56, 95% confidence interval 0.32-0.97) .

What molecular mechanisms govern ASRGL1 autoprocessing, and how can this process be enhanced experimentally?

ASRGL1 activation through autoprocessing is a complex molecular process that requires specific conformational changes:

In the inactive precursor protein, the distance between T168 hydroxyl and carbonyl carbon of G167 is 4.0 Å, which is unfavorable for the chemical events necessary for autoprocessing. This indicates the need for a conformational change to initiate cleavage . The T168A mutation, which prevents autoprocessing, causes a large increase in thermal stability (ΔTM=10°C), suggesting a mechanism of activation by steric tension. This tension results from the orientation of T168 in the inactive protein, where electrons in the methyl group create repulsive forces unfavorable to interaction .

Researchers have discovered several methods to enhance ASRGL1 autoprocessing experimentally:

• Glycine Supplementation: High concentrations of glycine (5 mM) in culture medium promote complete autoprocessing of ASRGL1 in human cell lines. Glycine anchors to the active site through multiple interactions: its amino group forms electrostatic interactions with aspartate 199 (D199) and hydrogen bonds with glycine 220 (G220) and a water molecule; its carboxyl group interacts with arginine 196 (R196) and forms hydrogen bonds with glycine 222 (G222) and water molecules .

• Protein Engineering: Karamitros and Konrad developed a strategy for identifying catalytically improved ASRGL1 variants, resulting in a variant with 6-fold better activity than wild-type ASRGL1 .

• Circular Permutation: The construction of ASRGL1 using circular permutation technique (CP-ASRGL1), in which N- and C-terminal regions are joined by a linker, has provided new insights into autoprocessing mechanisms .

These approaches offer methodological solutions to overcome the low efficiency of ASRGL1 autoprocessing in vitro.

What experimental models are available for studying ASRGL1 function in vivo, and what have they revealed?

Several experimental models have been developed to study ASRGL1 function in vivo, with knockout mouse models providing particularly valuable insights:

ASRGL1 Knockout Mouse Model: Researchers have generated an Asrgl1 knockout mouse model using CRISPR/Cas9 technology to investigate its role in retinitis pigmentosa. This model has revealed several important phenotypes and molecular changes:

• Retinal Degeneration Phenotype: Asrgl1 ablation led to attenuated electroretinogram (ERG) responses around 8 months of age. The thickness of the outer nuclei layer (ONL) began to decrease around 9 months and progressively worsened at 12 and 15 months .

• Cellular Changes: Immunostaining revealed thinner inner segment (IS) and outer segment (OS) layers, along with progressive degeneration of both rod and cone photoreceptor cells .

• Transcriptional Changes: RNA-seq analysis identified 149 differentially expressed genes (DEGs) in Asrgl1 KO retina. These DEGs were linked to various biological processes, including gastrointestinal disease and organismal injury and abnormalities .

• Signaling Pathway Alterations: Glucocorticoid receptor signaling was identified as the most significantly altered canonical pathway in Asrgl1 KO retina. Network analysis revealed several key molecules in the central nodes of interaction, including NFE2L2, IL-4, Foxp3, and Fos .

This mouse model provides a foundation for investigating the molecular mechanisms of ASRGL1-related retinitis pigmentosa and potential therapeutic interventions.

What proteomics approaches are most effective for studying ASRGL1 expression and function?

Modern proteomics offers several sophisticated approaches for studying ASRGL1 expression, processing, and function:

• Liquid Chromatography-Mass Spectrometry (LC-MS): LC-MS methods, particularly those employing MSE or HDMSE data acquisition, are effective for analyzing ASRGL1 in complex biological samples. These techniques enable both identification and quantification of ASRGL1 and its processed forms .

• Data Processing and Algorithms: Software packages like Protein Lynx Global Server (PLGS) and Progenesis can effectively process MSE data for ASRGL1 analysis. PLGS employs a three-stage process using an iterative ion depletion strategy: Apex3D performs noise subtraction and integrates ion current; Pep3D de-isotopes and performs charge-state reduction; and the database search algorithm matches exact mass and retention time (EMRT) clusters to peptides .

• Stable Isotope Labeling: Stable isotope labeling with amino acids in cell culture (SILAC) allows precise quantitation of ASRGL1 expression changes under different experimental conditions. This technique minimizes handling errors and run-to-run variability by processing samples together after differential labeling .

• Immunohistochemistry: For clinical samples, immunohistochemistry is valuable for assessing ASRGL1 expression intensity in tissue sections. In endometrial cancer research, ASRGL1 expression has been scored into four intensity classes, revealing prognostic correlations .

• Western Blotting: Western blotting can specifically detect the processed (25 and 17 kDa) and unprocessed forms of ASRGL1, making it useful for studying the activation status in different tissues or experimental conditions .

These methodologies provide complementary approaches for investigating ASRGL1 from molecular mechanisms to clinical applications.

What are the key challenges in developing human ASRGL1 as a therapeutic alternative to bacterial L-asparaginases for ALL treatment?

Despite the potential advantages of human ASRGL1 as a therapeutic agent, several challenges must be addressed:

• Enzymatic Activity: The primary challenge is ASRGL1's millimolar-range KM for L-asparagine, which is inadequate for therapeutic use compared to bacterial L-asparaginases with micromolar KM values . This lower substrate affinity results in reduced enzymatic efficiency in physiological conditions.

• Autoprocessing Efficiency: ASRGL1 must undergo autoprocessing to become active, which is a low-efficiency process in vitro that results in reduced enzymatic activity . Developing methods to enhance this process or creating pre-activated forms is essential.

• Protein Engineering Requirements: Significant protein engineering efforts are needed to develop ASRGL1 variants with improved catalytic properties while maintaining its advantages as a human protein .

• Therapeutic Dose Determination: Establishing appropriate dosing regimens that achieve therapeutic L-asparagine depletion while minimizing potential side effects requires extensive pharmacokinetic and pharmacodynamic studies.

• Scalable Production: Developing production methods that yield consistently active protein in quantities suitable for clinical application presents manufacturing challenges.

Despite these challenges, the potential benefits of a human L-asparaginase are substantial. Researchers have already made progress, with Karamitros and Konrad developing an ASRGL1 variant with 6-fold better activity than wild-type . The therapeutic potential is further supported by evidence from other human Ntn-hydrolases with antileukemic efficacy, such as AGA, which induced apoptosis in leukemia cells .

How can ASRGL1 expression intensity be effectively measured for prognostic applications in cancer?

The prognostic value of ASRGL1 in endometrial cancer highlights the importance of standardized measurement techniques:

• Immunohistochemistry Scoring: ASRGL1 expression intensity can be scored into four classes (negative, weak, moderate, strong) using immunohistochemistry. This classification has shown significant correlation with disease-specific survival in endometrial cancer patients .

• Integration with Molecular Classification: For optimal prognostic value, ASRGL1 expression should be assessed alongside molecular classification systems such as the Proactive Molecular Risk Classifier for Endometrial Cancer. This integrated approach provides more comprehensive risk stratification .

• Multimarker Panels: ASRGL1 expression should be evaluated in conjunction with other biomarkers, including L1CAM, estrogen receptor, and progesterone receptor, as these markers show significant associations with ASRGL1 status .

• Statistical Adjustments: To establish the independent prognostic value of ASRGL1, statistical analyses should adjust for confounding variables such as stage, grade, lymphovascular space invasion, and myometrial invasion. After such adjustments, strong ASRGL1 staining intensity has been associated with a lower risk for cancer-related deaths (hazard ratio 0.56, 95% confidence interval 0.32-0.97; P = 0.038) .

• Long-term Follow-up: Extended monitoring periods (median of 81 months in published studies) are necessary to establish the relationship between ASRGL1 expression and long-term outcomes .

These methodological approaches enable the effective incorporation of ASRGL1 assessment into cancer prognostication frameworks.