|Year : 2022 | Volume
| Issue : 1 | Page : 138-144
Immunogenicity assessment of antileukemic agent glutaminase from Escherichia coli, Pseudomonas sp., and Bacillus sp.
Jyotsna Parmar, Shikha Tripathi, Awanish Kumar
Department of Biotechnology, National Institute of Technology, Raipur, Chhattisgarh, India
|Date of Submission||06-Jul-2021|
|Date of Acceptance||16-Nov-2021|
|Date of Web Publication||11-Mar-2022|
Department of Biotechnology, National Institute of Technology, Raipur - 492 010, Chhattisgarh
Source of Support: None, Conflict of Interest: None
Background: L-glutaminase (L-glutaminase or glutamine amidohydrolase: EC 188.8.131.52) is an antileukemic agent which catalyzes the deamidation of glutamine to glutamic acid and ammonia. It is a highly potent antitumor drug solely or in combination with L-asparaginase. In the market, various microbial glutaminases are available, which are used in treatment. The high immunogenicity was reported with microbial glutaminase when they are introduced in the body during the treatment of acute lymphoblastic leukemia (ALL). Methods: This study was aimed to determine the immunogenicity of the less studied enzyme L-glutaminase from Escherichia coli, Pseudomonas sp., and Bacillus sp. to reduce the allergenicity caused by this enzyme. In the present study, we determined the immunogenicity and allergenicity of microbial glutaminase using an immunoinformatics approach to predict immunogenic and allergenic epitopes with their structural analysis also. Results: We found high immunogenicity of glutaminase from these three microbial sources but did not find a significant difference in their immunogenicity, while E. coli glutaminase showed a high relative frequency of allergenic epitopes. Conclusions: In our knowledge, this is the first research report that presented the immunogenic epitopes and structural allergenic determinants that warrant further work to minimize the immune response of glutaminase and could contribute to reducing the side effect and hypersensitivity response of glutaminase during the treatment.
Keywords: Epitope, glutaminase, immunogenic, immunoinformatics, microbial source
|How to cite this article:|
Parmar J, Tripathi S, Kumar A. Immunogenicity assessment of antileukemic agent glutaminase from Escherichia coli, Pseudomonas sp., and Bacillus sp. Biomed Biotechnol Res J 2022;6:138-44
|How to cite this URL:|
Parmar J, Tripathi S, Kumar A. Immunogenicity assessment of antileukemic agent glutaminase from Escherichia coli, Pseudomonas sp., and Bacillus sp. Biomed Biotechnol Res J [serial online] 2022 [cited 2022 Oct 1];6:138-44. Available from: https://www.bmbtrj.org/text.asp?2022/6/1/138/339357
| Introduction|| |
L-glutaminase is an effective antileukemic agent which catalyzes the deamidation of glutamine to glutamic acid and ammonia. Glutamine is very essential for the growth of tumor cells. Inhibition of tumor cell uptake of glutamine is one of the ways to restrict the growth of tumor cells. There is a different application of glutaminase majorly utilized in the food industry as a flavor enhancer, development of a biosensor for determination of glutamine as an analytical agent, and as a therapeutic agent not only in leukemia but very promising anti-HIV agent which inhibits HIV replication in an infected cell. Glutaminase came to attention due to its therapeutic application in cancer; it could be significant for the treatment of acute lymphoblastic leukemia (ALL) as an alternative to or in combination with L-asparaginase. Glutaminase exists in two forms: kidney-type glutaminase and liver-type glutaminase in human-derived from gls1 gene and gls2 gene, respectively. Expression of liver-type glutaminase is limited in the liver and kidney-type glutaminase distributed throughout the body which makes kidney-type glutaminase effective for a different types of cancers.
One of the leading types of malignant disorders is ALL. This disease is generally seen in childhood between 2 and 5 years which decreases with aging. Some of the factors responsible for causing ALL are viral infections, genetic factors, and exposure to chemical carcinogens. Treatment of ALL includes mainly chemotherapy, radiotherapy, and bone marrow transplantation. Chemotherapy involves the use of the enzymatic drug L-asparaginase with glutaminase. The use of this enzyme has some side effects such as pancreatitis and thrombosis, which are mainly due to the side activity of glutaminase. There are various complications associated with glutaminase side activity. It is very necessary to reduce glutaminases side effects for the treatment of ALL with the help of glutaminase alone or in combination with L-asparaginase. Glutaminase is widely distributed in animals, plants, and microorganisms, including bacteria, yeast, and fungi. Microbial sources are more studied for therapeutic enzyme extraction due to their biochemical diversity. Glutaminase is present in different bacterial sources such as Escherichia coli, Pseudomonas sp., Acetobacter sp., Bacillus sp., and Proteus morganii. Glutaminase is produced mostly by E. coli, Pseudomonas sp., and Bacillus sp.,,, The major problem with microbial L-glutaminase is that it is immunogenic and has a short life span. There are various commercial glutaminases available. Some of them are Flavorpro B73P from Bacillus sp. (Biocatalysts, UK), Gln from Bacillus (Kikkoman Corporation, Japan), and glutaminase 1 from E. coli.
The immunogenicity and allergenicity of this enzyme affect the efficacy and safety of biopharmaceuticals. It is needed to develop a new alternative of glutaminase or make a new derivative of it by a structural modification to overcome this disadvantage. For that, bioinformatics tools are very supportive, which are very useful to predict, reduce, and eliminate B-cell and T-cell epitopes from various immunogenic and therapeutic proteins. Intense improving of bioinformatics database and tools for immunogenicity prediction over time made predictions more efficient to complement with studies of molecular dynamic simulations, molecular docking, three dimensional (3D) structure modeling, and inter alia. Currently, various broadly used immunoinformatic tools and databases are available for the prediction of immunogenicity and allergenicity. In the present research, we determine the immunogenicity and allergenicity of glutaminases from E. coli, Bacillus sp., and Pseudomonas sp. associated with their structural analysis.
| Methods|| |
This article does not contain any studies involving human/animals performed by any of the authors.
Protein sequence dataset
FASTA sequence and 3D structure of E. coli, Pseudomonas sp. and Bacillus sp. Glutaminase was studied and downloaded from Protein Data Bank (http://www.rcbs.org/). Their PDB ID is 1U60, 4PGA, and 3AGF, respectively. Their monomer was used for further analysis.
T-cell epitope prediction and calculation of their epitope density
The major histocompatibility complex class II (MHC-II) binding predictions server (http://tools.iedb.org/mhcii/) of the immune epitope database (IEDB) was used for T-cell epitope prediction. For this, the consensus method of this program was selected and eight reference alleles such as HLA-DRB1 × 01:01, HLA-DRB1 × 03:03, HLA-DRB1 × 04:04, HLA-DRB1 × 07:07, HLA-DRB1 × 08:08, HLA-DRB1 × 11:11, HLA-DRB × 13:13, and HLA-DRB1 × 15:15 with wide global frequency were taken for calculating epitope density. Epitope density was calculated using the formula of relative frequency: relative frequency fi = ni/N, where ni = number of epitopes predicted within the threshold (which is ≤10 in this case) and N = total number of epitopes predicted by the program.
Comparative analysis of epitope density
The paired t-test for P < 0.05 and ANOVA were performed to compare the epitope density of all three enzymes with average epitope density with the help of QuickCalc GraphPad Software (San Diego, CA, USA).
B-cell epitope prediction
The B-cell epitope prediction was carried out to predict the antigenic determinants of all three enzymes with the help of the antibody epitope prediction tool (http://tools.iedb.org/bcell/).
Prediction of epitope allergenicity
The allergenicity prediction was carried out to predict the allergenic determinants by AllerTOP v. 2.0 server (http://www.ddg-pharmfac.net/AllerTOP/). Each unique T-cell epitope predicted was processed using this tool. This tool contains 2427 allergens and 2427 nonallergen protein datasets as training datasets which are accessible at http://www.ddg-pharmfac.net/AllergenFP/data.html, which is classified by a machine learning method using a k-nearest neighbor.
For structural analysis of these enzymes, allergenic epitopes predicted by Allertop2.0 software and T-cell epitope predicted by IEDB MHC-II binding site prediction tool were visualized using PyMOL (The PyMOL Molecular Graphics System, Version 2.3 Schrödinger) in the 3D structure of each enzyme.
| Results|| |
Estimating epitope density
In this study, by the concept of epitope density, we calculated the relative frequency of glutaminases from three different bacterial sources, E. coli, Pseudomonas sp., and Bacillus sp., having PDB ID 1U60, 4PGA, and 3AGF, respectively [Figure 1]. No significant difference was observed among these values (P = 0.2826), while Bacillus sp. glutaminase had a high relative frequency among them. For structural analysis, we perform epitope mapping of immunogenic epitopes [Figure 2]. After performing the epitope mapping on the three monomers, we found that the structure of Bacillus sp. glutaminase is covered mostly by immunogenic epitopes than other [Figure 2]e and [Figure 2]f. Multiple sequence alignment of E. coli, Pseudomonas sp., and Bacillus sp. glutaminases is shown in [Figure 3].
|Figure 2: Structural distribution of immunogenic and allergenic epitopes of glutaminase monomer. Red zones represent the immunogenic T-cell epitopes and the blue zones represent the allergenic epitopes in the above image. Image a and b. represent the Escherichia coli glutaminase, image c and d represent the Pseudomonas sp. Glutamiase and image e and f represent the Bacillus sp. glutaminase|
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|Figure 3: Multiple sequence alignment of Escherichia coli, Pseudomonas sp., and Bacillus sp. glutaminases|
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In addition, we performed B-cell epitope prediction to determine the ability of enzymes to generate the antibody. The pattern of a repertoire of all enzymes is shown in [Figure 4],[Figure 5],[Figure 6]. A slight difference was observed in predicted epitopes. According to this result, glutaminase from Pseudomonas sp. will generate more immune response than other glutaminase and E. coli glutaminase will generate the least immune response among these three sources of enzymes. The yellow peaks above the threshold represent the B-cell epitopes. The table below figure contains the predicted B-cell epitope and their respective position in the sequence, as it is returned by the software.
|Figure 4: B-cell epitope distribution patterns for Escherichia coli glutaminase|
Click here to view
Glutaminase is salt tolerable, potent antileukemic agent, and thermostable enzyme. To provide the characteristics of allergenicity to E. coli, Pseudomonas sp., and Bacillus sp., we have explored the structural determinants for immunogenicity prediction at the time of epitope mapping. For this purpose, allergenicity prediction of the epitopes predicted as immunogenic above was carried out, and their mapping showed that allergenic epitope of Bacillus sp. glutaminase covered a greater area than other enzymes in the structure. Both E. coli and Pseudomonas glutaminase have shown almost similar allergenic determinants frequency.
| Discussion|| |
An alternative measure to estimate immunogenicity is epitope density. Epitope density is proportional to the immunogenicity of proteins, i.e., protein with higher epitope density will be more immunogenic. In studies, epitope density has been described as a function of hot spots or regions with enriched MHC-II binding sites. Virus and bacterial peptides having binding specificity with MHC-II and TCR T-cell receptor activate an immune response. They tend to reduce their epitope density by mutation to evade the immune system., There are various immunoinformatic approaches available to calculate T-cell and B-cell-binding sites prediction of proteins which predict the relative ability of a peptide/MHC complex to elicit an immune response or ability to generate the antibody. Several studies have been reported on MHC-II binding site prediction and their epitope density calculation of various therapeutic enzymes and proteins for the development of potent vaccines.,,
As in this study, we found Bacillus sp. glutaminase had a high relative frequency and was covered mostly by immunogenic epitopes than the other three monomers. However, the pattern of B-cell epitope prediction shows that glutaminase from Pseudomonas sp. will generate more immune response than other glutaminase and E. coli glutaminase will generate the least immune response among these three sources of enzymes with the help of induced mutagenesis to deimmunize therapeutic protein by the depletion of T-cell and B-cell epitopes which would be a successful strategy for the production of safe biopharmaceuticals, permitting the development of new techniques for the structural modification with more effective result. There are some bioinformatics tools available for the structural modification of proteins or enzymes. After generating different mutants of glutaminase, testing their ability to elicit immune or hypersensitivity reaction, and finding one with lesser immunogenicity and allergenicity, we test their stability with the help of molecular docking and molecular dynamic simulation studies. The use of these techniques would be helpful to find a more potent therapeutic drug.
Their allergenicity prediction has also been done which shows that allergenic epitope of Bacillus sp. glutaminase covered a greater area than the other three monomers. This study is very helpful at the time of selecting a bacterial source to produce therapeutic glutaminases as a sole drug or in combination with asparaginase. The structural analysis of antigenic determinants of E. coli, Pseudomonas sp., and Bacillus sp. glutaminases that cause hypersensitivity was performed for the first time in this study with the help of bioinformatics tools. As similar to the immunogenicity, allergenic epitope prediction would also be very useful for the development of strategies to reduce allergenicity caused by these enzymes during treatment, and one can make more potential drugs from them.
| Conclusions|| |
In the present research, we applied an immunoinformatic approach to predict immunogenicity and allergenicity of glutaminases from three microbial sources E. coli, Pseudomonas sp., and Bacillus sp., by calculating the relative frequency of all enzymes. This study has insight on structure and location of allergenic and immunogenic epitopes in silico. We found no significant difference in the level of immunogenicity among all enzymes. However, it is observed that E. coli glutaminase has less B-cell epitope present on it than others. Hence, it may be implicit that E. coli glutaminase may generate lesser immune reasons. This insight could have an impact on the design of bacterial glutaminase (which will have less immunogenicity and allergenicity), using various bioengineering approaches viz through the modification of immunogenic or allergenic epitopes or modification of in site-specific amino acids or more extended residues for the development or improvement of therapeutic proteins like glutaminase.
Limitation of study
No limitation in the study samples.
The authors are grateful to the National Institute of Technology, Raipur (CG), India, for providing the facility and space for this work.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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