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Microbial Biotechnology (MB)  |  Synthetic Biology and Metabolic Engineering

Microbiol. Biotechnol. Lett. 2024; 52(2): 141-151

Received: September 22, 2023; Revised: September 27, 2023; Accepted: October 8, 2023

A Novel Draft Genome-Scale Reconstruction Model of Isochrysis sp: Exploring Metabolic Pathways for Sustainable Aquaculture Innovations

Abhishek Sengupta1, Tushar Gupta1, Aman Chakraborty1, Sudeepti Kulshrestha1, Ritu Redhu1, Raya Bhattacharjya2, Archana Tiwari2, and Priyanka Narad1*,#

1Systems Biology and Data Analytics Research Lab, Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh-201301, India
2Diatom Research Laboratory, Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh-201301, India
#Current Affiliation: Indian Council of Medical Research, New Delhi

Correspondence to :
Priyanka Narad,

Isochrysis sp. is a sea microalga that has become a species of interest because of the extreme lipid content and rapid growth rate of this organism indicating its potential for efficient biofuel production. Using genome sequencing/genome-scale modeling for the prediction of Isochrysis sp. metabolic utilities there is high scope for the identification of essential pathways for the extraction of byproducts of interest at a higher rate. In our work, we design and present iIsochr964, a genome-scale metabolic model of Isochrysis sp. including 4315 reactions, 934 genes, and 1879 metabolites, which are distributed among fourteen compartments. For model validation, experimental culture, and isolation of Isochrysis sp. were performed and biomass values were used for validation of the genome-scale model. OptFlux was instrumental in uncovering several novel metabolites that influence the organism's metabolism by increasing the flux of interacting metabolites, such as Malonyl-CoA, EPA, Protein and others. iIsochr964 provides a compelling resource of metabolic understanding to revolutionize its industrial applications, thereby fostering sustainable development and allowing estimations and simulations of the organism metabolism under varying physiological, chemical, and genetic conditions. It is also useful in principle to provide a systemic view of Isochrysis sp. metabolism, efficiently guiding research and granting context to omics data.

Keywords: Genome-scale metabolic model, Isochrysis, flux balance analysis, metabolic engineering, aquaculture, COBRA toolbox

Isochrysis, an algal genus, exhibits abilities as an effective source of bioactive compounds and can be utilized for superior-quality biomass for industrial applications in the field of aquaculture. According to a study, Isochrysis is recognized for its exceptional nutritional properties, which comprise omega-3 fatty acids, vitamins, and essential amino acids [1]. Furthermore, Isochrysis is known to synthesize diverse pigments, including fucoxanthin and chlorophyll, DHA, and other compounds that exhibit promising prospects in the domains of food, pharmaceuticals, and cosmetics. The microalgae genus is commonly ingested by various aquatic organisms such as fish, invertebrates, and zooplankton. Due to its elevated nutritional value and ease of large-scale cultivation, the species is a vital agricultural component [2]. However, there exists a gap in understanding the metabolic networks and fluxes of Isochrysis that are currently inadequate, thereby restricting its potential applications [3].

Genome-scale metabolic models (GEMs) have been a fascinating method for understanding metabolism in cells. GEMs have been meticulously curated computationally designed models that depict comprehensive metabolic networks of cells. These models are constructed by utilizing genomic observations and empirical data. The models include curated metabolic reactions that are balanced in mass and associations between genes and proteins that are involved in each reaction. This relationship between genes and proteins is established [4]. Through the utilization of GEM, it is possible to discern potential metabolic pathways that may be pursued to enhance lipid production and optimize the efficacy of biofuel production. The scarcity of flux balance analysis application to Isochrysis sp. can be attributed to the limited extent of prior research on its metabolic model. Additionally, there were no discernible avenues for developing a metabolic model tailored to the Isochrysis sp. as the genome of the species has not yet been sequenced and available in public repositories. To accomplish a draft reconstruction of an organism where sequencing has not been done, the approach is to map it to the most closely related organism for which the GEM is available.

In our work, we chose the most pertinent organism for our study based on phylogenetic resemblance to the organism under investigation, determining Emiliania Huxleyi as the optimal candidate. Through the construction of a metabolic network and subsequent analysis of fluxes in Isochrysis sp. with the aid of Flux balance analysis (FBA), it has become possible to generate predictions regarding the impacts of a diverse range of factors on the organism's metabolism and growth. The ability to predict outcomes is of significant value to researchers who seek to enhance cultivation conditions. This may involve modifying factors including light intensity, temperature, and availability of nutrients to achieve optimal biomass production or target compound synthesis. The results of our study exhibit considerable potential for propelling the domain of nutraceuticals and enhancing health outcomes in the human population. Several metabolites, L-cysteine, malonyl-CoA, EPA, protein, carbon dioxide, glyceraldehyde-3-phosphate, isocitrate, D-erythrose-4-phosphates, L-glycine, 5,10- methylenetetrahydrofolate, glyceraldehyde-3-phosphate, dihydroxyacetone-phosphate, D-fructose-6-phosphates, sedoheptulose-7-phosphate, dihydrofolic acid, dTMP, Dxylulose- 5-phosphates, L-serine, NADPH, pyridoxal, Dfructose, and D-glucose have been identified as having an impact on the organism when modified. In general, examining metabolic networks and fluxes of Isochrysis sp. through a constraint-based modeling approach [5] can yield significant findings regarding the organism's metabolism and potential practical uses. Furthermore, this approach can be useful for directing future research endeavours in this field.

The complete workflow of the protocol being used for the designing and evaluation of the Genome-scale metabolic model (GEM) of the Isochrysis sp. has been demonstrated in Fig. 1.

Figure 1.A concise workflow of the project utilized for designing and recreating the Genome-scale metabolic model of Isochrysis sp.

Reconstruction of metabolic network

The process of reconstructing the genome-scale model of any organism requires a set of pathways and reactions involved in the entire metabolism of the organism. Given the restricted availability of pathway data and complete genome sequence data for Isochrysis sp. in the NCBI database, a Basic local alignment search (BLASTn) search [6] was conducted for the identification of the organism that displayed the closest phylogenetic relationship with Isochrysis sp.

BLASTn search for a similar organism. BLASTn search was conducted to assess the degree of sequence similarity for Isochrysis sp. as the target sequence for the identification of the most closely related species. Emiliana huxleyi was selected as the organism for similarity search against Isochrysis sp. based on the similarity search on the BLASTn. The degree of similarity between the query sequence as compared to E. huxleyi and the database sequence was observed, intending to ascertain the number of similarities. The results indicated that the number of similarities exceeds 90%, with approximately 4097 hits.

Key metabolic Pathway acquisition and collection. The construction of a metabolic model requires the collection of appropriate data concerning an organism's metabolism. This data includes pathways, genes, metabolites, reactions, and compartments of these reactions. To ensure the comprehensiveness and availability of curated data, a range of resources including KEGG [7], BioCyc [8], and MetaCyc [9] were employed. The model's reaction compartments were acquired from the Metabolic Atlas and the pathways were retrieved using KEGG. The model reconstruction utilized the RHEA database [10] as the primary source for the reactions. Pathways and sub-pathways used for the generation of the model are mentioned in Supplementary File 1.

Pathway data annotation and curation. Initially, the entirety of the organism's genome was comprehensively inspected and annotated, in the form of all essential pathways including reactions and genes. The data gathered from diverse database sources were integrated, utilizing unique reaction identifiers and reaction equations, in conjunction with the corresponding genes and their respective compartments.

The format followed for the data annotation of reaction(s) in the model involved equating the unique reaction identifiers corresponding to their respective reaction equation comprising reactants and products along with their respective compartments separated by the directionality indicator and enclosed in the single quotes. An example is mentioned below.

r_0001='lactate[c] + 2ferricytochrome-c[m] -> 2ferrocytochrome- c[m] + pyruvate[c]'

The format followed for the data annotation of gene(s) corresponding to reactions in the model involved equating the unique gene identifier to the gene name and separated by “OR’ if multiple reactions are involved.

grule1='YJR048W OR YDL174C OR YEL039C OR YEL071W',

A similar format was followed for the acquisition of all the genes and reactions.

In the final step, the metabolic pathway reactions and genes were compiled and transformed into a mathematical model that conforms to the COBRA Toolbox's readability standards. Further, the model was enhanced by adding sophisticated reactions, referred to as the objective reaction. In our instance, the objective reaction pertains to the maximization of biomass and constitutes the primary objective of the model. This reaction involves the generation of several biomass precursors. In our scenario, this reaction effectively employs all desired metabolites to synthesize intricate vital metabolites that are of significance to our research. This data of desired metabolites for the biomass reaction is retrieved and validated by the experimental data.

Experimental validation for biomass concentrations

Pure culture of Isochrysis sp. was extracted from water sampled from the southern coast of India through serial dilution and single cell pickup method [11]. The cultures were cultivated and preserved in artificial seawater enriched with f/2 media [12] in 1 L Erlenmeyer flasks having a starting inoculum of 1 × 104 cells ml-1 under constant illumination (100 μmol m-2 s-1) and temperature (25℃) [13]. Every week until the stationary phase, growth was observed through cell count using a hemacytometer (Silverlite, India), and biomass concentration was estimated from the differential dry weights (Eppendorf BioSpectrometer) of the recovered biomass centrifuged at 5000 rpm using a cooling centrifuge (REMI, India) and freeze-dried (-4℃) until later use harvested cultures [14].

Following this, an aliquot of harvested biomass was used to estimate total protein, carbohydrate, and, secondary metabolites like phenol, flavonoids, and tocopherol content [15]. The chlorophyll and carotenoid compositions were estimated spectrophotometrically using 95% acetone algal extracts. The total lipids extracted according to [16] were first estimated gravimetrically (lipid% DW) [17], and then transesterified esterified for analyzing the fatty acid profile via gas chromatography (GC-FiD). The methylated fatty acids reported were identified based on the FAME standard (Supelco Mix, India) [18]. The experimental data obtained by the experimental data has been summarized in Supplementary File 2. This data is used for designing and creating the biomass reaction used as the objective reaction for the Genome-scale metabolic modeling of Isochrysis sp.

Model construction, refinement, and experimental data integration

After the data has been curated in the mathematical model format, it needs to be modeled and analyzed by the process of flux balance analysis (FBA). It is a highly preferred and in-demand methodology for the biochemical analysis of Genome-scale metabolic studies using the GEM reconstruction. After the initialization of the COBRA Toolbox in MATLAB, all the essential pathway data including reactions, genes, and their respective compartments including the biomass reaction and ATP maintenance (ATPm) reaction were imported into COBRA Toolbox in the mathematical format and the data was optimized to create a draft mode. duplicates were removed and the model was checked for blocked reaction. The final model was created and labeled ‘iIsochr964’. The model was then optimized by flux balance analysis to check the optimal growth and yield of the metabolic model. This model can help in simulating and predicting the metabolism of Isochrysis sp. in an insilco environment. This model is accompanied and validated by experimental data and related pathways for the crucial metabolites generated during the Isochrysis sp.'s metabolic processes. The model was then translated to standard SBML version 3 format for easy exportation and downstream analysis in a vast amount of software. The comprehensive code for the design and reconstruction of the model can be accessed on GitHub (accessible at:

Evaluation of model

Just as flux balance analysis is used to analyze the overall biomass production in the model, it can also be helpful to simulate the growth of other essential metabolites by varying the constraints of the reactions associated with the production of that metabolite. This is useful in principle in creating a non-natural environment for the simulation of the growth of the metabolites in the varied environment. Following the completion and refinement of the genome-scale model of Isochrysis sp., it is essential to conduct an analysis of the model for the purpose of strain optimization. This analysis entails the identification of metabolic targets that may be subjected to over or under-expression of reactions based on a set objective reaction. The objective reaction refers to the specific reaction inside a model that is intended to be either maximized or minimized. The objective reaction was maximized/minimized based on the over or underexpression of these several reactions using an opensource and modular software known as OptFlux. In the OptFlux software, distinct projects were established for each of the objective reactions selected for our investigation. These reactions comprised Protein production (Reaction ID: protein), Phenol production (Reaction ID: phenol), DHA production (Reaction ID: dha), EPA production (Reaction ID: epa), Flavonoid production (Reaction ID: flava), Tocopherol production (Reaction ID: toco), and Fucoxanthin production (Reaction ID: fuco). The SBML level 3 format was used to import the model for each project. The “Simulation” tool was used to conduct an initial optimization of the wild-type organism (model with no constraints) under default environmental conditions. The “wild-type” option was selected, and the objective function was selected. The simulation technique employed was Flux Balance Analysis (FBA), and the aim was set to maximize the objective function's value, which illustrates the flow values linked to the wild-type simulation. Following this, changes in environmental circumstances were induced by adjusting the upper and lower limits of flux in the aforementioned reactions, hence imposing limitations for each individual project. The model was constrained by increasing the upper bounds of reactions producing desired metabolites by 50% (1500) and by constraining the bounds of their respective exchange reactions with a 90% reduction (100). By employing the ensuing methodologies, one can identify the reactions that exerted a significant impact on generating desired metabolites. Similarly, the “Simulation” tool was utilized to conduct an optimization of diverse constraints under modulated environmental conditions. The “environmental condition for the varied constraints” option was selected, the objective function was selected, the simulation method was set to FBA, and the objective was set to maximize its value, which illustrates the flow values linked to the constraint-based simulation. Subsequently, the “Simulation comparison” feature of OptFlux was used to compare the wild-type and constraint-based simulations, with the aim of examining the fluxes of all reactions that exhibited variability as a consequence of the diverse limitations imposed within the model environment. The flow of related reactions in Wild-Type simulation exhibited diverse responses when limitations were applied. The change of reaction bounds in the reactions corresponding to desired metabolites and resulting in varied flux is illustrated in Supplementary S3.

The reconstruction of Isochrysis sp. was initiated through the compilation of data about the metabolic pathways of a similar organism Emiliania huxleyi. The study performed a compilation of gene lists, reaction equations, and formulae, along with their corresponding reaction IDs, from publicly available sources. The information was carefully curated and annotated in a mathematical format that was compatible with the COBRA Toolbox. The refinement of the reconstruction model was achieved through the incorporation of exchange reactions and the elimination of all instances of duplicated and blocked reactions. Subsequently, the Flux Balance Analysis was executed on the model to anticipate the growth and progression of the model and essential metabolites in constrained environments. This application of constraints was helpful for identifying potential targets within the genome-scale model of Isochrysis sp. that facilitates the modification of said targets to amplify the production of target metabolites.

General features of the refined recreated Genome-Scale metabolic model (GEM) of Isochrysis sp.

The final Isochrysis sp. model was constructed and a nomenclature for future reference was given as ‘iIsochr964’. The model consists of 964 genes, 4315 reactions, and 1879 metabolites, which are distributed among fourteen compartments namely cytoplasm, mitochondrial membrane, endoplasmic reticulum, nucleus, golgi, peroxisome, vacuole, cell envelope, lipid particle, golgi membrane, and mitochondrion. The reactions and gene data of the initial draft and final refined Genome-scale metabolic model for the Isochrysis sp. model (iIsochr964) are provided in Supplementary S4. Fig. 2 shows the number of reactions present in different compartments of cells of the Isochrysis sp.

Figure 2.Graphical representation of the total number of reactions present in different compartments of the cell.

The refined model included the Objective reaction (in our case Biomass reaction), ATP Maintenance reaction, and removal of duplicates and blocked reactions. The final refined model in the MATLAB (.mat) and SBML (.xml) format can be available upon request. The .mat file may be directly imported into the MATLAB environment with the COBRA Toolbox add-on and may be visualized and analyzed. The .xml file in the SBML format file has applications in various other visualizing webbased and standalone programs.

Model validation in OptFlux

On varying the bounds of the essential reactions and pathways related to desired metabolites, the changes in the flux values of different reactions have been illustrated in Fig. 3, Fig. 4, and Fig. 5. The data has been summarized in Supplementary S5.

Figure 3.Comparison of flux values in Wild Type simulation vs. Constraint-based simulation with the objective function set to (A) Total Biomass reaction, (B)Total Protein, (C) Phenol.
Figure 4.Comparison of flux values in Wild Type simulation vs. Constraint-based simulation with the objective function set to (A) Docosahexaenoic acid (DHA), (B) Eicosapentaenoic acid (EPA).
Figure 5.Comparison of flux values in Wild Type simulation vs. Constraint-based simulation with the objective function set to (A) Fucoxanthin, (B) Tocopherol, (C) Flavonoids.

Upon imposition of constraints on various reactions, a significant enhancement of 100 mmol/g dry cell weight/ hr was noted in the flux values of the “Protein” and “EPA” producing reactions.

The heightened frequency of protein synthesis in microalgae may suggest the adaptation to alterations or stressors in their surroundings, given that various proteins often participate in stress responses and cellular defense mechanisms. The phenomenon has the potential to impact the physiological processes, functional abilities, and ecological interactions of microalgae. The enhancement of protein levels results in an increase in nutrient density and desirability for a large number of essential metabolites, including aquaculture feed and nutritional supplements. Eicosapentaenoic acid (EPA), these noteworthy metabolites that influenced the organism, included malonyl-CoA, EPA, protein, carbon dioxide, glyceraldehyde-3-phosphate, isocitrate, D-erythrose- 4-phosphates, L-glycine, 5,10-methylenetetrahydrofolate, glyceraldehyde-3-phosphate, dihydroxyacetonephosphate, D-fructose-6-phosphates, sedoheptulose-7- phosphate, dihydrofolic acid, dTMP, D-xylulose-5-phosphates, L-serine, NADPH, pyridoxal, D-fructose, and Dglucose. The pathway map linking the pathways of the Protein and EPA has been depicted in Fig. 6. The Genome-scale metabolic model (GEM) ‘iIsochry964’ can be used by metabolic engineers to identify and modulate pathways, reactions, and genes of the Isochrysis specie metabolism for the over/under the production of the desired metabolite.

Figure 6.Pathway model establishing relations among different pathways used for model reconstruction.

Isochrysis is a member of the Prymnesiophyceae class, the Isochrysidales order, and the Isochrysidaceae family. The high nutritional profiles of Isochrysis sp. typically result in diverse applications within the biotechnology and nutraceutical sectors due to the presence of a vast number of essential macronutrients and micronutrients, including proteins, carbohydrates, lipids, vitamins, and minerals, found within them. Isochrysis sp. is utilized in aquaculture feed, biofuel production, bioremediation, and research and development. They also exhibit increased concentrations of different omega-3 fatty acids, including docosahexaenoic acid (DHA) (Matos et al., 2019), which is considered essential for human wellbeing [19]. They are known to efficiently remove surplus nutrients like nitrogen and phosphorus from agricultural runoff and wastewater, thereby mitigating water pollution. These species assimilate heavy metals and other contaminants, thereby contributing to the remediation of polluted aquatic environments [20]. This study of Genome-scale Metabolic modeling aims to develop a well-curated insilico genome-scale metabolic model (GEM) model with the aid of the computational biology approach of Flux balance analysis (FBA) that may be useful metabolic engineering to study the environmental constraints that promote the formation of the desired metabolite in the Isochrysis sp. metabolism. The study commenced with the selection of the most similar organism to our target organism due to the lack of metabolic data in the public repository about the Isochrysis genus. After the BLAST procedure for the target species, E. huxleyi, a similar diatom microalga was selected as the reference organism with over 90% similarity to the Isochrysis genus for pathway data extraction and curation. With the aid of multiple online databases and repositories, the pathway and subpathway data extracted related to the metabolism of the E. huxleyi metabolism. The pathway data extracted contained a pool of reactions, a set of genes, and their respective compartments. The extracted data was processed, curated, and annotated in the mathematical format to be read in the modeling software MATLAB. Experimental validation was conducted to understand the Isochrysis metabolism and metabolites being formed during the metabolism of the species. The experimental data were further used for designing the Biomass reaction for the model which further was assigned as the objective reaction for the model. The data including reactions, genes, and compartments were aligned according to the mathematical format, and biomass reaction and ATP Maintenance reaction were added to the stoichiometry of the model. The Constraint-based Reconstruction and Analysis (COBRA) Toolbox was initialized, and the data was integrated in the MATLAB environment. The data was validated for reaction stoichiometry and reconstructed in the metabolic model format. The model was then optimized, and the draft model was reconstructed. The model was further refined by removing duplicate and blocked reactions and integrating the model with the Objective (biomass) reaction and ATP maintenance reaction. The final refined model was named ‘iIsochr946’. The model was later converted to a highly applicable SBML format for further validation and analysis in the OptFlux software. The OptFlux software utilized the Flux Balance Analysis (FBA) algorithm in the user-friendly interface for the application of constraints and modulation of the environment for the growth of the compounds of interest. In this study, the application of OptFlux resulted in a notable elevation in the production rate of Protein and EPA, thereby exerting a significant influence on the organism. The increased frequency of protein synthesis in microalgae may suggest that they are adapting to alterations or stressors in their surroundings, given that proteins often participate in stress responses and cellular defense mechanisms. Along with Protein and EPA, several other compounds also showed variation in the Flux during the model progression that included Malonyl-CoA, dTMP, D-xylulose-5-phosphates, L-serine, NADPH, Pyridoxal, D-fructose, L-glycine, D-fructose-6- phosphates, Glyceraldehyde-3-phosphate, Sedoheptulose- 7-phosphate, EPA, Protein, Carbon dioxide, Isocitrate, L-cysteine, dihydrofolic acid, D-erythrose-4 -phosphates, 5,10-methylenetetrahydrofolate, dihydroxyacetonephosphate, glyceraldehyde-3-phosphate, and D-glucose. There is a need for the collection, curation, and assembling of more experimentally derived and validated data related to algae species and the generation of more databases that may boost the study and analysis of algae and other organisms in exploitation in the industrial application. A similar approach used in this study may be applied to designing metabolic models for other organisms and employed in metabolic engineering. The genome-scale reconstruction of Isochrysis, as described in the paper, is presented as an initial version. Our objectives are centered on subjecting this model to thorough experimental validation in the near future, ultimately leading to the presentation of a conclusive genome-scale reconstruction. The computational predictions made by this model are of utmost importance in this endeavour. The use of technologies such as OptFlux to generate predictions plays a pivotal role in establishing a fundamental basis for further study and investigation. The insights provided by these findings are of great use in understanding the metabolic pathways and regulatory networks that regulate Isochrysis sp. They serve as a good guide for the development of focused experimental designs. As we go towards experimental verification, the computational findings will serve as a guiding force, enriching our understanding of Isochrysis at a genomic scale. Utilizing kinetic networking, pathway analysis, and metabolic modeling is crucial for fully understanding and utilizing the hidden potential of various microbial species and their impact on humans [21, 22]. Therefore, this first draft reconstruction signifies a crucial advancement towards a thorough understanding, with computational methods acting as our guiding principle in navigating the complex realm of Isochrysis's biology.

In our study, we aimed to reconstruct the genomescale metabolic model of novel Isochrysis sp. For GEM reconstruction, we chose the most closely related organism E. Huxleyi. iIsochr964 model consists of 4315 reactions, 934 genes, and 1879 metabolites in 14 compartments, which in principle represents the metabolic constitution of the organism. We performed the validation of the model by identifying metabolic targets for over/under-expression of reactions based on a set objective reaction, utilizing the Flux Balance (FBA) analysis approach using OptFlux. The experimental simulation included reactions linked to ‘Total Biomass production’, ‘Protein production’, ‘Phenol production’, ‘DHA production’, ‘EPA production’, ‘Flavonoid production’, ‘Tocopherol production’, and ‘Fucoxanthin production’ reactions to gain effective understanding in the utility of the GEM reconstruction. The study revealed a notable increase in the production of metabolites, particularly in Eicosapentaenoic acid (EPA) and Proteins, which hold significant importance in marine algae. The results are suggestive that further experimental data would be useful for validating the knockout sets experimentally. The incorporation of regulatory mechanisms into GEMs will be beneficial for the users for predicting strategies of optimization of the strain more effectively. iIsochr964 could also be employed for other purposes such as metabolic engineering and can be utilized to investigate and evaluate different targets that have the property to enhance the creation of desired metabolites through target reactions and metabolites within the complete pathway.

The comprehensive code for the design and reconstruction of the model can be accessed on GitHub (accessible at: The model files in (.mat) and (.xml) format can be made available upon request.

Designing the research and method: Abhishek Sengupta, Priyanka Narad, Archana Tiwari; Performed the literature search: Tushar Gupta, Aman Chakraborty, and Sudeepti Kulshrestha; Performed the manual curation of data through literature: Tushar Gupta, Aman Chakraborty, Ritu Redhu, Raya Bhattacharya, Sudeepti Kulshrestha; conducted the data compilation: Raya Bhattacharya, Tushar Gupta, Aman Chakraborty, Sudeepti Kulshrestha, Ritu Redhu; Supervised the work and edited manuscript: Abhishek Sengupta, Priyanka Narad, Archana Tiwari. All the authors have read and approved the final manuscript.

We would like to acknowledge Dr. Ashok K. Chauhan, Founder President, Amity University Uttar Pradesh for providing us the opportunity to conduct research. We would also like thank Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University for providing us necessary resources.

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