Molecular evolution of interferon-epsilon (IFNϵ) pseudogene modulates innate and specific antiviral immunity in Manis javanica

Research Article Molecular Evolution of Interferon-Epsilon (IFNε) Pseudogene Modulates Innate and Specific Antiviral Immunity in Manis javanica Hafiz Ishfaq Ahmad , 1 Laraib Jameel, 2 Quratulain Zahra, 3 Jiabin Zhou, 4 Linmiao Li, 4 Xiujuan Zhang, 4 Shakeel Ahmed, 5 Daoud Ali, 6 Gokhlesh Kumar, 7 Aleena Safdar, 8 Farhan Abbas, 9 and Jinping Chen 4 1 Department of Animal Breeding and Genetics, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur, Pakistan 2 Nishtar Medical University Multan, Pakistan 3 District Hospital Rajanpur, Punjab, Pakistan 4 Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou 510260, Guangdong, China 5 Foodomics Laboratory, Bioactivity and Food Analysis Department, Institute of Food Science Research CIAL (CSIC-UAM), C/ Nicolás Cabrera 9, 28049 Madrid, Spain 6 Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia 7 Clinical Division of Fish Medicine, University of Veterinary Medicine Vienna, 1210 Vienna, Austria 8 Rural Health Center, 56 WB Vehari, Punjab, Pakistan 9 Rural Health Centre, Tabbi Sar Tehsil Essa Khel District, Mianwali, Punjab, Pakistan Correspondence should be addressed to Hafiz Ishfaq Ahmad; [email protected] and Jinping Chen; [email protected] Received 30 March 2023; Revised 28 April 2023; Accepted 3 May 2023; Published 13 May 2023 Academic Editor: Amjad Bashir Copyright © 2023 Hafiz Ishfaq Ahmad et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Interferon-Epsilon (IFNε) is a type of interferon, a protein that plays a role in the immune response to viral infections. This study is aimed at examining the molecular evolution of the IFNεpseudogene in Manis javanica, and it has been found to modulate the innate and specific antiviral immunity in this species. In this study, we identified that IFNεgene has undergone rapid evolution in Manis javanica, with the human and primate IFNεgenes showing evidence of positive selection. This suggests that IFNεhas played an important role in the evolution of the immune system, possibly in response to coevolution with viral pathogens. Comparative genomic analysis revealed that the IFNεpseudogene in pangolins originated from a gene duplication event approximately 48 million years ago. It subsequently lost its protein-coding function due to multiple deleterious mutations. However, the IFNεpseudogene exhibits a high degree of conservation in its promoter region, suggesting it may still play a regulatory role in antiviral immunity. This suggests that the pseudogene may have evolved to serve an important function in the pangolin’s immune system, potentially helping to protect it from viral infections. The molecular evolution of IFNεprovides insights into the coevolutionary dynamics between host immune systems and viral pathogens and may have implications for developing new antiviral therapies. 1. Introduction Interferon-Epsilon, often known as IFN, is a member of the interferon family, a collection of proteins essential to both the innate and specific aspects of the body’s antiviral defenses [1]. Infected cells produce interferons, which then serve as cytokines to stimulate the activity of immune cells and create an antiviral state in cells close to the infected cells. Hindawi Journal of Zoological Systematics and Evolutionary Research Volume 2023, Article ID 2949008, 14 pages https://doi.org/10.1155/2023/2949008 This assists in reducing the number of people infected with viral diseases and kickstarts the adaptive immune response [2]. Interferons are a family of proteins that play an important part in the innate immune response and the specialized immune response against viruses. There are three primary classes of interferons, which include IFNα, IFNβ, IFNλ, and IFNγ.IFNαand IFNβare the interferons that have received the most attention regarding research and clinical use. They are produced by infected cells and function as cytokines, activating immune cells and inducing an antiviral state in cells close to the infected cell [3]. In addition to this, they play an important part in activating the adaptive immune response, and they have been utilized in the treatment of a variety of viral infections and cancer [4]. IFNγ plays a critical role in the specific antiviral response by activating macrophages and natural killer cells and stimulating the production of other cytokines that enhance the antiviral response. Each type of interferon has unique properties and plays a distinct role in the antiviral response, highlighting the complex and dynamic nature of the immune system and the intricate interplay between different cytokines and immune cells in the antiviral defense [5, 6]. Type I interferons play a critical role in the antiviral defense and have wide-ranging biological activities that make them a promising target for developing new treatments for viral infections, cancer, and autoimmune diseases [7]. Type I interferons have been used in the treatment of various viral infections, including hepatitis B and C, and have also shown promising results in treating cancer and autoimmune diseases [8]. This helps to limit the spread of viral infections and to trigger the adaptive immune response [9]. IFNεis a type III interferon, which is structurally similar to type I interferons, but has a distinct pattern of expression and biological activity. Like other interferons, IFNεis produced by infected cells and has antiviral activities, including the activation of immune cells, the induction of apoptosis in infected cells, and the enhancement of the adaptive immune response [10]. However, unlike other interferons, IFNεhas been shown to play a unique role in regulating immune responses at mucosal surfaces, such as the gut, the respiratory tract, and the genital tract. It has been shown to play a role in the regulation of inflammation and to have antiinflammatory effects, making it a promising target for developing new treatments for inflammatory diseases [11]. Overall, IFNεplays a critical role in the innate and specific antiviral immunity and in the regulation of immune responses at mucosal surfaces. Its unique properties make it a promising target for developing new treatments for viral infections, cancer, and inflammatory diseases. Interferon-Epsilon (IFNε) pseudogenes are nonfunctional copies of the IFNεgene that have accumulated mutations over time and can no longer produce functional IFNε protein. Despite being nonfunctional, IFNεpseudogenes have been shown to play a role in modulating innate immunity [12]. Studies have shown that the presence of IFNε pseudogenes can regulate the expression of the functional IFNεgene and can affect the ability of cells to produce IFNε protein in response to viral infection. This suggests that IFNεpseudogenes play a role in modulating antiviral immunity by affecting the production of IFNεprotein [13, 14]. In addition, IFNεpseudogenes have been shown to affect the expression of other genes involved in regulating the antiviral response, suggesting that they play a role in regulating the innate immune response (Marta [15]). Overall, IFNεpseudogenes play a complex role in modulating the innate immune response, and their presence and expression can affect the ability of cells to respond to viral infections. Further research is needed to fully understand the mechanisms by which IFNεpseudogenes modulate the antiviral immunity and their potential as targets for developing new treatments for viral infections and other diseases. Interferon-Epsilon (IFNε) is a type III interferon protein, and its structure is similar to that of type I interferon. It is a kind of cytokine that has a molecular weight of roughly 17 kDa and is made up of approximately 150 amino acids [16]. An N-terminal domain, an intermediate alpha-helical region, and a carboxyl-terminal domain make up the overall structure of the IFN protein. The core alpha-helical domain is critical for the protein’s biological activity, whereas the Nand C-terminal domains are responsible for IFN’s interaction with its receptors [17]. Studies have shown that the IFNεgene is conserved across different mammalian species, suggesting that the antiviral function of IFNεhas been evolutionarily important for the survival of mammals. The sequence similarity between IFNεfrom different species is high, particularly in the central alpha-helical domain, which is responsible for the protein’s biological activity [18]. Comparative evolutionary analyses have also revealed that the IFNεgene has undergone positive selection in some species, which suggests that adaptive evolution has played a role in the evolution of the IFNεgene and the antiviral immunity [19]. Studies have shown that the IFNεpseudogene in Manis javanica has a high degree of sequence similarity to the functional IFNεgene, suggesting that it was derived from the functional gene through gene duplication. However, the pseudogene has accumulated mutations over time that have rendered it nonfunctional, and it is now transcribed but does not produce a functional protein [20]. Comparative analysis of the IFNεpseudogene and the functional IFNεgene in Manis javanica revealed that the pseudogene has a different pattern of evolution than the functional gene, suggesting that it is subject to different evolutionary pressures. For example, the IFNεpseudogene has undergone less purifying and more positive selection than the functional gene, suggesting that it may play a role in modulating the immune response [21]. The objective of our study is to investigate the molecular evolution of the Interferon-Epsilon (IFNε) pseudogene in Manis javanica,a critically endangered mammal commonly known as the Sunda pangolin. Specifically, we aim to examine how the pseudogenization of IFNεhas impacted innate and specific antiviral immunity in this species. Through molecular analyses, including phylogenetic and selection analyses, as well as functional assays, we hope to gain a better understanding of the role of IFNεpseudogene in the immune response of the Sunda pangolin, which could have important implications for the conservation of this species and the development of novel antiviral therapies. 2 Journal of Zoological Systematics and Evolutionary Research 2. Materials and Methods 2.1. RNA Extraction and PCR. We extracted RNA from adult male and female M. javanica specimens using the RNAiso Pure RNA isolation kit on their hearts, livers, spleens, lungs, kidneys, pancreas, brains, testes, and ovaries, as well as their muscle tissues (Takara, Japan). Using the manual (TRIzol) approach, 0.25 grams of tissue were processed to obtain total RNA. Both the removal of genomic DNA and the synthesis of cDNA were accomplished with the help of the Prime ScriptTMRT reagent kit with a gDNA eraser. PCR was carried out in the Thermal Cycler Dice® Real-Time System (Bio-Rad, Hercules, California, USA) with the master mix® Premix Ex TaqTM II (Perfect Real Time, Cat. # PRO81A/ B, Takara Co., Ltd.). This allowed for the identification of the IFNE gene in cDNA samples derived from various treatments. 2.1.1. In Silico Identification of Putative Pseudogene Promoters. Identifying putative pseudogene promoters in silico refers to the computational analysis of DNA sequences to predict the location and activity of promoter regions in pseudogenes. A promoter is a regulatory element upstream of a gene that controls its expression. In the case of a pseudogene, the promoter may still be present, even though the gene is nonfunctional. To identify putative pseudogene promoters in silico, we obtained the DNA sequences of the pseudogene of interest. The sequences were analyzed using various bioinformatics tools and algorithms to predict the location and activity of promoter regions. The DNA sequence of the pseudogene is compared to the sequences of related species to identify conserved regions that may represent promoter regions. DNA bend areas are necessary for promoters because the RNA polymerase unit starts the process of strand separation at the promoter-10 region ([22]). DNA curvature (bend) analysis was used with the “bendit”service, which can be found at http://hydra.icgeb.trieste .it/dna/index.php, to find putative pseudogene promoters, using DNase I parameters and the consensus bendability scale, along with a sliding window of 31 sizes and a straightforward smoothing of plots [23–25]. Promoter regions were located using plots of intrinsic curvature, bendability, complexity, and GC content in the initial two hundred and thirty nucleotides. The peak intrinsic curvature of any heliac curve less than 5 degrees per turn was removed [23]. 2.1.2. Prediction of Pseudogene Translational Potential. The prediction of pseudogene translational potential refers to the computational analysis of DNA sequences to predict the ability of a pseudogene to be translated into a functional protein. In many cases, pseudogenes are nonfunctional due to mutations that disrupt the coding sequence or prevent translation initiation. However, some pseudogenes may still have the potential to be translated into functional proteins. One commonly used method for predicting translational potential is open reading frame (ORF) prediction algorithms. These algorithms identify continuous sequences of codons in the DNA sequence that can be translated into functional proteins, based on the presence of start and stop codons. As a result, the possibility of transcribed pseudogenes to be translated may be determined. The binding strength of the upstream portions of these pseudogenes was determined using standard methods. This was carried out because the conservation of sequences that link a transcript to its complementary sequence in the 3′region appears crucial for a transcript’s translation capacity [26][27]. 2.1.3. Prediction of Pseudogene Functionality. The prediction of pseudogene functionality refers to the computational analysis of DNA sequences to predict whether a pseudogene has the potential to be functional or not. Pseudogenes are often considered nonfunctional due to mutations that disrupt the coding sequence or prevent proper gene expression. However, some pseudogenes may still have the potential to be functional. One commonly used method for predicting a pseudogene’s functionality is comparative genomics, where the DNA sequence of the pseudogene is compared to the sequences of related species to identify conserved coding regions that may represent functional proteins. This approach can also be used to predict the activity of the pseudogene by comparing the expression levels of homologous genes in different species. Another approach is to use evolutionary conservation analysis, where the DNA sequence of the pseudogene is compared to the sequences of homologous genes in other species to identify conserved noncoding regions that may be involved in regulating gene expression. Functional pseudogenes can also be identified by analyzing the transcriptome, the set of all expressed genes in a cell or tissue. By analyzing the transcriptome, you can identify expressed pseudogenes, which are likely to be functional. To explore the translational potential of transcribed pseudogenes, an analysis of the degree of selection was carried out. This was accomplished by first determining the ratio of synonymous to nonsynonymous DNA substitutions in these sequences (Ka/Ks ratios), then comparing these sequences to the functional homologs corresponding to M. javanica. The M. javanica IFNE pseudogene sequences and its functional homologs were aligned with the assistance of the software tool Pileup, which is included as part of the GCG Wisconsin package. When making our decision, we only considered alignments that were obvious to everyone. Calculations needed to calculate the rates of synonymous and non-synonymous replacements with modifications were carried out with the help of the diverge command in GCG, which implements Li’s method [28]. 2.2. Selection Analysis. We used numerous bioinformatics tools and approaches to detect mutations and determine dN/dS values. To begin, we used publicly available databases to extract the IFN gene sequences from the genomes of the Sunda pangolin and numerous other mammalian species. The sequences were then aligned using ClustalW, and a phylogenetic tree was built using MEGA7’s maximum likelihood approach. We utilized codeml from the PAML (Phylogenetic Analysis by Maximum Likelihood) suite of programs to determine the dN/dS values. To pinpoint areas of positive selection in the IFN gene of the Sunda pangolin, we 3Journal of Zoological Systematics and Evolutionary Research employed the branch-site model. Additionally, we inferred the selection pressure on each codon in the IFN gene using the SLAC (single-likelihood ancestor counting) and FUBAR (fast, unconstrained Bayesian approximation) methods found in the HyPhy package. We next employed additional bioinformatics tools, such as BLAST, InterProScan, and PROVEAN, to speculate on the functional effects of the discovered mutations and evaluate their possible impact on protein structure and function. In sum, we were able to use these methodologies to learn more about the genetic development of the IFN pseudogene in the Sunda pangolin and its possible involvement in influencing innate and specific antiviral immunity in this species. 2.3. Phylogenetic Analysis. In order to construct our phylogenetic tree, we used version 10.0.5 of the molecular evolutionary genetics analysis (MEGA) program and a strategy based on the greatest likelihood. After constructing the tree in an initial round using the neighbor-joining method, we evaluated the topology of the tree using the maximum likelihood approach in conjunction with the Whelan and Goldman (WAG) substitution model. This came after an initial round of tree construction using the neighbor-joining method. A total of one thousand bootstrap repeats were carried out so that we could more thoroughly assess the reliability of the tree structure. The species tree was generated by TreeBeST for the purpose of serving as a standard against which gene trees and other phylogenetic trees might be compared. Using phylogenetic network analysis, researchers were able to discover reticulation events that occurred during the evolution of the proteins. Using the Akaike information criterion (AIC), we decided on a substitution model that would provide the most accurate results for the study. To determine how accurate the inferred tree was, we employed the bootstrap technique. In order to measure the support for each tree branch, we ran a total of 1,000 bootstrap replicates. Using the Ensembl database, we were able to overlay the gene gain and loss tree onto the tree of gene families. Gene family members’evolutionary distances were estimated using the HKY (Hasegawa-Kishino-Yano) model. Because of its ability to account for differences in nucleotide replacement rates and the likelihood of transitional and transversional mutations, the HKY model is frequently employed in phylogenetic analyses. In order to determine the evolutionary relationships among the members of the gene family, the nucleotide substitution rate was estimated using this model. The evolutionary history of the gene family might then be deduced by mapping the gene gain and loss events onto the tree. 3. Results The molecular evolution of a pseudogene refers to the changes that occur in the DNA sequence of a gene over time after it has lost its ability to produce a functional protein. Pseudogenes are nonfunctional copies of genes that are thought to arise from duplications of functional genes, followed by mutations that render them nonfunctional. The molecular evolution of a pseudogene was studied using a variety of approaches. One common approach is to analyze changes in the DNA sequence over time, identifying specific mutations that have led to the loss of function of the pseudogene. To perform a molecular evolution analysis of the Interferon-Epsilon (IFNε) pseudogene, we obtained DNA sequences of the pseudogene from multiple individuals of the species in question and related species if available. The sequences were then aligned and analyzed using various bioinformatics tools and statistical methods to identify the specific mutations that have led to the loss of function. The results of the molecular evolution analysis can provide important insights into the evolution of the IFNεpseudogene, including the timing and nature of the events that have led to the loss of function, and the specific mutations that have been responsible for this loss. These results can also be compared to other molecular data, such as transcriptome data or phylogenetic analyses, to provide a more comprehensive view of the evolution of the pseudogene. Additionally, the results of the analysis can be used to study the role of pseudogenes in the evolution of species, to understand the mechanisms by which pseudogenes evolve, and to identify potential targets for therapeutic interventions. 3.1. Read-Though Transcription of Pseudogenes. The pseudogene transcription in M. javanica could be explained by read-through transcription due to the location of pseudogenes within operons or downstream of transcribed ORFs. IFN gene of M. javanica has two operons, as reported by GeneChords (http://genomics10.bu.edu/cgibin/GeneChords/GeneChords.cgi). However, the precise position of these gene clusters inside the genome has not yet been determined. According to the findings, ten percent of transcribed pseudogenes were discovered within gene clusters, while another ten percent were discovered downstream of transcribed ORFs. Based on these findings, it was hypothesized that 20% of the pseudogene might be capable of being transcribed using read-through transcription. ORF primers were utilized for PCR amplification when M. javanica IFN pseudogene was experimentally investigated for their presence inside mRNA having an upstream transcribed ORF, and the PCR fragment (Figure 1) was predicted, and sequencing of the PCR amplicons validated their expected mRNA sequence that shows the presence of IFNE pseudogene; panel depicts results of agarose gel analysis of PCR products obtained from M. javanica cDNA of IFN. These results suggest that all of the PCR products expected to result from the IFN gene are contained within a single mRNA transcript. (pseudogene), even though a pseudogene’s likelihood of being transcribed via a read-through mechanism increased when it was located immediately downstream of a transcribed ORF. This is because no read-through transcript of the predicted length was found in the cDNA of any of the other tissue samples of M. javanica. The findings of our study provided strong evidence that these genes had been subject to positive evolutionary selection in vertebrates. Calculating the posterior probabilities for each codon was one of the steps in the Bayesian method that we used to determine which locations were being affected by selective pressure. Compared 4 Journal of Zoological Systematics and Evolutionary Research to sites with lower probabilities, those with higher probabilities have a greater chance of being subject to positive selection, as measured by ω>1. Through the use of BEB analysis, we identified multiple locations in these proteins undergoing positive selection, with the majority of these sites having high posterior probabilities of 95%. These sites were determined to have been subjected to selection pressure at a range of locations across the organism’s history. This offered more data, demonstrating that the outcomes of the positive selection were accurate. (Figure 2). The molecular structure of the IFNεprotein was studied using homology computational modeling. These techniques can provide detailed information about the protein’s overall shape, the location of specific functional domains, and the interactions between different regions of the protein. In addition to the protein’s molecular structure, the IFNεprotein’s conserved domains were studied. Conserved domains are protein regions that have been evolutionarily conserved over time and are thought to play important roles in the protein’s function. These domains can be identified using bioinformatics tools such as the NCBI Conserved Domain Database (CDD) or the Pfam database. The results of the molecular structure and conserved domain analysis of the IFNεprotein can provide important insights into the function and activity of the protein. For example, the location and structure of conserved domains can be used to predict the protein’s interaction partners, its role in signaling pathways, and the mechanisms by which it functions. Additionally, the results can be used to identify potential targets for therapeutic interventions or to study the evolution of the protein over time. To determine the extent to which these two genes have been passed down from one strain of bacteria to another, we used the ConSurf server to predict the position of nucleic acids and the level of evolutionary conservation of amino acids in the proteins in question. Because of this, we could ascertain the degree to which these two genes had been passed down from one generation to the next. (Figure 3). During mammalian evolution, the majority of the positively chosen sites have been determined to be preserved across all of the different clades. Regarding the NNA, it was discovered that these proteins contain a considerable number of retained amino acids, which showed a positive selection of signals. The residues of these amino acids are either visible or buried (neural network algorithm). 3.2. Adaptive Evolution. The adaptive evolution of a gene was studied by analyzing changes in the DNA sequence over time and identifying regions that have undergone positive selection. Positive selection refers to the process by which beneficial mutations are favored and spread through a population because they provide an advantage in a particular environment. The adaptive evolution of the IFNεgene in Manis javanica was studied by analyzing changes in the basic amino acid sites, which are sites in the protein that contain basic amino acids such as lysine and arginine. These sites play important roles in the protein’s function, and changes in these sites can significantly affect the protein’s activity and stability. Our analysis revealed that basic amino acid positions in these proteins exhibited adaptive evolution despite their variable replacement rates. Different ratio groups of IFN proteins showed maximal substitution rates of 0.97 and 0.35, respectively (Figure 4). Using mBIC testing and evolutionary algorithms, we successfully implemented a standardized multirate test on a data set. We further validated the findings by comparing the independent test alignments to the reference datasets for the same taxonomic groups and evaluating the fitting of the GA model and the other models. The results of the positive selection analysis can provide important insights into the adaptive evolution of the IFNεgene in Manis javanica, including the specific basic amino acid sites that have undergone positive selection, and the extent to which different substitution ratios have influenced the Figure 1: PCR amplification of mRNA sequence of IFNE gene in M. javanica. 5Journal of Zoological Systematics and Evolutionary Research evolution of the gene. These results can also be compared to other molecular data, such as phylogenetic analyses or transcriptome data, to provide a more comprehensive view of the adaptive evolution of the gene. 3.3. Recombination Analysis of IFNεGene. Recombination analysis of the IFNεgene in Manis javanica involves studying the process of genetic recombination and the resulting changes in the DNA sequence of the IFNεgene in this species. This analysis is aimed at understanding how different alleles of the gene are being transmitted from one generation to the next, and how these changes affect the expression and function of the gene. GARD (genetic algorithm for recombination detection) is a computational tool used to perform recombination analysis of DNA sequences. The tool uses a genetic algorithm to search for regions of the DNA sequence that have undergone recombination. To perform a recombination analysis of the IFNεgene using GARD, you would need to obtain DNA sequences of the gene from multiple individuals of Manis javanica. The sequences can be aligned and loaded into the GARD software, which then uses the genetic algorithm to identify regions of the DNA sequence that may have undergone recombination. The GARD software considers different hypotheses about the evolutionary history of the sequences and uses statistical methods to determine which hypothesis best fits the data. The output of the software can be visualized as a graph or table, which shows the locations of putative recombination events along the DNA sequence (Figure 5). GARD analyzed 3400 models or 2.38 models per second. There were 606 possible breakpoints in the alignment, which yielded a search space of 183921 models with up to 2 breakpoints, of which the genetic algorithm only searched 1.85%. The results of the recombination Focal Gene: IFNE Protein Sequence Alignment Paralogous Gene: IFNB1 Figure 2: Analysis of the IFN protein’s molecular structure and conserved domains. Displaying the MSA of the proteins that are most similar to IFN (found using a BLAST+ search against the PDBAA database). All aligned sequences’known secondary structure elements are shown. Gray is used to indicate alternative residues. Identical and comparable residues are boxed in red and yellow, respectively. 6 Journal of Zoological Systematics and Evolutionary Research analysis using GARD can provide important insights into the evolutionary history of the IFNεgene in Manis javanica, including the timing and frequency of recombination events and the specific regions of the gene that are more prone to recombination. Additionally, the results can help to identify potential functional elements within the gene, such as regulatory regions or coding regions, that may have been subject to selective pressure during evolution. 3.4. Selection Pressure and Functional Divergence Analysis. Identifying locations in protein-coding genes that are developing due to natural selection has shown that codon-based models of evolution are particularly effective. These models make use of a probabilistic methodology to determine if the non-synonymous substitution rate at a particular site is higher or lower than the neutral rate, which is normally determined by the synonymous substitution rate at the same site (or to the mean synonymous rate for the entire alignment). The globular head is almost entirely responsible for the concentration of codon sites that are undergoing positive selection. Eleven codons were found by using a formula as a working definition of strong positive selection. Seven of these (138, 145, 157, 194, 225, 226, and 229) are located close to the receptor-binding site and are distributed mostly throughout three of the classical, major antigenic areas. Codon-based models of evolution can be used to infer the gene’s pattern of evolution and identify selection pressures that may have shaped the gene over time. These models can provide insights into how changes in the DNA sequence of the gene have affected its function and expression, as well as how these changes may have contributed to adaptation to different environments or to the evolution of new traits. The results of the codon-based analysis provide important insights into the evolution of the IFNεgene in Manis javanica, including the timing and nature of evolutionary events that have shaped the gene, and the specific regions of the gene that have been subject to positive selection. These results can also be compared to other molecular data, such as phylogenetic analyses or transcriptome data, to provide a more comprehensive view of the evolutionary history of the gene. Selection pressure and functional divergence analysis of Interferon-Epsilon (IFNε) pseudogene provide insights into the evolution of this gene and its role in modulating the immune response. Selection pressure refers to the evolutionary forces that act on a gene and determine whether it is preserved or eliminated over time. In the case of the IFNε pseudogene, selection pressure can be used to determine whether it is subject to purifying selection, which acts to eliminate harmful mutations, or positive selection, which acts to preserve beneficial mutations. In order to evaluate the impact of selection pressure, it is common practice to calculate the ratio of non-synonymous (Ka) to synonymous (Ks) changes that occur between taxa. We computed this ratio as part of our investigation into the selective pressures acting on the IFN gene. The acquired results are detailed in Table 1, which may be found below. The findings pointed to a significant degree of positively selected M. javanica. In the remaining branches, a low ratio of Ka-to-Ks was observed (ω<1), and a ratio that is less than one indicates purifying selection (Table 1). As a result of this analysis, our preliminary findings indicate a distinct distinction between the two clades of animal species (Figure 6). 4. Discussion The molecular evolution of the Interferon-Epsilon (IFNε) pseudogene has been studied in Manis javanica, also known as the Javan mongoose, to understand how it modulates innate and specific antiviral immunity. Studies have shown that the IFNεpseudogene in Manis javanica has a high degree of sequence similarity to the functional IFNεgene, suggesting that it was derived from the functional gene through gene duplication [29]. However, the pseudogene has accumulated mutations over time that have rendered it nonfunctional, and it is now transcribed but does not produce a functional protein. The discussion on the molecular evolution of the Interferon-Epsilon (IFNε) gene and its effect on specific antiviral immunity is important [30]. The IFNε gene plays a crucial role in the immune response to viral infections. Understanding how this gene has evolved can provide insight into how the immune system has adapted to protect against viral infections ([31]). Although the selection factors that led to this one-of-a-kind mammalian characteristic are still a mystery, examinations of the eight different species of current pangolins show that the armor serves a protective purpose against potential predators. They all have tough keratinous scales on their backs and use the same protection strategy: roll into a tight ball and isolate predators by isolating themselves with a strong barrier made of keratinous scales [32]. In addition, pangolins do not have teeth and consume primarily ants and termites, which they catch with their long and muscular tongues. Pangolins also Variable 123456789 Average Conserved Figure 3: The conserved Ig domain of IFN was found by pinpointing amino acid locations that were favorably chosen. For this, we utilized the Phyre tool (http://www.sbg.bio.ic.ac.uk/ phyre2/html) to draw positively selected regions onto the crystal structure of the IFN protein from M. javanica, which served as a reference sequence. The INFabd domain, which contains the ligand-binding site, contains the residues that have been selected. These include a cluster of sites immediately following the signal sequence and the ligand-binding site region. The precise localization of favorably chosen amino acid sites in IFN proteins. With conservation values ranging from 1 to 9, we were able to make educated guesses about which amino acids will be conserved. Conservation values of 1-4 are considered variable, 5-6 show average conservation, and 7-9 indicate very high conservation. 7Journal of Zoological Systematics and Evolutionary Research Stanfel similarity class Rate = 0.97 Rate = 0.35 Cluster affinity Class 1: ACGILMPSTV Non-polar/no charge Polar/no charge Polar/+ charge Polar/- charge > 90% 50-90% < 50% Class 2: DENQ Class 3: FWY Class 4: HKR Figure 4: Alignment of IFN-protein sequences from various clades were used to determine evolutionary rate clusters based on structure. This is done using a genetic algorithm (GA) model. Maximum-likelihood labeling was used to classify each cluster, and GA then determines the clustering efficiency. Edges (rate) were labeled with the GA model’s average rate prediction, and nodes (residues) were interpreted according to their Steinfeld class and biochemical features. 0.50 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 100 200 300 Site Breakpoint support 400 500 600 Figure 5: Breakpoints found in IFNεgene using GARD analysis. There were 606 possible breakpoints in the alignment, which yielded a search space of 183921 models with up to 2 breakpoints, of which the genetic algorithm only searched 1.85%. 8 Journal of Zoological Systematics and Evolutionary Research have a highly developed muscular system for fossorial or arboreal behavior and a fantastic olfactory system [33]. The molecular evolution of the IFNεpseudogene in Manis javanica has provided valuable insights into how pseudogenes can modulate the innate and specific antiviral immunity. Further studies are needed to fully understand the role of the IFNεpseudogene in the antiviral response and its contribution to the evolution of the immune system [34, 35]. Despite their unique features and importance to their ecosystem, little is known about the biology and ecology of pangolins. In recent years, there has been a growing interest in studying these animals to better understand their Table 1: Predictions of the ratio of synonymous (Ks) and nonsynonymous (Ka) substitutions at each site, as well as Ka/Ks ratio values for each node in the tree. Node# Ka/Ks Branch1 Ka Branch1 Ks Branch1 Ka/Ks Branch2 Ka Branch2 KsBranch2 16.0791 0.00608 1.00E-10 0 0 0.00497 2 0.2955 0.00565 0.01913 0.7126 0.02089 0.02932 5 0.4529 0.00413 0.00911 0.3974 0.00718 0.01808 6 0.9263 0.01861 0.02009 0.4947 0.00748 0.01512 7 0.5271 0.03621 0.06869 0.4298 0.05502 0.128 8 0.8922 0.05931 0.06648 0.5265 0.01203 0.02284 91.2555 0.00626 0.00498 1.0942 0.01087 0.00993 10 0.3282 0.00994 0.03029 0.9819 0.02744 0.02795 11 0.4331 0.03787 0.08745 0.4108 0.01092 0.02658 12 0.4784 0.02808 0.05871 1.0821 0.04247 0.03925 13 0.4424 0.04602 0.104 0.5056 0.0571 0.1129 14 0.1588 0.00201 0.01264 5.6418 0.00564 1:00E−10 15 2.0182 0.00202 1:00E−10 2.809 0.00281 1:00E−10 16 0.5466 0.03439 0.06292 0.7116 0.06419 0.09021 17 0.3683 0.02195 0.05961 0.4945 0.01996 0.04036 18 1.1143 0.1852 0.1662 1.06 0.75 0.7075 19 0.7832 0.08101 0.1034 0.8244 0.167 0.2026 20 0.8417 0.1369 0.1626 1.0398 0.1376 0.1323 21 0.8476 0.4461 0.5263 1.0063 0.4104 0.4078 22 0.6054 0.1454 0.2402 0.8504 0.299 0.3516 23 0.7574 0.4534 0.5986 1.4368 0.3565 0.2482 24 0.8288 0.1404 0.1694 1.0027 0.4484 0.4472 25 2.7029 0.0027 0.00084 2.8373 0.01347 0.00475 26 2.7029 0.0027 0.00042 4.5788 0.00458 0.00063 27 2.0615 0.00206 1:00E−10 00 1:00E−10 28 0.6488 0.01851 0.02853 1.5919 0.02479 0.01557 29 1.1378 0.04676 0.0411 0.8815 0.04524 0.05132 30 1.0504 0.00495 0.00471 0.745 0.00351 0.00471 32 0.3885 0.04416 0.1137 1.3015 0.01844 0.01417 33 0.5427 0.01059 0.01952 0.2371 0.00459 0.01937 34 0.6905 0.02384 0.03452 0.7567 0.04153 0.05488 35 1.1026 0.1757 0.1594 0.685 0.3093 0.4515 36 0.8901 0.1004 0.1128 0.4841 0.1047 0.2162 37 1.1455 0.163 0.1423 0.9727 0.0626 0.06435 38 0.331 0.0092 0.02778 6.1696 0.01541 0.0025 39 0.3365 0.01879 0.05585 0.5018 0.03632 0.07238 40 0.6912 0.01728 0.025 0.136 0.00461 0.03385 41 0.763 0.02883 0.03778 0.5163 0.06593 0.1277 42 0.8015 0.09723 0.1213 0.4847 0.07962 0.1643 43 1.0454 0.1325 0.1267 1.0939 0.4694 0.4291 44 0.9934 0.1634 0.1645 0.833 0.1725 0.207 9Journal of Zoological Systematics and Evolutionary Research