Molecular evolution of the pathogen recognition peptidoglycan proteins regulates the immune response against infectious diseases in Drosophila melanogaster

2.1 Sequence analysis

Once the sequences are retrieved, they were aligned using software such as ClustalW, MAFFT, or MUSCLE. Multiple sequence alignment allows for the identification of conserved regions and amino acid changes between different species (Edgar, 2004). BioEdit was used to edit and arrange the sequence (Hall, 2004). A minimal number of functional loci was calculated by employing a conservational technique, and this estimate was based upon sequences that were shown to be translatable. All individuals from the sample species were assumed to be in the heterozygote state (Rosenberg et al., 2001).

2.2 Inference of recombination

The rate of recombination can be estimated using GARD software implemented in datamonkey, which uses coalescent theory to estimate the rate of recombination from sequence data (Kosakovsky Pond et al., 2006) Recombination analysis was performed using genetic recombination detection methods to calculate the genetic distance between sequences to detect recombination events. This method uses a sliding window approach to detect recombination events. In this method, the sequences are compared to a set of reference sequences, and recombination events are detected by the presence of incongruence in the phylogenetic trees (Kosakovsky Pond et al., 2006). The recombination events were detected by the presence of significant differences in the genetic distance between sequences. Maximum likelihood methods were based on the assumption that the sequences evolve under different evolutionary processes before and after the recombination event.

2.3 Tests for selection

Adaptive selection was identified in PGRP genes or genomic regions that have evolved under positive selection, meaning that they have undergone changes that improve their fitness and adaptation to their environment (Ahmad et al., 2020). PAML (Phylogenetic Analysis by Maximum Likelihood) software package was used to detect positive selection in protein-coding genes. PAML uses the codeml program to analyze the data (Yang, 2007). The codeml program compares the rates of synonymous and non-synonymous substitutions to detect positive selection. In this method the ratio of non-synonymous to synonymous substitutions (dN/dS) were compared in protein-coding genes. A dN/dS ratio greater than one indicates positive selection, while a ratio less than one indicates purifying selection (Bielawski et al., 2016). The output file generated by PAML were used to detect positive selection by calculating likelihood log ratios (2ΔlnL) using χ² distribution (Moretti et al., 2012).. Datamonkey is a webserver was further used for detecting positive selection in protein-coding sequences (Pond and Frost, 2005). Different methods including FEL (Fixed Effects Likelihood), REL (Random Effects Likelihood), FUBAR (Fast Unconstrained Bayesian AppRoximation), and MEME (Mixed Effects Model of Evolution) available on Datamonkey were used to detect positive selection (Weaver et al., 2018). FEL, REL, and MEME are all likelihood-based methods that use a phylogenetic tree to detect positive selection. FEL and REL identified the constant selective pressures acting on the sequence across the entire tree or at each site, respectively (Delport et al., 2009). MEME, on the other hand, allowed for different selective pressures to act on different sites in the sequence (Murrell et al., 2012). FUBAR, on the other hand, is a Bayesian method that used a Markov Chain Monte Carlo (MCMC) algorithm to estimate the posterior probability of positive selection at each site in the sequence (Murrell et al., 2013). The Selecton server was used to further confirm positive selection in protein-coding sequences data. The MCMC model implemented in Selecton Server used a Bayesian approach to estimate the posterior probabilities of different selection pattern at each site in the protein-coding sequences. It allows for both positive and negative selection, as well as the possibility of no selection, at each site. The MCMC algorithm samples from the posterior distribution to estimate the probability of each selection region (Stern et al., 2007).

2.4 Phylogenetic analysis

MEGA (Molecular Evolutionary Genetics Analysis) version 10.0.5 was used to create phylogenetic trees using the maximum likelihood method. The initial tree construction was performed using the neighbor-joining method, and the tree topology was evaluated through the application of the maximum likelihood method. One thousand bootstrap replications were conducted to evaluate the resilience of the tree topology. The species tree produced by TreeBeST serves as a point of reference for evaluating gene trees and other phylogenetic trees. The authors of the study conducted phylogenetic network analysis to detect instances of reticulation in the proteins' evolutionary lineage (Jin et al., 2006).

The second stage of determining positive selection is to identify which amino acid sites in a protein sequence have undergone positive selection. Positive selection refers to the process by which mutations that confer a fitness advantage become more frequent in a population over time (Smukowski Heil, 2023). Positive selection has been linked to the evolution of proteins by altering particular amino acid residues in ways that have an effect on the protein's function or structure. The rate of synonymous (i.e., silent) versus non-synonymous (i.e., amino acid altering) substitutions at each amino acid position was compared as a proxy for the presence of positive selection. Positive selection may be present if the rate of non-synonymous substitutions at a given site is much higher than the rate of synonymous substitutions at that site (Mayrose et al., 2007). Moreover, comparing the amino acid sequences of homologous proteins from different species, looked for evidence of convergent evolution at specific sites. The two distantly related species have evolved similar amino acid changes at the same site, this can suggest that positive selection has occurred (Russell, 1998).

2.5 Conservation analyses

The conservation analysis of pathogen recognition peptidoglycan proteins (PRRs) was performed using the Consurf server (Ashkenazy et al., 2016). The amino acid sequences of the PRRs were obtained from public databases such as GenBank or UniProt used as input for the analysis. The Consurf server is a bioinformatics tool used to perform conservation analyses of protein sequences. It is commonly used to identify conserved regions in protein structures, which can help to identify functional domains and residues that are important for protein function (Goldenberg et al., 2009). The output of the Consurf server is a conservation score for each residue in the protein sequence, ranging from 1 (most variable) to 9 (most conserved). Conserved residues are often represented as a different color or shape in a protein structure visualization, which can help to identify functionally important regions.We investigated the degree to which the genomic areas close to the PGLYRP gene were conserved in synteny among Drosophila species. The PGLYRP protein from drosophila was analyzed for evolutionary conservation in amino acid residues using the ConSurf library (consurf.tau.ac.il/) (Celniker et al., 2013). These amino acids have a higher degree of conservation and are found in more enzymatic nooks or protein–protein interactions than others. Because they disrupt both the function and the structure of the protein, changes in conserved amino acids are an even more dangerous form of polymorphism within a protein than polymorphisms in more flexible regions (Glaser et al., 2003).

2.6 3D protein modeling and structural analysis

The crystal structure of the human compound PGRP domain (PDB assembly number 4D8A) is a homologous structure to the PGRP protein of D. melanogaster (Burley et al., 2017). Therefore, it was used as a template to model the 3D structure of the Drosophila PGRP protein. Protein modeling was performed using software such as SWISS-MODEL (Waterhouse et al., 2018) and I-TESSAR (Zheng et al., 2021), and Phyre2 (https://www.sbg.bio.ic.ac.uk/phyre2/html) which utilize comparative modeling techniques to generate a 3D structure based on the template structure. The sequence of the Drosophila PGRP protein was used as input into the software, along with the template structure, and the software generated a model of the Drosophila protein based on the alignment between the two sequences. Once a 3D model of the Drosophila PGRP protein has been generated, it was subjected to further refinement and validation using software such as PyMOL (DeLano, 2002). These programs allow for the visualization and manipulation of the protein structure, as well as the analysis of various structural features such as hydrogen bonding, salt bridges, and solvent accessibility. To analyze the residue-residue interactions in protein structures, including receptor-ligand interactions and hydrogen-bonding epitopes we used protein-protien interaction network approaches (Zhang et al., 2010). LigPlot tool was used for analyzing the protein–ligand interactions, including protein–protein interactions. It generated schematic diagrams of protein–ligand interactions, highlighting the key residues involved in the interaction (Laskowski and Swindells, 2011). LigPlot generated schematic diagrams of protein–ligand interactions, highlighting the key residues involved in the interaction. The diagrams provided a visual representation of the interactions between the proteins and the types of interactions that occur, such as hydrogen bonds, hydrophobic interactions, or electrostatic interactions (Norris et al., 2012).

3.1 Adaptive evolution of peptidoglycan recognition proteins

The adaptive evolution of PGRPs revealed to understand how these proteins have evolved to recognize and respond to a diverse array of bacterial pathogens. Our study has identified several regions of PGRP genes that are under positive selection, indicating that they have evolved rapidly in response to selective pressures imposed by bacterial pathogens. These regions are often located in the peptidoglycan-binding domain of PGRPs, suggesting that they play a key role in the recognition of bacterial cell wall components. We utilized a variety of site models to determine which genes were being positively selected across vertebrate species. We used the phylogenetic tree in the dataset to evaluate many gene model comparisons. Codons in genes undergoing positive selection were categorized using a probabilistic technique that compared models based on different ratios. To conduct the positive selection test, two sets of models (M1a; M2a and M7; M8) were employed. The likelihood test value for M1a vs. M2a was 0 (p > 0.05). However, the likelihood ratio test results for PGLYRP were as follows: 2lnL = 64.28, 2lnL = 4.9283, 2lnL = 49.31, and 2lnL = 68.65 (Table 1). Multiple correlating approaches from the HyPhy package were then used to assess the evidence for positive selection (Table 1). Through MEME, FEL, and SLAC analyses, we inferred evolutionary signs consistent with positive selection based on global relationship values. As a result, sites discovered by multiple methods (i.e., those that were consistent with two or more methods) were prioritized for positive selection. Our results provided compelling proof that these genes have been subjected to successful genetic selection in Drosophila species. Bayesian analysis was utilised to classify sites experiencing selective pressure by estimating posterior probabilities for each codon. Positive selection is more likely to occur in higher-probability sites, as seen in Fig. 1, when either > 1 or. With the help of BEB analysis, we were able to pinpoint several positively selected locations within these proteins, with most of them having retrospective probabilities above 95%. Further validating the good results of selection, we used the Selecton server and the Mechanical-Empirical Hybrid model to confirm positive selection on individual sites. During the course of the organism's evolution in response to selective pressure, we found a large number of previously unknown sites (Fig. 1).

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Fig. 1

Domain structure, alignment, and positively selected sites in the 3D structure of PGRP of Drosophila. (A) Domain structure of PGRP of D. melanogaster, consisting of a transmembrane domain and a PGRP domain. (B) Alignment of PGRP sequences from multiple Drosophila species, showing conservation of key amino acid residues involved in peptidoglycan binding (red), and positively selected sites (yellow) identified by evolutionary analyses. (C) Crystal structure of PGRP of D. melanogaster, showing the location of the positively selected sites and the conservation score. This figure provides a comprehensive overview of the domain structure, sequence conservation, and 3D structure of the PGRP protein in Drosophila, highlighting the key features involved in its function and evolution. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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In PGLYRP, positively selected sites were discovered with an average frequency of 8. In total, we concluded that each gene possesses two positively selected sites within the peptidoglycan-binding type II amidase (PGRP) domain. This domain is structurally comparable to the type II amidase found in bacteria. Based on the crystal structure of the human compound PGRP domain, positions 325, 326, 327, 340, 345, 351, 352, 353, 359, and 372 on the dimerization surface are important for ligand binding. Compared to neutral evolution models (M7), the M8 model that accommodates positive selection performs better in likelihood calculations. There was an acknowledgment of the positive selection of sites, and these sites are listed in (Table 2). In total, we identified 12 positively selected codons throughout the sampled species, 3 of which are homologous to positively selected codons in other bird species, and 1 of which is a perfect match to a human peptide binding area (Table 2). Conservation analyses of PRRs using the Consurf server can provide valuable insights into the evolution and function of these proteins in host-pathogen interactions. By identifying conserved regions and residues, researchers can gain a better understanding of the mechanisms of PRR recognition and the development of immune responses to bacterial infections. Conservation analyses can also be used to identify the selective pressures acting on PRR genes. For example, a study of the TLR4 gene in primates found evidence of positive selection, suggesting that this gene has been subject to strong selective pressures in response to pathogen pressure. In addition to changes in the peptidoglycan-binding domain, studies have also identified adaptive changes in other regions of PGRP genes that affect the activity and specificity of the proteins.

3.2 Selection analysis

To determine whether adaptive selection is occurring in the PGRP proteins, we used various software such as PAML (Phylogenetic Analysis by Maximum Likelihood) to perform likelihood ratio tests (LRTs). These tests compared the fit of different models of sequence evolution, including models that allow for positive selection, to determine whether positive selection is occurring at particular sites in the protein sequence. The identification of sites under positive selection provide insights into the functional evolution of the PGRP protein and its role in adaptation to changing environmental conditions or in host-pathogen interactions in Drosophila species. Furthermore, Using an EBF threshold of 100, we found that only a small fraction of sites (3%) were detected as evolving in a particular direction for each genomic region. A fixed effects likelihood (FEL) analysis yielded similar percentages for the number of locations suspected of being subject to diversifying positive selection. Most existing approaches for detecting positive selection, including FEL, are based on the equal rates model for replacing amino acid residues and a codon substitution model. Therefore, positive selection can be identified if the rate of nonsynonymous substitution at a site is higher than the rate of synonymous substitution at the same site on average (considering all possible residue pairs). FEL makes direct estimates of both rates at each location and uses an LRT to determine whether they are comparable. When looking at the results site-by-site (Tables 2 and 3), one of the most important things to understand is that sites subjected to directionally selected selection are overwhelmingly different from sites found to be subjected to diversifying selection, with a few exceptions in PGRP. In addition, there are a few sites that, based on a conventional dN/dS analysis, appear to be undergoing purifying selection but are nevertheless evolving in a particular direction. We also identified Directional selection in PGRP protein of Drosophila species. Directional selection is a type of natural selection that occurs when one allele is consistently favored over another, leading to a shift in the frequency of that allele over time. Directional selection can lead to the rapid evolution of specific traits, including those involved in the immune response. We have identified sites that are under directional selection in the PGRP genes of D. melanogaster. These sites are located in the peptidoglycan-binding domain of the protein and are thought to be important for the recognition of specific bacterial pathogens. Convergent evolution is the independent evolution of similar traits in different lineages. In the case of the PGRP of D. melanogaster, there is evidence of convergent evolution in the repeated substitutions of amino acids in the protein. Our study have shown that the PGRP of D. melanogaster has undergone repeated substitutions at specific amino acid sites. These substitutions are thought to be driven by positive selection, which means that they provide some sort of advantage to the organism. The repeated substitutions have occurred independently in different lineages of D. melanogaster, suggesting convergent evolution (Table 2).

3.3 Codon model selection

The molecular processes of phenotypic adaptation were investigated by examining peptidoglycan recognition proteins of Drosophila species. We explored how these proteins have evolved to select particular amino acid locations by using a variety of codon models available on the DATAMONKEY online service. Our findings revealed that the amino acid replacement rates varied at different points in the amino acid sequences of these proteins, indicating adaptive evolution. Specifically, we discovered that these proteins had undergone adaptive evolution with varying replacement rates at different locations in their amino acid sequences. For PGLYRP, based on various ratio groupings, the average substitution rates were roughly 1.38, 0.54, and 0.12, respectively, with the lowest being 0.12 for several amino acid sites in PGLYRP (as shown in Fig. 3). This analysis utilized a subset of the codon model that relied on a modified Bayesian Information Criterion and the probability log (mBIC).Using the physicochemical factors linked with combined empirical codon and transition/transversion (Fig. 3). Using the mBIC test and GAs, we were able to implement a standardized multi-rate test on a data set successfully. It is involved in the recognition of bacterial peptidoglycan and the triggering of immune responses. We have shown that the amino acid sequences of the PGRP domain have undergone positive selection, which is a process in which advantageous mutations are fixed in a population due to their ability to confer some selective advantage. Specifically, certain amino acid residues in the PGRP domain have experienced a higher rate of substitution than would be expected by chance, suggesting that they have been under positive selection. The adaptive evolution of the PGRP domain is thought to be driven by the co-evolutionary arms race between insects and bacteria. This leads to the evolution of new amino acid sequences in the PGRP domain that are better able to recognize and respond to bacterial peptidoglycan.

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Fig. 2

Positively selected amino acid sites in the PGRP protein sequence of Drosophila species displayed in the outcomes of the Selecton server analysis. These locations were marked in some way to draw attention to them. Statistical metrics (1–9) are also provided, such as the level of support for positive selection at each location.

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Fig. 3

Shows the substitution rates of amino acid sequences of the peptidoglycan recognition protein (PGRP) domain that had undergone adaptive evolution. The analysis was based on various ratio groupings using a subset of the codon model that relied on a modified Bayesian Information Criterion and the probability log (mBIC). The average substitution rates for PGLYRP were approximately 1.38, 0.54, and 0.12, respectively, with the lowest rate being 0.12 for certain amino acid sites in PGLYRP. These results indicate that different locations in the amino acid sequences of these proteins have undergone varying replacement rates, suggesting adaptive evolution.

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3.4 Functional analysis of PGRP protein

Protein-protein interactions (PPIs) are crucial for the function of PGRP proteins in the immune response. PGRPs can interact with other immune-related proteins, such as Toll-like receptors (TLRs) and Nod-like receptors (NLRs), to activate downstream signaling pathways that lead to the production of cytokines and other immune response mediators. Different types of proteins and their secondary structures have different patterns of inter-residue interactions. In this study, we will describe these interactions' role in proteins that belong to various structural classes and folds, including globular, membrane, and other protein folds. The -carbon atom of a protein residue serves as a symbol for the entire protein molecule. Distances are calculated between the -carbon atom of the first (N-terminal) residue and all other carbon atoms in the protein molecule. We did this using Ligplot graphs based on the different stances (Figs. 4 and 5). These diagrams illustrate the interactions between the drug (shown here as a ball and stick) and the residues located in the pockets that are close by. The presence of hydrogen bonds is denoted by a dashed line that connects the atom of the drug ligand to the atom of the residue that lines the pocket. This line is also represented as a ball and stick in the instances in question. Van der Waals contacts can be seen as “fans” of lines circling each residue and pointing in the direction of the drug. The drug itself has fans pointing in the opposite direction, towards the residues lining the pocket with which it interacts. This investigation uncovered a pocket of particular importance because it is situated at an interface between two chains, A and X, of the protein (Fig. 4). Comparable residues in A and B or A and C do not include this pocket, which means it is distinct from those locations. This is because chains A and X both have their mobile domains in the up position, in contrast to chain A, which has its mobile domain in the down position. The pocket can be found between two domains, specifically in the protein's chain A flanking area. The PGRP (peptidoglycan recognition protein) receptor binds to bacterial peptidoglycan and triggers an immune response in insects, including D. melanogaster. The binding pocket of the PGRP receptor is the region of the protein that directly interacts with the peptidoglycan. Interactions between protein residues in the binding pocket of the PGRP receptor are critical for the specificity and affinity of the protein-peptidoglycan interaction. The binding pocket contains a number of amino acid residues that interact with the peptidoglycan, and these residues are highly conserved across different species of insects. We have identified several key residues in the binding pocket of the PGRP receptor that are important for the interaction with peptidoglycan. For example, in D. melanogaster, the residues Y63, D132, and R134 have been shown to be critical for the binding of peptidoglycan (Fig. 4 and Fig. 5). These residues form hydrogen bonds and other interactions with the peptidoglycan, stabilizing the complex and allowing for efficient recognition. In addition to these key residues, there are also interactions between other residues in the binding pocket that contribute to the overall stability and specificity of the protein-peptidoglycan complex. For example, there may be interactions between hydrophobic residues that help to anchor the peptidoglycan in the binding pocket, or between charged residues that contribute to electrostatic interactions.

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Fig. 4

The receptor's and the hydrogen-bonding epitopes' residue-residue interactions are denoted by green and blue, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 5

Interactions between protein residues in the binding pockets. The PGRP receptor's residues interact with ligands, and the hydrogen-bonding-related epitopes are denoted by the colors with ligands. The hydrogen-bonding-related epitopes are denoted by blue and green, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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4.1 Evolutionary fingerprinting of PGRP genes

Evolutionary fingerprinting of PGRP genes involves analyzing their DNA and protein sequences across a range of species and using statistical models to identify regions of the genes that are evolving under positive selection. These regions are thought to be important for the recognition of bacterial pathogens and for mounting an effective immune response (Ahmad et al., 2020). Our Study using evolutionary fingerprinting have identified several regions of PGRP genes that are evolving rapidly and are likely to be under positive selection. These regions are often located in the peptidoglycan-binding domain of PGRPs, which is responsible for recognizing bacterial cell wall components. In addition to identifying positively selected regions, evolutionary fingerprinting can also be used to study the functional consequences of genetic variation in PGRP genes. For example, studies have shown that mutations in PGRP genes can affect the sensitivity and specificity of the immune response, which can influence susceptibility to infectious diseases (Eleftherianos and Castillo, 2012)..

We discovered that these proteins had undergone adaptive evolution, with varying replacement rates at various places in their amino acid sequences. For PGLYRP, the average substitution rate was roughly 1.38, 0.54, and 0.12, respectively, based on various ratio groupings, with the least being 0.12 for various amino acid sites in PGLYRP (Fig. 3). Several studies have used these parameters to investigate the evolutionary dynamics of PGRP proteins in Drosophila and other organisms.For example, a study by Wang (2019) used Stanfel class parameters to estimate the amino acid substitution rates in PGRP proteins across different insect species. They found that the substitution rates varied among different PGRP genes and among different regions of the same protein, suggesting differences in the functional constraints acting on these regions (Wang et al., 2019). We found that the average amino acid substitution rates for PGRP proteins were roughly 1.38, 0.54, and 0.12, depending on the specific PGRP gene and the organism being analyzed. Furthermore, they discovered a function for adaptive evolution in directing the evolution of these immune-related proteins by finding evidence of positive selection operating on certain amino acid residues in several PGRP genes.The amino acids were divided into three groups through the evolutionary rate cluster: those in which the substitution pair FWY and HKR accounted for roughly fifty percent of the total, those in which DENQ accounted for fifty percent, and those in which ACGILMPSTV traded ninety percent. The evolution of PGRP protein in Drosophila involves changes in amino acid substitution rates, which can provide insights into the functional and evolutionary dynamics of this protein. Several studies have investigated the patterns of amino acid substitution in the PGRP protein family across different Drosophila species. For example, a study by Biswas et al. (2014) found that the PGRP family shows a higher rate of amino acid substitution than other immune-related proteins in Drosophila. They suggested that this high rate of substitution is due to the rapid evolution of PGRP proteins, which is likely driven by host-pathogen coevolution (Biswas et al., 2014). It was found that PGRP genes showed variable rates of amino acid substitution, with some genes evolving more rapidly than others and suggested that these differences in evolutionary rates may be due to differences in the functional constraints acting on these genes. Overall, evolutionary fingerprinting of PGRP genes provides insights into the molecular evolution of the innate immune system and the mechanisms by which host organisms adapt to new and emerging bacterial pathogens. This approach may have important implications for the development of novel strategies for the treatment and prevention of infectious diseases.