Molecular Evolution Of The Bactericidal/Permeability-Increasing Protein (BPIFA1) Regulating The Innate Immune Responses in Mammals Part 2

3. Results

The BPIFA1 protein sequences encoded in the mammalian genome were studied to determine the role of adaptive selection and evolution. The protein BPIFA1 is the key mediator of innate signaling against microbial infections by bacteria and fungi. Once the sequences were combined using MSA, they were utilized to create Bayesian phylogenetic trees and undergo further investigation. To initiate intracellular signaling cascades, activating a set of genes identified in the appropriate mammalian species and possessing a functioning (LBP-BPI) domain is necessary. For the surfactant phospholipid dipalmitoylphosphatidylcholine (DPPC), this lipid-binding domain has a very high degree of selectivity. The upper airway’s innate immune system is activated in response to numerous genetic signals, such as increased non-synonymous substitution rates, significant homologous haplotypes, and an absence of genetic variation in BPIFA1 proteins, demonstrating that the presence of these proteins has been favored by positive selection.

Lipid Binding Domain (LBD) is a structural domain contained in many proteins, which can bind some specific lipid molecules to regulate the function or localization of proteins.

Several studies have shown that lipid-binding domains can affect immunity. For example, in some secondary lymphoid organs like the spleen and lymph nodes, a lipid molecule called S1P (sphingolipid-1-phosphate) regulates the migration and migration of T cells and B cells by interacting with lipid-binding domains. retain. In addition, some important immune cell surface receptors, such as TLR4, TLR7, and TLR8, also contain lipid-binding domains, which can bind different kinds of lipid molecules and regulate the activation and response of immune cells.

Therefore, there is a certain relationship between lipid-binding domains and immunity, which also provides new ideas for studying the regulation of immune responses by lipid-binding domains. It can be seen that we must improve our immunity to resist viruses. Cistanche can significantly improve immunity. Cistanche also has anti-virus and anti-cancer effects, which can strengthen the immune system’s ability to fight and improve the body’s immunity.

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3.1. Molecular Evolution of BPIFA1 Gene

In this work, we searched for signs of adaptation in the BPIFA1 gene, ranging from progressively weak to strong selection signals during adaptive evolution in the mammalian genome. The typical percentage of codons in the BPIFA1 gene undergoing adaptive evolution was determined. Following the same procedure for each coding sequence, we calculated the average proportion of positively selected codons across all branches. Using BUSTED and synonymous rate variation in carefully chosen test branches of the BPIFA1 phylogeny, we determined traces of gene-wide episodic diversifying selection. As a result, we concluded that divergent selection occurred along the three examined lines of descent. Using synonymous rate variation, we observed gene-wide episodic diversifying selection in the test branches of the BPIFA1 phylogeny. A gene-wide episodic diversifying selection was used to achieve this (LRT). Two test branches exhibited evidence of diversifying selection, suggesting that the site had been subjected to this type of evolution (Figure 1).

The average dN/dS ratios for BPIFA1 across all sites and lineages were greater than one. As a result, research was conducted on this protein to identify the signatures of positive selection. The protein was found to have a conserved structure of amino acids, making it possible to be purified, and it had an omega value greater than 1. A log-likelihood test was performed on this protein, all of its sites were analyzed, and the substitution rate was calculated. To assess whether or not a positive selection occurred, we used three different sets of likelihood models: M0 vs. M3, M1 vs. M2, and M7 vs. M8. The parameter estimates under M1 and M2 were compared and it was found that the M2 value for these proteins was positive. The percentages of positively selected sites were significant for the three models, with values of 422.86, 64.5, and 93.63, respectively (Table 1). 

To provide additional evidence to support the findings of positive selection, we applied the Mechanistic-Empirical Combination model to specific sites using the Selection server. During this process, we discovered that several sites had been identified as having been subjected to selective pressure at various points during evolution (Figure 1). Because of this, we could estimate the degree to which this gene has been evolutionarily conserved. We found that the vast majority of the positively selected sites had been conserved throughout the mammalian clades. This was because the conserved amino acids accounted for most of the signals used for positive selection in the neural network’s algorithm (Table 2).

The codon model selection method evaluated 9113 different models. The best model (log(L) = −18,910, mBIC = 39,340.92) contained three rates and was the most accurate. With this model, improvements of 218.66 log(L) and 398.33 mBIC points were achieved compared to a single rate model, in which all non-synonymous substitutions occurred at the same rate, as shown in Table 1. Each model in the credible set had an evidence ratio of at least 0.01 compared to the best model, meaning that it was within 9.21 mBIC units of the best model, or equivalently, that it had an evidence ratio of at least 0.01 compared to the best model. Model averaging estimated the rate of change in this collection of models (Figure 2). The evolutionary selection pattern on amino acid positions in the BPIFA1 protein was also assessed using codon model selection analysis, which showed that the substitution of amino acid sites occurred during adaptive evolution in the proteins. We revealed that the basic amino acid positions of the proteins exhibited adaptive evolution due to varying substitution ratios. Based on the distribution of amino acid sites in BPIFA1, the maximum substitution rate was approximately 1.19, while the lowest was.14 (Figure 2).

Identification of physiologically significant regions of a protein can be performed by contrasting the frequency of synonymous (Ks) and non-synonymous (Ka) substitutions in the protein. This provides the basis for concluding the existence of purifying selection and localized positive Darwinian selection. We used Selecton v. 2.2 (accessible at http://selection-bio info-tau.ac.il, accessed on 29 September 2021), a web server that automatically calculates the ratio of Ka to Ks (u) at each site in the protein. Different colors represent different types of selection (positive selection, purifying selection, and no selection) and are used to graphically display this ratio at each site. The Selection model is a collection of different evolutionary hypotheses that can be used to statistically test the likelihood that a given protein has been subjected to positive selection. It operates via a graphical user interface. The recently established mechanistic-empirical model influenced the amino acid’s physical properties (Table 3).

3.2. Adaptive Selection of BPIFA1 Gene

To determine the degree to which different mammalian species have adapted to their environments, we used multiple alignments of the coding sequences of the BPIFA1 gene from each of the 34 species. These tests can be employed individually or in combination. The most common variety of tests is known as a branch test. During the evolution of the vertebrate species, the selection of specific lineages was utilized to recognize distinct lineages as subject to selection pressure. Lineage-specific selection probabilities were calculated for each phylogenetic group using an adaptive branch-site random effects likelihood (aBS-REL) model. In addition, the aBS-REL technique was utilized to dissect each gene to determine which lineages had been subjected to adaptive selection at different times in evolutionary history. 

When applied to mammalian lineages, the aBS-REL model confirmed that the BUSTED-predicted genes were under positive selection. Our results, which suggested that selective pressure was acting on BPIFA1 genes in mammalian lineages, demonstrated that the two hypotheses were congruent (Table 4). In the phylogeny of the BPIFA1 gene, there was evidence of episodic diversifying selection in eight branches. The importance of the findings was evaluated using the Likelihood Ratio Test (p > 0.05), which was carried out after the outcomes of many other tests were considered (Figure 3). In total, 63 distinct lines were put through this specific test for diversifying selection. Multiple tests were carried out, and the significance of the findings was established by applying the Likelihood Ratio Test with a p-value threshold of 0.05.

This table reports a statistical summary of the models’ fit to the data. Baseline MG94xREV refers to the MG94xREV baseline model that infers a single ω rate category per branch. The full adaptive model refers to the adaptive aBS-REL model, which implies an optimized number of ω rate categories per branch.

During the evolutionary process, we examined the omega values by employing the SLAC, FUBAR, MEME, and FEL methods to locate indications of positive selection (Table 5). According to our findings, the BPIFA1 gene in mammalian clades has been subject to positive evolutionary selection. We could detect which regions of the genome were being subjected to selective pressure by using the Bayesian method. This technique involves determining the posterior probability for each codon. Sites with a greater number of possibilities are more likely to have undergone diversifying selection, which leads to higher rates of non-synonymous and synonymous substitution than sites with a lower number of probabilities (Table 2). Using BEB analysis, we found that several locations all across the bactericidal protein’s LBP-BPI domain had been subject to positive selection with a high posterior probability of 95%. This was the case for all sites. The sites were dispersed throughout the domain in various locations. The findings of PAML were examined using the dataset found in the Selection server. This server was able to identify adaptive selection at certain sites within the protein, which allowed us to validate the existence of positive selection. To determine the substitution rates, the MEC model was applied. The findings demonstrated that adaptive selection occurred at several locations in BPIFA1 (Table 5).

3.3. Recombination Analysis

For the BPIFA1 gene, a recombination analysis was performed to find potential evolutionary links between genes. The research revealed three recombination events. Each of the recombination sequences, including the major and minor parents, came from the BPIFA1 gene. We identified recombination breakpoints using GARD analysis. At a rate of 30.30 models per second, GARD inspected 5120 models. The search space of 72,874,879 models with up to three breakpoints was generated by the alignment’s 759 possible breakpoints, of which the genetic algorithm only examined 0.01%. With an evidence ratio of 100 or above, the multiple-tree model was preferred to the single-tree model, indicating that at least one of the breakpoints reflected a topological incongruity. This was validated by comparing the AICc scores of the best-fitting GARD model, which allowed for variable topologies across segments (37,996.2), and the model, which assumed the same tree for all of the partitions determined by GARD, but allowed varied branch lengths between partitions. Specifically, the AICc score of the best-fitting GARD model was 37,996.2, whereas the AICc score of the model was 37,996.2. (Figures 4 and 5).

3.4. Protein-Protein Interactions and Ligand Binding Analysis

We used the STRING database to search for proteins expressed with BPIFA1, identifying several pairs of protein-protein interactions. There were 13 nodes and 35 edges denoted by the proteins expressed with BPIFA1. The edges of the PPI diagram are the line networks that link the individual nodes (Figure 6). The average local clustering coefficient value was 0.978. PPI enrichment had a p-value of 5.25 × 10−12. The PPI network represented the BPIFA1 gene’s interactions with other co-expressed immune genes. COX7B2, BPIFB6, BPIFB4, BPIFB2, BPIFB3, PLTP, CETP, BPI, LBP, and ODF2L were the 10 genes involved in the PPI network of BPIFA1 (Figure 6).

The BPIFB6, BPIFB4, BPIFB2, and BPIFB3 genes were the most significant because they are involved in biological signaling pathways, which play an essential role in innate immunity against bacterial infection. In addition, these genes are upregulated by BPIFA1, which is another reason they were considered so significant (Table 6). The molecular pathways are essential in eradicating invading germs through membrane-disrupting activity comprised of all related proteins with varied roles. The membrane-disrupting activity was necessary for the elimination of invading germs. Two crucial proteins in the mediation of signals in response to lipopolysaccharides include LPS-binding protein (LPSBP) and bactericidal permeability-increasing protein (BPI). They displayed a strong affinity for Lipid A, a substance found in LPS, and were strikingly similar to one another. Despite having similar structures, LBP and BPI perform various biological functions that are distinctly different from one another. For instance, LBP frequently binds to LPS and greatly facilitates the presentation of LPS to CD14+ cells, such as macrophages and monocytes, whereas BPI inhibits and lowers the bioactivity of LPS. These two proteins are both present in bacteria.

Ligands are critical components in the process of controlling the expression and activity of proteins. Intermolecular binding forces, such as ionic bonds, hydrogen bonds, hydrophobic interaction, and Vander-Waals forces, contribute to the ligand-binding process. Due to interactions between ligands and proteins, the protein’s three-dimensional structure will be altered. Because of these changes in the conformational state of the protein, some of the protein’s functions may be either inhibited or activated. Therefore, we performed a protein-ligand binding interaction study using amino acid physiochemical characteristics to determine which residues interact with the ligand and which do not. To accomplish this, we used a website (http://crdd.osdd.net/raghava/lpicom, accessed on 18 October 2021) that calculates the fraction of residues that interact with a given ligand. Key residues, such as cysteine, glycine, alanine, lysine, aspartic acid, histidine, leucine, valine arginine, tryptophan, serine, threonine, and tyrosine, were shown to interact with seven ligands (1BP1, BPH, XE, NEH, CLA, CU, and MG) and PC1. Compared to the interaction with PC1, charged amino acids, especially essential amino acids, had a greater advantage when interacting with 1BP1, BPH, XE, NEH, CLA, CU, and MG (Figure 7). The small and polar amino acids that correlated with them were characterized in each of the three ligands.

We used two distinct approaches to make predictions regarding complementary binding sites: the first was predicated on comparing binding-specific substructures (TM-SITE), while the second was predicated on the alignment of the sequence profiles (S-SITE). These techniques assessed the BPIFA1 protein against 500 non-redundant proteins that combined with 814 organic, synthetic, and metal ion compounds. Beginning with predictions of low-resolution protein structures, the approaches successfully identified the binding residues of BPIFA1, achieving an average Matthews correlation coefficient (MCC) that was much higher. Additionally, the techniques uncovered ligands that bind with the residues (Table 7).

4. Discussion

Heterogeneous backgrounds offer platforms where populations undergoing divergent selection can be distinguished into natively adapted subpopulations [44]. The influence of selection on gene flow among populations, such as migration-selection balance, determines the possibility of innate adaptation and continued divergence. This is also known as the migration-selection balance. There is a tendency for local genetic variability within populations to become homogenized due to gene flow when the effect of selection is less significant than the effect of gene flow. Instead, genetic variants may accumulate and be retained across specific loci susceptible to powerful divergent selection if the selective pressure is greater than the integrative force of gene flow [45]. 

In the possible alternative outcome, the benefits of gene flow are limited by selection against immigrants who have a poor genetic fit, which also paves the way for local adaptation [45,46]. There must be a connection between gene flow and selection to understand population differences in the frequency of gene flow [46]. Under such circumstances, selection determines whether the population continues to evolve or diverge as a distinct group. The empirical Bayes approach calculated the LRT at each branch site and located all the different sites where diversified selection may occur. Based on the empirical Bayes approach, the Fast, Unconstrained Bayesian Approximation, also known as FUBAR, was applied to locate the diversifying selection occurring at the BPIFA1 gene. FUBAR allowed for site-to-site and branch-to-branch dispersion of codons and was utilized to explore the adaptive evolution that occurred at the gene level. The method of MEME was utilized to investigate the adaptive evolution that occurred at the gene level [25,32,47]. The episodic diversifying coding sites were found by SLAC with a p-value of less than 0.01 (Table 1). 

This model was used to estimate the synonymous and non-synonymous substitution rates, and coding sites with synonymous substitution rates greater than or equal to the non-synonymous rate were considered noteworthy for identifying sites that were undergoing diversifying selection. In MEME, maximum-likelihood estimations for the BPIFA1 gene’s codons 130, 167, 168, 190, 243, 265, and 289 were obtained (Table 2). Based on their non-significant signals, these codons were not identified as positively selected sites, which is due to the episodic character of natural selection. The natural selection that took place sporadically throughout brief intervals of adaptive evolution was masked by the frequent occurrence of either purifying or natural selection. Consequently, signs of adaptive evolution could not be found via sensitivity testing and positive selection [48].

We found seventeen sites that were favorably chosen using the PAML method, fifteen sites that were chosen using the IFEL algorithm, and four sites that were chosen using the FEL algorithm. The adaptive selection pressure on the BPIFA1 gene’s codon sequences was calculated using the MEC model. This resulted in the identification of seventy-four amino acids (Figure 1). A model of evolution based on positive selection was used, revealing differences at the codon level (M8). The MrBayes application on the Selection server utilized an MCMC model to previously determine differences in the MAVS gene in mammals at the codon level [49]. 

Based on the results of MAFFT protein alignments, previous studies have shown that the Ig domain remains in the MAVS coding sequences. These results suggest that alternative protein switches in purifying selected regions are deleterious and thus unlikely to be maintained throughout evolution [50,51]. Sites for multiple evolutionary pathways were identified using a multi-parameter rate distribution, a random effect model with a 95% confidence interval, and substantial Pr [β > α] values. Sites could then be located thanks to this method (Table 3). In the case of positive selection, the class rate weight was determined using a bivariate general discrete distribution for each coding site. Convergence of the MCMC model was demonstrated by the fact that the posterior mean estimates for BPIFA1 were found to be closer to the considered reduction factor value (Table 2). 

These values ranged from 0.95 to 0.99. During the process of diversifying selection, only the coding sites with empirical Bayes factor (EBF) values of more than 50 were considered. Calculations were performed using the net effective sample size to determine the EBF values for each coding site evaluated using positive selection. Inferring the distribution of gene-specific selection parameters could improve the detected selections across a large number of coding sites. The coding areas that were positively selected and identified give significant evidence of diversifying selection in BPIFA1 genes that are now undergoing selective lineage. As a result, some mutations that initially appear to be neutral (and have no immediate impact on fitness) can be “permissive,” allowing the protein to withstand later changes that would otherwise be harmful and cause phenotypic differences [52]. Neutral mutations in epistasis lay the foundation for later selection and adaptation, which has recently attracted much attention and been offered as a way to reconcile neutral and selection models of evolution [53].

The substitution rate for the pair FWY and HKR was approximately 50%, the substitution rate for DENQ was 50%, and the substitution rate for ACGILMPSTV was 90%. The PPI network represented the interactions of the BPIFA1 protein with other co-expressed immune proteins. COX7B2, BPIFB6, BPIFB4, BPIFB2, BPIFB3, PLTP, CETP, BPI, LBP, and ODF2L were the ten genes that we determined to be responsible for these protein interactions (Figure 6). The BPIFB6, BPIFB4, BPIFB2, and BPIFB3 genes are the most significant because they are involved in biological signaling pathways, which play an essential role in innate immunity against bacterial infection. In addition, these genes are upregulated by BPIFA1, providing another reason that they are so significant (Table 6). Interfaces contain clusters of conserved residues with an amino acid composition compatible with both the interface core (residues with the largest change in burial upon binding) and a conserved region [54], and hot regions evolving from the clustering of hot spots correspond to tightly packed and conserved regions. 

Thus, interfaces are under evolutionary pressure to sustain current connections while averting unfavorable, non-specific interactions. Certain physicochemical features can be altered to reduce the likelihood that protein-protein interfaces may form dysfunctional interactions [55]. As a result of our investigation, we found that values were more than 1 for positively selected codons presented in Table 1. This illustrates that the development of synonymous sites required more time than the development of non-synonymous sites (dN sites). This beneficial impact of Darwinian selection, which encourages novel variations and greater allelic polymorphism, operates as balancing or purifying selection [56], which causes an alteration in the structural protein and affects the signaling pathway [57]. Even though they originate from the same lineage, amino acid substitutions in the offspring of different species might have very different consequences [56,57]. This contrasts with the fact that their pedigree coincides with earlier submissions. The BPIFA1 genes chosen in this study provide some information for bioanalysis, which aims to select genes based on the evolutionary time scale from the most recent to longer-term periods. 

In addition, the fundamental evolutionary mechanism that has been uncovered as a result of recent research may be insufficient due to the absence of the structural and functional features of a large number of proteins in the genome. The evolution and adaptation of protein-coding genes in Drosophila melanogaster were thoroughly examined to determine the most relevant determinants of evolution and adaptation at the level of protein-coding genes. This was accomplished by comparing D. melanogaster to closely related species and their populations. Large-scale applications of bioinformatics and structural analysis were carried out by our team to ascertain the structural and functional features of proteins. Subsequently, we divided the residues into a variety of structural and functional sites using our categorization system. The rates of sequence evolution and adaptation were compared across a variety of proteins and locations, which enabled the identification of hotspots of adaptation across the whole genome. In addition, it has been demonstrated that fast-adaptive proteins interact with one another at rates that are higher than what would be predicted by chance; this discovery shows that coadaptation is likely ubiquitous among fast-adaptive proteins.

As a result of their physical connections, the following are examples of mechanisms that have the potential to contribute to coadaptation: (1) fast-adaptive proteins are often found to be enriched in similar chemical activities and exposed to similar selection pressure, and (2) fast-adaptive proteins coevolve. Two different instances of adaptive evolution in PPIs were demonstrated in this research, which leads the authors to hypothesize that these physical interactions may have played a role in the coadaptation of fast-adaptive proteins in D. melanogaster. In addition, we showed that the phenomenon of coadaptation may take place in a more general sense than only between fast-adaptive proteins. The rate of adaptation is typically higher in proteins that interact with fast-adaptive proteins. Given that molecular interactions play a role in adaptive evolution, it is fair to anticipate that these interactions may also govern coadaptation at a more global level. It has been postulated that the coevolution of physical contacts is the mechanism responsible for the similar evolutionary rates observed in interacting proteins.

5. Conclusions

Our goal was to identify the selective pressures that have contributed to the development of the plant and mammalian BPIFA1 system, the expression of which is modulated in a wide variety of diseases. The BPIFA1 protein rapidly evolved in response to selective pressure in the human lineage, and we were able to pinpoint the genetic selection determinants that account for its bactericidal activity. During its evolutionary history, positive selection may have had a crucial role in improving the virulence response to different stimuli, which could explain the observed diversity in the stability of the gene’s function. Our findings provide a more comprehensive understanding of the evolutionary history of BPIFA1 genes, which will enhance the functional genomics analysis of pathogenicity in biological processes. It is anticipated that these findings may also help to improve the understanding of disease prevention. Additionally, the study of these genes might facilitate the design of a unique method that could assist in determining the various virulence proteins present in bacterial pathogens. Our findings lead us to hypothesize that restrictions during the evolutionary process have played a key role in shaping our discoveries. As a result of these limitations, we were able to identify some numerical boundaries when we coupled characteristics such as protein length to complicated complexes. The unique characteristics of proteins are intriguing because they may provide an indication of unusual stressors or homeostatic adjustments that have enabled their presence in cells. Therefore, they are a promising choice for further research.

Author Contributions:

Conceptualization, H.I.A., and J.C.; methodology, H.I.A., M.A.K., F.A.K., S.I., R.W.A. and N.S.P.; software, H.I.A., W.N., N.S.P., R.W.A., and S.I.; validation, M.A.K., J.C., F.A.K., and H.I.A.; formal analysis, H.I.A., M.A.K., F.A.K., S.I., R.W.A., and N.S.P.; investigation, H.I.A., M.A.K., F.A.K., S.I., R.W.A., and N.S.P.; resources, H.I.A., M.A.K., and J.C.; data curation, H.I.A., M.A.K., F.A.K., S.I., R.W.A., and W.N.; writing—original draft preparation, H.I.A.; writing—review and editing, H.I.A., S.I., R.W.A., W.N. and N.S.P.; visualization, J.C. and M.A.K.; supervision, M.A.K., F.A.K., N.S.P., and W.N. All authors have read and agreed to the published version of the manuscript.

Funding:

This research received no external funding.

Institutional Review Board Statement:

Not applicable.

Informed Consent Statement:

Not applicable.

Data Availability Statement:

All data relevant to this article shall be openly available to readers.

Acknowledgments:

This study was supported by the 2022 Guangdong Provincial Financial Special Project for Ecological Forestry Construction.

Conflicts of Interest:

The authors declare no conflict of interest.

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