Molecular Evolution Of The Bactericidal/Permeability-Increasing Protein (BPIFA1) Regulating The Innate Immune Responses in Mammals Part 1

Abstract:

Bactericidal/permeability-increasing protein, a primary factor of the innate immune system of mammals, participates in natural immune protection against invading bacteria. BPIFA1 actively contributes to host defense via multiple mechanisms, such as antibacterial, surfactant, airway surface liquid control, and immunomodulatory activities. However, the evolutionary history and selection forces on the BPIFA1 gene in mammals during adaptive evolution are poorly understood. This study examined the BPIFA1 gene of humans compared with that of other mammalian species to estimate the selective pressure derived by adaptive evolution. To assess whether or not positive selection occurred, we employed several different possibility tests (M1 vs. M2 and M7 vs. M8). 

The proportions of positively selected sites were significant, with a likelihood log value of 93.63 for the BPIFA1 protein. The Selection server was used on the same dataset to reconfirm positive selection for specific sites by employing the Mechanistic-Empirical Combination model, thus providing additional evidence supporting the findings of positive selection. There was convincing evidence for positive selection signals in the BPIFA1 genes of mammalian species, which was more significant for selection signs and creating signals. 

BPIFA1 protein, also known as LPLUNC1 or Nasopharyngeal Carcinoma-associated Gene 6 (NAG6), is a protein highly expressed in the upper airway and oral cavity. BPIFA1 protein is closely related to immunity.

Studies have shown that the BPIFA1 protein has antibacterial and immunomodulatory effects. BPIFA1 protein can bind to invasive Escherichia coli and destroy its cell membrane, thereby playing an antibacterial role. In addition, the BPIFA1 protein plays an immunomodulatory role by activating Toll-like receptor (TLR) 4 and TLR2 pathways, promoting inflammatory responses, apoptosis, and immune cell activation.

The relationship between BPIFA1 protein and respiratory diseases has also been extensively studied. For example, studies have shown that the expression level of BPIFA1 protein in respiratory diseases such as bronchial asthma and chronic obstructive pulmonary disease (COPD) is closely related to the severity of the disease. Especially under infection conditions, the expression level of BPIFA1 protein can be significantly increased, thus participating in the regulation of respiratory system immune response and flora balance.

Therefore, the role of the BPIFA1 protein in the immune system cannot be ignored, and its relationship with respiratory diseases and infections needs further in-depth study. Therefore, we need to pay special attention to improving our immunity. Cistanche has a significant effect on improving immunity. The polysaccharides in the meat can regulate the immune response of the human immune system, improve the stress ability of immune cells, and strengthen immune cells. bactericidal effect.

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We performed probability tests comparing various models based on dN/dS ratios to recognize specific codons under positive selection pressure. We identified positively selected sites in the LBP-BPI domain of BPIFA1 proteins in the mammalian genome, including a lipid-binding domain with a very high degree of selectivity for DPPC. BPIFA1 activates the upper airway’s innate immune system in response to numerous genetic signals in the mammalian genome. These findings highlight evolutionary advancements in immunoregulatory effects that play a significant role in the antibacterial and antiviral defenses of mammalian species.

Keywords:

BPIFA1; bactericidal; immune system; molecular evolution; positive selection.

1. Introduction

Bactericidal permeability-increasing protein (BPI) is a highly effective antimicrobial protein that binds and neutralizes lipopolysaccharides released from the outer membrane of bacteria [1]. The BPI fold-containing family A member 1 (BPIFA1) gene is known to have effects on the local immune system, and these effects can potentially influence the growth and invasion of microorganisms [2]. One of the potential mechanisms that underlie this link is the ability of BPIFA1 to enhance the absorption of bacteria by phagocytic cells and their ability to destroy them [3]. 

Although they have minimal sequence similarity, BPI has two domains that adopt the same structural fold [1,4]. Invading Gram-negative bacteria result in an integrated host response facilitated by the presence of a lipopolysaccharide-binding protein (LBP). LBP is an endotoxin-binding protein closely linked to and coordinated with BPI [5]. BPIFA1 controls the mucosal microbiota and baseline interferon signaling. SPLUNC1 (formerly known as BPIFA1) is a protein fold-containing family member with antibacterial, surfactant, and immunomodulatory activities, all of which contribute to host protection. The respiratory system is the primary site of its expression [6]. SPLUNC1, the human homolog of the mouse gene PLUNC, exhibits the same expression pattern in the upper airways and nasopharyngeal areas as its mouse homolog. Antibacterial action against Gram-negative bacteria is displayed by the encoded antimicrobial protein [5]. In non-small cell lung cancer, it might serve as a potential molecular marker for locating micrometastasis. Multiple transcript variants have been discovered as a result of the alternative splicing of the 3’ untranslated region; however, the full-length nature of only three of these transcript variants is understood [7].

Both mice and humans have significant levels of BPIFA1 gene expression in the upper part of the trachea, but this expression diminishes with distance from the trachea, reaching a minimum at the bifurcation of the main stem bronchi and becoming undetectable in the lungs’ periphery. [8]. Extensive gene expression studies in mice and humans have failed to detect BPIFA1 in peripheral lung tissue [9]. Except for very low levels of BPIFA1 mRNA expression in the mouse thymus, rat heart, and olfactory mucosa, BPIFA1 is not expressed in any organs or tissues outside of the respiratory system of rodents [10]. There is no indication that BPIFA1 mRNA is present in any of the following human tissues: the heart, liver, brain, stomach, small intestine, placenta, skeletal muscle, pancreas, spleen, normal lymph nodes, peripheral lymphocytes, prostate, testis, or ovary [11]. 

The expression of BPIFA1 mRNA follows a distribution pattern that is highly comparable in embryonic and adult tissues. This pattern is observed in both types of tissues [1]. BPIFA1 overexpression in transgenic mice produced alveolar macrophages with enhanced opsonization and phagocytosis of carbon nanotubes in a model of controlled airway inflammation [12]. In addition, commensal Gram-negative nanobacteria were shown to co-localize with BPIFA1 within the epithelial cells of nasopharyngeal cancer tissues [13]. The samples were taken from patients who had previously been identified as having nasopharyngeal carcinoma. The findings of a recent study suggested that interactions between BPIFA1 and non-bacterial LPS can mitigate the body's inflammatory response caused by non-bacterial LPS. [1].

The range of its antibacterial effects and the preservation of its structure in air-breathing vertebrates imply that BPIFA1 has evolved to provide essential host-protective capacities [14]. However, due to its location in the proximal airways and its high level under basal conditions, BPIFA1 may be indispensable. This is because antimicrobial effectors are abundant in animals [15]. Consequently, BPIFA1 seems to have the most significant effects in avoiding infection and clearing it up before the invasion of pathogens. Bacterial infections in the respiratory system can be prevented thanks to these functions, which may signal the activation of immunity and improved regulation of other airway functions [15].

Adaptive changes in response to environmental demands are thought to be constrained by biophysical factors, but the structural aspects of sites that contain adaptive changes cannot be predicted by any evolutionary theory [16]. This is because biophysical constraints limit the types of substitutions that are allowed for protein function to be maintained [17]. Positive selection may be more prevalent in sections of proteins where mutations are expected to have a lower effect than in other parts of the protein, although this has not been proven (e.g., allosteric regulation sites) [18]. However, functional regions are expected to remain substantially conserved during evolution, even though adaptive alterations are associated with the rapid fixation of favorable mutations [19]. The molecular evolution of protein sequences is significantly influenced by the process of natural selection. 

Recent developments in genome sequencing and reliable inference methods at phylogenetic and population levels have made it possible to conduct a rapid and robust assessment of the evolutionary rates and adaptations that are driven by natural selection [20]. At both the phylogenetic and population levels, a substantial amount of work has been conducted to build inference methods. Furthermore, the increasing accessibility of protein structural and functional data has allowed researchers to examine the impact of structural and functional constraints on the evolution and adaptation of protein sequences [16]. Because of the limits imposed by their structures and their functions, the rates of evolution and adaptation are different for various proteins and sites within the same protein [19].

The bulk of a cell’s functions is intricately intertwined with the regulatory networks of gene expression that enable organisms to tolerate higher infection levels or mitigate the effects of those infections [21]. Most of the components that make up cellular physiology are intimately related to these gene expression regulatory systems, which are frequently old evolutionary adaptations [22]. These mechanisms have drawn a substantial degree of interest in research that has utilized a constrained set of model species for which genetic information is available [23]. However, little is known about the mechanisms that led to the evolution of these systems or how they adapted to diverse environmental settings as development progressed. 

This study aims to investigate the evolutionary origins of the BPIFA1 gene to reveal its physiochemical features and apply comparative genomics to provide an assessment of the gene in various mammalian species. We conducted in-depth comparative studies of the bactericidal/permeability-increasing protein (BPIFA1) gene, which regulates the innate immune response in mammals, to better understand how these genes work. There is a possibility that selective pressure will have a significant effect on the evolution of adaptation. In this study, we investigate the history of these genes in various vertebrate species, as well as how genetic diversity and natural selection have influenced the development of this gene family over time.

2. Materials and Methods

2.1. Sequence Retrieval and Analysis

The amino acid and coding nucleotide sequences of the BPIFA1 gene in 34 mammalian species, including humans as the reference species in this study, were collected from GenBank (https://www.ncbi.nlm.nih.gov/genbank, accessed on 20 September 2021), and they were aligned using the Clustal Omega tool in MEGA 6 software [24]. The maximum likelihood method was used in MEGA 6 software to generate the phylogenetic tree for the BPIFA1 gene. This tree was constructed based on the evolutionary relationships among the genes. The bootstrap test calculated the average number of substitutions per site and the average branch length by employing a maximum-likelihood method with 1000 repeats to determine taxonomic clustering. This method was used to pick a topology for more advanced log-likelihood values [25,26]. The species names and accession numbers used to study the BPIFA1 gene are provided in Supplementary Table S1.

2.2. Selection Analysis

Maximum likelihood approaches were used to compare the ratios of dN/dS for each codon site to identify specific codons in mammalian BPIFA1 gene sequences subjected to positive selection [27,28]. CODEML executed in PAML [29] and the DATA MONKEY web server (https://www.datamonkey.org, accessed on 29 September 2021) [30] were utilized for the analysis, and the outcomes were designated using substitution ratios of codons that were considerably higher than 1 for codons under positive selection. The initial step of this research was to determine whether or not positive selection occurred using the maximum likelihood ratio test. 

This analysis determined the presence of sites with a dN/dS ratio greater than one. In this study, we contrasted a discrete (generic) model that performed this function with a null model that prohibited the occurrence of sites with a value greater than 1 [31]. Analyses were compared using a likelihood log (2∆l) distribution with df = 4. The null hypothesis (M7) asserted that the distribution was bounded by the values 0 and 1. An alternative model (M8) with two parameters, omega (ω) and beta (β), allows for the derivation of a value from the dataset, which may be greater than 1 [27]. Analyses using fixed effect likelihood (FEL), single likelihood ancestor counting (SLAC), and random effect likelihood (REL) all found that the BPIFA1 gene was subject to positive selection when global values for synonymous and non-synonymous divergences at each site were compared [32].

The second stage was to utilize the maximum probability estimate to locate amino acid positions that were the subject of positive selection throughout evolution. The Bayes theorem, which predicts the posterior probabilities of the sites that are subject to positive selection, was used to accomplish this goal. Positive selection was observed operating at amino acid locations with posterior probabilities ranging from 95% to 99% [33]. Amino acid residues with a high probability that the value was greater than one were subjected to a selective procedure. The Swiss model and Phyre 2 (http://www. sbg.bio.ic.ec.k/pyre/html, accessed on 28 September 2021) are web-based applications that display the locations of favorably selected amino acids on protein structures [34]. 

We predicted the location of evolutionary conservation of nucleic acids and amino acids in the protein using the ConSurf tool (http://consurftest.tau.ac.il, accessed on 28 September 2021), which was based on the phylogenetic relationship between sequences [35]. The sequence of the aligned codon of BPIFA1 was examined in Selecton version 2.2 (http://secton.tau.ac.il, accessed on 28 September 2021), which permits determining the varied ratios of various codons inside the aligned sequences. These ratios were measured using the Bayesian inference approach through various likelihood tests. This was performed to confirm positively selected codons [36]. Moreover, the Selection results were shown in various colors to denote the various selection criteria.

2.3. Recombination Analysis

To find evidence of recombination, we performed a model selection procedure based on the statistical likelihood that can sift through many sequence alignments in search of breakpoints and spot likely recombinant DNA. This technique used a genetic algorithm to search the alignments of several sequences for recombination breakpoints to accomplish its goal. The GARD approach is simple to grasp, easily extensible, and highly parallelizable. Extensive simulation experiments have demonstrated that the method beats other current tools in almost all cases, particularly concerning accuracy. 

To investigate the evidence of recombination, the nucleotide sequences were first assessed to identify haplotypes (Na) and estimate the polymorphic sites (S), the average number of nucleotide differences (K), and nucleotide diversity (π) using DnaSP 5.10 software [37]. Detection of breakpoints and assessment of recombinant signals in nucleotide sequences were performed using the online GARD tool of the Datamonkey webserver [38]. Additionally, using GARD to screen sequences for recombination assures that methods focused on identifying positive selection have acceptable statistical features.

2.4. Protein-Protein Interactions Analysis

Much interest has been directed toward investigating how protein-protein interactions are preserved from one species to another. Since there are several hurdles in the experimental identification and confirmation of interactome data, it would be intriguing to understand a PPI transferred from a species that has been proven in another species [39]. The STRING databank is a free bioinformatics resource that contains information describing how proteins interact with one another as part of several pathways. The number of lines connecting each protein node and betweenness values are used to identify intermediate nodes, representing proteins that play important biological roles and are intimately linked to one another. Network creation was carried out using STRING and Cytoscape software (http://www.cytoscape.org, accessed on 29 September 2021) was used to display the network [40]. By identifying the protein-protein interactions of BPIFA1 among immune proteins and co-expression analysis using STRING version 9.1 (http://www.string-db.org,accessed on 29 September 2021), we were able to further determine how BPIFA1 functions at the molecular level.

2.5. Structural Analysis of BPIFA1 Protein

In this analysis, we built the crystal structure of the human BPIFA1 protein using homology modeling with online tools, such as the Swiss model (http://swissmodel.expasy. org, accessed on 29 September 2021) [41], I-TESSAR [42], and Phyre2 (http://www.sbg. bio1.ic.ac.uk/phyre2/html, accessed on 29 September 2021) [43]. The conjugate gradient method and Amber force field in UCSF Chimera 1.10.1 software were used to reduce the assembled target protein. In addition, the ProSA webserver was utilized to evaluate the stereochemical properties of the expected structure [22].

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