Hydrophilic But Not Hydrophobic Surfactant Protein Genetic Variants Are Associated With Severe Acute Respiratory Syncytial Virus Infection in Children

Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infection-related hospitalization in the first year of life. Surfactant dysfunction is central to pathophysiologic mechanisms of various pulmonary diseases including RSV. We hypothesized that RSV severity is associated with single nucleotide polymorphisms (SNPs) of surfactant proteins (SPs). We prospectively enrolled 405 RSV-positive children and divided them into moderate and severe RSV disease. DNA was extracted and genotyped for sixteen specific SP gene SNPs. SP-A1 and A2 haplotypes were assigned. The association of RSV severity with SP gene SNPs was investigated by multivariate logistic regression. A likelihood ratio test was used to test the goodness of fit between two models (one with clinical and demographic data alone and another that included genetic variants). p ≤ 0.05 denotes statistical significance. A molecular dynamics simulation was done to determine the impact of the SFTPA2 rs1965708 on the SP-A behavior under various conditions. Infants with severe disease were more likely to be younger, of lower weight, and exposed to household pets and smoking, as well as having co-infection on admission. A decreased risk of severe RSV was associated with the rs17886395_C of the SFTPA2 and rs2243639_A of the SFTPD, whereas an increased risk was associated with the rs1059047_C of the SFTPA1. RSV severity was not associated with SNPs of SFTPB and SFTPC. An increased risk of severe RSV was associated with the 1A⁰ genotype of SFTPA2 in its homozygous or heterozygous form with 1A³. A molecular dynamic simulation study of SP-A variants that differ in amino acid 223, an important amino acid change (Q223K) between 1A⁰ and 1A³, showed no major impact on the behavior of these two variants except for higher thermodynamic stability of the K223 variant. The likelihood ratio test showed that the model with multi-allelic variants along with clinical and demographic data was a better fit to predict RSV severity. In summary, RSV severity was associated with hydrophilic (but not with hydrophobic) SPs gene variants. Collectively, our findings show that SP gene variants may play a key role in RSV infection and have a potential role in prognostication.

Introduction

Respiratory syncytial virus (RSV) is a major burden to the health of children worldwide. It is the leading cause of lower respiratory tract infection and hospitalization in the first year of life in developed countries (1–5). In the United States, it is the most common viral cause of death in children under 5 years of age. While nearly all children are infected with RSV by 2 years of age, only 2-3% of these need hospitalization and of the hospitalized children, 5-10% require mechanical ventilation (2). In a small subset of infants, the risk of death from RSV is as high as 1% even in developed countries (5). Currently, there is no vaccine available for the prevention of RSV infection. Passive immunization is given using anti-RSV monoclonal antibody prophylactically only to high-risk children; for example, to those, who are born extremely premature (<29 weeks), with certain types of congenital heart diseases, chronic lung disease, and certain immunologic disorders (6).

RSV has a heterogeneous presentation and interactions, among virus, host, and environmental factors, all of which have been implicated in affecting RSV disease severity (7). Though environmental factors, sex, and socioeconomic status play a role in the severity of RSV, the underlying mechanisms of this wide spectrum of contributing factors, have not yet been understood. A study of > 12,000 twins showed that genetic factors contributed approximately 20% in determining RSV severity (8) and multiple genetic studies have shown RSV bronchiolitis–associated loci in genes encoding proteins such as surfactant proteins, toll-like receptors, Vitamin D receptor, and various cytokines (9, 10). Thus, genetic variability may partially explain the individual disease susceptibility to RSV infection.

Pulmonary surfactant, a lipoprotein complex, is essential for normal lung function. It prevents alveolar collapse at low lung volumes by lowering the surface tension at the alveolar air-liquid surface. Its components, and especially the surfactant proteins, play important roles both in surfactant-related functions and in innate immune host defense of the lung. In general, the hydrophobic surfactant proteins (SP-B and SP-C) play important roles in surfactant function and structure, and the hydrophilic surfactant proteins (SP-A and SP-D) in innate immunity, as well as in surfactant function (SP-A) (11–16). Because of the diverse functions of the surfactant proteins, any derangement in their structure, function, and/or composition could lead to the development of a wide variety of pulmonary disorders.

Human SP-A is encoded by two functional genes, SFTPA1 and SFTPA2 (16, 17), and several genetic polymorphisms are found frequently in the general population (16, 18–21). SP-B, SP-C, and SP-D are each encoded by a single gene, SFTPB, SFTPC, and SFTPD, respectively (22), and several polymorphisms have been described for each (23–26). Moreover, SP genetic polymorphisms have been shown to associate with RSV (27–30), as well as other pulmonary diseases, such as neonatal respiratory distress syndrome (RDS) (31–35), cystic fibrosis (CF) (36), chronic obstructive pulmonary disease (COPD) (37, 38), acute respiratory distress syndrome (ARDS) (23), hypersensitivity pneumonitis (39, 40), pediatric acute respiratory failure (ARF) (41), and persistent respiratory morbidity in pediatric ARF survivors (42). However, the majority of previous RSV association studies with SP genetic variants were case-control in design, comparing RSV-infected children with healthy children without RSV infection (27, 28, 30, 43). Predicting which infants are at risk of developing RSV infection is less important because virtually all infants are at risk. Hence, in the current study, we took the unique approach of enrolling only RSV-infected children, regardless of their risk factors, and categorizing them by their disease severity to study associations of severe RSV infection with SP genetic variants.

We hypothesized that severe RSV infection is associated with natural SP genetic variants and such associations, in addition to clinical demographic information, may help to identify at-risk children for severe RSV. We further hypothesized that the genetic variation of two significant SP-A haplotypes has an impact on the molecular dynamics of SP-A protein.

Materials and Methods

Subjects: We prospectively enrolled and collected blood samples from 416 children (ages between 7 days and 3 years old) admitted with a diagnosis of RSV infection at two academic children’s hospitals, Penn State Health Children’s Hospital and the University of Virginia Children’s Hospital, during three consecutive winters. The diagnosis of RSV was made by either direct fluorescent assay or viral culture of nasopharyngeal swabs. Clinical data were extracted from their medical records, including demographics, risk factors for RSV infection, history of parental smoking, pet exposure, length of stay, and co-infections.

For this study, severe RSV was defined a priori as the need for admission to the intensive care unit with or without mechanical ventilation. Children admitted to the general pediatric ward were considered as having moderate RSV infection. In the current study, 171 (42%) and 234 (58%) children were diagnosed with moderate and severe RSV disease, respectively. We observed 32 (out of 171) and 123 (out of 234) cases of co-infection in moderate and severe RSV groups, respectively. Out of the 32 cases of co-infection in the moderate RSV group, 8 cases were of otitis media, 9 were with clinical and radiographic pneumonia of unknown bacterial etiology, 2 cases of each Streptococcus pneumoniae and staphylococcus aureus pneumonia, 3 cases of influenza, 2 cases of sinus infection, 1 case of each adenovirus, Moraxella catarrhalis, group b streptococcus urinary tract infection, Hemophilus influenzae bacteremia, coagulase-negative staphylococcus bacteremia, and clostridium difficile. Out of the 123 cases of co-infection in the severe RSV group, the majority of them were due to bacterial infection, n=112 (Hemophilus influenzae = 31, Moraxella catarrhalis = 27, Streptococcus pneumoniae = 22, Staphylococcus aureus = 16, Klebsiella pneumoniae = 4, Escherichia coli = 3, Bordetella pertussis = 2, Enterobacter cloacae = 2, coagulase-negative staphylococcus =2, Pseudomonas aeruginosa = 1, Neisseria meningitidis = 1, Serratia marcescens = 1); 5 cases were each of viral (influenza = 2, 1 case each of adenovirus, rhinovirus, and parainfluenza) and fungal infections (candida albicans = 5) and 1 case of otitis media. The majority of the co-infections were due to a wide variety of bacteria; hence, we have clumped them together and adjusted them as a co-infection covariate.

For statistical analysis purposes, children with severe and moderate RSV infection were considered as cases and controls, respectively. The protocol to collect human samples in this study was approved by the Human Subjects Protection Office of The Pennsylvania State University College of Medicine and the Institutional Review Board for Health Sciences Research at The University of Virginia, and informed consent was obtained from each parent or guardian.

DNA Isolation

The samples received from the participating sites, were numbered sequentially upon arrival with no other identifiers. DNA extraction and genotyping, each was performed in a blinded fashion to reduce any potential bias. A total of 416 children were enrolled for the current study, however, eleven subjects were excluded from analysis due to lack of genotyping information secondary to degradation of the original sample quality, leaving the final sample size of 405 subjects.

Genomic DNA was extracted from blood samples using QIAamp Blood kit (Qiagen, Valencia, CA USA) following the manufacturer’s instructions as described earlier (19).

Selection of Genetic Variants

A total of 16 target SNPs of SP genes, SFTPA1, SFTPA2, SFTPB, SFTPC, and SFTPD were selected that included, 5 SNPs from SFTPA1, rs1059047, rs1136450, rs1136451, rs1059057, and rs4253527; 4 SNPs from SFTPA2, rs1059046, rs17886395, rs1965707, and 1965708; 3 SNPs from SFTPB, rs2077079, rs3024798, and rs1130866; 2 SNPs from SFTPC, rs4715 and rs1124; and 2 SNPs from SFTPD, rs721917 and rs2243639. Several acute and chronic pulmonary diseases of all age groups, such as, neonatal RDS (31–35), CF (36), COPD (37, 38), ARDS (23), hypersensitivity pneumonitis (39, 40), pediatric ARF (41), and persistent respiratory morbidity in pediatric ARF survivors (42) have been shown to be associated with the studied SNPs. The SP-A1 (6A, 6A^m, m=0-13) and SP-A2 (1A, 1Aⁿ, n=0-15) genotypes were assigned as described (19).

Genotype Analysis

We used polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), as described earlier (19), to analyze SFTPA1, SFTPA2, SFTPD (19, 23), SFTPB (23, 36), and SFTPC (44) gene polymorphisms. The PCR primer sequences and restriction enzymes used are given in Supplementary Table 1 and the detailed method is described elsewhere (36). This method was used for genotyping of approximately half of the samples. The other half of the samples were processed using a multiplexed polymerase chain reaction workflow of Ampliseq utilizing custom designed panels from Illumina, (Illumina, San Diego, CA) (Supplementary Table 2). The library was prepared according to manufacturer’s instructions. All reagents for the library preparation were from Illumina, San Diego, CA, unless specified otherwise. Briefly, 20 nanograms of DNA (in low EDTA TE buffer solution, 10 mM Tris-HCl +1 mM EDTA, pH 7.0) were used and mixed with 4.5 µl of ampliseq Hi-fi mix (Illumina, San Diego, CA) and 2 µl of custom primer design panels to a final volume of 20 µl (Supplementary Table 2). PCR was performed at 99°C for 2 min, 21 cycles of 99°C for 15 seconds, and 60°C for 8 minutes (instead of 4 minutes recommended in manufacturer’s instructions) to optimize amplification of the studied genes. Next, we used 2 µl of the FuPa Reagent (Illumina, San Diego, CA) to digest primer dimers and amplicons. The library prep was vortexed and then centrifuged briefly at 280 x g for 10 seconds. The following volumes of reagents were added in the order listed to each sample (switch solution - 4 µl, unique index adapters for each sample - 2 µl, and DNA ligase – 2 µl) to the 22 µl of amplicons to make the final volume of 30 µl to ligate the index adapters. The ligation program was performed at 22°C for 30 minutes, 68°C for 5 minutes, and then at 72°C for 5 minutes. Libraries were cleaned up using AMPure XP beads per manufacturer’s instructions (Beckman Coulter, CA, USA) and amplified a second time after adding 1X Lib Amp Mix (45 µl) and 10X Library Amp primers (5 µl) at 98°C for 2 min, 7 cycles of 98°C for 15 seconds, 64°C for 1 minute. The second cleanup of libraries was done using AMPure XP beads as noted above and sequenced at the Penn State College of Medicine Genome Sciences Facility (Hershey, PA). SNPs, rs1059047, rs1136450, rs2243639, were not included in the final analysis because it was challenging to make appropriate calls with the sequencing method.

Statistical Analysis

We used descriptive statistics to define the study population and a t-test to compare the two groups (moderate vs severe). For the genetic analysis, we used a total of 5 dummy variables to represent the six ancestral covariates, i.e. Hispanic, Black, Asian, others (Hawaiian and Pacific Islanders), and Mixed. The sixth ancestral category, White, was used as the baseline (0,0,0,0,0). Using these encodings as well as additional covariates of sex, age, smoking exposure, pet exposure, and co-infection, logistic regression models were constructed for each of the 16 SNPs using PLINK 2.0. Additional models, both logistic regression (PLINK 2.0) and associations (PLINK 1.9), were constructed via variation of the covariates as well as including additional interaction and dominant/recessive features.

Given that certain SNPs were in linkage disequilibrium, permutation tests were used to correct for the multiple comparisons. In each permutation, we shuffled the phenotype so that the connections between the phenotype and the genetic variants are broken and a null distribution can be generated. A regression model is then run on each permuted dataset in the same way it is used to analyze the original dataset. The minimal p-value of all tested SNPs in each permuted dataset forms an empirical distribution, which is used to determine the corrected p-value after controlling for the family-wise error rate. The corrected p-value for each SNP is determined by:

$$correctedP-value=\left(\#ofPermutedminimalP-values>OriginalP-value\right)/\left(Total\#ofPermutatations\right)$$

Odds ratios (OR) were calculated for the tested SNPs to determine whether variants were associated with risk (> 1) or protection (< 1). To test the 42 genotypes of multi-allelic variants of SP-A1 and SP-A2 i.e., 1A/1A⁰, 6A/6A², etc, a multivariate logistic model was used including all covariates previously described, following our published approach for analyzing multi-allelic variants in genetic association studies (45). A likelihood ratio test (LRT) was used to compare models of clinical demographic data only and clinical demographic data with genotype information to determine if the multi-allelic variants are associated. Analysis was performed using the R framework, with regression models using both PLINK 2.0 for individual SNP tests and the stats package for multi-allelic models and PLINK 1.9 for individual SNP association tests. The lmtest package was used for likelihood ratio test.

Software Versions:

PLINK – 1.9 & 2.0

R (stats package included) – 4.0.2

Readxl – 1.3.1

Dplyr – 1.0.4

Tidyr – 1.1.2

biomaRt – 2.44.4

forcats – 0.5.1

Biobase – 2.48.0

lmtest – 0.9-48

data.table – 1.14.0

kableExtra – 1.3.4

Molecular Modeling of Human SP-A

Next, we studied the impact of rs1965708 on the molecular dynamics of SP-A. This SNP changes a glutamine (Q) to lysine (K) at amino acid position 223 of SP-A2. The rationale for studying this particular SNP is based on the following: a) a previous association of RSV with the SFTPA2 rs1965708 has been observed (43); b) in the current study an association of RSV severity with SP-A2 protein variants was observed, as assessed by the logistic regression analysis (see results below); and c) the location of the rs1965708 SNP is in the SP-A C-terminal carbohydrate recognition domain (CRD), a region shown to bind carbohydrates on the surface of pathogens in a calcium-dependent manner to enable neutralization and clearance of pathogens including RSV (16, 46).

The three-dimensional structure of human SP-A is not available in any database, and one way to solve this problem is to apply comparative/homology modeling. This modeling is based on identifying the three-dimensional structure of known proteins that resemble the structure of the query sequence, producing an alignment between the amino acids of the known structure and the amino acid sequence of the desired structure (47, 48). To obtain the three-dimensional structure of human SP-A, we used as a template, the crystallographic structure of Rattus norvegicus SP-A, obtained from the protein data bank (49), code 5FFT (50) with a resolution of 2.20 Å. We obtained the SP-A three-dimensional structure through the Swiss-Model server (48, 51–54), and compared and verified the possible structural errors using the SPDB Viewer (55).

The SP-A variants investigated in the present study consisted of amino acids 104-244 and as such, these variants enabled the study of the amino acid change of interest at residue 223. The variation occurring at the amino acid Q223 was studied by using the PyMOL software by Schrödinger Inc. Applying the mutagenesis tool, we chose the conformation of amino acid K223. The molecular dynamics (MD) simulations of the SP-A K223 and the Q223 variants followed the same protocol and these simulations are described below. One of the smaller SP-A oligomers is a trimer, consisting of three monomer subunits, two SP-A1 and one SP-A2 monomers, i.e., a hetero-oligomer (56) or a homo-trimer consisting of three monomers of either SP-A1 or SP-A2 as shown by electrophoresis under non-denaturing conditions (57, 58). All the gene-specific differences are found in the collagen-like domain of SP-A, specifically at amino acid positions 66, 73, 81, 85 (16, 20, 21). Because the structural information of the first 100 amino acid residues of SP-A is not available in any database, we focused our attention on residues 104-244 to perform MD simulations, as this region contains the amino acid change at position 223 encoded by the rs1965708. An SP-A trimer was created using three identical monomers. The MD simulations mentioned below were performed once with each trimer but we analyzed the data of the three monomers separately in order to study the behavior of each SP-A trimer under various conditions.

MD simulations were performed applying the bonded and non-bonded parameters for the all-atom force field, OPLS-AA (59). The tri-dimensional coordinates and topology of the protein were generated by pdb2gmx, which is part of the GROMACS 2019 package (60–63), and used in this work to perform all MD simulations.

Protein and calcium were placed in an octahedron-shaped box, with a volume of 1393 nm³ and filled with 43602 water molecules of the TIP4P model (64–66). Periodic boundary conditions were applied on all axes of the box (67). The system was submitted to the minimization step applying the steepest descent algorithm with the convergence criterion of 100.00 kJ.mol^-1.nm^-1 or 20000 steps. The equilibration of pressure and temperature was achieved with a time of 100 picoseconds (ps) following a two-step through the simulation. Towards this we used: (i) the canonical ensemble (NVT) (68), keeping the number of particles, volume, and temperature constant, and (ii) the isothermal-isobaric ensemble (NpT) (69), keeping the number of particles, pressure, and temperature constant. After the equilibration step, the systems were submitted to a production step of 50 nanosecond (ns). All MD simulations were performed at 310 K and 1 bar, using 2 fs of integration time with the lists of pairs being updated at every 5 steps. The cut-off for Lennard Jones and Coulomb interactions was between 0 and 1.2 nm. The leap-frog algorithm was used in the production step with the Nose-Hoover thermostat (70) (τ = 0.5 ps) at 310 K and the Parrinello-Rahman barostat (68) (τ = 2.0 ps) at 1 bar. All Arg and Lys residues were assigned with positive charges and all Glu and Asp residues were assigned with negative charges. The Visual Molecular Dynamics (71) software were used to visualize the simulation trajectories.

We analyzed the root-mean-square deviation (RMSD), the root-mean-square fluctuation (RMSF), the radius of gyration, hydrogen bond, Dictionary of Secondary Structure of Proteins (DSSP), and principal component analysis (PCA), to assess the conformational influence of the Q223/K223 on SP-A behavior. The RMSD is calculated from the comparison of the overlap of two structures, one of which is taken as a reference (72). For example, in our work, each structure from the molecular dynamic trajectory was compared to the initial structure. The RMSF calculates the fluctuation of each atom of each amino acid from its average position, indicating the flexibility of the amino acids throughout the simulation. The radius of gyration of a protein is the measure of the stability of protein folding. If a protein is stably folded, it will likely maintain a relatively steady value of radius of gyration (Rg). Hydrogen bonds were determined based on the cutoffs of the angle formed between the hydrogen, the donor atom and the acceptor atom and the distance between the donor atom and the acceptor atom. The DSSP computes the secondary structure for each time frame and describes the stability of the secondary structure of proteins throughout the simulation (72). PCA is a statistical technique applied in molecular modeling to reduce the complexity of the data that characterize dominant conformational movements in proteins during molecular dynamics simulations (73). PCA describes them through the significance of the collective movements of the structure that are converted into main movements during the molecular dynamics simulations, that is, the number of dimensions necessary to describe the conformation dynamics is reduced by decomposing its movements from a larger spatial scale to those of a smaller scale (74, 75). For this work, the default values of the gmx hbond module were used from the Gromacs 2019 modules.

The torsional entropy of the proteins was calculated using the gmentro.py program (76). We generated a tabulated data file from the dynamics angle trajectory. Using Gaussian mixtures of the torsional data, gmentro.py was used to calculate the configurational entropy from a conformational ensemble of the whole SP-A structure (76).

Results

A. Associations of Severe RSV Risk With SP Polymorphisms and Demographic Variables

Out of the 405 enrolled RSV-positive children, 171 (42%) and 234 (58%) children were diagnosed with moderate and severe RSV disease, respectively. Table 1 shows the demographic data describing the study population. There were no statistically significant differences in sex, race, ethnicity, and measures of risk factors previously known to be associated with severe RSV disease (e.g. history of prematurity, congenital heart disease) between the two groups (Table 1). The majority of the patients (~ 85%) were white. Infants with severe RSV were more likely to be younger, of lower weight, had exposure to household pets and smoking; as well as more likely to have a co-infection on admission. As one would expect, children with severe RSV required a longer hospital stay and supplemental oxygen at discharge (p < 0.05). The logistic regression analysis of the clinical data showed that history of pet exposure (OR 3.05, 95% CI 1.25-7.46, p=0.014), and co-infection on admission (OR 3.98, 95% CI 2.30-6.87, p < 0.001) were associated with increased risk of severe RSV, whereas higher age was associated with decreased risk of severe RSV (OR 0.89, 95% CI 0.84-0.94, p < 0.001) (Figure 1).

TABLE 1

Table 1 Demographics of the cohort stratified by moderate and severe RSV disease.

FIGURE 1

Figure 1 Forest plot represents predictors of severe RSV using mutivariable logistic regression analysis. X-axis and Y-axis represents odds ratio (OR) and variables, respectively. Dotted line represents OR of 1. Based on OR, 1A⁰/1A⁰, 1A⁰/1A³, pet exposure and co-infection are associated with increase risk of severe RSV (OR > 1), whereas, older age is associated with decreased risk of severe RSV (OR < 1).

The observed frequency distribution of the majority of SNPs shown in Supplementary Table 3 was in Hardy-Weinberg equilibrium. The logistic regression analysis of SNPs showed that decreased risk of severe RSV associated with two SNPs, the rs17886395_C allele of the SFTPA2 (OR=0.63, 95% CI 0.46-0.85, p = 0.002) and the rs2243639_A allele of the SFTPD (OR 0.64, 95% CI 0.41-0.99, p = 0.045) after adjusting for significant covariates (age, ethnicity, co-infection, smoke and pet exposure), and increased risk of severe RSV with the rs1059047_C (OR= 3.8, 95% CI 1.1-12.6, p=0.032) allele of the SFTPA1 after adjusting for the same covariates (Table 2). As shown in Supplementary Table 4, the logistic regression analysis of genotypes after adjusting for the same covariates showed similar association with RSV severity.

TABLE 2

Table 2 Severe RSV vs moderate RSV using the multivariate logistic analysis after adjusting for covariates (age, ethnicity, co-infection, smoke and pet exposure).

As shown in Figure 1, an increased risk of severe RSV was associated with the SP-A2 1A⁰ variant in its homozygous (1A⁰/1A⁰, OR=5, 95% CI 1.5-16.4, p = 0.009) or heterozygous form with 1A³ (1A⁰/1A³, OR=7.2, 95% CI 1.1-47.2, p = 0.04).

To evaluate the overall addition of genotype information to demographic data, a likelihood ratio test was done. The model with the genotype information along with demographic data was a better fit to predict disease severity compared to demographic data alone (p = 0.029 and Chi sq of 57.445).

B. Impact of the SFTPA2 SNP rs1965708 on SP-A Structure

This SNP encodes the only amino acid change that exists between the SP-A2, 1A⁰ and 1A³ protein variants (16, 20, 77). A glutamine (Q) in 1A⁰ is changed into a lysine (K) in 1A³ at residue 223, which is located in the CRD of SP-A. Because, in the present study, increased risk of severe RSV was associated with 1A⁰/1A⁰ and 1A⁰/1A³ genotypes, we sought to investigate the impact of rs1965708 SNP on SP-A structural parameters via the use of SP-A simulated structural data.

We studied the RMSF of the Q223 and K223 SP-A variants and Figure 2 shows the RMSF of the backbone and sidechain of the Q223 and K223 variants. In the RMSF data, although the position of peaks did not differ significantly between Q223 and K223, we observed that the major peaks are in general, greater for Q223 compared to the K223 SP-A variant. The region around amino acid 223 (the site of variation), has more intense peaks indicating a region with great atomic movement. Furthermore, the RMSF values of each of the three identical monomers present in each trimer (Q223 or K223) analyzed did not differ significantly. The sidechain of both Q223 and K223 SP-A monomers showed higher RMSF values than the backbone (Figure 2) indicating a greater fluctuation of the sidechain atoms compared to the protein backbone.

FIGURE 2

Figure 2 Root-mean-square fluctuation (RMSF) in the backbone and sidechain of the Q223 and K223 SP-A variants. SP-A homo-trimers consisting of three identical monomers of SP-A1 or SP-A2 that included amino acids 104-224 were studied. The RMSF trimer data of each of the two variants (Q223 and K223) were analyzed for each of the 3 identical monomers present in each trimer to study the behavior of SP-A trimer. (A–C) represent monomers 1, 2, and 3, respectively, for Q223 trimer, and (D–F) represent monomers 1,2,3, respectively, for K223 trimer. The amino acid residue number is on the x-axis and RMSF is on the y-axis. The backbone and sidechain of the Q223 variant, are depicted in orange and gray colors, respectively. Whereas, blue and green colors depict the backbone and sidechain of the K223 SP-A, respectively. The position of peaks did not differ significantly between Q223 and K223 variants, however, the peak of each residue, in general, is greater for Q223 compared to the K223. The region around amino acid 223 (the site of variation), has more intense peaks, indicating a region with great atomic movement.

The thermodynamic data on the torsional entropy show a larger entropy value for the K223 compared to Q223 SP-A variant (Table 3). Although entropy change in the variant process (ΔS_mutation) seems small (< 70 J K-1 mol-1), it is not negligible and indicates larger stability of the K223 compared to the Q223 SP-A variant. These observations may also be associated with a greater fluctuation mostly in amino acids of the backbone and sidechain atoms of the K223 compared to the Q223 variant.

TABLE 3

Table 3 First order entropy change (ΔS_mutation) in the mutation process for protein and for the backbone chain.

We did not observe a major difference in the RMSD of the center of mass in either Q223 or K223 SP-A (Supplementary File 1), indicating that the amino acid change at the specific site (223) at the protein chain does not significantly influence atomic movement in relation to the center of mass. Furthermore, there were no major differences, among the three monomers of each trimer (Q223 and K223), in the radius of gyration (a measure of protein folding stability), the DSSP (a description of secondary structure stability), hydrogen bonds (a measure of overall protein stability), and PCA (to reduce the complexity of the data that characterize dominant conformational movements of the protein during MD simulations). (Supplementary File 1).

Discussion

Risk for RSV has been associated, in several studies, with genetic variants including SP genetic variants. However, most of the previous RSV association studies have used healthy children without RSV infection as controls. In the current study, we took the unique approach of enrolling only RSV-positive children to study association of disease severity with surfactant protein genetic variants. Our findings indicated that 1) a decreased risk of severe RSV is associated with the rs17886395_C of the SFTPA2 and the rs2243639_A of the SFTPD, whereas, an increased risk is associated with the rs1059047_C of the SFTPA1. 2) RSV severity is not associated with the studied SNPs of SFTPB and SFTPC. 3) An increased risk of severe RSV is associated with the 1A⁰ haplotype of SFTPA2 in its homozygous or heterozygous form with 1A³. 4) The model with the SP genetic information along with demographic data is a better fit to predict RSV disease severity compared to demographic data alone. 5) Based on the molecular dynamic studies, the K223 SP-A variant is more stable than the Q223 SP-A (the 1A³ haplotype has a lysine (K) and the 1A⁰ has a glutamine (Q) at residue 223).

A decreased risk of severe RSV is associated with the rs17886395_C allele of SFTPA2 (p < 0.002). This SNP results in an amino acid substitution from alanine to proline at position 91 within the coding region (16, 20, 77). Similar to our findings, a decreased risk of RSV in Finnish children (43) and a decreased risk of RDS in white American neonates (35) was associated with the same SNP. Although direct comparison between studies is difficult, due to differences in study design, it is interesting that the rs17886395_C allele is associated with decreased risk of RSV and RDS in different populations and disease processes. It is shown that replacing alanine with proline stabilizes collagen triple helices due to conformational restrictions of the pyrrolidine ring (78). Whether this affects the concentration of a well-functioning SP-A and confers protection against severe RSV, is unknown as direct experimental evidence is lacking.

An increased risk of severe RSV was associated with the rs1059047_C of the SFTPA1 (p=0.032) in our study. This SNP results in an amino acid substitution from valine to alanine at amino acid position 19, also within the coding region (16, 20, 26, 77), although it may or may not be part of all the molecules of the mature SP-A (57). Contrary to our observation, the aforementioned Finnish study found a protective effect of rs1059047_C on RSV disease (43). The difference in findings between the two studies could be due to differences a) in sample size (n=86 compared to n=405 in our study), b) definition of cases and controls, and c) patient population. The Finnish study used RSV-positive children as cases and healthy children without RSV as controls. In contrast, we enrolled only RSV-positive children and divided them between moderate (controls) and severe (cases) RSV disease. The fact that all children get infected with RSV by two years of age, it becomes less important to know who is at risk of getting RSV infection because virtually all children are at risk. For the current study, we enrolled only RSV-positive children to study RSV associations with SP SNPs, and determine who of the RSV-infected subjects is at risk for severe disease. If our findings are replicated and validated independently, the at-risk children for severe RSV could receive disease-modifying treatments without delay and be considered to receive prophylactic anti-RSV monoclonal antibodies in the future. The antibody treatment bears a considerable cost (79) and thus targeting it to those at the highest risk makes economic sense.

A decreased risk of severe RSV was associated with the rs2243639_A allele of SFTPD (p=0.045). This SNP results in an amino acid substitution from threonine to alanine at position 160 (26). Similar to our findings, previous studies have shown a decreased risk of severe RSV to associate with the rs2243639_A in the Chilean population (30) and in white American children (28). Although the exact mechanisms of how this SNP confers protection against severe RSV is not known, the rs2243639_A is shown to associate with a decreased level of serum SP-D and decreased risk of RDS in premature infants (80). Increased serum SP-D concentration is associated with worst respiratory outcomes in other viral infections, such as influenza and COVID-19 (81, 82), presumably, due to alveolar injury secondary to oxidative stress and infection. We did not measure SPs concentration in our patient population, and hence, we are unable to determine in the present study the impact of the rs2243639, if any, on SP-D concentration and its association with RSV.

An increased risk of severe RSV was associated with the 1A⁰ haplotype of SFTPA2, in its homozygous (1A⁰/1A⁰) or in its heterozygous (1A⁰/1A³) form. Previous studies have shown varying associations of SFTPA2 haplotypes with RSV (28, 30, 43). In line with our observation, one study found an association of increased risk of RSV with 1A⁰/1A³, but not with 1A⁰/1A⁰ (43). However, another study found an association of decreased risk of severe RSV with the 1A⁰/1A⁰ (29). Although the latter used the same definition for cases and controls as ours, the study population was very different. In that study, approximately 65% of the enrolled patients were African American compared to 85% of Caucasian children in our study. Liu et al. showed differences in frequencies of SP-A haplotypes based on race (83), therefore, the contrasting findings between the two studies could be due to baseline genetic differences in patient population. Furthermore, the sample size (29) was lower (n=277) than that in our study (n=405) and the authors compared the 1A⁰/1A⁰ genotype vs other SP-A genotypes combined due to the low frequency of the other haplotypes in their patient population (29). Nevertheless, the majority of studies have found associations of SFTPA2 haplotypes with RSV (28–30, 43). Similarly, a previous association study of SFTPA genetic variants with severity of Influenza A virus showed that the 1A⁰ of the SFTPA2 was associated with a need for mechanical ventilation, acute respiratory failure, and acute respiratory distress syndrome in an adult population (84). These findings are not surprising because SP-A2 (encoded by SFTPA2) compared to SP-A1 (encoded by SFTPA1) exhibits higher activity in innate host defense/inflammatory processes (16).

In addition to RSV infection, the same SNPs and haplotypes have been studied with other infectious diseases such as tuberculosis (85, 86), pneumonia (87), otitis media (88, 89), and allergic bronchopulmonary aspergillosis (26, 90). Of interest, none of the individual studied SNPs were associated with the above-mentioned infectious diseases after correcting for significant covariates. However, similar to our findings, an increased risk of tuberculosis was associated with the 1A³ haplotype in Ethiopian (86) and Mexican (85) adult populations. In the above-mentioned studies, there were other haplotypes of SP-A1 and SP-A2 (but not of the 1A⁰ and the 1A³ that are observed in the current study) associated with pneumonia, otitis media, and allergic bronchopulmonary aspergillosis. Nonetheless, associations of SFTPA1 and SFTPA2 SNPs and haplotypes with infectious diseases other than RSV underline the role of SP-A1 and SP-A2 in innate immunity and host defense functions.

The only amino acid difference, between SP-A2, 1A⁰ and 1A³ variants, is generated by the rs1965708 SNP at residue 223, where the amino acid is either a glutamine (Q) in 1A⁰ or a lysine (K) in 1A³ (16, 20). Glutamine is a neutral amino acid with a polar amide group, whereas lysine is a positively charged amino acid with a basic side chain. In simulation studies of molecular dynamics, we did not observe any major difference in structure or behavior of the SP-A variants, Q223 and K223, except a higher stability of the K223 as assessed by its higher conformational entropy compared to Q223 SP-A variant. In line with our findings, no significant differences were observed between the Q223K variants in a number of parameters studied in vitro, including their ability to form stable secreted complexes, oligomers, structural stability following limited proteolysis, and other (91). Yet, a transgenic mouse model of asthma carrying this SNP showed delayed resolution of eosinophils in bronchoalveolar lavage fluid of SP-A2 223K/K and SP-A knockout mice compared to SP-A2 223Q/Q > wild-type mice (92). It is well known that severe RSV infection early in life can predispose children to a later development of asthma (93, 94). Taken together, we speculate that the 1A⁰/1A³ genotype predisposes children to severe RSV secondary to decrease binding and clearance of RSV and may predispose them to develop asthma later in life. In the future, we plan to study the impact of Q223K on the binding ability of SP-A2 to RSV and its subsequent clearance.

Most importantly, a likelihood ratio test revealed that the model that included information about the patients’ genetic variants, along with clinical and demographic data is more accurate at predicting severe RSV infection compared to the model with only clinical and demographic data. The collective observations in this study contribute to an important foundation where, in the future, physicians could consider using this genetic information in clinical decision-making for high-risk children and identify those that may benefit from preventative measures such as an anti-RSV monoclonal antibody or aggressive early treatment such as surfactant replacement therapy.

Strengths of this study include the prospective study design and the well-characterized demographic, illness, and environmental exposure information for the cohort. There are a few limitations of our study. First, the relatively moderate sample size and homogeneous patient population may prevent the generalizability of our findings. Second, we did not serotype RSV in our patient population and RSV genetic variations may have accounted for altered disease severity rather than the host genetic variants (95, 96). Third, we used a direct fluorescent assay and viral culture to diagnose RSV infection instead of molecular testing. Although this holds the possibility of introducing a few false-positive results, a previous study has shown a good correlation between the direct fluorescent assay and molecular approaches, particularly for infants and young children (97). Subclinical infections with RSV are not common. Importantly, in the present study we have only enrolled children who were symptomatic and required hospitalization, hence, RSV was likely causally related to the illness. Therefore, we believe that the method of RSV detection may not change our conclusion. Fourth, we did not measure the level of inflammatory markers or SPs in our patient population, hence, the impact of these SNPs on these markers is unknown. Nonetheless, it is important that the associations observed in the present study be strengthened and validated by increasing the sample size and replicating the findings in other groups of heterogeneous patient populations with RSV, where measurements of other endpoints are performed.

In summary, multiple single SNPs and haplotypes of SFTPA1, SFTPA2, and SFTPD genes, encoding the hydrophilic SPs, but no SNPs or haplotypes of those (SFTPB and SFTPC) encoding the hydrophobic SPs were associated with RSV severity, indicating that SPs involved in innate immunity and host defense, play an important role in RSV severity. The model of genetic variants combined with demographic data was a better fit for predicting RSV severity than that of demographic data alone. This observation strengthens the argument that genetic variants do play a role along with environmental factors in the prediction of viral illness severity in children.

Data Availability Statement

The data presented in the study have been deposited in the ClinVar repository, accession number SCV002506610.

Ethics Statement

The studies involving human participants were reviewed and approved by Human Subjects Protection Office of The Pennsylvania State University College of Medicine and the Institutional Review Board for Health Sciences Research at The University of Virginia. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

Data curation: CG, LD, CR, NT. Molecular modeling data: JB, FS, AP. Statistical Analysis: KA-F, DL. Methodology: CG, NT, JF. Resources: JF, CG, NT. Supervision: NT, JF. Writing original draft: CG, LD, KA-F, AP. Writing, reviewing & editing: CG, NT, JF. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported in part by grants from Children’s Miracle Network to CG and by NIH R37 HL 34788 to JF. The computational work was supported by the Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) with the Rio Network of Innovation in nanosystems for the health (Nanohealth) - E-26/010.000983/2019. The National Council for Scientific and Technological Development (CNPq – 465259/2014-6), the Coordination for the Improvement of Higher Education Personnel (CAPES), the National Institute of Science and Technology Complex Fluids (INCT-FCx), and the São Paulo Research Foundation (FAPESP – 2014/50983-3) to ASP.

Conflict of Interest

The authors declare that the research conducted was in the absence of any commercial or financial relationships construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors thank the parents and study participants. We thank Susan DiAngelo for organizing and cataloging the incoming samples and for genotyping about half of the samples studied.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.922956/full#supplementary-material

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