Host Genetic Risk Factors for Community-acquired Pneumonia

Lyubov E. Salnikova, Dr. Sci. (Biol.)a,b,*, Tamara V. Smelaya, Dr. Sci. (Med.)b, Viktor V.

Moroz, Corresponding. Member of Russian Acad. Med. Sci.b, Arkady M. Golubev, Dr. Sci.

(Med.)b, Alexander V. Rubanovich, Dr. Sci. (Biol.)a

aN.I. Vavilov Institute of General Genetics, Russian Academy of Sciences, 3 Gubkin Street,

Moscow 117971, Russia

bV. A. Negovsky Research Institute of General Reanimatology, Russian Academy of Medical

Sciences, 25 Petrovka Street, Build.2, Moscow 107031, Russia

* Correspondence: Lyubov E. Salnikova, Institute of General Genetics, Russian Academy of

Sciences, 3 Gubkin Street, Moscow 117971, Russia, phone 74991328958, fax 4991328962

E-mail: salnikovalyubov@gmail.com

Abstract

This study was conducted to establish the contribution of genetic host factors in the

susceptibility to community acquired pneumonia (CAP) in Russian population. Patients with

CAP (n = 334), volunteers without a previous history of CAP, constantly exposed to

infectious agents, control A group (n=141) and a second control group B consisted of healthy

persons (n=314) were included in the study. All subjects were genotyped for 13 polymorphic

variants in the genes of xenobiotics detoxification CYP1A1 (rs2606345, rs4646903,

rs1048943), GSTM1 (Ins/del), GSTT1 (Ins/del), ABCB1 rs1045642); immune and

inflammation response IL-6 (rs1800795), TNF-a (rs1800629), MBL2 (rs7096206), CCR5

(rs333), NOS3 (rs1799983), angiotensin-converting enzyme ACE (rs4340), and occlusive

vascular disease/hyperhomocysteinemia MTHFR (rs1801133). Seven polymorphic variants in

genes CYP1A1, GSTM1, ABCB1, NOS3, IL6, CCR5 and ACE were associated with CAP. For

two genes CYP1A1 and GSTM1 associations remained significant after correction for

multiple comparisons. Multiple analysis by the number of all risk genotypes showed a highly

significant association with CAP (P=2.4 x 10-7, OR=3.03, 95% CI 1.98-4.64) with the

threshold for three risk genotypes. Using the ROC-analysis, the AUC value for multi-locus

model was estimated as 68.38.

Highlight:

To establish the contribution of genetic host factors in the susceptibility to community

acquired pneumonia (CAP) in Russian population. Rs2606345 (CYP1A1) is strongly

associated with the risk of CAP. Genetic predisposition to CAP is attributed to cumulative

contribution of polymorphisms at a number of genes involved in xenobiotics detoxification,

immune and inflammation response and renin-angiotensin system.

Key words:

Community-acquired pneumonia, gene polymorphism, multi-locus model, ROC analysis,

CYP1A1

Abbreviations:

AIC, Akaike information criterion; AUC, area under curve, CAP, community acquired

pneumonia; CI, confidence interval; CNV, copy number variations, Del (D), deletion; FDR,

false discovery rate; HWE, Hardy-Weinberg equilibrium; HWP, Hardy-Weinberg

probability; Ins (I), insertion; LD, linkage disequilibrium; OR, odds ratio; ROC, receiver

operating characteristic; SE, standard error; Se, sensitivity; SNP, single-nucleotide

polymorphism, Sp, specificity.

1. Introduction

An estimated 1500000 cases and 44400 deaths occur annually from community

acquired pneumonia in Russia. The analysis of genetic and environmental risk factors

predisposing to CAP is clearly warranted. Risk of CAP has been associated with pathogen

virulence, host susceptibility and epidemiologic factors. It has been shown that polymorphic

variants at some host genes can modify risk of CAP, including those critical for the host

response to CAP - innate immune system (Endeman et al., 2009; Endeman et al., 2008;

Endeman et al., 2011; Gallagher et al., 2003; Garcia-Laorden et al., 2008; Gentile et al.,

2003; Gomi et al., 2004; Herpers et al., 2009; Lingappa et al., 2011; Martín-Loeches et al.,

2012; Mukamal et al., 2010; Schaaf et al., 2005; Solé-Violán et al., 2010; Solé-Violán et al.,

2011; Waterer et al., 2001; Wunderink et al., 2002; Yuan et al., 2008; Zúñiga et al., 2012),

the lung's defense against inhaled microorganisms (García-Laorden et al., 2011; Lingappa et

al., 2011), inhibition of fibrinolysis (Yende et al., 2007) and renin-angiotensin system (de

Garde et al., 2008; Morimoto et al., 2002; Takahashi et al., 2005).

Given the results of the abovementioned studies, we have selected 13 polymorphic

variants which were assigned to 11 different genes based on their potential association with

molecular pathways implicated in CAP pathogenesis, including both previously unexplored

variations and variations reported to be associated with susceptibility to CAP, and/or its

severity and outcome for the latter to reveal significance of previously identified associations

in Russian population (IL-6 rs1800795, TNF-a rs1800629, MBL2 rs7096206, ACE rs4340).

Xenobiotics detoxification genes (CYP1A1 rs2606345, rs4646903, rs1048943, GSTM1

Ins/del, GSTT1 Ins/del, ABCB1 rs1045642) were also included in our study as they encode

enzymes involved in the detoxification and excretion of a broad range of exogenous and

endogenous compounds, thereby participating in general resistance to occupational hazards

and endogenous harmful metabolites as well as in biotransformation of drugs. Two more

genes involved in immune and inflammation response (CCR5 rs333, NOS3 rs1799983) were

selected as the receptor obliteration (CCR5) and nitric oxide production (NOS3) might

influence CAP initiation and progression. The MTHFR locus (rs1801133) was chosen as

gene-candidate for occlusive vascular disease and hyperhomocysteinemia, the both

syndromes possibly extending inflammation processes.

2. Materials and methods

2.1. Patients and controls

334 patients with CAP (age 26.93 ± 0.82 years, 91.9% males), hospitalized at the

clinical bases of V. A. Negovsky Research Institute of General Reanimatology, Moscow,

Russia, were included in the study. Confirmed diagnosis of pneumonia was based on the

presence of acute symptoms resulted from lower respiratory tract infection, confirmed by

clinical, radiological and laboratory data.

Initially control group was composed of 188 unrelated healthy volunteers without a

previous history of relevant infectious diseases. Volunteers were medical workers, the staff of

the auxiliaries (drivers, technicians, mechanics, electricians, security officers). The subjects

from the control group were in contact with patients and therefore they were constantly

exposed to infectious agents. All volunteers started to work in 2005 year or later and were

under medical supervision in 2007-2012 years. To date, 141 subjects from this group have

not contracted CAP and were therefore included in Control group A (age 21.06 ± 0.42 years,

92.5% males). A second control group (control B) consisted of 314 healthy persons (41.65 ±

1.03 years, 91.1% males).

All patients and control subjects were Russian and were residents of the Central Federal

District of European Russia. Investigated CAP and control A groups were rather

homogeneous as to the age of those under study (Figure 1) and their sex (91.9% and 92.2%

male in control A and CAP groups correspondingly).

The study was approved by the Inter-Institutional Ethical Committee and was conducted

according to the principles of the Declaration of Helsinki. All participants provided written

informed consent.

2.2. Genotyping

DNA was isolated from 200 μl of blood using gDNA purification kit Diatom DNA

Prep 200 (Isogene laboratory, Moscow, Russia). The genotyping was performed using an

allele-specific tetraprimer PCR developed to genotype a relatively large number of samples in

a cost-effective and time-saving manner. In this method, allele-specific DNA products are

amplified by means of applying appropriately designed two-pair primers (four primers) into

an ordinary PCR tube (Hamajima, 2001). Amplification was carried out on an ABI thermal

cycler using two external and two internal sequence-specific primers (Table 2) and tubes

PCR MasterMix (Isogene laboratory, Moscow, Russia) as previously described (Moroz et al.,

2011). For each SNP, 10% of randomly taken DNA samples (cases and controls) were

genotyped twice and no discrepancies were observed. The PCR data were validated by

sequencing (Evrogen, Moscow, Russia).

2.3. Statistical analysis

Exact Hardy Weinberg Equilibrium (HWE) tests were performed for each SNP

independently using the goodness-of-fit χ2 test to compare the observed and expected

genotype frequencies for CAP and control groups. Single SNP statistical analysis was

performed using WinSTAT software (Robert K. Fitch Software, Germany). Two-tailed

Fisher’s exact test was also performed to evaluate association between SNP and disease.

Odds ratios were estimated using WINPEPI software (Abramson, 2011). The distributions of

genotypes in cases and controls were compared using a logistic regression analysis.

Using SNPStats package, five genetic models were considered (codominant,

dominant, recessive, over-dominant and log-additive), and the best genetic model was

selected using AIC value. The lowest AIC value was considered the best-fitting model for the

fitted variant. For genotypes with minor allele frequencies <5% only dominant genetic

models were considered (minor homozygotes+heterozygotes vs. major homozygotes).

Bonferroni correction and FDR approach were used for adjustment for multiple

comparisons. FDR approach has been well-documented in the literature as a means to control

type I error while maximizing power of analysis (Storey, 2002; Benjamini and Yekutieli,

2002). In contrast to Bonferroni correction, false discovery rate controls the expected

proportion of false discoveries among all rejected hypotheses. WINPEPI test power and

sample size calculators were used to evaluate type II error.

Pairwise LDs were measured as Lewontin’s D’-values and estimated from genotype

data using the expectation-maximization algorithm implemented in SNPStats software (Sole

et al., 2006). The SNPStats software was also used to estimate haplotype frequencies in both

case and control groups as well as for evaluation of haplotype association with CAP by

individual haplotype P-values. To estimate the cumulative effect, variables were coded as 0

and for the protective and risk genotypes, respectively.

3. Results

In total, 304 patients with pneumonia and 455 control subjects were included in the

study. The main demographic and clinical variables of the patients and control subjects are

shown in Table 1. Patients and controls were predominantly male. Bilateral radiographic

opacities were

observed in 98 (29.3%) patients. In 108 (32.3%) cases a causative agent could be identified.

Streptococcus pneumoniae was the most common etiologic agent (70, 64.8% of the patients

with known causative microorganism).

V. A. Negovsky Research Institute of General Reanimatology specializes in the

general critical care. The clinical departments of the Institute include pulmonary divisions

and intensive care units of several hospitals. CAP patients with severe pneumonia and

pulmonary complications hospitalized at the clinical bases of the Institute often required more

intensive treatment, admission to an ICU and prolonged hospital stay. The overall median

duration of hospital stay was 27.40±10.16 days and 178 patients (53.3%) were admitted to

the intensive care ward.

3.1. Individual SNP analysis

13 polymorphic variants (11 SNPs and 2 CNV - GSTM1, GSTT1) were genotyped in

three groups from Russian population: 334 patients with community-acquired pneumonia

(CAP), 141 control subjects from Control group A (without CAP) and 314 healthy

individuals from general population (control B). The data are presented in Table 3. SNPs

were analyzed for Hardy-Weinberg equilibrium (HWE). The majority of SNPs were in HWE,

with just few showing a marginally-significant departure from HWE. Four of the 13 SNPs

showed a significant association with CAP. A comparison of distributions between cases and

Control group A revealed significant effects of SNPs – rs2606345T (CYP1A1), rs333Ins

(CCR5), rs4340Del (ACE), with the highest association detected for the CYP1A1 locus (SNP

rs2606345, P= 3.9 x 10-5). The frequency of deletion at the GSTM1 gene in control group A

slightly exceed those in cases and control group B. CAP susceptibility was also associated

with the GSTM1 gene insertion (homozygotes and heterozygotes) in case-control A study. It

should be noted that the effect of SNP rs2606345 is determined also in cases in comparison

with Control B group (P=1.4x10-5).

We additionally applied the logistic regression analysis and the Akaike information

criterion (AIC) to establish whether the effects of the alleles are best described by any of five

genetic models (codominant, dominant, overdominant, recessive, and log-additive). In case-

control A study the effects of rs1045642T (ABCB1) were best described by the log-additive

model (P=0.049, OR=1.34, 95% CI 1.01-1.78, AIC=577); the effects of rs1800795 (IL-6) by

the over-dominant model (G/G-C/C vs. G/C; P=0.033, OR=1.55, 95% CI 1.04-2.32,

AIC=563.7); and the effects of rs1799983 (NOS3) by the over-dominant model (G/G-T/T vs.

G/T; P=0.031, OR=1.56, 95% CI 1.04-2.32, AIC 567.8). Interestingly, for the both genes

regulating inflammation and immune response NOS3 and IL6 the protective effect for

heterozygotes was found.

To exclude the false-positive associations with CAP, we used Bonferroni correction

for multiple comparisons (13 polymorphic variants in cases and two control groups; 39

comparisons). The P-value after Bonferroni correction remained significant for CYP1A1 gene

(case-control A, P=0.01; case-control B, P=0.0036). The association between GSTM1 and

CAP was also confirmed in the case-control A using the 5% FDR test (P<0.0058).

3.2. Haplotype-based association analysis

Haplotype-based association analysis evaluates the joint effects of closely linked

genetic markers on a trait of interest. When compared to its single-marker counterparts, this

multi-marker approach can be more powerful to detect associations when the causal variants

are not genotyped (de Bakker et al., 2005; Zaitlen et al., 2007), have low frequency (de

Bakker et al., 2005, Schaid, 2004), or exhibit cis-acting effects (Clark, 2004; Schaid, 2004).

To evaluate the association between the haplotype CYP1A1 (rs2606345 and two other

CYP1A1 SNPs rs4646903 and rs1048943) and CAP, we estimated the standardized measure

of LD, D' (Table 4).

The values of LD did not significantly differ between the two control groups. The

analysis of CYP1A1 haplotype association with CAP was performed for case-control A and

case-control B studies. Protective effect of rs2606345G minor allele was pronounced for its

combination with two major alleles of rs4646903T and rs1048943A (haplotype N2), but

increased significantly when combined with variant allele rs4646903C (haplotype N3). We

therefore conclude that the interaction between these two SNPs can further increase the

protective effects of CYP1A1.

3.3. Multiple SNP analysis

We next established the cumulative effects of multiple SNPs at the analyzed genes on

CAP. A highly significant association between the number of risk genotypes (CYP1A1

rs2606345 T/T, GSTM1 I/*, ABCB1 T/T, IL-6 CC-GG, NOS3 TT-GG, CCR5 I/I , ACE D/D)

and CAP was found. Thus 85 and 111 subjects from control A and CAP group possessed 0-3

risk genotypes, respectively, whereas the incidence of subjects carrying from 4 to 7 risk

genotypes in the CAP group substantially exceed that in control (180 vs. 47; OR=3.03, 95%

CI 1.98-4.64, P=2.4 x 10-7). These effects were further confirmed by the comparison of

relative frequency of risk genotypes in the CAP group and controls (Figure 2).

We used area under the curve (AUC) statistics of the receiver operating characteristic

(ROC) to assess the overall performance of estimated risk in discriminating CAP cases and

controls. ROC is a plot of the sensitivity vs. (100-specificity) of classifying cases at various

thresholds. AUC quantifies the overall ability to discriminate between those who have the

disease and those who do not have the disease and ranges from 0.5 (no effect) to 1 (complete

effect). AUC for our panel, consisting of 7 genetic variations is 68.38 (Figure 3), which is

rather high for genetic markers.

After correction for multiple comparisons, 5 genes (ABCB1, NOS3, IL6, CCR5, ACE)

in case-control A study proved to be insignificantly associated with CAP. To assay if the two

associated in single gene analysis markers (CYP1A1 and GSTM1) are responsible for the

whole significance of the association effect, multiple gene analysis was carried out for either

these or five remaining SNPs. Two-gene analysis revealed a less pronounced association

between number of risk genotypes and CAP susceptibility. For no risk genotype (N=47 in

control, N=56 in CAP group) versus 1-2 risk genotypes (N=86 in control, N=174 in CAP

group) P-value was 3.8 x 10-5 (OR=2.67, 95% CI 1.70-4.22). At the same time, the remaining

SNPs (ABCB1, NOS3, IL6, CCR5, ACE) showed the cumulative effects of these genotypes on

CAP susceptibility. For 0-2 risk genotypes (N=76 in control, N=121 in CAP) versus 3-5 risk

genotypes (N=121 in control, N=176 in CAP) association was significant P=1.6 x 10-3,

OR=1.97, 95% CI 1.30-2.99. These results suggest that the five risk genotypes ABCB1 T/T,

NOS3 T/T-G/G, IL-6 C/C-G/G, CCR5 I/I and ACE D/D contribute to the whole association

enhancing its significance and OR.

3.4. Test power analysis for case-control association study

The statistical power of association studies strongly depends on the sample size,

which is particularly true for minor alleles with frequencies below 0.25. As a number of

minor alleles with the frequencies, p, ranging between 0.06 and 0.31 (CYP1A1 rs1048943G,

p=0.06, rs4646903C, p=0.25, GSTT1 D/D, p=0.16, TNF-α rs1800629A, p= 0.14, and MBL2

rs7096206G, p=0.31) did not show significant risk associations with CAP, we estimated the

statistical power of our study. To detect OR=1.5 in our sample size for the whole case-control

A group, the power for ranges from 11.9% to 42.91% for minor allele frequencies 0.06 and

0.31, respectively (two-sided Fisher’s test). The effects of minor alleles can therefore be

detected by profiling much larger number of control and CAP-affected subjects. On the other

hand, the results of Bonferroni correction and FDR test showed that in our study the type 1,

statistical error was avoided.

4. Discussion

The results of our study demonstrate that a number of SNPs show a significant

association with CAP. We found that three xenobiotics detoxification genes (CYP1A1 - first

stage of detoxification - activation, GSTM1 - second stage - actually detoxification and

ABCB1 - P-glycoprotein - efflux transporter) are apparently (CYP1A1 and GSTM1) or

nominally (ABCB1) associated with CAP in Russian population.

CYP1A1 is by far the most widely studied human pulmonary CYP gene regulating

enzyme, expressed mainly in the epithelium of the peripheral airways, i.e. bronchiolar,

terminal bronchiolar and alveolar epithelium. CYP1A1 is highly inducible by exposure to

PAH, polychlorinated biphenyls (PCBs) and other chemicals capable of binding to and

activating the Ah receptor. The CYP1A1 SNPs included in our study are functional. Thus, the

intron-located rs2606345G/T determines enhanced gene expression in the presence of

specific substrates (allele G) or in their absence (allele T) (Rotunno et al., 2009; Wang et al.

2008). The minor allele at the CYP1A1 SNP rs4646903C, located near Gene-3, also shows an

increased inducibilty (Meletiadis et al., 2006), and, according to our data, enhances the

protective effect of rs2606345G in haplotype G-C (Table 2). Another CYP1A1 SNP

rs1048943A/G (Ile/Val), resulted in a missense amino acid substitution, is characterized by

the substrate-specific increased activity for minor allele G and is not associated with CAP

susceptibility.

Resolution of inflammation depends on the removal of apoptotic cells as on active

suppression of inflammatory mediator production. The elimination of apoptotic cells and cell

bodies by phagocytes prevent exposure of surrounding tissue to potentially cytotoxic,

immunogenic, or inflammatory cellular content (Kang et al., 2010). Proinflammatory

cytokines and endogenous and exogenous xenobiotics (for example endogenous harmful

metabolites and drugs intermediates) may interact to potentiate both DNA damage and the

inflammatory response. This interaction is compounded by the fact that TNF-α inhibits

apoptotic cell clearance in the lung, even more exacerbating acute inflammation (Borges et

al., 2009). Genetically determined variability in CYP1A1 activity may contribute to

augmentation of inflammation.

According to our data, the GSTM1 insertion represents another important component

of genetic factors predisposing to CAP. However, in contrast to the CYP1A1 effects, there is

no evidence for the interaction with occupational exposure. As the GSTM1 enzyme has a

broad substrate specificity, at the beginning of the treatment the GSTM1(+) subjects can more

efficiently metabolize a number of drugs, thus belonging to a group of “fast metabolizers”.

Another hypothesis might deal with “proapoptotic” GSTM1 activity. It was shown that

during exposure to oxidative stress and other stress stimuli, the GSTM1 protein directly

regulates Ask1 (apoptosis signal-regulating kinase 1), an upstream activator of the stress-

activated protein kinase p38 (Cho et al., 2001). It was also shown that the Ask1 activation

involves the heat shock-induced dissociation of its inhibitor, glutathione S-transferase Mu1-1

GSTM1-1 (Dorion et al., 2002). Heat shock at the beginning of inflammation might be

attributed to the specific stress stimuli running GSTM1 mediated apoptosis extending

inflammation processes.

The ABCB1 rs1045642T/T genotype was nominally associated with CAP in our

study, though the results were insignificant after correction for multiple comparisons.

Nevertheless, this risk genotype can make a certain contribution to the overall cumulative

effect of CAP-associated risk genotypes (Figure 1). The ABCB1 gene encodes a membrane-

bound transporter that actively effluxes a wide range of compounds from cells. ABCB1 is

expressed in various tissues to protect them from the adverse effect of toxins. The

pharmacokinetics of drugs that represent substrates for the ABCB1 enzyme may also

influence disease outcome and treatment efficacy. Although ABCB1 is a well conserved gene,

there is increasing evidence that its polymorphisms affect substrate specificity and may be the

expression level and/or stability of its mRNA (Fung and Gottesman, 2009). For example, it

has been shown that under experimental acute inflammation ABCB1 mRNA expression is

significantly down-regulated in PBMCs (peripheral blood mononuclear cells) obtained from

the subjects harboring 3435C in comparison to those without it (Markova et al., 2006). We

therefore suggest that during inflammation the fast efflux of drugs in the subjects carrying

3435T/T genotype may contribute to the progression of the disease.

Three polymorphic variants of genes regulating immune response and inflammation

development were associated (though insignificantly after correction for multiple

comparisons) with CAP.

It should be noted that to date the results of a number of studies on the effects of IL-6

and NOS3 genes polymorphism on inflammation remain highly controversial. Thus according

to Terry and co-authors, the IL-6 − rs1800795G/C polymorphism affects transcription by

altering the serum levels of IL-6, with the C allele associated with significantly lower levels

of plasma IL-6 (Terry et al., 2000). However, the results of the association studies are indeed

controversial as they show the deleterious effects of the minor allele IL-6 174C (Martín-

Loeches et al., 2012; Schaaf et al., 2005), the absence of association (Chauhan and McGuire,

2008; Schlüter et al., 2002), and even suggest its protective role (Marshall et al., 2002).

Functional studies of NOS3 rs1799983G/T polymorphism have demonstrated that minor T

allele codes a protein with decreased enzymatic activity (Wang et al., 2000), leading to a

reduction in nitric oxide production (Veldman et al., 2002). For NOS3 polymorphism the

deleterious effect for rs1799983T allele was demonstrated (Ma et al., 2011) as well as the

protective effect of this minor allele which may be attributed to decreased inflammatory

signaling (Hildebrandt et al., 2010).

The immune response may vary depending on the causative pathogen, clinical and

ethnic heterogeneity of studied groups and some other factors (Cooper et al., 2002; Rosseau

et al., 2007). One of them might be molecular heterosis which is common in humans and may

occur in up to 50% of all gene associations (Comings and MacMurray, 2000). Molecular

heterosis is attributed to the phenomenon of overdominance, where the phenotype of

heterozygotes is superior to that of homozygotes. Heterozygosity may facilitate the dynamic

balance between proinflammatoty and antiinflammatory factors, thus providing better defense

against exposure to a wider range of causative agents, exogenous and endogenous pathogens.

It therefore appears that the abovementioned protective effects for heterozygotes at the NOS3

and IL-6 loci may be attributed to this phenomenon.

The CCR5 chemokine receptor is exploited by HIV-1 to gain entry into the CD4+ T

cells. A deletion mutation (del32) confers resistance against HIV by obliterating the

expression of the receptor on the cell surface. The allele exists at appreciable frequencies

only in Europe, and within Europe, the frequency is higher in the north. Several studies have

demonstrated that polymorphism at the CCR5 locus in both the coding and regulatory regions

affects susceptibility to HIV infection. The much-studied CCR5-del32 allele is a 32-bp

deletion that introduces a premature stop codon into the CCR5 chemokine-receptor locus and

thus obliterates the receptor. CCR5- del32 is at average allele frequency of 10% across

Europe, translating into a homozygote frequency of about 1% (Galvani and Novembre,

2005). Bubonic plague as well as smallpox could have posed sufficient selection to account

for the rise of the CCR5-del32 allele in Europe. The central role that chemokine receptors

play in the inflammatory immune response makes it likely that the obliteration of CCR5

would have negative fitness repercussions in the absence of a compensatory protective effect.

However, to date no effects associated with this deletion have been documented in clinical

studies. Moreover, nonfunctional CCR5- del32 increases the likelihood of recovery from

hepatitis B in humans (Thio et al., 2007), reduces the risk of acute rejection in liver

transplantation (Heidenhain et al., 2009), lowers the risk of developing acute graft-versus-

host disease after allogeneic hematopoietic stem cell transplantation (Bogunia-Kubik et al.,

2006), and also attenuates the adverse effects of inflammation on overall and cardiovascular

mortality in ESRD (end-stage renal disease) (Muntinghe et al., 2009). Our data on the

protective effects del32 in CAP group are therefore in line with these results.

We also detected the protective effect of ACE insertion in CAP-control A study. The

insertion/deletion (I/D) polymorphism at this gene substantially affects the serum level of the

ACE protein. The I/D polymorphism has been reported to account for 47% of the variance in

serum ACE level, whereas the DD genotype is associated with the highest levels of serum

ACE (Rigat et al., 1990). It has also been shown that the effects of I/D polymorphism on

serum ACE level may not be directly related to the presence of a deletion per se, but

attributed to its strong linkage with the G2350A ACE allele (Zhu et al., 2001). Given that the

DD homozygotes have a lower cough reflex compared with the carries of II and ID genotypes

(Takahashi et al., 2001) and the decreased serum level of the proinflammatory angiotensin-II

(Reyes-Engel et al., 2006), the ACE I/D polymorphism can also predispose to pneumonia.

The study presented some limitations that should be addressed. Our case and control

A groups were well-stratified, but patients were rather young predominantly between the ages

of 16 and 30 (271, 81%). Our results may not be generalizable to older women or men.

In summary, we have provided the first experimental evidence for the associations of

allelic variation at genes coding detoxification enzymes with the risk of CAP. Our results

also demonstrate that predisposition to CAP is strongly attributed to the effects of a number

of genes with low penetrance and therefore imply that inter-locus interactions may be

regarded as an important component of polygenic and multifactorial factors of susceptibility

to CAP. Finally, our data on the effects of heterozygosity at immune and inflammation genes

can potentially partly explain the existing inconsistency of the results of some recent

association studies.

Acknowledgements

This work was supported by a Grant of Presidium of the Russian Academy of Sciences

program “Fundamental sciences for medicine”-2012; Fundamental Research Programs of the

Russian Academy of Sciences "Biodiversity and dynamics of gene pools" (2006-2010);

"Biological Diversity", subdivision "Gene Pools and Genetic Diversity" (2011-2012).

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Table 1. Charateristics of the sample and control groups involved in the study.

Feature Control A n (%) Control B n (%) CAP n (%) Total number 141 314 334 Age 21.06 ± 0.42 41.65 ± 1.03 26.93±0.82 Sex Male Female

130 (92.2) 11 (7.8)

286 (91.1) 28 (8.9)

307 (91.9) 27 (8.1)

Progression Bilateral Unilateral

- - 98 (29.34) 236 (70.66)

Background diseases: No Yesa

- - 326 (97.6) 8 (2.4)

Co-morbidity: No Yesb

- - 303(90.72) 31(9.28)

ICU admission No Yes

- - 156 (46.71) 178 (53.29)

Infectious pathogens Streptococcus pneumoniae Monoflora S. pneumoniae with other infectious pathogens Other infectious pathogens

- - 108 (32.34)c 70 (39.33) 12 (17.14) 58 (82.86) 38 (21.35)

Duration of hospitalization - - 27.40±10.16 (days) Hospital mortality - - 12 (3.59) a Ischemic cardiopathy, diabetes, obesity. b The most frequently purulent antritis (51.61%).

cData available for 108 cases only.

Table 2. Summary on primer sequencesa, amplicons, and PCR cycling conditions for

individual SNPs analyzed in the study.

rs# of SNP Primer sequences 5’-3’, external forward/reverse, internal allele-specific forward/reverse

PCRb Amplicon length (bp)

CYP1A1 rs2606345

F gggagatggatggttcctaccac 94 оC (30s) 65.5 оC (18s) 72 оC (30s) 34x

388 R cctcctaagggtggcttgtcagt F(c) cctgcagttggcaatctgtcac 155 R(a) cctttgctgggagacaatcaggt 277

CYP1A1 rs4646903

F aggaggtagcagtgaagaggtg 94 оC (27s)

63.2 оC (18s) 72 оC (30s) 34x

373 R ggcgtaagtcagcacagtgatt F(c) tcactgtaacctccacctccc 250 R(t) ggagaatcgtgtgagccca 162

CYP1A1 rs1048943

F gagctccactcacttgacacttct 94 оC (30s) 58 оC (18s) 72 оC (30s) 32×

465 R cagtgtctatgagtttcaggctgaatctt F(a) gaagtgtatcggtgagacca 285 R(g) ctcccagcgggcaac 211

GSTM1 Ins/del GSTT1 Ins/Del

F gaactccctgaaaagctaaagc 94 оC (30s) 60 оC (18s) 72 оC (30s) 32x

219 R gttgggctcaaatatacggtgg F ttccttactggtcctcacatctc 459 R tcaccggatcatggccagca

ABCB1 rs1045642

F tgttttcagctgcttgatgg 94 оC (30s) 61.5 оC (20s) 72 оC (30s) 32x

377 R aacccaaacaggaagtgtgg

F(c) gtgttgtcacaggaagagatc 236 R(t) tcctttgctgccctcaca 179

ACE rs4340 CCR5 rs333

F ctgctgcctatacagtcacttt 94 оC (20s) 56 оC (20s) 72 оC (40s) 33x

436 R gtggccatcacattcgtcagat 149 F gtggtgacaagtgtgatcactt 240 R tcgacaccgaagcagagttttta 208

IL-6 rs1800795

F tgcatgacttcagctttactctttg 94 оC (40s) 64.5 оC (18s) 72 оC (40s)

375 R ggggagatagagcttctctttcgtt F(g) ccccctagttgtgtcttgcg 241

R(c) gcaatgtgacgtcctttagcatg 32x 176 TNF-α rs1800629

F cctcaaggactcagctttctgaag 94 оC (40s) 63 оC (20s) 72 оC (40s) 33x

312 R accttctgtctcggtttcttctcc F(g) caataggttttgaggggcatgg 195 R(a) gaggctgaaccccgtcct 156

NOS3 rs1799983

F ggagatgaaggcaggagacagt 94 оC (40s) 63 оC (20s) 72 оC (40s) 33x

465 R cgatctcagtgctcatgtaccag F(g) tgcaggccccagatgag 314 R(t) cagaaggaagagttctggggga 189

MBL2 rs7096206

F gcatgccctctgtcctacaatc 94 оC (40s) 63о C (20s) 72 оC (40s) 33x

349 R ccgaagaggacatggagagaaa

F(g) gctggaagactataaacatgctttcg 152 R(c) ccatttgttctcactgccacg 243

ACE rs4340 CCR5

F ctgctgcctatacagtcacttt 94 оC (20s) 56 оC (20s) 72 оC (40s)

436 R gtggccatcacattcgtcagat 149 F gtggtgacaagtgtgatcactt 240

rs333 R tcgacaccgaagcagagttttta 33x 208 MTHFR rs1801133

F gccactcactgttttagttcaggctgtg 94 оC (40s) 70.2 оC (18s) 72 оC (40s) 30x

390 R tggagggagcttatgggctctcc F(t) gaaggagaaggtgtctgcgggagt 174 R(c) aagctgcgtgatgatgaaatcgg 262

a The Primer3 algorithm was used to design external primer sequences.

b PCR conditions are provided as temperature (duration) of denaturation, annealing and

extension phase, respectively, followed by the number of cycles for a given SNP. Each PCR

reaction was finished by a final extension phase lasting 5 min.

Table 3. The distribution of genotypes among CAP and two control groupsa.

Genes and genotypes

Community-acquired pneumonia

Control subjects (main control group – control A)

P-valueb for dominant (dom) or recessive (rec)

modelc, OR, 95% CI (Case-control A)

Healthy control subjects (population control group –

control B)

P-valueb for dominant (dom) or recessive (rec)

modeld, OR, 95% CI (Case-control B)

Number (%) HWP Number (%) HWP Number (%) HWP

Xenobiotics detoxification

n=334 n=134 n=311

CYP1A1 rs2606345

G/G 37 (11.08) P=0.002

25 (18.66) P=0.677

P=3.9x10-5 (rec)e OR=2.40 1.59-3.64

38 (12.22) P=0.311

P=1.4x10-5 (rec)f OR=2.00 1.46-2.74

T/G 111 (33.23) 63 (47.01) 153 (49.20) T/T 186 (55.69) 46 (34.33) 120 (38.59)

n=323 n=131 n=280

CYP1A1 rs4646903

T/T 265 (82.04) P=0.256

98 (74.81)

P=0.099

P=0.093 OR=1.54 0.95-2.50

218 (77.86) P=0.128

P=0.220 OR=1.30 0.87-1.94 T/C 57 (17.65) 33 (25.19) 61 (21.79)

C/C 1 (0.31) 0 (0.00) 1 (0.36) n=323 n=132 n=281

CYP1A1 rs1048943 Ile462Val

A/A 291 (90.09)

P=0.349

124 (93.94)

P=0.720

P=0.207 OR=1.70 0.77-3.79

256 (91.10) P=0.435

P=0.780 OR=1.13 0.65-1.95 A/G 32 (9.91) 8 (6.06) 25 (8.90)

G/G 0 (0.00) 0 (0.00) 0 (0.0) n=331 n=140 n=307 GSTM1 Del(D/D)-Ins (I/*)

D/D 134 (40.48) -

78 (55.71) -

P=0.0032g OR=1.85 1.24-2.75

140 (45.60) -

P=0.20 OR=0.81 0.59-1.11 I/ 197 (59.52) 62 (44.29) 167 (54.40)

n=331 n=140 n=307

GSTT1 Del(D/D)-Ins (I/*)

D/D 63 (19.03) -

22 (15.71) -

P=0.433 OR=1.26 0.74-2.14

42 (13.68) -

P=0.112 OR=1.43 0.94-2.18 I/ 268 (80.97) 118 (84.29) 265 (86.32)

n=332 n=141 n=314

ABCB1 rs1045642 Ile1145=

C/C 53 (15.96) P=0.066

31 (21.99) P=0.261

P=0.081 (dom) OR=1.46 0.97-2.20

54 (17.20) P=0.890

P=0.053 (dom) OR=1.37 1.00-1.88

T/C 139 (41.87) 63 (44.68) 151 (48.09) T/T 140 (42.17) 47 (33.33) 109 (34.71)

Immune response regulation and inflammation

n=322 n=139 n=306

IL-6 rs1800795

C/C 69 (21.43) P=0.299

22 (15.83) P=0.052h

P=0.202 (dom) OR=1.45 0.86-2.45

54 (17.65) P=0.238

P=0.269 (dom) OR=1.27 0.86-1.89

C/G 150 (46.58) 80 (57.55) 137 (44.77) G/G 103 (31.99) 37 (26.62) 115 (37.58)

n=321 n=139 n=313

TNF-α rs1800629

A/A 5 (1.56) P=0.891

0 (0.00) P=0.054h

P=0.238 OR=1.32 0.84-2.08

0 (0.00) P=0.047h

P=0.632 OR=0.91 0.62-1.32

G/A 68 (21.18) 39 (28.06) 66 (21.09) G/G 248 (77.26) 100 (71.94) 247 (78.91)

n=321 n=141 n=314

NOS3 rs1799983 Asp298Glu

G/G 185 (57.63) P=0.751

67 (47.52)

P=0.109

P=0.054 (dom) OR=1.50 1.01-2.23

157 (50.00)

P=0.240

P=0.056 (dom) OR=0.74 0.54-1.00 G/T 116 (36.14) 66 (46.81) 136 (43.31)

T/T 20 (6.23) 8 (5.67) 21 (6.69) n=277 n=120 n=231

MBL2 rs7096206

C/C 194 (70.04)

P=0.345

85 (70.83)

P=0.580

P=0.905 OR=1.04 0.65-1.66

166 (71.86)

P=0.877

P=0.695 OR=1.09 0.74-1.60

C/G 73 (26.35) 31 (25.83) 60 (25.97)

G/G 10 (3.61) 4 (3.33) 5 (2.16)

n=319 n=141 n=313 CCR5 rs333 Del32

I/I 281 (88.09) P=0.851

113 (80.14) P=0.654

P=0.03 (rec) OR=1.83 1.07-3.12

259 (82.75) P=0.142

P=0.071 OR=1.54 0.99-2.41

I/D 37 (11.60) 27 (19.15) 49 (15.65) D/D 1 (0.31) 1 (0.71) 5 (1.60)

Renin-angiotensin system n=321 n=141 n=313 ACE rs4340 Alu-287 bp

I/I 78 (24.38) P=0.014

41 (29.08) P=0.990

P=0.014 (rec) OR=1.81 1.14-2.88

82 (26.20) P=0.337

P=0.098 (rec) OR=1.35 0.96-1.91

I/D 137 (42.81) 70 (49.65) 148 (47.28) D/D 105 (32.81) 30 (21.28) 83 (26.52)

Occlusive vascular disease n=316 n=131 n=278

MTHFR rs1801133 Ala222Val

C/C 134 (42.41) P=0.467

56 (42.75) P=0.217

P=0.495 (rec) OR=1.32 0.65-2.68

124 (44.60) P=0.216

P=0.409 (rec) OR=1.28 0.74-2.21

C/T 148 (46.84) 64 (48.85) 130 (46.76) T/T 34 (10.76) 11 (8.40) 24 (8.63)

aGenotypes associated with CAP are highlighted in grey; significant results are in bold.

bTwo-tailed P-value in Fisher’s test.

cThe genetic model is presented for risk genotype independently of its frequency.

dThe same genetic model as in ‘case-control A’.

e,fSignificant after Bonferroni correction for multiple comparisons: Pe=0.010, Pf=0.00364

gSignificant after 5%FDR correction for multiple comparisons P<0.00577

hBorderline significance for HWE in the control.

Table 4. LD statistics and CYP1A1 haplotype association with CAP

CYP1A1 (15q24.1) D’ statistics (over diagonal), P-value (under diagonal)

CAP Control A Control B rs2606345 rs4646903 rs1048943 rs2606345 rs4646903 rs1048943 rs2606345 rs4646903 rs1048943

rs2606345 0.687 0.729 rs2606345 0.999 0.997 rs2606345 0.966 0.998

rs4646903 0 0.632 rs4646903 0 0.839 rs4646903 0 0.951

rs1048943 8∙10-4 0 rs1048943 0 0 rs1048943 0 0

CYP1A1 haplotype associations rs4646903 rs1048943 rs2606345 Haplotype frequencies Case-Control A Case-Control B

Chr. position 75011641 75012985 75017176 CAP Control A Control B OR (95% CI) P value OR (95% CI) P value

N1 T A T 0.6990 0.5803 0.6295 1a --- 1 ---

N2 T A G 0.1928 0.2892 0.2511 0.58 (0.41-0.81) 0.0014 0.70 (0.54-0.92) 0.01 N3 C A G 0.0390 0.1002 0.0704 0.33 (0.18-0.62) 6∙10-4 0.52 (0.30-0.90) 0.019 rare * * * 0.0692 0.0303 0.049 1.93 (0.87-4.30) 0.11 1.26 (0.74-2.14) 0.39 aReference odds ratio (OR) was accepted as OR=1 for the major haplotype; significant results are in bold.

33

Figure 1. Age distribution of patients and controls.

34

Figure 1. Relative frequency distribution by the number of risk genotypes of the SNPs in genes

associated with CAP in case and control groups (CYP1A1 rs2606345, GSTM1, ABCB1, ACE,

CCR5, NOS3, IL-6). Mean and standard error (SE) of relative frequencies are shown.

35

Figure 3. ROC-curve for the aggregation of genetic markers CYP1A1 T606G, GSTM1, ABCB1,

ACE, CCR5, NOS3, IL-6 associated with CAP.