key: cord-0825719-095vxp36
authors: Guin, Debleena; Yadav, Saroj; Singh, Priyanka; Singh, Pooja; Thakran, Sarita; Kukal, Samiksha; Kanojia, Neha; Paul, Priyanka Rani; Pattnaik, Bijay; Sardana, Viren; Grover, Sandeep; Hasija, Yasha; Saso, Luciano; Agrawal, Anurag; Kukreti, Ritushree
title: Human genetic factors associated with pneumonia risk, a cue for COVID-19 susceptibility
date: 2022-05-08
journal: Infect Genet Evol
DOI: 10.1016/j.meegid.2022.105299
sha: e3cc042f30cf8542cdb703a0132db45e9c4c30a0
doc_id: 825719
cord_uid: 095vxp36

Pneumonia, an acute respiratory tract infection, is one of the major causes of mortality worldwide. Depending on the site of acquisition, pneumonia can be community acquired pneumonia (CAP) or nosocomial pneumonia (NP). The risk of pneumonia, is partially driven by host genetics. CYP1A1 is a widely studied pulmonary CYP family gene primarily expressed in peripheral airway epithelium. The CYP1A1 genetic variants, included in this study, alter the gene activity and are known to contribute in lung inflammation, which may cause pneumonia pathogenesis. In this study, we performed a meta-analysis to establish the possible contribution of CYP1A1 gene, and its three variants (rs2606345, rs1048943 and rs4646903) towards the genetic etiology of pneumonia risk. Using PRISMA guidelines, we systematically reviewed and meta-analysed case-control studies, evaluating risk of pneumonia in patients carrying the risk alleles of CYP1A1 variants. Heterogeneity across the studies was evaluated using I2 statistics. Based on heterogeneity, a random-effect (using maximum likelihood) or fixed-effect (using inverse variance) model was applied to estimate the effect size. Pooled odds ratio (OR) was calculated to estimate the overall effect of the risk allele association with pneumonia susceptibility. Egger's regression test and funnel plot were used to assess publication bias. Subgroup analysis was performed based on pneumonia type (CAP and NP), population, as well as age group. A total of ten articles were identified as eligible studies, which included 3049 cases and 2249 healthy controls. The meta-analysis findings revealed CYP1A1 variants, rs2606345 [T vs G; OR = 1.12 (0.75–1.50); p = 0.02; I2 = 84.89%], and rs1048943 [G vs T; OR = 1.19 (0.76–1.61); p = 0.02; I2 = 0.00%] as risk markers whereas rs4646903 showed no statistical significance for susceptibility to pneumonia. On subgroup analysis, both the genetic variants showed significant association with CAP but not with NP. We additionally performed a spatial analysis to identify the key factors possibly explaining the variability across countries in the prevalence of the coronavirus disease 2019 (COVID-19), a viral pneumonia. We observed a significant association between the risk allele of rs2606345 and rs1048943, with a higher COVID-19 prevalence worldwide, providing us important links in understanding the variability in COVID-19 prevalence.

. Host genetic factors that participate in these processes starting from pathogen entry, infection, inflammation, and resolution can all be considered as good candidates in genetic association studies of pneumonia and its complications (Cooke, 2001; Kumar, 2014) . The difference in epidemiology, pathogenesis, microbiology, common causative organism, and pathophysiology between CAP and NP depends on the mode of acquisition of pneumonia infection, on host risk factors and other environmental changes (Herold C.J., 2004; Torres A, 2021) . Thus, understanding the functional impact of genetic determinants of susceptibility to pneumonia, both CAP and NP, independently is crucial for determining the mechanisms behind pneumonia pathogenesis.

CYP1A1 is a critical enzyme mediating the metabolism of a broad spectrum of xenobiotics and endobiotics [9] . There are several reports highlighting the functionally relevant genetic variants of CYP1A1 to play a clinically important role in several disease phenotypes. A number of studies have investigated the genetic association as well as gene interaction of pneumonia risk with monooxygenase enzyme group, cytochrome P450 (CYP). In a study, a group of researchers identified CYP1A1 gene as a critical regulator of inflammatory responses and phagocytosis in sepsis through signalling pathways that may be promising targets for treating inflammatory diseases (Tian LX, 2020) . A study by Fang X et al. (2016) demonstrated lower CYP1A1 expression in pigs infected with Mycoplasma hyopneumoniae (M. hyopneumoniae). They further extended their efforts by studying this gene in pulmonary alveolar macrophages (PAM) cell lines suggest CYP1A1 supresses inflammatory response caused by pneumonia infection (Fang X 2016) .

Interestingly, few other studies indicated the role of CYP1A1 genetic polymorphisms in infectious diseases and consequently establishing its role in inflammatory responses. Previously, it was identified that genetic variants of some host genes (CYP1A1, ACE and IL-6) are associated with the diversity in response to CAP (Salnikova et al., 2014; Zhao et al., 2017) . The selection of this gene was established based on its role in physiological and pathological processes during pneumonia infection, particularly in the immune and inflammatory responses (Zhao et al., 2017) .

From the systematic literature search performed for association of CYP1A1 genetic variants and pneumonia, the most widely reported CYP1A1 single nucleotide polymorphisms (SNPs) obtained were rs2606345, rs1048943 and rs4646903. These SNPs had functional consequences which may ultimately be involved with a disease phenotype like pneumonia. The SNP rs2606345 (C>A), is located in the first intron of the gene, has a functional role of lower gene expression in the presence of allele A (Rotunno, 2009; Talwar P, 2017). Another SNP, rs1048943 (T>A, C, G), resulted in a missense amino acid substitution, is characterized by the substrate-specific increased activity for minor allele G (Salnikova et al., 2013c) . The presence of minor allele "C" of the 3" untranslated region (UTR) SNP, rs4646903, shows an increased inducibility of CYP1A1 gene expression (Meletiadis, 2006; Salnikova et al., 2013c) . Thus we can suggest that genetically determined alteration of CYP1A1 expression could contribute to lung inflammation pathogenesis.

While there are several evidence of association between CYP1A1 polymorphisms with risk of pneumonia, there are studies which show conflicting results as well (Muñoz B, 2012; Smith GB, 2001) .

In this study we used a meta-analysis approach: 1) to investigate the impact of CYP1A1 risk allele and the risk of pneumonia (including both CAP and NP). This may increase the odds of the incident pneumonia; 2) to determine whether any association between CYP1A1 and pneumonia is generalizable to the coronavirus disease 2019 (COVID-19) as the ongoing COVID-19 pandemic and its consequent prevalence has rarely been examined through the lens of pneumonia. This genetic predisposition with pneumonia may help us in understanding the genetic etiology of COVID-19 infection and its prevalence.

This meta-analysis was conducted as per the recommendations of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines (Alessandro Liberati, 2009) following the PICOS (Population, intervention, comparison, outcome and study design) strategy. used to identify relevant studies were "CYP1A1", "Pneumonia", "genetic variant", and "single nucleotide polymorphisms" using AND/ OR Boolean operators. Cross references of each study retrieved were also examined for inclusion in case they discuss the effect of CYP1A1 genetic variant and its risk in pneumonia.

Two investigators (DG and SY) independently reviewed each study for its inclusion in the metaanalysis. The inclusion criteria were: (1) studies conducted on human population only, (2) included the effect of CYP1A1 genetic variant with available numeric data, (3) adopted a casecontrol study design (4) provided a detailed assay method. Studies (1) any other type of lung inflammation apart from pneumonia or pneumonia as a consequence of any exposure or J o u r n a l P r e -p r o o f pneumoniae infection is the most common form of CAP, the data from this article is included with CAP cohort. Patients having frequently recurring (relapsing) pneumonia (J18, according to the ICD-10) with unspecified organism of infection, this has also been included with CAP for easy interpretation. Sensitivity analyses were also performed to assess heterogeneity, to estimate the influence of any individual datasets, by omitting one study at a time and examining their influence on the combined effect.

We additionally conducted a spatial analysis (among countries) of the factors that might account for the variability among COVID-19 prevalence (total cases per million). Here we used countryspecific demographic and socio-economic variables (such as population density, GDP, median age, and many others), to discover any association pattern. The COVID-19 dataset used in the study was downloaded from ourworldindata.org on May 24, 2021. The population specific allele frequency data for rs2606345 and rs1048943 were obtained from 1000 genome browser (Consortium, 2015) on March 08, 2021. For this purpose, we initially ran a linear regression fit between COVID-19 prevalence, and the country-wise distribution of risk allele of CYP1A1 SNPs (rs2606345, and rs1048943). To further strengthen the robustness and precision of above association, and to determine the influence of other confounding variables along with the allelic distribution of CYP1A1 variants with COVID-19 prevalence, we considered 20 other predictor variables (socio-economic and demographic factors). To reduce the skewness of the data, all the variables were log-transformed before entering into the regression models. We additionally removed predictor variables with >30% missing data. We next examined the univariate relationships between predictor variables and COVID-19 prevalence to find candidate variables for our final multivariable model. The variables with p < 0.05 were considered for the multivariable models. All the analyses were performed in R 3.6.3.

Through the initial search, a total of 1,406 articles were identified (7 from PubMed, 9 from Web of science, 245 from Science Direct and 1,145 from worldwidescience.org). Based on initial screening of titles and abstract 1,292 publications were excluded after removing duplicates (n=30), leaving 84 articles for full text review. Among them 17 articles were removed as they discussed some other gene but not CYP1A1 gene or its genetic variants and 59 articles were J o u r n a l P r e -p r o o f Journal Pre-proof removed as they did not discuss any genetic association. Finally, 8 case control studies and 2 studies from their cross-references that met the pre-defined criteria, were included for the quantitative analysis. The flow chart for the study selection process is represented in Figure 1 .

The current systematic search included ten studies totalling 5,298 subjects (3,049 Table   1 . For cumulative quality assessment, three of ten articles were deemed as good quality (cut off score of ≥7), six articles (≥5-6 score) were categorised under moderate, and finally any scores below 5 were judged as poor quality which included one article (Supplementary table 1) .

This meta-analysis compares pneumonia patients as cases, comprising CAP and NP subjects both, with healthy controls for association of CYP1A1 genetic variants with pneumonia susceptibility

To maintain the precision in assessing the effect size in each meta-analyses performed, we (Figure 4) , when compared with healthy controls.

J o u r n a l P r e -p r o o f

As shown in Supplementary figure 1, visualization of the Begg"s funnel plot suggested that

Egger"s linear regression test yielded evidence of publication bias among the included studies, therefore, we further performed subgroup analysis and sensitivity analysis to assess the robustness and consistency of our meta-analysis findings.

Due to heterogeneity in the meta-analysis, we attempted to subgroup the studies. Subgroup analysis was performed based on pneumonia type (CAP and NP), different population (China and Russia), and age group (<12 years and >12years) ( Table 4 , showing no change from overall effect size after removing one study at a time.

Since rs2606345 (A) is the major allele in Europeans (66.6%) but not in other populations (African 5%, Asian 5-30%) (Consortium, 2015) , we explored its possible association, and the risk allele (C) of rs1048943, with high variability in regional prevalence of the ongoing COVID-19

pandemic, through a spatial analysis. The COVID-19 prevalence varied widely across countries ( Figure 5) . To assess the pattern of association between country-specific prevalence with all the candidate predictor variables (as shown in Supplementary table 2) from a univariate regression analysis for each individual predictor. We found that all the variables except population, population density, diabetes prevalence, and male smokers were significantly associated with prevalence in these univariate models. Excluding these non-significant variables and variables 

This study is an attempt to investigate the role of host genetic factors in pneumonia susceptibility. Our study revealed the association of rs2606345 and rs1048943 with risk of pneumonia, particularly CAP. The alternate allele (A in plus-strand or T in minus-strand) of rs2606345 increased pneumonia susceptibility. Despite following a stringent criterion for inclusion of studies in the present study, the possibility of observer bias in diagnosing pneumonia cannot be ruled out when dealing with retrospective records. We, therefore advice readers caution, when interpreting the results. There is a need to conduct large scale cohort studies in future to validate our findings.

We also observed, this allele to be the major allele in European (66.6%) and Russian (~80%) population unlike in the other populations (African 5%, Asian 5-30%, American 39%) (Consortium, 2015) . Interestingly, on spatial analysis, we noted significant association of this allele frequency distribution with total cases per million due to the recent outbreak of COVID-19 ( Figure 6) . Likewise, for rs1048943, higher the risk allele "C" frequency also showed association with COVID-19 prevalence, across countries worldwide (Figure 7) . We would also like to warn J o u r n a l P r e -p r o o f that since this is an ongoing pandemic, the numbers are changing with time and this is a circumstantial evidence.

The COVID-19, a viral pneumonia (Berlin, 2020; Gandhi, 2020) , is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Kim, 2020; Wu, 2020; Yao, 2020; ). This activates the host immune system to release several inflammatory cytokines (IL-1, IL-6, TNFα, and IFNγ) (Li J, 2020). There is a very fine balance between the immune response produced by the host cells on encountering SARS-CoV-2 and lung damage. The immune cells accelerate the production of cytokines to kill the pathogen. In case of cytokine rush, such accelerated immune response may in turn, damage the host cells. Several studies correlated the downregulation of CYP1A1 with increasing pro-inflammatory cytokines levels (IL-6, TNF-α, IL-1, IL-1β) (Wang, 2022) . In this study we speculate that CYP1A1 could be crucial in COVID-19

prevalence. Since this study establishes the association of CYP1A1 genetic variants with pneumonia susceptibility and one of the major symptoms in patients dying with COVID-19

infection is pneumonia (Guan WJ, 2020; Surendra H, 2021). Pneumonia may be considered a proxy phenotype for studying association with CYP1A1 with COVID-19. We also observed in this study, there is a positive correlation of "A" allele frequency of CYP1A1 SNP (rs2606345)

with COVID-19 prevalence among populations worldwide. Since CYP1A1 plays a vital role in innate immune response (inflammatory responses) against any kind of infection (Stading R, 2020), specifically in lungs (Fang X 2016) . According to the GTEx database (https://gtexportal.org/home/gene/CYP1A1), the expression of this gene is the highest in lungs with >1500 transcript per million (TPM) expression. This is almost 3 times higher than the average expression of CYP1A1 in other tissues like adipose, breast, liver, and skin (where the TPM is ~500) (Consortium, 2013) . Also, interestingly, the biological role of the allele "A" or "T" of rs2606345 is known to have ~70-80% reduced CYP1A1 promoter activity thereby reducing the enzyme activity as well (Talwar P, 2017) . Therefore, we speculate that due to the reduced enzyme activity in individuals carrying A or T allele, they are at a higher risk of pneumonia. 

Financial support for this research work has been provided by the Council of Scientific and Industrial Research (CSIR), grant number OLP1154.

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.

We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). She is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The square and horizontal lines correspond to the study-specific odds ratio(OR) and 95% confidence interval (CI). The area of the square refers to the study specific weight (Fixed effect; inverse of variance).The diamond represents the summary of OR and 95% CI. The square and horizontal lines correspond to the study-specific odds ratio(OR) and 95% confidence interval (CI). The area of the square refers to the study specific weight (fixed effect; inverse of variance).The diamond represents the summary of OR and 95% CI. The data is obtained from population frequency data of the 1000genome browser on 8 March, 2021. The white coloured areas in the map show the absence of data. A half open intervals includes only one of its end-points and is denoted by mixing notations for open and closed intervals. For e.g., (0-1] means greater than 0 and less than or equal to 1 and [0,1) means greater than or equal to 0 and less than 1.

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