A Pilot Cross-Sectional Study of Immunological and Microbiome Profiling Reveals Distinct Inflammatory Profiles for Smokers, Electronic Cigarette Users, and Never-Smokers

Smokers (SM) have increased lung immune cell counts and inflammatory gene expression compared to electronic cigarette (EC) users and never-smokers (NS). The objective of this study is to further assess associations for SM and EC lung microbiomes with immune cell subtypes and inflammatory gene expression in samples obtained by bronchoscopy and bronchoalveolar lavage (n = 28). RNASeq with the CIBERSORT computational algorithm were used to determine immune cell subtypes, along with inflammatory gene expression and microbiome metatranscriptomics. Macrophage subtypes revealed a two-fold increase in M0 (undifferentiated) macrophages for SM and EC users relative to NS, with a concordant decrease in M2 (anti-inflammatory) macrophages. There were 68, 19, and 1 significantly differentially expressed inflammatory genes (DEG) between SM/NS, SM/EC users, and EC users/NS, respectively. CSF-1 and GATA3 expression correlated positively and inversely with M0 and M2 macrophages, respectively. Correlation profiling for DEG showed distinct lung profiles for each participant group. There were three bacteria genera-DEG correlations and three bacteria genera-macrophage subtype correlations. In this pilot study, SM and EC use were associated with an increase in undifferentiated M0 macrophages, but SM differed from EC users and NS for inflammatory gene expression. The data support the hypothesis that SM and EC have toxic lung effects influencing inflammatory responses, but this may not be via changes in the microbiome.

Saliva and Lung Microbiome Associations with Electronic Cigarette Use and Smoking

The microbiome has increasingly been linked to cancer. Little is known about the lung and oral cavity microbiomes in smokers, and even less for electronic cigarette (EC) users, compared with never-smokers. In a cross-sectional study (n = 28) of smokers, EC users, and never-smokers, bronchoalveolar lavage and saliva samples underwent metatranscriptome profiling to examine associations with lung and oral microbiomes. Pairwise comparisons assessed differentially abundant bacteria species. Total bacterial load was similar between groups, with no differences in bacterial diversity across lung microbiomes. In lungs, 44 bacteria species differed significantly (FDR < 0.1) between smokers/never-smokers, with most decreased in smokers. Twelve species differed between smokers/EC users, all decreased in smokers of which Neisseria sp. KEM232 and Curvibacter sp. AEP1-3 were observed. Among the top five decreased species in both comparisons, Neisseria elongata, Neisseria sicca, and Haemophilus parainfluenzae were observed. In the oral microbiome, 152 species were differentially abundant for smokers/never-smokers, and 17 between smokers/electronic cigarette users, but only 21 species were differentially abundant in both the lung and oral cavity. EC use is not associated with changes in the lung microbiome compared with never-smokers, indicating EC toxicity does not affect microbiota. Statistically different bacteria in smokers compared with EC users and never-smokers were almost all decreased, potentially due to toxic effects of cigarette smoke. The low numbers of overlapping oral and lung microbes suggest that the oral microbiome is not a surrogate for analyzing smoking-related effects in the lung.

PREVENTION RELEVANCE

The microbiome affects cancer and other disease risk. The effects of e-cig usage on the lung microbiome are essentially unknown. Given the importance of lung microbiome dysbiosis populated by oral species which have been observed to drive lung cancer progression, it is important to study effects of e-cig use on microbiome.

Memory B cell repertoire for recognition of evolving SARS-CoV-2 spike

Memory B cell reserves can generate protective antibodies against repeated SARS-CoV-2 infections, but with unknown reach from original infection to antigenically drifted variants. We charted memory B cell receptor-encoded antibodies from 19 COVID-19 convalescent subjects against SARS-CoV-2 spike (S) and found seven major antibody competition groups against epitopes recurrently targeted across individuals. Inclusion of published and newly determined structures of antibody-S complexes identified corresponding epitopic regions. Group assignment correlated with cross-CoV-reactivity breadth, neutralization potency, and convergent antibody signatures. Although emerging SARS-CoV-2 variants of concern escaped binding by many members of the groups associated with the most potent neutralizing activity, some antibodies in each of those groups retained affinity-suggesting that otherwise redundant components of a primary immune response are important for durable protection from evolving pathogens. Our results furnish a global atlas of S-specific memory B cell repertoires and illustrate properties driving viral escape and conferring robustness against emerging variants.

Memory B cell repertoire for recognition of evolving SARS-CoV-2 spike

Memory B cell reserves can generate protective antibodies against repeated SARS-CoV-2 infections, but with an unknown reach from original infection to antigenically drifted variants. We charted memory B cell receptor-encoded monoclonal antibodies (mAbs) from 19 COVID-19 convalescent subjects against SARS-CoV-2 spike (S) and found 7 major mAb competition groups against epitopes recurrently targeted across individuals. Inclusion of published and newly determined structures of mAb-S complexes identified corresponding epitopic regions. Group assignment correlated with cross-CoV-reactivity breadth, neutralization potency, and convergent antibody signatures. mAbs that competed for binding the original S isolate bound differentially to S variants, suggesting the protective importance of otherwise-redundant recognition. The results furnish a global atlas of the S-specific memory B cell repertoire and illustrate properties conferring robustness against emerging SARS-CoV-2 variants.

Abstract 1163: Lung and salivary microbiome in electronic cigarette users, never-smokers, and smokers: A pilot cross-sectional study

Background: Little is known about the microbiomes of the lung and oral cavity with electronic cigarette (e-cig) use and how they compare to those of smokers and never-smokers. E-cigs have been promoted as a safe alternative to smoking cigarettes. Given the recent outbreak of E-cigarette or Vaping product use Associated Lung Injury (EVALI) there is an urgent need for understanding the biological effects of e-cig use on the lung and oral cavity, including effects on the microbiome. Previous studies have been limited to 16S-rRNA sequencing which were used to detect bacteria genera. In this study, we used metatranscriptome profiling to study differentially abundant bacteria species in the oral and lung microbiome of never-smokers, smokers, and e-cig users.

Methods: A cross-sectional study of 10 never-smokers, 8 cigarette smokers, and 10 e-cig users was conducted, with saliva and bronchoalveolar lavage (BAL) collected for each study participant. RNA was extracted from saliva and BAL samples for total transcriptome RNA-seq analysis. Sequences were aligned with bowtie2 v.2.2.8 to the human genome (hg19) and non-aligned reads were aligned and annotated using NCBI metagenomes database and Kraken v.1. Differences in the microbiome by smoking status were determined by pairwise comparisons using limma-voom with FDR q-value cutoffs &lt;0.2.

Results: The distribution of richness and evenness of bacterial communities measured by Shannon diversity in our metatranscriptome data did not significantly differ between the three smoking status groups. When comparing levels of bacteria species between groups in the saliva, 234 were differentially abundant between smokers and never-smokers, and 39 were differentially abundant between smokers and e-cig users. In the lung, 87 bacterial species were differentially abundant between smokers and never-smokers and 36 were differentially abundant between smokers and e-cig users. Notably, no bacteria species were differentially abundant when comparing e-cig users and never-smokers in both the saliva and lung samples. There are 50 bacterial species found to be differentially abundant in both the lung and saliva samples, 47 of which are decreased in smokers. These 47 bacteria species included common commensal oral microbiome species such as Haemophilus parainfluenzae, Capnocytophaga gingivalis and Neisseria species. The 3 species that were increased in smokers were Lactobacillus species.

Conclusion: Our findings suggests that smoking cigarettes may alter populations of common commensal species in both the oral and lung microbiome. The lack of differentially abundant bacterial species between electronic cigarette users and never-smokers indicates that e-cigs may alter bacterial species to a lesser extent than smoking.

Citation Format: Kevin L. Ying, Min-Ae Song, Daniel Y. Weng, Quentin A. Nickerson, Joseph P. McElory, Theodore M. Brasky, Noah B. Whiteman, Mark D. Wewers, Jo L. Freudenheim, Ewy A. Mathé, Peter G. Shields. Lung and salivary microbiome in electronic cigarette users, never-smokers, and smokers: A pilot cross-sectional study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1163.

Abstract A34: Lung and salivary microbiome in electronic cigarette users, never-smokers, and smokers: A pilot cross-sectional study

Background: Little is known about the microbiomes of the lung and oral cavity with electronic cigarette (e-cig) use and how they compare to those of smokers and never-smokers. E-cigs have been promoted as a safe alternative to smoking cigarettes. Given the recent outbreak of E-cigarette or Vaping product use Associated Lung Injury (EVALI), there is an urgent need for understanding the biologic effects of e-cig use on the lung and oral cavity, including effects on the microbiome. Previous studies have been limited to 16S-rRNA sequencing, which was used to detect bacteria genera. In this study, we used metatranscriptome profiling to study differentially abundant bacteria species in the oral and lung microbiome of never-smokers, smokers, and e-cig users.

Methods: A cross-sectional study of 10 never-smokers, 8 cigarette smokers, and 10 e-cig users was conducted, with saliva and bronchoalveolar lavage (BAL) collected for each study participant. RNA was extracted from saliva and BAL samples for total transcriptome RNA-seq analysis. Sequences were aligned with bowtie2 v.2.2.8 to the human genome (hg19) and nonaligned reads were aligned and annotated using NCBI metagenomes database and Kraken v.1. Differences in the microbiome by smoking status were determined by pairwise comparisons using limma-voom with FDR q-value cutoffs &lt;0.2.

Results: The distribution of richness and evenness of bacterial communities measured by Shannon diversity in our metatranscriptome data did not significantly differ between the three smoking status groups. When comparing levels of bacteria species between groups in the saliva, 234 were differentially abundant between smokers and never-smokers, and 39 were differentially abundant between smokers and e-cig users. In the lung, 87 bacterial species were differentially abundant between smokers and never-smokers and 36 were differentially abundant between smokers and e-cig users. Notably, no bacteria species were differentially abundant when comparing e-cig users and never-smokers in both the saliva and lung samples. There are 50 bacterial species found to be differentially abundant in both the lung and saliva samples, 47 of which are decreased in smokers. These 47 bacteria species included common commensal oral microbiome species such as Haemophilus parainfluenzae, Capnocytophaga gingivalis, and Neisseria species. The 3 species that were increased in smokers were Lactobacillus species.

Conclusion: Our findings suggests that smoking cigarettes may alter populations of common commensal species in both the oral and lung microbiome. The lack of differentially abundant bacterial species between electronic cigarette users and never-smokers indicates that e-cigs may alter bacterial species to a lesser extent than smoking.

Citation Format: Kevin L. Ying, Min-Ae Song, Daniel Y. Weng, Quentin A. Nickerson, Joseph P. McElroy, Theodore M. Brasky, Noah B. Whiteman, Mark D. Wewers, Jo L. Freudenheim, Ewy A. Mathe, Peter G. Shields. Lung and salivary microbiome in electronic cigarette users, never-smokers, and smokers: A pilot cross-sectional study [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr A34.