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To further highlight the dependence of DEGs in vapers on cumulative e-cig exposure, but not on intensity and duration of past smoking (PY), we have visualized the results of primary and cum e-liq- and PY sensitivity analyses for several randomly selected target genes. Figure 2 shows the visualization results for six vape-specific DEGs (upper panel) and six common DEGs (lower panel) in vapers vs. controls, as determined by primary and cum e-liq- and PY sensitivity analyses. In all cases, the target genes showed concordant statistically significant differential expression results in primary and cum e-liq sensitivity analyses, but not in PY sensitivity analysis. The associations between differential expression of the target genes and vaping index were stronger in cum e-liq sensitivity analysis than in primary analysis, as reflected by the lower FDR (Fig. 2).

Vape V4 by elosa 0001.zip

Gene networks and toxicity functions analysis of differentially expressed genes in vapers and smokers by IPA. The top functional networks impacted in (A) vapers and (B) smokers show high enrichment of mitochondrial genes. Red and green nodes represent up-regulated and down-regulated DEGs, respectively. White nodes show molecules that are not included in the datasets but interact with other components of the network. Solid line, direct interaction; dashed line, indirect interaction. (C) The IPA-Tox analysis tool was used to catalogue sets of molecules in the list of DEGs that were known to be involved in a particular type of toxicity or phenotype. Major toxic effects associated with DEGs in vapers (dark blue) and smokers (light blue) include increased depolarization of the mitochondrial membrane and damage of the mitochondria.

Altogether, our IPA analysis shows that vapers, similarly to smokers, exhibit disruption of key functional pathways and gene networks in peripheral blood leukocytes. Notably, mitochondrial dysfunction and impaired innate immunity (inflammatory response) are highly associated with the DEGs detected in both vapers and smokers, although the extent of effects differs between the two groups. Supplementary Figure S1 summarizes the results of Top Diseases and BioFunctions analysis of DEGs in vapers and smokers. Table 1 lists major diseases and/or function annotations associated with the top disrupted gene networks in vapers and smokers, respectively.

To independently validate the RNA-seq expression results, we randomly selected several of the identified up- and down-regulated genes in vapers and smokers, and examined their transcription levels by RT-qPCR. Supplementary Figure S2 shows the correlation results for expression of the tested genes, as determined by RT-qPCR vs. RNA-seq. In all cases, the median normalized expression levels of the target genes determined by RT-qPCR were directly correlated to the normalized read counts in RNA-seq. Thus, we have validated the RNA-seq expression data by RT-qPCR analysis using triplicate samples from our study population. We stress that the Illumina sequencing data are proven to be highly replicable, with few systematic differences among technical replicates31,32. Therefore, for most applications, it suffices to sequence each mRNA sample only once31, considering the limiting source materials (tissue/cells), especially in population-based studies32. It is well-established that adding more technical replicates gives diminishing return on accuracy and statistical power to detect DEGs32,33. Conversely, adding biological replicates (i.e., more samples) and increasing the sequencing depth (up to a certain level) generate more informational reads, thereby significantly improving the sensitivity and statistical power to detect DEGs32.

In the present study, we have compared the biological consequences of e-cig use and cigarette smoking by constructing and analyzing the whole transcriptome in leukocytes of healthy adult vapers (with and without a history of smoking), exclusive cigarette smokers, and control nonsmokers non-vapers. Transcriptome analysis in peripheral blood leukocytes has been widely used to study the regulation of genes in a variety of diseases, including cardiovascular disease, immune-related (inflammatory) disease, respiratory disease, and cancer1,5,12,13. Specifically, gene expression analysis in leukocytes has been extensively exploited for investigating the effects of exposure to inhaled chemicals, such as tobacco smoke1,5,34. Through systemic circulation, blood cells interact with key organs, such as the lungs (in capillaries), liver (in sinusoids), and kidneys (in glomerus capillary plexus)35, all of which are major targets for tobacco-related diseases and inflammatory conditions and disorders13,36.

Comparative analysis of the gene networks and canonical pathways impacted in vapers and smokers showed strikingly similar biological outcomes, though the number of affected genes varied considerably between the two groups. Importantly, a significant percentage (12.0%) of DEGs in vapers consisted of mitochondrial genes, including one MT-rRNA and 10 MT-tRNAs (all over-expressed) (Supplementary Table S1), suggesting that vaping interferes with mitochondrial homeostasis. Likewise, 32 of all 37 mitochondrial genes24, including 13 protein-coding genes, two MT-rRNAs and 17 MT-tRNAs, were up-regulated in smokers (Supplementary Table S2). Notably, IPA analysis of the dysregulated genes in vapers and smokers confirmed a high enrichment of mitochondrial genes in both groups (Fig. 5A,B). Furthermore, major diseases and/or function annotations associated with the top disrupted gene networks in both vapers and smokers included several mitochondrial disorders that are characterized by structurally, functionally, or numerically abnormal mitochondria (Table 1)37. This together with the observed up-regulation of mitochondrial genes in both vapers and smokers is suggestive of occurrence of mitochondrial dysfunction and damage in both groups.

While the novel findings of the present study have significant implications for public health and regulation of tobacco products, we also acknowledge the limitations of our study, in terms of its representativeness for the general population. Future studies with larger sample size should verify the generalizability of our findings to the broad population of vapers and smokers. These follow-up studies should also investigate the health consequences of vaping combined with other lifestyle habits, including co-use with recreational drugs. Of note, marijuana vaping is on the rise, particularly among youth and young adults58,59.

In summary, we have demonstrated preferential targeting of the mitochondrial genes, important for innate immunity and inflammatory response, in peripheral blood leukocytes of vapers and smokers. We have also shown that e-cig use, but not past smoking, is significantly associated with dysregulation of gene transcription in chronic vapers. Together with the observation that most dysregulated genes in vapers (72.8%) are common to those found in smokers, our findings support that gene dysregulation in vapers is likely due to exposure to similar chemical(s) present in both e-cig vapor and cigarette smoke. Although the exact identity of these chemicals remains to be determined, potential candidates may include ROS-inducing chemicals and/or heavy metals13. Future studies are warranted to identify the constituents of e-cig vapor that are responsible for the observed dysregulation of genes in vapers, similarly to smokers. Lastly, we have shown accentuated transcriptomic effects in smokers relative to vapers, suggesting that smoking has greater and more pronounced adverse effects than vaping on biological systems. Altogether, the results of this research and future investigations into the health risks or potential benefits of vaping vs. smoking should provide scientific evidence to inform the regulation of tobacco products to protect public health.

The study was advertised in online forums, including Craigslist, Reddit, and myUSC ( ), and on social media (Twitter, Instagram, and Facebook). Also, flyers and leaflets were used to advertise the study in local colleges, universities, and vape shops. Furthermore, an online survey was developed, validated, and subsequently employed to solicit and query potential participants ( ). Individuals who appeared to have met the study criteria were contacted by phone to complete a screening questionnaire. Based on the information obtained during the phone screen, those who were deemed potentially eligible, were scheduled for an in-person visit to our laboratory. During the visit, an expanded version of the phone screen was administered to reconfirm eligibility, and informed consent was obtained, afterwards (see, below)15.

We performed post hoc ordinal sensitivity analysis23 to seek the relationship between DEGs in vapers and smokers and exposure indices, including cumulative e-liquid consumption and pack year. Whereas cumulative e-liquid consumption was calculated as the total volume of e-liquid (in milliliter) vaped by a person during his/her lifetime, pack year was estimated by multiplying the number of packs of cigarette a person smoked per day by the number of years he/she smoked15. We performed two separate sets of ordinal sensitivity analysis as follows: (I) to assess the persistency of the effects of past smoking on gene expression in vapers ex-smokers, we sought the association between DEGs and pack year; (II) to confirm the consistency and robustness of our analysis, we examined the dependence of DEGs in vapers and smokers on vaping and/or smoking indices, i.e., cumulative e-liquid consumption and pack year.

From 25 subjects that followed the protocol, sixteen succeeded in completing the RP and 8 the MP (32%). No significant differences in baseline characteristics were noted between subjects in the success and failure groups including markers of nicotine addiction, plasma cotinine levels or smoking history. Success subjects showed significantly longer puff duration (seconds per vape) and total overall vapor exposure (number of vapes x average vape duration or vape-seconds) in both study phases. Furthermore, subjects in the success group continued to increase the number of vapes, device voltage and wattage significantly as they transitioned into the MP. After an initial drop, subjects in the success group were able to regain plasma cotinine levels comparable to their TC use while subjects in the failure group could not. Cotinine levels significantly correlated with the average number of daily vapes and vapes-seconds, but not with other vaping parameters. 041b061a72