Serial ChIP as a tool to investigate the co-localization or exclusion of proteins on plant genes

<p>Abstract</p> <p>Background</p> <p>Establishing transcriptional regulatory networks that include protein-protein and protein-DNA interactions has become a key component to better understanding many fundamental biological processes. Although a variety of techniques are available to expose protein-protein and protein-DNA interactions, unequivocally establishing whether two proteins are targeted together to the same promoter or DNA molecule poses a very challenging endeavour. Yet, the recruitment of multiple regulatory proteins simultaneously to the same promoter provides the basis for combinatorial transcriptional regulation, central to the transcriptional regulatory network of eukaryotes. The serial ChIP (sChIP) technology was developed to fill this gap in our knowledge, and we illustrate here its application in plants.</p> <p>Results</p> <p>Here we describe a modified sChIP protocol that provides robust and quantitative information on the co-association or exclusion of DNA-binding proteins on particular promoters. As a proof of principle, we investigated the association of histone H3 protein variants with modified tails (H3K9ac and H3K9me2) with <it>Arabidopsis </it>RNA polymerase II (RNPII) on the promoter of the constitutively expressed <it>actin </it>gene (At5g09810), and the trichome-expressed <it>GLABRA3 </it>(<it>GL3</it>) gene. As anticipated, our results show a strong positive correlation between H3K9ac and RNPII and a negative correlation between H3K9me2 and RNPII on the actin gene promoter. Our findings also establish a weak positive correlation between both H3K9ac and H3K9me2 and RNPII on the <it>GL3 </it>gene promoter, whose expression is restricted to a discrete number of cell types. We also describe mathematical tools that allow the easy interpretation of sChIP results.</p> <p>Conclusion</p> <p>The sChIP method described here provides a reliable tool to determine whether the tethering of two proteins to the same DNA molecule is positively or negatively correlated. With the increasing need for establishing transcriptional regulatory networks, this modified sChIP method is anticipated to provide an excellent way to explore combinatorial gene regulation in eukaryotes.</p

Public perceptions of the FDA’s marketing authorization of Vuse on Twitter/X

IntroductionOn October 12, 2021, the FDA issued its first marketing granted orders for Vuse, the e-cigarette product by R.J. Reynolds Vapor Company. The public perceptions and reactions to the FDA’s Vuse authorization are prevalent on social media platforms such as Twitter/X. We aim to understand public perceptions of the FDA’s Vuse authorization in the US using Twitter/X data.MethodsThrough the Twitter/X streaming API (Application Programming Interface), 3,852 tweets between October 12, 2021, and October 23, 2021, were downloaded using the keyword of Vuse. With the elimination of retweets, irrelevant tweets, and tweets from other countries, the final dataset consisted of 523 relevant tweets from the US. Based on their attitudes toward the FDA authorization on Vuse, these tweets were coded into three major categories: positive, negative, and neutral. These tweets were further manually classified into different categories based on their contents.ResultsThere was a large peak on Twitter/X mentioning FDA’s Vuse authorization on October 13, 2021, just after the authorization was announced. Of the 523 US tweets related to FDA’s Vuse authorization, 6.12% (n=32) were positive, 26.77% (n=140) were negative, and 67.11% (n=351) were neutral. In positive tweets, the dominant subcategory was Cessation Claims (n=18, 56.25%). In negative tweets, the topics Health Risk (n=43, 30.71%), Criticize Authorization (n=42, 30.00%), and Big Tobacco (n=40, 38.57%) were the major topics. News (n=271, 77.21%) was the most prevalent topic among neutral tweets. In addition, tweets with a positive attitude tend to have more likes.DiscussionPublic perceptions and discussions on Twitter/X regarding the FDA’s Vuse authorization in the US showed that Twitter/X users were more likely to show a negative than a positive attitude with a major concern about health risks

Cross-Sectional Associations of Self-Reported Social/Emotional Support and Life Satisfaction with Smoking and Vaping Status in Adults

This study aimed to examine the cross-sectional association of self-reported social/emotional support and life satisfaction with smoking/vaping status in US adults. The study included 47,163 adult participants who self-reported social/emotional support, life satisfaction, and smoking/vaping status in the 2016 and 2017 BRFSS national survey data. We used multivariable weighted logistic regression models to measure the cross-sectional association of self-reported social/emotional support and life satisfaction with smoking/vaping status. Compared to never users, dual users and exclusive smokers were more likely to have low life satisfaction, with an adjusted odds ratio (aOR) = 1.770 (95% confidence interval [CI]: 1.135, 2.760) and an aOR = 1.452 (95% CI: 1.121, 1.880) respectively, especially for the age group 18&ndash;34. Exclusive cigarette smokers were more likely to have low life satisfaction compared to ex-smokers (aOR = 1.416, 95% CI: 1.095, 1.831). Exclusive cigarette smokers were more likely to have low social/emotional support (aOR = 1.193, 95% CI: 1.030, 1.381) than never users, especially those aged 65 and above. In addition, exclusive cigarette smokers were more likely to have low social/emotional support than ex-smokers, with an aOR = 1.279 (95% CI: 1.097, 1.492), which is more pronounced among the age group 18&ndash;34, as well as 65 and above. Our results suggest that life satisfaction and social/emotional support may play important roles in smoking and vaping, which should be incorporated into behavioral interventions to reduce tobacco use

Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study

BackgroundFlavored electronic cigarettes (e-cigarettes) have become very popular in recent years. e-Cigarette users like to share their e-cigarette products and e-cigarette use (vaping) experiences on social media. e-Cigarette marketing and promotions are also prevalent online. ObjectiveThis study aims to develop a method to identify new e-cigarette brands and flavors mentioned on Twitter and to monitor e-cigarette brands and flavors mentioned on Twitter from May 2021 to December 2021. MethodsWe collected 1.9 million tweets related to e-cigarettes between May 3, 2021, and December 31, 2021, by using the Twitter streaming application programming interface. Commercial and noncommercial tweets were characterized based on promotion-related keywords. We developed a depletion method to identify new e-cigarette brands by removing the keywords that already existed in the reference data set (Twitter data related to e-cigarettes from May 3, 2021, to August 31, 2021) or our previously identified brand list from the keywords in the target data set (e-cigarette–related Twitter data from September 1, 2021, to December 31, 2021), followed by a manual Google search to identify new e-cigarette brands. To identify new e-cigarette flavors, we constructed a flavor keyword list based on our previously collected e-cigarette flavor names, which were used to identify potential tweet segments that contain at least one of the e-cigarette flavor keywords. Tweets or tweet segments with flavor keywords but not any known flavor names were marked as potential new flavor candidates, which were further verified by a web-based search. The longitudinal trends in the number of tweets mentioning e-cigarette brands and flavors were examined in both commercial and noncommercial tweets. ResultsThrough our developed methods, we identified 34 new e-cigarette brands and 97 new e-cigarette flavors from commercial tweets as well as 56 new e-cigarette brands and 164 new e-cigarette flavors from noncommercial tweets. The longitudinal trend of the e-cigarette brands showed that JUUL was the most popular e-cigarette brand mentioned on Twitter; however, there was a decreasing trend in the mention of JUUL over time on Twitter. Menthol flavor was the most popular e-cigarette flavor mentioned in the commercial tweets, whereas mango flavor was the most popular e-cigarette flavor mentioned in the noncommercial tweets during our study period. ConclusionsOur proposed methods can successfully identify new e-cigarette brands and flavors mentioned on Twitter. Twitter data can be used for monitoring the dynamic changes in the popularity of e-cigarette brands and flavors

Perception of the Food and Drug Administration Electronic Cigarette Flavor Enforcement Policy on Twitter: Observational Study

BackgroundOn January 2, 2020, the US Food and Drug Administration (FDA) released the electronic cigarette (e-cigarette) flavor enforcement policy to prohibit the sale of all flavored cartridge–based e-cigarettes, except for menthol and tobacco flavors. ObjectiveThis research aimed to examine the public perception of this FDA flavor enforcement policy and its impact on the public perception of e-cigarettes on Twitter. MethodsA total of 2,341,660 e-cigarette–related tweets and 190,490 FDA flavor enforcement policy–related tweets in the United States were collected from Twitter before (between June 13 and August 22, 2019) and after (between January 2 and March 30, 2020) the announcement of the FDA flavor enforcement policy. Sentiment analysis was conducted to detect the changes in the public perceptions of the policy and e-cigarettes on Twitter. Topic modeling was used for finding frequently discussed topics about e-cigarettes. ResultsThe proportion of negative sentiment tweets about e-cigarettes significantly increased after the announcement of the FDA flavor enforcement policy compared with before the announcement of the policy. In contrast, the overall sentiment toward the FDA flavor enforcement policy became less negative. The FDA flavor enforcement policy was the most popular topic associated with e-cigarettes after the announcement of the FDA flavor enforcement policy. Twitter users who discussed about e-cigarettes started to talk about other alternative ways of getting e-cigarettes after the FDA flavor enforcement policy. ConclusionsTwitter users’ perceptions of e-cigarettes became more negative after the announcement of the FDA flavor enforcement policy

Potential Impact of the COVID-19 Pandemic on Public Perception of Water Pipes on Reddit: Observational Study

BackgroundSocializing is one of the main motivations for water pipe smoking. Restrictions on social gatherings during the COVID-19 pandemic might have influenced water pipe smokers’ behaviors. As one of the most popular social media platforms, Reddit has been used to study public opinions and user experiences. ObjectiveIn this study, we aimed to examine the influence of the COVID-19 pandemic on public perception and discussion of water pipe tobacco smoking using Reddit data. MethodsWe collected Reddit posts between December 1, 2018, and June 30, 2021, from a Reddit archive (PushShift) using keywords such as “waterpipe,” “hookah,” and “shisha.” We examined the temporal trend in Reddit posts mentioning water pipes and different locations (such as homes and lounges or bars). The temporal trend was further tested using interrupted time series analysis. Sentiment analysis was performed to study the change in sentiment of water pipe–related posts before and during the pandemic. Topic modeling using latent Dirichlet allocation (LDA) was used to examine major topics discussed in water pipe–related posts before and during the pandemic. ResultsA total of 45,765 nonpromotion water pipe–related Reddit posts were collected and used for data analysis. We found that the weekly number of Reddit posts mentioning water pipes significantly increased at the beginning of the COVID-19 pandemic (P<.001), and gradually decreased afterward (P<.001). In contrast, Reddit posts mentioning water pipes and lounges or bars showed an opposite trend. Compared to the period before the COVID-19 pandemic, the average number of Reddit posts mentioning lounges or bars was lower at the beginning of the pandemic but gradually increased afterward, while the average number of Reddit posts mentioning the word “home” remained similar during the COVID-19 pandemic (P=.29). While water pipe–related posts with a positive sentiment were dominant (12,526/21,182, 59.14% before the pandemic; 14,686/24,583, 59.74% after the pandemic), there was no change in the proportion of water pipe–related posts with different sentiments before and during the pandemic (P=.19, P=.26, and P=.65 for positive, negative, and neutral posts, respectively). Most topics related to water pipes on Reddit were similar before and during the pandemic. There were more discussions about the opening and closing of hookah lounges or bars during the pandemic. ConclusionsThis study provides a first evaluation of the possible impact of the COVID-19 pandemic on public perceptions of and discussions about water pipes on Reddit

Characteristics of and User Engagement With Antivaping Posts on Instagram: Observational Study

BackgroundAlthough government agencies acknowledge that messages about the adverse health effects of e-cigarette use should be promoted on social media, effectively delivering those health messages is challenging. Instagram is one of the most popular social media platforms among US youth and young adults, and it has been used to educate the public about the potential harm of vaping through antivaping posts. ObjectiveWe aim to analyze the characteristics of and user engagement with antivaping posts on Instagram to inform future message development and information delivery. MethodsA total of 11,322 Instagram posts were collected from November 18, 2019, to January 2, 2020, by using antivaping hashtags including #novape, #novaping, #stopvaping, #dontvape, #antivaping, #quitvaping, #antivape, #stopjuuling, #dontvapeonthepizza, and #escapethevape. Among those posts, 1025 posts were randomly selected and 500 antivaping posts were further identified by hand coding. The image type, image content, and account type of antivaping posts were hand coded, the text information in the caption was explored by topic modeling, and the user engagement of each category was compared. ResultsAnalyses found that antivaping images of the educational/warning type were the most common (253/500; 50.6%). The average likes of the educational/warning type (15 likes/post) were significantly lower than the catchphrase image type (these emphasized a slogan such as “athletesdontvape” in the image; 32.5 likes/post; P<.001). The majority of the antivaping posts contained the image content element text (n=332, 66.4%), followed by the image content element people/person (n=110, 22%). The images containing people/person elements (32.8 likes/post) had more likes than the images containing other elements (13.8-21.1 likes/post). The captions of the antivaping Instagram posts covered topics including “lung health,” “teen vaping,” “stop vaping,” and “vaping death cases.” Among the 500 antivaping Instagram posts, while most posts were from the antivaping community (n=177, 35.4%) and personal account types (n=182, 36.4%), the antivaping community account type had the highest average number of posts (1.69 posts/account). However, there was no difference in the number of likes among different account types. ConclusionsMultiple features of antivaping Instagram posts may be related to user engagement and perception. This study identified the critical elements associated with high user engagement, which could be used to design antivaping posts to deliver health-related information more efficiently