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EVI1 acts as an inducible negative-feedback regulator of NF-κB by inhibiting p65 acetylation.
Xu, Xiangbin,Woo, Chang-Hoon,Steere, Rachel R,Lee, Byung Cheol,Huang, Yuxian,Wu, Jing,Pang, Jinjiang,Lim, Jae Hyang,Xu, Haidong,Zhang, Wenhong,Konduru, Anuhya S,Yan, Chen,Cheeseman, Michael T,Brown, S Williams Wilkins 2012 JOURNAL OF IMMUNOLOGY Vol.188 No.12
<P>Inflammation is a hallmark of many important human diseases. Appropriate inflammation is critical for host defense; however, an overactive response is detrimental to the host. Thus, inflammation must be tightly regulated. The molecular mechanisms underlying the tight regulation of inflammation remain largely unknown. Ecotropic viral integration site 1 (EVI1), a proto-oncogene and zinc finger transcription factor, plays important roles in normal development and leukemogenesis. However, its role in regulating NF-κB-dependent inflammation remains unknown. In this article, we show that EVI1 negatively regulates nontypeable Haemophilus influenzae- and TNF-α-induced NF-κB-dependent inflammation in vitro and in vivo. EVI1 directly binds to the NF-κB p65 subunit and inhibits its acetylation at lysine 310, thereby inhibiting its DNA-binding activity. Moreover, expression of EVI1 itself is induced by nontypeable Haemophilus influenzae and TNF-α in an NF-κB-dependent manner, thereby unveiling a novel inducible negative feedback loop to tightly control NF-κB-dependent inflammation. Thus, our study provides important insights into the novel role for EVI1 in negatively regulating NF-κB-dependent inflammation, and it may also shed light on the future development of novel anti-inflammatory strategies.</P>
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Social Network Analysis of an Online Smoking Cessation Community to Identify Users’ Smoking Status
Adnan Muhammad Shah,Xiangbin Yan,Abdul Qayyum 대한의료정보학회 2021 Healthcare Informatics Research Vol.27 No.2
Objectives: Users share valuable information through online smoking cessation communities (OSCCs), which help peoplemaintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations existin identifying the smoking status of OSCC users (“quit” vs. “not quit”). Thus, the current study implicitly analyzed user-generatedcontent (UGC) to identify individual users’ smoking status through advanced computational methods and real datafrom an OSCC. Methods: Secondary data analysis was conducted using data from 3,833 users of BcomeAnEX.org. Domainexperts reviewed posts and comments to determine the authors’ smoking status when they wrote them. Seven types of featuresets were extracted from UGC (textual, Doc2Vec, social influence, domain-specific, author-based, and thread-based features,as well as adjacent posts). Results: Introducing novel features boosted smoking status recognition (quit vs. not quit) by 9.3%relative to the use of text-only post features. Furthermore, advanced computational methods outperformed baseline algorithmsacross all models and increased the smoking status prediction performance by up to 12%. Conclusions: The results ofthis study suggest that the current research method provides a valuable platform for researchers involved in online cessationinterventions and furnishes a framework for on-going machine learning applications. The results may help practitioners designa sustainable real-time intervention via personalized post recommendations in OSCCs. A major limitation is that onlyusers’ smoking status was detected. Future research might involve programming machine learning classification methods toidentify abstinence duration using larger datasets.
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