The inflammasome is a critical molecular complex that activates interleukin-1 driven inflammation in response to pathogen-and danger-associated signals. Germline mutations in the inflammasome sensor NLRP1 cause Mendelian systemic autoimmunity and skin cancer susceptibility, but its endogenous regulation remains less understood. Here we use a proteomics screen to uncover dipeptidyl dipeptidase DPP9 as a novel interacting partner with human NLRP1 and a related inflammasome regulator, CARD8. DPP9 functions as an endogenous inhibitor of NLRP1 inflammasome in diverse primary cell types from human and mice. DPP8/9 inhibition via small molecule drugs and CRISPR/Cas9-mediated genetic deletion specifically activate the human NLRP1 inflammasome, leading to ASC speck formation, pyroptotic cell death, and secretion of cleaved interleukin-1␤. Mechanistically, DPP9 interacts with a unique autoproteolytic domain (Function to Find Domain (FIIND)) found in NLRP1 and CARD8. This scaffolding function of DPP9 and its

Influence maximization, whose objective is to select k users (called seeds) from a social network such that the number of users influenced by the seeds (called influence spread) is maximized, has attracted significant attention due to its widespread applications, such as viral marketing and rumor control. However, in real-world social networks, users have their own interests (which can be represented as topics) and are more likely to be influenced by their friends (or friends' friends) with similar topics. We can increase the influence spread by taking into consideration topics. To address this problem, we study topic-aware influence maximization, which, given a topic-aware influence maximization (TIM) query, finds k seeds from a social network such that the topic-aware influence spread of the k seeds is maximized. Our goal is to enable online TIM queries. Since the topicaware influence maximization problem is NP-hard, we focus on devising efficient algorithms to achieve instant performance while keeping a high influence spread. We utilize a maximum influence arborescence (MIA) model to approximate the computation of influence spread. To efficiently find k seeds under the MIA model, we first propose a besteffort algorithm with 1 − 1 e approximation ratio, which estimates an upper bound of the topic-aware influence of each user and utilizes the bound to prune large numbers of users with small influence. We devise effective techniques to estimate tighter upper bounds. We then propose a faster topicsample-based algorithm with · (1 − 1 e ) approximation ratio for any ∈ (0, 1], which materializes the influence spread of some topic-distribution samples and utilizes the materialized information to avoid computing the actual influence of users with small influences. Experimental results show that our methods significantly outperform baseline approaches.

Human NLRP1 (NACHT, LRR, and PYD domain-containing protein 1) is an innate immune sensor predominantly expressed in the skin and airway epithelium. Here, we report that human NLRP1 senses the ultraviolet B (UVB)- and toxin-induced ribotoxic stress response (RSR). Biochemically, RSR leads to the direct hyperphosphorylation of a human-specific disordered linker region of NLRP1 (NLRP1 DR ) by MAP3K20/ZAKα kinase and its downstream effector, p38. Mutating a single ZAKα phosphorylation site in NLRP1 DR abrogates UVB- and ribotoxin-driven pyroptosis in human keratinocytes. Moreover, fusing NLRP1 DR to CARD8, which is insensitive to RSR by itself, creates a minimal inflammasome sensor for UVB and ribotoxins. These results provide insight into UVB sensing by human skin keratinocytes, identify several ribotoxins as NLRP1 agonists, and establish inflammasome-driven pyroptosis as an integral component of the RSR.

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