Interleukin-18: The Bouncer at the Mucosal Bar

retina (Macosko et al., 2015). In addition to measuring transcriptional output, new methods have also been developed to characterize transcriptional regulation by measuring methylated DNA, accessible chromatin, modified histones, and chromatin conformation in single cells (Schwartzman and Tanay, 2015). Of course, technical hurdles remain. Measuring genes expressed in single cells is noisy, and existing methods suffer from low sensitivity. Methods to characterize chromatin in single cells are even less mature and face harder limits on the dynamic range of their measurements. However, if history is a guide, these methods will be improved rapidly and together form a suite of tools to systemat-

ically discover new cell types and map the genetic control of their phenotype and function.

REFERENCES Gaublomme, J.T., Yosef, N., Lee, Y., Gertner, R.S., Yang, L.V., Wu, C., Pandolfi, P.P., Mak, T., Satija, R., Shalek, A.K., et al. (2015). Cell 163, this issue, 1400–1412. Ghoreschi, K., Laurence, A., Yang, X.-P., Tato, C.M., McGeachy, M.J., Konkel, J.E., Ramos, H.L., Wei, L., Davidson, T.S., Bouladoux, N., et al. (2010). Nature 467, 967–971. Harrington, L.E., Mangan, P.R., and Weaver, C.T. (2006). Curr. Opin. Immunol. 18, 349–356. Kurokawa, J., Arai, S., Nakashima, K., Nagano, H., Nishijima, A., Miyata, K., Ose, R., Mori, M., Kubota,

N., Kadowaki, T., et al. (2010). Cell Metab. 11, 479–492. Littman, D.R., and Rudensky, A.Y. (2010). Cell 140, 845–858. Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A.R., Kamitaki, N., Martersteck, E.M., et al. (2015). Cell 161, 1202–1214. Schwartzman, O., and Tanay, A. (2015). Nat. Rev. Genet. 16, 716–726. Stockinger, B., and Veldhoen, M. (2007). Curr. Opin. Immunol. 19, 281–286. Toh, M.-L., and Miossec, P. (2007). Curr. Opin. Rheumatol. 19, 284–288. Wang, C., Yosef, N., Gaublomme, J., Wu, C., Lee, Y., Clish, C.B., Kaminski, J., Xiao, S., Zu Horste, G.M., Pawlak, M., et al. (2015). Cell 163, this issue, 1413–1427.

Interleukin-18: The Bouncer at the Mucosal Bar Timothy W. Hand1,* 1Richard King Mellon Institute for Pediatric Research, Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15224, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.cell.2015.11.041

The fidelity of the intestinal barrier is critical to maintaining a healthy relationship between the immune system and the microbiota. Levy et al. and Nowarski et al. reveal how microbiota-derived metabolites modulate the activation of the inflammasome to influence the expression of the cytokine IL-18, intestinal barrier function, and intestinal inflammation. The mucosal immune system has a complex task, as it must be vigilant to pathogens while maintaining cordial relations with the relatively benign commensal microbiota. To complicate matters, inflammation in the intestine can allow the outgrowth of aggressive members of the microbiota, blurring the lines between ‘‘pathogens’’ and ‘‘commensals’’ and contributing to autoinflammatory conditions such as inflammatory bowel disease (Dalal and Chang, 2014). A primary mechanism of immune homeostasis in the gut is to limit the interaction with the microbiota via the physical barrier made of the intestinal epithelial cells (IECs), anti-microbial proteins, and the mucus, produced by goblet cells (Hooper and Macpherson, 2010). The inflammasome, a macromolecular structure that

supports the post-translational production of the cytokines IL-1b and IL-18, plays a critical role in supporting the intestinal barrier. As a result, mice deficient in inflammasome function and IL-18 production develop an invasive dysbiotic microbiota that exacerbates pathology in mouse models of chemically induced colitis (Elinav et al., 2013). Two papers in this issue of Cell now better elucidate how the inflammasome and microbiota interact via sensing of metabolites to induce IL-18 expression, modulate intestinal barrier function, and intestinal inflammation (Levy et al., 2015; Nowarski et al., 2015). Previous studies on mice deficient in key components of the inflammasome have indicated that this structure may support goblet cell secretion and there-

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fore intestinal barrier function, independent of the production of IL-18 (Wlodarska et al., 2014). Levy et al. (2015) now extend these findings to show that, at steady state, signals from the microbiota are necessary for inflammasome activation, IL-18 production, and the expression of certain anti-microbial proteins (AMPs). Critically, one of these AMPs, Ang4, is sufficient to restore microbial diversity, providing an explanation of how IL-18 supports the intestinal barrier and why abrogation of IL-18 may lead to commensal dysbiosis (see Figure 1). In contrast, during instances of acute inflammation, IL-18 may exacerbate disease. Using a series of genetic tools to parse the role of IL-18 during chemically induced colitis, Nowarski et al. (2015) show that IL-18 signaling specifically

Figure 1. Role of Inflammasome and IL-18 in Barrier Function (A) At steady state, a healthy bacterial microbiota produces metabolites, such as taurine, that support inflammasome-mediated production of IL-18 and anti-microbial proteins in the colon that promote microbial diversity and prevent commensal dysbiosis. (B) A dysbiotic microbiota is characterized by different metabolites, such as spermine, that inhibit the inflammasome and inhibit anti-microbial protein production, allowing for its invasive character. During inflammation-induced colitis, IL-18 prevents the development of goblet cells from uncommitted precursors, significantly reducing mucus production and intestinal barrier function.

to the IECs is a critical driver of pathology. The intestinal epithelium, including the goblet cells, turns over rapidly, with new cells developing from uncommitted stem cell precursors. Interestingly, IL-18 signaling specifically blocks the development of goblet cells, leading to reduced mucus in the colon and presumably increased bacterial access to the surface of the intestine (see Figure 1). It was unclear how a disruption in the dialog between the inflammasome and the microbiota leads to invasive dysbiosis or even what microbiota-derived signals modulate the inflammasome. One possibility was the sensing of ‘‘keystone metabolites’’ derived from the microbiota that can act as surrogates of commensal ecology (Belkaid and Hand, 2014). The best example of this phenomenon is the sensing of short-chain fatty acids (SCFA), which are bacterial by-products of fiber metabolism and induce regulatory immune responses (Arpaia and Rudensky, 2014). SCFA sensing by the inflammasome also has been shown to bolster the intestinal barrier (Macia et al., 2015). When Levy and colleagues analyzed the metabolome of their mouse models, they discovered

significant differences, notably that taurine, a bile acid conjugate, is decreased in the dysbiotic microbiota while the polyamine metabolite spermine is increased. These metabolites are shown to have little effect on the microbiota but instead acted upon the stability and function of the inflammasome positively (taurine) or negatively (spermine). Thus, these results describe how collections of bacteria can modulate host immune signaling by the production of metabolites that either support or disrupt intestinal function, in this case via the inflammasome (see Figure 1). This highlights the compelling idea that certain configurations of the microbiota may ‘‘highjack’’ the immune response via metabolite production to induce an environment conducive to their own growth. It seems unlikely that the host immune system would passively allow this kind of negative manipulation, and this type of metabolite/host interaction may have evolved as part of a greater response to enteric infections and other severe perturbations of the microbiota. Thus, the apparent invasiveness of dysbiosis in these models may also be an indicator of those bacteria that can sur-

vive in a certain inflammatory environment associated with a particular disrupted microbial metabolome. It will be of tremendous interest to study how various metabolites tune the immune response, as this will allow for the identification of key biomarkers and therapeutics. Indeed, Levy et al. (2015) raise the exciting possibility that administration of microbial metabolites such as taurine could be therapeutic in patients predisposed to IBD. Taken together, these two papers paint a complex portrait of IL-18 wherein its role at steady state is to bolster the barrier and prevent the outgrowth of more aggressive members of the microbiota, but during instances of severe inflammation, IL-18 expression leads to a loss of goblet cells, depleting barrier function. While it is clear that the effects of IL-18 in driving pathology during colitis are directed to IECs, the cellular targets and key signaling components of IL-18 in controlling the microbiota and mucosal immunity are not. For example, in contrast to germline IL-18 knockout animals, IEC-specific knockouts of IL-18 and IL-18R1 do not develop a microbiota that predisposes to colitis. As further illustration of the pleiotropic effects of IL-18 in the gut, in a T-cell-driven model of colitis, IL-18R1 expression on T cells is critical to both suppress IL-17 production and the function of regulatory T cells (Harrison et al., 2015). Finally, these two papers underscore the difficulties facing our understanding of autoinflammatory disorders at barrier sites that exist at the confluence of three interdependent factors: host genome, the metagenome of the microbiota, and the environment, all of which are unique to an individual. It is only through holistic studies of how host genetics interact with the microbiome and metabolome that we will understand the etiology of these complex diseases.

REFERENCES Arpaia, N., and Rudensky, A.Y. (2014). Proc. Natl. Acad. Sci. USA 111, 2058–2059. Belkaid, Y., and Hand, T.W. (2014). Cell 157, 121–141. Dalal, S.R., and Chang, E.B. (2014). J. Clin. Invest. 124, 4190–4196.

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Elinav, E., Henao-Mejia, J., and Flavell, R.A. (2013). Mucosal Immunol. 6, 4–13. Harrison, O.J., Srinivasan, N., Pott, J., Schiering, C., Krausgruber, T., Ilott, N.E., and Maloy, K.J. (2015). Mucosal Immunol. 8, 1226–1236. Hooper, L.V., and Macpherson, A.J. (2010). Nat. Rev. Immunol. 10, 159–169.

Levy, M., Thaiss, C.A., Zeevi, D., Dohnalova´, L., Zilberman-Schapira, G., Mahdi, J.A., David, E., Savidor, A., Korem, T., and Herzig, Y. (2015). Cell 163, this issue, 1428–1443.

Nowarski, R., Jackson, R., Gagliani, N., de Zoete, M.R., Palm, N.W., Bailis, W., Low, J.S., Harman, C.C.D., Graham, M., Elinav, E., and Flavell, R.A. (2015). Cell 163, this issue, 1444–1456.

Macia, L., Tan, J., Vieira, A.T., Leach, K., Stanley, D., Luong, S., Maruya, M., Ian McKenzie, C., Hijikata, A., Wong, C., et al. (2015). Nat. Commun. 6, 6734.

Wlodarska, M., Thaiss, C.A., Nowarski, R., HenaoMejia, J., Zhang, J.P., Brown, E.M., Frankel, G., Levy, M., Katz, M.N., Philbrick, W.M., et al. (2014). Cell 156, 1045–1059.

Genome Sequencing Fishes out Longevity Genes Vanisha Lakhina1 and Coleen T. Murphy1,* 1Department of Molecular Biology & LSI Genomics, Princeton University, Princeton NJ 08544, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.cell.2015.11.040

Understanding the molecular basis underlying aging is critical if we are to fully understand how and why we age—and possibly how to delay the aging process. Up until now, most longevity pathways were discovered in invertebrates because of their short lifespans and availability of genetic tools. Now, Reichwald et al. and Valenzano et al. independently provide a reference genome for the short-lived African turquoise killifish, establishing its role as a vertebrate system for aging research. Human aging is associated with reproductive and cognitive decline and an increased risk of cancer, diabetes, cardiovascular disease, and neurodegenerative disease. The discovery of long-lived mutants demonstrated that aging is a genetically regulated process. Most molecular insights into the biology of aging come from short-lived invertebrates, such as C. elegans and Drosophila (with lifespans of 3 weeks and 3 months, respectively), or single-celled organisms like Saccharomyces cerevisiae. For example, the insulin/IGF-1 signaling pathway, which plays a conserved role in lifespan regulation from yeast to humans, was first discovered in C. elegans; mutants of the daf-2 insulin receptor double lifespan (Kenyon et al., 1993). Other pathways that modulate lifespan, including the nutrientsensing TOR pathway, the HSF-1 heat shock pathway, and the JNK stress response pathway, were also discovered in these systems. Vertebrate model systems, such as zebrafish and mouse, have also been used to study aging and age-related decline, but their long lifespans (3.5 and 5 years, respectively;

Figure 1), make it difficult to rapidly conduct complex aging experiments. Thus, the establishment of a short-lived genetic and genomic model system would allow both testing of conserved pathways and the discovery of new longevity regulators. While the African turquoise killifish Nothobranchius furzeri has been recognized for its potential for aging research (Genade et al., 2005; Valenzano et al., 2006; Di Cicco et al., 2011, Kirschner et al., 2012, Harel et al., 2015), until now, its utility has been limited because of the lack of genomic resources. In this issue of Cell, two independent groups, Reichwald et al. (2015) and Valenzano et al. (2015), map the genome of this short-lived fish and bridge this gap. Nothobranchius furzeri is a short-lived vertebrate that lives in seasonal freshwater ponds in Zimbabwe and Mozambique (Genade et al., 2005). In laboratory conditions, N. furzeri exhibit a maximal lifespan of 4—6 months, making them the shortest-lived vertebrate that can be bred in captivity. They also exhibit age-related declines in fertility and cognitive ability, as well as age-related telomere shortening,

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impaired mitochondrial function, and cancer (Genade et al., 2005; Valenzano et al., 2006; Di Cicco et al., 2011), making them an ideal model system for lifespan studies. Indeed, N. furzeri have previously been used for mapping quantitative trait loci that control lifespan (Kirschner et al., 2012). Here, Reichwald et al. and Valenzano et al., describe their independent work to generate a reference genome for the highly inbred GRZ strain of N. furzeri. The authors then use the new genome information to provide novel insights into lifespan regulation and sex determination. Upon genome assembly and annotation, Valenzano et al. identified 497 genes under positive selection in the GRZ reference strain. These include genes associated with longevity in humans (IGFR1, INSRA, LMNA3, and XRCC5). Further, they found that distinct residues are under positive selection in humans versus turquoise killifish, suggesting that variants of the same gene might confer contextdependent short or long lifespans in turquoise killifish and humans, respectively. They also sequenced two longer-lived