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Gilbert C FAURE
November 14, 2018 8:40 AM
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Background: Mesenchymal Stromal Cells (MSCs) from Wharton’s Jelly (WJ) tissue express HLA-G, a molecule which exerts several immunological properties. This study aimed at the evaluation of HLA-G expression in MSCs derived from vitrified WJ tissue.
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Gilbert C FAURE
November 13, 2018 4:29 AM
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Biological therapy has led to marked improvements in treatment of patients with inflammatory bowel disease, and an increasing number of drugs has been approved for treatment. However, only a subgroup of patients responds to therapy, highlighting the need to identify biomarkers for therapeutic...
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Gilbert C FAURE
November 13, 2018 4:26 AM
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Gilbert C FAURE
November 13, 2018 2:27 AM
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Am J Respir Crit Care Med. 2017 Nov 27. doi: 10.1164/rccm.201710-1986OC.[Epub ahead of print]...
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Gilbert C FAURE
November 12, 2018 1:13 PM
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Author summary The olfactory organ is a vitally important chemosensory organ in vertebrates but it is also continuously stimulated by pathogenic microorganisms in the external environment. In mammals and birds, nasopharynx-associated lymphoid tissue (NALT) is considered one of the first lines of...
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Gilbert C FAURE
November 11, 2018 1:52 PM
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Abstract Helicobacter pylori (H. pylori) infection is associated with an inflammatory response in the gastric mucosa, leading to chronic gastritis, peptic ulcers, and gastric cancer. Increased T‐cell infiltration is found at sites of H. pylori infection. The CCR6+ subset of CD4+ regulatory T cells (Tregs), a newly characterized subset of Tregs, has been reported to contribute to local immune inhibition. However, whether CCR6+ Tregs are present in H. pylori gastritis, and what their relationship is to disease prognosis, remains to be elucidated. In this study, gastric infiltrating lymphocytes were isolated from endoscopic biopsy specimens of H. pylori gastritis patients and analyzed. We found that in gastric infiltrating lymphocytes, CCR6+ CD4+ CD25high Tregs, which express high levels of CD45RO, are positively associated with more severe inflammation in gastric mucosa during H. pylori infection. Furthermore, the frequency of CCR6+ Tregs in gastric infiltrating lymphocytes, but not CCR6− Tregs, is significantly increased in inflamed gastric tissues, which is inversely correlated with significantly lower expression of IFN‐γ+ CD8+ T cells. We also found that the frequency of CCR6+ Tregs is positively correlated with the frequency of CD4+ IFN‐γ+ T cells. In addition, the frequency of CCR6+ Tregs, but not that of CCR6− Tregs, is significantly correlated with increased inflammation in H. pylori gastritis. This study demonstrates that immunosuppression in H. pylori gastritis might be related to the activity of CCR6+ Tregs, which could influence disease prognosis. 1 INTRODUCTION Approximately 50% of the world's population is infected with Helicobacter pylori (H. pylori), a common bacteria often associated with chronic gastritis, peptic ulcer disease, and gastric cancer.1 The H. pylori infection process is influenced by H. pylori virulence factors, host gastric mucosal factors, and other environmental factors. H. pylori colonization induces systemic and mucosal immune responses. Increased T‐cell infiltration is found at sites of H. pylori infection.2 Recent studies have suggested that the number of T helper type 1 (Th1) cells is selectively increased during infection.3, 4 Th1 cytokines, such as gamma interferon (IFN‐γ) and tumor necrosis factor alpha (TNF‐α), can increase the release of proinflammatory cytokines, augmenting apoptosis induced by H. pylori.5 Many studies have confirmed that the pathogenesis of H. pylori infection is probably largely due to the host immune system, particularly the cellular immune response. However, the exact immunological mechanism of H. pylori infection is still not completely understood. In recent years, regulatory T cells (Tregs) and other related negative regulatory molecules have been a focus of research in to immune mechanisms of disease. Tregs represent approximately 5%‐10% of circulating CD4+ T cells in mice and humans,6 and they play an important role in the maintenance of immunological tolerance to both self and foreign antigens by suppressing aggressive T‐cell responses.7-9 Evidence indicates that removal or reduction of Tregs can enhance the immune response against infectious microbes.10-17 The transcription factor forkhead box protein 3 (Foxp3), expressed in CD4+ CD25high T cells, is the hallmark of regulatory T cells. Recent studies have further proposed that C‐C chemokine receptor type 6 (CCR6) expression defines regulatory effector/memory‐like cells within the CD25+ CD4+ T‐cell subset.18 Moreover, a recent study found that in situ prior proliferation of CD4+ CCR6+ Tregs, which could be facilitated by TGF‐β‐secreting dendritic cells (DCs), is crucial for their enrichment and suppression in tumor immunity.19 Our previous work has shown upregulation of CCL20 and recruitment of CCR6+ gastric infiltrating lymphocytes in H. pylori gastritis.20 We also showed expression of CD25high Tregs and PD‐1 in gastric infiltrating CD4+ T lymphocytes in patients with H. pylori infection.21 A recent study also revealed CCL20/CCR6‐mediated migration of Tregs to the H. pylori‐infected human gastric mucosa.22 With all these clues, we speculated that CCR6+ Tregs are crucial in the pathogenesis of H. pylori infection. In this study, we further investigated the distribution of CCR6+ Tregs and their association with the prognosis of H. pylori gastritis patients, to further understand the underlying disease mechanisms. 2 MATERIALS AND METHODS 2.1 Subjects and patients Biopsy specimens of the gastric antrum were obtained from patients undergoing gastric endoscopy for dyspepsia at theChina Medical University Hospital. Informed consent was obtained from all patients. The study was approved by the Medical Ethics Committee of the China Medical University Medical Center (Taichung, Taiwan; DMR97‐IRB‐263). A total of 3‐4 biopsy specimens were obtained from each patient. Peripheral blood samples from the same patients were also collected and served as the control. To avoid the influence of medical treatment, patients treated with proton pump inhibitors or antibiotics prior to the endoscopic exam were excluded from this study. The presence of H. pylori infection was determined by visualization through histology (including Giemsa staining), and/or by a positive rapid urease test performed on at least one additional biopsy sample. A total of 38 H. pylori gastritis patients were enrolled in this study. The degree of inflammation present in the histological specimens was classified according to the updated Sydney system, as per our previous study, with modifications.20, 21 Absent to mild inflammation in the updated Sydney system was referred to as mild inflammation, whereas moderate to severe inflammation in the updated Sydney system was referred to as marked inflammation in this study. 2.2 Isolation of gastric infiltrating lymphocytes Gastric infiltrating lymphocytes (GILs) were isolated using a modification ofour previously described technique.20, 21 Briefly, biopsy specimens were collected into sterile collection medium (calcium and magnesium‐free Hanks’ balanced salt solution [HBSS] with 5% fetal calf serum and penicillin plus streptomycin). The collected tissues were immediately placed in ice‐cold RPMI 1640 complete medium (GIBCO BRL, Gaithersburg, MD), which contained 10% fetal calf serum supplemented with penicillin (50 IU/mL), streptomycin (50 g/mL), L‐glutamine (2 mmol/L), and sodium pyruvate (1 mmol/L). The tissues were washed, diced into 1 mm3 pieces through a 380 µmol/L tissue sieve, and treated with collagenase (type I; 5 g/mL; Sigma Chemical, St. Louis, MO) and heparin (5 U/mL) in RPMI 1640 complete medium at 37°C for 60 min. The resulting cell suspensions were washed, and the viability of the mononuclear cells was determined by trypan blue exclusion. 2.3 Flow cytometry Flow cytometry was performed using a FACS Calibur (BD Biosciences) with CellQuest Pro software, using directly conjugated monoclonal antibodies against the following markers: anti‐CD4‐phycoerythrin (PE)‐Texas Red (ECD; Beckman Coulter, High Wycombe, UK); anti‐CD25‐PE‐cyanine 7 (Pc7), anti‐CD127‐PE, anti‐CD62L‐PE (eBioscience Systems, San Diego, USA); anti‐CCR6‐Alexa Fluor 647 (A647), CD45RO‐PE, and CTLA‐4‐PE, with corresponding isotype‐matched controls (either BD Biosciences or eBioscience Systems). Conjugation was carried out before cells were fixed in 0.5% formaldehyde. Cells permeabilized with Foxp3 Perm buffer set (BioLegend) were stained with anti‐Foxp3‐Alexa Fluor 488 (A488). To calculate the percentage of Tregs, lymphocytes were gated by plotting forward and side scatter plots and then gating for CD4+ T cells. The gated cells were then analyzed for CD25 and CCR6 expression. CD4+ CD25high CCR6+ and CD4+ CD25high CCR6− Tregs from GILs were purified using FACS sorting. CD8+ T‐cell or CD4+ T‐cell subsets and intracellular IFN‐γ staining assays were analyzed by flow cytometry as previously described.23-25 2.4 Proliferation assays We needed at least 5 × 104 GILs to do the proliferation assay. Because of the scarcity of cells, Tregs isolated from several gastric biopsies from different patients were pooled together to do each proliferation assay. We repeated the proliferation assay in six sets of patients, involving a total of 36 H. pylori‐infected patients. Briefly, 5 × 104 CD4+ CD25− cells were treated with 2 μg/mL anti‐CD3 (eBioscience Systems) and anti‐CD28 (eBioscience Systems), as per our previous method,23 for 12 hours as effector cells, then incubated with or without CCR6+ Tregs at a ratio of 2:1 for 72 hours in complete RPMI 1640 medium (Sigma, St. Louis, MO), supplemented with 5% FCS. [3H] thymidine (0.5 μCi/well) was added for the last 18 hours of culture to test the suppression activity of CD4+ CD25high CCR6+ Tregs as previously described.26 2.5 Statistical analysis All data were analyzed using SPSS 15.0 for Windows27 and PRISM 4.0 (GraphPad Software Inc, San Diego, CA, USA). Data are shown with standard deviation. For comparison among multiple groups, data were analyzed by one‐way analysis of variance (ANOVA). For comparison between paired GIL and PBL data from the same patient, a non‐parametric Mann‐Whitney U test was used. A Spearman correlation test was used for correlation analyses. P < 0.05 was considered statistically significant. 3 RESULTS 3.1 CCR6+ CD4+ CD25high Tregs were upregulated at inflammation sites To determine whether CCR6+ Tregs are present at the inflammation site in patients with H. pylori gastritis, peripheral blood lymphocytes (PBLs) and gastric infiltrating lymphocytes (GILs) were isolated as per our previous studies.20, 21 We first analyzed the frequency of CD4+ CD25high Tregs in PBLs and GILs in H. pylori gastritis patients, as shown in our previous article.21 In addition, the frequency of CCR6+ Treg subpopulations was analyzed. We found that the frequency of CCR6+ Tregs was higher in GILs (6.9%) than in PBLs (1.4%). Compared with CCR6− Tregs, the frequency of CCR6+ Tregs in GILs was also significantly higher (6.9% vs 2.5%; Figure 1). These data indicate that CCR6+ Tregs are not only present, but also upregulated, at the site of inflammation in patients with clinical H. pylori gastritis. 3.2 CCR6+ Tregs exhibit a memory‐like phenotype and effectively inhibit the proliferation of CD4+ CD25− T cells Next, we investigated the surface molecule expression in CCR6+ CD4+ CD25high Tregs, and their suppression ability. As shown in Figure 2A, like their CCR6− equivalents, CCR6+ Tregs express high levels of Foxp3, CTLA‐4 and low levels of CD127, representative of the Treg phenotype. In addition, CCR6+ Tregs express higher levels of CD45RO and lower levels of CD62L than CCR6− Tregs (Figure 2A). CCR6+ Tregs can also inhibit the proliferation of CD4+ CD25− T cells more efficiently than CCR6− Tregs (Figure 2B). In addition, when CCR6+ Tregs were separated from responder T cells by a trans‐well membrane,20 the inhibitory effect of CCR6+ Tregs on CD4+ CD25− T cells was abolished, indicating that the inhibitory effect of CCR6+ Tregs is contact‐dependent (Figure 2C). Our studies demonstrate that CCR6+ Tregs exhibit a memory‐like phenotype and stronger inhibitory capacity than CCR6− Tregs. 3.3 The prevalence of CCR6+ Tregs is related to the degree of inflammation in patients with H. pylori gastritis To further investigate the role of CCR6+ Tregs in H. pylori gastritis, patients were divided into two groups according to the updated Sydney score with modification: mild inflammation and marked inflammation. The frequency of CD4+ CD25high Tregs was not significantly different in the PBLs from the two groups. For GILs, however, the frequency of CD4+ CD25high Tregs was significantly higher in the marked inflammation group than that of the mild group (Figure 3). There was no difference between the Treg frequency in PBLs in patients and healthy donors (P > 0.05; Figure 3). With regard to the CCR6+ subset of GILs Tregs, the frequency of CCR6+ Tregs was significantly higher in the marked inflammation group than in the mild inflammation group (Figure 4A). The frequency of CCR6− subsets of T GILs Tregs was not significantly different between the two groups (Figure 4B,C). Our results imply that the frequency of the CCR6+ subset of GILs Tregs is positively correlated with the degree of inflammation. 3.4 CCR6+ Tregs were inversely correlated with the percentage of IFN‐γ+ CD8+ T cells, and positively correlated with the percentage of CD4+ IFN‐γ+ T cells We then analyzed the frequency ofIFN‐γ‐secreting T cells. The frequency of CD8+ and CD4+ GILs was similar in both marked and mild inflammation groups (Figure 5A,B). But as shown in Figure 5C, the frequency of the IFN‐γ+ CD8+ T‐cell subset of GILs is significantly lower in the marked group than in the mild group, indicating a compromised anti‐inflammatory response. It has been shown that gastric Tregs are able to inhibit H. pylori‐induced T‐cell proliferation and CD8+ T‐cell IFN‐γ production.28 In our study, we found that for GILs, the frequency of both CCR6+ and CCR6− Tregs is not related to the frequency of CD8+ T cells (Figure 5D,E). However, the frequency of both CCR6+ and CCR6− Tregs is inversely correlated with the frequency of CD8+ IFN‐γ+ T cells (Figure 5F,G). The frequency of CCR6+ Tregs is positively correlated with the frequency of CD4+ IFN‐γ+ T cells (Figure 6A), while the frequency of CCR6− Tregs is not (Figure 6B). 4 DISCUSSION Previous studies, including our previous work, have found enrichment of CCR6+ T cells in inflammatory tissue under different clinical conditions.20, 29 This study further demonstrated that the frequency of the CCR6+ subset of GIL Tregs was positively correlated with the degree of inflammation in H. pylori gastritis. This observation was consistent with a previous report that CD4+ CD25high Tregs expressed high levels of CCR6 in breast cancer patients.30 Our current study also demonstrated that CCR6+ Tregs can inhibit the proliferation of CD4+ CD25− T cells more prominently than their CCR6− counterpart in vitro, through a contact‐dependent manner, which might be correlated with the localized immunosuppression associated with the pathogenesis of H. pylori gastritis. Moreover, in GILs, the frequency of CD4+ CD25high CCR6+ Tregs is inversely correlated with the frequency of CD8+ IFN‐γ+ cells, which could be correlated with the suppression of the host defense mechanism in H. pylori infection. Previous studies suggest that the inhibition of IFN‐γ expression in CD8+ T cells by Tregs is moderated by direct T‐cell‐to‐T‐cell contact, which was replicated in our current study. CD13731, 32 and GITR expression might be associated with this process.31-33 However, the exact mechanism by which CCR6+ Tregs inhibit the IFN‐γ‐secreting CD8+ T cells still requires further investigation. We also found that the frequency of CCR6+ Tregs is positively correlated with the frequency of CD4+ IFN‐γ+ T cells. One possible explanation for this observation is that CD4+ IFN‐γ+ T cells might be more associated with tissue destruction, while CD8+ IFN‐γ+ T cells might be associated more with host defense in H. pylori infection. Further studies are needed to confirm our speculation. In conclusion, our research discovered that the expression of CCR6+ CD4+ CD25high Tregs is positively correlated with the degree of inflammation in patients with H. pylori gastritis. These CCR6+ CD4+ CD25high Tregs can actively suppress the proliferation of CD4+ CD25− T cells. Moreover, their frequency is inversely correlated with the frequency of CD8+ IFNγ+ T cells. These findings suggest an immunosuppressive role for CCR6+ Tregs in the pathogenesis of H. pylori gastritis. Our study helps elucidate the disease mechanism of H. pylori gastritis and may have clinical implications. However, further studies are still needed to help better understand the underlying disease mechanisms. ACKNOWLEDGEMENTS This work was financially supported by the Chinese Medicine Research Center, China Medical University, from The Featured Areas Research Center Program, within the framework of the Higher Education Sprout Project, by the Ministry of Education (MOE) in Taiwan (CMU107‐CR‐02; CMRC‐CHM‐5) and the National Science Council, Taiwan (NSC 98‐2320‐B‐039 ‐012 ‐MY3). We would like to thank Uni‐edit (www.uni-edit.net) for editing and proofreading this manuscript. DISCLOSURES OF INTERESTS No potential conflicts of interest are disclosed. REFERENCES Notes : Ying‐Ming Chiu contributed equally to correspondence.
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Gilbert C FAURE
November 11, 2018 1:51 PM
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A healthcare social media research article published in Clinical Kidney Journal, 2017. Graham-Brown, M., Vasilica, C., Oates, T., et. Al. (2017). Study protocol: responding to the needs of patients with IgA nephropathy, a social media approach, Clinical Kidney Journal, , sfx131.
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Gilbert C FAURE
November 8, 2018 11:28 AM
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We read with interest the paper of Rostami et al , which was recently published in Gut . Counting intraepithelial lymphocytes (IEL) is considered highly useful in the histological evaluation of small intestinal biopsy specimens.
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Gilbert C FAURE
November 8, 2018 11:27 AM
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The diagnosis of Coeliac disease (CD) requires a combination of sign/symptoms, positivity of specific antibodies and duodenal histological evidence of villous atrophy. Duodenal villous atrophy, despite representing the CD landmark, is not specific since it is found in many gastrointestinal...
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Gilbert C FAURE
November 8, 2018 11:26 AM
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In humans, niche-specific gastrointestinal microbiomes influence the colonization success of probiotic microbes. Microbiome reconstitution following antimicrobial perturbation is most successful using preperturbation autofecal microbial transplant.
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Gilbert C FAURE
from Immunology and Biotherapies
November 8, 2018 9:09 AM
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Existing treatments for debilitating gut inflammation can offer only temporary relief. An array of potential therapies look set to unlock lasting remission.
Via Krishan Maggon , Gilbert C FAURE
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Gilbert C FAURE
November 6, 2018 1:31 PM
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Suggested by
Société Francaise d'Immunologie
November 5, 2018 9:39 AM
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Research ArticleGastroenterologyImmunology Free access | 10.1172/JCI120453 Interaction between smoking and ATG16L1T300A triggers Paneth cell defects in Crohn’s disease Ta-Chiang Liu,1 Justin T. Kern,1 Kelli L. VanDussen,1 Shanshan Xiong,1 Gerard E. Kaiko,1 Craig B. Wilen,1 Michael W. Rajala,2 Roberta Caruso,2 Michael J. Holtzman,3 Feng Gao,4 Dermot P.B. McGovern,5 Gabriel Nunez,2 Richard D. Head,1 and Thaddeus S. Stappenbeck1 First published August 23, 2018 - More info Related article: Abstract It is suggested that subtyping of complex inflammatory diseases can be based on genetic susceptibility and relevant environmental exposure (G+E). We propose that using matched cellular phenotypes in human subjects and corresponding preclinical models with the same G+E combinations is useful to this end. As an example, defective Paneth cells can subtype Crohn’s disease (CD) subjects; Paneth cell defects have been linked to multiple CD susceptibility genes and are associated with poor outcome. We hypothesized that CD susceptibility genes interact with cigarette smoking, a major CD environmental risk factor, to trigger Paneth cell defects. We found that both CD subjects and mice with ATG16L1T300A (T300A; a prevalent CD susceptibility allele) developed Paneth cell defects triggered by tobacco smoke. Transcriptional analysis of full-thickness ileum and Paneth cell–enriched crypt base cells showed the T300A-smoking combination altered distinct pathways, including proapoptosis, metabolic dysregulation, and selective downregulation of the PPARγ pathway. Pharmacologic intervention by either apoptosis inhibitor or PPARγ agonist rosiglitazone prevented smoking-induced crypt apoptosis and Paneth cell defects in T300A mice and mice with conditional Paneth cell–specific knockout of Atg16l1. This study demonstrates how explicit G+E can drive disease-relevant phenotype and provides rational strategies for identifying actionable targets. Introduction Subtyping of complex immune diseases, such as inflammatory bowel disease (IBD), has traditionally been done by categorizing clinical phenotypes and disease presentations (1, 2). Incorporating key pathogenic elements, namely genetic susceptibility and relevant environmental exposure (G+E) (3), into subclassification schemes may facilitate identification of therapeutic targets in these subtypes. The identification of more than 200 susceptibility SNPs and recently identified prognosis-associated SNPs (4, 5) for Crohn’s disease (CD), one major form of IBD, poses a challenge to this approach, as many of these SNPs have been associated with gene expression and functional changes in various cell types, such as immune (6–9) and epithelial cells (10, 11). While development of gene scores has shown promise in subtyping patients (12), such scores do not account for environmental exposures that are likely to trigger phenotypes and disease. Likewise, environmental factors identified by epidemiologic studies require relevant and functional testing in preclinical models where exposure to these environmental factors is controlled. Conversely, environmental factors identified in preclinical models need to be confirmed in patients. Development of a surrogate phenotype/biomarker that can integrate the effects from both genetics and environmental factors will facilitate subtyping of IBD. In CD, morphologic patterns of small intestinal Paneth cells (Paneth cell phenotype) are a surrogate phenotype that stratifies CD into prognostically distinct subtypes (13–15). We and others have shown that, in mouse models, knockout of CD-associated genes (Atg16l1, Xbp1, Irgm, Lrrk2) resulted in Paneth cell defects manifested as secretory granule abnormalities (11, 16–18) that are similar to those observed in CD subjects (11, 14, 15), with potential additive effects between genes (14). We previously showed that administration of a chronic strain of murine norovirus (MNV) could induce Paneth cell defects in Atg16l1HM (hypomorph) mice, which express low levels of Atg16l1 protein (19). In human subjects, Paneth cell defects in CD are associated with microbiota changes (20) and poor clinical outcome (14, 15). Thus, Paneth cell phenotypes are biologically and clinically relevant surrogate phenotypes ideally suited for mechanistic studies and identification of potential therapeutics in CD. One G+E trigger for Paneth cell defects in mouse models, MNV (19), as yet has no correlate in human subjects (21, 22). Therefore, our goal was to identify an environmental trigger for Paneth cell defects that occurs in both CD subjects and analogous mouse models. Among the known CD environmental risk factors (1, 23), cigarette smoking is one of the most reproducible (23, 24). It is also associated with an aggressive disease course in patients with established CD (25). A recent study suggested potential interactions between genetics and cigarette smoking (26). Based on these findings, we hypothesized that smoking would induce Paneth cell defects in genetically susceptible CD patients. As a proof of concept, we investigated the correlation of smoking exposure, Paneth cell defects, and postoperative recurrence after ileal/ileocolonic resections in CD subjects with ATG16L1T300A, the most prevalent CD susceptibility SNP in White patients (4). We then performed functional studies using the Atg16l1T300A mouse model to identify host factors that mediated smoking-induced Paneth cell defects. Finally, we validated rationally designed therapeutic strategies targeting these factors that result in Paneth cell defects. Results CD subjects with ATG16L1T300A were susceptible to smoking-associated Paneth cell defects. We found that in CD subjects (demographics described in Supplemental Table 1; supplemental material available online with this article; https://doi.org/10.1172/JCI120453DS1) who received ileocolonic anastomosis and postoperative immunomodulatory and/or biologics prophylactic therapy (a known confounder for outcome; n = 128), smoking status and Paneth cell phenotype were prognosticators of recurrence (Supplemental Figure 1) and the combination of these factors further stratified patients into prognostically distinct subgroups (Figure 1A). In addition, CD subjects who were of the ATG16L1T300A genotype and who were also smokers (T300A-smoking group) showed significantly shorter time to recurrence after surgery (Supplemental Figure 2). We therefore hypothesized that cigarette smoking was a trigger for Paneth cell defects in CD subjects. Given that the most common risk allele for CD susceptibility known to be associated with Paneth cell defects was ATG16L1T300A (11), we further hypothesized that smoking triggers Paneth cell defects preferentially in CD subjects who harbored the ATG16L1T300A risk allele(s). In support of this hypothesis, the ATG16L1T300A genotype in CD subjects who were smokers was associated with a lower percentage of normal Paneth cells, whereas subjects with no-risk (NR) allele were not (Figure 1, B and C, and Supplemental Table 2). We have previously described several distinct classes of abnormal Paneth cell morphology (14, 27). We determined the distribution of each subclass of abnormal Paneth cells and found that the majority of the abnormal Paneth cells were of the D2 subclass (decreased granules) (Supplemental Figure 3); this was similar to previous findings in adult CD (14, 15, 27). None of the individual abnormal morphology subclasses showed a significantly different distribution across the groups; rather, the sum percentage of these abnormal classes (or conversely, the percentage of normal Paneth cells) provided the most robust association in the T300A-smoking group (Figure 1C). Figure 1 CD subjects with ATG16L1T300A genotype (T300A) were more susceptible to cigarette smoking–associated Paneth cell defects. (A) In a cohort of CD subjects (n = 186) who underwent ileocolectomy, 126 received postoperative prophylaxis. Within this prophylaxis subset, smokers with type I Paneth cell phenotype (<80% Paneth cells with normal granule morphology) showed the shortest time to disease recurrence (P = 0.0183 by log-rank test). (B) Representative HD5 immunofluorescence. Scale bar: 10 μm. Asterisks indicate abnormal Paneth cells. (C) Cigarette smoking was associated with lower percentage of normal Paneth cells in patients with ATG16L1T300A allele or alleles, while no significant differences in Paneth cell defects were seen between NR patients with or without smoking history (overall P =0.001). NR-nonsmoking, n = 25; NR-smoking, n = 14; T300A-nonsmoking, n = 84; T300A-smoking, n = 62. Data were analyzed by Kruskal-Wallis test followed by Dunn’s multiple comparison tests between groups and represent mean ± SEM. P values for comparisons between groups are shown in Supplemental Table 2. *P < 0.05; **P < 0.01. Given that NOD2 is the other CD susceptibility gene known to be associated with Paneth cell defects in North American CD cohorts (14), we also examined the correlation among common NOD2 variant (R702W, G908R, and L1007fs) carrier status, smoking status, and Paneth cell phenotype. There were no significant changes in the percentage of normal Paneth cells in subjects carrying NOD2 variants that were smokers (Supplemental Figure 4A). We further correlated the total numbers of ATG16L1T300A and NOD2 risk alleles, smoking status, and Paneth cell phenotype. There was no significant difference in the genetic burden regarding Paneth cell phenotype and smoking status (Supplemental Figure 4B). Therefore, smoking-induced Paneth cell defect correlated specifically with ATG16L1T300A alleles in this cohort. Atg16l1T300A mice were susceptible to smoking-induced Paneth cell defects. We modeled the 4 patient populations above in a mouse model representative of the G+E interactions by exposing Atg16l1T300A mice (28) and WT littermates to cigarette smoke for 4 weeks (Figure 2A). Paneth cell defects were triggered only in the Atg16l1T300A mice (Figure 2B and Supplemental Table 3), recapitulating the findings in CD subjects. We also examined the distribution of each class of abnormal Paneth cells. In observations similar to those in the human cohort, the abnormal Paneth cells were predominantly of the D2 subclass, with a small percentage of D3 subclass (diminished) (Supplemental Figure 5A). Increased percentages of these 2 subclasses of abnormal Paneth cells were largely responsible for the decreased percentage of normal Paneth cells in the T300A-smoking group (Supplemental Figure 5, B–D). Figure 2 Atg16l1T300A mice were more susceptible to Paneth cell defects after exposure to cigarette smoking. (A) Schematic illustration of experimental design. Atg16l1T300A (T300A) mice and WT littermates were treated with or without cigarette smoking for 4 weeks, and Paneth cell morphology was assessed. (B) Smoking induced more Paneth cell defects, specifically in Atg16l1T300A mice (overall P < 0.0001). (A and B) WT-nonsmoking, n = 12; WT-smoking, n = 21; T300A-nonsmoking, n = 19; T300A-smoking, n = 25. Results are from 6 independent experiments. Data were analyzed by 2-way ANOVA. (C) Two weeks of cigarette smoking was sufficient to induce Paneth cell defects in Atg16l1T300A mice (P = 0.0054), while no additional Paneth cell defects were seen with longer exposure time, up to 6 weeks (P > 0.9999). Nonexposed, n = 17; 2 weeks, n = 10; 4 weeks, n = 10. (D) After 4 weeks of cessation of cigarette exposure, the percentage of normal Paneth cells of the Atg16l1T300A mice returned to a level comparable to that of unexposed status (P = 0.0027). Baseline, n = 6; 2 weeks, n = 7; 4 weeks, n = 7. (C and D) Data were analyzed by 1-way ANOVA, followed by Mann-Whitney U tests between groups. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. (B–D) Data represent mean ± SEM. We also performed transmission electron microscopy (TEM) to investigate potential ultrastructural changes in Paneth cells. We found that Paneth cells of the T300A mice contained cytoplasmic vesicles and degenerative mitochondria (Supplemental Figure 6, A–C), which was similar to our previous observations in Atg16l1HM mice (11). Importantly, these changes were more frequent in T300A mice exposed to smoking compared with those that were not (Supplemental Figure 6D). Paneth cells from CD patients that were of the ATG16L1T300A genotype and smokers also possessed similar features (Supplemental Figure 6, E and F). To exclude the possibility that the Paneth cell defects in the T300A-smoking mice were the result of stress associated with the physical presence in the smoking chamber rather than exposure to cigarette smoke, T300A mice were placed in the smoking chamber and exposed to normal air pumped through the machine (i.e., no exposure to cigarette smoke). Physical presence in the smoking chamber alone with exposure to normal air did not result in Paneth cell defects in these mice (Supplemental Figure 7). We next altered the duration of smoking to determine the impact on Paneth cell phenotypes. We found that a 2-week exposure was sufficient to trigger Paneth cell defects in Atg16l1T300A mice, but that exposure beyond 2 weeks did not increase the percentage of defective Paneth cells (Figure 2C). We also tested the durability of smoking-induced Paneth cell defects. Paneth cell phenotype was examined in Atg16l1T300A mice at the end of a 4-week smoking period (baseline), followed by a 2- or 4-week washout period where smoking was discontinued. Four weeks of washout (but not 2 weeks) were required to restore normal Paneth cell morphology (Figure 2D). Therefore, Paneth cell defects in Atg16l1T300A mice after short-term smoking exposure were reversible upon smoking cessation. We also determined whether administration of nicotine, a major component in cigarettes, would elicit similar effects on Paneth cells. We administered a daily dose of nicotine (0.7 mg/mouse/d) to mice instead of cigarette smoke. This dose is greater than the predicted absorbed nicotine dose (approximately 0.42 mg/mouse/d) achieved in the cigarette-smoking experiments based on the nicotine content of the cigarettes and known absorption kinetics (29, 30). Surprisingly, nicotine administration did not induce Paneth cell defects or crypt base apoptosis in Atg16l1T300A mice (Supplemental Figure 8). Gut microbiota did not alter smoking-induced Paneth cell defects. Paneth cell function is important for maintaining the homeostasis of the gut microbial community (31–33), and dysbiosis can develop as a downstream effect of Paneth cell defects or loss (20, 34, 35). Thus, we examined whether microbiota changes occurred upstream of or as part of a feedback loop with Paneth cell defects in the context of G+E. We first compared the microbial compositions in Atg16l1T300A mice and littermates with and without smoking. There was no significant difference in microbial composition between Atg16l1T300A mice and littermates without smoking (Supplemental Figure 9A). Smoking did not result in significant changes in α (Supplemental Figure 9, B and C) or β diversity (Supplemental Figure 9, D and E) in either Atg16l1T300A mice or littermates. Deeper examination of specific microbial taxa showed only limited differences between the groups of mice. For example, smoking induced relatively increased abundance of Lactobacillales and Turicibacterales and reduced the abundance of Alphaproteobacteria and Betaproteobacteria in Atg16l1T300A mice, whereas it only increased the abundance of Coriobacterales and Turicibacterales in the WT littermates (Supplemental Figure 9, F and G). Therefore, smoking only modestly altered the composition of the gut microbiota, regardless of genotype. Because we did detect small differences in the microbiota composition that depended on smoking in Atg16l1T300A mice, we functionally tested the microbiota for its ability to induce Paneth cell defects. We cohoused Atg16l1T300A mice and WT littermates exposed to smoking (microbiota donors) with mice of the same genotypes not exposed to smoking (microbiota recipients) (Figure 3A). Recipients were pretreated with antibiotics (36) to allow successful colonization of donor microbiota. The recipients showed microbiota compositions indistinguishable from those of their respective donors after 4 weeks (Supplemental Figure 10). Cohousing of donors and recipients did not induce Paneth cell defects in Atg16l1T300A recipients (Figure 3B). Therefore, the limited differences in microbial composition observed with smoking in the Atg16l1T300A mice did not contribute to Paneth cell defects. Figure 3 Smoking-associated Paneth cell defects in Atg16l1T300A mice were not horizontally transmissible by cohousing. (A) Schematic illustration of experimental design. Smoking Atg16l1T300A mice and WT littermates were used as microbiota donors and cohoused with nonsmoking, antibiotic-pretreated mice of the same genotypes as recipients. Cohousing lasted 4 weeks, during which the donors continued to receive exposure to cigarette smoking. (B) The Paneth cell defects of the Atg16l1T300A microbiota donor mice did not transfer to recipient mice. WT donors, n = 5; WT recipients, n = 15; T300A donors, n = 5; T300A recipients, n = 17. Data analyzed by Kruskal-Wallis test followed by Dunn’s multiple comparison tests between groups. *P < 0.05; **P < 0.01. Data represent mean ± SEM. Smoking-induced Paneth cell phenotype did not correlate with lung or systemic inflammation. To determine whether Paneth cell defects could be due to secondary changes of lung and/or systemic inflammation, we further examined the lungs for histopathology and serum for inflammatory markers. No overt inflammation was seen in the lungs in any of the mice (Supplemental Figure 11), consistent with a previous report that longer smoking exposure may be required to elicit lung inflammation (37). Likewise, none of the samples showed detectable TNF-α in serum (Supplemental Figure 12A). In addition, there was no significant difference in serum myeloperoxidase, RAGE, CXCL1, CXCL2, IL-6, or IL-1β levels among the groups (Supplemental Figure 12, B–G). Therefore, there was no correlation between lung or systemic inflammation and G+E-associated Paneth cell defects. Natural MNV infection was an unlikely cause for smoking-induced Paneth cell defects. We previously showed that administration of a chronic strain of MNV could induce Paneth cell defects in Atg16l1HM mice (19). To exclude the possibility that natural MNV infection, not infrequently encountered in animal facilities (19), could result in Paneth cell defects in Atg16l1T300A mice exposed to smoking, we also determined the MNV titers in fecal samples. We found that 16% of mice were indeed infected with MNV. However, among the Atg16l1T300A mice exposed to smoking, there was no significant difference in the percentages of normal Paneth cells between MNV-uninfected and MNV-infected mice (Supplemental Figure 13). Therefore, natural MNV infection was an unlikely cause for the Paneth cell defects observed in this study. Smoking and Atg16l1T300A genotype interaction led to unique host transcriptomic changes. The lack of a causative link among the microbiota, systemic inflammatory markers, and Paneth cell defects indicates that the underlying mechanisms most likely stemmed from the host intestine per se. To comprehensively analyze the effect of G+E in all ileal cell types, we performed global RNA sequencing (RNA-seq) using mRNAs isolated from full-thickness ileal sections from Atg16l1T300A mice and WT littermates with or without smoking. Identified transcriptomic differences were categorized as associated with either genetics (G patterns), smoking (E patterns), or combinatorial effects of genetics and smoking (G+E patterns) (Supplemental Figure 14A). The G+E patterns could be further classified into T300A-smoking and WT-smoking patterns (Figure 4, A and B, and Supplemental Figure 13, B–E). The T300A-smoking pattern was striking, as it included signatures associated with the promotion of apoptosis and the downmodulation of insulin signaling, predominantly through Ppara/g regulation (Figure 4A, Supplemental Figure 14, B–E, and Supplemental Table 4). Interestingly, Pparg activity has been shown to modulate Paneth cell function during high-fat–diet exposure (38). Figure 4 G+E interactions resulted in unexpected transcriptomic findings. Unique transcriptomic patterns associated with (A and B) combination from both genetics and smoking interactions (G+E patterns), (C) genetics alone (G patterns), or (D) smoking exposure alone (E patterns). Full-thickness ileum from Atg16l1T300A mice and WT littermates with or without cigarette smoking exposure was analyzed by RNA-seq. The x axes in all panels represent log P values. Significantly enriched pathways are shown. The WT-smoking pattern included induction of genes associated with Hnf4α and Ppar regulation in addition to lipid and amino acid metabolism (Figure 4B, Supplemental Figure 14F, and Supplemental Table 5). These molecular responses to smoking were either attenuated or not activated in the Atg16l1T300A mice. The G patterns specific to the Atg16l1 T300A ileum included genes that function in metabolism and complement activation (Figure 4C, Supplemental Figure 14, G and H, and Supplemental Table 6). We had previously found that deletion of Atg16l1 in mouse intestinal epithelial cells targets metabolism and acute-phase reactants such as complement (11). The E patterns enriched with smoking included genes that function in cell death, IFN signaling, cyclooxygenase pathway, and gluconeogenesis (Figure 4D, Supplemental Figure 14, I and J, and Supplemental Tables 7 and 8). Of note, smoking can induce apoptosis in a mouse model of emphysema (39). Apoptosis was a central mechanism behind smoking-associated Paneth cell defects in CD subjects and mice with ATG16L1T300A. The analysis of the transcriptomics data suggested that apoptosis-associated cell death might be a critical mediator of G+E-induced Paneth cell defects. We found that in CD subjects, the highest level of crypt base apoptosis was present in Atg16l1T300A-smoking as compared with all other groups (Figure 5A, Supplemental Figure 15A, and Supplemental Table 2). Further analysis showed that Paneth cells themselves were sensitive to apoptosis in ATG16L1T300A-smoking through defensin 5 (HD5)/TUNEL colocalization (Figure 5B and Supplemental Table 2). In addition, ATG16L1T300A-smoking, but not NR-smoking, also had lower Paneth cell numbers/crypt (Figure 5C and Supplemental Table 2). The effects of smoking and genotype showed no detectable effect on crypt proliferation, the other major function of epithelial cells in the crypt base (Supplemental Figure 16, A and B, and Supplemental Table 2). Figure 5 Paneth cell defects were mediated by apoptosis. (A) Smoking was associated with more crypt base apoptosis in CD subjects with the Atg16l1T300A genotype (P < 0.0001), compared with NR subjects (P > 0.9999). (B) Smoking was associated with more apoptotic Paneth cells in CD subjects with ATG16L1T300A (P = 0.01) compared with NR subjects (P > 0.9999). (C) ATG16L1T300A subjects who were smokers had reduced Paneth cell numbers/crypt (P = 0.0103) compared with NR subjects (P > 0.9999). (A–C) Sample sizes and data analysis were as in Figure 1. (D) In mice, smoking induced more profound crypt base apoptosis specifically in Atg16l1T300A mice (P < 0.0001). (E) More Paneth cells were undergoing apoptosis in Atg16l1T300A mice exposed to cigarette smoking (P = 0.0018). (F) Smoking did not induce significant alterations in Paneth cell numbers/crypt, irrespective of genotype (P = 0.0948). (G) Smoking did not induce increased apoptosis in the villi, irrespective of genotype (P = 0.5058). (D–G) Sample sizes and data analysis were as in Figure 2. Pan-caspase inhibitor Z-VAD-FMK administration prevented smoking-induced (H) Paneth cell defects and (I) crypt base apoptosis in Atg16l1T300A mice. (H and I) Control, n = 8; pan-caspase inhibitor, n = 10; nonsmoking, n = 7. Data were analyzed by Kruskal-Wallis tests followed by Dunn’s multiple comparison tests between groups. *P < 0.05; **P < 0.01, ***P < 0.001; ****P < 0.0001. Data represent mean ± SEM. Atg16l1T300A mice exposed to smoking also showed increased crypt apoptosis (Figure 5D, Supplemental Figure 15B, and Supplemental Table 3) and specifically increased Paneth cell apoptosis (Figure 5E and Supplemental Table 3), confirming that apoptosis-associated cell death in the crypt base compartment was directly linked to smoking-induced Paneth cell defects. Of note, the Paneth cells that coexpressed cleaved caspase-3 and lysozyme were exclusively of the abnormal morphology (human: 92% D2, 8% D3; mouse: 97% D2, 3% D3). Atg16l1T300A mice exposed to smoking tended to have fewer Paneth cells as compared with smoking WT littermate controls (WT-smoking), but this was not significant (Figure 5F and Supplemental Table 3). We also found that smoking did not induce apoptosis in villus epithelial cells (Figure 5G and Supplemental Figure 15C), further demonstrating that smoking and genotype specifically affect Paneth cells. Finally, crypt proliferation was not altered by exposing mice of either genotype to smoke (Supplemental Figure 16, C and D, and Supplemental Table 3). Therefore, crypt base apoptosis was a specific response to G+E, and the process did not elicit compensatory alterations in proliferation. To determine whether apoptosis mediated the smoking-induced Paneth cell defects in Atg16l1T300A mice, we administered pan-caspase inhibitor carbobenzoxy-valyl-alanyl-aspartyl-[O-methyl]-fluoromethylketone (Z-VAD-FMK) to the Atg16l1T300A mice. Z-VAD-FMK prevented the Paneth cell defects (Figure 5H) and crypt apoptosis (Figure 5I) induced by smoking, confirming that apoptosis is upstream of Paneth cell defects. In addition, we also determined the potential role of necroptosis in mediating Paneth cell defects (40), as a recent report has suggested a link between Atg16l1 and necroptosis (41). Administration of the necroptosis inhibitor nec-1 did not prevent the Paneth cell defect (Supplemental Figure 17A) nor crypt base apoptosis (Supplemental Figure 17B) phenotypes in T300A-smoking mice, confirming the lack of association with necroptosis in this experimental design. Repressed Pparg activation resulted in smoking-induced crypt apoptosis and Paneth cell defects. The unique G+E patterns in the full-thickness ileal transcriptomic analysis demonstrated an attenuation or repression of Ppara/g activation in the Atg16l1T300A mice exposed to smoking as compared with WT animals (Supplemental Figure 14, C–E, Supplemental Table 9). These data suggested the possibility that Ppara/g activation in WT-smoking mice may be protective of the normal Paneth cell phenotype. In a subsequent analysis of the G+E patterns using Enrichr to probe the GEO drug perturbations data sets (refs. 42, 43, and Supplemental Figure 18A), the WT-smoking pattern genes uniquely matched compounds with multiple highly significant adjusted P value entries (adjusted P < 0.01) (Supplemental Figure 18B). The analysis revealed that selective PPARγ agonists, including rosiglitazone, pioglitazone, and troglitazone (especially among the drug-like molecules) regulate the genes in this potentially protective pattern, whereas fibrates (PPARα agonists) were not detected in this analysis. This suggested the general Ppara/g signature observed in the pathway-level analysis may be more specific to Pparg. To further justify analysis of this pathway in Paneth cells, we performed global transcriptional analysis of crypt base material (enriched for Paneth cells) from these mice collected by laser capture microdissection (LCM) (Figure 6A). We found that Atg16l1T300A mice exposed to smoking showed significantly diminished expression of many Pparg-associated genes as compared with the other groups of mice in this experiment (Figure 6B and Supplemental Table 10). We also found a similar enrichment of downregulated PPARγ pathway genes in 2 specific G+E groups from our previous LCM-procured Paneth cell data sets: (a) Atg16l1HM mice infected with MNV (ref. 19, Figure 6B, and Supplemental Table 11), and importantly, (b) CD subjects who were smokers and of the ATG16L1T300A genotype (ref. 14, Figure 6B, and Supplemental Table 12). These data collectively suggest that the PPARγ pathway is a central mechanism closely linked to Paneth cell defects in CD subjects and relevant mouse models as a result of G+E interaction. Figure 6 Pparg-associated metabolism dysregulation resulted in crypt base apoptosis and Paneth cell defects. (A) Workflow for Paneth cell LCM of the mice in the T300A-smoking study. (B) Significantly downregulated Pparg-associated genes were found in all 3 data sets, including the Atg16l1T300A-smoking mice (n = 108 genes), Atg16l1HM MNV-infected mice (n = 298 genes), and CD subjects that were ATG16L1T300A-smokings (n = 166 genes). Adjusted P value for each group (by ChEA, https://omictools.com/chea-tool): #P = 0.0044; ##P = 0.0004; ###P = 0.038. Ppar-γ agonist rosiglitazone (Rosi) treatment rescued (C) Paneth cell defects (P < 0.0001) and (D) crypt base apoptosis (P < 0.0001) in Atg16l1T300A mice exposed to cigarette smoking. (C and D) Total n = 9/group for nonsmoking groups; n 0/group for smoking groups. Rosiglitazone administration prevented the (E) Paneth cell defects and (F) crypt apoptosis of PC-Cre+ smoking mice (n = 4/group). (C–E) Statistical analysis was performed using 2-way ANOVA. *P < 0.05, ****P < 0.0001. Data represent mean ± SEM. We next functionally tested the role of the PPARγ pathway in smoking-induced Paneth cell defects. Administration of the PPARγ agonist rosiglitazone rescued the smoking-induced Paneth cell defects (Figure 6C) and crypt base apoptosis (Figure 6D) in the Atg16l1T300A mice. In parallel, we also generated mice with intestinal epithelium–specific Pparg deletion (Pparg/Villin-Cre mice). These mice showed reduced percentages of normal Paneth cells, reduced Paneth cell numbers/crypt, and increased crypt base apoptosis compared with the Ppargfl/fl littermate controls (Supplemental Figure 19). Therefore, the PPARγ pathway is a critical mediator of crypt apoptosis and Paneth cell defects. G+E interactions directly affected Paneth cells and precursors. The crypt base transcriptomic data also suggested that the G+E interaction induced effects directly on Paneth cells themselves. To test this hypothesis in vivo, we utilized a conditional knockout model in which Atg16l1 was deleted exclusively in Paneth cells (α-defensin-4-IRES-Cre Atg16l1fl/fl mice; herein termed PC-Cre+ mice) (44). As seen in Supplemental Figure 20A, the PC-Cre+ mice elicited Paneth cell defects when exposed to cigarette smoking, whereas Atg16l1fl/fl mice (herein termed PC-Cre– mice) did not, as expected. The PC-Cre+ mice also showed increased crypt base apoptosis (Supplemental Figure 20B) and increased Paneth cell apoptosis (Supplemental Figure 20C). Finally, rosiglitazone administration also prevented smoking-induced Paneth cell defects and crypt apoptosis in PC-Cre+ mice (Figure 6, E and F). These data further support the notion that the G+E effect can act directly on Paneth cells and their Defa4-expressing precursors, although we cannot exclude Paneth cell–independent mechanisms with certainty. TNF antagonism rescued smoking-induced Paneth cell defects. We previously suggested that TNF-α, a major therapeutic target in CD, may be an important mediator of Paneth cell defects in CD subjects (15). We have also shown that anti–TNF-α treatment can ameliorate the intestinal pathology in Atg16l1HM mice infected with MNV and treated with dextran sodium sulfate (DSS) (19). Interestingly, it has also been shown that PPARγ antagonism in preadipocytes conferred increased sensitivity to TNF-α–induced apoptosis (45) and that treatment with PPARγ agonist blocked TNF-α–induced apoptosis in vitro (46), suggesting that a defective PPARγ pathway could prime the host tissue to TNF-α–induced apoptosis. In Atg16l1T300A mice, anti–TNF-α treatment prevented Paneth cell defects (Figure 7A) and crypt apoptosis (Figure 7B). Anti–TNF-α administration did not alter the expression of genes involved in the PPARγ pathway (Supplemental Figure 21), suggesting that TNF-α acts downstream of PPARγ and mediates apoptosis induction. We further tested the role of TNF-α by crossing Atg16l1T300A mice to TNF receptor 1–deficient (Tnfr1–deficient) mice. Atg16l1T300A Tnfr1–/– mice were resistant to smoking-induced Paneth cell defects (Figure 7C) and crypt base apoptosis (Figure 7D). Therefore, the TNF-α–signaling pathway is a key mediator and therapeutic target for smoking-induced crypt apoptosis and Paneth cell defects in Atg16l1T300A mice. Figure 7 Anti–TNF-α prevented Paneth cell defects. Administration of anti–TNF-α prevented smoking-induced (A) Paneth cell defects (P < 0.0001) and (B) crypt base apoptosis (P = 0.0011) in Atg16l1T300A mice. (A and B) Control, n = 9; anti–TNF-α, n = 10. Compared with the Atg16l1T300A mice, Atg16l1T300A Tnfr–/– mice exposed to cigarette smoking showed significantly (C) fewer Paneth cell defects (P = 0.0022) and (D) less crypt base apoptosis (P = 0.0087). (C and D) n = 6/group. Data were analyzed by Mann-Whitney U test. **P < 0.01; ****P < 0.0001. Data represent mean ± SEM. Discussion We previously showed that Paneth cell phenotypes are associated with CD genotypes, microbiota composition, a pathologic hallmark, unique transcriptomic profiles, and clinical outcome (11, 14, 15, 19, 20). However, clinically relevant environmental trigger or triggers and the mechanism or mechanisms driving Paneth cell defects were unclear (Figure 8A). In the current study, we show that relevant environmental stimuli can trigger Paneth cell defects in genetically susceptible hosts, confirming this phenotype as a unique readout to functionally test potential genetic and environmental interactions. We also show that the G+E interactions resulted in previously unpredicted intestinal metabolic dysregulation, leading to crypt base apoptosis and Paneth cell defects mediated by PPARγ that could additionally be blocked by anti–TNF-α (Figure 8B). Finally, we show that the G+E interactions directly affect Paneth cells and their Defa4-expressing precursors, although other cell types may also be affected. The Paneth cells that underwent apoptosis were exclusively of the abnormal morphology patterns. Therefore, the abnormal Paneth cells may undergo apoptosis; however, they may also revert to normal morphology once smoking is discontinued. Figure 8 Previous and current models of G+E resulting in Paneth cell defects. (A) Previous knowledge suggests G+E interaction may lead to Paneth cell defects, but the mechanisms have been unclear. (B) Proposed mechanistic model based on the current study. Clinically relevant genetic and environmental factors of CD could interact and induce metabolism dysfunction (defective PPARγ signaling), leading to TNF-α–mediated crypt base apoptosis and Paneth cell defects. We focused on Atg16l1T300A, as a knockin model that possesses the same polymorphism as CD patients, providing a mechanistic advantage over the whole gene–knockout models in select cell types. Similar mouse models for Nod2 polymorphisms exist, but this allele is much less common in CD cohorts of European ancestry (4). Other CD susceptibility genes associated with abnormal Paneth cells do not yet have mouse models of their respective genetic polymorphisms. In addition, while we have previously shown that Paneth cell defects are induced in hypomorphic Atg16l1 mice after MNV infection, we are unable to yet identify such a link in CD patients (21). Even so, smoking is a clinically relevant environmental trigger (23, 47). We show that the combination of relevant host genetic and environmental factors can provide insight into disease pathogenesis and therapeutic targets, as has been recently demonstrated in studies such as those of nonalcoholic fatty liver disease (48, 49). The majority of the microbiome studies on the effect of smoking have centered on the oral cavity microbiota (50). A recent population-based microbiome study showed that smoking status and history showed modest effect on Bray-Curtis distance without significant associations for individual species or pathways (51). A small cross-sectional study of CD patients showed that smoking is associated with reduced microbiota diversity, with reduced abundance of limited taxa at the genera level (52). Our in vivo study was consistent with these findings. Along with the failure of horizontal transmission of Paneth cell defects in cohousing experiments, our data strongly suggest that microbiota changes were not a cause of Paneth cell defects. Overall, this supports our working model (53) showing that Paneth cell defects promote dysbiosis only in the presence of active inflammation. In addition, recent studies have suggested that necroptosis modulates Paneth cell function (40, 41). In our study, smoking-induced Paneth cell defects in T300A mice were only rescued by apoptosis inhibition, but not necroptosis inhibition, suggesting that different injuries may elicit different predominant cell death responses. The value of the unbiased global transcriptomics approach is highlighted by the identification of Pparg as a central mediator in the T300A-smoking patterns, a finding that was not deducible from examining the effect of each single factor alone. PPARg has been shown to be downregulated in smoking-associated emphysema (54), suggesting that the modulation of this pathway by smoking is likely a general tissue response. In addition, Pparg is linked to reduced Paneth cell numbers in mice fed a high-fat diet (38). Given that agents targeting this pathway (e.g., rosiglitazone) are readily available for routine clinical use, further clinical studies using these agents to treat CD patients who are smokers with Paneth cell defects will validate the importance of this pathway in CD. Furthermore, metabolic dysregulation is tightly connected with the TNF-α–associated apoptosis pathway. Therefore, previously recognized important genetic factors, an environmental factor, and inflammatory pathways converged to affect Paneth cell health and clinical prognosis. Of note, our complementary approaches (crypt base LCM, PC-Cre+ mice) support the transcriptomics analysis from full-thickness ileum indicating that Paneth cells are the main target of the G+E effect. Our data also suggest that smoking cessation may be beneficial for ATG16L1T300A CD subjects with smoking-associated Paneth cell defects. Other potential intervention approaches include nicotine patch, PPARγ agonists, and anti–TNF-α. Anti–TNF-α is a major treatment modality for CD (1), and rosiglitazone has been shown to be efficacious in ulcerative colitis (another major form of IBD) (55). Our data indicate that the Paneth cell phenotype may be used to stratify CD patients who may benefit from these therapies. One limitation of our study is that, due to the physical restraint of the smoking chamber, the cigarette-smoking experiments could not exceed 6 weeks. Therefore, the effect of long-term cigarette smoking on Paneth cell defects, in particular the reversibility of the approaches described above, is unclear. In addition, while neither nicotine, lung pathology, nor systemic inflammatory signals were shown to affect Paneth cells in this model, it is possible that the changes in gut transcriptomics and subsequent Paneth cell defects are the results of processes initiated external to the diseased/target organ (gut), similar to what occurs in rheumatoid arthritis (56), in which cigarette smoking has been shown to alter transcriptomic changes of the joints (57). One such possibility could be the lung-gut axis, such that cigarette smoking affects the lung on a molecular level (potentially through lung microbiome and/or metabolites) (58, 59), which may in turn affect gut transcriptomics. In addition, the PPARγ pathway has best been studied in liver, skeletal muscle, and adipocytes in the context of metabolism (60). For example, based on the known PPARγ upstream regulatory mechanisms, we also speculate that the combination of G+E could affect either fatty acid–binding proteins or fatty acid transporters, which would potentially involve liver-gut crosstalk (61). In summary, we show that genetic and environmental factors synthesize to trigger unique biologic processes, resulting in a clinically relevant phenotype. Our data also provide complementary mechanistic insights into the role of Paneth cells in mediating CD pathogenesis (16, 62) and identification of actionable therapeutic targets. Methods Study design. The overall objective of our study was to determine the associations between the ATG16L1T300A genotype and exposure to cigarette smoking in triggering Paneth cell defects. For human subjects, based on the prevalence of adult CD subjects harboring type I Paneth cell phenotype (14, 15) and the natural history of CD after resection (1), 90 subjects were required to achieve a power of 80%. For in vivo experiments, we used a previously described mouse strain (Atg16l1T300A) that is known to possess Paneth cell defects (28). Cigarette smoking was performed following a previously described protocol (63, 64), with the cigarette filters removed. Paneth cell analysis was performed using immunofluorescence (14, 15) on distal ileum. All the experiments were performed in several replicates over the course of 2 years. At least 3 to 6 biological replicates were used for each group/experiment. The mice were randomized, and the investigator performing the histologic analysis was blinded to the sample identity. The design for microbiome studies included proper littermate controls and cohousing (65), and microbiome composition was analyzed using 16S rRNA-seq. Transcriptomic analysis was performed using RNA-seq. All data were included (no outliers were excluded). Additional details, including the total numbers per study group, are included in the respective figure legends. CD subjects. CD subjects who underwent ileocolectomy between 1999 and 2010 at Washington University or Cedars-Sinai Medical Center were previously described (14). A second CD cohort from Washington University composed of consecutive CD patients who underwent ileocolectomy between 2011 and 2013 were additionally included. Deidentified tissue samples from ileal resection margins that were free of acute inflammation were used for Paneth cell phenotype analysis. The following information was retrieved from the medical record: sex, age at operation, smoking history (never smoker vs. active/exsmoker), medication history (including immunomodulators and biologics), and endoscopic findings at the first visit 6 to 12 months after surgery. Recurrence was defined by endoscopy (Rutgeerts score ≥ i2). The genotypes of the patients were obtained using ImmunoChip (14) or through TaqMan genotyping assay (Thermo Fisher Scientific), with genomic DNA extracted from formalin-fixed, paraffin-embedded tissue based on the manufacturer’s instructions. Mice. Atg16l1T300A mice have been described before (28) and were a gift from Ramnik Xavier (Harvard Medical School, Boston, Massachusetts, USA). Heterozygotes were used to breed Atg16l1T300A and littermate controls. Atg16l1T300A mice were crossed with Tnfrsf1atm1/lmxmice (Tnfr1 KO; The Jackson Laboratory, 3244) to generate Atg16l1T300A Tnfr1–/– mice. PC-Cre mice were generated by introducing Cre recombinase gene driven by the α-defensin-4 promoter in the embryonic stem (ES) cells (44). The PC-Cre mice were subsequently crossed with Atg16l1fl/fl mice to generate PC-Cre+ mice. Mice with intestinal epithelium–specific knockout of Pparg were generated by crossing Ppargfl/fl mice (The Jackson Laboratory, catalog 004584) with Villin-Cre mice (The Jackson Laboratory, catalog 4586). All mice were on a C57BL/6 genetic background. The distal ileum of the mice (distal, 3 cm) was used for analysis in this study. Mouse treatments. Four- to six-week-old mice were exposed to cigarette smoking at 4 cigarettes per day for 5 days per week using Kentucky research cigarette 3R4F (with filters removed) (University of Kentucky, Lexington, Kentucky, USA) and a previously described protocol (64, 66). Mice were exposed to 2 weeks of smoking unless otherwise indicated and then sacrificed for tissue collection. For cohousing experiments, Atg16l1T300A and WT littermates designated as microbial recipients were exposed to an antibiotic cocktail of vancomycin, neomycin, ampicillin, and metronidazole for 2 weeks (36), followed by cohousing with mice of the same genotype that were exposed to cigarette smoking (microbial donors). Cohousing lasted 4 weeks, and the microbial donors continued to be exposed to cigarette smoking during this period. For the nicotine experiment, nicotine was added into the drinking water at a concentration of 0.1 mg/ml for 4 weeks. In other experiments to study effects of pharmacological agents in preventing cigarette smoking–induced Paneth cell defect, mice with the Atg16l1T300A genotype were exposed to smoking for 2 weeks. During this period, mice were administered either pan-caspase inhibitor Z-VAD-FMK (10 mg/kg/d) intraperitoneally (ApexBio) or Ultra-LEAF anti-mouse TNF-α antibody (0.5 mg/mouse/injection, 2 injections/wk) (BioLegend). For nec-1 inhibitor, mice received intraperitoneal administration of necrostatin (Sigma-Aldrich) at 4 mg/kg/d for 2 weeks. For rosiglitazone administration, mice received daily oral gavage with either PBS or rosiglitazone (Sigma-Aldrich) at a dose of 20 mg/kg/d for 2 weeks. Paneth cell phenotype analysis. Lysozyme and HD5 immunofluorescence was interpreted (by T.-C. Liu), as described previously (11, 14, 19, 20, 27). For both human and mouse samples, each Paneth cell was classified into normal or 1 of the 5 abnormal categories, including the following: disordered (abnormal distribution and size of the granules), diminished (≤10 granules), diffuse (smear of lysozyme or defensin within the cytoplasm with no recognizable granules), excluded (majority of the granules do not contain stainable material), and enlarged (rare, megagranules) (14, 27). The last 2 categories were only observed in human samples. The Paneth cell phenotypes of CD subjects (used for outcome correlation) were then defined by the percentage of total abnormal Paneth cells in the sample. The type I Paneth cell phenotype was defined as 20% or more of total Paneth cells showing abnormal morphology patterns, whereas the type II Paneth cell phenotype was defined as less than 20% of total Paneth cells showing morphologic defects (14). Statistics. Clinical outcome correlation was performed using log-rank test. For analysis between different genotype and smoking exposure combinations, Kruskal-Wallis tests followed by Dunn’s tests between groups were performed. For Paneth cell phenotype and various cellular readout comparisons in mouse experiments, 2-way ANOVA followed by Tukey’s multiple comparison adjustment was used. For microbiome studies, principle coordinate analysis was performed using analysis of similarities (ANOSIM) with 999 permutations. Relative operational taxonomic unit (OTU) abundance data were input into linear discriminant analysis effect size (LEfSe) to determine biomarkers with significant linear discriminant analysis effect size (67). The determination of sample size and data analysis for animal studies followed the general guideline of Festing and Altman (68). Based on the law of diminishing returns, Mead recommended that a degree of freedom (DF) of 10 to 20 associated with error term in an ANOVA would be adequate to estimate preliminary information (69). All tests were 2 tailed, and a P value of less than 0.05 was considered significant. Data were plotted and analyzed using GraphPad Prism (version 6.05) and SAS version 9.4 (SAS Institute). Data represent mean ± SEM. Study approval. The study was approved by the institutional review boards of Washington University School of Medicine and Cedars-Sinai Medical Center. Subjects provided written, informed consent. The animal studies were approved by the ethical committee at Washington University School of Medicine. Data and materials availability. All Atg16l1T300A mouse study full-thickness ileal RNA-seq data were deposited in the EMBL-EBI’s ArrayExpress database (E-MTAB-5707). All Atg16l1T300A mouse study LCM-procured Paneth cell microarray data were deposited in the ArrayExpress database (E-MTAB-6168). All 16s rRNA-seq data for Atg16l1T300A mice were deposited in the ArrayExpress database (E-MTAB-5717). All 16s rRNA-seq data for the cohousing experiment were deposited in the ArrayExpress database (E-MTAB-5720). Author contributions TCL, DPBM, and TSS designed the study. TCL, JTK, SX, GEK, and CBW acquired the data. DPBM recruited patients. MJH provided instruments for the experiments. MWR, RC, and GN provided mouse strains. TCL, KLV, FG, RDH, and TSS analyzed data. TCL and TSS drafted the manuscript. All the coauthors agreed on the content of the manuscript. Supplemental material Acknowledgments The study was supported by the Leona M. and Harry B. Helmsley Charitable Trust (to DPBM and TSS), Doris Duke Charitable Foundation grant 2014103 (to TCL), Crohn’s and Colitis Foundation grant 274415, NIH grants 1R56DK095820 (to DPBM and TSS), and P01 DK046763 and U01 DK062413 (to DPBM), K01DK109081 (to KLV), and UL1 TR000448 (to TCL). The authors thank the Genome Technology Access Center at Washington University School of Medicine for help with RNA-seq analysis. The Center is partially supported by National Cancer Institute grant P30 CA91842 and by Institute for Clinical and Translational Science/Clinical and Translational Sciences Award grant UL1 TR000448 from the NIH. The authors also thank Wandy Beatty in the Molecular Microbiology Imaging Facility for assistance with TEM and Robert Schmidt for interpretation of the TEM images. Footnotes Conflict of interest: The authors have declared that no conflict of interest exists. 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3 RESULTS A total of 10 638 citations were identified from the electronic and manual search. After removing duplicates (n = 767) and non‐applicable references (n = 9620), 251 citations were screened, of which 92 met the inclusion criteria (Figure 1).14-105 Baseline characteristics of the included manuscripts, study quality and studies reporting negative results are provided in Table S1, S2 and S3 respectively. Data from several included studies are only presented in tables.14-29 Figures 2 and 3 provide an overview of different predictive molecular markers grouped by underlying biological mechanism. 3.1 Anti‐TNF therapy (IFX // ADA // GLM // CZP) 3.1.1 (Pharmaco) genomics An overview of predictive genetic biomarkers for anti‐TNF response is presented in Figure 2 and Tables 1 and 4. Reference (year) Response definition Biomarker Association Predictive value Bank (2014) Clin Resp: based on three‐step scale CD SNP rs5030728 (TLR4 G>A) Clin NR OR (adj): 0.38 [0.15‐0.96] P = 0.04 SNP rs352139 (TLR9 1174G>A) Clin NR OR (adj): 0.38 [0.16‐0.94] P = 0.04 SNP rs4149570 (TNFRSF1A‐609G>T) Clin Resp OR (adj): 5.16 [1.14‐23.39] P = 0.03 UC SNP rs11938228 (TLR2 C > A) Clin NR OR (adj): 0.26 [0.08‐0.92] P = 0.04 SNP rs4696480 (TLR2 A > T) Clin NR OR (adj): 0.29 [0.12‐0.70] P = 0.01 SNP rs3804099 (TLR2 597 T>C) Clin Resp OR (unadj): 2.47 [0.98‐6.23] P = 0.05 SNP rs5030728 (TLR4 G > A) Clin Resp OR (unadj): 2.59 [1.08‐6.18] P = 0.03 Bank (2018) Clin Resp: three‐step scale to reflect max response 22 W after initiation CD heterozygous genotype of TLRS 936 T>C (rs5744174) NR OR: 0.24 [0.11‐0.53], P = 0.008 homozygous variant genotype of IFNGR2 T>C (rs8126756) NR OR: 0.36, 95% CI: 0.16‐0.81, P = 0.01 UC heterozygous genotype of IL128B ‐ 10993 G>C (rs3212217) NR OR: 0.09, 95% CI; 0.01‐0.65, P = 0.02 heterozygous genotype of IFNGRI ‐ 56 T> C (rs2234711) NR OR: 0.29, 95°/o CI: 0.11‐0.78, P = 0.01 homozygous genotype of TBX21 ‐ 1514 T>C (rs17250932) NR OR: 0.06, 95% CI: 0.01‐0.80, P = 0.03 homozygous genotype of JAK2 T>C (rs12343867) NR OR: 0.17, 95% CI: 0.03‐0.85, P = 0.03 hetero+homoz. genotypes of IL128 −10993 G > C (rs3212217) NR OR: 033, 95% CI: 0.15‐0.69, P = 0.004 hetero+homoz. genotypes of IL 18‐607 C > A (rs1946518) Clin Resp OR: 2.14, 95% CI: 1.06‐4.32, P = 0.03 CD +UC: heterozygous genotype of IFNGRI ‐ 56 T>C (rs2234711) NR OR: 0.57, 95% CI: 034‐0.96, P = 0.04 homozygous genotype of IL 126 −10993 G > C (rs3212217) Clin Resp OR: 10.40, 95% CI: 131‐82.41, P = 0.03 hetero+homoz. genotypes NLRP3 29940 C > G (rs10754558) Clin Resp OR: 1.60, 95% CI: 1.02‐2.52, P = 0.04 Barber (2016) Clin PNR: Clin NR by W12 or Th cessation within 4 months of induction. DR: Clin response ≥24 M prediction model: 15 SNPs (GRS 5‐20) Clin PNR AUC 0.929; OR 2.65 [1.94‐3.61], P = 6 x 10‐10 prediction model: 16 SNPs (GRS 6‐23) Clin DR AUC 0.83; OR 1.6 [1.41‐1.83], P = 2 x 10‐12 prediction model: clinical and genome Clin DR AUC 0.85 prediction model: clinical and genome Th cessation HR 0.89 [0.85‐0.93], P = 4 x 10‐8 Billiet (2016) Biochem; Not Specified FCGRT promotor VNTR2/VNTR3 genotype Drug expos Not specified Dezelak (2016) Clin Resp: IBDQ >170 or increase in >22 pts compared to baseline. Biochem Resp: CRP <3 mg/L or decrease >25% from baseline SNP rs9373839 (ATG5) Allele C and haplotypes C/C & C/T Clin Resp 4.2‐fold higher chance for CResp; P = 1.13x10‐0 SNPs rs9373839 and rs510432 (ATG5) haplotype C/G Clin Resp 4.9% higher IBDQ score; P = 1.31x10‐06 SNPs rs9373839 and rs510432 (ATG5) Allele C and haplotypes C/G Clin Resp 36% higher chance for CResp; P = 8.34x10‐7 SNP rs1130864 (CRP) ≥1 allele C Clin&Biochem Resp 10‐fold higher chance for CResp and fourfold higher chance for BResp Gonsky (2014) Clin; Not Specified SNP rs1861494 (IFNG +2109) T allele carriage (T/T and T/C) vs C allele (C/C) ASCA+ P < 0.015, No quant pred data Haritunians (2009) MR‐UC: UC requiring colectomy (refractory to drugs). Non‐MR‐UC: colectomy for cancer or no colectomy. Prediction model: 46 SNPs MR‐UC 48% of the variance for colectomy explained. HR 1.313; AUC 0.91; Sens 0.793; Spec 0.858 Score of 44 & 47 generated test Sens and Spec of over 90% Hlavaty (2005) Clin Resp (partial): LCD: decrease≥70 pts (W4). FCD: decrease ≥50% in fistula drainage (W10) Clin Rem: LCD: CDAI<150 (W4). FCD: cessation of fistula drainage (W10). SNP rs1800682 (FAS) haplotype C/C or C/T vs T/T (minor allele: T) Clin Resp OR 0.11 [0.08‐0.56]; P = 0.002 SNP rs4645983 (CASP9) allotypes T/T vs C/C or C/T (minor allele: T) Clin Resp OR 1.50 [1.34‐1.68]; P = 0.04 SNP rs763110 (FAS)Fas ligand −843 CC/CT genotype Clin Resp OR 1.66 [1.21‐2.29], P = 0.002 Hlavaty (2007) Clin Resp (partial): LCD: decrease by ≥70 points at W4. FCD: decrease ≥50% fistula drainage at W10. Clin Rem: LCD: CDAI <150 at W4. FCD: complete cessation of fistula drainage at W10. Prediction model: Apoptotic pharmacogenetic index (API) (ranging from low to high apoptotic response (0‐3)) consisting of three SNPs: Fas ligand −843 C/T (0 pt: if TT and 1 point if CT/CC) Fas −670 G/A (0 pt if TT and 1 pt if AG/AA) Caspase9 93 C/T (0 pt if TT/CT and 1 pt if CC) Clin Resp API ≤1 (n = 59): R in LCD (50%) and FCD (39.5%). API of 2 (n = 158): CR and CResp in LCD (56.1% & 73.8%) and in FCD (44.9% & 85.7%). API =3 (n = 10), CR and CResp in LCD (85.7% & 100%) and in FCD (0% & 100%) No quant pred data Koder (2015) Biochem Resp: decrease of CRP to <3 mg/L or drop in CRP >25% SNP rs10210302 (ATG16L1) genotypes C/T or T/T vs C/C Biochem Resp OR 9.44 [2.49‐35.83], P(corrected) = 0.027 Lopez‐Hernandez (2014) Clin Resp: improvement in HBI or clinical observation. Clin NR: no change in HBI or exacerbation SNP rs1800629 (TNFα −308G/A) allele A and G/A vs G/G Clin NR Cp<0.05 No quant pred data %Resp vs %NR: Clin NR Clin Resp Clin NR ‐308A allele: 6% vs 27% Cp=0.026 ‐308GG genotype: 88% vs 44% Cp=0.029 ‐308GA genotype: 12% vs 55% Cp=0.017 No quant pred data Louis (2004) Biochem Resp (complete): normal CRP at W4. Biochem Resp (partial): decrease ≥25% at W4 SNP rs396991 (FCGR3A) allotype T/T vs T/G or G/G (WT) Biochem Resp RR 1.43 [1.27‐1.61], P = 0.0002 Louis (2006) Biochem Resp: ≥25% reduction in CRP at both W2 and W6 from BL SNP rs396991 (FCGR3A) allotype V/V vs V/F and F/F Biochem Resp W6 CRP reduction: −79.4%, −76.5% and −64.3%; P = 0,085, No quant pred data Mascheretti (2002) Clin Resp: cohort 1: decrease CDAI >70 points from baseline to W4; cohort 2: decrease CDAI>70 points from baseline to W2. Clin Rem: CDAI<150 SNP rs1800629 (TNF), haplotype G/A or A/A vs G/G (minor allele: A) SNP rs1061622 (TNFRSF1B), haplotype T/G or G/G vs T/T (minor allele: G) Clin Resp Clin Resp Not specified OR 0.88 [0.57‐1.3] Matsukura (2008) Clin Resp: improvement in HBI or clinical observation. Clin NR: no improvement or exacerbation SNP rs767455 (TNFRSF1A), A/G and G/G vs A/A (WT) SNP rs1061624 & rs3397) (TNFRSF1B), rs1061624 A allele and rs3397 T allele Clin NR Clin NR OR 0.22 [0.06‐0.76] & OR 0.26 [0.08‐0.90]; P < 0.05 5% Resp vs 18% NR, χ2 = 6.4, P = 0.01, No quant pred data Medrano (2014) Clin Resp (partial): LCD: decrease HBI >3 and no CS. FCD: reduction ≥50% in the number of draining fistulas. Clin Rem: LCD: HBI<4 and no CS. FCD: complete closure of fistulas. SNP rs1061624 (TNFRSF1B), haplotype A‐rs3397_T Clin NR OR 1.78 [1.09‐2.90], P = 0.015 Moroi (2013) Biochem Resp: ΔCRP & ΔCRP% SNP rs396991 (FCGR3A‐158), allotype V/V vs V/F or F/F Biochem Resp ΔCRP, P < 0.001; ΔCRP%, P = 0.044, No quant pred data Netz (2017) Clin Resp: in accordance with the treating physician decision Fas ligand SNP (rs763110) A (minor) TNF gene −308 allele NR NR OR 4.30 [1.45‐12.8], P = 0.009 OR 2.88 [1.1‐8.22], P = 0.049 Pierik (2004) Biochem Resp: >12 mg/L reduction in CRP. Biochem Rem: CRP <3 mg/L. SNP rs767455 (TNFRSF1A), A36G carriage Biochem Resp OR 0.47 [0.234‐0.946], P = 0.0343 Repnik (2016) Clin Resp: increase in IBDQ >22 or IBDQ >170 SNP rs2071303 (HFE), A/G or G/G vs A/A genotype Clin Resp W12: OR 3.467 [1.149‐10.463] P = 0.037. W20: OR 4.832 [1.519‐15.367] P = 0.009. Allele G vs A carriage: W20: OR 3.542 [1.355‐9.258] P = 0.0082, No quant pred data Spalinger (2016) Clin NR: 1. breakthrough/LOR, 2. PNR 3. Stop due to side effects/intolerance. SNP rs1893217 (PTPN2), variant haplotypes C/T or C/C vs T/T Clin Resp (n)Resp vs (n)NR Genotype: CC: 7 vs 5 ‐ CT: 37 vs 39 ‐ TT: 71 vs 109 Existence of C‐allele: CC/CT: 44 vs 44 ‐ TT: 71 vs 109 P = 0.05, No quant pred data Steenholdt (2012) Clin Resp: PGA +biochemical markers SNP rs1061622 (TNFRSF1B) minor allele carriage SNP rs976881 (TNFRSF1B), minor allele carriage Clin Resp DR LOR OR 4.2 [1.2‐18.2], P = 0.014 OR 5.5 [1.5‐25.5], P = 0.007 OR 3,3 [1.2‐9.1], P = 0.014) (Homozygosity: OR 19, P = 0.006) IBD: inflammatory bowel disease; CD: Crohn's disease; UC: ulcerative colitis; LCD: luminal Crohn's disease; FCD: fistulising Crohn's disease; MR‐UC: medical refractory ulcerative colitis; M: months; W: weeks; D: days; FU: follow‐up; CDAI: Crohn's Disease Activity Index; CAI: colitis activity index; HBI: Harvey Bradshaw Index; PCDAI: Paediatric Crohn's Disease Activity Index; PUCAI: Paediatric Ulcerative Colitis Activity Index; PGA: Physician's Global Assessment score; ULN: upper limit of normal; Hb: haemoglobin; CRP: C‐reactive protein; ESR: erythrocyte sedimentation rate; FC: faecal calprotectin; CK: cytokine; PBMC: peripheral blood mononuclear cell; TNF: tumour necrosis factor; IFX: infliximab; ADA: adalimumab: UST: ustekinumab; PCB: placebo; CS: corticosteroids A‐D‐A: anti‐drug antibodies; Clin: clinical; Biochem: biochemical; Rem: remission; Resp: Response; NR: nonresponse; PNR: primary nonresponse; DR: durable response; Sust: stained; LOR: loss of response; Th cessation: Therapy cessation; Drug expos: Drug exposure; SSFR: sustained steroid free remission; RB: rectal bleeding; pts: points; OR: odds ratio; HR: hazard ratio; Sens: sensitivity; Spec: specificity; PPV: positive predictive value; NPV: negative predictive value; SNP: single nucleotide polymorphism; WT: wild type; GRS: genetic risk score; LH: likelihood; FCGRT: Fc Fragment Of IgG Receptor And Transporter; MPV: mean platelet volume; IL: interleukin; Cp: Corrected p‐value; (n): number; No quant. pred. data: No quantitative predictive data. Fc‐receptors Several studies reported on the association between FCGR3A genotype and response to anti‐TNF treatment.28-31 The V/V allotype was associated with a significantly higher proportion of CD patients (100%) achieving a CRP decrease in response to infliximab compared to the V/F or F/F allotypes (70%).30 This finding was confirmed by Moroi et al and in a sub‐analysis of the ACCENT I trial.29, 31 Due to close location of the FCGR3A and the CRP genes, a potential false‐positive association was investigated and excluded.32 Genetic variations within the neonatal Fc‐receptor (FcRn gene) were associated with pharmacokinetic changes for IFX and ADA. Heterozygosity for the VNTR2/VNTR3 genotype led to a 14% (P = 0.03) and 25% (P = 0.007) reduction in induction drug concentrations compared to homozygosity VNTR3 for IFX and ADA respectively.33 ATG16L1 The autophagy related gene ATG16L1 was linked to anti‐TNF response in two studies.28, 34 Patients with the ATG16L1 T/T and C/T genotypes had a significantly greater CRP response to adalimumab than patients with the C/C genotype (85% vs 37.5%; OR 9.44 [2.49‐35.83], P = 0.027).28 More recently however, the association between autophagy and anti‐TNF response was not confirmed in a large retrospective study.35 Apoptosis A number of studies reported an association between genes related to apoptosis and anti‐TNF response.28, 34, 36, 37 Fas ligand TT genotype and caspase‐9 CC/CT genotypes were associated with low response to IFX in luminal CD. In fistulising CD, Fas ligand‐843 CC/CT genotype was the only predictor of response.36 Conversely, the CC genotype vs a TC or TT genotype was associated with primary nonresponse in CD (OR 4.3 [1.45‐12.8], P = 0.009).38 TNF/TNF‐receptor genes Several studies looked at TNF‐signalling pathways and anti‐TNF response.39-47 A genetic alteration in the TNF region (the lymphotoxin α locus, also known as TNF‐β) was associated with clinical nonresponse to a single infusion of infliximab in CD (P = 0.007).39 Other studies found no significant association between variation in the TNF region (eg, the TNF promoter region) and treatment response.40, 41 Recently however, the presence of the minor allele (A) for the TNF gene (rs1800629) was associated with primary nonresponse (OR 2.88 [1.1‐8.22], P = 0.049).38 Several studies reported on single nucleotide polymorphisms (SNPs) within the TNF‐receptor superfamilies (TNFRSF) 1A and 1B. Regarding TNFRSF1A, higher response rates were observed in CD patients homozygous for the TNFRSF1A‐609G>T genotype (OR 5.16 [1.14‐23.39], P = 0.03).42 Minor allele carriage (G) of SNP rs767455 was associated with low clinical anti‐TNF response (OR 0.47 [0.23‐0.95], P = 0.034), a finding that was replicated by a different study group (OR 0.26 [0.08‐0.90]).43, 44 With respect to TNFRSF1B, an increased frequency in the haplotype conformed by two SNPs (rs1061624_A‐rs3397_T) was found in anti‐TNF nonresponders in both a Japanese and Caucasian CD population (P = 0.01 and OR 1.78 [1.09‐2.90], P = 0.015, respectively).43, 45 Conversely, minor allele (G) carriage of the TNFRSF1B SNP (rs1061622) was associated with improved initial (OR 4.2 [1.2‐18.2], P = 0.014) and sustained clinical response to anti‐TNF (OR 5.5 [1.5‐25.5], P = 0.007).46 However, the latter association (OR 8.5 [0.9‐206.3]) could not be confirmed.47 Recently, a similar association between this specific SNP (TNFRSF1B, rs1061622) and clinical response was reported (OR 1.52 [0.79‐3.04], P = 0.023).45 In addition, minor allele carriage of TNFRSF1B (rs976881) was also discovered to be associated with loss of response to IFX (OR 3.3 [1.2‐9.1], P = 0.014).46 Finally, Pierik et al did not find an association between TNFRSF1B (rs1061622) and anti‐TNF response.44 NOD2/CARD15, CRP, IL23R and IL12 No significant association with treatment response was found either for the IBD susceptibility variants of NOD2/CARD15 or for variations of the CRP genotype.32, 48-51 UC patients carrying the homozygous IBD risk‐increasing IL23R SNPs were more likely to respond to infliximab than patients with the risk‐decreasing variants (74.1% vs 36.6%, P = 0.001).52 A recent study observed that the heterozygous genotype of IL12B—10993 G > C (rs3212217) was associated with nonresponse within 22 weeks after initiation in UC (OR 0.24 [0.11‐0.53], P = 0.008).53 Genetic risk models Several predictive genetic models for treatment response to anti‐TNF therapy have been suggested.34, 37, 54, 55 Hlavaty et al developed an algorithm—comprising three apoptotic SNPs (Fas ligand −843 C/T, Fas −670 G/A and caspase‐9 93 C/T)—to predict clinical response to the initial IFX infusion in CD.37 Another risk model with 46 SNPs, was found to be predictive of colectomy as a measure of refractory UC (Area under the curve value (AUC) 0.91, sensitivity 0.79 and specificity 0.85).55 Furthermore, three genetic IBD susceptibility SNPs, rs2836878 (21q22/BRWD1) combined with the microbial biomarker pANCA predicted primary nonresponse to anti‐TNF in paediatric UC patients (AUC 0.98, sensitivity 0.95, specificity 0.88 and positive likelihood ratio 8).54 Recently, two additional models were developed for primary nonresponse and sustained response to anti‐TNF treatment in CD. The genetic risk score—comprising 15 SNPs—was the only independent predictor of primary nonresponse (OR 2.65 [1.94‐3.61], P < 0.0001). In the risk model for sustained response—comprising 16 SNPs—each one‐point increase was associated with a 60% higher likelihood of sustained response (AUC 0.929; OR 1.6 [1.41‐1.83], P = 2x10–12).34 Recently, the combination of two‐risk genotypes, involving both apoptosis and the TNF region, was associated with primary anti‐TNF nonresponse (OR 4.76 [1.35‐16.77], P = 0.015).38 3.1.2 Gene expression An overview of predictive gene expression biomarkers is presented in Tables 2 and 4. Reference (year) Response definition Biomarker Association Predictive value Peripheral blood Li (2016) Endo Resp: Mayo/SES‐CD/Rutgeerts score; Not Specified lamina propria CD4+ T‐cell subsets. Endo Resp Reduced prevalence of Gata3‐expressing cells in the CD cohort (P = 0.002), No quant pred data Intestinal tissue Arijs (2009) Endo Resp: MH = Mayo ≤1. H Resp: Geboes ≤1 osteoprotegerin, (TNFRSF11B), stanniocalcin‐1 (STC1), prostaglandin‐endoperoxide synthase 2 (PTGS2), IL‐13 receptor α 2 (IL13Rα2) and IL‐11 Endo&Hist Resp Accuracy of 89.1% (41/46). Sens: 95% (19/20); Spec: 85% (22/26) Arijs (2010) Endo Resp: MH, Hist Resp: ≥ 3 pts decrease on the geboes score TNF induced protein 6 (TNFAIP6), S100A8, IL11, G0/G1switch 2 (G0S2) and S100A9 Endo&Hist Resp CDc: top 5 genes: (accuracy 100%). CDi: no association, No quant pred data Atreya (2014) Clin Resp: CDAI decrease ≥100 pts (12 W). Endo Resp: MH = absence of ulcers Number of membrane‐bound TNF (mTNF) expressing cells Clin&Endo Resp High mTNF‐group (≥20 cells/confocal image) vs low‐group (<20): higher probability of R (92% vs 15%). Accuracy 88%; Sens 92%; Spec 85%; PPV 85%; NPV 92% Halloran (2014) Endo Resp: total mayo of 0 or 1 Principal component 1 (PC1) (disturbance in gene expression seen in TCMR (kidney/heart) Endo Resp Higher pre‐treatment PC1 (UC: P < 0.03; CD: P < 0.001), No quant pred data Viazis (2017) Endo Resp: MH=endoscopic mayo 0 lower Th1‐defining T‐bet and higher Th17‐defining Rorc‐p Endo Resp P < 0.05, No quant pred data West (2017) Endo&H Resp: complete disease resolution BL Oncostatin M‐associated inflammatory module expression: Module‐low vs Module‐high Endo&Hist Resp Relative risk = 5, 95% CI = 1.4‐17.9, P = 0.0006 Yamada (2017) CD: Clin Resp: decrease ≥100 from BL, or <150 when BL was≤248 UC: Clin Resp: decrease ≥3 full Mayo or ≥2 points in clinical Mayo with decrease ≥1 in RB Higher expression of Bmi1 PNR Relative Bmi1 expression Resp vs NR: 1.28 ± 0.14 vs 2.10 ± 0.23, P = 0.0039, No quant pred data IBD: inflammatory bowel disease; CD: Crohn's disease; CDc: crohn's colitis; CDi; crohn's ileitis: UC: ulcerative colitis; M: months; W: weeks; FU: follow‐up; BL: baseline; CDAI: Crohn's Disease Activity Index; HBI: Harvey Bradshaw Index; SES‐CD: simple endoscopic score for Crohn's disease; TNF: tumour necrosis factor; IFX: infliximab; ADA: adalimumab: Clin: clinical; Endo: endoscopic; Hist: histological; Resp: response; NR: nonresponse; PNR: primary nonresponse; MH: mucosal healing; pts: points; TCMR: T‐cell‐mediated rejection of organ transplants; Sens: sensitivity; Spec: specificity; PPV: positive predictive value; NPV: negative predictive value; No quant pred data: No quantitative predictive data. Peripheral blood A reduced number of Gata3+‐expressing T helper cells was associated with endoscopic response to anti‐TNF in CD but not UC, which could be associated with a reduced number of eosinophils.56 Recently, a decrease in peripheral blood mononuclear cell (PBMC) expression of CD14 and CD86 at both week 2 and week 14 compared to baseline was shown to be associated with short term clinical and endoscopic response to infliximab.57 Intestinal tissue A mucosal gene signature for response to infliximab in both colonic CD and UC was identified.58, 59 In colonic CD, the top five differentially expressed genes (TNFAIP6, S100A8, S100A9, IL‐11 and G0S2) predicted endoscopic and histological response with 100% accuracy.59 In UC, a combination of five innate immunity gene expression profiles (TNFRSF11B, STC1, PTGS2, IL13Ralpha2 and IL11) predicted response with 89% accuracy (sensitivity 95% and specificity 85%).58 An association between mRNA patterns—reflecting general inflammation, dedifferentiation and tissue damage—and nonresponse to anti‐TNF has been reported. This pattern was similar to that seen in organ transplant rejection.60 Ongoing increased regulatory T‐cell CD39 expression was associated with anti‐TNF response and resulted in decreased suppression of inflammation in a consecutive IBD mouse model.61 Another approach to predict response is by direct visualisation of a target molecule. A high number of membrane bound TNF‐expressing mucosal cells, as identified by in vivo molecular imaging techniques, was associated with increased probability of anti‐TNF response (92% vs 15%; accuracy 88%, positive predictive value (PPV) 85%, negative predictive value (NPV) 92%).62 Furthermore, clinical response to anti‐TNF in UC was associated with low baseline mucosal mRNA expression of IL‐6, IL‐1β, IL‐17A and IFN‐γ.63 Finally, mucosal oncostatin M and its receptor expression were recently shown to be associated with disease activity and pre‐treatment expression was associated with subsequent endoscopic anti‐TNF nonresponse in both CD and UC.64 3.1.3 Proteins An overview of predictive protein markers is presented in Tables 3 and 4. Reference (year) Response definition Biomarker Association Predictive value Anti‐TNF Algaba (2014) Clin Resp: ≥5 pt reduction in modified Truelove‐Witts index or ≥70 pt reduction in CDAI VEGF; Ang1 Clin Resp NR Low VEGF levels at BL (R vs NR: 453.5 ± 250.7 vs 667.5 ± 153.9 pg/mL) and before 2nd and 3rd dose (P < 0.05). High Ang1 at BL (AUC 0.7) High VEGF at BL (AUC = 0.8. Cutoff: 658.36 pg/mL; Sens 85.7%; Spec 78.6% Baird (2016) Clin Resp: CDAI<150; CAI ≤4, Biochem Resp: CRP≤10 mg/L TNF, IL‐1β, IL‐10, IFN‐γ, IL‐6, plasmacytoid DC, IRAK4 NR Clin&Biochem Resp UC: Increased TNF, IL‐1β, IL‐10 and IFN‐γ at BL. Lower TNF & IL‐1β production by TLRs and reduced TLR0‐induced IL‐6 and TLR‐3/4/8/9‐induced IL‐10. Lower pDC frequency's (P < 0.01). Increased IRAK4 upon TLR‐9 stimulation compared to BL (P = 0.03). No IRAK4 reduction upon TLR‐2/4/7 stimulation. No quant pred data Lower total IRAK4 levels upon TLR stimulation, No quant pred data Biancheri (2015) Clin Rem: Not Specified, Endo Resp: complete MH Serum levels of cleaved IgG and antihinge autoantibodies PNR NR have higher serum levels of MMP3‐/MMP12‐cleaved endogenous IgG than both healthy controls and Resp and higher levels of antihinge autoantibodies than Resp (both significant), No quant pred data Billiet (2017) Clin Resp: PGA clear clinical benefit after three infusions. PNR: absence of clinical improvement TNF/CRP ratio at BL higher IL8 at BL IFN‐ɣ & IL‐6 decrease between BL and W2 and W6 resp PNR PNR Clin Resp OR 2.8 [1.4‐5.5], P = 0.003 BL levels IL8 Resp vs PNR (median(IQR)): 11.3 pg/mL (7.3‐18.7) vs 20.3 pg/mL (8.9‐61.1), P = 0.01, No quant pred data P < 0.05, No quant pred data Caneparo (2016) UC: Clin Rem: partial Mayo ≤2 with no subscore >1, Clin Resp: decrease partial Mayo ≥3 and ≥30% from BL with decrease RB ≥1 or absolute RB of 1 or 0. CD: Clin Rem: HBI<4, Clin Resp: decrease HBI≥3 compared to previous IgG anti‐IFI16 antibodies at BL Clin Resp OR =0.143; 95% CI 0.027‐0.761. Feng (2017) Clin Rem: CDAI<150. Clin Resp: CDAI decrease >100. Endo Rem: MH =absence ulcers IL‐9 levels at W14 to predict Resp at W30 Clin Rem Endo Rem NR AUC: 0.803, cut‐off 17,4 (Sens 0,80; spec 0.75; PPV 44.4; NPV 93.7) AUC: 0.752, cut‐off 15.5 (Sens 0.63; spec 0.87; PPV 85.0; NPV 66.7) higher IL‐9 levels at W14 P = 0.003, No quant pred data Ferrante (2007) Clin Resp: absence of diarrhoea and RB. Partial Clin Resp: improvement but with RB. pANCA+/ASCA‐ (n = 87) Clin Resp (suboptimal) OR 0.4 [0.16‐1.00] P = 0.049 Kevans (2015) Clin Resp at 1 year: no symptoms, CS, salicylate therapy and no colectomy. Anti‐OmpC positivity NR OR 0.14 [0.03‐0.6], P = 0,04; in multivariable model: OR 0.14 [0.03‐0.6] P = 0.006 Magnusson (2013) Clin Resp: decrease in Mayo ≥3 compared with BL, prior to fourth treatment dose. CD4+ and CD8+CD25+ T cells (W2) Annexin V + CD4+ and CD8+ T cells (W2) Serum sCD25 (W2) Serum TNFRII (W2) Clin Resp Clin Resp Clin Resp Clin Resp Cut‐off CD4+CD25+: decrease ≥0.65%; Sens 84.6%. Spec 87.5%. Cut‐off CD8+CD25+ T cell: decrease ≥0.14%; Sens 69.2%; Spec 93.8% Cut‐off: decrease ≥98 pg/mL with Sens 66.7%; Spec 77.8% Cut‐off: decrease ≥98 pg/mL with Sens 66.7%; Spec 77.8% Cut‐off: decrease ≥255 pg/ml; Sens 75%; Spec 72.2% Magnusson (2015) Clin Resp: reduction in Mayo score ≥3 compared with BL (evaluation before 4th infusion). Prediction model: T‐cell surface activation receptor CD25 & IL‐5 Clin Resp R2=0.63; prediction accuracy: 85% Nishida (2017) Clin Resp: partial mayo reduction ≥3 with a decrease ≥30% from BL and decrease RB ≥1 or absolute RB 0 or 1. LOR: clinical relapse BL higher Neutrophil‐to‐lymphocyte ratio (NLR) LOR ROC: cut‐off 4.488 Sens 78.6% Spec 78.3% HR: 3.86 P = 0.023 Reich (2016) Clin Rem: modified HBI<5. Clin Resp: reduction HBI ≥3. NR: surgery or escalation before W14 Clin Rem W14: OR 3.5 [1.3‐9.7], W22: OR 4.7 [1.3‐17.2] Santos Antunes (2016) Clin NR based on PGA and/or need for surgery ANA (positive if ≥1/100) Vit D deficiency: 25(OH)D < 20 ng/mL) Clin NR Clin NR OR 20.107 [2101‐192 388] Association between VD deficiency and positive pre‐treatment ANA OR 6.015 [1,054‐51,599]) Sobolewska (2016) DR: Clin Resp for 52 W. (Clin Resp: CDAI decrease≥25% & ≥70 pts.) LOR: 1) CDAI≥175 & increase CDAI≥35% & ≥70 at ≥2 visits (≥3 W), 2) CD surgery,3) switch to episodic treatment. MPV and ΔMPV DR LOR MPV >10.3 fl at W14: Sens 67% and Spec 80%; P = 0.0019. ΔMPV between BL and W14 >0.4 fl: Sens 87%; Spec: 93% Lower ΔMPV between BL & W14 (P = 0.0003), No quant pred data Steenholdt (2016) Clin Resp: CDAI reduction ≥70 pts from BL IL‐6 and sTNF‐R2 NR (PD) Lower levels after 12 W of treatment in primary PK than PD failures (<3.1 vs 4.0, P = 0.02; 3209 vs 4740, P = 0.04), No quant pred data Zator (2014) DR: time to discontinuation of the index anti‐TNF (drug with VD level within 3 M prior to start) Vit D (<30 ng/mL) NR HR 2.13 [1.03‐4.39] P = 0.04 (with supplementation at index measure) HR 3.4 [1.14‐10.19] (no supplementation) Anti‐IL23p19 Sands (2017) Clin Rem: CDAI<150 at W8, Clin Resp: CDAI decrease ≥100 BL IL‐22 Clin Resp ≥15.6 pg/mL, P = 0.04, No quant pred data IBD: inflammatory bowel disease; CD: Crohn's disease; UC: ulcerative colitis; MR‐UC: medical refractory ulcerative colitis; M: months; W: weeks; D: days; FU: follow‐up; CDAI: Crohn's Disease Activity Index; CAI: colitis activity index; HBI: Harvey Bradshaw Index; PCDAI: Paediatric Crohn's Disease Activity Index; PUCAI: Paediatric Ulcerative Colitis Activity Index; PGA: Physician's Global Assessment score; ULN: upper limit of normal; Hb: haemoglobin; CRP: C‐reactive protein; ESR: erythrocyte sedimentation rate; GM‐CSF: granulocyte‐macrophage colony stimulating factor: IFN‐γ: interferon‐gamma; FC: faecal calprotectin; CK: cytokine; PBMC: peripheral blood mononuclear cell; TNF: tumour necrosis factor; IFX: infliximab; ADA: adalimumab: CS: corticosteroids A‐D‐A: anti‐drug antibodies; Clin: clinical; Bio: biochemical: Endo: endoscopic; Rem: remission; Resp: response; NR: nonresponse; PNR: primary nonresponse; DR: durable response; LOR: loss of response; SSFR: sustained steroid free remission; RB: rectal bleeding; BL: Baseline; PK: pharmacokinetic; PD: pharmacodynamics; pts: points; OR: odds ratio; HR: hazard ratio; Sens: sensitivity; Spec: specificity; PPV: positive predictive value; NPV: negative predictive value; SNP: single nucleotide polymorphism; GRS, genetic risk score; LH: likelihood; FCGRT: Fc Fragment Of IgG Receptor And Transporter; MPV: mean platelet volume; IL: interleukin; No quant pred data: No quantitative predictive data. Reference (year) Response definition Biomarker Association Predictive value Peripheral blood Dubinsky (2010) CD: Clin PNR: increase HBI (W10 or 4 W after last dose) or no decrease ≥3 pts. UC: Clin PNR: no decrease ≥2 pts in partial Mayo score and no improvement in the PGA subscore (W10 or 4 W after last dose) Prediction model: UC, pANCA, 3 SNPs and rs2836878 (21q22/BRWD1) Clin PNR Model: R2 = 0.82 ≥ 3/6 risk factors: AUC 0.98; Sens 0.95; Spec 0.88; accuracy 0.92 and PLR 8 Jurgens (2010) Clin Resp: CAI decrease ≥5 pts or CAI<10pts. Clin Rem: CAI ≤4 Seronegativity of pANCA IL23R variants (homozygous carriers) Clin Resp Clin Resp %seropos vs %seroneg: Resp: 15.6% vs 84.4% NR: 37.1% vs 62.9%, P = 0.037, No quant pred data Clin Resp vs NR:74.1% vs 36.6%; P = 0.001, No quant pred data Lacruz (2013) Clin Resp: CDAI >70 decrease, Clin Rem: CDAI<150. Biochem Resp: CRP >25% decrease Serum IL1‐β SNP rs1143634 (IL1‐β) (C allele) Clin Resp NR <0.64 pg/ml: 6/7 Resp vs 3/10 NR; P = 0.036, No quant pred data 50% Resp vs 84% NR, P = 0.027, No quant pred data Taylor (2001) Clin Resp: decrease in CDAI score ≥70 pnts at W4. pANCA & sANCA seropositivity LTA TNFc‐aa13L‐aa26 haplotype 1‐1‐1‐1 NR NR P = 0.003 (W4), No quant pred data P = 0.007, No quant pred data Peripheral blood & Intestinal tissue Dahlen (2015) Clin Resp: Mayo decrease ≥3 pts (W14) High mucosal mRNA expression of IL6, IL‐1β, IL‐17A and IFN‐γ Low mucosal mRNA expression of IL‐6, IL‐1β, IL‐17A, TNF and IFN‐γ NR Clin Resp Not Specified Not Specified Magnusson (2015) Clin Resp: Mayo decrease ≥3 compared with BL. Endo Resp: reduction ≥1 in Mayo (endoscopy) score (W14) Reduced CD14&CD86 expression (tissue) Reduced CCL2 (W14) (tissue) Reduced PBMC expression of CD14 & 86 (blood) Reduced CCL2 (W2 & W14) (blood) Reduced tenascin C (W2 & W14) (tissue) NR Clin&Endo Resp Clin&Endo Resp Clin&Endo Resp Clin&Endo Resp Not Specified Not Specified Not Specified Not Specified Not Specified Peripheral blood & intestinal tissue & microbiota Magnusson (2016) Clin Resp: decrease total Mayo ≥3 (incl. Endo Resp: decrease ≥1 endoscopic mayo) higher DEFA5 and ECP; lower CATH (tissue) higher circulating ECP (blood) higher abundance of F. prausnitzii (microbiota) higher levels of cFOS and EGR‐1 (tissue) higher IL‐6, IL‐12a, IL‐17A, TNF, CD154 (tissue) higher IL‐6, IL‐12a, IL‐17A, TNF expression (tissue) Clin&Endo Resp Clin&Endo Resp Clin&Endo Resp Clin&Endo Resp NR NR P = 0.001, P = 0.02, P = 0.04 resp, No quant pred data Not specified P = 0.01, No quant pred data Scatter plot, Not specified Scatter plot, Not specified median [25%‐75%] Resp vs NR: IL‐6:0.77 [0.24‐1.46] vs 1.67 [1.09‐2.34] P = 0.06 IL12a: 0.53 [0.29‐0.83] vs 0.91 [0.53‐2.81] P = 0.03 IL17a: 0.92 [0.46‐1.57] vs 1.65 [1.42‐2.27] P = 0.03 TNF: 0.26 [0.18‐1.01] vs 0.65 [0.47‐0.81] P = 0.05 No quant pred data CD: Crohn's disease; UC: ulcerative colitis; pts: points; BL: baseline; W: weeks; CDAI: Crohn's Disease Activity Index; CAI: colitis activity index; RB: rectal bleeding; SF: stool frequency; HBI: Harvey Bradshaw Index; PGA: Physician's Global Assessment score; CRP: C‐reactive protein; Clin: clinical; Biochem: biochemical: Endo: endoscopic; Rem: remission; Resp: response; NR: nonresponse; PNR: primary nonresponse; CS: corticosteroids; PBMC: peripheral blood mononuclear cell; Ig: immunoglobulin; TNF: tumour necrosis factor; IFNγ: interferon gamma; mRNA: messenger RNA; pANCA: perinuclear anti‐neutrophil cytoplasmic antibody; IL: interleukin; CCL2: C‐C motf chemokine ligand 2; Sens: sensitivity; Spec: specificity; PLR: positive likelihood ratio; SNP: single nucleotide polymorphism; No quant pred data: No quantitative predictive data. Peripheral Blood Cytokines and inflammatory cells Low circulating IL‐1β concentrations were associated with clinical response to IFX in CD.41 Decreased frequencies of CD25+ T cells, increased annexin V+ CD4+CD8+ T cells and decreased concentrations of serum CD25 and TNFR2 were also associated with initial response.65 In addition, a combination of T‐cell receptor CD25 suppression and a decrease in IL‐5 secretion was predictive of treatment response with 85% accuracy.66 Reduction in tenascin C (an extracellular matrix glycoprotein) predicted downregulation of CCL2 (a proinflammatory chemokine), which was associated with response at week 14.57 Nonresponders to anti‐TNF dose intensification had high IL‐6 and sTNFR2 compared to responders.67 In UC, anti‐TNF nonresponse was associated with high baseline TNF, IL‐1β, IL‐10 and IFN‐γ, and decreased TNF, IL‐1β, IL‐6 and IL‐10 production upon peripheral blood mononuclear cell stimulation in vitro.68 The TNF/CRP ratio was associated with clinical response (OR 2.8 [1.4‐5.5], P = 0.003), which may underscore the contribution of TNF‐independent inflammation. An elevated IL‐8 concentration at baseline was also associated with nonresponse.69 Finally, a decrease in serum IL‐9 between baseline and week 14 could accurately predict both clinical remission and mucosal healing at week 30 (AUC 0.803 and 0.752 respectively).70 Vitamin D Vitamin D deficiency (<30 ng/mL) predicted earlier cessation of anti‐TNF therapy (HR 2.13 [1.03‐4.39], P = 0.04).71 Severe vitamin D deficiency (<20 ng/mL) was linked with ANA seropositivity (OR 6.015 [1.054‐51.599]), which was in turn associated with anti‐TNF failure (adjusted OR 20.107 [2.101‐192.388]).72 Conversely, an association between low baseline vitamin D concentration and improved clinical anti‐TNF response was recently reported (80% vs 23%; OR 3.5 [1.3‐99.7]).73 Microbial biomarkers As previously described, ANA seropositivity has been associated with anti‐TNF failure.72 In addition, a significantly larger proportion of pANCA negative CD patients achieved clinical response as compared to pANCA positive patients.39 In a multivariable analysis, pANCA+/ASCA‐ status was the only independent predictor of failure to achieve early clinical response to infliximab in UC patients (OR 0.4 [0.16‐1.00], P = 0.049).74 Similarly, pANCA seronegativity in UC patients was associated with early clinical anti‐TNF response in UC patients (P = 0.037). However, the likelihood of achieving clinical remission was not significantly increased (P = 0.557).52 Furthermore, anti‐OmpC status was associated with both anti‐TNF response (OR 0.14 [0.03‐0.6], P = 0.04) and therapy discontinuation at 1 year (HR 2.2 [1.1‐4.7], P = 0.03).75 Finally, low baseline IFI16 IgG level was associated with clinical response to infliximab induction treatment (OR 0.143 [0.027‐0.761]).76 Albumin, haemoglobin, CRP and neutrophil‐to‐lymphocyte ratio The value of albumin,61, 77 haemoglobin78, 79 and CRP40, 77-83 for predicting anti‐TNF response has been reported in several studies (Table S7). Evidence from one study suggests that baseline neutrophil‐to‐lymphocyte ratio predicts secondary loss of response to IFX treatment in UC patients (HR 3.86 [1.20‐12.4], P = 0.023; AUC 0.798).84 Faeces An overview of faecal biomarkers as predictors of therapeutic response is presented in Table S3. In acute severe UC, nonresponse to IFX (as defined by the need for colectomy during admission) was associated with higher faecal calprotectin (FC) values at time of hospitalisation (1795 µg/g vs 920 µg/g, AUC 0.77, P = 0.06).85 Conversely, in UC patients treated with ADA, high pre‐treatment FC values were shown to be associated with sustained clinical and endoscopic response at week 52 (585.5 µg/g [300‐689] vs 372 µg/g [232.5‐450.5], P = 0.049).88 A steep decrease in FC between baseline and week 2 could predict endoscopic remission at week 10 (P < 0.01; β = 0.7 [0.27‐1.12]).89 Week two concentrations of FC (AUC 0.82 and 0.61), faecal lactoferrin (AUC 0.84 and 0.80) and the dimeric M2 isoform of pyruvate kinase (AUC 0.88 and 0.78) could accurately predict both clinical and endoscopic response to IFX induction treatment.90 Finally, several authors have reported the association between low post‐induction FC levels and the decrease between baseline and post‐induction FC values and subsequent sustained response.90-93 3.1.4 Microbiota High baseline abundance of Bifidobacterium, Clostridium colinum, Eubacterium rectale, uncultured Clostridiales and Vibrio, together with a lower abundance of Streptococcus mitis, predicted response to anti‐TNF therapy in one study (Table S4).94 Recently, a lower baseline dysbiosis index and higher abundance of Faecalibacterium prausnitzii were associated with subsequent clinical and endoscopic response, however these findings lacked statistical significance.95 3.2 Anti‐integrins (VDZ// etrolizumab (ETZ)) Three studies reported on predictive biomarkers for therapeutic response to anti‐integrins (Tables 5 and S6 and Figure 3).96-101 In ETZ‐treated patients, higher mucosal baseline levels of granzyme A mRNA and integrin αE mRNA were associated with clinical remission and mucosal healing. Patients with high, compared to low, granzyme A mRNA had significantly increased CCL5 and decreased CXCL1—proteins implicated in the recruitment of memory T cells—at baseline.97 Baseline mucosal integrin α4β7 expression has been linked to subsequent VDZ response with contradictory findings. High integrin α4β7 expression on both T‐ and B‐cell subsets was associated with subsequent VDZ response in IBD patients (P = 0.0047).99 Conversely, low numbers of baseline integrin α4β7 expressing T lymphocytes were also associated with clinical response to VDZ in UC (n = 17), however, not in CD.100 At the microbiome level, high baseline α‐diversity, an abundance of butyrate‐producing species (including Roseburia inulinivorans and Burkholderiales) and increased branched chain amino acid synthesis were associated with clinical response to VDZ at week 14. With an AUC value of 0.872, the optimal neural network prediction model (which incorporates both clinical and microbial variables) accurately predicted treatment response.101 Reference (year) Response definition Biomarker Association Predictive value Gene expression and proteins Fuchs (2017) UC: Clin Resp: MCS (mayo clinical subscore) decrease ≥2 at W16 α4β7 expression at baseline α4β1 expression on CD4+ Tcells at W6 predicts W16 Clin Resp Clin Resp lower expression P < 0.01,No quant pred data cut‐off +4,2%, Sens 100%, Spec 100% P < 0,01 Tew (2016) Clin Rem: total Mayo Clinic Score ≤2, no individual subscore >1. Endo Rem: MH (=Endo score≤1) BL higher level of granzyme A mRNA BL higher level of integrin αE mRNA Clin Rem Endo Rem PPV/NPV (all pts) 41%/94%; (aTNF‐naïve) 75%/88% P < 0.05 Not specified Proteins Boden (2018) Endo Resp: improvement from BL moderate/severe to mild inflammation or reduction extent >50%. Clin Resp: decrease HBI or SSCAI ≥3 or ≤3 within 180 days higher pre‐treatment α4β7 expression Endo&Clin Resp multiple subsets of T, B, and NK cells, with terminal effector memory (mean CD4: 41% ± 2.6% vs 25% ± 3.3%, P = .0009, mean CD8: 43% ± 3.5% vs 28% ± 3.2%, P = .0043) and NK cells (mean =34% ± 3.2% vs 17% ± 4.4%, P = .0047) Stallmach (2016) SFCR at W30 and W54. (Clin Rem: CD: HBI ≤4; UC: pMayo≤1 with RB subscore of 0 at W54) Lower FC (W14) Lower CRP (W14) SFCR SFCR median 354vs3000µg/kg P = 0.002,No quant pred data P = 0.003, No quant pred data Intestinal microbiota Ananthakrishnan (2017) CD: Clin Rem: HBI<4, Clin Resp: decrease HBI>3. UC: Clin Rem: SSCAI<2, Clin Resp: decrease SSCAI>3. Manually curated list of 40 microbiome variables Community alpha‐diversity at baseline Community beta‐diversity at baseline Roseburia inulinivorans and Burkholderiales species 13 pathways, including branched chain amino acid biosynthesis pathways Clin Rem Clin Rem Clin Rem Clin Rem Clin Rem AUC 0,872, >80% true pos <25% false neg α diversity sign higher (q < 0.1), β diversity sign lower, higher BL(q = 0,0914, q = 0,0614) significantly enriched, CD: Crohn's disease; UC: ulcerative colitis; W: weeks; BL: baseline; HBI: Harvey Bradshaw Index; Clin: clinical; Endo: endoscopic; Rem: remission; Resp: response; SFCR: steroid free clinical remission; RB: rectal bleeding; MH: mucosal healing; FC: faecal calprotectin; CRP: C‐reactive protein; PPV: positive predictive value; NPV: negative predictive value; sign: significant; No quant pred data: No quantitative predictive data. 3.3 Anti‐IL23 Thus far, no studies have explored molecular markers as predictors of response to ustekinumab. However, a recent phase 2a CD study of a selective anti‐IL23p19 antibody demonstrated that a significantly higher proportion of patients with elevated baseline IL‐22 serum concentration achieved clinical response and remission at week 8 compared to patients with low IL‐22 levels.102 So far, this observation has not been confirmed with other p19 inhibitors. 3.4 Immunomodulators Despite an abundance of observational studies examining the relationship between molecular markers and adverse events related to immunomodulator use in IBD, evidence regarding biomarkers as predictors of treatment efficacy are scarce (Table S5 and Figure 3).15, 19-21, 103-105 Homozygous carriers for IL23R‐G9T were more likely to respond to azathioprine therapy.15 Moreover, the Rac1 SNP (rs34932801) genotype GC was associated with lower rates of clinical response compared to the wild type (46% vs 76%; OR 0.26 [0.08‐0.91], P = 0.036).20 However, these results could not be replicated in a paediatric IBD population.104 A complete deletion of the GSTM1 gene (glutathione S‐transferase) was associated with poor clinical response to thiopurine treatment (OR 9.22 [1.018‐78.62], P = 0.042).105 Furthermore, a BMI <25 kg/m2 (HR 3.95 [1.55‐5.83], P = 0.001) and albumin concentration <4 g/dL (HR 1.98 [0.97‐4], P = 0.06) were associated with thiopurine failure.103 No studies of methotrexate met the pre‐defined inclusion criteria.
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Gilbert C FAURE
November 13, 2018 4:27 AM
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In most industrialized countries, allergies have increased in frequency quite dramatically during the past 50 years. Estimates show that 20–30% of the populations are affected. Allergies have thereby become one of the major medical challenges ...
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Gilbert C FAURE
November 13, 2018 2:28 AM
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The field of Human Microbiome research and development is apparently one of the most popular hubs of the biotechnology industry. While the Human Microbiome Project, MetaHIT and other huge studies of human microbiota, have garnered a lot of attention over that past few years, the microbiome space has literally exploded in terms of both basic and applied biomedical research. This report focuses on biomedical aspects of research, development, and commercial endeavors in the human microbiome space. It includes essential background information, evolution of the field, advances in basic research, events in the emerging commercial market, deal activity, interviews with experts, and trends in microbiome research and commerce. Primary sources of information for this report include the scientific literature, discussions with experts, and an online survey of individuals working in this space. This Report Covers: Advances in Research on the Human Microbiome Commercial Aspects of Microbiome Research and Development Current Deal Activity Over 25 Companies Profiled Survey data from exclusive Insight Pharma Reports Survey Interviews with: Lee Jones, Founder CEO, Rebiotix Brian Varnum, PhD, Chief Development Officer C3 Jian Yanjiao Zhou, MD, PhD, Research Scientist, The Jackson Laboratory for Genomic Medicine Dr Bernard Malfroy-Camine, President and CEO, ViThera Pharmaceuticals Mark L. Heiman, Ph.D., FTOS, Vice President, Research and CSO, MicroBiome Therapeutics (formerly NuMe Health) Larry Weiss, MD, Chief Medical Officer, AOBiome, LLC Karen E. Nelson, PhD, President, J. Craig Venter Institute (JCVI), Head, Microbiome Program, Human Longevity Institute (HLI) Sara Malcus, PhD, CEO, MetaboGen AB
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Gilbert C FAURE
November 12, 2018 1:18 PM
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MAIT cells are abundant in the lungs and confer protection against bacterial pathogens. Whilst activation of these cells has been described during viral infections, here van Wilgenburg and colleagues show that in a murine model MAIT cells contribute to the protective host immune response to...
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Gilbert C FAURE
from Immunology and Biotherapies
November 12, 2018 4:36 AM
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Abstract United airways disease (UAD) is the concept that the upper and lower airways, which are anatomically and immunologically related, form a single organ. According to this concept, upper and lower airway diseases are frequently comorbid because they reflect manifestations of a single underlying disease at different sites of the respiratory tract. Allergic asthma‐allergic rhinitis is the archetypal UAD, but emerging data indicate that UAD is a heterogeneous condition and consists of multiple phenotypes (observable clinical characteristics) and endotypes (pathobiologic mechanisms). The UAD paradigm also extends to myriad sinonasal diseases (eg, chronic rhinosinusitis with or without nasal polyps) and lower airway diseases (eg, bronchiectasis, chronic obstructive pulmonary disease). Here, we review currently known phenoendotypes of UAD and propose a “treatable traits” approach for the classification and management of UAD, wherein pathophysiological mechanisms and factors contributing to disease are identified and targeted for treatment. Treatable traits in UAD can be analyzed according to a framework comprising airway inflammation (eosinophilic, neutrophilic), impaired airway mucosal defense (impaired mucociliary clearance, antibody deficiency), and exogenous cofactors (allergic sensitizers, tobacco smoke, microbes). Appreciation of treatable traits is necessary in advancing the effort to deliver precise treatments and achieve better outcomes in patients with UAD. 1 INTRODUCTION The respiratory tract is divided into the upper and lower airways at the level of the vocal cords. Despite this artificial distinction, they are anatomically contiguous and immunologically related. United airways disease (UAD) is the concept that upper and lower airways form a single organ, with upper and lower airway diseases co‐occurring frequently because they reflect different manifestations of a single underlying disease process.1 Possible mechanisms linking the upper and lower airways include aspiration of nasal inflammatory cells and mediators into the tracheobronchial tree, hematogenous propagation of inflammatory mediators from one part of the airway to another, stimulation of bone marrow progenitor cells, or the nasobronchial reflex.2 The archetypal UAD is allergic asthma‐rhinitis, but emerging data indicate that UAD is complex and heterogenous, and may present with other clinical phenotypes of sinonasal disease (eg, chronic rhinosinusitis with or without nasal polyps) and lower airway disease (eg, bronchiectasis, chronic obstructive pulmonary disease [COPD]). Even within a given clinical phenotype, there is further variability based on the underlying pathobiologic mechanisms, known as endotypes. Current guideline‐based management approaches (summarized in Table 1) stratify patients according to symptoms, disease control, future risk, and occasionally broad clinical phenotypes, but do not sufficiently consider complexity and variability in other important dimensions such as airway inflammation, airway mucosal defense, and exogenous cofactors. Failure to recognize this complexity or treat both upper and lower compartments of the airways inevitably leads to suboptimal outcomes for patients. Site Disease Approach to stratification of patients and selection of therapies Patient stratification groups Recommended treatments according to patient stratification Other possible treatments Upper airway Allergic rhinitis Depends on symptoms, time course, severity, control, patient preferences, availability of treatment, and cost. One approach is a step‐up/step‐down approach based on assessment of control Step 1: mild symptoms Intranasal or oral non‐sedating H1‐antihistamines Leukotriene antagonists Allergen and irritant avoidance Allergen immunotherapy Nasal filters or saline Step 2: moderate‐to‐severe symptoms or persistent AR Intranasal corticosteroids Step 3: uncontrolled at step 2 (current or historical) Combination intranasal corticosteroids and intranasal H1‐antihistamines Step 4: uncontrolled at step 3 Short course oral steroids Chronic rhinosinusitis Based on clinical phenotype CRSsNP Intranasal corticosteroids Antibiotics (second line) Consider short course of oral steroids Saline irrigation Sinus surgery for medically refractory disease CRSwNP Intranasal corticosteroids Short course of oral steroids Antibiotic if suspicion of infection Consider leukotriene receptor for acetylsalicylic acid sensitivity Lower airway Asthma Assessment of symptoms, exacerbations, and lung function followed by stepwise escalation/de‐escalation of therapy Step 1 Consider low‐dose ICS Leukotriene receptor antagonists Theophylline Allergen avoidance Allergen immunotherapy for patients with allergic rhinitis and sensitized to house dust mites, frequent exacerbations, and FEV1 > 70% Vaccinations Weight reduction for obese asthma patients Step 2 Low‐dose ICS Step 3 Low‐dose ICS + LABA Step 4 Medium‐/high‐dose ICS + LABA Step 5 Low‐dose oral corticosteroids Add‐on treatments: tiotropium, anti‐IgE, anti‐IL5, bronchial thermoplasty Patients may benefit from phenotyping and endotyping at Step 5 Chronic obstructive pulmonary disease Assessment of symptoms (CAT, mMRC), exacerbations, airflow obstruction (FEV1) Group A (CAT < 10, mMRC < 2, infrequent exacerbations) Bronchodilator Smoking cessation (including pharmacologic treatment) Education, self‐management, and pulmonary rehabilitation Pneumococcal and influenza vaccinations Long‐term oxygen therapy in the presence of severe resting hypoxemia Long‐term noninvasive ventilation for patients with severe chronic hypercapnia and a history of hospitalization for acute respiratory failure Bronchoscopic or surgical lung volume reduction surgery for selected patients Group B (CAT ≥ 10, mMRC ≥ 2, infrequent exacerbations) LABA or LAMA Increase to LAMA + LABA if persistent symptoms Group C (CAT < 10, mMRC < 2, frequent exacerbations) LAMA Increase to LAMA + LABA or change to LABA + ICS if further exacerbations Group D (CAT ≥ 10, mMRC ≥ 2, frequent exacerbations) LAMA + LABA Increase to LAMA + LABA + ICS if persistent symptoms/further exacerbations Consider macrolides Consider roflumilast if FEV1 < 50% predicted and patient has chronic bronchitis Bronchiectasis Assessment of microbial infection, exacerbations and airway symptoms Pseudomonas aeruginosa infection with frequent exacerbations Long‐term inhaled antibiotic treatment Escalate to combined oral and inhaled antibiotic treatment if inadequate response Physiotherapy, pulmonary rehabilitation, and/or airway clearance Pneumococcal and influenza vaccinations Long‐acting bronchodilators for patients with significant breathlessness Non‐Pseudomonas aeruginosa infection with frequent exacerbations Long‐term macrolide treatment Escalate to combined oral and inhaled antibiotic treatment if inadequate response Multiple sequential airway treatments Short‐ or long‐acting bronchodilators → Mucolytic/physiotherapy adjuncts → airway clearance → inhaled antibiotics AR, allergic rhinitis; CAT, COPD Assessment Tool; CRSsNP, chronic rhinosinusitis without nasal polyps; CRSwNP, chronic rhinosinusitis with nasal polyps; FEV1, forced expiratory volume in one second; ICS, inhaled corticosteroids; ICS + LABA, combination inhaled corticosteroids and long‐acting beta agonist; LABA, long‐acting beta agonist; LAMA, long‐acting muscarinic antagonist; mMRC, modified Medical Research Council dyspnea score. The treatable traits approach has been described for lower airway diseases,3 and in this review, we extend its application to combined upper and lower airway disease. We summarize the current evidence for known clinicopathobiologic subtypes (“phenoendotypes”) of UAD. We have grouped the clinical manifestations of UAD according to the “treatable traits” of airway inflammation, airway mucosal defense dysfunction, and exogenous cofactors (Figure 1 and Table 2). These traits are not mutually exclusive and multiple traits may contribute to disease in an individual at any one point in time. Treatable traits may also vary over time. It is with the intention of drawing the readers’ attention to traits that are common to both airway compartments that we have artificially classified the upper and lower airway conditions as presented in this review. We believe that targeting common disease processes, when used in combination with current patient stratification approaches, is likely to lead to clinical improvements at both sites of the respiratory tract. Trait Endotype/pathophysiology Method of assessment/biomarker Clinical phenotype Targeted treatment Upper airway Lower airway Inflammation Eosinophilic, high type 2/TH2‐cytokines IL‐4, IL‐5, IL‐13 Blood periostin, high FeNO, sputum eosinophilia, blood eosinophilia CRSwNP Eosinophilic asthma Corticosteroids (systemic, inhaled, intranasal), anti‐IL‐5, IL‐4, IL‐13, anti‐TSLP, ?CRTh2 antagonist Allergic, systemic CD4+ TH2 cells Blood total IgE, blood specific IgEs, skin prick tests Allergic rhinitis Allergic asthma Allergen avoidance, corticosteroids, antileukotrienes, anti‐IgE, immunotherapy Allergic, local Local nasal and bronchial mucosa specific IgE production Blood total IgE not raised, negative blood specific IgEs, negative skin prick tests, local IgE and specific IgE production, nasal allergen provocation test Local allergic rhinitis Intrinsic asthma ?Omalizumab, ?immunotherapy Non‐allergic eosinophilic IL‐25, IL‐33, TSLP, group 2 innate lymphoid cells Blood periostin, high FeNO, sputum eosinophilia, blood eosinophilia, non‐reactive skin prick tests, absent specific IgE in blood Non‐allergic rhinitis with eosinophilia syndrome (NARES) Adult onset eosinophilic asthma Anti‐TSLP, anti‐IL‐4, IL‐5, IL‐13, ?CRTh2 antagonist Neutrophilic TH17 cells, IL‐17, CXCL1, CXCL8 IL‐8, sputum neutrophilia CRSsNP COPD, neutrophilic asthma Smoking cessation, macrolides Impaired airway mucosal defense Cystic fibrosis Autosomal recessive disorder in CFTR gene leading to improper salt balance and viscous airway surface liquid Genetic testing, sweat chloride test CRSwNP (acid mucin) Bronchiectasis CFTR modulators Primary ciliary dyskinesia Autosomal recessive mutations causing dynein and radial spoke defects Low nasal nitric oxide, ciliary motion and ultrastructure CRSsNP Bronchiectasis No validated PCD‐specific therapies Primary antibody deficiency Deficient immunoglobulins or functional antibody response Quantitative serum immunoglobulin levels, functional antibody responses CRS Bronchiectasis Immunoglobulins, vaccinations Exogenous cofactors Occupational exposures High molecular weight sensitizers: IgE‐mediated type 2 inflammation Low molecular weight sensitizers: haptens Irritant‐induced: no immunological reaction Total IgE, skin prick tests Peak flow monitoring Specific bronchoprovocation challenge Occupational rhinitis, reactive upper airways dysfunction syndrome Occupational asthma, reactive airways dysfunction syndrome Exposure avoidance, respiratory protection devices, ?anti‐IgE Aspirin/non‐steroidal anti‐inflammatories Constitutive overexpression of 5‐lipoxygenase and leukotriene C4 synthase Aspirin/NSAID challenge, urinary leukotriene E4 CRSwNP Severe asthma Avoidance, desensitization, antileukotrienes Staphylococcus aureus enterotoxin Superantigens elicit Th1, Th17 and Th2 cytokines, including IL‐4, IL‐5, IL‐13 Staph aureus enterotoxin IgE's CRSwNP Severe asthma ?anti‐IgE Aspergillus/fungal hypersensitivity TH2 cells, IL‐4, IL‐5, IL‐13, IgE, eosinophilia Aspergillus skin test positivity, Aspergillus specific IgE, IgE > 1000 IU/mL, Blood eosinophil >500/μL, precipitating antibodies to Aspergillus Allergic fungal rhinosinusitis Allergic bronchopulmonary aspergillosis (asthma, bronchiectasis) Steroids, antifungal, ?anti‐IgE Upper respiratory tract infection Variable depending on microbe and underlying disease Respiratory cultures and nucleic acid tests Exacerbation of rhinitis/rhinosinusitis Exacerbation of asthma, COPD or bronchiectasis Antivirals, antibiotics, vaccinations Typical upper and lower airway clinical phenotypes associated with underlying treatable traits are listed. Taxonomy based on clinical phenotypes has significant limitations: each phenotype may represent comprise treatable traits, for example, CRSwNP and asthma may be neutrophilic or eosinophilic inflammation depending on the population studied. Hence, a more precise approach is needed to identify contributory treatable trait(s) in each case, which can aid in selection of targeted treatments. Question mark (?) indicates targeted therapies under investigation. Treatable traits and targeted therapies may be used to complement the current approaches for patient stratification and treatment selection—including non‐targeted treatments—shown in Table 1. 2 AIRWAY INFLAMMATION Airway inflammation is important in the pathogenesis of many upper and lower airway diseases. It is a dynamic trait which varies over time and is modulated by factors such as treatment, infection, environmental exposures, and natural history of disease.4 Clinicians commonly assume that asthma and chronic rhinosinusitis with nasal polyps (CRSwNP) are stereotypically eosinophilic in nature, and that chronic rhinosinusitis without nasal polyps (CRSsNP) or COPD is stereotypically neutrophilic. As discussed below, while these generalizations may hold true for many cases there is substantial variability in the underlying pathophysiological mechanisms for each clinical phenotype or disease. Identification and quantification of airway inflammation can help to direct appropriate treatment. Current methods to assess upper and lower airway inflammation are summarized in Table 3. Method Comments Strengths Limitations Nasal lavage Luminal proteins, cytokines and cells Simple and safe Correlates with, but less invasive than induced sputum Variable fluid recovery and dilution with lavages No sampling of the epithelium Nasal brush or scraping Samples the nasal epithelium Simple and minimally invasive Does not sample deeper layers of the mucosa or the nasal lumen Nasal biopsy Identifies tissue inflammatory cellular subtypes of rhinitis or rhinosinusitis Samples the total nasal mucosa Technically difficult No information about nasal lumen Blood periostin Marker of Th2, eosinophilic airway inflammation Predicts severity of chronic rhinosinusitis Predicts chronic rhinosinusitis with nasal polyps phenotype Noninvasive Not widely available Conflicting evidence on whether periostin is associated with sputum eosinophilia Sputum induction Neutrophil count ≥65% or >500 × 104/mL identifies neutrophilic airway inflammation Eosinophil count ≥3% identifies eosinophilic airway inflammation Noninvasive Well‐validated Not widely available Logistically, technically and practically difficult High failure rate Risk of inducing bronchoconstriction Peripheral blood eosinophils Eosinophil count ≥2% identifies eosinophilic airway inflammation Noninvasive, minimal risk Widely available, cheap Predicts with important clinical outcomes such as exacerbation risk and response to treatment Wide diurnal variation of readings (40%) within the same subject May not be applicable when only local mucosal inflammation occurs Fractional exhaled nitric oxide (FeNO) FeNO < 25 ppb indicates low likelihood of airway eosinophilia FeNO > 47 ppb suggests eosinophilic airway inflammation and corticosteroid responsiveness Noninvasive Quick, easy, and safe to perform Readings may be affected by smoking, respiratory tract infections Sputum eosinophilia and FeNO are not always concordant Urinary leukotriene E4 (LTE4) Marker of cysteinyl leukotriene activity Higher levels observed in aspirin‐exacerbated respiratory disease Collection of urine is simple and of no risk Special equipment and training are required to measure LTE4 2.1 Allergic asthma‐rhinitis The link between allergic asthma and allergic rhinitis (AR) is well established. Up to 40% of AR patients have asthma and almost all patients with asthma have AR.5 Many patients with AR also exhibit nonspecific bronchial hyperresponsiveness.6 IgE‐mediated hypersensitivity to aeroallergens results in type 2/eosinophilic inflammation in the respiratory tract, manifesting as rhinitis in the upper airways and asthma in the lower airways. Upon allergen exposure, sensitization occurs whereby specific IgE (sIgE) are generated, circulate in the blood, and attach to the surface of mast cells and basophils, including those that reside in the airway mucosa. Subsequent exposure to allergen triggers mast cells degranulation, release of chemical mediators, and synthesis of leukotrienes and cytokines, resulting in an immediate allergic airway reaction. The late phase response involves Th2 lymphocyte recruitment with production of inflammatory cytokines such as IL‐4, IL‐5, and IL‐13, as well as activation of eosinophils. The central role of sIgE in allergic asthma‐rhinitis is highlighted by a population‐based birth cohort study showing that presence of peripheral blood sIgE directed against common allergens is associated with increased odds of asthma and rhinitis in childhood.7 In adults with AR, experimental nasal provocation with house dust mite allergens induces not only nasal eosinophilia but also bronchial eosinophilic inflammation, bronchoconstriction, and bronchial hyperresponsiveness,8 suggesting inflammatory crosstalk between upper and lower airways. The allergic asthma‐rhinitis phenoendotype usually begins in childhood and can be identified by measuring peripheral blood total IgE which is elevated, plus demonstration of atopy by the presence of raised sIgE levels in blood or skin prick test positivity. Omalizumab, an anti‐IgE monoclonal antibody, is efficacious for both severe allergic asthma9 and uncontrolled AR.10 Therapeutic response to omalizumab is predicted by higher baseline periostin levels and reduced free serum IgE levels during period of treatment.11 In some cases, specific causative allergens can be identified and targeted with allergen immunotherapy. In both AR12 and allergic asthma,13 immunotherapy improves symptoms and reduces the need for pharmacotherapy in the short term, albeit with limited evidence for longer‐term efficacy. Treatment of AR patients with grass pollen sublingual immunotherapy tablets was associated with slower AR progression, less frequent asthma onset, and slower asthma progression.14 Candidate biomarkers that can identify and/or predict response to allergen immunotherapy include the sIgE:total IgE ratio, specific IgG4 and IgE‐FAB.15 2.2 Local allergic rhinitis‐non‐allergic asthma: local mucosal IgE production A subset of asthma and rhinitis patients demonstrate type 2/eosinophilic allergic airway responses mediated by sIgE which is produced and sequestered in the airway mucosa, with skin prick test non‐reactivity and no detectable circulating sIgE in peripheral blood. In the upper airway, this phenoendotype is known as local allergic rhinitis (LAR), a distinct entity characterized by local production of sIgE in the nasal mucosa without evidence of systemic atopy. LAR does not appear to represent a forme fruste of AR,16 and is associated with increased risk of asthma compared to healthy individuals without LAR.17 The lower airway analog of LAR may correspond to a subtype of non‐allergic asthma which is currently not well defined, but whose existence is supported by studies (Supporting Information: References S1‐S4). In the aforementioned entity, intramucosal IgE synthesis is observed in the lower airways, and functional sIgE can be found in the sputum despite negative skin prick tests and blood sIgE levels. Bronchial inhalational challenge with allergen induces a clinical lower airway allergic response characterized by sputum eosinophilia and bronchial hyperresponsiveness. It is not known why individuals with allergic asthma‐rhinitis demonstrate positive skin prick tests and elevated sIgE in blood, whereas non‐allergic asthmatics and patients with LAR do not although local mucosal IgE synthesis occurs in all those conditions. One possibility is that local mucosal IgE synthesis may not reach sufficient quantities to spill over into peripheral blood. Alternatively, IgE may be sequestered within the bronchial mucosa. Regardless, efficacy of omalizumab has been shown in non‐atopic asthma with evidence of local mucosal IgE production. Pillai et al. (Supporting Information: References S5) randomized 18 such subjects and obtained bronchial biopsies from subjects before and after treatment with 12‐14 weeks of omalizumab. The patients subsequently underwent supervised dose reduction of their regular asthma therapy. Compared to placebo, omalizumab therapy significantly reduced total bronchial mucosal IgE+ cells. Lung function declined in the placebo‐treated group but improved in the omalizumab‐treated group. This small but important study serves as proof‐of‐concept that anti‐IgE can target disease associated with local mucosal IgE production. 2.3 Non‐allergic rhinitis with eosinophilia syndrome‐non‐allergic eosinophilic asthma Type 2 (eosinophilic) inflammation can occur in UAD without evidence of IgE‐mediated hypersensitivity. In the upper airways, this endotype has been termed non‐allergic rhinitis with eosinophilia syndrome (NARES) and is characterized by persistent rhinitis and marked eosinophilia of the nasal mucosa in the absence of local or systemic IgE production, suggesting allergen‐independent induction of eosinophilic airway inflammation.18 Almost half of patients with NARES who do not report asthmatic symptoms nevertheless demonstrate bronchial hyperresponsiveness and sputum eosinophilia, supporting a link between NARES and asthma.19 The corresponding asthma phenoendotype is non‐allergic eosinophilic asthma, in which eosinophilic inflammation and type 2 cytokine production are not driven by allergen‐activated Th2 lymphocytes, but is instead postulated to be initiated by type 2 innate lymphoid cells (ILC2).20 ILC2s are a recently discovered population of lymphocytes capable of producing large amounts of type 2 cytokines (IL‐5 and IL‐13) and orchestrate eosinophilic inflammation in the absence of adaptive immunity, including Th2 cells, B cells, and IgE antibody.21 ILC2s are activated by epithelium‐derived cytokines IL‐25, IL‐33, and thymic stromal lymphopoietins (TSLP), which are released by epithelial cells in response to epithelial damage caused by a wide range of stimuli including microbiota and pollutants. ILC2s are increased in peripheral blood in eosinophilic asthmatics compared to non‐eosinophilic asthmatics and their numbers in sputum correlate with eosinophil counts.22 Increased ILC2s has also been observed in eosinophilic nasal polyps,23 but evidence for the involvement of ILC2 cells in NARES and non‐allergic eosinophilic asthma is lacking and should be the focus of future studies. Potential targeted treatments for this pathway include monoclonal antibodies targeting any of the type 2 cytokines, discussed in the section below. TSLP, targeted by the monoclonal antibody tezepelumab, was shown in a recent dose‐ranging proof‐of‐concept study to reduce exacerbations in patients with uncontrolled asthma, including in those with low IgE (<100 IU/mL).24 Antagonists of CRTH2, the receptor for prostaglandin D2 on ILC2 cells, are currently undergoing evaluation to assess clinical efficacy (Supporting Information: References S6, S7). 2.4 Asthma‐chronic rhinosinusitis Chronic rhinosinusitis (CRS) is broadly defined as an inflammatory disorder of the paranasal sinuses and mucosa of the nasal passages that lasts 12 weeks or longer. The condition is heterogeneous, with 2 broad clinical phenotypes, with nasal polyps (CRSwNP) and without nasal polyps (CRSsNP), as well as diverse endotypes.25 Epidemiologic studies have demonstrated higher prevalence of CRS in asthmatics compared to non‐asthmatics across different populations.26, 27 However, the inflammatory endotype of CRS varies according to the population being studied. In Caucasian subjects, CRSwNP is predominantly eosinophilic and CRSsNP is predominantly neutrophilic, whereas in Chinese subjects CRSwNP is mainly neutrophilic, with fewer Chinese subjects demonstrating eosinophilic nasal polyps.28 Various studies have elucidated the detailed phenoendotypes of either CRS or asthma separately, but there is a paucity of data about phenoendotypes of combined asthma‐CRS. Despite this, phenoendotypes of asthma and CRS are remarkably analogous to each other. Based on sputum inflammatory cells, asthma can be eosinophilic, neutrophilic, mixed eosinophilic and neutrophilic, or paucigranulocytic.29 Based on bronchoepithelial cell gene expression profiles, asthma inflammatory signatures can be partitioned into “Th2 high” (associated with elevated IL‐4, IL‐5, IL‐13, sputum, and blood eosinophilia) or “Th2 low”.30 CRS endotypes can be similarly classified into “IL‐5 high” and “IL‐5 low” clusters.31 Subjects with IL‐5 high CRS have high eosinophilic cationic protein and IgE from nasal specimens, and present clinically with CRSwNP and asthma. Low IL‐5 clusters clinically exhibit the CRSsNP phenotype. “IL‐5 high” CRS appears to be the upper airway analog of “Th2‐high” asthma. The inflammatory pattern of the upper and lower airways in patients with nasal polyps and comorbid asthma was evaluated in a recent study using nasal polyp biopsies and exhaled nitric oxide.32 Approximately, two‐thirds of patients were eosinophilic and one‐third was non‐eosinophilic, with concordance demonstrated between the upper and lower airways in individual subjects. More studies are needed to clarify concordance between upper and lower airway phenoendotypes across the spectrum of inflammatory endotypes in asthma and CRS. Systemic, inhaled, or intranasal corticosteroids are cost‐effective treatments for eosinophilic upper and lower airway inflammation. In cases that are refractory to corticosteroids or in patients who are unable to tolerate high doses of corticosteroids, several biologics targeting eosinophilic inflammation can be considered, such as anti‐IL‐13 (tralokinumab), anti‐IL‐5 (mepolizumab and reslizumab), antibodies against alpha chain of IL‐5 receptor (benralizumab), and alpha subunit of the IL‐4 receptor (dupilumab). Mepolizumab is efficacious in both eosinophilic nasal polyposis and severe eosinophilic asthma,33, 34 reducing polyp burden and need for surgery in the former and reducing exacerbations in the latter. Similarly, dupilumab was demonstrated in small phase 2 trials to reduce polyp burden in patients with chronic sinusitis and nasal polyposis,35 and was found to decrease exacerbations and improve forced expiratory volume in one second (FEV1) in patients with severe eosinophilic asthma.36 Patients with asthma and nasal polyps show greater improvement in Asthma Control Questionnaire score with reslizumab compared to asthmatics without nasal polyps.37 2.5 Chronic obstructive pulmonary disease‐chronic rhinosinusitis Chronic obstructive pulmonary disease is a disease characterized by chronic respiratory symptoms and persistent airflow limitation caused by significant exposure to noxious particles or gases, most commonly tobacco smoke. Although COPD has traditionally been thought to be a purely lower airway disease, there is increasing evidence to suggest frequent concomitant involvement of the upper airways. Multiple epidemiologic studies have shown a high prevalence of nasal symptoms among COPD patients. An early population‐based epidemiologic study conducted in the Swedish general population in 1992 found that 40.1% of individuals with self‐reported chronic bronchitis or emphysema had symptoms of rhinosinusitis, compared with 32.7% in the total study sample.38 In a follow‐up study in 2000, those who reported thick, yellow, nasal discharge and nasal blockage in 1992 had a higher incidence of COPD diagnosis over an 8‐year follow‐up period.39 Another cross‐sectional population‐based Canadian study also found an association between CRS and COPD, both self‐reported.40 Self‐report may lead to misclassification errors of COPD and sinonasal disease but are an inherent limitation of large‐scale epidemiologic studies. Medical record diagnoses, which are considered more reliable than self‐reported medical history, were used in one study from Taiwan which extracted International Classification of Diseases diagnostic codes from a national insurance database and found an increased risk of CRSsNP among COPD patients.41 Recent studies have utilized more stringent and objective criteria for classifying COPD and rhinosinusitis. Bergqvist et al.42 used spirometry for identifying COPD, defined as FEV1/forced vital capacity (FVC) ratio <0.7, and found that the 5‐year incidence of nasal symptoms without having a cold was higher in subjects who demonstrated obstruction on spirometry compared to subjects without baseline spirometric evidence of airflow limitation. Hansen et al43 evaluated the prevalence of paranasal sinus opacification at magnetic resonance imaging in COPD and asthma patients compared to a control group. COPD was defined using similar spirometric criteria as the Bergqvist et al study, whereas asthma was self‐reported. COPD and asthma patients were, respectively, found to be 6 and 2 times more likely than healthy controls to demonstrate paranasal sinus opacification. These findings support the link between upper and lower airway diseases in both asthma and COPD, but we cannot conclude from this study that the association with sinonasal disease is stronger for COPD than for asthma because misclassification bias may have occurred among asthma patients, who were diagnosed based on self‐report instead of objective evidence of variable airflow limitation. Similarly, uncontrolled COPD cohort studies have also found a high prevalence of up to 75% of sinonasal disease, when using spirometry for COPD diagnosis and CT paranasal sinuses for diagnosis of CRS.44, 45 An important limitation of all the aforementioned studies is the use of prebronchodilator, instead of postbronchodilator, FEV1/FVC ratio for identifying cases of COPD. Such a definition could result in other obstructive lung diseases (such as asthma or bronchiectasis) being misclassified as COPD. Only one small study thus far has evaluated COPD using postbronchodilator spirometry and found higher nasal symptoms scored on a visual analogue scale compared to healthy age‐matched controls.46 Although prevalence of sinonasal disease does not increase with declining FEV1,44, 45 the presence of sinonasal disease has profound effects on other important COPD‐related outcomes such as symptom morbidity and exacerbations, which are known to be relatively independent of FEV1. Upper airway symptoms worsen over the natural history of COPD,47 and are associated with lower airway symptoms44 and impaired quality of life.48 Sinonasal symptoms also feature prominently during COPD exacerbations, and are associated with frequent exacerbations47 as well as treatment failure of acute exacerbation of COPD.49 Tobacco smoke forms the common link between COPD and CRS,50 likely by inducing neutrophilic inflammation in the nasal and bronchial mucosa. Compared with non‐smoking healthy controls, smokers without COPD present with nasal inflammation characterized by increased neutrophils and CD8 T lymphocytes51 although this has not been consistently demonstrated.52 Nasal and bronchial IL‐8 levels, neutrophil counts, and CD8 Th1 cells are even higher in smoking COPD patients than in smoking “healthy” controls matched for pack‐year smoking history.51, 53 Nasal and sputum IL‐8 concentrations correlate in COPD patients,53 indicating concordance of neutrophilic inflammation between upper and lower airways in keeping with the UAD hypothesis. Release of neutrophil‐derived proteases such as elastase and matrix metalloproteinase‐9 causes alveolar wall destruction and mucous hypersecretion characteristic of COPD, and may contribute to tissue destruction and remodeling of the sinonasal mucosa seen in CRS. Neutrophilic inflammation results in airflow limitation in the lower airways in COPD, which is mirrored in the upper airways by reduced nasal patency54 and higher nasal resistance.55 Severity of lower airway obstruction, as measured by FEV1, correlates with severity of CRS symptoms.45 Smoking cessation alters the progressive clinical course of COPD by reducing lung function decline and mortality, hence remaining a cornerstone of COPD treatment in addition to other medical therapies. Additionally, in CRS, smoking cessation improves symptoms, quality of life and decreases CRS‐related antibiotic use.56Neutrophilic airway inflammation per se may potentially be targeted for treatment using macrolides, which exert a repertory of effects that suppress neutrophilic inflammation, including downregulation of IL‐8, elastase and matrix metalloproteinase‐9, and reduction of neutrophil chemotaxis and survival.57 Macrolides have proven efficacy in decreasing the frequency of COPD exacerbations,58 and in a small double‐blind randomized controlled trial was associated with improvement in symptoms, endoscopic scores, and nasal lavage IL‐8 levels specifically in Th1, neutrophilic CRS patients.59 Further studies are needed to confirm the efficacy of macrolides. In addition, long‐term macrolides should only be used in carefully selected patients after optimization of other standard medical therapies because of potential cardiac and otological adverse effects, as well as risk of inducing antibiotic resistance. 3 IMPAIRED AIRWAY MUCOSAL DEFENCE Major components of the airway mucosal defense mechanism include the mucociliary escalator, immunoglobulins, antimicrobial peptides, the epithelial barrier, and cells of the innate and adaptive immune system. Impaired mucosal airway defense has a causal role in several phenoendotypes of UAD. The predominant lower airway phenotype in UAD due to impaired mucosal defense is bronchiectasis, which is characterized by abnormal and irreversible dilatation of the lower airways. Frequent coexistence of bronchiectasis and sinonasal disease has long been recognized.60 Patients with bronchiectasis have a high prevalence of CRS when assessed using European Position Paper on Rhinosinusitis and Nasal Polyps criteria, that is, presence of symptoms and positive findings on either nasoendoscopy or CT of paranasal sinuses.61, 62 Conversely, patients with CRS more frequently demonstrate CT evidence of dilated airways compared to individuals without CRS.63 3.1 Cystic fibrosis: bronchiectasis‐chronic rhinosinusitis due to airway surface liquid viscosity Airway surface liquid (ASL) viscosity is regulated in part by the cystic fibrosis transmembrane regulator (CFTR) protein, which mediates ion transport at the airway epithelial surface. Pathological processes which impair CFTR activity can result in abnormal ion flux at the epithelial surface, increased sodium reabsorption, periciliary liquid depletion, and increased mucous viscosity, thereby impairing mucociliary clearance.64 Loss of CFTR‐mediated bicarbonate secretion in the airway epithelium also results in acidification of the ASL, which impairs the action of antimicrobial peptides in the ASL, rendering the airway more susceptible to colonization and infection by microbes.64 Cystic fibrosis (CF) is due to heterogeneous autosomal recessive mutations in the CFTR gene and affects mucociliary clearance at the sinoepithelial lining, lower airways, gastrointestinal tract, and exocrine organs. Sinonasal disease is ubiquitous in CF and characterized by neutrophilic polyps with acid mucin glands, mucoceles, or pseudomucoceles.65 Lower airway disease is characterized by intense neutrophilic inflammation resulting in bronchiectasis, which occurs in 70%‐75% of children by 5 years of age and represents the major cause of morbidity and death in this condition. Treatment of CF has been transformed by the discovery of novel CFTR modulators aimed at addressing the specific defects causing reduced CFTR activity. The most common gene mutation in CF is F508del which causes a processing defect that severely reduces CFTR levels at the epithelial surface and also disrupts channel opening. The latter gating defect is also seen in other mutations, such as the G551D mutation. Lumacaftor and tezacaftor are both CFTR correctors, which improve trafficking of CFTR to the epithelial cell surface. Ivacaftor is a CFTR potentiator and acts by increasing the probability of the channel remaining open. Randomized controlled trials have proven that treatment with these compounds is associated with improved lung function in CF,66-69 and improvement of CF‐associated CRS following treatment with CFTR modulators has been reported anecdotally.70, 71 There is emerging evidence that CFTR dysfunction is a trait in other UADs and may be a target for treatment. CF carrier mutations are implicated in CRS patients who do not have CF.72 Constituents in tobacco smoke such as cadmium, arsenic, and acrolein can cause CFTR dysfunction,73 either by reducing CFTR expression or by inducing gating defects. CFTR decrements are also seen in COPD, including reduced nasal and lower airway potential difference74 and elevated sweat chloride,75 and the degree of CFTR impairment correlates with severity of symptoms and airflow limitation.74, 75 Ivacaftor reverses the acute reduction of CFTR activity after exposure to cigarette smoke in human bronchoepithelial cells,76 and has been demonstrated to ameliorate chronic bronchitis symptoms and improve CFTR activity in a preliminary study.77 Roflumilast, a phosphodiesterase‐4 inhibitor, can stimulate CFTR by increasing cyclic adenosine monophosphate and reverses smoke‐induced inhibition of CFTR activity.78 Roflumilast is approved for reducing exacerbations in severe COPD associated with chronic bronchitis. 3.2 Primary ciliary dyskinesia (PCD): bronchiectasis‐chronic rhinosinusitis due to ciliary dysfunction Chronic rhinosinusitis and bronchiectasis are universal in adults with PCD. The underlying pathophysiology is absent or impaired ciliary motility due to heterogeneous recessive mutations affecting the biogenesis of cilia lining the upper and lower airways.79 In turn, this leads to impaired mucociliary clearance manifesting in the neonatal period as difficulty feeding due to nasal blockage/rhinitis, neonatal respiratory distress syndrome and later in life as recurrent sinopulmonary infections, and bronchiectasis. Nasal nitric oxide is an excellent screening test for patients with a history suggestive of PCD.80 Other tests to assess for ciliary dyskinesia include analysis of ciliary ultrastructure by electron microscopy, assessment of ciliary beat frequency and pattern by high resolution, high‐speed video microscopy, and immunofluorescent staining of ciliary proteins. Ciliary dysfunction occurs in other airway diseases but is usually acquired, for example, due to smoking or infection. In CRS, blunted ciliary response to environmental stimuli has been demonstrated, and this is reversed once the tissue is removed from infected/inflamed environment.81 In COPD, cigarette smoke is associated with ciliary abnormalities in both bronchial82 and nasal mucosae.83 Sputum from atopic and non‐atopic asthmatics have ciliary inhibitory effect on human bronchoepithelial cell cultures, which was reversible upon removal of the sputum.84 Bronchial biopsies from asthma patients demonstrate reduced ciliary number which recovers after treatment with inhaled corticosteroids.85 There are currently no validated effective therapies for primary ciliary dyskinesia and this should be a focus for future research. In acquired cases, removal of the causative factor may reverse ciliary impairment. 3.3 Primary antibody deficiency The most common immunodeficiencies associated with CRS and bronchiectasis are primary antibody deficiencies,86 of which common variable immunodeficiency (CVID), characterized by reduced serum IgG concentrations, is the most common in adults. One‐third and two‐thirds of patients with CVID have bronchiectasis and recurrent sinusitis, respectively.87 Specific antibody deficiency, which is associated with normal immunoglobulin and IgG subclass levels but impaired antibody response to encapsulated respiratory pathogens such as Streptococcus pneumoniae and Hemophilus influenza, is also associated with a high prevalence of CRS and bronchiectasis, accounting for as many as 50% of idiopathic bronchiectasis.88 Patients with primary antibody deficiency most likely require lifelong immunoglobulin replacement, vaccinations, and prophylactic antibiotics. There is also some evidence that antibody deficiency may be a treatable underlying contributor in other airway diseases. For example, low circulating IgG levels are associated with higher rate of exacerbations and shorter time to next exacerbation in two COPD cohorts.89 Treatment with immunoglobulin replacement is associated with significant reduction in exacerbations and rescue antibiotic use in a small case series of 14 COPD patients (Supporting Information: References S8), warranting future prospective, randomized clinical trials to assess the efficacy of IVIG in preventing exacerbations in selected COPD patients with antibody deficiency. A potential contributing role of antibody deficiency in asthma has also been suggested by a small case‐control study which found that asthma patients are more likely to have a diagnosis of CVID or selective IgA deficiency than non‐asthmatic individuals, potentially accounting for the increased risk of bacterial infections in some individuals with asthma.90 A preliminary proof‐of‐concept study found that IVIG significantly reduces the frequency of asthma exacerbations and improved asthma control in adults with IgG subclass deficiency.91 4 EXOGENOUS COFACTORS The respiratory tract is continuously in contact with air from the environment which contains particulates such as microbes, pollutants, and other noxious agents that can cause or aggravate UAD. Aeroallergens such as house dust mites, pollen, and animal dander are the archetypal examples of how specific exposures can cause and worsen allergic asthma‐rhinitis. There are several other well‐delineated phenoendotypes of UAD in which specific inciting or aggravating cofactors are implicated. 4.1 Occupational asthma‐rhinitis More than 90% of individuals with occupational asthma (OA) also have occupational rhinitis (OR).92 Different occupational exposures can cause OA‐OR by different mechanisms. High molecular weight sensitizers, such as proteins and glycopeptides, often cause symptoms after a latency period via IgE‐mediated hypersensitivity.93 In contrast, the mechanism by which low molecular weight agents induces OA‐OR is poorly understood. Single or multiple exposures to high concentrations of irritants can lead to nonspecific inflammation in both upper and lower airways, clinically recognized as reactive upper airways dysfunction syndrome and reactive airways dysfunction syndrome, respectively. In addition to usual care for asthma and rhinitis, the management of OA‐OR is centered around exposure avoidance and use of appropriate respiratory protection equipment. The efficacy of omalizumab for sensitizing agents with IgE‐dependent reactions requires future studies. 4.2 Aspirin‐exacerbated respiratory disease The clinical phenotype of aspirin‐exacerbated respiratory disease (AERD) consists of a triad of asthma, CRSwNP, and exacerbation of upper and lower respiratory tract symptoms within 30 minutes to 3 hours after exposure to aspirin and other cyclooxygenase‐1‐inhibiting drugs. Nasal polyps in AERD can be distinguished from CRSwNP in individuals without AERD by the presence of enhanced eosinophilia, GM‐CSF, and decreased tissue plasminogen activator.94 The endotype of AERD is not fully understood but current available data suggest an underlying dysregulation of arachidonic acid metabolism, with constitutive overproduction of proinflammatory cysteinyl leukotrienes and decreased levels of anti‐inflammatory mediators such as prostaglandin E2.95 This dysregulation is aggravated by cyclooxygenase inhibition following exposure to aspirin and other non‐steroidal anti‐inflammatory drugs in susceptible individuals. The gold standard for diagnosing AERD is aspirin challenge, but challenge testing carries the risk of severe bronchospasm. Urinary leukotriene E4, the end‐product of arachidonic acid metabolism, is raised in AERD and may have a role in diagnosing AERD.96 Treatment options include avoidance of aspirin and NSAIDs, desensitization, and leukotriene receptor antagonists. Omalizumab and mepolizumab may be helpful in AERD as suggested by small trials involving subsets of patients with AERD (Supporting Information: References S9, S10). Whether the observed clinical efficacy of these biologics can be attributed to target AERD‐specific endotype above and beyond their effect on type‐2 eosinophilic inflammation is unclear. 4.3 Asthma‐CRS: hypersensitivity to Staphylococcus aureus enterotoxins Staphylococcus aureus is consistently found in nasal tissues of CRS patients, and evidence supports a role for S. aureus in the pathogenesis of CRS‐asthma. S. aureus colonizes nasal polyps and secretes proteins, including enterotoxins and serine protease‐like proteins in the nasal mucosa, which have the ability to elicit a polyclonal IgE response resulting in an intense type 2, IL‐5 high, eosinophilic inflammation which is likely responsible for chronic severe inflammation of the airways. Presence of sIgE to S. aureus enterotoxins in nasal polyp tissue is strongly associated with asthma in patients with CRSwNP,31, 97 providing an etiological link between asthma‐CRS. In a small placebo‐controlled, proof‐of‐concept study, omalizumab was found to ameliorate nasal polyps and airway symptoms in a small cohort of patients with asthma‐CRSwNP who demonstrated sIgE toward S. aureus enterotoxins.98 4.4 Allergic bronchopulmonary aspergillosis‐allergic aspergillus rhinosinusitis: fungal hypersensitivity IgE‐mediated hypersensitivity to Aspergillus hyphae is the underlying endotype for allergic bronchopulmonary aspergillosis (ABPA) and allergic aspergillus rhinosinusitis (AARS). Upper airway disease is characterized by pansinusitis, passage of nasal plugs, and recurrent nasal polyposis. Lower airway disease is characterized by mucus inspissation within the bronchi and severe asthma, central bronchiectasis, mucous plugging, and fleeting pulmonary infiltrates. In a case series, 22/95 patients of ABPA had also concomitant AARS. All these patients had positive skin tests, positive precipitins, and raised total and sIgE (Supporting Information: References S11). This combination of ABPA and AAS has been termed “sinobronchial allergic mycosis” to recognize their shared etiology and association. The optimal treatment of ABPA and AAS is not known but includes the use of steroids, antifungals, or omalizumab. 5 CONCLUSION The evidence for UAD is strongest for asthma and AR/CRS. However, close associations between upper airway diseases and other lower respiratory tract disorders can also be appreciated. UAD is complex and heterogeneous but a “treatable traits” paradigm, based on assessment of airway inflammation, mucosal defense dysfunction, and exogenous cofactors can help to identify pathophysiological mechanisms involved in the development of disease and potential targets for intervention in individuals presenting with combined upper and lower airway disease. A given individual with disease can demonstrate multiple traits which may vary over time. We have summarized the various treatable traits in UAD and their proven or notional targeted treatments in Table 2. Appreciation of these treatable traits is necessary toward advancing the precision medicine approach in UAD. CONFLICT OF INTEREST The authors declare that they have no conflicts of interest. Supporting Information REFERENCES Notes : AR, allergic rhinitis; CAT, COPD Assessment Tool; CRSsNP, chronic rhinosinusitis without nasal polyps; CRSwNP, chronic rhinosinusitis with nasal polyps; FEV1, forced expiratory volume in one second; ICS, inhaled corticosteroids; ICS + LABA, combination inhaled corticosteroids and long‐acting beta agonist; LABA, long‐acting beta agonist; LAMA, long‐acting muscarinic antagonist; mMRC, modified Medical Research Council dyspnea score. Typical upper and lower airway clinical phenotypes associated with underlying treatable traits are listed. Taxonomy based on clinical phenotypes has significant limitations: each phenotype may represent comprise treatable traits, for example, CRSwNP and asthma may be neutrophilic or eosinophilic inflammation depending on the population studied. Hence, a more precise approach is needed to identify contributory treatable trait(s) in each case, which can aid in selection of targeted treatments. Question mark (?) indicates targeted therapies under investigation. Treatable traits and targeted therapies may be used to complement the current approaches for patient stratification and treatment selection—including non‐targeted treatments—shown in Table 1. Citing Literature Number of times cited: 2 Ana Margarida Pereira, Cristina Jácome, Rute Almeida and João Almeida Fonseca, How the Smartphone Is Changing Allergy Diagnostics, Current Allergy and Asthma Reports, 10.1007/s11882-018-0824-4, 18, 12, (2018). Crossref Umut Can Kucuksezer, Cevdet Ozdemir, Mubeccel Akdis and Cezmi A. Akdis, Precision/Personalized Medicine in Allergic Diseases and Asthma, Archivum Immunologiae et Therapiae Experimentalis, 10.1007/s00005-018-0526-6, (2018). Crossref
Via Krishan Maggon
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Scooped by
Gilbert C FAURE
November 11, 2018 1:51 PM
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Serodominant group 1 allergens of house dust mites (HDMs) are cysteine protease digestive enzymes. By increasing the detection of any allergen by dendritic antigen presenting cells, upregulating inflammatory signalling molecules, and activating cells crucial to the transition from innate to...
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Scooped by
Gilbert C FAURE
November 11, 2018 11:21 AM
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Mucosal-associated invariant T (MAIT) cells are characterized by a semi-invariant TCR that recognizes vitamin B metabolite Ags presented by the MHC-related molecule MR1. Their Ag restriction determines a unique developmental lineage, imbuing a tissue-homing, preprimed phenotype with antimicrobial function. A growing body of literature indicates that MR1-restricted T cells are more diverse than the MAIT term implies. Namely, it is increasingly clear that TCR α- and TCR β-chain diversity within the MR1-restricted repertoire provides a potential mechanism of Ag discrimination, and context-dependent functional variation suggests a role for MR1-restricted T cells in diverse physiological settings. In this paper, we summarize MR1-restricted T cell biology, with an emphasis on TCR diversity, Ag discrimination, and functional heterogeneity.
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Gilbert C FAURE
November 8, 2018 11:27 AM
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Introduction Galactose-deficient IgA1 (Gd-IgA1) is a critical pathogenic factor for IgA nephropathy (IgAN), but its value as a disease-specific biomarker remains controversial. We aimed to clarify the clinical significance of Gd-IgA1 in patients with IgAN.
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Scooped by
Gilbert C FAURE
November 8, 2018 11:26 AM
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Bacterial communities are known to impact human health and disease. Mixed species biofilms, mostly pathogenic in nature, have been observed in dental and gastric infections as well as in intestinal diseases, chronic gut wounds and colon cancer.
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Scooped by
Gilbert C FAURE
November 8, 2018 11:25 AM
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The induction of self-renewing memory alveolar macrophages by viral infection requires priming by CD8 T cells, illustrating how signaling from adaptive to innate immune systems generates trained immunity key to protection from re-infection.
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Rescooped by
Gilbert C FAURE
from Immunology and Biotherapies
November 8, 2018 3:03 AM
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Scooped by
Gilbert C FAURE
November 5, 2018 11:30 AM
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