Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE1 of 15MICROBIOMEMicrobial ecology perturbation in human IgA deficiencyJehane Fadlallah,1* Hela El Kafsi,1* Delphine Sterlin,1,2 Catherine Juste,3 Christophe Parizot,2 Karim Dorgham,1 Gaëlle Autaa,1 Doriane Gouas,1 Mathieu Almeida,4 Patricia Lepage,3 Nicolas Pons,5 Emmanuelle Le Chatelier,5 Florence Levenez,5 Sean Kennedy,5 Nathalie Galleron,5 Jean-Paul Pais de Barros,6,7 Marion Malphettes,8 Lionel Galicier,8 David Boutboul,8,9 Alexis Mathian,10 Makoto Miyara,1,2 Eric Oksenhendler,8,11 Zahir Amoura,1,10 Joel Doré,3,5 Claire Fieschi,8,9 S. Dusko Ehrlich,5,12 Martin Larsen,1,2†‡ Guy Gorochov1,2†‡Paradoxically, loss of immunoglobulin A (IgA), one of the most abundant antibodies, does not irrevocably lead to severe infections in humans but rather is associated with relatively mild respiratory infections, atopy, and auto-immunity. IgA might therefore also play covert roles, not uniquely associated with control of pathogens. We show that human IgA deficiency is not associated with massive quantitative perturbations of gut microbial ecology. Metagenomic analysis highlights an expected pathobiont expansion but a less expected depletion in some typi-cally beneficial symbionts. Gut colonization by species usually present in the oropharynx is also reminiscent of spatial microbiota disorganization. IgM only partially rescues IgA deficiency because not all typical IgA targets are efficiently bound by IgM in the intestinal lumen. Together, IgA appears to play a nonredundant role at the fore-front of the immune/microbial interface, away from the intestinal barrier, ranging from pathobiont control and regulation of systemic inflammation to preservation of commensal diversity and community networks.INTRODUCTIONEukaryotes have developed spectacular ways not only to protect them-selves from pathogens but also to benefit from unique and essential features of surrounding organisms (symbionts). Mammals are indeed highly dependent on their consortia of symbionts (microbiota) that serve both to optimize processing of nutrients and to protect from opportunistic agents by competition. Innate immune mechanisms con-trolling host-microbiota mutualism are physically localized, activated by bacterial contact, culminating into breach of the gut mucosal firewall. Immediate nonspecific host responses involve the secretion of de-fensins and the intraluminal recruitment of innate immune cells such as neutrophils that encapsulate commensals and limit their contact with surrounding gut epithelium (1). Such a potent response comes with a fitness cost to the commensal community and the benefits this brings to the host. It is therefore postulated that an evolutionary pressure took place to acquire mechanisms acting in the lumen, which would regulate microbial communities, thereby reducing the frequency of breaching the gut barrier and activation of innate immunity. This role could be played by antibodies and, most likely, by secretory immunoglobulin A (IgA) (2, 3). However, whereas antibody responses to pathogens have been intensively studied, much less interest has been devoted to the study of antibody relations with symbionts. Murine models of IgA deficiency (IgAd) have been studied and display modifications of the microbiome-immune interface. In such models, IgAd was obtained by inducing (i) defects in IgA class switch recombination (CSR) (4–6), (ii) defects in IgA transport into the gut lumen [pIgR−/− mice (7, 8) and J-chain−/− mice (9)], or (iii) reduction of IgA repertoire diversity with-out altering IgA levels [(activation-induced cytidine deaminase knock-in mutation (10), PD-1−/− mice (11), and FoxP3+CD4+–depleted mice (12)]. Models impairing CSR mechanisms (i) are associated with a gut dysbiosis defined by an expansion of anaerobes, predominantly seg-mented filamentous bacteria (SFB) and Clostridiales, as well as nodular hyperplasia secondary to hyperactivation of germinal center B cells induced by microbial antigens (4, 5). Models impairing IgA secretion (ii) into the gut lumen were associated with altered microbiota compo-sition, increased susceptibility to induced colitis, higher bacterial trans-location to mesenteric lymph nodes after Salmonella typhimurium challenge, and lack of protective immunity against cholera toxin (7, 8). Notably, alterations of gut microbiota ecosystems were observed in the small intestine, whereas large intestine communities were much less affected by the absence of IgA (5), possibly because IgA predomi-nantly targets commensal bacteria in the small intestine, but not in the colon, as shown by Bunker et al. (13) both in mice and in humans. Together, altered microbiome composition, increased susceptibility to microbial translocation, reduced microbial diversity, and reduced microbial fitness are shared features of IgA deficiency models.These results are in line with the initial conception of IgA func-tion, mainly presented as a neutralizing antibody, whose role would mainly be to exclude potentially harmful microbes and toxins from intimate contact with intestinal epithelia, thereby preserving mucosal barrier integrity (14). IgAd is relatively common in human adults, occurring in about 1 in 500 Caucasian individuals (15, 16). Although 1Sorbonne Université, INSERM, Centre d’Immunologie et des Maladies Infectieuses– Paris (CIMI-Paris), 75013 Paris, France. 2Assistance Publique-Hôpitaux de Paris (AP-HP), Groupement Hospitalier Pitié-Salpêtrière, Département d’Immunologie, 75013 Paris, France. 3UMR1319 Micalis, Institut National de la Recherche Agronomique (INRA), Jouy-en-Josas, France. 4Center for Bioinformatics and Computational Biology, University of Maryland, Paint Branch Road, College Park, MD 20742, USA. 5INRA, US1367 MetaGenoPolis, 78350 Jouy en Josas, France. 6INSERM, LNC UMR866, Uni-versity Bourgogne Franche-Comté, F-21000 Dijon, France. 7LIPoprotéines et Santé prévention Traitement des maladies Inflammatoires et du Cancer (LipSTIC) LabEx, Fondation de Coopération Scientifique Bourgogne-Franche Comté, F-21000 Dijon, France. 8Département d’Immunologie Clinique, Hôpital Saint-Louis, AP-HP, 75010 Paris, France. 9INSERM U1126, Université Paris Diderot Paris 7, 75010 Paris, France. 10Assistance Publique-Hôpitaux de Paris (AP-HP), Groupement Hospitalier Pitié-Salpêtrière, Service de Médecine Interne 2, Institut E3M, 75013 Paris, France. 11Uni-versité Paris Diderot Paris 7, EA3518, 75010 Paris, France. 12King’s College London, Centre for Host-Microbiome Interactions, Dental Institute Central Office, Guy’s Hospital, London, UK.*These authors contributed equally to this work.†These authors jointly directed this work.‡Corresponding author. Email: martin.larsen@upmc.fr (M.L.); guy.gorochov@upmc.fr (G.G.)Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE2 of 15human selective IgA deficiency (SIgAd) (that is, patients deficient for IgA but sufficient for all other antibody isotype) was, for a long time, considered asymptomatic, recent longitudinal studies have revealed that 80% of patients are symptomatic, when assessing complications more broadly (17) or when follow-up is extended (18). Human IgAd is associated with recurrent mucosal infections, autoimmunity, and intestinal disorders such as inflammatory bowel disease (IBD) and lymphoid hyperplasia (19, 20). To explain the mild phenotype observed in SIgAd patients, it is proposed that IgM may effectively replace IgA as the predominant antibody in secretions (21, 22). However, if IgA indeed represents a merely redundant component of the immune system, then it appears paradoxical that it was so well conserved in evolution and that it is massively produced at the individual level [about 66 mg/kg per day of IgA is secreted everyday (23)]. IgA also appears to orchestrate the beneficial tolerance established between the host and its gut commensal microbiome. These mutualistic host- microbial relationships were emphasized in animal models with an immune system reduced to a single monoclonal antibody of known bacterial specificity challenged by a limited microbial diversity. Although anti-body binding was reducing bacterial fitness, it also resulted in reduced intestinal production of proinflammatory signals, hence allowing bacterial tolerance by the host (24). Similarly, flagellin-specific secre-tory IgA (sIgA) may promote tolerance by reducing bacterial motility through modulation of flagellin transcription (25).Although these models have provided important examples and possible mechanisms through which antibodies can imprint specific microbes, the targets of polyclonal IgA and their global impact on the microbiome remain poorly defined. Although it was recently shown in an animal model that IgA-coated bacteria include proinflammatory elements (26), it is not known whether IgA preferentially binds po-tentially harmful bacteria and/or commensals (27). It is not known either whether IgM can indeed replace IgA at no expense to host/bacterial homeostasis. Finally, the relations between SIgAd and sys-temic autoimmunity are not well understood.Here, we studied the composition of sIgA-bound gut microbiota in healthy individuals and evaluated alterations of this bacterial consor-tium in SIgAd patients. To get insights into the specific contributions of IgA on host/microbial symbiosis, we also explored systemic im-mune responses in these patients.RESULTSPatients lacking IgA-producing B cells and seric IgA also lack free and bacteria-bound digestive IgASIgAd represents a bioclinical entity that is defined by serologic means, namely, undetectable seric IgA titers ( 0.07 mg/ml) with normal IgG concentration (28). SIgAd patients are known to present low or un-detectable salivary IgA levels (21, 22), but their gut IgA status had not been assessed. We serologically confirmed a status of IgA deficiency in 21 patients that could be included in this study because, among other exclusion criteria, they did not receive antibiotic treatment 3 months before inclusion. Compared to age- and sex-matched healthy donors (HDs) (n = 34), these SIgAd patients had undetectable seric (fig. S1A) and scarce digestive IgA levels [43 (0 to 206) g in HDs versus 0 (0 to 21) g of free IgA per gram of stool in SIgAd, P 0.0001; Fig.1A], whereas their seric IgG levels were preserved (fig. S1B).Circulating IgA+ B cells were undetectable in all patients except one [0.1 (0 to 1.3)% in SIgAd versus 7.1 (2.4 to 14.4)% in HDs, P 0.0001], whereas proportion of CD19+IgG+ cells among B cells was similar in both groups [12.55 (5.32 to 29.6)% versus 13.1 (0.793 to 37.6)%, P = 0.8004; Fig.1B]. Compared to controls, SIgAd patients are also characterized by a depletion of CD19+CD27+IgD− switched memory B cells among total CD19+ B cells [20 (5.17 to 34.1)% versus 14.9 (3.3 to 38.1)%, P = 0.0328; Fig.1C]. These data show that sIgA deficiency affects both peripheral blood and distant organs, such as the intestine, at both the cellular and the protein level.The clinical spectrum of digestive SIgAd-associated symptoms varies from very mild to severe, and it remains unknown whether residual digestive IgA would account for pauci-symptomatic presen-tations. We therefore used a flow cytometry assay, derived from our previously published technology (29), to test whether traces of IgA might be detected at the surface of the fecal microbiota, although free digestive IgA is usually not detectable in SIgAd, as shown above. The protocol was modified to assess levels of mucosal antibodies targeting colonic microbiota in vivo (Fig.1D). IgA, as the main mucosal anti-body in HDs, binds a median percentage of 7.6 (0.8 to 17.6)% of the whole fecal microbiota (Fig.1D). Close examination of flow cytom-etry profiles also reveals that IgA-bound microbiota can be sub-divided into IgAdim and IgAbright bacterial populations (Fig.1D, left). These IgA+ subsets were absent in all SIgAd patients except one (P 0.0001; Fig.1D, right). Notably, this patient also had detectable IgA+ circulating B cells (Fig.1B). This interesting case confirms that some patients diagnosed with SIgAd by serologic means indeed retain sIgA, which can be detected by the very sensitive bacterial flow cytometry technique. These data nevertheless establish that microbiota-bound IgA is usually undetectable in SIgAd patients.Global microbiome diversity is preserved in SIgAd patientsTo study the global impact of digestive IgA deficiency on the gut microbiome, we performed shotgun sequencing (30, 31) of fecal sam-ples in 34 HDs and 17 SIgAd patients. High-quality reads from each sample were mapped to a reference catalog of 3.9 million genes (32). Taxonomic abundances were computed at the level of co-abundance gene groups (CAGs) and, subsequently, binned at broader taxonomic levels (genus, family, order, class, and phylum). CAGs contain at least 50 different genes. Metagenomic species (MGSs) are defined as larger CAGs with very high connectivity and a defined minimal size of at least 700 genes. This approach to study microbiome composition presents the advantage to overcome the limited resolution of previous methods used for metagenomic or 16S amplicon data analysis that rely on comparisons to reference genomes, offering the possibility to com-prehensively profile the diversity of a clinical sample and to poten-tially identify previously uncharacterized microbes (32).Metagenomic analysis revealed a similar representation of the dominant phyla in the two groups [HDs versus SIgAd: 44.55% versus 45.52% (P = 1) for Bacteroidetes, 50.52% versus 48.35% (P = 0.7774) for Firmicutes, 2.19% versus 3% (P = 1) for Proteobacteria, 0.65% versus 0.84% (P = 0.99) for Actinobacteria, and 1.5% versus 1.41% (P = 1) for unclassified phyla; fig. S2A]. Microbiota diversity was not different between the two groups, either when including all MGS regardless of their phylum [median Shannon index, HDs versus SIgAd: 4.008 (2.501 to 4.698) versus 3.946 (1.690 to 4.511), P = 0.6344] or when comparing MGS diversity within each phylum [HDs versus SIgAd: 2.529 (1.225 to 3.380) versus 2.564 (0.8064 to 3.234), P = 0.8180 for Bacteroidetes; 3.978 (3.067 to 4.619) versus 3.779 (2.735 to 4.286), P = 0.05 for Firmicutes; 1.688 (1.170 to 2.223) versus 1.699 (0.5898 to 2.131), P = 0.8421 for Actinobacteria; 1.432 (0.1518 to 2.132) versus at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE3 of 151.432 (0.3349 to 2.014), P = 0.9042 for Proteobacteria; and 2.283 (0.6089 to 2.830) versus 2.032 (1.161 to 2.687), P = 0.1357 for un-classified phyla; fig. S2B]. Furthermore, no significant difference was observed in terms of median MGS richness between the two groups [454 (215 to 694) different MGS detected in HDs versus 451 (160 to 641) in patients, P = 0.7553; fig. S2C] nor in gene count [395841 (236166 to 636394) genes in HDs versus 379482 (248232 to 514298) genes in patients, P = 0.4167; fig. S2C]. To estimate the stability of the gut microbiota profiles, we analyzed the gut microbiota composition of three healthy subjects sampled longitudinally (two samples, 12 months apart). Hierarchical cluster analysis outlines strong structural intra-individual sample proximity, suggesting overall temporal stability of individual gut microbiota profiles, at least in healthy subjects (fig. S3). Together, these data reveal that the diversity of SIgAd and control fecal bacterial repertoires does not differ significantly. Our data also suggest that an individual’s profile remains stable over time.IgA deficiency gut microbiota displays mild dysbiosisWe reasoned that IgA deficiency might affect relatively discrete bac-terial populations, without affecting global microbiome structure at the analysis level applied above. A gene biomarker approach (33) was used to determine which bacteria were expanded or depleted in IgA deficiency, comparing the whole unsorted microbiota of 34 HDs with 17 SIgAd patients (fig. S4). We found 31 differential MGSs between the two groups; 17 being were overrepresented, whereas 14 were un-derrepresented in SIgAd patients. Differential MGSs, between controls and patients, were ranked according to increasing statistical signifi-cance (Fig.2A). Most MGSs depleted in IgA deficiency (13 of 14) belong to the Firmicutes phylum, whereas only one belongs to the Bacteroidetes phylum. Among depleted Firmicutes, more than half of them (7 of 13) belong to the Lachnospiraceae family, and two are Ruminococcaceae (Faecalibacterium genus, n = 2 of 2) (Fig.2B).Conversely, among the 17 expanded MGSs in IgA deficiency, 10 are Firmicutes, 4 are Bacteroidetes, and 3 are Proteobacteria (Gamma-proteobacteria exclusively, including Escherichia coli). These 17 MGSs belong to 11 different families and, thus, are more diverse than depleted species. Three of seventeen expanded MGSs are usually present in the oropharynx flora (Streptococcus sanguinis, Veillonella parvula, and Haemophilus parainfluenzae). In addition, we found two different species of Prevotella to be overrepresented in SIgAd (compare Fig.2A).IgA targets more likely decline than expand in the absence of IgAWe then postulated that microbial ecology perturbation in the ab-sence of IgA might be appreciated in a different manner if we could focus on bacteria, more specifically, IgA-targeted in healthy controls. We previously verified that metagenomic analysis could reliably be performed on sorted bacterial subsets with a determined lowest ana-lyzable sample size of 108 bacteria (34). IgA+ fractions were enriched by magnetic sorting in 30 healthy controls, allowing IgA+ and IgA− fraction sequencing and differential metagenomic analysis (Fig.3A). The same gene biomarker identification approach as above (fig. S4) allowed the identification of 24 different MGS overrepresented in the IgA+ fraction of healthy controls (fig. S5). These \"IgA+ MGSs” were ranked according to increasing statistical significance at the lowest tax-onomic level available (Fig.3B). Among the 24 IgA+ MGSs, 19 belong to the Firmicutes phylum (among which 12 belong to the Clostridia class, 1 of these 12 belonging to the Faecalibacterium genus), 2 are bacteria from unclassified phylum, 1 is Proteobacteria (one E. coli species), 1 be-longs to the Actinobacteria phylum (Bifidobacterium bifidum), and the last identified MGS belong to the Bacteroidetes phylum (Fig.3C). We finally compared the prevalence of IgA+ MGSs between SIgAd patients and controls and found that the prevalence of only four of them was significantly altered (Fig.3D). Whereas E. coli (CAG 4) was ACDBFig. 1. Immunological phenotype of stool and blood from SIgAd patients. (A) Free IgA levels in fecal water were measured by enzyme-linked immunosorbent assay (ELISA) in HDs (n = 34) and SIgAd patients (n = 21). (B) CD19+IgA+ and CD19+IgG+ cells (C) peripheral CD19+CD27+ (memory B cells) and CD19+CD27+IgD− (switched memory B cells) cells were detected by flow cytometry surface staining. (D) SIgA-binding levels of ex vivo purified and fixed microbiota are measured by a flow cytome-try–based assay. Gray histograms represent isotype control, and black lines represent anti-IgA–stained microbiota. In the graphs, each symbol represents a donor (34 HDs and 21 SIgAd). In all sections, horizontal bars represent medians. Indicated P values wer e calculated using a Mann-Whitney test. An HD with nondetectable IgA binding of gut microbiota and an SIgAd patient with detectable IgA binding of gut microbiota are indicated with a gre en and red symbol, r espectively. at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE4 of 15found to be expanded in patients, we rather observed that Coprococcus comes (CAG 19), Clostridium sp. (CAG 138), and Dorea sp. (CAG 73) were depleted in SIgAd (Fig.3E). Together, the prevalence of most IgA+ MGSs does not vary significantly in SIgAd. Furthermore, an IgA+ MGS does not systematically expand in the context of SIgAd.IgM digestive secretion partially rescues microbiota antibody coating in SIgAd patientsWe then postulated that compensatory immune mechanisms might explain why IgA deficiency is not associated with massive perturba-tions of gut microbial ecology, as previously suggested (21, 22, 35). Microbial flow cytometry analysis was used to detect other antibody iso-types on the surface of SIgAd microbiomes. IgM was indeed detected at the surface of SIgAd microbiota in all SIgAd patients analyzed (Fig.4A). IgM bound 6.26 (0.625 to 45)% (n = 21) of the whole microbiome in patients, whereas IgM binding was observed in minimal amounts in only 2 of 34 healthy controls [0.05 (0 to 2.4)%, P 0.0001; Fig.4B]. Measured free fecal IgM levels were consistent with IgM-bound levels [0.83 (0 to 28.1) g and 39.9 (0 to 436.1) g of free IgA per gram of feces in HDs and SIgAd patients, respectively, P = 0.0004; Fig.4B]. ABFig. 2. Bacterial repertoire shift associated with IgA deficiency. (A) Differential MGS (n = 31) between SIgAd patients and HDs assigned at their lowest taxonomic level and ranked by statistical difference. White histograms represent MGSs that are underrepresented in IgA deficiency (n = 14), whereas gray histograms represent MGSs that are overrepresented in IgA deficiency (n = 17), compared to HDs. (B) Taxon omic distribution at phylum (top row) and family (bottom row) level of underrepresented MGS (left column) and overrepresented (right column) MGS in IgA deficiency. at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE5 of 15In blood, so-called IgM-only B cells (36) (CD19+CD27+IgM+IgD−) were increased in patients [5.67 (2.88 to 12.73)% in HDs versus 9.14 (3.83 to 43.75)% in SIgAd patients, P = 0.0048], whereas marginal zone-like cells (CD19+CD27+IgM+IgD+) are similar in both groups [40.2 (17.8 to 66.3)% and 38.85 (9.29 to 76.8)% in HDs and SIgAd patients, respectively, P = 0.7588; Fig.4C]. Therefore, in the absence of IgA, IgM is secreted to the digestive tract, where it binds gut commen-sals. Our data also suggest that CD19+CD27+IgM+IgD− B cell expan-sion could account for this compensatory measure.We then asked whether IgM could be looked upon as a surrogate for IgA at the immune-microbiota interface. To test whether IgM and IgA bind overlapping repertoires of bacteria, we separated IgM+ and IgM− bacteria from patient’s microbiomes (n = 10) using flow cy-tometry. Metagenomic analysis could not be performed on sorted samples because the lowest analyzable sample size of 108 bacteria was not reached for all IgM+ fractions. Bacterial fractions were then identified by 16S rRNA sequencing (Fig.4D, top). Hierarchical clus-tering of n = 33 dominant taxa abundance ratios [log2 (IgM+/IgM−)] displays no evidence of a common pattern of IgM recognition (Fig.4D, middle). Thus, much like IgA responses (fig. S6), IgM responses also seem to be highly variable interindividually.We then focused our analysis on the taxa bound by SIgA, or dif-fering between HDs and SIgAd patients, (confer Figs.3B and 2A, respectively). Using the median log2 (IgM+/IgM−) ratios of all the donors, we found that (i) Veillonellaceae family, Prevotella genus, Porphyromonadaceae family, Pseudomonas genus, Lachnospira ge-nus, Faecalibacterium genus, Clostridium genus, Bifidobacterium genus, and Bacteroides genus are overrepresented in IgM+ fraction; (ii) Enterobacteriaceae family, Streptococcus genus, Ruminococcus genus, Dorea genus, Coprococcus genus, and Blautia genus are overrepresented in IgM− fraction; and (iii) Acinetobacter genus, Erysipelotrichaceae family, Anaerostipes genus, Eubacterium genus ABDCEFig. 3. IgA+ bacterial repertoire in HDs at MGS level. (A) IgA+ and IgA− fractions of HD gut microbiota (n = 30) were enriched by magnetic sorting, sequenced, and analyzed. (B) Twenty-four indicated MGSs were over-represented in the IgA+ fractions. Bar diagram shows the 24 MGSs assigned at their lowest taxonomic level and ranked by the statistical significance with which it is found to be overrepresented in the IgA+ fraction. (C) IgA+ MGS represented at phylum (left) and family level (right). (D) IgA+ MGS frequencies in HDs compared to SIgAd patients. Ends of whiskers plots represent mini-mum and maximum values, vertical line represents me-dians, and boxes represent data spreading (25th and 75th percentiles). (E) Abundances of the four IgA+ MGS significantly different between HDs and SIgAd pa-tients. Horizontal lines represent medians, and P values were calculated with a Mann-Whitney test. Significant differences were indicated with *P 0.05, **P 0.025, and ***P 0.01. at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE6 of 15ACDEBFig. 4. IgM binding to SIgAd microbiota. (A) Rep-resentative flow cytometry–based IgM detection on purified microbiota in an HD and an SIgAd pa-tient. Gray histograms represent isotype controls, and black lines represent IgM surface staining. (B) IgM binding in HDs (n = 34) and SIgAd patients (n = 21) measured by flow cytometry (left). Free IgM levels in fecal waters measured by ELISA (r ight) . (C) Peripheral CD19+CD27+IgM+IgD+ (circulating marginal zone B cells) and CD19+CD27+IgM+IgD− (\"IgM only” B cells) frequencies in HDs and patients. (D) IgM+ and IgM− fractions of gut microbiota were sorted by flow cytometry in 10 patients, and their composition was analyzed by 16S ribosomal RNA (rRNA) gene sequencing (top). Taxa enrichment in IgM+ fraction is measured as the log2(IgM+/IgM−) ratio of OTU abundances. The taxa enrichment mea-sures of 33 dominant OTUs (rows) were grouped by hierarchical cluster analysis according to Ward’s method and plotted as a heat map. Each column represents a patient. Yellow and cyan colors rep-resent the lowest and highest ratios, respectively (range of the ratios: −6 to +6, middle). Medians of the IgM+ enrichment ratio. Each horizontal bar represents the median ratio of the 10 donors by OTU (bottom). (E) Paired analysis of the abundances in IgM+ and IgM− fractions for each OTU of interest. In (B) and (C), horizontal bars represent medians, and Mann-Whitney test was used to calculate P values. In (E), P values were calculated with a paired-rank Wilcoxon test. FSC, forward scatter. at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE7 of 15are equally present in both fractions (Fig.4D, bottom). Paired analysis performed on these 19 taxa revealed that Clostridium and Pseudomonas genera are significantly enriched in the IgM+ fraction (P = 0.013). Bac-teria belonging to the Enterobacteriaceae family are poorly bound by IgM. Together, not all typical IgA targets are bound by digestive IgMs in SIgAd patients (Fig.4E).IgM responses are correlated with commensal diversity in IgA deficiencyGiven that the recent literature substantiates that IgA shapes microbiota composition and diversity in mice (12), we wanted to know whether IgM could play this role in IgA deficiency. Median gut IgM+ enrich-ment ratios based on operational taxonomic unit (OTU) abundances calculated at phylum level in IgM+- and IgM−-sorted fractions were correlated with the Shannon diversity index (fig. S2B) within each dominant phylum in nine SIgAd patients (Fig.5A). As shown, IgM+ enrichment ratio is positively correlated with Actinobacteria phy-lum diversity (Spearman coefficient, r = 0.7167; P = 0.0369), whereas no statistical correlation is observed for the three other dominant phyla. Notably, the very narrow range of IgM binding to Firmicutes, and to some extent Proteobacteria, might have precluded identification of po-tential correlations between IgM binding and diversity in these phyla. ABCDρρFig. 5. IgM binding and microbial diversity. (A) Scatter graphs represent the correlation between phylum enrichment in the IgM+ fraction {log2[(IgM+)/(IgM−)], where (IgM+) and (IgM−) represent phylum abundances in the IgM+ and IgM− fractions, respectively} and microbial diversity within each of the top four most dominant phyla (Shannon diversity index) calculated from metagenomic sequencing (confer Fig.1) in n = 9 patients with SIgAd. Spearman coefficient (ρ) and P values (P) are indicated. (B) Phyla distribution, (C) MGS richness, and (D) diversity are shown for n = 7 patients with common variable immunodeficiency (CVID). Diversity is calculated with Shannon’s diversity index either for all the MGS (white whiskers plot) or within each phylum. Ends of whiskers represent the minimum and maximum of all the data, and boxes represent data spreading (1 SD). Horizontal bars represent medians, and P values were calculated with a Mann-Whitney test. at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE8 of 15Serum IgM responses against two strains belonging to the Actinobacte-ria phylum (Bifidobacterium adolescentis and Bifidobacterium longum) were confirmed in 16 HDs by flow cytometry (fig. S7A). We then pos-tulated that Actinobacteria diversity could be further reduced in the absence of IgM. We therefore explored CVID patients with total IgA deficiency and very low or undetectable IgM digestive levels [0 (0 to 115) g of free IgM per gram of stool; fig. S7B]. As expected, IgM gut microbiota binding is minimal [0.297 (0.08 to 3.84)%, n = 15; fig. S7C] in CVID. Shotgun sequencing of the whole CVID gut mi-crobiota and metagenomic analysis (Fig.5B) suggested a significant global loss of MGS richness [454 (305 to 590) in HDs (n = 34) versus 375 (294 to 491) in CVID (n = 7), P = 0.0615; Fig.5C]. A significant loss in Actinobacteria phylum diversity was observed in these patients [Shannon diversity index, 1.688 (1.170 to 2.223) in HDs versus 1.342 (0.319 to 1.827) in CVID; P = 0.05]. A loss, yet more moderate, of the Firmicutes phylum diversity was also observed (P = 0.065; Fig.5D). Together, the data suggest that, in the absence of intestinal IgA, IgM binding preserves Actinobacteria diversity, although this conclusion needs to be validated on a larger CVID cohort.IgA deficiency is associated with systemic inflammationWe then asked whether the lack of sIgA could induce perturbations in host systemic inflammatory versus regulatory responses, in spite of the presence of mucosal IgM responses. Cytokine-secreting cir-culating CD4+ T cells were measured in both groups (Fig.6A). Pro-portions of interferon- (IFN-)+CD4+ T cells were not different [14.6 (3.3 to 25.7)% in HDs versus 16.75 (2.28 to 47.8)% in SIgAd, P = 0.3932], whereas interleukin-17 (IL-17)+CD4+ and IL-22+CD4+ T cells were increased in IgA deficiency [0.422 (0.04 to 1.96)% ver-sus 1.49 (0.06 to 3.65)%, P = 0.0137 and 0.136 (0.06 to 0.769)% ver-sus 0.866(0.02 to 4)%, P = 0.0104, respectively]. Double-positive IL-17+IL- 22+ CD4+ T cells were also increased in IgA deficiency [0.05 (0.02 to 0.44)% versus 0.2 (0 to 0.75)%, P = 0.0058]. Seric IL-6, IL-10, and IL-17 concentrations were all elevated in patients [0.6 (0.33 to 2.4) pg/ml versus 1 (0.25 to 34.37) pg/ml (P = 0.0315), 0.47 (0 to 1.41) pg/ml versus 0.87 (0.37 to 5.2) pg/ml (P = 0.0001), and 0.06 (0 to 1.47) pg/ml versus 0.21 (0.007 to 0.92) pg/ml (P = 0.0215), respectively; Fig.6B]. sCD14 was increased in patient’s sera [2063 (1147 to 4283] pg/ml versus 2841 (1399 to 5187) pg/ml, P = 0.0023; Fig.6C], although LPS concentration was not significantly increased in the same samples [54.15 (23.40 to 77.92) versus 48.10 (34.57 to 97.33); Fig.6C]. We also observed an increase in CD4+PD-1+ cells [7.11 (1.86 to 16.9)% versus 14 (3.13 to 31.6)%, P = 0.0093; Fig.6C] in SIgAd patients. The circulating regulatory T cell (Treg) compartment was not altered by IgA deficiency, because frequency of naïve Tregs (CD45RA+FoxP3+ CD4+ T cells), effector Tregs (CD45RA−FoxP3bright CD4+ T cells), and activated conventional T cells (CD4+CD45RA−FoxP3low) was similar between patients and controls (Fig.6D). Thus, SIgAd patients display a circulating skewed CD4+ T cell pheno-type toward T helper 17 (TH17) differentiation associated with an increase of seric sCD14, which is a marker of monocyte activation. Patients with malabsorption were excluded from the study because mu-cosal defects might grossly affect gut microbial ecology. We stratified patients in an effort to determine whether other clinical features such as infection, autoimmunity, or historic antibiotic treatments (at least 3 months before sampling) might be preferentially associated with in-flammation. We found no correlations between clinical status and any of the elevated immunological markers mentioned above (Fig.6, A to D). Together, the immunological and inflammatory modifications as-sociated with SIgAd are not contributed by isolated patients presenting \"extreme” clinical phenotypes.IgA deficiency is associated with a disturbed bacterial dependency networkBacterial symbiosis in the human gut notably implies that some bacteria depend on other bacteria for their persistence. Within such networks, and by definition, a dependent bacterium, called \"satellite,” never occurs independently of another, coined \"host,” in a given sample. Conversely, the same host may occur in a given sample independently of its satellites (Fig.7A). This concept was initially promoted by Nielsen et al. (32), who identified a minimal obligatory network of 45 MGS-MGS de-pendency associations involving 60 MGSs (the same MGS can make several associations). To investigate the potential impact of IgA defi-ciency on bacterial dependency associations, we tested whether this minimal obligatory network was disturbed in IgA deficiency.Main links defined by Nielsen et al. (32) were confirmed in HDs (90 to 100%), whereas the confirmed link percentage was more dis-persed in SIgAd patients, ranging from 75 to 100% (Fig.7B). On the basis of our HD MGS-MGS co-presence distribution, the 99% con-fidence interval was calculated according to a  distribution defining a lower threshold of 84% of MGS-MGS co-presence, below which the population-level link was considered absent. In our HD cohort, 41 of 45 links were maintained at population level, whereas only 30 links were confirmed in SIgAd patients. The 11 links lost in SIgAd patients involved 21 MGS: 7 Faecalibacterium sp., 4 Firmicutes, 2 Clostrid-iales, 2 Ruminococcus sp., 2 Prevotella, 2 Butyricimonas virosa, and 2 unknown MGSs (data file S1).The resulting network of remaining bacterial dependencies for HDs and SIgAd patients is shown in Fig.7C. In summary, IgA defi-ciency is associated with a disturbed bacterial dependency associa-tion network.DISCUSSIONSimilar to what was observed in murine models of IgAd (5), we found that IgA deficiency does not markedly alter global fecal microbiota composition in affected patients. However, our observations are limited to feces, and a more severe dysbiosis may be present in the small intes-tine, as shown in mouse models of IgA deficiency (5, 11–13). The com-bination of flow cytometry and metagenomic analysis proved useful to increase the resolution of the analysis on human fecal samples by monitoring alterations of commensals specifically bound by sIgA in healthy controls. More work will be necessary to determine whether IgAdim and IgAbright bacterial populations correspond to bacteria bound by high- versus low-affinity IgA, respectively, and whether these s ubsets have overlapping bacterial repertoires or not. Notably, IgA interac-tions with very low affinity are also suggested and could account for the very discrete, but global, bacterial flow cytometry profile shift consistently observed in HDs but not observed in SIgAd patients.It could have been expected that commensals bound by sIgA in healthy subjects would all tend to expand in IgAd patients. In mu-rine models of immune deficiency, IgA targets, such as SFB (26), ex-pand in the absence of an effective IgA response (4, 12). We rather observed that MGSs defined in Fig.3B as main IgA targets in controls do not systematically bloom in SIgAd patients. Most notably, C. comes (CAG 19) is underrepresented in SIgAd patients compared to con-trols. By contrast, another typical IgA target, such as E. coli (CAG 4), is overrepresented in SIgAd patients by an order of magnitude, compared at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE9 of 15to controls. These data suggest that E. coli (CAG 4) expansion in the micr o-biota could indeed be negatively influenced by sIgA, whereas C. comes thrives in the presence of sIgA. More generally, at the family level, we ob-serve that IgA+ bacteria are more likely found to be underrepresented than overrepresented in SIgAd patients. These data emphasize the protective role that sIgA directly (or indirectly) plays in humans on commensal ecology, as previously observed in animal models (12).Using the gene biomarker analytical approach described in Qin et al. (33), we identified bacterial targets of sIgA in HDs by meta geno mic anal-ysis of microbiota preparations enriched for sIgA-bound commensals. ABCDFig. 6. Systemic inflammatory and bacterial trans-location markers. (A) Peripheral cytokine-secreting CD4+ T cells after phorbol 12-myristate 13-acetate–calcium ionophore stimulation (6 hours). On the left, intracellular detection of IFN-/IL-17 or IL-17/IL-22 in an HD (left column) and a patient (right column). On the right, graphs showing percentages of peripheral CD4+IFN-+, CD4+IL-17+, CD4+IL-22+, and CD4+ IFN-+IL-17+ cells in both groups. (B) Seric IL-6, IL-10, and IL-17 (in picograms per milliliter) measured by single molecule array (SIMOA) technology stratified according to donor status. (C) Seric soluble CD14 (sCD14) (in pi-cograms per milliliter) measured by ELISA (left), seric lipopolysaccharide (LPS) (in nanomolars) measured by mass spectrometry (middle), and peripheral PD-1 expressing CD4+ T cells were detected by flow cy-tometry (right). (D) CD4+ Tregs were measured by flow cytometry, naïve Treg cells (CD4+CD45RA+FoxP3+), effector Treg cells (CD4+CD45RA−FoxP3bright), and ac-tivated conventional CD4+ T cells (CD4+CD45RA−FoxP3low). For all panels, horizontal bars represent medians, and P values were calculated with a Mann- Whitney test. A color code indicates the main clinical features associated with each SIgAd case. at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE10 of 15Notably, enriched samples retained detectable levels of non– IgA- bound commensals. This analytical approach (33) was chosen, because it allows identification based on differential abundance, which is less sensitive to sample purity. We show that sIgA targets preferentially Firmicutes, Actinobacteria, and Proteobacteria, whereas Bacteroidetes are largely underrepresented compared to total microbiota composition. Only 1 of the 24 MGSs identified as preferred sIgA targets belongs to the Bacteroidetes phylum, despite its dominance in the human colon. Our results are consistent with previous mouse (12, 26) and human (26, 37–40) studies.Although preferred IgA targets belong to the Firmicutes phylum, underrepresented bacteria in SIgAd patients are also Firmicutes, such as Faecalibacterium, a genus well known to exert anti-inflammatory effects on the gut mucosa (41) and which is notably depleted in IBDs (42, 43). Conversely, potentially proinflammatory Gammaproteo-bacteria and Prevotella (44) are overrepresented in SIgAd patients. Overrepresented bacteria are more diverse than underrepresented bacteria. Three MGSs usually belonging to the oral flora (V. parvula, S. sanguinis, and H. parainfluenzae) are overrepresented in gut mi-crobiomes of SIgAd patients. These bacteria are involved in biofilm formation and could exert inflammatory effects (45, 46). V. parvula has also been described as responsible for sepsis in an X-linked agammaglobulinemia patient (47). Finally, the genera usually de-scribed as pathogenic in patients (Salmonella and Campylobacter) were not found to be overrepresented.Overall, we observed depletion of anti-inflammatory species, an expansion of proinflammatory species, and a lower digestive tract localization of constituents of the oral flora in IgAd patients. It was recently shown that ectopic localization of human salivary microbiota can elicit severe gut inflammation in susceptible host animals (48). It is therefore possible that ectopic oral microbiota could also exert a proinflammatory role in the context of SIgAd.ABCFig. 7. Bacterial dependency networks in IgA deficiency and HDs. (A) For each individual and each MGS-MGS dependency, there are four possible situations. If the satellite MGS is present without corresponding host MGS, then the link is considered lost. If the satellite and host are both present, then the link is considered conserved. If the host is present in the absence of the satellite, or if both host and satellite are absent, then no information is given about the link. (B) Distribution of the percentage of confirmed MGS-MGS dependency links. (C) MGS-MGS dependency network compared between HDs (left) and SIgAd patients (right). Each of the two network maps shows the expected 45 highly significant and directional dependencies among 60 MGSs, as defined by Nielsen et al. (32). Blue circles represent host MGSs. Purple triangles represent satellite MGSs. A purple triangle within a blue circle indicates both satellite and host status. Black arrows indicate the confirmed dependencies among MGSs (directional arrow from the satellite MGS to the host MGS). Red crosses indicate lost dependencies in HDs and IgA deficiency. at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE11 of 15Mechanisms underlying these findings are unknown, but one could speculate that sIgA, on the one hand, excludes (14, 49) and facilitates elimination of fast-growing pathobionts (50). On the other hand, sIgA could protect commensals by agglutination (51) and by localizing these bacteria in a favorable habitat like the mucus (fig. S8). Finally, IgA-mediated \"intraluminal trapping” effects should prevent intimate bacterial proximity with the epithelial barrier and, therefore, host in-flammation and associated negative effects on commensals.In SIgAd patients, we show that the microbiota remains substan-tially bound by IgM. This observation, together with the recent char-acterization of dually coated (IgA+ IgM+) mucus-embedded bacteria with increased richness compared to IgA-only–coated bacteria (52), suggests that IgM may compensate IgA deficiency. We show that microbial IgM binding is highly variable between patients. When focusing the analysis on commensals bound by sIgA in healthy sub-jects, or on differential genera between healthy controls and SIgAd patients, we noticed that IgM, like IgA, tends to preferentially bind Clostridium, Bifidobacterium, and Faecalibacterium (all Gram-positive bacteria). However, we show that IgM does not bind Enterobacteriaceae, nor Prevotellaceae families, which are overrepresented in SIgAd pa-tients and involved in proinflammatory events (44). These data sug-gest that IgM is only partially efficient at controlling pathobionts, Table 1. Demographic and clinical cohort summary. HDs SIgAd P value*Number of individuals 34 21Median age 32.9 (23–61) 36 (18–67) 0.1289Sex ratio (F/M) 18:16 14:7 0.4708IMC (index de masse corporelle) 21.9 (18.7–33.9) 22 (19.1–36) 0.3599Ethnic origin†0.1149 Caucasian 23 (68%) 18 (86%) North Africa 4 (11%) 3 (14%) Africa 1 (3%) 0 (0%) Middle eastern 4 (11%) 0 (0%) Asiatic 2 (5%) 0 (0%)Recurrent infections 9 (43%) Upper and lower respiratory tract 7 (33%) Intestinal 3 (14%) Vaginal 6 of 14 (43%)Antibiotics ( 1 per year)‡12 (57%)Autoimmune condition 11 (52%) Cytopenias 5 (24%) Systemic lupus erythematosus (SLE) 4 (19%) Thyroiditis 4 (19%) Celiac disease 2 (10%) Biermer anemia 1 (5%) Vitiligo 2 (10%) Type 1 diabetes 1 (5%) Ankylosing spondylitis 1 (5%)Intestinal symptoms (chronic diarrhea and/or chronic abdominal pain leading to digestive endoscopy)9 (43%)Immunomodulatory therapy 3 (14%) Steroids§3 (14%) Methotrexate 1 (5%) Hydroxychloroquine 3 (14%)Associated IgG4 subclass deficiency 2 (10%)Median IgG levels (mg/ml) 10.10 (6.7–15.2) 11.8 (7.14–18) 0.2475*Continuous and discrete variables were assessed statistically with Mann-Whitney and 2 test for homogeneity, respectively. †All donors domesticated in France. ‡Four SIgAd patients having received antibiotics within 3 months from sampling were excluded for gut microbiota analysis but were included in the immunological phenotype analysis (compare Fig. 6). §Median dose of 10 mg/day. at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE12 of 15accounting for the susceptibility to enteropathogens in SIgAd. Our data sugg est that sIgA has the same positive impact at least on Firmicutes and Actinobacteria diversity in humans, whereas IgM would only favor the latter. Notably, the very narrow range of IgM binding to Firmicutes, and to some extent Proteobacteria, might have precluded identification of po-tential correlations between IgM binding and diversity in these phyla.In an effort to determine whether Actinobacteria diversity would also be lost in the absence of IgM, we studied CVID gut microbiomes. We observed that Actinobacteria diversity is drastically decreased in CVID, an \"IgM-deficient” model. Moreover, a similar trend is observed for Firmicutes. These results were confirmed in an independent CVID study. Jørgensen et al. (53) recently showed that CVID patients display dysbiotic gut microbiota with reduced  diversity, reduced abun-dance of Actinobacteria, and increased abundance of Gammaproteo-bacteria. It should be underlined that, although we took care to include CVID that did not receive antibiotics within 3 months before sam-pling, the analysis of CVID patients is potentially hampered by several confounders such as previous antibiotic courses and other treatments. It is, however, interesting to note that Actinobacteria diversity is preferentially decreased in two independent CVID studies, whereas other phyla are comparatively less affected in this IgM-deficient co-hort, while, allegedly, confounding factors might have similarly af-fected all phyla. There is clinical relevance for this concept because it is well established that patients selectively lacking IgA rarely develop IBD, whereas this complication is more frequent and severe in those lacking both IgA and IgM (54).The impact of IgA deficiency on bacterial symbiosis is further evi-denced by the perturbation of bacterial networks. The presence in SIgAd patients of satellite bacteria (such as CAG 97, CAG 133, CAG 488, CAG 206, and CAG 328), in the absence of their previously de-scribed host (32), most probably reflects the establishment of novel dependency links. Such modifications underscore the role played by IgA well beyond its recognized neutralizing activity.SIgAd has also profound systemic repercussions. The TH17 bias that we observed is potentially connected to intestinal dysbiosis because Klemola et al. (55) have already shown the presence of activated T cells in gut mucosa of IgAd patients. As described by Perreau et al. (56), in CVID, CD4+PD-1+ cells are increased in SIgAd patients. Al-though LPS was not found to be elevated in SIgAd serum, increased sCD14 and PD-1 up-regulation could reflect T cell exhaustion in-duced by repeated bacterial translocation (57). Finally, and from a therapeutic perspective, a much larger cohort of patients would be needed to extract a potentially beneficial microbial signature associ-ated with elevated IL-10 observed in some patients.Together, we show that SIgAd in humans is associated with a mild intestinal dysbiosis, characterized by expansion of proinflammatory bacteria, depletion of anti-inflammatory commensals, and a pertur-bation in the \"obligatory” bacterial network. Dysbiosis could be partly explained by the fact that IgA deficiency is not totally compensated by IgM secretion and by the loss of a nonredundant chaperone-like effect of IgA on microbial diversity.MATERIALS AND METHODSStudy designPatients and controlsWe conducted a cross-sectional study of patients with IgA deficiency compared to healthy controls. Fresh stool and blood samples were collected simultaneously at a single time point in 21 patients with SIgAd (table S1) and compared with 34 age- and sex-matched HDs (Table1). We furthermore recruited seven CVID patients with IgA, IgG, and/or IgM deficiency, thus displaying a global antibody production defect. Patients were recruited from two French referral centers of clin-ical immunology (Department of Clinical Immunology in Saint Louis Hospital and Department of Internal Medicine in Pitié-Salpêtrière Hospital, Paris), where they were followed for clinical manifestations associated with antibody deficiencies. IgAd patient’s inclusion criteria were undetectable seric IgA levels ( 0.07 mg/ml) in at least three pre-vious samples in the past year. SIgAd is defined by serological means, namely, undetectable seric IgA titers ( 0.07 mg/ml) with normal IgG levels. CVID patients are characterized by a marked decrease of seric IgG (at least 2 SD below the age-dependent mean) and a marked de-crease in at least one of the isotypes IgM or IgA. For our study, we furthermore requested that the patients should be deficient in seric IgA ( 0.07 mg/ml).Exclusion criteriaAntibiotic therapy and laxative drugs use in the last 3 months before stool collection (inclusion for metagenomic analysis: 17 SIgAd patients, 7 CVID, and 34 HDs). Clinical and biological data were collected at in-clusion time. Oral and written consent were obtained from patients before inclusion in the study.Bioinformatical analysisCAGs matrix constructionThe projection of genes into CAGs was performed using a predefined list of 7381 CAGs (32). Every predefined CAG is a vector of genes ordered by increasing connectivity. For a bacterial genome, the most connected genes correspond to the marker genes present in all indi-viduals carrying this organism (core genome). Starting with the gene frequency matrix, we projected the list of connected genes in each predefined CAG and extracted the corresponding frequency profile of the 50 most connected ones. Provided that at least 10% of marker genes are found, each frequency profile was used to compute the mean vector corresponding to the CAG frequency.Differential CAGs identificationGenes from the gene profile matrix were used to identify those that were differentially abundant between the SIgAd patients and HD groups as described in (30). Briefly, Wilcoxon tests were used to compute the probabilities that gene frequency profiles did not differ between SIgAd patients and HD groups by chance alone. Benjamini and Hochberg multiple-test correction was applied to the P values. By performing a selection based on a significance threshold of P 0.01, we identified 18,025 genes that were differentially abundant between the two groups. The same method and P value were applied to identify the differen-tially abundant genes between the IgA+/IgA− fractions in HDs. We identified 110,558 genes that were differentially abundant between the two groups. In a second step, the differentially abundant genes were projected into CAGs defined in the 3.9 million genes catalog. To validate a differential CAG, we used a threshold with a minimum of 50 differentially abundant genes per CAG (CAGs are kept only if they are equal to or exceed 50 connected genes) including at least 10% of marker genes. The frequency profile of the 50 most connected genes was used to compute the mean vector corresponding to the CAG frequency. Of the 18,025 differential genes between SIgAd pa-tients and HDs, 8191 fell into 33 CAGs composed of 53 to 997 genes after the projection step (fig. S2B and data file S2). Thirty-one of the 33 CAGs are considered as MGS according to their gene size (≥700 genes). For the IgA+ and IgA− fraction comparisons, 80,415 of at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE13 of 15110,558 differential genes fell into 119 CAGs of 50 to 3105 genes each. Thirty CAGs are overrepresented in the IgA+ fraction, and 89 are over represented in the IgA− fraction. Twenty-four of the 30 IgA+ CAGs are considered MGS according to their gene size (fig. S5 and data file S3).Wilcoxon tests were used to compute the probabilities that CAG frequency profiles did not differ between the two compared groups (SIgAd/HDs and IgA−/IgA+ fractions) by chance alone. Benjamini and Hochberg multiple-test correction was applied to the P values. The CAG taxonomical annotation was performed as previously described (58).Dependency associationsDependency associations between CAGs were defined as described previously (32). This analysis relies on the detection of CAGs that are systematically associated with other CAGs in n = 396 HDs. Briefly, dependencies between CAGs were based on their sample-wise over-lapping detection. First, a Fisher exact test was used to identify statis-tically significant CAGs detection overlap. Second, the dependencies were validated only when the dependent CAGs were systematically ob-served with their associated host CAG. Thus, a dependent CAG, called satellite CAG, should never occur independently of the host CAG. Using these criteria, 886 dependencies were identified, most of which involved a small CAG as a satellite, corresponding to phage/bacteria or pan-genome/core-genome relationship. Moreover, 45 MGS- MGS dependency associations were detected, involving MGS as satellite and host microorganisms that may reflect bacterial symbiosis events. We have considered the 45 MGS-MGS dependency associations de-tected by Nielsen et al. (32) as a minimal obligatory network that should be retrieved in HDs, and we hypothesized that it could be disturbed in IgA deficiency. The presence of the 45 links was subsequently inves-tigated in our cohort, and for each link, we have determined the fre-quency of its presence in both groups. On the basis of our HD frequency distributions, we have determined the 99% confidence interval accord-ing to the  distribution law defining a threshold of frequency of 84%, below which the link was considered significantly decreased at t he pop-ulation level. The investigation of the 45-link presence- absence pro-files in our cohort allowed us to distinguish four possible situations. For each link and each individual, if the MGS satellite is present in the absence of the host MGS, we consider the link as lost. If the MGS satellite is present in the presence of the host MGS, then we consider the link as confirmed. If the MGS host is present in the absence of the satellite MGS or if the two are absent, then the situation is not taken into account (no information about the link).Statistical analysisLog2 ratio of IgM+/IgM− bacterial abundances was performed at the lowest taxonomic identification level (genus or family) to analyze IgM+ relative binding. Only dominant taxa were taken into account, that is, taxa that are present in at least 50% of the patients. Zero was normalized in pairs and adjusted to the lowest abundance of all taxa in the paired samples.Flow cytometry analysis was performed using FlowJo (9.3.2) TreeStar software and datamined with Funky Cells ToolBox software (version 0.1.2; www.FunkyCells.com). Statistical nonparametric tests were used whenever necessary: Mann-Whitney was used when com-paring two groups, Kruskal-Wallis with multiple comparisons post-test of Dunn’s was used when comparing three groups or more, Fisher’s exact test was used for contingency, Wilcoxon paired rank test was used for paired analysis, and Spearman coefficient was used for correlations. R v3.2.1 and GraphPad Prism version 6 were used to perform statistical analysis.SUPPLEMENTARY MATERIALSwww.sciencetranslationalmedicine.org/cgi/content/full/10/439/eaan1217/DC1Materials and MethodsFig. S1. Serum IgA and IgG concentration in IgAd patients.Fig. S2. Global gut microbiota composition.Fig. S3. Longitudinal stability of 24 IgA+ MGS abundances in three healthy subjects.Fig. S4. Differential CAG abundance analysis between healthy subjects and IgAd patients.Fig. S5. Differential CAG abundance analysis between IgA+ and IgA− microbes.Fig. S6. Gut microbiota IgA-binding patterns.Fig. S7. Gut microbiota IgA and IgM binding in CVID patients with complete IgA deficiency.Fig. S8. Model of the ecological impact of sIgA binding on commensals.Table S1. Individual clinical description of SIgAd patients.Data file S1. MGS-MGS dependency network confirmation in IgAd patients (Excel file).Data file S2. Thirty-three CAGs differentiating HDs and IgAd patients (Excel file).Data file S3. One hundred nineteen CAGs differentiating gut microbiota in vivo bound by sIgA or not in HDs (Excel file).References (59–71)REFERENCES AND NOTES 1. M. J. Molloy, J. R. Grainger, N. Bouladoux, T. W. Hand, L. Y. Koo, S. Naik, M. Quinones, A. K. Dzutsev, J.-L. Gao, G. Trinchieri, P. M. Murphy, Y. Belkaid, Intraluminal containment of commensal outgrowth in the gut during infection-induced dysbiosis. Cell Host Microbe 14, 318–328 (2013). 2. S. Hapfelmeier, M. A. E. Lawson, E. Slack, J. K. Kirundi, M. Stoel, M. Heikenwalder, J. Cahenzli, Y. Velykoredko, M. L. Balmer, K. Endt, M. B. Geuking, R. Curtiss III, K. D. McCoy, A. J. Macpherson, Reversible microbial colonization of germ-free mice reveals the dynamics of IgA immune responses. Science 328, 1705–1709 (2010). 3. L. V. Hooper, A. J. Macpherson, Immune adaptations that maintain homeostasis with the intestinal microbiota. Nat. Rev. Immunol. 10, 159–169 (2010). 4. K. Suzuki, B. Meek, Y. Doi, M. Muramatsu, T. Chiba, T. Honjo, S. Fagarasan, Aberrant expansion of segmented filamentous bacteria in IgA-deficient gut. Proc. Natl. Acad. Sci. U.S.A. 101, 1981–1986 (2004). 5. S. Fagarasan, M. Muramatsu, K. Suzuki, H. Nagaoka, H. Hiai, T. Honjo, Critical roles of activation-induced cytidine deaminase in the homeostasis of gut flora. Science 298, 1424–1427 (2002). 6. G. R. Harriman, M. Bogue, P. Rogers, M. Finegold, S. Pacheco, A. Bradley, Y. Zhang, I. N. Mbawuike, Targeted deletion of the IgA constant region in mice leads to IgA deficiency with alterations in expression of other Ig isotypes. J. Immunol. 162, 2521–2529 (1999). 7. D. H. Reikvam, M. Derrien, R. Islam, A. Erofeev, V. Grcic, A. Sandvik, P. Gaustad, L. A. Meza-Zepeda, F. L. Jahnsen, H. Smidt, F.-E. Johansen, Epithelial-microbial crosstalk in polymeric Ig receptor deficient mice. Eur. J. Immunol. 42, 2959–2970 (2012). 8. O. L. C. Wijburg, T. K. Uren, K. Simpfendorfer, F.-E. Johansen, P. Brandtzaeg, R. A. Strugnell, Innate secretory antibodies protect against natural Salmonella typhimurium infection. J. Exp. Med. 203, 21–26 (2006). 9. N. Lycke, L. Erlandsson, L. Ekman, K. Schön, T. Leanderson, Lack of J chain inhibits the transport of gut IgA and abrogates the development of intestinal antitoxic protection. J. Immunol. 163, 913–919 (1999). 10. M. Wei, R. Shinkura, Y. Doi, M. Maruya, S. Fagarasan, T. Honjo, Mice carrying a knock-in mutation of Aicda resulting in a defect in somatic hypermutation have impaired gut homeostasis and compromised mucosal defense. Nat. Immunol. 12, 264–270 (2011). 11. S. Kawamoto, T. H. Tran, M. Maruya, K. Suzuki, Y. Doi, Y. Tsutsui, L. M. Kato, S. Fagarasan, The inhibitory receptor PD-1 regulates IgA selection and bacterial composition in the gut. Science 336, 485–489 (2012). 12. S. Kawamoto, M. Maruya, L. M. Kato, W. Suda, K. Atarashi, Y. Doi, Y. Tsutsui, H. Qin, K. Honda, T. Okada, M. Hattori, S. Fagarasan, Foxp3+ T cells regulate immunoglobulin a selection and facilitate diversification of bacterial species responsible for immune homeostasis. Immunity 41, 152–165 (2014). 13. J. J. Bunker, T. M. Flynn, J. C. Koval, D. G. Shaw, M. Meisel, B. D. McDonald, I. E. Ishizuka, A. L. Dent, P. C. Wilson, B. Jabri, D. A. Antonopoulos, A. Bendelac, Innate and adaptive humoral responses coat distinct commensal bacteria with immunoglobulin A. Immunity 43, 541–553 (2015). 14. C. R. Stokes, J. F. Soothill, M. W. Turner, Immune exclusion is a function of IgA. Nature 255, 745–746 (1975). 15. L. Yel, Selective IgA deficiency. J. Clin. Immunol. 30, 10–16 (2010). 16. N. Wang, L. Hammarström, IgA deficiency: What is new? Curr. Opin. Allergy Clin. Immunol. 12, 602–608 (2012). 17. G. H. Jorgensen, A. Gardulf, M. I. Sigurdsson, S. T. Sigurdardottir, I. Thorsteinsdottir, S. Gudmundsson, L. Hammarström, B. R. Ludviksson, Clinical symptoms in adults with selective IgA deficiency: A case-control study. J. Clin. Immunol. 33, 742–747 (2013). 18. S. Koskinen, Long-term follow-up of health in blood donors with primary selective IgA deficiency. J. Clin. Immunol. 16, 165–170 (1996). at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE14 of 15 19. A. Aghamohammadi, T. Cheraghi, M. Gharagozlou, M. Movahedi, N. Rezaei, M. Yeganeh, N. Parvaneh, H. Abolhassani, Z. Pourpak, M. Moin, IgA deficiency: Correlation between clinical and immunological phenotypes. J. Clin. Immunol. 29, 130–136 (2009). 20. J. F. Ludvigsson, M. Neovius, L. Hammarström, Association between IgA deficiency other autoimmune conditions: A population-based matched cohort study. J. Clin. Immunol. 34, 444–451 (2014). 21. L. Mellander, J. Björkander, B. Carlsson, L. Hanson, Secretory antibodies in IgA-deficient and immunosuppressed individuals. J. Clin. Immunol. 6, 284–291 (1986). 22. P. Brandtzaeg, G. Karlsson, G. Hansson, B. Petruson, J. Björkander, L. A. Hanson, The clinical condition of IgA-deficient patients is related to the proportion of IgD- and IgM-producing cells in their nasal mucosa. Clin. Exp. Immunol. 67, 626–636 (1987). 23. W. B. Chodirker, T. B. Tomasi Jr., Gamma-globulins: Quantitative relationships in human serum and nonvascular fluids. Science 142, 1080–1081 (1963). 24. D. A. Peterson, N. P. McNulty, J. L. Guruge, J. I. Gordon, IgA response to symbiotic bacteria as a mediator of gut homeostasis. Cell Host Microbe 2, 328–339 (2007). 25. T. C. Cullender, B. Chassaing, A. Janzon, K. A. Kumar, C. E. Muller, J. J. Werner, L. T. Angenent, M. E. Bell, A. G. Hay, D. A. Peterson, J. Walter, M. Vijay-Kumar, A. T. Gewirtz, R. E. Ley, Innate and adaptive immunity interact to quench microbiome flagellar motility in the gut. Cell Host Microbe 14, 571–581 (2013). 26. N. W. Palm, M. R. de Zoete, T. W. Cullen, N. A. Barry, J. Stefanowski, L. Hao, P. H. Degnan, J. Hu, I. Peter, W. Zhang, E. Ruggiero, J. H. Cho, A. L. Goodman, R. A. Flavell, Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell 158, 1000–1010 (2014). 27. N. J. Mantis, N. Rol, B. Corthésy, Secretory IgA’s complex roles in immunity and mucosal homeostasis in the gut. Mucosal Immunol. 4, 603–611 (2011). 28. W. Al-Herz, A. Bousfiha, J.-L. Casanova, H. Chapel, M. E. Conley, C. Cunningham-Rundles, A. Etzioni, A. Fischer, J. L. Franco, R. S. Geha, L. Hammarström, S. Nonoyama, L. D. Notarangelo, H. D. Ochs, J. M. Puck, C. M. Roifman, R. Seger, M. L. K. Tang, Primary immunodeficiency diseases: An update on the classification from the International Union of Immunological Societies Expert Committee for Primary Immunodeficiency. Front. Immunol. 2, 54 (2011). 29. K. Moor, J. Fadlallah, A. Toska, D. Sterlin, M. L. Balmer, A. J. Macpherson, G. Gorochov, M. Larsen, E. Slack, Analysis of bacterial-surface-specific antibodies in body fluids using bacterial flow cytometry. Nat. Protoc. 11, 1531–1553 (2016). 30. J. Qin, R. Li, J. Raes, M. Arumugam, K. S. Burgdorf, C. Manichanh, T. Nielsen, N. Pons, F. Levenez, T. Yamada, D. R. Mende, J. Li, J. Xu, S. Li, D. Li, J. Cao, B. Wang, H. Liang, H. Zheng, Y. Xie, J. Tap, P. Lepage, M. Bertalan, J.-M. Batto, T. Hansen, D. Le Paslier, A. Linneberg, H. B. Nielsen, E. Pelletier, P. Renault, T. Sicheritz-Ponten, K. Turner, H. Zhu, C. Yu, S. Li, M. Jian, Y. Zhou, Y. Li, X. Zhang, S. Li, N. Qin, H. Yang, J. Wang, S. Brunak, J. Doré, F. Guarner, K. Kristiansen, O. Pedersen, J. Parkhill, J. Weissenbach; MetaHIT Consortium, P. Bork, S. D. Ehrlich, J. Wang, A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010). 31. M. Arumugam, J. Raes, E. Pelletier, D. Le Paslier, T. Yamada, D. R. Mende, G. R. Fernandes, J. Tap, T. Bruls, J.-M. Batto, M. Bertalan, N. Borruel, F. Casellas, L. Fernandez, L. Gautier, T. Hansen, M. Hattori, T. Hayashi, M. Kleerebezem, K. Kurokawa, M. Leclerc, F. Levenez, C. Manichanh, H. B. Nielsen, T. Nielsen, N. Pons, J. Poulain, J. Qin, T. Sicheritz-Ponten, S. Tims, D. Torrents, E. Ugarte, E. G. Zoetendal, J. Wang, F. Guarner, O. Pedersen, W. M. de Vos, S. Brunak, J. Doré; MetaHIT Consortium, J. Weissenbach, S. D. Ehrlich, P. Bork, Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011). 32. H. B. Nielsen, M. Almeida, A. S. Juncker, S. Rasmussen, J. Li, S. Sunagawa, D. R. Plichta, L. Gautier, A. G. Pedersen, E. Le Chatelier, E. Pelletier, I. Bonde, T. Nielsen, C. Manichanh, M. Arumugam, J.-M. Batto, M. B. Q. dos Santos, N. Blom, N. Borruel, K. S. Burgdorf, F. Boumezbeur, F. Casellas, J. Doré, P. Dworzynski, F. Guarner, T. Hansen, F. Hildebrand, R. S. Kaas, S. Kennedy, K. Kristiansen, J. R. Kultima, P. Léonard, F. Levenez, O. Lund, B. Moumen, D. Le Paslier, N. Pons, O. Pedersen, E. Prifti, J. Qin, J. Raes, S. Sørensen, J. Tap, S. Tims, D. W. Ussery, T. Yamada; MetaHIT Consortium, P. Renault, T. Sicheritz-Ponten, P. Bork, J. Wang, S. Brunak, S. D. Ehrlich, Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014). 33. N. Qin, F. Yang, A. Li, E. Prifti, Y. Chen, L. Shao, J. Guo, E. Le Chatelier, J. Yao, L. Wu, J. Zhou, S. Ni, L. Liu, N. Pons, J. M. Batto, S. P. Kennedy, P. Leonard, C. Yuan, W. Ding, Y. Chen, X. Hu, B. Zheng, G. Qian, W. Xu, S. D. Ehrlich, S. Zheng, L. Li, Alterations of the human gut microbiome in liver cirrhosis. Nature 513, 59–64 (2014). 34. F. Plaza Onate, J.-M. Batto, C. Juste, J. Fadlallah, C. Fougeroux, D. Gouas, N. Pons, S. Kennedy, F. Levenez, J. Dore, S. D. Ehrlich, G. Gorochov, M. Larsen, Quality control of microbiota metagenomics by k-mer analysis. BMC Genomics 16, 183 (2015). 35. P. Brandtzaeg, I. Fjellanger, S. T. Gjeruldsen, Immunoglobulin M: Local synthesis and selective secretion in patients with immunoglobulin A deficiency. Science 160, 789–791 (1968). 36. U. Klein, R. Küppers, K. Rajewsky, Evidence for a large compartment of IgM-expressing memory B cells in humans. Blood 89, 1288–1298 (1997). 37. A. L. Kau, J. D. Planer, J. Liu, S. Rao, T. Yatsunenko, I. Trehan, M. J. Manary, T.-C. Liu, T. S. Stappenbeck, K. M. Maleta, P. Ashorn, K. G. Dewey, E. R. Houpt, C.-S. Hsieh, J. I. Gordon, Functional characterization of IgA-targeted bacterial taxa from undernourished Malawian children that produce diet-dependent enteropathy. Sci. Transl. Med. 7, 276ra24 (2015). 38. J. D. Planer, Y. Peng, A. L. Kau, L. V. Blanton, I. M. Ndao, P. I. Tarr, B. B. Warner, J. I. Gordon, Development of the gut microbiota and mucosal IgA responses in twins and gnotobiotic mice. Nature 534, 263–266 (2016). 39. G. D’Auria, F. Peris-Bondia, M. Džunková, A. Mira, M. C. Collado, A. Latorre, A. Moya, Active and secreted IgA-coated bacterial fractions from the human gut reveal an under-represented microbiota core. Sci. Rep. 3, 3515 (2013). 40. M. Dzidic, T. Abrahamsson, A. Artacho, B. Björksten, M. C. Collado, A. Mira, M. Jenmalm, Aberrant IgA responses to the gut microbiota during infancy precede asthma and allergy development. J. Allergy Clin. Immunol. 139, 1017–1025.e14 (2017). 41. H. Sokol, B. Pigneur, L. Watterlot, O. Lakhdari, L. G. Bermúdez-Humarán, J.-J. Gratadoux, S. Blugeon, C. Bridonneau, J.-P. Furet, G. Corthier, C. Grangette, N. Vasquez, P. Pochart, G. Trugnan, G. Thomas, H. M. Blottière, J. Doré, P. Marteau, P. Seksik, P. Langella, Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl. Acad. Sci. U.S.A. 105, 16731–16736 (2008). 42. K. Machiels, M. Joossens, J. Sabino, V. De Preter, I. Arijs, V. Eeckhaut, V. Ballet, K. Claes, F. Van Immerseel, K. Verbeke, M. Ferrante, J. Verhaegen, P. Rutgeerts, S. Vermeire, A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis. Gut 63, 1275–1283 (2014). 43. H. N. Sokol, P. Seksik, J. P. Furet, O. Firmesse, I. Nion-Larmurier, L. Beaugerie, J. Cosnes, G. Corthier, P. Marteau, J. Dore, Low counts of Faecalibacterium prausnitzii in colitis microbiota. Inflamm. Bowel Dis. 15, 1183–1189 (2009). 44. E. Elinav, T. Strowig, A. L. Kau, J. Henao-Mejia, C. A. Thaiss, C. J. Booth, D. R. Peaper, J. Bertin, S. C. Eisenbarth, J. I. Gordon, R. A. Flavell, NLRP6 inflammasome regulates colonic microbial ecology and risk for colitis. Cell 145, 745–757 (2011). 45. B. van den Bogert, M. Meijerink, E. G. Zoetendal, J. M. Wells, M. Kleerebezem, Immunomodulatory properties of Streptococcus and Veillonella isolates from the human small intestine microbiota. PLOS ONE 9, e114277 (2014). 46. I. Mashima, F. Nakazawa, The influence of oral Veillonella species on biofilms formed by Streptococcus species. Anaerobe 28, 54–61 (2014). 47. M. Strach, M. Siedlar, D. Kowalczyk, M. Zembala, T. Grodzicki, Sepsis caused by Veillonella parvula infection in a 17-year-old patient with X-linked agammaglobulinemia (Bruton’s disease). J. Clin. Microbiol. 44, 2655–2656 (2006). 48. K. Atarashi, W. Suda, C. Luo, T. Kawaguchi, I. Motoo, S. Narushima, Y. Kiguchi, K. Yasuma, E. Watanabe, T. Tanoue, C. A. Thaiss, M. Sato, K. Toyooka, H. S. Said, H. Yamagami, S. A. Rice, D. Gevers, R. C. Johnson, J. A. Segre, K. Chen, J. K. Kolls, E. Elinav, H. Morita, R. J. Xavier, M. Hattori, K. Honda, Ectopic colonization of oral bacteria in the intestine drives TH1 cell induction and inflammation. Science 358, 359–365 (2017). 49. K. Moor, M. Diard, M. E. Sellin, B. Felmy, S. Y. Wotzka, A. Toska, E. Bakkeren, M. Arnoldini, F. Bansept, A. Dal Co, T. Völler, A. Minola, B. Fernandez-Rodriguez, G. Agatic, S. Barbieri, L. Piccoli, C. Casiraghi, D. Corti, A. Lanzavecchia, R. R. Regoes, C. Loverdo, R. Stocker, D. R. Brumley, W.-D. Hardt, E. Slack, High-avidity IgA protects the intestine by enchaining growing bacteria. Nature 544, 498–502 (2017). 50. S. Okai, F. Usui, S. Yokota, Y. Hori-i, M. Hasegawa, T. Nakamura, M. Kurosawa, S. Okada, K. Yamamoto, E. Nishiyama, H. Mori, T. Yamada, K. Kurokawa, S. Matsumoto, M. Nanno, T. Naito, Y. Watanabe, T. Kato, E. Miyauchi, H. Ohno, R. Shinkura, High-affinity monoclonal IgA regulates gut microbiota and prevents colitis in mice. Nat. Microbiol. 1, 16103 (2016). 51. A. Mathias, B. Corthésy, Recognition of gram-positive intestinal bacteria by hybridoma- and colostrum-derived secretory immunoglobulin A is mediated by carbohydrates. J. Biol. Chem. 286, 17239–17247 (2011). 52. G. Magri, L. Comerma, M. Pybus, J. Sintes, D. Lligé, D. Segura-Garzón, S. Bascones, A. Yeste, E. K. Grasset, C. Gutzeit, M. Uzzan, M. Ramanujam, M. C. van Zelm, R. Albero-Gonzále z, I. Vazquez, M. Iglesias, S. Serrano, L. Márquez, E. Mercade, S. Mehandru, A. Cerutti, Human secretory IgM emerges from plasma cells clonally related to gut memory B cells and targets highly diverse commensals. Immunity 47, 118–134.e8 (2017). 53. S. F. Jørgensen, M. Trøseid, M. Kummen, J. A. Anmarkrud, A. E. Michelsen, L. T. Osnes, K. Holm, M. L. Høivik, A. Rashidi, C. P. Dahl, M. Vesterhus, B. Halvorsen, T. E. Mollnes, R. K. Berge, B. Moum, K. E. A. Lundin, B. Fevang, T. Ueland, T. H. Karlsen, P. Aukrust, J. R. Hov, Altered gut microbiota profile in common variable immunodeficiency associates with levels of lipopolysaccharide and markers of systemic immune activation. Mucosal Immunol. 9, 1455–1465 (2016). 54. S. Agarwal, L. Mayer, Pathogenesis and treatment of gastrointestinal disease in antibody deficiency syndromes. J. Allergy Clin. Immunol. 124, 658–664 (2009). 55. T. Klemola, E. Savilahti, A. Arato, T. Ormälä, J. Partanen, C. Eland, S. Koskimies, Immunohistochemical findings in jejunal specimens from patients with IgA deficiency. Gut 37, 519–523 (1995). 56. M. Perreau, S. Vigano, F. Bellanger, C. Pellaton, G. Buss, D. Comte, T. Roger, C. Lacabaratz, P.-A. Bart, Y. Levy, G. Pantaleo, Exhaustion of bacteria-specific CD4 T cells and microbial translocation in common variable immunodeficiency disorders. J. Exp. Med. 211, 2033–2045 (2014). at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Fadlallah et al., Sci. Transl. Med. 10, eaan1217 (2018) 2 May 2018SCIENCE TRANSLATIONAL MEDICINE | RESEARCH ARTICLE15 of 15 57. C. Chenevier-Gobeaux, D. Borderie, N. Weiss, T. Mallet-Coste, Y.-E. Claessens, Presepsin (sCD14-ST), an innate immune response marker in sepsis. Clin. Chim. Acta 450, 97–103 (2015). 58. E. Le Chatelier, T. Nielsen, J. Qin, E. Prifti, F. Hildebrand, G. Falony, M. Almeida, M. Arumugam, J.-M. Batto, S. Kennedy, P. Leonard, J. Li, K. Burgdorf, N. Grarup, T. Jørgensen, I. Brandslund, H. B. Nielsen, A. S. Juncker, M. Bertalan, F. Levenez, N. Pons, S. Rasmussen, S. Sunagawa, J. Tap, S. Tims, E. G. Zoetendal, S. Brunak, K. Clément, J. Doré, M. Kleerebezem, K. Kristiansen, P. Renault, T. Sicheritz-Ponten, W. M. de Vos, J.-D. Zucker, J. Raes, T. Hansen; MetaHIT consortium, P. Bork, J. Wang, S. D. Ehrlich, O. Pedersen, E. Guedon, C. Delorme, S. Layec, G. Khaci, M. van de Guchte, G. Vandemeulebrouck, A. Jamet, R. Dervyn, N. Sanchez, E. Maguin, F. Haimet, Y. Winogradski, A. Cultrone, M. Leclerc, C. Juste, H. Blottière, E. Pelletier, D. LePaslier, F. Artiguenave, T. Bruls, J. Weissenbach, K. Turner, J. Parkhill, M. Antolin, C. Manichanh, F. Casellas, N. Boruel, E. Varela, A. Torrejon, F. Guarner, G. Denariaz, M. Derrien, J. E. van Hylckama Vlieg, P. Veiga, R. Oozeer, J. Knol, M. Rescigno, C. Brechot, C. M’Rini, A. Mérieux, T. Yamada, Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013). 59. D. M. Rissin, C. W. Kan, T. G. Campbell, S. C. Howes, D. R. Fournier, L. Song, T. Piech, P. P. Patel, L. Chang, A. J. Rivnak, E. P. Ferrell, J. D. Randall, G. K. Provuncher, D. R. Walt, D. C. Duffy, Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. Nat. Biotechnol. 28, 595–599 (2010). 60. J.-P. Pais de Barros, T. Gautier, W. Sali, C. Adrie, H. Choubley, E. Charron, C. Lalande, N. Le Guern, V. Deckert, M. Monchi, J.-P. Quenot, L. Lagrost, Quantitative lipopolysaccharide analysis using HPLC/MS/MS and its combination with the limulus amebocyte lysate assay. J. Lipid Res. 56, 1363–1369 (2015). 61. M. Miyara, Y. Yoshioka, A. Kitoh, T. Shima, K. Wing, A. Niwa, C. Parizot, C. Taflin, T. Heike, D. Valeyre, A. Mathian, T. Nakahata, T. Yamaguchi, T. Nomura, M. Ono, Z. Amoura, G. Gorochov, S. Sakaguchi, Functional delineation and differentiation dynamics of human CD4+ T cells expressing the FoxP3 transcription factor. Immunity 30, 899–911 (2009). 62. M. Miyara, D. Chader, E. Sage, D. Sugiyama, H. Nishikawa, D. Bouvry, L. Claër, R. Hingorani, R. Balderas, J. Rohrer, N. Warner, A. Chapelier, D. Valeyre, R. Kannagi, S. Sakaguchi, Z. Amoura, G. Gorochov, Sialyl Lewis x (CD15s) identifies highly differentiated and most suppressive FOXP3high regulatory T cells in humans. Proc. Natl. Acad. Sci. U.S.A. 112, 7225–7230 (2015). 63. C. Juste, D. P. Kreil, C. Beauvallet, A. Guillot, S. Vaca, C. Carapito, S. Mondot, P. Sykacek, H. Sokol, F. Blon, P. Lepercq, F. Levenez, B. Valot, W. Carré, V. Loux, N. Pons, O. David, B. Schaeffer, P. Lepage, P. Martin, V. Monnet, P. Seksik, L. Beaugerie, S. D. Ehrlich, J.-F. Gibrat, A. Van Dorsselaer, J. Doré, Bacterial protein signals are associated with Crohn’s disease. Gut 63, gutjnl-2012-303786 (2014). 64. J. J. Godon, E. Zumstein, P. Dabert, F. Habouzit, R. Moletta, Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis. Appl. Environ. Microbiol. 63, 2802–2813 (1997). 65. E. R. Mardis, The impact of next-generation sequencing technology on genetics. Trends Genet. 24, 133–141 (2008). 66. B. Langmead, Aligning short sequencing reads with Bowtie. Curr. Protoc. Bioinformatics 32, 11.7.1–11.7.14 (2010). 67. M.-A. Dillies, A. Rau, J. Aubert, C. Hennequet-Antier, M. Jeanmougin, N. Servant, C. Keime, G. Marot, D. Castel, J. Estelle, G. Guernec, B. Jagla, L. Jouneau, D. Laloë, C. Le Gall, B. Schaëffer, S. Le Crom, M. Guedj, F. Jaffrézic, A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform. 14, 671–683 (2013). 68. K. H. Wilson, R. B. Blitchington, R. C. Greene, Amplification of bacterial 16S ribosomal DNA with polymerase chain reaction. J. Clin. Microbiol. 28, 1942–1946 (1990). 69. J. G. Caporaso, J. Kuczynski, J. Stombaugh, K. Bittinger, F. D. Bushman, E. K. Costello, N. Fierer, A. G. Peña, J. K. Goodrich, J. I. Gordon, G. A. Huttley, S. T. Kelley, D. Knights, J. E. Koenig, R. E. Ley, C. A. Lozupone, D. McDonald, B. D. Muegge, M. Pirrung, J. Reeder, J. R. Sevinsky, P. J. Turnbaugh, W. A. Walters, J. Widmann, T. Yatsunenko, J. Zaneveld, R. Knight, QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010). 70. W. Li, A. Godzik, Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006). 71. J. R. Cole, Q. Wang, E. Cardenas, J. Fish, B. Chai, R. J. Farris, A. S. Kulam-Syed-Mohideen, D. M. McGarrell, T. Marsh, G. M. Garrity, J. M. Tiedje, The Ribosomal Database Project: Improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141–D145 (2009).Acknowledgments: We thank E. Slack and J.-M. Batto for discussions and advice, C. Fougeroux for assistance during lymphocyte and stool immunophenotyping initial setup, and J. R. Hov and S. F. Jorgensen for discussing their own unpublished microbiota diversity results in CVID patients. Funding: The study was financed by the following funding bodies: INSERM, Agence Nationale de la Recherche (ANR) (MetAntibody, ANR-14-CE14-0013), Fondation pour l’Aide a la Recherche sur la Sclerose En Plaques, and Université Pierre et Marie Curie Emergence (MycELIA). LPS measurements were supported by a French government grant managed by the French National Research Agency under the program \"Investissements d’Avenir” with reference ANR-11 LABX-0021. Author contributions: J.F., M. Malphettes, L.G., D.B., A.M., M. Miyara, E.O., Z.A., and C.F. recruited the patients and collected the clinical data. J.F., D.S., D.G., C.J., and M.L. prepared the biospecimens (aliquoting and purification of live stool commensals) and cryopreserved them to generate biobank. J.F., D.S., C.P., C.J., and M.L. enriched the Ig-bound commensals. F.L., S.K., N.G., and N.P. generated the whole-genome metagenomic data. G.A., M.L., and P.L. generated the 16S data. J.F., C.P., D.G., D.S., and M.L. performed the flow cytometry analysis of patient lymphocytes and commensals. J.F., D.S., and K.D. measured the cytokine and sCD14 levels in donor serum. J.-P.P.d.B. measured the LPS levels in donor serum. J.F, H.E.K., M.A., E.L.C., and M.L. performed the data mining and biostatistical analysis. J.F., H.E.K., M.L., and G.G. designed the study, prepared the figures, and wrote the manuscript. S.D.E., J.D., P.L., and C.J. reviewed the manuscript. S.D.E., J.D., and C.J. provided support for the design of the study. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The data presented in our study are available at http://IgAdef.immulab.fr.Submitted 7 March 2017Resubmitted 7 December 2017Accepted 12 March 2018Published 2 May 201810.1126/scitranslmed.aan1217Citation: J. Fadlallah, H. El Kafsi, D. Sterlin, C. Juste, C. Parizot, K. Dorgham, G. Autaa, D. Gouas, M. Almeida, P. Lepage, N. Pons, E. Le Chatelier, F. Levenez, S. Kennedy, N. Galleron, J.-P. P. de Barros, M. Malphettes, L. Galicier, D. Boutboul, A. Mathian, M. Miyara, E. Oksenhendler, Z. Amoura, J. Doré, C. Fieschi, S. D. Ehrlich, M. Larsen, G. Gorochov, Microbial ecology perturbation in human IgA deficiency. Sci. Transl. Med. 10, eaan1217 (2018). at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Microbial ecology perturbation in human IgA deficiencyand Guy GorochovMathian, Makoto Miyara, Eric Oksenhendler, Zahir Amoura, Joel Doré, Claire Fieschi, S. Dusko Ehrlich, Martin Larsen Kennedy, Nathalie Galleron, Jean-Paul Pais de Barros, Marion Malphettes, Lionel Galicier, David Boutboul, AlexisDoriane Gouas, Mathieu Almeida, Patricia Lepage, Nicolas Pons, Emmanuelle Le Chatelier, Florence Levenez, Sean Jehane Fadlallah, Hela El Kafsi, Delphine Sterlin, Catherine Juste, Christophe Parizot, Karim Dorgham, Gaëlle Autaa,DOI: 10.1126/scitranslmed.aan1217, eaan1217.10Sci Transl Med that, in humans, IgA is not solely responsible for controlling infections but does shape the microbiome.concluded that IgM could partially compensate for the lack of IgA in patients, but not entirely. Their results suggest were predominant in the patients. They investigated which bacteria were bound by different isotypes andindividuals in comparison to those deficient in IgA. Overall bacterial diversity was comparable, but different genera . examined the fecal microbiomes of healthyet alspecifically in IgA often have only mild symptoms. Fadlallah enforce the gut barrier to prevent dangerous bacteria from damaging the host. However, humans deficient IgA is the most abundant mucosal antibody, and experiments with animal models suggest that it mayIgA leads the way in the gutARTICLE TOOLS http://stm.sciencemag.org/content/10/439/eaan1217MATERIALSSUPPLEMENTARY http://stm.sciencemag.org/content/suppl/2018/04/30/10.439.eaan1217.DC1CONTENTRELATED http://stm.sciencemag.org/content/scitransmed/11/507/eaau9356.fullhttp://science.sciencemag.org/content/sci/360/6390/795.fullhttp://stm.sciencemag.org/content/scitransmed/8/343/343ra81.fullhttp://stm.sciencemag.org/content/scitransmed/9/376/eaaf9655.fullhttp://stm.sciencemag.org/content/scitransmed/7/276/276ra24.fullREFERENCES http://stm.sciencemag.org/content/10/439/eaan1217#BIBLThis article cites 71 articles, 22 of which you can access for freePERMISSIONS http://www.sciencemag.org/help/reprints-and-permissionsTerms of ServiceUse of this article is subject to the registered trademark of AAAS. is aScience Translational MedicineScience, 1200 New York Avenue NW, Washington, DC 20005. The title (ISSN 1946-6242) is published by the American Association for the Advancement ofScience Translational Medicine of Science. No claim to original U.S. Government WorksCopyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement at INSTITUT PASTEUR on January 17, 2020http://stm.sciencemag.org/Downloaded from Citations (117)References (76)... Paradoxically, IgA-deficiency is often asymptomatic or mild symptoms in humans, which intuitively challenge that pivotal role as a fundamental mechanism to recognize our commensal microbiome. The effect of IgA-deficiency on human microbiome composition has been addressed in mice-models 11 and in the human gut, fully ignoring the effect in other niches of our body, such as the oral mucosal and cavity. In the human gut, on one side, it has been proposed that IgA-deficient humans exhibit a gut microbiota dysbiosis 12 , while other experimental data support that IgA deficiency does not lead to massive, major perturbations in the gut microbiome 11 . ...... The effect of IgA-deficiency on human microbiome composition has been addressed in mice-models 11 and in the human gut, fully ignoring the effect in other niches of our body, such as the oral mucosal and cavity. In the human gut, on one side, it has been proposed that IgA-deficient humans exhibit a gut microbiota dysbiosis 12 , while other experimental data support that IgA deficiency does not lead to massive, major perturbations in the gut microbiome 11 . Furthermore, all these recent microbiome studies have fully ignored our extremely abundant commensal viruses. ...... The dominant source of the salivary microbiome is most likely bacterial communities on the mucosal surfaces 20 and it has been demonstrated that IgA not only serve for arresting pathogens at epithelial border but also to bind to commensal oral microbes and anchor them to our mucous barriers 7 www.nature.com/scientificreports/ the reported IgA role 7-9 , more profound changes would be expected in the oral microbiome. It has been proposed that IgM might compensate for a lack of secretory IgA, although recent data challenge that view, since IgM only partially rescue IgA deficiency 11,12 . Here, we have also included three CVID patients (one sample with undetectable IgM levels), which did not segregate more than other IgA-deficiency or control samples (e.g. ...Minimal-moderate variation of human oral virome and microbiome in IgA deficiencyArticleFull-text availableJul 2021Maria Jose de la Cruz Peña Luis Ignacio Gonzalez-Granado Inmaculada Garcia-Heredia Manuel Martinez GarciaImmunoglobulin A (IgA) is the dominant antibody found in our mucosal secretions and has long been recognized to play an important role in protecting our epithelium from pathogens. Recently, IgA has been shown to be involved in gut homeostatic regulation by ‘recognizing’ and shaping our commensal microbes. Paradoxically, yet selective IgA-deficiency is often described as asymptomatic and there is a paucity of studies only focused on the mice and human gut microbiome context fully ignoring other niches of our body and our commensal viruses. Here, we used as a model the human oral cavity and employed a holistic view and studied the impact of IgA deficiency and also common variable IgA and IgM immunodeficiencies (CVID), on both the human virome and microbiome. Unexpectedly, metagenomic and experimental data in human IgA deficiency and CVID indicate minimal-moderate changes in microbiome and virome composition compared to healthy control group and point out to a rather functional, resilient oral commensal viruses and microbes. However, a significant depletion (two fold) of bacterial cells (p-value 0.01) and viruses was observed in IgA-deficiency. Our results demonstrate that, within the limits of our cohort, IgA role is not critical for maintaining a rather functional salivary microbiome and suggest that IgA is not a major influence on the composition of abundant commensal microbes.ViewShow abstract... Since IgA is the dominant mucosal immunoglobulin maintaining intestinal homeostasis (10), CVID is expected to impact the gut microbiome. Indeed, several studies showed changes in bacterial composition of the gut associated with CVID and with severity of the disease (11)(12)(13)(14)(15). While previous studies analyzed CVID microbiome using sequencing of 16S rRNA gene, we performed metagenome deep-sequencing for identification of differences among bacterial species and genetic functions. ...... On higher taxonomic levels than species, we did not observe microbial enrichment in CVID, which agrees with the previous 16S rRNA microbiome studies showing that CVID patients, when selecting those without complications, did not differ in adiversity/richness from healthy controls (11,13). Moreover, a metagenomic analysis by Fadlallah et al. (15) did not find differences between controls and patients with selective IgA deficiency, which is likely a result of the fact that selective IgA deficiency could be considered a very mild, frequently asymptomatic, form of CVID and because the authors did not use controls living in the same household. On the other hand, Berbers et al. (40) showed enrichment of oropharyngeal microbiota in CVID patients and its association with severity of the immunodeficiency; however, this finding cannot be directly extrapolated to the fecal microbiome without additional experimental support. ...... Although we did not observe significant differences in relative abundance of bacterial phyla between CVID and control samples, a trend for expansion of Proteobacteria (5.19% vs. 1.49%, q=0.092) among patients was observed ( Figure S2), which is in accordance with increase of Proteobacteria taxa in previous studies (11,13). Moreover, the expansion of Proteobacteria could correspond to fact that IgA immunity preferentially targets Proteobacteria, e.g., E. coli (15,57). ...Patients With Common Variable Immunodeficiency (CVID) Show Higher Gut Bacterial Diversity and Levels of Low-Abundance Genes Than the Healthy HousematesArticleFull-text availableMay 2021 Juraj Bosak Matej Lexa Kristyna Fiedorova D. SmajsCommon variable immunodeficiency (CVID) is a clinically and genetically heterogeneous disorder with inadequate antibody responses and low levels of immunoglobulins including IgA that is involved in the maintenance of the intestinal homeostasis. In this study, we analyzed the taxonomical and functional metagenome of the fecal microbiota and stool metabolome in a cohort of six CVID patients without gastroenterological symptomatology and their healthy housemates. The fecal microbiome of CVID patients contained higher numbers of bacterial species and altered abundance of thirty-four species. Hungatella hathewayi was frequent in CVID microbiome and absent in controls. Moreover, the CVID metagenome was enriched for low-abundance genes likely encoding nonessential functions, such as bacterial motility and metabolism of aromatic compounds. Metabolomics revealed dysregulation in several metabolic pathways, mostly associated with decreased levels of adenosine in CVID patients. Identified features have been consistently associated with CVID diagnosis across the patients with various immunological characteristics, length of treatment, and age. Taken together, this initial study revealed expansion of bacterial diversity in the host immunodeficient conditions and suggested several bacterial species and metabolites, which have potential to be diagnostic and/or prognostic CVID markers in the future.ViewShow abstract... Fecal bacteria were purified by gradient purification as previously described. 18,19 Bacterial extracts were suspended in 1xPBS (phosphate buffer saline)-10% glycerol, immediately frozen in liquid nitrogen, and then stored at −80°C. Genomic DNA was extracted from whole stool samples as previously described. ...... We first compared IgA-bound fecal microbiota levels in healthy donors and patients with MS. In accordance with previous studies, 19 we observed in the control group used for the present study that IgA binds a median percentage of 7.6% We then evaluated whether the IgA/ microbiota interface could be more perturbed in severely affected patients. Disease severity at the time of serum and microbiota sampling was evaluated using the Extended Disability Status Scale (EDSS). ...Perturbed Microbiota/Immune Homeostasis in Multiple SclerosisArticleFull-text availableJul 2021Delphine Sterlin Martin LarsenJehane FadlallahGuy GorochovObjectiveBased on animal models and human studies, there is now strong suspicion that host/microbiota mutualism in the context of gut microbial dysbiosis could influence immunity and multiple sclerosis (MS) evolution. Our goal was to seek evidence of deregulated microbiota-induced systemic immune responses in patients with MS.MethodsWe investigated gut and systemic commensal-specific antibody responses in healthy controls (n = 32), patients with relapsing-remitting MS (n = 30), and individuals with clinically isolated syndromes (CISs) (n = 15). Gut microbiota composition and diversity were compared between controls and patients by analysis of 16S ribosomal ribonucleic acid (rRNA) sequencing. Autologous microbiota and cultivable bacterial strains were used in bacterial flow cytometry assays to quantify autologous serum IgG and secretory IgA responses to microbiota. IgG-bound bacteria were sorted by flow cytometry and identified using 16S rRNA sequencing.ResultsWe show that commensal-specific gut IgA responses are drastically reduced in patients with severe MS, disease severity being correlated with the IgA-coated fecal microbiota fraction ( r = −0.647, p 0.0001). At the same time, IgA-unbound bacteria elicit qualitatively broad and quantitatively increased serum IgG responses in patients with MS and CIS compared with controls (4.1% and 2.5% vs 1.9%, respectively, p 0.001).ConclusionsGut and systemic microbiota/immune homeostasis are perturbed in MS. Our results argue that defective IgA responses in MS are linked to a breakdown of systemic tolerance to gut microbiota leading to an enhanced triggering of systemic IgG immunity against gut commensals occurring early in MS.ViewShow abstract... In one example, natural IgA protects against Salmonella Thyphimurium and against necrotizing enterocolitis in newborn infants (42,43). IgA may also protect against pathogens by indirect actions such as enhancing colonization by protective (anti-inflammatory) commensal species (44). For example, Bacteroides fragilis capsule induces specific IgA which in turn, increases its adherence to intestinal epithelial cells (33), and 20 commensals such as Bacteroides thetaiotaomicron utilize IgA to support mutualism (41). ...TNFRSF13B polymorphisms counteract microbial adaptation to natural IgAArticleFull-text availableJun 2021Jeffrey L. Platt Mayara Garcia de Mattos BarbosaDaniel Huynh Marilia CascalhoTNFRSF13B encodes the transmembrane-activator and CAML-interactor (TACI) receptor, which drives plasma cell differentiation. Although TNFRSF13B supports host defense, dominant-negative TNFRSF13B alleles are common in humans and other species and only rarely associate with disease. We reasoned the high frequency of disruptive TNFRSF13B alleles reflects balancing selection, the loss of function conferring advantage in some settings. Testing that concept, we asked whether and how a common human dominant negative variant, TNFRSF13B A181E, imparts resistance to enteric pathogens. Mice engineered to express mono-allelic or bi-allelic A144E variants of tnrsf13B, corresponding to A181E exhibited striking resistance to pathogenicity and transmission of C. rodentium, a murine pathogen that models enterohemorrhagic E. coli, and resistance was principally owed to deficiency of natural IgA in the intestine. In wild type mice with gut IgA and in mutant mice fed IgA, binding of Ig induces expression of LEE encoded virulence genes, which confer pathogenicity and transmission. C. rodentium and probably some other enteric organisms thus appropriated binding of otherwise protective antibodies to signal induction of the virulence program and the high prevalence of TNFRSF13B dominant negative variants thus reflects balancing selection.ViewShow abstract... On the other hand, previous studies have found that Candida albicans is signi cantly associated with dental caries (35,36) and periodontal disease (37) through induction of oral microbial dysbiosis (38) and in ammatory responses (39). Immunode ciency is also a predisposing factor for oral microbial dysbiosis (40,41) and periodontal diseases (42). Similarly, we demonstrated that patients with immunode ciency or systemic disease had a high prevalence rate of E. gingivalis infection (64%). ...Current global Status and the Epidemiology of Entamoeba gingivalis in humans: a systematic review and meta-analysisPreprintFull-text availableMay 2021Milad Badri Aida Vafae Eslahi Meysam OlfatifarPurpose Entamoeba gingivalis (E. gingivalis) is one of the members of the wide range of oral resident pathogens in humans, particularly found in dental plaques, surfaces of gingiva or teeth, interdental spaces and carious lesions. The purpose of the current review and meta-analysis was to determine the global prevalence of E. gingivalis infection and its association with oral diseases based on published literatures.Methods Multiple English databases (PubMed, Scopus, Science Direct, Web of Science and Google Scholar) were explored for papers published until August 2020. A total of 52 studies (including 7596 participants) met the inclusion criteria.Results The overall prevalence of E. gingivalis was estimated to be 37% (95% CI: 29% - 46%). With regard to different countries, the highest and lowest pooled prevalence of E. gingivalis infection were related to Jordan with 87% (95% CI: 81% - 92%) and Portugal with 3% (95% CI: 0% - 10%), respectively. Based on WHO regions, the highest prevalence was related to the region of the Americas with 56% (95% CI: 31%-79%). The infection was most prevalent in 46-55 mean age groups [61% (95% CI: 21% - 94%)]. Among different diagnostic methods, the highest rate of the pooled prevalence was related to the molecular [53% (95% CI: 24% - 81%)] and the direct methods [36% (95% CI: 25% - 47%)], respectively. Our analyses revealed that E. gingivalis infection was associated with 4.34-fold increased risk of oral diseases (P 0.05).Conclusion Our findings revealed a high prevalence rate of the infection among periodontal disease patients with 37% (95% CI: 20% - 57%). To conclude, it must be considered that E. gingivalis can be a risk factor associated with oral diseases and a wide range of research is needed to specify its role in the pathogenesis of these disorders.ViewShow abstract... These two strategies are referred to as Nested and Standard throughout the manuscript. The semi-nested PCR was performed as previously described 40 Bioinformatics for 16S rRNA gene analysis. Further processing of demultiplexed sequence reads followed the DADA2 workflow for Big Data (https:// benjj neb. ...Refinement of 16S rRNA gene analysis for low biomass biospecimensArticleFull-text availableMay 2021 Rémy VilletteGaëlle AutaaSophie Hind Martin LarsenHigh-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, high-throughput amplicon sequencing and bioinformatic analysis. Low biomass microbiota samples (e.g. biopsies, tissue swabs and lavages) are receiving increasing attention, but optimal standardization for analysis of low biomass samples has yet to be developed. Here we tested the lower bacterial concentration required to perform 16S rRNA gene analysis using three different DNA extraction protocols, three different mechanical lysing series and two different PCR protocols. A mock microbiota community standard and low biomass samples (108, 107, 106, 105 and 104 microbes) from two healthy donor stools were employed to assess optimal sample processing for 16S rRNA gene analysis using paired-end Illumina MiSeq technology. Three DNA extraction protocols tested in our study performed similar with regards to representing microbiota composition, but extraction yield was better for silica columns compared to bead absorption and chemical precipitation. Furthermore, increasing mechanical lysing time and repetition did ameliorate the representation of bacterial composition. The most influential factor enabling appropriate representation of microbiota composition remains sample biomass. Indeed, bacterial densities below 106 cells resulted in loss of sample identity based on cluster analysis for all tested protocols. Finally, we excluded DNA extraction bias using a genomic DNA standard, which revealed that a semi-nested PCR protocol represented microbiota composition better than classical PCR. Based on our results, starting material concentration is an important limiting factor, highlighting the need to adapt protocols for dealing with low biomass samples. Our study suggests that the use of prolonged mechanical lysing, silica membrane DNA isolation and a semi-nested PCR protocol improve the analysis of low biomass samples. Using the improved protocol we report a lower limit of 106 bacteria per sample for robust and reproducible microbiota analysis.ViewShow abstract... IgA binding can potentially also alter the functionality of obesityassociated microbes, such as global gene expression patterns, affecting their motility and local occupancy within the intestinal environment 79 . Although the antibody isotype IgM can partially compensate for a loss in IgA in some circumstances, humans with selective IgA deficiency (a form of primary immunodeficiency disease) have an altered intestinal microbiota despite secreting compensatory IgM 80,81 . Nonetheless, IgM is polyreactive to many bacterial species and whether intestinal production and functionality of this antibody is also altered during metabolic disease has not been examined. ...Emerging concepts in intestinal immune control of obesity-related metabolic diseaseArticleFull-text availableDec 2021Saad KhanHelen LuckShawn WinerDaniel A. WinerThe intestinal immune system is an important modulator of glucose homeostasis and obesity-associated insulin resistance. Dietary factors, the intestinal microbiota and their metabolites shape intestinal immunity during obesity. The intestinal immune system in turn affects processes such as intestinal permeability, immune cell trafficking, and intestinal hormone availability, impacting systemic insulin resistance. Understanding these pathways might identify mechanisms underlying treatments for insulin resistance, such as metformin and bariatric surgery, or aid in developing new therapies and vaccination approaches. Here, we highlight evolving concepts centered on intestinal immunity, diet, and the microbiota to provide a working model of obesity-related metabolic disease.ViewShow abstractGut IgA puts pathogens under pressureArticleJul 2021 Reiko ShinkuraSome gut bacterial pathogens can escape antibody-mediated immunity by changing surface-exposed antigens, such as O-antigens. But by using vaccines targeting specific O-antigens to induce immunoglobulin A responses in the gut, such pathogens can also be directed to evolve towards expressing O-antigen variants that impair gut colonization.ViewShow abstractImmunohistochemical distribution of Immunoglobulin-A in relation to the intestinal microbiota of Cairina moschata (Muscovy) duckArticleFull-text availableJun 2021J Phys Conf R. Susanti Wulan Christijanti Ari YuniastutiThe intestinal mucosal was a physical barrier of host defense against foreign pathogens. This research was conducted to elaborate the distribution of IgA and its relation to the abundance of muscovy duck intestinal microbes. Muscovy duck samples were obtained from local community farms in Gunungpati Subdistrict, Semarang City, which were maintained in the barn (non-cage). Each muscovy duck sample was slaughtered and dissected the chest cavity to the abdomen and then taken the intestinal organs. A total of 5g of intestinal contents was taken aseptically and used for NGS analysis. Furthermore, intestinal tissue was made into histology slides for immunohistochemical IgA analysis. The results of the immunohistochemical analysis showed that the IRS score of the small and large intestine were 4 (moderate). Muscovy duck in this study was healthy or normal, so the IRS score was in position 4 (moderate). Intestinal bacteria were dominated by Firmicutes phyla (48.71%), followed by Proteobacteria (32.87%) and Actinobacteria (8.32%). At the ordo level, bacterial composition was dominated by the ordo Enterobacteriales (32.08%), Clostridiales (21.04%), Bacillales (14.84%) and Lactobacillales (13.41%). In this intestinal muscovy duck, there was an equilibrium of microbiota components and there was no exogenous microorganisms that stimulate the overexpression of IgA production.ViewShow abstractCurrent Global Status and the Epidemiology of Entamoeba gingivalis in Humans: A Systematic Review and Meta-analysisArticleFull-text availableMay 2021ACTA PARASITOL Milad Badri Meysam Olfatifar Amir Abdoli Aida Vafae EslahiPurposeEntamoeba gingivalis (E. gingivalis) is one of the members of the wide range of oral resident pathogens in humans, particularly found in dental plaques, surfaces of gingiva or teeth, interdental spaces and carious lesions. The purpose of the current review and meta-analysis was to determine the global prevalence of E. gingivalis infection and its association with oral diseases based on published literatures.Materials and MethodsMultiple English databases (PubMed, Scopus, Science Direct, Web of Science and Google Scholar) were explored for papers published until August 2020. A total of 52 studies (including 7596 participants) met the inclusion criteria.ResultsThe overall prevalence of E. gingivalis was estimated to be 37% (95% CI 29–46%). With regard to different countries, the highest and lowest pooled prevalence of E. gingivalis infection were related to Jordan with 87% (95% CI 81–92%) and Portugal with 3% (95% CI 0–10%), respectively. Based on WHO regions, the highest prevalence was related to the region of the Americas with 56% (95% CI 31–79%). The infection was most prevalent in 46–55 mean age groups [61% (95% CI 21–94%)]. Among different diagnostic methods, the highest rate of the pooled prevalence was related to the molecular [53% (95% CI 24–81%)] and the direct methods [36% (95% CI 25–47%)], respectively. Our analyses revealed that E. gingivalis infection was associated with 4.34-fold increased risk of oral diseases (P 0.05).ConclusionOur findings revealed a high prevalence rate of the infection among periodontal disease patients with 37% (95% CI 20–57%). To conclude, it must be considered that E. gingivalis can be a risk factor associated with oral diseases and a wide range of research is needed to specify its role in the pathogenesis of these disorders.ViewShow abstractShow moreIgA Deficiency: Correlation Between Clinical and Immunological PhenotypesArticleFull-text availableAug 2008J Clin ImmunolSpringer Science + BusinessMedia Asghar Aghamohammadi Mostafa MoinIgA deficiency (IGAD) is the most common primary antibody deficiency. Although many affected individuals have no apparent symptom, selected patients suffer from recurrent mucosal infections, allergies, and autoimmune diseases. We aimed to investigate the clinical features in relation to immune function of Iranian patients with symptomatic IGAD.Thirty-seven patients (21 male and 16 female), aged 4-32 years, were evaluated in this study. Patients were followed for a total of 131 patient years with a mean follow-up of 3.5 years per patient.The most prevalent presentations were recurrent infections occurring in 27 subjects, followed by allergy in eight cases and autoimmunity in two patients. However, during the follow-up period, 35 patients developed infections in respiratory and gastrointestinal tracts, necessitating medical care. Apart from infections, allergy was the most frequent complaint (31 cases); the major features were asthma, atopic dermatitis, and allergic rhinoconjunctivitis. Autoimmune diseases were documented in ten cases; thyroiditis was the most common. In 31 patients who received unconjugated pneumococcal polyvalent vaccine, antibody response against polysaccharide antigen was measured before and 28 days after vaccination. One fourth of vaccinated patients were hyporesponsive to vaccine; four of these patients developed bronchiectasis. The patients with IGAD were classified into two groups: group 1 (14 cases) consisted of patients with IGAD and other associated immune defects, such as immunoglobulin G (IgG) subclass deficiency and defective specific antibody production. Group 2 (23 cases) had isolated IGAD without other immunological abnormalities. There was a significantly increased number of lower respiratory tract infections in group 1 compared with group 2 (P = 0.006). Moreover, four patients of group 1 had bronchiectasis whereas none of the patients in group 2 developed this complication (P = 0.015).Subclassification of IGAD regarding the existence of associated immune defects is useful in terms of morbidity and planning for medical care. IgA-deficient patients with concomitant immune defects such as defects in specific antibody production have higher rates of recurrent infections and bronchiectasis, which necessitates more effective monitoring.ViewShow abstractHuman Secretory IgM Emerges from Plasma Cells Clonally Related to Gut Memory B Cells and Targets Highly Diverse CommensalsArticleFull-text availableJul 2017IMMUNITYGiuliana Magri Laura ComermaMarc PybusAndrea CeruttiSecretory immunoglobulin A (SIgA) enhances host-microbiota symbiosis, whereas SIgM remains poorly understood. We found that gut IgM⁺ plasma cells (PCs) were more abundant in humans than mice and clonally related to a large repertoire of memory IgM⁺ B cells disseminated throughout the intestine but rare in systemic lymphoid organs. In addition to sharing a gut-specific gene signature with memory IgA⁺ B cells, memory IgM⁺ B cells were related to some IgA⁺ clonotypes and switched to IgA in response to T cell-independent or T cell-dependent signals. These signals induced abundant IgM which, together with SIgM from clonally affiliated PCs, recognized mucus-embedded commensals. Bacteria recognized by human SIgM were dually coated by SIgA and showed increased richness and diversity compared to IgA-only-coated or uncoated bacteria. Thus, SIgM may emerge from pre-existing memory rather than newly activated naive IgM⁺ B cells and could help SIgA to anchor highly diverse commensal communities to mucus.ViewShow abstractAberrant IgA responses to the gut microbiota during infancy precede asthma and allergy developmentArticleFull-text availableAug 2016J Allergy Clin Immunol Majda Dzidic Thomas R Abrahamsson Alejandro Artacho Maria C JenmalmBackground: While a reduced gut microbiota diversity and low mucosal total IgA levels in infancy have been associated with allergy development, IgA responses to the gut microbiota have not yet been studied.Objective: We sought to determine the proportions of IgA coating together with the characterization of the dominant bacteria, bound to IgA or not, in infant stool samples in relation to allergy development.Methods: A combination of flow cytometry cell sorting and deep sequencing of the 16S rDNA gene was used to characterize the bacterial recognition patterns by IgA in stool samples collected at 1 and 12 month of age from children staying healthy or developing allergic symptoms up to seven years of age.Results: The children developing allergic manifestations, particularly asthma, during childhood had a lower proportion of IgA bound to fecal bacteria at 12 months of age compared with healthy children. These alterations cannot be attributed to differences in IgA levels or bacterial load between the two groups. Moreover, the bacterial targets of early IgA responses (including the coating of Bacteroides genus) as well as the IgA recognition patterns differed between healthy children and children developing allergic manifestations. Altered IgA recognition patterns in children developing allergy were observed also already at 1 month of age, when the IgA antibodies are predominantly maternally derived in breast fed children.Conclusion: An aberrant IgA responsiveness to the gut microbiota during infancy precedes asthma and allergy development, possibly indicating an impaired mucosal barrier function in allergic children.Key message: Aberrant and reduced IgA responses to the gut microbiota during infancy precede development of asthma and allergic disease during the first seven years of life.ViewShow abstractHigh-affinity monoclonal IgA regulates gut microbiota and prevents colitis in miceArticleFull-text availableJul 2016Shinsaku OkaiFumihito UsuiShuhei Yokota Reiko ShinkuraImmunoglobulin A (IgA) is the main antibody isotype secreted into the intestinal lumen. IgA plays a critical role in the defence against pathogens and in the maintenance of intestinal homeostasis. However, how secreted IgA regulates gut microbiota is not completely understood. In this study, we isolated monoclonal IgA antibodies from the small intestine of healthy mouse. As a candidate for an efficient gut microbiota modulator, we selected a W27 IgA, which binds to multiple bacteria, but not beneficial ones such as Lactobacillus casei. W27 could suppress the cell growth of Escherichia coli but not L. casei in vitro, indicating an ability to improve the intestinal environment. Indeed W27 oral treatment could modulate gut microbiota composition and have a therapeutic effect on both lymphoproliferative disease and colitis models in mice. Thus, W27 IgA oral treatment is a potential remedy for inflammatory bowel disease, acting through restoration of host–microbial symbiosis.ViewShow abstractEctopic colonization of oral bacteria in the intestine drives T H 1 cell induction and inflammationArticleOct 2017SCIENCE Koji Atarashi Wataru SudaChengwei Luo Kenya HondaIntestinal colonization by bacteria of oral origin has been correlated with several negative health outcomes, including inflammatory bowel disease. However, a causal role of oral bacteria ectopically colonizing the intestine remains unclear. Using gnotobiotic techniques, we show that strains of Klebsiella spp. isolated from the salivary microbiota are strong inducers of T helper 1 (TH1) cells when they colonize in the gut. These Klebsiella strains are resistant to multiple antibiotics, tend to colonize when the intestinal microbiota is dysbiotic, and elicit a severe gut inflammation in the context of a genetically susceptible host. Our findings suggest that the oral cavity may serve as a reservoir for potential intestinal pathobionts that can exacerbate intestinal disease.ViewShow abstractHigh-avidity IgA protects the intestine by enchaining growing bacteriaArticleApr 2017NATURE Kathrin Schumann-Moor Diard Médéric Mikael E Sellin Emma SlackVaccine-induced high-avidity IgA can protect against bacterial enteropathogens by directly neutralizing virulence factors or by poorly defined mechanisms that physically impede bacterial interactions with the gut tissues ( immune exclusion ). IgA-mediated cross-linking clumps bacteria in the gut lumen and is critical for protection against infection by non-typhoidal Salmonella enterica subspecies enterica serovar Typhimurium (S. Typhimurium). However, classical agglutination, which was thought to drive this process, is efficient only at high pathogen densities (≥10(8) non-motile bacteria per gram). In typical infections, much lower densities (10(0)-10(7) colony-forming units per gram) of rapidly dividing bacteria are present in the gut lumen. Here we show that a different physical process drives formation of clumps in vivo: IgA-mediated cross-linking enchains daughter cells, preventing their separation after division, and clumping is therefore dependent on growth. Enchained growth is effective at all realistic pathogen densities, and accelerates pathogen clearance from the gut lumen. Furthermore, IgA enchains plasmid-donor and -recipient clones into separate clumps, impeding conjugative plasmid transfer in vivo. Enchained growth is therefore a mechanism by which IgA can disarm and clear potentially invasive species from the intestinal lumen without requiring high pathogen densities, inflammation or bacterial killing. Furthermore, our results reveal an untapped potential for oral vaccines in combating the spread of antimicrobial resistance.ViewShow abstractAnalysis of bacterial-surface-specific antibodies in body fluids using bacterial flow cytometryArticleAug 2016Nat Protocol Kathrin Schumann-MoorJehane Fadlallah Lena Toska Emma SlackAntibacterial antibody responses that target surfaces of live bacteria or secreted toxins are likely to be relevant in controlling bacterial pathogenesis. The ability to specifically quantify bacterial-surface-binding antibodies is therefore highly attractive as a quantitative correlate of immune protection. Here, binding of antibodies from various body fluids to pure-cultured live bacteria is made visible with fluorophore-conjugated secondary antibodies and measured by flow cytometry. We indicate the necessary controls for excluding nonspecific binding and also demonstrate a cross-adsorption technique for determining the extent of cross-reactivity. This technique has numerous advantages over standard ELISA and western blotting techniques because of its independence from scaffold binding, exclusion of cross-reactive elements from lysed bacteria and ability to visualize bacterial subpopulations. In addition, less than 10(5) bacteria and less than 10 μg of antibody are required per sample. The technique requires 3-4 h of hands-on experimentation and analysis. Moreover, it can be combined with automation and mutliplexing for high-throughput applications.ViewShow abstractDevelopment of the gut microbiota and mucosal IgA responses in twins and gnotobiotic miceArticleMay 2016NATUREJoseph D. Planer Yangqing PengAndrew L. KauJeffrey I GordonImmunoglobulin A (IgA), the major class of antibody secreted by the gut mucosa, is an important contributor to gut barrier function(1-3). The repertoire of IgA bound to gut bacteria reflects both T-cell-dependent and -independent pathways(4,5), plus glycans present on the antibody s secretory component(6). Human gut bacterial taxa targeted by IgA in the setting of barrier dysfunction are capable of producing intestinal pathology when isolated and transferred to gnotobiotic mice(7,8). A complex reorientation of gut immunity occurs as infants transition from passively acquired IgA present in breast milk to host-derived IgA(9-11). How IgA responses co-develop with assembly of the microbiota during this period remains poorly understood. Here, we (1) identify a set of age-discriminatory bacterial taxa whose representations define a program of microbiota assembly and maturation during the first 2 postnatal years that is shared across 40 healthy twin pairs in the USA; (2) describe a pattern of progression of gut mucosal IgA responses to bacterial members of the microbiota that is highly distinctive for family members (twin pairs) during the first several postnatal months then generalizes across pairs in the second year; and (3) assess the effects of zygosity, birth mode, and breast feeding. Age-associated differences in these IgA responses can be recapitulated in young germ-free mice, colonized with faecal microbiota obtained from two twin pairs at 6 and 18 months of age, and fed a sequence of human diets that simulate the transition from milk feeding to complementary foods. Most of these responses were robust to diet, suggesting that intrinsic properties of community members play a dominant role in dictating IgA responses. The approach described can be used to define gut mucosal immune development in health and disease states and to help discover ways of repairing or preventing perturbations in this facet of host immunity.ViewShow abstractAltered gut microbiota profile in common variable immunodeficiency associates with levels of lipopolysaccharide and markers of systemic immune activationArticleMar 2016MUCOSAL IMMUNOLSilje F Jørgensen Marius TrøseidMartin Kummen Johannes R HovCommon variable immunodeficiency (CVID) is the most common symptomatic primary immunodeficiency characterized by low immunoglobulin (Ig)G and IgA, and/or IgM. In addition to bacterial infections, a large subgroup has noninfectious inflammatory and autoimmune complications. We performed 16S ribosomal RNA-based profiling of stool samples in 44 CVID patients, 45 patients with inflammatory bowel disease (disease controls), and 263 healthy controls. We measured plasma lipopolysaccharide (LPS) and markers of immune cell activation (i.e., soluble (s) CD14 and sCD25) in an expanded cohort of 104 patients with CVID and in 30 healthy controls. We found a large shift in the microbiota of CVID patients characterized by a reduced within-individual bacterial diversity (alpha diversity, P 0.001) without obvious associations to antibiotics use. Plasma levels of both LPS (P=0.001) and sCD25 (P 0.0001) were elevated in CVID, correlating negatively with alpha diversity and positively with a dysbiosis index calculated from the taxonomic profile. Low alpha diversity and high dysbiosis index, LPS, and immune markers were most pronounced in the subgroup with inflammatory and autoimmune complications. Low level of IgA was associated with decreased alpha diversity, but not independently from sCD25 and LPS. Our findings suggest a link between immunodeficiency, systemic immune activation, LPS, and altered gut microbiota.ViewShow abstractInnate and Adaptive Humoral Responses Coat Distinct Commensal Bacteria with Immunoglobulin AArticleAug 2015IMMUNITYJeffrey J. Bunker Theodore M FlynnJason C. KovalAlbert BendelacImmunoglobulin A (IgA) is prominently secreted at mucosal surfaces and coats a fraction of the intestinal microbiota. However, the commensal bacteria bound by IgA are poorly characterized and the type of humoral immunity they elicit remains elusive. We used bacterial flow cytometry coupled with 16S rRNA gene sequencing (IgA-Seq) in murine models of immunodeficiency to identify IgA-bound bacteria and elucidate mechanisms of commensal IgA targeting. We found that residence in the small intestine, rather than bacterial identity, dictated induction of specific IgA. Most commensals elicited strong T-independent (TI) responses that originated from the orphan B1b lineage and from B2 cells, but excluded natural antibacterial B1a specificities. Atypical commensals including segmented filamentous bacteria and Mucispirillum evaded TI responses but elicited T-dependent IgA. These data demonstrate exquisite targeting of distinct commensal bacteria by multiple layers of humoral immunity and reveal a specialized function of the B1b lineage in TI mucosal IgA responses.Copyright © 2015 Elsevier Inc. All rights reserved.ViewShow abstractShow moreAdvertisementRecommendationsDiscover more about: IgA DeficiencyProjectGut immunity in health and disease Martin LarsenMore info: www.immulab.fr and www.Funkycells.comBriefly, I aim to identify distorted gut microbial immunity and investigate how such distortions may affect gut microbial metabolism as well as syst emic host immunity in the context of human autoimmunity and allergic diseases. ... [more]View projectProjectHost/microbiota relation through IgA regulation. Rémy Villette Martin LarsenInquire host/microbiota relation in immune system of gut, focusing on IgA coating of microbiota and human metabolome. View projectProjectAutoimmune diseases Karim DorghamView projectProjectClinical Study of the Human Microbiota and its Role in Health and Disease Sean Kennedy Kenzo-Hugo Hillion Claire PoyartWork with hospitals and clinicians to establish cohorts for metagenomic analysis of the human microbiota using high-throughput -omics technologies. Use of statistics and machine learning to uncover biomarkers associated with health and disease states. Development of actionable results to improve patient care. ... [more]View projectArticleFull-text availableMinimal-moderate variation of human oral virome and microbiome in IgA deficiencyJuly 2021 · Scientific ReportsMaria Jose de la Cruz Peña Luis Ignacio Gonzalez-Granado Inmaculada Garcia-Heredia[...] Manuel Martinez GarciaImmunoglobulin A (IgA) is the dominant antibody found in our mucosal secretions and has long been recognized to play an important role in protecting our epithelium from pathogens. Recently, IgA has been shown to be involved in gut homeostatic regulation by ‘recognizing’ and shaping our commensal microbes. Paradoxically, yet selective IgA-deficiency is often described as asymptomatic and there is ... [Show full abstract] a paucity of studies only focused on the mice and human gut microbiome context fully ignoring other niches of our body and our commensal viruses. Here, we used as a model the human oral cavity and employed a holistic view and studied the impact of IgA deficiency and also common variable IgA and IgM immunodeficiencies (CVID), on both the human virome and microbiome. Unexpectedly, metagenomic and experimental data in human IgA deficiency and CVID indicate minimal-moderate changes in microbiome and virome composition compared to healthy control group and point out to a rather functional, resilient oral commensal viruses and microbes. However, a significant depletion (two fold) of bacterial cells (p-value 0.01) and viruses was observed in IgA-deficiency. Our results demonstrate that, within the limits of our cohort, IgA role is not critical for maintaining a rather functional salivary microbiome and suggest that IgA is not a major influence on the composition of abundant commensal microbes.View full-textArticleFull-text availableSynergistic convergence of microbiota-specific systemic IgG and secretory IgADecember 2018 · Journal of Allergy and Clinical ImmunologyJehane FadlallahDelphine SterlinGuy Gorochov[...]Claire FieschiBackground: Commensals induce local IgA responses essential to the induction of tolerance to gut microbiota, but it remains unclear whether antimicrobiota responses remain confined to the gut.Objective: The aim of this study was to investigate systemic and intestinal responses against the whole microbiota under homeostatic conditions and in the absence of IgA.Methods: We analyzed blood and ... [Show full abstract] feces from healthy donors, patients with selective IgA deficiency (SIgAd), and patients with common variable immunodeficiency (CVID). Immunoglobulin-coated bacterial repertoires were analyzed by using combined bacterial fluorescence-activated cell sorting and 16S rRNA sequencing. Bacterial lysates were probed by using Western blot analysis with healthy donor sera.Results: Although absent from the healthy gut, serum antimicrobiota IgG are present in healthy subjects and increased in patients with SIgAd. IgG converges with nonoverlapping secretory IgA specificities to target the same bacteria. Each individual subject targets a diverse microbiota repertoire with a proportion that correlates inversely with systemic inflammation. Finally, intravenous immunoglobulin preparations target CVID gut microbiota much less efficiently than healthy microbiota.Conclusion: Secretory IgA and systemic IgG converge to target gut microbiota at the cellular level. SIgAd-associated inflammation is inversely correlated with systemic anticommensal IgG responses, which might serve as a second line of defense. We speculate that patients with SIgAd could benefit from oral IgA supplementation. Our data also suggest that intravenous immunoglobulin preparations can be supplemented with IgG from IgA-deficient patient pools to offer better protection against gut bacterial translocations in patients with CVID.View full-textArticleFull-text availableQuality control of microbiota metagenomics by k-mer analysisDecember 2015 · BMC Genomics Catherine Juste Martin LarsenFlorian Plaza Oñate[...] Jean-Michel BattoThe biological and clinical consequences of the tight interactions between host and microbiota are rapidly being unraveled by next generation sequencing technologies and sophisticated bioinformatics, also referred to as microbiota metagenomics. The recent success of metagenomics has created a demand to rapidly apply the technology to large case-control cohort studies and to studies of microbiota ... [Show full abstract] from various habitats, including habitats relatively poor in microbes. It is therefore of foremost importance to enable a robust and rapid quality assessment of metagenomic data from samples that challenge present technological limits (sample numbers and size). Here we demonstrate that the distribution of overlapping k-mers of metagenome sequence data predicts sequence quality as defined by gene distribution and efficiency of sequence mapping to a reference gene catalogue.We used serial dilutions of gut microbiota metagenomic datasets to generate well-defined high to low quality metagenomes. We also analyzed a collection of 52 microbiota-derived metagenomes. We demonstrate that k-mer distributions of metagenomic sequence data identify sequence contaminations, such as sequences derived from empty ligation products. Of note, k-mer distributions were also able to predict the frequency of sequences mapping to a reference gene catalogue not only for the well-defined serial dilution datasets, but also for 52 human gut microbiota derived metagenomic datasets.We propose that k-mer analysis of raw metagenome sequence reads should be implemented as a first quality assessment prior to more extensive bioinformatics analysis, such as sequence filtering and gene mapping. With the rising demand for metagenomic analysis of microbiota it is crucial to provide tools for rapid and efficient decision making. This will eventually lead to a faster turn-around time, improved analytical quality including sample quality metrics and a significant cost reduction. Finally, improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies.View full-textArticleImmune/microbial interface perturbation in human IgA deficiencyNovember 2018 · Gut MicrobesDelphine SterlinJehane FadlallahClaire Fieschi[...]Marion MalphettesIn a recently published article we report the metagenomic analysis of human gut microbiomes evolved in the absence of immunoglobulin A (IgA). We show that human IgA deficiency is not associated with massive quantitative perturbations of gut microbial ecology. While our study underlines a rather expected pathobiont expansion, we at the same time highlight a less expected depletion in some ... [Show full abstract] typically beneficial symbionts. We also show that IgM partially supply IgA deficiency, explaining the relatively mild clinical phenotype associated with the early steps of this condition. Microbiome studies in patients should consider potential issues such as cohort size, human genetic polymorphism and treatments. In this commentary, we discuss how such issues were taken into account in our own study.Read moreArticleFull-text availablePerturbed Microbiota/Immune Homeostasis in Multiple SclerosisJuly 2021 · Neurology Neuroimmunology NeuroinflammationDelphine Sterlin Martin LarsenJehane Fadlallah[...]Guy GorochovObjectiveBased on animal models and human studies, there is now strong suspicion that host/microbiota mutualism in the context of gut microbial dysbiosis could influence immunity and multiple sclerosis (MS) evolution. Our goal was to seek evidence of deregulated microbiota-induced systemic immune responses in patients with MS.MethodsWe investigated gut and systemic commensal-specific antibody ... [Show full abstract] responses in healthy controls (n = 32), patients with relapsing-remitting MS (n = 30), and individuals with clinically isolated syndromes (CISs) (n = 15). Gut microbiota composition and diversity were compared between controls and patients by analysis of 16S ribosomal ribonucleic acid (rRNA) sequencing. Autologous microbiota and cultivable bacterial strains were used in bacterial flow cytometry assays to quantify autologous serum IgG and secretory IgA responses to microbiota. IgG-bound bacteria were sorted by flow cytometry and identified using 16S rRNA sequencing.ResultsWe show that commensal-specific gut IgA responses are drastically reduced in patients with severe MS, disease severity being correlated with the IgA-coated fecal microbiota fraction ( r = −0.647, p 0.0001). At the same time, IgA-unbound bacteria elicit qualitatively broad and quantitatively increased serum IgG responses in patients with MS and CIS compared with controls (4.1% and 2.5% vs 1.9%, respectively, p 0.001).ConclusionsGut and systemic microbiota/immune homeostasis are perturbed in MS. Our results argue that defective IgA responses in MS are linked to a breakdown of systemic tolerance to gut microbiota leading to an enhanced triggering of systemic IgG immunity against gut commensals occurring early in MS.View full-textInterested in research on IgA Deficiency?Join ResearchGate to discover and stay up-to-date with the latest research from leading experts in IgA Deficiency and many other scientific topics.Join for free ResearchGate iOS AppGet it from the App Store now.InstallKeep up with your stats and moreAccess scientific knowledge from anywhere orDiscover by subject areaRecruit researchersJoin for freeLoginEmail Tip: Most researchers use their institutional email address as their ResearchGate loginPasswordForgot password? Keep me logged inLog inorContinue with GoogleWelcome back! Please log in.Email · HintTip: Most researchers use their institutional email address as their ResearchGate loginPasswordForgot password? 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