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Microbiome. 2020 Jul 27;8(1):113
Michael Smith Laboratories
The Author(s)
Huus, Kelsey E.
Rodriguez-Pozo, André
Kapel, Nathalie
Nestoret, Alison
Habib, Azimdine
Dede, Michel
Manges, Amee R.
Collard, Jean-Marc
Sansonetti, Philippe J.
Vonaesch, Pascale
Finlay, B. Brett, 1959-
Background Child undernutrition is a global health issue that is associated with poor sanitation and an altered intestinal microbiota. Immunoglobulin (Ig) A mediates host-microbial homeostasis in the intestine, and acutely undernourished children have been shown to have altered IgA recognition of the fecal microbiota. We sought to determine whether chronic undernutrition (stunting) or intestinal inflammation were associated with antibody recognition of the microbiota using two geographically distinct populations from the Afribiota project. Fecal bacteria from 200 children between 2 and 5 years old in Antananarivo, Madagascar, and Bangui, Central African Republic (CAR), were sorted into IgA-positive (IgA+) and IgA-negative (IgA−) populations by flow cytometry and subsequently characterized by 16S rRNA gene sequencing to determine IgA-bacterial targeting. We additionally measured IgG+ fecal bacteria by flow cytometry in a subset of 75 children. Results Stunted children (height-for-age z-score ≤ −2) had a greater proportion of IgA+ bacteria in the fecal microbiota compared to non-stunted controls. This trend was consistent in both countries, despite the higher overall IgA-targeting of the microbiota in Madagascar, but lost significance in each country individually. Two of the most highly IgA-recognized bacteria regardless of nutritional status were Campylobacter (in CAR) and Haemophilus (in both countries), both of which were previously shown to be more abundant in stunted children; however, there was no association between IgA-targeting of these bacteria and either stunting or inflammatory markers. IgG-bound intestinal bacteria were rare in both stunted and non-stunted children, similar to levels observed in healthy populations. Conclusions Undernourished children carry a high load of intestinal pathogens and pathobionts. Our data suggest that stunted children have a greater proportion of IgA-recognized fecal bacteria. We moreover identify two putative pathobionts, Haemophilus and Campylobacter, that are broadly targeted by intestinal IgA. This study furthers our understanding of host-microbiota interactions in undernutrition and identifies immune-recognized microbes for future study.
RESEARCH Open AccessImmunoglobulin recognition of fecalbacteria in stunted and non-stuntedchildren: findings from the Afribiota studyKelsey E. Huus1,2, André Rodriguez-Pozo3, Nathalie Kapel4, Alison Nestoret4, Azimdine Habib5, Michel Dede6,Amee Manges7, Jean-Marc Collard8, Philippe J. Sansonetti3,9†, Pascale Vonaesch3,10†, B. Brett Finlay1,2,11*† and forthe Afribiota InvestigatorsAbstractBackground: Child undernutrition is a global health issue that is associated with poor sanitation and an alteredintestinal microbiota. Immunoglobulin (Ig) A mediates host-microbial homeostasis in the intestine, and acutelyundernourished children have been shown to have altered IgA recognition of the fecal microbiota. We sought todetermine whether chronic undernutrition (stunting) or intestinal inflammation were associated with antibodyrecognition of the microbiota using two geographically distinct populations from the Afribiota project. Fecalbacteria from 200 children between 2 and 5 years old in Antananarivo, Madagascar, and Bangui, Central AfricanRepublic (CAR), were sorted into IgA-positive (IgA+) and IgA-negative (IgA−) populations by flow cytometry andsubsequently characterized by 16S rRNA gene sequencing to determine IgA-bacterial targeting. We additionallymeasured IgG+ fecal bacteria by flow cytometry in a subset of 75 children.Results: Stunted children (height-for-age z-score ≤ −2) had a greater proportion of IgA+ bacteria in the fecalmicrobiota compared to non-stunted controls. This trend was consistent in both countries, despite the higheroverall IgA-targeting of the microbiota in Madagascar, but lost significance in each country individually. Two of themost highly IgA-recognized bacteria regardless of nutritional status were Campylobacter (in CAR) and Haemophilus(in both countries), both of which were previously shown to be more abundant in stunted children; however, therewas no association between IgA-targeting of these bacteria and either stunting or inflammatory markers. IgG-bound intestinal bacteria were rare in both stunted and non-stunted children, similar to levels observed in healthypopulations.(Continued on next page)© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you giveappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtainpermission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.* Correspondence: bfinlay@msl.ubc.ca†Philippe J. Sansonetti, Pascale Vonaesch and B. Brett Finlay Co-last authors1Michael Smith Laboratories, University of British Columbia, Vancouver, BC,Canada2Department of Microbiology and Immunology, University of BritishColumbia, Vancouver, BC, CanadaFull list of author information is available at the end of the articleHuus et al. Microbiome (2020) 8:113 https://doi.org/10.1186/s40168-020-00890-1(Continued from previous page)Conclusions: Undernourished children carry a high load of intestinal pathogens and pathobionts. Our data suggestthat stunted children have a greater proportion of IgA-recognized fecal bacteria. We moreover identify two putativepathobionts, Haemophilus and Campylobacter, that are broadly targeted by intestinal IgA. This study furthers ourunderstanding of host-microbiota interactions in undernutrition and identifies immune-recognized microbes forfuture study.Keywords: Microbiome, Immunoglobulin A, Immunoglobulin G, Undernutrition, Stunting, Environmental Entericdysfunction, Sub-Saharan AfricaBackgroundUndernutrition is responsible for nearly half of all deathsin children less than five years old [1]. Surprisingly, evenwhen undernourished children are fed nutrient-richtherapeutic foods, they often fail to regain normal heightand weight [2]; it has been estimated that the universalimplementation of all existing nutritional interventionswould only reduce stunting by one-third [3]. This sug-gests that factors other than diet contribute to malnutri-tion. Currently, the contribution of non-dietary factorsto stunting represents an area of intensive research andinterest for the prevention and treatment of undernutri-tion, and the intestinal microbiota has been increasinglyimplicated [4–6].Undernutrition is associated with dysbiosis and imma-turity of the gut microbiota [7–10] and increased sus-ceptibility to diarrheal disease [11]. Recently, we showedthat stunted children have a dysbiotic fecal microbiotacharacterized by an overabundance of pathobionts andof bacteria typically associated with the oral cavity, in-cluding Haemophilus, Campylobacter, and Escherichia-Shigella [12]. Using duodenal aspirates to sample themicrobiota of the upper gastrointestinal tract, we furthershowed that small intestinal bacterial overgrowth (SIBO)was common and that oral taxa were abundant in theupper gastrointestinal tract of stunted children [12].In low-income regions with poor sanitation, undernu-trition is frequently associated with chronic intestinal in-flammation and permeability, a condition referred to asenvironmental enteropathy or environmental entericdysfunction (EED) [9, 10, 13]. EED is hypothesized tointerfere with nutrient absorption and thus exacerbateundernutrition, but it is a heterogeneous condition thatis difficult to define or diagnose, and its etiology is un-clear [5]. Nevertheless, multiple studies have found anassociation between intestinal inflammation and lineargrowth in children [5], indicating that intestinal homeo-stasis is important for child nutrition.Immunoglobulin (Ig) A regulates host-bacterial homeo-stasis in the mammalian gut [14, 15]. Studies of IgA-targeted bacteria indicate that acute undernutrition andinflammatory bowel disease are both associated with al-tered interactions between IgA and the intestinalmicrobiota [16–19]. Acutely malnourished children hadhigher IgA-targeting of Bacteroidales and Escherichia coli,while patients with inflammatory bowel disease (IBD) hadhigher targeting of a variety of bacteria including Hae-mophilus and E. coli [16–18], and these IgA+ bacteria in-duced intestinal inflammation and disease whentransferred into germ-free mice.Moreover, chronic under-nutrition in mice directly alters IgA recognition of themicrobiota, in part by driving bacterial dietary adaptation[19]. Here, we investigated whether chronic undernutri-tion and EED are also associated with altered Ig-recognition of the microbiota in children, using two geo-graphically distinct populations from the Afribiota study.ResultsIgA+ fecal bacteria are higher in stunted childrenTwo hundred children aged 2 to 5 years old who wereenrolled in the Afribiota study in Madagascar and theCentral African Republic (CAR) [20] were selected foranalysis of the IgA-targeted microbiota (Fig. S1). IgA-coating of fecal bacteria was measured using flow cytom-etry (Fig. S2), and cytometric data was ultimately avail-able for 188 children of whom 98 were stunted and 90were non-stunted (Fig. S1). The proportion of fecal bac-teria coated by IgA (IgA+) varied widely, from zero tonearly 50%, which is consistent with findings from otherhuman studies [15, 17]. The percentage of IgA+ bacteriain a sample by FACS correlated strongly with total fecalIgA levels (Fig. 1a, p = 7.8e−06, rho = 0.35 by Spear-man’s correlation), validating our technique and indicat-ing a real biological variation in the availability ofsecretory IgA within the gut.The population of IgA+ fecal bacteria was significantlyhigher in children from Madagascar than in childrenfrom CAR (Fig. 1b, p = 2.1e−05 by Wilcoxon rank sumtest). It did not vary by age or by sex (Fig. 1c, d). Inter-estingly, there was a modest association between IgA-coating and height-for-age z-score (HAZ; Fig. S3A, p =0.039, rho = −0.019 by Spearman’s correlation), withstunted children having a greater proportion of IgA-coated bacteria in the fecal microbiota compared tonon-stunted controls (Fig. S3B, p = 0.029 by Wilcoxonrank sum test). The proportion of IgA-coated bacteriaHuus et al. Microbiome (2020) 8:113 Page 2 of 16also increased stepwise with the severity of stunting(Fig. 1e, p = 0.030 by Jonkheere’s non-parametrictrend test). Further, IgA-coating was increased instunted children compared to non-stunted children inboth countries individually, although it did not reachstatistical significance in the country subsets (Fig. 1f;p = 0.13 and p = 0.22 by Wilcoxon rank sum test inMadagascar and in CAR, respectively). Removing 12outliers also led to borderline significance (p = 0.054by Wilcoxon rank sum test of stunted vs non-stuntedin the pooled dataset). This may be partly due to re-duced statistical power in smaller sample sizes butcould also reflect bias in the data. Due to the largenumber of samples, IgA-sorting was performed in twomajor batches over several days each: although therewas some variation in the data by batch, trend direc-tions by stunting and by country were consistent inboth main sorting batches (Fig. S3C–F). Further, theassociation between %IgA+ and HAZ was robust tonon-parametric bootstrapping when including country(p = 0.045) or batch (p = 0.042) as grouping factorsto constrain permutations.Total fecal IgA was higher in Madagascar than in CAR,mirroring the flow cytometry data, but to a much smallerdegree (Fig. S3G, p = 0.072 by Wilcoxon rank sum test).In contrast, there was no difference in total fecal IgA bystunting (Fig. S3H, p = 0.571 by Wilcoxon rank sum test).We additionally did not find any association between%IgA-targeting and the inflammatory “EED” biomarkersfecal calprotectin, fecal alpha-1-antitrypsin (AAT), orserum C-reactive protein (CRP), although all trends werepositive (Fig S4A–C; p = 0.862 for CRP by Wilcoxonrank sum test; p = 0.139 and p = 0.103 for AAT and cal-protectin, respectively, by Spearman’s correlation).Together, our data indicate a large difference in IgA recog-nition of fecal microbiota between geographically distinctpopulations. IgA recognition of fecal bacteria may moreoverbe higher in chronically undernourished children; however,larger sample sizes are needed to validate this result.Highly IgA-targeted taxa include Firmicutes, Prevotella,and HaemophilusTo characterize the IgA-targeted microbiota, 16S rRNAgene sequencing of the V4 region was applied to theFig. 1 IgA-coating of fecal bacteria. The percentage of IgA-positive (%IgA+) fecal bacteria was measured by flow cytometry relative to an isotypecontrol. a–d The relationship between %IgA+ bacteria and a the total concentration of fecal IgA in each sample (ng/g of feces, wet weight); bthe country of origin; c child age in months and d sex. e %IgA+ bacteria by stunting severity. NN, non-stunted; MCM, moderately stunted; MCS,severely stunted. f Percent IgA-targeting by stunting status in each country individually. Mada, Madagascar; CAR, Central African Republic. N = 188total children; N = 93 in Madagascar and N = 95 in CAR. Significance was determined by Spearman’s correlation (a, c), Wilcoxon rank sum test (b,d, f), and Jonkheere’s trend test (e). A linear fit is shown in a and c.Huus et al. Microbiome (2020) 8:113 Page 3 of 16sorted IgA+ and IgA− fractions in a method known as“IgA-SEQ” [16]. After filtering and rarefaction, a total of138 children had valid sequencing data from both theIgA+ and IgA− fractions (Fig. S1), allowing us to calcu-late a log-normalized IgA Index as reported previously[16]. Demographics of this IgA-SEQ subpopulation arereported in Table 1 and Table S1.IgA-targeting of the microbiota was taxonomically di-verse, differing between closely related amplicon se-quence variants (ASVs) as has been reported in previousstudies (Fig. S5A) [17, 21, 22]. At the genus level,twenty-one taxa were considered to be significantly IgA-targeted in the total dataset (Fig. 2a; FDR-adjusted p <0.05 by one-sided Wilcoxon rank sum test, and meanIgA Index > 0). Of these, 12 (57.1%) belonged to the Fir-micutes phylum, 5 (23.8%) to the Bacteroidetes, and theremainder to the Proteobacteria, Actinobacteria, Cyano-bacteria, and Epsilonbacteraeota. The distribution wassimilar at the ASV level (Fig. S5A), with 24 out of 35highly targeted ASVs (68.6%) belonging to the Firmi-cutes. These data are consistent with previous findingsthat Firmicutes are the most frequently IgA-recognizedtaxa in human feces [15].To identify the most highly IgA-targeted taxa in a givensubpopulation, we selected taxa with (a) an IgA Index sig-nificantly different from zero (FDR-adjusted p < 0.05) and(b) a positive median IgA Index. Notably, because IgA-targeting is highly variable between individuals, the use ofTable 1 Demographics of stunted and non-stunted children with valid IgA-seq data (N = 138)Description Non-stunted (N = 67) Stunted (N = 71) PCountryMadagascar 35/67 (52.2%) 43/71 (60.6%) 0.3909Central African Republic (CAR) 32/67 (47.8%) 28/71 (39.4%)SexMale 29/67 (43.3%) 35/71 (49.3%) 0.4993Female 38/67 (56.7%) 36/71 (50.7%)AgeMedian (months) 42.7 38.4 0.35532–3 years 24/67 (35.8%) 30/71 (42.2%)3–4 years 18/67 (26.9%) 17/71 (23.9%)4–5+ years 25/67 (37.3%) 24/71 (33.8%)Nutritional statusMedian HAZ score − 1.05 − 3.48 2.2e−16Median WHZ score − 0.21 − 0.54 0.0600Median hemoglobin (g/100mL serum) 11.6 10.9 0.0040Presence of anemia 18/64 (28.1%) 34/66 (51.5%) 0.0076BreastfeedingMedian duration (months) 20 20 0.8842Inflammation 0.4979Median AAT (mg/g dry fecal weight) 42.0 43.5Median calprotectin (μg/g dry fecal weight) 367.5 405.0 0.7026Elevated CRP (> 10 mg/l serum) 4/61 (6.5%) 13/66 (19.7%) 0.0374ParasitesPresence of helminths 34/59 (57.6%) 36/66 (54.5%) 0.8569Presence of Giardia 11/59 (18.6%) 17/66 (25.7%) 0.3942Sequencing batchBatch 1 26/67 (38.8%) 27/71 (38.0%) 1.000Batch 2 41/67 (61.2%) 44/71 (62.0%)Values are presented as the group median (continuous variable) or as counts (categorical variable) with missing values excluded. Significance was determined byWilcoxon rank sum test (continuous variable) or Fisher’s exact test (categorical variable). Hemoglobin was adjusted by altitude (− 0.2 g/100 mL in Madagascar toaccount for the height above sea level). Anemia was defined as an adjusted hemoglobin level below 11 g/100 mL. Elevated CRP was defined as > 10 mg/l serum.Breastfeeding duration represents the total number of months a child was previously breastfed for; all but four children had been weaned by the timeof samplingHuus et al. Microbiome (2020) 8:113 Page 4 of 16Fig. 2 Highly IgA-targeted taxa at genus level. a IgA-targeting profiles at genus level in the full dataset (All), Madagascar (Mada), and CentralAfrican Republic (CAR). Genera are included if the IgA Index was significantly different from zero by a one-sided Wilcoxon (FDR-adjusted p < 0.05)in at least one subset. Color of circles indicates the direction of IgA-targeting (red = positive IgA Index, blue = negative IgA Index), and saturationof the color represents FDR-corrected statistical significance. The size of the circle indicates overall effect size as measured by average IgA Index.b–d Most highly IgA-targeted taxa in b the full dataset, c Madagascar, and d CAR, as defined by a median IgA Index greater than zero with FDR-adjusted p < 0.05. N = 138 total children; N = 78 in Madagascar and N = 60 in CARHuus et al. Microbiome (2020) 8:113 Page 5 of 16median IgA index excluded many taxa identified abovewhere targeting may have been driven by a small number ofindividuals. By these criteria, therefore, eight genera werehighly and consistently targeted in the total study population:six members of the phylum Firmicutes (Eubacterium, Copro-coccus, Dorea, Lachnoclostridium, unclassified Ruminococca-ceae UCG-002, and Subdoligranulum), one Bacteroidetes(Prevotella 2), and one Gammaproteobacterium (Haemophi-lus) (Fig. 2b). Most of these taxa were targeted in both coun-tries: Eubacterium, Coprococcus, Subdoligranulum, Prevotella2, and Haemophilus were significantly targeted inMadagascar and in CAR (Fig. 2c, d). In addition, Solobacter-ium was highly recognized in Madagascar (Fig. 2c), andCampylobacter was highly recognized in CAR (Fig. 2d).These patterns were similar at the ASV level and oftenseemed to be driven by one or two dominant ASVs (Fig.S5B–D). A larger number of taxa met the criteria for “con-sistently untargeted” (FDR < 0.05 and median negative IgAindex) than for significantly targeted, in the pooled datasetand in each country individually, including Streptococcus,Faecalibacterium, Pseudomonas, and several Alphaproteo-bacteria of likely environmental origin (Table S2). As ex-pected, for most taxa, there was no relationship betweenIgA-targeting and their relative abundance in the unsortedintestinal microbiota of the same children; the exception wasa negative correlation in Faecalibacterium (Fig. S6A; FDR-corrected p = 0.003, rho = − 0.35 by Spearman’s correlation).Together, these data suggest a relatively conserved pat-tern of IgA-targeting in Madagascar and CAR, with highIgA recognition of Haemophilus, Prevotella, and mul-tiple Firmicutes.IgA-targeting of putative pathobionts does not correlatewith stunting or inflammationTwo of the most targeted taxa, Haemophilus (in bothcountries) and Campylobacter (in CAR), are putativepathobionts that were previously found to be moreabundant in the fecal microbiota of stunted children[12]. Both Haemophilus and Campylobacter were posi-tively IgA-targeted in stunted and non-stunted children,regardless of country (Fig. S6B–C). Notably, Campylo-bacter was significantly IgA-positive in Madagascar (aswell as in CAR) when mean IgA Index, rather than me-dian IgA index, was used as a criterion, suggesting apositive but less well-distributed IgA recognition of thistaxon (Fig. 2a). In addition, both taxa had a mean posi-tive IgA Index in each sequencing batch, although therewas some batch variability in the magnitude of the index(Fig. S6D–E). We did not find any association betweenthe IgA Index of either Haemophilus or Campylobacterand the inflammatory biomarkers fecal calprotectin, fecalAAT, or serum CRP (Fig. S7A–F; p > 0.1 for all tests bySpearman’s correlation (calprotectin, AAT) or Wilcoxonrank sum test (binary CRP levels)).Together, these data indicate that both Campylobacterand Haemophilus are highly recognized by IgA, with IgArecognition of Haemophilus being particularly wide-spread among children in Madagascar and CAR. How-ever, there is no significant association between IgArecognition of these taxa and chronic undernutrition orinflammatory markers.Variation in IgA-targeting is influenced by geography,age, and breastfeedingPrinciple component analysis of the IgA Index showshigh inter-individual variability of IgA-bacterial targeting(Fig. 3a). To identify factors which explain variation inIgA-targeting between individuals, we performed PERMANOVA analysis of the IgA Index against a set of ra-tionally chosen metadata variables (Fig. 3b). Country oforigin had a large and statistically significant effect onthe IgA Index (Fig. 3b); indeed, several variables whichmight influence IgA or the microbiota (ex., breastfeedingduration and helminth carriage) differed significantly bycountry (Table S1). In addition to a strong country ef-fect, sources of technical variation (sequencing batchand the relative sequencing depth of the IgA+ and IgA−fractions before rarefaction) influenced the IgA Index(Fig. 3b). Notably, sorting and sequencing batches largelycorresponded (i.e., the same samples were included ineach), and therefore, we have only included one batchvariable here, although both processes might be contrib-uting to the batch effect. To account for country andbatch variation in PERMANOVA, we repeated this ana-lysis with (a) these variables as grouping factors to con-strain permutations (using the “strata” parameter) and(b) analysis performed separately in each country and ineach batch (fully stratified data). Significance was deter-mined by an FDR-corrected p < 0.05.None of the tested measures of nutrition (HAZ,weight-for-height z-score (WHZ), anemia) explainedvariation in the IgA Index in either full or stratified data-sets (Fig. 3b). Note that WHZ was within the normalrange for this study and that no children were consid-ered acutely undernourished; however, WHZ tended tobe lower in stunted children (Table 1) and was thereforeincluded for analysis. The inflammatory markers calpro-tectin and CRP were also non-significant. Fecal AATwas significant in the pooled dataset and when permuta-tions were constrained by country (Fig. S8A); however,AAT was not a significant factor in either country indi-vidually (Fig. 3b). There was also some variability in thesignificance of these nutritional and inflammatorymarkers between batches (Fig. S8A), indicating noisinessin the data. Thus, nutritional or inflammatory markersmeasured in this study do not have a consistent measur-able influence on the IgA-targeting of fecal bacteria.Huus et al. Microbiome (2020) 8:113 Page 6 of 16Helminth carriage was also significant in pooled ana-lysis but was almost fully confounded by country (88.5%prevalence in Madagascar versus 2.1% in CAR; Table S1)and was no longer significant when data were stratifiedor constrained by country (Fig. 3b, S8A).The prior duration of breastfeeding (i.e., total monthsthe child was breastfed for; all but four children hadbeen weaned by the time of sampling) was also signifi-cant in pooled analysis but differed by country (TableS1). Duration of breastfeeding was no longer significantin country-constrained PERMANOVA after FDR correc-tion; however, it was a marginally significant factor inCAR alone (FDR-adjusted p = 0.06).In general, PERMANOVA results for these variableswere similar if data were binned taxonomically at thegenus level rather than the ASV level (Fig. S8B).-However, in genus-level data, age was a statisticallysignificant factor after FDR correction in the fulldataset (Fig. S8B) and was marginally significant inMadagascar alone (FDR-adjusted p = 0.084). Age wasalso a marginally significant factor at the ASV levelbut did not pass FDR correction (FDR-adjusted p =0.07, p = 0.23, and p = 0.35 in the pooled dataset,Madagascar, and CAR, respectively).Taken together, variation in IgA-targeting among chil-dren in this population is strongly influenced by geog-raphy. Age and prior duration of breastfeeding mightalso contribute, but the influence of these variables isless significant and differs by country. In contrast,markers of undernutrition and inflammation do notshow an important influence.IgA-targeting of specific taxa differs by geography butnot by stuntingWe further explored whether the IgA-targeting of any in-dividual taxa was correlated with chronic undernutritionor with additional factors identified by PERMANOVA(country of origin, age, breastfeeding duration). Reflectingthe contribution of technical factors, a handful of taxa cor-related significantly with batch effect by FDR-correctedWilcoxon Rank Sum test (Table S3); of these, however,most had already been identified as “significantly untar-geted” by IgA (Table S2), underlining the utility of IgA-SEQ in distinguishing host-interacting bacteria.The IgA-targeting of several taxa differed significantlyby country in FDR-corrected linear models adjusted forsequencing depth, batch effect, age, and sex (Fig. S8C–F).Although linear models were used as an exploratoryFig. 3 Distribution of IgA-targeting by study metadata. a Principal coordinate analysis based on Euclidian distance of the IgA Index, colored bycountry of origin. b Summary of PERMANOVA analysis of the IgA Index in the full dataset (All) and in each country individually. Analysis is basedon taxa maintained at the ASV level. Starred variables are significant with an FDR-corrected p < 0.05. Each variable was tested individually in thePERMANOVA without other covariates. CAR, Central African Republic; Mada, Madacascar; Coef, the coefficient of variance by PERMANOVA. Briefly,the tested variables were as follows: whz_cont, weight-for-height z-score; total IgA, total fecal IgA (ng/g of feces, wet weight); percent IgA+, thepercentage of IgA+ bacteria by flow cytometry; helminth, presence or absence of helminths; haz, height-for-age z-score; depth, the relativesequencing depth of the IgA+ fraction compared to the IgA− fraction of a given sample; crp, serum c-reactive protein in mg/l; calprotectin, fecalcalprotectin in μg/g dry weight; breastfeeding, total duration of breastfeeding in months; batch, sequencing batch; anemia, presence or absenceof anemia; age, child’s age in months; aat, fecal alpha-1 antitrypsin in mg/g dry weight. N = 138 total; N = 78 in Madagascar and N = 60 in CARHuus et al. Microbiome (2020) 8:113 Page 7 of 16approach to allow for the inclusion of covariates, we veri-fied that significant hits were also robust to non-parametric methods, including non-parametric bootstrap-ping of the model and basic uncorrected Wilcoxon ranksum test (all taxa in Fig. S8C–F remained significant bythese tests at p < 0.05; see R Markdown for further detail).Interestingly, differences in IgA Index by country did notseem to reflect differences in unsorted relative abundanceof the same taxa (Fig. S8G–J), although differences in theprocessing of these two datasets could have biased detec-tion. Notably, in the case of Solobacterium ASV 461 (Fig.S8C, G), this 16S sequence was barely detected in the un-sorted relative abundance data (Fig. S8G).No taxa were correlated with age or with breastfeed-ing in the full dataset at FDR-corrected p < 0.05. How-ever, at a more relaxed cutoff of p < 0.1, the IgA Indexof Intestinimonas was negatively correlated with age ina linear model adjusted for sequencing depth, batch ef-fect, country of origin, and sex of the child (FDR-ad-justed p = 0.09). This association was also significant byuncorrected Spearman’s correlation (Fig. S9A; rho = −0.29, p = 0.0004). An ASV of Christensenellaceae wasalso modestly associated with months of breastfeedingin a linear model adjusted for depth, batch, country,age, and sex (FDR-adjusted p = 0.09). However, Chris-tensenellaceae was not significant by uncorrectedSpearman’s (Fig. S9B; rho = 0.14, p = 0.10 in the fulldataset). Both Intestinimonas and Christensenellaceaewere sparsely detected, and associations with these taxawere also significant after removal of zero values (sup-plemental R markdown; rho = − 0.50 and p = 0.0003for Intestinimonas and age; rho = 0.24 and p = 0.025for Christensenellaceae and breastfeeding, by Spear-man’s correlation). No taxa were significant by age orby breastfeeding in either country individually.We next looked for taxa whose IgA Index correlatedwith chronic undernutrition (as either a continuous vari-able (HAZ) or binary factor (stunting)) in linear modelscorrected for relative sequencing depth, batch effect,country, and age. No taxa were associated with HAZ orwith stunting in the full dataset or in CAR using anFDR-adjusted p < 0.1. In Madagascar, a single ASV ofLachnospiraceae NK4A136 was negatively associatedwith HAZ (FDR-corrected p = 0.042) and with stuntingstatus (FDR-corrected p = 0.012) and this associationremained significant after non-parametric bootstrappingor by uncorrected Spearman’s correlation (Fig. S9C; p =6.7e−05, rho = − 0.43).Together, these data indicate that the IgA-targetingof specific taxa varies strongly by geography. In con-trast, while a small number of taxa correlate with age,breastfeeding status, and HAZ, these correlations areinconsistent by geography and should be interpretedwith caution.IgG targeting of fecal bacteria is rare in stunted and non-stunted childrenGiven that bacterially-targeted IgG is elevated in the in-testines of IBD patients [23] and that many biomarkersof intestinal inflammation appear to be shared betweenEED and IBD [24], we investigated whether IgG target-ing of intestinal bacteria was similarly elevated in thispopulation. Fecal samples from 75 children werescreened, and most samples demonstrated little to nomeasurable IgG+ bacteria when compared to an isotypecontrol. However, a small number of samples did showIgG-coated bacterial populations, with 12 out of 75 chil-dren having an IgG+ population ≥ 2% above backgroundand one child displaying a population of nearly 20%IgG+ bacteria (Fig. 4). IgG-coating did not correlate withlevels of total IgG in the feces (Fig. 4a; p = 0.6 and rho =0.07 by Spearman’s correlation). Fecal IgG+ bacteriawere significantly higher in children from Madagascarthan CAR (Fig. 4b, p = 0.0008 by Wilcoxon rank sumtest), similar to the observed increase in IgA+ bacteria inthese children (Fig. 1b). There was no significant rela-tionship between IgG+ bacteria and stunting (Fig. 4c; p= 0.39 by Wilcoxon rank sum test) or markers of inflam-mation (p = 0.425 and p = 0.347 for calprotectin andAAT, respectively, by Spearman’s correlation and p =0.152 for CRP by Wilcoxon rank sum test). However, allof these observations should be interpreted with cautiondue to the outlier-skewed distribution of the data.We selected five highly IgG-targeted samples for apilot analysis of IgG-targeted microbiota in this popula-tion (data points highlighted in red, Fig. 4b, c). All ofthese children were from Madagascar; four out of fivewere severely stunted; two reported a history of diarrhealillness in early life; and three had higher than averageAAT in the fecal sample (Table S4). IgG+ and IgG−populations of these five children were sorted, se-quenced, and used to calculate an IgG Index analogousto the IgA Index above. IgG+ bacteria in these childrenincluded many members of the normal and IgA+ micro-biota, including multiple Clostridiales and Prevotella-ceae, as well as Bifidobacterium and Escherichia-Shigella(Fig. 4d). Although the sample size is too small for statis-tical analysis, these data support previous reports thatIgG broadly recognizes many members of the intestinalmicrobiota [22]. Together, our findings indicate that un-like IBD, chronic undernutrition and EED are not gener-ally associated with elevated IgG-coating of themicrobiota; however, occasional IgG+ populations exist,and IgG may broadly recognize fecal bacteria.DiscussionStudies on the microbiota of undernourished childrenhave established a signature of dysbiosis that includes in-creased abundance of a variety of pathogens andHuus et al. Microbiome (2020) 8:113 Page 8 of 16pathobionts and that may drive the inflammatory intes-tinal condition called EED [10, 12, 25–27]. IgA is a crit-ical mediator of intestinal homeostasis, and recentstudies suggest that IgA recognition of the intestinalmicrobiota is altered during undernutrition and intes-tinal inflammation [16–19]. We examined IgA-bacterialtargeting in almost 200 stunted and non-stunted chil-dren across two study sites, constituting one of the lar-gest datasets examined to date and providing valuableinsights into IgA-bacterial interactions during chronicundernutrition.We found here that the overall proportion of fecal mi-crobes bound by IgA was slightly but significantly higherin stunted children. Child stunting has been previouslycorrelated with increased serum antibodies against fla-gellin and LPS [28–30]. Interestingly, high IgA recogni-tion of fecal bacteria is also consistently observed in IBDpatients, who exhibit an intestinal dysbiosis and inflam-mation that shares many biomarkers with EED [17, 18,24, 31]. High IgA recognition of intestinal bacteria instunted children might reflect past or current disruptionof intestinal homeostasis and is consistent with the in-creased pathogen loads of these children [25, 27]. How-ever, although the difference observed here wassignificant in pooled datasets and was consistent in bothcountries, it was non-significant in stratified data andmay have been biased by outliers. Reproduction of thisfinding in other populations of stunted children wouldtherefore be valuable.We did not find that IgA recognition of specific bac-teria correlated with stunting or inflammatory markers.However, several of the highly IgA-recognized bacteriain both stunted and non-stunted children are note-worthy for their potential as pathogens or pathobionts.Oral bacteria have been proposed as pathobionts in bothundernutrition and IBD [12, 23, 32], and several of thehighly targeted genera in this study, including Haemoph-ilus, Prevotella, Solobacterium, and Campylobacter, areFig. 4 IgG targeting of the fecal microbiota. The percentage of IgG-positive (%IgG+) fecal bacteria by flow cytometry relative to an isotypecontrol and a total IgG concentration (ng/g wet weight); b country of origin and c stunting status. N = 75. Significance was determined bySpearman’s correlation (a) and Wilcox rank sum test (b, c). Red datapoints in b and c indicate the five children selected for IgG-SEQ analysis. dTop IgG-targeted taxa in five children with high IgG-coating of the fecal microbiota. Color saturation reflects the numerical IgG Index (red =positively targeted, blue = negatively targeted). The top 10 most and least-targeted taxa in each child were selected to display in the heatmap.Huus et al. Microbiome (2020) 8:113 Page 9 of 16traditional members of oral microbiomes [33]. More-over, multiple species of both Haemophilus and Cam-pylobacter cause human disease. Both Haemophilus andCampylobacter are more abundant in stunted childrenof the larger Afribiota population [12], and Campylobac-ter has been strongly and consistently associated withlinear growth faltering in children [12, 34, 35], suggest-ing an important relationship with undernutrition. BothHaemophilus and Campylobacter are additionally associ-ated with host inflammation, including the species H.parainfluenzae to which the ASV observed here wasmost closely related [34, 36, 37]. In fact, in a populationof adults from high-income countries, highly IgA-coatedH. parainfluenzae was unique to IBD patients [17].Overall, these data thus highlight immunogenic mem-bers of the microbiota, to which children in Madagascarand CAR are widely exposed.It is noteworthy that Haemophilus, in particular, seemsto have an important functional relationship with IgA.Abundance of intestinal Haemophilus expands in pa-tients with secretory IgA deficiency [15], and commensalPasteurellaceae, the family which Haemophilus belongs,are persistently expanded in mice born to IgA-deficientdams [38]. Indeed, pathogenic strains of H. influenzae,which are a major cause of bacterial pneumonia in chil-dren [39], encode IgA proteases as an important viru-lence factor [40]. The literature therefore supports theimportance of IgA in controlling Haemophiluscolonization, suggesting a functional relevance for theIgA-Haemophilus interaction observed so pervasively inthis population.Another major finding of this study was the large dif-ference by geography. Total IgA recognition of fecal bac-teria was significantly higher in Madagascar than inCAR; in addition, several taxa were differentially tar-geted. The unsorted fecal microbiota of children inMadagascar and CAR differs significantly [12], and manyenvironmental and immunological differences exist be-tween these populations (Table S1). This makes it diffi-cult to conclude whether differences in IgA-bacterialtargeting are driven by host or microbial factors. How-ever, as has been observed previously, IgA-targetingtended not to correlate with taxonomic abundance [19,21]. One notable difference between the two study popu-lations is the burden of intestinal helminths, which arealmost absent in CAR due to successful dewormingcampaigns but are widespread in Madagascar [41]. Hel-minths are known to elicit robust IgA responses, andsome of this induced IgA has off-target specificity to thebacterial microbiota [42], offering a possible explanationfor the increased IgA-targeting in Madagascar. Althoughit is not possible to infer causality, these data showclearly for the first time that IgA-targeting patterns aredifferent between geographically distinct populations. Inaddition, we saw trends in IgA-targeting by both age andhistory of breastfeeding, two factors well known to influ-ence the development of the microbiota and of the mu-cosal immune system [21, 43, 44].Although IgA is the major mucosal antibody, otherisotypes may also bind to the intestinal microbiota [45].In addition to IgA, bacterially-targeted IgG is elevated inthe intestines of IBD patients [31, 46], and specific taxamay be differentially recognized by IgG during IBD [23,47]. As noted, many biomarkers of intestinal inflamma-tion are shared between EED and IBD, such as elevatedfecal calprotectin and blunting of small intestinal villi[24]: we therefore hypothesized that children with EEDmarkers might also have increased IgG-recognized fecalbacteria. However, fecal IgG-coating of the microbiotawas low in almost all samples measured, similar tovalues reported in healthy human populations [22].Interestingly, a small number of samples in Madagascardid have measurable IgG-coating of the microbiota; twoout of five of these samples had reported a previousdiarrheal infection and another two had elevated levelsof fecal inflammatory markers (AAT or calprotectin).We speculate that transient IgG-coating reflects recentor ongoing intestinal infections. IgG targeting in thissmall set of samples also supports findings that IgG, likeIgA, recognizes diverse members of the bacterial micro-biota [22, 48].Our study has several limitations. Ig Index data is ex-tremely heterogeneous and sparse, and popular analysistools like Deseq2 cannot be applied to normalized datalike the log-adjusted IgA Index, making it challenging tofind appropriate statistical methods. Published studiesthat have detected phenotypes by IgA-SEQ have usedrelatively lenient cut-offs for differences in the IgAIndex, including defining “IgA-targeted” as any taxonrecognized in at least one patient [17] and using themicrobiota of a single twin pair replicated multiple timesinto recipient gnotobiotic mice for analysis [16]. OurFDR-corrected models were comparatively stringent andwere further limited by the use of non-parametricmethods; thus, although our sample size was large for astudy of this kind, we were likely underpowered to de-tect small differences after FDR correction. We maytherefore have underestimated differences in IgA-targeting by stunting or inflammation. We additionallyhad significant technical variation between sorting andsequencing batches, and although we attempted to cor-rect and account for this as rigorously as possible, tech-nical variation further limited our statistical power. Ofnote, we also found that the IgA Index was influencedby the difference in sequencing depth between the IgA+and IgA− fractions, even after rarefaction; to our know-ledge, this depth bias has not been previously accountedfor in IgA-SEQ datasets. Nevertheless, our studyHuus et al. Microbiome (2020) 8:113 Page 10 of 16benefited from the acknowledgement of these technicalbiases, as well as from a relatively large sample size andfrom geographically distinct populations which allowedus to find reproducible trends. Given the high inter-individual variability in bacterial IgA-targeting, we feelthe detection of consistent signatures between countriesis particularly valuable.ConclusionsA correlation between undernutrition and poor sanita-tion has now been recognized for many years, but it isnot certain how these factors are linked or how they re-late to the heterogenous disease EED [5]: a better under-standing of host-microbial interactions during humanundernutrition is required. Our data suggest that stuntedchildren have a greater proportion of IgA-recognizedbacteria in the fecal microbiota; validation of this findingin other populations would be valuable. We moreoveridentify two putative pathobionts, Haemophilus andCampylobacter, that are highly targeted by intestinalIgA. These data improve our understanding of immune-microbiota interactions during undernutrition and mayinform causative studies on the role of intestinal mi-crobes in child growth.MethodsStudy participants and sample collectionStudy participants were recruited as part of the Afribiotaproject [12, 20]. The study protocol for Afribiota was ap-proved by the Institutional Review Board of the InstitutPasteur (2016-06/IRB), the National Ethical Review Boardsof Madagascar (55/MSANP/CE) and the Central AfricanRepublic (173/UB/FACSS/CSCVPER/16), and the HumanEthics Board of the University of British Columbia (H18-01108). All participants received oral and written informa-tion about the study, and the legal representatives of thechildren provided written consent to participate in thestudy [20]. Subject characteristics such as age, sex, andbreastfeeding history were assessed by standardized ques-tionnaire. The detailed inclusion and exclusion criteriaand recruitment procedures of the Afribiota study are de-scribed in the published protocol [20].All children were aged 2–5 years living in Antananarivo,Madagascar, and in Bangui, Central African Republic.Children were categorized as either stunted cases (height-for-age z-score ≤ − 2) or non-stunted controls (height-for-age z-score > − 2), using the World Health Organization(WHO) Anthro Software and growth standards [49]. Forthe analysis presented in this study, samples from 100children in Madagascar and 100 children in CAR were se-lected for IgA-sorting and sequencing. Participants wereselected from the full study based on sample availability;within these constraints, samples were divided equally be-tween stunted and non-stunted children and matched forcountry, sex, and age (± 3months). Stunted children wereinitially included if they met the criteria for severe stunting(height-for-age z-score ≤ − 3) in order to better differenti-ate cases and controls; however, 12 out of 96 stunted chil-dren were re-categorized after inclusion as moderatelystunted (height-for-age z-score > − 3 and ≤ − 2) and weremaintained in the analysis.Biobanking of fecal samples was performed by theClinical Investigation and Access to BioResources Plat-form (ICAReB) at the Pasteur Institute, Paris, and by thePasteur Institutes of Madagascar and of Bangui. An ali-quot of fecal material for each sample was shipped ondry ice to Vancouver, Canada, and frozen immediatelyupon arrival at − 70 °C.Ig-sortingIgA-sequencing was performed as described by Kau et al[16]. Approximately 50 mg of each fecal sample (± 10mg) were homogenized in 1 mL of phosphate bufferedsaline (PBS; HyClone DPBS−/−, SH30028.02) and spungently to settle debris; intestinal bacteria were then fil-tered through a 0.7-μm filter. A volume of suspensionequal to 5 mg of sample was washed in FACS buffer(PBS containing 1% bovine serum albumin) and blockedfor 20 min in FACS buffer containing 10% fetal bovineserum. Samples were then stained with anti-human IgA-PE (Miltenyl 130-093-128) or an isotype control(eBioscience, 12-4714-42) at 1:25 dilution for 30 min inthe dark. For IgG-sorting, an anti-human IgG Fc APC(BioLegend 409306) and isotype control (BioLegendMouse IgG2a kappa isotype, 400222) were used at thesame 1:25 dilution. Samples were washed twice moreand fixed overnight in 2% paraformaldehyde (PFA) at4 °C in the dark without shaking. The next day, the PFAwas washed off and samples were stained with SYTO-BC (1:4000 dilution) for bacterial DNA, washed again,and sorted by flow cytometry into Ig-positive and Ig-negative populations. A minimum of 50 000 events arecollected in the IgA+ and IgA− fraction and frozen at −20 °C for further analysis. Each sample was stained withboth an anti-human IgA antibody and an isotype controlto distinguish between specific and non-specific binding,and the final percentage of IgA-positive bacteria was re-ported after subtraction of the isotype-positive popula-tion. Samples in which the isotype and antibody-specificpopulations could not be distinguished were excludedfrom further sequencing analysis.16S Library preparationSorted bacterial suspensions were boiled for 15 min at100 °C, and 2 μL of lysate was used as template for 16SPCR, using Illumina-tagged and barcoded primers spe-cific for the 16S V4 region [50]. PCR was performedwith Phusion polymerase under the following cyclingHuus et al. Microbiome (2020) 8:113 Page 11 of 16conditions: 5 min initial denaturation at 98 °C, 30 cyclesof 20 s at 98 °C, 15 s at 55 °C, 30 s at 72 °C, and 10 minfinal extension at 72 °C. Reactions were run on a gel toensure successful amplification and were purified andnormalized using the 96well Sequel-Prep kit (Thermo-Fisher A1051001). All reactions were subsequentlypooled and gel extracted (GeneJet K0692) to removerprimer-dimers. Sequencing was performed on an Illu-mina MiSeq using a v2 kit for 2x250 bp reads with 30%PhiX at the Biomedical Research Centre (BRC) Sequen-cing Core of the University of British Columbia.Bioinformatics analysis of 16S rRNA dataDemultiplexed forward reads were analyzed in QIIME2(https://qiime2.org/) [51], using the Dada2 option [52] forsequence quality control and trimming to 250 bp. Taxo-nomic assignment was performed using the SILVA data-base [53]. Further filtering was then performed in R usingphyloseq [54]. Filtering included removal of unintendedtargets (archaea, mitochondria, and chloroplast), removalof singleton taxa, and rarefaction to 5000 reads. A log-adjusted IgA index was calculated as described previously[16, 21]: IgAIndex ¼ − ð logðIgAþtaxonÞ − logðIgA − taxonÞlogðIgAþtaxonÞþ logðIgA − taxonÞ Þ , afteradding a pseudocount of 0.0000001 relative abundance toallow for zero values. Taxa were maintained at either theASV level or the genus level for calculation of the IgAIndex. IgA Index data was further filtered for prevalencewithin each sequencing batch, by excluding taxa in which≥ 75% of samples had zero values in either batch individu-ally. Multiple additional statistical methods were exploredto further reduce batch effect (ex., ComBat package in R,percentile normalization in qiime2), but were discarded asthey did not substantially reduce batch effect in our data.Instead, sequencing batch was taken as a covariate in allmodels (see statistical analysis) and analyses were repeatedto ensure similar trends in each batch individually. Rawsequencing data has been deposited to the Sequence ReadArchive (SRA) under BioProject PRJNA603512 and isavailable at the following reviewer-access link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA603512?re-viewer=vk3tkm0bkrm8nbqkegvmtqs5vs.Quantification of total fecal IgA and IgGFor total Ig quantification, 200 mg of fecal samples werehomogenized in 1 mL of PBS containing Protease Inhibi-tor Cocktail (Roche Diagnostics GmbH, Mannheim,Germany), incubated for 30 min on ice, and centrifugedfor 10 min at 10 000 g at 4 °C. The supernatant wastaken for analysis and stored at − 80 °C. The concentra-tion of antibodies (IgA, IgG1, IgG2, IgG3, IgG4) wasmeasured on supernatants at a dilution of 1/100–1/10000 with the Bio-Plex Pro Human Isotyping Panel(Bio-Rad Laboratories, Hercules, CA, USA) using theDropArray method (Curiox Biosystems Pte Ltd,Singapore) on a Bio-Plex 200 machine (LUMINEX) ac-cording to manufacturer instructions. Values were mea-sured in duplicates, and all samples with more than 20%difference in between the two measurements or valuesbelow or above the standard curve were repeated. Finalvalues were normalized to the initial fecal weight usedfor extraction. Analysis was performed with Bio-PlexManager Software version 6.1.1.Assessment of inflammatory markersFecal calprotectin and alpha-1 antitrypsin (AAT) weremeasured as described previously [55]. Briefly, stoolsamples were diluted 1:5 in 0.15M NaCl and vortexedvigorously until complete homogenization; the hom-ogenate was then centrifuged and the supernatant col-lected for analysis. Calprotectin concentrations wereassayed in duplicate by sandwich ELISA using a poly-clonal antibody system (Calprest; Eurospital, Italy) ac-cording to the manufacturer’s instructions. Fecal alpha-1antitrypsin (AAT) was measured using an immuno-nephelemetric method adapted on the BN ProSpec sys-tem (Siemens, Germany) [55].To measure serum C-reactive protein (CRP), venousblood was collected through the AFRIBIOTA projectusing Ethylene Diamine Tetra Acetic Acid (EDTA)microtainer tubes (BD Vacutainer). C-reactive protein(CRP) was measured at the Clinical Biology Center ofthe Institut Pasteur the Madagascar and the Laboratoired’Analyse Médicale at the Institut Pasteur de Banguiwithin 4 h after blood collection according to accreditedmethods.Helminth detectionFecal samples were examined microscopically for thepresence of helminths using the Merthiolate-Iodine-Formaldehyde (MIFs) and Kato-Katz (KK) techniques[56]. Briefly, MIF solution (200mL merthiolate tincture,25 mL formalin 10%, 5 mL glycerin, 250 mL distilledwater) was prepared, and fecal samples were trituratedin the MIF solution in a haemolysis tube at approxi-mately 250 mg stool per 2.5 mL. Stool was allowed tostand in the MIF solution at room temperature for 30min. Using a Pasteur pipette, the stool was then re-moved and deposited in a microscope slide, and thesample was visually examined for the presence of hel-minth eggs or larva, and on the surface of the sedimentfor protozoa trophozoites, cysts, or oocysts. For KK tech-nique, a solution of 3% malachite green-glycerol solutionwas prepared in advance and cellophane strips the sizeof a slide were immersed in the solution for 24 h priorto use. After thorough homogenization of the samples,approximately 1 gram of feces was placed on a tissuepaper and covered with a wire mesh. With the aid of aHuus et al. Microbiome (2020) 8:113 Page 12 of 16spatula, pressure was applied and the feces that passedthrough the mesh were deposited onto a standard tem-plate holding 41.7 mg of feces located on a glass slide.The template was then removed, and the sample wasspread onto a strip of cellophane paper embedded in the3% malachite green-glycerol solution. Finally, the prepar-ation was left at rest for at least 1 h before observationunder optical microscope (× 10 objective) for the visualidentification of helminth eggs.Statistical analysisAll statistical analysis was performed in R studio usingthe phyloseq and ggplot2 packages as indicated in the at-tached R markdown file (Supplemental File 1). Unlessotherwise indicated, all analyses were performed both inthe pooled dataset and in each country individually, aswell as in each batch individually, and multiple correc-tion of statistical tests was applied using the false discov-ery rate (FDR). Data structure of the IgA Index wasexplored using a principle component analysis based onEuclidean distance. A PERMANOVA analysis was itera-tively applied to each variable of interest to determine itscontribution to the distribution of the IgA Index. Non-parametric Wilcoxon rank sum tests were used to com-pare data with two groups (ex. stunted versus non-stunted or Madagascar versus CAR). A one-sided Wil-coxon rank sum test was used to identify taxa with anIgA Index significantly different from zero. Spearman’scorrelation was applied for continuous variables. To lookfor associations between the IgA Index and metadata ofinterest, we iteratively fitted a linear model correcting forconfounders (batch, relative sequencing depth (the differ-ence in sequencing depth between IgA+ and IgA− frac-tions for a given taxon), country, age, and sex) for eachindividual taxon and corrected the p using FDR. Whereindicated, bootstrapping analysis was further applied tomodels that contained covariates, in order to obtain anon-parametric estimate of the statistical significance.Supplementary informationSupplementary information accompanies this paper at https://doi.org/10.1186/s40168-020-00890-1.Additional file 1:. Fig. S1. Subject inclusion/exclusion tree. Mada,Madagascar; CAR, Central African Republic; NS, non-stunted; S, stuntedAdditional file 2:. Fig. S2 Representative flow cytometry gating. Fecalsamples were processed unstained (A) or were stained with SYTO-GreenFITC to detect bacterial DNA (B–C) and with an isotype control PE (B) oran anti-human IgA-PE (anti-hIgA) (C). Top panels: bacterially-sized eventswere selected by forward scatter (FSC) and side scatter (SSC). Bottompanels: events were further gated by FITC and PE to detect IgA+ and IgA− bacterial populationsAdditional file 3: Fig. S3 IgA-bacterial targeting and stunting, by countryand batch. (A–B) The percentage of IgA-positive fecal bacteria by flow cy-tometry (%IgA+) versus height-for-age z-score (HAZ) (A), and stunting sta-tus (B) in the full dataset. (C–D) Trend in stunting status by (C) majorsorting batch and (D) daily sorting batch. (E–F) Country effect by (E)major sorting batch and (F) daily sorting batch. (G–H) Total fecal IgA bycountry (G) and stunting status (H). Mada, Madagascar; CAR, Central Afri-can Republic. N = 188 total samples. N = 93 in Madagascar and N = 95 inCAR; N = 91 in Batch 2017 and N = 88 in Batch 2018. An additional N = 9samples were sorted in a single day in 2019 and are thus included indaily batch effect. Statistical significance was determined by Spearman’scorrelation (A) and Wilcoxon Rank Sum test (B, C, E, G, H)Additional file 4: Fig. S4 IgA-bacterial targeting by inflammatorymarkers. Associations between the percentage of IgA-positive fecal bac-teria by flow cytometry (%IgA+) and serum C-reactive protein (CRP) (A),fecal alpha-1 antitrypsin (AAT) (B), and fecal calprotectin (C). N = 188.“High” CRP was defined as > 10 mg/l. Statistical significance was deter-mined by Wilcoxon rank sum test (A) and Spearman’s correlation (B–C)Additional file 5: Fig. S5 Highly IgA-targeted taxa at amplicon sequencevariant (ASV) level. (A) IgA-targeting profiles at ASV level in the full dataset(All), Madagascar (Mada), and Central African Republic (CAR). ASVs are in-cluded if the IgA Index was significantly different from zero by a one-sided Wilcoxon (FDR-adjusted p < 0.05) in at least one subset. Color ofcircles indicates the direction of IgA-targeting (red = positively targeted,blue = negatively targeted), and saturation of the color represents FDR-corrected statistical significance. The size of the circle indicates overall ef-fect size as measured by average IgA Index. (B–D) Most highly IgA-targeted taxa in (B) the full dataset, (C) Madagascar, and (D) CAR, as de-fined by a median IgA Index greater than zero with FDR-adjusted p <0.05. N = 138 total; N = 78 in Madagascar and N = 60 in CARAdditional file 6: Fig. S6 Abundance and IgA Index correlations in maintargeted taxa. (A) Correlation between IgA-targeting and unsorted relativeabundance at genus level. The most- and least-targeted taxa, as definedby median IgA Index in Fig. 2 and Table S1, are shown. Color representsSpearman’s rho. A star (*) represents significant correlation at FDR-corrected p < 0.05. (B–C) IgA Index of Haemophilus and Campylobacter bystunting status in Madagascar (Mada) and Central African Republic (CAR).(D–E) IgA Index of Haemophilus and Campylobacter by sequencing batch.Statistical significance determined by Wilcoxon Rank Sum test (B–E). N =138 total; N = 78 in Madagascar and N = 60 in CAR; N = 53 in Batch 1and N = 85 in Batch 2Additional file 7: Fig. S7 IgA-targeting of Haemophilus and Campylobac-ter does not correlate with inflammatory markers. Serum C-reactive pro-tein (CRP) levels (A, D), fecal calprotectin (B, E) and fecal alpha-1-antitrypsin (AAT) (C, F) by the IgA Index of Haemophilus (A–C) and Cam-pylobacter (D–F). Statistical significance was determined by Wilcoxon RankSum Test (A, D) or Spearman’s correlation (B,C,E,F). “High” CRP was de-fined as > 10 mg/l. N = 138Additional file 8: Fig. S8 Distribution of IgA-targeting by study metadataaccording to batch and country. (A) Summary of PERMANOVA analysis ofthe IgA Index in the full dataset when permutations were constrained byeither sequencing batch (All ~Batch) or country (All ~Country) using the‘strata’ parameter, or when analysis was performed in each batch indi-vidually (Batch1 and Batch2). Analysis is based on taxa maintained at theASV level. (B) Summary of PERMANOVA analysis of the IgA Index in thefull dataset and in each batch and country individually, based on taxabinned at the genus level. Starred variables are significant with an FDR-corrected p < 0.05. Each variable was tested individually in the PERMANOVA without other covariates. (C–F) IgA Index of taxa that differentsignificantly by country in IgA-targeting. Statistical significance was deter-mined by FDR-corrected linear models that incorporated sequencingdepth, batch effect, age and sex. (G–J) Unsorted relative abundance ofthese same taxa. Numbers and letters following a genus indicate the firstdigits of the qiime2 feature code for a unique ASV. CAR, Central AfricanRepublic; Mada, Madacascar; Coef, the coefficient of variance by PERMANOVA; whz_cont, weight-for-height z-score; haz, height-for-age z-score;crp, serum c-reactive protein; aat, fecal alpha-1 antitrypsin. N = 138 total;N = 78 in Madagascar and N = 60 in CAR; N = 53 in Batch 1 and N = 85in Batch 2.Additional file 9: Fig. S9 IgA-targeting of taxa that correlated with age,breastfeeding or HAZ. Taxa in (A–C) were selected by linear modelswhich incorporated country (A–B only), age, sex, batch and sequencingHuus et al. Microbiome (2020) 8:113 Page 13 of 16depth as covariates, at an FDR-corrected p < 0.1. Statistical significanceand rho as presented in the plots derive from uncorrected Spearman’scorrelation. All, full dataset; Mada, Madgascar; Lachno., Lachnospiraceae. N= 138 in full dataset (A–B); N = 78 in Madagascar (C)Additional file 10: Table S1. Demographics of children with valid IgA-seq data (n = 138) by country. Values are presented as the group median(continuous variable) or as counts (categorical variable) with missingvalues excluded. Significance was determined by Wilcoxon Rank Sum test(continuous variable) or Fisher’s exact test (categorical variable).Hemoglobin was adjusted by altitude (− 0.2 g/100 mL in Madagascar toaccount for the height above sea level). Anemia was defined as an ad-justed hemoglobin level below 11 g/100 mL. Elevated CRP was defined as> 10 mg/l serum. Breastfeeding duration represents the total number ofmonths a child was previously breastfed for; all but four children hadbeen weaned by the time of sampling.Additional file 11: Table S2. Least IgA-targeted taxa at genus level, asdefined by a median IgA Index < 0 and an FDR-adjusted p < 0.05 byone-sided Wilcoxon test. Mada, Madagascar; CAR, Central AfricanRepublicAdditional file 12: Table S3. Batch-associated taxa. Taxa which correlatesignificantly with flow cytometry sorting batch (sort) or 16S rDNA sequen-cing batch (seq). Batch (es) indicates which type of batch the taxon wassignificant in, according to Wilcoxon Rank Sum test with an FDR-corrected p < 0.05. Taxa in bold were also significantly untargeted by IgAAdditional file 13:. Table S4. Characteristics of children selected for IgG-SEQ analysis. HAZ, height-for-age z-score; WHZ, weight-for-age z-score;AAT, fecal alpha-1-antitrypsin (mg/g dry fecal weight); Calpro, fecal cal-protectin (μg/g dry fecal weight); CRP, serum C-reactive protein (mg/lserum)Additional file 14:. Supplementary Files. File S1. R markdown code andstatistical analysis used in this studyAbbreviationsIgA: Immunoglobulin AIgGImmunoglobulin GCARCentral AfricanRepublicEEDEnvironmental enteric dysfunctionIBDInflammatory boweldiseaseASVAmplicon sequence variantFDRFalse discovery rateHAZHeight-for-age z-scoreWHZWeight-for-height z-scoreAATAlpha-1-antitrypsinCRPC-Reactive proteinAcknowledgementsWe are deeply grateful to the children and families who participated inAfribiota and to the large team of community health workers, nurses,doctors, technicians, administrators, and others without whom this studywould not have been possible. We would like to thank Andy Johnson andJustin Wong at UBC for their invaluable assistance in the flow cytometryfacility, as well as Lisa Thorson and Adrian Carney for their help in arrangingand receiving sample shipments at the Michael Smith Laboratories. We arefurther grateful to Vincent Guillemot and Antoine Menard at the InstitutPasteur for their advice on biostatistic analysis and to the members of theSansonetti research group and the Finlay laboratory for their support andfeedback. The Afribiota Investigators are: (in alphabetical order):Authors’ contributionsKEH and PV designed the study. KEH performed the Ig-SEQ experiments, an-alyzed the data, and wrote the manuscript. ARP and PV performed immuno-globulin quantification experiments. NK and AN measured the fecalinflammatory biomarkers. AH provided the helminth data. JMC providedsupervision for AH, assisted with fecal biobanking, and provided critical edit-ing of the manuscript. AM provided critical statistical and epidemiological re-view of the manuscript. PJS, PV, and BBF obtained funding, assisted withdata analysis and interpretation, and provided critical editing of the manu-script. The authors read and approved the final manuscript.FundingThis research was supported by grants from the Canadian Institutes ofHealth Research (CIHR), the Bill and Melinda Gates Foundation and the TotalFoundation. Work in PJS’s laboratory is supported by grants from theEuropean Research Commission. Work in BBF’s laboratory was supported bygrants from CIHR. KEH was supported by a CIHR Vanier Graduate scholarship,a Killam fellowship and a Four-Year Fellowship. PV was supported by a SwissNational Science Foundation Early and Advanced Postdoctoral Fellowship aswell as Return Grant, a Roux-Cantarini Postdoctoral Fellowship and a fellow-ship from L’Oréal-UNESCO for Women in Science.Availability of data and materialsRaw sequencing data has been deposited to the Sequence Read Archive(SRA) under BioProject PRJNA603512 and is available at the followingreviewer-access link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA603512?reviewer=vk3tkm0bkrm8nbqkegvmtqs5vs.Ethics approval and consent to participateThe study protocol for Afribiota was approved by the Institutional ReviewBoard of the Institut Pasteur (2016-06/IRB), the National Ethical ReviewBoards of Madagascar (55/MSANP/CE) and the Central African Republic (173/UB/FACSS/CSCVPER/16), and the Human Ethics Board of the University ofBritish Columbia (H18-01108). All participants received oral and writteninformation about the study and the legal representatives of the childrenprovided written consent to participate in the study [20].Consent for publicationNot applicable (none of the data presented is individually identifiable).Competing interestsThe authors declare no competing interests.Author details1Michael Smith Laboratories, University of British Columbia, Vancouver, BC,Canada. 2Department of Microbiology and Immunology, University of BritishColumbia, Vancouver, BC, Canada. 3Unité de Pathogénie MicrobienneMoléculaire, Institut Pasteur, Paris, France. 4Laboratoire de coprologiefonctionnelle, APHP.SU, Hôpital de la Pitié-Salpêtrière, Paris, France. 5Unitédes Helminthiases, Institut Pasteur de Madagascar, Antananarivo,Madagascar. 6Laboratoire d’Analyse médicale, Institut Pasteur de Bangui,Bangui, Central African Republic. 7School of Population and Public Health,University of British Columbia, Vancouver, BC, Canada. 8Unité deBactériologie Expérimentale, Institut Pasteur de Madagascar, Antananarivo,Madagascar. 9Current address: Center for Microbes, Development and Health,Institut Pasteur de Shanghai, Shanghai, China. 10Current address: Human andAnimal Health Unit, Swiss Tropical and Public Health Institute & University ofBasel, Basel, Switzerland. 11Department of Biochemistry and MolecularBiology, University of British Columbia, Vancouver, BC, Canada.Received: 22 May 2020 Accepted: 5 July 2020References1. 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Article
eng
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Vancouver : University of British Columbia Library
BioMed Central
Attribution 4.0 International (CC BY 4.0)
Faculty
Microbiome
Immunoglobulin A
Immunoglobulin G
Undernutrition
Stunting
Environmental Enteric dysfunction
Sub-Saharan Africa
Immunoglobulin recognition of fecal bacteria in stunted and non-stunted children: findings from the Afribiota study
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