Introduction: Neonates are protected from colonizing bacteria by antibodies secreted into maternal milk. Necrotizing Enterocolitis (NEC) is a disease of neonatal preterm infants with high morbidity and mortality that is associated with intestinal inflammation driven by the microbiota 1-3 . The incidence of NEC is significantly lower in infants fed with maternal milk, though the mechanisms underlying this benefit are not clear 4-6 . Here, we show that maternal Immunoglobulin A (IgA) is an important factor in protection against NEC. Analysis of IgA-binding of fecal bacteria from preterm infants indicated that maternal milk was the predominant source of IgA in the first month of life and that a relative decrease in IgA-bound bacteria is associated with the development of NEC. Sequencing of IgA-bound and unbound bacteria revealed that prior to disease onset, NEC was associated with increasing domination of the IgA-unbound microbiota by Enterobacteriaceae. Further, we confirmed that IgA is critical in preventing NEC in a murine model, where pups reared by IgA deficient mothers are susceptible to disease despite exposure to maternal milk. Our findings show that maternal IgA shapes the host-microbiota relationship of preterm neonates and that IgA is a critical and necessary factor in maternal milk for the prevention of NEC. Main NEC is associated with an intestinal microbiota of decreased diversity and increased Enterobacteriaceae, but this association is not sufficient for disease 7,8 . Bioactive components of maternal milk, including IgA antibodies, shape the neonatal microbiota 9-12 . It is not known how the anti-bacterial IgA repertoire of maternal milk varies between women, but mammary gland IgA-producing B cells traffic from the intestine and thus may differ between mothers as a result of individualized microbiomes and infectious histories 13-15 . We hypothesized that differential binding of the preterm microbiota by maternal IgA is a central feature of NEC pathogenesis. To analyze immunoglobulin (Ig) binding of gut bacteria in preterm infants we stained fecal samples (Table 1a) with anti-human IgA, IgM and IgG antibodies and measured the Ig-bound populations with flow cytometry 16,17 . This initial sample set contained 30 samples collected at the time of NEC diagnosis and 39 samples from age-matched controls. Surveyed across all samples, the percentage of IgA-bound bacteria was far greater than the percentages of IgM- and IgG-bound bacteria and samples from maternal milk-fed infants contained a far greater abundance of IgA positive bacteria compared to formula-fed infants (Figure 1a,b and Extended Data 1a,b). Although a majority (11/19) of formula-fed infants had 99%) in our IgA separations, complicating our ability to discriminate IgA positive and negative bacteria (Extended Data 4a). Post-sort flow cytometric analysis of each sample allowed us to deconvolve the contamination, significantly increasing the correlation between the percent IgA positive bacteria in the unsorted sample and the ratio of IgA positive and negative bacteria reads from IgSeq (Extended Data 4b-d). Deconvolution is particularly important in samples with low levels of IgA positive bacteria, which both validates that our technique is correcting for contamination and improves analysis of the samples most critical to explaining our observation of reduced IgA positive bacteria in NEC (Extended Data 4c and d). Longitudinal IgSeq analysis of deconvolved samples revealed that control infants showed a significant increase in bacterial diversity amongst IgA negative bacteria over time, while no significant changes were detected in the IgA positive sample (Figure 2c). Conversely, among infants that progressed to NEC, the diversity of both IgA positive and negative fecal bacteria significantly decreased over time (Figure 2c) 21 . In accord with our analysis of the total microbiome (Figure 2b), IgSeq also revealed that over time the IgA negative intestinal microbiota of NEC infants became dominated by Enterobacteriaceae, while anaerobes (notably Clostridiales and Bifidobacteriales) were virtually undetectable (Figure 2d,e and Extended Data 5). Conversely, amongst controls, the relative abundance of IgA negative anaerobes increased and the relative abundance of IgA negative Enterobacteriaceae decreased, confirming that Enterobacteriaceae are bound at relatively higher frequencies over time in infants that do not develop NEC (Figure 2b,d,e and Extended Data 5). While the IgA positive and negative fractions from controls differed in both diversity and the relative abundance of Enterobacteriaceae, in NEC patients these fractions were not discernibly different (Figure 2c and d). The lack of differences between IgA positive and negative fractions of NEC infants may be explained by very low diversity and domination by Enterobacteriaceae, so essentially there are few taxa available for IgA to bind (Extended Data 5). An advantage of IgSeq deconvolution is that it allows for a meaningful comparison of taxon abundance between IgA positive and IgA negative samples, as their ratio corresponds to the abundance of IgA-bound bacteria in the unsorted sample (Extended Data 4). When we calculated the ratio of the paired IgA negative and positive reads, we observe a unique increase over time in IgA negative total and Enterobacteriaceae reads in infants who will go on to develop NEC, that correlates well with IgA binding data from flow cytometry (Figure 1e and 2f). In contrast, control infants show no significant shifts in the relative abundances of IgA positive and negative total bacteria, Enterobacteriaceae or anaerobes and only show significant shifts in less abundant OTUs (Figure 2f and Extended Data 6). As Enterobacteriaceae is the most abundant OTU in preterm infants and the only taxa uniquely increasing in the IgA negative fraction of the microbiota prior to disease, the increase in IgA negative Enterobactericeae is the most likely driver of the reduced IgA bound bacteria seen preceding NEC (Figure 1e, 2a, 2f and Extended Data 6). Thus, we have identified that NEC infants uniquely fail to diversify their intestinal microbiota with anaerobic bacteria and instead remain dominated by IgA-unbound Enterobacteriaceae. To test the possibility that the increase in Enterobacteriaceae was driven by a rapid bloom we measured the number of each bacterial taxa per unit mass in fecal samples 22,23 . Although we saw increased Enterobacteriaceae amongst some infants developing NEC, inter-individual variation was high and there were no statistically significant differences between cases and controls (Extended Data 7). However, our analysis may not adequately represent bacteria in the small intestine so we cannot rule out the possibility of focal expansions of Enterobacteriaceae associated with NEC 24 . Nonetheless, we favor a model where loss of IgA binding of the microbiota is induced either by mutation or by transcriptional modifications that allow sub-populations of Enterobacteriaceae to escape maternal IgA but do not lead to increases in their total number. To further define the contribution of maternal IgA to disease pathogenesis, we turned to an experimental murine model of NEC 25,26 . We bred mice so that heterozygote wild-type pups were fed by mothers that either can (C57BL/6) or cannot produce IgA (Rag1−/− or Igha−/−), and compared them to formula-fed positive controls (Figure 3a). We confirmed that mice, like humans, produce little IgA during their first two weeks of life and that dams are the primary source of neonatal IgA 18,27,28 (Figure 3b). We also determined that Enterobacter spp. gavaged into pups (C57BL/6 dams) to induce NEC was enriched in the IgA positive fraction, indicating that murine dams may produce protective IgA without being vaccinated (Extended Data 8). Strikingly, pups undergoing the NEC protocol that were breast-fed by mothers lacking IgA (Rag1−/− or Igha−/−) showed a phenotype that was indistinguishable from formula-fed controls. Specifically, they exhibited increased mortality, and severe intestinal damage characterized by shortened necrotic villi and mucosal sloughing (Figure 3c-e). Furthermore, pups fed by Igha−/− mothers exhibited a significant reduction in weight gain compared to pups fed by wild-type mothers (Figure 3f). We have thus shown, using an experimental model of NEC, that maternal milk only protects against NEC when it contains IgA. Discussion Previous studies have shown associations between the abundance of Enterobacteriaceae and NEC 7 . We now show that IgA-unbound Enterobacteriaceae is more closely linked to NEC development than total Enterobacteriaceae abundance. Animal studies have indicated that both host and maternal IgA is important in controlling Enterobacteriaceae and establishing a mature microbiota characterized by fastidious anaerobic bacteria 29,30 . Our results indicate that binding of bacteria by maternally-derived IgA may promote diversity in the microbiome and the acquisition of anaerobic bacteria during the critical window when infants make little or no IgA of their own, perhaps by limiting inflammation driven by Enterobacteriaceae 31-33 . Future studies will be required to elucidate the mechanism by which IgA controls and modifies gut bacterial colonization in newborns. IgA has been shown to modify bacterial surface protein expression and motility which may limit the ability of bacteria to gain access to the intestinal epithelium 17,34 . IgA may accomplish these tasks by ‘enchaining’ bacterial cells, allowing for easier expulsion and preventing gene transfer 35 . Importantly, the current study did not discriminate between bacteria at the strain level. Thus, it remains to be determined whether the loss of IgA binding results from the appearance of new organisms not constrained by the existing IgA repertoire, or alternatively from changes to bacterial genomes and/or gene expression that allow early colonizers to escape IgA binding 36,37 . Temporal changes in bacterial binding could also result from shifts in the maternal IgA repertoire. Previous attempts to prevent NEC with intravenous immunoglobulins have largely failed to show efficacy 38,39 . However, the repertoire of intravenous antibodies may differ from that of secretory IgA and bacterial specificity was not accounted for in these studies. Future efforts might allow for precision microbiome-informed strategies that enable augmentation of milk or preterm infant formulas with rationally selected protective antibodies. Methods Mice C57BL/6 mice were purchased from Taconic. Rag1−/− mice were obtained from Jackson Laboratories. Igha−/− mice were obtained from Dr. Yasmine Belkaid (NIH/NIAID). All mice were maintained at and all experiments were performed in an American Association for the Accreditation of Laboratory Animal Care-accredited animal facility at the University of Pittsburgh and housed in accordance with the procedures outlined in the Guide for the Care and Use of Laboratory Animals under an animal study proposal approved by the Institutional Animal Care and Use Committee of the University of Pittsburgh. Mice were housed in specific pathogen-free (SPF) conditions. Human Fecal Samples The human study protocol was approved by the Institutional Review Board (Protocol Nos. PRO16030078, PRO09110437) of the University of Pittsburgh. Fecal samples were collected fresh or from the diaper of preterm infants at UPMC Magee-Womens Hospital and frozen immediately at −80°C. The samples were later divided into age-matched controls and NEC depending on the incidence of NEC. Fecal IgA Flow Cytometry and Magnetic Sorting of IgA+ and IgA− Bacteria Either fecal pellets collected from mice after sacrifice or ~50 mg of frozen human fecal material was placed in 1.5ml Eppendorf tubes and 1ml Phosphate Buffered Saline (PBS) was added. The fecal material was disrupted by a combination of vortexing and pipetting and passed through a 40μm filter to remove food/fibrous material. The fecal material is diluted with PBS to obtain a bacterial OD of ~0.4 to maintain equality between samples and to prevent the magnetic columns from clogging. A volume of 200μl of the suspended bacterial material was then frozen as an ‘unsorted’ control. An additional 200μl of the suspended material was divided equally on a 96-well plate for IgA staining and isotype control for each sample to eliminate non-specific binding. The fractions were washed with twice with staining buffer (1% Bovine Serum Albumin (Sigma) in PBS-filtered through a 2.2μm filter). The bacteria were stained with Syto BC (Green Fluorescent nuclear acid stain, Invitrogen-1:400), APC Anti-Human IgA (Miltenyi Biotec clone IS11-8E10) (1:10)/ Anti-Human IgA APC (Miltenyi Biotec clone REA1014) (1:50), Anti-Human IgM BV421 (BD Biosciences clone G20-127) (1:30)/ BV421 Mouse Anti-Human IgG (BD Biosciences clone G18-145) (1:10) or PE-conjugated Anti-Mouse IgA (eBioscience clone mA-6E1) (1:500), Anti-Mouse Rat IgM BV421 (BD Biosciences clone R6-60.2) (1:30)/ Anti-Mouse Rat IgG2a isotype (BD Biosciences clone R35-95), Anti-Mouse IgG FITC (BioLegend clone Poly4060) (1:30) and blocking buffer of 20% Normal Mouse Serum for human or 20% Normal Rat Serum for mouse samples (ThermoFisher). The isotype control was stained similarly using APC Mouse IgG1 isotype control (Miltenyi Biotec clone-IS5-21F5) (1:10) or PE-conjugated Rat Anti-Mouse IFNγ (eBioscience clone XMG1.2). The stained samples were incubated in the dark for an hour at 4°C. Samples were then washed three times with 200μl of staining buffer before flow-cytometric analysis (LSRFortessa-BD Biosciences). For magnetic activated cell sorting (MACS), we used 500μl of the suspended fecal material to compensate for the loss of material during sorting and scaled our staining volume accordingly. Anti-IgA stained fecal bacterial pellets were incubated in 1ml per sample of staining buffer containing 45μl of anti-APC or anti-PE MACS Microbeads (Miltenyi Biotec) (20 min at 4°C in the dark), washed twice with 1 ml Staining Buffer (8000 x rpm, 5 min, 4°C), and then sorted using MS columns (Miltenyi Biotec). The flow-through was collected as IgA-unbound (IgA-negative) fraction. Columns were washed with 70% ethanol and sterile PBS between separations. The IgA-bound fraction was added in the column and the steps mentioned above were repeated four times for maximum enrichment. 100μl each of the IgA-bound and IgA-unbound fraction was used for post-sort flow cytometric analysis (along with unsorted sample). Absolute bacterial counts were determined by adding a known number of AccuCheck Counting beads (Life Technologies) to antibody stained fecal samples of a given mass, which allows for the calculation of the total number of SYTO (DNA)+ events in any given sample. This can then be multiplied by the measured abundance of any OTU to represent the number of bacteria of that taxon/mass in any sample. DNA Extraction All microbial DNA was extracted using the MO BIO PowerSoil DNA Isolation kit (single tube extractions). The unsorted, IgA-bound and IgA-unbound pellets were resuspended in Solution TD1 by pipetting and vortexing and ~200μl of 0.1mm diameter Zirconia/Silica beads (Biospec) were added and shaken horizontally on a lab mixer for 12-18 min at maximum speed using a MO BIO vortex adaptor. All remaining steps followed the manufacturer’s protocol. The DNA extracted was stored at −20°C for further 16S amplicon PCR and sequencing. 16S Amplicon PCR, Sequencing and Analysis PCR amplification of the small subunit ribosomal RNA gene (16S rRNA) was performed in triplicate 25μl reactions. Reactions were held at 94°C for 3 min to denature the DNA, with amplification performed for 30 cycles at 94°C for 45 s, 50°C for 60 s, and 72°C for 90 s; followed by a final extension of 10 min at 72°C. Amplicons were produced utilizing primers adapted for the Illumina MiSeq. Amplicons target the V4 region and primers utilized either the Illumina adaptor, primer pad and linker (forward primer) or Illumina adaptor, Golay barcode, primer pad and linker (reverse primer) followed by a sequence targeting a conserved region of the bacterial 16S rRNA gene as described 40,41 . The only deviation from the protocol was that PCR was run for 30 cycles. Amplicons were cleaned using the Qiagen UltraClean 96 PCR Cleanup Kit. Quantification of individual amplicons was performed with the Invitrogen Quant-iT dsDNA High Sensitivity Assay Kit. Amplicons were then pooled in equimolar ratio. Agarose gel purification was performed to further purify the amplicon pool and remove undesired PCR products prior to submission for paired-end sequencing on the Illumina MiSeq. Read pairing, clustering and core diversity statistics were generated through PEAR, UPARSE and QIIME and R 42,43 . LEfSe was used to compare family level relative abundances between NEC and control groups 44 . Raw 16S rRNA data has been uploaded to NCBI BioSample/SRA and is available under accession number PRJNA526906. Deconvolution and microbiome data analysis: Flow cytometry was used to determine the percentage of IgA positive and IgA negative bacteria in each sample (unsorted, IgA positive, IgA negative) post magnetic separation. We assumed that contamination affected each OTU equally and that the IgA positive and IgA negative samples are reciprocal (fractions of the same whole). The raw reads from sequencing of 16S rRNA genes were then deconvolved by summing the proportion of IgA bound or IgA unbound (as measured by flow cytometry) across the paired (infant and time point [Day of life]) IgA positive and IgA negative samples for each OTU (Extended Data 3c). Example to solve for IgA positive from one time point and one OTU ‘X’: Total IgA positive ‘ X ’ = ‘ X ’ correctly bound to IgA positive + ‘ X ’ contaminating IgA negative Total IgA positive ‘ X ’ = ( % IgA+ in bound ) ∗ ( # ‘ X ’ reads in bound ) + ( % IgA+ in unbound ) ∗ ( # ‘ X ’ reads in unbound ) The deconvoluted data was processed through the QIIME2 workflow to create alpha diversity metrics with sampling depth chosen based on alpha rarefaction plotting. Abundance of individual or ‘pooled’ (Anaerobes) OTUs was then calculated using the deconvolved values. The algorithm for deconvolution is available on GitHub (https://github.com/handlab/IgA_Seq_Deconvolution). If IgSeq is accurate, the ratio of the read numbers between IgA positive and negative (IgA+/IgA++IgA−) samples should roughly correspond to the percent IgA+ bacteria in the unsorted sample from where they were derived. This relationship does not hold for samples with low levels of IgA positive bacteria (Extended Data 4c) prior to deconvolution but is much improved after our method has been applied (Extended Data 4d) We categorized all of the following OTUs as ‘Anaerobes’: Bifidobacteriaceae, Prevotellaceae, Bacteroidiales_S24-7, Clostridiaceae, Lachnospiraceae, Peptostreptococcaceae, Ruminococcaceae, Veillonellaceae, Tissierellaceae (Figure 2e and Extended Data 6b). Quantitative PCR for 16S rRNA. PCR amplification of the small subunit ribosomal RNA gene (16S rRNA) was performed in triplicate 10μl reactions. Reactions were held at 95°C for 3 min to denature the DNA, with amplification performed for 35 cycles (95°C for 10 s and 60°C for 30 s). The forward primer sequence of 16S is ACTCCTACGGGAGGCAGCAGT and the reverse primer sequence of 16S ATTACCGCGGCTGCTGGC. Quantitative PCR for Enterobacter spp. PCR amplification of the small subunit ribosomal RNA gene (23S rRNA) was performed in triplicate 10μl reactions. Reactions were held at 95°C for 3 min to denature the DNA, with amplification performed for 35 cycles (95°C for 10 s and 60°C for 30 s). The forward primer sequence of Enterobacter 23S is AGTGGAACGGTCTGGAAAGG and the reverse primer sequence of Enterobacter 23S TCGGTCAGTCAGGAGTATTTAGC 45 . Induction of NEC NEC is induced in 7- to 8-day-old mice by hand-feeding mice formula via gavage 5 times/day (22-gauge needle; 200μl volume; Similac Advance infant formula [Ross Pediatrics, Columbus, Ohio]/ Esbilac canine milk replacer 2:1). The formula is supplemented with 107 CFUs of Enterobacter spp. (99%) and Enterococcus spp. (1%) and mice are rendered hypoxic (5%O2, 95% N2) for 10 minutes in a hypoxic chamber (Billups-Rothenberg, Del Mar, CA) twice daily for 4 days 46,47 . We used males and females in all experiments. Disease is monitored by weighing mice daily prior to the second feed. The severity of disease was determined on histologic sections of the entire length of the small intestines stained with hematoxylin and eosin by trained personnel who were blinded to the study conditions according to previously published scoring system from 0 (normal) to 4(severe) 48 . Statistics Statistical tests used are indicated in the figure legends. Lines in scatter bar charts represent the mean of that group. Group sizes were determined based on the results of preliminary experiments. Mouse studies were performed in a non-blinded fashion. Statistical significance was determined with the two-tailed unpaired Student’s t-test or non-parametric Mann-Whitney test when comparing two groups and one-way ANOVA with multiple comparisons, when comparing multiple groups. All statistical analyses were calculated using Prism software (GraphPad). Differences were considered to be statistically significant when p < 0.05. Data Availability Statement Patient-related data not included in the paper were generated as part of clinical trials and may be subject to patient confidentiality. The human study protocol was approved by the Institutional Review Board (Protocol Nos. PRO16030078, PRO09110437) of the University of Pittsburgh. All raw and analyzed sequencing data can be found at the NCBI Sequence Read Archive (accession number: PRJNA526906). Algorithm for deconvolution of IgSeq data available on GitHub (https://github.com/handlab/IgA_Seq_Deconvolution). Biological Materials Availability Statement All materials used in the production of this paper are available upon request (timothy.hand@chp.edu). Some reagents may require a Material Transfer Agreement through the University of Pittsburgh. Extended Data Extended Data Fig. 1. Maternal milk-derived antibodies binding to intestinal bacteria from preterm infants. Extended Data Fig. 2. Fraction of intestinal bacteria bound by IgA in preterm infants. Extended Data Fig. 3. Linear discriminant analysis of the microbiota of infants that will develop NEC and controls. Extended Data Fig. 4. Deconvolution method to decrease the effect of contamination in IgSeq. Extended Data Fig. 5. Longitudinal analysis of the intestinal microbiota of preterm infants. Extended Data Fig. 6. Ratio of IgA− to IgA+ reads for low-abundance taxa. Extended Data Fig. 7. Absolute number of bacteria and number of bacteria associated with the dominant taxa in preterm infants. Extended Data Fig. 8. Enterobacter spp. is enriched in the IgA+ fraction of breast-fed mouse pups.
