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ONLINE SUPPLEMENTARY MATERIAL AND METHODS

MATERIAL AND METHODS

Study design

Faecal sample collection

Caregivers were carefully instructed on faecal sample collection and were given plastic

diapers, spatulas and screw-cap plastic containers containing a carbon dioxide generator

system to create an anaerobic atmosphere (Microbiology Anaerocult A mini, Merck,

Darmstadt, Germany). The faecal samples were collected at home into the containers, and

the study team aliquoted and stored them at -20°C the same day until further analysis.

Laboratory methods

Faecal samples

Faecal samples were thawed at 4°C before measurement of faecal pH and faecal

calprotectin. For measurement of pH, 100 mg (±10%) of faeces were added to 1 mL

ultrapure water (>18 MΩ·cm), vortexed for 30 sec and centrifuged for 3 min at 5000 rpm at

4°C; pH in the liquid phase was measured using a digital pH meter (Metrohm, Zofingen,

Switzerland). We measured faecal calprotectin using the Calprest ELISA assay for stools,

following the manufacturer’s procedures (Eurospital,Trieste, Italy).

Faecal samples were thawed at 4°C before DNA extraction and quantification. 700 µL

S.T.A.R. buffer (Roche, Indianapolis, IN, USA) and 250 (±10%) mg of faeces were added to

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0.5 g of 0.1 mm sterilized zirconia beads in a 2.0 mL screw-cap tube. Lysis was performed in

a FastPrep instrument (MP Biomedicals, Santa Ana, CA, USA) at 5.5 ms for 3 times 1 min at

room temperature. Samples were then incubated, while shaking at 100 rpm, at 95°C for 15

min, after which samples were centrifuged at 16000 g for 5 min at 4°C. The supernatant was

collected and kept on ice. The stool pellet was subjected to another lysis round as described

above, except only 350 µL S.T.A.R. buffer was added. The resulting supernatant was pooled

with the supernatant of the first lysis round. Purification of DNA was performed on the

automated Maxwell instrument (Promega, Madison, WI, USA) by applying the Maxwell 16

Tissue LEV Total RNA Purification Kit (Promega) according to the manufacturer’s

instructions. 250 µL of the supernatant was added to the first well of the Maxwell cartridge

and 50 µL of RNAse/DNAse free water was used for elution of the DNA. Quantification was

carried out with a Nanodrop ND-1000 Spectrophotometer (Witec AG, Luzern, Switzerland) at

260 nm.

Using qPCR, we targeted selected enteropathogenic bacteria: Bacillus cereus,

Campylobacter jejuni/Campylobacter coli, Clostridium difficile, Clostridium perfringens,

enterohemorrhagic E. coli producing shiga toxin 1 (EHEC stx1), enterohemorrhagic E. coli

producing shiga toxin 2 (EHEC stx2), enteropathogenic E. coli (EPEC), enterotoxigenic E.

coli producing heat-labile enterotoxin (ETEC LT), enterotoxigenic E. coli producing heat-

stable enterotoxin (ETEC ST), Salmonella spp and Staphylococcus aureus. The primers

used for qPCR are shown in supplementary table 1.

For preparation of the DNA for qPCR we performed pre-amplification using primers shown in

supplementary table 1, Fluidigm Preamp Master Mix (Fluidigm, PN 100-5580, California,

United States) and the 12 recommended cycles as described by the manufacturer (Fluidigm

Quick Reference for Pre-amplification of cDNA for Gene Expression with Delta Gene Assay,

PN 100-5875 C1, Fluidigm). The final reaction products were diluted 5-fold. We prepared

sample pre-mix and assay mix and run the qPCR with BioMark 96.96 Gene Expression

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Dynamic Arrays (PN BMK-M-96.69, Fluidigm, South San Francisco, USA) as described by

the manufacturer (Advanced Development Protocol 41: Using EvaGreen DNA Binding Dye

for Gene Expression with the 48.48 and 96.96 Dynamic Array IFCs, Fluidigm). To generate

standards, target genes were amplified from representative strains. Amplicons were purified

and were cloned into PGEMT Easy Vector and heterologously expressed in E. coli according

to supplier instructions (Promega). Standard curves were prepared from 10-fold dilutions of

linearised plasmids harbouring the gene of interest. Concentrations of the plasmids were

measured using a Qubit dsDNA BR assay kit (Q32850, Thermo Fisher Scientific) in triplicate

on a Spark M10 plate reader (Tecan Group Ltd., Maennedorf, Switzerland). We processed

data with the Fluidigm Real-Time PCR Analysis Software including melting curve analysis.

We used Excel (Microsoft Office 2010) for analysis of the qPCR data. The standard curves

were generated by linear regression analysis of the CT (threshold cycle) values versus the

amounts of the template DNA of the standard (log gene copies/µL). The goodness of fit (r2)

was calculated for each linear regression. Primer efficiency was calculated for each target.

The limit of quantification (LOQ) was set at the last point of the standard line falling in the

linear range. Samples below the LOQ were assigned the log gene copies/g faeces defined

as LOQ/2 and defined as below detection limit. A summary variable was created for all

virulence and toxin genes (VTGs) of pathogenic E. coli (EHEC stx1, EHEC stx2, EPEC

eaeA, ETEC LT and ETEC ST) and the sum of all pathogens (B. cereus, C. difficile,

C. perfringens, EHEC stx1, EHEC stx2, EPEC eaeA, ETEC LT, ETEC ST, Salmonella spp

and S. aureus). To correct for multiple 16S rDNA genes in C. difficile, gene copy numbers

were divided by 11. We simultaneously detected C. jejuni/C. coli using a targeted TaqMan

assay based on the housekeeping gene fusA (encoding elongation factor G) [1,2]. Primers

and probes are given in supplementary table 1. Each sample was tested in duplicate in a

total volume of 25 µL, containing 2.5 µL DNA and 22.5 µL of a premix composed of 1X

TaqMan Fast Advances Master Mix (Thermo Fisher Scientific), 300 nM of each primer, 200

nM of each probe, and 0.5X internal positive control (TaqMan Exogenous Internal Positive

Control Reagents, Thermo Fisher Scientific). Samples were run and analysed on an ABI

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7500 Fast Real-Time PCR System (Applied Biosystems) using standard conditions. For the

analysis a threshold of 0.02 was applied. For quantification, standard curves were prepared

from 10-fold dilutions of genomic DNA of C. jejuni.

Barcoded amplicons from the V3-V4 region of 16S rRNA genes were generated using a 2-

step PCR, and universal primers appended with Illumina adaptors were used for initial

amplification of the V3-V4 part of the 16S rRNA gene with the following sequences: forward

primer, ‘5-CCTACGGGAGGCAGCAG-3’ (broadly conserved bacterial primer 357F); reverse

primer, ‘5-TACNVGGGTATCTAAKCC’ (broadly conserved bacterial primer (with

adaptations) 802R) appended with Illumina adaptor sequences. PCR amplification mixture

contained: 1 µL faecal sample DNA, 1 µL bar-coded forward primer, 15 µL master mix (1 µL

KOD Hot Start DNA Polymerase (1 U/µL; Novagen, Madison, WI, USA), 5 µL KOD-buffer

(10×), 3 µL MgSO4 (25 mM), 5 µL dNTP mix (2 mM each), 1 µL (10 µM) of reverse primer)

and 33 µL sterile water (total volume 50 µL). PCR conditions were: 95°C for 2 min followed

by 35 cycles of 95°C for 20 sec, 55°C for 10 sec, and 70°C for 15 sec. The approximately

500 bp PCR amplicon was subsequently purified using the MSB Spin PCRapace kit (Invitek,

Berlin, Germany) and concentration and quality was subsequently checked with a Qubit

fluorometer (Thermo Fisher Scientific). Purified PCR products were shipped to BaseClear BV

(Leiden, The Netherlands) and used for the second PCR in combination with sample-specific

barcoded primers. PCR products were purified, checked on a Bioanalyzer (Agilent) and

quantified, followed by multiplexing, clustering, and sequencing on an Illumina MiSeq with

the paired-end (2x) 300 bp protocol and indexing. The sequencing run was analysed with the

Illumina CASAVA pipeline (v1.8.3) with demultiplexing based on sample-specific barcodes.

The raw sequencing data produced was processed removing the sequence reads of too low

quality (only "passing filter" reads were selected) and discarding reads containing adaptor

sequences or PhiX control with an in-house filtering protocol. A quality assessment on the

remaining reads was performed using the FASTQC quality control tool version 0.10.0.

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Supplementary Table 1. Primers used for quantitative polymerase chain reaction.

Target Target gene

(description)

PCR primer and sequence (5’-3’) Product

size (bp)

Ref.

Bacillus cereus

Hemolysin

BC-1, CTGTAGCGAATCGTACGTATC

BC-2, TACTGCTCCAGCCACATTAC

185 [3]

Campylobacter

jejuni/

Campylobacter coli

fusA gene

(encoding

elongation

factor G)

Ccj_fusA-L1, GCCTTGAGGAAATTAAAACTGGTATT

Ccj_fusA-L2, GCCTTGAAGAGATTAAAACAGGGATT

Ccj_fusA-R1, TTTAAATGCAGTTCCACAAAGCA

Ccj_fusA-R2, TTTAAACGCTGTACCGCAAAGCA

Cj_fusA-probe, FAM-AAGTCTTTCTATCGTTCC-MGBNFQ

Cc_fusA-probe, FAM-AAGTCTTTCTATTGTTCC-MGBNFQ

[1,2]

Clostridium difficile

16S rRNA

gene

cdF, TTGAGCGATTTACTTCGGTAAAGA

cdR, CCATCCTGTACTGGCTCACCT

157 [4]

Clostridium

perfringens

pcl (alpha

toxin)

plcF, AAGTTACCTTTGCTGCATAATCCC

plcR, ATAGATACTCCATATCATCCTGCT

283 [5]

Enterohemorrhagic

Escherichia coli

(EHEC)

Stx1 (shiga

toxin)

JMS1F, GTCACAGTAACAAACCGTAACA

JMS1R, TCGTTGACTACTTCTTATCTGGA

95 [3]

Enterohemorrhagic

Escherichia coli

(EHEC)

Stx2 (shiga

toxin)

JMS2F, CGACCCCTCTTGAACATA

JMS2G, GATAGACATCAAGCCCTCGT

108 [3]

Enteropathogenic

Escherichia coli

(EPEC)

eaeA (E. coli

attaching and

effacing)

EAE-a, ATGCTTAGTGCTGGTTTAGG

EAE-b, GCCTTCATCATTTCGCTTTC

248 [3]

Enterotoxigenic

Escherichia coli

(ETEC)

LT (heat-labile

enterotoxin)

LT-1, AGCAGGTTTCCCACCGGATCACCA

LT-2, GTGCTCAGATTCTGGGTCTC

132 [3]

Enterotoxigenic

Escherichia coli

(ETEC)

ST (heat-

stable

enterotoxin)

STa-F, GCTAATGTTGGCAATTTTTATTTCTGTA

STa-R, AGGATTACAACAAAGTTCACAGCAGTAA

190 [3]

Salmonella spp.

invA (invasion)

invA,139, GTGAAATTATCGCCACGTTCGGGCAA

invA, 141, TCATCGCACCGTCAAAGGAACC

284 [3]

Staphylococcus

aureus

Nuclease SA-1, GCGATTGATGGTGATACGGTT

SA-2, CAAGCCTTGACGAACTAAAGC

276 [3]

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Statistical methods

We analysed the 16S rRNA gene sequences using a workflow based on Qiime 1.8 [6].

Operational taxonomic unit (OTU) clustering (open reference), taxonomic assignment and

reference alignment were done with the pick_open_reference_otus.py workflow script of

Qiime, using uclust as clustering method (97% identity) and GreenGenes v13.8 as reference

database for taxonomic assignment. Reference-based chimera removal was done with

Uchime [7]. The RDP classifier version 2.2 was performed for taxonomic classification [8].

For the Bifidobacterium genus specific analysis OTUs assigned to Bifidobacterium were re-

clustered at the 99% identity level and then analysed further on composition and diversity.

Statistical tests were performed as implemented in SciPy (https://www.scipy.org/),

downstream of the Qiimebased workflow.

Multivariate redundancy analysis (RDA) was performed in Canoco V.5.0.4 using default

settings of the analysis type “Constrained” [9]. For the RDA, relative abundance values of

OTUs were used as response data and as explanatory variable we used: 1) group effect

(Fe+Ab+, Fe-Ab+, Fe+Ab-, Fe-Ab-) and 2) antibiotic effect (Ab+ groups (Fe+Ab+ and Fe-Ab+) and

Ab- groups (Fe+Ab- and Fe-Ab-)). For visualization purposes families or genera (and not

OTUs) were plotted as supplementary variables. RDA calculates p-values by permutating

(Monte Carlo) the sample status. Longitudinal effects of intervention were tested by

calculating 2log ratios in which the relative abundance of an OTU at D5, D10, D20 and D40

was divided by the relative abundance of an OTU at D0. These ratios were used as response

variables in RDAs (ratio RDAs), and were weighted based on the average relative

abundance of each OTU in all infants. Taxa names in brackets are annotations supplied by

the Greengenes database and are not officially accepted by the Society for General

Microbiology.

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We analysed the relative abundances of the main taxa of interest of the 16S rDNA

sequencing (Bacteroidetes, Bifidobacterium, Clostridiales, Enterobacteriaceae,

Lactobacillus), using the R statistical programming environment (R 3.4.1 software; R Core

Team). Between groups differences of the relative abundances of the main taxa of interest at

each time point were tested using Kruskal Wallis test with Dunn’s post hoc test using the

Benjamini & Hochberg method to adjust for multiple comparisons. Within group differences

from D0 to D5, from D0 to D10, from D0 to D20 and from D0 to D40 were tested using

Wilcoxon rank sum tests. The 2log ratio in which the relative abundance of the taxa at D5,

D10, D20 and D40, was divided by the relative abundance of the taxa at D0, was calculated

for each main taxon of interest and to assess between group differences we used Kruskal

Wallis test with Dunn’s post hoc test using the Benjamini & Hochberg method to adjust for

multiple comparisons.

We analysed demographic data, faecal calprotectin and pH, entheropathogen abundances

and phylogenetic distances (weighted UniFrac), alpha diversity (phylogenetic diversity (PD),

observed species and Shannon diversity indices), using the R statistical programming

environment (R 3.4.1 software; R Core Team). We ran descriptive statistics for all variables.

We assessed normality by testing the distribution of continuous variables against a normal

distribution using Shapiro-Wilk W test. When departing significantly from normality, the

variables were transformed with the use of log(x), sqrt(x), or -1/x to correct positive skewness

or x2, x3, or antilog(x) to correct negative skewness. When W > 0.97 could be achieved by

transformation, parametric tests were used for further data analyses; otherwise non

parametric tests were applied. Values in the tables are presented as means ± SDs for

normally distributed data and as medians (IQRs) for non-normal data. We tested for group

differences at D0 using one-way ANOVA or Kruskal Wallis tests for continuous variables and

Chi-square tests for binomial variables. We used paired t-tests or Wilcoxon rank sum tests to

test within group changes from D0 to D5, from D0 to D10, from D0 to D20 and from D0 to

D40. We used one-way ANOVA with Tukey’s hones significance post hoc test or Kruskal

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Wallis test with Dunn’s post hoc test using the Benjamini & Hochberg method to adjust for

multiple comparisons, to test between group differences at each time point. We used

Spearman correlations to test the associations between faecal calprotectin and the sum of

VTGs of pathogenic E. coli, the sum of all pathogens and faecal pH.

We assessed infant morbidity as longitudinal prevalence, calculated as days with illness (RTI

or diarrhoea and/or mucus in stool) from day 6 to day 40, divided by the total days of

observation (from day 6 to day 40). We calculated longitudinal prevalence ratio (LPR) and

95% CI using Poisson regression with robust standard errors [10]. We calculated p-values as

described by Altman DG and Bland JM [11].

Due to the small number of subjects, we evaluated not only statistically significant

differences, but also individual trends. We mention p values < 0.10 as statistical trends as

they may provide insights for other researchers in this new research area linking iron,

antibiotics and the gut microbiome. We considered p values < 0.05 as statistically significant.

RESULTS

Gut microbiome by 16S rDNA sequencing

Between group differences in Bifidobacterium OTUs

The ratio RDA analysis on Bifidobacterium OTUs showed significant differences in

Bifidobacterium composition between groups. In the ratio RDA from D0 to D5 and from D0 to

D10 there were significant differences in Bifidobacterium composition when comparing both

Ab+ with both Ab- groups (7.1%, p=0.016 and 10.3%, p=0.010, respectively). The Ab+ groups

were enriched for Bifidobacterium longum like OTUs, while the Ab- groups were more often

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associated with OTUs related to B. adolescentis, B. bifidum, B. breve and B. ruminantium.

When looking at the four groups separately, there was a significant difference in

Bifidobacterium composition in the ratio RDA from D0 to D10 (16.8%, p=0.006)

(supplementary figure 4A): the Fe+Ab+ group was predominantly associated with B. longum

OTUs, while the other 3 groups were more often associated with B. adolescentis, B. bifidum

and B. ruminantium related OTUs. Phylogenetic diversity (PD) and observed species index

showed no significant between group differences indicating that the number of different

Bifidobacterium species/phylotypes was not significantly affected (supplementary figure 4B

and 4C). However, both, PD and observed species index, tended to decrease over time in

the groups receiving antibiotics and/or iron whereas they remained more stable over time in

the Fe-Ab- group (supplementary figure 4A and 4B). Diversity analysis of Bifidobacterium

OTUs using Shannon index, that reflects both the abundance and evenness of species,

showed significant differences between groups, indicating that within the Bifidobacterium

genus shifts occur that alter the distribution of Bifidobacterium species. The change in the

Shannon index was significantly different between groups for D0 to D5 (p=0.017), for D0 to

D10 (trend, p=0.079) and for D0 to D40 (trend, p=0.052), with a significant decrease in the

Fe-Ab+ group from D0 to D5 (p=0.031), and a significant decrease in the Fe+Ab+ group from

D0 to D10 and D0 to D20 (p=0.031 and p=0.047, respectively) (low values indicating uneven

distribution of Bifidobacterium spp). In contrast, there were no within group differences and

overall higher values (indicating even distribution) in the Fe-Ab- group (online supplementary

figure 4D).

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SUPPLEMENTARY FIGURES

Supplementary Figure 1. Gut microbiome composition as assessed by 16S rDNA

sequencing among Kenyan infants at baseline (D0; n=28). The fraction of 16S rDNA reads

(in %) attributed to specific taxonomic levels is given below the taxon name.

domain phylum class order family genus species

BacteriaBacteriaBacteriaBacteriaBacteria99.98%99.98%

ActinobacteriaActinobacteriaActinobacteriaActinobacteriaActinobacteria67.25%67.25%

BacteroidetesBacteroidetesBacteroidetesBacteroidetesBacteroidetes1.09%1.09%

FirmicutesFirmicutesFirmicutesFirmicutesFirmicutes28.98%28.98%

ProteobacteriaProteobacteriaProteobacteriaProteobacteriaProteobacteria2.48%2.48%

ActinobacteriaActinobacteriaActinobacteriaActinobacteriaActinobacteria57.21%57.21%

CoriobacteriiaCoriobacteriiaCoriobacteriiaCoriobacteriiaCoriobacteriia10.03%10.03%

BacteroidiaBacteroidiaBacteroidiaBacteroidiaBacteroidia1.09%1.09%

BacilliBacilliBacilliBacilliBacilli15.00%15.00%

ClostridiaClostridiaClostridiaClostridiaClostridia13.08%13.08%

ErysipelotrichiErysipelotrichiErysipelotrichiErysipelotrichiErysipelotrichi0.89%0.89%

GammaproteobacteriaGammaproteobacteriaGammaproteobacteriaGammaproteobacteriaGammaproteobacteria2.45%2.45%

BifidobacterialesBifidobacterialesBifidobacterialesBifidobacterialesBifidobacteriales57.05%57.05%

CoriobacterialesCoriobacterialesCoriobacterialesCoriobacterialesCoriobacteriales10.03%10.03%

BacteroidalesBacteroidalesBacteroidalesBacteroidalesBacteroidales1.09%1.09%

LactobacillalesLactobacillalesLactobacillalesLactobacillalesLactobacillales14.97%14.97%

ClostridialesClostridialesClostridialesClostridialesClostridiales13.08%13.08%

ErysipelotrichalesErysipelotrichalesErysipelotrichalesErysipelotrichalesErysipelotrichales0.89%0.89%

EnterobacterialesEnterobacterialesEnterobacterialesEnterobacterialesEnterobacteriales2.42%2.42%

BifidobacteriaceaeBifidobacteriaceaeBifidobacteriaceaeBifidobacteriaceaeBifidobacteriaceae57.05%57.05%

CoriobacteriaceaeCoriobacteriaceaeCoriobacteriaceaeCoriobacteriaceaeCoriobacteriaceae10.03%10.03%

BacteroidaceaeBacteroidaceaeBacteroidaceaeBacteroidaceaeBacteroidaceae0.37%0.37%

PrevotellaceaePrevotellaceaePrevotellaceaePrevotellaceaePrevotellaceae0.68%0.68%

EnterococcaceaeEnterococcaceaeEnterococcaceaeEnterococcaceaeEnterococcaceae0.48%0.48%

LactobacillaceaeLactobacillaceaeLactobacillaceaeLactobacillaceaeLactobacillaceae4.97%4.97%

StreptococcaceaeStreptococcaceaeStreptococcaceaeStreptococcaceaeStreptococcaceae9.42%9.42%

ClostridiaceaeClostridiaceaeClostridiaceaeClostridiaceaeClostridiaceae0.63%0.63%

LachnospiraceaeLachnospiraceaeLachnospiraceaeLachnospiraceaeLachnospiraceae5.80%5.80%

RuminococcaceaeRuminococcaceaeRuminococcaceaeRuminococcaceaeRuminococcaceae3.40%3.40%

VeillonellaceaeVeillonellaceaeVeillonellaceaeVeillonellaceaeVeillonellaceae2.62%2.62%

ErysipelotrichaceaeErysipelotrichaceaeErysipelotrichaceaeErysipelotrichaceaeErysipelotrichaceae0.89%0.89%

EnterobacteriaceaeEnterobacteriaceaeEnterobacteriaceaeEnterobacteriaceaeEnterobacteriaceae2.42%2.42%

BifidobacteriumBifidobacteriumBifidobacteriumBifidobacteriumBifidobacterium57.05%57.05%

CollinsellaCollinsellaCollinsellaCollinsellaCollinsella7.04%7.04%

BacteroidesBacteroidesBacteroidesBacteroidesBacteroides0.37%0.37%

PrevotellaPrevotellaPrevotellaPrevotellaPrevotella0.68%0.68%

EnterococcusEnterococcusEnterococcusEnterococcusEnterococcus0.48%0.48%

LactobacillusLactobacillusLactobacillusLactobacillusLactobacillus4.97%4.97%

StreptococcusStreptococcusStreptococcusStreptococcusStreptococcus9.40%9.40%

BlautiaBlautiaBlautiaBlautiaBlautia2.53%2.53%

CoprococcusCoprococcusCoprococcusCoprococcusCoprococcus0.82%0.82%DoreaDoreaDoreaDoreaDorea0.73%0.73%

[Ruminococcus][Ruminococcus][Ruminococcus][Ruminococcus][Ruminococcus]0.71%0.71%

FaecalibacteriumFaecalibacteriumFaecalibacteriumFaecalibacteriumFaecalibacterium1.13%1.13%

MegasphaeraMegasphaeraMegasphaeraMegasphaeraMegasphaera1.51%1.51%

VeillonellaVeillonellaVeillonellaVeillonellaVeillonella0.85%0.85%

[Eubacterium][Eubacterium][Eubacterium][Eubacterium][Eubacterium]0.32%0.32%

Bifidobacterium_longumBifidobacterium_longumBifidobacterium_longumBifidobacterium_longumBifidobacterium_longum24.49%24.49%

Collinsella_aerofaciensCollinsella_aerofaciensCollinsella_aerofaciensCollinsella_aerofaciensCollinsella_aerofaciens6.99%6.99%

Prevotella_copriPrevotella_copriPrevotella_copriPrevotella_copriPrevotella_copri0.62%0.62%

Lactobacillus_ruminisLactobacillus_ruminisLactobacillus_ruminisLactobacillus_ruminisLactobacillus_ruminis2.69%2.69%

[Ruminococcus]_gnavus[Ruminococcus]_gnavus[Ruminococcus]_gnavus[Ruminococcus]_gnavus[Ruminococcus]_gnavus0.43%0.43%

Faecalibacterium_prausnitziiFaecalibacterium_prausnitziiFaecalibacterium_prausnitziiFaecalibacterium_prausnitziiFaecalibacterium_prausnitzii1.13%1.13%

Veillonella_disparVeillonella_disparVeillonella_disparVeillonella_disparVeillonella_dispar0.57%0.57%

[Eubacterium]_biforme[Eubacterium]_biforme[Eubacterium]_biforme[Eubacterium]_biforme[Eubacterium]_biforme0.27%0.27%

11

Supplementary Figure 2. Redundancy analysis (RDA) by group. Kenyan infants (n=28)

consuming daily a micronutrient powder with iron (Fe+ groups) or without iron (Fe- groups)

and after receiving antibiotic treatment for 5 days (Ab+ groups) or no antibiotic treatment (Ab-

groups). With ratio RDAs the change in relative abundance over time (2log ratios) within

individuals was analysed. In the ratio RDA of D0 to D5 (A) the variation in the microbiota that

could be explained by group was 4.9% (trend, p=0.066). In the ratio RDA of D0 to D5 (B) the

variation in the microbiota that could be explained by antibiotic treatment was 3.3% (trend,

p=0.054) and in the ratio RDA of D0 to D10 (C) the variation in the microbiota that could be

explained by antibiotic treatment was 5.4% (p=0.010).

C

A B

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Supplementary Figure 3. (A) Alpha diversity (phylogenetic diversity (PD) whole tree metric)

and (B) phylogenetic distance (weighted UniFrac), by group. Differences in these variables

among Kenyan infants (n=28) at D0 and at D5, D10 and D40 after consuming daily a

micronutrient powder with iron (Fe+ groups) or without iron (Fe- groups) and after receiving

antibiotic treatment for 5 days (Ab+ groups) or no antibiotic treatment (Ab- groups). Within

group differences of the phylogenetic diversity from D0 to D5, from D0 to D10 and from D0 to

D40 were tested using paired t-tests; between group differences were tested using one-way

ANOVA with Tukey’s post hoc test with correction for multiple comparison; *p<0.05. Between

group differences of the UniFrac distance were tested using Kruskal Wallis test with Dunn’s

post hoc test.

13

Supplementary Figure 4. (A) Redundancy analysis (RDA) of Bifidobacterium OTUs, by

group shows significant differences in Bifidobacterium composition in the ratio RDA from D0

to D10 (16.8%, p=0.006). The Fe+Ab+ group was predominantly associated with B. longum

OTUs, while the other 3 groups were more often associated with B. adolescentis, B.bifidum

and B. ruminantium related OTUs. (B) Phylogenetic diversity, (C) observed species index

and (D) Shannon index of Bifidobacterium species, by group. Differences in these metrics

among Kenyan infants (n=28) at D0 and at D5, D10, D20 and D40 after consuming daily a

micronutrient powder with iron (Fe+ groups) or without iron (Fe- groups) and after receiving

antibiotic treatment for 5 days (Ab+ groups) or no antibiotic treatment (Ab- groups). Between

group difference using Kruskal Wallis test with Dunn’s post hoc test to adjust for multiple

comparisons; *p<0.05. aWithin group difference from D0 to D5 and from D0 to D10 using

Wilcoxon rank sum tests; *p<0.05.

14

REFERENCES

1. Schnider A, Overesch G, Korczak MB, et al. Comparison of Real-Time PCR Assays for Detection, Quantification, and Differentiation of Campylobacter jejuni and Campylobacter coli in Broiler Neck Skin Samples. Journal of Food Protection 2010;73:1057-63.

2. Haas K, Overesch G, Kuhnert P. A Quantitative Real-Time PCR Approach for Assessing Campylobacter jejuni and Campylobacter coli Colonization in Broiler Herds. Journal of Food Protection 2017;80:604-08.

3. Fukushima H, Tsunomori Y, Seki R. Duplex Real-Time SYBR Green PCR Assays for Detection of 17 Species of Food- or Waterborne Pathogens in Stools. Journal of Clinical Microbiology 2003;41:5134-46.

4. Rinttila T, Kassinen A, Malinen E, et al. Development of an extensive set of 16S rDNA-targeted primers for quantification of pathogenic and indigenous bacteria in faecal samples by real-time PCR. J Appl Microbiol 2004;97:1166-77.

5. Rinttila T, Lyra A, Krogius-Kurikka L, et al. Real-time PCR analysis of enteric pathogens from fecal samples of irritable bowel syndrome subjects. Gut Pathog 2011;3:6.

6. Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010;7:335-6.

7. Edgar RC, Haas BJ, Clemente JC, et al. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011;27:2194-200.

8. Cole JR, Wang Q, Cardenas E, et al. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 2009;37:D141-5.

9. Braak CJF, Smilauer P. CANOCO Reference manual anc CanoDraw for Windows User's guide: Software for Canonical Communiy Ordination. Microcomputer Power (Ithaca, NY, USA) 2002.

10. Zou G. A Modified Poisson Regression Approach to Prospective Studies with Binary Data. American Journal of Epidemiology 2004;159:702-06.

11. Altman DG, Bland JM. How to obtain the P value from a confidence interval. Bmj 2011;343:d2304-d04.