Development of gut microbiota in infants not exposed to medical interventions

Development of gut microbiota in infants not exposed to medicalinterventions

Merete Eggesbø1,2, Birgitte Moen3, Shyamal Peddada4, Donna Baird2, Jarle Rugtveit5,Tore Midtvedt6, Pierre R. Bushel4, Monika Sekelja3, and Knut Rudi31 Department of Genes and Environment, Division of Epidemiology, Norwegian Institute of PublicHealth, Oslo, Norway2 Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes ofHealth, Department of Health and Human Services, Research Triangle Park, North Carolina, USA3 The Norwegian Institute of Food, Fisheries and Aquaculture, Nofima, Osloveien 1, N-1430 Ås,Norway4 Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes ofHealth, Department of Health and Human Services, Research Triangle Park, North Carolina, USA5 Departments of Pediatrics, Oslo University Hospital, Ullevaal, Oslo, Norway6 Laboratory of Medical Microbial Ecology, Karolinska Institute, Stockholm, Sweden

SummaryKnowledge of the composition of a normal healthy gut microbiota during infancy is important forunderstanding the role of gut microbiota in disease. A limitation of previous studies is that they arebased on infants who have been subject to factors which can have a profound disruptive effect onthe natural colonization process.

We describe the colonization process, during the first four months after birth, in 85 infants whohave experienced no major medical or dietary interventions. They were all vaginally delivered,healthy, term infants, who were not exposed to antibiotics, exclusively breastfed during their firstmonth of life and at least partially breastfed up to four months. Selected microbial groups wereidentified by targeting small subunit microbial ribosomal RNA genes.

In contrast to more recent studies, but in agreement with older studies, almost all our infantsharbored γ-proteobacteria and Bifidobacterium..Yet undefined non-cultivable species belonging toBacteroides, as well as microbes identified as Lachnospiraceae 2, were common. Strongassociations were observed between some specific constituents of microbiota at day 4 and theconcentration of specific microbial groups at day 120, indicating that early gut microbiota mayinfluence later microbiota. Novel information of the undisturbed composition of early gutmicrobiota in babies is presented.

KeywordsGut microbiota; infants; colonization; bifidobacterium; enterobacterium; microarray

Correspondence to: Dr. Merete Eggesbø. Department of Genes and Environment, Division of Epidemiology, Norwegian Institute ofPublic Health, P.O. Box 4404 Nydalen, N - 0403 OSLO, NORWAY. Visiting address: Marcus Thranesgt 6, Oslo.Phone: +47 21 70 8206 Telefax: 47 21 70 82 52, [email protected].

NIH Public AccessAuthor ManuscriptAPMIS. Author manuscript; available in PMC 2012 January 1.

Published in final edited form as:APMIS. 2011 January ; 119(1): 17–35. doi:10.1111/j.1600-0463.2010.02688.x.

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IntroductionGut microbiota has received an increasing amount of attention over the last decades as wehave come to understand its crucial role in host biology and human health (1-6). Gutmicrobiota in early infancy is of special interest due to the presence of developmentalwindows that relies on microbial stimulus from the gut, involving development of tolerance(7,8), angiogenesis (9) and stress responses (10). Furthermore, early gut microbiota is ofinterest as a determinant of the final stable adult like microbiota (11-14).

However, we have limited knowledge of early gut microbiota (15-17). A major limitation ofprevious studies is that they are based on infants who have been subject to factors which canhave a profound disruptive effect on the natural colonization process. This has been clearlydemonstrated for cesarean delivery (18-22), use of antibiotics in the perinatal period (23,24)and neonatal intensive care (25,26). Since caesarean deliveries have increased up to tenfoldsince the early 19nth century, and up to one-third of babies are now delivered by a sterilemode of birth (27) this complicates the comparison of older and more recent studies inunselected populations. More importantly, some of these gut modifying factors, specificallycesarean delivery and early exposure to antibiotics, may be associated with a dysfunctionalgut microbiota and a possible increased risk of immune related diseases in childhood(28-34). This underscores the importance of studying infants who have not been subjected tomedical interventions when aiming at describing a healthy natural gut microbiota.

The aim of this paper is to characterize the composition of early gut microbiota in 85 infantswho have experienced no major medical or dietary interventions. We use 22 selected geneticprobes that were developed for this study, encompassing both known and unknown gutmicrobes, to describe the composition of gut microbiota four days after delivery and up tofour months of age. We also identify microbial clusters and show how neonatal gutmicrobiota is associated with microbiota at 4 months of age.

Materials and MethodsCohort design

We analyzed gut microbiota from a subset of infants enrolled in The Norwegian MicrofloraStudy (“NoMic”), a birth-cohort which was established for the overall purpose of studyingthe establishment of gut microbiota during infancy and its consequences for child health.Participating mothers were recruited to the NoMic study by a pediatrician at the maternityward in a county hospital in South-Norway. When a preterm-birth mother was enrolled, twomothers of consecutively born term-infants were recruited. The recruitment started inNovember 2002 and was completed in May 2005. Mothers who were fluent in Norwegianand who were resident in the geographical area were eligible for the cohort. The study wasapproved by the Norwegian Data Inspectorate and the Regional Ethics Committee forMedical Research.

Containers for fecal samples and a questionnaire were handed out to the participants at thematernity ward after the informed consent form had been signed. Mothers were asked tocollect and freeze one fecal samples from herself at postpartum day four, as well as samplesfrom her infant when it was 4, 10, 30 and 120 days old. Date and time of collection wererecorded for each sample, as well as the time until the samples were placed in the freezer.Study personnel retrieved samples and kept them frozen during transport. Samples werestored at −20 °C at the Biobank of the Norwegian Institute of Public Health upon arrival.Further questionnaires were sent to the families when their infants were 6, 12, 18 and 24months. Further follow up is planned at 7 and 12 years.

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Study sampleSix hundred and one mothers agreed to participate in the NoMic study, but 86 (14%) ofthese mothers never returned any fecal samples, leaving 524 infants from whom we hadavailable fecal samples, at one or more occasions. In the present paper we describe the infantmicrobiota from birth to four months of age among infants without major dietary or medicalinterventions. Thus, we restricted the analysis in the current paper to the subset of infantswho had been vaginally delivered, had not been transferred to an intensive care unit, wereexclusively breastfed for one month and still breastfed (partially or exclusively) at 4 monthsof age, had not received any antibiotics, and their mothers had not received antibiotics at anytime during the last three months before delivery, nor the first four months after delivery.We also included only infants born at term with a birth weight above 2500g. In total 87infants fulfilled these criteria. Two of these were excluded because they were missing morethan one of the four fecal samples that we analyzed (4, 10, 30 and 120 days). The remaining85 maternal infant pairs constitute the study sample.

Data collection of infant and maternal characteristics—For the present paper weused data from the questionnaires filled in by the mothers after delivery (median age 9.5weeks, mean 11.0), to obtain information on birth weight, cesarean delivery, maternal age,education, body mass index at beginning of pregnancy, parity, maternal smoking afterdelivery, ethnicity and whether the infant had been transferred to an intensive care unit. Wealso used this questionnaire, as well as the questionnaire filled out when the infant was sixmonth, to obtain data on breastfeeding, the age at when milk substitutes were first given andmaternal and infant use of antibiotics. We obtained information on gestational age andpreterm delivery from the Medical Birth Registry of Norway (MBR) (35). We validatedmaternal reports by comparing these with information in the Norwegian Medical BirthRegistry on cesarean delivery, perinatal infections and systemic antibiotic use. None of theinfants in our study sample had been subjected to cesarean delivery or perinatal infectionsaccording to this registry.

Analysis of faecal samplesTo develop the probes for determining the microbial constituents of our infants, ten childrenwere randomly selected among those who had been delivered by cesarean section and tenchildren among those who had had a normal delivery. 16S rRNA gene clone libraries wereconstructed from DNA extracted from the fecal samples obtained on day 4, 10, 30 and 120.The sequences were transformed into multimer frequencies (n _ 5) by the in-house-developed computer program PhyloMode(www.matforsk.no/web/sampro.nsf/downloadE/Microbial_community). The transformationwas based on sliding a window of 5 nucleotides along a DNA sequence and counting thefrequencies of the different multimers encountered. The sizes of the multimer windows werechosen as tradeoffs between detecting phylogenetic signals (homologous multimerequalities), avoiding base composition biases due to nonhomologous multimer equalities,and the requirements for computer operation time (36). The multimer frequency data werecompressed using principal component analysis (PCA) as previously described for thealignment-independent bilinear multivariate modeling (AIBIMM) approach (36). Thephylogenetic content in the PCA was evaluated by cross-validation, while the potentialpresence of chimerical sequences was determined empirically by conflicting multimerloadings. AIBIMM is related to the Tetra approach previously published by Teeling et al.(37). The main difference, however, is that Tetra is designed for the detection of skewedtetranucleotide distribution in whole genomes, while AIBIMM is designed to detectphylogenetic signals in single genes (36). A detailed description for the phylogeneticinterpretation of the AIBIMM data has been given elsewhere (38).

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http://www.matforsk.no/web/sampro.nsf/downloadE/Microbial_community

The probes were constructed using a beta version of the probe construction softwareTNTProbeTool, a software under development for efficient design of Single NucleotideExtension (SNE) probes for analysis of microbial communities. The basic process is toidentify probes which are unique to a target phylogroup, and hybridize neither to otherlabeling probes (cross-labeling) nor to other complementary probes immobilized on thearray (cross-binding). The selected labeling probes should not form secondary structures,which may result in self-labeling.

The format of input file for TNTProbeTool is “.pcam”. This format stands for PCA model ofgene phylogeny and is obtained using a 16S rRNA gene sequence file (in “.fasta” or “.txt”format) and PhyloMode program. The sequence file should ideally contain a comprehensivelibrary of all 16S rRNA gene sequences likely to be found in samples in question. The usercan then find selective probes by defining manually the probes' target and non-targetbacteria. This process is defined into several steps, where each step consists of a report onresults from the last step and a choice of parameters for the next. The probes that passthrough all filters and tests are then presented visually (where they are supposed to hybridizeto target sequences) and/or stored in a “.fasta”/”.txt” file.

The probes were constructed based on a limited 389 clone dataset and subsequentlyevaluated on a 3845 clone dataset (16,39), using Basic Local Alignment Search Tool(BLAST)(40,41) on a local database containing the dataset. The homologues identified byBlast were manually examined using the same criteria as in TNTProbeTool for determiningpotential target sequences. All constructed probes were also evaluated experimentally bycloned target sequences, and closest non-target sequences. Finally, the compatible probesthat satisfied the criteria for target detection, and exclusion of non-target were included inthe array.

Automated DNA purification—Feces were prepared for analysis by adding one milliliterSolution 1 (50 mM glucose, 25 mM Tris–HCl pH 8.0, 10 mM EDTA pH 8.0) per 0.2 gfaeces. The samples were mixed by vortexing and left for 30-60 min on ice before 400 μl ofthe supernatant was diluted 1:2 in 4 M guanidinium thiocyanate (GTC). Five hundredmicroliters of sample were transferred to a sterile FastPrep®-tube (Qbiogene Inc., Carlsbad,CA, USA) containing 250 mg glass beads (106 microns and finer, Sigma-Aldrich,Steinheim, Germany), and samples were homogenised for 40 seconds in FastPrep®

Instrument (Qbiogene). Wells in a 96-well Greiner U-plate (Greiner bio-one, Frickenhausen,Germany) were filled with 170 μl sample and 10 μl Silica particles (Merck, Darmstadt,Germany) and transferred to a Biomek® 2000 Workstation (Beckman Coulter, Fullerton,CA, USA). One percent Sarkosyl was added, and the plate was incubated at 65 °C for 10min and at room temperature for 10 min. The supernatant was removed, and theparamagnetic beads were washed twice with 50% ethanol. DNA was eluted from the silicaparticles by suspension of the particles in 100 μl Buffer C (1 mM EDTA pH 8.0, 10 mMTris–HCl pH 8.0) at 65 °C for 30 min.

PCRThe PCR was performed using 16S universal primers (42). This is a relatively short ¨460 bpamplicon, which is a trade-off between PCR robustness and level of phylogeneticinformation. The PCR mixture contained 0.2 μM of each primer, 1Unit DyNAzyme™ II Hotstart DNA polymerase (Finnzymes), 1×Hot Start Buffer, 200 μM Deoxyribonucleotidetriphosphate (dNTP) mix, and 2 μl DNA in a 25 μl PCR reaction. The amplification profilewas developed with an initial step at 95°C for 10 min, then 30 cycles of 95°C for 30 s, 60°Cfor 30 s, and 72°C for 45 s, and a final extension at 72°C for 7 min. 3U Shrimp AlkalinePhosphatase was added to 10 μl PCR product to remove the phosphate groups from the

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excess dNTP's left over from the PCR. The reaction was incubated at 37°C for one hour andthen inactivated by incubation at 80°C for 15 min.

Labeling—Cyclic labelling reaction was performed using a probe mixture of all 27 probesand the 16S universal probe. The reaction mixture consisted of 0.1 μM of each of the 27probes and 0.3 μM of the 16S universal probe, 1×Thermo Sequenase™ Buffer, 1 μMTAMRA-labelled ddCTP, 3.2U Termo Sequenase™ DNA polymerase (GE Healthcare), and2 μl of SAP treated PCR product in a 10 μl reaction volume. The cyclic labelling profile was10 cycles of 96°C for 10 s, 50°C for 5 s, and 60°C for 30 s. The labelled product was thenSAP treated as described above using 2U SAP in a 10 μl reaction volume. One μl of theSAP treated labelling product was run on a 2.5% agarose gel to check the labelling.

Microarrays, prehybridization and hybridization—The arrays were printed byTeleChem International, Inc. (ArrayIt) on superepoxy slides.The arrays were prehybridizedin BlockIt (ArrayIt) for 2 hours at room temperature. The prehybridized arrays were thenwashed in room temperature 2×SSC/0.03% sarkosyl for 2 min, 2×SSC for 2 min, boilingH2O for 2 min, and ice cold 100% EtOH for < 2 min. The arrays were dried bycentrifugation at 90 times gravity for 12 min and used within one hour. The SAP treatedlabeling product was mixed with 60 μl hybridization buffer (7.2% PEG 8000/1.2xSSC) and0.1μl of fluorescent spike-mix (1:4 TAMRA-labeled fluorescent spike to unlabelled spike).This fluorescent spike was later used in the normalization of signals. The samples wereheated to 95°C for 2 minutes and then cooled to 45° C before being applied to the array. Thearrays were hybridized over night at 45°C with gentle agitation. The arrays were thenwashed in 2×SSC/0.03% Sarkosyl for 5 min, 2×SSC for 5 min, and then 0.2×SSC for 10 s.The arrays were dried by centrifugation 90×g for 12 min and scanned the same day. Thearrays were scanned with the Tecan LS Reloaded basic scanner following the manufacturer'sguidelines. The scanning intensity was set so that none of the spots reached saturation. Thefluorescent spot intensities were quantified using Imagene™ 5.6.1 (BioDiscovery Inc.)software. The data were pre-processed in DataGetter (Text S1) and normalized in Excel asdescribed by the following formula: ([(probe X sample Y / average fluorescent spike sampleY) - (probe X NTC / average fluorescent spike NTC)] / average 16S Universal sample Y) *1×10−6 ). The universal 16S rDNA probe was used for normalization since this probe regionis universally distributed among bacteria. The samples were analyzed in two differentbatches (that is two separate oligo synthesis for array printing).

Data analysis—Labeled DNA extracted from the fecal samples were spotted on the arraysin triplicates. Because there is no absolute cutoff that can be empirically demonstrated toidentify absence of a microbial group, we required that a microbial group be detected in atleast two of the three replicates. The lowest value detected when at least two replicatesyielded detectable values was thus treated as the detection limit for that probe for thatperson. We chose this approach rather than some arbitrary detection level because when weexamined samples for which all of the replicates showed detectable levels, we found that allprobes included samples with very low concentrations (Table 1). This also has the advantageof taking into account the obvious differential performance of different probes (Table 2).

For the analysis of concentration, samples for which a probe was non-detectable weretreated as “0” concentration. Since the concentration data on each replicate exhibited a longtail distribution, we log transformed the data after adding ‘1’ to all observations to avoidcomputing log of zero. The resulting transformed data exhibited approximately symmetricdistribution, consequently means of the transformed triplicate values were used in analysesof concentration.

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We calculated the prevalence of each probe; the proportion of infants with detectable levels(non-zero concentration (present) or zero concentration (absent) at each time point, andmade a graph showing the changes in these data over time. We also made a graph on themean change in concentration (expression level) for each probe over time relative to theconcentration at day 4. Tests for significant changes in these pattern analyses (both bivariatecomparing day 4 and day 120, as well as trend tests) were conducted using a software calledORIOGEN 3.0 (Order Restricted Inference for Ordered Gene ExpressioN)(43,44) which isdesigned to detect significant increases and decreases in expression values over time.

We performed hierarchical clustering to detect clusters among microbial groups. Results aredepicted using heat-maps produced by the Partek Genomics Suite software (6.08.0325). Wealso examined the degree to which the concentrations of 22 microbial groups on day 120associated with presence or absence of the 22 microbes on day 4 using standard stepwiselinear regression analysis.

RESULTSThe characteristics of the 85 mother-infant pairs are listed in Table 3. Median maternal agewas thirty years and 10.6% were smokers. Infants were all still breastfed at 4 months(eligibility criteria), but 38.8% had been given supplemental milk formula starting betweenone and four months of age. Most were of Norwegian ethnicity. One mother participatedwith two infants.

Probe specificityWe evaluated our set of 27 probes (Table 4). The experimental evaluation of these probes ontarget and non-target clones showed that the probes gave the expected signals. Bybioinformatic evaluation using the clone libraries published by Palmer (16) based on 3845clone sequences, we found that the probes in our array covered 48.2% of the clonesidentified by Palmer. Our universal probe covered 96.5% of all the clones. The probes usedfor our analysis are shown in a phylogenetic tree in Figure 1 and the composition of theprobes in Table 4.

Prevalence of microbial groups at day 4 and day 120The Staphylococcus and γ-proteobacteria probes gave signals, above the detection limit andusing our threshold definitions, in nearly all infants at day 4 (Figure 2). Only 5% of allinfants had no detectable microbes belonging to the phyla γ-proteobacteria at day 4(Covered by the probes: γ-proteobacteria, Enterobacteriacea 1 and 2, and Pseudomonas inFigure 2), decreasing to 1% at day 120. Only 17% of all infants had no detectable signal toeither of the Enterobacteriacea probes 1 & 2 at day 4, decreasing to 7% at day 120. Almostas prevalent were the Bifidobacterium species: B.longum, Bifidobacterium 2 (including B.thermophilum and B. adolescentis), B.bifidum and Enterobacteriaceae 2, whileBifidobacterium 1 (mainly B. breve) was less common and detected in less than 50% of theinfants at day 4 (Figure 2). Only 4 infants (4.8%) had no detectable bacteria belonging toBifidobacterium species at day 4. Twenty five percents of all infants had no detectableLactobacillus (1 or 2a) at day 4, decreasing to 16% at day 120. Veillonella andLachnospiraceae 1 were the least common groups at day 4 but not at day 120.

The prevalence of microbial groups changed significantly with the age of the infant (Figure2 and 4). An increase in prevalence over time was most pronounced for the Veillonella, andLachnospiraceae 2 probes (Figure 2). In contrast, the prevalence of Staphylococcus,Uncultured human fecal bacterium and Streptococcus declined from day 4 to day 120, thedecline most pronounced for Staphylococcus.

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Diversity in microbiotaIn the newborns the median number of probes that gave a signal was 12 (of the 22), but therange was wide; 3 to 22. The number of detectable groups showed a small increase, duringthe four-month study (median number at four months; 15, range 7 to 21). ForBifidobacterium, the median number of probes that gave a signal was 3 out of 4 at all timepoints (range 0-4 at day 4 and 10, range 1-4 at day 30 and 120). Correspondingly, themedian number of signals to the four probes detecting γ-proteobacteria was 3 out of 4 at day4 to 30, increasing to 4 at day 120 (range 0 to 4 at all time points). The median number ofsignals to the two Lactobacillus probes was 1 out of 2, range 0-2, all time points.

Changes in concentration of each microbial group over timeWe observed an increase in concentration over time, as assessed by the signal intensity ofthe probes, during the 4 month period, for a large number of microbial groups as can be seenin Figure 3 and 4. The largest increases, relative to day 4, were observed for the probesencompassing the microbial groups; Lachnospiracea 1, Varibaculum, Veillonella,Lachnospiracea 2, Lactobacillus 2a, and Clostridium. In contrast we observed a markeddecline in concentration during the four month period for the Staphylococcus, Streptococcus,and Uncultured human fecal bacterium groups (Figure 3). In general an increase in therelative concentration was mirrored by an increase in prevalence (Figure 4). However, someexceptions are noted, for instance the relative change in signal intensity for the Lactobacillus1 probe is unparalleled by a change in prevalence (Figure 4). For other probes the change inconcentration is more marked than the change in prevalence, as for Varibaculum.

Microbial clusters at day 4Some microbial groups clustered within infants at day 4. A subpopulation of infants withhigher signals to the Bifidobacterium probes (especially Bifidobacterium 1) than theircounterparts was observed (Figure 5, cluster 5). These infants had almost no signal to theStreptococcus probe (Figure 5). We also observed a subset of infants with lower signalintensity to the four γ-proteobacterium probes (γ-proteobacteria, Enterobacteriacea 1 and 2,and Pseudomonas) (Figure 5, cluster 1 and 2). Higher signal intensity to the Unculturedhuman fecal bacterium probe was observed for some of these infants (cluster 1). The othersin this subset had a relative lack of Bifidobacterium and a higher concentration ofStreptococcus and Staphylococcus (cluster 2).

Microbial clusters at day 120We observed different clusters of microbial groups at day 120 although these were lessdistinct than at day 4. One cluster had a relatively high concentration of the Bifidobacteriumgroup, and especially the Bifidobacterium 1 group (Figure 6, cluster 4). This cluster had lowsignals to the Enterobacteriaceae 1 and 2 probes, but signals to the broader group, γ-proteobacterium (probe 18, figure 1) in the same phyla. Infants in this subset had no or lowsignals to the Staphylococcus and Streptococcus probes.

Correspondingly a cluster of infants with low concentrations of all Bifidobacterium groupsat day 120 was observed (Figure 6, cluster 1). Interestingly, among these were the infantswith the highest signals to the Veillonella and Clostridium perfringens probes and amongthem also the infants with the highest concentration of Staphylococcus and Streptococcus.

Tracking clusters over timeWe also tracked the specific clusters observed at day 4 over time, to see how each clusterevolved. However, in general the clusters identified at day 4 did not share specificmicrobiota component at day 120 (Figure 7). However, the tracking of clusters over time

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revealed that infants with initially high concentrations of Bifidobacterium still werecharacterized by higher concentrations of Bifidobacterium, especially Bifidobacterium 1, atday 120 than the other clusters. In contrast the marked differences between clusters insignaling to the probes detecting γ-proteobacterium and Enterobacteriacea 1 and 2 at day 4was no longer visible, due to a decline in concentrations of these microbes in clusters thatwere originally high and an increase in concentrations among clusters with originally lowvalues.

Associations among concentrations of microbial groups at day 4 and day 120Since we could not observe cluster specific evolvement, we also evaluated the associationsbetween the presence of a specific microbial group at day 4 and the composition ofmicrobiota at day 120. The presence (or absence) of a microbial group at day 4 was oftensignificantly associated with the concentration of other microbes at day 120 (Table 5). Forinstance, the concentration of Staphylococcus sp. by day 120 was negatively associated withthe presence of Enterobacteriacea 1, and Bacteroides fragilis at day 4. The concentration ofLachnospiracea 2 at day 120 was associated with several constituents of early microbiota,especially γ-proteobacteria..

The Lachnospiracea incerta sedis 2 group at day 4 had the highest number of associationswith later microbiota (Table 5). Noteworthy, is the positive association with theconcentration of C. perfringens at day 120. Also the presence of Lachnospiracea incertasedis 3, Enterobacteriacea 1 and Lactobacillus 2a at day 4, showed significant associationswith a number of other microbial groups at day 120.

In contrast, the presence of B.longum and Pseudomonas at day 4 was not associated withany specific constituents of microbiota at day 120. Also, no microbial groups at day 4 weresignificantly associated with either Bifidobacterium 2 or Bacteriodes fragilis at day 120.

The early presence of a microbe was usually not associated with later concentrations of thatsame specific microbe, although some exceptions were noted, and especially strongassociations were noted for Bifidobacterium 1, Enterobacteriacea 1 and Enterococcus(Table 5).

DISCUSSIONThis is the first study on the composition of gut microbiota and the colonization process ininfants who have experienced limited medical or dietary interventions. We also limited ouranalysis to the first four months of life to avoid the period of weaning, since a compositionalchange in gut microbiota occurs in this period (45,46). Since medical interventions may beassociated with increased risk of immune-related diseases in childhood (30,34), studiesdescribing a healthy gut microbiota needs to be based on children who have not been subjectto such interventions. This study provides novel information of the undisturbed colonizationprocess.

The 22 probes used in this analysis cover the most common groups identified by Palmer andcoworker with several thousand probes and about 50% of their total (16). The broadgroupings used in the present paper allow for study of change in composition over time thatis difficult to do when thousands of probes are used to characterize the biota.

No previous studies are directly comparable to our study, since they have all includedinfants exposed to at least one or more of the gut modifying factors already mentioned. Thedifferent methodology used in the studies further complicates comparison. An extensivemapping of infant gut microbiota, has previously been conducted in fourteen infants by

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Palmer and coworker (16). Palmer's study included infants who had been delivered bycesarean section, had been given antibiotics, had stayed at the intensive care unit, and/orwere not breastfed (information on antibiotics to the mother not given in the paper). In factonly four out of the fourteen infants in the Palmer study had not been subject to one or moresuch factors (16), therefore most of these infants probably did not exhibit an unaffectedcolonization process. To illustrate this, Bifidobacterium has been reported as the dominantmember of the gut in healthy breastfed infants (47-50), which is in accordance with ourobservations, but they were only minor constituents of infant microbiota in Palmer's study.Inefficient cell lysis of Gram positive bacteria which may reduce availability ofchromosomal DNA, has been offered as an explanation for low detection of Bifidobacteriumin studies identifying microbes based on DNA. However, other studies based on DNA, apartfrom ours, have also reported a high prevalence of Bifidobacterium (17,51,52). Lack ofbreastfeeding, antibiotics, preterm birth, and caesarean delivery are all factors which havebeen associated with a lack of or reduction in Bifidobacterium species in infants(22,24,45,53). The relative lack of Bifidobacterium in some of the recent studies maytherefore instead reflect differential exposure to gut microbiota modifying factors.

Also E.coli has been an ubiquitous inhabitant in the gut of new born infants according toolder classical studies, performed in the 1970s and 80s (46,48-50,50,54,55), however nolonger detected in an increasing proportion of Swedish infants (56-58). In contrast, in ourstudy Enterobacteriaceae 1 (which mainly covers E.coli and Shigella) was detected inseventy percent of our infants. The reported decline in E.coli in the microbiota of Swedishinfants may not be a universal trend but may also reflect exposure to gut modifying factorsin subpopulations of the infants studied.

Interestingly, several clusters of infants with quite different composition of early gutmicrobiota were observed in our study (Figure 5 & 6). The factors involved in shaping thesedistinct sub clusters are unknown and will be further studied by our research group.However, such heterogeneity even within this subset of infants selected for “natural” earlylife experience highlights the large variation in infant gut composition and the need to studylarger samples when aiming at describing gut composition and the importance of taking intoaccount the many factors involved in shaping gut microbiota. Interestingly, we could notobserve that the distinct clusters formed at day 4 evolved differently from each other overtime. Day-to-day variation in microbial content may add random variation, making itdifficult to detect such patterns. However, such random variation should not reduce thevalidity of our group-level findings and the trend patterns support this (Figure 4).

Overall, few longitudinal studies on the evolvement of the colonization process exists andalmost all are limited to cultured or species known beforehand to be constituents of the gut.Previous longitudinal studies on infant gut microbiota not restricted to already knownspecies have been small (15-17,24) . Our study encompassed 85 infants and the large samplesize enhances the chance of identifying yet unknown constituents of microbiota and toestablish both the prevalence of novel microbial constituents and their development overtime with higher accuracy. More than 60% of our infants harbored microbes picked up bythe Uncultured human fecal bacterium probe which detected yet undefined non-cultivableBacteroides species (as well as B. dorei og B.vulgatus). Also frequent were signaling to thedifferent Lachnospiracea probes. Forty-five percent of the 85 newborns had signal to theprobe Lachnospiraceae 2, which detects species belonging to Ruminococcus. This finding isin accordance with the other studies based on DNA methodology, including cross sectionalstudies, (15,21,59), where species belonging to Ruminococcus were proposed to be acommon novel constitute (15,17). Noteworthy, the proportion of infants who harboredspecies covered by the Lachnospiracea 2 probe, showed one of the most significantincreases over time (Figure 3). Moreover, it was the microbial group at day 120 which

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seemed most influenced by early concentration of other microbes (Table 5, column 12),indicating that it could be susceptible to alterations in early gut microbiota. However, anumber of other significant associations between early and later microbiota was alsoobserved (Table 5), indicating that the presence or absence of specific microbes at an earlyage may be important for subsequent composition of microbiota.

Molecular methods are appropriate for studying changes over time in concentration of agiven microbial group and enables elucidation of the colonization process, which is the mainfocus of this study. Furthermore, by assuming that a null signal is an absence of the microbein a given sample, we have precise data on prevalence of each microbial group over time.

A limitation in all data based on fecal samples is the inability to differentiate betweencolonizing and transient bacteria. Also, the composition of the colonizing microbiota differsamong different segments of the gastrointestinal tract (60,61). However, culture basedstudies comparing fecal and intestinal mucosal samples have shown that a strong associationexists between the two (62), and an even stronger association could be expected when usingmolecular methods since microbial DNA is preserved during the passage through theintestium, supporting the use of fecal content to be used as an approximate of gutmicrobiota.

ConclusionThe present study describes the early colonization of gut bacteria in infants who have notexperienced major dietary or medical interventions. It confirms the dominant role ofBifidobacterium and Enterobacteriacea reported in the older classical studies. The relativelack of these microbes in more recent studies in unselected infants could be due to theincrease in medical interventions such as cesarean section.

Undefined non-cultivable species belonging to Bacteroides, as well as microbes identified asLachnospiracea, were commonly found. Though we could not observe any cluster specificevolvement over time, strong associations were observed between some specific constituentsof microbiota at day 4 and the concentration of specific microbial groups at day 120. Thus,early gut microbiota may influence later microbiota. Knowledge of the composition of anormal healthy gut microbiota and understanding the community dynamics that take placeduring infancy is a prerequisite for understanding the role of gut microbiota in disease andmore studies among infants who have not been subject to medical interventions are neededin order to provide such knowledge.

AcknowledgmentsThis project has been partly financed by a grant from the South-Eastern Norway Regional Health Authority and, inpart, by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences [Z01ES045005-14]. We thank Bente Kvenshagen for her contribution in the recruitment process, and for organizingsample pick up. Finally, we thank Palmer et al for access to their clone library (16) and Sue Edelstein for graphicdesign.

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56. Adlerberth I, Jalil F, Carlsson B, Mellander L, Hanson LA, Larsson P, et al. High turnover rate ofEscherichia coli strains in the intestinal flora of infants in Pakistan. Epidemiology & Infection.1998; 121:587–98. [PubMed: 10030708]

57. Nowrouzian F, Hesselmar B, Saalman R, Strannegard IL, Aberg N, Wold AE, et al. Escherichiacoli in Infants' Intestinal Microflora: Colonization Rate, Strain Turnover, and Virulence GeneCarriage. Pediatr Res. 2003; 54:8–14. [PubMed: 12700366]

58. Gareau FE, Mackel DC, Boring JR III, Payne FJ, Hammett FL. The acquisition of fecal flora byinfants from their mothers during birth. J Pediatr. 1959; 54:313–8. [PubMed: 13631584]

59. Hopkins MJ, Macfarlane GT, Furrie E, Fite A, Macfarlane S. Characterisation of intestinal bacteriain infant stools using real-time PCR and northern hybridisation analyses. FEMS Microbiol Ecol.2005; 54:77–85. [PubMed: 16329974]

60. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, et al. Diversity of thehuman intestinal microbial flora. Science. 2005; 308:1635–8. [PubMed: 15831718]

61. Zoetendal EG, von WA, Vilpponen-Salmela T, Ben-Amor K, Akkermans AD, De Vos WM.Mucosa-associated bacteria in the human gastrointestinal tract are uniformly distributed along thecolon and differ from the community recovered from feces. Appl Environ Microbiol. 2002;68:3401–7. [PubMed: 12089021]

62. Ouwehand AC, Salminen S, Arvola T, Ruuska T, Isolauri E. Microbiota composition of theintestinal mucosa: association with fecal microbiota? Microbiol Immunol. 2004; 48:497–500.[PubMed: 15272194]

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Figure 1.Cartoon tree representing the phylogenetic grouping of the probes used in the analyses of thedata.The numbers before the punctuation for the labeling of branches represent the number ofthat phylogroup on the array. If there is a number after the punctuation, more than one probewas evaluated for that particular phylogroup. If the naming of the terminal nodes is notdistinct we included numbering for distinction.

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Figure 2.Prevalence of microbial groups at day 4 and day 120.The percentage of infants in which each microbial group was detected (above threshold) atday 4 (n=83) and at day 120 (n=83) is plotted. Bacterial groups are ordered by meanprevalence at day 4. The p-values are calculated based on 81 infants with data for both days.**** P-value <0.0001, *** p-value <0.001, ** p-value<0.01 and * p-value<0.05.

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Figure 3.The mean change in concentration/ expression pattern at day 120 relative to day 4 is shown.Concentrations are defined by log transformed expression intensities. Bacterial groups areordered by differences in concentrations.* p-value<0.05, ** p-value<0.01 ***, p-value <0.001****, P-value <0.0001.

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Figure 4.Prevalence of microbial groups, and changes in concentration. The mean prevalence rate forday 4, 10, 30 and day 120 is plotted, as well as the mean change in concentration/expressionpattern for day 10, 30 and 120, all relative to day 4, based on the log scale.We had data from 83, 84, 82 and 83 infants, at age 4, 10, 30 and 120 days, respectively and77 infants had samples from all four ages. The p-values are calculated based on the 77infants with full data. * p-value<0.05, ** p-value<0.01 ***, p-value <0.001****, P-value<0.0001.

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Figure 5.Heatmap of the concentration of each microbial group for each infant on day 4, withhierarchical clustering among study subjects performed using Spearman correlationcoefficients as the similarity index. Each column represents one study subject, and the treeshows the subjects who were clustered together. Wards method was used to form theclusters. The scale of the concentration ranges from 0 concentration (deep blue) to thehighest concentration (bright red). (Concentrations should only be compared acrossindividuals and not across probes).

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Figure 6.Heatmap of the concentration of each probe on day 120 for each infant, with hierarchicalclustering among study subjects performed using Spearman correlation coefficients as thesimilarity index. Each column represents one study subject, and the tree shows the subjectswho were clustered together. Wards method was used to form the clusters. The scale of theconcentration ranges from 0 concentration (deep blue) to the highest concentration (brightred). (Concentrations should only be compared across individuals and not across probes)

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Figure 7. Clustering of infants at day 120 based on the concentration of microbial groups at day4Heatmaps in which the infants have been clustered according to the concentration ofmicrobial group at day 4 (see color code in Figure 5) and these clusters kept at day 120. Thecolor on top of each column, below the tree, shows the cluster the infant has been assignedto at day 4. Hierarchical clustering among study subjects was performed using Spearmancorrelation coefficients as the similarity index and Ward's method to form the clusters. Thescale of the concentration ranges from 0 concentration (deep blue) to the highestconcentration (bright red). Concentrations should only be compared across individuals andnot across probes.

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Table 1

Lowest expression values detected within each probe (across all sets of triplicates with detectable values andall time points) in the whole NoMic dataset (n=524).

ProbeLowest valuemeasured

Bifidobacterium longum 0,006

Bifidobacterium 1 0,001

Bifidobacterium bifidum 0,000

Bifidobacterium 2 0,000

Varibaculum 0,002

Pseudomonas 0,001

Enterobacteriaceae 1 0,004

Enterobacteriaceae 2 0,002

gamma Proteobacteria 0,006

Lachnospiraceae 1 0,000

Lachnospiraceae 2 0,002

Lachnospiraceae Inc_2 0,002

Lachnospiraceae_Inc_3 0,000

Veillonella 1c 0,012

Clostridium perfringens 0,001

Streptococcus 0,082

Staphylococcus sp. 0,000

Lactobacillus 1 0,000

Lactobacillus 2a 0,003

Enterococcus 0,018

Bacteroides fragilis 0,001

Uncultured human fecal bacterium 0,002


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Table 2

shows the distribution of the four possible sets of triplicates (e.g. 0,0,0 and 0,0,>0 and 0,>0,>0 and >0,>0,>0)for each gene probe at each time point. This reveals that some probes have a larger proportion ofheterogeneous triplicates than others (45% for Enterococcus, 12% for the best performing probes), suggestingthat they are not performing as well as others. However, when evaluating this we also needed to considerwhether the probe identified microbes that were present in low concentration because this also influences theproportion of heterogeneous triplicates. The influence of low concentration can be observed clearly for probesidentifying microbes that increased in concentration over time. When the concentration increased, the numberof heterogeneous triplicates decreased, and vica versa (Veillonella and Staphylococcus sp.are good examples).Rather than choosing an arbitary threshold value, we decided that if two out of the three triplicate values werezero, we would assign a value of 0 for that individual at that time point for that probe. This has the advantageof taking into account the obvious differential performance of different probes. The presence and absence datawere then used to assign concentrations of “0” to absent probes. The value “0” was chosen because all probeshad some very low values within the useable data as shown in the new table (Table 1). For probes that met ourcriteria for present, we then added “1” to each of the three replicate values and took the mean of the three log-transformed to use for analysis. The resulting transformed data exhibited approximately symmetricdistribution, consequently means of the transformed triplicate values were used in analyses of concentration.The description has been expanded in methods

% of triplicates

0,0,0a 0,0,1b 0, 1, 1c 1,1,1d

Bifidobacterium longum

Day 4 14,7 7,7 9,6 67,9

Day 10 12,7 5,8 9,4 72,1

Day 30 12,8 3,6 10,5 73,1

Day 120 6,3 3,4 7,9 82,5

Bifidobacterium 1

Day 4 42,5 9,6 5,3 42,5

Day 10 39,1 8,6 6,7 45,7

Day 30 32,5 11,1 6,2 50,2

Day 120 27,6 4,5 6,1 61,8

Bifidobacteriumbifidum

Day 4 20,1 11,3 13,5 55,1

Day 10 16,1 11,2 12,0 60,7

Day 30 11,3 10,0 13,5 65,2

Day 120 8,5 5,4 10,1 76,0

Bifidobacterium 2

Day 4 28,2 6,8 8,3 56,6

Day 10 24,7 7,9 8,2 59,2

Day 30 20,3 7,9 7,7 64,1

Day 120 16,6 7,2 7,2 69,0

Varibaculum

Day 4 34,8 37,4 22,6 5,1

Day 10 35,0 38,0 22,5 4,5

Day 30 35,3 37,6 20,5 6,6


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% of triplicates

0,0,0a 0,0,1b 0, 1, 1c 1,1,1d

Day 120 28,8 31,2 24,3 15,7

Pseudomonas

Day 4 15 12,4 19,4 53,2

Day 10 13,5 13,5 20 53

Day 30 18,6 12,6 22,4 46,4

Day 120 18,4 14,8 18,4 48,3

Enterobacteriaceae 1

Day 4 34,4 12,8 7,5 45,3

Day 10 35,4 9,9 7,9 46,8

Day 30 28,2 12 7,1 52,8

Day 120 15,7 8,1 8,3 67,9

Enterobacteriaceae 2

Day 4 13,9 6,8 7,5 71,8

Day 10 8,8 6 7,1 78,1

Day 30 9,4 4,9 6,4 79,3

Day 120 6,1 3,1 5,6 85,2

gamma Proteobacteria

Day 4 5,8 6 10,3 78

Day 10 2,1 4,3 11,4 82,2

Day 30 2,4 3,2 8,8 85,7

Day 120 1,6 1,3 9,2 87,9

Lachnospiraceae 1

Day 4 49,6 23,6 9,7 17,2

Day 10 46,4 21,6 11,5 20,5

Day 30 32,1 13,5 9,7 44,7

Day 120

Lachnospiraceae 2

Day 4 38,2 9,8 9,8 42,1

Day 10 30,7 10,9 9 49,4

Day 30 25,9 8,3 8,3 57,5

Day 120 14,8 5,2 7,4 72,6

Lachnospiraceae Inc_2

Day 4 41,9 9,4 11,5 37,2

Day 10 38 8,8 10,5 42,7

Day 30 34,8 6,2 12,6 46,4

Day 120 21,8 5,6 9,7 62,9

Lachnospiraceae_Inc_3

Day 4 15 24,1 29,9 31

Day 10 11,8 27,5 33,5 27,3

Day 30 11,3 26,7 28,8 33,1

Day 120 11,7 22,7 29,2 36,4


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% of triplicates

0,0,0a 0,0,1b 0, 1, 1c 1,1,1d

Veillonella

Day 4 59,2 10,3 4,3 26,3

Day 10 41,2 9,7 6,4 42,7

Day 30 23,5 7,7 7,9 60,9

Day 120 21,6 7 7,4 64

Clostridium perfringens

Day 4 45,3 12,4 12,6 29,7

Day 10 41,6 11,4 10,1 36,9

Day 30 37,4 9,8 8,5 44,2

Day 120 25,6 8,8 4,9 60,7

Streptococcus

Day 4 45,5 10,5 9,8 34,2

Day 10 43,8 12,9 11,6 31,8

Day 30 42,7 11,8 13,5 32,1

Day 120 55,3 14,2 13,3 17,3

Staphylococcus sp.

Day 4 3,6 3,2 13,7 79,5

Day 10 1,9 3,6 13,9 80,5

Day 30 9 6,4 15,2 69,4

Day 120 26,5 10,8 15,3 47,4

Lactobacillus 1

Day 4 54,9 12,8 9,8 22,4

Day 10 50,4 12,9 9,4 27,3

Day 30 41,2 9,6 11,1 38,0

Day 120 48,8 13,0 10,3 27,9

Lactobacillus 2a

Day 4 35,3 16 17,5 31,2

Day 10 33,5 16,1 20 30,5

Day 30 28,2 15 17,1 39,7

Day 120 17,5 12,1 12,4 58

Enterococcus

Day 4 22,4 19,2 25,2 33,1

Day 10 19,7 24,7 25,1 30,5

Day 30 20,7 22,6 26,3 30,3

Day 120 11 23,4 27,6 38

Bacteroides fragilis

Day 4 66,5 11,1 5,6 16,9

Day 10 62 14,2 5,8 18

Day 30 64,1 12,6 6,2 17,1

Day 120 65,6 14,2 4,3 16

Uncultured human fecal


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% of triplicates

0,0,0a 0,0,1b 0, 1, 1c 1,1,1d

bacterium

Day 4 20,9 29,1 14,7 35,3

Day 10 23,6 34,1 13,9 28,3

Day 30 21,8 30,8 20,1 27,4

Day 120 22,9 29,9 15,7 31,5

aAll triplicates 0 value

btwo triplicates 0 value, one triplicate with value above 0.

cOne triplicates 0 value, one triplicate with value above 0

dall triplicates values above 0. N at day 4, 10, 30 and 120, 468, 466, 468 and 445, respectively, for all probes.


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Table 3

Characteristics of the 85 infants in the NoMic cohort who fulfilled eligible criteria; vaginally delivered, notbeen exposed to antibiotics (directly or indirectly through the mother), were exclusively breastfed for onemonth and still breastfed (partially or exclusively) at 4 months of age.

%

Maternal age (years)

<=27 år 17.6

28-31 41.2

>32 41.2

Childs birth weight (g)

2500- 3499 43.5

3500 to 3999 31.8

>4000 24.7

Gestational age(days)

260-279 21.2

280-294 67.1

>294 11.8

Maternal BMI (kg/m2) a

10-18.49 2.5

18.5-24.9 70.0

25-29.9 25.0

>30 2.5

Maternal parity (%)

First child 33.7

Second child 49.4

Third child ormore

16.9

Maternal smoking (%)b

No 89.5

Occasional or 10.6

Child sex (%)

Female 50.6

Male 49.4

Maternal education (%)

<12 years 3.6

12 years 24.1

13 to 16 years 41.0

More than 16years

31.3

Nationality (%)

Norwegian ethnicity 91.8

Not Norwegian 8.2

Exclusive breastfeeding at 4 months


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%

No 38.8

Yes 61.2

aPre-pregnancy body mass index.

bInformation on smoking collected after delivery.

Missing information for education; n=2, BMI; n=3, smoking; n=1. One mother participated with two infants. No twins.


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Table 4

Probe number, name, labeling sequence and target bacteria according to search in the Ribosomal databaseproject (RDP) database is shown. The table also shows the number of hits in the Palmer clone library (3845clone sequences).

Probe nr Probe name (bacteria) Target bacteriaa

Hits in thePalmerclone library(3845clonesequences) Labelling probes

1 EnterococcusEnterococcus sp. (e.g. E.faecium) 44 TCATCCCTTGACGGTATCTAA

2.1 Lactobacillus 1

Lactobacillus sp. (L.gassery, L. helveticus, L.acidophilus, L. johnsoniietc.) 50 GTCAAATAAAGGCCAGTTACTA

3.2 Lactobacillus 2a

Lactobacillus sp. (L. casei,L. rhamnosous, L.rennaquilify, L. paracasei) 1 CAGTTACTCTGCCGACCATT

3.4 Lactobacillus 2b

Lactobacillus sp. (L. casei,L. rhamnosous, L.rennaquilify, L. paracasei) 0 AGTAACTGTTGTCGGCGTGA

4.1 Staphylococcus sp. Staphylococcus sp. 11 ACACATATGTTCTTCCCTAATAA

5 Streptococcus Streptococcus sp. 149 AGTGTGAGAGTGGAAAGTTCA

6 Clostridium perfringensClostridium sp. (C.perfringens) 95 TCAACTTGGGTGCTGCATTC

7.1 Veillonella 1a Veillonella sp. 11 GTCAATCCTTCTCACTATTCG

7.3 Veillonella 1b Veillonella sp. 1 CGAACTTTTAAGACAGACTGAC

7.5 Veillonella 1c Veillonella sp. 49 GATTGGCAGTTTCCATCCCAT

8 Lachnospiraceae 1

Lachnospireaceae sp.(Dorea, L. Incerteasedis) 2 AGCTAGAGTGTCGGAGAGG

9.1Lachnospiraceae incertaesedis 1

Lachnospireaceae sp. (L.Incertea sedis) 2 GGCGGGGGCTCAACCCC

10Lachnospiraceae incertaesedis 2

Lachnospireaceae sp. (L.Incertea sedis) 7 TATCAGCAGGAAGATAGTGA

11 Lachnospiraceae 2

Lachnospireaceae sp.(Dorea, L. Incerteasedis) 1 AGTCAGGTACCGTCATTTTCT

12.3Lachnospiraceae incertaesedis 3

Lachnospireaceae sp. (L.Incertea sedis) 1 ACTGCTTTGGAAACTGCAGAT

15.1 Pseudomonas 1a Pseudomonas sp. 0 GTCAAAACAGCAAAGTATTAATTTA

15.2 Pseudomonas 1b Pseudomonas sp. 0 GTAGAGGGTGGTGGAATTTC

16.1 Enterobacteriaceae 1Enterobacteriaceae(Esherichia coli, Shigella) 221 GAGCAAAGGTATTAACTTTACTC

17 Enterobacteriaceae 2

Enterobacteriaceae(Enterobacter sp.(Pantoeasp), Klebsilla, Erwina,Citrobacter,Bulliauxella) 696 CGAAACTGGCAGGCTAGAGT

18 Gamma (γ)-Proteobacteria

(γ)-Proteobacteria(Alteromonadales,Vibronales, Aeromonadales,Enterobacteriales) 1399 CCTGGACAAAGACTGACGCT


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Probe nr Probe name (bacteria) Target bacteriaa

Hits in thePalmerclone library(3845clonesequences) Labelling probes

19.1 VaribaculumVaribaculum sp. (Vcambriense) 3 TTGAGTGTAGGGGTTGATTAG

22 Bifidobacterium longum Bifidobacterium (B. longum) 114 GAGCAAGCGTGAGTAAGTTTA

23 Bifidobacterium bifidum Bifidobacterium (B. bifidum) 12 CCGAAGGCTTGCTCCCAAA

24.3 Bifidobacterium 1 Bifidobacterium (B. breve,) 38 CACTCAACACAAAGTGCCTTG

25 Bifidobacterium 2

Bifidobacterium (B.thermophilum, B.adolescentis) 7 GCTTATTCGAAAGGTACACTCACCCCGAAGGG

26 Bacteroides fragilis Bacteriodes sp. (B. fragilis) 1 GGGCGCTAGCCTAACCAG

27.2Uncultured human fecalbacterium Bacteroides sp. 179 ATGCATACCCGTTTGCATGTA

16Suniversal 16S rDNA Bacteria 3710 (96.5%) CGTATTACCGCGGCTGCTGGCA

aProbe Matchsearch (using the labelling probe+ C) in the RDP database (http://rdp.cme.msu.edu/probematch/search.jsp)

*One error allowed in search sequence


http://rdp.cme.msu.edu/probematch/search.jsp

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Table 5

The association between the presence or absence of a microbe at day 4 (first column) and the concentration ofeach of the other microbes/microbial groups at day 120 (columns) is shown. Each row is the result of aseparate model.


Development of gut microbiota in infants not exposed to medical interventions

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