{"paper_id":"45f671e6-d9e7-411a-afb8-13cba41a9dec","body_text":"The impact of the COVID-19 pandemic and associated lifestyle changes on early-life microbiome development: a natural experiment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The impact of the COVID-19 pandemic and associated lifestyle changes on early-life microbiome development: a natural experiment Evgenia Dikareva, Niels van Best, Liene Bervoets, Christina E. West, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7565364/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The COVID-19 pandemic triggered rapid, population-wide behavioral and environmental changes, offering a unique natural experiment to study how early-life microbiome development responds to abrupt shifts in social and hygiene-related exposures. Using longitudinal data from 139 infants in the Dutch LucKi Gut study, we compared gut microbiome development in fecal samples collected before and during the pandemic. Whole metagenome sequencing of 808 stool samples was performed across nine time points in the first 14 months of life. An exposure index (EI) capturing variation in household-level pandemic-related behaviors was constructed to quantify variations in social distancing, lifestyle and hygiene measures. Microbial richness and diversity increased with age, following established developmental trajectories. However, from 6 months onward, the COVID-19 pandemic independently shaped gut microbial composition, explaining up to 2.7% of variation by 11 months of age. Forty-four species were differentially abundant in pandemic-era samples, including depletion of Gordonibacter pamelaeae and several Actinomyces species. Notably, greater environmental exposure (higher EI scores) was associated with lower abundance of G. pamelaeae, a microbe implicated in bile acid and immunomodulatory metabolism. This is the first longitudinal whole-genome sequencing study to demonstrate that pandemic-related behavioral changes measurably altered infant gut microbiota maturation. These findings highlight the sensitivity of microbiome development to societal-level environmental disruptions and suggest that early-life microbial exposures, modulated by hygiene and social behavior, may carry long-term implications for child health. COVID-19 pandemic hygiene microbiome social distancing Figures Figure 1 Figure 2 Figure 3 INTRODUCTION An increasing number of studies emphasize the significance of the gut microbiome in infant health. The early-life gut microbiome plays a crucial role in shaping long-term health by influencing immune development, host metabolism, and disease susceptibility [ 1 – 7 ]. The establishment of the microbiome during childhood follows a dynamic progression that can be divided into three phases: a developmental phase, a transition phase, and a stable phase [ 8 ]. This process is largely orchestrated by microbial transmission from the mother, family members, and the environment while genetic factors, infant feeding practices, and dietary transitions also play important roles. However, while breastfeeding and complementary feeding are key determinants of microbiota composition [ 8 – 12 ], they account for only a fraction of the interindividual variation in microbial community structure [ 13 ]. Given the established links between early-life microbiota and the risk of allergic and autoimmune diseases [ 14 – 17 ], identifying additional factors that shape or disrupt microbiota establishment is essential. The \"hygiene hypothesis\" proposes that reduced exposure to infections contributes to the rise in non-communicable diseases [ 18 , 19 ], whereas the \"old friends hypothesis\" suggests that evolutionary shifts in microbial exposure, rather than a lack of infections per se, underlie these trends [ 20 , 21 ]. Although changes in social interactions, hygiene practices, and lifestyle typically occur gradually over time, the onset of the COVID-19 pandemic led to abrupt and widespread behavioral and environmental shifts [ 22 – 24 ]. This unprecedented global event presents a unique opportunity to investigate how such factors influence gut microbiome development in early life. During the pandemic, the incidence of infectious diseases—including gastrointestinal infections—and antibiotic prescriptions for respiratory infections declined significantly in the Netherlands [ 25 , 26 ], largely due to social distancing measures and daycare closures [ 27 ]. Additionally, lifestyle changes varied across the population, with some individuals adopting healthier dietary and physical activity habits, while others exhibited the opposite trend [ 24 , 28 ]. These pandemic-induced behavioral shifts have the potential to alter infant gut microbiota development. A study of 12-month-old American infants reported reduced alpha diversity and decreased abundances of Pasteurellaceae and Haemophilus in pandemic-era samples compared to pre-pandemic cohorts [ 29 ]. Similarly, the CORAL study in Ireland found that pandemic-era infants had increased Bifidobacterium levels and decreased Clostridium abundance, possibly due to reduced exposure to individuals outside the household [ 30 ]. Comparable findings were observed in a Chinese cohort, where pandemic-born infants exhibited reduced microbial diversity, altered community composition, and decreased antimicrobial resistance gene carriage [ 31 ]. However, these studies either analyzed microbiota at a single time point [ 29 ] or lacked a pre-pandemic comparison within the same cohort [ 30 , 31 ], limiting their ability to assess longitudinal microbiota development across pandemic and non-pandemic periods in the same population. Here we study the impact of the COVID-19 pandemic on gut microbiota establishment during infancy within a single cohort, the Dutch Lucki Gut Study. This longitudinal birth cohort was designed to track the longitudinal development of the infant gut microbiome, began recruitment in 2016 and continued enrolling participants throughout the pandemic. This provided a unique opportunity to examine the impact of the COVID-19 pandemic on gut microbiota establishment within a single cohort. We hypothesized that lockdown and strict hygiene measures would have an impact on the infants’ gut microbiota development. To test this, we performed whole metagenome sequencing on samples collected pre and during the COVID-19 pandemic. The resolution of the sequencing allowed us to examine differences at species level. METHODS Study population and sample collection The LucKi Gut study is an on-going longitudinal study among newborns and their families. Pregnant women residing in the South Limburg region of the Netherlands were recruited through obstetrics and gynaecology clinics, lactation information sessions, and advertisements at pregnancy yoga classes, baby clothing stores, and on social media. Infants born prematurely (gestational age ≤ 32 weeks) were excluded. Infant fecal samples were collected at 1–2 weeks post-partum and again at 1, 2, 4, 5, 6, 9, 11 and 14 months of age (Supplementary Table 1A). These time-points align with the ages at which children have scheduled visits to well-baby clinics. Participants received fecal sampling starter kits consisting of stool collection tubes (Sarstedt, REF 80.623.022), cold transport containers (Sarstedt, REF 95.1123), a safety bag, gloves, questionnaires, and instructions. The samples were collected at home and immediately stored at -20°C in their home freezers. Samples were thereafter transported to the family’s well-baby clinic using a frozen transport container to preserve the cold chain. From there, samples were transported to the laboratory, where frozen fecal matter was aliquoted and stored at -80°C until further analyses. At each fecal sampling time-point, parents also completed a questionnaire gathering information on the infant's lifestyle, health, development, medication use, and feeding practices, as well as maternal health (during pregnancy), diet, and medication use. Alongside the questionnaires we collected 808 fecal samples from 139 infants recruited between August 2016 and November 2022. After the onset of COVID-19 pandemic, the newly enrolled families and families that were still in follow-up (n = 36) also filled in a separate questionnaire on social distancing, protective measures, hygiene and other pandemic related measures (Supplementary Table 1B). Informed consent was provided by all parents. The study was approved by the medical ethics committee of the Maastricht University Hospital (METC 15-4-237). Metadata processing Information on perinatal determinants, lifestyle, diet, medication use, and health outcomes was collected through self-reported questionnaires collected around birth (pregnancy questionnaire and paternal and maternal questionnaires) and at each subsequent sampling time point. The following variables were included for the purpose of the present study: gestational age (weeks), birthweight (grams), maternal weight gain (kilograms), age at complementary food introduction (weeks), delivery type (C-section, vaginal), maternal atopy (no, yes), paternal atopy (no, yes), infant antibiotic use since previous follow-up moment (no, yes), breastfeeding since previous follow-up moment (yes, no), formula feeding since previous follow-up moment (yes, no), day care attendance (no, yes), maternal smoking before pregnancy (no, yes), older siblings (no, yes), siblings in the cohort (no, yes), furry pets at home (no, yes), sex (male, female), delivery place (hospital, at home), infant hospital admission upon birth (no, yes), maternal antibiotics during delivery (no, yes) and maternal antibiotics during pregnancy (no, yes) (Supplementary Table 2). For the follow-up time points where less than 5% of the children were reported to have used antibiotics, the variable on antibiotic use was omitted from the analyses. Missing information for breast and formula feeding was imputed as follows: if information on infant feeding was available and identical at both the preceding and subsequent time-point, then this value was imputed for the intermediate time-points. If breast milk was not given at the first time-points (1–2 and 4 weeks), then “no breastfeeding” was imputed for subsequent time-points for which information was missing. Otherwise, missing values were not imputed. For data on furry pets missing values were imputed as follows: if the information was available at two times-points (at birth and 14 months, or at birth and 6 months) and it was identical, then this value was imputed for the missing time-point. Otherwise, missing values were not imputed. For missing numeric values (gestational age, birthweight, maternal weight gain and age of complementary food introduction), the mean was calculated and used to impute missing values (Supplementary Table 2). The frequencies of the variables for each time point can be found in Supplementary Tables 3. The “ pandemic ” variable categorized samples into two groups based on their collection date relative to the onset of the COVID-19 pandemic in the Netherlands. Samples collected before February 27, 2020, (the date of the first confirmed infection by the SARS-CoV-2 virus in the Netherlands) were assigned to the \"pre-pandemic\" group, while samples collected on or after this date were assigned to the \"pandemic\" group (Supplementary Table 1B). For the subgroup of 36 families in which at least one fecal sample was collected during the COVID-19 pandemic, we additionally created an “ exposure index ” (EI) to estimate the level of social interactions, lifestyle and hygiene measures. To this end, we initially selected 58 questions related to the COVID-19 pandemic, excluding those related to SARS-CoV-2 infections directly such as diagnostic testing and symptoms. For highly correlated variables (Pearson’s rho > 0.8) one of the variables was removed and remaining variables were further processed. From these individual variables (Supplementary Table 4) we created new variables that aggregated the level of exposure (Supplementary Fig. 1, Supplementary Table 5). For example, for variables on exposure in indoor public spaces, the number of days per week people had social interactions in public space, the number of times they kept distance and/or were wearing a mask during such social interactions were combined into one new variable that provides a summed score with the highest value for individuals with frequent unprotected interactions in indoor public spaces. All variables were coded in such a way that a higher score was related to more exposure (Supplementary Table 4). Next, we rescaled these newly created variables according to the median for the answers (0, when original value was 0, 1 when below median and 2 when above the median). Binary variables were kept as binary variables. Finally, these rescaled variables were summed into the “exposure index” (EI). Two versions of the EI were initially constructed: one incorporating responses related to social interactions, hygiene practices, parental occupation, and protective measures used by both parents, and another excluding paternal (or second parent) responses. Given the high level of correlation (Spearman’s ρ = 0.940, p = 0.000) between both indices (Supplementary Fig. 2), we ultimately continued with the latter index since information on questions related to the father (or second parent) was only available for 32 out of the 36 families. DNA isolation, whole metagenomic sequencing and data pre-processing Approximately 100 mg of aliquoted fecal sample, was sent to MGI Tech Latvia (Mārupe, Latvia) for DNA extraction and whole metagenomic sequencing (WMGS). Metagenomic DNA was isolated using the MagPure Stool DNA LQ kit according to the manufacturer’s protocol (Magin Biotech, Guangzhou, China), with the additional inclusion of a mechanical bead-beating step using 0.1 mm glass beads as described previously [ 32 ]. Library preparation and shotgun metagenomic sequencing were performed on the BGISEQ-500 platform using the paired-end 150 mode. Pre-processing of sequencing reads was performed according to the \"remove-host\" standard operating procedure in MMHP. To standardize the pipeline a workflow manager Snakemake v. 5.14.0 [ 33 ] was used. Quality filtering was performed using Fastp v.0.20.1 with default quality threshold of Q15, a minimum read length of 60 bp, and rejection of any reads containing N bases [ 34 ]. The same tool was used to trim the BGI-SEQ adapters [ 35 ] “AAGTCGGAGGCCAAGCGGTCTTAGGAAGACAA” for forward and “AAGTCGGATCGTAGCCATGTCGTTCTGTGAGCCAAGGAGTTG” for reverse reads. Human reads were subsequently removed using Bowtie 2 v.2.3.5.1 using the very-sensitive preset and the maximum length of paired-end alignments: 600 bp, index: chm13.draft_v1.0_plusY, downloaded 14.10.2020) [ 36 ]. Paired reads where both mates failed to align were retained using Samtools v.1.9 [ 37 ] (-f 12 -F 256). As the reference a human reference genome from the Telomere-to-telomere consortium CHM13 project (version 1.0) [ 38 ] and the Y chromosome, compiled into index by Bowtie2 was used. Then forward and reverse reads were used to identify taxonomic composition using MetaPhlAn v.3.0 [ 39 ] by aligning marker genes to a reference database (species-markers database from January 2019 CHOCOPhlAn v.30). Statistical analysis and data visualization Statistical analysis and data visualisation were performed on Rstudio (v.2023.06.2 + 561) with integrated R (v.4.1.3) [ 40 ]. With the phyloseq (v.1.38.0) [ 41 ] and tidyverse (v.2.0.0) [ 42 ] packages the phyloseq object was constructed. For data visualisation microViz (v.0.10.8) [ 43 ] and viridis (v.0.6.2) [ 44 ] packages were used. To reduce the sparsity of the data, bacterial taxa were filtered out at species level with a prevalence of < 5% (microViz, tax_filter(min_prevalence = 0.05, tax_level = \"Species\") across all samples. These filtered data were used in all downstream analysis except for alpha diversity analysis where unfiltered reads were used. Alpha diversity and linear regression analysis The following ecological diversity distances were calculated with vegan package (v.2.6-4) [ 45 ]: Shannon index and observed richness. The Effective Number of Species (ENS) was subsequently calculated from the Shannon index using the base R exponential function (exp()) [ 46 ]. Given the non-normal distribution of the data, non-parametric tests for all indices and time-points were applied, and the median with the interquartile range was used to summarize alpha diversity metrics. Alpha diversity indices were compared between subsequent time-points using the paired Wilcoxon Rank Sum Test (p.adjust.method = \"BH\", alternative = \"two.sided\", paired = T). The p-values were adjusted with false discovery rate (FDR) correction using the Benjamini–Hochberg procedure [ 47 ] with function p.adjust from stats package (v.4.1.3) for each alpha diversity metric separately. To analyse whether variables were associated with alpha diversity, linear regression analysis was performed for each time point using function lm() from stats package. The numeric variables were scaled using the R basic function scale(). We performed backward elimination by iteratively excluding variables with p-value > 0.2. Principal component analysis and beta diversity evaluation The Aitchison’s distance [ 48 ] was used to analyse beta-diversity and overall variation between the samples. Ordination of infant fecal samples was performed by principal component analysis (PCA) using microViz package. The reads were center log ratio transformed on “Species” level (tax_transform(\"clr\", rank = \"Species\")) upon which samples were arranged by similarity into new dimensions to form PCA (ord_calc(method = \"PCA\")). To visualize the species composition of samples, a circular compositional barplot (IRIS plot) sorted by the PCA ordination angle was created (microViz, tax_transform(\"clr\", rank = \"Species\")). A permutational multivariate analysis of variance (PERMANOVA) was conducted using the pairwise.adonis() function from vegan package to assess differences between the ages. The Skillings–Mack test was used to compare Aitchison distance between the time points with a Wilcoxon paired signed rank test as a post hoc test. Dirichlet Multinomial Mixture clustering Dirichlet Multinomial Mixture (DMM) clustering was performed using the Dirichlet Multinomial package (v.1.36.0) at the species level. Samples were assigned to a specific cluster according to the Laplace approximation score, which represents a specific enterotype or a signature composition of microbes. The clustering procedure [ 49 ] and transition analyses [ 8 ] were conducted as previously described. For clustering visualisation, we used a non-metric multidimensional scaling (NMDS) analysis that was performed on Bray-Curtis dissimilarities. The samples were coloured according to the DMM clustering. Marginal Permutational Multivariate Analysis of Variance To identify variables that were significantly associated with the microbial community structure, a Marginal PERMANOVA based on marginal sum of squares was performed with microViz . PERMANOVA was conducted on filtered reads with 999 permutations. For each time point, Aitchison distance matrices were calculated on species-level using microViz . For the first three time points (1, 4, and 8 weeks), the following variables were selected: parental atopy, birthweight, breastfeeding and formula feeding, delivery place and type, hospital admission upon delivery, maternal antibiotic use during pregnancy and delivery, maternal weight gain during pregnancy, older siblings, presence of siblings inside the cohort, gestational age, sex, maternal smoking before pregnancy and the pandemic variable. For the remaining time points, the following additional variables were added: age at the introduction of complementary foods and daycare attendance. Only the last three time points had enough observations (> 5% of “yes” answers) for infant antibiotic exposure since previous sampling time point. For each time point, a series of PERMANOVAs were performed, and the backward elimination method was implemented. Variables with the highest p-value were removed iteratively until only those variables with p ≤ 0.200 remained. For each time point, a distinct set of variables was retained in the final model. These were used in further analysis and are referred to as the 'final set of variables' for each respective time point. Sensitivity analysis To ensure that the impact of the pandemic on the infant microbiome was not a technical artefact related to sequencing batch effects, we ran a sensitivity analysis including the sequencing batch as an additional covariate. This could however only be performed in case samples were sufficiently spread across sequencing batches (e.g., not for 9 and 11 months of age). Sensitivity analyses revealed that while technical variation significantly explained up to 5% of interindividual variation in microbiome profiles, it did not eliminate the effect of the pandemic at 6 months (p = 0.018) and as such rules out that observed findings were due to technical artefacts (Supplementary Fig. 3, Supplementary Table 6). Differential abundance analysis To detect bacterial species that were significantly different in abundance between time points, a linear model for differential abundance analysis (LinDa) was performed using the LinDa package (v.0.1.0) [ 50 ] (alpha = 0.2, prev.cut = 0, lib.cut = 1, winsor.quan = NULL, corr.cut = 0.1, p.adj.method = 'BH',type = \"count\"). Separate models were run for each follow-up time with default settings with the “final set of variables”. To reduce the false discovery rate, the p-values were adjusted using the Benjamini-Hochberg procedure within each time point, and only FDR-adjusted p-values ≤ 0.2 [ 51 ] are presented. The relative taxon abundances were visualised in heatmaps presenting the log fold differences in bacterial abundance. Impact if exposure index on alpha and beta diversity and abundance of individual species To investigate the effect of the EI for the 5–14-month time points, we used samples collected after the onset of the pandemic. The EI variable was used in place of the original pandemic variable. For the 6-month time point, the variable \"delivery place\" was excluded due to collinearity with the \"siblings in cohort\" variable. The impact of EI on alpha diversity was assessed as previously described for ENS and observed richness. Beta diversity was analysed using PCA plots and PERMANOVA, as outlined above. Differential abundance analysis was also performed as described previously. RESULTS A total of 808 fecal samples collected from 139 infants at the ages of 1–2, 4 and 8 weeks, 4, 5, 6, 9, 11 and 14 months were successfully sequenced (Supplementary Table 1A). Out of the 808 samples, 253 (31.3%) were collected during the COVID-19 pandemic (Supplementary Table 1A). Additional questionnaire data on societal interactions, lifestyle and hygiene measures during the pandemic were collected for the 36 participating families in which at least one stool sample from the child was collected during the COVID-19 pandemic (Supplementary Table 1B). Of the 139 included children, 80 (57.6%) were boys, 117 were born vaginally (84.2%) and 121 were born in the hospital (87.1%). Moreover, 59 (42.2%) infants had older siblings (Supplementary Table 2). Breastfeeding was initially given to most infants (88.1%) during the first weeks of life and over half (52.3%) of the infants were still being breastfed by 6 months of age. Antibiotic exposure during the first 6 months of life was rare (Supplementary Table 3). By the age of 18 weeks, complementary foods had been introduced to half of the infants (median: 18 weeks, Interquartile range (IQR): 4.0 weeks, Supplementary Table 2). Temporal changes in the infant microbiome: microbial richness and diversity increase over time, with the infant gut being dominated by Bifidobacterium species In line with previous studies [ 52 – 55 ], we observed an increase in microbial richness and diversity with age. Observed richness was statistically significantly different between the ages 1 and 4 weeks (Wilcoxon rank-sum test, W = 703, Q value (FDR) = 0.049); 4 and 8 weeks (W = 885, Q value = 0.049); 8 weeks and 4 months (W = 1124, Q value = 0.049); 6 and 9 months (W = 570, Q value = 0.002); 9 and 11 months (W = 476, Q value = 0.000) and 11 and 14 months (W = 503, Q value = 0.004, Fig. 1A, Supplementary Table 7). The difference for Effective Number of Species (ENS) was significant between 6 and 9 months (W = 645, Q value = 0.01) and 9 and 11 months (W = 786, Q value = 0.03, Supplementary Fig. 4, Supplementary Table 7). Age was also the primary factor explaining variation in microbial community structure, as evidenced by the separation of samples in the PCA plot (Fig. 1B). A Skillings–Mack test used to compared Aitchison distance between the time points indicated a statistically significant difference between the ages (statistics = 185.82, p = 0.000). The most pronounced shift in microbial community composition occurred between 6 and 9 months of age (p = 0.000), as reflected by the increased Aitchison distances between adjacent time points (Supplementary Fig. 5, Supplementary Table 8). Across all time points, Bifidobacterium species, such as B. longum and B. breve , along with Escherichia coli , were among the most abundant species. At later time points, various Bacteroides species, Collinsella aerofaciens , Phocaeicola vulgatus (previous name - Bacteroides vulgatus [ 56 ]), and Faecalibacterium prausnitzii increased in relative abundance (Fig. 1C). We next applied Dirichlet multinomial mixtures (DMM) modelling to cluster bacterial communities over time (Fig. 1D). Each cluster was defined by distinct microbial compositions (Fig. 1E, Supplementary Table 9), reflecting progressive microbiome maturation (Supplementary Fig. 6). Most infants initially belonged to cluster 1, dominated by B. longum , B. breve , and E. coli , and transitioned to cluster 5, characterised by dominance of B. longum , B. breve , and B. bifidum and reduced E. coli —by 4 months of age. By 9 months, most had shifted to cluster 6, followed by further transition to cluster 7 in later months, marked by the emergence of F. prausnitzii . The COVID-19 pandemic affected microbial diversity and composition To understand the effect of various variables on infants’ gut microbial richness and diversity, we performed linear regressions on alpha diversity metrics. The observed microbial richness was most strongly influenced by infant feeding, with breastfeeding being associated with a lower and formula feeding with a higher microbial richness at several time-points during the first 6 months of life (Fig. 2A, Supplementary Table 10). Maternal and paternal atopy was associated with a lower microbial richness in the infant microbiome, and early complementary food introduction was associated with an change in microbial richness. Besides these well-known drivers of microbial richness, the COVID-19 pandemic was associated with a statistically significant increase in observed richness at 9 months of age (estimate = 5.9, 95% CI [0.7, 11.2], p = 0.027) (Fig. 2A, Supplementary Table 10). Moreover, the fecal microbial diversity was marginally higher in 11-month-old infants sampled during the pandemic as compared to infants sampled prior to the pandemic (estimate = 1.8, 95% CI [-0.02, 3.7], p = 0.052, Supplementary Figs. 7, Supplementary Table 10). The pandemic began to influence infant microbial community composition at 5 months of age and became a statistically significant factor from 6 months onwards (PERMANOVA; Fig. 2B, Supplementary Table 11). The proportion of variance explained by the pandemic gradually increased over time, reaching a maximum of 2.7% at 11 months (p = 0.001). We identified multiple perinatal, environmental, dietary, and health-related factors (Supplementary Figs. 7, Supplementary Table 10) contributing to inter-individual variation in gut microbiota composition. Mode of delivery was a major determinant, exerting its strongest influence between 1 and 2 weeks of age and explaining over 3% of the variation, with effects persisting up to 9 months. Maternal atopy and breastfeeding had a continuous impact on microbiota composition throughout the first 11 months of life. Day care attendance emerged as a significant factor from 4 months of age, coinciding with the end of maternity leave in the Netherlands. The presence of older siblings influenced the infant microbiota from the earliest time point (1–2 weeks of age), suggesting early household microbial transmission. In contrast, introduction of complementary feeding contributed to microbial variation only during the initial transition to complementary foods (4–6 months) but not thereafter, highlighting a transient effect at this developmental stage. As the impact of the pandemic became more pronounced at later time points and persisted in sensitivity analysis performed using the samples collected at age 6 months (Supplementary Fig. 3, Supplementary Table 6), we further examined differences in bacterial species abundance between pre-pandemic and pandemic samples. The most pronounced shifts in species composition were observed at 9–14 months (Fig. 2C, Supplementary Table 12). Here, LinDa revealed that 44 out of 216 species were statistically significantly differentially abundant in samples collected during as compared to prior the start of the pandemic. The most pronounced depleted species in pandemic samples were Actinomyces sp. ICM47, particularly at 14 months (Q value = 0.008, log2-fold change = -6.7), as well as 9 (Q value = 0.004, log2-fold change = -6.6) and 11 months (Q value = 0.017, log2-fold change = -6.3). Other taxa, including Eggerthella lenta, Actinomyces odontolyticus and Gordonibacter pamelaeae , also showed decreased relative abundances in pandemic samples at later time points. Conversely, several species were significantly enriched in pandemic samples, notably Bifidobacterium adolescentis (9 months, Q value = 0.028, log2-fold change = 7.8), Ralstonia sp MD27 (9mon, Q value = 0.002, log2-fold change = 7.0; 1wk, Q value = 0.000, log2-fold change = 5.7), Haemophilus parainfluenza (9mon, Q value = 0.017, log2-fold change = 6.1; 11mon, Q value = 0.035, log2-fold change = 5.8) and Streptococcus sp A12 (11mon, Q value = 0.016, log2-fold change = 5.6).These data further confirm a shift in microbial composition during the pandemic period. Linking behavioural exposure during the pandemic to shifts in infant gut microbial composition To investigate whether shifts in bacterial species abundance during the pandemic were linked to changes in environmental exposure and parental behavior, we developed an Exposure Index (EI) based on questionnaire data from families with at least one infant fecal sample collected during the pandemic period (Supplementary Fig. 8). The final EI (Supplementary Fig. 9), derived from 36 families, consisted of 10 grouped variables (Fig. 3A) and followed a normal distribution (Shapiro-Wilk test: p = 0.171, W = 0.957). Scores ranged from 4 to 18 (mean = 11.4, SD = 3.8), with higher values reflecting greater exposure—characterized by fewer hygiene measures, reduced protective behaviors, and increased social interactions (Supplementary table 4). With increasing EI—indicating greater environmental exposure—we observed significant shifts in microbial richness across multiple time points. Upon adjustment for other covariates, richness decreased at 5 months (estimate = -7.0, 95% CI [-11.6, -2.5], p = 0.005) and 11 months (estimate = -12.1, 95% CI [-16.4, -7.7], p = 0.001), whereas an increase in microbial richness was detected at 9 months (estimate = 4.9, 95% CI [0.9, 8.8], p = 0.018) and 14 months (estimate = 12.0, 95% CI [2.9, 21.2], p = 0.020) (Fig. 3B, Supplementary table 13). Similarly, effective number of species (ENS) decreased at 6 months (estimate = -1.5, 95% CI [-2.5, -0.4], p = 0.013), 9 months (estimate = -3.9, 95% CI [-4.9, -3.0], p = 0.000), and 11 months (estimate = -1.6, 95% CI [-2.9, -0.2], p = 0.031), while increasing at 14 months (estimate = 3.6, 95% CI [0.9, 6.2], p = 0.019) (Supplement Fig. 10, Supplementary table 13). We analysed beta-diversity in pandemic-era samples collected between 5 and 14 months of age, the period when the pandemic effect was most pronounced. Although no clear overall separation by EI was observed, likely due to the dominant influence of age on sample clustering in PCA space (Fig. 3C), at 6 months the EI accounted for 5.7% of the variation (p = 0.029, Supplementary Fig. 11, Supplementary table 14). Next, we assessed the impact of EI on the abundance of bacterial species that were significantly differentially abundant in pandemic-era samples. Of the 44 species identified, Blautia obeum and Prevotella timonensis were excluded as too few samples contained these species (Supplementary Fig. 12). Higher EI was associated with a reduction in G. pamelaeae at 9 months (Q value = 0.024, fold = -4.7) and 14 months (Q value = 0.16, fold = -6.3) (Fig. 3C, Supplementary Table 15). Interestingly, the samples of infants with an EI above the mean ( ≥ 11.48) had a lower abundance of G. pamelaeae when compared to the samples of infants with an EI below the mean (Supplementary Fig. 12, Supplementary Fig. 13). Altogether this indicates that infants of whom samples were collected during the pandemic had lower levels of G. pamelaeae as compared to pre-pandemic infants of the same age, an effect that was most pronounced for infants with the highest EI scores. DISCUSSION In this study, we made use of the unique timing of the COVID-19 pandemic to explore how abrupt lifestyle, and behavioural changes affected the gut microbiome development during infancy. Within the well-phenotyped prospective LucKi Gut study, we showed that microbiota maturation followed age-related trajectories that aligned with previous studies [ 8 , 12 ] but was measurably impacted by the pandemic from 6 months of age onwards. First, we investigated infant gut microbiota development. As anticipated, microbial richness and diversity increased with age [ 52 – 55 ], and community composition matured toward a more complex, adult-like profile over time. Our results align with previous studies demonstrating distinct microbial phases in infancy, characterized by early dominance of Bifidobacterium species [ 57 ] and a gradual increase of other taxa including Bacteroides , Faecalibacterium , and Collinsella upon cessation of breastfeeding and the introduction of complementary foods [ 52 – 55 ]. We, moreover, confirmed well-established associations between birth mode [ 53 , 58 ], infant feeding [ 8 , 9 ], daycare attendance [ 59 ] and older siblings [ 60 ] with infant microbiota composition, further validating the robustness of our data. Second, we investigated the effect of the pandemic on infant gut microbiota maturation. To our knowledge, this is the first large-scale longitudinal study using whole metagenome sequencing to assess the pandemic’s impact on infant gut microbiota development. Notably, microbial richness was significantly increased in pandemic samples at 9 months of age, and community composition began to diverge measurably from pre-pandemic samples at 6 months. Moreover, 44 bacterial species differed significantly in abundance between pre- and post-pandemic fecal samples of infants of the same age. Together this suggests that reduced exposure to social and environmental microbes during the pandemic may have disrupted typical microbial succession This effect becomes mainly apparent once infants are weaned and the dominant effect of infant feeding on the microbiome composition starts to diminish. Interestingly, several Actinomyces species were significantly lower in pandemic as compared to pre-pandemic samples at later time points. Bailey et al. previously reported a positive correlation between fecal Actinomyces levels and air pollution levels, particularly with particulate matter PM 2.5 and gas NO 2 [ 61 ]. The significant global reduction in air pollution during the pandemic, including reductions in PM 2.5 and NO 2 emissions [ 62 , 63 ], could potentially explain the decreased levels of Actinomyces in pandemic samples. Beyond reducing environmental pollution, the pandemic has resulted in various changes in social interactions and hygiene practices that might have all affected infant gut microbiota. To explore how variation in family behavior during the pandemic contributed to microbiota outcomes, we next developed a novel Exposure Index (EI) capturing hygiene practices, social contacts, and lifestyle changes. Interestingly, higher EI scores—indicating more environmental exposure and fewer protective measures—were associated with both increased and decreased microbial richness, depending on the infant's age. While these associations might seem inconsistent, they likely reflect age-dependent windows of microbial vulnerability or resilience. We speculate that pandemic effect may be cumulative, depending on the duration of exposure to pandemic-related conditions such as social distancing and lifestyle changes. In our cohort, only six infants were already several months old when the pandemic began—for example, infants who were 5 months old at its onset had experienced only one month of pandemic conditions by the time their 6-month samples were collected. In contrast, infants born during the pandemic were continuously exposed to these conditions from birth, so their 6-month samples reflect six full months of exposure. This difference in cumulative exposure could contribute to the inconsistencies we observed in alpha diversity metrics, where the impact differed across ages. Moreover, one bacterial species, Gordonibacter pamelaeae , was significantly less abundant in infants with higher exposure at the ages of at 9 and 14 months, suggesting sensitivity to environmental conditions. Given its role in bile acid metabolism and production of immunomodulatory compounds such as 3-oxolithocholic acid and urolithins [ 64 – 66 ] reduced abundance of G. pamelaeae could have functional implications for immune development. While speculative, our findings raise the possibility that altered microbial exposure may impact immune programming via loss of specific microbial functions. To uncover such functional consequences, future studies are needed that include fecal metabolomics and immune profiling. Prior to our study, several cross-sectional studies and cross-cohort comparisons have explored the association between the pandemic and the infant gut microbiota. A study among American infants [ 27 ] reported a lower abundance of Haemophilus in fecal samples collected during the pandemic. The authors attributed this decrease to more intensive cleaning and disinfection practices. In contrast, our results showed higher abundance of several Haemophilu s species in pandemic samples. Specifically, we observed significant increases in H. parainfluenzae , and H. sputorum at 9 months and Haemophilus sp. HMSC71H05 at 11 months of age. Our findings are consistent with another study [ 59 ] showing that infants cared for at home have higher levels of Haemophilus than those attending daycare. This pattern may help explain the discrepancy between our results and the American cohort and underscores the need for caution in generalizing results across cohorts. The Irish CORAL study reported lower levels of environmentally transmitted Clostridia , including Hungatella , and higher levels of fecal bifidobacteria in their pandemic infant cohort as compared to pre-pandemic cohorts [ 67 ]. We also observed reduced levels of Hungatella hathewayi in 11-month-old infants during the pandemic, while levels of B. pseudocatenulatum and B. adolescentis were enriched between 9 to 14 months of age among infants sampled during the pandemic. Altogether, our findings are partly consistent with, yet extend, the few previous cross-sectional studies in the gut microbiome of pandemic-era infants [ 29 – 31 ] by incorporating longitudinal data and whole metagenome shotgun sequencing. Unlike these previous studies, we could demonstrate that pandemic effects emerge gradually and are most pronounced during later stages of infant microbial development. This refined understanding was facilitated by some of the key strengths of our study, including its longitudinal design, the application of deep sequencing, and the use of a detailed behavioral exposure index. However, some limitations should be acknowledged. The exposure index was available for a subset of 36 families, potentially limiting power. In addition, we lacked concurrent functional (e.g., metabolomics) and immunological data to determine the physiological consequences of compositional shifts, particularly regarding the potential roles of G. pamelaeae . In conclusion, by drastically altering patterns of social and environmental exposure, the COVID-19 pandemic provided a natural experiment to explore determinants of early-life microbiome development. Our findings indicate that such abrupt shifts in exposure can profoundly affect the infant gut microbiome during critical windows of microbiome maturation. Future research should examine whether these compositional changes translate into functional or clinical consequences later in life. Abbreviations BH Benjamini-Hochberg DMM Dirichlet Multinomial Mixture EI Exposure Index IQR InterQuartile Range LinDA Linear models for Differential Abundance analysis NMDS Non-metric MultiDimensional Scaling PC Principal Components PCA Principal Component Analysis Declarations Acknowledgements The authors would like to sincerely thank all families for their participation during this study, as well as the participating midwifery practices in the Limburg area of the Netherlands. This project is part of the Million Microbiomes from Humans Project (MMHP) consortium. We also would like to thank members of the John Penders lab for their help with this study. ChatGPT (OpenAI) was used to assist with language editing and improving grammar in this manuscript. The authors take full responsibility for the content and interpretation of the results. Author Contributions Conceptualization and design: L.B., J.P., N.v.B. and M.M.; Data collection: L.B., M.M., and E.D.; Methodology: E.D., L.B., J.P., N.v.B. and M.M.; Formal analysis: E.D., C.D.; Data curation: M.M., and E.D.; writing—original draft preparation: E.D., and J.P.; Writing—review and editing, all; supervision: N.v.B. and J.P.; project administration: C.D., M.M., and E.D.; funding acquisition: M.M. and J.P. All authors have read and agreed to the published version of the manuscript. Availability of data and materials Trimmed and quality filtered reads, with removed human reads can be found at European Nucleotide Archive under project number PRJEB88182. The data presented in this study are available on request from the corresponding authors (JP and MM). The data are not publicly available due to the potentially identifiable nature of the data and privacy concerns. Competing interests No potential conflict of interest was reported by the authors. Consent for publication Not applicable. Materials and Correspondence Correspondence to John Penders or Monique Mommers Ethics approval and consent to participate Written informed consent was obtained from both parents/legal caregivers prior to enrolment in the study. This research confirmed to the principles of the Helsinki Declaration. Ethical approval was obtained by the Medical Ethical Committee of Maastricht University Medical Center (study number: METC-15-4-237 ). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7565364\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":525527570,\"identity\":\"9b175618-1a0f-4a28-821c-61f5c97bbb85\",\"order_by\":0,\"name\":\"Evgenia Dikareva\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Maastricht University Medical Centre+\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Evgenia\",\"middleName\":\"\",\"lastName\":\"Dikareva\",\"suffix\":\"\"},{\"id\":525527571,\"identity\":\"aad26aa4-e1b6-4393-9fef-49c9157d4880\",\"order_by\":1,\"name\":\"Niels van Best\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Maastricht University Medical Centre+\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Niels\",\"middleName\":\"van\",\"lastName\":\"Best\",\"suffix\":\"\"},{\"id\":525527572,\"identity\":\"254bdfcb-654e-4bf0-8a7e-9e3be8a7c2ef\",\"order_by\":2,\"name\":\"Liene Bervoets\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Maastricht University Medical Centre+\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Liene\",\"middleName\":\"\",\"lastName\":\"Bervoets\",\"suffix\":\"\"},{\"id\":525527574,\"identity\":\"47f256fd-c243-40c3-a14f-6ad1cfcfb206\",\"order_by\":3,\"name\":\"Christina E. 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08:01:13\",\"extension\":\"html\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":151304,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7565364/v1/bb9a59b84722ac78e2bb664c.html\"},{\"id\":93017674,\"identity\":\"051da6c5-3d3e-4ed5-a35f-5a044e36811e\",\"added_by\":\"auto\",\"created_at\":\"2025-10-08 08:17:12\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":369787,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTemporal dynamics and taxonomic composition of infant microbiome\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA.\\u003c/strong\\u003e Changes in observed richness over time. Individual samples are displayed as jittered points. The FDR adjusted p-values are represented with stars, where “***” indicates Q values \\u0026lt; 0.001, “**” indicates Q values between 0.001 and 0.01, and “*” indicates Q values between 0.01 and 0.05.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eB.\\u003c/strong\\u003e Beta-diversity at different time points. The PCA is based on centered log-ratio (clr)-transformed reads using Aitchison distance at the species level. The arrows represent taxon loading vectors that drive the majority of the variation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eC.\\u003c/strong\\u003e Iris plot displaying the top 12 abundant species. The samples are arranged in the same sequence as in the PCA plot.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eD.\\u003c/strong\\u003e Infants' microbiota transition across Dirichlet Multinomial Mixture (DMM) clusters over time. The thickness of the lines represents transition frequency, and the size of the nodes represents the number of infants in each particular cluster at each time point (in days).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eE.\\u003c/strong\\u003e Heatmap displaying the mean relative abundance of the most dominant bacterial species for each DMM cluster. Only 21 bacteria species with a relative abundance \\u0026gt; 0.01 are presented.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7565364/v1/2165988f793b10616bf54bd4.png\"},{\"id\":93017232,\"identity\":\"7bc6941f-79b1-4f3a-8c52-9b63a46044cb\",\"added_by\":\"auto\",\"created_at\":\"2025-10-08 08:09:12\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":344515,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCOVID-19 pandemic is one of the determinants affecting microbial composition, diversity and species abundance.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA. \\u003c/strong\\u003eBar charts depicting the\\u003cstrong\\u003e \\u003c/strong\\u003eregression coefficients of each variable in association to the observed richness at each time point. Significant p-values are indicated with stars: “***” indicates p-values \\u0026lt; 0.001, “**” indicates p-values between 0.001 and 0.01, and “*” indicates p-values between 0.01 and 0.05.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eB.\\u003c/strong\\u003e Circular bar charts depicting the explained variance (R²) by each variable per time point (PERMANOVA). Significant p-values are indicated with stars: “***” indicates p-values \\u0026lt; 0.001, “**” indicates p-values between 0.001 and 0.01, and “*” indicates p-values between 0.01 and 0.05.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eC.\\u003c/strong\\u003e Heat map of the 44 bacterial species which showed statistically significant differences in abundance in pandemic as compared to pre-pandemic samples across each time point (LinDa). The color gradient ranges from blue (lower abundance) to red (higher abundance), representing log changes. Statistically significantly differentially abundant species are indicated with stars: “**” indicates Q values \\u0026lt; 0.05 and “*” for Q values between 0.05 and 0.2.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7565364/v1/8186f9224a80405567f6938f.png\"},{\"id\":93015881,\"identity\":\"1148fa02-d874-4aad-a748-d3d7c6f8d73a\",\"added_by\":\"auto\",\"created_at\":\"2025-10-08 08:01:12\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":284860,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePandemic Exposure index and its effect on the gut microbiota\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eA. \\u003c/strong\\u003eGraphical representation of the number and type of variables included in the Exposure Index (EI)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eB. \\u003c/strong\\u003eBar charts depicting regression coefficients of each variable in association to observed richness at 5-14 months for pandemic samples. Significant p-values are indicated with stars: “***” indicates p-values \\u0026lt; 0.001, “**” indicates p-values between 0.001 and 0.01, and “*” indicates p-values between 0.01 and 0.05.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eC. \\u003c/strong\\u003ePrincipal Component Analysis ordinating all 5–14-month\\u003cstrong\\u003e \\u003c/strong\\u003efecal samples collected during the pandemic. The PCA was performed on centered log-ratio (clr)-transformed reads using Aitchison distance at the species level. Samples are color-coded by index values, with arrows representing taxon loading vectors driving the largest variation along the first two Principal Components.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eD. \\u003c/strong\\u003eHeat-map\\u003cstrong\\u003e \\u003c/strong\\u003evisualizing how\\u003cstrong\\u003e \\u003c/strong\\u003ethe\\u003cstrong\\u003e \\u003c/strong\\u003e44 bacterial species which were differentially abundant in pandemic samples at 5-14 months of age are associated with the EI (LinDa). The color gradient ranges from blue (lower abundance) to red (higher abundance), representing log changes. Statistically significantly differentially abundant species are indicated with stars: “**” indicates Q values \\u0026lt; 0.05 and “*” for Q values between 0.05 and 0.2.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7565364/v1/a52b6cebdb64f28e4c91d639.png\"},{\"id\":93018926,\"identity\":\"75251554-165e-4d3a-9e19-413490fd246b\",\"added_by\":\"auto\",\"created_at\":\"2025-10-08 08:25:13\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1950325,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7565364/v1/36fbdae1-059b-42e5-811a-cc614bd8014d.pdf\"},{\"id\":93015889,\"identity\":\"8470933c-47c5-4513-a721-5a5950a315a8\",\"added_by\":\"auto\",\"created_at\":\"2025-10-08 08:01:13\",\"extension\":\"xlsx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":3451002,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"TablesCovidManuscript.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7565364/v1/214d60d05c79c12f774d9fdc.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The impact of the COVID-19 pandemic and associated lifestyle changes on early-life microbiome development: a natural experiment \",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eAn increasing number of studies emphasize the significance of the gut microbiome in infant health. The early-life gut microbiome plays a crucial role in shaping long-term health by influencing immune development, host metabolism, and disease susceptibility [\\u003cspan additionalcitationids=\\\"CR2 CR3 CR4 CR5 CR6\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. The establishment of the microbiome during childhood follows a dynamic progression that can be divided into three phases: a developmental phase, a transition phase, and a stable phase [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. This process is largely orchestrated by microbial transmission from the mother, family members, and the environment while genetic factors, infant feeding practices, and dietary transitions also play important roles.\\u003c/p\\u003e\\u003cp\\u003eHowever, while breastfeeding and complementary feeding are key determinants of microbiota composition [\\u003cspan additionalcitationids=\\\"CR9 CR10 CR11\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e], they account for only a fraction of the interindividual variation in microbial community structure [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Given the established links between early-life microbiota and the risk of allergic and autoimmune diseases [\\u003cspan additionalcitationids=\\\"CR15 CR16\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e], identifying additional factors that shape or disrupt microbiota establishment is essential. The \\\"hygiene hypothesis\\\" proposes that reduced exposure to infections contributes to the rise in non-communicable diseases [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e], whereas the \\\"old friends hypothesis\\\" suggests that evolutionary shifts in microbial exposure, rather than a lack of infections per se, underlie these trends [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eAlthough changes in social interactions, hygiene practices, and lifestyle typically occur gradually over time, the onset of the COVID-19 pandemic led to abrupt and widespread behavioral and environmental shifts [\\u003cspan additionalcitationids=\\\"CR23\\\" citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. This unprecedented global event presents a unique opportunity to investigate how such factors influence gut microbiome development in early life.\\u003c/p\\u003e\\u003cp\\u003eDuring the pandemic, the incidence of infectious diseases\\u0026mdash;including gastrointestinal infections\\u0026mdash;and antibiotic prescriptions for respiratory infections declined significantly in the Netherlands [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e], largely due to social distancing measures and daycare closures [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Additionally, lifestyle changes varied across the population, with some individuals adopting healthier dietary and physical activity habits, while others exhibited the opposite trend [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThese pandemic-induced behavioral shifts have the potential to alter infant gut microbiota development. A study of 12-month-old American infants reported reduced alpha diversity and decreased abundances of \\u003cem\\u003ePasteurellaceae\\u003c/em\\u003e and \\u003cem\\u003eHaemophilus\\u003c/em\\u003e in pandemic-era samples compared to pre-pandemic cohorts [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Similarly, the CORAL study in Ireland found that pandemic-era infants had increased \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e levels and decreased \\u003cem\\u003eClostridium\\u003c/em\\u003e abundance, possibly due to reduced exposure to individuals outside the household [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Comparable findings were observed in a Chinese cohort, where pandemic-born infants exhibited reduced microbial diversity, altered community composition, and decreased antimicrobial resistance gene carriage [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eHowever, these studies either analyzed microbiota at a single time point [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e] or lacked a pre-pandemic comparison within the same cohort [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e], limiting their ability to assess longitudinal microbiota development across pandemic and non-pandemic periods in the same population.\\u003c/p\\u003e\\u003cp\\u003eHere we study the impact of the COVID-19 pandemic on gut microbiota establishment during infancy within a single cohort, the Dutch Lucki Gut Study. This longitudinal birth cohort was designed to track the longitudinal development of the infant gut microbiome, began recruitment in 2016 and continued enrolling participants throughout the pandemic. This provided a unique opportunity to examine the impact of the COVID-19 pandemic on gut microbiota establishment within a single cohort. We hypothesized that lockdown and strict hygiene measures would have an impact on the infants\\u0026rsquo; gut microbiota development. To test this, we performed whole metagenome sequencing on samples collected pre and during the COVID-19 pandemic. The resolution of the sequencing allowed us to examine differences at species level.\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStudy population and sample collection\\u003c/h2\\u003e\\u003cp\\u003eThe LucKi Gut study is an on-going longitudinal study among newborns and their families. Pregnant women residing in the South Limburg region of the Netherlands were recruited through obstetrics and gynaecology clinics, lactation information sessions, and advertisements at pregnancy yoga classes, baby clothing stores, and on social media. Infants born prematurely (gestational age\\u0026thinsp;\\u0026le;\\u0026thinsp;32 weeks) were excluded. Infant fecal samples were collected at 1\\u0026ndash;2 weeks post-partum and again at 1, 2, 4, 5, 6, 9, 11 and 14 months of age (Supplementary Table\\u0026nbsp;1A). These time-points align with the ages at which children have scheduled visits to well-baby clinics.\\u003c/p\\u003e\\u003cp\\u003eParticipants received fecal sampling starter kits consisting of stool collection tubes (Sarstedt, REF 80.623.022), cold transport containers (Sarstedt, REF 95.1123), a safety bag, gloves, questionnaires, and instructions. The samples were collected at home and immediately stored at -20\\u0026deg;C in their home freezers. Samples were thereafter transported to the family\\u0026rsquo;s well-baby clinic using a frozen transport container to preserve the cold chain. From there, samples were transported to the laboratory, where frozen fecal matter was aliquoted and stored at -80\\u0026deg;C until further analyses.\\u003c/p\\u003e\\u003cp\\u003eAt each fecal sampling time-point, parents also completed a questionnaire gathering information on the infant's lifestyle, health, development, medication use, and feeding practices, as well as maternal health (during pregnancy), diet, and medication use. Alongside the questionnaires we collected 808 fecal samples from 139 infants recruited between August 2016 and November 2022.\\u003c/p\\u003e\\u003cp\\u003eAfter the onset of COVID-19 pandemic, the newly enrolled families and families that were still in follow-up (n\\u0026thinsp;=\\u0026thinsp;36) also filled in a separate questionnaire on social distancing, protective measures, hygiene and other pandemic related measures (Supplementary Table\\u0026nbsp;1B). Informed consent was provided by all parents. The study was approved by the medical ethics committee of the Maastricht University Hospital (METC 15-4-237).\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eMetadata processing\\u003c/h3\\u003e\\n\\u003cp\\u003eInformation on perinatal determinants, lifestyle, diet, medication use, and health outcomes was collected through self-reported questionnaires collected around birth (pregnancy questionnaire and paternal and maternal questionnaires) and at each subsequent sampling time point.\\u003c/p\\u003e\\u003cp\\u003eThe following variables were included for the purpose of the present study: gestational age (weeks), birthweight (grams), maternal weight gain (kilograms), age at complementary food introduction (weeks), delivery type (C-section, vaginal), maternal atopy (no, yes), paternal atopy (no, yes), infant antibiotic use since previous follow-up moment (no, yes), breastfeeding since previous follow-up moment (yes, no), formula feeding since previous follow-up moment (yes, no), day care attendance (no, yes), maternal smoking before pregnancy (no, yes), older siblings (no, yes), siblings in the cohort (no, yes), furry pets at home (no, yes), sex (male, female), delivery place (hospital, at home), infant hospital admission upon birth (no, yes), maternal antibiotics during delivery (no, yes) and maternal antibiotics during pregnancy (no, yes) (Supplementary Table\\u0026nbsp;2). For the follow-up time points where less than 5% of the children were reported to have used antibiotics, the variable on antibiotic use was omitted from the analyses. Missing information for breast and formula feeding was imputed as follows: if information on infant feeding was available and identical at both the preceding and subsequent time-point, then this value was imputed for the intermediate time-points. If breast milk was not given at the first time-points (1\\u0026ndash;2 and 4 weeks), then \\u0026ldquo;no breastfeeding\\u0026rdquo; was imputed for subsequent time-points for which information was missing. Otherwise, missing values were not imputed. For data on furry pets missing values were imputed as follows: if the information was available at two times-points (at birth and 14 months, or at birth and 6 months) and it was identical, then this value was imputed for the missing time-point. Otherwise, missing values were not imputed. For missing numeric values (gestational age, birthweight, maternal weight gain and age of complementary food introduction), the mean was calculated and used to impute missing values (Supplementary Table\\u0026nbsp;2). The frequencies of the variables for each time point can be found in Supplementary Tables\\u0026nbsp;3.\\u003c/p\\u003e\\u003cp\\u003eThe \\u0026ldquo;\\u003cem\\u003epandemic\\u003c/em\\u003e\\u0026rdquo; variable categorized samples into two groups based on their collection date relative to the onset of the COVID-19 pandemic in the Netherlands. Samples collected before February 27, 2020, (the date of the first confirmed infection by the SARS-CoV-2 virus in the Netherlands) were assigned to the \\\"pre-pandemic\\\" group, while samples collected on or after this date were assigned to the \\\"pandemic\\\" group (Supplementary Table\\u0026nbsp;1B).\\u003c/p\\u003e\\u003cp\\u003eFor the subgroup of 36 families in which at least one fecal sample was collected during the COVID-19 pandemic, we additionally created an \\u0026ldquo;\\u003cem\\u003eexposure index\\u003c/em\\u003e\\u0026rdquo; (EI) to estimate the level of social interactions, lifestyle and hygiene measures. To this end, we initially selected 58 questions related to the COVID-19 pandemic, excluding those related to SARS-CoV-2 infections directly such as diagnostic testing and symptoms. For highly correlated variables (Pearson\\u0026rsquo;s rho\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.8) one of the variables was removed and remaining variables were further processed. From these individual variables (Supplementary Table\\u0026nbsp;4) we created new variables that aggregated the level of exposure (Supplementary Fig.\\u0026nbsp;1, Supplementary Table\\u0026nbsp;5). For example, for variables on exposure in indoor public spaces, the number of days per week people had social interactions in public space, the number of times they kept distance and/or were wearing a mask during such social interactions were combined into one new variable that provides a summed score with the highest value for individuals with frequent unprotected interactions in indoor public spaces. All variables were coded in such a way that a higher score was related to more exposure (Supplementary Table\\u0026nbsp;4).\\u003c/p\\u003e\\u003cp\\u003eNext, we rescaled these newly created variables according to the median for the answers (0, when original value was 0, 1 when below median and 2 when above the median). Binary variables were kept as binary variables. Finally, these rescaled variables were summed into the \\u0026ldquo;exposure index\\u0026rdquo; (EI).\\u003c/p\\u003e\\u003cp\\u003eTwo versions of the EI were initially constructed: one incorporating responses related to social interactions, hygiene practices, parental occupation, and protective measures used by both parents, and another excluding paternal (or second parent) responses. Given the high level of correlation (Spearman\\u0026rsquo;s ρ\\u0026thinsp;=\\u0026thinsp;0.940, p\\u0026thinsp;=\\u0026thinsp;0.000) between both indices (Supplementary Fig.\\u0026nbsp;2), we ultimately continued with the latter index since information on questions related to the father (or second parent) was only available for 32 out of the 36 families.\\u003c/p\\u003e\\n\\u003ch3\\u003eDNA isolation, whole metagenomic sequencing and data pre-processing\\u003c/h3\\u003e\\n\\u003cp\\u003eApproximately 100 mg of aliquoted fecal sample, was sent to MGI Tech Latvia (Mārupe, Latvia) for DNA extraction and whole metagenomic sequencing (WMGS). Metagenomic DNA was isolated using the MagPure Stool DNA LQ kit according to the manufacturer\\u0026rsquo;s protocol (Magin Biotech, Guangzhou, China), with the additional inclusion of a mechanical bead-beating step using 0.1 mm glass beads as described previously [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Library preparation and shotgun metagenomic sequencing were performed on the BGISEQ-500 platform using the paired-end 150 mode.\\u003c/p\\u003e\\u003cp\\u003ePre-processing of sequencing reads was performed according to the \\\"remove-host\\\" standard operating procedure in MMHP. To standardize the pipeline a workflow manager Snakemake v. 5.14.0 [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e] was used. Quality filtering was performed using Fastp v.0.20.1 with default quality threshold of Q15, a minimum read length of 60 bp, and rejection of any reads containing N bases [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. The same tool was used to trim the BGI-SEQ adapters [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e] \\u0026ldquo;AAGTCGGAGGCCAAGCGGTCTTAGGAAGACAA\\u0026rdquo; for forward and \\u0026ldquo;AAGTCGGATCGTAGCCATGTCGTTCTGTGAGCCAAGGAGTTG\\u0026rdquo; for reverse reads. Human reads were subsequently removed using Bowtie 2 v.2.3.5.1 using the very-sensitive preset and the maximum length of paired-end alignments: 600 bp, index: chm13.draft_v1.0_plusY, downloaded 14.10.2020) [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. Paired reads where both mates failed to align were retained using Samtools v.1.9 [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e] (-f 12 -F 256). As the reference a human reference genome from the Telomere-to-telomere consortium CHM13 project (version 1.0) [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e] and the Y chromosome, compiled into index by Bowtie2 was used. Then forward and reverse reads were used to identify taxonomic composition using MetaPhlAn v.3.0 [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e] by aligning marker genes to a reference database (species-markers database from January 2019 CHOCOPhlAn v.30).\\u003c/p\\u003e\\n\\u003ch3\\u003eStatistical analysis and data visualization\\u003c/h3\\u003e\\n\\u003cp\\u003eStatistical analysis and data visualisation were performed on Rstudio (v.2023.06.2\\u0026thinsp;+\\u0026thinsp;561) with integrated R (v.4.1.3) [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. With the \\u003cem\\u003ephyloseq\\u003c/em\\u003e (v.1.38.0) [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e] and \\u003cem\\u003etidyverse\\u003c/em\\u003e (v.2.0.0) [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e] packages the phyloseq object was constructed. For data visualisation \\u003cem\\u003emicroViz\\u003c/em\\u003e (v.0.10.8) [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e] and \\u003cem\\u003eviridis\\u003c/em\\u003e (v.0.6.2) [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e] packages were used.\\u003c/p\\u003e\\u003cp\\u003eTo reduce the sparsity of the data, bacterial taxa were filtered out at species level with a prevalence of \\u0026lt;\\u0026thinsp;5% (microViz, tax_filter(min_prevalence\\u0026thinsp;=\\u0026thinsp;0.05, tax_level = \\\"Species\\\") across all samples. These filtered data were used in all downstream analysis except for alpha diversity analysis where unfiltered reads were used.\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u003c/h3\\u003e\\n\\u003cdiv class=\\\"Heading\\\"\\u003e\\u003cem\\u003eAlpha diversity and linear regression analysis\\u003c/em\\u003e\\u003c/div\\u003e\\u003cp\\u003eThe following ecological diversity distances were calculated with \\u003cem\\u003evegan\\u003c/em\\u003e package (v.2.6-4) [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]: Shannon index and observed richness. The Effective Number of Species (ENS) was subsequently calculated from the Shannon index using the base R exponential function (exp()) [\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eGiven the non-normal distribution of the data, non-parametric tests for all indices and time-points were applied, and the median with the interquartile range was used to summarize alpha diversity metrics. Alpha diversity indices were compared between subsequent time-points using the paired Wilcoxon Rank Sum Test (p.adjust.method = \\\"BH\\\", alternative = \\\"two.sided\\\", paired\\u0026thinsp;=\\u0026thinsp;T). The p-values were adjusted with false discovery rate (FDR) correction using the Benjamini\\u0026ndash;Hochberg procedure [\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e] with function p.adjust from \\u003cem\\u003estats\\u003c/em\\u003e package (v.4.1.3) for each alpha diversity metric separately.\\u003c/p\\u003e\\u003cp\\u003eTo analyse whether variables were associated with alpha diversity, linear regression analysis was performed for each time point using function lm() from \\u003cem\\u003estats\\u003c/em\\u003e package. The numeric variables were scaled using the R basic function scale(). We performed backward elimination by iteratively excluding variables with p-value\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.2.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePrincipal component analysis and beta diversity evaluation\\u003c/h2\\u003e\\u003cp\\u003eThe Aitchison\\u0026rsquo;s distance [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e] was used to analyse beta-diversity and overall variation between the samples. Ordination of infant fecal samples was performed by principal component analysis (PCA) using microViz package. The reads were center log ratio transformed on \\u0026ldquo;Species\\u0026rdquo; level (tax_transform(\\\"clr\\\", rank = \\\"Species\\\")) upon which samples were arranged by similarity into new dimensions to form PCA (ord_calc(method = \\\"PCA\\\")). To visualize the species composition of samples, a circular compositional barplot (IRIS plot) sorted by the PCA ordination angle was created (microViz, tax_transform(\\\"clr\\\", rank = \\\"Species\\\")). A permutational multivariate analysis of variance (PERMANOVA) was conducted using the pairwise.adonis() function from vegan package to assess differences between the ages. The Skillings\\u0026ndash;Mack test was used to compare Aitchison distance between the time points with a Wilcoxon paired signed rank test as a post hoc test.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eDirichlet Multinomial Mixture clustering\\u003c/h3\\u003e\\n\\u003cp\\u003eDirichlet Multinomial Mixture (DMM) clustering was performed using the Dirichlet Multinomial package (v.1.36.0) at the species level. Samples were assigned to a specific cluster according to the Laplace approximation score, which represents a specific enterotype or a signature composition of microbes. The clustering procedure [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e] and transition analyses [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e] were conducted as previously described. For clustering visualisation, we used a non-metric multidimensional scaling (NMDS) analysis that was performed on Bray-Curtis dissimilarities. The samples were coloured according to the DMM clustering.\\u003c/p\\u003e\\n\\u003ch3\\u003eMarginal Permutational Multivariate Analysis of Variance\\u003c/h3\\u003e\\n\\u003cp\\u003eTo identify variables that were significantly associated with the microbial community structure, a Marginal PERMANOVA based on marginal sum of squares was performed with \\u003cem\\u003emicroViz\\u003c/em\\u003e. PERMANOVA was conducted on filtered reads with 999 permutations. For each time point, Aitchison distance matrices were calculated on species-level using \\u003cem\\u003emicroViz\\u003c/em\\u003e.\\u003c/p\\u003e\\u003cp\\u003eFor the first three time points (1, 4, and 8 weeks), the following variables were selected: parental atopy, birthweight, breastfeeding and formula feeding, delivery place and type, hospital admission upon delivery, maternal antibiotic use during pregnancy and delivery, maternal weight gain during pregnancy, older siblings, presence of siblings inside the cohort, gestational age, sex, maternal smoking before pregnancy and the pandemic variable.\\u003c/p\\u003e\\u003cp\\u003eFor the remaining time points, the following additional variables were added: age at the introduction of complementary foods and daycare attendance. Only the last three time points had enough observations (\\u0026gt;\\u0026thinsp;5% of \\u0026ldquo;yes\\u0026rdquo; answers) for infant antibiotic exposure since previous sampling time point.\\u003c/p\\u003e\\u003cp\\u003eFor each time point, a series of PERMANOVAs were performed, and the backward elimination method was implemented. Variables with the highest p-value were removed iteratively until only those variables with p\\u0026thinsp;\\u0026le;\\u0026thinsp;0.200 remained. For each time point, a distinct set of variables was retained in the final model. These were used in further analysis and are referred to as the 'final set of variables' for each respective time point.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eSensitivity analysis\\u003c/h2\\u003e\\u003cp\\u003eTo ensure that the impact of the pandemic on the infant microbiome was not a technical artefact related to sequencing batch effects, we ran a sensitivity analysis including the sequencing batch as an additional covariate. This could however only be performed in case samples were sufficiently spread across sequencing batches (e.g., not for 9 and 11 months of age). Sensitivity analyses revealed that while technical variation significantly explained up to 5% of interindividual variation in microbiome profiles, it did not eliminate the effect of the pandemic at 6 months (p\\u0026thinsp;=\\u0026thinsp;0.018) and as such rules out that observed findings were due to technical artefacts (Supplementary Fig.\\u0026nbsp;3, Supplementary Table\\u0026nbsp;6).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eDifferential abundance analysis\\u003c/h2\\u003e\\u003cp\\u003eTo detect bacterial species that were significantly different in abundance between time points, a linear model for differential abundance analysis (LinDa) was performed using the \\u003cem\\u003eLinDa\\u003c/em\\u003e package (v.0.1.0) [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e] (alpha\\u0026thinsp;=\\u0026thinsp;0.2, prev.cut\\u0026thinsp;=\\u0026thinsp;0, lib.cut\\u0026thinsp;=\\u0026thinsp;1, winsor.quan\\u0026thinsp;=\\u0026thinsp;NULL, corr.cut\\u0026thinsp;=\\u0026thinsp;0.1, p.adj.method = 'BH',type = \\\"count\\\"). Separate models were run for each follow-up time with default settings with the \\u0026ldquo;final set of variables\\u0026rdquo;. To reduce the false discovery rate, the p-values were adjusted using the Benjamini-Hochberg procedure within each time point, and only FDR-adjusted p-values\\u0026thinsp;\\u0026le;\\u0026thinsp;0.2 [\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e] are presented. The relative taxon abundances were visualised in heatmaps presenting the log fold differences in bacterial abundance.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eImpact if exposure index on alpha and beta diversity and abundance of individual species\\u003c/h2\\u003e\\u003cp\\u003eTo investigate the effect of the EI for the 5\\u0026ndash;14-month time points, we used samples collected after the onset of the pandemic. The EI variable was used in place of the original pandemic variable. For the 6-month time point, the variable \\\"delivery place\\\" was excluded due to collinearity with the \\\"siblings in cohort\\\" variable.\\u003c/p\\u003e\\u003cp\\u003eThe impact of EI on alpha diversity was assessed as previously described for ENS and observed richness. Beta diversity was analysed using PCA plots and PERMANOVA, as outlined above. Differential abundance analysis was also performed as described previously.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003eA total of 808 fecal samples collected from 139 infants at the ages of 1\\u0026ndash;2, 4 and 8 weeks, 4, 5, 6, 9, 11 and 14 months were successfully sequenced (Supplementary Table\\u0026nbsp;1A). Out of the 808 samples, 253 (31.3%) were collected during the COVID-19 pandemic (Supplementary Table\\u0026nbsp;1A). Additional questionnaire data on societal interactions, lifestyle and hygiene measures during the pandemic were collected for the 36 participating families in which at least one stool sample from the child was collected during the COVID-19 pandemic (Supplementary Table\\u0026nbsp;1B).\\u003c/p\\u003e\\u003cp\\u003eOf the 139 included children, 80 (57.6%) were boys, 117 were born vaginally (84.2%) and 121 were born in the hospital (87.1%). Moreover, 59 (42.2%) infants had older siblings (Supplementary Table\\u0026nbsp;2). Breastfeeding was initially given to most infants (88.1%) during the first weeks of life and over half (52.3%) of the infants were still being breastfed by 6 months of age. Antibiotic exposure during the first 6 months of life was rare (Supplementary Table\\u0026nbsp;3). By the age of 18 weeks, complementary foods had been introduced to half of the infants (median: 18 weeks, Interquartile range (IQR): 4.0 weeks, Supplementary Table\\u0026nbsp;2).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eTemporal changes in the infant microbiome: microbial richness and diversity increase over time, with the infant gut being dominated by\\u003c/b\\u003e \\u003cb\\u003eBifidobacterium\\u003c/b\\u003e \\u003cb\\u003especies\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn line with previous studies [\\u003cspan additionalcitationids=\\\"CR53 CR54\\\" citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e], we observed an increase in microbial richness and diversity with age. Observed richness was statistically significantly different between the ages 1 and 4 weeks (Wilcoxon rank-sum test, W\\u0026thinsp;=\\u0026thinsp;703, Q value (FDR)\\u0026thinsp;=\\u0026thinsp;0.049); 4 and 8 weeks (W\\u0026thinsp;=\\u0026thinsp;885, Q value\\u0026thinsp;=\\u0026thinsp;0.049); 8 weeks and 4 months (W\\u0026thinsp;=\\u0026thinsp;1124, Q value\\u0026thinsp;=\\u0026thinsp;0.049); 6 and 9 months (W\\u0026thinsp;=\\u0026thinsp;570, Q value\\u0026thinsp;=\\u0026thinsp;0.002); 9 and 11 months (W\\u0026thinsp;=\\u0026thinsp;476, Q value\\u0026thinsp;=\\u0026thinsp;0.000) and 11 and 14 months (W\\u0026thinsp;=\\u0026thinsp;503, Q value\\u0026thinsp;=\\u0026thinsp;0.004, Fig.\\u0026nbsp;1A, Supplementary Table\\u0026nbsp;7). The difference for Effective Number of Species (ENS) was significant between 6 and 9 months (W\\u0026thinsp;=\\u0026thinsp;645, Q value\\u0026thinsp;=\\u0026thinsp;0.01) and 9 and 11 months (W\\u0026thinsp;=\\u0026thinsp;786, Q value\\u0026thinsp;=\\u0026thinsp;0.03, Supplementary Fig.\\u0026nbsp;4, Supplementary Table\\u0026nbsp;7).\\u003c/p\\u003e\\u003cp\\u003eAge was also the primary factor explaining variation in microbial community structure, as evidenced by the separation of samples in the PCA plot (Fig.\\u0026nbsp;1B). A Skillings\\u0026ndash;Mack test used to compared Aitchison distance between the time points indicated a statistically significant difference between the ages (statistics\\u0026thinsp;=\\u0026thinsp;185.82, p\\u0026thinsp;=\\u0026thinsp;0.000). The most pronounced shift in microbial community composition occurred between 6 and 9 months of age (p\\u0026thinsp;=\\u0026thinsp;0.000), as reflected by the increased Aitchison distances between adjacent time points (Supplementary Fig.\\u0026nbsp;5, Supplementary Table\\u0026nbsp;8).\\u003c/p\\u003e\\u003cp\\u003eAcross all time points, \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e species, such as \\u003cem\\u003eB. longum\\u003c/em\\u003e and \\u003cem\\u003eB. breve\\u003c/em\\u003e, along with \\u003cem\\u003eEscherichia coli\\u003c/em\\u003e, were among the most abundant species. At later time points, various \\u003cem\\u003eBacteroides\\u003c/em\\u003e species, \\u003cem\\u003eCollinsella aerofaciens\\u003c/em\\u003e, \\u003cem\\u003ePhocaeicola vulgatus\\u003c/em\\u003e (previous name - \\u003cem\\u003eBacteroides vulgatus\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]), and \\u003cem\\u003eFaecalibacterium prausnitzii\\u003c/em\\u003e increased in relative abundance (Fig.\\u0026nbsp;1C).\\u003c/p\\u003e\\u003cp\\u003eWe next applied Dirichlet multinomial mixtures (DMM) modelling to cluster bacterial communities over time (Fig.\\u0026nbsp;1D). Each cluster was defined by distinct microbial compositions (Fig.\\u0026nbsp;1E, Supplementary Table\\u0026nbsp;9), reflecting progressive microbiome maturation (Supplementary Fig.\\u0026nbsp;6). Most infants initially belonged to cluster 1, dominated by \\u003cem\\u003eB. longum\\u003c/em\\u003e, \\u003cem\\u003eB. breve\\u003c/em\\u003e, and \\u003cem\\u003eE. coli\\u003c/em\\u003e, and transitioned to cluster 5, characterised by dominance of \\u003cem\\u003eB. longum\\u003c/em\\u003e, \\u003cem\\u003eB. breve\\u003c/em\\u003e, and \\u003cem\\u003eB. bifidum and\\u003c/em\\u003e reduced \\u003cem\\u003eE. coli\\u003c/em\\u003e\\u0026mdash;by 4 months of age. By 9 months, most had shifted to cluster 6, followed by further transition to cluster 7 in later months, marked by the emergence of \\u003cem\\u003eF. prausnitzii\\u003c/em\\u003e.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eThe COVID-19 pandemic affected microbial diversity and composition\\u003c/h2\\u003e\\u003cp\\u003eTo understand the effect of various variables on infants\\u0026rsquo; gut microbial richness and diversity, we performed linear regressions on alpha diversity metrics. The observed microbial richness was most strongly influenced by infant feeding, with breastfeeding being associated with a lower and formula feeding with a higher microbial richness at several time-points during the first 6 months of life (Fig.\\u0026nbsp;2A, Supplementary Table\\u0026nbsp;10). Maternal and paternal atopy was associated with a lower microbial richness in the infant microbiome, and early complementary food introduction was associated with an change in microbial richness.\\u003c/p\\u003e\\u003cp\\u003eBesides these well-known drivers of microbial richness, the COVID-19 pandemic was associated with a statistically significant increase in observed richness at 9 months of age (estimate\\u0026thinsp;=\\u0026thinsp;5.9, 95% CI [0.7, 11.2], p\\u0026thinsp;=\\u0026thinsp;0.027) (Fig.\\u0026nbsp;2A, Supplementary Table\\u0026nbsp;10). Moreover, the fecal microbial diversity was marginally higher in 11-month-old infants sampled during the pandemic as compared to infants sampled prior to the pandemic (estimate\\u0026thinsp;=\\u0026thinsp;1.8, 95% CI [-0.02, 3.7], p\\u0026thinsp;=\\u0026thinsp;0.052, Supplementary Figs.\\u0026nbsp;7, Supplementary Table\\u0026nbsp;10).\\u003c/p\\u003e\\u003cp\\u003eThe pandemic began to influence infant microbial community composition at 5 months of age and became a statistically significant factor from 6 months onwards (PERMANOVA; Fig.\\u0026nbsp;2B, Supplementary Table\\u0026nbsp;11). The proportion of variance explained by the pandemic gradually increased over time, reaching a maximum of 2.7% at 11 months (p\\u0026thinsp;=\\u0026thinsp;0.001).\\u003c/p\\u003e\\u003cp\\u003eWe identified multiple perinatal, environmental, dietary, and health-related factors (Supplementary Figs.\\u0026nbsp;7, Supplementary Table\\u0026nbsp;10) contributing to inter-individual variation in gut microbiota composition. Mode of delivery was a major determinant, exerting its strongest influence between 1 and 2 weeks of age and explaining over 3% of the variation, with effects persisting up to 9 months. Maternal atopy and breastfeeding had a continuous impact on microbiota composition throughout the first 11 months of life. Day care attendance emerged as a significant factor from 4 months of age, coinciding with the end of maternity leave in the Netherlands. The presence of older siblings influenced the infant microbiota from the earliest time point (1\\u0026ndash;2 weeks of age), suggesting early household microbial transmission. In contrast, introduction of complementary feeding contributed to microbial variation only during the initial transition to complementary foods (4\\u0026ndash;6 months) but not thereafter, highlighting a transient effect at this developmental stage.\\u003c/p\\u003e\\u003cp\\u003eAs the impact of the pandemic became more pronounced at later time points and persisted in sensitivity analysis performed using the samples collected at age 6 months (Supplementary Fig.\\u0026nbsp;3, Supplementary Table\\u0026nbsp;6), we further examined differences in bacterial species abundance between pre-pandemic and pandemic samples. The most pronounced shifts in species composition were observed at 9\\u0026ndash;14 months (Fig.\\u0026nbsp;2C, Supplementary Table\\u0026nbsp;12). Here, LinDa revealed that 44 out of 216 species were statistically significantly differentially abundant in samples collected during as compared to prior the start of the pandemic. The most pronounced depleted species in pandemic samples were \\u003cem\\u003eActinomyces sp.\\u003c/em\\u003e ICM47, particularly at 14 months (Q value\\u0026thinsp;=\\u0026thinsp;0.008, log2-fold change = -6.7), as well as 9 (Q value\\u0026thinsp;=\\u0026thinsp;0.004, log2-fold change = -6.6) and 11 months (Q value\\u0026thinsp;=\\u0026thinsp;0.017, log2-fold change = -6.3). Other taxa, including \\u003cem\\u003eEggerthella lenta, Actinomyces odontolyticus\\u003c/em\\u003e and \\u003cem\\u003eGordonibacter pamelaeae\\u003c/em\\u003e, also showed decreased relative abundances in pandemic samples at later time points.\\u003c/p\\u003e\\u003cp\\u003eConversely, several species were significantly enriched in pandemic samples, notably \\u003cem\\u003eBifidobacterium adolescentis\\u003c/em\\u003e (9 months, Q value\\u0026thinsp;=\\u0026thinsp;0.028, log2-fold change\\u0026thinsp;=\\u0026thinsp;7.8), \\u003cem\\u003eRalstonia sp MD27\\u003c/em\\u003e (9mon, Q value\\u0026thinsp;=\\u0026thinsp;0.002, log2-fold change\\u0026thinsp;=\\u0026thinsp;7.0; 1wk, Q value\\u0026thinsp;=\\u0026thinsp;0.000, log2-fold change\\u0026thinsp;=\\u0026thinsp;5.7), \\u003cem\\u003eHaemophilus parainfluenza\\u003c/em\\u003e (9mon, Q value\\u0026thinsp;=\\u0026thinsp;0.017, log2-fold change\\u0026thinsp;=\\u0026thinsp;6.1; 11mon, Q value\\u0026thinsp;=\\u0026thinsp;0.035, log2-fold change\\u0026thinsp;=\\u0026thinsp;5.8) and \\u003cem\\u003eStreptococcus sp A12\\u003c/em\\u003e (11mon, Q value\\u0026thinsp;=\\u0026thinsp;0.016, log2-fold change\\u0026thinsp;=\\u0026thinsp;5.6).These data further confirm a shift in microbial composition during the pandemic period.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eLinking behavioural exposure during the pandemic to shifts in infant gut microbial composition\\u003c/h2\\u003e\\u003cp\\u003eTo investigate whether shifts in bacterial species abundance during the pandemic were linked to changes in environmental exposure and parental behavior, we developed an Exposure Index (EI) based on questionnaire data from families with at least one infant fecal sample collected during the pandemic period (Supplementary Fig.\\u0026nbsp;8).\\u003c/p\\u003e\\u003cp\\u003eThe final EI (Supplementary Fig.\\u0026nbsp;9), derived from 36 families, consisted of 10 grouped variables (Fig.\\u0026nbsp;3A) and followed a normal distribution (Shapiro-Wilk test: p\\u0026thinsp;=\\u0026thinsp;0.171, W\\u0026thinsp;=\\u0026thinsp;0.957). Scores ranged from 4 to 18 (mean\\u0026thinsp;=\\u0026thinsp;11.4, SD\\u0026thinsp;=\\u0026thinsp;3.8), with higher values reflecting greater exposure\\u0026mdash;characterized by fewer hygiene measures, reduced protective behaviors, and increased social interactions (Supplementary table 4).\\u003c/p\\u003e\\u003cp\\u003eWith increasing EI\\u0026mdash;indicating greater environmental exposure\\u0026mdash;we observed significant shifts in microbial richness across multiple time points. Upon adjustment for other covariates, richness decreased at 5 months (estimate = -7.0, 95% CI [-11.6, -2.5], p\\u0026thinsp;=\\u0026thinsp;0.005) and 11 months (estimate = -12.1, 95% CI [-16.4, -7.7], p\\u0026thinsp;=\\u0026thinsp;0.001), whereas an increase in microbial richness was detected at 9 months (estimate\\u0026thinsp;=\\u0026thinsp;4.9, 95% CI [0.9, 8.8], p\\u0026thinsp;=\\u0026thinsp;0.018) and 14 months (estimate\\u0026thinsp;=\\u0026thinsp;12.0, 95% CI [2.9, 21.2], p\\u0026thinsp;=\\u0026thinsp;0.020) (Fig.\\u0026nbsp;3B, Supplementary table 13).\\u003c/p\\u003e\\u003cp\\u003eSimilarly, effective number of species (ENS) decreased at 6 months (estimate = -1.5, 95% CI [-2.5, -0.4], p\\u0026thinsp;=\\u0026thinsp;0.013), 9 months (estimate = -3.9, 95% CI [-4.9, -3.0], p\\u0026thinsp;=\\u0026thinsp;0.000), and 11 months (estimate = -1.6, 95% CI [-2.9, -0.2], p\\u0026thinsp;=\\u0026thinsp;0.031), while increasing at 14 months (estimate\\u0026thinsp;=\\u0026thinsp;3.6, 95% CI [0.9, 6.2], p\\u0026thinsp;=\\u0026thinsp;0.019) (Supplement Fig.\\u0026nbsp;10, Supplementary table 13).\\u003c/p\\u003e\\u003cp\\u003eWe analysed beta-diversity in pandemic-era samples collected between 5 and 14 months of age, the period when the pandemic effect was most pronounced. Although no clear overall separation by EI was observed, likely due to the dominant influence of age on sample clustering in PCA space (Fig.\\u0026nbsp;3C), at 6 months the EI accounted for 5.7% of the variation (p\\u0026thinsp;=\\u0026thinsp;0.029, Supplementary Fig.\\u0026nbsp;11, Supplementary table 14).\\u003c/p\\u003e\\u003cp\\u003eNext, we assessed the impact of EI on the abundance of bacterial species that were significantly differentially abundant in pandemic-era samples. Of the 44 species identified, \\u003cem\\u003eBlautia obeum\\u003c/em\\u003e and \\u003cem\\u003ePrevotella timonensis\\u003c/em\\u003e were excluded as too few samples contained these species (Supplementary Fig.\\u0026nbsp;12). Higher EI was associated with a reduction in \\u003cem\\u003eG. pamelaeae\\u003c/em\\u003e at 9 months (Q value\\u0026thinsp;=\\u0026thinsp;0.024, fold = -4.7) and 14 months (Q value\\u0026thinsp;=\\u0026thinsp;0.16, fold = -6.3) (Fig.\\u0026nbsp;3C, Supplementary Table\\u0026nbsp;15). Interestingly, the samples of infants with an EI above the mean (\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003e\\u0026ge;\\u003c/span\\u003e\\u0026thinsp;11.48) had a lower abundance of \\u003cem\\u003eG. pamelaeae\\u003c/em\\u003e when compared to the samples of infants with an EI below the mean (Supplementary Fig.\\u0026nbsp;12, Supplementary Fig.\\u0026nbsp;13). Altogether this indicates that infants of whom samples were collected during the pandemic had lower levels of \\u003cem\\u003eG. pamelaeae\\u003c/em\\u003e as compared to pre-pandemic infants of the same age, an effect that was most pronounced for infants with the highest EI scores.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eIn this study, we made use of the unique timing of the COVID-19 pandemic to explore how abrupt lifestyle, and behavioural changes affected the gut microbiome development during infancy. Within the well-phenotyped prospective LucKi Gut study, we showed that microbiota maturation followed age-related trajectories that aligned with previous studies [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e] but was measurably impacted by the pandemic from 6 months of age onwards.\\u003c/p\\u003e\\u003cp\\u003eFirst, we investigated infant gut microbiota development. As anticipated, microbial richness and diversity increased with age [\\u003cspan additionalcitationids=\\\"CR53 CR54\\\" citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e], and community composition matured toward a more complex, adult-like profile over time. Our results align with previous studies demonstrating distinct microbial phases in infancy, characterized by early dominance of \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e species [\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e] and a gradual increase of other taxa including \\u003cem\\u003eBacteroides\\u003c/em\\u003e, \\u003cem\\u003eFaecalibacterium\\u003c/em\\u003e, and \\u003cem\\u003eCollinsella\\u003c/em\\u003e upon cessation of breastfeeding and the introduction of complementary foods [\\u003cspan additionalcitationids=\\\"CR53 CR54\\\" citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eWe, moreover, confirmed well-established associations between birth mode [\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e], infant feeding [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e], daycare attendance [\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e] and older siblings [\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e] with infant microbiota composition, further validating the robustness of our data.\\u003c/p\\u003e\\u003cp\\u003eSecond, we investigated the effect of the pandemic on infant gut microbiota maturation. To our knowledge, this is the first large-scale longitudinal study using whole metagenome sequencing to assess the pandemic\\u0026rsquo;s impact on infant gut microbiota development.\\u003c/p\\u003e\\u003cp\\u003eNotably, microbial richness was significantly increased in pandemic samples at 9 months of age, and community composition began to diverge measurably from pre-pandemic samples at 6 months. Moreover, 44 bacterial species differed significantly in abundance between pre- and post-pandemic fecal samples of infants of the same age. Together this suggests that reduced exposure to social and environmental microbes during the pandemic may have disrupted typical microbial succession This effect becomes mainly apparent once infants are weaned and the dominant effect of infant feeding on the microbiome composition starts to diminish.\\u003c/p\\u003e\\u003cp\\u003eInterestingly, several \\u003cem\\u003eActinomyces\\u003c/em\\u003e species were significantly lower in pandemic as compared to pre-pandemic samples at later time points. Bailey et al. previously reported a positive correlation between fecal \\u003cem\\u003eActinomyces\\u003c/em\\u003e levels and air pollution levels, particularly with particulate matter PM 2.5 and gas NO\\u003csub\\u003e2\\u003c/sub\\u003e [\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e]. The significant global reduction in air pollution during the pandemic, including reductions in PM\\u003csub\\u003e2.5\\u003c/sub\\u003e and NO\\u003csub\\u003e2\\u003c/sub\\u003e emissions [\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e], could potentially explain the decreased levels of \\u003cem\\u003eActinomyces\\u003c/em\\u003e in pandemic samples. Beyond reducing environmental pollution, the pandemic has resulted in various changes in social interactions and hygiene practices that might have all affected infant gut microbiota. To explore how variation in family behavior during the pandemic contributed to microbiota outcomes, we next developed a novel Exposure Index (EI) capturing hygiene practices, social contacts, and lifestyle changes.\\u003c/p\\u003e\\u003cp\\u003eInterestingly, higher EI scores\\u0026mdash;indicating more environmental exposure and fewer protective measures\\u0026mdash;were associated with both increased and decreased microbial richness, depending on the infant's age. While these associations might seem inconsistent, they likely reflect age-dependent windows of microbial vulnerability or resilience.\\u003c/p\\u003e\\u003cp\\u003eWe speculate that pandemic effect may be cumulative, depending on the duration of exposure to pandemic-related conditions such as social distancing and lifestyle changes. In our cohort, only six infants were already several months old when the pandemic began\\u0026mdash;for example, infants who were 5 months old at its onset had experienced only one month of pandemic conditions by the time their 6-month samples were collected. In contrast, infants born during the pandemic were continuously exposed to these conditions from birth, so their 6-month samples reflect six full months of exposure. This difference in cumulative exposure could contribute to the inconsistencies we observed in alpha diversity metrics, where the impact differed across ages.\\u003c/p\\u003e\\u003cp\\u003eMoreover, one bacterial species, \\u003cem\\u003eGordonibacter pamelaeae\\u003c/em\\u003e, was significantly less abundant in infants with higher exposure at the ages of at 9 and 14 months, suggesting sensitivity to environmental conditions. Given its role in bile acid metabolism and production of immunomodulatory compounds such as 3-oxolithocholic acid and urolithins [\\u003cspan additionalcitationids=\\\"CR65\\\" citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e] reduced abundance of \\u003cem\\u003eG. pamelaeae\\u003c/em\\u003e could have functional implications for immune development. While speculative, our findings raise the possibility that altered microbial exposure may impact immune programming via loss of specific microbial functions. To uncover such functional consequences, future studies are needed that include fecal metabolomics and immune profiling.\\u003c/p\\u003e\\u003cp\\u003ePrior to our study, several cross-sectional studies and cross-cohort comparisons have explored the association between the pandemic and the infant gut microbiota. A study among American infants [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e] reported a lower abundance of \\u003cem\\u003eHaemophilus\\u003c/em\\u003e in fecal samples collected during the pandemic. The authors attributed this decrease to more intensive cleaning and disinfection practices. In contrast, our results showed higher abundance of several \\u003cem\\u003eHaemophilu\\u003c/em\\u003es species in pandemic samples. Specifically, we observed significant increases in \\u003cem\\u003eH. parainfluenzae\\u003c/em\\u003e, and \\u003cem\\u003eH. sputorum\\u003c/em\\u003e at 9 months and \\u003cem\\u003eHaemophilus sp.\\u003c/em\\u003e HMSC71H05 at 11 months of age. Our findings are consistent with another study [\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e] showing that infants cared for at home have higher levels of \\u003cem\\u003eHaemophilus\\u003c/em\\u003e than those attending daycare. This pattern may help explain the discrepancy between our results and the American cohort and underscores the need for caution in generalizing results across cohorts.\\u003c/p\\u003e\\u003cp\\u003eThe Irish CORAL study reported lower levels of environmentally transmitted \\u003cem\\u003eClostridia\\u003c/em\\u003e, including \\u003cem\\u003eHungatella\\u003c/em\\u003e, and higher levels of fecal bifidobacteria in their pandemic infant cohort as compared to pre-pandemic cohorts [\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e]. We also observed reduced levels of \\u003cem\\u003eHungatella hathewayi\\u003c/em\\u003e in 11-month-old infants during the pandemic, while levels of \\u003cem\\u003eB. pseudocatenulatum\\u003c/em\\u003e and \\u003cem\\u003eB. adolescentis\\u003c/em\\u003e were enriched between 9 to 14 months of age among infants sampled during the pandemic.\\u003c/p\\u003e\\u003cp\\u003eAltogether, our findings are partly consistent with, yet extend, the few previous cross-sectional studies in the gut microbiome of pandemic-era infants [\\u003cspan additionalcitationids=\\\"CR30\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e] by incorporating longitudinal data and whole metagenome shotgun sequencing. Unlike these previous studies, we could demonstrate that pandemic effects emerge gradually and are most pronounced during later stages of infant microbial development. This refined understanding was facilitated by some of the key strengths of our study, including its longitudinal design, the application of deep sequencing, and the use of a detailed behavioral exposure index. However, some limitations should be acknowledged. The exposure index was available for a subset of 36 families, potentially limiting power. In addition, we lacked concurrent functional (e.g., metabolomics) and immunological data to determine the physiological consequences of compositional shifts, particularly regarding the potential roles of \\u003cem\\u003eG. pamelaeae\\u003c/em\\u003e.\\u003c/p\\u003e\\u003cp\\u003eIn conclusion, by drastically altering patterns of social and environmental exposure, the COVID-19 pandemic provided a natural experiment to explore determinants of early-life microbiome development. Our findings indicate that such abrupt shifts in exposure can profoundly affect the infant gut microbiome during critical windows of microbiome maturation. Future research should examine whether these compositional changes translate into functional or clinical consequences later in life.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eBH\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eBenjamini-Hochberg\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eDMM\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eDirichlet Multinomial Mixture\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eEI\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eExposure Index\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eIQR\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eInterQuartile Range\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eLinDA\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eLinear models for Differential Abundance analysis\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003eNMDS\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003eNon-metric MultiDimensional Scaling\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003ePC\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003ePrincipal Components\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e\\u003cdiv class=\\\"Term\\\"\\u003ePCA\\u003c/div\\u003e\\u003cdiv class=\\\"Description\\\"\\u003e\\u003cp\\u003ePrincipal Component Analysis\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eAcknowledgements\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors would like to sincerely thank all families for their participation during this study, as well as the participating midwifery practices in the Limburg area of the Netherlands. This project is part of the Million Microbiomes from Humans Project (MMHP) consortium. We also would like to thank members of the\\u0026nbsp;John Penders lab\\u0026nbsp;for their help with this study.\\u0026nbsp;ChatGPT (OpenAI) was used to assist with language editing and improving grammar in this manuscript. The authors take full responsibility for the content and interpretation of the results.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eAuthor Contributions\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConceptualization and design: L.B., J.P., N.v.B. and M.M.; Data collection: L.B., M.M., and E.D.; Methodology: E.D., L.B., J.P., N.v.B. and M.M.; Formal analysis: E.D., C.D.; Data curation: M.M., and E.D.; writing\\u0026mdash;original draft preparation: E.D., and J.P.; Writing\\u0026mdash;review and editing, all; supervision: N.v.B. and J.P.; project administration: C.D., M.M., and E.D.; funding acquisition: M.M. and J.P. All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eAvailability of data and materials\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTrimmed and quality filtered reads, with removed human reads can be found at European Nucleotide Archive under project number PRJEB88182. The data presented in this study are available on request from the corresponding authors (JP and MM). The data are not publicly available due to the potentially identifiable nature of the data and privacy concerns.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eCompeting interests\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo potential conflict of interest was reported by the authors.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eConsent for publication\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eMaterials and Correspondence\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCorrespondence to John Penders or Monique Mommers\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eEthics approval and consent to participate\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWritten\\u0026nbsp;informed consent was obtained from both parents/legal caregivers prior to enrolment in the study. This research confirmed to the principles of the Helsinki Declaration. Ethical approval was obtained by the Medical Ethical Committee of Maastricht University Medical Center (study number: METC-15-4-237 ).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eFunding\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe LucKi Gut study was funded by a grant from The Netherlands Organization for Health Research and Development (ZonMw) through the European Union Joint Programming Initiative\\u0026mdash;A Healthy Diet for a Healthy Life (received by J.P. and M.M.; project #: 529051010).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eYao, Y., et al., \\u003cem\\u003eThe role of microbiota in infant health: from early life to adulthood\\u003c/em\\u003e. Frontiers in immunology, 2021. 12: p. 708472.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSarkar, A., et al., \\u003cem\\u003eThe association between early-life gut microbiota and long-term health and diseases\\u003c/em\\u003e. Journal of clinical medicine, 2021. 10(3): p. 459.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBisgaard, H., et al., \\u003cem\\u003e25 Years of translational research in the Copenhagen Prospective Studies on Asthma in Childhood (COPSAC)\\u003c/em\\u003e. Journal of Allergy and Clinical Immunology, 2023. 151(3): p. 619\\u0026ndash;633.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePenders, J., et al., \\u003cem\\u003eEstablishment of the intestinal microbiota and its role for atopic dermatitis in early childhood\\u003c/em\\u003e. Journal of Allergy and Clinical Immunology, 2013. 132(3): p. 601\\u0026ndash;607. e8.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eB\\u0026eacute;lteky, M., et al., \\u003cem\\u003eInfant gut microbiome composition correlated with type 1 diabetes acquisition in the general population: the ABIS study\\u003c/em\\u003e. Diabetologia, 2023. 66(6): p. 1116\\u0026ndash;1128.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLiu, L., et al., \\u003cem\\u003eGut microbiota: A new insight into neurological diseases\\u003c/em\\u003e. Chinese Medical Journal, 2023. 136(11): p. 1261\\u0026ndash;1277.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVatanen, T., et al., \\u003cem\\u003eThe human gut microbiome in early-onset type 1 diabetes from the TEDDY study\\u003c/em\\u003e. Nature, 2018. 562(7728): p. 589\\u0026ndash;594.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eStewart, C.J., et al., \\u003cem\\u003eTemporal development of the gut microbiome in early childhood from the TEDDY study\\u003c/em\\u003e. Nature, 2018. 562(7728): p. 583\\u0026ndash;588.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePannaraj, P.S., et al., \\u003cem\\u003eAssociation between breast milk bacterial communities and establishment and development of the infant gut microbiome\\u003c/em\\u003e. JAMA pediatrics, 2017. 171(7): p. 647\\u0026ndash;654.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGalazzo, G., et al., \\u003cem\\u003eDevelopment of the microbiota and associations with birth mode, diet, and atopic disorders in a longitudinal analysis of stool samples, collected from infancy through early childhood\\u003c/em\\u003e. 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Science, 2016. 352(6285): p. 560\\u0026ndash;564.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHaahtela, T., et al., \\u003cem\\u003eHunt for the origin of allergy\\u0026ndash;comparing the Finnish and Russian Karelia\\u003c/em\\u003e. Clinical \\u0026amp; Experimental Allergy, 2015. 45(5): p. 891\\u0026ndash;901.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAzad, M.B., et al., \\u003cem\\u003eInfant gut microbiota and food sensitization: associations in the first year of life\\u003c/em\\u003e. Clinical \\u0026amp; Experimental Allergy, 2015. 45(3): p. 632\\u0026ndash;643.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMiyauchi, E., et al., \\u003cem\\u003eThe impact of the gut microbiome on extra-intestinal autoimmune diseases\\u003c/em\\u003e. 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Pediatric Allergy and Immunology, 2023. 34(9): p. e14013.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"genome-medicine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Genome Medicine](https://genomemedicine.biomedcentral.com/)\",\"snPcode\":\"13073\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/13073/3\",\"title\":\"Genome Medicine\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"COVID-19, pandemic, hygiene, microbiome, social distancing\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7565364/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7565364/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe COVID-19 pandemic triggered rapid, population-wide behavioral and environmental changes, offering a unique natural experiment to study how early-life microbiome development responds to abrupt shifts in social and hygiene-related exposures.\\u003c/p\\u003e\\u003cp\\u003eUsing longitudinal data from 139 infants in the Dutch LucKi Gut study, we compared gut microbiome development in fecal samples collected before and during the pandemic. Whole metagenome sequencing of 808 stool samples was performed across nine time points in the first 14 months of life. An exposure index (EI) capturing variation in household-level pandemic-related behaviors was constructed to quantify variations in social distancing, lifestyle and hygiene measures.\\u003c/p\\u003e\\u003cp\\u003eMicrobial richness and diversity increased with age, following established developmental trajectories. However, from 6 months onward, the COVID-19 pandemic independently shaped gut microbial composition, explaining up to 2.7% of variation by 11 months of age. Forty-four species were differentially abundant in pandemic-era samples, including depletion of Gordonibacter pamelaeae and several Actinomyces species. Notably, greater environmental exposure (higher EI scores) was associated with lower abundance of G. pamelaeae, a microbe implicated in bile acid and immunomodulatory metabolism.\\u003c/p\\u003e\\u003cp\\u003eThis is the first longitudinal whole-genome sequencing study to demonstrate that pandemic-related behavioral changes measurably altered infant gut microbiota maturation. 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