The influence of early life exposures on the infant gut virome

The influence of early life exposures on the infant gut virome | 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 influence of early life exposures on the infant gut virome Yichang Zhang, Josué L. Castro-Mejía, Ling Deng, Shiraz A. Shah, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4205731/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The factors influencing the establishment of the gut bacterial community in early life are fairly well studied. However, the factors shaping the infant gut virome remain elusive. Most gut viruses are bacteriophages (phages), i.e., viruses attacking bacteria in a host specific manner, and to a lesser extent, but also widely present, eukaryotic viruses, including viruses attacking human cells. Interestingly, early life gut virome imbalances have recently been linked with increased risk of developing diseases like type 1 diabetes and asthma. We utilized the deeply phenotyped COPSAC2010 cohort to investigate how environmental factors influence the gut virome at one year age. Results We demonstrate that presence of older siblings as well as residental location (urban or rural) had the strongest impact on gut virome composition at one year of age. A total of 16,118 species-level clustered viral representative contigs (here termed viral Operational Taxonomic Units – vOTUs) were identified and of these 2105 vOTUs varied in abundance with environmental exposure. Of these vOTUs 94.1% were phages mainly predicted to infect Bacteroidaceae , Prevotellaceae , and Ruminococcaceae . Strong co-abundance of phages and their bacterial hosts was confirmed underlining the predicted phage-host connections. Furthermore, we found some gut viruses affected by environmental factors encode enzymes involved in the utilization and degradation of major dietary components, potentially affecting infant health by influencing the bacterial host metabolic capacity. Genes encoding enzymes significantly associated with early life exposures were found in a total of 42 vOTUs. Eigtheen of these vOTUs had their life styles predicted, with 17 of them having a temperate lifestyle. Conclusion Given the importance of the gut microbiome in early life for maturation of the immune system and maintenance of metabolic health, these findings provide avaluable insights for understanding early life factors that predispose to autoimmune and metabolic disorders. Environmental exposures Infant Gut microbiota Virus Phagenome Phage Bacterial host Metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Early life gut microbiome (GM) establishment plays a fundamental role in shaping host physiology and health [ 1 , 2 ] with early life GM imbalances being linked to onset and progression of chronic diseases later in life, such as obesity [ 3 ], diabetes [ 4 , 5 ], and asthma [ 2 ]. To date, GM research has generally focused on understanding the importance of the bacterial GM component, but recent findings indicate that the vast and diverse population of viruses found in the gut (collectively called the “gut virome”) also play a prominent role in gut microbial ecology [ 6 – 9 ]. Amidst these biological entities, bacterial viruses, also termed bacteriophages (phages), are the most diverse and abundant particles of the GM [ 9 – 11 ] representing a major reservoir of genetic diversity influencing not only GM composition, but also the GM metabolic potential [ 12 , 13 ]. Disease-specific alterations in the gut virome have been reported in several chronic conditions [ 14 ] such as inflammatory bowel disease [ 15 ], colorectal cancer [ 16 ], necrotizing enterocolitis in preterm infants [ 17 ], severe acute malnutrition [ 18 ], type-1 diabetes [ 19 , 20 ] and other autoimmune diseases such as rheumatoid arthritis [ 21 ]. Very recently we have demonstrated that infant gut virome imbalance in the temperate phage pool at the age of one year is associated with increased risk of developing asthma before school age. Interestigly it was found, that this effect was additive to the increaed risk of developing asthma due to imbalanced gut bacteriome at the age of one year, meaning that the influence of gut virome composition on asthma is not only mediated via influencing the bacterial gut component, but also independent of the bacteriome [ 22 ]. The role of the gut virome in shaping the GM is further underlined by the observation that fecal virome transfer from healthy donors to recipients with a dysbiotic GM prevent or ameliorate symptoms associated with metabolic [ 23 ] and gastrointestinal [ 24 , 25 ] disorders. While various early-life factors such as birth mode, siblings, diet and exposure to antibiotics have been found to influence development of the gut bacterial community [ 1 , 26 ], little is known about which factors shape the gut virome. The few attempts that have characterized the gut virome early in life have revealed that its composition is highly dynamic [ 27 – 29 ], affected by delivery mode [ 6 ] and the first bacterial colonizers [ 30 ] as well as being enriched in phages belonging to the Microviridae family [ 10 , 28 ]. Moreover, its transmission-dynamics after birth follows a stepwise assembly, with breastfeeding playing a protective role against eukaryotic viral infections [ 31 , 32 ]. Understanding how environmental exposures and phenotypes intertwine the vector space conformed by viruses, bacteria, host, and their functional attributes remains hitherto an unsolved task. In a recent detailed investigation of the infant gut virome, we showed a massive diversity of hitherto undescribed phages [ 9 ]. In this cross-sectional study of the gut virome of 645 infants at one year of age enrolled in the COPSAC 2010 cohort [ 33 ] more than ten thousand viral species distributed over 248 viral families and 17 viral order-level clades were detected [ 9 ]. Recently gut virome imbalance was as mentioned above linked to increased risk of developing asthma before school age in this cohort [ 22 ]. Early life environmental exposures have influence gut bacteriome development early in life [ 2 , 26 , 34 , 35 ]. Here we expand these findings by investigating how social, pre-, peri- and postnatal factors influence the gut virome composition at one year of age. Our findings demonstrate how early life exposures are linked to the abundance of specific viruses, as well as their co-abundance and concordance with their predicted bacterial hosts. Metabolic functions encoded in the genomes of these viruses displayed enrichment of genes important for bacterial physiology in response to exposures, some of which are likely associated with dietary elements (e.g., degradation of complex carbohydrates) and others that may influence infant growth and health. Results Composition of DNA viruses in the gut of Danish infants A total of 645 stool samples from 1-year old infants in the COPSAC 2010 cohort [ 33 ] were obtained and analyzed [ 9 ]. Virions were isolated, concentrated and their genome was sequenced using a shotgun metagenome strategy [ 9 , 36 ]. Following assembly, a total of 16,118 species-level clustered viral representative contigs (here termed viral Operational Taxonomic Units – vOTUs) were obtained. Around 70% of the vOTUs were affiliated to five viral classes ( Arfiviricetes, Caudoviricetes, Faserviricetes, Malgrandaviricetes and Tectiliviricetes ) (Fig. 1 A and 1 I). Almost 18.8% of the vOTUs (n = 3,029) were considered putative satellite phages as contigs lacked genes coding for structural proteins but encoded other viral proteins (e.g., integrases or replicases) and were conserved in size and gene content across multiple samples. In addition, 11.8% of the vOTUs (n = 1,895) were categorized as unclassified viral fragments (Fig. 1 A). The largest genomes (> 10 kb) were observed among Caudoviricetes , which constituted the vast majority of vOTUs (Fig. 1 B). The genomes, dominated by Caudoviricetes (tailed, double-stranded DNA phages) and Malgrandaviricetes (non-tailed, single-stranded DNA phages), followed a bi-/multi-modal distribution (Hartigans' Dip test, P < 0.0001) based on their genome sizes (Fig. 1 B). Bacterial hosts as well as lifestyle (temperate/virulent) of the vOTUs were predicted using CRISPR spacers and the presence of integrases [ 9 ], respectively (Fig. 1 C). Because phages tend to have comparable k- mer frequencies to those of their hosts [ 37 , 38 ], we also performed dimensionality reduction on tetramer vectors to confirm global host associations as a complement to our viral taxonomy [ 9 ]. Using unsupervised stochastic neighbor embedding (t-SNE) dimensionality reduction, vOTUs targeting the same hosts as determined by CRISPR spacers (Fig. 1 D) or belonging to the same viral classes (Fig. 1 E) were found to clearly cluster together. Previously only Enterobacteriaceae and Bacteroidetes have been shown to be the hosts of non-tailed Malgrandaviricetes [ 39 ], but when examining the bacterial hosts, we observed that in addition to Bacteroidetes , also Ruminococcaceae, Clostridiaceae, Erysipelotrichaceae and Sutterellaceae are predicted as hosts of Malgrandaviridetes viruses (Fig. 1 C and S1). With respect to lifestyle, Streptococcaceae and most families of the Bacteroidetes are predicted hosts of a greater proportion of vOTUs recognized as virulent than temperate (Fig. 1 C and Table S2). The distribution of vOTUs was very individual-specific, with less than 5% of vOTUs appearing in more than 50% of the samples (Fig. 1 F). However, this still adds up to around 800 vOTUs that are shared among a majority of infants and representing, on average, more than 20% of the sequencing reads (Fig. 1 F). The proportion of vOTUs classified as Caudoviricetes (Fig. 1 G) as well as those infecting Bacteroidaceae and Bifidobacteriaceae (Fig. 1 H) increased as a function of prevalance. Environmental exposures influence viral diversity A range of pre-, peri-, and postnatal as well as social factors were recorded for the enrolled infants and their families (Supplementary Table S1 ). Having older siblings was associated with higher vOTU richness (linear mixed model, P = 0.048, estimate = 69.14, 95% CI = [0.58, 137.52]) and lower evenness (Shannon H’ ) (linear mixed model, P = 0.003, estimate = -0.30, 95% CI = [-0.50, -0.10]) (Fig. 2 A-B and 2 E-F) at one year of age. Likewise, a higher birth weight was linked to higher vOTU richness (linear mixed model, P = 0.007, estimate = -85.76, 95% CI = [-153.98, -17.56]) (Fig. 2 A and 2 E). Dietary factors were also found to influence the gut virome at one year of age, with late introduction of eggs in the diet being associated with lower viral evenness (Shannon H’ ) (linear mixed model, P = 0.012, estimate = 0.25, 95% CI = [0.05, 0.45]) (Fig. 2 A and 2 F). The mothers were enrolled in a nested randomized placebo-controlled trial of fish oil to the mothers during the third trimester of pregnancy [ 40 , 41 ]. Receiving fish oil during pregnancy was associated with increased gut vOTU richness (linear mixed model, P = 0.038, estimate = 71.60, 95% CI = [3.90, 139.22]) of the infants at one year of age (Fig. 2 A). The design also examined the difference in vitamin D between high and standard doses [ 42 ], which had no effect on the viral community in our analysis. Interestingly, other factors that have been found to influence the bacterial GM component during infancy such as birth mode, use of antibiotics, and duration of exclusive breastfeeding [ 1 , 26 ] did not seem to influence gut virome alpha-diversity measures at one year of age in this cohort (Fig. 2 A-B). Regarding virome composition, Bray-Curtis dissimilarity analysis (weighted measure, which is therefore mainly influenced by more abundant vOTUs) showed a link (PERMANOVA, P = 0.049, R2 = 0.0016) between maternal body mass index (BMI) and virome composition at one year of age (Fig. 2 C, 2 G and S1A); while Sørensen-Dice distance (unweighted binary metric and therefore mainly influenced by more rare vOTUs) revealed that a number of pre- and perinatal exposures were linked with virome composition differences (PERMANOVA, P ≤ 0.05), namely weight at birth, fish oil supplementation during pregnancy, hospitalization after birth, and preeclampsia (Fig. 2 D, 2 H and S2B). Both Bray-Curtis and Sørensen-Dice metrics showed significant differences in virome composition for children having older siblings (PERMANOVA, P = 0.006, R2 = 0.0018 and P = 0.001, R2 = 0.0029 for Bray-Curtis and Sorensen-Dice, respectively), and whether the family was living in an urban or a rural area (PERMANOVA, P = 0.003, R2 = 0.0019 and P = 0.049, R2 = 0.0016 for Bray-Curtis and Sorensen-Dice, respectively) (Fig. 2 C-D, 2 H and S2A-B). Environmental exposure variables influence the abundance of specific vira Subsequently, we determined how the distribution of vOTUs differed between the nine exposures (Fig. 2 C-D) found to significantly influence overall gut virome composition (preeclampsia was not included due to highly unbalanced sample size, see Supplementary Table S1 ). A total of 2,105 differentially abundant vOTUs affiliated to 173 viral families and 19 families of bacterial hosts were identified by DESeq2, with having older siblings being associated with 822 differential abundant vOTUs, while being hospitalized after birth being associated with 212 differential abundant vOTUs (Fig. 3 ). For perinatal covariates, vOTUs differing in abundance were predicted to infect a range of different hosts, but interestingly revealed a pronounced lower abundance of vOTUs predicted as having Bacteroidaceae , Ruminococcaceae and Streptococcaceae as hosts with with maternal antibiotic usage and hospitalization after birth (Fig. 3 ). Postnatal factors like specific dietary patterns (late introduction of eggs in the diet), presence of older siblings in the house and living in a rural environment, were associated with a higher abundance of vOTUs infecting Bifidobacteriaceae , Bacteroidaceae , Prevotellaceae , Tannerellaceae , Ruminococcaceae and Sutterellaceae . To further integrate these findings in the context of the gut bacterial component, we used 16S rRNA gene (V4 region) amplicon sequencing (bacterial OTUs - bOTUs) data previously published for this cohort [ 2 ] to determine virus-host co-abundances. Spearman correlation coefficients (ρ) were calculated between the abundance of the above identified differentially different abundant vOTUs and bOTUs across samples. Only bOTUs that were strongly associated (ρ ≥ 0.3) with at least one vOTU were retained. If a vOTU was correlated with a bOTU, the bOTU family tended to be consistent with the predicted host family of the vOTU (Figure S3A). These virus-host co-abundances indicate there is a high degree of inter-relatedness between phages and their host in response to environmental exposures. This was supported by the fact that the same perinatal and postnatal covariates were also significantly associated with bOTU diversity and composition (Figure S4A-D). Overall, the 91 co-abundant vOTUs (ρ ≥ 0.3), vOTUs that infect the same bacterial host family were in most cases closely related genetically, indicating a high degree of co-evolution between bacterial hosts and the phages that infect them (Figure S3B). To confirm the above-mentioned findings, we repeated the analysis of virus-host co-abundances using shotgun metagenomic data from the same cohort [ 43 ]. We found again that viruses and their bacterial hosts were highly correlated supporting the same conclusion as above (Figure S4E). Functional profiles of gut viruses are linked with environmental exposures Differentially abundant vOTUs were subjected to gene (open reading frame, ORF) prediction, and annotated based on KEGG Orthology (KO) using KofamScan [ 44 ]. As seen from figure S5A, 0.82% of genes matched known metabolism-related orthologs, while the remaining genes with KO assignments (8.48% of predicted genes) encoded genes related to genetic information processing and signaling and cellular processes, representing typical viral-associated traits required to accomplish replication [ 45 ]. The remaining 90.7% of the predicted genes were not annotated by the database. Next, we focused on determining genes with metabolic functions having the potential to enhance host fitness and drive metabolic reprogramming of the bacterial host [ 46 ]. The gut virome of infants with older siblings were enriched in genes related to O − antigen nucleotide sugar biosynthesis and seleno-compound metabolism, while infants without siblings were enriched in genes related to carbon fixation in photosynthetic organisms (Fisher's exact test, P < 0.05; Fig. 4 A) (the link to photosynthetic microorganisms may be caused by the KEGG database not being optimized for vira). The gut of infants living in rural areas or that were introduced to eggs in their diet later in life (above the median age when eggs were introduced in the diet) were enriched in viral encoded genes associated with glycolysis/gluconeogenesis and O − antigen nucleotide sugar biosynthesis, whereas the gut of infants living in urban areas or that were introduced to eggs relatively early in life were enriched in viral genes associated with thiamine metabolism. Infants with birth weight above the median or whose mothers who had a pre-pregnancy BMI < 25 also encoded genes involved in diverse pathways involved in e.g. vitamin synthesis. Further, the gut virome of infants whose mothers received fish oil during pregnancy or were prescribed antibiotics during delivery encoded genes related to purine metabolism (Fig. 4 A). To determine how virally encoded gene functions associate with the microbial composition, we linked back enriched genes to the vOTU of origin (Fig. 4 B). 94% of lifestyle predicted vOTUs (n = 17) were temperate. Genes associated with two classes of amino acid metabolisms (i.e. alanine, aspartate and glutamate metabolism and lysine biosynthesis) were conserved across Alistipes and Faecalibacterium targeting vOTUs, respectively. In addition, multiple carbohydrate metabolism enzyme encoding genes were found to be widely encoded by Blautia , Prevotella , Ruminococcus and Faecalibacterium targeting vOTUs. These encoded enzymes including L − lactate dehydrogenase, ribose − phosphate pyrophosphokinase and aldose 1 − epimerase (Figure S5D). Energy metabolism genes were found in Prevotella and Faecalibacterium targeting vOTUs, while nicotinate and nicotinamide metabolism genes were mapped in Ruminococcus and Escherichia targeting vOTUs (Fig. 4 B). Phage-host co-abundance (Figure S3A), was further confirmed by Procrustes analysis. The linking of the virome and bacteriome compositions revealed a strong correlation one to another ( P < 0.001, r = 0.52) (Figure S5B-C). The cumulative abundance of all bOTUs belonging to the bacterial genera Ruminococcus, Prevotella and Faecalibacterium , which were found to be the main bacterial host of vOTUs carrying the above metabolic genes (Fig. 4 B) and having previously been reported to be highly associated with stable viral communities [ 47 ], was highly correlated with rural vs. urban living and having older siblings (Fig. 4 C-E). These results emphasize the potential role of phage-host association in metabolic regulation. Discussion The gut of healthy newborns is usually devoid of viruses at birth, but it is rapidly colonized afterwards [ 28 , 31 ]. Still relatively few studies have focused on the assembly of the gut virome within the first year of life and the factors that influence it [ 6 , 27 , 28 , 31 ] and even less is known about the environmental exposures that shape the gut virome. Here, we leveraged a massive gut virome dataset from healthy infants at 1-year of age, and integrated measures of viral diversity such as sequence composition, viral hosts, and phage lifestyles [ 9 ], (see Fig. 1 ) with social, pre-, peri- and postnatal environmental exposures. We revealed the effects of these exposures on viral community and the possible effects on metabolism. In previous reports, Crassvirales (class Caudoviricetes ) and Microviridae (class Malgrandaviricetes ) phages were found to be the two most abundant viral groups in the adult human gut, with their relative abundance being negatively correlated [ 19 , 47 – 50 ]. Here, in one-year-old infants, a similar observation was made, members of the Caudoviricetes and Malgrandaviricetes classes were the most abundant phages. Interestingly, ongoing exposures such as having older siblings and residential location, as well as past exposures (e.g., birth weight, preeclampsia) were linked with gut virome composition at one year of age. However, it is still possible that the prenatal and perinatal exposures still influenced the immune education earlier in life and remnants of the interplay are still tangible at 1-year of age [ 5 ]. Among the exposures significantly influencing the gut virome composition, the largest effect sizes were from residential location (rural vs. urban) and having older siblings (see Fig. 2 C and 2 D). Interestingly, urbanization has been reported to have a significant impact on the composition of the adult viral community, with individuals living in urban areas having higher abundance of Lactococcus (family Streptococcaceae ) phages [ 51 ]. The latter is presumably associated with the consumption of dairy products. We show that the living environment also affects the gut virome of infants, and that Streptococcaceae targeting phages are also more abundant in infants living in urban areas, possibly reflecting differences in dietary habits rather than residence per se (Fig. 3 ). Having older siblings influences the development of the bacterial community in early life [ 34 , 52 , 53 ] and here we show that having older siblings is also associated with gut virome composition at one year of age. Importantly, focussing on the curated DNA phage community (i.e. not the total vOTU pool, but only the fraction predicted as being bacteriophages) we recently demonstrated that phageome imbalances are associated with increased risk of developing before school age, but also that having older siblings was negatively associated with higher virome asthma signature score, implying a lower asthma rate, showing how the influence of environmental exposures on virome composition also have implications for child health [ 22 ]. Importantly, from a translational angle, early-life exposures may affect the establishment of health phenotypes, such as the protective role of breastfeeding against eukaryotic-viral infections in the neonatal period [ 31 ]. Combining gut bacterial compositional data with gut virome composition (Figure S3A and S5B-C) in our cohort elucidates the co-abundance of phages and their hosts, underlying the role of phage-host interactions in shaping the GM. Most of these viruses (Figure S3A) have temperate lifestyles, as evidenced by the presence of genes coding for integrases. Thus, these temperate phages appear to have the ability to integrate their genome into the bacterial hosts and become prophages at some point. Gut virome members have the potential to modulate biochemical processes [ 12 , 13 , 54 ]. The functional prediction of the genes derived from vOTUs co-varying with exposures, revealed up to 90% of genes with unknown functions. It emphasizes that proteins with yet uncharacterized functions are potentially playing a role in the regulation of human host phenotypes. Certain predicted gene functions linked to metabolic activities, such as alanine, aspartate and glutamate metabolism, amino sugar and nucleotide sugar metabolism and glycolysis/gluconeogenesis, which are likely associated with dietary intake and degradation of macronutrients, were associated with fish in the diet, birth weight, residence location and egg in the diet (Fig. 4 A). Birth weight also show negative correlation with higher virome asthma signature score (asthma rate) [ 22 ], and time-to-asthma analysis proved that the effects of phages on asthma were additive and statistically independent of the bacterial gut microbiome component [ 22 ]. Maternal obesity alters fatty acid metabolism and changes in gene expression of lipid metabolism in infants, which cause a higher risk of developing obesity and its complications, neuropsychiatric disorders and asthma [ 55 , 56 ]. We find here that viral genes associated with normal weight mothers were predominantly enriched in fatty acid biosynthesis compared to obese mothers, which may be an intermediate pathway by which maternal obesity affects child health. In addition, for biotin metabolism, which is known to be impaired by severe obesity [ 57 ], many phage genes are also observed to be enriched in infants from mothers with BMI below 25 in our data. The mothers enrolled in the cohort participated in a randomized clinical trial where they were randomized to receiving fish oil or a placebo from week 24 of pregnancy to one week after birth [ 41 , 58 ]. The design also examined the difference in vitamin D between high and standard doses [ 42 ], which had no effect on the viral community in our analysis. Of note, the supplementation of fish oil during pregnancy was not found to influence the gut bacterial component at age one year. Here we report that the same intervention has some influence on the gut virome at age one year, but the effect is only borderline significant. The infants of mothers that received fish oil had viral genes involved in lysine biosynthesis, glycerophospholipid metabolism, and purine metabolism – metabolic activities that have been associated to fish oil supplementation [ 59 , 60 ], but never attributed to gut virome composition. Interestingly, most of these metabolism-related genes were conserved across temperate vOTUs targeting Ruminococcus , Faecalibacterium and Prevotella spp. (Fig. 4 B). These genera have been consistently reported to be enriched in Danish and American subjects with a diet rich in carbohydrates, resistant starch, and fibers, and being determinants of the so-called Prevotella -enterotype [ 61 , 62 ]. The Prevotella -enterotype is established early in life (between 9–36 months of age) [ 63 – 65 ] and have been previously suggested as markers of GM maturity at age one year [ 2 , 66 , 67 ]. Stokholm et al. (2018) reported delayed GM maturation as a risk factor for later development of asthma indicating the importance of these microbes for immune maturation. Although our study is currently unable to assess how these gut virome associated genes are actively involved in either enhancing either phage or host fitness, or both, our data as well as our recent finding, that early life gut virome imbalance is associated with increased asthma before school age risk [ 22 ] underlines the potential importance of bacteriophage-encoded metabolic genes and delivers an initial insight of the type of metabolic content conveyed by the gut virome in association to environmental variables. In summary, our data provides detailed insight into the influence of common environmental factors that shape the gut virome during early life. We also uncover that key gut metabolic functions can be encoded by viral genes, which suggest that, in addition of shaping gut bacteriome composition, phages may directly play a role in metabolic activities. Methods Study participants Participants belong to the COPSAC2010 cohort [ 33 ]. Fecal samples for virome extraction sequencing and analysis were collected from all infants at age 1 year. Sample collection, sequencing, virome assembly Preparation of fecal samples, and extraction and sequencing of virions was carried out using a previously described protocol [ 36 ]. Briefly, viral-associated DNA was subjected to short MDA amplification and libraries were prepared following using manufacturer’s procedures for the Nextera XT kit (FC-131-1096 Ilumina, California). Libraries were single-end high-throughput sequenced on the Illumina HiSeq X platform. Details of the pipeline for data processing, de-novo assembly, quality control, bacterial-host and lifestyle predictions, abundance-mapping (vOTU table), and taxonomy of complete and partial viral genomes (here termed vOTUs) can be found in Shah et al. (2021). 16S rRNA gene amplicon data (bOTU table) from the same cohort’s participants were retrieved from Stokholm et al. (2018). Environmental exposures Briefly, during scheduled visits to the COPSAC clinic, information on a wide range of exposures was collected. A total of 30 environmental exposures were investigated and were grouped into social (n = 6), pre- (n = 4), peri- (n = 9) and postnatal (n = 11) exposures based on whether they occurred or existed before birth. See Supplementary Table S1 for a complete list of the exposures. Statistics and data analysis Analyses on diversity were carried out on contingency tables gathering vOTUs abundance. Abundance data was normalized by reads per kilobase per million (RPKM). Alpha-diversity (Observed vOTUs and Shannon Index) indices and Beta-diversity (Bray-Curtis and Sørensen-Dice distances) matrices were generated using the package phyloseq (version 1.42.0) [ 68 ]. The contribution of each covariate to explain vOTUs community structure (as determined by Sørensen-Dice similarity and Bray-Curtis dissimilarity metrics) was calculated using distance-based redundancy analysis (db-RDA) models coupled to adonis PERMANOVA (n permutations = 999) in package vegan (version 2.6-2) [ 69 ], while the effect size of the same covariates on alpha-diversity was calculated with linear mixed models from the package lmerTest (version 3.1-3) [ 70 ]. All linear mixed models accounted for technical variation between runs using sequencing lane as the random effect. Different differential abundance analysis methods were evaluated by DAtest [ 71 ]. DESeq2 (version 1.36.0) performed well with a low false positive rate and a high ability to detect differential vOTUs for our data [ 72 ]. The sequencing lane was considered as a factor-covariate. The raw reads count table of each sample for vOTUs were prepared as input. All parameters are default except for sfType which is set to poscount. Benjamini and Hochberg method was adapted to correct the p-values. vOTUs with adjusted p-value ≤ 0.001 and log 2 fold change ≥ |1| were selected for downstream analyses. Spearman’s rank correlations were used to test univariate associations of continuous data, and results were visualized in a heatmap. MAFFT [ 73 ] was used to generate the phylogenetic tree file for those highly correlated vOTUs. The phylogenetic tree was visualized using the R package ggtree (version 3.4.0) [ 74 ]. Procrustes analysis (R package vegan) was performed on vOTUs as target block and 16S rRNA gene data as rotatory block (n permutations = 999), while using the first two constrained components (CAP1 and CAP2) of db-RDA models for each data block. ORF calling on selected vOTUs was executed with Prodigal [ 75 ]. To determine metabolic function, genes were annotated based on KEGG Orthology using KofamScan [ 44 ] and filtered by default thresholds. Enricher function in clusterProfiler package (version 4.6.0) was applied to detect whether genes in differently abundant vOTUs were enriched in the metabolic pathway [ 76 ]. All analyses were carried out in R (version 4.0.2) and results were visualized with the package ggplot2 (version 3.3.6) [ 77 ]. Declarations Acknowledgements We express our deepest gratitude to the children and families of the COPSAC 2010 cohort study for all their support and commitment. We acknowledge and appreciate the unique efforts of the COPSAC research team. Funding This work is supported by the Joint Programming Initiative ‘Healthy Diet for a Healthy Life’, specifically here, the Danish Agency for Science and Higher Education, Institut National de la Recherche Agronomique (INRA), and the Canadian Institutes of Health Research (Team grant on Intestinal Microbiomics, Institute of Nutrition, Metabolism, and Diabetes, grant number 143924). JT is supported by the BRIDGE Translational Excellence Program (bridge.ku.dk) at the Faculty of Health and Medical Sciences, University of Copenhagen, funded by the Novo Nordisk Foundation (grant no. NNF18SA0034956). S.M. holds the Tier 1 Canada Research Chair in Bacteriophages [950-232136]. JS and DSN are recipients of Novo Nordisk Foundation grant NNF20OC0061029. Author contributions L.D., S.M., M.A.P., J.S. and D.S.N. conceived the project and supervised all the research; B.C., K.B., J.S., S.J.S., L.J. and M.D. collected the samples and/or information; Y.Z., J.L.C.M., S.A.S. and D.S.N. analyzed the data; Y.Z., J.L.C.M. and D.S.N. wrote the manuscript with the assistance of L.D., S.A.S., J.T., C.L.R., S.M., M.A.P. and J.S.; L.D. prepared the virome and sequencing libraries; all authors contributed to, revised and approved the final manuscript. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and was approved by The National Committee on Health Research Ethics (H-B-2008-093) and the Danish Data Protection Agency (2015-41-3696). Both parents gave written informed consent before enrollment. Consent for publication Not applicable. No data on individual persons is reported. Availability of data and materials Sequencing FASTQ files are available on ENA under project number PRJEB46943. All cohort participants' individual-level data are protected by Danish and European law and are not publicly available. However, participant-level data can be made available under a data transfer agreement as part of a collaboration effort. Codes for data analyses are available from the authors upon request. Competing interests All authors declare no conflicts of interest related to the present study. References Tamburini S, Shen N, Wu HC, Clemente JC. The microbiome in early life: implications for health outcomes. Nat Med. 2016;22(7):713-22; doi: 10.1038/nm.4142. Stokholm J, Blaser MJ, Thorsen J, Rasmussen MA, Waage J, Vinding RK, et al. Maturation of the gut microbiome and risk of asthma in childhood. Nat Commun. 2018;9:141; doi: 10.1038/s41467-017-02573-2. Pihl AF, Fonvig CE, Stjernholm T, Hansen T, Pedersen O, Holm JC. The role of the gut microbiota in childhood obesity. Child Obes. 2016;12(4):292-9; doi: 10.1089/chi.2015.0220. Kostic AD, Gevers D, Siljander H, Vatanen T, Hyotylainen T, Hamalainen AM, et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe. 2015;17(2):260-73; doi: 10.1016/j.chom.2015.01.001. Vatanen T, Kostic AD, d'Hennezel E, Siljander H, Franzosa EA, Yassour M, et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell. 2016;165(4):842-53; doi: 10.1016/j.cell.2016.04.007. McCann A, Ryan FJ, Stockdale SR, Dalmasso M, Blake T, Ryan CA, et al. Viromes of one year old infants reveal the impact of birth mode on microbiome diversity. Peerj. 2018;6:e4694; doi: 10.7717/peerj.4694. Sutton TDS, Hill C. Gut bacteriophage: current understanding and challenges. Front Endocrinol. 2019;10:784; doi: 10.3389/fendo.2019.00784. Shkoporov AN, Hill C. Bacteriophages of the human gut: the “known unknown'' of the microbiome. Cell Host Microbe. 2019;25(2):195-209; doi: 10.1016/j.chom.2019.01.017. Shah SA, Deng L, Thorsen J, Pedersen AG, Dion MB, Castro-Mejía JL, et al. Expanding known viral diversity in the healthy infant gut. Nat Microbiol. 2023;8(5):986–98; doi: 10.1038/s41564-023-01345-7. Gregory AC, Zablocki O, Zayed AA, Howell A, Bolduc B, Sullivan MB. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe. 2020;28(5):724-40; doi: 10.1016/j.chom.2020.08.003. Roux S, Paez-Espino D, Chen IMA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2021;49(D1):D764-D75; doi: 10.1093/nar/gkaa946. Kieft K, Zhou ZC, Anderson RE, Buchan A, Campbell BJ, Hallam SJ, et al. Ecology of inorganic sulfur auxiliary metabolism in widespread bacteriophages. Nat Commun. 2021;12(1):3503; doi: 10.1038/s41467-021-23698-5. Bi L, Yu DT, Du S, Zhang LM, Zhang LY, Wu CF, et al. Diversity and potential biogeochemical impacts of viruses in bulk and rhizosphere soils. Environ Microbiol. 2021;23(2):588-99; doi: 10.1111/1462-2920.15010. Tisza MJ, Buck CB. A catalog of tens of thousands of viruses from human metagenomes reveals hidden associations with chronic diseases. P Natl Acad Sci USA. 2021;118(23):e2023202118; doi: 10.1073/pnas.2023202118. Clooney AG, Sutton TDS, Shkoporov AN, Holohan RK, Daly KM, O'Regan O, et al. Whole-virome analysis sheds light on viral dark matter in inflammatory bowel disease. Cell Host Microbe. 2019;26(6):764-78; doi: 10.1016/j.chom.2019.10.009. Zhao LY, Shi Y, Lau HC, Liu WX, Luo GW, Wang GP, et al. Uncovering 1,058 novel human enteric DNA viruses through deep long-read third-generation sequencing and their clinical impact. Gastroenterology. 2022;162(7):S96-S. Kaelin EA, Rodriguez C, Hall-Moore C, Hoffmann JA, Linneman LA, Ndao IM, et al. Longitudinal gut virome analysis identifies specific viral signatures that precede necrotizing enterocolitis onset in preterm infants. Nat Microbiol. 2022;7(5):653-62; doi: 10.1038/s41564-022-01096-x. Reyes A, Blanton LV, Cao S, Zhao G, Manary M, Trehan I, et al. Gut DNA viromes of Malawian twins discordant for severe acute malnutrition. P Natl Acad Sci USA. 2015;112(38):11941-6; doi: 10.1073/pnas.1514285112. Zhao GY, Vatanen T, Droit L, Park A, Kostic AD, Poon TW, et al. Intestinal virome changes precede autoimmunity in type I diabetes-susceptible children. P Natl Acad Sci USA. 2017;114(30):E6166-E75; doi: 10.1073/pnas.1706359114. Vehik K, Lynch KF, Wong MC, Tian XJ, Ross MC, Gibbs RA, et al. Prospective virome analyses in young children at increased genetic risk for type 1 diabetes. Nat Med. 2019;25(12):1865-72; doi: 10.1038/s41591-019-0667-0. Tomofuji Y, Kishikawa T, Maeda Y, Ogawa K, Nii T, Okuno T, et al. Whole gut virome analysis of 476 Japanese revealed a link between phage and autoimmune disease. Ann Rheum Dis. 2021;81:278-88; doi: 10.1136/annrheumdis-2021-221267. Leal Rodriguez C, Shah SA, Rasmussen MA, Thorsen J, Boulund U, Pedersen CT, et al. The infant gut virome is associated with preschool asthma risk independently of bacteria. Nat Med. 2023; doi: 10.1038/s41591-023-02685-x. Rasmussen TS, Mentzel CMJ, Kot W, Castro-Mejia JL, Zuffa S, Swann JR, et al. Faecal virome transplantation decreases symptoms of type 2 diabetes and obesity in a murine model. Gut. 2020;69(12):2122-30; doi: 10.1136/gutjnl-2019-320005. Ott SJ, Waetzig GH, Rehman A, Moltzau-Anderson J, Bharti R, Grasis JA, et al. Efficacy of sterile fecal filtrate transfer for treating patients with clostridium difficile infection. Gastroenterology. 2017;152(4):799-811; doi: 10.1053/j.gastro.2016.11.010. Brunse A, Deng L, Pan XY, Hui Y, Castro-Mejia JL, Kot W, et al. Fecal filtrate transplantation protects against necrotizing enterocolitis. ISME J. 2022;16(3):686-94; doi: 10.1038/s41396-021-01107-5. Stewart CJ, Ajami NJ, O'Brien JL, Hutchinson DS, Smith DP, Wong MC, et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature. 2018;562(7728):583-8; doi: 10.1038/s41586-018-0617-x. Breitbart M, Haynes M, Kelley S, Angly F, Edwards RA, Felts B, et al. Viral diversity and dynamics in an infant gut. Res Microbiol. 2008;159(5):367-73; doi: 10.1016/j.resmic.2008.04.006. Lim ES, Zhou YJ, Zhao GY, Bauer IK, Droit L, Ndao IM, et al. Early life dynamics of the human gut virome and bacterial microbiome in infants. Nature Medicine. 2015;21(10):1228-34; doi: 10.1038/nm.3950. Taboada B, Moran P, Serrano-Vazquez A, Isa P, Rojas-Velazquez L, Perez-Juarez H, et al. The gut virome of healthy children during the first year of life is diverse and dynamic. PLoS One. 2021;16(4):e0240958; doi: 10.1371/journal.pone.0240958. Liang G, Bushman FD. The human virome: assembly, composition and host interactions. Nat Rev Microbiol. 2021;19(8):514-27; doi: 10.1038/s41579-021-00536-5. Liang GX, Zhao CY, Zhang HJ, Mattei L, Sherrill-Mix S, Bittinger K, et al. The stepwise assembly of the neonatal virome is modulated by breastfeeding. Nature. 2020;581(7809):470-4; doi: 10.1038/s41586-020-2192-1. Walters WA, Granados AC, Ley C, Federman S, Stryke D, Santos Y, et al. Longitudinal comparison of the developing gut virome in infants and their mothers. Cell Host Microbe. 2023;31(2):187-98; doi: 10.1016/j.chom.2023.01.003. Bisgaard H, Vissing NH, Carson CG, Bischoff AL, Folsgaard NV, Kreiner-Moller E, et al. Deep phenotyping of the unselected COPSAC2010 birth cohort study. Clin Exp Allergy. 2013;43(12):1384-94; doi: 10.1111/cea.12213. Christensen ED, Hjelmso MH, Thorsen J, Shah S, Redgwell T, Poulsen CE, et al. The developing airway and gut microbiota in early life is influenced by age of older siblings. Microbiome. 2022;10(1):106; doi: 10.1186/s40168-022-01305-z. Gasparrini AJ, Wang B, Sun X, Kennedy EA, Hernandez-Leyva A, Ndao IM, et al. Persistent metagenomic signatures of early-life hospitalization and antibiotic treatment in the infant gut microbiota and resistome. Nat Microbiol. 2019;4(12):2285-97; doi: 10.1038/s41564-019-0550-2. Deng L, Silins R, Castro-Mejia JL, Kot W, Jessen L, Thorsen J, et al. A protocol for extraction of infective viromes suitable for metagenomics sequencing from low volume fecal samples. Viruses. 2019;11(7):667; doi: 10.3390/v11070667. Edwards RA, McNair K, Faust K, Raes J, Dutilh BE. Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol Rev. 2016;40(2):258-72; doi: 10.1093/femsre/fuv048. Pride DT, Wassenaar TM, Ghose C, Blaser MJ. Evidence of host-virus co-evolution in tetranucleotide usage patterns of bacteriophages and eukaryotic viruses. BMC Genom. 2006;7:1-13; doi: 10.1186/1471-2164-7-8. Krupovic M, Forterre P. Microviridae goes temperate: microvirus-related proviruses reside in the genomes of bacteroidetes. PLoS One. 2011;6(5):e19893; doi: 10.1371/journal.pone.0019893. Vinding RK, Stokholm J, Sevelsted A, Chawes BL, Bonnelykke K, Barman M, et al. Fish oil supplementation in pregnancy increases gestational age, size for gestational age, and birth weight in infants: a randomized controlled trial. J Nutr. 2019;149(4):628-34; doi: 10.1093/jn/nxy204. Bisgaard H, Stokholm J, Chawes BL, Vissing NH, Bjarnadottir E, Schoos AMM, et al. Fish oil-derived fatty acids in pregnancy and wheeze and asthma in offspring. New Engl J Med. 2016;375(26):2530-9; doi: 10.1056/NEJMoa1503734. Chawes BL, Bonnelykke K, Stokholm J, Vissing NH, Bjarnadottir E, Schoos AMM, et al. Effect of vitamin D-3 supplementation during pregnancy on risk of persistent wheeze in the offspring: a randomized clinical trial. JAMA. 2016;315(4):353-61; doi: 10.1001/jama.2015.18318. Li XJ, Stokholm J, Brejnrod A, Vestergaard GA, Russel J, Trivedi U, et al. The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition. Cell Host Microbe. 2021;29(6):975-87; doi: 10.1016/j.chom.2021.03.017. Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020;36(7):2251-2; doi: 10.1093/bioinformatics/btz859. Sanz-Gaitero M, Seoane-Blanco M, van Raaij MJ. Structure and function of bacteriophages. Bacteriophages: Biology, Technology, Therapy. 2019:1-73; doi: 10.1007/978-3-319-40598-8_1-1. Warwick-Dugdale J, Buchholz HH, Allen MJ, Temperton B. Host-hijacking and planktonic piracy: how phages command the microbial high seas. Virol J. 2019;16:1-13; doi: 10.1186/s12985-019-1120-1. Shkoporov AN, Clooney AG, Sutton TDS, Ryan FJ, Daly KM, Nolan JA, et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe. 2019;26(4):527-41; doi: 10.1016/j.chom.2019.09.009. Kim KH, Bae JW. Amplification methods bias metagenomic libraries of uncultured single-stranded and double-stranded DNA viruses. Appl Environ Microbiol. 2011;77(21):7663-8; doi: 10.1128/Aem.00289-11. Roux S, Solonenko NE, Dang VT, Poulos BT, Schwenk SM, Goldsmith DB, et al. Towards quantitative viromics for both double-stranded and single-stranded DNA viruses. Peerj. 2016;4:e2777; doi: 10.7717/peerj.2777. Yutin N, Makarova KS, Gussow AB, Krupovic M, Segall A, Edwards RA, et al. Discovery of an expansive bacteriophage family that includes the most abundant viruses from the human gut. Nat Microbiol. 2018;3(1):38-46; doi: 10.1038/s41564-017-0053-y. Zuo T, Sun Y, Wan YT, Yeoh YK, Zhang F, Cheung CP, et al. Human-gut-DNA virome variations across geography, ethnicity, and urbanization. Cell Host Microbe. 2020;28(5):741-51; doi: 10.1016/j.chom.2020.08.005. Laursen MF, Zachariassen G, Bahl MI, Bergstrom A, Host A, Michaelsen KF, et al. Having older siblings is associated with gut microbiota development during early childhood. BMC Microbiol. 2015;15:1-9; doi: 10.1186/s12866-015-0477-6. Hedin CR, van der Gast CJ, Stagg AJ, Lindsay JO, Whelan K. The gut microbiota of siblings offers insights into microbial pathogenesis of inflammatory bowel disease. Gut Microbes. 2017;8(4):359-65; doi: 10.1080/19490976.2017.1284733. Brown EM, Arellano-Santoyo H, Temple ER, Costliow ZA, Pichaud M, Hall AB, et al. Gut microbiome ADP-ribosyltransferases are widespread phage-encoded fitness factors. Cell Host Microbe. 2021;29(9):1351-65; doi: 10.1016/j.chom.2021.07.011. Alvarez D, Munoz Y, Ortiz M, Maliqueo M, Chouinard-Watkins R, Valenzuela R. Impact of maternal obesity on the metabolism and bioavailability of polyunsaturated fatty acids during pregnancy and breastfeeding. Nutrients. 2020;13(1):19; doi: 10.3390/nu13010019. Costa SM, Isganaitis E, Matthews TJ, Hughes K, Daher G, Dreyfuss JM, et al. Maternal obesity programs mitochondrial and lipid metabolism gene expression in infant umbilical vein endothelial cells. Int J Obes (Lond). 2016;40(11):1627-34; doi: 10.1038/ijo.2016.142. Belda E, Voland L, Tremaroli V, Falony G, Adriouch S, Assmann KE, et al. Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism. Gut. 2022;71(12):2463-80; doi: 10.1136/gutjnl-2021-325753. Hjelmso MH, Shah SA, Thorsen J, Rasmussen M, Vestergaard G, Mortensen MS, et al. Prenatal dietary supplements influence the infant airway microbiota in a randomized factorial clinical trial. Nat Commun. 2020;11(1):426; doi: 10.1038/s41467-020-14308-x. Hishikawa D, Valentine WJ, Iizuka-Hishikawa Y, Shindou H, Shimizu T. Metabolism and functions of docosahexaenoic acid-containing membrane glycerophospholipids. FEBS Lett. 2017;591(18):2730-44; doi: 10.1002/1873-3468.12825. Cao Y, Gao Q, Li X, Zhou Y, Dong S, Wang Y, et al. Integrated analysis of metabolomics and transcriptomics for assessing effects of fish meal and fish oil replacement on the metabolism of rainbow trout (Oncorhynchus mykiss). Front Mar Sci. 2022;9:208; doi: 10.3389/fmars.2022.843637. Roager HM, Vogt JK, Kristensen M, Hansen LBS, Ibrugger S, Maerkedahl RB, et al. Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: a randomised cross-over trial. Gut. 2019;68(1):83-93; doi: 10.1136/gutjnl-2017-314786. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science. 2011;334(6052):105-8; doi: 10.1126/science.1208344. Bergstrom A, Skov TH, Bahl MI, Roager HM, Christensen LB, Ejlerskov KT, et al. Establishment of intestinal microbiota during early life: a longitudinal, explorative study of a large cohort of danish infants. Appl Environ Microbiol. 2014;80(9):2889-900; doi: 10.1128/Aem.00342-14. Backhed F, Roswall J, Peng YQ, Feng Q, Jia HJ, Kovatcheva-Datchary P, et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe. 2015;17(5):690-703; doi: 10.1016/j.chom.2015.04.004. Roswall J, Olsson LM, Kovatcheva-Datchary P, Nilsson S, Tremaroli V, Simon MC, et al. Developmental trajectory of the healthy human gut microbiota during the first 5 years of life. Cell Host Microbe. 2021;29(5):765-76; doi: 10.1016/j.chom.2021.02.021. Larsen JM. The immune response to Prevotella bacteria in chronic inflammatory disease. Immunology. 2017;151(4):363-74; doi: 10.1111/imm.12760. Park H, Shin JW, Park SG, Kim W. Microbial communities in the upper respiratory tract of patients with asthma and chronic obstructive pulmonary disease. PLoS One. 2014;9(10):e109710; doi: 10.1371/journal.pone.0109710. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217; doi: 10.1371/journal.pone.0061217. Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14(6):927-30; doi: DOI 10.1111/j.1654-1103.2003.tb02228.x. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: tests in linear mixed effects models. J Stat Softw. 2017;82(13):1-26; doi: DOI 10.18637/jss.v082.i13. Russel J, Thorsen J, Brejnrod AD, Bisgaard H, Sørensen SJ, Burmølle M. DAtest: a framework for choosing differential abundance or expression method. bioRxiv. 2018:241802; doi: 10.1101/241802. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550; doi: 10.1186/s13059-014-0550-8. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772-80; doi: 10.1093/molbev/mst010. Yu GC, Smith DK, Zhu HC, Guan Y, Lam TTY. GGTREE: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol. 2017;8(1):28-36; doi: 10.1111/2041-210x.12628. Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:1-11; doi: 10.1186/1471-2105-11-119. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2(3):100141; doi: 10.1016/j.xinn.2021.100141. Hadley W. ggplot2: elegant graphics for data analysis. Springer-Verlag New York. 2016. 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Dion","email":"","orcid":"","institution":"Université Laval","correspondingAuthor":false,"prefix":"","firstName":"Moïra","middleName":"B.","lastName":"Dion","suffix":""},{"id":287841023,"identity":"78e740cb-aa7d-4dba-a41c-5c6e4b4217fb","order_by":8,"name":"Bo Chawes","email":"","orcid":"","institution":"Copenhagen University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Chawes","suffix":""},{"id":287841024,"identity":"c3850359-ae92-4861-a507-1aca35ad37c9","order_by":9,"name":"Klaus Bønnelykke","email":"","orcid":"","institution":"Copenhagen University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Klaus","middleName":"","lastName":"Bønnelykke","suffix":""},{"id":287841025,"identity":"98bfa2e4-4929-4a53-883c-79d22961cff1","order_by":10,"name":"Søren J. 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Nielsen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAn0lEQVRIiWNgGAWjYDCCA8wHPjAwMDNIANmSDcRpYUucQaoWHkMStfDd7vnY8KHCmkFyRgLjzRnEaJG8c3Zj44wz6QzSEgnMlhuI0WJwI3f7Y962wwxyEglskg+I05LzsJlkLYxgLdIgLUQ5TPJGmiHILzySPQ+bLYnyPt+N5IegEJOTOJ588GYPMVpggIeBgbGBFA2jYBSMglEwCvABAA71NLUCiCjjAAAAAElFTkSuQmCC","orcid":"","institution":"University of Copenhagen","correspondingAuthor":true,"prefix":"","firstName":"Dennis","middleName":"S.","lastName":"Nielsen","suffix":""}],"badges":[],"createdAt":"2024-04-02 10:00:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4205731/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4205731/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54449533,"identity":"f3058861-3dd5-4766-a636-0042ea4d2aea","added_by":"auto","created_at":"2024-04-10 17:38:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":515899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVirome structure of the infants enrolled in the COPSAC\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2010\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Distribution of the 16118 vOTUs identified colored by their taxonomic class annotation.\u003c/p\u003e\n\u003cp\u003e(B) Cumulative frequency of viral genomes (kb) identified by their taxonomic class annotation.\u003c/p\u003e\n\u003cp\u003e(C) Circular diagram showing the distribution of vOTUs colored by their targeted bacterial hosts (at phylum and family levels), viral class and lifestyle.\u003c/p\u003e\n\u003cp\u003e(D-E) t-Stochastic Neighbor Embedding (t-SNE) plots clustering \u003cem\u003etetra\u003c/em\u003e-mer vOTUs profiles identified according to host family (D) and viral class (E).\u003c/p\u003e\n\u003cp\u003e(F-H) Percentage of vOTUs that appear at a specific prevalence (F), and vOTUs' distribution colored by their taxonomic class (G) and host family (H).\u003c/p\u003e\n\u003cp\u003e(I) Relative abundance of vOTUs across all samples at the class level. Samples were sorted by \u003cem\u003eMalgrandaviricetes \u003c/em\u003eabundance.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4205731/v1/4850148bbddefcfadeefa6ba.png"},{"id":54449534,"identity":"1fd89ba2-e664-403e-9ddc-25a19e8cfe1b","added_by":"auto","created_at":"2024-04-10 17:38:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":362850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVirome diversity and composition covariates with early life exposures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-D) Barplot showing the strength of associations (-log\u003csub\u003e10\u003c/sub\u003e p-value) of the alpha diversity metrics Observed vOTUs (A) and Shannon Index (B) across different exposures (linear mixed model) as well as beta diversity using distance-based redundancy analysis (db-RDA) on Bray-Curtis dissimilarity (C) and Sorensen-Dice distance (D) matrices.\u003c/p\u003e\n\u003cp\u003e(E-F) Distribution of Observed vOTUs for weight at birth and siblings (E) and Shannon Index for siblings and dietary introduction of egg (F). Dietary introduction of egg is indicated in days.\u003c/p\u003e\n\u003cp\u003e(G-H) db-RDA constrained-components based on Bray-Curtis distances for location and siblings (G), and Sorensen-Dice distances for weight at birth and dietary introduction of fish (H).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4205731/v1/29667fc1bc3857a15bcab117.png"},{"id":54449532,"identity":"7b30c5e3-e416-45cd-a93d-057f061f8ff4","added_by":"auto","created_at":"2024-04-10 17:38:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":375504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eViral host family, relative abundance and lifestyle associate with environmental exposures at one year of age.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisualization of differential abundance analysis of 2105 vOTUs across the nine exposures significantly associated with virome diversity and composition. Log\u003csub\u003e2\u003c/sub\u003e fold change panel displays the change in abundance between the two groups for each exposure. The viral families to which vOTU belongs, surrounded by red boxes, are labeled. Adjusted P ≤ 0.001 and Log\u003csub\u003e2\u003c/sub\u003e-Fold changes ≥ |1| were used to select differentially abundant vOTUs.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4205731/v1/9a8cea7877e3f071aae36732.png"},{"id":54449535,"identity":"d557af12-190f-4cae-a66c-fb3fc43dd20a","added_by":"auto","created_at":"2024-04-10 17:38:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":636112,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAbundance of phage accessory genes differ in connection with exposures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Abundance of genes (3\u003csup\u003erd\u003c/sup\u003e level KEGG pathway) in the virome of infants with significant (P ≤ 0.05) enzymatic enrichments that are associated with the presence of siblings and residential location.\u003c/p\u003e\n\u003cp\u003e(B) Viral host families that contribute to metabolism pathways.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(C-E) Bacterial points extracted from the procrustes analysis (E). The points are colored according to the abundance of the specific genus in each sample.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4205731/v1/60bbf0e662b8c69626b43127.png"},{"id":57328390,"identity":"7bcc4bc9-3568-4957-89d8-9e8eda392d65","added_by":"auto","created_at":"2024-05-29 07:45:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2536991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4205731/v1/fd400aaf-4db4-4c18-8bd2-e595d73c7050.pdf"},{"id":54449537,"identity":"fc6a2456-0ebf-44bb-9b8d-ac4bed8085b2","added_by":"auto","created_at":"2024-04-10 17:38:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15242741,"visible":true,"origin":"","legend":"","description":"","filename":"supplement0402.docx","url":"https://assets-eu.researchsquare.com/files/rs-4205731/v1/8efe06cdcfd36bc6785e31fe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The influence of early life exposures on the infant gut virome","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEarly life gut microbiome (GM) establishment plays a fundamental role in shaping host physiology and health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] with early life GM imbalances being linked to onset and progression of chronic diseases later in life, such as obesity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], diabetes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and asthma [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo date, GM research has generally focused on understanding the importance of the bacterial GM component, but recent findings indicate that the vast and diverse population of viruses found in the gut (collectively called the \u0026ldquo;gut virome\u0026rdquo;) also play a prominent role in gut microbial ecology [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Amidst these biological entities, bacterial viruses, also termed bacteriophages (phages), are the most diverse and abundant particles of the GM [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] representing a major reservoir of genetic diversity influencing not only GM composition, but also the GM metabolic potential [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Disease-specific alterations in the gut virome have been reported in several chronic conditions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] such as inflammatory bowel disease [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], colorectal cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], necrotizing enterocolitis in preterm infants [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], severe acute malnutrition [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], type-1 diabetes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and other autoimmune diseases such as rheumatoid arthritis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Very recently we have demonstrated that infant gut virome imbalance in the temperate phage pool at the age of one year is associated with increased risk of developing asthma before school age. Interestigly it was found, that this effect was additive to the increaed risk of developing asthma due to imbalanced gut bacteriome at the age of one year, meaning that the influence of gut virome composition on asthma is not only mediated via influencing the bacterial gut component, but also independent of the bacteriome [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The role of the gut virome in shaping the GM is further underlined by the observation that fecal virome transfer from healthy donors to recipients with a dysbiotic GM prevent or ameliorate symptoms associated with metabolic [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and gastrointestinal [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] disorders.\u003c/p\u003e \u003cp\u003eWhile various early-life factors such as birth mode, siblings, diet and exposure to antibiotics have been found to influence development of the gut bacterial community [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], little is known about which factors shape the gut virome. The few attempts that have characterized the gut virome early in life have revealed that its composition is highly dynamic [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], affected by delivery mode [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and the first bacterial colonizers [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] as well as being enriched in phages belonging to the \u003cem\u003eMicroviridae\u003c/em\u003e family [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, its transmission-dynamics after birth follows a stepwise assembly, with breastfeeding playing a protective role against eukaryotic viral infections [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Understanding how environmental exposures and phenotypes intertwine the vector space conformed by viruses, bacteria, host, and their functional attributes remains hitherto an unsolved task.\u003c/p\u003e \u003cp\u003eIn a recent detailed investigation of the infant gut virome, we showed a massive diversity of hitherto undescribed phages [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In this cross-sectional study of the gut virome of 645 infants at one year of age enrolled in the COPSAC\u003csub\u003e2010\u003c/sub\u003e cohort [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] more than ten thousand viral species distributed over 248 viral families and 17 viral order-level clades were detected [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recently gut virome imbalance was as mentioned above linked to increased risk of developing asthma before school age in this cohort [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly life environmental exposures have influence gut bacteriome development early in life [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Here we expand these findings by investigating how social, pre-, peri- and postnatal factors influence the gut virome composition at one year of age. Our findings demonstrate how early life exposures are linked to the abundance of specific viruses, as well as their co-abundance and concordance with their predicted bacterial hosts. Metabolic functions encoded in the genomes of these viruses displayed enrichment of genes important for bacterial physiology in response to exposures, some of which are likely associated with dietary elements (e.g., degradation of complex carbohydrates) and others that may influence infant growth and health.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eComposition of DNA viruses in the gut of Danish infants\u003c/h2\u003e \u003cp\u003eA total of 645 stool samples from 1-year old infants in the COPSAC\u003csub\u003e2010\u003c/sub\u003e cohort [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] were obtained and analyzed [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Virions were isolated, concentrated and their genome was sequenced using a shotgun metagenome strategy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Following assembly, a total of 16,118 species-level clustered viral representative contigs (here termed viral Operational Taxonomic Units \u0026ndash; vOTUs) were obtained. Around 70% of the vOTUs were affiliated to five viral classes (\u003cem\u003eArfiviricetes, Caudoviricetes, Faserviricetes, Malgrandaviricetes\u003c/em\u003e and \u003cem\u003eTectiliviricetes\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI). Almost 18.8% of the vOTUs (n\u0026thinsp;=\u0026thinsp;3,029) were considered putative satellite phages as contigs lacked genes coding for structural proteins but encoded other viral proteins (e.g., integrases or replicases) and were conserved in size and gene content across multiple samples. In addition, 11.8% of the vOTUs (n\u0026thinsp;=\u0026thinsp;1,895) were categorized as unclassified viral fragments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe largest genomes (\u0026gt;\u0026thinsp;10 kb) were observed among \u003cem\u003eCaudoviricetes\u003c/em\u003e, which constituted the vast majority of vOTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The genomes, dominated by \u003cem\u003eCaudoviricetes\u003c/em\u003e (tailed, double-stranded DNA phages) and \u003cem\u003eMalgrandaviricetes\u003c/em\u003e (non-tailed, single-stranded DNA phages), followed a bi-/multi-modal distribution (Hartigans' Dip test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) based on their genome sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eBacterial hosts as well as lifestyle (temperate/virulent) of the vOTUs were predicted using CRISPR spacers and the presence of integrases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Because phages tend to have comparable \u003cem\u003ek-\u003c/em\u003emer frequencies to those of their hosts [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], we also performed dimensionality reduction on tetramer vectors to confirm global host associations as a complement to our viral taxonomy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Using unsupervised stochastic neighbor embedding (t-SNE) dimensionality reduction, vOTUs targeting the same hosts as determined by CRISPR spacers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) or belonging to the same viral classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) were found to clearly cluster together. Previously only \u003cem\u003eEnterobacteriaceae\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e have been shown to be the hosts of non-tailed \u003cem\u003eMalgrandaviricetes\u003c/em\u003e [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], but when examining the bacterial hosts, we observed that in addition to \u003cem\u003eBacteroidetes\u003c/em\u003e, also \u003cem\u003eRuminococcaceae, Clostridiaceae, Erysipelotrichaceae\u003c/em\u003e and \u003cem\u003eSutterellaceae\u003c/em\u003e are predicted as hosts of \u003cem\u003eMalgrandaviridetes\u003c/em\u003e viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and S1). With respect to lifestyle, \u003cem\u003eStreptococcaceae\u003c/em\u003e and most families of the \u003cem\u003eBacteroidetes\u003c/em\u003e are predicted hosts of a greater proportion of vOTUs recognized as virulent than temperate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and Table S2).\u003c/p\u003e \u003cp\u003eThe distribution of vOTUs was very individual-specific, with less than 5% of vOTUs appearing in more than 50% of the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). However, this still adds up to around 800 vOTUs that are shared among a majority of infants and representing, on average, more than 20% of the sequencing reads (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). The proportion of vOTUs classified as \u003cem\u003eCaudoviricetes\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG) as well as those infecting \u003cem\u003eBacteroidaceae\u003c/em\u003e and \u003cem\u003eBifidobacteriaceae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH) increased as a function of prevalance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental exposures influence viral diversity\u003c/h2\u003e \u003cp\u003eA range of pre-, peri-, and postnatal as well as social factors were recorded for the enrolled infants and their families (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Having older siblings was associated with higher vOTU richness (linear mixed model, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048, estimate\u0026thinsp;=\u0026thinsp;69.14, 95% CI = [0.58, 137.52]) and lower evenness (Shannon \u003cem\u003eH\u0026rsquo;\u003c/em\u003e) (linear mixed model, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, estimate = -0.30, 95% CI = [-0.50, -0.10]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F) at one year of age. Likewise, a higher birth weight was linked to higher vOTU richness (linear mixed model, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, estimate = -85.76, 95% CI = [-153.98, -17.56]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Dietary factors were also found to influence the gut virome at one year of age, with late introduction of eggs in the diet being associated with lower viral evenness (Shannon \u003cem\u003eH\u0026rsquo;\u003c/em\u003e) (linear mixed model, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, estimate\u0026thinsp;=\u0026thinsp;0.25, 95% CI = [0.05, 0.45]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). The mothers were enrolled in a nested randomized placebo-controlled trial of fish oil to the mothers during the third trimester of pregnancy [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Receiving fish oil during pregnancy was associated with increased gut vOTU richness (linear mixed model, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038, estimate\u0026thinsp;=\u0026thinsp;71.60, 95% CI = [3.90, 139.22]) of the infants at one year of age (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The design also examined the difference in vitamin D between high and standard doses [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which had no effect on the viral community in our analysis. Interestingly, other factors that have been found to influence the bacterial GM component during infancy such as birth mode, use of antibiotics, and duration of exclusive breastfeeding [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] did not seem to influence gut virome alpha-diversity measures at one year of age in this cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding virome composition, Bray-Curtis dissimilarity analysis (weighted measure, which is therefore mainly influenced by more abundant vOTUs) showed a link (PERMANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049, R2\u0026thinsp;=\u0026thinsp;0.0016) between maternal body mass index (BMI) and virome composition at one year of age (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG and S1A); while S\u0026oslash;rensen-Dice distance (unweighted binary metric and therefore mainly influenced by more rare vOTUs) revealed that a number of pre- and perinatal exposures were linked with virome composition differences (PERMANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05), namely weight at birth, fish oil supplementation during pregnancy, hospitalization after birth, and preeclampsia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH and S2B). Both Bray-Curtis and S\u0026oslash;rensen-Dice metrics showed significant differences in virome composition for children having older siblings (PERMANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, R2\u0026thinsp;=\u0026thinsp;0.0018 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, R2\u0026thinsp;=\u0026thinsp;0.0029 for Bray-Curtis and Sorensen-Dice, respectively), and whether the family was living in an urban or a rural area (PERMANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, R2\u0026thinsp;=\u0026thinsp;0.0019 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049, R2\u0026thinsp;=\u0026thinsp;0.0016 for Bray-Curtis and Sorensen-Dice, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH and S2A-B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental exposure variables influence the abundance of specific vira\u003c/h2\u003e \u003cp\u003eSubsequently, we determined how the distribution of vOTUs differed between the nine exposures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D) found to significantly influence overall gut virome composition (preeclampsia was not included due to highly unbalanced sample size, see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of 2,105 differentially abundant vOTUs affiliated to 173 viral families and 19 families of bacterial hosts were identified by DESeq2, with having older siblings being associated with 822 differential abundant vOTUs, while being hospitalized after birth being associated with 212 differential abundant vOTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For perinatal covariates, vOTUs differing in abundance were predicted to infect a range of different hosts, but interestingly revealed a pronounced lower abundance of vOTUs predicted as having \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eStreptococcaceae\u003c/em\u003e as hosts with with maternal antibiotic usage and hospitalization after birth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Postnatal factors like specific dietary patterns (late introduction of eggs in the diet), presence of older siblings in the house and living in a rural environment, were associated with a higher abundance of vOTUs infecting \u003cem\u003eBifidobacteriaceae\u003c/em\u003e, \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003ePrevotellaceae\u003c/em\u003e, \u003cem\u003eTannerellaceae\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eSutterellaceae\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further integrate these findings in the context of the gut bacterial component, we used 16S rRNA gene (V4 region) amplicon sequencing (bacterial OTUs - bOTUs) data previously published for this cohort [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] to determine virus-host co-abundances. Spearman correlation coefficients (ρ) were calculated between the abundance of the above identified differentially different abundant vOTUs and bOTUs across samples. Only bOTUs that were strongly associated (ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.3) with at least one vOTU were retained. If a vOTU was correlated with a bOTU, the bOTU family tended to be consistent with the predicted host family of the vOTU (Figure S3A). These virus-host co-abundances indicate there is a high degree of inter-relatedness between phages and their host in response to environmental exposures. This was supported by the fact that the same perinatal and postnatal covariates were also significantly associated with bOTU diversity and composition (Figure S4A-D). Overall, the 91 co-abundant vOTUs (ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.3), vOTUs that infect the same bacterial host family were in most cases closely related genetically, indicating a high degree of co-evolution between bacterial hosts and the phages that infect them (Figure S3B). To confirm the above-mentioned findings, we repeated the analysis of virus-host co-abundances using shotgun metagenomic data from the same cohort [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. We found again that viruses and their bacterial hosts were highly correlated supporting the same conclusion as above (Figure S4E).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional profiles of gut viruses are linked with environmental exposures\u003c/h2\u003e \u003cp\u003eDifferentially abundant vOTUs were subjected to gene (open reading frame, ORF) prediction, and annotated based on KEGG Orthology (KO) using KofamScan [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. As seen from figure S5A, 0.82% of genes matched known metabolism-related orthologs, while the remaining genes with KO assignments (8.48% of predicted genes) encoded genes related to genetic information processing and signaling and cellular processes, representing typical viral-associated traits required to accomplish replication [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The remaining 90.7% of the predicted genes were not annotated by the database.\u003c/p\u003e \u003cp\u003eNext, we focused on determining genes with metabolic functions having the potential to enhance host fitness and drive metabolic reprogramming of the bacterial host [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The gut virome of infants with older siblings were enriched in genes related to O\u0026thinsp;\u0026minus;\u0026thinsp;antigen nucleotide sugar biosynthesis and seleno-compound metabolism, while infants without siblings were enriched in genes related to carbon fixation in photosynthetic organisms (Fisher's exact test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) (the link to photosynthetic microorganisms may be caused by the KEGG database not being optimized for vira). The gut of infants living in rural areas or that were introduced to eggs in their diet later in life (above the median age when eggs were introduced in the diet) were enriched in viral encoded genes associated with glycolysis/gluconeogenesis and O\u0026thinsp;\u0026minus;\u0026thinsp;antigen nucleotide sugar biosynthesis, whereas the gut of infants living in urban areas or that were introduced to eggs relatively early in life were enriched in viral genes associated with thiamine metabolism. Infants with birth weight above the median or whose mothers who had a pre-pregnancy BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 also encoded genes involved in diverse pathways involved in e.g. vitamin synthesis. Further, the gut virome of infants whose mothers received fish oil during pregnancy or were prescribed antibiotics during delivery encoded genes related to purine metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo determine how virally encoded gene functions associate with the microbial composition, we linked back enriched genes to the vOTU of origin (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). 94% of lifestyle predicted vOTUs (n\u0026thinsp;=\u0026thinsp;17) were temperate. Genes associated with two classes of amino acid metabolisms (i.e. alanine, aspartate and glutamate metabolism and lysine biosynthesis) were conserved across \u003cem\u003eAlistipes\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e targeting vOTUs, respectively. In addition, multiple carbohydrate metabolism enzyme encoding genes were found to be widely encoded by \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e targeting vOTUs. These encoded enzymes including L\u0026thinsp;\u0026minus;\u0026thinsp;lactate dehydrogenase, ribose\u0026thinsp;\u0026minus;\u0026thinsp;phosphate pyrophosphokinase and aldose 1\u0026thinsp;\u0026minus;\u0026thinsp;epimerase (Figure S5D). Energy metabolism genes were found in \u003cem\u003ePrevotella and Faecalibacterium\u003c/em\u003e targeting vOTUs, while nicotinate and nicotinamide metabolism genes were mapped in \u003cem\u003eRuminococcus\u003c/em\u003e and \u003cem\u003eEscherichia\u003c/em\u003e targeting vOTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003ePhage-host co-abundance (Figure S3A), was further confirmed by Procrustes analysis. The linking of the virome and bacteriome compositions revealed a strong correlation one to another (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.52) (Figure S5B-C). The cumulative abundance of all bOTUs belonging to the bacterial genera \u003cem\u003eRuminococcus, Prevotella\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e, which were found to be the main bacterial host of vOTUs carrying the above metabolic genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) and having previously been reported to be highly associated with stable viral communities [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], was highly correlated with rural vs. urban living and having older siblings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E). These results emphasize the potential role of phage-host association in metabolic regulation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe gut of healthy newborns is usually devoid of viruses at birth, but it is rapidly colonized afterwards [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Still relatively few studies have focused on the assembly of the gut virome within the first year of life and the factors that influence it [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and even less is known about the environmental exposures that shape the gut virome.\u003c/p\u003e \u003cp\u003eHere, we leveraged a massive gut virome dataset from healthy infants at 1-year of age, and integrated measures of viral diversity such as sequence composition, viral hosts, and phage lifestyles [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with social, pre-, peri- and postnatal environmental exposures. We revealed the effects of these exposures on viral community and the possible effects on metabolism.\u003c/p\u003e \u003cp\u003eIn previous reports, \u003cem\u003eCrassvirales\u003c/em\u003e (class \u003cem\u003eCaudoviricetes\u003c/em\u003e) and \u003cem\u003eMicroviridae\u003c/em\u003e (class \u003cem\u003eMalgrandaviricetes\u003c/em\u003e) phages were found to be the two most abundant viral groups in the adult human gut, with their relative abundance being negatively correlated [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Here, in one-year-old infants, a similar observation was made, members of the \u003cem\u003eCaudoviricetes\u003c/em\u003e and \u003cem\u003eMalgrandaviricetes\u003c/em\u003e classes were the most abundant phages.\u003c/p\u003e \u003cp\u003eInterestingly, ongoing exposures such as having older siblings and residential location, as well as past exposures (e.g., birth weight, preeclampsia) were linked with gut virome composition at one year of age. However, it is still possible that the prenatal and perinatal exposures still influenced the immune education earlier in life and remnants of the interplay are still tangible at 1-year of age [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among the exposures significantly influencing the gut virome composition, the largest effect sizes were from residential location (rural vs. urban) and having older siblings (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Interestingly, urbanization has been reported to have a significant impact on the composition of the adult viral community, with individuals living in urban areas having higher abundance of \u003cem\u003eLactococcus\u003c/em\u003e (family \u003cem\u003eStreptococcaceae\u003c/em\u003e) phages [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The latter is presumably associated with the consumption of dairy products. We show that the living environment also affects the gut virome of infants, and that \u003cem\u003eStreptococcaceae\u003c/em\u003e targeting phages are also more abundant in infants living in urban areas, possibly reflecting differences in dietary habits rather than residence \u003cem\u003eper se\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHaving older siblings influences the development of the bacterial community in early life [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and here we show that having older siblings is also associated with gut virome composition at one year of age. Importantly, focussing on the curated DNA phage community (i.e. not the total vOTU pool, but only the fraction predicted as being bacteriophages) we recently demonstrated that phageome imbalances are associated with increased risk of developing before school age, but also that having older siblings was negatively associated with higher virome asthma signature score, implying a lower asthma rate, showing how the influence of environmental exposures on virome composition also have implications for child health [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Importantly, from a translational angle, early-life exposures may affect the establishment of health phenotypes, such as the protective role of breastfeeding against eukaryotic-viral infections in the neonatal period [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Combining gut bacterial compositional data with gut virome composition (Figure S3A and S5B-C) in our cohort elucidates the co-abundance of phages and their hosts, underlying the role of phage-host interactions in shaping the GM. Most of these viruses (Figure S3A) have temperate lifestyles, as evidenced by the presence of genes coding for integrases. Thus, these temperate phages appear to have the ability to integrate their genome into the bacterial hosts and become prophages at some point.\u003c/p\u003e \u003cp\u003eGut virome members have the potential to modulate biochemical processes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The functional prediction of the genes derived from vOTUs co-varying with exposures, revealed up to 90% of genes with unknown functions. It emphasizes that proteins with yet uncharacterized functions are potentially playing a role in the regulation of human host phenotypes. Certain predicted gene functions linked to metabolic activities, such as alanine, aspartate and glutamate metabolism, amino sugar and nucleotide sugar metabolism and glycolysis/gluconeogenesis, which are likely associated with dietary intake and degradation of macronutrients, were associated with fish in the diet, birth weight, residence location and egg in the diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Birth weight also show negative correlation with higher virome asthma signature score (asthma rate) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and time-to-asthma analysis proved that the effects of phages on asthma were additive and statistically independent of the bacterial gut microbiome component [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Maternal obesity alters fatty acid metabolism and changes in gene expression of lipid metabolism in infants, which cause a higher risk of developing obesity and its complications, neuropsychiatric disorders and asthma [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. We find here that viral genes associated with normal weight mothers were predominantly enriched in fatty acid biosynthesis compared to obese mothers, which may be an intermediate pathway by which maternal obesity affects child health. In addition, for biotin metabolism, which is known to be impaired by severe obesity [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], many phage genes are also observed to be enriched in infants from mothers with BMI below 25 in our data. The mothers enrolled in the cohort participated in a randomized clinical trial where they were randomized to receiving fish oil or a placebo from week 24 of pregnancy to one week after birth [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The design also examined the difference in vitamin D between high and standard doses [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which had no effect on the viral community in our analysis. Of note, the supplementation of fish oil during pregnancy was not found to influence the gut bacterial component at age one year. Here we report that the same intervention has some influence on the gut virome at age one year, but the effect is only borderline significant. The infants of mothers that received fish oil had viral genes involved in lysine biosynthesis, glycerophospholipid metabolism, and purine metabolism \u0026ndash; metabolic activities that have been associated to fish oil supplementation [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], but never attributed to gut virome composition. Interestingly, most of these metabolism-related genes were conserved across temperate vOTUs targeting \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e spp. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These genera have been consistently reported to be enriched in Danish and American subjects with a diet rich in carbohydrates, resistant starch, and fibers, and being determinants of the so-called \u003cem\u003ePrevotella\u003c/em\u003e-enterotype [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The \u003cem\u003ePrevotella\u003c/em\u003e-enterotype is established early in life (between 9\u0026ndash;36 months of age) [\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] and have been previously suggested as markers of GM maturity at age one year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Stokholm et al. (2018) reported delayed GM maturation as a risk factor for later development of asthma indicating the importance of these microbes for immune maturation.\u003c/p\u003e \u003cp\u003eAlthough our study is currently unable to assess how these gut virome associated genes are actively involved in either enhancing either phage or host fitness, or both, our data as well as our recent finding, that early life gut virome imbalance is associated with increased asthma before school age risk [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] underlines the potential importance of bacteriophage-encoded metabolic genes and delivers an initial insight of the type of metabolic content conveyed by the gut virome in association to environmental variables.\u003c/p\u003e \u003cp\u003eIn summary, our data provides detailed insight into the influence of common environmental factors that shape the gut virome during early life. We also uncover that key gut metabolic functions can be encoded by viral genes, which suggest that, in addition of shaping gut bacteriome composition, phages may directly play a role in metabolic activities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eParticipants belong to the COPSAC2010 cohort [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Fecal samples for virome extraction sequencing and analysis were collected from all infants at age 1 year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSample collection, sequencing, virome assembly\u003c/h2\u003e \u003cp\u003ePreparation of fecal samples, and extraction and sequencing of virions was carried out using a previously described protocol [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Briefly, viral-associated DNA was subjected to short MDA amplification and libraries were prepared following using manufacturer\u0026rsquo;s procedures for the Nextera XT kit (FC-131-1096 Ilumina, California). Libraries were single-end high-throughput sequenced on the Illumina HiSeq X platform. Details of the pipeline for data processing, de-novo assembly, quality control, bacterial-host and lifestyle predictions, abundance-mapping (vOTU table), and taxonomy of complete and partial viral genomes (here termed vOTUs) can be found in Shah et al. (2021). 16S rRNA gene amplicon data (bOTU table) from the same cohort\u0026rsquo;s participants were retrieved from Stokholm et al. (2018).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental exposures\u003c/h2\u003e \u003cp\u003eBriefly, during scheduled visits to the COPSAC clinic, information on a wide range of exposures was collected. A total of 30 environmental exposures were investigated and were grouped into social (n\u0026thinsp;=\u0026thinsp;6), pre- (n\u0026thinsp;=\u0026thinsp;4), peri- (n\u0026thinsp;=\u0026thinsp;9) and postnatal (n\u0026thinsp;=\u0026thinsp;11) exposures based on whether they occurred or existed before birth. See Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for a complete list of the exposures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistics and data analysis\u003c/h2\u003e \u003cp\u003eAnalyses on diversity were carried out on contingency tables gathering vOTUs abundance. Abundance data was normalized by reads per kilobase per million (RPKM). Alpha-diversity (Observed vOTUs and Shannon Index) indices and Beta-diversity (Bray-Curtis and S\u0026oslash;rensen-Dice distances) matrices were generated using the package phyloseq (version 1.42.0) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The contribution of each covariate to explain vOTUs community structure (as determined by S\u0026oslash;rensen-Dice similarity and Bray-Curtis dissimilarity metrics) was calculated using distance-based redundancy analysis (db-RDA) models coupled to adonis PERMANOVA (n permutations\u0026thinsp;=\u0026thinsp;999) in package vegan (version 2.6-2) [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], while the effect size of the same covariates on alpha-diversity was calculated with linear mixed models from the package lmerTest (version 3.1-3) [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. All linear mixed models accounted for technical variation between runs using sequencing lane as the random effect.\u003c/p\u003e \u003cp\u003eDifferent differential abundance analysis methods were evaluated by DAtest [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. DESeq2 (version 1.36.0) performed well with a low false positive rate and a high ability to detect differential vOTUs for our data [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. The sequencing lane was considered as a factor-covariate. The raw reads count table of each sample for vOTUs were prepared as input. All parameters are default except for sfType which is set to poscount. Benjamini and Hochberg method was adapted to correct the p-values. vOTUs with adjusted p-value\u0026thinsp;\u0026le;\u0026thinsp;0.001 and log\u003csub\u003e2\u003c/sub\u003e fold change \u0026ge; |1| were selected for downstream analyses.\u003c/p\u003e \u003cp\u003eSpearman\u0026rsquo;s rank correlations were used to test univariate associations of continuous data, and results were visualized in a heatmap. MAFFT [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] was used to generate the phylogenetic tree file for those highly correlated vOTUs. The phylogenetic tree was visualized using the R package ggtree (version 3.4.0) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Procrustes analysis (R package vegan) was performed on vOTUs as target block and 16S rRNA gene data as rotatory block (n permutations\u0026thinsp;=\u0026thinsp;999), while using the first two constrained components (CAP1 and CAP2) of db-RDA models for each data block.\u003c/p\u003e \u003cp\u003eORF calling on selected vOTUs was executed with Prodigal [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. To determine metabolic function, genes were annotated based on KEGG Orthology using KofamScan [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and filtered by default thresholds. Enricher function in clusterProfiler package (version 4.6.0) was applied to detect whether genes in differently abundant vOTUs were enriched in the metabolic pathway [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll analyses were carried out in R (version 4.0.2) and results were visualized with the package ggplot2 (version 3.3.6) [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our deepest gratitude to the children and families of the COPSAC\u003csub\u003e2010\u0026nbsp;\u003c/sub\u003ecohort study for all their support and commitment. We acknowledge and appreciate the unique efforts of the COPSAC research team.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by the Joint Programming Initiative \u0026lsquo;Healthy Diet for a Healthy Life\u0026rsquo;, specifically here, the Danish Agency for Science and Higher Education, Institut National de la Recherche Agronomique (INRA), and the Canadian Institutes of Health Research (Team grant on Intestinal Microbiomics, Institute of Nutrition, Metabolism, and Diabetes, grant number 143924). JT is supported by the BRIDGE Translational Excellence Program (bridge.ku.dk) at the Faculty of Health and Medical Sciences, University of Copenhagen, funded by the Novo Nordisk Foundation (grant no. NNF18SA0034956). S.M. holds the Tier 1 Canada Research Chair in Bacteriophages [950-232136].\u0026nbsp;JS and DSN are recipients of Novo Nordisk Foundation grant NNF20OC0061029.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.D., S.M., M.A.P., J.S. and D.S.N. conceived the project and supervised all the research; B.C., K.B., J.S., S.J.S., L.J. and M.D. collected the samples and/or information; Y.Z., J.L.C.M., S.A.S. and D.S.N. analyzed the data; Y.Z., J.L.C.M. and D.S.N. wrote the manuscript with the assistance of L.D., S.A.S., J.T., C.L.R., S.M., M.A.P. and J.S.; L.D. prepared the virome and sequencing libraries; all authors contributed to, revised and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and was approved by The National Committee on Health Research Ethics (H-B-2008-093) and the Danish Data Protection Agency (2015-41-3696). Both parents gave written informed consent before enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No data on individual persons is reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing FASTQ files are available on ENA under project number PRJEB46943. All cohort participants\u0026apos; individual-level data are protected by Danish and European law and are not publicly available. However, participant-level data can be made available under a data transfer agreement as part of a collaboration effort. Codes for data analyses are available from the authors upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eAll authors declare no conflicts of interest related to the present study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTamburini S, Shen N, Wu HC, Clemente JC. The microbiome in early life: implications for health outcomes. Nat Med. 2016;22(7):713-22; doi: 10.1038/nm.4142.\u003c/li\u003e\n\u003cli\u003eStokholm J, Blaser MJ, Thorsen J, Rasmussen MA, Waage J, Vinding RK, et al. Maturation of the gut microbiome and risk of asthma in childhood. Nat Commun. 2018;9:141; doi: 10.1038/s41467-017-02573-2.\u003c/li\u003e\n\u003cli\u003ePihl AF, Fonvig CE, Stjernholm T, Hansen T, Pedersen O, Holm JC. The role of the gut microbiota in childhood obesity. Child Obes. 2016;12(4):292-9; doi: 10.1089/chi.2015.0220.\u003c/li\u003e\n\u003cli\u003eKostic AD, Gevers D, Siljander H, Vatanen T, Hyotylainen T, Hamalainen AM, et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe. 2015;17(2):260-73; doi: 10.1016/j.chom.2015.01.001.\u003c/li\u003e\n\u003cli\u003eVatanen T, Kostic AD, d\u0026apos;Hennezel E, Siljander H, Franzosa EA, Yassour M, et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell. 2016;165(4):842-53; doi: 10.1016/j.cell.2016.04.007.\u003c/li\u003e\n\u003cli\u003eMcCann A, Ryan FJ, Stockdale SR, Dalmasso M, Blake T, Ryan CA, et al. Viromes of one year old infants reveal the impact of birth mode on microbiome diversity. Peerj. 2018;6:e4694; doi: 10.7717/peerj.4694.\u003c/li\u003e\n\u003cli\u003eSutton TDS, Hill C. Gut bacteriophage: current understanding and challenges. Front Endocrinol. 2019;10:784; doi: 10.3389/fendo.2019.00784.\u003c/li\u003e\n\u003cli\u003eShkoporov AN, Hill C. Bacteriophages of the human gut: the \u0026ldquo;known unknown\u0026apos;\u0026apos; of the microbiome. Cell Host Microbe. 2019;25(2):195-209; doi: 10.1016/j.chom.2019.01.017.\u003c/li\u003e\n\u003cli\u003eShah SA, Deng L, Thorsen J, Pedersen AG, Dion MB, Castro-Mej\u0026iacute;a JL, et al. Expanding known viral diversity in the healthy infant gut. Nat Microbiol. 2023;8(5):986\u0026ndash;98; doi: 10.1038/s41564-023-01345-7.\u003c/li\u003e\n\u003cli\u003eGregory AC, Zablocki O, Zayed AA, Howell A, Bolduc B, Sullivan MB. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe. 2020;28(5):724-40; doi: 10.1016/j.chom.2020.08.003.\u003c/li\u003e\n\u003cli\u003eRoux S, Paez-Espino D, Chen IMA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2021;49(D1):D764-D75; doi: 10.1093/nar/gkaa946.\u003c/li\u003e\n\u003cli\u003eKieft K, Zhou ZC, Anderson RE, Buchan A, Campbell BJ, Hallam SJ, et al. Ecology of inorganic sulfur auxiliary metabolism in widespread bacteriophages. Nat Commun. 2021;12(1):3503; doi: 10.1038/s41467-021-23698-5.\u003c/li\u003e\n\u003cli\u003eBi L, Yu DT, Du S, Zhang LM, Zhang LY, Wu CF, et al. Diversity and potential biogeochemical impacts of viruses in bulk and rhizosphere soils. Environ Microbiol. 2021;23(2):588-99; doi: 10.1111/1462-2920.15010.\u003c/li\u003e\n\u003cli\u003eTisza MJ, Buck CB. A catalog of tens of thousands of viruses from human metagenomes reveals hidden associations with chronic diseases. P Natl Acad Sci USA. 2021;118(23):e2023202118; doi: 10.1073/pnas.2023202118.\u003c/li\u003e\n\u003cli\u003eClooney AG, Sutton TDS, Shkoporov AN, Holohan RK, Daly KM, O\u0026apos;Regan O, et al. Whole-virome analysis sheds light on viral dark matter in inflammatory bowel disease. Cell Host Microbe. 2019;26(6):764-78; doi: 10.1016/j.chom.2019.10.009.\u003c/li\u003e\n\u003cli\u003eZhao LY, Shi Y, Lau HC, Liu WX, Luo GW, Wang GP, et al. Uncovering 1,058 novel human enteric DNA viruses through deep long-read third-generation sequencing and their clinical impact. Gastroenterology. 2022;162(7):S96-S.\u003c/li\u003e\n\u003cli\u003eKaelin EA, Rodriguez C, Hall-Moore C, Hoffmann JA, Linneman LA, Ndao IM, et al. Longitudinal gut virome analysis identifies specific viral signatures that precede necrotizing enterocolitis onset in preterm infants. Nat Microbiol. 2022;7(5):653-62; doi: 10.1038/s41564-022-01096-x.\u003c/li\u003e\n\u003cli\u003eReyes A, Blanton LV, Cao S, Zhao G, Manary M, Trehan I, et al. Gut DNA viromes of Malawian twins discordant for severe acute malnutrition. P Natl Acad Sci USA. 2015;112(38):11941-6; doi: 10.1073/pnas.1514285112.\u003c/li\u003e\n\u003cli\u003eZhao GY, Vatanen T, Droit L, Park A, Kostic AD, Poon TW, et al. Intestinal virome changes precede autoimmunity in type I diabetes-susceptible children. P Natl Acad Sci USA. 2017;114(30):E6166-E75; doi: 10.1073/pnas.1706359114.\u003c/li\u003e\n\u003cli\u003eVehik K, Lynch KF, Wong MC, Tian XJ, Ross MC, Gibbs RA, et al. Prospective virome analyses in young children at increased genetic risk for type 1 diabetes. Nat Med. 2019;25(12):1865-72; doi: 10.1038/s41591-019-0667-0.\u003c/li\u003e\n\u003cli\u003eTomofuji Y, Kishikawa T, Maeda Y, Ogawa K, Nii T, Okuno T, et al. Whole gut virome analysis of 476 Japanese revealed a link between phage and autoimmune disease. Ann Rheum Dis. 2021;81:278-88; doi: 10.1136/annrheumdis-2021-221267.\u003c/li\u003e\n\u003cli\u003eLeal Rodriguez C, Shah SA, Rasmussen MA, Thorsen J, Boulund U, Pedersen CT, et al. The infant gut virome is associated with preschool asthma risk independently of bacteria. Nat Med. 2023; doi: 10.1038/s41591-023-02685-x.\u003c/li\u003e\n\u003cli\u003eRasmussen TS, Mentzel CMJ, Kot W, Castro-Mejia JL, Zuffa S, Swann JR, et al. Faecal virome transplantation decreases symptoms of type 2 diabetes and obesity in a murine model. Gut. 2020;69(12):2122-30; doi: 10.1136/gutjnl-2019-320005.\u003c/li\u003e\n\u003cli\u003eOtt SJ, Waetzig GH, Rehman A, Moltzau-Anderson J, Bharti R, Grasis JA, et al. Efficacy of sterile fecal filtrate transfer for treating patients with clostridium difficile infection. Gastroenterology. 2017;152(4):799-811; doi: 10.1053/j.gastro.2016.11.010.\u003c/li\u003e\n\u003cli\u003eBrunse A, Deng L, Pan XY, Hui Y, Castro-Mejia JL, Kot W, et al. Fecal filtrate transplantation protects against necrotizing enterocolitis. ISME J. 2022;16(3):686-94; doi: 10.1038/s41396-021-01107-5.\u003c/li\u003e\n\u003cli\u003eStewart CJ, Ajami NJ, O\u0026apos;Brien JL, Hutchinson DS, Smith DP, Wong MC, et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature. 2018;562(7728):583-8; doi: 10.1038/s41586-018-0617-x.\u003c/li\u003e\n\u003cli\u003eBreitbart M, Haynes M, Kelley S, Angly F, Edwards RA, Felts B, et al. Viral diversity and dynamics in an infant gut. Res Microbiol. 2008;159(5):367-73; doi: 10.1016/j.resmic.2008.04.006.\u003c/li\u003e\n\u003cli\u003eLim ES, Zhou YJ, Zhao GY, Bauer IK, Droit L, Ndao IM, et al. Early life dynamics of the human gut virome and bacterial microbiome in infants. Nature Medicine. 2015;21(10):1228-34; doi: 10.1038/nm.3950.\u003c/li\u003e\n\u003cli\u003eTaboada B, Moran P, Serrano-Vazquez A, Isa P, Rojas-Velazquez L, Perez-Juarez H, et al. The gut virome of healthy children during the first year of life is diverse and dynamic. PLoS One. 2021;16(4):e0240958; doi: 10.1371/journal.pone.0240958.\u003c/li\u003e\n\u003cli\u003eLiang G, Bushman FD. The human virome: assembly, composition and host interactions. Nat Rev Microbiol. 2021;19(8):514-27; doi: 10.1038/s41579-021-00536-5.\u003c/li\u003e\n\u003cli\u003eLiang GX, Zhao CY, Zhang HJ, Mattei L, Sherrill-Mix S, Bittinger K, et al. The stepwise assembly of the neonatal virome is modulated by breastfeeding. Nature. 2020;581(7809):470-4; doi: 10.1038/s41586-020-2192-1.\u003c/li\u003e\n\u003cli\u003eWalters WA, Granados AC, Ley C, Federman S, Stryke D, Santos Y, et al. Longitudinal comparison of the developing gut virome in infants and their mothers. Cell Host Microbe. 2023;31(2):187-98; doi: 10.1016/j.chom.2023.01.003.\u003c/li\u003e\n\u003cli\u003eBisgaard H, Vissing NH, Carson CG, Bischoff AL, Folsgaard NV, Kreiner-Moller E, et al. Deep phenotyping of the unselected COPSAC2010 birth cohort study. Clin Exp Allergy. 2013;43(12):1384-94; doi: 10.1111/cea.12213.\u003c/li\u003e\n\u003cli\u003eChristensen ED, Hjelmso MH, Thorsen J, Shah S, Redgwell T, Poulsen CE, et al. The developing airway and gut microbiota in early life is influenced by age of older siblings. Microbiome. 2022;10(1):106; doi: 10.1186/s40168-022-01305-z.\u003c/li\u003e\n\u003cli\u003eGasparrini AJ, Wang B, Sun X, Kennedy EA, Hernandez-Leyva A, Ndao IM, et al. Persistent metagenomic signatures of early-life hospitalization and antibiotic treatment in the infant gut microbiota and resistome. Nat Microbiol. 2019;4(12):2285-97; doi: 10.1038/s41564-019-0550-2.\u003c/li\u003e\n\u003cli\u003eDeng L, Silins R, Castro-Mejia JL, Kot W, Jessen L, Thorsen J, et al. A protocol for extraction of infective viromes suitable for metagenomics sequencing from low volume fecal samples. Viruses. 2019;11(7):667; doi: 10.3390/v11070667.\u003c/li\u003e\n\u003cli\u003eEdwards RA, McNair K, Faust K, Raes J, Dutilh BE. Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol Rev. 2016;40(2):258-72; doi: 10.1093/femsre/fuv048.\u003c/li\u003e\n\u003cli\u003ePride DT, Wassenaar TM, Ghose C, Blaser MJ. Evidence of host-virus co-evolution in tetranucleotide usage patterns of bacteriophages and eukaryotic viruses. BMC Genom. 2006;7:1-13; doi: 10.1186/1471-2164-7-8.\u003c/li\u003e\n\u003cli\u003eKrupovic M, Forterre P. Microviridae goes temperate: microvirus-related proviruses reside in the genomes of bacteroidetes. PLoS One. 2011;6(5):e19893; doi: 10.1371/journal.pone.0019893.\u003c/li\u003e\n\u003cli\u003eVinding RK, Stokholm J, Sevelsted A, Chawes BL, Bonnelykke K, Barman M, et al. Fish oil supplementation in pregnancy increases gestational age, size for gestational age, and birth weight in infants: a randomized controlled trial. J Nutr. 2019;149(4):628-34; doi: 10.1093/jn/nxy204.\u003c/li\u003e\n\u003cli\u003eBisgaard H, Stokholm J, Chawes BL, Vissing NH, Bjarnadottir E, Schoos AMM, et al. Fish oil-derived fatty acids in pregnancy and wheeze and asthma in offspring. New Engl J Med. 2016;375(26):2530-9; doi: 10.1056/NEJMoa1503734.\u003c/li\u003e\n\u003cli\u003eChawes BL, Bonnelykke K, Stokholm J, Vissing NH, Bjarnadottir E, Schoos AMM, et al. Effect of vitamin D-3 supplementation during pregnancy on risk of persistent wheeze in the offspring: a randomized clinical trial. JAMA. 2016;315(4):353-61; doi: 10.1001/jama.2015.18318.\u003c/li\u003e\n\u003cli\u003eLi XJ, Stokholm J, Brejnrod A, Vestergaard GA, Russel J, Trivedi U, et al. The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition. Cell Host Microbe. 2021;29(6):975-87; doi: 10.1016/j.chom.2021.03.017.\u003c/li\u003e\n\u003cli\u003eAramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020;36(7):2251-2; doi: 10.1093/bioinformatics/btz859.\u003c/li\u003e\n\u003cli\u003eSanz-Gaitero M, Seoane-Blanco M, van Raaij MJ. Structure and function of bacteriophages. Bacteriophages: Biology, Technology, Therapy. 2019:1-73; doi: 10.1007/978-3-319-40598-8_1-1.\u003c/li\u003e\n\u003cli\u003eWarwick-Dugdale J, Buchholz HH, Allen MJ, Temperton B. Host-hijacking and planktonic piracy: how phages command the microbial high seas. Virol J. 2019;16:1-13; doi: 10.1186/s12985-019-1120-1.\u003c/li\u003e\n\u003cli\u003eShkoporov AN, Clooney AG, Sutton TDS, Ryan FJ, Daly KM, Nolan JA, et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe. 2019;26(4):527-41; doi: 10.1016/j.chom.2019.09.009.\u003c/li\u003e\n\u003cli\u003eKim KH, Bae JW. Amplification methods bias metagenomic libraries of uncultured single-stranded and double-stranded DNA viruses. Appl Environ Microbiol. 2011;77(21):7663-8; doi: 10.1128/Aem.00289-11.\u003c/li\u003e\n\u003cli\u003eRoux S, Solonenko NE, Dang VT, Poulos BT, Schwenk SM, Goldsmith DB, et al. Towards quantitative viromics for both double-stranded and single-stranded DNA viruses. Peerj. 2016;4:e2777; doi: 10.7717/peerj.2777.\u003c/li\u003e\n\u003cli\u003eYutin N, Makarova KS, Gussow AB, Krupovic M, Segall A, Edwards RA, et al. Discovery of an expansive bacteriophage family that includes the most abundant viruses from the human gut. Nat Microbiol. 2018;3(1):38-46; doi: 10.1038/s41564-017-0053-y.\u003c/li\u003e\n\u003cli\u003eZuo T, Sun Y, Wan YT, Yeoh YK, Zhang F, Cheung CP, et al. Human-gut-DNA virome variations across geography, ethnicity, and urbanization. Cell Host Microbe. 2020;28(5):741-51; doi: 10.1016/j.chom.2020.08.005.\u003c/li\u003e\n\u003cli\u003eLaursen MF, Zachariassen G, Bahl MI, Bergstrom A, Host A, Michaelsen KF, et al. Having older siblings is associated with gut microbiota development during early childhood. BMC Microbiol. 2015;15:1-9; doi: 10.1186/s12866-015-0477-6.\u003c/li\u003e\n\u003cli\u003eHedin CR, van der Gast CJ, Stagg AJ, Lindsay JO, Whelan K. The gut microbiota of siblings offers insights into microbial pathogenesis of inflammatory bowel disease. Gut Microbes. 2017;8(4):359-65; doi: 10.1080/19490976.2017.1284733.\u003c/li\u003e\n\u003cli\u003eBrown EM, Arellano-Santoyo H, Temple ER, Costliow ZA, Pichaud M, Hall AB, et al. Gut microbiome ADP-ribosyltransferases are widespread phage-encoded fitness factors. Cell Host Microbe. 2021;29(9):1351-65; doi: 10.1016/j.chom.2021.07.011.\u003c/li\u003e\n\u003cli\u003eAlvarez D, Munoz Y, Ortiz M, Maliqueo M, Chouinard-Watkins R, Valenzuela R. Impact of maternal obesity on the metabolism and bioavailability of polyunsaturated fatty acids during pregnancy and breastfeeding. Nutrients. 2020;13(1):19; doi: 10.3390/nu13010019.\u003c/li\u003e\n\u003cli\u003eCosta SM, Isganaitis E, Matthews TJ, Hughes K, Daher G, Dreyfuss JM, et al. Maternal obesity programs mitochondrial and lipid metabolism gene expression in infant umbilical vein endothelial cells. Int J Obes (Lond). 2016;40(11):1627-34; doi: 10.1038/ijo.2016.142.\u003c/li\u003e\n\u003cli\u003eBelda E, Voland L, Tremaroli V, Falony G, Adriouch S, Assmann KE, et al. Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism. Gut. 2022;71(12):2463-80; doi: 10.1136/gutjnl-2021-325753.\u003c/li\u003e\n\u003cli\u003eHjelmso MH, Shah SA, Thorsen J, Rasmussen M, Vestergaard G, Mortensen MS, et al. Prenatal dietary supplements influence the infant airway microbiota in a randomized factorial clinical trial. Nat Commun. 2020;11(1):426; doi: 10.1038/s41467-020-14308-x.\u003c/li\u003e\n\u003cli\u003eHishikawa D, Valentine WJ, Iizuka-Hishikawa Y, Shindou H, Shimizu T. Metabolism and functions of docosahexaenoic acid-containing membrane glycerophospholipids. FEBS Lett. 2017;591(18):2730-44; doi: 10.1002/1873-3468.12825.\u003c/li\u003e\n\u003cli\u003eCao Y, Gao Q, Li X, Zhou Y, Dong S, Wang Y, et al. Integrated analysis of metabolomics and transcriptomics for assessing effects of fish meal and fish oil replacement on the metabolism of rainbow trout (Oncorhynchus mykiss). Front Mar Sci. 2022;9:208; doi: 10.3389/fmars.2022.843637.\u003c/li\u003e\n\u003cli\u003eRoager HM, Vogt JK, Kristensen M, Hansen LBS, Ibrugger S, Maerkedahl RB, et al. Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: a randomised cross-over trial. Gut. 2019;68(1):83-93; doi: 10.1136/gutjnl-2017-314786.\u003c/li\u003e\n\u003cli\u003eWu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science. 2011;334(6052):105-8; doi: 10.1126/science.1208344.\u003c/li\u003e\n\u003cli\u003eBergstrom A, Skov TH, Bahl MI, Roager HM, Christensen LB, Ejlerskov KT, et al. Establishment of intestinal microbiota during early life: a longitudinal, explorative study of a large cohort of danish infants. Appl Environ Microbiol. 2014;80(9):2889-900; doi: 10.1128/Aem.00342-14.\u003c/li\u003e\n\u003cli\u003eBackhed F, Roswall J, Peng YQ, Feng Q, Jia HJ, Kovatcheva-Datchary P, et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe. 2015;17(5):690-703; doi: 10.1016/j.chom.2015.04.004.\u003c/li\u003e\n\u003cli\u003eRoswall J, Olsson LM, Kovatcheva-Datchary P, Nilsson S, Tremaroli V, Simon MC, et al. Developmental trajectory of the healthy human gut microbiota during the first 5 years of life. Cell Host Microbe. 2021;29(5):765-76; doi: 10.1016/j.chom.2021.02.021.\u003c/li\u003e\n\u003cli\u003eLarsen JM. The immune response to Prevotella bacteria in chronic inflammatory disease. Immunology. 2017;151(4):363-74; doi: 10.1111/imm.12760.\u003c/li\u003e\n\u003cli\u003ePark H, Shin JW, Park SG, Kim W. Microbial communities in the upper respiratory tract of patients with asthma and chronic obstructive pulmonary disease. PLoS One. 2014;9(10):e109710; doi: 10.1371/journal.pone.0109710.\u003c/li\u003e\n\u003cli\u003eMcMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217; doi: 10.1371/journal.pone.0061217.\u003c/li\u003e\n\u003cli\u003eDixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14(6):927-30; doi: DOI 10.1111/j.1654-1103.2003.tb02228.x.\u003c/li\u003e\n\u003cli\u003eKuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: tests in linear mixed effects models. J Stat Softw. 2017;82(13):1-26; doi: DOI 10.18637/jss.v082.i13.\u003c/li\u003e\n\u003cli\u003eRussel J, Thorsen J, Brejnrod AD, Bisgaard H, S\u0026oslash;rensen SJ, Burm\u0026oslash;lle M. DAtest: a framework for choosing differential abundance or expression method. bioRxiv. 2018:241802; doi: 10.1101/241802.\u003c/li\u003e\n\u003cli\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550; doi: 10.1186/s13059-014-0550-8.\u003c/li\u003e\n\u003cli\u003eKatoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30(4):772-80; doi: 10.1093/molbev/mst010.\u003c/li\u003e\n\u003cli\u003eYu GC, Smith DK, Zhu HC, Guan Y, Lam TTY. GGTREE: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol. 2017;8(1):28-36; doi: 10.1111/2041-210x.12628.\u003c/li\u003e\n\u003cli\u003eHyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:1-11; doi: 10.1186/1471-2105-11-119.\u003c/li\u003e\n\u003cli\u003eWu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2(3):100141; doi: 10.1016/j.xinn.2021.100141.\u003c/li\u003e\n\u003cli\u003eHadley W. ggplot2: elegant graphics for data analysis. Springer-Verlag New York. 2016.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Environmental exposures, Infant, Gut microbiota, Virus, Phagenome, Phage, Bacterial host, Metabolism","lastPublishedDoi":"10.21203/rs.3.rs-4205731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4205731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe factors influencing the establishment of the gut bacterial community in early life are fairly well studied. However, the factors shaping the infant gut virome remain elusive. Most gut viruses are bacteriophages (phages), i.e., viruses attacking bacteria in a host specific manner, and to a lesser extent, but also widely present, eukaryotic viruses, including viruses attacking human cells. Interestingly, early life gut virome imbalances have recently been linked with increased risk of developing diseases like type 1 diabetes and asthma. We utilized the deeply phenotyped COPSAC2010 cohort to investigate how environmental factors influence the gut virome at one year age.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe demonstrate that presence of older siblings as well as residental location (urban or rural) had the strongest impact on gut virome composition at one year of age. A total of 16,118 species-level clustered viral representative contigs (here termed viral Operational Taxonomic Units \u0026ndash; vOTUs) were identified and of these 2105 vOTUs varied in abundance with environmental exposure. Of these vOTUs 94.1% were phages mainly predicted to infect \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003ePrevotellaceae\u003c/em\u003e, and \u003cem\u003eRuminococcaceae\u003c/em\u003e. Strong co-abundance of phages and their bacterial hosts was confirmed underlining the predicted phage-host connections. Furthermore, we found some gut viruses affected by environmental factors encode enzymes involved in the utilization and degradation of major dietary components, potentially affecting infant health by influencing the bacterial host metabolic capacity. Genes encoding enzymes significantly associated with early life exposures were found in a total of 42 vOTUs. Eigtheen of these vOTUs had their life styles predicted, with 17 of them having a temperate lifestyle.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eGiven the importance of the gut microbiome in early life for maturation of the immune system and maintenance of metabolic health, these findings provide avaluable insights for understanding early life factors that predispose to autoimmune and metabolic disorders.\u003c/p\u003e","manuscriptTitle":"The influence of early life exposures on the infant gut virome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 17:38:32","doi":"10.21203/rs.3.rs-4205731/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23377aec-edac-436b-a8ba-08f5c3d298c3","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-29T07:37:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 17:38:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4205731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4205731","identity":"rs-4205731","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}