The role of breast-feeding in infant immune system: a systems perspective on the intestinal microbiome
© Praveen et al. 2015
Received: 23 March 2015
Accepted: 26 August 2015
Published: 24 September 2015
The human intestinal microbiota changes from being sparsely populated and variable to possessing a mature, adult-like stable microbiome during the first 2 years of life. This assembly process of the microbiota can lead to either negative or positive effects on health, depending on the colonization sequence and diet. An integrative study on the diet, the microbiota, and genomic activity at the transcriptomic level may give an insight into the role of diet in shaping the human/microbiome relationship. This study aims at better understanding the effects of microbial community and feeding mode (breast-fed and formula-fed) on the immune system, by comparing intestinal metagenomic and transcriptomic data from breast-fed and formula-fed babies.
We re-analyzed a published metagenomics and host gene expression dataset from a systems biology perspective. Our results show that breast-fed samples co-express genes associated with immunological, metabolic, and biosynthetic activities. The diversity of the microbiota is higher in formula-fed than breast-fed infants, potentially reflecting the weaker dependence of infants on maternal microbiome. We mapped the microbial composition and the expression patterns for host systems and studied their relationship from a systems biology perspective, focusing on the differences.
Our findings revealed that there is co-expression of more genes in breast-fed samples but lower microbial diversity compared to formula-fed. Applying network-based systems biology approach via enrichment of microbial species with host genes revealed the novel key relationships of the microbiota with immune and metabolic activity. This was supported statistically by data and literature.
The intestinal microbiota and its human host develop a strong mutual relationship . Several vital functions (e.g., metabolism and innate and adaptive immunity) are affected by the intestinal microbiota. The microbiota of older individuals displays greater inter-individual variation than that of younger adults [2, 3]. The infant intestinal microbiota is more variable in its composition and is less stable over time. In the first 2 years of life, the infant intestinal tract progresses from sterility (pre-natal colonization might influence the sterility ) to extremely dense colonization, ending with a mixture of microbes that is broadly very similar to that found in the adult intestine [4–6]. The Human Microbiome Project  has investigated the stability and individual-level variability of human microbiota depending on geography, environment, lifestyle, and other factors . However, the assembly of the microbial community during infancy remains poorly understood despite being essential to human health [8, 9]. Despite the fact that bacterial cells outnumber the total number of human cells in the body, the human gut contains a surprisingly limited number of dominant enterotypes . Starting primarily with facultative anaerobes, e.g., Escherichia coli, the microbial community is later diversified with anaerobes, e.g., Bifidobacterium and Clostridium . The factors that affect the colonization process after birth include feeding, proboitic treatment, and environmental factors. An atypical intestinal colonization during the first weeks of life may alter the nutritional and immunological functions of the host microbiota  and increase susceptibility to immunological and metabolic diseases .
Despite the fact that bacterial cells outnumber the total number of human cells in the body, the human gut contains a surprisingly limited number of dominant enterotypes . Starting primarily with facultative anaerobes, e.g., E. coli, the microbial community is later diversified with anaerobes, e.g., Bifidobacterium and Clostridium . The factors that affect the colonization process after birth include feeding, proboitic treatment, and environmental factors. An atypical intestinal colonization during the first weeks of life may alter the nutritional and immunological functions of the host microbiota  and increase susceptibility to immunological and metabolic diseases .
A systems biology approach to study the relationships between the host and the microbiota requires the measurement of biomolecular activity within the host (e.g., via transcriptomic data) and the quantification of host microbiota under similar conditions . The acquisition and diversity of the gut microbiota in term neonates have been the subject of several studies. Jiménez et al.  studied the microbiota at pre-birth stages in the umbilical cord, showing the possibility of a pre-natal colonization of infant gut. Then, Neu et al.  investigated the effect of mode of birth (caesarean and non-caesarean) showing the effects on the microbiota. The effect of diet on the gut microbiota in piglets and humans was demonstrated by Poroyko et al.  and Marques et al. , showing the differences between breast-fed and formula-fed piglets. Works by Eckburg et al. , Azad et al. [20, 21], and Thompson et al.  revealed the differences in the diversity of the microbiota in breast-fed and formula-fed infants. Schwartz et al.  made one of the first attempts to show the genome and transcriptome cross talk in breast-fed and formula-fed infants showing the effect of feeding mode on microbiome and transcriptome. Rogier et al.  demonstrated the role of breast-feeding on the microbiota and host gene expression at individual levels. However, a detailed cross-species study at the systems level is still missing. Molecular and metagenomic techniques together with rigorous computational analysis from systems biology perspective can approach this goal . A systems perspective can add novel information on key relationships among the interacting individual components of this rich ecosystem, apart from their pure composition and abundance distribution . Moreover, differences between the two feeding types can be quantified and visualized.
Our study aims to observe cross talk at microbial and transcript level, in order to understand the effect of feeding mode on the host system, on the microbiome, and on the interaction between the two. We present a detailed analysis of the data studied in , from systems biology perspective, to elucidate the relationship between the feeding mode, the microbiota, and host cell activities. The data come from samples under two feeding conditions: exclusively breast-feeding (BF) and exclusively formula-feeding (FF). The formula food was Enfamil® LIPIL®: a commercially available formula.
Our analysis is an extension of the work of Schwartz et al.  with the systems biology point of view in terms of relation between the feeding mode, the microbiota, and host cell activities. We identify relations between the microbial species and host genes using the enrichment analysis of microbiome with bibliographic data, followed by the verification of the relations via bivariate analysis on experimental data. Finally, we present a system level overview of a host gene-microbiota relationship at network level in order to distinguish the infant systems based on the modes of feeding.
Our results indicated the effect of feeding mode both on the microbiota and the expression patterns of certain genes.
The FF microbial community network is ~1.5 times denser than the corresponding BF network. This can be seen as a direct implication of the higher diversity observed in FF infant microbiota and leads to an intricate mutual dependence of microbes on one another. Furthermore, among the three Bifidobacterium species detected in our analysis, B. breve and B. bifidum were detected mostly in BF infants, whereas B. longum and R. gnavus were detected in FF samples (Fig. 2b). This also shows that the network at the phylum may look alike but at lower level (here, species level), the two systems (BF and FF) can be differentiated. The core of the BF network consisting of the largest connected component (11 nodes) was retained in the FF network but extended with several other co-occurrence relationships observed (Fig. 3).
Relation between microbes and human systems
A functional enrichment analysis with GO terms (Biological Process) of the genes related to differentially abundant species indicated their role in the immune system (Fig. 5b; details in Additional file 2). Similar enrichment analysis for the genes related to all the species was found to be rich with immune system, metabolic- and transport-related terms (Fig. 5b). A pathway enrichment analysis based on Reactome  gave a similar result with several metabolic pathways, e.g., carbohydrate digestion, interleukin processing, and antigen processing.
Differences at transcriptomic level in host
The analysis of the gene expression data obtained from the epithelial cells in fecal samples of the infants (see “Methods”) distinguishes the two feeding types at the transcription level (see Additional file 1). The functional enrichment analysis of the 477 differentially expressed genes revealed a significant abundance of immune system-related activities (Fig. 5a). We extended our analysis to obtain two co-expression networks of genes under each feeding condition. As a higher number of array probes provided overexpression signals in the transcriptomic data for BF infants, the BF gene co-expression networks (see “Methods” section) were ~2.1 denser than the FF network at the level of gene co-expression unlike our finding at microbial community level (see “The microbiota” section).
The feeding mode microbiome host system
The main focus of this study was to complement the work of Schwartz et al. with a systems level analysis for understanding the colonization of infant gut as a function of feeding mode and its interaction between the microbiota and the human system. The feeding mode seemed to directly affect the microbiota as well as the host system. We complemented the approach of Schwartz et al. in three directions: (1) mining the relationships between the microbiota and the host genes from literature and their validation with the gene expression and microbial abundance data; (2) a differential abundance analysis of the microbiota at species level; and (3) combining these findings at a systems biology level depicting the overall relations between the feeding mode, the microbiota, and host biomolecular activities.
Feeding mode has been shown to affect the microbiota composition as well as gene expression in infants [23, 14, 21]. The presence of Bacteroides in BF samples was unusual, but it can be explained, as recent findings have shown that Bacteroides colonize infant gut with time [32, 33]. The samples we used were from 12-week-old infants; this likely explains the presence of Bacteroides in BF samples. Furthermore, it has also been shown that certain Bacteroides species can use the sugar in human milk via mucosal layers . It has also been shown that the formula-fed infants had increased richness of species compared to the breast-fed infants [21, 35]. Our findings confirmed that formula-feeding increases the microbial diversity. This finding was in line with existing knowledge [19–22]. Nevertheless, the core of the co-occurrence microbial network was retained. In terms of microbial enrichment, we showed that B. longum was abundant in the FF infants. B. longum is one of the widely used probiotic in formula food . The genes associated with differentially abundant microbes were highly involved in immunological activities depicting the role of these microbes in the functioning of the human system.
The picture was different at the level of human genes. The gene network among the BF sample was found to have a higher number of interactions (~2.1 times the FF network). Thus, the phenotypic outcomes observed in the host system are affected by feeding conditions. The BF samples do not exhibit a great diversity in the microbiota network . The higher transcriptomic activities as inferred from a denser co-expression networks in BF infants indicates the presence of bioactive compounds in the breast milk . These compounds can activate numerous pathways compared to formula . The presence of genes related to metabolism in our co-expression network extends the effect of feeding mode to these activities together with the immune system [39, 40]. The difference at metabolomic level has been shown before by Martin et al. .
The conservation of the core of microbial co-occurrence networks and the differentially abundant microbes being associated with genes that already have higher degree, leads to the question of the true functional importance of diversity in the microbiota. However, a core microbiota is undoubtedly required for optimal health  and it has also been observed in our study, where the core of the community network was retained under both BF and FF conditions. At the level of network biology, the gain in microbial diversity was not found to add many new relationships/effects to the host system (genes) but rather presented alternative paths to the existing ones in the network for BF infants. However, the role of bioactive components of breast milk played a prominent role in activity at the host (human) level. As observed in the overall microbe-host gene system (Fig. 6b), the sharp increase in reachability of differentially expressed genes in BF infants is leveraged by the bioactive compounds in breast milk. The difference in the reachability of transient human genes (linked to microbial species) remained more consistent since new microbes had indifferent relationships to human genes. Nevertheless, they might provide alternate ways to affect the microbiota-host relationships.
Our findings based on small but unbiased samples leveraged by robust statistical checks suggested that the development of human immune system during infancy should not be seen as an effect of the diet or the microbiota at individual level, rather these factors should be studied together in a system level approach. Our results indicated how feeding affected the expression pattern of genes as well as the microbial community and how these two factors together impact the host system (infants).
We used a network model with multiple components (microbial species and human genes) to study the effects of feeding conditions in infants on the microbiota and on the overall human system and its role in host immune system. The components included the microbial system and the host genes together with the interplay between these components. The analysis on the microbiota revealed a feeding mode-dependent difference in terms of diversity of the microbiota as well as of the network describing the interactions among them. The comparison of the results based on literature and data showed the validity of our findings. The network among the differentially expressed genes, computed via co-expression, was found to be much denser in the case of BF infants than FF infants, depicting higher biomolecular cross talk depending on the feeding mode. Many of the edges in the gene network for BF and FF samples could be mapped onto immune, signaling, or metabolic pathways. Mapping the relations between microbes and human genes was another major advancement achieved during the study. We complemented and extended the work of Schwartz et al. by detecting the differences in the microbiota based on feeding mode. The study of the microbiota and the host system together helped us to study a meta-system consisting of the host with the fellow microbes residing within the host rather than individual organism systems. Our results revealed that the integration of -omics data from the host and the microbiota with existing knowledge (in this case, literature) could yield useful insights into the system or rather the meta-system.
We used (1) metagenomic and (2) transcriptomic data [14, 43] from a previous study by Schwartz et al. The metagenomic data was obtained from EBI Short Read Archive (SRA) accession number ERP001038. It consisted of microbial DNA profiles in full term 3-month-old infants obtained from the fecal samples via metagenomic pyrosequencing. The transcriptomic data in the form of gene expression profiles came from NCBI Gene Expression Omnibus (GEO) accession GSE31075. The mRNA measurements were performed on intact sloughed epithelial cells from fecal samples. Both types of data were taken from the same set of infants categorized into the two exclusive feeding conditions (BF and FF).
Our approach aimed to use the metagenomic and transcriptomic data integratively in order to infer the relation between the microbiota, the host (human) system, and the feeding mode.
Expression data analysis
The limma package  from Bioconductor was used to identify genes that were differentially expressed between feeding types. Limma uses an empirical Bayes method to test the differential expression of genes . A p value cutoff of 0.05 (after multiple testing correction based on Benjamini-Hochberg method ) and a log fold change ≥2 were used to select the differentially expressed genes, resulting in 477 genes that were differentially expressed under the two feeding conditions.
Metagenomic data analysis
We analyzed the metagenome data in the form of sequence reads in order to obtain the enrichment of microbes in the samples. We used Bowtie 2  to align the reads to the microbial genome database. Then, we performed RAST analysis  to produce the microbial and functional annotation that identified the biomolecular functions of the sequences detected in the metagenomic data. We also computed the species abundance in the metagenomic sample using MetaPhlAn . The data were then filtered to the species level to obtain the abundances of species in the metagenomic samples. A limma analysis  was used to detect the differential abundance of the species in the samples (for details, see ).
Inferring microbial community network
Inferring microbial community network is a major challenge due to ambiguities about the operational taxonomic unit (OTU) definition and corresponding variation in quantification data based on OTUs. However, we define “microbial species” as the OTU in our investigation for simplicity and in order to follow the standards in the literature. The abundance table (see Additional file 4) computed for the metagenome from the samples showed many entries equal to zero. We adopted the Bray-Curtis similarity to obtain the pairwise distances between the microbial species based on their corresponding abundances across the samples. In this way, we infer the relationships between pairs of organisms, based on their co-occurrence patterns. We used Bray-Curtis similarity because (1) it is insensitive to sparse count data (i.e., the occurrence of zeroes in the count data because of the absence or below detection level abundance of a microbe ) and (2) correlation coefficients cannot serve the purpose due to the small sample size (six BF and six FF). Similarities with mean scores less than least 2 standard deviations above 0 were filtered out to disallow large variance in the similarity matrix, thereby removing spurious edges in the network (i.e., a kind of sampling noise). This matrix was used to infer the relations among the microbial species.
Extracting microbe-gene relations from text
The knowledge of potential bipartite relations between the microbes and the human genes/proteins is important to establish effects of feeding modes and microbes on the human mechanisms at system level. To extract these relations, we performed text mining on MEDLINE abstracts [50–52]. We identified the abstracts containing the co-occurrence of human genes in context with the microbial species under investigation. This list went through a filter to include only the text that had relationship terms like “activates,” “interacts with,” “depends on,” etc. Thus, a set of genes/proteins for each microbial species was created. This can be interpreted as microbial species enriched in relationships with certain human genes. To identify statistically significant genes/protein for each microbial species, we did a Fisher’s exact test on this enrichment. The relationships with p values less than 0.05 were accepted for further analysis (see Additional file 5). The approach used the statistical test score to establish relations between a microbe and genes retrieving only significant and specific relations from the text and minimizing noise.
Computing the gene-gene co-expression network
Based on the set of differentially expressed genes extracted from the expression data, together with those mined from literature, the gene-gene relationships were computed. A co-expression gene network based on the Pearson correlation coefficient was created based on these data for two different conditions (breast-fed and formula-fed samples). The cutoff for the correlation was set to 0.8. For simplicity, unweighted networks were constructed. The networks were joined with the microbial community networks and the results from text mining procedure to get an overall picture.
We did a hyper-geometric test on the genes in the data from different sources (expression, literature) to understand the biological roles of the human genes involved by means of a functional enrichment of these gene sets. A detailed analysis of this functional enrichment was done at individual and combinatorial level. The differentially expressed genes and text mining genes underwent functional enrichment with GO Biological Process terms  and Reactome pathways .
The authors thank the other team members at the Microsoft Research-University of Trento Centre for Computational and Systems Biology for the many interesting discussions on the topic. Funding for the work was provided by the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto (Italy).
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- Sommer F, Bäckhed F. The gut microbiota? Masters of host development and physiology. Nat Rev Microbiol. 2013;4:227–38. doi:10.1038/nrmicro2974.View ArticleGoogle Scholar
- Claesson MJ, Cusack S, O’Sullivan O, Greene-Diniz R, de Weerd H, Flannery E, et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc Natl Acad Sci. 2011;108(Suppl 1):4586–91. doi:10.1073/pnas.1000097107. http://www.pnas.org/content/108/Supplement_1/4586.long.
- Claesson MJ, Jeffery IB, Conde S, Power SE, O’Connor EM, Cusack S, et al. Gut microbiota composition correlates with diet and health in the elderly. Nature. 2012;488(7410):178–84.View ArticlePubMedGoogle Scholar
- Matamoros S, Gras-Leguen C, Vacon FL, Potel G, de La Cochetiere M-F. Development of intestinal microbiota in infants and its impact on health. Trends Microbiol. 2013;21(4):167–73. doi:10.1016/j.tim.2012.12.001.View ArticlePubMedGoogle Scholar
- Koenig JE, Spor A, Scalfone N, Fricker AD, Stombaugh J, Knight R, et al. Succession of microbial consortia in the developing infant gut microbiome. Proc Natl Acad Sci U S A. 2011;108(Suppl):4578–85.PubMed CentralView ArticlePubMedGoogle Scholar
- Vaishampayan PA, Kuehl JV, Froula JL, Morgan JL, Ochman H, Francino MP. Comparative metagenomics and population dynamics of the gut microbiota in mother and infant. Genome Biol Evol. 2010;2:53–66.PubMed CentralView ArticlePubMedGoogle Scholar
- Turnbaugh PJ, Ruth E, Ley MH, Fraser-Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature. 2007;449(7164):804–10. doi:10.1038/nature06244.PubMed CentralView ArticlePubMedGoogle Scholar
- Tremaroli V, Bäckhed F. Functional interactions between the gut microbiota and host metabolism. Nature. 2012;489(7415):242–9.View ArticlePubMedGoogle Scholar
- Hemarajata P, Versalovic J. Effects of probiotics on gut microbiota : mechanisms of intestinal immunomodulation and neuromodulation. Therap Adv Gastroenterol. 2012. doi:10.1177/1756283X12459294.Google Scholar
- Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, et al. Enterotypes of the human gut microbiome. Nature. 2011;473(7346):174–80. doi:10.1038/nature09944.PubMed CentralView ArticlePubMedGoogle Scholar
- Jiménez E, María L, Marín RM, Odriozola JM, Olivaresc M, Xaus J, et al. Is meconium from healthy newborns actually sterile? Res Microbiol. 2008;159(3):187–93.View ArticlePubMedGoogle Scholar
- Hooper LV, Gordon JI. Commensal host-bacterial relationships in the gut. Science. 2001;292(5519):1115–18. doi:10.1126/science.1058709. http://www.sciencemag.org/content/292/5519/1115.long.
- Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500(7464):541–6.Google Scholar
- Schwartz S, Friedberg I, Ivanov IV, Davidson LA, Goldsby JS, Dahl DB, et al. A metagenomic study of diet-dependent interaction between gut microbiota and host in infants reveals differences in immune response. Genome Biol. 2012;13(4):32.Google Scholar
- Jiménez E, Fernández L, Marín M, Martín R, Odriozola JM, Nueno-Palop C, et al. Isolation of commensal bacteria from umbilical cord blood of healthy neonates born by cesarean section. Curr Microbiol. 2005;51(4):270–4. doi:10.1007/s00284-005-0020-3.
- Neu J, Rushing J. Cesarean versus vaginal delivery: long-term infant outcomes and the hygiene hypothesis. Clin Perinatol. 2011;38(2):321–31.PubMed CentralView ArticlePubMedGoogle Scholar
- Poroyko V, White JR, Wang M, Donovan S, Alverdy J, Liu DC, et al. Gut microbial gene expression in mother-fed and formula-fed piglets. PLoS ONE. 2010;5(8):12459. doi:10.1371/journal.pone.0012459.View ArticleGoogle Scholar
- Marques TM, Wall R, Ross RP, Fitzgerald GF, Ryan CA, Stanton C. Programming infant gut microbiota: influence of dietary and environmental factors. Curr Opin Biotechnol. 2010;21(2):149–56. doi:10.1016/j.copbio.2010.03.020. Food biotechnology – Plant biotechnology.View ArticlePubMedGoogle Scholar
- Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, et al. Diversity of the human intestinal microbial flora. Science. 2005;308(5728):1635–8. doi:10.1126/science.1110591. http://www.sciencemag.org/content/308/5728/1635.long.
- Azad M, Konya T, Guttman D, Field C, Sears M, Becker A, Scott J, Kozyrskyj A. Breastfeeding, infant gut microbiota, and early childhood overweight (637.4). FASEB J. 2014; 28(1 Supplement)Google Scholar
- Azad MB, Konya T, Maughan H, Guttman DS, Field CJ, Chari RS, et al. Gut microbiota of healthy canadian infants: profiles by mode of delivery and infant diet at 4 months. Can Med Assoc J. 2013;185(5):385–94. doi:10.1503/cmaj.121189. http://www.cmaj.ca/content/185/5/385.long.
- Thompson AL, Monteagudo-Mera A, Cadenas MB, Lampl ML, Azcarate-Peril MA. Milk- and solid-feeding practices and daycare attendance are associated with differences in bacterial diversity, predominant communities, and metabolic and immune function of the infant gut microbiome. Front Cell Infect Microbiol. 2015. doi:10.3389/fcimb.2015.00003.Google Scholar
- Rogier EW, Frantz AL, Bruno MEC, Wedlund L, Cohen D, Stromberg AJ, et al. Secretory antibodies in breast milk promote long-term intestinal homeostasis by regulating the gut microbiota and host gene expression. Proc Natl Acad Sci U S A. 2014;111(8):3074–9. doi:10.1073/pnas.1315792111.
- Musso G, Gambino R, Cassader M. Interactions between gut microbiota and host metabolism predisposing to obesity and diabetes. Annu Rev Med. 2011;62:361–80. doi:10.1146/annurev-med-012510-175505.View ArticlePubMedGoogle Scholar
- Waldor MK, Tyson G, Borenstein E, Ochman H, Moeller A, Finlay BB, et al. Where next for microbiome research? PLoS Biol. 2015;13(1):1002050. doi:10.1371/journal.pbio.1002050.
- Ley RE, Bäckhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A. 2005;102(31):11070–5. doi:10.1073/pnas.0504978102.PubMed CentralView ArticlePubMedGoogle Scholar
- Shannon CE. A mathematical theory of communication. SIGMOBILE Mob Comput Commun Rev. 2001;5(1):3–55. doi:10.1145/584091.584093.View ArticleGoogle Scholar
- Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods. 2012;9(8):811–4. doi:10.1038/nmeth.2066.PubMed CentralView ArticlePubMedGoogle Scholar
- Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9. doi:10.1038/nmeth.1923. #14603.PubMed CentralView ArticlePubMedGoogle Scholar
- Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39(Database-Issue):691–7.Google Scholar
- Watts DJ, Strogatz SH. Collective dynamics of “small-world” networks. Nature. 1998;393:440–2.View ArticlePubMedGoogle Scholar
- Sánchez E, De Palma G, Capilla A, Nova E, Pozo T, Castillejo G, et al. Influence of environmental and genetic factors linked to celiac disease risk on infant gut colonization by bacteroides species. Appl Environ Microbiol. 2011;77(15):5316–23. doi:10.1128/AEM.00365-11. http://aem.asm.org/content/77/15/5316.long.
- Guaraldi F, Salvatori G. Effect of breast and formula feeding on gut microbiota shaping in newborns. Front Cell Infect Microbiol. 2012. doi:10.3389/fcimb.2012.00094.PubMed CentralPubMedGoogle Scholar
- Marcobal A, Barboza M, Sonnenburg ED, Pudlo N, Martens EC, Desai P, et al. Bacteroides in the infant gut consume milk oligosaccharides via mucus-utilization pathways. Cell Host and Microbe. 2011;10(5):507–14. doi:10.1016/j.chom.2011.10.007.
- Penders J, Thijs C, Vink C, Stelma FF, Snijders B, Kummeling I, et al. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics. 2006;118(2):511–21. doi:10.1542/peds.2005-2824. http://pediatrics.aappublications.org/content/118/2/511.
- Brenner DM, Moeller MJ, Chey WD, Schoenfeld PS. The utility of probiotics in the treatment of irritable bowel syndrome: a systematic review. Am J Gastroenterol. 2009;104(4):1033–49.View ArticlePubMedGoogle Scholar
- Donovan SM. Role of human milk components in gastrointestinal development: current knowledge and future needs. J Pediatr. 2006;149(5):49–61.View ArticleGoogle Scholar
- Ballard O, Morrow AL. Human milk composition: nutrients and bioactive factors. Pediatr Clin North Am. 2013;60(1):49–74. doi:10.1016/j.pcl.2012.10.002. Breastfeeding Updates for the Pediatrician.PubMed CentralView ArticlePubMedGoogle Scholar
- M’Rabet L, Vos AP, Boehm G, Garssen J. Breast-feeding and its role in early development of the immune system in infants: consequences for health later in life. J Nutr. 2008;138(9):1782–90. http://jn.nutrition.org/content/138/9/1782S.full.Google Scholar
- Maynard CL, Elson CO, Hatton RD, Weaver CT. Reciprocal interactions of the intestinal microbiota and immune system. Nature. 2012;489(7415):231–41. doi:10.1038/nature11551.PubMed CentralView ArticlePubMedGoogle Scholar
- Martin F-PJ, Moco S, Montoliu I, Collino S, Da Silva L, Rezzi S, et al. Impact of breast-feeding and high- and low-protein formula on the metabolism and growth of infants from overweight and obese mothers. Pediatr Res. 2014;75(4):535–43.Google Scholar
- Lozupone C, Stombaugh J, Gordon J, Jansson J, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature. 2012;489(7415):220. doi:10.1038/nature11550.PubMed CentralView ArticlePubMedGoogle Scholar
- Chapkin RS, Zhao C, Ivanov I, Davidson LA, Goldsby JS, Lupton JR, et al. Noninvasive stool-based detection of infant gastrointestinal development using gene expression profiles from exfoliated epithelial cells. Am J Physiol Gastrointest Liver Physiol. 2010;298(5):582–9. doi:10.1152/ajpgi.00004.2010.
- Smyth GK. Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W, editors. Bioinformatics and computational biology solutions using R and bioconductor. New York: Springer; 2005. p. 397–420.View ArticleGoogle Scholar
- Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3(1):3.Google Scholar
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300. doi:10.2307/2346101.Google Scholar
- Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The seed and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res. 2013. doi:10.1093/nar/gkt1226. http://nar.oxfordjournals.org/content/early/2013/11/29/nar.gkt1226.full
- Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Meth. 2013;10(12):1200.View ArticleGoogle Scholar
- Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol. 2012;8(7):1002606. doi:10.1371/journal.pcbi.1002606.
- Zhu F, Patumcharoenpol P, Zhang C, Yang Y, Chan J, Meechai A, et al. Biomedical text mining and its applications in cancer research. J Biomed Inform. 2013;46(2):200–11. doi:10.1016/j.jbi.2012.10.007.
- Ongenaert M, Dehaspe L. Integrating automated literature searches and text mining in biomarker discovery. BMC Bioinformatics. 2010;11 Suppl 5:5. doi:10.1186/1471-2105-11-S5-O5.View ArticleGoogle Scholar
- Younesi E, Toldo L, Muller B, Friedrich C, Novac N, Scheer A, et al. Mining biomarker information in biomedical literature. BMC Med Inform Decis Mak. 2012;12(1):148. doi:10.1186/1472-6947-12-148.
- Ashburner M. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9.PubMed CentralView ArticlePubMedGoogle Scholar