A metagenomic study of diet-dependent interaction between gut microbiota and host in infants reveals differences in immune response

Scott Schwartz, Iddo Friedberg, Ivan V Ivanov, Laurie A Davidson, Jennifer S Goldsby, David B Dahl, Damir Herman, Mei Wang, Sharon M Donovan, Robert S Chapkin, Scott Schwartz, Iddo Friedberg, Ivan V Ivanov, Laurie A Davidson, Jennifer S Goldsby, David B Dahl, Damir Herman, Mei Wang, Sharon M Donovan, Robert S Chapkin

Abstract

Background: Gut microbiota and the host exist in a mutualistic relationship, with the functional composition of the microbiota strongly affecting the health and well-being of the host. Thus, it is important to develop a synthetic approach to study the host transcriptome and the microbiome simultaneously. Early microbial colonization in infants is critically important for directing neonatal intestinal and immune development, and is especially attractive for studying the development of human-commensal interactions. Here we report the results from a simultaneous study of the gut microbiome and host epithelial transcriptome of three-month-old exclusively breast- and formula-fed infants.

Results: Variation in both host mRNA expression and the microbiome phylogenetic and functional profiles was observed between breast- and formula-fed infants. To examine the interdependent relationship between host epithelial cell gene expression and bacterial metagenomic-based profiles, the host transcriptome and functionally profiled microbiome data were subjected to novel multivariate statistical analyses. Gut microbiota metagenome virulence characteristics concurrently varied with immunity-related gene expression in epithelial cells between the formula-fed and the breast-fed infants.

Conclusions: Our data provide insight into the integrated responses of the host transcriptome and microbiome to dietary substrates in the early neonatal period. We demonstrate that differences in diet can affect, via gut colonization, host expression of genes associated with the innate immune system. Furthermore, the methodology presented in this study can be adapted to assess other host-commensal and host-pathogen interactions using genomic and transcriptomic data, providing a synthetic genomics-based picture of host-commensal relationships.

Figures

Figure 1
Figure 1
Effect of diet on host transcriptional responses. Genes known a priori to be involved in intestinal biology or immunity and defense mechanisms were enriched for differential expression between BF and FF infants. (a-d) The distribution of P-values (a,b) and the distribution of q-values (c,d). (a,c) Intestinal biology: 459 genes known to be related to intestinal biology passed the quality control measures and were tested for differential expression between the BF and FF infants - 146/459 genes (32%) had FDR corrected q-values <0.2. (b,d) Immunity and defense: 660 genes known to be related to immunity and defense that passed the quality control measures and tested for differential expression between the BF and FF infants - 191/660 genes (29%) had FDR corrected q-values <0.2.
Figure 2
Figure 2
Effect of diet on infant microbiota. BF (breast-fed) infants (green) exhibited more heterogeneity than FF (formula-fed) infants (blue) with respect to phylogenetic composition. (a) Taxon assignment (phylum level) variability for BF and FF samples using 16S rRNA alignments to GreenGenes (see Materials and methods). A diet label permutation test using the statistic ∑s |∑iεBF pis/6 - ∑iεFF pis/6|, where s indexes phylum and iεBF and iεFF denote that sample i is BF or FF infant, respectively, and p denotes the associated taxon proportion, rejected the null hypothesis that variability in phylogenetic composition was unrelated to BF/FF status with a P-value of 0.011. (b) Taxon assignments for all the shotgun reads (not just 16S rRNA homologs) using PhymmBL [17]. (c) Shannon-Weiner index for BF and FF infants, indicating alpha-diversity for each sample.
Figure 3
Figure 3
Functional analysis of metagenomic data. Top panel: SEED level 1 categories for which all BF or all FF samples had at least 200 reads mapped. At least 2% of the total number of mapped reads were tested for differences between BF (breast-fed) infants (green) and FF (formula-fed) infants (blue). A permutation test on the test statistic ∑iεBF pi/6 - ∑iεFF pi/6, where iεBF and iεFF denote that sample i is BF or FF infant, respectively, and p denotes the associated taxon proportion, was performed. The FDR corrected q-value for the virulence category was 0.058. Bottom panel: differences between BF and FF infants in the SEED level 2 virulence assignment (within the SEED level 1 virulence category) was assessed using a permutation test on the test statistic ∑s |∑iεBF pis/6 - ∑iεFF pis/6|, where s indexes the SEED level 2 virulence categories, and P = 0.0140.
Figure 4
Figure 4
First and second canonical correlations between host gene sets and microbial virulence characteristics. Horizontal lines in the density plots are at 0.5, and the vertical lines are at 0.85. These cutoffs were chosen arbitrarily to emphasize enrichment in the upper-right quadrant of the plot that is suggestive of increased multivariate structure as identified by CCA. (a) First and second canonical correlations between triples of immunity and defense genes and virulence variables are shown. There are increased canonical correlations in the upper-right corner of the plot, suggesting an enriched multivariate relationship between the immunity and defense genes and microbiome virulence characteristics as compared to, for example, the set of random genes shown in (d). (b) Intestinal biology genes did not show the same level of enrichment of canonical correlations as the immunity and defense genes. (c) We analyzed 1,000 random sets each containing 660 genes in an analogous manner to the immunity and defense gene analysis (a). Of these, 969 random sets resulted in less than 12% of analyzed gene triples having first canonical correlation >0.85 and second canonical correlation >0.5. (d) An example random gene CCA plot. Additional examples are given in Additional file 6.
Figure 5
Figure 5
Frequency of host genes appearing in triples. Sets of gene triples were included when the first canonical correlation was at least 0.85 and the second canonical correlation was at least 0.65. These levels were chosen arbitrarily to represent strong multivariate structure as identified by CCA. Genes were ranked by their prevalence of top performing triples. This provided a qualitative profile to select genes that empirically show the strongest potential for being related to the virulence characteristics of the microbiome. (a,b) Genes related to immunity and defense far outperformed the other functional categories. For example, the best two performing intestinal biology genes were in fact also co-listed as immunity and defense genes. (c) In contrast, randomly selected genes did not display any strong multivariate structure with respect to the virulence characteristics of the microbiome.
Figure 6
Figure 6
Relative performance of the top 11 immunity and defense host genes and virulence characteristics. Data were assessed by the ranking described in Figure 5 with respect to multivariate association between annotated genes and the virulence characteristics of the microbiome. The size of the nodes reflects the number of triples of genes whose first canonical correlation was at least 0.85 and whose second canonical correlation was at least 0.5. The thickness of the edges connecting the nodes reflects the number of triples whose first canonical correlation was at least 0.85 and whose second canonical correlation was at least 0.5. This plot summarizes the potential relationships between genes with respect to the virulence characteristics of the microbiome. ALOX5, arachidonate 5-lipoxygenase; AOC3, amine oxidase, copper containing 3 (vascular adhesion protein); BPIL1, bactericidal/permeability-increasing protein-like 1; DUOX2, dual oxidase 2; IL1A, interleukin 1 alpha; KLRF1, killer cell lectin-like receptor subfamily F, member 1; NDST1, N-deacetylase/N-sulfotransferase (heparan glucosaminyl) 1; REL, v-rel reticuloendotheliosis viral oncogene homolog; SP2, Sp2 transcription factor; TACR1, tachykinin receptor 1; VAV2, vav 2 guanine nucleotide exchange factor.

References

    1. Willing BP, Van Kessel AG. Enterocyte proliferation and apoptosis in the caudal small intestine is influenced by the composition of colonizing commensal bacteria in the neonatal gnotobiotic pig. J Anim Sci. 2007;85:3256–3266. doi: 10.2527/jas.2007-0320.
    1. Willing BP, Van Kessel AG. Intestinal microbiota differentially affect brush border enzyme activity and gene expression in the neonatal gnotobiotic pig. J Anim Physiol Anim Nutr (Berl) 2009;93:586–595. doi: 10.1111/j.1439-0396.2008.00841.x.
    1. Meurens F, Berri M, Siggers RH, Willing BP, Salmon H, Van Kessel AG, Gerdts V. Commensal bacteria and expression of two major intestinal chemokines, TECK/CCL25 and MEC/CCL28, and their receptors. PLoS One. 2007;2:e677. doi: 10.1371/journal.pone.0000677.
    1. Van den Abbeele P, Van de Wiele T, Verstraete W, Possemiers S. The host selects mucosal and luminal associations of coevolved gut microorganisms: a novel concept. FEMS Microbiol Rev. 2011;35:681–704. doi: 10.1111/j.1574-6976.2011.00270.x.
    1. Chowdhury SR, King DE, Willing BP, Band MR, Beever JE, Lane AB, Loor JJ, Marini JC, Rund LA, Schook LB, Van Kessel AG, Gaskins HR. Transcriptome profiling of the small intestinal epithelium in germfree versus conventional piglets. BMC Genomics. 2007;8:215. doi: 10.1186/1471-2164-8-215.
    1. Phelan VV, Liu WT, Pogliano K, Dorrestein PC. Microbial metabolic exchange-the chemotype-to-phenotype link. Nat Chem Biol. 2012;8:26–35.
    1. Dumas ME. The microbial-mammalian metabolic axis: beyond simple metabolism. Cell Metab. 2011;13:489–490. doi: 10.1016/j.cmet.2011.04.005.
    1. Poroyko V, White JR, Wang M, Donovan S, Alverdy J, Liu DC, Morowitz MJ. Gut microbial gene expression in mother-fed and formula-fed piglets. PLoS One. 2010;5:e12459. doi: 10.1371/journal.pone.0012459.
    1. Adlerberth I, Wold AE. Establishment of the gut microbiota in Western infants. Acta Paediatr. 2009;98:229–238. doi: 10.1111/j.1651-2227.2008.01060.x.
    1. 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;21:149–156. doi: 10.1016/j.copbio.2010.03.020.
    1. Chapkin RS, McMurray DN, Lupton JR. Colon cancer, fatty acids and anti-inflammatory compounds. Curr Opin Gastroenterol. 2007;23:48–54. doi: 10.1097/MOG.0b013e32801145d7.
    1. Chapkin RS, Zhao C, Ivanov I, Davidson LA, Goldsby JS, Lupton JR, Mathai RA, Monaco MH, Rai D, Russell WM, Donovan SM, Dougherty ER. Noninvasive stool-based detection of infant gastrointestinal development using gene expression profiles from exfoliated epithelial cells. Am J Physiol Gastrointest Liver Physiol. 2010;298:G582–589. doi: 10.1152/ajpgi.00004.2010.
    1. Zhao C, Ivanov I, Dougherty ER, Hartman TJ, Lanza E, Bobe G, Colburn NH, Lupton JR, Davidson LA, Chapkin RS. Noninvasive detection of candidate molecular biomarkers in subjects with a history of insulin resistance and colorectal adenomas. Cancer Prev Res (Phila) 2009;2:590–597. doi: 10.1158/1940-6207.CAPR-08-0233.
    1. Donovan SM, Monaco MH, Drnevich JM, Hernell O, Kvistgaard AS, Lonnerdal B. Transcriptional responses of the neonatal rhesus intestine to osteopontin. J Pediatr Gastroenterol Nutr. 2011;52:E62.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Met. 1995;57:289–300.
    1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–29. doi: 10.1038/75556.
    1. Brady A, Salzberg SL. Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models. Nat Methods. 2009;6:673–676. doi: 10.1038/nmeth.1358.
    1. Koenig JE, Spor A, Scalfone N, Fricker AD, Stombaugh J, Knight R, Angenent LT, Ley RE. Succession of microbial consortia in the developing infant gut microbiome. Proc Natl Acad Sci USA. 2011;108(Suppl 1):4578–4585.
    1. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, Formsma K, Gerdes S, Glass EM, Kubal M, Meyer F, Olsen GJ, Olson R, Osterman AL, Overbeek RA, McNeil LK, Paarmann D, Paczian T, Parrello B, Pusch GD, Reich C, Stevens R, Vassieva O, Vonstein V, Wilke A, Zagnitko O. The RAST Server: rapid annotations using subsystems technology. BMC Genomics. 2008;9:75. doi: 10.1186/1471-2164-9-75.
    1. Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, de Crecy-Lagard V, Diaz N, Disz T, Edwards R, Fonstein M, Frank ED, Gerdes S, Glass EM, Goesmann A, Hanson A, Iwata-Reuyl D, Jensen R, Jamshidi N, Krause L, Kubal M, Larsen N, Linke B, McHardy AC, Meyer F, Neuweger H, Olsen G, Olson R, Osterman A, Portnoy V. et al.The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 2005;33:5691–5702. doi: 10.1093/nar/gki866.
    1. Cooley WW, Lohnes PR. Multivariate Data Analysis. New York: Wiley; 1971.
    1. Dunteman GH. Introduction to Multivariate Analysis. Beverly Hills: Sage Publications; 1984.
    1. Krzanowski WJ. Principles of Multivariate Analysis: a User's Perspective. Revised. New York: Oxford University Press; 2000.
    1. de Santa Barbara P, van den Brink GR, Roberts DJ. Development and differentiation of the intestinal epithelium. Cell Mol Life Sci. 2003;60:1322–1332. doi: 10.1007/s00018-003-2289-3.
    1. Palmer AC. Nutritionally mediated programming of the developing immune system. Adv Nutr. 2011;2:377–395.
    1. Cummins AG, Thompson FM. Postnatal changes in mucosal immune response: a physiological perspective of breast feeding and weaning. Immunol Cell Biol. 1997;75:419–429. doi: 10.1038/icb.1997.67.
    1. Donovan SM. Role of human milk components in gastrointestinal development: Current knowledge and future needs. J Pediatr. 2006;149:S49–S61. doi: 10.1016/j.jpeds.2006.06.052.
    1. Donovan SM, Odle J. Growth factors in milk as mediators of infant development. Annu Rev Nutr. 1994;14:147–167. doi: 10.1146/annurev.nu.14.070194.001051.
    1. Mulder IE, Schmidt B, Stokes CR, Lewis M, Bailey M, Aminov RI, Prosser JI, Gill BP, Pluske JR, Mayer CD, Musk CC, Kelly D. Environmentally-acquired bacteria influence microbial diversity and natural innate immune responses at gut surfaces. BMC Biol. 2009;7:79. doi: 10.1186/1741-7007-7-79.
    1. Gianoulis TA, Raes J, Patel PV, Bjornson R, Korbel JO, Letunic I, Yamada T, Paccanaro A, Jensen LJ, Snyder M, Bork P, Gerstein MB. Quantifying environmental adaptation of metabolic pathways in metagenomics. Proc Natl Acad Sci USA. 2009;106:1374–1379. doi: 10.1073/pnas.0808022106.
    1. Sanger GJ. Neurokinin NK1 and NK3 receptors as targets for drugs to treat gastrointestinal motility disorders and pain. Br J Pharmacol. 2004;141:1303–1312. doi: 10.1038/sj.bjp.0705742.
    1. Geiszt M, Witta J, Baffi J, Lekstrom K, Leto TL. Dual oxidases represent novel hydrogen peroxide sources supporting mucosal surface host defense. FASEB J. 2003;17:1502–1504.
    1. Lipinski S, Till A, Sina C, Arlt A, Grasberger H, Schreiber S, Rosenstiel P. DUOX2-derived reactive oxygen species are effectors of NOD2-mediated antibacterial responses. J Cell Sci. 2009;122:3522–3530. doi: 10.1242/jcs.050690.
    1. Schmitter T, Pils S, Sakk V, Frank R, Fischer KD, Hauck CR. The granulocyte receptor carcinoembryonic antigen-related cell adhesion molecule 3 (CEACAM3) directly associates with Vav to promote phagocytosis of human pathogens. J Immunol. 2007;178:3797–3805.
    1. Steinbrecher KA, Harmel-Laws E, Sitcheran R, Baldwin AS. Loss of epithelial RelA results in deregulated intestinal proliferative/apoptotic homeostasis and susceptibility to inflammation. J Immunol. 2008;180:2588–2599.
    1. Swanson PA, Kumar A, Samarin S, Vijay-Kumar M, Kundu K, Murthy N, Hansen J, Nusrat A, Neish AS. Enteric commensal bacteria potentiate epithelial restitution via reactive oxygen species-mediated inactivation of focal adhesion kinase phosphatases. Proc Natl Acad Sci USA. 2011;108:8803–8808. doi: 10.1073/pnas.1010042108.
    1. Salmi M, Jalkanen S. VAP-1: an adhesin and an enzyme. Trends Immunol. 2001;22:211–216. doi: 10.1016/S1471-4906(01)01870-1.
    1. Kuttruff S, Koch S, Kelp A, Pawelec G, Rammensee HG, Steinle A. NKp80 defines and stimulates a reactive subset of CD8 T cells. Blood. 2009;113:358–369.
    1. Nagashima T, Ichimiya S, Kikuchi T, Saito Y, Matsumiya H, Ara S, Koshiba S, Zhang J, Hatate C, Tonooka A, Kubo T, Ye RC, Hirose B, Shirasaki H, Izumi T, Takami T, Himi T, Sato N. Arachidonate 5-lipoxygenase establishes adaptive humoral immunity by controlling primary B cells and their cognate T-cell help. Am J Pathol. 2011;178:222–232. doi: 10.1016/j.ajpath.2010.11.033.
    1. Yu Z, Morrison M. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques. 2004;36:808–812.
    1. Davidson LA, Lupton JR, Miskovsky E, Fields AP, Chapkin RS. Quantification of human intestinal gene expression profiles using exfoliated colonocytes: a pilot study. Biomarkers. 2003;8:51–61. doi: 10.1080/1354750021000042268.
    1. Smyth GK, Speed T. Normalization of cDNA microarray data. Methods. 2003;31:265–273. doi: 10.1016/S1046-2023(03)00155-5.
    1. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003;13:2129–2141. doi: 10.1101/gr.772403.
    1. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13. doi: 10.1093/nar/gkn923.
    1. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57.
    1. Sima C, Dougherty ER. What should be expected from feature selection in small-sample settings. Bioinformatics. 2006;22:2430–2436. doi: 10.1093/bioinformatics/btl407.
    1. Niu B, Fu L, Sun S, Li W. Artificial and natural duplicates in pyrosequencing reads of metagenomic data. BMC Bioinformatics. 2010;11:187. doi: 10.1186/1471-2105-11-187.
    1. Meyer F, Paarmann D, D'Souza M, Olson R, Glass EM, Kubal M, Paczian T, Rodriguez A, Stevens R, Wilke A, Wilkening J, Edwards RA. The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics. 2008;9:386. doi: 10.1186/1471-2105-9-386.
    1. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–5072. doi: 10.1128/AEM.03006-05.
    1. Genome Reference Constortium.
    1. Hotelling H. Relations between two sets of variates. Biometrika. 1936;28:321–377.
    1. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–210. doi: 10.1093/nar/30.1.207.

Source: PubMed

스폰서 및 공동 작업자

건강 상태

약물 개입

3
구독하다