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Microbiota and infant development

Malnutrition in children is a persistent challenge that is not always remedied by improvements in nutrition. This is because a characteristic community of gut microbes seems to mediate some of the pathology. Human gut microbes can be transplanted effectively into germ-free mice to recapitulate their associated phenotypes. Using this model, Blanton et al. found that the microbiota of healthy children relieved the harmful effects on growth caused by the microbiota of malnourished children. In infant mammals, chronic undernutrition results in growth hormone resistance and stunting. In mice, Schwarzer et al. showed that strains of Lactobacillus plantarum in the gut microbiota sustained growth hormone activity via signaling pathways in the liver, thus overcoming growth hormone resistance. Together these studies reveal that specific beneficial microbes could potentially be exploited to resolve undernutrition syndromes.
Science, this issue p. 10.1126/science.aad3311, p. 854

Structured Abstract

INTRODUCTION

As we come to appreciate how our microbial communities (microbiota) assemble following birth, there is an opportunity to determine how this facet of our developmental biology relates to the healthy or impaired growth of infants and children. Childhood undernutrition is a devastating global health problem whose long-term sequelae, including stunting, neurodevelopmental abnormalities, and immune dysfunction, remain largely refractory to current therapeutic interventions.

RATIONALE

To test the hypothesis that perturbations in the normal development of the gut microbiota are causally related to undernutrition, we first applied random forests (RF), a machine learning method, to bacterial 16S ribosomal RNA data sets generated from fecal samples that were collected serially from healthy Malawian infants and children during their first 3 postnatal years. Age-discriminatory bacterial taxa were identified with distinctive time-dependent changes in their relative abundances; they were used to construct a sparse RF-derived model describing a program of normal postnatal gut microbiota development that is shared across biologically unrelated individuals. A metric based on this model (microbiota-for-age Z-score) was used to define the state of development (maturation) of fecal microbiota from infants and children with varying degrees of undernutrition. Fecal samples obtained from 6- and 18-month-old children with healthy growth patterns or with varying degrees of undernutrition were transplanted into young germ-free mice that were fed a representative Malawian diet. The recipient animals’ rate of lean body mass gain was characterized by serial quantitative magnetic resonance, their metabolic phenotypes were determined by targeted mass spectrometry, and their femoral bone morphologic features were delineated by microcomputed tomography.

RESULTS

Undernourished children in the Malawian birth cohort that we studied have immature gut microbiota. Unlike microbiota from healthy children, immature microbiota transmit impaired growth, altered bone morphology, and metabolic abnormalities in the muscle, liver, and brain to recipient gnotobiotic mice. The representation of several age-discriminatory taxa in the transplanted microbiota harbored by recipient animals correlated with their growth rates. Microbiota from 6-month-old infants produced greater effects on growth than did microbiota from 18-month-old children, although in each age bin, the growth effects produced by a healthy donor’s community were greater than those produced by an undernourished donor’s community. Cohousing coprophagic mice shortly after they received microbiota from healthy or severely stunted and underweight 6-month-old infants resulted in the invasion of age- and growth-discriminatory taxa from the former into the latter microbiota in the recipient animals, with associated prevention of growth impairments. Introducing cultured members from this group of invasive species ameliorated growth and metabolic abnormalities in recipients of microbiota from undernourished donors.

CONCLUSION

These preclinical findings provide evidence that gut microbiota immaturity is causally related to childhood undernutrition. The age- and growth-discriminatory taxa that we identified should help direct studies of the effects of host and environmental factors on gut microbial community development, and they represent therapeutic targets for repairing or preventing gut microbiota immaturity.
Preclinical evidence that gut microbiota immaturity is causally related to childhood undernutrition.
(A) A model of normal gut microbial community development in Malawian infants and children, based on the relative abundances of 25 bacterial taxa that provide a microbial signature defining the “age,” or state of maturation, of an individual’s (fecal) microbiota. (Hierarchical clusterings of operational taxonomic units are indicated on the left.) (B) Fecal samples from healthy (H) or stunted and underweight (Un) infants and children were transplanted into separate groups of young germ-free mice that were fed a Malawian diet. The immature microbiota of Un donors transmitted impaired growth phenotypes to the mice. (C) Evidence that a subset of age-discriminatory taxa are also growth-discriminatory. Cohousing mice shortly after they received microbiota from 6-month-old healthy or undernourished donors resulted in the invasion of taxa from the healthy donor’s microbiota (HCH) into the undernourished donor’s microbiota (UnCH) among recipient animals and prevented growth impairments. Adding cultured invasive growth-discriminatory taxa directly to the Un donor’s microbiota (Un+) improved growth. 

Abstract

Undernourished children exhibit impaired development of their gut microbiota. Transplanting microbiota from 6- and 18-month-old healthy or undernourished Malawian donors into young germ-free mice that were fed a Malawian diet revealed that immature microbiota from undernourished infants and children transmit impaired growth phenotypes. The representation of several age-discriminatory taxa in recipient animals correlated with lean body mass gain; liver, muscle, and brain metabolism; and bone morphology. Mice were cohoused shortly after receiving microbiota from healthy or severely stunted and underweight infants; age- and growth-discriminatory taxa from the microbiota of the former were able to invade that of the latter, which prevented growth impairments in recipient animals. Adding two invasive species, Ruminococcus gnavus and Clostridium symbiosum, to the microbiota from undernourished donors also ameliorated growth and metabolic abnormalities in recipient animals. These results provide evidence that microbiota immaturity is causally related to undernutrition and reveal potential therapeutic targets and agents.
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Supplementary Material

Summary

Materials and Methods
Supplementary Text
Figs. S1 to S10
Tables S1 to S17
References (2441)

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REFERENCES AND NOTES

1
Victora C. G., Adair L., Fall C., Hallal P. C., Martorell R., Richter L., Sachdev H. S., and Maternal and Child Undernutrition Study Group, Maternal and child undernutrition: Consequences for adult health and human capital. Lancet 371, 340–357 (2008).
2
Gaayeb L., Sarr J. B., Cames C., Pinçon C., Hanon J. B., Ndiath M. O., Seck M., Herbert F., Sagna A. B., Schacht A. M., Remoue F., Riveau G., and Hermann E., Effects of malnutrition on children’s immunity to bacterial antigens in Northern Senegal. Am. J. Trop. Med. Hyg. 90, 566–573 (2014).
3
Kosek M., Haque R., Lima A., Babji S., Shrestha S., Qureshi S., Amidou S., Mduma E., Lee G., Yori P. P., Guerrant R. L., Bhutta Z., Mason C., Kang G., Kabir M., Amour C., Bessong P., Turab A., Seidman J., Olortegui M. P., Quetz J., Lang D., Gratz J., Miller M., Gottlieb M., and MAL-ED network, Fecal markers of intestinal inflammation and permeability associated with the subsequent acquisition of linear growth deficits in infants. Am. J. Trop. Med. Hyg. 88, 390–396 (2013).
4
Waber D. P., Bryce C. P., Girard J. M., Zichlin M., Fitzmaurice G. M., and Galler J. R., Impaired IQ and academic skills in adults who experienced moderate to severe infantile malnutrition: A 40-year study. Nutr. Neurosci. 17, 58–64 (2014).
5
Black R. E., Victora C. G., Walker S. P., Bhutta Z. A., Christian P., de Onis M., Ezzati M., Grantham-McGregor S., Katz J., Martorell R., Uauy R., and Maternal and Child Nutrition Study Group, Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382, 427–451 (2013).
6
Richard S. A., McCormick B. J., Miller M. A., Caulfield L. E., and Checkley W., Modeling environmental influences on child growth in the MAL-ED cohort study: Opportunities and challenges. Clin. Infect. Dis. 59, S255–S260 (2014).
7
Keusch G. T., Denno D. M., Black R. E., Duggan C., Guerrant R. L., Lavery J. V., Nataro J. P., Rosenberg I. H., Ryan E. T., Tarr P. I., Ward H., Bhutta Z. A., Coovadia H., Lima A., Ramakrishna B., Zaidi A. K., Hay Burgess D. C., and Brewer T., Environmental enteric dysfunction: Pathogenesis, diagnosis, and clinical consequences. Clin. Infect. Dis. 59, S207–S212 (2014).
8
Dewey K. G., The challenge of meeting nutrient needs of infants and young children during the period of complementary feeding: An evolutionary perspective. J. Nutr. 143, 2050–2054 (2013).
9
de Onis M., Garza C., Victora C. G., Onyango A. W., Frongillo E. A., and Martines J., The WHO Multicentre Growth Reference Study: Planning, study design, and methodology. Food Nutr. Bull. 25, S15–S26 (2004).
10
de Onis M., Onyango A. W., Van den Broeck J., Chumlea W. C., and Martorell R., Measurement and standardization protocols for anthropometry used in the construction of a new international growth reference. Food Nutr. Bull. 25, S27–S36 (2004).
11
Subramanian S., Huq S., Yatsunenko T., Haque R., Mahfuz M., Alam M. A., Benezra A., DeStefano J., Meier M. F., Muegge B. D., Barratt M. J., VanArendonk L. G., Zhang Q., Province M. A., Petri W. A. Jr., Ahmed T., and Gordon J. I., Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014).
12
Breiman L., Random forests. Mach. Learn. 45, 5–32 (2001).
13
Smith M. I., Yatsunenko T., Manary M. J., Trehan I., Mkakosya R., Cheng J., Kau A. L., Rich S. S., Concannon P., Mychaleckyj J. C., Liu J., Houpt E., Li J. V., Holmes E., Nicholson J., Knights D., Ursell L. K., Knight R., and Gordon J. I., Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 339, 548–554 (2013).
14
Edgar R. C., Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
15
Wang Q., Garrity G. M., Tiedje J. M., and Cole J. R., Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
16
Ashorn P., Alho L., Ashorn U., Cheung Y. B., Dewey K. G., Harjunmaa U., Lartey A., Nkhoma M., Phiri N., Phuka J., Vosti S. A., Zeilani M., and Maleta K., The impact of lipid-based nutrient supplement provision to pregnant women on newborn size in rural Malawi: A randomized controlled trial. Am. J. Clin. Nutr. 101, 387–397 (2015).
17
Sjögren K., Engdahl C., Henning P., Lerner U. H., Tremaroli V., Lagerquist M. K., Bäckhed F., and Ohlsson C., The gut microbiota regulates bone mass in mice. J. Bone Miner. Res. 27, 1357–1367 (2012).
18
Ridaura V. K., Faith J. J., Rey F. E., Cheng J., Duncan A. E., Kau A. L., Griffin N. W., Lombard V., Henrissat B., Bain J. R., Muehlbauer M. J., Ilkayeva O., Semenkovich C. F., Funai K., Hayashi D. K., Lyle B. J., Martini M. C., Ursell L. K., Clemente J. C., Van Treuren W., Walters W. A., Knight R., Newgard C. B., Heath A. C., and Gordon J. I., Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).
19
Knights D., Kuczynski J., Charlson E. S., Zaneveld J., Mozer M. C., Collman R. G., Bushman F. D., Knight R., and Kelley S. T., Bayesian community-wide culture-independent microbial source tracking. Nat. Methods 8, 761–763 (2011).
20
Cleary B., Brito I. L., Huang K., Gevers D., Shea T., Young S., and Alm E. J., Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning. Nat. Biotechnol. 33, 1053–1060 (2015).
21
Kau A. L., Planer J. D., Liu J., Rao S., Yatsunenko T., Trehan I., Manary M. J., Liu T. C., Stappenbeck T. S., Maleta K. M., Ashorn P., Dewey K. G., Houpt E. R., Hsieh C. S., and Gordon J. I., Functional characterization of IgA-targeted bacterial taxa from undernourished Malawian children that produce diet-dependent enteropathy. Sci. Transl. Med. 7, 276ra24 (2015).
22
Crane R. J., Jones K. D. J., and Berkley J. A., Environmental enteric dysfunction: An overview. Food Nutr. Bull. 36, S76–S87 (2015).
23
Dewey K. G. and Adu-Afarwuah S., Systematic review of the efficacy and effectiveness of complementary feeding interventions in developing countries. Matern. Child Nutr. 4, 24–85 (2008).
24
Magoč T. and Salzberg S. L., FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
25
Goodman A. L., Kallstrom G., Faith J. J., Reyes A., Moore A., Dantas G., and Gordon J. I., Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc. Natl. Acad. Sci. U.S.A. 108, 6252–6257 (2011).
26
Ferrara C. T., Wang P., Neto E. C., Stevens R. D., Bain J. R., Wenner B. R., Ilkayeva O. R., Keller M. P., Blasiole D. A., Kendziorski C., Yandell B. S., Newgard C. B., and Attie A. D., Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling. PLOS Genet. 4, e1000034 (2008).
27
An J., Muoio D. M., Shiota M., Fujimoto Y., Cline G. W., Shulman G. I., Koves T., Stevens R. D., Millington D. S., and Newgard C. B., Hepatic expression of malonyl-CoA decarboxylase reverses muscle, liver and whole-animal insulin resistance. Nat. Med. 10, 268–274 (2004).
28
Jensen M. V., Joseph J. W., Ilkayeva O., Burgess S., Lu D., Ronnebaum S. M., Odegaard M., Becker T. C., Sherry A. D., and Newgard C. B., Compensatory responses to pyruvate carboxylase suppression in islet beta-cells. Preservation of glucose-stimulated insulin secretion. J. Biol. Chem. 281, 22342–22351 (2006).
29
Deutsch J., Grange E., Rapoport S. I., and Purdon A. D., Isolation and quantitation of long-chain acyl-coenzyme A esters in brain tissue by solid-phase extraction. Anal. Biochem. 220, 321–323 (1994).
30
Magnes C., Sinner F. M., Regittnig W., and Pieber T. R., LC/MS/MS method for quantitative determination of long-chain fatty acyl-CoAs. Anal. Chem. 77, 2889–2894 (2005).
31
Minkler P. E., Kerner J., Ingalls S. T., and Hoppel C. L., Novel isolation procedure for short-, medium-, and long-chain acyl-coenzyme A esters from tissue. Anal. Biochem. 376, 275–276 (2008).
32
Merrill A. H. Jr., Sullards M. C., Allegood J. C., Kelly S., and Wang E., Sphingolipidomics: High-throughput, structure-specific, and quantitative analysis of sphingolipids by liquid chromatography tandem mass spectrometry. Methods 36, 207–224 (2005).
33
Chevreux B., Pfisterer T., Drescher B., Driesel A. J., Müller W. E., Wetter T., and Suhai S., Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 14, 1147–1159 (2004).
34
Seemann T., Prokka: Rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).
35
Markowitz V. M., Chen I. M., Palaniappan K., Chu K., Szeto E., Grechkin Y., Ratner A., Jacob B., Huang J., Williams P., Huntemann M., Anderson I., Mavromatis K., Ivanova N. N., and Kyrpides N. C., IMG: The Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Res. 40, D115–D122 (2012).
36
Overbeek R., Olson R., Pusch G. D., Olsen G. J., Davis J. J., Disz T., Edwards R. A., Gerdes S., Parrello B., Shukla M., Vonstein V., Wattam A. R., Xia F., and Stevens R., The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 42, D206–D214 (2014).
37
Rodionov D. A., Comparative genomic reconstruction of transcriptional regulatory networks in bacteria. Chem. Rev. 107, 3467–3497 (2007).
38
Novichkov P. S., Kazakov A. E., Ravcheev D. A., Leyn S. A., Kovaleva G. Y., Sutormin R. A., Kazanov M. D., Riehl W., Arkin A. P., Dubchak I., and Rodionov D. A., RegPrecise 3.0 – A resource for genome-scale exploration of transcriptional regulation in bacteria. BMC Genomics 14, 745 (2013).
39
Ravcheev D. A., Godzik A., Osterman A. L., and Rodionov D. A., Polysaccharides utilization in human gut bacterium Bacteroides thetaiotaomicron: Comparative genomics reconstruction of metabolic and regulatory networks. BMC Genomics 14, 873 (2013).
40
Rodionov D. A., Rodionova I. A., Li X., Ravcheev D. A., Tarasova Y., Portnoy V. A., Zengler K., and Osterman A. L., Transcriptional regulation of the carbohydrate utilization network in Thermotoga maritima. Front. Microbiol. 4, 244 (2013).
41
Rodionov D. A., Yang C., Li X., Rodionova I. A., Wang Y., Obraztsova A. Y., Zagnitko O. P., Overbeek R., Romine M. F., Reed S., Fredrickson J. K., Nealson K. H., and Osterman A. L., Genomic encyclopedia of sugar utilization pathways in the Shewanella genus. BMC Genomics 11, 494 (2010).

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Science
Volume 351 | Issue 6275
19 February 2016

Submission history

Received: 28 August 2015
Accepted: 23 December 2015
Published in print: 19 February 2016

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Acknowledgments

We are indebted to the parents and children from Malawi for their participation in this study. We thank S. Wagoner, D. O’Donnell, M. Karlsson, and J. Serugo for their assistance with gnotobiotic mouse husbandry; D. Leib and M. Silva for their assistance with bone morphology assays; J. Guruge for help with anaerobic microbiology; B. Dankenbring for assistance with maintenance of the biospecimen repository; and M. Meier, S. Deng, and J. Hoisington-Lopez for their contribution to various facets of the DNA sequencing pipeline. This work was supported by the Bill and Melinda Gates Foundation and the NIH (grant DK30292). Additional funding was provided by the Office of Health, Infectious Diseases and Nutrition, Bureau for Global Health, U.S. Agency for International Development under the terms of cooperative agreement no. AID-OAA-A-12-00005, through the Food and Nutrition Technical Assistance III Project managed by FHI 360. Data management and statistical analysis for theiLiNS-DYAD-M clinical study were also funded by the Academy of Finland (grant 252075) and the Medical Research Fund of Tampere University Hospital (grant 9M004). Micro-CT analysis was performed in the Washington University Musculoskeletal Research Center, which is supported by NIH grant P30 AR057235. L.V.B. received stipend support from NIH predoctoral training grants NIH T32 AI007172 and T32 GM007067 and from the Lucille P. Markey Special Emphasis Pathway in Human Pathobiology. D.A.R. and S.A.L. were supported by the Russian Science Foundation (grant 14-14-00289). Collection of the human specimens included in this study was approved by the University of Malawi College of Medicine Research Ethics Committee. Specimens were provided to Washington University in St. Louis under a materials transfer agreement (MTA) between the University of Malawi and Washington University, and their collection and use for this study was approved by the Washington University Human Research Protection Office (Federalwide Assurance no. FWA00002284). 16S rRNA sequences, generated from fecal samples in raw format before post-processing and data analysis, and shotgun sequencing data sets, generated from the R. gnavus TS8243C and C. symbiosum TS8243C genomes, have been deposited in the European Nucleotide Archive under accession number PRJEB9853. J.I.G. is a cofounder of Matatu, a company characterizing the role of diet-by-microbiota interactions in animal health. A.L.O. is an adjunct vice president for research for Buffalo BioLabs. L.V.B., M.R.C., and J.I.G. designed the gnotobiotic mouse studies; L.V.B. and M.R.C. performed the experiments with gnotobiotic animals; I.T. and M.J.M. designed and implemented the clinical monitoring and sampling for the twin study and participated in patient recruitment, sample collection and preservation, and/or clinical evaluations; K.M.M., Y.F., J.M.J., K.G.D., and P.A. designed and oversaw the clinical studies, sample collection and processing, and/or clinical monitoring and evaluations in the iLiNS-DYAD-M study; L.V.B. generated the 16S rRNA data; L.V.B., M.R.C., S.V., and O.I. generated the metabolomics data; T.S. and L.V.B. cultured bacterial isolates; B.H., D.A.R., S.A.L., and A.L.O. performed metabolic reconstructions of the R. gnavus and C. symbiosum genomes; L.V.B., M.R.C., M.J.B., S.S., C.B.N., and J.I.G. analyzed the data; and L.V.B. and J.I.G. wrote the paper.

Authors

Affiliations

Laura V. Blanton
Center for Genome Sciences and Systems Biology and Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63108, USA.
Mark R. Charbonneau
Center for Genome Sciences and Systems Biology and Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63108, USA.
Tarek Salih
Center for Genome Sciences and Systems Biology and Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63108, USA.
Michael J. Barratt
Center for Genome Sciences and Systems Biology and Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63108, USA.
Siddarth Venkatesh
Center for Genome Sciences and Systems Biology and Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63108, USA.
Olga Ilkaveya
Sarah W. Stedman Nutrition and Metabolism Center and Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC 27710, USA.
Sathish Subramanian
Center for Genome Sciences and Systems Biology and Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63108, USA.
Mark J. Manary
Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA.
School of Public Health and Family Medicine, College of Medicine, University of Malawi, Chichiri, Blantyre 3, Malawi.
Indi Trehan
Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA.
Department of Paediatrics and Child Health, College of Medicine, University of Malawi, Chichiri, Blantyre 3, Malawi.
Josh M. Jorgensen
Department of Nutrition and Program in International and Community Nutrition, University of California–Davis, Davis, CA 95616, USA.
Yue-mei Fan
Department for International Health, University of Tampere School of Medicine, Tampere 33014, Finland.
Bernard Henrissat
Architecture et Fonction des Macromolécules Biologiques, Centre National de la Recherche Scientifique and Aix-Marseille Université, 13288 Marseille Cedex 9, France.
Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
Semen A. Leyn
A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow 127994, Russia.
Dmitry A. Rodionov
A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow 127994, Russia.
Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.
Andrei L. Osterman
Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA.
Kenneth M. Maleta
School of Public Health and Family Medicine, College of Medicine, University of Malawi, Chichiri, Blantyre 3, Malawi.
Christopher B. Newgard
Sarah W. Stedman Nutrition and Metabolism Center and Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC 27710, USA.
Department of Pharmacology and Cancer Biology and Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA.
Per Ashorn
Department for International Health, University of Tampere School of Medicine, Tampere 33014, Finland.
Department of Pediatrics, Tampere University Hospital, Tampere 33521, Finland.
Kathryn G. Dewey
Department of Nutrition and Program in International and Community Nutrition, University of California–Davis, Davis, CA 95616, USA.
Jeffrey I. Gordon*
Center for Genome Sciences and Systems Biology and Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63108, USA.

Notes

*Corresponding author. E-mail: [email protected]

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