A sparse covarying unit that describes healthy and impaired human gut microbiota development

Malnutrition and dietary repair Childhood malnutrition is accompanied by growth stunting and immaturity of the gut microbiota. Even after therapeutic intervention with standard commercial complementary foods, children may fail to thrive. Gehrig et al. and Raman et al. monitored metabolic parameters in healthy Bangladeshi children and those recovering from severe acute malnutrition. The authors investigated the interactions between therapeutic diet, microbiota development, and growth recovery. Diets were then designed using pig and mouse models to nudge the microbiota into a mature post-weaning state that might be expected to support the growth of a child. These were first tested in mice inoculated with age-characteristic gut microbiota. The designed diets entrained maturation of the children's microbiota and put their metabolic and growth profiles on a healthier trajectory. Science, this issue p. eaau4732, p. eaau4735 Health-linked microbiota can be used to monitor the effects of potentially therapeutic dietary components on recovery from malnutrition. INTRODUCTION Ecosystems such as the human gut microbiota are typically described by a “parts list” with enumeration of component members. Accordingly, the abundances of community components are commonly used as a metric for relating its configuration to features of its habitat and to the biological state of the host. Although this approach has provided much insight, the structure and function of biological systems are emergent, arising from the collective action of constituent parts rather than each part acting in isolation. This characteristic demands a different approach to describing the form of a microbiota—one that takes into consideration the abundances as well as the interactions between members. RATIONALE Borrowing from the fields of econophysics and protein evolution, where identification of conserved covariation has provided insights about the organization of complex dynamic systems, we searched for features amidst the seemingly intractable complexity of human gut microbial communities that could serve as a framework for understanding how they assemble and function. RESULTS A statistical workflow was developed to identify conserved bacterial taxon-taxon covariance in the gut communities of healthy members of a Bangladeshi birth cohort who provided fecal samples monthly from postnatal months 1 to 60. The results revealed an “ecogroup” of 15 bacterial taxa that together exhibited consistent covariation by 20 months of age and beyond. Ecogroup taxa also described gut microbiota development in healthy members of birth cohorts residing in Bangladesh, India, and Peru to an extent comparable to what is achieved when considering all detected bacterial taxa; this finding suggests that the ecogroup network is a conserved general feature of microbiota organization. Moreover, the ecogroup provided a framework for characterizing the state of perturbed microbiota development in Bangladeshi children with severe acute malnutrition (SAM) and moderate acute malnutrition (MAM), as well as a quantitative metric for defining the efficacy of standard versus microbiota-directed therapeutic foods in reconfiguring their gut communities toward a state seen in age-matched healthy children living in the same locale. These results highlight the importance of the ecogroup as a descriptor, both for fundamental and practical uses. A consortium of cultured ecogroup taxa, introduced into gnotobiotic piglets, reenacted changes in their relative abundances that were observed in human communities as the animals transitioned from exclusive milk feeding to a fully weaned state consuming a prototypic Bangladeshi diet. This pattern of change correlated with the representation of a sparse set of metabolic pathways in the genomes of these organisms and, in the fully weaned state, with their expression. CONCLUSION The ecogroup represents a simplified feature of community organization and components that could play key roles in community assembly and function. As the gut microbiota constantly faces environmental challenges, “embedding” a sparse network of covarying taxa in a larger framework of independently varying organisms could represent an elegant architectural solution developed by nature to maintain robustness while enabling adaptation. The approach used to identify and characterize the sparse network of covarying ecogroup taxa is, in principle, generalizable to a wide variety of ecosystems. Ecogroup as a concise description of microbiota form. (Top) Network diagram of covarying taxa where node (taxon) color indicates ecogroup (green) or non-ecogroup (gray), node size indicates number of mutually covarying taxa, and connection between nodes indicates covariance between two taxa. (Bottom) Measuring the representation of ecogroup taxa reveals that children with SAM treated with standard therapeutic foods have an ecogroup profile similar to that of children with untreated MAM, indicating persistent perturbations in their gut community relative to healthy children. In contrast, children with MAM treated with a therapeutic food designed to target the microbiota (MDCF-2) have an ecogroup profile that overlaps nearly entirely with that of healthy children. Characterizing the organization of the human gut microbiota is a formidable challenge given the number of possible interactions between its components. Using a statistical approach initially applied to financial markets, we measured temporally conserved covariance among bacterial taxa in the microbiota of healthy members of a Bangladeshi birth cohort sampled from 1 to 60 months of age. The results revealed an “ecogroup” of 15 covarying bacterial taxa that provide a concise description of microbiota development in healthy children from this and other low-income countries, and a means for monitoring community repair in undernourished children treated with therapeutic foods. Features of ecogroup population dynamics were recapitulated in gnotobiotic piglets as they transitioned from exclusive milk feeding to a fully weaned state consuming a representative Bangladeshi diet.

[1]  Richard J. Giannone,et al.  Effects of microbiota-directed foods in gnotobiotic animals and undernourished children , 2019, Science.

[2]  Simona Cocco,et al.  Inverse statistical physics of protein sequences: a key issues review , 2017, Reports on progress in physics. Physical Society.

[3]  Michael J. Barratt,et al.  The effects of micronutrient deficiencies on bacterial species from the human gut microbiota , 2017, Science Translational Medicine.

[4]  Jesse R. Zaneveld,et al.  Normalization and microbial differential abundance strategies depend upon data characteristics , 2017, Microbiome.

[5]  Mehdi Layeghifard,et al.  Disentangling Interactions in the Microbiome: A Network Perspective , 2016, Trends in Microbiology.

[6]  R. Ranganathan,et al.  Origins of Allostery and Evolvability in Proteins: A Case Study , 2016, Cell.

[7]  Sophie J. Weiss,et al.  Correlation detection strategies in microbial data sets vary widely in sensitivity and precision , 2016, The ISME Journal.

[8]  Paul J. McMurdie,et al.  DADA2: High resolution sample inference from Illumina amplicon data , 2016, Nature Methods.

[9]  Michael J. Barratt,et al.  Sialylated Milk Oligosaccharides Promote Microbiota-Dependent Growth in Models of Infant Undernutrition , 2016, Cell.

[10]  Peer Bork,et al.  Metabolic dependencies drive species co-occurrence in diverse microbial communities , 2015, Proceedings of the National Academy of Sciences.

[11]  Christian L. Müller,et al.  Sparse and Compositionally Robust Inference of Microbial Ecological Networks , 2014, PLoS Comput. Biol..

[12]  R. Sang,et al.  Evolution of mosquito preference for humans linked to an odorant receptor , 2014, Nature.

[13]  Robert E Black,et al.  The MAL-ED study: a multinational and multidisciplinary approach to understand the relationship between enteric pathogens, malnutrition, gut physiology, physical growth, cognitive development, and immune responses in infants and children up to 2 years of age in resource-poor environments. , 2014, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[14]  Rashidul Haque,et al.  Members of the human gut microbiota involved in recovery from Vibrio cholerae infection , 2014, Nature.

[15]  Torsten Seemann,et al.  Prokka: rapid prokaryotic genome annotation , 2014, Bioinform..

[16]  Qunyuan Zhang,et al.  Persistent Gut Microbiota Immaturity in Malnourished Bangladeshi Children , 2014, Nature.

[17]  D. Gordon The Ecology of Collective Behavior , 2014, PLoS biology.

[18]  Fangfang Xia,et al.  The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST) , 2013, Nucleic Acids Res..

[19]  Susan P. Holmes,et al.  Waste Not , Want Not : Why Rarefying Microbiome Data is Inadmissible . October 1 , 2013 , 2013 .

[20]  F. J. Poelwijk,et al.  The spatial architecture of protein function and adaptation , 2012, Nature.

[21]  Jonathan Friedman,et al.  Inferring Correlation Networks from Genomic Survey Data , 2012, PLoS Comput. Biol..

[22]  J. Raes,et al.  Microbial interactions: from networks to models , 2012, Nature Reviews Microbiology.

[23]  Curtis Huttenhower,et al.  Microbial Co-occurrence Relationships in the Human Microbiome , 2012, PLoS Comput. Biol..

[24]  S. Leibler,et al.  Contingency and Statistical Laws in Replicate Microbial Closed Ecosystems , 2012, Cell.

[25]  J. Clemente,et al.  Human gut microbiome viewed across age and geography , 2012, Nature.

[26]  C. Sander,et al.  Direct-coupling analysis of residue coevolution captures native contacts across many protein families , 2011, Proceedings of the National Academy of Sciences.

[27]  J. Faith,et al.  Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice , 2011, Proceedings of the National Academy of Sciences.

[28]  William A. Walters,et al.  QIIME allows analysis of high-throughput community sequencing data , 2010, Nature Methods.

[29]  G. Wagner,et al.  Mutational robustness can facilitate adaptation , 2010, Nature.

[30]  Najeeb M. Halabi,et al.  Protein Sectors: Evolutionary Units of Three-Dimensional Structure , 2009, Cell.

[31]  Danielle M. Varda,et al.  A Network Perspective , 2009 .

[32]  Leslie G. Valiant,et al.  Evolvability , 2009, JACM.

[33]  H. Pan,et al.  WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age , 2006 .

[34]  Naryttza N. Diaz,et al.  The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes , 2005, Nucleic acids research.

[35]  S. Carpenter,et al.  ESTIMATING COMMUNITY STABILITY AND ECOLOGICAL INTERACTIONS FROM TIME‐SERIES DATA , 2003 .

[36]  V. Plerou,et al.  Random matrix approach to cross correlations in financial data. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  R. Ranganathan,et al.  Evolutionarily conserved pathways of energetic connectivity in protein families. , 1999, Science.

[38]  E. R. Miller,et al.  The pig as a model for human nutrition. , 1987, Annual review of nutrition.

[39]  W. Z. Lidicker,et al.  A Clarification of Interactions in Ecological Systems , 1979 .

[40]  J. Hedgpeth Manton on Arthropods , 1979 .

[41]  J. Gerring A case study , 2011, Technology and Society.