Assembly, stability, and dynamics of the infant gut microbiome are linked to bacterial strains and functions in mother's milk.

The establishment of the gut microbiome in early life is critical for healthy infant development. Although human milk is recommended as the sole source of nutrition for the human infant, little is known about how variation in milk composition, and especially the milk microbiome, shapes the microbial communities in the infant gut. Here, we quantified the similarity between the maternal milk and the infant gut microbiome using 507 metagenomic samples collected from 195 mother-infant pairs at one, three, and six months postpartum. We found that the microbial taxonomic overlap between milk and the infant gut was driven by bifidobacteria, in particular by B. longum. Infant stool samples dominated by B. longum also showed higher temporal stability compared to samples dominated by other species. We identified two instances of strain sharing between maternal milk and the infant gut, one involving a commensal (B. longum) and one a pathobiont (K. pneumoniae). In addition, strain sharing between unrelated infants was higher among infants born at the same hospital compared to infants born in different hospitals, suggesting a potential role of the hospital environment in shaping the infant gut microbiome composition. The infant gut microbiome at one month compared to six months of age was enriched in metabolic pathways associated with de-novo molecule biosynthesis, suggesting that early colonisers might be more versatile and metabolically independent compared to later colonizers. Lastly, we found a significant overlap in antimicrobial resistance genes carriage between the mother's milk and their infant's gut microbiome. Taken together, our results suggest that the human milk microbiome has an important role in the assembly, composition, and stability of the infant gut microbiome.

Multi-omic approaches for host-microbiome data integration.

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Integration of 168,000 samples reveals global patterns of the human gut microbiome.

Understanding the factors that shape variation in the human microbiome is a major goal of research in biology. While other genomics fields have used large, pre-compiled compendia to extract systematic insights requiring otherwise impractical sample sizes, there has been no comparable resource for the 16S rRNA sequencing data commonly used to quantify microbiome composition. To help close this gap, we have assembled a set of 168,484 publicly available human gut microbiome samples, processed with a single pipeline and combined into the largest unified microbiome dataset to date. We use this resource, which is freely available at microbiomap.org, to shed light on global variation in the human gut microbiome. We find that Firmicutes, particularly Bacilli and Clostridia, are almost universally present in the human gut. At the same time, the relative abundance of the 65 most common microbial genera differ between at least two world regions. We also show that gut microbiomes in undersampled world regions, such as Central and Southern Asia, differ significantly from the more thoroughly characterized microbiomes of Europe and Northern America. Moreover, humans in these overlooked regions likely harbor hundreds of taxa that have not yet been discovered due to this undersampling, highlighting the need for diversity in microbiome studies. We anticipate that this new compendium can serve the community and enable advanced applied and methodological research.

Human Cytomegalovirus in breast milk is associated with milk composition, the infant gut microbiome, and infant growth.

Human cytomegalovirus (CMV) is a highly prevalent herpesvirus that is often transmitted to the neonate via breast milk. Postnatal CMV transmission can have negative health consequences for preterm and immunocompromised infants, but any effects on healthy term infants are thought to be benign. Furthermore, the impact of CMV on the composition of the hundreds of bioactive factors in human milk has not been tested. Here, we utilize a cohort of exclusively breastfeeding full term mother-infant pairs to test for differences in the milk transcriptome and metabolome associated with CMV, and the impact of CMV in breast milk on the infant gut microbiome and infant growth. We find upregulation of the indoleamine 2,3- dioxygenase (IDO) tryptophan-to-kynurenine metabolic pathway in CMV+ milk samples, and that CMV+ milk is associated with decreased Bifidobacterium in the infant gut. Our data indicate a complex relationship between milk CMV, milk kynurenine, and infant growth; with kynurenine positively correlated, and CMV viral load negatively correlated, with infant weight-for-length at 1 month of age. These results suggest CMV transmission, CMV-related changes in milk composition, or both may be modulators of full term infant development.

Microbes with higher metabolic independence are enriched in human gut microbiomes under stress.

A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health versus IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.

Universal gut microbial relationships in the gut microbiome of wild baboons.

Ecological relationships between bacteria mediate the services that gut microbiomes provide to their hosts. Knowing the overall direction and strength of these relationships is essential to learn how ecology scales up to affect microbiome assembly, dynamics, and host health. However, whether bacterial relationships are generalizable across hosts or personalized to individual hosts is debated. Here, we apply a robust, multinomial logistic-normal modeling framework to extensive time series data (5534 samples from 56 baboon hosts over 13 years) to infer thousands of correlations in bacterial abundance in individual baboons and test the degree to which bacterial abundance correlations are 'universal'. We also compare these patterns to two human data sets. We find that, most bacterial correlations are weak, negative, and universal across hosts, such that shared correlation patterns dominate over host-specific correlations by almost twofold. Further, taxon pairs that had inconsistent correlation signs (either positive or negative) in different hosts always had weak correlations within hosts. From the host perspective, host pairs with the most similar bacterial correlation patterns also had similar microbiome taxonomic compositions and tended to be genetic relatives. Compared to humans, universality in baboons was similar to that in human infants, and stronger than one data set from human adults. Bacterial families that showed universal correlations in human infants were often universal in baboons. Together, our work contributes new tools for analyzing the universality of bacterial associations across hosts, with implications for microbiome personalization, community assembly, and stability, and for designing microbiome interventions to improve host health.

Communities of bacteria living in the guts of humans and other animals perform essential services for their hosts such as digesting food, degrading toxins, or fighting viruses and other bacteria that cause disease. These services emerge from so-called 'ecological' relationships between different species of bacteria. One species, for example, may break down a molecule in human food into another compound that is, in turn, digested by another species into a small molecule that the human gut can absorb and use. The bacteria involved in such a process may become more or less common together in their host. In other situations, some bacteria may have opposing roles to each other, meaning that if one species becomes more abundant it may reduce the level of the other. However, it is not known if relationships between different bacteria are consistent across hosts (i.e., universal) or unique to each host (personalized). In other words, if a pair of bacteria increase and decrease in abundance together in one host, do they do the same in other hosts? Microbes often swap genes with each other to gain new traits; as each host harbors a distinctive set of gut microbes, it may be possible for microbial relationships to change depending on the bacterial species present in a specific environment. To investigate, Roche et al. studied the bacteria in thousands of samples of feces collected from 56 baboons over a 13-year period. These samples came from a long-term research project in Amboseli, Kenya which has been studying a population of wild baboons continuously since 1971. Roche et al. measured the abundance of hundreds of gut bacteria in the feces to understand the relationships between pairs. This revealed that connections between species were largely universal rather than personalized to each baboon. Furthermore, the pairs of bacteria with the strongest positive or negative associations had the most consistent relationships across the baboons. Microbial relationships that have strong effects on the microbiome's composition might therefore be especially universal. Further analyses measuring gut bacteria in human babies also found that relationships between pairs of bacteria were largely universal. Hence, individual species of bacteria may fill similar ecological roles in each host across humans and other primates, and perhaps also in other mammals. These findings suggest that it may be possible to leverage the ecological relationships between bacteria to develop universal therapies for human diseases associated with gut bacteria, such as inflammatory bowel disease or Clostridium difficile infection.

How longitudinal data can contribute to our understanding of host genetic effects on the gut microbiome.

A key component of microbiome research is understanding the role of host genetic influence on gut microbial composition. However, it can be difficult to link host genetics with gut microbial composition because host genetic similarity and environmental similarity are often correlated. Longitudinal microbiome data can supplement our understanding of the relative role of genetic processes in the microbiome. These data can reveal environmentally contingent host genetic effects, both in terms of controlling for environmental differences and in comparing how genetic effects differ by environment. Here, we explore four research areas where longitudinal data could lend new insights into host genetic effects on the microbiome: microbial heritability, microbial plasticity, microbial stability, and host and microbiome population genetics. We conclude with a discussion of methodological considerations for future studies.

For humans and animals, host genes play a role in shaping the gut microbiome. However, measuring these effects is difficult because host genetic and environmental similarities are often correlated. For instance, relatives often live together and share similar diets and lifestyles­forces that can also change gut microbial communities. Watching the microbiome over time, through longitudinal sampling, can help solve this problem by breaking gene-environment correlations. Here, we review this idea and several other research areas where longitudinal data will be helpful for understanding host genetic effects on the microbiome. We believe this approach will shed new light on the evolution of host­microbe relationships and can inform new microbiome therapies.

Human milk variation is shaped by maternal genetics and impacts the infant gut microbiome.

Human milk is a complex mix of nutritional and bioactive components that provide complete nutrition for the infant. However, we lack a systematic knowledge of the factors shaping milk composition and how milk variation influences infant health. Here, we used multi-omic profiling to characterize interactions between maternal genetics, milk gene expression, milk composition, and the infant fecal microbiome in 242 exclusively breastfeeding mother-infant pairs. We identified 487 genetic loci associated with milk gene expression unique to the lactating mammary gland, including loci that impacted breast cancer risk and human milk oligosaccharide concentration. Integrative analyses uncovered connections between milk gene expression and infant gut microbiome, including an association between the expression of inflammation-related genes with IL-6 concentration in milk and the abundance of Bifidobacteria in the infant gut. Our results show how an improved understanding of the genetics and genomics of human milk connects lactation biology with maternal and infant health.

Codiversification of gut microbiota with humans.

The gut microbiomes of human populations worldwide have many core microbial species in common. However, within a species, some strains can show remarkable population specificity. The question is whether such specificity arises from a shared evolutionary history (codiversification) between humans and their microbes. To test for codiversification of host and microbiota, we analyzed paired gut metagenomes and human genomes for 1225 individuals in Europe, Asia, and Africa, including mothers and their children. Between and within countries, a parallel evolutionary history was evident for humans and their gut microbes. Moreover, species displaying the strongest codiversification independently evolved traits characteristic of host dependency, including reduced genomes and oxygen and temperature sensitivity. These findings all point to the importance of understanding the potential role of population-specific microbial strains in microbiome-mediated disease phenotypes.

Synchrony and idiosyncrasy in the gut microbiome of wild baboons.

Human gut microbial dynamics are highly individualized, making it challenging to link microbiota to health and to design universal microbiome therapies. This individuality is typically attributed to variation in host genetics, diets, environments and medications but it could also emerge from fundamental ecological forces that shape microbiota more generally. Here, we leverage extensive gut microbial time series from wild baboons-hosts who experience little interindividual dietary and environmental heterogeneity-to test whether gut microbial dynamics are synchronized across hosts or largely idiosyncratic. Despite their shared lifestyles, baboon microbiota were only weakly synchronized. The strongest synchrony occurred among baboons living in the same social group, probably because group members range over the same habitat and simultaneously encounter the same sources of food and water. However, this synchrony was modest compared to each host's personalized dynamics. In support, host-specific factors, especially host identity, explained, on average, more than three times the deviance in longitudinal dynamics compared to factors shared with social group members and ten times the deviance of factors shared across the host population. These results contribute to mounting evidence that highly idiosyncratic gut microbiomes are not an artefact of modern human environments and that synchronizing forces in the gut microbiome (for example, shared environments, diets and microbial dispersal) are not strong enough to overwhelm key drivers of microbiome personalization, such as host genetics, priority effects, horizontal gene transfer and functional redundancy.

BiomeHorizon: Visualizing Microbiome Time Series Data in R.

Despite playing a key role in the health of their hosts, host-associated microbial communities demonstrate considerable variation over time. These communities comprise thousands of temporally dynamic taxa, which makes visualizing microbial time series data challenging. As such, a method to visualize both the proportional and absolute change in the relative abundance of multiple taxa across multiple subjects over time is needed. To address this gap, we developed BiomeHorizon, the first automated, open-source R package that visualizes longitudinal compositional microbiome data using horizon plots. BiomeHorizon is available at https://github.com/blekhmanlab/biomehorizon/ and a guide with step-by-step instructions for using the package is provided at https://blekhmanlab.github.io/biomehorizon/. IMPORTANCE Host-associated microbiota (i.e., the number and types of bacteria in the body) can have profound impacts on an animal's day-to-day functioning as well as their long-term health. Recent work has shown that these microbial communities change substantially over time, so it is important to be able to link changes in the abundance of certain microbes with host health outcomes. However, visualizing such changes is difficult because the microbiome comprises thousands of different microbes. To address this issue, we developed BiomeHorizon, an R package for visualizing longitudinal microbiome data using horizon plots. BiomeHorizon accepts a range of data formats and was developed with two common microbiome study designs in mind: human health studies, where the microbiome is sampled at set time points, and observational wildlife studies, where samples may be collected at irregular time intervals. BiomeHorizon thus provides a flexible, user-friendly approach to microbiome time series data visualization and analysis.

Identification of shared and disease-specific host gene-microbiome associations across human diseases using multi-omic integration.

While gut microbiome and host gene regulation independently contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here we developed a machine learning-based framework to jointly analyse paired host transcriptomic (n = 208) and gut microbiome (n = 208) profiles from colonic mucosal samples of patients with colorectal cancer, inflammatory bowel disease and irritable bowel syndrome. We identified associations between gut microbes and host genes that depict shared as well as disease-specific patterns. We found that a common set of host genes and pathways implicated in gastrointestinal inflammation, gut barrier protection and energy metabolism are associated with disease-specific gut microbes. Additionally, we also found that mucosal gut microbes that have been implicated in all three diseases, such as Streptococcus, are associated with different host pathways in each disease, suggesting that similar microbes can affect host pathophysiology in a disease-specific manner through regulation of different host genes. Our framework can be applied to other diseases for the identification of host gene-microbiome associations that may influence disease outcomes.

Public human microbiome data are dominated by highly developed countries.

The importance of sampling from globally representative populations has been well established in human genomics. In human microbiome research, however, we lack a full understanding of the global distribution of sampling in research studies. This information is crucial to better understand global patterns of microbiome-associated diseases and to extend the health benefits of this research to all populations. Here, we analyze the country of origin of all 444,829 human microbiome samples that are available from the world's 3 largest genomic data repositories, including the Sequence Read Archive (SRA). The samples are from 2,592 studies of 19 body sites, including 220,017 samples of the gut microbiome. We show that more than 71% of samples with a known origin come from Europe, the United States, and Canada, including 46.8% from the US alone, despite the country representing only 4.3% of the global population. We also find that central and southern Asia is the most underrepresented region: Countries such as India, Pakistan, and Bangladesh account for more than a quarter of the world population but make up only 1.8% of human microbiome samples. These results demonstrate a critical need to ensure more global representation of participants in microbiome studies.

Interspecies variation in hominid gut microbiota controls host gene regulation.

The gut microbiome exhibits extreme compositional variation between hominid hosts. However, it is unclear how this variation impacts host physiology across species and whether this effect can be mediated through microbial regulation of host gene expression in interacting epithelial cells. Here, we characterize the transcriptional response of human colonic epithelial cells in vitro to live microbial communities extracted from humans, chimpanzees, gorillas, and orangutans. We find that most host genes exhibit a conserved response, whereby they respond similarly to the four hominid microbiomes. However, hundreds of host genes exhibit a divergent response, whereby they respond only to microbiomes from specific host species. Such genes are associated with intestinal diseases in humans, including inflammatory bowel disease and Crohn's disease. Last, we find that inflammation-associated microbial species regulate the expression of host genes previously associated with inflammatory bowel disease, suggesting health-related consequences for species-specific host-microbiome interactions across hominids.

The gut microbiome in konzo.

Konzo, a distinct upper motor neuron disease associated with a cyanogenic diet and chronic malnutrition, predominately affects children and women of childbearing age in sub-Saharan Africa. While the exact biological mechanisms that cause this disease have largely remained elusive, host-genetics and environmental components such as the gut microbiome have been implicated. Using a large study population of 180 individuals from the Democratic Republic of the Congo, where konzo is most frequent, we investigate how the structure of the gut microbiome varied across geographical contexts, as well as provide the first insight into the gut flora of children affected with this debilitating disease using shotgun metagenomic sequencing. Our findings indicate that the gut microbiome structure is highly variable depending on region of sampling, but most interestingly, we identify unique enrichments of bacterial species and functional pathways that potentially modulate the susceptibility of konzo in prone regions of the Congo.

Multi-Omics Analyses Show Disease, Diet, and Transcriptome Interactions With the Virome.

BACKGROUND & AIMS: The gut virome includes eukaryotic viruses and bacteriophages that can shape the gut bacterial community and elicit host responses. The virome can be implicated in diseases, such as irritable bowel syndrome (IBS), where gut bacteria play an important role in pathogenesis. We provide a comprehensive and longitudinal characterization of the virome, including DNA and RNA viruses and paired multi-omics data in a cohort of healthy subjects and patients with IBS. METHODS: We selected 2 consecutive stool samples per subject from a longitudinal study cohort and performed metagenomic sequencing on DNA and RNA viruses after enriching for viral-like particles. Viral sequence abundance was evaluated over time, as well as in the context of diet, bacterial composition and function, metabolite levels, colonic gene expression, host genetics, and IBS subsets. RESULTS: We found that the gut virome was temporally stable and correlated with the colonic transcriptome. We identified IBS-subset-specific changes in phage populations; Microviridae, Myoviridae, and Podoviridae species were elevated in diarrhea-predominant IBS, and other Microviridae and Myoviridae species were elevated in constipation-predominant IBS compared to healthy controls. We identified correlations between subsets of the virome and bacterial composition (unclassifiable "dark matter" and phages) and diet (eukaryotic viruses). CONCLUSIONS: We found that the gut virome is stable over time but varies among subsets of patients with IBS. It can be affected by diet and potentially influences host function via interactions with gut bacteria and/or altering host gene expression.

Gut microbiome heritability is nearly universal but environmentally contingent.

Relatives have more similar gut microbiomes than nonrelatives, but the degree to which this similarity results from shared genotypes versus shared environments has been controversial. Here, we leveraged 16,234 gut microbiome profiles, collected over 14 years from 585 wild baboons, to reveal that host genetic effects on the gut microbiome are nearly universal. Controlling for diet, age, and socioecological variation, 97% of microbiome phenotypes were significantly heritable, including several reported as heritable in humans. Heritability was typically low (mean = 0.068) but was systematically greater in the dry season, with low diet diversity, and in older hosts. We show that longitudinal profiles and large sample sizes are crucial to quantifying microbiome heritability, and indicate scope for selection on microbiome characteristics as a host phenotype.

A global metagenomic map of urban microbiomes and antimicrobial resistance.

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.

Traditional Human Populations and Nonhuman Primates Show Parallel Gut Microbiome Adaptations to Analogous Ecological Conditions.

Compared with urban-industrial populations, small-scale human communities worldwide share a significant number of gut microbiome traits with nonhuman primates. This overlap is thought to be driven by analogous dietary triggers; however, the ecological and functional bases of this similarity are not fully understood. To start addressing this issue, fecal metagenomes of BaAka hunter-gatherers and traditional Bantu agriculturalists from the Central African Republic were profiled and compared with those of a sympatric western lowland gorilla group (Gorilla gorilla gorilla) across two seasons of variable dietary intake. Results show that gorilla gut microbiomes shared similar functional traits with each human group, depending on seasonal dietary behavior. Specifically, parallel microbiome traits were observed between hunter-gatherers and gorillas when the latter consumed more structural polysaccharides during dry seasons, while small-scale agriculturalist and gorilla microbiomes showed significant functional overlap when gorillas consumed more seasonal ripe fruit during wet seasons. Notably, dominance of microbial transporters, transduction systems, and gut xenobiotic metabolism was observed in association with traditional agriculture and energy-dense diets in gorillas at the expense of a functional microbiome repertoire capable of metabolizing more complex polysaccharides. Differential abundance of bacterial taxa that typically distinguish traditional from industrialized human populations (e.g., Prevotella spp.) was also recapitulated in the human and gorilla groups studied, possibly reflecting the degree of polysaccharide complexity included in each group's dietary niche. These results show conserved functional gut microbiome adaptations to analogous diets in small-scale human populations and nonhuman primates, highlighting the role of plant dietary polysaccharides and diverse environmental exposures in this convergence.IMPORTANCE The results of this study highlight parallel gut microbiome traits in human and nonhuman primates, depending on subsistence strategy. Although these similarities have been reported before, the functional and ecological bases of this convergence are not fully understood. Here, we show that this parallelism is, in part, likely modulated by the complexity of plant carbohydrates consumed and by exposures to diverse xenobiotics of natural and artificial origin. Furthermore, we discuss how divergence from these parallel microbiome traits is typically associated with adverse health outcomes in human populations living under culturally westernized subsistence patterns. This is important information as we trace the specific dietary and environmental triggers associated with the loss and gain of microbial functions as humans adapt to various dietary niches.