Biology subjects
Publications and source records attributed to Youngblut, N. D..
This study presents shotgun gut metagenomes of 430 men and women (18-62 years old) from diverse urban areas of Colombia. Metagenome assembly resulted in 2 266 medium- and high-quality metagenome-assembled genomes (MAGs). The metagenomes and MAGs complement previously published 16S rRNA gene and host data from the same cohort.
Cellular responses to perturbations are a cornerstone for understanding biological mechanisms and selecting drug targets. While machine learning models offer tremendous potential for predicting perturbation effects, they currently struggle to generalize to unobserved cellular contexts. Here, we introduce SO_SCPLOWTATEC_SCPLOW, a transformer model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. SO_SCPLOWTATEC_SCPLOW predicts perturbation effects across sets of cells and is trained using gene expression data from over 100 million perturbed cells. SO_SCPLOWTATEC_SCPLOW improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling and chemical perturbations with significantly improved accuracy. Using its cell embedding trained on observational data from 167 million cells, SO_SCPLOWTATEC_SCPLOW identified strong perturbations in novel cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that highlights SO_SCPLOWTATEC_SCPLOWs ability to detect cell type-specific perturbation responses, such as cell survival. Overall, the performance and flexibility of SO_SCPLOWTATEC_SCPLOW sets the stage for scaling the development of virtual cell models.
Single-cell RNA sequencing has transformed cell biology by enabling precise transcriptomic measurements of individual cells. The Sequence Read Archive (SRA) is the largest public repository of sequencing reads, yet much of it remains underutilized due to unstandardized metadata and the cost of processing reads. Here, we introduce scBaseCount, a single-cell RNA sequencing database that leverages an AI agent to automate discovery and metadata extraction, and standardize data processing. Built by directly mining all 10x Genomics datasets from SRA, scBaseCount is the largest freely accessible public repository of single-cell gene expression data, comprising over 502 million cells across 27 organisms and 75 tissues, offering an unbiased view of the composition of data within SRA. Uniform processing enables measurement of both intronic and exonic reads, non-coding gene expression and improves alignment across experiments as well as the performance of AI models trained on this phenotypically diverse data. Moreover, scBaseCount provides a blueprint for how AI can be leveraged to curate and autonomously update large biological data repositories.
Soil-transmitted helminths (STH) are common in (sub)tropical regions and primarily affect impoverished populations. STHs reside in the gut, interacting both with the gut microbiota and host immunity. Clinical STH detection is laborious and often not performed within the context of gut microbiome studies. Here, we assessed whether fecal metagenome data could be used to assess STH infection, and to relate STH infection to microbiome diversity. We generated 310 gut metagenomes for mother-child pairs from two different locations in Gabon: one rural and one semi-urban. The presence and abundance of four STH parasites (Ascaris lumbricoides, Strongyloides stercoralis, Trichuris trichiura, and Necator americanus) were assessed using both microscopy and qPCR. Sequence data were used to characterize the microbiomes and to detect the presence of these four STH parasites. We found that metagenome data could accurately detect the presence of all four STHs, as reflected by high sensitivity and specificity compared to microscopy or qPCR detection. Furthermore, the number of STH species present in stool was associated with microbiome diversity and with the abundances of specific taxa, most notably in young children. We applied this approach to microbiome data from five other populations in Africa and corroborated our findings. Our results demonstrate that human intestinal parasites can be accurately detected via metagenomic sequencing, and highlight how infection with multiple STH parasites is linked to consistent features in the gut microbiome in populations in Africa.
This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiotas role in NPD and its interactions with pharmacological treatments are needed.
Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in the growth performance of three wild-type strains and one genetically engineered strain in two independent chemostat bioreactor experiments. In the first experiment, with molecular hydrogen and carbon dioxide, we found the highest methane production rate for Methanothermobacter thermautotrophicus {Delta}H, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. Systems biology investigations, including implementing a pan-model that contains combined reactions from all three microbes, allowed us to perform an interspecies comparison. This comparison enabled us to identify crucial differences in formate anabolism. In the second experiment, with sodium formate, we found stable growth with an M. thermautotrophicus {Delta}H plasmid-carrying strain with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. Our findings reveal that formate anabolism influences the diversion of carbon to biomass and methane with implications for biotechnological applications of Methanothermobacter spp. in power-to-gas technology and for chemical production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/522236v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1d468aeorg.highwire.dtl.DTLVardef@24a595org.highwire.dtl.DTLVardef@1f2dd60org.highwire.dtl.DTLVardef@1a3d8fc_HPS_FORMAT_FIGEXP M_FIG C_FIG Broader contextRenewable energy sources (e.g., wind and solar) provide carbon-free electric power. However, their intermittency and offset between peak production and demand generate the need to store this electric power. Furthermore, these technologies alone do not satisfy the demand for carbon-based commodities. Power-to-gas technology provides a means to store intermittent renewable electric power with concomitant carbon dioxide recycling into a chemical energy carrier, such as methane, on a centralized and decentralized scale. This is particularly important to establish equitable energy strategies for all countries, as is highlighted by the United Nations Sustainable Development Goals. With this work, we provide an integrated systems-biology platform for Methanothermobacter spp. to optimize biological power-to-gas technology and formulate strategies to produce other value-added products besides methane.
The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging to detect for taxonomically novel genomic data. Assembly errors can affect all downstream analyses of the assemblies. Accuracy for the state of the art in reference-free misassembly prediction does not exceed an AUPRC of 0.57, and it is not clear how well these models generalize to real-world data. Here, we present the Residual neural network for Misassembled Contig identification (ResMiCo), a deep learning approach for reference-free identification of misassembled contigs. To develop ResMiCo, we first generated a training dataset of unprecedented size and complexity that can be used for further benchmarking and developments in the field. Through rigorous validation, we show that ResMiCo is substantially more accurate than the state of the art, and the model is robust to novel taxonomic diversity and varying assembly methods. ResMiCo estimated 4.7% misassembled contigs per metagenome across multiple real-world datasets. We demonstrate how ResMiCo can be used to optimize metagenome assembly hyperparameters to improve accuracy, instead of optimizing solely for contiguity. The accuracy, robustness, and ease-of-use of ResMiCo make the tool suitable for general quality control of metagenome assemblies and assembly methodology optimization. Author summaryMetagenome assembly quality is fundamental to all downstream analyses of such data. The number of metagenome assemblies, especially metagenome-assembled genomes (MAGs), is rapidly increasing, but tools to assess the quality of these assemblies lack the accuracy needed for robust quality control. Moreover, existing models have been trained on datasets lacking complexity and realism, which may limit their generalization to novel data. Due to the limitations of existing models, most studies forgo such approaches and instead rely on CheckM to assess assembly quality, an approach that only utilizes a small portion of all genomic information and does not identify specific misassemblies. We harnessed existing large genomic datasets and high-performance computing to produce a training dataset of unprecedented size and complexity and thereby trained a deep learning model for predicting misassemblies that can robustly generalize to novel taxonomy and varying assembly methodologies.
Flagellin, the protein unit of the bacterial flagellum, stimulates the innate immune receptor Toll-like receptor (TLR)5 following pattern recognition, or evades TLR5 through lack of recognition. This binary response fails to explain the weak agonism of flagellins from commensal bacteria, raising the question of how TLR5 response is tuned. Here, we describe a novel class of flagellin-TLR5 interaction, termed silent recognition. Silent flagellins are weak agonists despite high affinity binding to TLR5. This dynamic response is tuned by TLR5-flagellin interaction distal to the site of pattern recognition. Silent flagellins are produced primarily by the abundant gut bacteria Lachnospiraceae and are enriched in non-Western populations. These findings provide a mechanism for the innate immune system to tolerate commensal-derived flagellins. One-Sentence SummaryTLR5 sensitively recognizes, but responds weakly to, flagellins from gut commensal bacteria.
Tracking the metabolic activity of whole soil communities can improve our understanding of the transformation and fate of carbon in soils. We used stable isotope metabolomics to trace 13C from nine labeled carbon sources into the water-soluble metabolite pool of an agricultural soil over time. Soil was amended with a mixture of all nine sources, with one source isotopically labeled in each treatment. We compared changes in the 13C-enrichment of metabolites with respects to carbon source and time over a 48-day incubation and contrasted differences between soluble versus insoluble sources. Whole soil metabolite profiles varied singularly by time, while the composition of 13C-labeled metabolites differed primarily by carbon source (R2 = 0.68) rather than time (R2 = 0.07) with source-specific differences persisting throughout incubations. The 13C-labeling of metabolites from insoluble carbon sources occurred at a slower rate than soluble sources but yielded a higher average atom % 13C in metabolite markers of biomass (amino acids and nucleic acids). The 13C-enrichment of metabolite markers of biomass stabilized at between 5 - 15 atom % 13C by the end of incubations. Temporal patterns in the 13C-enrichment of TCA cycle intermediates, nucleobases (uracil and thymine), and by-products of DNA salvage (allantoin) closely tracked microbial activity. Our results demonstrate that metabolite production in soils is driven by the carbon source supplied to the community, and that the fate of carbon in metabolite profiles do not tend to converge over time as a result of ongoing microbial processing and recycling. ImportanceCarbon metabolism in soil remains poorly described due to the inherent difficulty of obtaining information on the microbial metabolites produced by complex soil communities. Our study demonstrates the use of stable isotope probing (SIP) to study carbon metabolism in soil by tracking 13C from supplied carbon sources into metabolite pools and biomass. We show that differences in the metabolism of sources influences the fate of carbon in soils. Heterogeneity in 13C-metabolite profiles corresponded with compositional differences in the metabolically active populations, providing a basis for how microbial community composition is correlated with the quality of soil carbon. Our study demonstrates the application of SIP-metabolomics in studying soils and identifies several metabolite markers of growth, activity, and other aspects of microbial function.
Soil dwelling microorganisms are key players in the terrestrial carbon cycle, driving both the degradation and stabilization of soil organic matter. Bacterial community structure and function vary with respect to land-use, yet the ecological drivers of this variation remain poorly described and difficult to predict. We conducted a multi-substrate DNA-stable isotope probing experiment across cropland, old-field, and forest habitats to link carbon mineralization dynamics with the dynamics of bacterial growth and carbon assimilation. We tracked the movement of 13C derived from five distinct carbon sources as it was assimilated into bacterial DNA over time. We show that carbon mineralization, community composition, and carbon assimilation dynamics all differed with respect to land-use. We also show that microbial community dynamics affect carbon assimilation dynamics and are predictable from soil DNA content. Soil DNA yield is easy to measure and it predicts microbial community dynamics linked to soil carbon cycling. Originality-Significance StatementSoil dwelling microorganisms are key players in the terrestrial carbon cycle, driving both the degradation and stabilization of soil organic matter. Microbial communities vary with respect to land-use, but we still have an incomplete understanding of how variation in community structure links to variation in community function. DNA stable isotope probing (DNA-SIP) is a high-resolution method that can identify specific microbial taxa that assimilate carbon in situ. We conducted a large-scale multi-substrate DNA-SIP experiment to explore differences in bacterial activity across land-use regimes. We show that microbial community dynamics vary with land-use, that these dynamics are linked to soil carbon cycling, and that they are predicted from easily measured soil properties.
BackgroundTree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only yield limited insights into how microbial taxa or genomic content may be associated. Results: We developed endoR, a method to interpret a fitted tree ensemble model. First, endoR simplifies the fitted model into a decision ensemble from which it then extracts information on the importance of individual features and their pairwise interactions and also visualizes these data as an interpretable network. Both the network and importance scores derived from endoR provide insights into how features, and interactions between them, contribute to the predictive performance of the fitted model. Adjustable regularization and bootstrapping help reduce the complexity and ensure that only essential parts of the model are retained. We assessed the performance of endoR on both simulated and real metagenomic data. We found endoR to infer true associations with more or comparable accuracy than other commonly used approaches while easing and enhancing model interpretation. Using endoR, we also confirmed published results on gut microbiome differences between cirrhotic and healthy individuals. Finally, we utilized endoR to gain insights into components of the microbiome that predict the presence of human gut methanogens, as these hydrogen-consumers are expected to interact with fermenting bacteria in a complex syntrophic network. Specifically, we analyzed a global metagenome dataset of 2203 individuals and confirmed the previously reported association between Methanobacteriaceae and Christensenellales. Additionally, we observed that Methanobacteriaceae are associated with a network of hydrogen-producing bacteria. Conclusion: Our method accurately captures how tree ensembles use features and interactions between them to predict a response. As demonstrated by our applications, the resultant visualizations and summary outputs facilitate model interpretation and enable the generation of novel hypotheses about complex systems. An implementation of endoR is available as an open-source R-package on GitHub (https://github.com/leylabmpi/endoR).
Some gut microbes have cospeciated with hominids, but whether they further codiversified with human populations is unclear. Here, we identify predominant gut microbial species sharing a parallel evolutionary history with human populations. Patterns of strain transfer between populations are generally consistent with an African origin, and suggest long-term vertical transmission over thousands of generations. We show the same strains also faithfully transmit between mothers and their children. Consistent with the development of intimate symbiosis, species with strongest patterns of codiversification have the smallest genomes. This study reveals long-term fidelity of gut microbiota with human populations through transmission among individuals living in close proximity. Dominance of specific strains in different populations is based in part on vertical transmission and they may provide population-specific health benefits. One-sentence summaryIdentification of gut microbes that codiversified with human populations.
Soil microorganisms determine the fate of soil organic matter (SOM), and their activities comprise a major component of the global carbon (C) cycle. We employed a multi-substrate DNA-stable isotope probing experiment to track bacterial assimilation of C derived from distinct sources that varied in bioavailability. This approach allowed us to measure microbial contributions to SOM processing by measuring the C assimilation dynamics of diverse microorganisms as they interact within soil. We identified and tracked 1,286 bacterial taxa that assimilated 13C in an agricultural soil over a period of 48 days. Overall 13C-assimilation dynamics of bacterial taxa, defined by the source and timing of the 13C they assimilated, exhibited low phylogenetic conservation. We identified bacterial guilds comprised of taxa that had similar 13C assimilation dynamics. We show that C source bioavailability explained significant variation in both C mineralization dynamics and guild structure. In addition, guild structure explained significant variation in bacterial growth dynamics. We demonstrate that the observed guild structure is consistent with predictions made by bacterial life history theory. We also demonstrate that the guild structure explains significant variation in the biogeographical distribution of bacteria at continental and global scales. We interpret these findings in the context of bacterial life history strategies and their relationship to terrestrial C-cycling.
Commonly used 16S rRNA gene primers miss much of the archaeal diversity present in the vertebrate gut, leaving open the question of which archaea are host associated, the specificities of such associations, and the major factors influencing archaeal diversity. We applied 16S rRNA amplicon sequencing with Archaea-targeting primers to a dataset of 311 fecal/gut samples spanning 5 taxonomic classes (Mammalia, Aves, Reptilia, Amphibia, and Actinopterygii) and obtained from mainly wild individuals (76% were wild). We obtained sufficient archaeal sequence data from 185 samples comprising 110 species that span all 5 classes. We provide evidence for novel Archaea-host associations, including Bathyarchaeia and Methanothermobacter -- the latter of which was prevalent among Aves and enriched in higher body temperatures. Host phylogeny more strongly explained archaeal diversity than diet, while specific taxa were associated with each factor. Co-phylogeny was significant and strongest for mammalian herbivores. Methanobacteria was the only class predicted to be present in the last command ancestors of mammals and all host species. Archaea-Bacteria interactions seem to have a limited effect on archaeal diversity. These findings substantially expand on the paradigm of Archaea-vertebrate associations and the factors that explain those associations. SignificanceArchaea play key roles in the vertebrate gut such as promoting bacterial fermentation via consumption of waste products. Moreover, gut-inhabiting methanogenic Archaea in livestock are a substantial source of greenhouse gas production. Still, much is not known of the archaeal diversity in most vertebrates, especially since 16S rRNA sequence surveys often miss much of the archaeal diversity that is present. By applying Archaea-targeted gut microbiome sequencing to a large collection of diverse vertebrates, we reveal new Archaea-host associations such as a high prevalence of Methanothermobacter in birds. We also show that host evolutionary history explains archaeal diversity better than diet, and certain genera in one particular class of Archaea (Methanobacteria) were likely pervasive in the ancestral vertebrate gut.
Host-adapted microbiota are generally thought to have evolved from free-living ancestors. This process is in principle reversible, but examples are few. The genus Psychrobacter (family Moraxellaceae, phylum Gamma-Proteobacteria) includes species inhabiting diverse and mostly polar environments, such as sea ice and marine animals. To probe Psychrobacters evolutionary history, we analyzed 85 Psychrobacter strains by comparative genomics and phenotyping under 24 different growth conditions. Genome-based phylogeny shows Psychrobacter are derived from Moraxella, which are warm-adapted pathobionts. Psychrobacter strains form two ecotypes based on growth temperature: flexible (FE, growth at 4 - 37{degrees}C), and restricted (RE, 4 - 25{degrees}C). FE strains, which can be either phylogenetically basal or derived, have smaller genomes and higher transposon copy numbers. RE strains have larger genomes, and show genomic adaptations towards a psychrophilic lifestyle and are phylogenetically derived only. We then assessed Psychrobacter abundance in 86 mostly wild polar bear stools and tested persistence of select strains in germfree mice. Psychrobacter (both FE and RE) was enriched in stool of polar bears feeding on mammals, but only FE strains persisted in germfree mice. Together these results indicate growth at 37{degrees}C is ancestral in Psychrobacter, lost in many derived species, and likely necessary to colonize the mammalian gut.
The genera Catabacter (family Catabacteraceae) and Christensenella (family Christensenellaceae) are close relatives within the phylum Firmicutes. Members of these genera are strictly anaerobic, non-spore forming, short straight rods with diverse phenotypes. Phylogenetic analysis of 16S rRNA genes suggest that Catabacter splits Christensenella into a polyphyletic clade. In an effort to ensure that family/genus names represent monophyletic clades, we performed a whole-genome based analysis of the genomes available for the cultured representatives of these genera: four species of Christensenella and two strains of Catabacter hongkongensis. A concatenated alignment of 135 shared protein sequences indicates that C. hongkongensis is indeed nested within the Christensenella clade. Based on their evolutionary relationship, we propose the transfer of Catabacter hongkongensis to the new genus as Christensenella hongkongensis comb.nov.
Archaea of the order Methanomassiliicoccales use methylated-amines such as trimethylamine as a substrate for methane production. They form two large phylogenetic clades and reside in diverse environments, from soil to the human gut. Two genera, one from each clade, inhabit the human gut: Methanomassiliicoccus, which has one cultured representative, and "candidatus Methanomethylophilus", which has none. Questions remain regarding their distribution across different biomes and human populations, their association with other taxa in the human gut, and whether host genetics correlate with their abundance. To gain insight into the Methanomassiliicoccales, and the human-associated members in particular, we performed a genomic comparison of 72 Methanomassiliicoccales genomes and assessed their presence in metagenomes derived from the human gut (n=4472 representing 25 populations), nonhuman animal gut (n=145) and nonhost environments (n=160). Our analyses showed that all taxa are generalists: they were detected in animal gut and environmental samples. We confirmed two large clades, one enriched in the gut, the other enriched in the environment, with notable exceptions. Genomic adaptations to the gut include genome reduction, a set of adhesion factors distinct from that of environmental taxa, and genes involved in the shikimate pathway and bile resistance. Genomic adaptations differed by clade, not habitat preference, indicating convergent evolution between the clades. In the human gut, the relative abundance of Methanomassiliicoccales correlated with trimethylamine-producing bacteria and was unrelated to host genotype. Our results shed light on the microbial ecology of this group may help guide Methanomassiliicoccales-based strategies for trimethylamine mitigation in cardiovascular disease. ImportanceMethanomassiliicoccales are a lesser known component of the human gut microbiota. This archaeal order is composed of methane producers that use methylated amines, such as trimethylamine, in methane production. This group has only one cultured representative; how they adapted to inhabit the mammalian gut and how they interact with other microbes is largely unknown. Using bioinformatics methods applied to DNA from a wide range of samples, we profiled the relative abundances of these archaea in environmental and host-associated microbial communities. We observed two groups of Methanomassiliicoccales, one largely host-associated and one largely found in environmental samples, with some exceptions. When host-associated, these archaea have a distinct set of genes related to adhesion and possess genes related to bile resistance. We did not detect Methanomassiliicoccales in all human populations tested but when present, they are correlated with Bacteria known to produce trimethylamine. Since trimethylamine is linked to cardiovascular disease risk, these intriguing Archaea may also be involved.
One of the strongest associations between human genetics and the gut microbiome is a greater relative abundance of Bifidobacterium in adults with lactase gene (LCT) SNPs associated with lactase-non persistence (GG genotypes), versus lactase persistence (AA/AG genotypes). To gain a finer grained phylogenetic resolution of this association, we interrogated 1,680 16S rRNA libraries and 245 metagenomes from gut microbiomes of adults with varying lactase persistence genotypes. We further employed a novel genome-capture based enrichment of Bifidobacterium DNA from a subset of these metagenomes, including monozygotic (MZ) twin pairs, each sampled 2 or 3 times. B. adolescentis and B. longum were the most abundant Bifidobacterium species regardless of host LCT-genotype. LCT- genotypes could not be discriminated based on relative abundances of Bifidobacterium species or Bifidobacterium community structure. Metagenomic analysis of Bifidobacterium-enriched DNA revealed intra-individual temporal stability of B. longum, B. adolescentis, and B. bifidum strains against the background of a changeable microbiome. We also observed greater strain sharing within MZ twin pairs compared to unrelated individuals, and within GG compared to AA/AG individuals, but no effect of host LCTgenotype on Bifidobacterium strain composition. Our results support a "rising tide lift all boats" model for the dominant Bifidobacteria in the adult gut: their higher abundance in lactase-non persistent compared to lactase-persistent individuals results from an expansion at the genus level. Bifidobacterium species are known to be transmitted from mother to child and stable within individuals in infancy and childhood: our results extend this stability into adulthood. IMPORTANCEWhen human populations domesticated animals to drink their milk they adapted genetically with the ability to digest milk into adulthood (lactase persistence). The gut microbiomes of lactase non-persistent people (LNP) differ from those of lactase-persistent people (LP) by containing more bacteria belonging to the Bifidobacteria. These beneficial gut bacteria, which fall into many species, are known to degrade milk in the baby gut. Here, we asked if adult LP and LNP microbiomes differ in the species of Bifidobacteria present. We studied the gut microbiomes of LP and LNP adults, including twins, sampled at several times. In particular, we used a technique to selectively pull out the DNA belonging to the Bifidobacteria: analysis of these DNA segments allowed us to compare Bifidobacteria at the strain level. Our results show that the LNP enhance the abundance of Bifidobacteria regardless of species. We also noted that a persons specific strains are recoverable several years later, and twins tend to share the same ones. Given that Bifidobacteria are inherited from mother to child, strain stability over time in adulthood suggests long term, multi-generational inheritance.