Biology subjects

Christakis, N. A.

Publications and source records attributed to Christakis, N. A..

5 recordsLinked to original sources

bioRxiv2026

Social network cycle motifs and gut microbiome strain-sharing

Microbes are commonly transmitted through human social interactions, yet little is known about how higher-order social network structures shape microbial circulation. Here, we analyze network cycles, i.e., closed loops of individuals, in both friendship networks and microbial networks, across 1,787 individuals from 18 isolated Honduran villages. Using strain-level resolution, we construct species-specific microbial networks, study their cyclic structure, and compare them with the social networks in the same population. Cycles were strongly over-represented relative to degree-preserving randomized networks in both social and microbial networks for most species, indicating that microbial transmission frequently occurs within recurrent and clustered groups of hosts. However, the overlap between microbial and social cycles varies substantially across species and individuals, and regression analyses identify a small subset of species whose cyclic sharing patterns are associated with social cycle participation. Notably, several anaerobic species show negative associations, suggesting reliance on repeated local exposures or shared environments only partially aligned with social ties. Consistent with this, many species exhibit niche-like transmission pathways independent of social network structure. Together, these findings show that microbial sharing networks exhibit rich higher-order organization that only partially mirrors human social networks and that reveals that network cycles can provide a useful framework for understanding how repeated exposure might contribute to microbial spread.

microbiology↗

bioRxiv2025

High-Fidelity Tuning of Olfactory Mixture Distances in the Perceptual Space of Smell Through a Community Effort

A central goal in sensory science is to establish quantitative mappings between physical stimuli and perceptual responses. While such mappings are well characterized in vision and audition, they remain poorly defined in olfaction, limiting progress toward understanding the representations of smell. Predicting perceptual similarity between odor mixtures offers a promising route to formalize these relationships. To advance this effort, the DREAM (Dialogue for Reverse Engineering Assessment and Methods) Olfactory Mixtures Prediction Challenge assembled a curated, cross-study dataset describing the similarity of 507 mixture pairs and an unpublished test set of 46 mixture pairs. Teams competed to predict the perceptual similarity of mixture pairs, and then collaborated post-challenge to create an ensemble combining top-performing models that notably improves predictions over the existing state-of-the-art models. Moreover, ensemble model maintains high predictive accuracy in novel validation set. Our model provides a reproducible framework for neuroscientists, chemists, and engineers to compare odor mixtures and provides a foundation for future efforts towards better understanding the olfactory properties of mixtures.

neuroscience↗

bioRxiv2025

Metagenomic polymorphic toxin effector and immunity profiling predicts microbiome development and disease-related dysbiosis

Bacteria use antagonistic interbacterial weapons such as polymorphic toxin secretion systems (TSS) to compete for niches in the human gut microbiome. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including [~]200 effector and immunity genes and applied it to [~]15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states. Decision tree machine learning models integrating bacterial taxonomy with PolyProf had near-perfect accuracy (ROC area 1.00) for all 12 disease states. During microbiome development in the first year of life, PolyProf alpha diversity increases, and beta diversity becomes increasingly like the maternal microbiome, influenced by vertical transfer, delivery mode, and breastfeeding. PolyProf is related to strain sharing among adults through social interactions. In summary, interbacterial antagonism with TSS shapes microbiome development and interpersonal strain sharing. Since PolyProf distinguishes diverse adult disease statuses, these dynamics may contribute to non-genetic inheritance.

microbiology↗

bioRxiv2025

Characterization of gut microbiomes in rural Honduras reveals novel species and associations with human genetic variation

The gut microbiome is integral to human health, yet research data to date has emphasized industrialized populations. Here, we performed large-scale shotgun metagenomic sequencing on 1,889 individuals from rural Honduras, providing the most comprehensive microbiome dataset from Central America. We identify a distinct microbial composition enriched in Prevotella species, with 861 previously unreported bacterial species. Functional profiling reveals unique carbohydrate metabolism adaptations consistent with high-fiber diets. Longitudinal analysis over two years reveals microbiome instability, with shifts in taxonomic diversity and metabolic potential, including changes associated with SARS-CoV-2 infection. Additionally, we characterize the gut virome and eukaryotic microbiome, identifying novel viral taxa, including Crassvirales phages, and a high prevalence of Blastocystis species in individuals with greater microbial diversity. Finally, by integrating host genomic data obtained from low-pass saliva whole-genome sequencing, we uncover significant host-microbiome associations, highlighting the influence of human genetic variation on microbial composition. People who are more genetically similar also have more similar gut microbiomes. These findings expand our understanding of microbiome diversity in non-industrialized populations, highlighting the uniqueness of those microbiomes and underscoring the need for global microbiome research.

microbiology↗

bioRxiv2023

Detailed Social Network Interactions and Gut Microbiome Strain-Sharing Within Isolated Honduras Villages

When humans assemble into face-to-face social networks, they create an extended environment that permits exposure to the microbiome of other members of a population. Social network interactions may thereby also shape the composition and diversity of the microbiome at individual and population levels. Here, we use comprehensive social network and detailed microbiome sequencing data in 1,098 adults across 9 isolated villages in Honduras to investigate the relationship between social network structure and microbiome composition. Using both species-level and strain-level data, we show that microbial sharing occurs between many relationship types, notably including non-familial and non-household connections. Using strain-sharing data alone, we can confidently predict a wide variety of relationship types (AUC ~0.73). This strain-level sharing extends to second-degree social connections in a network, suggesting the importance of the extended network with respect to microbiome composition. We also observe that socially central individuals are more microbially similar to the overall village than those on the social periphery. Finally, we observe that clusters of microbiome species and strains occur within clusters of people in the village social networks, providing the social niches in which microbiome biology and phenotypic impact are manifested.

microbiology↗