Biology subjects

Lozupone, C.

Publications and source records attributed to Lozupone, C..

12 recordsLinked to original sources

bioRxiv2026

Altered Tonsillar Microbiome in Children with Down Syndrome and Obstructive Sleep Apnea

Background and ObjectivesChildren with Down syndrome (DS) have a high prevalence of obstructive sleep apnea (OSA) due to anatomic, neuromuscular, immunological and metabolic factors, yet the contribution of the tonsillar microbiome to airway obstruction in this population remains unexplored. We hypothesized that DS-associated OSA would be associated with a distinct tonsillar microbiome compared to non-DS OSA. MethodsTonsillar tissue from 22 DS and 18 NDS participants were analyzed by 16S rRNA sequencing. Alpha and beta diversity were assessed using Faiths phylogenetic diversity and UniFrac distances, respectively, and significantly different taxa were identified with ANCOM-BC and Mann-Whitney testing. ResultsAlthough overall microbial richness and community structure were similar between groups, overweight DS participants demonstrated increased phylogenetic diversity compared to normal-weight DS peers. Taxonomic profiling of the entire patient cohort revealed that in DS tonsils there were selective alterations in key genera with selective depletion of Haemophilus and enrichment of Staphylococcus, Rothia, and Lactobacillales. Haemophilus abundance correlated positively with tonsil weight in both cohorts. ConclusionsThese findings suggest that while global diversity is preserved, specific microbial shifts distinguish the DS tonsillar niche, potentially reflecting altered immune and metabolic environments associated with trisomy 21. Understanding these microbial differences may reveal mechanisms underlying the higher incidence and persistence of OSA in DS and inform targeted therapeutic strategies.

microbiology↗

bioRxiv2026

Amoxicillin induces gut dysbiosis leading to long term suppression of type-17 immune tone in the lungs

T-helper (Th)-17 lymphocytes are central mediators of adaptive type 17 immunity. Decreased type-17 signaling increases severity of infections in humans and mice. However, detrimental effects of excessive type 17 responses in autoimmune and other inflammatory diseases highlight a need for type-17 immune calibration to support beneficial host defense requirements. Mechanisms of type 17 calibration are poorly understood. A gut-lung axis has been proposed to coordinate homeostatic protection and acute host defense. Factors that acutely alter the gut microbiome are heterogeneous and include acute intestinal infections, non-infectious colitis, and medical treatments such as antibiotics. How changes in the gut microbiome affect lung immune tone during homeostasis and acute pulmonary infections are also poorly understood. Prior studies have shown that antibiotics reduce expression of IL-17-mediated host defense in the gut. Since gut microbial homeostasis influences Th17 cell numbers in both the intestine and remote tissues, we postulated that antibiotic treatment would result in gut dysbiosis and weakened type-17 host defense in the lungs. We found that amoxicillin induces significant dysbiosis that is long-lasting and that there is a long-term decrease in type-17 tone in the lungs. We also found that in mice lacking the gut mucin, Muc2, Th17 cells increased in the lungs following inflammatory challenge. These findings suggest that antibiotic-induced dysbiosis can decrease lung immune defenses for long periods of time after cessation of antibiotic treatment.

immunology↗

bioRxiv2026

Enabling Megascale Microbiome Analysis with DartUniFrac

We introduce a new algorithm, DartUniFrac, and a near-optimal implementation with GPU acceleration, up to three orders of magnitude faster than the state of the art and scaling to millions of samples (pairwise) and billions of taxa. DartUniFrac connects UniFrac with weighted Jaccard similarity and exploits sketching algorithms for fast computation. We benchmark DartUniFrac against exact UniFrac implementations, demonstrating that DartUniFrac is statistically indistinguishable from them on real-world microbiome and metagenomic datasets.

bioinformatics↗

bioRxiv2025

The impact of western versus agrarian diet consumption on gut microbiome composition and immune dysfunction in people living with HIV in rural and urban Zimbabwe

BackgroundPeople living with HIV (PLWH) suffer from chronic inflammation even with effective antiretroviral therapy (ART). A high-fat, low-fiber western-type diet has been linked with inflammation, in part through gut microbiome changes. In sub-Saharan Africa (SSA), a region with high HIV burden, urbanization has been linked with a shift from traditional agrarian towards westernized diets, and with changes in food security. To explore the relationship between diet, inflammation, and the gut microbiome in PLWH, we enrolled 1) ART Naive PLWH who provided samples before and after 24 weeks of ART, 2) PLWH on ART at both timepoints and 3) HIV-seronegative controls. Individuals were evenly recruited from rural and urban Zimbabwe (locations were 145 kilometers/90 miles apart). Using a food frequency survey designed to measure intake of agrarian versus western-type food items in Zimbabwe, we determined how diet differs with urbanization, HIV-infection and treatment, and is related to inflammation and the gut microbiome. ResultsIndividuals residing in a rural area of Zimbabwe less frequently consumed high-fat, low-fiber western type food items and had lower consumption of diverse food items overall, except for sadza-a subsistence staple-processed from home-grown grains. Consumption of a more western-type diet correlated with lower CD4+ T cell percentage in untreated and treated PLWH and with increased T cell exhaustion in PLWH on ART. PLWH on ART at time of enrollment also consumed diverse food items at a lower frequency and more often were underweight. Low food consumption correlated with muted improvements in T cell exhaustion after 24 weeks of ART. Individuals residing in the rural area had more Prevotella-rich/Bacteroides-poor microbiomes, but this did was not significantly mediated by diet. western diet consumption reduced the diversity of carbohydrate substrate degradation capabilities in the microbiome, based on predictions made using metagenomic polysaccharide utilization loci. ConclusionsTaken together, this work supports that consumption of more high-fat/low-fiber type food items has the potential to exacerbate HIV pathogenesis in a sub-Saharan setting where HIV burden is high and reinforces the importance of nutritional support for promoting immunologic response to ART in PLWH in SSA.

microbiology↗

bioRxiv2025

Ecological relationships between human gut bacteria predicted from analysis of dense microbiome time series data from US travelers in Bangladesh

Gut microbiomes provide critical host homeostatic functions, resulting from a complex web of ecological interactions among community members. We studied these interactions using a time-lagged correlational strategy of dense longitudinal sequence data from Western individuals traveling abroad to Bangladesh who experienced diarrhea. We identified both negative (140) and positive (78) relationships between bacterial pairs. Positive relationships occurred in pairs that were significantly more phylogenetically distant, such as inter-order associations between Clostridiales and Bacteroidales, while negative relationships were more between more phylogenetically related pairs. Further analysis of computationally predicted genome content and metabolic pathways revealed that cooperative bacterial pairs overlapped less in function and offered each other metabolic support, while competitive pairs were more likely to compete for the same resources. Predicted levels of B vitamins (B5 and B3), enoyl acyl- carrier protein (acp) reductase II (FabK) and its metabolites, and nucleotide/nucleoside derivatives were able to differentiate negatively and positively associated microbe pairs. Ultimately, our findings show that combining time-series analysis with metabolic/genomic network analysis can identify relationships between bacteria with plausible causal mechanisms that are consistent with existing ecological and biochemical observations. IMPORTANCEUnderstanding how microbes in the gut interact with each other is important for devising strategies to target the human gut microbiome therapeutically. For instance, understanding competitive relationships, where a shared need of similar limited resources limits the degree to which two microbes can co-exist, can inform strategies for limiting colonization of undesirable microbes. Understanding cooperative relationships, where one microbe provides the other with substrates needed for growth, can inform strategies to promote desirable microbes. By evaluating dense time-series gut microbiome data from individuals who experienced diarrhea while traveling, we were able to predict both cooperative and competitive relationships among human gut microbes as those whose abundances were significantly related within an individual over time. Strikingly, in subsequent analyses performed using inferred genomic information, pairs with negative associations from the time series analysis were predicted to compete over more metabolic substrates, and pairs with positive associations had significantly more metabolic complementarity. These predictions regarding the underlying molecular bases of interactions could inform how nutritional environment will impact interactions between gut microbiome community members.

microbiology↗

bioRxiv2024

Appearance of green tea compounds in plasma following green tea consumption is modulated by the gut microbiome in mice

Studies have suggested that phytochemicals in green tea have systemic anti-inflammatory and neuroprotective effects. However, the mechanisms behind these effects are poorly understood, possibly due to differential metabolism of phytochemicals resulting from variation in gut microbiome composition. To unravel this complex relationship, our team utilized a novel combined microbiome analysis and metabolomics approach applied to low complexity microbiome (LCM) and human colonized (HU) gnotobiotic mice treated with an acute dose of powdered matcha green tea. A total of 20 LCM mice received 10 distinct human fecal slurries for an n=2 mice per human gut microbiome; 9 LCM mice remained un-colonized with human slurries throughout the experiment. We performed untargeted metabolomics on green tea and plasma to identify green tea compounds that were found in plasma of LCM and HU mice that had consumed green tea. 16S ribosomal RNA gene sequencing was performed on feces of all mice at study end to assess microbiome composition. We found multiple green tea compounds in plasma associated with microbiome presence and diversity (including acetylagmatine, lactiflorin, and aspartic acid negatively associated with diversity). Additionally, we detected strong associations between bioactive green tea compounds in plasma and specific gut bacteria, including associations between spiramycin and Gemmiger, and between wildforlide and Anaerorhabdus. Additionally, some of the physiologically relevant green tea compounds are likely derived from plant-associated microbes, highlighting the importance of considering foods and food products as meta-organisms. Overall, we describe a novel workflow for discovering relationships between individual food compounds and composition of the gut microbiome. ImportanceFoods contain thousands of unique and biologically important compounds beyond the macro- and micro-nutrients listed on nutrition facts labels. In mammals, many of these compounds are metabolized by the community of microbes in the colon. These microbes may impact the thousands of biologically important compounds we consume; therefore, understanding microbial metabolism of food compounds will be important for understanding how foods impact health. We used metabolomics to track green tea compounds in plasma of mice with and without complex microbiomes. From this, we can start to recognize certain groups of green tea-derived compounds that are impacted by mammalian microbiomes. This research presents a novel technique for understanding microbial metabolism of food-derived compounds in the gut, which can be applied to other foods.

systems biology↗

bioRxiv2024

EXPLANA: A user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies

MotivationLongitudinal microbiome studies (LMS) are increasingly common but have analytic challenges including non-independent data requiring mixed-effects models and large amounts of data that motivate exploratory analysis to identify factors related to outcome variables. Although change analysis (i.e. calculating deltas between values at different timepoints) can be powerful, how to best conduct these analyses is not always clear. For example, observational LMS measurements show natural fluctuations, so baseline might not be a reference of primary interest; whereas, for interventional LMS, baseline is a key reference point, often indicating the start of treatment. ResultsTo address these challenges, we developed a feature selection workflow for cross-sectional and LMS that supports numerical and categorical data called EXPLANA (EXPLoratory ANAlysis). Machine-learning methods were combined with different types of change calculations and downstream interpretation methods to identify statistically meaningful variables and explain their relationship to outcomes. EXPLANA generates an interactive report that textually and graphically summarizes methods and results. EXPLANA had good performance on simulated data, with an average area under the curve (AUC) of 0.91 (range: 0.79-1.0, SD = 0.05), outperformed an existing tool (AUC: 0.95 vs. 0.56), and identified novel order-dependent categorical feature changes. EXPLANA is broadly applicable and simplifies analytics for identifying features related to outcomes of interest.

bioinformatics↗

bioRxiv2023

Hypothesizing mechanistic links between microbes and disease using knowledge graphs

Knowledge graphs have found broad biomedical applications, providing useful representations of complex knowledge. Although plentiful evidence exists linking the gut microbiome to disease, mechanistic understanding of those relationships remains generally elusive. Here we demonstrate the potential of knowledge graphs to hypothesize plausible mechanistic accounts of host-microbe interactions in disease. To do so, we constructed a knowledge graph of linked microbes, genes and metabolites called MGMLink. Using a semantically constrained shortest path search through the graph and a novel path prioritization methodology based on cosine similarity, we show that this knowledge supports inference of mechanistic hypotheses that explain observed relationships between microbes and disease phenotypes. We discuss specific applications of this methodology in inflammatory bowel disease and Parkinsons disease. This approach enables mechanistic hypotheses surrounding the complex interactions between gut microbes and disease to be generated in a scalable and comprehensive manner.

bioinformatics↗

bioRxiv2023

Characterization of methods for mechanistic inference of the gut microbiome in disease

MotivationKnowledge graphs have found broad biomedical applications, providing useful representations of complex knowledge. Although plentiful evidence exists linking the gut microbiome to disease, mechanistic understanding of those relationships remains generally elusive. A structured analysis of existing resources is necessary to characterize the resources and methodologies needed to facilitate mechanistic inference. ResultsHere we demonstrate the potential of knowledge graphs to hypothesize plausible mechanistic accounts of host-microbe interactions in disease and define the need for semantic constraint in doing so. We constructed a knowledge graph of linked microbes, genes and metabolites called MGMLink, and one of microbial traits, environments, and human pheno-types called KG-microbe-phenio. Using a shortest path search and a pattern based semantically constrained path search through the graphs, we highlight the need for a microbiome-disease resource and semantically informed search methods to enable mechanistic inference. AvailabilityThe software to create MGMLink is openly available at https://github.com/bsantan/MGMLink, and KG-microbe is available at https://github.com/Knowledge-Graph-Hub/kg-microbe and KG-phenio is available at https://github.com/Knowledge-Graph-Hub/kg-phenio. Contactbrook.santangelo@cuanschutz.edu

bioinformatics↗

bioRxiv2020

Hemagglutination by B. fragilis is mediated by capsular polysaccharides and is influenced by host ABO blood type.

Bacterial hemagglutination of red blood cells (RBCs) is mediated by interactions between bacterial cell components and RBC envelope glycans that vary across individuals by ABO blood type. ABO glycans are also expressed on intestinal epithelial cells and in most individuals secreted into the intestinal mucosa, indicating that hemagglutination by bacteria may be informative about bacteria-host interactions in the intestine. Bacteroides fragilis, a prominent member of the human gut microbiota, can hemagglutinate RBCs by an unknown mechanism. Using a novel technology for quantifying bacterial hemagglutination, genetic knockout strains of B. fragilis and blocking antiserums, we demonstrate that the capsular polysaccharides of B. fragilis, polysaccharide B (PSB), and PSC are both strong hemagglutinins. Furthermore, the capacity of B. fragilis to hemagglutinate was much stronger in individuals with Type O blood compared to Types A and B, an adaptation that could impact the capacity of B. fragilis to colonize and thrive in the host. Importance StatementThis study found that the human pathobiont, B. fragilis, hemagglutinates human red blood cells using specific capsular polysaccharides (PSB and PSC) which are known to be important for interacting with and influencing host immune responses. Because the factors found on red blood cells are also abundantly expressed on other tissues and in the mucous, the ability to hemagglutinate sheds light on interactions between bacteria and host throughout the body. Intriguingly, the strength of hemagglutination varied based on the ABO blood type of the host, a finding which could have implications for understanding if an individuals blood type may influence interactions with B. fragilis and its potential as a pathogen versus a commensal.

microbiology↗

bioRxiv2018

AMON: Annotation of metabolite origins via networks to better integrate microbiome and metabolome data

MotivationUntargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live with the host, or from other exposures such as diet or the environment.\n\nResultsWe address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to determine the degree to which annotated compounds in the metabolome may have been produced by bacteria present, the host, either (i.e. both the bacteria and host are capable of production), or neither (i.e. neither the human or the fecal microbiome are predicted to be capable of producing the observed metabolite).\n\nAvailability and ImplementationThis software is available at https://github.com/lozuponelab/AMON as well as via pip.\n\nContactcatherine.lozupone@ucdenver.edu

bioinformatics↗

bioRxiv2018

VOLARE: Visual analysis of disease-associated microbiome-immune system interplay

BackgroundRelationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. High-dimensional \"omic\" methodologies such as 16S ribosomal RNA (rRNA) sequencing and Time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features is one approach for quantifying relationships between \"omes\", and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts need to vet candidate results for follow-up experiments.\n\nResultsTo support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies--microbiome:cytokine data from fecal samples in HIV, microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that, in using VOLARE, domain experts frequently generated detailed regression plots, suggesting that this detail aids the vetting of results.\n\nConclusionsSystematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

bioinformatics↗