Biology subjects

Blaser, M. J.

Publications and source records attributed to Blaser, M. J..

11 recordsLinked to original sources

bioRxiv2026

Urea is a dynamic and functional component of mammalian breast milk

Urea is a constituent of human milk, yet its regulation and interactions with the infant gut microbiome remain unclear. Combining three human cohorts, a controlled mouse model, and complementary microbiome analyses, we show that milk urea increases across lactation, is conserved across mammals, and is not explained by circulating urea levels or maternal diet. Instead, we identify lactation-dependent remodeling of mammary urea-metabolism and transport pathways, including dynamic AQP3 localization in secretory epithelium, indicative of regulated transfer of circulating urea into breast milk. Alongside urea, maternal milk and stool provide complementary reservoirs of urea-metabolizing bacteria that help establish the infant "ureabiome", whose urea-utilization capacity is supported by metagenomics. This maternal-infant axis is context-dependent: infant formulas provide substantially higher urea levels than human milk, while functional assays show that urea supports both commensal and opportunistic bacteria in a concentration-dependent manner. Together, these findings define a maternal nitrogen axis through which lactation can shape the assembly and function of the infant gut microbiome.

microbiology↗

bioRxiv2025

Early-life Helicobacter pylori infection worsens metabolic state in mice receiving a high-fat diet

Perturbations to early-life microbial colonization can shape immune and metabolic development, predisposing a host to obesity. We hypothesized that neonatal infection with the ancient human symbiont Helicobacter pylori exacerbates diet-induced metabolic responses and is accompanied by alterations in endocrine and inflammatory regulation. To test this, C57BL/6JRj neonatal mice were infected with H. pylori or left uninfected and, after weaning, exposed to a high-fat diet in a long-term (5-month) study of microbiome composition and a short-term (3-week) study of circulating biomarkers and microbiome composition. In the short-term intervention, infected mice showed increased visceral adiposity. Infection was associated with altered ghrelin and leptin responses across fasting conditions, accompanied by elevated MCP-1 and IL-6, particularly in males. H. pylori infection also reinforced diet-induced shifts in gastrointestinal microbiome composition. In the long-term study, there were no differences in adiposity between infected and control mice, suggesting that diet was the dominant determinant of adiposity at this stage. Together, our findings suggest that early-life H. pylori infection amplifies the initial endocrine, inflammatory, and adipose responses to a high-fat diet.

evolutionary biology↗

bioRxiv2025

The 16p11.2 microdeletion influences how early-life microbiota perturbations affect hippocampal development and behavior throughout the lifespan

Neurodevelopmental disorders (NDDs) arise from interactions between genetic factors and environmental exposures, with infancy representing a critical period of vulnerability. This exploratory, preclinical study investigated whether the 16p11.2 microdeletion (16pDel), a NDD-associated genetic variant, exacerbates the effects of early-life therapeutic antibiotic exposure on the gut microbiome, hippocampal development, and behavior. Cefdinir, selected for its epidemiological association with NDD risk, acutely perturbed the gut microbiome, causing sustained reductions in Lachnospiraceae. These changes were followed by alterations in sociability, risk assessment, and associative learning. Notably, only cefdinir-exposed 16pDel mice exhibited altered hippocampal stem cell dynamics and gene expression, demonstrating genotype-dependent susceptibility. Increased intestinal permeability and alterations in arginine biosynthesis and glycerophospholipid metabolism implicate gut barrier dysfunction as a contributing factor. Our findings suggest that genetic composition can exacerbate neurodevelopmental consequences of early-life microbiome perturbations, identify metabolic pathways for potential interventions, and support cautious antibiotic use during infancy, especially in genetically vulnerable populations.

neuroscience↗

bioRxiv2025

Gut microbiota phospholipids regulate intestinal gene expression and can counteract the effects of antibiotic treatment

The gut microbiome influences immune and metabolic homeostasis. Our research using non-obese diabetic (NOD) mice revealed that early-life antibiotic exposure remodels the gut microbiome affecting metabolism and accelerating type 1 diabetes (T1D) incidence, with cecal material transplant (CMT) mitigating the damage. Now examining murine intestinal lipidomic profiles, we identified 747 compounds. Comparing the lipidomic profiles of cecal contents of conventional and germ-free mice and their diets, we identified 87 microbially-produced lipids reduced by antibiotic exposure but CMT-restored. Parallel analysis of human fecal lipid profiles after azithromycin-exposure showed significant alterations with substantial overlap with mice. In vitro co-culture with mouse macrophages or small intestinal epithelial cells and human colonic epithelial cells identified phospholipids that repress inflammation through the NF{kappa}B pathway. Oral administration of these phospholipids to antibiotic-treated NOD mice reduced expression of ileal genes involved in early stages of T1D pathogenesis. These findings indicate potential therapeutic anti-inflammatory roles of microbially-produced lipids.

microbiology↗

bioRxiv2025

Revisiting the cancer microbiome using PRISM

Recent controversy around the cancer microbiome highlights the need for improved microbial analysis methods for human genomics data. We developed PRISM, a computational approach for precise microorganism identification and decontamination from low-biomass sequencing data. PRISM removes spurious signals and achieves excellent performance when benchmarked on a curated dataset of 62,006 known true- and false-positive taxa. We then use PRISM to detect microbes in 8 cancer types from the CPTAC and TCGA datasets. We identify rich microbiomes in gastrointestinal tract tumors in CPTAC and identify bacteria in a subset of pancreatic tumors that are associated with altered glycoproteomes, more extensive smoking histories, and higher tumor recurrence risk. We find relatively sparse microbes in other cancer types and in TCGA, which we demonstrate may reflect differing sequencing parameters. Overall, PRISM does not replace gold-standard controls, but it enables higher-confidence analyses and reveals tumor-associated microorganisms with potential molecular and clinical significance.

genomics↗

bioRxiv2025

Epigenetic phase variation in the gut microbiome enhances bacterial adaptation

The human gut microbiome within the gastrointestinal tract continuously adapts to variations in diet, medications, and host physiology. A strategy for bacterial genetic adaptation is epigenetic phase variation (ePV) mediated by bacterial DNA methylation, which can regulate gene expression, enhance clonal heterogeneity, and enable a single bacterial strain to exhibit variable phenotypic states. Genome-wide and site-specific ePVs have been characterized in human pathogens antigenic variation and virulence factor production. However, the role of ePV in facilitating adaptation within the human microbiome remains poorly understood. Here, we comprehensively cataloged genome-wide and site-specific ePV in human infant and adult gut microbiomes. First, using long-read metagenomic sequencing, we detected genome-wide ePV mediated by complex structural variations of DNA methyltransferases, highlighting those associated with antibiotics or fecal microbiota transplantation. Second, we analyzed a collection of public short-read metagenomic sequencing datasets, uncovering a great prevalence of genome-wide ePV in the human gut microbiome. Third, we quantitatively detected site-specific ePVs using single-molecule methylation analysis to identify dynamic variation associated with antibiotic treatment or probiotic engraftment. Finally, we performed an in-depth assessment of an Akkermansia muciniphila isolate from an infant, highlighting that ePVs can regulate gene expression and enhance the bacterial adaptive capacity by employing a bet-hedging strategy to increase tolerance to differing antibiotics. Our findings indicate that epigenetic modifications are a common strategy used by gut bacteria to adapt to the fluctuating environment.

microbiology↗

bioRxiv2022

Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues

We developed SAHMI, a computational resource to identify truly present microbial nucleic acids and filter contaminants and spurious false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. In benchmark studies, SAHMI correctly identifies known microbial infections present in diverse tissues. The application of SAHMI to single-cell and spatial genomic data enables co-detection of somatic cells and microorganisms and joint analysis of host-microbiome ecosystems.

genomics↗

bioRxiv2020

Joint Modeling of Zero-inflated Longitudinal Proportions and Time-to-event Data with Application to a Gut Microbiome Study

Recent studies have suggested that the temporal dynamics of the human microbiome may have associations with human health and disease. An increasing number of longitudinal microbiome studies, which record time to disease onset, aim to identify candidate microbes as biomarkers for prognosis. Owing to the ultra-skewness and sparsity of microbiome proportion (relative abundance) data, directly applying traditional statistical methods may result in substantial power loss or spurious inferences. We propose a novel joint modeling framework [JointMM], which is comprised of two sub-models: a longitudinal sub-model called zero-inflated scaled-Beta generalized linear mixed-effects regression to depict the temporal structure of microbial proportions among subjects; and a survival sub-model to characterize the occurrence of an event and its relationship with the longitudinal microbiome proportions. JointMM is specifically designed to handle the zero-inflated and highly skewed longitudinal microbial proportion data and examine whether the temporal pattern of microbial presence and/or the non-zero microbial proportions are associated with differences in the time to an event. The longitudinal sub-model of JointMM also provides the capacity to investigate how the (time-varying) covariates are related to the temporal microbial presence/absence patterns and/or the changing trend in non-zero proportions. Comprehensive simulations and real data analyses are used to assess the statistical efficiency and interpretability of JointMM.

bioinformatics↗

bioRxiv2020

Quantifying bacterial evolution in the wild: a birthday problem for Campylobacter lineages

Measuring molecular evolution in bacteria typically requires estimation of the rate at which nucleotide changes accumulate in strains sampled at different times that share a common ancestor. This approach has been useful for dating ecological and evolutionary events that coincide with the emergence of important lineages, such as outbreak strains and obligate human pathogens. However, in multi-host (niche) transmission scenarios, where the pathogen is essentially an opportunistic environmental organism, sampling is often sporadic and rarely reflects the overall population, particularly when concentrated on clinical isolates. This means that approaches that assume recent common ancestry are not applicable. Here we present a new approach to estimate the molecular clock rate in Campylobacter that draws on the popular probability conundrum known as the birthday problem. Using large genomic datasets and comparative genomic approaches, we use isolate pairs that share recent common ancestry to estimate the rate of nucleotide change for the population. Identifying synonymous and non-synonymous nucleotide changes, both within and outside of recombined regions of the genome, we quantify clock-like diversification to estimate synonymous rates of nucleotide change for the common pathogenic bacteria Campylobacter coli (2.4 x 10-6 s/s/y) and Campylobacter jejuni (3.4 x 10-6 s/s/y). Finally, using estimated total rates of nucleotide change, we infer the number of effective lineages within the sample time-frame - analogous to a shared birthdays - and assess the rate of turnover of lineages in our sample set over short evolutionary timescales. This provides a generalizable approach to calibrating rates in populations of environmental bacteria and shows that multiple lineages are maintained, implying that large-scale clonal sweeps may take hundreds of years or more in these species. Author SummaryGrowth and reproduction in living organisms require DNA replication but this process is error prone. Along with variation introduced by horizontal gene transfer, it can lead to alterations in the nucleotide sequence. These nucleotide changes accumulate over time in successive generations at an approximately constant rate termed the molecular clock. Therefore, if this rate is known, one can estimate the date when two or more lineages diverged. In bacteria, this can be informative for understanding the time-scale of emergence and spread of pathogenic strains. Such analyses are robust when the ancestral population is known, such as for obligate pathogens that only infect humans. However, when the bacterium inhabits multiple hosts or niches it is difficult to infer direct ancestry from one strain to another, reducing the accuracy of molecular clock estimates. Here we focus on one such multi-host organism, Campylobacter, a leading cause of food-borne gastroenteritis. Reconstructing the population history by estimating empirical nucleotide change rates from carefully selected isolate pairs, and evaluating the maintenance of multiple lineages over time, we provide information about strain diversification. Our method is a new addition to the bacterial genomics toolkit that will help in understanding the spread of opportunistic pathogens.

genomics↗

bioRxiv2020

Microbial trend analysis for common dynamic trend, group comparison and classification in longitudinal microbiome study

MotivationThe human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time. ResultsWe propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects on the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different in groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice. ConclusionsThe proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.

bioinformatics↗

bioRxiv2019

Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data

MotivationRecent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data.\n\nResultsWe propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight.

bioinformatics↗