Biology subjects

Kostic, A. D.

Publications and source records attributed to Kostic, A. D..

8 recordsLinked to original sources

bioRxiv2026

The Oral Microbiome Is a Population-Scale Readout of the Exposome, Age, and Systemic Health

The oral microbiome interfaces humans and the environment and is implicated in diseases from caries to cardiovascular conditions. Yet, few studies systematically interrogate oral taxa associations with the host phenome and exposome in diverse populations. We developed a comprehensive oral microbiome atlas, deploying a Microbiome Association Study (MAS) evaluating relationships between host features including exposome, disease, and physiology and the microbiome in a 10,000-person representative US population. Evaluating demographics, 133 phenotypes, 473 exposures, and 20 disease outcomes across 1,349 taxa yielded >800k relationships and 45,757 FDR-significant associations. Age emerged as a major organizing axis, with genera following non-linear life-course patterns. Oral disease, smoking, and dietary sugar correlated with aciduric and anaerobic taxa, whereas oral health featured oxygen-tolerant Proteobacteria. The exposome and cardiovascular/respiratory disease linked to diverse taxa. These results establish the oral microbiome as a sensitive, population-scale indicator of the exposome, phenome, and systemic health.

microbiology↗

bioRxiv2024

Yanomami skin microbiome complexity challenges prevailing concepts of healthy skin.

The adult skin microbiome contributes to skin homeostasis and generally comprises relatively low microbial complexity, especially sebaceous sites, where lipophilic Cutibacterium and Malassezia spp. predominate. Current understanding of the healthy skin microbiome derives predominantly from studies of western, industrialized populations, with limited representation of diverse cultures and lifestyles. In this study, the skin microbiome of a remote indigenous Yanomami community was investigated, revealing a complex microbial community comprised of 115 novel bacterial genomes. The bacterial community composition included genera common to western skin microbiota and additional diverse taxa, which formed multiplex interactions with a dominant eukaryote, Malassezia globosa. Metatranscriptome-derived functional attributes of the microbial communities contributed to skin homeostasis, fortifying barrier integrity via lipid metabolism and acid production and protecting against oxidative stress. The Yanomami skin microbiome comprised a suite of microbial taxa co-detected within their various surroundings. Longitudinal monitoring of Western expeditioner microbiome revealed acquisition of the Yanomami skin microbiome following immersion into the Amazon and subsequent loss upon return to an industrialized setting. These findings challenge the prevailing Western-centric view of what comprises a healthy adult skin microbiome, suggesting a diverse community that includes bacteria of environmental origin confers benefits not recognized in the current model of healthy skin. Importantly, we highlight the malleability of adult skin microbiome composition, which includes changes sustained by lifestyle modification.

microbiology↗

bioRxiv2023

Whole mitogenome analysis highlights demographic history and shared connections among distal Indigenous groups of Mexico Complete mitogenome sequencing from 60 Mexican Native American groups

The study of mitochondrial DNA is a valuable tool to delve into the demographic history of human populations. Particularly in the Americas, five widespread Native American specific mitochondrial lineages have been identified. Here we included the complete mitogenome sequencing of 572 Indigenous individuals belonging to 60 populations spanning the Mexican territory. Our results show a great diversity of matrilineages widespread across the country, revealing shared mtDNA haplogroups in populations from distant regions. We identified all the five main Native American haplogroups clades, including 83 different subhaplogroups, from which nine are novel. The most frequent of the novel haplogroups was A2+64. A phylogenetic inference suggests that A2+64 comes from an ancestral maternal lineage that spread into the Caribbean islands. Additionally, a demographic reconstruction from whole mitogenomes showed an exponential increase in female Ne around 10 Ka ago in all the tested regions. All these findings suggest a genetic persistence through Mexico and possibly the Americas, in agreement with the model of the Mesoamerican-related expansion into the Caribbean and South America.

genetics↗

bioRxiv2023

Single-cell RNA-seq reveals TCR clonal expansion and a high frequency of transcriptionally distinct double-negative T cells in NOD mice

T cells primarily drive the autoimmune destruction of pancreatic beta cells in Type 1 diabetes (T1D). However, the profound yet uncharacterized diversity of the T cell populations in vivo has hindered obtaining a clear picture of the T cell changes that occur longitudinally during T1D onset. This study aimed to identify T cell clonal expansion and distinct transcriptomic signatures associated with T1D progression in Non-Obese Diabetic (NOD) mice. Here we profiled the transcriptome and T cell receptor (TCR) repertoire of T cells at single-cell resolution from longitudinally collected peripheral blood and pancreatic islets of NOD mice using single-cell RNA sequencing technology. Surprisingly, we detected a considerable high frequency of islet-matching T cell clones in the peripheral circulation and blood-matching T cell clones in the islets. Our analysis showed that transcriptional signatures of the T cells are associated with the matching status of the T cells, suggesting potential future applications as a marker for early prediction of diabetes onset using peripheral T cells. In addition, we discovered a high frequency of transcriptionally distinct double negative (DN) T cells that might arise from naive and effector backgrounds through the loss of CD4 or CD8 in a yet unknown biological pathway. This study provides a single-cell level transcriptome and TCR repertoire atlas of T cells in NOD mice and opens the door for more research into the causes of type 1 diabetes and inflammatory autoimmune disease using mouse models.

immunology↗

bioRxiv2022

Quantifying shared and unique gene content across 17 microbial ecosystems

Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies including -- 6 human-associated, 7 non-human-host-associated, and 4 in other non-human host environments. In total, we identified 117,629,181 non-redundant genes. The vast majority of genes (66%) occurred in only one sample (i.e. "singletons"). By contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report datasets of other ecology-associated genes (e.g. abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g. at gene-sequence identifies that are too restrictive). We provide our results and the sets of environmentally-differentiating genes described above at http://www.microbial-genes.bio. ImportanceThe amount of shared genetic elements has not been quantified between the human microbiome and other host and non-host associated microbiomes. Here we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as "near-complete" are far from it. Additionally, over two-thirds of all genes only appear in a single sample and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.

microbiology↗

bioRxiv2022

The impact of multigenerational high-fat diet feeding on the gut microbiome and host metabolism

Although human genetics partially explain the heritability of obesity and type 2 diabetes (T2D), the human gut microbiome also plays a significant role. While changes in human genetics at the population level occur only after many generations, the gut microbiome evolves over a shorter time span. The gut microbiome is also vertically transmitted across generations, therefore, changes in one generation can be passed on to subsequent generations. However, it is unknown whether the gut microbiome influences natural selection of its host under obesogenic pressure. Here, we show that C57BL/6 mice fed a high-fat diet (60% fat, HFD) over four generations develop resistance to obesity and the metabolic syndrome (MetS). Unexpectedly, the mice were increasingly leaner as well as more glucose tolerant and insulin sensitive across generations. This phenomenon was attributed to the most obese mice not yielding progenies, whereas the leanest mice successfully reproduced, and their offspring were also resistant to obesity. In other words, a population bottleneck was observed. Because all the mice were nearly genetically identical inbred C57BL/6J mice, the large variation in body weight gain in response to HFD feeding was likely independent of genetics. We explored whether microbial factors enriched in obesity-resistant mice promote healthier host metabolic phenotypes under HFD feeding, thereby contributing to the heterogeneity in body weight gain and providing an adaptive advantage to the host. Pearson correlation analysis revealed that body weight gain was positively correlated with Lactococcus lactis, as well as negatively correlated with Lactobacillus johnsonii and pathways for coenzyme A biosynthesis, amino acid biosynthesis (lysine, isoleucine, valine), and nucleotide biosynthesis (adenosine, guanosine). Overall, we observed multigenerational adaptation in the gut microbiome correlated with improved metabolism, yet further studies are needed to validate that these adaptations drive metabolic health.

microbiology↗

bioRxiv2020

The predictive power of the microbiome exceeds that of genome-wide association studies in the discrimination of complex human disease

Over the past decade, studies of the human genome and microbiome have deepened our understanding of the connections between human genes, environments, microbes, and disease. For example, the sheer number of indicators of the microbiome and human genetic common variants associated with disease has been immense, but clinical utility has been elusive. Here, we compared the predictive capabilities of the human microbiome versus human genomic common variants across 13 common diseases. We concluded that microbiomic indicators outperform human genetics in predicting host phenotype (overall Microbiome-Association-Study [MAS] area under the curve [AUC] = 0.79 [SE = 0.03], overall Genome-Wide-Association-Study [GWAS] AUC = 0.67 [SE = 0.02]). Our results, while preliminary and focused on a subset of the totality of disease, demonstrate the relative predictive ability of the microbiome, indicating that it may outperform human genetics in discriminating human disease cases and controls. They additionally motivate the need for population-level microbiome sequencing resources, akin to the UK Biobank, to further improve and reproduce metagenomic models of disease.

bioinformatics↗

bioRxiv2017

Aether: Leveraging Linear Programming For Optimal Cloud Computing In Genomics

Across biology we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective, and scalable framework that uses linear programming (LP) to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis while maximizing its efficiency and speed. As a test, we used Aether to de novo assemble 1572 metagenomic samples, a task it completed in merely 13 hours with cost savings of approximately 80% relative to comparable methods.

bioinformatics↗