Biology subjects

Ryon, K. A.

Publications and source records attributed to Ryon, K. A..

6 recordsLinked to original sources

bioRxiv2026

Diverse high-fat diets drive multi-omic reprogramming that persists after dietary reversal

Dietary fat composition modulates host physiology and the gut microbiome, but the long-term effects of specific fat sources and the extent to which these changes resolve after dietary reversal remain incompletely defined. Here, we present a longitudinal multi-omic resource of mice maintained for one year on a purified control diet, seven high-fat diets differing in predominant fat source, or reversal regimens in which animals were switched from high-fat to control diet after 4 or 9 months. We further incorporated two cohorts with distinct pre-existing microbiome configurations to determine how baseline community structure shapes diet-induced remodeling of the gut microbiome ecosystem. By integrating longitudinal phenotyping, fecal metagenomics, fecal metabolomics, plasma metabolomics and lipidomics, and intestinal single-cell RNA sequencing, we defined the shared and dietary fat-specific responses across host and microbiome compartments. Baseline microbiome composition strongly influenced microbial responses to diet, indicating that pre-existing community structure is a major determinant of dietary ecosystem remodeling. Although many altered features shifted toward baseline after dietary reversal, only approximately half of diet-associated microbial changes recovered within the study window. A subset of taxa exhibited persistent alterations, including sustained depletion of Lactobacillus johnsonii and Bifidobacterium pseudolongum and sustained enrichment of Alistipes finegoldii, consistent with a "microbiome memory" of prior high-fat diet exposure. This memory effect is mirrored in the host, by sustained suppression of major histocompatibility complex class II (MHC-II) gene expression in intestinal epithelial cells after dietary reversal. These findings indicate that dietary fats leave a lasting imprint on the host-microbiome interactome that survives dietary intervention. Together, these data establish a resource for defining how dietary fat source, baseline microbiome composition, and dietary history shape host-microbiome states. The entire resource is available online as an RShiny app.

systems biology↗

bioRxiv2025

The Two Frontiers Project Field Handbook and OpenTools: Standardizing microbial fieldwork for biobank-scale sequencing and culturomics

From medicines to materials, our planets microbial diversity comprises an enormous wellspring of biotechnological potential. For centuries, microbiologists have developed tools for interrogating microbial function, ranging from microscopy and culturing to, more recently, metagenomics. However, deploying these tools during fieldwork requires substantial forward planning, interdisciplinary technical expertise, and plans for navigating permitting and the ethical implications of bioprospecting. To address these challenges, we built The Two Frontiers Project Handbook and OpenTools Resource, which aggregates our expertise in high-throughput sampling, sequencing, and culturing of microbes from thousands of samples. We provide our full suite of fieldwork methods as well as relevant software and hardware. We lay our standards for team roles and construction, general expedition planning, sample transport, permitting, and numerous other key aspects of executing a successful field campaign. The version-controlled resource is available at https://two-frontiers-project.github.io/ and is open for non-commercial use.

microbiology↗

bioRxiv2025

XTree enables memory-efficient, accurate short and long sequence alignment to millions of genomes across the tree of life

XTree is a k-mer-based aligner enabling rapid, memory-efficient alignment of sequencing reads to whole-genome reference databases with up to millions of genomes. Here, we detail XTrees performance on short and long read sequencing data and demonstrate its high accuracy across diverse bacterial, viral, and eukaryotic genomes. Benchmarking demonstrates superior and/or comparable precision and recall over existing tools, with more scalable indexing and efficient memory mapping. We additionally provide pre-indexed databases, including (1) the Genome Taxonomy Database (versions r214-r226), (2) representative GenBank fungi and protozoan genomes and (3) the Pan-Viral-Compendium, a bespoke data resource spanning 6.6 million, quality-controlled, viral genomes.

bioinformatics↗

bioRxiv2025

Modular metagenomic analysis of pan-domain symbioses with MAGUS

Metagenomic analysis of deeply sequenced, eukaryotic-dominant symbiotic communities can be difficult for many metagenomic workflows. Here, we present MAGUS, a bioinformatic toolkit that uses a suite of custom bioinformatic methods for iterative genome assembly and filtering of pan-domain communities, where eukaryotes, bacteria, viruses, and functionally annotated gene catalogs are resolved and analyzed over a series of interconnected, modular software components. We evaluated MAGUS using deeply sequenced (median depth: 579 million reads) ten samples of hard corals, soft corals, and hydrozoans, which comprise complex, eukaryote-dominated symbiotic communities. We successfully resolved phylogenetically comparable host (N = 10), algal (N = 6), bacterial (N = 55), and viral (N = 160,925) genomes, as well as a gene catalog comprising 15,369,684 non-redundant genes (7.6% functionally annotated). MAGUS is available on GitHub (https://github.com/two-frontiers-project/2FP_MAGUS/).

bioinformatics↗

bioRxiv2023

Cyanobacteria newly isolated from marine volcanic seeps display rapid sinking and robust, high density growth

Cyanobacteria are photosynthetic organisms that play important roles in carbon cycling as well as promising bioproduction chassis. Here, we isolate two novel cyanobacteria, UTEX 3221 and UTEX 3222, from a unique marine environment with naturally elevated CO2. We describe complete genome sequences for both isolates and, focusing on UTEX 3222 due to its planktonic growth in liquid, characterize biotechnologically-relevant growth and biomass characteristics. UTEX 3222 outpaces other fast-growing model strains on solid medium. It can double every 2.35 hours in a liquid medium and grows to high density (>31g/L biomass dry weight) in batch culture, nearly double that of Synechococcus sp. PCC 11901, whose high-density growth was recently reported. In addition, UTEX 3222 sinks readily, settling more quickly than other fast-growing strains, suggesting improved de-watering of UTEX 3222 biomass. This settling behavior can be explained in part by larger cell volume. These traits may make UTEX 3222 a compelling choice for photosynthetic bioproduction from CO2. Overall, we find that bio-prospecting in environments with naturally elevated CO2 may uncover novel CO2-metabolizing organisms with unique characteristics.

microbiology↗

bioRxiv2022

Supervised Machine Learning Enables Geospatial Microbial Provenance

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset - the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. We then used these results to develop Cassandra, a random-forest-based classifier that identifies indicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).

microbiology↗