About

Ryan Blaustein is an Assistant Professor in the Department of Nutrition & Food Science at the University of Maryland, College Park. His research focuses on microbial ecology, microbial genomics, and food safety. He holds a Ph.D. in Microbial Ecology from the University of Florida, obtained in 2017, and has additional training as a Postdoctoral Fellow at NIH-NHGRI in Microbial Genomics from 2020 to 2022, as well as a Postdoctoral Fellowship at Northwestern University from 2017 to 2020. Blaustein's academic background also includes a Master's degree in Environmental Microbiology and a Bachelor's degree in Biology from the University of Maryland, College Park. His professional experience includes a research associate position at the USDA-ARS Environmental Microbial Food Safety Laboratory from 2011 to 2015. His teaching responsibilities include courses such as Food Microbiology, Food Microbiology Laboratory, and R for Applied Genomics. His research aims to advance understanding in microbial ecology and genomics with applications in food safety.

Research topics

• Biology
• Microbiology
• Ecology
• Botany
• Genetics
• Biochemistry
• Horticulture
• Medicine
• Environmental health

Selected publications

• <i>Candida auris</i> metabolism and growth preferences in physiologically relevant skin-like conditions

  mBio · 2026-03-13 · 1 citations

  articleOpen access

  ABSTRACT Candida auris is an opportunistic, multidrug-resistant yeast with a high capacity for human skin colonization in healthcare settings, which can lead to subsequent infections with high mortality rates. Despite the recent emergence of at least four distinct clades at the global scale, little remains known about how C. auris is so adept at growing on skin and the key genes and pathways it utilizes to metabolize the scarce nutrients available. Here, we identify the roles that conventional and alternative carbon metabolism genes and metabolic pathways have in facilitating C. auris growth through laboratory-based experiments and bioinformatics analyses. In artificial skin-like media, all four clades of C. auris were more capable of growing than Candida albicans SC5314, a clinically relevant counterpart. By investigating the differential regulation of C. auris when growing in skin-like media as compared to rich fungal media, we uncovered hundreds of genes in multiple metabolic pathways. To further test the mechanisms of these metabolic pathways, we deleted several non-essential gene candidates including FOX2 (B9J08_002847), CAT2 (B9J08_000010), and ICL1 (B9J08_003374). The mutant strains all exhibited abrogated growth in skin-like media and demonstrated nutrient preferences that differed from the wild type. Thus, we propose a model of how C. auris has the capacity to metabolize nutrients that are available on skin by optimizing its metabolic profile. Targeting these metabolic pathways to mitigate C. auris growth on skin is a potential avenue to explore in controlling the spread of this emerging human fungal pathogen. IMPORTANCE Candida auris is an emerging fungal pathogen with human skin as its primary site of colonization and subsequent transmission. Here, we show the importance of conventional and alternative carbon metabolism for the ability of C. auris to grow in artificial skin-like media. This knowledge provides a better understanding of C. auris metabolism and sheds light on genes and pathways that could be targeted to interfere with persistent skin colonization.

  PublisherDOI

• Effective growth restriction of <i>Botrytis cinerea</i> and <i>Colletotrichum fioriniae</i> by wine grape-associated native non- <i>Saccharomyces</i> yeasts

  Journal of Applied Microbiology · 2026-02-27

  article

  AIMS: Fungal biological control agents (BCAs) have been widely researched as alternatives to conventional fungicides. As the wine grape industry relies on intensive sprays, bio-based solutions, especially those that may also enhance wine fermentations, may hold potential for reducing chemical inputs while improving wine quality. This study aimed to isolate and identify yeast strains from vineyards to evaluate their biocontrol efficacy against major fruit rot pathogens and additional value as fermentation starters. METHODS AND RESULTS: Wild yeasts were isolated from vineyards in Maryland. Recovered isolates (n=234) were characterized with ITS and 26S sequencing analysis and processed in a series of experiments to determine potential antagonistic activity against Botrytis cinerea and Colletotrichum fioriniae. Capabilities for micro-fermentation of grape juice was also evaluated for selected biocontrol candidates. Five yeasts belonging to Aureobasidium (JB951), Pichia (JB524, JB543, and JB767), and Zygoascus (JB624) showed significant antagonism, each restricting the mycelial growth of both pathogens in vitro. Among these yeasts, Pichia JB767 and Aureobasidium JB951 significantly inhibited pathogen spore germination in vivo as well. While some of the yeasts only performed well either as BCAs or fermenters, Pichia JB524 and JB767, and Zygoascus JB624 tested demonstrated dual functions. CONCLUSIONS: This study suggests that these yeasts may not only be valuable in disease control but could also serve as effective fermentation starters, warranting further validation in a more practical setting.

  PublisherDOI

• A Triple-Modality Peptide-Antibiotic-Phage Therapy Eradicates Multidrug-Resistant <i>Serratia marcescens</i> Biofilms

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-09

  articleOpen access

  Abstract Serratia marcescens is an opportunistic pathogen that causes severe hospital-acquired infections, notable for its biofilm formation abilities and development of extensive antibiotic resistance. Here we evaluated the efficacy of bacteriophages, antibiotics, and antimicrobial peptides (BAP), alone and in combination, against fourteen multi-drug-resistant (MDR) S. marcescens isolates sourced from hospitals and other environmental settings in an in vitro biofilm model. Phage combination with a cocktail of sub-minimal inhibitory concentration (MIC) of penicillin-streptomycin, kanamycin, and ciprofloxacin, reduced biofilm biomass, however, complete decolonization was not achieved. Incorporating an antimicrobial peptide cocktail into this regimen eradicated 99.99% of multi-drug-resistant isolates grown planktonically or in surface-associated biofilms. Microscopy and viability assays confirmed extensive biofilm disruption and bacterial clearance without regrowth. These findings reveal that simultaneous interference of cell wall synthesis, protein translation, DNA replication, and membrane integrity can overcome S. marcescens antimicrobial defenses, establishing a multifaceted therapeutic framework for managing device-associated infections caused by MDR pathogens.

  PublisherDOI

• Clonal Candida auris and ESKAPE pathogens on the skin of residents of nursing homes

  Nature · 2025-02-26 · 25 citations

  article
  PublisherDOI

• Multidrug resistance in bacteria associated with leafy greens and soil in urban agriculture systems

  Frontiers in Plant Science · 2025-09-22 · 5 citations

  articleOpen accessSenior authorCorresponding

  Urban farms and community gardens support local food production, though these agroecosystems can contain emerging environmental contaminants that may contribute to the dissemination of antimicrobial resistance (AMR). Our previous research enumerated AMR bacteria associated with leafy vegetable production environments in the greater Washington, D.C. area, identifying &amp;gt;100 isolates with multidrug-resistant (MDR) phenotypes. Here, we performed whole genome sequencing analysis of 87 of these strains recovered from leafy greens (n=29), root zone soil (n=42), and bulk soil (n=16) to comprehensively characterize their MDR genotypes, including taxonomy and any encoded ARGs, stress response genes, and mobile genetic elements (MGEs; e.g., plasmids, phages, conjugative elements). The MDR isolates spanned 4 phyla and 14 genera, with the majority identified as Pseudomonas (n = 29), Serratia (n = 22), Providencia (n = 11), and Bacillus (n = 11). Most of the ARGs were linked to multidrug efflux, while other abundant ARG classes reflected resistance to beta-lactams and tetracyclines. While the genotypes were often conserved within respective species and even genera, the observed phenotypes within taxonomic groups slightly varied, suggesting the potential roles of uncharacterized genetic elements in MDR function. Moreover, all of the MDR isolates encoded at least one gene annotated as a MGE, and there were 19 distinct ARGs located within 5,000 bp upstream or downstream of these sequences, suggesting potential implications for mobilization. Overall, our results indicate that the MDR bacteria in urban agriculture systems, including on fresh produce, are dominated by general soil-associated taxa that carry diverse ARGs and MGEs.

  PublisherOA PDFDOI

• Genomic diversity of Cronobacter sakazakii across the food system to consumers at the global scale

  International Journal of Food Microbiology · 2025-07-02 · 5 citations

  articleOpen accessSenior authorCorresponding

  Deciphering how foodborne pathogens adapt to changing environments is essential for improving food safety monitoring and control. Cronobacter sakazakii , a persistent opportunistic pathogen associated with powdered infant formula outbreaks, poses critical health risks to neonates and other vulnerable populations. This study tested the hypothesis that genetic variation in C. sakazakii correlates with specific isolation sources and geographic origins across the global food system. We conducted a pangenomics meta-analysis of C. sakazakii derived from food, environmental, and clinical sources spanning North America, Asia, and Europe. A robust fine-tuned Generative Pre-trained Transformer (GPT) model was developed to standardize the categorization of isolate metadata descriptors. C. sakazakii genome assemblies (n = 748) were used to build and annotate the pangenome, and genome size and accessory gene profiles were found to be significantly associated with source type and continent of origin. Isolates from powdered foods, compared to those sourced from alternative foods, had larger genomes and were enriched in functions annotated to Clusters of Orthologous Genes (COG) category L for DNA replication, recombination and repair (e.g., transposase, integrase), among other features. Random forest models using both accessory genes and the subset of virulence factor homologs accurately predicted source attributions, identifying type VI secretion system and heavy metal response genes as key indicators of isolate origins. Several antimicrobial resistance genes associated with efflux (i.e., arlR , facT , oprZ ) also exhibited patterns for biogeography. Overall, this study uncovered the distribution of key accessory genetic elements of C. sakazakii throughout the food system, revealing putative adaptations for its persistence and transmission. Our reproducible and automated workflow has potential applications in molecular surveillance for emerging food safety concerns. • Characterized the pangenome of C. sakazakii from diverse sample types at global scale • Developed an AI fine tune model for automated categorization of isolation sources • Machine learning accurately predicted biogeography of accessory genes. • Genomes of isolates from powdered foods were distinct from other sources. • Key genes of isolate origins included DNA repair, T6SS, and heavy metal response.

  PublisherDOI

• Benchmarking Metagenomic Pipelines for the Detection of Foodborne Pathogens in Simulated Microbial Communities

  Journal of Food Protection · 2025-07-17 · 5 citations

  articleOpen access

  Foodborne pathogens pose a significant public health threat worldwide, despite modern advances in food safety. While molecular detection of pathogens in complex food matrices has gained attention to support tracking and preventing outbreaks, thorough benchmarking is needed to optimize workflows for specific scenarios. This study evaluated the performance of four metagenomic classification tools: Kraken2, Kraken2/Bracken, MetaPhlAn4, and Centrifuge, for estimating pathogen presence and abundance in simulated microbial communities representing three food products. Specifically, we evaluated workflow performance in predicting varying levels of Campylobacter jejuni, Cronobacter sakazakii, and Listeria monocytogenes in metagenomes of chicken meat, dried food, and milk products. Metagenomes were simulated to include the respective pathogen at defined relative abundance levels (0%-control, 0.01%, 0.1%, 1%, and 30%) within the respective food microbiome. Performance evaluations demonstrated that Kraken2/Bracken achieved the highest classification accuracy, with consistently higher F1-scores across all food metagenomes, whereas Centrifuge exhibited the weakest performance. MetaPhlAn4 also performed well, particularly in predicting C. sakazakii in dried food metagenomes, but was limited in detecting pathogens at the lowest abundance level (0.01%). Overall, Kraken2/Bracken and Kraken2 exhibited the broadest detection range, correctly identifying pathogen sequence reads down to the 0.01% level, whereas MetaPhlAn4 and Centrifuge had higher limits of detection. Our results highlight Kraken2/Bracken as an effective tool for pathogen detection, with MetaPhlAn4 serving as a valuable alternative depending on pathogen prevalence. These findings provide crucial insights for selecting metagenomic tools for applications in food safety and pathogen surveillance applications.

  PublisherDOI

• Water metagenomes reflect physicochemical water quality throughout a model agricultural pond

  Frontiers in Microbiology · 2025-06-04 · 3 citations

  articleOpen access1st authorCorresponding

  Agricultural ponds are essential irrigation resources, though may also serve as reservoirs for pathogens and antimicrobial resistance (AMR) genes. While monitoring microbiological water quality is critical for food safety, the influence of sampling factors (e.g., when and where to collect samples) in making risk assessments and potential applications for using environmental covariates as indicators remain unclear. Here, we explored the hypothesis that metagenomes of agricultural waters change with spatiotemporal shifts in physicochemical water quality, i.e., across water depths over time. Water samples and underlying sediments were collected at a model pond at the surface and within the water column (0, 1, 2 m depths) throughout one day (i.e., 9:00, 12:00, 15:00). All samples were processed for shotgun metagenomic sequencing analysis and enumeration of various water quality parameters (e.g., temperature, nutrient concentrations, turbidity, pH, culturable Escherichia coli ). At the pond surface, Microcystis aeruginosa and members of Cyanobacteria, along with genes encoding pathways related to photosynthesis and nucleotide biosynthesis, were enriched throughout the day. In contrast, within the water column (1–2 m depths) and sediments, diverse members of Proteobacteria and Actinobacteria were more dominant, along with encoded pathways related to respiration and amino acid biosynthesis. Various aspects of water quality (i.e., chlorophyll dissolved organic matter, ammonia, E. coli concentrations) correlated with water metagenome diversity, albeit not with any specific AMR genes or virulence factors. Nevertheless, de novo assembly of sequenced reads uncovered 22 unique strains encoding several AMR, virulence, or stress response genetic elements, thus linking metagenome functional potential to key taxa. Overall, our findings highlight distinctions in agricultural pond water metagenomes at the surface and in the water column and demonstrate the potential for metagenomic surveillance in water quality monitoring to support food safety.

  PublisherOA PDFDOI

• Bioinformatics combined with machine learning unravels differences among environmental, seafood, and clinical isolates of Vibrio parahaemolyticus

  Frontiers in Microbiology · 2025-03-19 · 3 citations

  articleOpen access

  Vibrio parahaemolyticus is the leading cause of illnesses and outbreaks linked to seafood consumption across the globe. Understanding how this pathogen may be adapted to persist along the farm-to-table supply chain has applications for addressing food safety. This study utilized machine learning to develop robust models classifying genomic diversity of V. parahaemolyticus that was isolated from environmental ( n = 176), seafood ( n = 975), and clinical ( n = 865) sample origins. We constructed a pangenome of the respective genome assemblies and employed random forest algorithm to develop predictive models to identify gene clusters encoding metabolism, virulence, and antibiotic resistance that were associated with isolate source type. Comparison of genomes of all seafood-clinical isolates showed high balanced accuracy (≥0.80) and Area Under the Receiver Operating Characteristics curve (≥0.87) for all of these functional features. Major virulence factors including tdh , trh , type III secretion system-related genes, and four alpha-hemolysin genes ( hlyA , hlyB , hlyC , and hlyD ) were identified as important differentiating factors in our seafood-clinical virulence model, underscoring the need for further investigation. Significant patterns for AMR genes differing among seafood and clinical samples were revealed from our model and genes conferring to tetracycline, elfamycin, and multidrug (phenicol antibiotic, diaminopyrimidine antibiotic, and fluoroquinolone antibiotic) resistance were identified as the top three key variables. These findings provide crucial insights into the development of effective surveillance and management strategies to address the public health threats associated with V. parahaemolyticus .

  PublisherOA PDFDOI

• <i>Candida auris</i> Metabolism and Growth Preferences in Physiologically Relevant Skin-like Conditions

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-19 · 1 citations

  preprintOpen access

  ABSTRACT Candida auris is an opportunistic, multidrug-resistant yeast with high capacity of skin colonization in healthcare settings, which can lead to subsequent infections with high mortality rates. Given the recent emergence of at least four distinct clades at the global scale, little remains known about how C. auris is so adept at growing on skin and the key genes and pathways it utilizes to metabolize the scarce nutrients available. Here, we identify the roles that conventional and alternative carbon metabolism genes and metabolic pathways have in facilitating C. auris growth through laboratory-based experiments and bioinformatics analyses. In artificial skin-like media, all four clades of C. auris were more capable of growing than C. albicans SC5314, a clinically relevant counterpart. By investigating the differential regulation of C. auris when growing in skin-like media as compared to rich fungal media, we uncovered hundreds of genes in multiple metabolic pathways. To further test the mechanisms of these metabolic pathways, we deleted several non-essential gene candidates including FOX2 (B9J08_002847), CAT2 (B9J08_000010), and ICL1 (B9J08_003374). The mutant strains all exhibited abrogated growth in skin-like media and demonstrated nutrient preferences that differed from the wild type. Thus, we propose a model of how C. auris has the capacity to metabolize nutrients that are naturally available on skin by changing its metabolic profile. Targeting these metabolic pathways to mitigate C. auris growth on skin is a potential avenue to explore in controlling the spread of this emerging human fungal pathogen. IMPORTANCE Candida auris is an emerging fungal pathogen with skin as its primary site of colonization and subsequent transmission. Here, we show the importance of conventional and alternative carbon metabolism for C. auris’ ability to grow in artificial skin-like media. This knowledge provides a better understanding of C. auris metabolism and sheds light on genes and pathways that could be targeted to interfere with persistent skin colonization.

  PublisherOA PDFDOI

Frequent coauthors

• Yakov Pachepsky

  20 shared

• Erica M. Hartmann

  16 shared

• Daniel R. Shelton

  Agricultural Research Service

  15 shared

• Matthew Stocker

  United States Department of Agriculture

  8 shared

• Robert L. Hill

  University of Maryland, College Park

  8 shared

• K. Allison Perry‐Dow

  7 shared

• Sarah Ben Maamar

  Cornell University

  7 shared

• A. Glawe

  Northwestern University

  6 shared

Education

• Ph.D., Soil and Water Sciences

  University of Florida

  2017