About

Garret Suen is a Professor of Bacteriology at the University of Wisconsin-Madison and holds the Alfred Toepfer Faculty Fellowship. His research focuses on microbial ecology, rumen microbiology, metagenomics, and biofuels. He is involved in studying the effects of rumen to blood pathways on early lactation performance in transition dairy cows, microbial dynamics following dietary interventions in cattle, and the microbiota of wooden cheese-ripening boards as a source of antimicrobial-producing bacteria. His work also includes investigating host-microbiome mutualism during hibernation, the impact of dietary supplements on microbiota composition, and the microbiome's role in disease and health in various animals, including dairy cows, horses, and humans. Suen's research contributes to understanding microbial communities' disturbance-diversity relationships, zoonotic pathogen carriage, and microbiome responses to environmental and physiological factors, advancing knowledge in microbiology, veterinary science, and agricultural health.

Research topics

• Biology
• Genetics
• Computer Science
• Microbiology
• Computational biology
• Evolutionary biology
• Animal science
• Endocrinology
• Medicine
• Immunology
• Library science
• Astronomy
• Data science
• World Wide Web
• Food science
• Biochemistry
• Ecology
• Internal medicine
• Veterinary medicine
• Physiology

Selected publications

• In vitro comparative analysis of steamed wood and other lignocellulosic substrates on ruminal fermentation and microbiota

  Frontiers in Veterinary Science · 2026-01-30

  articleOpen access

  Lignocellulosic biomass such as wood is increasingly recognized as a promising low-opportunity-cost feed (LCF) that does not compete with human-edible food. In this study, we evaluated rumen fermentation characteristics and microbial community responses using an in vitro batch culture system with a diverse set of substrates, including steamed and untreated woods, xylo-oligosaccharides, spent mushroom substrates, and conventional feeds. Hierarchical clustering based on bacterial community composition revealed five distinct microbial clusters. Certain steamed woods and xylo-oligosaccharides formed separate clusters from untreated woods and conventional feeds, and were associated with the dominance of specific genera such as Succinivibrio and Selenomonas . These microbial shifts may reflect differences in substrate characteristics, potentially related to hemicellulose- and oligosaccharide -derived components. The results suggest that both steamed wood and xylo-oligosaccharides enhance fermentability and are associated with distinct microbial community structures under in vitro conditions. These substrates show promise as sustainable feed ingredients, and further in vivo studies are needed to evaluate their efficacy and long-term impacts on animal health and productivity.

  PublisherOA PDFDOI

• The microbiota of wooden cheese-ripening boards is a rich source of antimicrobial-producing bacteria against <i>Listeria monocytogenes</i>

  Microbiology Spectrum · 2026-01-07

  articleOpen access

  ABSTRACT Wooden boards are essential tools in cheese ripening, and there is accumulating evidence that they have antimicrobial effects against foodborne bacterial pathogens, such as Listeria monocytogenes . However, poor bacterial recovery of bacteria from wood can confound the quantification of pathogen burden. To assess L. monocytogenes survival on wooden cheese boards, we applied a disruptive grinding method and tracked native board-associated bacterial counts as controls. Our data revealed that L. monocytogenes declines on clean zones of wooden boards but can replicate on areas where there is suitable cheese. Our microbiota analysis revealed diverse bacterial communities on wooden board surfaces, with a prominent presence of bacteria in the genera Brevibacterium , Brachybacterium , and Staphylococcus . We further identified seven bacterial species that inhibit L. monocytogenes , belonging to Bacillus , Staphylococcus , and Serratia phyla, in addition to Lactococcus lactis . One isolate, Bacillus safensis , was found to be a potential biocontrol agent, potently inhibiting L. monocytogenes via secreted antimicrobial factors. Our genomic, bioinformatic, and biochemical analyses indicate that those factors are likely antimicrobial peptides encoded by multiple biosynthetic gene clusters, many of which have not been characterized. A sub-inhibitory concentration of B. safensis supernatant induces a significant downregulation of prophage elements and an upregulation of antimicrobial stress responses in L. monocytogenes . Taken together, our findings indicate that the wooden board microbiota is a rich source of antimicrobial-producing bacteria with potential applications in foodborne pathogen control strategies. IMPORTANCE Despite stringent food safety measures, L. monocytogenes foodborne outbreaks remain frequent with high hospitalization and mortality rates. Removal of L. monocytogenes from food processing environments is extremely challenging because this pathogen is ubiquitous and encodes a wide array of stress response mechanisms that enable it to thrive under harsh conditions. Our study found that clean wooden boards used in cheese ripening inhibit L. monocytogenes , causing a noticeable decline in pathogen population following surface inoculation. Bacterial communities on wooden cheese boards are rich and diverse and harbor many species that produce antimicrobial compounds against L. monocytogenes , with the example of a new B. safensis isolate. Therefore, the wooden cheese-ripening board microbiota is a promising source for future antimicrobial discovery efforts.

  PublisherDOI

• Rumen and cecum microbial dynamics following narasin inclusion in Nellore cattle diets

  Frontiers in Microbiology · 2026-02-13

  articleOpen access

  This study investigated the effects of narasin supplementation on the ruminal and cecal bacterial communities of feedlot Nellore cattle. We hypothesized that narasin would selectively modulate microbial populations in distinct gastrointestinal compartments without causing broad-scale disruption of overall community diversity. Sixty-four Nellore bulls (393 ± 24 kg) were assigned to a completely randomized block design and fed finishing diets containing either 0 or 20 ppm of narasin for 112 days. Rumen and cecal contents were collected at slaughter and analyzed using 16S rRNA gene sequencing to characterize bacterial community structure and composition. Overall, the rumen exhibited greater bacterial diversity and richness than the cecum, regardless of dietary treatment. Narasin supplementation did not affect Shannon diversity in either the rumen ( p = 0.182) or the cecum ( p = 0.298); however, Chao richness was reduced in the rumen of narasin-fed cattle ( p = 0.028). Beta-diversity analyses based on Bray–Curtis and Jaccard dissimilarities revealed no significant differences in overall community structure between treatments in either compartment ( p &amp;gt; 0.198). At the phylum level, narasin supplementation was associated with a reduction in Firmicutes and a concomitant increase in Bacteroidetes in the rumen. In contrast, Firmicutes predominated in the cecum, and narasin significantly increased the relative abundance of this phylum, particularly members of the order Clostridiales ( p = 0.05). In conclusion, narasin exerts selective effects on specific bacterial populations rather than inducing widespread shifts in microbial diversity. These results provide novel insights into how narasin modulates microbial ecology in both the rumen and the understudied cecum, highlighting compartment-specific responses that may contribute to improved feed efficiency in beef cattle.

  PublisherOA PDFDOI

• Multi-omics revealed the effects of rumen to blood path on early lactation performance in transition dairy cows

  Figshare · 2026-04-07

  otherOpen access

  Abstract Background The transition period is vitally important to the life cycle of dairy cows. However, the function of the microbiota during both pre- and post-partum and their relationship with ruminal, plasma, and milk metabolites still require systematic investigation. To address this, the 7 highest- and 7 lowest-performing animals among a cohort of 100 dairy cows were selected based on their postpartum energy-corrected milk yield. Rumen fluid and plasma samples were collected during both pre- and post-partum periods, whereas milk samples were obtained postpartum. Shotgun metagenomics of rumen contents in addition to metabolomics of rumen, plasma, and milk samples were performed to evaluate the associations between ruminal microbes and early lactation performance in transition dairy cows. Results Compared with prepartum cows, postpartum high-yield cows had greater concentrations of ruminal volatile fatty acids and plasma total bile acid. Moreover, plasma urea nitrogen and most amino acids, peptides, and their derivatives in plasma and milk were increased in postpartum high-yield cows, relative to postpartum low-yield cows. Metagenomic analysis revealed that the relative abundances of several species within the Prevotella, Succinimonas, Succinatimonas, and Methanosphaera increased, while other bacteria belong to Alistipes and Bacteroides, and archaeal Methanobrevibacter species decreased in postpartum cows, particularly in postpartum high-yield cows. Co-occurrence network and correlation analysis suggested that Prevotella and Succinatimonas were negatively correlated to Alistipes, Bacteroides, and Methanobrevibacter, potentially contributing to the nutritionally efficient phenotype of postpartum high-yield cows. A metabolic pathway analysis of our metagenomic data revealed that postpartum high-yield cows possessed more microbial genes involved in starch utilization and amino acid synthesis, while a wide range of microbial genes involved in cellulose utilization, acetogenesis, and amino acid degradation were found in prepartum cows with low-yield in postpartum. A structural equation model analysis showed that the increased relative abundances of Prevotella tf.2–5 and Succinatimonas CAG_777 were related to greater concentrations of plasma chenodeoxycholic acid glycine conjugate, milk 5-Methoxytryptophan, and energy-corrected milk yield. Finally, pan-genomic analysis confirmed that Alistipes, Bacteroides, and Methanobrevibacter possess genetic conservation of both hydrogenases and dehydrogenases, which may contribute to energy loss in the rumen via hydrogen dissipation. Conclusion In summary, our findings provide a fundamental understanding of how microbiome-dependent mechanisms contribute to early lactation performance in dairy cows during the transition period. The increased abundance of Prevotella, Succinimonas, and Succinatimonas in postpartum cows suggest that they are important microbes during the transition period and may help in coping with metabolic challenges, while improving nutrient utilization efficiency during this period. Our study underscores the importance of the ruminal microbiome during the transition period and highlights the need for rumen-based nutritional intervention strategies to improve production efficiency in ruminants. Video Abstract

  PublisherDOI

• Multi-omics revealed the effects of rumen to blood path on early lactation performance in transition dairy cows

  Microbiome · 2026-04-07

  articleOpen access

  BACKGROUND: The transition period is vitally important to the life cycle of dairy cows. However, the function of the microbiota during both pre- and post-partum and their relationship with ruminal, plasma, and milk metabolites still require systematic investigation. To address this, the 7 highest- and 7 lowest-performing animals among a cohort of 100 dairy cows were selected based on their postpartum energy-corrected milk yield. Rumen fluid and plasma samples were collected during both pre- and post-partum periods, whereas milk samples were obtained postpartum. Shotgun metagenomics of rumen contents in addition to metabolomics of rumen, plasma, and milk samples were performed to evaluate the associations between ruminal microbes and early lactation performance in transition dairy cows. RESULTS: Compared with prepartum cows, postpartum high-yield cows had greater concentrations of ruminal volatile fatty acids and plasma total bile acid. Moreover, plasma urea nitrogen and most amino acids, peptides, and their derivatives in plasma and milk were increased in postpartum high-yield cows, relative to postpartum low-yield cows. Metagenomic analysis revealed that the relative abundances of several species within the Prevotella, Succinimonas, Succinatimonas, and Methanosphaera increased, while other bacteria belong to Alistipes and Bacteroides, and archaeal Methanobrevibacter species decreased in postpartum cows, particularly in postpartum high-yield cows. Co-occurrence network and correlation analysis suggested that Prevotella and Succinatimonas were negatively correlated to Alistipes, Bacteroides, and Methanobrevibacter, potentially contributing to the nutritionally efficient phenotype of postpartum high-yield cows. A metabolic pathway analysis of our metagenomic data revealed that postpartum high-yield cows possessed more microbial genes involved in starch utilization and amino acid synthesis, while a wide range of microbial genes involved in cellulose utilization, acetogenesis, and amino acid degradation were found in prepartum cows with low-yield in postpartum. A structural equation model analysis showed that the increased relative abundances of Prevotella tf.2-5 and Succinatimonas CAG_777 were related to greater concentrations of plasma chenodeoxycholic acid glycine conjugate, milk 5-Methoxytryptophan, and energy-corrected milk yield. Finally, pan-genomic analysis confirmed that Alistipes, Bacteroides, and Methanobrevibacter possess genetic conservation of both hydrogenases and dehydrogenases, which may contribute to energy loss in the rumen via hydrogen dissipation. CONCLUSION: In summary, our findings provide a fundamental understanding of how microbiome-dependent mechanisms contribute to early lactation performance in dairy cows during the transition period. The increased abundance of Prevotella, Succinimonas, and Succinatimonas in postpartum cows suggest that they are important microbes during the transition period and may help in coping with metabolic challenges, while improving nutrient utilization efficiency during this period. Our study underscores the importance of the ruminal microbiome during the transition period and highlights the need for rumen-based nutritional intervention strategies to improve production efficiency in ruminants. Video Abstract.

  PublisherDOI

• Seasonality regulates ruminal microbiome structure and protozoal abundance in grazing cattle, even under grass–legume intercropping

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Multi-omics revealed the effects of rumen to blood path on early lactation performance in transition dairy cows

  Figshare · 2026-04-07

  otherOpen access

  Abstract Background The transition period is vitally important to the life cycle of dairy cows. However, the function of the microbiota during both pre- and post-partum and their relationship with ruminal, plasma, and milk metabolites still require systematic investigation. To address this, the 7 highest- and 7 lowest-performing animals among a cohort of 100 dairy cows were selected based on their postpartum energy-corrected milk yield. Rumen fluid and plasma samples were collected during both pre- and post-partum periods, whereas milk samples were obtained postpartum. Shotgun metagenomics of rumen contents in addition to metabolomics of rumen, plasma, and milk samples were performed to evaluate the associations between ruminal microbes and early lactation performance in transition dairy cows. Results Compared with prepartum cows, postpartum high-yield cows had greater concentrations of ruminal volatile fatty acids and plasma total bile acid. Moreover, plasma urea nitrogen and most amino acids, peptides, and their derivatives in plasma and milk were increased in postpartum high-yield cows, relative to postpartum low-yield cows. Metagenomic analysis revealed that the relative abundances of several species within the Prevotella, Succinimonas, Succinatimonas, and Methanosphaera increased, while other bacteria belong to Alistipes and Bacteroides, and archaeal Methanobrevibacter species decreased in postpartum cows, particularly in postpartum high-yield cows. Co-occurrence network and correlation analysis suggested that Prevotella and Succinatimonas were negatively correlated to Alistipes, Bacteroides, and Methanobrevibacter, potentially contributing to the nutritionally efficient phenotype of postpartum high-yield cows. A metabolic pathway analysis of our metagenomic data revealed that postpartum high-yield cows possessed more microbial genes involved in starch utilization and amino acid synthesis, while a wide range of microbial genes involved in cellulose utilization, acetogenesis, and amino acid degradation were found in prepartum cows with low-yield in postpartum. A structural equation model analysis showed that the increased relative abundances of Prevotella tf.2–5 and Succinatimonas CAG_777 were related to greater concentrations of plasma chenodeoxycholic acid glycine conjugate, milk 5-Methoxytryptophan, and energy-corrected milk yield. Finally, pan-genomic analysis confirmed that Alistipes, Bacteroides, and Methanobrevibacter possess genetic conservation of both hydrogenases and dehydrogenases, which may contribute to energy loss in the rumen via hydrogen dissipation. Conclusion In summary, our findings provide a fundamental understanding of how microbiome-dependent mechanisms contribute to early lactation performance in dairy cows during the transition period. The increased abundance of Prevotella, Succinimonas, and Succinatimonas in postpartum cows suggest that they are important microbes during the transition period and may help in coping with metabolic challenges, while improving nutrient utilization efficiency during this period. Our study underscores the importance of the ruminal microbiome during the transition period and highlights the need for rumen-based nutritional intervention strategies to improve production efficiency in ruminants. Video Abstract

  PublisherDOI

• Additional file 1 of Multi-omics revealed the effects of rumen to blood path on early lactation performance in transition dairy cows

  Figshare · 2026-04-07

  articleOpen access

  Additional file 1: Fig. S1. Experimental design for this study. Healthy multiparous Chinese Holstein dairy cows (n = 100) with similar last 305-d milk yields, parity, body condition scores, and due date were selected. Ruminal fluid and blood samples of all cows were collected at −14 d (14 days before calving) and 14 d (14 days after calving) 2 hours after the morning feeding. Milk samples of all cows were collected at 14 d. Average milk yield at 13 d, 14 d, and 15 d was used to calculate the yield of energy corrected milk (ECM) of all cows. Based on the ECM yield of all cows at 14 d, the 7 cows with highest ECM yield (51.07 ± 7.76 kg/d, mean ± SD) and the 7 cows with the lowest ECM yield (31.19 ± 3.36 kg/d, mean ± SD) were used for the following analysis. The selected cows were compared based on their pregnancy status: prepartum (PREP, n = 14) and postpartum (POSP, n = 14), or based on a combination of ECM and pregnancy status: prepartum cows with high ECM postpartum (PREP_H, n = 7); prepartum cows with low ECM postpartum (PREP_L, n = 7); postpartum cows with high ECM (POSP_H, n = 7); postpartum cows with low ECM (POSP_L, n = 7). Fig. S2. Correlation analysis of ECM production with differential ruminal fermentation and plasma parameters (n = 14). In the heatmap visualization, color gradients represent correlation magnitudes, with red indicating positive correlations and blue representing negative correlations. Correlations were assessed using Spearman's correlation. * indicates q &lt; 0.05, ** indicates q &lt; 0.01, and *** indicates q &lt; 0.001. ECM: energy-corrected milk yield; A/G: albumin to globulin ratio; ALT: alanine aminotransferase; A/P: acetate to propionate; BUN: blood urea nitrogen; NH3-N: ammonia nitrogen; TVFA: total volatile fatty acids. Fig S3. Identification of differential ruminal bacterial and archaeal species in high and low yield dairy cows during the transition period. A Differential bacterial species between PREP and POSP group (n = 14). B Differential bacterial species among PREP_H, PREP_L, POSP_H, and POSP_L group (n = 7). C Differential archaeal species between PREP and POSP group (n = 14). D Differential archaeal species among PREP_H, PREP_L, POSP_H, and POSP_L group (n = 7). Significant differences were identified by linear discriminant analysis (LDA) effect size with LDA scores &gt; 2.5 and q &lt; 0.05. ECM: energy-corrected milk yield; PREP_H: prepartum cows with high ECM postpartum; PREP_L: prepartum cows with low ECM postpartum; POSP_H: postpartum cows with high ECM; POSP_L: postpartum cows with low ECM. Fig. S4. Correlation analysis between differential ruminal microbes and fermentation parameters, plasma metabolites, and energry-corrected milk (ECM). A Correlation analysis of differential bacterial species with ruminal fermentation (n = 28), plasma parameters (n = 28), and ECM yield (n = 14). B Correlation analysis of differential archaeal species with ruminal fermentation (n = 28), plasma parameters (n = 28), and ECM yield (n = 14). In the heatmap visualization, color gradients represent correlation magnitudes, with red indicating positive correlations and blue representing negative correlations. In Spearman's correlation, * indicates q &lt; 0.05, ** indicates q &lt; 0.01, and *** indicates q &lt; 0.001. A/G: albumin to globulin ratio; ALT: alanine aminotransferase; A/P: acetate to propionate; BUN: blood urea nitrogen; NH3-N: ammonia nitrogen; TVFA: total volatile fatty acids. PREP_H: prepartum cows with high ECM postpartum; PREP_L: prepartum cows with low ECM postpartum; POSP_H: postpartum cows with high ECM; POSP_L: postpartum cows with low ECM. Fig. S5. Visualization of microbial interactions with ruminal microbial functions and metabolites (n = 28). Red lines indicated positive correlations, and blue lines indicate negative correlations. The thickness of the lines represents the magnitude of the correlation coefficients. The colors of nodes indicate the group to which the species/enzyme genes/metabolites belong. Correlations were assessed using Spearman's correlation and all correlations with a q value &lt; 0.05 were considered statistically significant. Fig S6. Pan-genomes and core-genomes of Prevotella (A), Succinivibrionaceae (Succinimonas and Succinatimonas) (B), Methanosphaera (C), Alistipes (D), Bacteroides (E), and Methanobrevibacter (F). The red line represents the pan-genome size for each strain combination, while the blue line indicates the corresponding core genome size.

  PublisherDOI

• Additional file 1 of Multi-omics revealed the effects of rumen to blood path on early lactation performance in transition dairy cows

  Figshare · 2026-04-07

  articleOpen access

  Additional file 1: Fig. S1. Experimental design for this study. Healthy multiparous Chinese Holstein dairy cows (n = 100) with similar last 305-d milk yields, parity, body condition scores, and due date were selected. Ruminal fluid and blood samples of all cows were collected at −14 d (14 days before calving) and 14 d (14 days after calving) 2 hours after the morning feeding. Milk samples of all cows were collected at 14 d. Average milk yield at 13 d, 14 d, and 15 d was used to calculate the yield of energy corrected milk (ECM) of all cows. Based on the ECM yield of all cows at 14 d, the 7 cows with highest ECM yield (51.07 ± 7.76 kg/d, mean ± SD) and the 7 cows with the lowest ECM yield (31.19 ± 3.36 kg/d, mean ± SD) were used for the following analysis. The selected cows were compared based on their pregnancy status: prepartum (PREP, n = 14) and postpartum (POSP, n = 14), or based on a combination of ECM and pregnancy status: prepartum cows with high ECM postpartum (PREP_H, n = 7); prepartum cows with low ECM postpartum (PREP_L, n = 7); postpartum cows with high ECM (POSP_H, n = 7); postpartum cows with low ECM (POSP_L, n = 7). Fig. S2. Correlation analysis of ECM production with differential ruminal fermentation and plasma parameters (n = 14). In the heatmap visualization, color gradients represent correlation magnitudes, with red indicating positive correlations and blue representing negative correlations. Correlations were assessed using Spearman's correlation. * indicates q &lt; 0.05, ** indicates q &lt; 0.01, and *** indicates q &lt; 0.001. ECM: energy-corrected milk yield; A/G: albumin to globulin ratio; ALT: alanine aminotransferase; A/P: acetate to propionate; BUN: blood urea nitrogen; NH3-N: ammonia nitrogen; TVFA: total volatile fatty acids. Fig S3. Identification of differential ruminal bacterial and archaeal species in high and low yield dairy cows during the transition period. A Differential bacterial species between PREP and POSP group (n = 14). B Differential bacterial species among PREP_H, PREP_L, POSP_H, and POSP_L group (n = 7). C Differential archaeal species between PREP and POSP group (n = 14). D Differential archaeal species among PREP_H, PREP_L, POSP_H, and POSP_L group (n = 7). Significant differences were identified by linear discriminant analysis (LDA) effect size with LDA scores &gt; 2.5 and q &lt; 0.05. ECM: energy-corrected milk yield; PREP_H: prepartum cows with high ECM postpartum; PREP_L: prepartum cows with low ECM postpartum; POSP_H: postpartum cows with high ECM; POSP_L: postpartum cows with low ECM. Fig. S4. Correlation analysis between differential ruminal microbes and fermentation parameters, plasma metabolites, and energry-corrected milk (ECM). A Correlation analysis of differential bacterial species with ruminal fermentation (n = 28), plasma parameters (n = 28), and ECM yield (n = 14). B Correlation analysis of differential archaeal species with ruminal fermentation (n = 28), plasma parameters (n = 28), and ECM yield (n = 14). In the heatmap visualization, color gradients represent correlation magnitudes, with red indicating positive correlations and blue representing negative correlations. In Spearman's correlation, * indicates q &lt; 0.05, ** indicates q &lt; 0.01, and *** indicates q &lt; 0.001. A/G: albumin to globulin ratio; ALT: alanine aminotransferase; A/P: acetate to propionate; BUN: blood urea nitrogen; NH3-N: ammonia nitrogen; TVFA: total volatile fatty acids. PREP_H: prepartum cows with high ECM postpartum; PREP_L: prepartum cows with low ECM postpartum; POSP_H: postpartum cows with high ECM; POSP_L: postpartum cows with low ECM. Fig. S5. Visualization of microbial interactions with ruminal microbial functions and metabolites (n = 28). Red lines indicated positive correlations, and blue lines indicate negative correlations. The thickness of the lines represents the magnitude of the correlation coefficients. The colors of nodes indicate the group to which the species/enzyme genes/metabolites belong. Correlations were assessed using Spearman's correlation and all correlations with a q value &lt; 0.05 were considered statistically significant. Fig S6. Pan-genomes and core-genomes of Prevotella (A), Succinivibrionaceae (Succinimonas and Succinatimonas) (B), Methanosphaera (C), Alistipes (D), Bacteroides (E), and Methanobrevibacter (F). The red line represents the pan-genome size for each strain combination, while the blue line indicates the corresponding core genome size.

  PublisherDOI

• Metabolomic shifts in beef steers rotationally grazing toxic endophyte-infected tall fescue under fall conditions

  Frontiers in Veterinary Science · 2026-05-15

  articleOpen access

  Background Fescue toxicosis (FT) results from ingestion of tall fescue infected with the ergot alkaloid (EA)-producing endophyte Epichloë coenophiala . While the vascular system is a major EA target, their biogenic amine-like properties can trigger wider physiological effects. This study used untargeted metabolomics and targeted volatile fatty acid (VFA) analysis to characterize EA-induced metabolic disruption in steers under rotational tall fescue grazing. Methods 18 steers grazed toxic (E+), novel (NE), or endophyte-free (E-) fescue pastures. After 14 days, groups switched diets (toxic to nontoxic and vice versa). Urine, saliva, plasma, rumen fluid (RF), and feces were collected. Untargeted high-resolution metabolomics (HRM) was performed on liquid matrices, and gas chromatography-mass spectrometry quantified VFAs in RF and feces. Results Total and individual VFA increased in RF during E+ exposure and returned to baseline after removal. Discriminative analyses showed that E+ steers had a distinct metabolome, while previously exposed steers in period 2 resembled those never exposed. Pathway analysis revealed downward shifts in beta-oxidation, fatty acid and arachidonic acid metabolism in E+, whereas aromatic amino acids (e.g., tyrosine, tryptophan), branched-chain amino acids, vitamin B6, and carbohydrate (e.g., gluconeogenesis) pathways shifted upwards. Upward-shifted pathways were mainly amino acid-related (45%), and downward-shifted mostly lipid-related (60%). Several metabolites, including tyramine, methyltyramine, methoxytyramine, and dopamine, were discriminatory for E+. HRM detected clavine alkaloids, and lysergic acid derivatives in all matrices except plasma, rising and returning to baseline within 2 days of E+ exposure/removal. Conclusion Grazing E+ disrupts metabolism in steers, shifting energy use from lipids toward amino acids and carbohydrates. The main detected EAs were clavine-type alkaloids and simple lysergic acid amides, not ergopeptines, suggesting extensive biotransformation. The EA dynamics and the similar metabolic profile of previously and never-exposed steers in period 2, indicate minimal or no lasting effects metabolomic after exposure ends. The upward shift in metabolite abundance associated with aromatic amino acid pathways (e.g., tyrosine and tryptophan), vitamin B6 metabolism (a key cofactor for aromatic amino acid decarboxylases), and their downstream products, biogenic and trace amines, suggests a coordinated metabolic shift that may contribute to and/or amplify the FT pathophysiology.

  PublisherOA PDFDOI

Frequent coauthors

• Cameron R. Currie

  University of Wisconsin–Madison

  92 shared

• Paul J. Weimer

  University of Wisconsin–Madison

  61 shared

• Nasia Safdar

  University of Wisconsin–Madison

  38 shared

• Jarrod J. Scott

  Smithsonian Tropical Research Institute

  36 shared

• Ashley E. Kates

  William S. Middleton Memorial Veterans Hospital

  31 shared

• Frank O. Aylward

  Virginia Tech

  29 shared

• Lynne Goodwin

  Los Alamos National Laboratory

  29 shared

• Andrew J. Steinberger

  University of Wisconsin–Madison

  25 shared

Labs

• Suen LabPI

Education

• Ph.D. Biology, Biology

  Syracuse University

  2008

• M. Sc. Computer Sciences

  University of Calgary

  2004

• B. Sc. Computer Sciences

  University of Calgary

  2002

• B.Sc. Biological Sciences

  University of Calgary

  2000

Awards & honors

• Alfred Toepfer Faculty Fellow