Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics (2023)
Journal Article
Chio, H., Guest, E. E., Hobman, J. L., Dottorini, T., Hirst, J. D., & Stekel, D. J. (2023). Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics. Journal of Molecular Graphics and Modelling, 123, Article 108508. https://doi.org/10.1016/j.jmgm.2023.108508

Antibiotics enter the environment through waste streams, where they can exert selective pressure for antimicrobial resistance in bacteria. However, many antibiotics are excreted as partly metabolized forms, or can be subject to partial breakdown in w... Read More about Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics.

Activated tissue resident memory T-cells (CD8+CD103+CD39+) uniquely predict survival in left sided “immune-hot” colorectal cancers (2023)
Journal Article
Talhouni, S., Fadhil, W., Mongan, N. P., Field, L., Hunter, K., Makhsous, S., …Ramage, J. M. (2023). Activated tissue resident memory T-cells (CD8+CD103+CD39+) uniquely predict survival in left sided “immune-hot” colorectal cancers. Frontiers in Immunology, 14, Article 1057292. https://doi.org/10.3389/fimmu.2023.1057292

Introduction: Characterization of the tumour immune infiltrate (notably CD8+ T-cells) has strong predictive survival value for cancer patients. Quantification of CD8 T-cells alone cannot determine antigenic experience, as not all infiltrating T-cells... Read More about Activated tissue resident memory T-cells (CD8+CD103+CD39+) uniquely predict survival in left sided “immune-hot” colorectal cancers.

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock (2022)
Journal Article
Maciel-Guerra, A., Baker, M., Hu, Y., Wang, W., Zhang, X., Rong, J., …Dottorini, T. (2023). Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock. ISME Journal, 17, 21-35. https://doi.org/10.1038/s41396-022-01315-7

A debate is currently ongoing as to whether intensive livestock farms may constitute reservoirs of clinically relevant antimicrobial resistance (AMR), thus posing a threat to surrounding communities. Here, combining shotgun metagenome sequencing, mac... Read More about Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock.

Identifying associations between management practices and antimicrobial resistances of sentinel bacteria recovered from bulk tank milk on dairy farms (2022)
Journal Article
McLaughlin, D., Bradley, A., Dottorini, T., Giebel, K., Leach, K., Hyde, R., & Green, M. (2022). Identifying associations between management practices and antimicrobial resistances of sentinel bacteria recovered from bulk tank milk on dairy farms. Preventive Veterinary Medicine, 204, Article 105666. https://doi.org/10.1016/j.prevetmed.2022.105666

There is increasing emphasis on the need to reduce antimicrobial use (AMU) on dairy farms to reduce the emergence of resistant bacteria which could compromise animal health and impact human medicine. In addition to AMU, the role of farm management is... Read More about Identifying associations between management practices and antimicrobial resistances of sentinel bacteria recovered from bulk tank milk on dairy farms.

Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming (2022)
Journal Article
Peng, Z., Maciel-Guerra, A., Baker, M., Zhang, X., Hu, Y., Wang, W., …Dottorini, T. (2022). Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming. PLoS Computational Biology, 18(3), Article e1010018. https://doi.org/10.1371/journal.pcbi.1010018

Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence of antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs... Read More about Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming.

Novel SCCmec type XV (7A) and two pseudo-SCCmec variants in foodborne MRSA in China (2022)
Journal Article
Wang, W., Hu, Y., Baker, M., Dottorini, T., Li, H., Dong, Y., …Li, F. (2022). Novel SCCmec type XV (7A) and two pseudo-SCCmec variants in foodborne MRSA in China. Journal of Antimicrobial Chemotherapy, 77(4), 903-909. https://doi.org/10.1093/jac/dkab500

Background Staphylococcal cassette chromosome mec (SCCmec) elements are highly diverse and have been classified into 14 types. Novel SCCmec variants have been frequently detected from humans and animals but rarely from food. Objectives To charac... Read More about Novel SCCmec type XV (7A) and two pseudo-SCCmec variants in foodborne MRSA in China.

Transcriptomic Analysis of Cardiomyocyte Extracellular Vesicles in Hypertrophic Cardiomyopathy Reveals Differential snoRNA Cargo (2021)
Journal Article
James, V., Nizamudeen, Z. A., Lea, D., Dottorini, T., Holmes, T. L., Johnson, B. B., …Smith, J. G. (2021). Transcriptomic Analysis of Cardiomyocyte Extracellular Vesicles in Hypertrophic Cardiomyopathy Reveals Differential snoRNA Cargo. Stem Cells and Development, 30(24), 1215-1227. https://doi.org/10.1089/scd.2021.0202

Hypertrophic cardiomyopathy (HCM) is characterised by increased left ventricular wall thickness that can lead to devastating conditions such as heart failure and sudden cardiac death. Despite extensive study, the mechanisms mediating many of the asso... Read More about Transcriptomic Analysis of Cardiomyocyte Extracellular Vesicles in Hypertrophic Cardiomyopathy Reveals Differential snoRNA Cargo.

A Multi-Factorial Observational Study on Sequential Fecal Microbiota Transplant in Patients with Medically Refractory Clostridioides difficile Infection (2021)
Journal Article
Monaghan, T. M., Duggal, N. A., Rosati, E., Griffin, R., Hughes, J., Roach, B., …Kao, D. H. (2021). A Multi-Factorial Observational Study on Sequential Fecal Microbiota Transplant in Patients with Medically Refractory Clostridioides difficile Infection. Cells, 10(11), Article 3234. https://doi.org/10.3390/cells10113234

Fecal microbiota transplantation (FMT) is highly effective in recurrent Clostridioides difficile infection (CDI); increasing evidence supports FMT in severe or fulminant Clostridioides difficile infection (SFCDI). However, the multifactorial mechanis... Read More about A Multi-Factorial Observational Study on Sequential Fecal Microbiota Transplant in Patients with Medically Refractory Clostridioides difficile Infection.

Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India (2021)
Journal Article
Monaghan, T. M., Biswas, R. N., Nashine, R. R., Joshi, S. S., Mullish, B. H., Seekatz, A. M., …Kashyap, R. S. (2021). Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India. Microorganisms, 9(7), Article 1485. https://doi.org/10.3390/microorganisms9071485

Background: Non-communicable diseases (NCDs) have become a major cause of morbidity and mortality in India. Perturbation of host–microbiome interactions may be a key mechanism by which lifestyle-related risk factors such as tobacco use, alcohol consu... Read More about Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India.

Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms (2021)
Journal Article
Pearcy, N., Hu, Y., Baker, M., Maciel-Guerra, A., Xue, N., Wang, W., …Dottorini, T. (2021). Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms. mSystems, 6(4), Article e00913-20. https://doi.org/10.1128/mSystems.00913-20

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug re... Read More about Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms.