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Biomedical subjects

Lisa Crossman

Publications and source records attributed to Lisa Crossman.

2 recordsLinked to original sources

Plasticity in a bacterial global regulatory switch that drives a shift in antibiotic resistance and virulence.

Antibiotic resistance and expression of virulence factors impact the outcome of infection by Pseudomonas aeruginosa. Pathogenesis is often modelled using the PAO1 reference strain but laboratory lineages vary in the sequence and activity of MexT, a global regulator impacting virulence, biofilm formation, and ciprofloxacin resistance. We defined the impact of active versus inactive MexT in PAO1 and observed transcriptomic changes affecting the expression of ~900 genes. Phenotyping revealed altered metabolism, antibiotic resistance, and virulence, resulting in striking variation across a 'single' model organism. We propose that antibiotic resistance promotes plasticity in mexT accounting for variation across lineages. We introduced antibiotic resistance into clinical P. aeruginosa isolates and observed mutations in mexT when selective pressure was removed, supporting the proposed evolutionary pathway. Overall, we have demonstrated the transcriptomic basis of MexT as a phenotypic switch in PAO1 and implicated antibiotic resistance as a cause of changes in mexT. Furthermore, MexS/MexT-regulated efflux is implicated in the antibiotic stress response and virulence, helping identify the mechanisms for rapid phenotypic switching through mexT and confirming that PAO1 is unlike most isolates. Improved understanding of the regulatory changes linked to antibiotic resistance is particularly relevant to P. aeruginosa where cycles of antibiotic treatment are common.

antibiotic resistance

Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.

Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.

Artificial Intelligence