Biology subjects

Littler, J. L.

Publications and source records attributed to Littler, J. L..

3 recordsLinked to original sources

bioRxiv2026

Biofilm-deficient mutants of Pseudomonas aeruginosa have wild-type levels of antibiotic tolerance in a model of cystic fibrosis lung infection

1Opportunistic, biofilm-forming pathogens such as Pseudomonas aeruginosa can employ an array of strategies to reduce the impact of antibiotics on their survival. The biofilm matrix can prevent antibiotics from reaching bacteria embedded within it; general changes in metabolic activity alter susceptibility to specific drugs dependent on the target; changes in the membrane and the expression of channel or pump proteins embedded within it affect drug uptake and efflux; and production of antibiotic-degrading enzymes can remove the threat. In this study, we report that biofilm-deficient mutants of two well-studied lab strains of P. aeruginosa (PA14 and PAO1) have wild-type (WT) levels of tolerance to colistin and meropenem when allowed to establish mature populations in an ex vivo pig lung model of cystic fibrosis lung infection. This result was unexpected, given previous work in our lab that suggested physical protection of cells from colistin by the biofilm matrix was important for tolerance. The biofilm defects in the mutants were confirmed using electron and light microscopy, and cryo scanning electron microscopy was used to visualise the hydrated biofilm matrix in the WT. Using RNA sequencing of the PA14 WT and an isogenic mutant lacking the pel polysaccharide, we were able to identify a small number of differences in the responses of the two genotypes to the lung environment and to exposure to sub-bactericidal colistin in the lung model. Notably, there was differential upregulation of the MexXY-OprM and MexEF-OprN multidrug efflux pumps. Hence, mature biofilm structure is not essential for tolerance, as other mechanisms may be sufficient in this environment. However, the relative roles of biofilm matrix versus cellular changes in physiology in conferring antibiotic tolerance in this environment remain to be fully elucidated.

microbiology↗

bioRxiv2025

Transfer learning enables discovery of sub-micromolar antibacterials for ESKAPE pathogens from ultra-large chemical spaces

The rise of antimicrobial resistance, especially among gram-negative ESKAPE pathogens, presents an urgent global health threat. However, the discovery of new antibiotics is hampered by sparse publicly available antibacterial data, complex bacterial defenses, and weak economic incentives. Here, we introduce a transfer learning framework using deep graph neural networks (DGNNs) to identify antibacterials from ultra-large chemical libraries. DGNNs were first pre-trained on large molecular datasets of protein-ligand simulations, binding affinities, and physicochemical properties to learn generalizable chemical features, and then fine-tuned on limited antibacterial screening data. Compared to classical methods, transfer learning significantly improved enrichment factors and predictive performance in cross-dataset benchmarks. Applying this strategy to the ChemDiv and Enamine libraries, we virtually screened over a billion compounds and prioritized 156 candidates. Experimental testing against Escherichia coli revealed that 54% of compounds exhibited antibacterial activity (MIC [≤]64 {micro}g/ml), with several demonstrating sub-micromolar potency and broad-spectrum efficacy against gram-positive and gram-negative pathogens, including three ESKAPE species. Of 18 broad-spectrum candidates, 15 showed minimal cytotoxicity and no hemolytic activity. These results validate our approach for navigating underexplored chemical space and identifying potent, low-toxicity compounds with antibiotic activity. We release open-source models and a scalable workflow to accelerate antibacterial discovery in the face of data scarcity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/669197v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@d1e793org.highwire.dtl.DTLVardef@ba6c85org.highwire.dtl.DTLVardef@12ab303org.highwire.dtl.DTLVardef@4ea019_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Transcriptome analysis of Pseudomonas aeruginosa biofilm infection in an ex vivo pig model of the cystic fibrosis lung

Pseudomonas aeruginosa is the predominant cause of chronic biofilm infections that form in the lungs of people with cystic fibrosis (CF). These infections are highly resistant to antibiotics and persist for years in the respiratory tract. One of the main research challenges is that current laboratory models do not accurately replicate key aspects of a chronic P. aeruginosa biofilm infection, highlighted by previous RNA-sequencing studies. We compared the P. aeruginosa PA14 transcriptome in an ex vivo pig lung (EVPL) model of CF and a well-studied synthetic cystic fibrosis sputum medium (SCFM). P. aeruginosa was grown in the EVPL model for 1, 2 and 7 days, and in vitro in SCFM for 1 and 2 days. The RNA was extracted and sequenced at each time point. Our findings demonstrate that expression of antimicrobial resistance genes was cued by growth in the EVPL model, highlighting the importance of growth environment in determining accurate resistance profiles. The EVPL model created two distinct growth environments: tissue-associated biofilm and the SCFM surrounding tissue, each of which cued a transcriptome distinct from that seen in SCFM in vitro. The expression of quorum sensing associated genes in the EVPL tissue-associated biofilm at 48 h relative to in vitro SCFM was found to be similar to CF sputum versus in vitro conditions. Hence, the EVPL model can replicate key aspects of in vivo biofilm infection that are missing from other current models and provides a more accurate P. aeruginosa growth environment for determining antimicrobial resistance. Significance StatementPseudomonas aeruginosa lung infections that affect people with cystic fibrosis are a well-researched area, yet they continue to evade antimicrobial treatments. The lack of a laboratory model that captures all key aspects of these infections hinders not only research progression but also clinical diagnostics. We used transcriptome analysis to demonstrate how a model using pig lungs can more accurately replicate key characteristics of P. aeruginosa lung infection, including mechanisms of antibiotic resistance and infection establishment. Therefore, this model may be used in the future to further understand infection dynamics to develop novel treatments and more accurate treatment plans. This could improve clinical outcomes as well as quality of life for individuals affected by these infections.

microbiology↗