Biology subjects

Dillon, L.

Publications and source records attributed to Dillon, L..

6 recordsLinked to original sources

bioRxiv2025

Genomic constraints shape the evolution of alternative routes to drug resistance in prokaryotes.

Antimicrobial resistance (AMR) is often modelled as the accumulation of resistance genes leading to multidrug resistance (MDR). We show that gene co-occurrence patterns in two opportunistic pathogens are consistent with fitness trade-offs that constrain which combinations of resistance mechanisms coexist. We applied a combined pangenomic and machine-learning analysis to 9,584 Escherichia coli genomes (99.2% phylogroup B2) and 7,057 Pseudomonas aeruginosa genomes. In E. coli, we identified eight cases of mutually exclusive gene pairs that independently predicted the same MDR phenotype, suggesting alternative routes to resistance whose components are typically not co-inherited. In a separate dataset of 352 strains with paired minimum inhibitory concentration (MIC) data, these dissociated combinations co-occurred more often in resistant than susceptible strains, consistent with the constraints being conditional on antibiotic selection. 33 gene pairs showed opposing association patterns between the two species, with combinations significantly associated in one species and significantly dissociated in the other (e.g. associated in E. coli and dissociated in P. aeruginosa, or vice versa). This indicates that genomic context modifies the contribution of individual genes to resistance phenotypes, and offers one explanation for the observation that 106 ARGs are present in >95% of strains yet do not predict resistance phenotype on their own. The findings are consistent with resistance evolution being shaped by fitness trade-offs and suggest that the dissociation patterns we identify could be targets for follow-up experimental work on resistance-associated fitness costs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/671315v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@7a717corg.highwire.dtl.DTLVardef@b08e64org.highwire.dtl.DTLVardef@154820forg.highwire.dtl.DTLVardef@126b9ee_HPS_FORMAT_FIGEXP M_FIG C_FIG IMPACT STATEMENTMultidrug resistance threatens global health; however, resistance acquisition remains poorly understood. We show that genomes cannot simply accumulate all available resistance mechanisms. Instead, species-specific grammatical rules render certain gene combinations typically incompatible. Analysing 9,584 Escherichia coli genomes, we identify eight mutually exclusive routes to resistance, demonstrating that resistance is more constrained than previously thought. Strikingly, 33 gene pairs that cooperate in E. coli actively disassociate in Pseudomonas aeruginosa, revealing that genomic context determines resistance outcomes. This explains why 106 resistance genes present in all strains fail to confer universal resistance, challenging current diagnostics. Our findings transform our understanding of resistance evolution from an unlimited accumulation model to a grammatically constrained pathways model, presenting new therapeutic strategies that exploit evolutionary dead ends.

bioinformatics↗

bioRxiv2025

Whole-Genome Sequencing, Annotation and Taxonomic Confirmation of a Multidrug-Resistant Escherichia coli Isolated from the Blood of a Sepsis Patient

Sepsis (blood stream infection) caused by multidrug-resistant (MDR) bacteria, particularly Escherichia coli, represents a significant global health threat due to high morbidity, mortality, and limited treatment options. E. coli, a major causative agent of bloodstream infections, has evolved highly virulent and MDR strains, which contribute to the increasing burden of antimicrobial resistance (AMR), complicating clinical management and reducing the efficacy of conventional antibiotic therapies. In this study, we characterised the genomic and phenotypic drug resistance mechanism of E. coli 266631E isolated from a sepsis patient, highlighting the negative implications of MDR E. coli in sepsis. Antimicrobial susceptibility testing and minimum inhibitory concentration analysis revealed resistance to multiple antibiotics, including amoxicillin, cefotaxime, ciprofloxacin, gentamicin, and tobramycin. Whole genome sequencing identified a broad array of AMR genes encoding resistance to various antibiotic classes, such as macrolides, fluoroquinolones, aminoglycosides, carbapenems, and cephalosporins. Notably, the CTX-M-15 gene, a key extended-spectrum {beta}-lactamase determinant, was found in both the bacterial chromosome and an IncF-type plasmid, emphasizing the potential for horizontal gene transfer and rapid dissemination of resistance. Confirming the taxonomy of the novel and unidentified bacterial strain through querying its 16S rRNA sequence and genome in recognised bacterial taxonomic databases presented a challenge. The isolate showed genetic similarity to E. coli, E. fergusonii, and Shigella species despite their phenotypic differences and variations in their pathogenic traits. However, a simple phenotypic laboratory procedure, based on the biochemical and cultural differences among these bacteria in Coliform ChromoSelect Agar, confirmed the isolate as E. coli. This study underscores the critical importance of integrating phenotypic methods with genomic tools for the accurate identification of clinically significant bacteria. It also highlights the need for both phenotypic and genetic surveillance of key MDR variants in healthcare settings to enable timely, precise diagnosis and targeted treatment of life-threatening infections such as sepsis. DATA SUMMARYThe outputs of the MALDI-TOF analysis, AMR analysis using AMRFinderPlus, starAMR, and RGI, the query of bacterial 16S rRNA in the NCBI, Greengenes2, and SILVA databases, as well as Sourmash and pangenome analyses, are available in the supplementary material. The bacterial 16S rRNA gene sequence has been deposited in the NCBI GenBank database under accession number PQ871642. The isolates complete genome sequence has been submitted to the NCBI Genome database under BioProject accession PRJNA1220687 and BioSample accession SAMN46722626. The chromosome is available under GenBank accession number CP183489, while plasmids and other contigs are available under accession numbers CP183490-CP183498. The scripts used in the following bioinformatics analysis - Sourmash, Roary, and Prokka (for pangenome analysis) are available at: https://github.com/LucyDillon/MDR_isolate. IMPACT STATEMENTThis study presents a comprehensive genomic and phenotypic characterization of a multidrug-resistant Escherichia coli strain implicated in sepsis, uncovering extensive antimicrobial resistance genes across both the chromosome and plasmids. It exposes taxonomic ambiguity with closely related species such as E. coli, Shigella, and E. fergusonii, underscoring gaps in current genomic databases and reinforcing the necessity of phenotypic testing. By integrating whole-genome sequencing with biochemical differentiation, the research strengthens diagnostic precision and informs clinical decision-making for life-threatening infections. Overall, it advances AMR and bacterial taxonomy research by demonstrating the value of an integrated genomic-phenotypic framework to accurately identify and guide the treatment and surveillance of emerging MDR pathogens in clinical settings.

genomics↗

bioRxiv2023

Accessory genes define species-specific pathways to antibiotic resistance.

BackgroundThe rise of antimicrobial resistance (AMR) is a growing concern globally and a deeper understanding of AMR gene carriage vs usage is vital for future responses to reduce the spread of AMR. Identification of AMR phenotype by laboratory-based assays are often hindered by difficulties in establishing cultures. This issue could be resolved by rapid computational assessment of an organisms genome, however, AMR gene finder tools are not intended to infer AMR phenotype which is likely to be a product of multiple gene interactions. MethodsTo understand the importance of multi-gene interactions to the relationship between AMR genotype and AMR phenotype, we applied machine learning approaches to 16,950 genomes from microbial isolates representing 28 different genera with 1.2 million corresponding laboratory-determined MICs for 23 different antibiotics. We then elucidated the genomic paths to phenotypic antimicrobial resistance with the aim of allowing for the development of rapid determination of AMR phenotype from genomes or even whole microbiomes. ResultsThe application of machine learning models resulted in a >1.5-fold increase in average prediction accuracy of AMR phenotype across the 23 antibiotic models. Interpretation of these models revealed 528 distinct genomic pathways to phenotypic resistance, many of which were species-specific and involved genes which have not previously been associated with AMR phenotype. This is the first study to demonstrate the utility of machine learning models in the prediction of AMR phenotype for a wide range of clinically relevant organisms and antibiotics. This could be applied as a rapid and affordable alternative to culture-based techniques, estimating taxonomy in addition to AMR phenotype, and providing real-time monitoring of multi-drug resistant pathogens. Availability and implementationO_ST_ABSContactC_ST_ABSldillon05@qub.ac.uk View supplementary information at this linkhttps://osf.io/cj4bq/?view_only=c0ee87b7609543b688953089be4c376f See Code Availability for scripts used.

bioinformatics↗

bioRxiv2023

Environmental effects on reproductive performance in preindustrial women

Early life environments can have long-lasting effects on adult reproductive performance, but disentangling the influence of early and adult life environments on fitness is challenging, especially for long-lived species. Using a detailed dataset spanning over two centuries, we studied how early and adult life environments impact reproductive performance in preindustrial women. We assessed the effect of environmental conditions associated with the parish of birth and the parish where the women had their offspring. We considered resource availability differences between rural, urban, northern, and southern parishes by comparing women who switched environments during their lifetime with those who did not. We found that urban-born women had an earlier age at first reproduction and lower offspring survival to adulthood than rural-born women, while South Quebec women had more offspring born than North Quebec women. Moreover, switching from urban to rural led to higher offspring survival, while the reverse had the opposite effect. In addition, moving from South to North resulted in fewer offspring born and surviving, whereas moving from North to South had the inverse effect. Finally, women who changed from rural to urban and from South to North had their first child at an older age compared to those who stayed in the same urbanity type. Our study underscores the complex and interactive effects of early and adult life environments on reproductive traits, highlighting the need to consider both when studying environmental effects on reproductive outcomes.

ecology↗

bioRxiv2022

Widespread, depth-dependent microstructural damage in the cortex of children with drug resistant focal epilepsy: A quantitative T1 and T2 mapping study

ObjectiveTissue abnormalities in focal epilepsy may extend beyond the presumed focus. The underlying pathophysiology of these broader changes is unclear, and it is not known whether they result from ongoing disease processes, treatment-related side-effects, or whether they emerge earlier. Few studies have focused on the period of onset for most focal epilepsies, childhood. Fewer still have utilised quantitative MRI, which may provide a more sensitive and interpretable measure of tissue microstructural change. Here, we aimed to determine common spatial modes of changes in cortical architecture in children with heterogeneous drug-resistant focal epilepsy and, secondarily, whether changes were related to disease severity. MethodsTo assess cortical microstructure, quantitative T1 and T2 relaxometry (qT1 and qT2) was measured in 43 children with drug-resistant focal epilepsy [age-range=4-18 years] and 46 typically-developing children [age-range=2-18 years]. We assessed depth-dependent qT1 and qT2 values across the neocortex, as well as their gradient of change across cortical depths. We also determined whether global changes seen in group analyses were driven by focal pathologies in individual patients. Finally, as a proof-of-concept, we trained a classifier using qT1 and qT2 gradient maps from patients with radiologically-defined abnormalities (MRI-positive) and healthy controls, and tested if this could classify patients without reported radiological abnormalities (MRI-negative). ResultsWe uncovered depth-dependent qT1 and qT2 increases in widespread cortical areas in patients, likely representing microstructural alterations in myelin or gliosis. Changes did not correlate with disease severity measures, suggesting they may represent antecedent neurobiological alterations. Using a classifier trained with MRI-positive patients and controls, sensitivity was 62% at 100% specificity on held-out MRI-negative patients. SignificanceThese findings suggest the presence of a potential imaging endophenotype of focal epilepsy, detectable irrespective of radiologically-identified abnormalities, and potentially evident pre-symptomatically. Key PointsO_LIWe assessed cortical microstructure in children with focal epilepsy C_LIO_LIQuantitative T1 and T2 relaxometry (qT1 and qT2) was measured in the neocortex C_LIO_LIPatients showed extensive qT1/qT2 increases and intracortical organization changes C_LIO_LIAlterations may appear during cerebral development, prior to disease onset C_LI

neuroscience↗

bioRxiv2019

Extrachromosomal circular DNA, microDNA, without canonical promoters produce short regulatory RNAs that suppress gene expression

Interest in extrachromosomal circular DNA (eccDNA) molecules has increased recently because of their widespread presence in normal cells across every species ranging from yeast to humans, their increased levels in cancer cells, and their overlap with oncogenic and drug-resistant genes. However, the majority of eccDNA (microDNA) are too small to carry protein coding genes. We have tested functional capabilities of microDNA, by creating artificial microDNA molecules mimicking known microDNA sequences and have discovered that they express functional small regulatory RNA including microRNA and novel si-like RNA. MicroDNA is transcribed in vitro and in vivo independent of a canonical promoter sequence. MicroDNA which carry miRNA genes form transcripts which are processed into mature miRNA molecules, through the endogenous RNA-interference pathway, which repress a luciferase reporter gene as well as endogenous mRNA targets of the miRNA. Further, microDNA containing sequences of exons repress the endogenous gene from which the microDNA was derived through the formation of novel si-like RNA. We also show that endogenous microDNA associate with RNA polymerases subunits POLR2H and POLR3F. Together, these results suggest that microDNA may modulate gene expression through the production of both known and novel regulatory small RNA.

molecular biology↗