Biology subjects

Monk, J. M.

Publications and source records attributed to Monk, J. M..

13 recordsLinked to original sources

bioRxiv2025

Multi-strain Analysis of Pseudomonas putida Reveals the Metabolic and Genetic Diversity of the Species.

Pseudomonas putida is a gram-negative bacterial species increasingly utilized in biotechnology due to its robust growth, ability to degrade aromatic compounds, solvent tolerance, and genetic tractability. In this study, we report a comprehensive multi-strain analysis of 164 P. putida strains. We performed whole-genome sequencing and hybrid assembly for 40 strains, contributing a [~]8% increase to the available genomic data for P. putida. Furthermore, high-throughput phenotypic profiling using the Biolog phenotype microarray system for 24 strains on 190 unique carbon sources, along with 15 aromatic compounds not present on Biolog plates, yielded 4,920 unique strain-phenotype measurements. These data were leveraged to curate GEMs for 24 representative strains, including a refined model for strain KT2440, which comprised 1,480 genes and 2,191 metabolites, achieving a prediction accuracy of 91.2% in carbon utilization. Systematic comparison of genomes and GEMs revealed both conserved core pathways and significant allelic and functional divergence across strains, highlighting strain-specific variation in aromatic degradation. While pathways for protocatechuate and phenylacetate degradation were widely conserved, metabolic capabilities for compounds such as ferulate, phenol, and cresols varied markedly, suggesting adaptation to distinct ecological niches. Alleleome analysis of enzymes such as PcaI and PcaJ revealed distinct, functionally similar clades, indicating possible convergent evolution or horizontal gene transfer. These results provide computable resources and models for selecting P. putida strains with desired traits for biomanufacturing and bioremediation and offer insights into the evolution and phylogeny of the P. putida species.

bioengineering↗

bioRxiv2024

A metabolic atlas of the Klebsiella pneumoniae species complex reveals lineage-specific metabolism that supports persistent co-existence of diverse lineages

The Klebsiella pneumoniae species complex inhabits a wide variety of hosts and environments, and is a major cause of antimicrobial resistant infections. Genomics has revealed the population comprises multiple species/subspecies and hundreds of distinct co-circulating sub-lineages that are associated with distinct gene complements. A substantial fraction of the pan-genome is predicted to be involved in metabolic functions and hence these data are consistent with metabolic differentiation as a driver of population structure. However, this has so far remained unsubstantiated because in the past it was not possible to explore metabolic variation at scale. Here we used a combination of comparative genomics and high-throughput genome-scale metabolic modelling to systematically explore metabolic diversity across the K. pneumoniae species complex (n=7,835 genomes). We simulated growth outcomes for each isolate using carbon, nitrogen, phosphorus and sulfur sources under aerobic and anaerobic conditions (n=1,278 conditions per isolate). We showed that the distributions of metabolic genes and growth capabilities are structured in the population, and confirmed that sub-lineages exhibit unique metabolic profiles. In vitro co-culture experiments demonstrated reciprocal commensalistic cross-feeding between sub-lineages, effectively extending the range of conditions supporting individual growth. We propose that these substrate specialisations promote the existence and persistence of co-circulating sub-lineages by reducing nutrient competition and facilitating commensal interactions via negative frequency-dependent selection. Our findings have implications for understanding the eco-evolutionary dynamics of K. pneumoniae and for the design of novel strategies to prevent opportunistic infections caused by this World Health Organization priority antimicrobial resistant pathogen.

microbiology↗

bioRxiv2024

The Pangenome of Escherichia coli

Thousands of complete genome sequences for strains of a species that are now available enable the advancement of pangenome analytics to a new level of sophistication. We collected 2,377 publicly-available complete genomes of Escherichia coli for detailed pangenome analysis. The core genome and accessory genomes consisted of 2,398 and 5,182 genes, respectively. We developed a machine learning approach to define the accessory genes characterizing the major phylogroups of E. coli plus Shigella: A, B1, B2, C, D, E, F, G, and Shigella. The analysis resulted in a detailed structure of the genetic basis of the phylogroups differential traits. This pangenome structure was largely consistent with a housekeeping-gene-based MLST distribution, sequence-based Mash distance, and the Clermont quadruplex classification. The rare genome consisted of 163,619 genes, about 79% of which represented variations of 315 underlying transposon elements. This analysis generated a mathematical definition of the genetic basis for a species.

genomics↗

bioRxiv2023

A validated pangenome-scale metabolic model for the Klebsiella pneumoniae species complex

The Klebsiella pneumoniae Species Complex (KpSC) is a major source of nosocomial infections globally with high rates of resistance to antimicrobials. Consequently, there is growing interest in understanding virulence factors and their association with cellular metabolic processes for developing novel anti-KpSC therapeutics. Phenotypic assays have revealed metabolic diversity within the KpSC, but metabolism research has been neglected due to experiments being difficult and cost-intensive. Genome-scale metabolic models (GSMMs) represent a rapid and scalable in silico approach for exploring metabolic diversity, which compiles genomic and biochemical data to reconstruct the metabolic network of an organism. Here we use a diverse collection of 507 KpSC isolates, including representatives of globally distributed clinically-relevant lineages, to construct the most comprehensive KpSC pan-metabolic model to-date, KpSC pan v2. Candidate metabolic reactions were identified using gene orthology to known metabolic genes, prior to manual curation via extensive literature and database searches. The final model comprised a total of 3,550 reactions, 2,403 genes and can simulate growth on 360 unique substrates. We used KpSC pan v2 as a reference to derive strain-specific GSMMs for all 507 KpSC isolates, and compared these to GSMMs generated using a prior KpSC pan-reference (KpSC pan v1) and two single-strain references. We show that KpSC pan v2 includes a greater proportion of accessory reactions (8.8%) than KpSC pan v1 (2.5%). GSMMs derived from KpSC pan v2 also result in more accuracy growth predictions than those derived from other references in both aerobic (median accuracy = 95.4%) and anaerobic (median accuracy = 78.8%). KpSC pan v2 also generates more accurate growth predictions, with high median accuracies of 95.4% (aerobic, n=37 isolates) and 78.8% (anaerobic, n=36 isolates) for 124 matched carbon substrates. KpSC pan v2 is freely available at https://github.com/kelwyres/KpSC-pan-metabolic-model, representing a valuable resource for the scientific community, both as a source of curated metabolic information and as a reference to derive accurate strain-specific GSMMs. The latter can be used to investigate the relationship between KpSC metabolism and traits of interest, such as reservoirs, epidemiology, drug resistance or virulence, and ultimately to inform novel KpSC control strategies. Significance as a BioResource to the communityKlebsiella pneumoniae and its close relatives in the K. pneumoniae Species Complex (KpSC) are priority antimicrobial resistant pathogens that exhibit extensive genomic diversity. There is growing interest in understanding KpSC metabolism, and genome scale metabolic models (GSMMs) provide a rapid, scalable option for exploration of whole cell metabolism plus phenotype prediction. Here we present a KpSC pan-metabolic model representing the cellular metabolism of 507 diverse KpSC isolates. Our model is the largest and most comprehensive of its kind, comprising >2,400 genes associated with >3,500 metabolic reactions, plus manually curated evidence annotations. These data alone represent a key knowledge resource for the Klebsiella research community; however, our models greatest impact lies in its potential for use as a reference from which highly accurate strain-specific GSMMs can be derived to inform in depth strain-specific and/or large-scale comparative analyses. Data summaryO_LIKlebsiella pneumoniae species complex (KpSC) pan v2 metabolic model available at https://github.com/kelwyres/KpSC-pan-metabolic-model. C_LIO_LIAll KpSC isolate whole genome sequences used in this work were reported previously and are available under Bioprojects PRJEB6891, PRJNA351909, PRJNA493667, PRJNA768294, PRJNA253462, PRJNA292902 and PRJNA391323. Individual accessions listed in Table S1. C_LIO_LIStrain-specific GSMMs used for comparative analyses (deposited in Figshare - 10.6084/m9.figshare.24871914), plus their associated MEMOTE reports (indicates completeness and annotation quality), reaction and gene presence-absence matrices across all isolates. C_LIO_LIGrowth phenotype predictions derived from strain-specific GSMMs are available in Table S4. C_LIO_LIBinarised Biolog growth phenotype data for n=37 isolates (plates PM1 and PM2, aerobic and anaerobic conditions) are available in Tables S6 & S7. C_LIO_LIAdditional growth assay data for six substrates not included on Biolog plates PM1 and PM2 (deposited in Figshare - 10.6084/m9.figshare.24871914). C_LI

genomics↗

bioRxiv2023

Global pathogenomic analysis identifies known and novel genetic antimicrobial resistance determinants in twelve species

Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. Applied to 12 species, 27,155 genomes, and 69 drugs, we 1) found AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrated that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identified 142 novel AMR gene candidates. Validation of two candidates in E. coli BW25113 revealed cases of conditional resistance:{Delta} cycA conferred ciprofloxacin resistance in minimal media with D-serine, and frdD V111D conferred ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

bioinformatics↗

bioRxiv2023

Bactabolize: A tool for high-throughput generation of bacterial strain-specific metabolic models

Metabolic capacity can vary substantially within a bacterial species, leading to ecological niche separation, as well as differences in virulence and antimicrobial susceptibility. Genome-scale metabolic models are useful tools for studying the metabolic potential of individuals, and with the rapid expansion of genomic sequencing there is a wealth of data that can be leveraged for comparative analysis. However, there exist few tools to construct strain-specific metabolic models at scale. Here we describe Bactabolize (github.com/kelwyres/Bactabolize), a reference-based tool which rapidly produces strain-specific metabolic models and growth phenotype predictions. We describe a pan reference model for the priority antimicrobial-resistant pathogen, Klebsiella pneumoniae (github.com/kelwyres/KpSC-pan-metabolic-model), and a quality control framework for using draft genome assemblies as input for Bactabolize. The Bactabolize-derived model for K. pneumoniae reference strain KPPR1 performed comparatively or better than currently available automated approaches CarveMe and gapseq across 507 substrate and 2317 knockout mutant growth predictions. Novel draft genomes passing our systematically-defined quality control criteria resulted in models with a high degree of completeness ([≥]99% genes and reactions captured compared to models derived from matched complete genomes) and high accuracy (mean 0.97, n=10). We anticipate the tools and framework described herein will facilitate large-scale metabolic modelling analyses that broaden our understanding of diversity within bacterial species and inform novel control strategies for priority pathogens.

bioinformatics↗

bioRxiv2021

Characterization of antibiotic resistance in clinical isolates of Klebsiella pneumoniae in Denmark

Klebsiella pneumoniae (KP) is a major global health problem as it leads to hospital outbreaks all over the world and is becoming more difficult to treat due to its increasing antimicrobial resistance (AMR). Optimization and development of new treatments of KP requires understanding of its population structure and AMR properties. Therefore, in this study, we collected and sequenced 491 KP strains from four major Danish microbiology departments covering 51% of the Danish population. The isolates were whole genome sequenced (WGS), phenotypically characterized and compared with 2,124 KP strains from 13 different countries (PATRIC strains). We found that while genomic content varies significantly across the Danish strains, they also differ significantly from strains from other countries, due to the lack of certain AMR sequence types (e.g. ST258 and ST307) in Denmark. Genomic and experimental analysis suggest that Danish strains contain fewer virulence mechanisms and are more susceptible to antimicrobials compared to strains from other countries, likely due to the relatively low antibiotic usage in Denmark where 70% of hospital antibiotic usage is penicillins. We also identified potential novel AMR determinants to tigecycline through statistical analysis of genomic and phenotypic data. To conclude, we obtained a more comprehensive understanding of the KP strains in Denmark and provided valuable insights for future experiments and strategies to combat AMR in KP.

genomics↗

bioRxiv2021

Transmission of Klebsiella strains and plasmids within and between Grey-headed flying fox colonies

The Grey-headed flying fox (Pteropus poliocephalus) is an endemic Australian fruit bat, known to carry pathogens with zoonotic potential. We recently showed these bats harbour the bacterial pathogens Klebsiella pneumoniae and closely related species in the K. pneumoniae species complex (KpSC). However, the dynamics of Klebsiella transmission and gene flow within flying fox colonies were not explored and remain poorly understood. Here we report a high-resolution genomic comparison of 39 KpSC isolates from Greyheaded flying foxes. Illumina whole genome sequences (n=39) were assembled de novo and the Kleborate genotyping tool was used to infer sequence types (STs). Oxford Nanopore sequences were generated for 13 isolates (one for each distinct ST) in order to generate high-quality completed reference genomes. Read mapping and variant calling was used to identify single nucleotide variants (SNVs) within each ST, using the relevant reference genome. In silico genome-scale metabolic models were generated to predict and compare substrate usage to 59 previously published KpSC models for isolates from human and environmental sources, which indicated no distinction on the basis of metabolic capabilities. High-resolution genome comparisons identified five putative strain transmission clusters (four intra- and one inter-colony, n=2-15 isolates each, [≤]25 pairwise SNVs). Inter-colony transmission of Klebsiella africana was found between two flying fox populations located within flying distance. The 13 completed genomes harboured 11 plasmids, all of which showed 37-98% coverage (mean 73%) and [≥]95% identity to those previously reported from human-associated KpSC. Comparison of plasmids from different flying fox associated KpSC indicated an interspecies horizontal plasmid transmission between K. pneumoniae and K. africana for a 98 kbp plasmid, pFF1003. These data indicate that KpSC are able to transmit directly via flying fox populations or indirectly via a common source, and that these isolates can harbour plasmids with similarity to those found in human derived KpSC, indicating gene flow is occurring between isolates from Grey-headed flying fox KpSC and human clinical isolates.

microbiology↗

bioRxiv2019

Pangenome Analysis of Enterobacteria Reveals Richness of Secondary Metabolite Gene Clusters and their Associated Gene Sets

The growing number of sequenced genomes enables the study of secondary metabolite biosynthetic gene clusters (BGC) in phyla beyond well-studied soil bacteria. We mined 2627 enterobacterial genomes to detect 8604 BGCs, including nonribosomal peptide synthetases, siderophores, polyketide-nonribosomal peptide hybrids, and 60 other BGC types, with an average of around 3.3 BGCs per genome. These BGCs represented 212 distinct BGC families, of which only 20 have associated products in the MIBiG standard database with functions such as siderophores, antibiotics, and genotoxins. Pangenome analysis identified genes associated with a specific BGC encoding for colon cancer-related colibactin. In one example, we associated genes involved in the type VI secretion system with the presence of a colibactin BGC in Escherichia. This richness of BGCs in enterobacteria opens up the possibility to discover novel secondary metabolites, their physiological roles and provides a guide to identify and understand PKS associated gene sets.

systems biology↗

bioRxiv2019

Impact of insertion sequences on convergent evolution of Shigella species

Shigella species are specialised lineages of Escherichia coli that have converged to become human-adapted and cause dysentery by invading human gut epithelial cells. Most studies of Shigella evolution have been restricted to comparisons of single representatives of each species; and population genomic studies of individual Shigella species have focused on genomic variation caused by single nucleotide variants and ignored the contribution of insertion sequences (IS) which are highly prevalent in Shigella genomes. Here, we investigate the distribution and evolutionary dynamics of IS within populations of Shigella dysenteriae Sd1, Shigella sonnei and Shigella flexneri. We find that five IS (IS1, IS2, IS4, IS600 and IS911) have undergone expansion in all Shigella species, creating substantial strain-to-strain variation within each population and contributing to convergent patterns of functional gene loss within and between species. We find that IS expansion and genome degradation are most advanced in S. dysenteriae and least advanced in S. sonnei; and using genome-scale models of metabolism we show that Shigella species display convergent loss of core E. coli metabolic capabilities, with S. sonnei and S. flexneri following a similar trajectory of metabolic streamlining to that of S. dysenteriae. This study highlights the importance of IS to the evolution of Shigella and provides a framework for the investigation of IS dynamics and metabolic reduction in other bacterial species.

microbiology↗

bioRxiv2018

BOFdat: generating biomass objective function stoichiometric coefficients from experimental data

Genome-scale models (GEMs) rely on a biomass objective function (BOF) to predict phenotype from genotype. Here we present BOFdat, a Python package that offers functions to generate biomass objective function stoichiometric coefficients (BOFsc) from macromolecular cell composition and relative abundances of macromolecules obtained from omic datasets. Growth-associated and non-growth associated maintenance (GAM and NGAM) costs can also be calculated by BOFdat.\n\nBOFdat is freely available on the Python Package Index (pip install BOFdat). The source code and an example usage (Jupyter Notebook and example files) are available on GitHub (https://github.com/jclachance/BOFdat). The documentation and API are available through ReadTheDocs (https://bofdat.readthedocs.io).\n\nContactjean-christophe.lachance@usherbrooke.ca, zaking@eng.ucsd.edu, pierre-etienne.jacques@usherbrooke.ca

systems biology↗

bioRxiv2017

Multi-scale model of the proteomic and metabolic consequences of reactive oxygen species

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can completely inhibit cell growth when unmanaged and thus elicits an essential stress response that is universal and fundamental in biology. We develop a computable multi-scale description of the ROS stress response in Escherichia coli. We show that this quantitative framework allows for the understanding and prediction of ROS stress responses at three levels: 1) pathways: amino acid auxotrophies, 2) networks: the systemic response to ROS stress, and 3) genetic basis: adaptation to ROS stress during laboratory evolution. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

systems biology↗

bioRxiv2017

ssbio: A Python Framework for Structural Systems Biology

SummaryWorking with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows.\n\nAvailability and Implementationssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb.\n\nContactnmih@ucsd.edu\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

systems biology↗