Biology subjects

Garcia-Gonzalez, N.

Publications and source records attributed to Garcia-Gonzalez, N..

5 recordsLinked to original sources

bioRxiv2026

Integrative mobilizable elements are pervasive throughout Pseudomonadota

Integrative mobilizable elements (IMEs) are mobile genetic elements that reside stably integrated into chromosomes and rely on helper conjugative elements for horizontal transfer. Here, we identify and characterize a widespread family of IMEs, named Pseudomonadota Integrative Mobilizable Elements (PIMEs), which are distributed exclusively in the Pseudomonadota phylum. Genome and phylogenomic analyses reveal [~]1,000 putative PIMEs, comprising at least four distinct PIME subfamilies defined by distinctive genomic organizations and conserved hallmark features. Characterized PIMEs depend on helper conjugative plasmids of the incompatibility group P (IncP) and, upon induction, PIMEs excise, replicate and mobilize intra- and inter-species. Remarkably, the representative PIME and its helper conjugative plasmid engages in cross-complementation, revealing an unrecognized level of functional interplay between hijacker and helper element. We also demonstrate that PIMEs act as reservoirs of known and novel prokaryotic immune systems. Overall, our findings uncover an overlooked and disseminated family of IMEs, which likely plays an important role in bacterial ecology and evolution.

microbiology↗

bioRxiv2024

Improved prediction of antimicrobial resistance in Klebsiella pneumoniae using machine learning

Klebsiella pneumoniae is an important cause of healthcare-associated infections, with high levels of antimicrobial resistance (AMR) to critical antibiotics such as carbapenems and third-generation cephalosporins (3GCs). Accurate antimicrobial susceptibility detection is essential for guiding appropriate treatment. In this study, we evaluated the efficacy of machine learning (ML) models for predicting AMR phenotypes in K. pneumoniae particularly for antibiotics for which rule-based approaches fail. We analyzed a dataset of 5,907 K. pneumoniae genomes from public databases and a genomic surveillance project in Spanish hospitals. ML models were trained to predict AMR phenotypes using genomic features, and their performance was compared to ResFinder, which implements a conventional rule-based approach. Models were evaluated based on predictive accuracy across antibiotics. Additionally, we conducted a detailed analysis of the genomic features associated with AMR identified by ML to identify new putative AMR determinants. ResFinder exhibited low prediction accuracy for amikacin, fosfomycin, and piperacillin/tazobactam, whereas ML models significantly improved the prediction accuracy for these antibiotics. Moreover, we provide insights into why rule-based methods failed in these cases, specifically related to the genes acc(6)-Ib-cr, fosA, and blaOXA-1, respectively. Finally, we found possible genetic factors related to resistance for each antibiotic. Our findings underscore the value of ML models in AMR prediction based on genome information for K. pneumoniae, especially in challenging cases where traditional methods have low success rates. Continued evaluation and refinement of ML approaches are essential for applying these methods to enhance AMR detection in clinical and public health contexts. ImportanceTo combat antimicrobial resistance (AMR), the rapid and accurate identification of resistance phenotypes is essential for guiding appropriate therapy. In this study, we demonstrate the significant potential of machine learning (ML) to improve AMR prediction in Klebsiella pneumoniae using genomic data. Our findings reveal that gold standard rule-based methods for predicting AMR from genomic data underperform for antibiotics such as amikacin, fosfomycin, and piperacillin/tazobactam. In this study, we identified the genomic determinants that mislead resistance predictions in rule-based methods providing insights that can refine existing rule-based approaches. Moreover we used ML models that improved the prediction accuracy for these antibiotics and used these models to uncover potential new AMR-associated genes that contribute to a deeper understanding of resistance mechanisms. While these findings are specific to K. pneumoniae, the ML approach is broadly applicable to other pathogens facing similar challenges, enabling improved AMR prediction without reliance on prior knowledge.

genomics↗

bioRxiv2022

Tracking the emergence and dissemination of a blaNDM-23 Gene in a Multi-Drug Resistance Plasmid of Klebsiella pneumoniae

ObjectivesSince the discovery of blaNDM-1, NDM beta-lactamases have become one of the most widespread carbapenemases worldwide. To date, 28 different NDM variants have been reported but some, such as blaNDM-23, have not been characterized in detail yet. Here, we describe the emergence of a novel blaNDM-23 allele from a blaNDM-1 ancestor and the multidrug resistant plasmid that has disseminated it through a K. pneumoniae ST407 clone in several Spanish hospitals. MethodsBetween 2016 and 2019, 1,972 isolates were collected in an epidemiological survey for ESBL-producing Klebsiella pneumoniae in the Comunitat Valenciana (Spain). Three carbapenem resistant strains failed to be detected by CPE screening tests. These isolates carried a blaNDM-23 gene. To characterize this gene, its emergence, and dissemination, we performed antimicrobial susceptibility tests, hybrid sequencing with Illumina and Nanopore technologies, and phylogenetic analyses. ResultsThe MICs of the blaNDM-23 variant were identical to those of the blaNDM-1. The blaNDM-23 variant was found in 14 isolates in a 97 Kb non-mobilizable, multidrug-resistant plasmid carrying 19 resistance genes for 9 different antimicrobial families. In this plasmid, the blaNDM-23 gene is located in the variable region of a complex class-1 integron with a singular genetic environment. The short genetic distance between blaNDM-23-producing isolates reflects a 5-year-long clonal dispersion involving several hospitals and interregional spread. ConclusionsWe have characterized the genomic and epidemiological contexts in the emergence and community spread of a new blaNDM-23 allele in an MDR-plasmid of Klebsiella pneumoniae. TweetGenomic, epidemiologic and phylogenetic analyses of the emergence of a new NDM allele provide information on the rapid changes underlying the spread of antimicrobial resistance genes and strains in Klebsiella pneumoniae. ImportanceAt a time when antimicrobial resistance has become one of the biggest concerns worldwide, the emergence of novel alleles and extremely drug-resistant plasmids are a threat to public health worldwide. More so when they produce carbapenem resistance in one of the most problematic pathogens in clinical settings, such as Klebsiella pneumoniae. Here, we have used genomic epidemiology to describe the emergence of a novel NDM-23 allele and identify it in a MDR plasmid that has been disseminated through a K. pneumoniae ST407 clone in several hospitals in a Spanish region. By means of bioinformatic and phylogenetic analyses, we have been able to trace the evolutionary and epidemiological route of the new allele, the hosting plasmid, and the strain that carried both of them from Pakistan to Spain. A better understanding of the NDM-producing K. pneumoniae populations and its plasmids has made evident the spread of this clone through the region, enhancing the importance of genomic surveillance in the control of antimicrobial resistance.

microbiology↗

bioRxiv2022

Genetic determinants of host tropism in Klebsiella phages

Bacteriophages play key roles in bacterial ecology and evolution and are potential antimicrobials. However, the determinants of phage-host specificity remain elusive. Here, we used 46 newly-isolated phages to challenge 138 representative clinical isolates of Klebsiella pneumoniae, a widespread opportunistic pathogen. Spot tests revealed a narrow host range for most phages, with <2% of 6319 phage-host combinations tested yielding detectable interactions. Bacterial capsule diversity was the main factor restricting phage host range. Consequently, phage-encoded depolymerases were key determinants of host tropism, and we identified depolymerase sequence types associated with the ability to infect specific capsular types across phage families. Phages showing a capsule-independent mode of entry exhibited a much broader host range, but their infectivity was still restricted by complex intracellular defense mechanisms. These findings expand our knowledge of the complex interactions between bacteria and their viruses, and have implications for the biomedical and biotechnological use of phages.

microbiology↗

bioRxiv2020

One is not enough: on the effects of reference genome for the mapping and subsequent analyses of short-reads

Mapping of high-throughput sequencing (HTS) reads to a single arbitrary reference genome is a frequently used approach in microbial genomics. However, the choice of a reference may represent a source of errors that may affect subsequent analyses such as the detection of single nucleotide polymorphisms (SNPs) and phylogenetic inference. In this work, we evaluated the effect of reference choice on short-read sequence data from five clinically and epidemiologically relevant bacteria (Klebsiella pneumoniae, Legionella pneumophila, Neisseria gonorrhoeae, Pseudomonas aeruginosa and Serratia marcescens). Publicly available whole-genome assemblies encompassing the genomic diversity of these species were selected as reference sequences, and read alignment statistics, SNP calling, recombination rates, dN/dS ratios, and phylogenetic trees were evaluated depending on the mapping reference. The choice of different reference genomes proved to have an impact on almost all the parameters considered in the five species. In addition, these biases had potential epidemiological implications such as including/excluding isolates of particular clades and the estimation of genetic distances. These findings suggest that the single reference approach might introduce systematic errors during mapping that affect subsequent analyses, particularly for data sets with isolates from genetically diverse backgrounds. In any case, exploring the effects of different references on the final conclusions is highly recommended. Author summaryMapping consists in the alignment of reads (i.e., DNA fragments) obtained through high-throughput genome sequencing to a previously assembled reference sequence. It is a common practice in genomic studies to use a single reference for mapping, usually the reference genome of a species --a high-quality assembly. However, the selection of an optimal reference is hindered by intrinsic intra-species genetic variability, particularly in bacteria. Biases/errors due to reference choice for mapping in bacteria have been identified. These are mainly originated in alignment errors due to genetic differences between the reference genome and the read sequences. Eventually, they could lead to misidentification of variants and biased reconstruction of phylogenetic trees (which reflect ancestry between different bacterial lineages). However, a systematic work on the effects of reference choice in different bacterial species is still missing, particularly regarding its impact on phylogenies. This work intended to fill that gap. The impact of reference choice has proved to be pervasive in the five bacterial species that we have studied and, in some cases, alterations in phylogenetic trees could lead to incorrect epidemiological inferences. Hence, the use of different reference genomes may be prescriptive to assess the potential biases of mapping.

genomics↗