Biology subjects

Marvig, R. L.

Publications and source records attributed to Marvig, R. L..

11 recordsLinked to original sources

bioRxiv2026

Thirty years of Achromobacter ruhlandii evolution reveal pathways to epidemic lineages

BackgroundAchromobacter spp. are emerging opportunistic pathogens, associated with chronic infections, antimicrobial resistance, and poor clinical outcomes. The Danish epidemic strain (DES) of A. ruhlandii is highly drug-resistant and adapted to the cystic fibrosis (CF) airway, yet its evolutionary history and defining genomic features remain poorly understood. MethodsWe analysed genome and antibiotic susceptibility testing data for 58 longitudinally collected DES isolates sampled over 21 years at Rigshospitalet, Denmark. We combined these with 79 publicly available A. ruhlandii genomes and applied phylogenomics to infer DES emergence and transmission, and genome-wide association studies (GWAS) to identify lineage-specific and adaptive genomic features. ResultsDES forms a distinct monophyletic clade within A. ruhlandii, estimated to have emerged around 1990, with no evidence of dissemination beyond Denmark. GWAS identified key lineage-defining traits, including acquisition of large mobile genetic elements, plasmid integration events, and enrichment of resistance and iron acquisition genes. In addition, we detected other epidemic A. ruhlandii lineages with evidence of long-term persistence and inter-country spread, sharing similar genetic signatures of adaptation. ConclusionsThis study elucidates the genomic features associated with chronic infection and epidemic potential in A. ruhlandii. The DES lineage illustrates how extensive horizontal gene transfer, high intrinsic resistance potential, and enhanced host-adaptation traits, such as increased iron acquisition, can facilitate the emergence and persistence of successful epidemic lineages. These findings highlight shared evolutionary signatures of epidemic A. ruhlandii and underscore the need for continued genomic surveillance to detect and monitor emerging high-risk lineages in chronic infections.

bioinformatics↗

bioRxiv2024

Recommendations for Bioinformatics in Clinical Practice

Next Generation Sequencing (NGS) is increasingly used in clinical diagnostics, largely driven by the success and robustness of Whole Genome Sequencing (WGS). Whereas updated guidelines exist for how to interpret and report on variants that are identified from NGS using bioinformatics pipelines, there is a need for standardised bioinformatics practices for diagnostics to ensure clinical consensus, accuracy, reproducibility and comparability of the results. This article presents consensus recommendations developed by 13 clinical bioinformatics units taking part in the Nordic Alliance for Clinical Genomics (NACG), by expert bioinformaticians working in clinical production. The recommendations are based on clinical practice and focus on analysis types, test and validation, standardisation and accreditation, as well as core competencies and technical management required for clinical bioinformatics operations. Key recommendations include adopting the hg38 genome build as the reference and a standard set of recommended analyses, including the use of multiple tools for structural variant (SV) calling and in-house data sets for filtering recurrent calls. Clinical bioinformatics production should operate under the ISO 15189 standard, utilising off-grid clinical-grade high-performance computing systems, standardised file formats, and strict code version control. Containerized software containers or environment management systems are needed to ensure reproducibility. Pipelines should be rigorously documented and tested for accuracy and reproducibility, minimally covering unit, integration, and end-to-end testing. Standard truth sets such as GIAB and SEQC2 for germline and somatic variant calling, respectively, should be supplemented by recall testing of previously validated clinical cases. Data integrity must be verified using file hashing, and sample identity should be checked via sample fingerprinting and genetically inferred identification markers such as sex and relatedness. Finally, clinical bioinformatics teams should encompass diverse skills, including software development, data management, quality assurance, and domain expertise in human genetics. These recommendations provide a consensus framework for standardising bioinformatics practices across clinical WGS applications and can serve as a practical guide to facilities that are new to large-scale sequencing-based diagnostics, or as a reference for those who already run high-volume clinical production using NGS.

bioinformatics↗

bioRxiv2024

Chronic wounds and adaptive Pseudomonas aeruginosa: A phenotypic and genotypic characterization

Phenotypic and genetic diversity is found in varying prevalence in clinical populations where beneficial adaptations enable the bacteria to avoid recognition and eradication by the host immune system. This study aimed to investigate the presence of Pseudomonas aeruginosa in chronic venous leg ulcers wounds over an 8-week time course. This was performed using genomic and phenotypic approaches to understand the survival and persistence of Pseudomonas strains. The findings of this study show that the two patients were colonized with a recurring P. aeruginosa genotype with only minor phenotypic differences and few SNP differences, suggesting that the Pseudomonas isolates present in the wound can survive and proliferate in the hosts hostile environment. The results provided from this study will allow us to understand clinically important traits in P. aeruginosa and provide insight into the dynamics and adaptations (or the lack thereof) in chronic wounds. HighlightsO_LIEach patient is colonised overtime with a unique MLST C_LIO_LIThe total number of detected SNPs was 18 and 20 within each patient, respectively C_LIO_LIOne MLST belonged to the sequence type ST132, often observed in clinical isolates, whereas the other belonged to ST3244, previously seen inenvironmental isolates C_LI

microbiology↗

bioRxiv2021

A comparison of tools for copy-number variation detection in germline whole exome and whole genome sequencing data

BackgroundCopy-number variations (CNVs) have important clinical implications for several diseases and cancers. The clinically relevant CNVs are hard to detect because CNVs are common structural variations that define large parts of the normal human genome. CNV calling from short-read sequencing data has the potential to leverage available cohort studies and allow full genomic profiling in the clinic without the need for additional data modalities. Questions regarding performance of CNV calling tools for clinical use and suitable sequencing protocols remain poorly addressed, mainly because of the lack of good reference data sets. MethodsWe reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a unique reference cohort encompassing 39 whole genome sequencing (WGS) samples paired with analysis by the current clinical standard --SNP-array based CNV calling. Additionally, for nine of these samples we performed whole exome sequencing (WES) performed, in order to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). ResultsTool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Filtering output by CNV ranks from tools did not salvage precision. Several tools had better performance patterns for NA12878, and we hypothesize that this is the result of overfitting during the tool development. ConclusionsWe suggest combining tools with the best recall: GATK gCNV, Lumpy, DELLY, and cn.MOPS. These tools also capture different CNVs. Further improvements in precision requires additional development of tools, reference data sets, and annotation of CNVs, potentially assisted by the use of background panels for filtering of frequently called variants.

bioinformatics↗

bioRxiv2021

Achromobacter genetic adaptation in cystic fibrosis

Achromobacter is an emerging pathogen in patients with cystic fibrosis (CF) and Achromobacter caused infections are associated with more severe disease outcomes and high intrinsic antibiotic resistance. While conventional CF pathogens are studied extensively, little is known about the genetic determinants leading to antibiotic resistance and the genetic adaptation in Achromobacter infections. Here, we analyzed 101 Achromobacter genomes from 51 patients with CF isolated during the course of up to 20 years of infection to identify within-host adaptation, mutational signatures, and genetic variation associated with increased antibiotic resistance. We found that the same regulatory and inorganic ion transport genes were frequently mutated in persisting clone types within and between Achromobacter species indicating convergent genetic adaptation. Genome-wide association study (GWAS) of six antibiotic resistance phenotypes revealed the enrichment of associated genes involved in inorganic ion transport genes, transcription gene enrichment in {beta}-lactams, and energy production and translation gene enrichment in the trimethoprim/sulfonamide group. Overall, we provide insights into the pathogenomics of Achromobacter infections in patients with CF airways. Since emerging pathogens are increasingly recognised as an important healthcare issue, our findings on evolution of antibiotic resistance and genetic adaptation can facilitate better understanding of disease progression and how mutational changes have implications for patients with CF.

microbiology↗

bioRxiv2020

Transmission and genetic adaptation of Achromobacter in cystic fibrosis

Achromobacter species are increasingly being detected in patients with cystic fibrosis (CF), and this emerging pathogen is associated with antibiotic resistance and more severe disease outcomes. Nonetheless, little is known about the extent of transmission and antibiotic resistance development in Achromobacter infections. We sequenced the genomes of 101 clinical isolates of Achromobacter (A. xylosoxidans based on MALDI-TOF/API N20 typing) collected from 51 patients with CF--the largest longitudinal dataset to-date. We performed phylogenetic analysis on the genomes and combined this with epidemiological and antibiotic resistance data to identify patient-to-patient transmission and development of antibiotic resistance. We found that MALDI-TOF/API N20 was not sufficient for Achromobacter species-level typing, and that the population of Achromobacter isolates was composed of five different species where A. xylosoxidans accounted for 52% of infections. Most patients were infected by unique Achromobacter clone types; nonetheless, suspected patient-to-patient transmission cases identified by shared clone types were observed in 35% (N=18) of patients. In 15 of 16 cases the suspected transmissions were further supported by genome- or clinic visit-based epidemiological analysis. Finally, we found that resistance developed over time. We show that whole-genome sequencing (WGS) is essential for Achromobacter species typing and patient-to-patient transmission identification which was identified in A. ruhlandii, A. xylosoxidans and, for the first time, A. insuavis. Furthermore, we show that the development of antibiotic resistance is associated with chronic Achromobacter infections. Our findings emphasize that transmission and antibiotic resistance should be considered in future treatment strategies.

genomics↗

bioRxiv2020

Gene loss and acquisition in lineages of bacteria evolving in a human host environment

While genome analyses have documented that there are differences in the gene repertoire between evolutionary distant lineages of the same bacterial species, less is known about micro-evolutionary dynamics of gene loss and acquisition within lineages of bacteria as they evolve over the timescale of years. This knowledge is valuable to understand both the basic mutational steps that on long timescales lead to evolutionary distant bacterial lineages, and the evolution of the individual lineages themselves. In the case that lineages evolve in a human host environment, gene loss and acquisition may furthermore have implication for disease. We analyzed the genomes of 45 Pseudomonas aeruginosa lineages evolving in the lungs of cystic fibrosis patients to identify genes that are lost or acquired during the first years of infection in each of the different lineages. On average, the lineage genome content changed with 88 genes (range 0-473). Genes were more often lost than acquired, and prophage genes were more variable than bacterial genes. We identified genes that were lost or acquired independently across different clonal lineages, i.e. convergent molecular evolution. Convergent evolution suggests that there is a selection for loss and acquisition of certain genes in the host environment. We find that a significant proportion of such genes are associated with virulence; a trait previously shown to be important for adaptation. Furthermore, we also compared the genomes across lineages to show that within-lineage variable genes more often belonged to genomic content not shared across all lineages. Finally, we used 4,760 genes shared by 446 P. aeruginosa genomes to develop a stable and discriminatory typing scheme for P. aeruginosa clone types (Pactyper, https://github.com/MigleSur/Pactyper). In sum, our analysis adds to the knowledge on the pace and drivers of gene loss and acquisition in bacteria evolving over multiple years in a human host environment and provides a basis to further understand how gene loss and acquisition plays a role in lineage differentiation and host adaptation. Data SummaryP. aeruginosa genome sequencing data has been made publicly available by Marvig et al. (2015) and is deposited in Sequence Read Archive (SRA) under accession ERP004853.

evolutionary biology↗

bioRxiv2019

Hypermutator Pseudomonas aeruginosa exploits multiple genetic pathways to develop multidrug resistance during long term infections in the airways of cystic fibrosis patients

Pseudomonas aeruginosa exploits intrinsic and acquired resistance mechanisms to resist almost every antibiotic used in chemotherapy. Antimicrobial resistance in P. aeruginosa isolated from cystic fibrosis (CF) patients is further enhanced by the occurrence of hypermutator strains, a hallmark of chronic CF infections. However, the within-patient genetic diversity of P. aeruginosa populations related to antibiotic resistance remains unexplored. Here, we show the evolution of the mutational resistome profile of a P. aeruginosa hypermutator lineage by performing longitudinal and transversal analyses of isolates collected from a CF patient throughout 20 years of chronic infection. Our results show the accumulation of thousands of mutations with an overall evolutionary history characterized by purifying selection. However, mutations in antibiotic resistance genes appear to be positively selected, driven by antibiotic treatment. Antibiotic resistance increased as infection progressed towards the establishment of a population constituted by genotypically diversified coexisting sub-lineages, all of which converged to multi-drug resistance. These sub-lineages emerged by parallel evolution through distinct evolutionary pathways, which affected genes of the same functional categories. Interestingly, ampC and fstI, encoding the {beta}-lactamase and penicillin-binding protein 3, respectively, were found among the most frequently mutated genes. In fact, both genes were targeted by multiple independent mutational events, which led to a wide diversity of coexisting alleles underlying {beta}-lactam resistance. Our findings indicate that hypermutators, apart from boosting antibiotic resistance evolution by simultaneously targeting several genes, favor the emergence of adaptive innovative alleles by clustering beneficial/compensatory mutations in the same gene, hence expanding P. aeruginosa strategies for persistence.\n\nIMPORTANCEBy increasing mutation rates, hypermutators boost antibiotic resistance evolution by enabling bacterial pathogens to fully exploit their genetic potential and achieve resistance mechanisms for almost every known antimicrobial agent. Here, we show how co-existing clones from a P. aeruginosa hypermutator lineage that evolved during 20 years of chronic infection and antibiotic chemotherapy, converged to multidrug resistance by targeting genes from alternative genetic pathways that are part of the broad P. aeruginosa resistome. Within this complex assembly of combinatorial genetic changes, in some specific cases, multiple mutations are needed in the same gene to reach a fine tuned resistance phenotype. Hypermutability enables this genetic edition towards higher resistance profiles by recurrently targeting these genes, thus promoting new epistatic relationships and the emergence of innovative resistance-conferring alleles. Our findings help to understand this link between hypermutability and antibiotic resistance, a key challenge for the design of new therapeutic strategies.

microbiology↗

bioRxiv2019

GenAPI: a tool for gene absence-presence identification in fragmented bacterial genome sequences

Bacterial gene loss and acquisition is a well-known phenomenon which contributes to bacterial adaptation through changes in important phenotypes such as virulence, antibiotic resistance and metabolic capability. While advances in DNA sequencing have accelerated our ability to generate short-read genome sequencing to disentangle phenotypic changes caused by gene loss and acquisition, the short-read genome sequencing often results in fragmented genome assemblies as a basis for identification of gene loss and acquisition events. However, sensitive and precise determination of gene content change for fragmented genome assemblies remain challenging as analysis needs to account for cases when only a fragment of the gene is assembled or when the gene assembly is split in more than one contig.\n\nWe developed GenAPI, a command-line tool that is designed to compare the gene content of bacterial genomes for which only fragmented genome assemblies are available. GenAPI, unlike other available tools of similar purpose, accounts for genome assembly imperfections and aims to compensate for them. We tested the performance of GenAPI on three different datasets to show that GenAPI has high sensitivity while it maintains precision when dealing with partly assembled genes in both simulated and real datasets. Furthermore, we compared and evaluated the performance of GenAPI with six popular tools for gene presence-absence identification. While we find that the compared tools have the same precision and recall rates when analyzing complete genome sequences, GenAPI performs better than the other tools on fragmented genome assemblies.

bioinformatics↗

bioRxiv2018

Privatisation rescues function following loss of cooperation

A single cheating mutant can lead to the invasion and eventual eradication of cooperation from a population. Consequently, cheat invasion is often considered as \"game over\" in empirical and theoretical studies of cooperator-cheat dynamics, especially when cooperation is necessary for fulfilling an essential function. But is cheat invasion necessarily \"game over\" in nature? By following a population of bacteria through loss of cooperation and beyond, we observed that individuals evolved to replace cooperation with a selfish, or \"private\" behaviour. Specifically, we show that when cheating caused the loss of cooperative iron acquisition in a collection of Pseudomonas aeruginosa isolates from cystic fibrosis patients, a private uptake system that only benefits the focal individual was upregulated. This observation highlights the importance of social dynamics of natural populations and emphasizes the potential impact of past social interaction on the evolution of private traits.

evolutionary biology↗

bioRxiv2018

Virulence evolution in the opportunistic bacterial pathogen Pseudomonas aeruginosa

Bacterial opportunistic pathogens are feared for their difficult-to-treat nosocomial infections and for causing morbidity in immunocompromised patients. Here, we study how such a versatile opportunist, Pseudomonas aeruginosa, adapts to conditions inside and outside its model host Caenorhabditis elegans, and use phenotypic and genotypic screens to identify the mechanistic basis of virulence evolution. We found that virulence significantly dropped in unstructured environments both in the presence and absence of the host, but remained unchanged in spatially structured environments. Reduction of virulence was either driven by a substantial decline in the production of siderophores (in treatments without hosts) or toxins and proteases (in treatments with hosts). Whole-genome sequencing of evolved clones revealed positive selection and parallel evolution across replicates, and showed an accumulation of mutations in regulator genes controlling virulence factor expression. Our study identifies the spatial structure of the non-host environment as a key driver of virulence evolution in an opportunistic pathogen.

microbiology↗