Biology subjects

Puranen, S.

Publications and source records attributed to Puranen, S..

5 recordsLinked to original sources

bioRxiv2021

Cross-species evolutionary rewiring in the enteric bacterium Campylobacter

Significance StatementGenetic exchange among bacteria shapes the microbial world. From the acquisition of antimicrobial resistance genes to fundamental questions about the nature of bacterial species, this powerful evolutionary force has preoccupied scientists for decades. However, the mixing of genes between species rests on a paradox. On one hand, promoting adaptation by conferring novel functionality, on the other potentially introducing disharmonious gene combinations (negative epistasis) that will be selected against. Taking an interdisciplinary approach to analyse natural populations of the enteric bacteria Campylobacter, an ideal example of long-range admixture, we demonstrate that genes can independently transfer across species boundaries and rejoin in epistasis in a recipient genome. This challenges conventional ideas and highlights the possibility of single step evolution by saltation. AbstractRecombination of short DNA fragments via horizontal gene transfer (HGT) can both introduce beneficial alleles and create genomic disharmony through negative epistasis. For non-core (accessory) genes, the negative epistatic cost is likely to be minimal because the incoming genes have not co-evolved with the recipient genome. By contrast, for the core genome, interspecific recombination is expected to be rare because disruptive allelic replacement is likely to introduce negative epistasis. Why then is homologous recombination common in the core of bacterial genomes? To understand this enigma we take advantage of an exceptional model system, the common enteric pathogens Campylobacter jejuni and Campylobacter coli, that are known for very high magnitude interspecies gene flow in the core genome. As expected, HGT does indeed disrupt co-adapted allele pairings (negative epistasis). However, multiple HGT events enable recovery of the genomes co-adaption between introgressing alleles, even in core metabolism genes (e.g., formate dehydrogenase). These findings demonstrate that, even for complex traits, genetic coalitions can be decoupled, transferred and independently reinstated in a new genetic background - facilitating transition between fitness peaks. In this example, the two-step recombinational process is associated with C. coli that are adapted to the agricultural niche.

microbiology↗

bioRxiv2020

Genomic rearrangements uncovered by genome-wide co-evolution analysis of a major nosocomial pathogen Enterococcus faecium

Enterococcus faecium is a gut commensal of the gastro-digestive tract, but also known as nosocomial pathogen among hospitalized patients. Population genetics based on whole-genome sequencing has revealed that E. faecium strains from hospitalized patients form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here we further explored the adaptive evolution of E. faecium using a genome-wide co-evolution study (GWES) to identify co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are not proximal to each other based on the completely assembled chromosome of clade A1 reference hospital isolate AUS0004. Close examination of these regions revealed that they are located at the borders of four different types of large-scale genomic rearrangements, insertion sites of two different genomic islands and an IS30-like transposon. In non-clade A1 isolates, these regions are adjacent to each other and they lack the insertions of the genomic islands and IS30-like transposon. Additionally, among the clade A1 isolates there is one group of pet isolates lacking the genomic rearrangement and insertion of the genomic islands, suggesting a distinct evolutionary trajectory. In silico analysis of the biological functions of the genes encoded in three regions revealed a common link to a stress response. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. In conclusion, to our knowledge, this is the first study using GWES to identify genomic rearrangements, suggesting that there is considerable untapped potential to unravel hidden evolutionary signals from population genomic data. Impact statementEnterococcus faecium has emerged as an important nosocomial pathogen around the world. Population genetics revealed that clinical E. faecium strains form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here, the adaptive evolution of E. faecium was further explored using an unsupervised machine learning method (SuperDCA) to identify genome-wide co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are separated by a large chromosomal distance in a clinical clade A1 reference isolate, but appeared adjacent to each other in non-clade A1 isolates. We identified four different types of large-scale genomic rearrangements and in all cases, we found insertion of two different genomic islands and an insertion element at the border. In contrast, no genomic rearrangement and insertions were identified among a group of clade A1 pet isolates, suggesting a distinct evolutionary trajectory. Based on the in silico predicted biological functions, we found a common link to a stress response for the genes encoded in three regions. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. Data summaryRaw core-genome alignment (1.1 MB, Harvest suite v1.1.2), including the 1,644 Clade A isolates and the complete E. faecium AUS0004 (accession number CP003351) as a reference is available under the following gitlab repository https://gitlab.com/sirarredondo/efm_gwes.

genomics↗

bioRxiv2019

Genomes of a major nosocomial pathogen Enterococcus faecium are shaped by adaptive evolution of the chromosome and plasmidome

Enterococcus faecium is a gut commensal of many mammals but is also recognized as a major nosocomial human pathogen, as it is listed on the WHO global priority list of multi-drug resistant organisms. Previous research has suggested that nosocomial strains have multiple zoonotic origins and are only distantly related to those involved in human commensal colonization. Here we present the first comprehensive population-wide joint genomic analysis of hospital, commensal and animal isolates using both short- and long-read sequencing techniques. This enabled us to investigate the population plasmidome, core genome variation and genome architecture in detail, using a combination of machine learning, population genomics and genome-wide co-evolution analysis. We observed a high level of genome plasticity with large-scale inversions and heterogeneous chromosome sizes, collectively painting a high-resolution picture of the adaptive landscape of E. faecium, and identified plasmids as the main indicator for host-specificity. Given the increasing availability of long-read sequencing technologies, our approach could be widely applied to other human and animal pathogen populations to unravel fine-scale mechanisms of their evolution.

genomics↗

bioRxiv2019

Genome-wide epistasis and co-selection study using mutual information

Discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level co-variation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which is demonstrated to maintain a very low rate of false positive findings among those SNP pairs highlighted to deviate significantly from the null hypothesis of neutral co-evolution in simulated data. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Application of the method to large population genomic data sets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

genomics↗

bioRxiv2017

SuperDCA for genome-wide epistasis analysis

The potential for genome-wide modeling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has earlier been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 104-105 polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here we introduce a novel inference method (SuperDCA) which employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 105 polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA thus holds considerable potential in building understanding about numerous organisms at a systems biological level.\n\nAuthor SummaryRecent work has demonstrated the emerging potential in statistical genome-wide modeling to uncover co-selection and epistatic interactions between polymorphisms in bacterial chromosomes from densely sampled population data. Here we develop the Potts model based approach further into a fully mature computational method which can be applied to most existing bacterial population genomic data sets in a straightforward manner. Our advances are relying on more efficient parameter scoring, highly optimized and parallelized open source C++ code, which does not rely on the computation-intensive polymorphism subsampling approximations used earlier. By analyzing the two largest available population samples of Streptococcus pneumoniae (the pneumococcus), we highlight several biological discoveries related to the survival of the pneumococcus and co-evolution of penicillin-binding loci, which were not uncovered by the earlier analyses. Our method holds considerable potential for building understanding about numerous organisms at a systems biological level.

genomics↗