Biology subjects

Pensar, J.

Publications and source records attributed to Pensar, J..

7 recordsLinked to original sources

bioRxiv2023

PAN-GWES: Pangenome-spanning epistasis and co-selection analysis via de Bruijn graphs

Studies of bacterial adaptation and evolution are hampered by the difficulty of measuring traits such as virulence, drug resistance and transmissibility in large populations. In contrast, it is now feasible to obtain high-quality complete assemblies of many bacterial genomes thanks to scalable and affordable long-read sequencing technology. To exploit this opportunity we introduce a phenotype- and alignment-free method for discovering co-selected and epistatically interacting genomic variation from genome assemblies covering both core and accessory parts of genomes. Our approach uses a compact coloured de Bruijn graph to approximate the intra-genome distances between pairs of loci for a collection of bacterial genomes to account for the impacts of linkage disequilibrium (LD). We demonstrate the versatility of our approach to efficiently identify associations between loci linked with drug resistance and adaptation to the hospital niche in the major human bacterial pathogens Streptococcus pneumoniae and Enterococcus faecalis.

bioinformatics↗

bioRxiv2021

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

bioinformatics↗

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↗

bioRxiv2020

Co-evolutionary signals from Burkholderia pseudomallei population genomics highlight its survival strategy in a hostile environment

BackgroundThe soil bacterium Burkholderia pseudomallei is the causative agent of melioidosis. It kills up to 40% of cases and contributes to human morbidity and mortality in many tropical and sub-tropical countries. As no vaccines are currently available, prevention is the key health policy and is achieved by avoiding direct contact with soil and standing water. The pathogen notoriously persists in ranges of environmental conditions which make disease prevention difficult. We aimed to scan B. pseudomallei genomes for signals of evolutionary adaptations that allow it to thrive across environmental conditions, which should ultimately inform prevention policy. MethodsWe conducted three layers of analyses: a genome-wide epistasis and co-selection study (GWES) on 2,011 B. pseudomallei genomes to detect signals of co-selection; gene expression analyses across 82 diverse physical, chemical, biological and infectious conditions to identify specific conditions in which such selection might have acted; and gene knockout assays to confirm the function of the co-selection hotspot. FindingsWe uncovered 13,061 mutation pairs in distinct genes and non-coding RNA that have been repeatedly co-selected through B. pseudomallei evolution. Genes under co-selection displayed marked expression correlation when B. pseudomallei was subjected to physical stress conditions including temperature stress, osmotic stress, UV radiation, and nutrient deprivation; highlighting these conditions as the major evolutionary driving forces for this bacterium. We identified a putative adhesin (BPSL1661) as a hub of co-selection signals, experimentally confirmed the role of BPSL1661 under nutrient deprivation, and explored the functional basis of the co-selection gene network surrounding BPSL1661 in facilitating bacterial survival under nutrient depletion. InterpretationOur findings suggest that B. pseudomallei has a selective advantage to survive nutrient-limited conditions. Anthropogenic activities such as shifting cultivation systems with more frequent rotations of cropping and shortened fallow periods or continuous cultivation of cash crops could directly or indirectly contribute to loss of soil nutrient; these may lead to the preferential survival of B. pseudomallei and a subsequent rise of melioidosis. Successful disease control for melioidosis needs to consider improving environmental health in addition to current preventive efforts. FundingWellcome Trust, European Research Council, UK Department of Health, Thailand Research Fund and Khon Kaen University Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed with terms (co-selection AND bacteria AND population) with no date or language restrictions from database inception until April 11, 2021. We identified 44 publications of which four were conducted at a genome-wide scale. These four studies were performed on human-restricted pathogens, detected co-selection of antibiotic resistance gene networks which highlight the use of antibiotics as major selection pressures and further inform treatment options. However, none of these studies were performed on Burkholderia pseudomallei or other opportunistic pathogens that have been adapted to both natural and host environments. The selection pressures exerted on these pathogens and the genetic determinants allowed for their adaptations remain unclear, which limit our understanding on the bacterial biology and the information used for disease control. Added value of this studyBased on genomes of 2,011 B. pseudomallei collected from melioidosis endemic areas, we identified and confirmed genetic signals for co-selection. Using transcriptome profiling covering a broad spectrum of conditions and exposures, we showed that genes under co-selection displayed marked expression correlation under physical stress conditions with the gene at the co-selection hotspot conditionally expressed under nutrient starvation. Furthermore, we experimentally validated the function of the hotspot gene and demonstrated that unlike host-restricted pathogens, the B. pseudomallei co-selection network does not facilitate host infection but is focused on bacterial survival in a harsh environment, particularly under nutrient depletion. Aside from providing a data resource, the study also showcases the power of combined genetics, transcriptomics and functional analysis as a tool for biology discovery. Implications of all available evidenceOur findings provide evolutionary and biological evidence for preferential survival of B. pseudomallei under nutrient starvation. Agricultural practice that induces soil loss, which is not uncommon in melioidosis endemic areas has been linked to soil nutrient depletion and may contribute to the prevalence of B. pseudomallei and a consequent rise of melioidosis in these regions. Successful melioidosis control has to consider environmental health in addition to existing prevention policy.

microbiology↗

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↗