Browsing by Subject "R PACKAGE"

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  • Heino, Jani; Soininen, Janne; Alahuhta, Janne; Lappalainen, Jyrki; Virtanen, Risto (2015)
    Most metacommunity studies have taken a direct mechanistic approach, aiming to model the effects of local and regional processes on local communities within a metacommunity. An alternative approach is to focus on emergent patterns at the metacommunity level through applying the elements of metacommunity structure (EMS; Oikos, 97, 2002, 237) analysis. The EMS approach has very rarely been applied in the context of a comparative analysis of metacommunity types of main microbial, plant, and animal groups. Furthermore, to our knowledge, no study has associated metacommunity types with their potential ecological correlates in the freshwater realm. We assembled data for 45 freshwater metacommunities, incorporating biologically highly disparate organismal groups (i.e., bacteria, algae, macrophytes, invertebrates, and fish). We first examined ecological correlates (e.g., matrix properties, beta diversity, and average characteristics of a metacommunity, including body size, trophic group, ecosystem type, life form, and dispersal mode) of the three elements of metacommunity structure (i.e., coherence, turnover, and boundary clumping). Second, based on those three elements, we determined which metacommunity types prevailed in freshwater systems and which ecological correlates best discriminated among the observed metacommunity types. We found that the three elements of metacommunity structure were not strongly related to the ecological correlates, except that turnover was positively related to beta diversity. We observed six metacommunity types. The most common were Clementsian and quasi-nested metacommunity types, whereas Random, quasi-Clementsian, Gleasonian, and quasi-Gleasonian types were less common. These six metacommunity types were best discriminated by beta diversity and the first axis of metacommunity ecological traits, ranging from metacommunities of producer organisms occurring in streams to those of large predatory organisms occurring in lakes. Our results showed that focusing on the emergent properties of multiple metacommunities provides information additional to that obtained in studies examining variation in local community structure within a metacommunity.
  • Syrjälä, Essi; Nevalainen, Jaakko; Peltonen, Jaakko; Takkinen, Hanna-Mari; Hakola, Leena; Åkerlund, Mari; Veijola, Riitta; Ilonen, Jorma; Toppari, Jorma; Knip, Mikael; Virtanen, Suvi M. (2019)
    Several dietary factors have been suspected to play a role in the development of advanced islet autoimmunity (IA) and/or type 1 diabetes (T1D), but the evidence is fragmentary. A prospective population-based cohort of 6081 Finnish newborn infants with HLA-DQB1-conferred susceptibility to T1D was followed up to 15 years of age. Diabetes-associated autoantibodies and diet were assessed at 3-to 12-month intervals. We aimed to study the association between consumption of selected foods and the development of advanced IA longitudinally with Cox regression models (CRM), basic joint models (JM) and joint latent class mixed models (JLCMM). The associations of these foods to T1D risk were also studied to investigate consistency between alternative endpoints. The JM showed a marginal association between meat consumption and advanced IA: the hazard ratio adjusted for selected confounding factors was 1.06 (95% CI: 1.00, 1.12). The JLCMM identified two classes in the consumption trajectories of fish and a marginal protective association for high consumers compared to low consumers: the adjusted hazard ratio was 0.68 (0.44, 1.05). Similar findings were obtained for T1D risk with adjusted hazard ratios of 1.13 (1.02, 1.24) for meat and 0.45 (0.23, 0.86) for fish consumption. Estimates from the CRMs were closer to unity and CIs were narrower compared to the JMs. Findings indicate that intake of meat might be directly and fish inversely associated with the development of advanced IA and T1D, and that disease hazards in longitudinal nutritional epidemiology are more appropriately modeled by joint models than with naive approaches.
  • Kess, Tony; Bentzen, Paul; Lehnert, Sarah J.; Sylvester, Emma V.A.; Lien, Sigbjorn; Kent, Matthew P.; Sinclair-Waters, Marion; Morris, Corey J.; Regular, Paul; Fairweather, Robert; Bradbury, Ian R. (2019)
    Chromosome structural variation may underpin ecologically important intraspecific diversity by reducing recombination within supergenes containing linked, coadapted alleles. Here, we confirm that an ancient chromosomal rearrangement is strongly associated with migratory phenotype and individual genetic structure in Atlantic cod (Gadus morhua) across the Northwest Atlantic. We reconstruct trends in effective population size over the last century and reveal declines in effective population size matching onset of industrialized harvest (after 1950). We find different demographic trajectories between individuals homozygous for the chromosomal rearrangement relative to heterozygous or homozygous individuals for the noninverted haplotype, suggesting different selective histories across the past 150 years. These results illustrate how chromosomal structural diversity can mediate fine-scale genetic, phenotypic, and demographic variation in a highly connected marine species and show how overfishing may have led to loss of biocomplexity within Northern cod stock.
  • White, Brian S.; Khan, Suleiman A.; Mason, Mike J.; Ammad-ud-din, Muhammad; Potdar, Swapnil; Malani, Disha; Kuusanmäki, Heikki; Druker, Brian J.; Heckman, Caroline; Kallioniemi, Olli; Kurtz, Stephen E.; Porkka, Kimmo; Tognon, Cristina E.; Tyner, Jeffrey W.; Aittokallio, Tero; Wennerberg, Krister; Guinney, Justin (2021)
    The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this "general response across drugs" (GRD) is associated with FLT3-ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses.
  • Parravicini, Valeriano; Casey, Jordan M.; Schiettekatte, Nina M. D.; Brandl, Simon J.; Pozas-Schacre, Chloe; Carlot, Jeremy; Edgar, Graham J.; Graham, Nicholas A. J.; Harmelin-Vivien, Mireille; Kulbicki, Michel; Strona, Giovanni; Stuart-Smith, Rick D. (2020)
    Understanding species' roles in food webs requires an accurate assessment of their trophic niche. However, it is challenging to delineate potential trophic interactions across an ecosystem, and a paucity of empirical information often leads to inconsistent definitions of trophic guilds based on expert opinion, especially when applied to hyperdiverse ecosystems. Using coral reef fishes as a model group, we show that experts disagree on the assignment of broad trophic guilds for more than 20% of species, which hampers comparability across studies. Here, we propose a quantitative, unbiased, and reproducible approach to define trophic guilds and apply recent advances in machine learning to predict probabilities of pairwise trophic interactions with high accuracy. We synthesize data from community-wide gut content analyses of tropical coral reef fishes worldwide, resulting in diet information from 13,961 individuals belonging to 615 reef fish. We then use network analysis to identify 8 trophic guilds and Bayesian phylogenetic modeling to show that trophic guilds can be predicted based on phylogeny and maximum body size. Finally, we use machine learning to test whether pairwise trophic interactions can be predicted with accuracy. Our models achieved a misclassification error of less than 5%, indicating that our approach results in a quantitative and reproducible trophic categorization scheme, as well as high-resolution probabilities of trophic interactions. By applying our framework to the most diverse vertebrate consumer group, we show that it can be applied to other organismal groups to advance reproducibility in trait-based ecology. Our work thus provides a viable approach to account for the complexity of predator-prey interactions in highly diverse ecosystems.
  • Marques, Francine Z.; Prestes, Priscilla R.; Byars, Sean G.; Ritchie, Scott C.; Wurtz, Peter; Patel, Sheila K.; Booth, Scott A.; Rana, Indrajeetsinh; Minoda, Yosuke; Berzins, Stuart P.; Curl, Claire L.; Bell, James R.; Wai, Bryan; Srivastava, Piyush M.; Kangas, Antti J.; Soininen, Pasi; Ruohonen, Saku; Kahonen, Mika; Lehtimaki, Terho; Raitoharju, Emma; Havulinna, Aki; Perola, Markus; Raitakari, Olli; Salomaa, Veikko; Ala-Korpela, Mika; Kettunen, Johannes; McGlynn, Maree; Kelly, Jason; Wlodek, Mary E.; Lewandowski, Paul A.; Delbridge, Lea M.; Burrell, Louise M.; Inouye, Michael; Harrap, Stephen B.; Charchar, Fadi J. (2017)
    Background-Cardiac hypertrophy increases the risk of developing heart failure and cardiovascular death. The neutrophil inflammatory protein, lipocalin-2 (LCN2/NGAL), is elevated in certain forms of cardiac hypertrophy and acute heart failure. However, a specific role for LCN2 in predisposition and etiology of hypertrophy and the relevant genetic determinants are unclear. Here, we defined the role of LCN2 in concentric cardiac hypertrophy in terms of pathophysiology, inflammatory expression networks, and genomic determinants. Methods and Results-We used 3 experimental models: a polygenic model of cardiac hypertrophy and heart failure, a model of intrauterine growth restriction and Lcn2-knockout mouse; cultured cardiomyocytes; and 2 human cohorts: 114 type 2 diabetes mellitus patients and 2064 healthy subjects of the YFS (Young Finns Study). In hypertrophic heart rats, cardiac and circulating Lcn2 was significantly overexpressed before, during, and after development of cardiac hypertrophy and heart failure. Lcn2 expression was increased in hypertrophic hearts in a model of intrauterine growth restriction, whereas Lcn2-knockout mice had smaller hearts. In cultured cardiomyocytes, Lcn2 activated molecular hypertrophic pathways and increased cell size, but reduced proliferation and cell numbers. Increased LCN2 was associated with cardiac hypertrophy and diastolic dysfunction in diabetes mellitus. In the YFS, LCN2 expression was associated with body mass index and cardiac mass and with levels of inflammatory markers. The single-nucleotide polymorphism, rs13297295, located near LCN2 defined a significant cis-eQTL for LCN2 expression. Conclusions-Direct effects of LCN2 on cardiomyocyte size and number and the consistent associations in experimental and human analyses reveal a central role for LCN2 in the ontogeny of cardiac hypertrophy and heart failure.
  • Scala, Giovanni; Serra, Angela; Marwah, Veer Singh; Saarimaki, Laura Aliisa; Greco, Dario (2019)
    BackgroundFunctional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Several tools are already available to the community to represent and compare functional profiles of omics experiments. However, when the number of experiments and/or enriched functional terms is high, it becomes difficult to interpret the results even when graphically represented. Therefore, there is currently a need for interactive and user-friendly tools to graphically navigate and further summarize annotations in order to facilitate results interpretation also when the dimensionality is high.ResultsWe developed an approach that exploits the intrinsic hierarchical structure of several functional annotations to summarize the results obtained through enrichment analyses to higher levels of interpretation and to map gene related information at each summarized level. We built a user-friendly graphical interface that allows to visualize the functional annotations of one or multiple experiments at once. The tool is implemented as a R-Shiny application called FunMappOne and is available at https://github.com/grecolab/FunMappOne.ConclusionFunMappOne is a R-shiny graphical tool that takes in input multiple lists of human or mouse genes, optionally along with their related modification magnitudes, computes the enriched annotations from Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, or Reactome databases, and reports interactive maps of functional terms and pathways organized in rational groups. FunMappOne allows a fast and convenient comparison of multiple experiments and an easy way to interpret results.
  • Marwah, Veer Singh; Kinare, Pia Anneli Sofia; Serra, Angela; Scala, Giovanni; Lauerma, Antti; Fortino, Vittorio; Grecot, Dario (2018)
    The Summary: Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps.
  • DiLeo, Michelle F.; Husby, Arild; Saastamoinen, Marjo (2018)
    There is now clear evidence that species across a broad range of taxa harbor extensive heritable variation in dispersal. While studies suggest that this variation can facilitate demographic outcomes such as range expansion and invasions, few have considered the consequences of intraspecific variation in dispersal for the maintenance and distribution of genetic variation across fragmented landscapes. Here, we examine how landscape characteristics and individual variation in dispersal combine to predict genetic structure using genomic and spatial data from the Glanville fritillary butterfly. We used linear and latent factor mixed models to identify the landscape features that best predict spatial sorting of alleles in the dispersal-related gene phosphoglucose isomerase (Pgi). We next used structural equation modeling to test if variation in Pgi mediated gene flow as measured by F-st at putatively neutral loci. In a year when the population was recovering following a large decline, individuals with a genotype associated with greater dispersal ability were found at significantly higher frequencies in populations isolated by water and forest, and these populations showed lower levels of genetic differentiation at neutral loci. These relationships disappeared in the next year when metapopulation density was high, suggesting that the effects of individual variation are context dependent. Together our results highlight that (1) more complex aspects of landscape structure beyond just the configuration of habitat can be important for maintaining spatial variation in dispersal traits and (2) that individual variation in dispersal plays a key role in maintaining genetic variation across fragmented landscapes.
  • Morita, Wataru; Morimoto, Naoki; Jernvall, Jukka (2020)
    A major challenge in evolutionary developmental biology is to understand how genetic mutations underlie phenotypic changes. In principle, selective pressures on the phenotype screen the gene pool of the population. Teeth are an excellent model for understanding evolutionary changes in the genotype-phenotype relationship since they exist throughout vertebrates. Genetically modified mice (mutants) with abnormalities in teeth have been used to explore tooth development. The relationship between signaling pathways and molar shape, however, remains elusive due to the high intrinsic complexity of tooth crowns. This hampers our understanding of the extent to which developmental factors explored in mutants explain developmental and phenotypic variation in natural species that represent the consequence of natural selection. Here we combine a novel morphometric method with two kinds of data mining techniques to extract data sets from the three-dimensional surface models of lower first molars: i) machine learning to maximize classification accuracy of 22 mutants, and ii) phylogenetic signal for 31 Murinae species. Major shape variation among mutants is explained by the number of cusps and cusp distribution on a tooth crown. The distribution of mutant mice in morphospace suggests a nonlinear relationship between the signaling pathways and molar shape variation. Comparative analysis of mutants and wild murines reveals that mutant variation overlaps naturally occurring diversity, including more ancestral and derived morphologies. However, taxa with transverse lophs are not fully covered by mutant variation, suggesting experimentally unexplored developmental factors in the evolutionary radiation of Murines. Author summary Teeth are found in almost all vertebrates, and they show many different morphologies. In mammals, especially the cheek teeth or molars are highly diverse in shape, reflecting a vast range of dietary habits and efficiency of occlusion. As teeth are the most durable part of the body, they preserve well in the fossil record. The diversity of molar fossils has been useful in reconstructing the diet and phylogeny of extinct mammals. Genetically modified mice (mutants) show diverse modifications of their molar morphology, but we lack computational tools to test to what extent mutant morphologies account for the natural diversity found in the wild. We developed data mining using machine learning and phylogeny-based methods to analyze three-dimensional molar shapes in mouse mutants and natural species. Although many mutants and species have comparable features, most of the mutant molar variation covers the more evolutionarily ancestral than the more evolutionary derived shapes. Yet to be explored developmental factors may underly the more extreme shapes.
  • Shu, Le; Zhao, Yuqi; Kurt, Zeyneb; Byars, Sean Geoffrey; Tukiainen, Taru; Kettunen, Johannes; Orozco, Luz D.; Pellegrini, Matteo; Lusis, Aldons J.; Ripatti, Samuli; Zhang, Bin; Inouye, Michael; Makinen, Ville-Petteri; Yang, Xia (2016)
    Background: Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies. Results: We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package. Conclusion: Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.
  • Kess, Tony; Bentzen, Paul; Lehnert, Sarah J.; Sylvester, Emma V.A.; Lien, Sigbjørn; Kent, Matthew P.; Sinclair-Waters, Marion; Morris, Corey J.; Wringe, Brendan; Fairweather, Robert; Bradbury, Ian R. (2020)
    Genomic architecture and standing variation can play a key role in ecological adaptation and contribute to the predictability of evolution. In Atlantic cod (Gadus morhua), four large chromosomal rearrangements have been associated with ecological gradients and migratory behavior in regional analyses. However, the degree of parallelism, the extent of independent inheritance, and functional distinctiveness of these rearrangements remain poorly understood. Here, we use a 12K single nucleotide polymorphism (SNP) array to demonstrate extensive individual variation in rearrangement genotype within populations across the species range, suggesting that local adaptation to fine-scale ecological variation is enabled by rearrangements with independent inheritance. Our results demonstrate significant association of rearrangements with migration phenotype and environmental gradients across the species range. Individual rearrangements exhibit functional modularity, but also contain loci showing multiple environmental associations. Clustering in genetic distance trees and reduced differentiation within rearrangements across the species range are consistent with shared variation as a source of contemporary adaptive diversity in Atlantic cod. Conversely, we also find that haplotypes in the LG12 and LG1 rearranged region have diverged across the Atlantic, despite consistent environmental associations. Exchange of these structurally variable genomic regions, as well as local selective pressures, has likely facilitated individual diversity within Atlantic cod stocks. Our results highlight the importance of genomic architecture and standing variation in enabling fine-scale adaptation in marine species.
  • Pavlek, Martina; Mammola, Stefano (2021)
    Abstract Aim To disentangle the role of evolutionary history, competition and environmental filtering in driving the niche evolution of four closely related subterranean spiders, with the overarching goal of obtaining a mechanistic description of the factors that determine species' realized distribution in simplified ecological settings. Location Dinaric karst, Balkans, Europe. Taxon Dysderidae spiders (Stalita taenaria, S. pretneri, S. hadzii and Parastalita stygia). Methods We resolved phylogenetic relationships among species and modelled each species' distribution using a set of climatic and habitat variables. We explored the climatic niche differentiation among species with n-dimensional hypervolumes and shifts in their trophic niche using morphological traits related to feeding specialization. Results Climate was the primary abiotic factor explaining our species' distributions, while karstic and soil features were less important. Generally, there was a high niche overlap among species, reflecting their phylogenetic relatedness, but on a finer scale, niche shifts explained the realized distribution patterns. Trophic interaction was another important factor influencing species distributions ? the non-overlapping distributions of three morphologically indistinguishable Stalita species is seemingly the outcome of competitive exclusion dynamics. The distribution of the fourth species, Parastalita stygia, overlaps with that of the other species, with several instances of coexistence within caves. As inferred from the morphology of the mouthparts, the mechanism that minimizes interspecific competition is the shift in the trophic niche of P. stygia towards a more specialized diet. Main conclusions We showed that similarity in niches only partly correlated with the phylogenetic distance among species, and that overlaps in species distributions are possible only when a parallel shift in diet occurs. Our work emphasized how even simplified environments still maintain the potential for diversification via niche differentiation. Ultimately, we provide an ecological explanation for the diversification of life in an important hotspot of subterranean diversity.
  • Rosenström, Tom; Härmä, Mikko; Kivimäki, Mika; Ervasti, Jenni; Virtanen, Marianna; Hakola, Tarja; Koskinen, Aki; Ropponen, Annina (2021)
    Objectives Data mining can complement traditional hypothesis-based approaches in characterizing unhealthy work exposures. We used it to derive a hypothesis-free characterization of working hour patterns in shift work and their associations with sickness absence (SA). Methods In this prospective cohort study, complete payroll-based work hours and SA dates were extracted from a shift-scheduling register from 2008 to 2019 on 6029 employees from a hospital district in Southwestern Finland. We applied permutation distribution clustering to time series of successive shift lengths, between-shift rest periods, and shift starting times to identify clusters of similar working hour patterns over time. We examined associations of clusters spanning on average 23 months with SA during the following 23 months. Results We identified eight distinct working hour patterns in shift work: (i) regular morning (Myevening (E) work, weekends off; (ii) irregular M work; (iii) irregular M/E/night (N) work; (iv) regular M work, weekends off; (v) irregular, interrupted WE/N work; (vi) variable M work, weekends off, (vii) quickly rotating WE work, non-standard weeks; and (viii) slowly rotating WE work, non-standard weeks. The associations of these eight working-hour clusters with risk of future SA varied. The cluster of irregular, interrupted M/E/N work was the strongest predictor of increased SA (days per year) with an incidence rate ratio of 1.77 (95% confidence interval 1.74-1.80) compared to regular M/E work, weekends off. Conclusions This data-mining suggests that hypothesis-free approaches can contribute to scientific understanding of healthy working hour characteristics and complement traditional hypothesis-driven approaches.
  • Arredondo-Alonso, S.; Top, J.; McNally, A.; Puranen, S.; Pesonen, M.; Pensar, J.; Marttinen, P.; Braat, J. C.; Rogers, M. R. C.; van Schaik, W.; Kaski, S.; Willems, R. J. L.; Corander, J.; Schurch, A. C. (2020)
    Enterococcus faecium is a gut commensal of humans and animals but is also listed on the WHO global priority list of multidrug-resistant pathogens. Many of its antibiotic resistance traits reside on plasmids and have the potential to be disseminated by horizontal gene transfer. Here, we present the first comprehensive population-wide analysis of the pan-plasmidome of a clinically important bacterium, by whole-genome sequence analysis of 1,644 isolates from hospital, commensal, and animal sources of E. faecium. Long-read sequencing on a selection of isolates resulted in the completion of 305 plasmids that exhibited high levels of sequence modularity. We further investigated the entirety of all plasmids of each isolate (plasmidome) using a combination of short-read sequencing and machine-learning classifiers. Clustering of the plasmid sequences unraveled different E. faecium populations with a clear association with hospitalized patient isolates, suggesting different optimal configurations of plasmids in the hospital environment. The characterization of these populations allowed us to identify common mechanisms of plasmid stabilization such as toxin-antitoxin systems and genes exclusively present in particular plasmidome populations exemplified by copper resistance, phosphotransferase systems, or bacteriocin genes potentially involved in niche adaptation. Based on the distribution of k-mer distances between isolates, we concluded that plasmidomes rather than chromosomes are most informative for source specificity of E. faecium. IMPORTANCE Enterococcus faecium is one of the most frequent nosocomial pathogens of hospital-acquired infections. E. faecium has gained resistance against most commonly available antibiotics, most notably, against ampicillin, gentamicin, and vancomycin, which renders infections difficult to treat. Many antibiotic resistance traits, in particular, vancomycin resistance, can be encoded in autonomous and extrachromosomal elements called plasmids. These sequences can be disseminated to other isolates by horizontal gene transfer and confer novel mechanisms to source specificity. In our study, we elucidated the total plasmid content, referred to as the plasmidome, of 1,644 E. faecium isolates by using short- and long-read whole-genome technologies with the combination of a machine-learning classifier. This was fundamental to investigate the full collection of plasmid sequences present in our collection (pan-plasmidome) and to observe the potential transfer of plasmid sequences between E. faecium hosts. We observed that E. faecium isolates from hospitalized patients carried a larger number of plasmid sequences compared to that from other sources, and they elucidated different configurations of plasmidome populations in the hospital environment. We assessed the contribution of different genomic components and observed that plasmid sequences have the highest contribution to source specificity. Our study suggests that E. faecium plasmids are regulated by complex ecological constraints rather than physical interaction between hosts.
  • van Bergen, Erik; Dallas, Tad; DiLeo, Michelle F.; Kahilainen, Aapo; Mattila, Anniina L. K.; Luoto, Miska; Saastamoinen, Marjo (2020)
    Abstract The ecological impacts of extreme climatic events on population dynamics and/or community composition are profound and predominantly negative. Here, using extensive data of an ecological model system, we test whether predictions from ecological models remain robust when environmental conditions are outside the bounds of observation. First, we report a 10-fold demographic decline of the Glanville fritillary butterfly metapopulation on the Åland islands (Finland). Next, using climatic and satellite data we show that the summer of 2018 was an anomaly in terms of water balance and vegetation productivity indices across the habitats of the butterfly, and demonstrate that population growth rates are strongly associated with spatio-temporal variation in climatic water balance. Finally, we demonstrate that covariates that have previously been identified to impact the extinction probability of local populations in this system are less informative when populations are exposed to (severe) drought during the summer months. Our results highlight the unpredictable responses of natural populations to extreme climatic events. Article impact statement: A demographic crash of an iconic metapopulation reveals that extreme climatic events reduce the value of predictive models. This article is protected by copyright. All rights reserved
  • Harvey, Michael G.; Bravo, Gustavo A.; Claramunt, Santiago; Cuervo, Andres M.; Derryberry, Graham E.; Battilana, Jaqueline; Seeholzer, Glenn F.; McKay, Jessica Shearer; O'Meara, Brian C.; Faircloth, Brant C.; Edwards, Scott; Perez-Eman, Jorge; Moyle, Robert G.; Sheldon, Frederick H.; Aleixo, Alexandre; Smith, Brian Tilston; Chesser, R. Terry; Silveira, Luis Fabio; Cracraft, Joel; Brumfield, Robb T.; Derryberry, Elizabeth P. (2020)
    The tropics are the source of most biodiversity yet inadequate sampling obscures answers to fundamental questions about how this diversity evolves. We leveraged samples assembled over decades of fieldwork to study diversification of the largest tropical bird radiation, the suboscine passerines. Our phylogeny, estimated using data from 2389 genomic regions in 1940 individuals of 1287 species, reveals that peak suboscine species diversity in the Neotropics is not associated with high recent speciation rates but rather with the gradual accumulation of species over time. Paradoxically, the highest speciation rates are in lineages from regions with low species diversity, which are generally cold, dry, unstable environments. Our results reveal a model in which species are forming faster in environmental extremes but have accumulated in moderate environments to form tropical biodiversity hotspots.