Biology subjects

Marttinen, P.

Publications and source records attributed to Marttinen, P..

15 recordsLinked to original sources

bioRxiv2025

OMIDIENT: Multiomics Integration for Cancer by Dirichlet Auto-Encoder Networks

To achieve a more comprehensive understanding of cancer, novel computational methods are required for the integrative analysis of data from different molecular layers, such as genomics, transcriptomics, and epigenomics. Here, we present a novel multi-omics integrative method that performs unsupervised representation learning, referred to as OMIDIENT: multiOMics Integration for cancer by DIrichlet auto-ENcoder neTworks. OMIDIENT provides a natural framework for modeling sparse and compositional latent representations by employing a deep generative model, where the latent space is distributed as the product of Dirichlet distributions. Applied to five different cancers, we demonstrate that OMIDIENT outperforms the top state-of-the-art unsupervised multi-omics integrative analysis approaches in clustering, classification, and reconstruction of missing data using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, we provide interpretability analyses for OMIDIENT that not only support its improved performance, but also offer valuable insights into the underlying structure captured by the learned representations.

bioinformatics↗

bioRxiv2024

Basic reproduction number for pandemic Escherichia coli clones is comparable to typical pandemic viruses

Extra-intestinal pathogenic Escherichia coli (ExPEC) ubiquitously colonize the human gut and represent clinically the most significant bacterial species causing urinary tract infections and bacteremia in addition to contributing to meningitis in neonates. During the last two decades, new E. coli multi-drug resistant clones such as ST131, particularly its clades C1 and C2, have spread globally, as has their generally less resistant sister clade ST131-A. Phylodynamic coalescent modeling has indicated exponential growth in the populations corresponding to these clades during the early 2000s. However, it remains unknown how their transmission dynamics compare to viral epidemics and pandemics in terms of key epidemiological quantities such as the basic reproduction number (R0). Estimation of R0 for opportunistic pathogenic bacteria poses a difficult challenge compared to viruses causing acute infections, since data on E. coli infections accumulate with a much longer delay, even in the most advanced public health reporting systems. Here, we developed a compartmental model for asymptomatic gut colonization and onward transmission coupled with a stochastic epidemiological observation model for bacteremia and fitted the model to annual Norwegian national E. coli disease surveillance and bacterial population genomics data. Approximate Bayesian Computation leveraged by the ELFI software package was used to infer R0 for the pandemic ST131 clades. The resulting R0 estimates for ST131-A, ST131-C1 and ST131-C2 were 1.47 (1.31-1.60), 1.18 (1.12-1.20) and 1.13 (1.08-1.20), respectively, indicating that the ST131-A transmission potential can be even comparable to pandemic influenza viruses, such as H1N1. The significantly lower transmissibility of ST131-C1 and ST131-C2 suggests that their global dissemination has been aided by antibiotic selection pressure and that they may be more effectively transmitted through healthcare facilities instead of primarily community-driven transmission. In summary our results provide a fundamental advance in understanding the relative transmissibility of these opportunistic pathogens and that it can vary markedly even between very closely related E. coli.

microbiology↗

bioRxiv2023

Bayesian modeling of the impact of antibiotic resistance on the efficiency MRSA decolonization

Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of morbidity and mortality and one of the most serious infectious disease threats. Colonization by MRSA increases the risk of infection and transmission, underscoring the importance of decolonization efforts. However, success of these decolonization protocols varies, raising the possibility that some MRSA strains may be more persistent than others. Here, we studied how the persistence of MRSA colonization correlates with genomic presence of antibiotic resistance genes. Our analysis using a Bayesian mixed effects survival model found that high-level resistance to mupirocin was strongly associated with failure of the decolonization protocol. However, we did not see a similar effect with resistance to chlorhexidine or other antibiotics. Including strain-specific random effects improved the predictive performance, indicating that some strain characteristics other than resistance also contributed to persistence. Study subject-specific random effects did not improve the model. Our results highlight the need to consider the properties of the colonizing MRSA strain when deciding which treatments to include in the decolonization protocol. Author summaryMethicillin-resistant Staphylococcus aureus (MRSA) is responsible for a high burden of morbidity and mortality. MRSA colonization incurs risk of MRSA infection and transmission, highlighting the need for highly effective decolonization protocols. However, decolonization protocols have had mixed success. The extent to which this mixed success is attributable to MRSA strain variations and their resistance to antibiotics, including those like mupirocin that are commonly used for decolonization, versus study subject factors, has been unclear. Here, we characterized the effect of antibiotic resistance genes on the efficiency of an MRSA decolonization protocol. We found that mupirocin resistance and strain-specific effects were associated with reduced effectiveness of an MRSA decolonization protocol, but that resistance to other antibiotics, including purported chlorhexidine resistance genes, and subject-specific effects had no discernible impact on protocol success. Our results highlight the need to consider the properties of the colonizing MRSA strain when deciding which treatments to include in the decolonization protocol.

microbiology↗

bioRxiv2022

HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes

Existing methods for simulating synthetic genotype and phenotype datasets have limited scalability, constraining their usability for large-scale analyses. Moreover, a systematic approach for evaluating synthetic data quality and a benchmark synthetic dataset for developing and evaluating methods for polygenic risk scores are lacking. We present HAPNEST, a novel approach for efficiently generating diverse individual-level genotypic and phenotypic data. In comparison to alternative methods, HAPNEST shows faster computational speed and a lower degree of relatedness with reference panels, while generating datasets that preserve key statistical properties of real data. These desirable synthetic data properties enabled us to generate 6.8 million common variants and nine phenotypes with varying degrees of heritability and polygenicity across 1 million individuals. We demonstrate how HAPNEST can facilitate biobank-scale analyses through the comparison of seven methods to generate polygenic risk scoring across multiple ancestry groups and different genetic architectures.

bioinformatics↗

bioRxiv2021

Improving Neural Networks for Genotype-Phenotype Prediction Using Published Summary Statistics

Phenotype prediction is a necessity in numerous applications in genetics. However, when the size of the individual-level data of the cohort of interest is small, statistical learning algorithms, from linear regression to neural networks, usually fail due to insufficient data. Fortunately, summary statistics from genome-wide association studies (GWAS) on other large cohorts are often publicly available. In this work, we propose a new regularization method, namely, main effect prior (MEP), for making use of GWAS summary statistics from external datasets. The main effect prior is generally applicable for machine learning algorithms, such as neural networks and linear regression. With simulation and real-world experiments, we show empirically that MEP improves the prediction performance on both homogeneous and heterogeneous datasets. Moreover, deep neural networks with MEP outperform standard baselines even when the training set is small.

bioinformatics↗

bioRxiv2021

Crop loss identification at field parcel scale using satellite remote sensing and machine learning

Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop loss are lacking; third, there are many ways to define crop loss. This study investigates the feasibility of using satellite images to train machine learning (ML) models to classify agricultural field parcels into those with and without crop loss. The reference data for this study was provided by Finnish Food Authority (FFA) containing crop loss information of approximately 1.4 million field parcels in Finland covering about 3.5 million ha from 2000 to 2015. This reference data was combined with Normalised Difference Vegetation Index (NDVI) derived from Landsat 7 images, in which more than 80% of the possible data are missing. Despite the hard problem with extremely noisy data, among the four ML models we tested, random forest (with mean imputation and missing value indicators) achieved the average AUC (area under the ROC curve) of 0.688 {+/-} 0.059 over all 16 years with the range [0.602, 0.795] in identifying new crop-loss fields based on reference fields of the same year. To our knowledge, this is one of the first large scale benchmark study of using machine learning for crop loss classification at field parcel scale. The classification setting and trained models have numerous potential applications, for example, allowing government agencies or insurance companies to verify crop-loss claims by farmers and realise efficient agricultural monitoring.

bioinformatics↗

bioRxiv2021

Gene-Gene Interaction Detection with Deep Learning

We do not know the extent to which genetic interactions affect the observed phenotype in diseases, because the current interaction detection approaches are limited: they only consider interactions between the top SNPs of each gene, and only simple forms of interaction. We introduce methods for increasing the statistical power of interaction detection by taking into account all SNPs and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a gene interaction neural network (NN), and the interactions are quantified by the Shapley score between hidden nodes, which are gene representations that optimally combine information from all SNPs in the gene. Additionally, we design a new permutation procedure tailored for NNs to assess the significance of interactions. The new approach outperformed existing alternatives on simulated datasets, and in a cholesterol study on the UK Biobank it detected six interactions which replicated on an independent FINRISK dataset, four of them novel findings.

bioinformatics↗

bioRxiv2021

A non-adaptive demographic mechanism for genome expansion in Streptomyces

The evolution of microbial genome size is driven by gene acquisition and loss events that occur at scales from individual genomes to entire pangenomes. The equilibrium between gene gain and loss is shaped by evolutionary forces, including selection and drift, which are in turn influenced by population demographics. There is a well-known bias towards deletion in microbial genomes, which promotes genome streamlining. Less well described are mechanisms that promote genome expansion, giving rise to the many microbes, such as Streptomyces, that have unusually large genomes. We find evidence of genome expansion in Streptomyces sister-taxa, and we hypothesize that a recent demographic range expansion drove increases in genome size through a non-adaptive mechanism. These Streptomyces sister-taxa, NDR (northern-derived) and SDR (southern-derived), represent recently diverged lineages that occupy distinct geographic ranges. Relative to SDR genomes, NDR genomes are larger, have more genes, and their genomes are enriched in intermediate frequency genes. We also find evidence of relaxed selection in NDR genomes relative to SDR genomes. We hypothesize that geographic range expansion, coupled with relaxed selection, facilitated the introgression of non-adaptive horizontally acquired genes, which accumulated at intermediate frequencies through a mechanism known as genome surfing. We show that similar patterns of pangenome structure and genome expansion occur in a simulation that models the effects of population expansion on genome dynamics. We show that non-adaptive evolutionary phenomena can explain expansion of microbial genome size, and suggest that this mechanism might explain why some bacteria with large genomes can be found in soil.

microbiology↗

bioRxiv2020

Population analysis of Legionella pneumophila reveals the basis of resistance to complement-mediated killing

Legionella pneumophila is the most common cause of the severe respiratory infection known as Legionnaires disease. L. pneumophila is typically a symbiont of free-living amoeba, and our understanding of the bacterial factors that determine human pathogenicity is limited. Here we carried out a population genomic study of 900 L. pneumophila isolates from human clinical and environmental samples to examine their genetic diversity, global distribution and the basis for human pathogenicity. We found that although some clones are more commonly associated with clinical infections, the capacity for human disease is representative of the breadth of species diversity. To investigate the bacterial genetic basis for human disease potential, we carried out a genome-wide association study that identified a single gene (lag-1), to be most strongly associated with clinical isolates. Molecular evolutionary analysis showed that lag-1, which encodes an O-acetyltransferase responsible for lipopolysaccharide modification, has been distributed horizontally across all major phylogenetic clades of L. pneumophila by frequent recent recombination events. Functional analysis revealed a correlation between the presence of a functional lag-1 gene and resistance to killing in human serum and bovine broncho-alveolar lavage. In addition, L. pneumophila strains that express lag-1 escaped complement-mediated phagocytosis by neutrophils. Importantly, we discovered that the expression of lag-1 confers the capacity to evade complement-mediated killing by inhibiting deposition of classical pathway molecules on the bacterial surface. In summary, our combined population and functional analyses identified L. pneumophila genetic traits linked to human disease and revealed the molecular basis for resistance to complement-mediated killing, a previously elusive trait of direct relevance to human disease pathogenicity. SignificanceLegionella pneumophila is an environmental bacterium associated with a severe pneumonia known as Legionnaires disease. A small number of L. pneumophila clones are responsible for a large proportion of human infections suggesting they have enhanced pathogenicity. Here, we employed a large-scale population analysis to investigate the evolution of human pathogenicity and identified a single gene (lag-1) that was more frequently found in clinical isolates. Functional analysis revealed that the lag-1-encoded O-acetyltransferase, involved in modification of lipopolysaccharide, conferred resistance to the classical pathway of complement in human serum. These findings solve a long-standing mystery in the field regarding L. pneumophila resistance to serum killing, revealing a novel mechanism by which L. pneumophila may avoid immune defences during infection.

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↗

bioRxiv2018

A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation

Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.git.\n\nAuthor summaryAs colonizing bacterial populations are the source for much transmission and a reservoir for infection, they are a major focus of interest clinically and epidemiologically. Understanding the dynamics of colonization depends on being able to confidently identify acquisition and clearance events given intermittent sampling of hosts. To do so, we need a model of within-host bacterial population evolution from acquisition through the time of sampling that enables estimation of whether two samples are derived from the same population. Past efforts have frequently relied on empirical genetic distance thresholds that forgo an underlying model or employ a simple molecular clock model. Here, we present an inferential method that accounts for the timing of sample collection and population diversification, to provide a probabilistic estimate for whether two isolates represent the same colonizing strain. This method has implications for understanding the dynamics of acquisition and clearance of colonizing bacteria, and the impact on these rates by factors such as sensitivity of the sampling method, pathogen genotype, competition with other carriage bacteria, host immune response, and antibiotic exposure.

bioinformatics↗

bioRxiv2017

Modelling GxE with historical weather information improves genomic prediction in new environments

Interaction between the genotype and the environment (GxE) has a strong impact on the yield of major crop plants. Although influential, taking GxE explictily into account in plant breeding has remained difficult. Recently GxE has been predicted from environmental and genomic covariates, but existing works have not shown that generalization to new environments and years without access to in-season data is possible and practical applicability remains unclear. Using data from a Barley breeding program in Finland, we construct an in-silico experiment to study the viability of GxE prediction under practical constraints. We show that the response to the environment of a new generation of untested Barley cultivars can be predicted in new locations and years using genomic data, machine learning and historical weather observations for the new locations. Our results highlight the need for models of GxE: non-linear effects clearly dominate linear ones and the interaction between the soil type and daily rain is identified as the main driver for GxE for Barley in Finland. Our study implies that genomic selection can be used to capture the yield potential in GxE effects for future growth seasons, providing a possible means to achieve yield improvements, needed for feeding the growing population.

bioinformatics↗

bioRxiv2017

Bacmeta: simulation for genomic evolution in bacterial metapopulations

The advent of genomic data from densely sampled bacterial populations has created a need for flexible simulators by which models and hypotheses can be efficiently investigated in the light of empirical observations. Bacmeta provides fast stochastic simulation of neutral evolution within a large collection of interconnected bacterial populations with completely adjustable connectivity network. Stochastic events of mutations, recombinations, insertions/deletions, migrations and microepidemics can be simulated in discrete non-overlapping generations with a Wright-Fisher model that operates on explicit sequence data of any desired genome length. Each model component, including locus, bacterial strain, population, and ultimately the whole metapopulation, is efficiently simulated using C++ objects, and detailed metadata from each level of the simulation can be acquired. The software can be executed in a cluster environment using simple textual input files, enabling, e.g., large-scale simulations and likelihood-free inference. Bacmeta is implemented with C++ for Linux, Mac and Windows. It is available at https://bitbucket.org/aleksisipola/bacmeta under the BSD 3-clause license.\n\nContactaleksi.sipola@helsinki.fi,\n\njukka.corander@medisin.uio.no\n\nSupplementary informationSupplementary data are available online at bioRxiv.

genomics↗

bioRxiv2016

Speciation trajectories in recombining bacterial species

While speciation in eukaryotes is well-studied, a controversy over the nature of bacterial species due to recombination between distant strains continues. It is generally agreed that bacterial diversity can be classified into genetically and ecologically cohesive units, but what produces such variation is a topic of intensive research. Recombination may maintain coherent species of frequently recombining bacteria, but the emergence of distinct clusters within a recombining species, and the impact of habitat structure in this process are not well described, limiting our understanding of how new species are created. Here we present a model of bacterial evolution in overlapping habitat space. We show three different outcomes are possible for a pair of clusters, depending on the size of their habitat overlap: fast divergence with little interaction between the clusters, slow divergence with frequent recombination between the clusters, and stationary or near stationary population structure, where the clusters remain at an equilibrium distance for an indefinite time. We fit our model to two data sets. In Streptococcus pneumoniae, we find a genomically and ecologically distinct subset, held at a relatively constant genetic distance from the majority of the population through frequent recombination with it, while in Campylobacter jejuni, we find a minority population we predict will continue to diverge at a higher rate. This approach may predict and define speciation trajectories in multiple bacterial species.

microbiology↗

bioRxiv2016

biMM: Efficient estimation of genetic variances andcovariances for cohorts with high-dimensional phenotype measurements

Genetic research utilizes a decomposition of trait variances and covariances into genetic and environmental parts. Our software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals.\n\nAvailabilityImplementation in R freely available at www.iki.fi/mpirinen.

genetics↗