Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Drug-induced resistance evolution necessitates less aggressive treatment

Abstract: Evolution of drug resistance to anticancer, antimicrobial and antiviral therapies is widespread among cancer and pathogen cell populations. Classical theory posits strictly that genetic and phenotypic variation is generated in evolving populations independently of the selection pressure. However, recent experimental findings among antimicrobial agents, traditional cytotoxic chemotherapies and targeted cancer therapies suggest that treatment not only imposes selection but also affects the rate of adaptation via altered mutational processes. Here we analyze a model with drug-induced increase in mutation rate and explore its consequences for treatment optimization. We argue that the true biological cost of treatment is not limited to the harmful side-effects, but instead realizes even more profoundly by fundamentally changing the underlying eco-evolutionary dynamics within the microenvironment. Factoring in such costs (or collateral damage) of control is at the core of successful therapy design and can unify different evolution-based approaches to therapy optimization. Using the concept of evolutionary rescue, we formulate the treatment as an optimal control problem and solve the optimal elimination strategy, which minimizes the probability of evolutionary rescue. Our solution exploits a trade-off, where increasing the drug concentration has two opposing effects. On the one hand, it reduces de novo mutations by decreasing the size of the target cell population faster; on the other hand, a higher dosage generates a surplus of treatment-induced mutations. We show that aggressive elimination strategies, which aim at eradication as fast as possible and which represent the current standard of care, can be detrimental even with modest drug-induced increases (fold change ≤10) to the baseline mutation rate. Our findings highlight the importance of dose dependencies in resistance evolution and motivate further investigation of the mutagenicity and other hidden collateral costs of therapies that promote resistance. Author summary The evolution of drug resistance is a particularly problematic and frequent outcome of cancer and antimicrobial therapies. Recent research suggests that these treatments may enhance the evolvability of the target population not only via inducing intense selection pressures but also via altering the underlying mutational processes. Here we investigate the consequences of such drug-induced evolution by considering a mathematical model with explicitly dose-dependent mutation rate. We identify, characterize and exploit a trade-off between decreasing the target population size as fast as possible and generating a surplus of treatment-induced de novo mutations. By formulating the treatment as an optimal control problem over the evolution of the target population, we find the optimal treatment strategy, which minimizes the probability of evolutionary rescue. We show that this probability changes non-monotonically with the cumulative drug concentration and is minimized at an intermediate dosage. Our results are immediately amenable to experimental investigation and motivate further study of the various mutagenic and other hidden collateral costs of treatment. Taken together, our results add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and stimulate further clinical trials on alternative treatment strategies.

ViraPipe: scalable parallel pipeline for viral metagenome analysis from next generation sequencing reads.

Abstract: Motivation Next Generation Sequencing (NGS) technology enables identification of microbial genomes from massive amount of human microbiomes more rapidly and cheaper than ever before. However, the traditional sequential genome analysis algorithms, tools, and platforms are inefficient for performing large-scale metagenomic studies on ever-growing sample data volumes. Currently, there is an urgent need for scalable analysis pipelines that enable harnessing all the power of parallel computation in computing clusters and in cloud computing environments. We propose ViraPipe, a scalable metagenome analysis pipeline that is able to analyze thousands of human microbiomes in parallel in tolerable time. The pipeline is tuned for analyzing viral metagenomes and the software is applicable for other metagenomic analyses as well. ViraPipe integrates parallel BWA-MEM read aligner, MegaHit De novo assembler, and BLAST and HMMER3 sequence search tools. We show the scalability of ViraPipe by running experiments on mining virus related genomes from NGS datasets in a distributed Spark computing cluster. Results ViraPipe analyses 768 human samples in 210 minutes on a Spark computing cluster comprising 23 nodes and 1288 cores in total. The speedup of ViraPipe executed on 23 nodes was 11x compared to the sequential analysis pipeline executed on a single node. The whole process includes parallel decompression, read interleaving, BWA-MEM read alignment, filtering and normalizing of non-human reads, De novo contigs assembling, and searching of sequences with BLAST and HMMER3 tools. Contact ilari.maarala@aalto.fi. Availability and implementation https://github.com/NGSeq/ViraPipe.

SwiftLink: parallel MCMC linkage analysis using multicore CPU and GPU.

Abstract: MOTIVATION: Linkage analysis remains an important tool in elucidating the genetic component of disease and has become even more important with the advent of whole exome sequencing, enabling the user to focus on only those genomic regions co-segregating with Mendelian traits. Unfortunately, methods to perform multipoint linkage analysis scale poorly with either the number of markers or with the size of the pedigree. Large pedigrees with many markers can only be evaluated with Markov chain Monte Carlo (MCMC) methods that are slow to converge and, as no attempts have been made to exploit parallelism, massively underuse available processing power. Here, we describe SWIFTLINK, a novel application that performs MCMC linkage analysis by spreading the computational burden between multiple processor cores and a graphics processing unit (GPU) simultaneously. SWIFTLINK was designed around the concept of explicitly matching the characteristics of an algorithm with the underlying computer architecture to maximize performance. RESULTS: We implement our approach using existing Gibbs samplers redesigned for parallel hardware. We applied SWIFTLINK to a real-world dataset, performing parametric multipoint linkage analysis on a highly consanguineous pedigree with EAST syndrome, containing 28 members, where a subset of individuals were genotyped with single nucleotide polymorphisms (SNPs). In our experiments with a four core CPU and GPU, SWIFTLINK achieves a 8.5× speed-up over the single-threaded version and a 109× speed-up over the popular linkage analysis program SIMWALK. AVAILABILITY: SWIFTLINK is available at https://github.com/ajm/swiftlink. All source code is licensed under GPLv3.

Prediction of post-vaccine population structure of Streptococcus pneumoniae using accessory gene frequencies

Abstract: Predictions of how a population will respond to a selective pressure are valuable, especially in the case of infectious diseases, which often adapt to the interventions we use to control them. Yet attempts to predict how pathogen populations will change, for example in response to vaccines, are challenging. Such has been the case with Streptococcus pneumoniae , an important human colonizer and pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use recent advances in knowledge of negative-frequency dependent selection (NFDS) acting on frequencies of accessory genes (i.e., flexible genome) to predict the changes in the pneumococcal population after intervention. Implementing a deterministic NFDS model using the replicator equation, we can accurately predict which pneumococcal lineages will increase after intervention. Analyzing a population genomic sample of pneumococci collected before and after vaccination, we find that the predicted fitness of a lineage post-vaccine is significantly and positively correlated with the observed change in its prevalence. Then, using quadratic programming to numerically solve the frequencies of non-vaccine type lineages that best restored the pre-vaccine accessory gene frequencies, we accurately predict the post-vaccine population composition. Additionally, we also test the predictive ability of frequencies of core genome loci, a subset of metabolic loci, and naive estimates of prevalence change based on pre-vaccine lineages frequencies. Finally, we show how this approach can assess the migration and invasion capacity of emerging lineages, on the basis of their accessory genome. In general, we provide a method for predicting the impact of an intervention on pneumococcal populations and other bacterial pathogens for which NFDS is a main driving force.