Kim Philipp Jablonski

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Jablonski

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Kim Philipp

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0000-0002-4166-4343

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Publications 1 - 10 of 19

Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data
Nadeau, Sarah Ann; Vaughan, Timothy G.; Beckmann, Christiane; et al. (2023)
Science Translational Medicine
Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Switzerland in 2020 - the sixth largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrasted these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland’s border closures de-coupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86–98% reduction in introductions during Switzerland’s strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Last, we quantified local transmission dynamics once introductions into Switzerland occurred, using a phylodynamic model. We found that transmission slowed 35–63% upon outbreak detection in summer 2020, but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.
V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation
Fuhrmann, Lara; Jablonski, Kim Philipp; Topolsky, Ivan; et al. (2024)
GigaScience
The large amount and diversity of viral genomic datasets generated by next-generation sequencing technologies poses a set of challenges for computational data analysis workflows, including rigorous quality control, scaling to large sample sizes, and tailored steps for specific applications. Here, we present V-pipe 3.0, a computational pipeline designed for analyzing next-generation sequencing data of short viral genomes. It is developed to enable reproducible, scalable, adaptable, and transparent inference of genetic diversity of viral samples. By presenting 2 large-scale data analysis projects, we demonstrate the effectiveness of V-pipe 3.0 in supporting sustainable viral genomic data science.
Quantifying SARS-CoV-2 spread in Switzerland based on genomic sequencing data
Nadeau, Sarah Ann; Beckmann, Christiane; Topolsky, Ivan; et al. (2020)
medRxiv
Pathogen genomes provide insights into their evolution and epidemic spread. We sequenced 1,439 SARS-CoV-2 genomes from Switzerland, representing 3-7% of all confirmed cases per week. Using these data, we demonstrate that no one lineage became dominant, pointing against evolution towards general lower virulence. On an epidemiological level, we report no evidence of cryptic transmission before the first confirmed case. We find many early viral introductions from Germany, France, and Italy and many recent introductions from Germany and France. Over the summer, we quantify the number of non-traceable infections stemming from introductions, quantify the effective reproductive number, and estimate the degree of undersampling. Our framework can be applied to quantify evolution and epidemiology in other locations or for other pathogens based on genomic data.
Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data
Sutcliffe, Steven G.; Kraemer, Susanne A.; Ellmen, Isaac; et al. (2024)
Microbial Genomics
Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.
Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020
Alm, Eric; Broberg, Eeva K.; Connor, Thomas; et al. (2020)
Eurosurveillance
We show the distribution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three genomic nomenclature systems to all sequence data from the World Health Organization European Region available until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation, compare the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2.
Contribution of 3D genome topological domains to genetic risk of cancers: a genome-wide computational study
Jablonski, Kim Philipp; Carron, Leopold; Mozziconacci, Julien; et al. (2022)
Human Genomics
Background Genome-wide association studies have identified statistical associations between various diseases, including cancers, and a large number of single-nucleotide polymorphisms (SNPs). However, they provide no direct explanation of the mechanisms underlying the association. Based on the recent discovery that changes in three-dimensional genome organization may have functional consequences on gene regulation favoring diseases, we investigated systematically the genome-wide distribution of disease-associated SNPs with respect to a specific feature of 3D genome organization: topologically associating domains (TADs) and their borders. Results For each of 449 diseases, we tested whether the associated SNPs are present in TAD borders more often than observed by chance, where chance (i.e., the null model in statistical terms) corresponds to the same number of pointwise loci drawn at random either in the entire genome, or in the entire set of disease-associated SNPs listed in the GWAS catalog. Our analysis shows that a fraction of diseases displays such a preferential localization of their risk loci. Moreover, cancers are relatively more frequent among these diseases, and this predominance is generally enhanced when considering only intergenic SNPs. The structure of SNP-based diseasome networks confirms that localization of risk loci in TAD borders differs between cancers and non-cancer diseases. Furthermore, different TAD border enrichments are observed in embryonic stem cells and differentiated cells, consistent with changes in topological domains along embryogenesis and delineating their contribution to disease risk. Conclusions Our results suggest that, for certain diseases, part of the genetic risk lies in a local genetic variation affecting the genome partitioning in topologically insulated domains. Investigating this possible contribution to genetic risk is particularly relevant in cancers. This study thus opens a way of interpreting genome-wide association studies, by distinguishing two types of disease-associated SNPs: one with an effect on an individual gene, the other acting in interplay with 3D genome organization.
Quantitative measures of within-host viral genetic diversity
Fuhrmann, Lara; Jablonski, Kim Philipp; Beerenwinkel, Niko (2021)
Current Opinion in Virology
The genetic diversity of virus populations within their hosts is known to influence disease progression, treatment outcome, drug resistance, cell tropism, and transmission risk, and the study of dynamic changes of genetic heterogeneity can provide insights into the evolution of viruses. Several measures to quantify within-host genetic diversity capturing different aspects of diversity patterns in a sample or population are used, based on incidence, relative frequencies, pairwise distances, or phylogenetic trees. Here, we review and compare several of these measures.
Detection and surveillance of SARS-CoV-2 genomic variants in wastewater
Jahn, Katharina; Dreifuss, David; Topolsky, Ivan; et al. (2021)
medRxiv
The emergence of SARS-CoV-2 mutants with altered transmissibility, virulence, or immunogenicity emphasizes the need for early detection and epidemiological surveillance of genomic variants. Wastewater samples provide an opportunity to assess circulating viral lineages in the community. We performed genomic sequencing of 122 wastewater samples from three locations in Switzerland to analyze the B.1.1.7, B.1.351, and P.1 variants of SARS-CoV-2 on a population level. We called variant-specific signature mutations and monitored variant prevalence in the local population over time. To enable early detection of emerging variants, we developed a bioinformatics tool that uses read pairs carrying multiple signature mutations as a robust indicator of low-frequency variants. We further devised a statistical approach to estimate the transmission fitness advantage, a key epidemiological parameter indicating the speed at which a variant spreads through the population, and compared the wastewater-based findings to those derived from clinical samples. We found that the local outbreak of the B.1.1.7 variant in two Swiss cities was observable in wastewater up to 8 days before its first detection in clinical samples. We detected a high prevalence of the B.1.1.7 variant in an alpine ski resort popular among British tourists in December 2020, a time when the variant was still very rare in Switzerland. We found no evidence of local spread of the B.1.351 and P.1 variants at the monitored locations until the end of the study (mid February) which is consistent with clinical samples. Estimation of local variant prevalence performs equally well or better for wastewater samples as for a much larger number of clinical samples. We found that the transmission fitness advantage of B.1.1.7, i.e. the relative change of its reproductive number, can be estimated earlier and based on substantially fewer wastewater samples as compared to using clinical samples. Our results show that genomic sequencing of wastewater samples can detect, monitor, and evaluate genetic variants of SARS-CoV-2 on a population level. Our methodology provides a blueprint for rapid, unbiased, and cost-efficient genomic surveillance of SARS-CoV-2 variants.
Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC
Jahn, Katharina; Dreifuss, David; Topolsky, Ivan; et al. (2022)
Nature Microbiology
The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK.
Beyond reproducibility: Knocking on sustainability's door
Jablonski, Kim Philipp (2022)
The following thesis presents three independent studies which were carried out as part of the author's doctoral studies in the Computational Biology Group at the Department of Biosystems Science and Engineering at ETH Zurich in Basel. These projects deal with the development of statistical methods for the detection of pathway dysregulations, and the processing and analysis of next-generation sequencing data with a particular focus on the importance of benchmarking the methods' performances in a sustainable way. The first two studies are based on the fact that cancer is a heterogeneous disease where the same phenotype can arise from different mutational patterns and propose novel methods for the computation of pathway enrichments. The first study takes a causal approach and computes edge-specific pathway dysregulations while the second study computes global pathway dysregulation scores while accounting for term-term relations. Both studies include an extensive benchmark workflow which tests both the performance on synthetic and real data sets as well as runs exploratory analyses. The third study describes the development of a pipeline for the analysis of viral high-throughput sequencing data and an extensive benchmark of global haplotype reconstruction methods. The dissertation is organized in the following way. The first chapter provides an overview of different workflow management systems which can be used to create reproducible benchmarking workflows, a comment on the distinction between reproducible and sustainable data science, and their relevance in the fields of cancer genomics as well as virology. The second chapter presents \emph{dce}, a computational method for the edge-specific detection of pathway dysregulations using a causal framework. The third chapter presents \emph{pareg}, a regression-based method which addresses the issue of large and redundant pathway databases by incorporating term-term relations into the enrichment computation. It accomplishes this goal by adding regularization terms to the loss function of a generalized linear model. The fourth chapter presents a scalable, reproducible and transparent pipeline for the analysis of viral sequencing data as well as a benchmark of global haplotype reconstruction methods. The fifth chapter concludes the thesis by summarizing its findings as well as suggesting potential future directions.

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Kim Philipp Jablonski

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