Biology subjects

Jablonski, K. P.

Publications and source records attributed to Jablonski, K. P..

4 recordsLinked to original sources

bioRxiv2022

Coherent pathway enrichment estimation by modeling inter-pathway dependencies using regularized regression

Gene set enrichment methods are a common tool to improve the interpretability of gene lists as obtained, for example, from differential gene expression analyses. They are based on computing whether dysregulated genes are located in certain biological pathways more often than expected by chance. Gene set enrichment tools rely on pre-existing pathway databases such as KEGG, Reactome, or the Gene Ontology. These databases are increasing in size and in the number of redundancies between pathways, which complicates the statistical enrichment computation. Here, we address this problem and develop a novel gene set enrichment method, called pareg, which is based on a regularized generalized linear model and directly incorporates dependencies between gene sets related to certain biological functions, for example, due to shared genes, in the enrichment computation. We show that pareg is more robust to noise than competing methods. Additionally, we demonstrate the ability of our method to recover known pathways as well as to suggest novel treatment targets in an exploratory analysis using breast cancer samples from TCGA. pareg is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/pareg.html) as well as on https://github.com/cbg-ethz/pareg. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.

bioinformatics↗

bioRxiv2021

Contribution of 3D genome topological domains to genetic risk of cancers

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 3-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. 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 display such a preferential location 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 TAD border enrichment in risk loci differ between cancers and non-cancer diseases. Different TAD border enrichments are observed in embryonic stem cells and differentiated cells, which agrees with an evolution along embryogenesis of the 3D genome organization into topological domains. 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 a direct effect on an individual gene, the other acting in interplay with 3D genome organization. Author summaryGenome-wide association studies comparing patients and healthy subjects have evidenced correlations between diseases and the presence of pointwise genetic variations known as single-nucleotide polymorphisms (SNPs). We exploit and extend this statistical analysis by investigating the location of risk loci, i.e. disease-associated SNPs, with respect to the 3D organization of the genome into spatially-insulated domains, the topologically-associating domains (TADs). We show that for certain diseases, mostly cancers, their associated risk loci are preferentially located in the borders of these topological domains. The predominance of cancers among these diseases is confirmed and even enhanced when considering only intergenic SNPs. A different enrichment behavior is observed in embryonic stem cells and derived cell lines at an early developmental stage, presumably due to the not fully mature TAD structure in these cells. Overall, our results show that genome variations in specific TAD borders may increase the risk of developing certain diseases, especially cancers. Our work underlines the importance of considering the genetic risk loci within their 3D genomic context, and suggests a role of 3D genome partitioning into topological domains in the genetic risk which differs between cancers and non-cancer diseases.

genetics↗

bioRxiv2021

Identifying cancer pathway dysregulations using differential causal effects

Signaling pathways control cellular behavior. Dysregulated pathways, for example due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer. We introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, artificial signals from, for example, batch effects have less influence on the result and dce has a higher chance to detect the biological signals. We show that dce outperforms competing methods on synthetic data sets and on CRISPR knockout screens. In an exploratory analysis on breast cancer data from TCGA, we recover known and discover new genes involved in breast cancer progression.

bioinformatics↗

bioRxiv2020

Within-patient genetic diversity of SARS-CoV-2

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

genomics↗