Biology subjects

Rodenburg, S. Y. A.

Publications and source records attributed to Rodenburg, S. Y. A..

3 recordsLinked to original sources

bioRxiv2020

Mis-annotated multi nucleotide variants in public cancer genomics datasets can lead to inaccurate mutation calls with significant implications

BackgroundNext generation sequencing is widely used in cancer to profile tumors and detect variants. Most somatic variant callers used in these pipelines identify variants at the lowest possible granularity - single nucleotide variants (SNVs). As a result, multiple adjacent SNVs are called individually instead of as a multi-nucleotide variant (MNV). The problem with this level of granularity is that the amino acid change from the individual SNVs within a codon could be different from the amino acid change based on the MNV that results from combining the SNVs. Most variant annotation tools do not account for this, leading to incorrect conclusions about the downstream effects of the variants. MethodHere, we used Variant Call Files (VCFs) from the TCGA Mutect2 caller, and developed a solution to merge SNVs to MNVs. Our custom script takes the phasing information from the SNV VCFs and based on a gene model, determines if SNVs are at the same codon and need to be merged into a MNV prior to variant annotation. ResultsWe analyzed 10,383 VCFs from TCGA and found 12,141 MNVs that were incorrectly annotated. Strikingly, the analysis of seven commonly mutated genes from 178 studies from cBioPortal revealed that MNVs were consistently missed in 20 of these studies, while they were correctly annotated in 15 more recent studies. The best and most common example of MNVs was found at the BRAF V600 locus, where several public datasets reported separate BRAF V600E and BRAF V600M variants, instead of a single merged V600K variant. ConclusionWhile some datasets merged MNVs correctly, many public datasets have not been corrected for this problem. As a best practice for variant calling, we recommend that MNVs be accounted for in NGS processing pipelines, thus improving analyses on the impact of somatic variants in cancer genomics.

genomics↗

bioRxiv2020

Oomycete metabolism is highly dynamic and reflects lifestyle adaptations

Pathogen-host symbiosis drives metabolic adaptations. Animal and plant pathogenic oomycetes are thought to adapt their metabolism to facilitate interactions with their hosts. Here, we performed a large-scale comparison of oomycete metabolism and uncovered considerable variation in oomycete metabolism that could be linked to differences in lifestyle. Pathway comparisons revealed that plant pathogenic oomycetes can be divided in two parts; a conserved part and an accessory part. The accessory part could be associated with the degradation of plant compounds produced during defence responses. Obligate biotrophic oomycetes have smaller metabolic networks, and this group displays converged evolution by repeated gene losses affecting the same metabolic pathways. A comparison of the metabolic networks of obligate biotrophic oomycetes with those of plant pathogenic oomycetes as a whole revealed that the losses of metabolic enzymes in biotrophs are not random and that the network of biotrophs contracts from the periphery inwards. Our analyses represent the first metabolism-focused comparison of oomycetes at this scale and will contribute to a better understanding of the evolution and relationship between metabolism and lifestyle adaptation. ORIGINALITY & SIGNFICANCE STATEMENTThe intimate interaction between pathogens and their hosts exerts strong selection pressure leading to rapid adaptation. How this shapes the metabolism of pathogens is largely unknown. Here, we used comparative genomics to systematically characterize the metabolisms of animal and plant pathogenic oomycetes, a group of eukaryotes comprising many important animal and plant pathogens with significant economic and ecological impact. Core- and pan-genome as well as metabolic network analyses of distantly related oomycetes and their non-pathogenic relatives revealed considerable lifestyle- and lineage-specific adaptations. Extreme lifestyle adaptation could be observed in the metabolism of obligate biotrophic oomycetes - a group of pathogens that require a living host for proliferation. The metabolic networks of obligate biotrophic oomycetes reflect profound patterns of reductive evolution, converging to a loss the same metabolic enzymes during acquisition of an obligate parasitic lifestyle. These findings contribute to a be better understanding of oomycete evolution and the relationship between metabolism and lifestyle adaptation.

evolutionary biology↗

bioRxiv2017

Genome-wide characterization of Phytophthora infestansmetabolism: a systems biology approach

Genome-scale metabolic models (GEMs) provide a functional view of the complex network of biochemical reactions in the living cell. Initially mainly applied to reconstruct the metabolism of model organisms, the availability of increasingly sophisticated reconstruction methods and more extensive biochemical databases now make it possible to reconstruct GEMs for less characterized organisms as well, and have the potential to unravel the metabolism in pathogen-host systems. Here we present a GEM for the oomycete plant pathogen Phytophthora infestans as a first step towards an integrative model with its host. We predict the biochemical reactions in different cellular compartments and investigate the gene-protein-reaction associations in this model to get an impression of the biochemical capabilities of P. infestans. Furthermore, we generate life stage-specific models to place the transcriptomic changes of genes encoding metabolic enzymes into a functional context. In sporangia and zoospores there is an overall downregulation, most strikingly reflected in the fatty acid biosynthesis pathway. To investigate the robustness of the GEM, we simulate gene deletions to predict which enzymes are essential for in vitro growth. While there is room for improvement, this first model is an essential step towards an understanding of P. infestans and its interactions with plants as a system, which will help to formulate new hypotheses on infection mechanisms and disease prevention.

systems biology↗