Biology subjects

Schaap, P.

Publications and source records attributed to Schaap, P..

5 recordsLinked to original sources

bioRxiv2024

Comparative genomics of Rickettsiella bacteria reveal variable metabolic traits involved in symbiotic interactions with arthropods

Members of the Rickettsiella genus (order: Legionellales) are emerging as widespread bacteria associated with insects, arachnids, and crustaceans. While some Rickettsiella strains are highly virulent pathogens, others are maternally inherited endosymbionts that manipulate arthropod phenotypes, including the induction of defensive symbiosis and cytoplasmic incompatibility. However, the genomic diversity of Rickettsiella remains largely unexplored, and their genetic potential to induce complex phenotypes in arthropods is only partially understood. In this study, we sequenced five new Rickettsiella genomes isolated from three tick species. Through comparative genomics, we observed that Rickettsiella members share similar metabolic capabilities, and collectively lack virulence genes from pathogenic Legionellales. Additional analysis of Rickettsiella genomes revealed significant variability in metabolic properties related to endosymbiosis. Specifically, their capacity to biosynthesize certain B vitamins and heme varies, suggesting a functional role of some Rickettsiella strains in the nutrition of their arthropod hosts. Some Rickettsiella genomes harbour homologs of Wolbachia cif genes, the cause of Wolbachia-induced cytoplasmic incompatibility,, suggesting that Rickettsiella may use a similar molecular mechanism to manipulate the reproduction of their arthropod hosts. Phylogenomics further revealed that tick-borne Rickettsiella exhibit distinct evolutionary origins within the genus, indicating that Rickettsiella have undergone repeated horizontal transfers between ticks and other arthropods.

evolutionary biology↗

bioRxiv2021

Machine learning approaches to predict the plant-associated phenotype of Xanthomonas strains

The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding strain pathogenicity over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Recursive feature extraction provided further insights into the virulence enabling factors, but also yielded domains linked to traits not present in pathogenic strains.

microbiology↗

bioRxiv2020

Abundantly expressed class of non-coding RNAs conserved through the multicellular evolution of dictyostelid social amoebae

BackgroundAggregative multicellularity has evolved multiple times in diverse groups of eukaryotes. One of the most well-studied examples is the development of dictyostelid social amoebae, e.g. Dictyostelium discoideum. However, it is still poorly understood why multicellularity emerged in these amoebae while the great majority of other members of Amoebozoa are unicellular. Previously a novel type of non-coding RNA, Class I RNAs, was identified in D. discoideum and demonstrated to be important for normal multicellular development. In this study we investigated Class I RNA evolution and its connection to multicellular development. ResultsNew Class I RNA genes were identified by constructing a co-variance model combined with a scoring system based on conserved up-stream sequences. Multiple genes were predicted in representatives of each major group of Dictyostelia and expression analysis validated that our search approach can identify expressed Class I RNA genes with high accuracy and sensitivity. Further studies showed that Class I RNAs are ubiquitous in Dictyostelia and share several highly conserved structure and sequence motifs. Class I RNA genes appear to be unique to dictyostelid social amoebae since they could not be identified in searches in outgroup genomes, including the closest known relatives to Dictyostelia. ConclusionOur results show that Class I RNA is an ancient abundant class of ncRNAs, likely to have been present in the last common ancestor of Dictyostelia dating back at least 600 million years. Taken together, our current knowledge of Class I RNAs suggests that they may have been involved in evolution of multicellularity in Dictyostelia.

evolutionary biology↗

bioRxiv2018

Consistency, Inconsistency and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome Scale Metabolic Modelling

Genome scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in eleven biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

bioinformatics↗

bioRxiv2018

The diurnal transcriptional landscape of the microalga Tetradesmus obliquus

Tetradesmus obliquus is a promising oleaginous microalga. We functionally annotated its genome and characterized the transcriptional landscape of T. obliquus adapted to 16:8h light dark (LD) cycles in turbidostat culture conditions at very high temporal resolution (1h intervals). Revealing a cycle of cellular events, six distinct expression profiles were obtained, each with transcriptional phenotypes correlating with measurements of biochemical composition.\n\nThe impact of starch deficiency was studied using the starchless mutant slm1. Significant changes in the transcriptional landscape were observed. Starch deficiency resulted in incapacity to supply energy during dark period, resulting in early or late time shift for energy demanding processes. Our study provides new perspectives on the role of starch and the adaptation to LD cycles of oleaginous microalgae.

plant biology↗