Biology subjects

Koehler, S.

Publications and source records attributed to Koehler, S..

8 recordsLinked to original sources

bioRxiv2018

The tumor suppressor p53 promotes carcinoma invasion and collective cellular migration

SummaryLoss of function of the tumor suppressor p53 is generally thought to increase cell motility and invasiveness. Using 2-D confluent and 3-D spheroidal cell motility assays with bladder carcinoma cells and colorectal carcinoma cells, we report, to the contrary, that loss of p53 can decrease cell motility and invasion.\n\nAbstractFor migration of the single cell studied in isolation, loss of function of the tumor suppressor p53 is thought to increase cell motility. Here by contrast we used the 2-D confluent cell layer and the 3-D multicellular spheroid to investigate how p53 impacts dissemination and invasion of cellular collectives. We used two human carcinoma cell lines, the bladder carcinoma EJ and the colorectal carcinoma HCT116. We began by replicating single cell invasion in the traditional Boyden chamber assay, and found that the number of invading cells increased with loss of p53, as expected. In the confluent 2-D cell layer, however, for both EJ and HCT, speeds and effective diffusion coefficients for the p53 null types compared to their p53 expressing counterparts were significantly smaller. Compared to p53 expressers, p53 null cells exhibited more organized cortical actin rings together with reduced front-rear cell polarity. Furthermore, loss of p53 caused cells to exert smaller traction forces upon their substrates, and reduced formation of cryptic lamellipodia. In a 3-D collagen matrix, p53 consistently promoted invasion of the multicellular spheroids into surrounding matrix. Together, these results show that p53 expression in these carcinoma model systems increases collective cellular migration and invasion. As such, these studies point to paradoxical contributions of p53 in single cell versus collective cellular migration.

cancer biology↗

bioRxiv2018

Phenotero: annotate as you write

Controlled vocabularies and ontologies have become a valuable resource for knowledge representation, data integration, and downstream analyses in the biomedical domain. In precision medicine, especially in clinical genetics, the Human Phenotype Ontology (HPO) as well as disease ontologies like the Orphanet Rare Disease Ontology (ORDO) or Medical Subject Headings (MeSH) are often used for deep phenotyping of patients and coding of clinical diagnoses. However, the process of assigning ontology classes (annotating) to patient descriptions is often disconnected from the process of writing patient reports or manuscripts in word processing software such as Microsoft Word or LibreOffice. This additional workload and the requirement to install dedicated software may discourage usage of ontologies for parts of the target audience.\n\nTo improve this situation, we present Phenotero, a freely available and simple solution to annotate patient phenotypes and diseases at the time of writing clinical reports or manuscripts. We adopt Zotero, a well-established, actively developed citation management software to generate a tool which allows to reference classes from ontologies within clinical reports or manuscripts at the time of writing. We expect this approach to decrease the additional workload to a minimum while ensuring high quality associations with ontology classes. Standardised collection of phenotypic information at the time of describing the patient allows for streamlining of clinic workflow, efficient data entry, and will subsequently promote clinical and molecular diagnosis, remove ambiguousness from manuscripts, and allow sharing of anonymised patient phenotype data with ultimate goal of a better understanding of the disease. Thus, we hope that our integrated approach will further promote the usage of ontologies and controlled vocabularies in the clinical setting and in the biomedical domain.

bioinformatics↗

bioRxiv2017

Characterization of Glycosylphosphatidylinositol Biosynthesis Defects by Clinical Features, Flow Cytometry, and Automated Image Analysis

BackgroundGlycosylphosphatidylinositol Biosynthesis Defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of Hyperphosphatasia with Mental Retardation Syndrome (HPMRS) and to distinguish them from another subset of GPIBDs, termed Multiple Congenital Anomalies Hypotonia Seizures syndrome (MCAHS). However, the increasing number of individuals with a GPIBD shows that hyperphosphatasia is a variable feature that is not ideal for a clinical classification.\n\nMethodsWe studied the discriminatory power of multiple GPI-linked substrates that were assessed by flow cytometry in blood cells and fibroblasts of 39 and 14 individuals with a GPIBD, respectively. On the phenotypic level, we evaluated the frequency of occurrence of clinical symptoms and analyzed the performance of computer-assisted image analysis of the facial gestalt in 91 individuals.\n\nResultsWe found that certain malformations such as Morbus Hirschsprung and Diaphragmatic defects are more likely to be associated with particular gene defects (PIGV, PGAP3, PIGN). However, especially at the severe end of the clinical spectrum of HPMRS, there is a high phenotypic overlap with MCAHS. Elevation of AP has also been documented in some of the individuals with MCAHS, namely those with PIGA mutations. Although the impairment of GPI-linked substrates is supposed to play the key role in the pathophysiology of GPIBDs, we could not observe gene-specific profiles for flow cytometric markers or a correlation between their cell surface levels and the severity of the phenotype. In contrast, it was facial recognition software that achieved the highest accuracy in predicting the disease-causing gene in a GPIBD.\n\nConclusionsDue to the overlapping clinical spectrum of both, HPMRS and MCAHS, in the majority of affected individuals, the elevation of AP and the reduced surface levels of GPI-linked markers in both groups, a common classification as GPIBDs is recommended. The effectiveness of computer-assisted gestalt analysis for the correct gene inference in a GPIBD and probably beyond is remarkable and illustrates how the information contained in human faces is pivotal in the delineation of genetic entities.

genetics↗

bioRxiv2017

Ontology-based similarity calculations with an improved annotation model

A typical use case of ontologies is the calculation of similarity scores between items that are annotated with classes of the ontology. For example, in differential diagnostics and disease gene prioritisation, the Human Phenotype Ontology (HPO) is often used to compare a query phenotype profile against gold-standard phenotype profiles of diseases or genes. The latter have long been constructed as flat lists of ontology classes, which, as we show in this work, can be improved by exploiting existing structure and information in annotation datasets or full text disease descriptions.\n\nWe derive a study-wise annotation model of diseases and genes and show that this can improve the performance of semantic similarity measures. Inferred weights of individual annotations are one reason for this improvement, but more importantly using the study-wise structure further boosts the results of the algorithms according to precision-recall analyses. We test the study-wise annotation model for diseases annotated with classes from the HPO and for genes annotated with Gene Ontology (GO) classes. We incorporate this annotation model into similarity algorithms and show how this leads to improved performance.\n\nThis work adds weight to the need for enhancing simple list-based representations of disease or gene annotations. We show how study-wise annotations can be automatically derived from full text summaries of disease descriptions and from the annotation data provided by the GO Consortium and how semantic similarity measure can utilise this extended annotation model.

bioinformatics↗

bioRxiv2017

A compartmentalized, self-extinguishing signaling network mediates crossover control in meiosis

Meiotic recombination between homologous chromosomes is tightly regulated to ensure proper chromosome segregation. Each chromosome pair typically undergoes at least one crossover event (crossover assurance) but these exchanges are also strictly limited in number and widely spaced along chromosomes (crossover interference). This has implied the existence of chromosome-wide signals that regulate crossovers, but their molecular basis remains mysterious. Here we characterize a family of four related RING finger proteins in C. elegans. These proteins are recruited to the synaptonemal complex between paired homologs, where they act as two heterodimeric complexes, likely as E3 ubiquitin ligases. Genetic and cytological analysis reveals that they act with additional components to create a self-extinguishing circuit that controls crossover designation and maturation. These proteins also act at the top of a hierarchical chromosome remodeling process that enables crossovers to direct stepwise segregation. Work in diverse phyla indicates that related mechanisms mediate crossover control across eukaryotes.

cell biology↗

bioRxiv2017

"Opposite-of"-information improves similarity calculations in phenotype ontologies

One of the most important use cases of ontologies is the calculation of similarity scores between a query and items annotated with classes of an ontology. The hierarchical structure of an ontology does not necessarily reflect all relevant aspects of the domain it is modelling, and this can reduce the performance of ontology-based search algorithms. For instance, the classes of phenotype ontologies may be arranged according to anatomical criteria, but individual phenotypic features may affect anatomic entities in opposite ways. Thus, \"opposite\" classes may be located in close proximity in an ontology; for example enlarged liver and small liver are grouped under abnormal liver size. Using standard similarity measures, these would be scored as being similar, despite in fact being opposites.\n\nIn this paper, we use information about opposite ontology classes to extend two large phenotype ontologies, the human and the mammalian phenotype ontology. We also show that this information can be used to improve rankings based on similarity measures that incorporate this information. In particular, cosine similarity based measures show large improvements. We hypothesize this is due to the natural embedding of opposite phenotypes in vector space.\n\nWe support the idea that the expressivity of semantic web technologies should be explored more extensively in biomedical ontologies and that similarity measures should be extended to incorporate more than the pure graph structure defined by the subclass or part-of relationships of the underlying ontologies.

bioinformatics↗

bioRxiv2016

Tuning the course of evolution on the biophysical fitness landscape of an RNA virus

Predicting viral evolution remains a major challenge with profound implications for public health. Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics device, the \"Evolution Chip\", which propagates millions of independent viral sub-populations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs shape viral evolution.

evolutionary biology↗

bioRxiv2016

The Monarch Initiative: Insights across species reveal human disease mechanisms

The principles of genetics apply across the whole tree of life: on a cellular level, we share mechanisms with species from which we diverged millions or even billions of years ago. We can exploit this common ancestry at the level of sequences, but also in terms of observable outcomes (phenotypes), to learn more about health and disease for humans and all other species. Applying the range of available knowledge to solve challenging disease problems requires unified data relating genomics, phenotypes, and disease; it also requires computational tools that leverage these multimodal data to inform interpretations by geneticists and to suggest experiments. However, the distribution and heterogeneity of databases is a major impediment: databases tend to focus either on a single data type across species, or on single species across data types. Although each database provides rich, high-quality information, no single one provides unified data that is comprehensive across species, biological scales, and data types. Without a big-picture view of the data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) is an international consortium dedicated to providing computational tools that leverage a computational representation of phenotypic data for genotype-phenotype analysis, genomic diagnostics, and precision medicine on the basis of a large-scale platform of multimodal data that is deeply integrated across species and covering broad areas of disease.

evolutionary biology↗