Heimo Müller

Genetic background effects of keratin 8 and 18 in a DDC-induced hepatotoxicity and Mallory-Denk body formation mouse model

Keratin 8 (K8) and keratin 18 (K18) form the major hepatocyte cytoskeleton. We investigated the impact of genetic loss of either K8 or K18 on liver homeostasis under toxic stress with the hypothesis that K8 and K18 exert different functions. krt8−/− and krt18−/− mice crossed into the same 129-ola genetic background were treated by acute and chronic administration of 3,5-diethoxy-carbonyl-1,4-dihydrocollidine (DDC). In acutely DDC-intoxicated mice, macrovesicular steatosis was more pronounced in krt8−/− and krt18−/− compared with wild-type (wt) animals. Mallory-Denk bodies (MDBs) appeared in krt18−/− mice already at an early stage of intoxication in contrast to krt8−/− mice that did not display MDB formation when fed with DDC. Keratin-deficient mice displayed significantly lower numbers of apoptotic hepatocytes than wt animals. krt8−/−, krt18−/− and control mice displayed comparable cell proliferation rates. Chronically DDC-intoxicated krt18−/− and wt mice showed a similarly increased degree of steatohepatitis with hepatocyte ballooning and MDB formation. In krt8−/− mice, steatosis was less, ballooning, and MDBs were absent. krt18−/− mice developed MDBs whereas krt8−/− mice on the same genetic background did not, highlighting the significance of different structural properties of keratins. They are independent of the genetic background as an intrinsic factor. By contrast, toxicity effects may depend on the genetic background. krt8−/− and krt18−/− mice on the same genetic background show similar sensitivity to DDC intoxication and almost resemble wt animals regarding survival, degree of porphyria, liver-to-body weight ratio, serum bilirubin and liver enzyme levels. This stands in contrast to previous work where krt8−/− and krt18−/− mice on different genetic backgrounds were investigated.

Analysis of biomedical data with multilevel glyphs

BackgroundThis paper presents multilevel data glyphs optimized for the interactive knowledge discovery and visualization of large biomedical data sets. Data glyphs are three- dimensional objects defined by multiple levels of geometric descriptions (levels of detail) combined with a mapping of data attributes to graphical elements and methods, which specify their spatial position.MethodsIn the data mapping phase, which is done by a biomedical expert, meta information about the data attributes (scale, number of distinct values) are compared with the visual capabilities of the graphical elements in order to give a feedback to the user about the correctness of the variable mapping. The spatial arrangement of glyphs is done in a dimetric view, which leads to high data density, a simplified 3D navigation and avoids perspective distortion.ResultsWe show the usage of data glyphs in the disease analyser a visual analytics application for personalized medicine and provide an outlook to a biomedical web visualization scenario.ConclusionsData glyphs can be successfully applied in the disease analyser for the analysis of big medical data sets. Especially the automatic validation of the data mapping, selection of subgroups within histograms and the visual comparison of the value distributions were seen by experts as an important functionality.

Overexpression of eIF3a in Squamous Cell Carcinoma of the Oral Cavity and Its Putative Relation to Chemotherapy Response

The eukaryotic translation initiation factor eIF3a is one of the core subunits of the translation initiation complex eIF3, responsible for ribosomal subunit joining and mRNA recruitment to the ribosome. It is known to play an important role in general translation initiation as well as in the specific translational regulation of various gene products, among which many influence tumour development, progression, and the therapeutically important pathways of DNA damage repair. Therefore, beyond its role in protein synthesis, eIF3a is emerging as regulator in tumour pathogenesis and therapy response and, therefore, a potential tumor marker. By means of a tissue microarray (TMA) for histopathological and statistical assessment, we here show eIF3a expression in 103 cases of squamous cell carcinoma of the oral cavity (OSCC), representing tissues from 103 independent patients. A subset of the study cohort was treated with platinum based therapy. Our results show that the 170 kDa protein is upregulated in OSCC and correlates with good overall survival. Overexpressing tumors respond better to platinum-based chemotherapy, suggesting eIF3a as a putative predictive as well as prognostic tumor marker in OSCC.

State-of-the-Art and Future Challenges in the Integration of Biobank Catalogues

Biobanks are essential for the realization of P4-medicine, hence indispensable for smart health. One of the grand challenges in biobank research is to close the research cycle in such a way that all the data generated by one research study can be consistently associated to the original samples, therefore data and knowledge can be reused in other studies. A catalogue must provide the information hub connecting all relevant information sources. The key knowledge embedded in a biobank catalogue is the availability and quality of proper samples to perform a research project. Depending on the study type, the samples can reflect a healthy reference population, a cross sectional representation of a certain group of people (healthy or with various diseases) or a certain disease type or stage. To overview and compare collections from different catalogues, we introduce visual analytics techniques, especially glyph based visualization techniques, which were successfully applied for knowledge discovery of single biobank catalogues. In this paper, we describe the state-of-the art in the integration of biobank catalogues addressing the challenge of combining heterogeneous data sources in a unified and meaningful way, consequently enabling the discovery and visualization of data from different sources. Finally we present open questions both in data integration and visualization of unified catalogues and propose future research in data integration with a linked data approach and the fusion of multi level glyph and network visualization.

Interactive Patient Records

Researches in humanities and social sciences indicate that people using more visual information would be more creative and benefit from the power of the human mind to a higher degree. Therefore information should not only be recorded as static text. It is rather conceivable to construct new visual languages that are not based on letters but on icons. We developed a set of medical symbols that can be used in visual representations of medical records and that may also be used to build complex medical statements. Each of the basic symbols is available in 4 abstraction levels, which can be combined in a ‘visual sentence’. Complex visual representations are recognized more easily than simplified versions, while on the other hand the simple version can be perceived and memorized in a faster way than complex signs. To prove this hypothesis and to evaluate the perception of the symbols in a medical context we created a web-based evaluation tool covering two perception tests. With the help of the symbol library we developed a visual language for patient records and an interactive browser.

Connecting Genes with Diseases

This paper presents a visual data mining approach using the combination of clinical data, pathways and gene-expression data. The visual exploration of medical data using pathways to navigate and filter the data allows a more systematic and efficient investigation of problems in modern life science. A multiplicity of hypothesis can be evaluated in the same period of time, enabling a much better exploitation of the data. We present a system for data preprocessing and automatic classification, a set of visualization views and finally the integration in the Caleydo visualization framework, which enables the “coupling” of genetic and a broad spectrum of clinical data. With the help of the Caleydo framework the medical expert can identify connections between genetic parameters, patient subgroups, and drug responses.

From the evaluation of existing solutions to an all-inclusive package for biobanks

The domain of biobanking has gone through many stages and as a result there are a wide range of commercial and open source software solutions available. The utilization of these software tools requires different levels of domain and technical skills for installation, configuration and ultimate us of these biobank software tools. To compound this complexity the biobanking community are required to work together in order to share knowledge and jointly build solutions to underpin the research infrastructure. We have evaluated the available tools, described them in a catalogue (BiobankApps) and made a selection of tools available to biobanks in a reference toolbox (BIBBOX) that are use-case driven. In the BiobankApps tool catalogue, both commercial and open source software solutions related to the biobanking domain are included, classified and evaluated. The evaluation covers: 1) “user review” by an authenticated user 2) domain expert: quick analysis by BBMRI members and 3) domain expert: detailed analysis and test installation with real world data. The evaluation is paired with a survey across the more “advanced” (from a technology perspective) biobanks to investigate what tools are currently used and summarises known benefits/drawbacks of the respective packages. In the second step we recommend tools for specific use cases, and install, configure and connect these in the BIBBOX framework. This service also builds on the existing work in the United Kingdom in seeking to establish the motivations for different stakeholders to become involved and therefore assisting in prioritising the use-cases based on the level of need and support within the research community. All tools associated to a use-case are available as BIBBOX applications (technically this is achieved by docker containers), which are integrated in the BIBBOX framework with central identification and user management. In future work we plan to share the acquired knowledge with other networks, develop an Application Programmable Interface (API) for the exchange of metadata with other tool catalogues and work on an ontology for the evaluation of biobank software.

Machine Learning and Knowledge Extraction in Digital Pathology Needs an Integrative Approach

During the last decade pathology has benefited from the rapid progress of image digitizing technologies, which led to the development of scanners, capable to produce so-called Whole Slide images (WSI) which can be explored by a pathologist on a computer screen comparable to the conventional microscope and can be used for diagnostics, research, archiving and also education and training. Digital pathology is not just the transformation of the classical microscopic analysis of histological slides by pathologists to just a digital visualization. It is a disruptive innovation that will dramatically change medical work-flows in the coming years and help to foster personalized medicine. Really powerful gets a pathologist if she/he is augmented by machine learning, e.g. by support vector machines, random forests and deep learning. The ultimate benefit of digital pathology is to enable to learn, to extract knowledge and to make predictions from a combination of heterogenous data, i.e. the histological image, the patient history and the *omics data. These challenges call for integrated/integrative machine learning approach fostering transparency, trust, acceptance and the ability to explain step-by-step why a decision has been made.

Adaptive Visual Symbols for Personal Health Records

As a hub of information controlled by the patient, personal health records (PHR) collect information from the patient medical history including a wide variety of data sources as patients observations, lab results, clinical findings and in the future maybe even personal genetic data and automatic recordings from monitoring devices. This development will on the one hand make health care more personalized and user controlled but on the other hand also overloads consumers with a huge amount of data. To address this issue we developed a framework for adaptive visual symbols (AVS). An AVS can adapt its appearance and level of detail during the communication process. Finally we demonstrate the AVS principle for the visualization of personal health records.

Design, User Experience and Usability Requirements for NGS Workflows in Clinical Applications

Next-generation sequencing (NGS) has transformed genomic research by decreasing the cost of sequencing and increasing the throughput. Today, translation of NGS technology to (cancer) diagnostics and therapeutics is widely anticipated. In this paper we give an overview of design, user experience and usability (DUXU) requirements for NGS applications with a special focus on clinical and diagnostic settings.