Rationalized development of a campus-wide cell line dataset for implementation in the biobank LIMS system at Bioresource center Ghent

The Bioresource center Ghent is the central hospital-integrated biobank of Ghent University Hospital. Our mission is to facilitate translational biomedical research by collecting, storing and providing high quality biospecimens to researchers. Several of our biobank partners store large amounts of cell lines. As cell lines are highly important both in basic research and preclinical screening phases, good annotation, authentication, and quality of these cell lines is pivotal in translational biomedical science. A Biobank Information Management System (BIMS) was implemented as sample and data management system for human bodily material. The samples are annotated by the use of defined datasets, based on the BRISQ (Biospecimen Reporting for Improved Study Quality) and Minimum Information About Biobank data Sharing (MIABIS) guidelines completed with SPREC (Standard PREanalytical Coding) information. However, the defined dataset for human bodily material is not ideal to capture the specific cell line data. Therefore, we set out to develop a rationalized cell line dataset. Through comparison of different datasets of online cell banks (human, animal, and stem cell), we established an extended cell line dataset of 156 data fields that was further analyzed until a smaller dataset-the survey dataset of 54 data fields-was obtained. The survey dataset was spread throughout our campus to all cell line users to rationalize the fields of the dataset and their potential use. Analysis of the survey data revealed only small differences in preferences in data fields between human, animal, and stem cell lines. Hence, one essential dataset for human, animal and stem cell lines was compiled consisting of 33 data fields. The essential dataset was prepared for implementation in our BIMS system. Good Clinical Data Management Practices formed the basis of our decisions in the implementation phase. Known standards, reference lists and ontologies (such as ICD-10-CM, animal taxonomy, cell line ontology...) were considered. The semantics of the data fields were clearly defined, enhancing the data quality of the stored cell lines. Therefore, we created an essential cell line dataset with defined data fields, useable for multiple cell line users

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

What Pediatricians Should Know Before Studying Gut Microbiota

Billions of microorganisms, or "microbiota", inhabit the gut and affect its homeostasis, influencing, and sometimes causing if altered, a multitude of diseases. The genomes of the microbes that form the gut ecosystem should be summed to the human genome to form the hologenome due to their influence on human physiology; hence the term "microbiome" is commonly used to refer to the genetic make-up and gene-gene interactions of microbes. This review attempts to provide insight into this recently discovered vital organ of the human body, which has yet to be fully explored. We herein discuss the rhythm and shaping of the microbiome at birth and during the first years leading up to adolescence. Furthermore, important issues to consider for conducting a reliable microbiome study including study design, inclusion/exclusion criteria, sample collection, storage, and variability of different sampling methods as well as the basic terminology of molecular approaches, data analysis, and clinical interpretation of results are addressed. This basic knowledge aims to provide the pediatricians with a key tool to avoid data dispersion and pitfalls during child microbiota study

APPLICATIONS OF MACHINE LEARNING IN MICROBIAL FORENSICS

Microbial ecosystems are complex, with hundreds of members interacting with each other and the environment. The intricate and hidden behaviors underlying these interactions make research questions challenging – but can be better understood through machine learning. However, most machine learning that is used in microbiome work is a black box form of investigation, where accurate predictions can be made, but the inner logic behind what is driving prediction is hidden behind nontransparent layers of complexity. Accordingly, the goal of this dissertation is to provide an interpretable and in-depth machine learning approach to investigate microbial biogeography and to use micro-organisms as novel tools to detect geospatial location and object provenance (previous known origin). These contributions follow with a framework that allows extraction of interpretable metrics and actionable insights from microbiome-based machine learning models. The first part of this work provides an overview of machine learning in the context of microbial ecology, human microbiome studies and environmental monitoring – outlining common practice and shortcomings. The second part of this work demonstrates a field study to demonstrate how machine learning can be used to characterize patterns in microbial biogeography globally – using microbes from ports located around the world. The third part of this work studies the persistence and stability of natural microbial communities from the environment that have colonized objects (vessels) and stay attached as they travel through the water. Finally, the last part of this dissertation provides a robust framework for investigating the microbiome. This framework provides a reasonable understanding of the data being used in microbiome-based machine learning and allows researchers to better apprehend and interpret results. Together, these extensive experiments assist an understanding of how to carry an in-silico design that characterizes candidate microbial biomarkers from real world settings to a rapid, field deployable diagnostic assay. The work presented here provides evidence for the use of microbial forensics as a toolkit to expand our basic understanding of microbial biogeography, microbial community stability and persistence in complex systems, and the ability of machine learning to be applied to downstream molecular detection platforms for rapid and accurate detection

Methods to discover and validate biofluid-based biomarkers in neurodegenerative dementias

Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids, or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer's disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluid-based biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluid-based biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective

Development and application of methodologies and infrastructures for cancer genome analysis within Personalized Medicine

[eng] Next-generation sequencing (NGS) has revolutionized biomedical sciences, especially in the area of cancer. It has nourished genomic research with extensive collections of sequenced genomes that are investigated to untangle the molecular bases of disease, as well as to identify potential targets for the design of new treatments. To exploit all this information, several initiatives have emerged worldwide, among which the Pan-Cancer project of the ICGC (International Cancer Genome Consortium) stands out. This project has jointly analyzed thousands of tumor genomes of different cancer types in order to elucidate the molecular bases of the origin and progression of cancer. To accomplish this task, new emerging technologies, including virtualization systems such as virtual machines or software containers, were used and had to be adapted to various computing centers. The portability of this system to the supercomputing infrastructure of the BSC (Barcelona Supercomputing Center) has been carried out during the first phase of the thesis. In parallel, other projects promote the application of genomics discoveries into the clinics. This is the case of MedPerCan, a national initiative to design a pilot project for the implementation of personalized medicine in oncology in Catalonia. In this context, we have centered our efforts on the methodological side, focusing on the detection and characterization of somatic variants in tumors. This step is a challenging action, due to the heterogeneity of the different methods, and an essential part, as it lays at the basis of all downstream analyses. On top of the methodological section of the thesis, we got into the biological interpretation of the results to study the evolution of chronic lymphocytic leukemia (CLL) in a close collaboration with the group of Dr. Elías Campo from the Hospital Clínic/IDIBAPS. In the first study, we have focused on the Richter transformation (RT), a transformation of CLL into a high-grade lymphoma that leads to a very poor prognosis and with unmet clinical needs. We found that RT has greater genomic, epigenomic and transcriptomic complexity than CLL. Its genome may reflect the imprint of therapies that the patients received prior to RT, indicating the presence of cells exposed to these mutagenic treatments which later expand giving rise to the clinical manifestation of the disease. Multiple NGS- based techniques, including whole-genome sequencing and single-cell DNA and RNA sequencing, among others, confirmed the pre-existence of cells with the RT characteristics years before their manifestation, up to the time of CLL diagnosis. The transcriptomic profile of RT is remarkably different from that of CLL. Of particular importance is the overexpression of the OXPHOS pathway, which could be used as a therapeutic vulnerability. Finally, in a second study, the analysis of a case of CLL in a young adult, based on whole genome and single-cell sequencing at different times of the disease, revealed that the founder clone of CLL did not present any somatic driver mutations and was characterized by germline variants in ATM, suggesting its role in the origin of the disease, and highlighting the possible contribution of germline variants or other non-genetic mechanisms in the initiation of CLL