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Objective: This paper reports on the development of a dynamic data management planning questionnaire to guide data stewards of the European Reference Network (ERN) rare disease patient registries to make their data findable, accessible, interoperable, and reusable (FAIR). As part of this work, the questionnaire was validated through expert review and aligned with existing resources on rare diseases and FAIR data management. Materials and Methods: The questionnaire was developed for the Data Stewardship Wizard, a tool for data management planning. Knowledge sources on FAIR data, ERN patient registries, and data management were used to compose questions. Ten domain experts validated the questionnaire. The topics in the questionnaire were aligned with existing knowledge bases. Results: A total of 57 questions were included in the questionnaire. Twenty-three references to the FAIR Cookbook and Research Data Management toolkit for Life Sciences were added. Expert validation provided a total of 166 comments on content, structure, and software-related issues. A public instance of the Data Stewardship Wizard was deployed for use by data stewards of ERN patient registries. Discussion: The questionnaire addresses issues that ERNs encounter when making their registries FAIR and follows the implementation choices made by the European rare disease community. A challenging task for future research is to extend the questionnaire to other types of registries and to validate with users. Conclusion: This smart questionnaire is the first model created for the Data Stewardship Wizard that helps ERN patient registries with making their data FAIR. It will assist data stewards in aligning their efforts and providing guidance on FAIR data.

Background: Data sharing amongst existing Rare Disease (RD) registries, even though being a process that presents multiple barriers, would enrich and ease research, as well as facilitate interoperability between the registries themselves. Methods: To understand their preferences on sharing data, we surveyed 24 European Reference Networks (ERNs) from the RD Domain. Results: The answers show that most ERNs are willing to share a set of Common Data Elements for free with authenticated users at an aggregated or pseudonymized level the moment the data is collected. The one exception is the industry sector, to which ERNs prefer to ask for a fee. Objective: Our aim is to create a reference for how most RD registries are willing to share their data, improving the ability of other stakeholders to make informed decisions to make their data interoperable.

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Background The European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all RD registries. Interoperability, however, requires decisions beyond data elements - including data models, formats, and semantics. Within the European Joint Programme on Rare Diseases (EJP RD), we aim to further the goals of the EU RD Platform by generating reusable RD semantic model templates that follow the FAIR Data Principles. Results Through a team-based iterative approach, we created semantically grounded models to represent each of the CDEs, using the SemanticScience Integrated Ontology as the core framework for representing the entities and their relationships. Within that framework, we mapped the concepts represented in the CDEs, and their possible values, into domain ontologies such as the Orphanet Rare Disease Ontology, Human Phenotype Ontology and National Cancer Institute Thesaurus. Finally, we created an exemplar, reusable ETL pipeline that we will be deploying over these non-coordinating data repositories to assist them in creating model-compliant FAIR data without requiring site-specific coding nor expertise in Linked Data or FAIR. Conclusions Within the EJP RD project, we determined that creating reusable, expert-designed templates reduced or eliminated the requirement for our participating biomedical domain experts and rare disease data hosts to understand OWL semantics. This enabled them to publish highly expressive FAIR data using tools and approaches that were already familiar to them.

Introduction: Rare disease patient data are typically sensitive, present in multiple registries controlled by different custodians, and non-interoperable. Making these data Findable, Accessible, Interoperable, and Reusable (FAIR) for humans and machines at source enables federated discovery and analysis across data custodians. This facilitates accurate diagnosis, optimal clinical management, and personalised treatments. In Europe, twenty-four European Reference Networks (ERNs) work on rare disease registries in different clinical domains. The process and the implementation choices for making data FAIR (‘FAIRification’) differ among ERN registries. For example, registries use different software systems and are subject to different legal regulations. To support the ERNs in making informed decisions and to harmonise FAIRification, the FAIRification steward team was established to work as liaisons between ERNs and researchers from the European Joint Programme on Rare Diseases. Results: The FAIRification steward team inventoried the FAIRification challenges of the ERN registries and proposed solutions collectively with involved stakeholders to address them. Ninety-eight FAIRification challenges from 24 ERNs’ registries were collected and categorised into “training” (31), “community” (9), “modelling” (12), “implementation” (26), and “legal” (20). After curating and aggregating highly similar challenges, 41 unique FAIRification challenges remained. The two categories with the most challenges were “training” (15) and “implementation” (9), followed by “community” (7), and then “modelling” (5) and “legal” (5). To address all challenges, eleven types of solutions were proposed. Among them, the provision of guidelines and the organisation of training activities resolved the “training” challenges, which ranged from less-technical “coffee-rounds” to technical workshops, from informal FAIR Games to formal hackathons. Obtaining implementation support from technical experts was the

BackgroundNecrotising soft tissue infections (NSTIs) are rapidly progressing bacterial infections usually caused by either several pathogens in unison (polymicrobial infections) or Streptococcus pyogenes (mono-microbial infection). These infections are rare and are associated with high mortality rates. However, the underlying pathogenic mechanisms in this heterogeneous group remain elusive.MethodsIn this study, we built interactomes at both the population and individual levels consisting of host-pathogen interactions inferred from dual RNA-Seq gene transcriptomic profiles of the biopsies from NSTI patients.ResultsNSTI type-specific responses in the host were uncovered. The S. pyogenes mono-microbial subnetwork was enriched with host genes annotated with involved in cytokine production and regulation of response to stress. The polymicrobial network consisted of several significant associations between different species (S. pyogenes, Porphyromonas asaccharolytica and Escherichia coli) and host genes. The host genes associated with S. pyogenes in this subnetwork were characterised by cellular response to cytokines. We further found several virulence factors including hyaluronan synthase, Sic1, Isp, SagF, SagG, ScfAB-operon, Fba and genes upstream and downstream of EndoS along with bacterial housekeeping genes interacting with the human stress and immune response in various subnetworks between host and pathogen.ConclusionsAt the population level, we found aetiology-dependent responses showing the potential modes of entry and immune evasion strategies employed by S. pyogenes, congruent with general cellular processes such as differentiation and proliferation. After stratifying the patients based on the subject-specific networks to study the patient-specific response, we observed different patient groups with different collagens, cytoskeleton and actin monomers in association with virulence factors, immunogenic proteins and housekeeping genes which we utilised to postulat

Mycoplasma hyopneumoniae (M. hyopneumoniae) causes enzootic pneumonia in pigs but it is still largely unknown which host-pathogen interactions enable persistent infection and cause disease. In this study, we analyzed the host and bacterial transcriptomes during infection using RNA sequencing. Comparison of the transcriptome of lung lesion tissue from infected pigs with lung tissue from non-infected animals, identified 424 differentially expressed genes (FDR < 0.01 and fold change > 1.5LOG2). These genes were part of the following major pathways of the immune system: interleukin signaling (type 4, 10, 13, and 18), regulation of Toll-like receptors by endogenous ligand and activation of C3 and C5 in the complement system. Besides analyzing the lung transcriptome, a sampling protocol was developed to obtain enough bacterial mRNA from infected lung tissue for RNA sequencing. This was done by flushing infected lobes in the lung, and subsequently enriching for bacterial RNA. On average, 2.2 million bacterial reads were obtained per biological replicate to analyze the bacterial in vivo transcriptome. We compared the in vivo bacterial transcriptome with the transcriptome of bacteria grown in vitro and identified 22 up-regulated and 30 down-regulated genes (FDR < 0.01 and fold change > 2LOG2). Six out of seven genes in the operon encoding the mycoplasma specific F1-like ATPase (MHP_RS02445-MHP_RS02475) and all genes in the operon MHP_RS01965-MHP_RS01990 with functions related to nucleotide metabolism, spermidine transport and glycerol-3-phoshate transport were up-regulated in vivo. Down-regulated in vivo were genes related to glycerol uptake, cilium adhesion (P102), cell division and myo-inositol metabolism. In addition to providing a novel method to isolate bacterial mRNA from infected lung, this study provided insights into changes in gene expression during infection, which could help development of novel treatment strategies against enzootic pneumonia caused by M. hyopneu

In this project we aimed to perform dual mRNA sequencing on M. hyopneumoniae infection using whole tissue and lung flush RNA extracts. To assess bacterial transcriptome changes during infection, we developed a sampling method to isolate sufficient quantities of bacterial RNA from lung flushes and compared the transcriptome of M. hyopneumoniae during in vivo growth to culture grown (in vitro) cells. We analyzed the host response to infection by transcriptome comparison of infected tissue mRNA with non-infected tissue mRNA. In this project unique insight was gained in the bacterial in vivo transcriptome which could be used to further develop novel vaccine candidates or treatment strategies., In this project we aimed to perform dual mRNA sequencing on M. hyopneumoniae infection using whole tissue and lung flush RNA extracts. To assess bacterial transcriptome changes during infection, we developed a sampling method to isolate sufficient quantities of bacterial RNA from lung flushes and compared the transcriptome of M. hyopneumoniae during in vivo growth to culture grown (in vitro) cells. We analyzed the host response to infection by transcriptome comparison of infected tissue mRNA with non-infected tissue mRNA. In this project unique insight was gained in the bacterial in vivo transcriptome which could be used to further develop novel vaccine candidates or treatment strategies.

The gastrointestinal tract is subject of much research for its role in an organism’s health owing to its role as gatekeeper. The tissue acts as a barrier to keep out harmful substances like pathogens and toxins while absorbing nutrients that arise from the digestion of dietary components in in the lumen. There is a large population of microbiota that plays an important role in the functioning of the gut. All these sub-systems of the gastrointestinal tract contribute to the normal functioning of the gut. Due to its various functionalities, the gut is able to respond to different types of stimuli and bring the system back to homeostasis after perturbations. The work done in this thesis uses several bioinformatic tools to improve our understanding of the functioning of the gut. This was achieved with data from model animals, mice and pigs which were subjected to changing environments before their gastrointestinal response was measured. Different types of stimuli were studied (eg, antibiotic exposure, changing diets and infection with pathogens) in order to understand the response of the gut to varying environments. This data was analysed using different data integration techniques that provide a holistic view of the gut response. Vertical data integration techniques look for associations between different types of ~omics data to highlight possible interactions between the measured variables. Lateral integration techniques allow the study of one type of ~omics data over several time points or several experimental conditions. Using these techniques, we show proof of interactions between different sub-systems of the gut and the functional plasticity of the gut. Of the several hypotheses generated in this thesis we have validated several using existing literature and one using an in-vitro system. Further validation of these hypotheses will increase understanding of the responses of the gut and the interactions involved.

The genotype and external phenotype of organisms are linked by so-called internal phenotypes which are influenced by environmental conditions. In this study, we used five existing -omics datasets representing five different layers of internal phenotypes, which were simultaneously measured in dietarily perturbed mice. We performed 10 pair-wise correlation analyses verified with a null model built from randomized data. Subsequently, the inferred networks were merged and literature mined for co-occurrences of identified linked nodes. Densely connected internal phenotypes emerged. Forty-five nodes have links with all other data-types and we denote them "connectivity hubs." In literature, we found proof of 6% of the 577 connections, suggesting a biological meaning for the observed correlations. The observed connectivities between metabolite and cytokines hubs showed higher numbers of literature hits as compared to the number of literature hits on the connectivities between the microbiota and gene expression internal phenotypes. We conclude that multi-level integrated networks may help to generate hypotheses and to design experiments aiming to further close the gap between genotype and phenotype. We describe and/or hypothesize on the biological relevance of four identified multi-level connectivity hubs.

Dietary protein sources can have profound effects on host-microbe interactions in the gut that are critically important for immune resilience. However more knowledge is needed to assess the impact of different protein sources on gut and animal health. Thirty-six wildtype male C57BL/6J mice of 35 d age (n = 6/group; mean ± SEM body weight 21.9 ± 0.25 g) were randomly assigned to groups fed for four weeks with semi synthetic diets prepared with one of the following protein sources containing (300 g/kg as fed basis): soybean meal (SBM), casein, partially delactosed whey powder, spray dried plasma protein, wheat gluten meal and yellow meal worm. At the end of the experiment, mice were sacrificed to collect ileal tissue to acquire gene expression data, and mammalian (mechanistic) target of rapamycin (mTOR) activity, ileal digesta to study changes in microbiota and serum to measure cytokines and chemokines. By genome-wide transcriptome analysis, we identified fourteen high level regulatory genes that are strongly affected in SBM-fed mice compared to the other experimental groups. They mostly related to the mTOR pathway. In addition, an increased (P < 0.05) concentration of granulocyte colony-stimulating factor was observed in serum of SBM-fed mice compared to other dietary groups. Moreover, by 16S rRNA sequencing, we observed that SBM-fed mice had higher (P < 0.05) abundances of Bacteroidales family S24-7, compared to the other dietary groups. We showed that measurements of genome-wide expression and microbiota composition in the mouse ileum reveal divergent responses to diets containing different protein sources, in particular for a diet based on SBM.

Semantic Role Labeling plays a key role in many text mining applications. The development of SRL systems for the biomedical domain is frustrated by the lack of large domain specific corpora that are labeled with semantic roles. In this paper we proposed a method for building corpus that are labeled with semantic roles for the domain of biomedicine. The method is based on the theory of frame semantics, and uses domain knowledge provided by ontologies. By using the method, we have built a corpus for transport events strictly following the domain knowledge provided by GO biological process ontology. We compared one of our frames to a BioFrameNet frame. We also examined the gaps between the semantic classification of the target words in this domain-specific corpus and in FrameNet and PropBank/VerbNet data. The successful corpus construction demonstrates that ontologies, as a formal representation of domain knowledge, can instruct us and ease all the tasks in building this kind of corpus. Furthermore, ontological domain knowledge leads to well-defined semantics exposed on the corpus, which will be very valuable in text mining applications.

Semantic Role Labeling (SRL) plays a key role in many NLP applications. The development of SRL systems for the biomedical domain is frustrated by the lack of large domain-specific corpora that are labeled with semantic roles. Corpus development has been very expensive and time-consuming. In this paper we propose a method for building frame-based corpus on the basis of domain knowledge provided by ontologies. We believe that ontologies, as a structured and semantic representation of domain knowledge, can instruct and ease the tasks in building the corpora. In the paper we present a corpus built by using the method. We compared it to BioFrameNet, and examined the gaps between the semantic classification of the target words in the domain-specific corpus and in FrameNet and Prop-Bank/VerbNet.