Interactions and functionalities of the gut revealed by computational approaches

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.</p

Towards FAIRification of sensitive and fragmented rare disease patient data:challenges and solutions in European reference network registries

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 solution type for tackling the “implementation” challenges. CONCLUSION: This work shows that a dedicated team of FAIR data stewards is an asset for harmonising the various processes of making data FAIR in a large organisation with multiple stakeholders. Additionally, multi-levelled training activities are required to accommodate the diverse needs of the ERNs. Finally, the lessons learned from the experience of the FAIRification steward team described in this paper may help to increase FAIR awareness and provide insights into FAIRification challenges and solutions of rare disease registries. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13023-022-02558-5

Analysis of host-pathogen gene association networks reveals patient-specific response to streptococcal and polymicrobial necrotising soft tissue infections

Background: Necrotising 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. Methods: In 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. Results: NSTI 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. Conclusions: At 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 postulate differing modes of entry and immune evasion for different bacteria in relationship to the patients’ phenotype.publishedVersio

Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data

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

A Resource for Guiding Data Stewards to Make European Rare Disease Patient Registries FAIR

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

High-level integration of murine intestinal transcriptomics data highlights the importance of the complement system in mucosal homeostasis.

BACKGROUND: The mammalian intestine is a complex biological system that exhibits functional plasticity in its response to diverse stimuli to maintain homeostasis. To improve our understanding of this plasticity, we performed a high-level data integration of 14 whole-genome transcriptomics datasets from samples of intestinal mouse mucosa. We used the tool Centrality based Pathway Analysis (CePa), along with information from the Reactome database. RESULTS: The results show an integrated response of the mouse intestinal mucosa to challenges with agents introduced orally that were expected to perturb homeostasis. We observed that a common set of pathways respond to different stimuli, of which the most reactive was the Regulation of Complement Cascade pathway. Altered expression of the Regulation of Complement Cascade pathway was verified in mouse organoids challenged with different stimuli in vitro. CONCLUSIONS: Results of the integrated transcriptomics analysis and data driven experiment suggest an important role of epithelial production of complement and host complement defence factors in the maintenance of homeostasis

Dissenting opinion in Latvia

Bakalaura darbā aplūkots tiesnešu atsevišķo domu institūts, kurš Latvijas tiesību zinātnē ir maz pētīts tā iemesla dēļ, ka likumdevējs salīdzinoši nesen izšķīries par nepieciešamību reglamentēt šo institūtu normatīvajos aktos. Darba mērķis ir noskaidrot, vai šobrīd spēkā esošais regulējums nodrošina iespējami efektīvāko tiesneša atsevišķo domu institūta darbību. Autore darbā aplūko tiesību literatūrā paustos viedokļus par atsevišķo domu institūta būtību, tā saturu un iedalījumu, analizē atsevišķas Latvijas tiesiskā regulējuma īpatnības un izvērtēs šā institūta nozīmi. Darbā iegūtie rezultāti: autore piedāvā kritērijus, pēc kuriem klasificēt un sistematizēt atsevišķo domu institūtu, kā arī izvirza priekšlikumus iespējamiem atsevišķo domu institūta uzlabojumiem un attīstības virzieniem Latvijā. Atslēgas vārdi: tiesneša atsevišķās domas, tiesneša neatkarība, Satversmes tiesa, Augstākā tiesa, spriedums, lēmums.The Bachelor thesis discusses judges’ dissenting opinion. The legislator has made the decision to introduce this institute and has established the legal regulation in Latvian legal system comparatively recently, this is why no significant research has been done in this field. The objective of the thesis is to explore whether legal regulation provides the most effective functioning of the dissenting opinion institute. The author examines the ideas expressed in legal literature regarding the nature, content and classification of dissenting opinion, analyses some particularities of Latvian legal regulation and assesses the role of this institute. The results of the research: the author offers criteria for classification and systematisation of dissenting opinion, as well as provides the solutions to improve and develop dissenting opinion institute in Latvia. Key words: dissenting opinion, judicial independence, Constitutional Court, Supreme Court, judgment, decision

Corpus construction based on Ontological domain knowledge

The purpose of this thesis is to contribute a corpus for sentence level interpretation of biomedical language. The available corpora for the biomedical domain are small in terms of amount of text and predicates. Besides that these corpora are developed rather intuitively. In this effort which we call BioOntoFN, we created a corpus from the domain knowledge provided by an ontology. By doing this we believe that we can provide a rough set of rules to create corpora from ontologies. Besides that we also designed an annotation tool specifically for building our corpus. We built a corpus for biological transport events. The ontology we used is the piece of Gene Ontology pertaining to transport, the term transport GO: 0006810 and all of its child concepts, which could be called a sub-ontology. The annotation of the corpus follows the rules of FrameNet and the output is annotated text that is in an XML format similar to that of FrameNet. The text for the corpus is taken from abstracts of MEDLINE articles. The annotation tool is a GUI created using Java

Assessing resolvability, parsability, and consistency of RDF resources: a use case in rare diseases

Abstract Introduction Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement. Methods Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema. Results For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the ‘owl:ObjectProperty’ type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources. Conclusion We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources