SOX9 Governs Gastric Mucous Neck Cell Identity and Is Required for Injury-Induced Metaplasia.

BACKGROUND & AIMS: Acute and chronic gastric injury induces alterations in differentiation within the corpus of the stomach called pyloric metaplasia. Pyloric metaplasia is characterized by the death of parietal cells and reprogramming of mitotically quiescent zymogenic chief cells into proliferative, mucin-rich spasmolytic polypeptide-expressing metaplasia (SPEM) cells. Overall, pyloric metaplastic units show increased proliferation and specific expansion of mucous lineages, both by proliferation of normal mucous neck cells and recruitment of SPEM cells. Here, we identify Sox9 as a potential gene of interest in the regulation of mucous neck and SPEM cell identity in the stomach. METHODS: We used immunostaining and electron microscopy to characterize the expression pattern of SRY-box transcription factor 9 (SOX9) during murine gastric development, homeostasis, and injury in homeostasis, after genetic deletion of Sox9 and after targeted genetic misexpression of Sox9 in the gastric epithelium and chief cells. RESULTS: SOX9 is expressed in all early gastric progenitors and strongly expressed in mature mucous neck cells with minor expression in the other principal gastric lineages during adult homeostasis. After injury, strong SOX9 expression was induced in the neck and base of corpus units in SPEM cells. Adult corpus units derived from Sox9-deficient gastric progenitors lacked normal mucous neck cells. Misexpression of Sox9 during postnatal development and adult homeostasis expanded mucous gene expression throughout corpus units including within the chief cell zone in the base. Sox9 deletion specifically in chief cells blunts their reprogramming into SPEM. CONCLUSIONS: Sox9 is a master regulator of mucous neck cell differentiation during gastric development. Sox9 also is required for chief cells to fully reprogram into SPEM after injury.

Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports.

BACKGROUND: Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. METHODS: In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). RESULTS: Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. CONCLUSION: The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine.

An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content.

BACKGROUND: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. METHODS: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. RESULTS: Ontologies for "food and nutrition" (n = 37), "disease and specific population" (n = 100), "data description" (n = 21), "research description" (n = 35), and "supplementary (meta) data description" (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. CONCLUSION: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology.

REDESIGN: RDF-based Differential Signaling Framework for Precision Medicine Analytics.

Pathway-based analysis holds promise to be instrumental in precision and personalized medicine analytics. However, the majority of pathway-based analysis methods utilize "fixed" or "rigid" data sets that limit their ability to account for complex biological inter-dependencies. Here, we present REDESIGN: RDF-based Differential Signaling Pathway informatics framework. The distinctive feature of the REDESIGN is that it is designed to run on "flexible" ontology-enabled data sets of curated signal transduction pathway maps to uncover high explanatory differential pathway mechanisms on gene-to-gene level. The experiments on two morphoproteomic cases demonstrated REDESIGN's capability to generate actionable hypotheses in precision/personalized medicine analytics.

RDF SKETCH MAPS - KNOWLEDGE COMPLEXITY REDUCTION FOR PRECISION MEDICINE ANALYTICS.

Realization of precision medicine ideas requires significant research effort to be able to spot subtle differences in complex diseases at the molecular level to develop personalized therapies. It is especially important in many cases of highly heterogeneous cancers. Precision diagnostics and therapeutics of such diseases demands interrogation of vast amounts of biological knowledge coupled with novel analytic methodologies. For instance, pathway-based approaches can shed light on the way tumorigenesis takes place in individual patient cases and pinpoint to novel drug targets. However, comprehensive analysis of hundreds of pathways and thousands of genes creates a combinatorial explosion, that is challenging for medical practitioners to handle at the point of care. Here we extend our previous work on mapping clinical omics data to curated Resource Description Framework (RDF) knowledge bases to derive influence diagrams of interrelationships of biomarker proteins, diseases and signal transduction pathways for personalized theranostics. We present RDF Sketch Maps - a computational method to reduce knowledge complexity for precision medicine analytics. The method of RDF Sketch Maps is inspired by the way a sketch artist conveys only important visual information and discards other unnecessary details. In our case, we compute and retain only so-called RDF Edges - places with highly important diagnostic and therapeutic information. To do this we utilize 35 maps of human signal transduction pathways by transforming 300 KEGG maps into highly processable RDF knowledge base. We have demonstrated potential clinical utility of RDF Sketch Maps in hematopoietic cancers, including analysis of pathways associated with Hairy Cell Leukemia (HCL) and Chronic Myeloid Leukemia (CML) where we achieved up to 20-fold reduction in the number of biological entities to be analyzed, while retaining most likely important entities. In experiments with pathways associated with HCL a generated RDF Sketch Map of the top 30% paths retained important information about signaling cascades leading to activation of proto-oncogene BRAF, which is usually associated with a different cancer, melanoma. Recent reports of successful treatments of HCL patients by the BRAF-targeted drug vemurafenib support the validity of the RDF Sketch Maps findings. We therefore believe that RDF Sketch Maps will be invaluable for hypothesis generation for precision diagnostics and therapeutics as well as drug repurposing studies.