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.

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.