LPS-induced inflammation potentiates dental pulp stem cell odontogenic differentiation through C5aR and p38.

AIM: Inflammation is a complex host response to harmful infection or injury, and it seems to play a crucial role in tissue regeneration both positively and negatively. We have previously demonstrated that the activation of the complement C5a pathway affects dentin-pulp regeneration. However, limited information is available to understand the role of the complement C5a system related to inflammation-mediated dentinogenesis. The aim of this study was to determine the role of complement C5a receptor (C5aR) in regulating lipopolysaccharide (LPS)-induced odontogenic differentiation of dental pulp stem cells (DPSCs). MATERIAL AND METHODS: Human DPSCs were subjected to LPS-stimulated odontogenic differentiation in dentinogenic media treated with the C5aR agonist and antagonist. A putative downstream pathway of the C5aR was examined using a p38 mitogen-activated protein kinase (p38) inhibitor (SB203580). RESULTS: Our data demonstrated that inflammation induced by the LPS treatment potentiated DPSC odontogenic differentiation and that this is C5aR dependent. C5aR signaling controlled the LPS-stimulated dentinogenesis by regulating the expression of odontogenic lineage markers like dentin sialophosphoprotein (DSPP) and dentin matrix protein 1 (DMP-1). Moreover, the LPS treatment increased the total p38, and the active form of p38 expression, and treatment with SB203580 abolished the LPS-induced DSPP and DMP-1 increase. CONCLUSIONS: These data suggest a significant role of C5aR and its putative downstream molecule p38 in the LPS-induced odontogenic DPSCs differentiation. This study highlights the regulatory pathway of complement C5aR/p38 and a possible therapeutic approach for improving the efficiency of dentin regeneration during inflammation.

MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images.

Increasingly, highly multiplexed tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is the lack of robust cell segmentation tools for tissue sections. We present Multiplexed Image Resegmentation of Internal Aberrant Membranes (MIRIAM) that combines (a) a pipeline for cell segmentation and quantification that incorporates machine learning-based pixel classification to define cellular compartments, (b) a novel method for extending incomplete cell membranes, and (c) a deep learning-based cell shape descriptor. Using human colonic adenomas as an example, we show that MIRIAM is superior to widely utilized segmentation methods and provides a pipeline that is broadly applicable to different imaging platforms and tissue types.

TRRUST: a reference database of human transcriptional regulatory interactions.

The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data, and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs.