Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients

Systematic Postoperative Assessment of a Minimally-Invasive Sheep Model for the Treatment of Osteochondral Defects

To assess the clinical course of a sheep stifle joint model for osteochondral (OC) defects, medial femoral condyles (MFC) were exposed without patella luxation using medial parapatellar skin (3–4 cm) and deep incisions (2–3 cm). Two defects (7 mm diameter; 10 mm depth; OC punch) were left empty or refilled with osteochondral autologous transplantation cylinders (OATS) and explanted after six weeks. Incision-to-suture time, anesthesia time, and postoperative wound or impairment scores were compared to those in sham-operated animals. Implant performance was assessed by X-ray, micro-computed tomography, histology, and immunohistology (collagens 1, 2; aggrecan). There were no surgery-related infections or patellar luxations. Operation, anesthesia, and time to complete stand were short (0.5, 1.4, and 1.5 h, respectively). The wound trauma score was low (0.4 of maximally 4; day 7). Empty-defect and OATS animals reached an impairment score of 0 significantly later than sham animals (7.4 and 4.0 days, respectively, versus 1.5 days). Empty defects showed incomplete healing and dedifferentiation/heterotopic differentiation; OATS-filled defects displayed advanced bone healing with remaining cartilage gaps and orthotopic expression of bone and cartilage markers. Minimally-invasive, medial parapatellar surgery of OC defects on the sheep MFC allows rapid and low-trauma recovery and appears well-suited for implant testing.

Effects of rheumatoid arthritis associated transcriptional changes on osteoclast differentiation network in the synovium

Background

Osteoclast differentiation in the inflamed synovium of rheumatoid arthritis (RA) affected joints leads to the formation of bone lesions. Reconstruction and analysis of protein interaction networks underlying specific disease phenotypes are essential for designing therapeutic interventions. In this study, we have created a network that captures signal flow leading to osteoclast differentiation. Based on transcriptome analysis, we have indicated the potential mechanisms responsible for the phenotype in the RA affected synovium.

Method

We collected information on gene expression, pathways and protein interactions related to RA from literature and databases namely Gene Expression Omnibus, Kyoto Encyclopedia of Genes and Genomes pathway and STRING. Based on these information, we created a network for the differentiation of osteoclasts. We identified the differentially regulated network genes and reported the signaling that are responsible for the process in the RA affected synovium.

Result

Our network reveals the mechanisms underlying the activation of the neutrophil cytosolic factor complex in connection to osteoclastogenesis in RA. Additionally, the study reports the predominance of the canonical pathway of NF-κB activation in the diseased synovium. The network also confirms that the upregulation of T cell receptor signaling and downregulation of transforming growth factor beta signaling pathway favor osteoclastogenesis in RA. To the best of our knowledge, this is the first comprehensive protein–protein interaction network describing RA driven osteoclastogenesis in the synovium.

Discussion

This study provides information that can be used to build models of the signal flow involved in the process of osteoclast differentiation. The models can further be used to design therapies to ameliorate bone destruction in the RA affected joints.

Community structure informs species geographic distributions

Understanding what determines species’ geographic distributions is crucial for assessing global change threats to biodiversity. Measuring limits on distributions is usually, and necessarily, done with data at large geographic extents and coarse spatial resolution. However, survival of individuals is determined by processes that happen at small spatial scales. The relative abundance of coexisting species (i.e. ‘community structure’) reflects assembly processes occurring at small scales, and are often available for relatively extensive areas, so could be useful for explaining species distributions. We demonstrate that Bayesian Network Inference (BNI) can overcome several challenges to including community structure into studies of species distributions, despite having been little used to date. We hypothesized that the relative abundance of coexisting species can improve predictions of species distributions. In 1570 assemblages of 68 Mediterranean woody plant species we used BNI to incorporate community structure into Species Distribution Models (SDMs), alongside environmental information. Information on species associations improved SDM predictions of community structure and species distributions moderately, though for some habitat specialists the deviance explained increased by up to 15%. We demonstrate that most species associations (95%) were positive and occurred between species with ecologically similar traits. This suggests that SDM improvement could be because species co-occurrences are a proxy for local ecological processes. Our study shows that Bayesian Networks, when interpreted carefully, can be used to include local conditions into measurements of species’ large-scale distributions, and this information can improve the predictions of species distributions.

Computational modeling of cardiac fibroblasts and fibrosis

Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.

How to Predict Molecular Interactions between Species?

Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species interactions, we recently discussed dual RNA-seq experiments for host-pathogen interactions and extended the applied tool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use of measurement variances in the algorithmic procedure and accepts gene expression time series data with missing values. Additionally, we tested multiple modeling scenarios regarding the stimuli functions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015) and put it into a broader context. We review various studies making use of the dual transcriptomics approach to investigate the molecular basis of interacting species. Besides the application to host-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic and commensalistic interactions. Furthermore, we give a short introduction into additional approaches for the prediction of gene regulatory networks and discuss their application to dual transcriptomics data. We conclude that the application of network inference on dual-transcriptomics data is a promising approach to predict molecular inter-species interactions.

Candida albicans infection leads to barrier breakdown and a MAPK/NF-κB mediated stress response in the intestinal epithelial cell line C2BBe1

Intestinal epithelial cells (IEC) form a tight barrier to the gut lumen. Paracellular permeability of the intestinal barrier is regulated by tight junction proteins and can be modulated by microorganisms and other stimuli. The polymorphic fungus Candida albicans, a frequent commensal of the human mucosa, has the capacity of traversing this barrier and establishing systemic disease within the host. Infection of polarized C2BBe1 IEC with wild-type C. albicans led to a transient increase of transepithelial electric resistance (TEER) before subsequent barrier disruption, accompanied by a strong decline of junctional protein levels and substantial, but considerably delayed cytotoxicity. Time-resolved microarray-based transcriptome analysis of C. albicans challenged IEC revealed a prominent role of NF-κB and MAPK signalling pathways in the response to infection. Hence, we inferred a gene regulatory network based on differentially expressed NF-κB and MAPK pathway components and their predicted transcriptional targets. The network model predicted activation of GDF15 by NF-κB was experimentally validated. Furthermore, inhibition of NF-κB activation in C. albicans infected C2BBe1 cells led to enhanced cytotoxicity in the epithelial cells. Taken together our study identifies NF-κB activation as an important protective signalling pathway in the response of epithelial cells to C. albicans.