A novel method to efficiently differentiate human osteoclasts from blood-derived monocytes.

Background: Osteoclasts are the tissue-specific macrophage population of the bone and unique in their bone-resorbing activity. Hence, they are fundamental for bone physiology in health and disease. However, efficient protocols for the isolation and study of primary human osteoclasts are scarce.

Deletion of activin A in mesenchymal but not myeloid cells ameliorates disease severity in experimental arthritis.

Objective: The aim of this study was to assess the extent and the mechanism by which activin A contributes to progressive joint destruction in experimental arthritis and which activin A-expressing cell type is important for disease progression.

Methods: Levels of activin A in synovial tissues were evaluated by immunohistochemistry, cell-specific expression and secretion by PCR and ELISA, respectively. Osteoclast (OC) formation was assessed by tartrat-resistant acid phosphatase (TRAP) staining and activity by resorption assay.

A myostatin-CCL20-CCR6 axis regulates Th17 cell recruitment to inflamed joints in experimental arthritis.

The interactions of fibroblast-like synoviocyte (FLS)-derived pro-inflammatory cytokines/chemokines and immune cells support the recruitment and activation of inflammatory cells in RA. Here, we show for the first time that the classical myokine myostatin (GDF-8) is involved in the recruitment of Th17 cells to inflammatory sites thereby regulating joint inflammation in a mouse model of TNFalpha-mediated chronic arthritis. Mechanistically, myostatin-deficiency leads to decreased levels of the chemokine CCL20 which is associated with less infiltration of Th17 cells into the inflamed joints.

What Markov State Models Can and Cannot Do: Correlation versus Path-Based Observables in Protein-Folding Models.

Markov state models (MSMs) have been widely applied to study the kinetics and pathways of protein conformational dynamics based on statistical analysis of molecular dynamics (MD) simulations. These MSMs coarse-grain both configuration space and time in ways that limit what kinds of observables they can reproduce with high fidelity over different spatial and temporal resolutions. Despite their popularity, there is still limited understanding of which biophysical observables can be computed from these MSMs in a robust and unbiased manner, and which suffer from the space-time coarse-graining intrinsic in the MSM model.