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Abstract Background This short communication focuses the on articular cartilage and the subchondral bone, both of which play important roles in the development of osteoarthritis (OA). There are indications that estrogen-deficiency, as the post-menopausal state, accelerate the development of OA. Findings We investigated, which extracellular matrix (ECM) protein, proteases and different pro-inflammatory factors was up- or down-regulated in the knee joint tissue in response to estrogen-deficiency in rats induced by ovariectomy. These data support previous findings that several metalloproteinases (MMPs) and cysteine proteases are co-regulated with numerous collagens and proteoglycans that are important for cartilage integrity. Furthermore quite a few pro-inflammatory cytokines were regulated by estrogen deprivation. Conclusion We found multiple genes where regulated in the joint by estrogen-deficiency, many of which correspond well with our current knowledge of the pathogenesis of OA. It supports that estrogen-deficiency (e.g. OVX) may accelerate joint deterioration. However, there are also data that draw attention the need for better understanding of the synergy between proteases and tissue turnover.

Background: Omalizumab, a humanized recombinant monoclonal anti‐IgE antibody, proved to be effective in patients with chronic spontaneous urticaria (CSU), including severe and treatment‐refractory CSU. Here, we report omalizumab's effect on gene expression in skin biopsies from CSU patients enrolled in a double‐blind, placebo‐controlled study. Methods: Chronic spontaneous urticaria patients (18‐75 years) were randomized to either 300 mg omalizumab (n = 20) or placebo (n = 10) administered s.c. every 4 weeks for 12 weeks (NCT01599637). Lesional and nonlesional skin biopsies were collected from the same area of consenting patients and assessed at baseline and on Day 85 compared with skin biopsies from the same area of 10 untreated healthy volunteers (HVs). Gene expression data were generated using Affymetrix HG‐U133Plus2.0 microarrays. Statistical analyses were performed using R packages. Results: At baseline, 63 transcripts were differentially expressed between lesional and nonlesional skin. Two‐thirds of these lesional signatures were also differentially expressed between lesional and HV skin. Upon treatment with omalizumab, >75% of lesional signatures changed to reflect nonlesional skin expression levels (different vs placebo, P < 0.01). Transcripts upregulated in lesional skin (vs nonlesional and/or HV skin) suggested increased mast cell/leukocyte infiltration (FCER1G, C3AR1, CD93, S100A8, and S100A9), increased oxidative stress, vascularization (CYR61), and skin repair events (KRT6A, KRT16). Lesional signatures were not modulated by treatment in nonresponders (defined based on UAS7 longitudinal changes ≥16). Conclusion: Omalizumab, in treatment responders, reverted transcriptional signatures associated with CSU lesion phenotype to reflect nonlesional/HV expression levels; this is consistent with observed omalizumab‐mediated clinical improvement observed in patients with CSU. The gene expression of the lesional skin of patients with chronic spontaneous urticaria shows a distinct signature when compared to their nonlesional skin and the skin of healthy volunteers. The pattern of gene expression in the lesional skin of omalizumab‐treated patients are different than before treatment, but not in their nonlesional skin. In treatment responders, omalizumab treatment shows reverted transcriptional signatures associated with CSU lesion phenotype to reflect nonlesional/healthy volunteer expression levels consistent with observed clinical improvement. [ABSTRACT FROM AUTHOR]

Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies. We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently identifies the globally optimal solution while preserving non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell or tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. We tested our methods on several well controlled benchmark data sets with known mixing fractions of pure cell or tissue types and mRNA expression profiling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed. In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types (<10% total population). Furthermore, accurate changes in leukocyte trafficking associated with Fingolomid (FTY720) treatment were identified that were consistent with previous results generated by both cell counts and flow cytometry. These data suggest that our method can solve one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment. [ABSTRACT FROM AUTHOR]