Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum.
Methods
: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays.
Results
: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B).
Conclusion
: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.
The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.
The purpose of this review was to evaluate the efficacy and safety of acupuncture therapies in the treatment of psoriasis vulgaris. Embase, CENTRAL, PubMed, AMED, CINAHL, CNKI, CQVIP, CBM, and Wanfang databases were searched from inceptions to May 2013 for prospective randomized controlled trials evaluating acupuncture therapies for psoriasis vulgaris. No language limitations were applied. Studies were assessed using the Cochrane risk of bias tool. The primary outcome was Psoriasis Area Severity Index (PASI) score. Six studies (involving 522 participants) met the eligibility criteria for this review, and 5 were included in quantitative analysis. Due to the diversity of interventions, comparators and reported outcomes, meta-analysis was not possible. Results from single studies produced conflicting results for the outcomes PASI reduction, lesion reduction (non-PASI), PASI score, and relapse rate. There is some evidence of benefit of acupuncture therapies for the treatment of psoriasis vulgaris. However, the conclusions are limited by the small number of included trials and conflicting results from single studies. More research is needed to clarify the effect of acupuncture therapies for psoriasis vulgaris.
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