Combination of abundant protein depletion and multi-lectin affinity chromatography (M-LAC) for plasma protein biomarker discovery

We report on the development of a robust and relatively high-throughput method for in-depth proteomic analysis of human plasma suitable for biomarker discovery. The method consists of depletion of albumin and IgG and multi-lectin affinity chromatography (M-LAC), followed by nanoLC-MS/MS analysis of digested proteins and label-free comparative quantitation of proteins. The performance of the method is monitored by multiple quality control points to ensure reproducibility of the analysis. The method identifies proteins that are reported to be present in normal plasma at concentrations of 10-100 ng/mL and that may be of particular interest when studying a variety of disease conditions. Numerous tissue leakage proteins of potentially even lower concentrations are also identified. When the method was used in a study to identify potential biomarkers of psoriasis, the differential abundance of proteins present at low mug/mL level was quantitated and later verified by ELISA measurements.10 page(s

Increased Plasma Concentrations of Cytoskeletal and Ca2+-Binding Proteins and Their Peptides in Psoriasis Patients

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Feasibility of the use of combinatorial chemokine arrays to study blood and CSF in multiple sclerosis.

Meningeal inflammation, including the presence of semi-organized tertiary lymphoid tissue, has been associated with cortical pathology at autopsy in secondary progressive multiple sclerosis (SPMS). Accessible and robust biochemical markers of cortical inflammation for use in SPMS clinical trials are needed. Increased levels of chemokines in the cerebrospinal fluid (CSF) can report on inflammatory processes occurring in the cerebral cortex of MS patients. A multiplexed chemokine array that included BAFF, a high sensitivity CXCL13 assay and composite chemokine scores were developed to explore differences in lymphoid (CXCL12, CXCL13, CCL19 and CCL21) and inflammatory (CCL2, CXCL9, CXCL10 and CXCL11) chemokines in a small pilot study. Paired CSF and serum samples were obtained from healthy controls (n=12), relapsing-remitting MS (RRMS) (n=21) and SPMS (N=12). A subset of the RRMS patients (n = 9) was assessed upon disease exacerbation and 1 month later following iv methylprednisone. SPMS patients were sampled twice to ascertain stability. Both lymphoid and inflammatory chemokines were elevated in RRMS and SPMS with the highest levels found in the active RRMS group. Inflammatory and lymphoid chemokine signatures were defined and generally correlated with each other. This small exploratory clinical study shows the feasibility of measuring complex and potentially more robust chemokine signatures in the CSF of MS patients during clinical trials. No differences were found between stable RRMS and SPMS. Future trials with larger patient cohorts with this chemokine array are needed to further characterize the differences, or the lack thereof, between stable RRMS and SPMS

Stability and predictive value of anti-JCV antibody index in multiple sclerosis: A 6-year longitudinal study.

BACKGROUND:Risk of natalizumab-related progressive multifocal leukoencephalopathy is associated with the presence of anti-JC-virus (JCV) antibodies. OBJECTIVE:To investigate the longitudinal evolution of anti-JCV antibody index and to determine the predictive value of baseline anti-JCV antibody index for long-term stability of anti-JCV antibody status. METHODS:MS patients from the MS centre of Medical University of Innsbruck, who had serum sampling for a time period of 4-6 years at intervals of 6±3 months, were included in this retrospective, longitudinal study. Anti-JCV antibody serological status and index were determined by 2-step second-generation anti-JCV antibody assay. RESULTS:154 patients were included in this study. Median follow-up time was 63.7 months, with median 11 samples available per patient. At baseline, 111 (72.1%) patients were anti-JCV antibody positive. Baseline anti-JCV antibody index significantly correlated with age (R = 0.22, p = 0.005); there was no difference with respect to sex, disease duration or previously used disease-modifying treatment. During follow-up anti-JCV antibody status changed from negative to positive or vice versa in 17% of patients. In seronegative patients at baseline, baseline anti-JCV antibody index was significantly lower in those remaining seronegative at follow-up compared to those converting to seropositivity (median 0.16 vs. 0.24, p = 0.002). In seropositive patients at baseline, index was higher in those remaining seropositive compared to those reverting to seronegativity (2.6 vs. 0.45, p0.90 predicted stable positive serostatus (sensitivity 88.7%, specificity 96.5%) and <0.20 stable negative serostatus (sensitivity 61.3%, specificity 97.6%). CONCLUSIONS:Anti-JCV antibody index remained relatively stable over 6-year follow-up with annual serostatus change of ~3%. Baseline anti-JCV antibody index predicted stable negative and stable positive JCV serostatus

Clinical and baseline demographic data.

<p>Clinical and baseline demographic data.</p

Power to predict stability or change of long-term anti-JCV antibody status.

<p>Power to predict stability or change of long-term anti-JCV antibody status.</p

Percentages of patients switching between different anti-JCV antibody index categories.

<p>(A) Percentage of patients with anti-JCV antibody index ≤0.9 at baseline (n = 66) switching to higher index categories at least once during follow-up. (B) Percentage of patients with positive anti-JCV serostatus and antibody index ≤0.9 at baseline (n = 23) switching to higher index categories at least once during follow-up. (C) Percentage of patients with anti-JCV antibody index >0.9 and ≤1.5 at baseline (n = 19) switching to higher or lower index categories at least once during follow-up. There was no patient remaining within the baseline index category during follow-up. (D) Percentage of patients with anti-JCV antibody index >1.5 at baseline (n = 69) switching to lower index categories at least once during follow-up.</p

Trend analyses for each analyte for those individuals with two CSF samples.

<p>Both EX-RRMS and SPMS patients are grouped in the same graph.</p