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International audience; Proteomic technologies have been recently adapted to the new field of clinical proteomics. The origin of errors and biases has been well-identified in the pre-analytical steps, leading to the measurement of clinical analytes. One possible source of inadequacy in clinical proteomics is linked to sample pooling. This practice is usually related to low sample availability, variability, experiment time/cost. In this study, we first asked whether sample pooling in top-down proteomics is suitable to obtain a relevant biological average. Our second objective was to identify inflammatory biomarkers of outlier samples in our population of Creutzfeldt-Jakob disease patients. Our results demonstrated that, in a proteomics study, sample pooling as well as the inflammation status was an important source of errors: missed detection of biomarkers and false identification of others. Pooled samples were not equivalent to the average of biological values. In addition, this procedure reduced the statistical value of the identified biomarkers due to a stabilization of their standard deviation and rendered outlier samples difficult to detect. We identified serum amyloid A as a candidate biomarker of outlier samples. The presence of this protein, which could be explained by inflammatory processes, induced major modifications in the sample profiles.

International audience; Multiple myeloma (MM) is a plasma cell cancer with poor survival, characterized by the expansion of multiple myeloma cells (MMCs) in the bone marrow. Using a microarray-based genome-wide screen for genes responding to DNA methyltransferases (DNMT) inhibition in MM cells, we identified RECQ1 among the most downregulated genes. RecQ helicases are DNA unwinding enzymes involved in the maintenance of chromosome stability. Here we show that RECQ1 is significantly overexpressed in MMCs compared to normal plasma cells and that increased RECQ1 expression is associated with poor prognosis in three independent cohorts of patients. Interestingly, RECQ1 knockdown inhibits cells growth and induces apoptosis in MMCs. Moreover, RECQ1 depletion promotes the development of DNA double-strand breaks, as evidenced by the formation of 53BP1 foci and the phosphorylation of ataxia-telangiectasia mutated (ATM) and histone variant H2A.X (H2AX). In contrast, RECQ1 overexpression protects MMCs from melphalan and bortezomib cytotoxicity. RECQ1 interacts with PARP1 in MMCs exposed to treatment and RECQ1 depletion sensitizes MMCs to poly(ADP-ribose) polymerase (PARP) inhibitor. DNMT inhibitor treatment results in RECQ1 downregulation through miR-203 deregulation in MMC. Altogether, these data suggest that association of DNA damaging agents and/or PARP inhibitors with DNMT inhibitors may represent a therapeutic approach in patients with high RECQ1 expression associated with a poor prognosis.

Background Apolipoprotein E (Apo E) is a 36 Kda glycoprotein involved in lipid transport. It exists in 3 major isoforms: E2, E3 and E4. ApoE status is known to be a major risk factor for late-onset Alzheimer's and cardiovascular diseases. Genotyping is commonly used to obtain ApoE status but can show technical issues with ambiguous determinations. Phenotyping can be an alternative, not requiring genetic material. We evaluated the ability to accurately type ApoE isoforms by 2 phenotyping tests in comparison with genotyping. Methods Two phenotyping techniques were used: (1) LC–MS/MS detection of 4 ApoE specific peptides (6490 Agilent triple quadripole): After its denaturation, serum was either reduced and alkylated, or only diluted, and then trypsin digested. Before analysis, desalting, evaporation and resuspension were performed. (2) Isoelectric focusing and immunoprecipitation: serum samples were neuraminidase digested, delipidated and electrophoresed on Hydragel ApoE (Sebia agarose gel) using Hydrasys 2 Scan instrument (Sebia, Lisses, France). ApoE isoforms bands were directly immunofixed in the gel using a polyclonal anti human ApoE antibody. Then, incubation of the gel with HRP secondary antibody followed by TTF1/TTF2 substrate allowed the visualization of ApoE bands. The results of the two techniques were compared to genotyping. Results Sera from 35 patients previously genotyped were analyzed with the 2 phenotyping techniques. 100% concordance between both phenotyping assays was obtained for the tested phenotypes (E2/E2, E2/E3, E2/E4, E3/E3, E3/E4, E4/E4). When compared to genotyping, 3 samples were discordant. After reanalyzing them by both phenotyping tests and DNA sequencing, 2/3 discrepancies were confirmed. Those can be explained by variants or rare ApoE alleles or by unidentified technical issues. 102 additional samples were then tested on LC–MS/MS only and compared to genotyping. The data showed 100% concordance. Conclusion Our 2 phenotyping methods represent a valuable alternative to genotyping. LC–MS/MS has the advantage of being fully specific, with identification of the different isoforms and can be considered as a reference method. Sebia isofocusing technique was concordant with LC–MS/MS. Plus, it is a rapid, semi-automated assay that can be easily implemented in clinical laboratories.

International audience; Background: Proteomics analysis of human cerebrospinal fluid (CSF) is a major tool for identifying novel biomarkers for neurological diseases. However, the complexity and wide dynamic range of CSF represent a major challenge for detecting specific low-abundance biomarkers. One way to overcome this problem is to rely on different pre-fractionation techniques. However, the most relevant technique remains to be determined. Methods: This study compared three different well-known pre-fractionation methods: immuno-depletion of major proteins (Seppro® IgY14), hydrophobic solid phase extraction (Oasis® HLB), and lipophilic sorbent concentration (Liposorb™). Unfractionated and pre-fractionated CSF was digested with trypsin and analyzed by RP-LC-MS/MS with an OrbitrapTM mass spectrometer. We documented the number of peptides detected and sets of proteins identified. Experiments were repeated to minimize pre-analytical and analytical variability.Results: Compared to unfractionated CSF, the OASIS® HLB fractionated CSF method showed a significant 28% increase in the total number of proteins identified, while the Liposorb™ capture resulted in a significant 46% decrease. Interestingly, results based on the number of peptides detected were different. We also evaluated the capacity of these pre-fractionation methods to detect different proteins in terms of their molecular weight, isoelectrophoretic point (IEP) or nature. Each of these pre-fractionation methods identified a specific subset of proteins, when compared to unfractionated CSF, and/or other methods. This was particularly obvious for the lipophilic sorbent, which allowed the detection of many lipoproteins.Conclusion: Direct analysis of digested CSF led to the identification of several proteins despite matrix complexity. As expected, single pre-fractionation methods that can be included in simple and cost-effective workflows, yielded significant differences in terms of number, or range of proteins identified. This suggests that a single pre-fractionation method cannot cover the full range of protein species present in a complex sample

Cerebrospinal fluid (CSF) biomarkers are now widely used for diagnosis of Alzheimer disease (AD) in atypical clinical forms, for differential and early diagnosis, or for stratification of patients in clinical trials. Among these biomarkers, different forms of amyloid peptides (Aβ) produced by the cleavage of a transmembrane precursor protein called APP (amyloid precursor protein) have a major role. Aβ peptides exist in different length the most common ones having 40 (Aβ40), 42 (Aβ42), or 38 (Aβ38) amino acids in length. APP processing by gamma-secretase releases also an amino-terminal secreted fragment called sAβPP-beta while an alternative nonamyloidogenic cleavage of APP, through an alpha-secretase, liberates another fragment called sAβPP-alpha. To decipher the molecular and pathological mechanisms leading to the production and the detection of these entities is essential for the comprehension and the prevention of AD. In this report, we present the results of the multiplex measurement of CSF Aβ38, Aβ40, Aβ42, sAβPP-alpha, and sAβPP-beta in 60 patients mostly with dementia eventually segregated between neurochemical dementia diagnostic (NDD) positive and negative groups. The NDD classification was based on our routine Tau, P-tau(181), and Aβ(42) cutoff values. We confirmed previous findings regarding the correlation between sAβPP-alpha and sAβPP-beta, as well as the potential interest of these new biomarkers. We also studied the correlation between sAβPPs and Aβ peptides, as well as between Aβ peptides themselves. We observed a strong correlation between Aβ38 and sAβPP-beta which suggested that the production of this peptide was in direct relation with β secretase activities. We also reported a strong correlation between Aβ38 and Aβ40, while Aβ42 was correlated to these fragments only in nonpathological situations. These results enlighten the complex relationships between these molecular markers in both physiological and pathological situations. Our results are important for the further use of these analytes for AD diagnosis as well as for validating the cell biological hypotheses of APP processing and Aβ fragment production.

International audience; Depletion of major blood proteins is one of the most promising approaches to access low abundant biomarkers using proteomics. Immunocapture columns often used for this purpose exist in different formats depending on the number of major proteins removed. In this article, we compared the relative interest of depleting either one (albumin), six (albumin, IgG, IgA, transferrin, α1-antitrypsin, and haptoglobin), twelve (the previous six and apo A-I and-II, orosomucoid, α2-macroglobulin, fibrinogen, IgM) or twenty blood proteins (the previous twelve and IgD, ceruloplasmin, apo B, complement C1q, C3, C4, plasminogen, and prealbumin). Such study raises interesting issues related to the reproducibility, practicability, specificity of the immunocapture, and to the impact of removing not only the selected molecules, but also associated peptides and proteins. Depleted sera were here analysed using different proteomic approaches, including two dimensional electrophoresis and SELDI–TOF. Altogether, our results clearly confirmed the interest of depleting major blood proteins for the proteomic detection of low abundant components. However, we observed that increasing the number of depleted proteins from twelve to twenty had a limited beneficial impact and might increase drawbacks in removing associated peptides and proteins. This conclusion is however related to the technologies that we have used, and we believe that it is necessary to adapt the immunocapture to the analytical method employed, and to the ratio between wanted and unwanted proteins removed.

Background Surface-Enhanced Laser Desorption/Ionization – Time Of Flight (SELDI-TOF) has been proposed as new approach for blood biomarker discovery. However, results obtained so far have been often disappointing as this technique still has difficulties to detect low-abundant plasma and serum proteins. Results We used a serum depletion scheme using chicken antibodies against various abundant proteins to realized a pre-fractionation of serum prior to SELDI-TOF profiling. Depletion of major serum proteins by immunocapture was confirmed by 1D and 2D gel electrophoresis. SELDI-TOF analysis of bound and unbound (depleted) serum fractions revealed that this approach allows the detection of new low abundant protein peaks with satisfactory reproducibility. Conclusion The combination of immunocapture and SELDI-TOF analysis opens new avenues into proteomic profiling for the discovery of blood biomarkers.

In this manuscript, we compared serum profiles obtained with two related technologies, SELDI-TOF and Clinprot, using a single bioinformatic algorithm. These two approaches rely on mass spectrometry to detect proteins and peptides initially selected by binding to various chromatographic matrices. They are proposed by two different companies, and they are competing for being the reference in high throughput serum profiling for clinical proteomics. This independent evaluation of these two technologies put the light on some of their differences, suggests that they address different proteome fractions and, thus, could be complementary. Taken together, our data could contribute to the parameters relevant for the choice of one technology or the other.