QUANTITATIVE PROTEOMIC ANALYSES OF HUMAN PLASMA: APPLICATION OF MASS SPECTROMETRY FOR THE DISCOVERY OF CLINICAL DELIRIUM BIOMARKERS

The biomarker discovery pipeline is a multi-step endeavor to identify potential diagnostic or prognostic markers of a disease. Although the advent of modern mass spectrometers has revolutionized the initial discovery phase, a significant bottleneck still exists when validating discovered biomarkers. In this doctoral research, I demonstrate that the discovery, verification and validation of biomarkers can all be performed using mass spectrometry and apply the biomarker pipeline to the context of clinical delirium. First, a systematic review of recent literature provided a birds-eye view of untargeted, discovery proteomic attempts for biomarkers of delirium in the geriatric population. Here, a comprehensive search from five databases yielded 1172 publications, from which eight peer-reviewed studies met our defined inclusion criteria. Despite the paucity of published studies that applied systems- biology approaches for biomarker discovery on the subject, lessons learned and insights from this review was instrumental in the study designing and proteomics analyses of plasma sample in our cohort. We then performed a targeted study on four biomarkers for their potential mediation role in the occurrence of delirium after high-dose intra-operative oxygen treatment. Although S100B calcium binding protein (S100B), gamma enolase (ENO2), chitinase-3-like protein 1 (CHI3L1) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) have well-documented associations with delirium, we did not find any such associations in our cohort. Of note, this study demonstrates that the use of targeted approaches for the purposes of biomarker discovery, rather than an untargeted, systems-biology approach, is unavoidably biased and may lead to misleading conclusions. Lastly, we applied lessons learned and comprehensively profiled the plasma samples of delirium cases and non-delirium cases, at both pre- and post-surgical timepoints. We found 16 biomarkers as signatures of cardiopulmonary bypass, and 11 as potential diagnostic candidates of delirium (AuROC = 93%). We validated the discovered biomarkers on the same mass spectrometry platform without the use of traditional affinity-based validation methods. Our discovery of novel biomarkers with no know association with delirium such as serum amyloid A1 (SAA1) and A2 (SAA2), pepsinogen A3 (PEPA3) and cathepsin B (CATB) shed new lights on possible neuronal pathomechanisms

The Chemical Probes Portal: an expert review-based public resource to empower chemical probe assessment, selection and use.

We describe the Chemical Probes Portal (https://www.chemicalprobes.org/), an expert review-based public resource to empower chemical probe assessment, selection and use. Chemical probes are high-quality small-molecule reagents, often inhibitors, that are important for exploring protein function and biological mechanisms, and for validating targets for drug discovery. The publication, dissemination and use of chemical probes provide an important means to accelerate the functional annotation of proteins, the study of proteins in cell biology, physiology, and disease pathology, and to inform and enable subsequent pioneering drug discovery and development efforts. However, the widespread use of small-molecule compounds that are claimed as chemical probes but are lacking sufficient quality, especially being inadequately selective for the desired target or even broadly promiscuous in behaviour, has resulted in many erroneous conclusions in the biomedical literature. The Chemical Probes Portal was established as a public resource to aid the selection and best-practice use of chemical probes in basic and translational biomedical research. We describe the background, principles and content of the Portal and its technical development, as well as examples of its applications and use. The Chemical Probes Portal is a community resource and we therefore describe how researchers can be involved in its content and development

Combining Clustering techniques and Formal Concept Analysis to characterize Interestingness Measures

Formal Concept Analysis "FCA" is a data analysis method which enables to discover hidden knowledge existing in data. A kind of hidden knowledge extracted from data is association rules. Different quality measures were reported in the literature to extract only relevant association rules. Given a dataset, the choice of a good quality measure remains a challenging task for a user. Given a quality measures evaluation matrix according to semantic properties, this paper describes how FCA can highlight quality measures with similar behavior in order to help the user during his choice. The aim of this article is the discovery of Interestingness Measures "IM" clusters, able to validate those found due to the hierarchical and partitioning clustering methods "AHC" and "k-means". Then, based on the theoretical study of sixty one interestingness measures according to nineteen properties, proposed in a recent study, "FCA" describes several groups of measures.Comment: 13 pages, 2 figure

Representing and coding the knowledge embedded in texts of Health Science Web published articles

Despite the fact that electronic publishing is a common activity to scholars electronic journals are still based in the print model and do not take full advantage of the facilities offered by the Semantic Web environment. This is a report of the results of a research project with the aim of investigating the possibilities of electronic publishing journal articles both as text for human reading and in machine readable format recording the new knowledge contained in the article. This knowledge is identified with the scientific methodology elements such as problem, methodology, hypothesis, results, and conclusions. A model integrating all those elements is proposed which makes explicit and records the knowledge embedded in the text of scientific articles as an ontology. Knowledge thus represented enables its processing by intelligent software agents The proposed model aims to take advantage of these facilities enabling semantic retrieval and validation of the knowledge contained in articles. To validate and enhance the model a set of electronic journal articles were analyzed

Validation of a Multivariate Serum Profile for Epithelial Ovarian Cancer Using a Prospective Multi-Site Collection

In previous studies we described the use of a retrospective collection of ovarian cancer and benign disease samples, in combination with a large set of multiplexed immunoassays and a multivariate pattern recognition algorithm, to develop an 11-biomarker classification profile that is predictive for the presence of epithelial ovarian cancer. In this study, customized, Luminex-based multiplexed immunoassay kits were GMP-manufactured and the classification profile was refined from 11 to 8 biomarkers (CA-125, epidermal growth factor receptor, CA 19-9, C-reactive protein, tenascin C, apolipoprotein AI, apolipoprotein CIII, and myoglobin). The customized kits and the 8-biomarker profile were then validated in a double-blinded manner using prospective samples collected from women scheduled for surgery, with a gynecologic oncologist, for suspicion of having ovarian cancer. The performance observed in model development held in validation, demonstrating 81.1% sensitivity (95% CI 72.6 – 87.9%) for invasive epithelial ovarian cancer and 85.4% specificity (95% CI 81.1 – 88.9%) for benign ovarian conditions. The specificity for normal healthy women was 95.6% (95% CI 83.6 – 99.2%). These results have encouraged us to undertake a second validation study arm, currently in progress, to examine the performance of the 8-biomarker profile on the population of women not under the surgical care of a gynecologic oncologist