Jonathan L. Lustgarten

Discovery and verification of amyotrophic lateral sclerosis biomarkers by proteomics

Recent studies using mass spectrometry have discovered candidate biomarkers for amyotrophic lateral sclerosis (ALS). However, those studies utilized small numbers of ALS and control subjects. Additional studies using larger subject cohorts are required to verify these candidate biomarkers. Cerebrospinal fluid (CSF) samples from 100 patients with ALS, 100 disease control, and 41 healthy control subjects were examined by mass spectrometry. Sixty‐one mass spectral peaks exhibited altered levels between ALS and controls. Mass peaks for cystatin C and transthyretin were reduced in ALS, whereas mass peaks for posttranslational modified transthyretin and C‐reactive protein (CRP) were increased. CRP levels were 5.84 ± 1.01 ng/ml for controls and 11.24 ± 1.52 ng/ml for ALS subjects, as determined by enzyme‐linked immunoassay. This study verified prior mass spectrometry results for cystatin C and transthyretin in ALS. CRP levels were increased in the CSF of ALS patients, and cystatin C level correlated with survival in patients with limb‐onset disease. Our biomarker panel predicted ALS with an overall accuracy of 82%. Muscle Nerve 42: 104–111, 2010

A Metaproteomic Approach to Study Human-Microbial Ecosystems at the Mucosal Luminal Interface

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI.

Application of an efficient Bayesian discretization method to biomedical data

BackgroundSeveral data mining methods require data that are discrete, and other methods often perform better with discrete data. We introduce an efficient Bayesian discretization (EBD) method for optimal discretization of variables that runs efficiently on high-dimensional biomedical datasets. The EBD method consists of two components, namely, a Bayesian score to evaluate discretizations and a dynamic programming search procedure to efficiently search the space of possible discretizations. We compared the performance of EBD to Fayyad and Iranis (FI) discretization method, which is commonly used for discretization.ResultsOn 24 biomedical datasets obtained from high-throughput transcriptomic and proteomic studies, the classification performances of the C4.5 classifier and the naïve Bayes classifier were statistically significantly better when the predictor variables were discretized using EBD over FI. EBD was statistically significantly more stable to the variability of the datasets than FI. However, EBD was less robust, though not statistically significantly so, than FI and produced slightly more complex discretizations than FI.ConclusionsOn a range of biomedical datasets, a Bayesian discretization method (EBD) yielded better classification performance and stability but was less robust than the widely used FI discretization method. The EBD discretization method is easy to implement, permits the incorporation of prior knowledge and belief, and is sufficiently fast for application to high-dimensional data.

EPO-KB

UNLABELLED The knowledge base EPO-KB (Empirical Proteomic Ontology Knowledge Base) is based on an OWL ontology that represents current knowledge linking mass-to-charge (m/z) ratios to proteins on multiple platforms including Matrix Assisted Laser/Desorption Ionization (MALDI) and Surface Enhanced Laser/Desorption Ionization (SELDI)--Time of Flight (TOF). At present, it contains information on m/z ratio to protein links that were extracted from 120 published research papers. It has a web interface that allows researchers to query and retrieve putative proteins that correspond to a user-specified m/z ratio. EPO-KB also allows automated entry of additional m/z ratio to protein links and is expandable to the addition of gene to protein and protein to disease links. AVAILABILITY http://www.dbmi.pitt.edu/EPO-KB

Alefacept for Severe Alopecia Areata: A Randomized, Double-blind, Placebo-Controlled Study

OBJECTIVE To assess the efficacy of alefacept for the treatment of severe alopecia areata (AA). DESIGN Multicenter, double-blind, randomized, placebo-controlled clinical trial. SETTING Academic departments of dermatology in the United States. PARTICIPANTS Forty-five individuals with chronic and severe AA affecting 50% to 95% of the scalp hair and resistant to previous therapies. Intervention Alefacept, a US Food and Drug Administration-approved T-cell biologic inhibitor for the treatment of moderate to severe plaque psoriasis. Main Outcome Measure Improved Severity of Alopecia Tool (SALT) score over 24 weeks. RESULTS Participants receiving alefacept for 12 consecutive weeks demonstrated no statistically significant improvement in AA when compared with a well-matched placebo-receiving group (P = .70). Conclusion Alefacept is ineffective for the treatment of severe AA.

Distinguishing Historical from Current Problems in Clinical Reports -- Which Textual Features Help?

Determining whether a condition is historical or recent is important for accurate results in biomedicine. In this paper, we investigate four types of information found in clinical text that might be used to make this distinction. We conducted a descriptive, exploratory study using annotation on clinical reports to determine whether this temporal information is useful for classifying conditions as historical or recent. Our initial results suggest that few of these feature values can be used to predict temporal classification.

Knowledge-based variable selection for learning rules from proteomic data

BackgroundThe incorporation of biological knowledge can enhance the analysis of biomedical data. We present a novel method that uses a proteomic knowledge base to enhance the performance of a rule-learning algorithm in identifying putative biomarkers of disease from high-dimensional proteomic mass spectral data. In particular, we use the Empirical Proteomics Ontology Knowledge Base (EPO-KB) that contains previously identified and validated proteomic biomarkers to select m/z s in a proteomic dataset prior to analysis to increase performance.ResultsWe show that using EPO-KB as a pre-processing method, specifically selecting all biomarkers found only in the biofluid of the proteomic dataset, reduces the dimensionality by 95% and provides a statistically significantly greater increase in performance over no variable selection and random variable selection.ConclusionKnowledge-based variable selection even with a sparsely-populated resource such as the EPO-KB increases overall performance of rule-learning for disease classification from high-dimensional proteomic mass spectra.