Prem Gurnani

Regulation of Oxidative Stress by the Anti-aging Hormone Klotho

klotho is an aging suppressor gene and extends life span when overexpressed in mice. Klotho protein was recently demonstrated to function as a hormone that inhibits insulin/insulin-like growth factor-1 (IGF-1) signaling. Here we show that Klotho protein increases resistance to oxidative stress at the cellular and organismal level in mammals. Klotho protein activates the FoxO forkhead transcription factors that are negatively regulated by insulin/IGF-1 signaling, thereby inducing expression of manganese superoxide dismutase. This in turn facilitates removal of reactive oxygen species and confers oxidative stress resistance. Thus, Klotho-induced inhibition of insulin/IGF-1 signaling is associated with increased resistance to oxidative stress, which potentially contributes to the anti-aging properties of klotho.

Klotho Protects Dopaminergic Neuron Oxidant-Induced Degeneration by Modulating ASK1 and p38 MAPK Signaling Pathways.

Klotho transgenic mice exhibit resistance to oxidative stress as measured by their urinal levels of 8-hydroxy-2-deoxyguanosine, albeit this anti-oxidant defense mechanism has not been locally investigated in the brain. Here, we tested the hypothesis that the reactive oxygen species (ROS)-sensitive apoptosis signal-regulating kinase 1 (ASK1)/p38 MAPK pathway regulates stress levels in the brain of these mice and showed that: 1) the ratio of free ASK1 to thioredoxin (Trx)-bound ASK1 is relatively lower in the transgenic brain whereas the reverse is true for the Klotho knockout mice; 2) the reduced p38 activation level in the transgene corresponds to higher level of ASK1-bound Trx, while the KO mice showed elevated p38 activation and lower level of–bound Trx; and 3) that 14-3-3ζ is hyper phosphorylated (Ser-58) in the transgene which correlated with increased monomer forms. In addition, we evaluated the in vivo robustness of the protection by challenging the brains of Klotho transgenic mice with a neurotoxin, MPTP and analyzed for residual neuron numbers and integrity in the substantia nigra pars compacta. Our results show that Klotho overexpression significantly protects dopaminergic neurons against oxidative damage, partly by modulating p38 MAPK activation level. Our data highlight the importance of ASK1/p38 MAPK pathway in the brain and identify Klotho as a possible anti-oxidant effector.

Proteomic biomarker identification for diagnosis of early relapse in ovarian cancer.

Ovarian cancer recurs at the rate of 75% within a few months or several years later after therapy. Early recurrence, though responding better to treatment, is difficult to detect. Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry has showed the potential to accurately identify disease biomarkers to help early diagnosis. A major challenge in the interpretation of SELDI-TOF data is the high dimensionality of the feature space. To tackle this problem, we have developed a multi-step data processing method composed of t-test, binning and backward feature selection. A new algorithm, support vector machine-Markov blanket/recursive feature elimination (SVM-MB/RFE) is presented for the backward feature selection. This method is an integration of minimum weight feature elimination by SVM-RFE and information theory based redundant/irrelevant feature removal by Markov Blanket. Subsequently, SVM was used for classification. We conducted the biomarker selection algorithm on 113 serum samples to identify early relapse from ovarian cancer patients after primary therapy. To validate the performance of the proposed algorithm, experiments were carried out in comparison with several other feature selection and classification algorithms.

An Extended Markov Blanket Approach to Proteomic Biomarker Detection From High-Resolution Mass Spectrometry Data

High-resolution matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has recently shown promise as a screening tool for detecting discriminatory peptide/protein patterns. The major computational obstacle in finding such patterns is the large number of mass/charge peaks (features, biomarkers, data points) in a spectrum. To tackle this problem, we have developed methods for data preprocessing and biomarker selection. The preprocessing consists of binning, baseline correction, and normalization. An algorithm, extended Markov blanket, is developed for biomarker detection, which combines redundant feature removal and discriminant feature selection. The biomarker selection couples with support vector machine to achieve sample prediction from high-resolution proteomic profiles. Our algorithm is applied to recurrent ovarian cancer study that contains platinum-sensitive and platinum-resistant samples after treatment. Experiments show that the proposed method performs better than other feature selection algorithms. In particular, our algorithm yields good performance in terms of both sensitivity and specificity as compared to other methods.

Prostate cancer biomarker discovery using high performance mass spectral serum profiling

Prostate-specific antigen (PSA) is the most widely used serum biomarker for early detection of prostate cancer (PCA). Nevertheless, PSA level can be falsely elevated due to prostatic enlargement, inflammation or infection, which limits the PSA test specificity. The objective of this study is to use a machine learning approach for the analysis of mass spectrometry data to discover more reliable biomarkers that distinguish PCA from benign specimens. Serum samples from 179 prostate cancer patients and 74 benign patients were analyzed. These samples were processed using ProXPRESSION Biomarker Enrichment Kits (PerkinElmer). Mass spectra were acquired using a prOTOF 2000 matrix-assisted laser desorption/ionization orthogonal time-of-flight (MALDI-O-TOF) mass spectrometer. In this study, we search for potential biomarkers using our feature selection method, the Extended Markov Blanket (EMB). From the new marker selection algorithm, a panel of 26 peaks achieved an accuracy of 80.7%, a sensitivity of 83.5%, a specificity of 74.4%, a positive predictive value (PPV) of 87.9%, and a negative predictive value (NPV) of 68.2%. On the other hand, when PSA alone was used (with a cutoff of 4.0ng/ml), a sensitivity of 66.7%, a specificity of 53.6%, a PPV of 73.5%, and a NPV of 45.4% were obtained.

Fluorescence-Based Analysis of Cellular Protein Lysate Arrays Using Quantum Dots

Reverse-phase protein microarrays (RPPMAs) enable heterogeneous mixtures of proteins from cellular extracts to be directly spotted onto a substrate (such as a protein biochip) in minute volumes (nanoliter-to-picoliter volumes). The protein spots can then be probed with primary antibodies to detect important posttranslational modifications such as phosphorylations that are important for protein activation and the regulation of cellular signaling. Previously, we relied on chromogenic signals for detection. However, quantum dots (QDs) represent a more versatile detection system because the signals can be time averaged and the narrow-emission spectra enable multiple protein targets to be quantified within the same spot. We found that commercially available pegylated, streptavidin-conjugated QDs are effective detection agents, with low-background binding to heterogeneous protein mixtures. This type of test, the RPPMAs, is at the forefront of an exciting, clinically-oriented discipline that is emerging, namely tissue or clinical proteomics.

Prediction of labor for pregnant women using high-resolution mass spectrometry data

High-resolution MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry has shown promise as a screening tool for detecting discriminatory protein patterns. The major computational obstacle in analyzing MALDI-TOF data is a large number of mass/charge peaks (a.k.a. features, data points). With the number of data points easily going beyond one million for a single sample, efficient feature selection is critical for unequivocal protein pattern discovery. To tackle this problem, we have developed a multi-step strategy for data preprocessing and afterwards feature selection. The preprocessing is composed of binning, baseline correction, and normalization. For the preprocessed data, we propose a new feature subset selection method that is a hybrid filter/wrapper approach. Based on the two feature subsets for each feature, high and low correlated subsets, a feature is assigned a weight which indicates the extent of feature importance. Our scheme is applied to the analysis of labor dataset to predict delivery time of pregnant women. To validate the performance of the proposed algorithm, experiments are performed in comparison with other feature selection and classification methods. We show that our proposed approach outperforms other algorithms

Biomarker Selection for Predicting Alzheimer Disease Using High-Resolution MALDI-TOF Data

High-resolution MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry has shown promise as a screening tool for detecting discriminatory peptide/protein patterns. The major computational obstacle in analyzing MALDI-TOF data is the large number of mass/charge peaks (a.k.a. features, data points). With such a huge number of data points for a single sample, efficient feature selection is critical for unequivocal protein pattern discovery. In this paper, we propose a feature selection method and a new biclassification algorithm based on error-correcting output coding (ECOC) in multiclass problems. Our scheme is applied to the analysis of alzheimers disease (AD) data. To validate the performance of the proposed algorithm, experiments are performed in comparison with other methods. We show that our proposed framework outperforms not only the standard ECOC framework but also other algorithms.

Multicategory Classification using Extended SVM-RFE and Markov Blanket on SELDI-TOF Mass Spectrometry Data

Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers for disease to help early detection of the disease. Recently, support vector machine (SVM) algorithm based on recursive feature elimination (RFE) was proposed to find a set of genes for cancer classification. In our study, we extend the SVM-RFE such that it can be used in the multicategory classification work using SELDI-TOF mass spectrometry data and propose a new feature selection algorithm (SVM-MB/RFE : SVM-Markov Blanket/Recursive Feature Elimination). In the preprocessing task of SVM-MB/RFE, ANOVA (Analysis of Variance) and binning methods are used for feature filtering. We demonstrate that the performance is improved through the preprocessing work. Compared with other methods such as not only SVM-RFE and Markov blanket but also PCA (Principle Components Analysis)+LDA (Linear Discriminant Analysis) and other feature selection algorithms, SVM-MB/RFE performs better than them.

Classification of Relapse Ovarian Cancer on MALDI-TOF Mass Spectrometry Data

Ovarian cancer recurs at the rate of 75% within a few months or several years later after therapy. Early recurrence, though responding better to treatment, is difficult to detect. Recently, high-resolution MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry has shown promise as a screening tool for detecting discriminatory protein patterns. The major computational obstacle in analyzing MALDI-TOF data is a large number of mass/charge peaks (a.k.a. features, data points). To tackle this problem, we have developed a multi-step strategy for data preprocessing and afterwards feature selection. The preprocessing is composed of binning, baseline correction, and normalization. For the preprocessed data, we propose a new feature subset selection method. Our scheme is applied to the analysis of ovarian cancer dataset to predict early relapse in ovarian cancer. To validate the performance of the proposed algorithm, experiments are performed in comparison with other feature selection and classification methods. We show that our proposed approach outperforms other algorithms