Vanathi Gopalakrishnan

Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is characterized by degeneration of motor neurons. We tested the hypothesis that proteomic analysis will identify protein biomarkers that provide insight into disease pathogenesis and are diagnostically useful. To identify ALS specific biomarkers, we compared the proteomic profile of cerebrospinal fluid (CSF) from ALS and control subjects using surface‐enhanced laser desorption/ionization‐time of flight mass spectrometry (SELDI‐TOF‐MS). We identified 30 mass ion peaks with statistically significant (p < 0.01) differences between control and ALS subjects. Initial analysis with a rule‐learning algorithm yielded biomarker panels with diagnostic predictive value as subsequently assessed using an independent set of coded test subjects. Three biomarkers were identified that are either decreased (transthyretin, cystatin C) or increased (carboxy‐terminal fragment of neuroendocrine protein 7B2) in ALS CSF. We validated the SELDI‐TOF‐MS results for transthyretin and cystatin C by immunoblot and immunohistochemistry using commercially available antibodies. These findings identify a panel of CSF protein biomarkers for ALS.

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.

A Multiplexed Serum Biomarker Immunoassay Panel Discriminates Clinical Lung Cancer Patients from High-Risk Individuals Found to be Cancer-Free by CT Screening

Introduction: Clinical decision making in the setting of computed tomography (CT) screening could benefit from accessible biomarkers that help predict the level of lung cancer risk in high-risk individuals with indeterminate pulmonary nodules. Methods: To identify candidate serum biomarkers, we measured 70 cancer-related proteins by Luminex xMAP (Luminex Corporation) multiplexed immunoassays in a training set of sera from 56 patients with biopsy-proven primary non–small-cell lung cancer and 56 age-, sex-, and smoking-matched CT-screened controls. Results: We identified a panel of 10 serum biomarkers—prolactin, transthyretin, thrombospondin-1, E-selectin, C-C motif chemokine 5, macrophage migration inhibitory factor, plasminogen activator inhibitor, receptor tyrosine-protein kinase, erbb-2, cytokeratin fragment 21.1, and serum amyloid A—that distinguished lung cancer patients from controls with an estimated balanced accuracy (average of sensitivity and specificity) of 76.0 ± 3.8% from 20-fold internal cross-validation. We then iteratively evaluated this model in an independent test and verification case/control studies confirming the initial classification performance of the panel. The classification performance of the 10-biomarker panel was also analytically validated using enzyme-linked immunosorbent assays in a second independent case/control population, further validating the robustness of the panel. Conclusions: The performance of this 10-biomarker panel–based model was 77.1% sensitivity/76.2% specificity in cross-validation in the expanded training set, 73.3% sensitivity/93.3% specificity (balanced accuracy 83.3%) in the blinded verification set with the best discriminative performance in stage I/II cases: 85% sensitivity (balanced accuracy 89.2%). Importantly, the rate of misclassification of CT-screened controls was not different in most control subgroups with or without airflow obstruction or emphysema or pulmonary nodules. These biomarkers have potential to aid in the early detection of lung cancer and more accurate interpretation of indeterminate pulmonary nodules detected by CT screening.

Comparison of probabilistic combination methods for protein secondary structure prediction

MOTIVATION Protein secondary structure prediction is an important step towards understanding how proteins fold in three dimensions. Recent analysis by information theory indicates that the correlation between neighboring secondary structures are much stronger than that of neighboring amino acids. In this article, we focus on the combination problem for sequences, i.e. combining the scores or assignments from single or multiple prediction systems under the constraint of a whole sequence, as a target for improvement in protein secondary structure prediction. RESULTS We apply several graphical chain models to solve the combination problem and show that they are consistently more effective than the traditional window-based methods. In particular, conditional random fields (CRFs) moderately improve the predictions for helices and, more importantly, for beta sheets, which are the major bottleneck for protein secondary structure prediction.

Lung Cancer Serum Biomarker Discovery Using Label-Free Liquid Chromatography-Tandem Mass Spectrometry

Introduction: Lung cancer remains the leading cause of cancer-related death with poor survival due to the late stage at which lung cancer is typically diagnosed. Given the clinical burden from lung cancer and the relatively favorable survival associated with early-stage lung cancer, biomarkers for early detection of lung cancer are of important potential clinical benefit. Methods: We performed a global lung cancer serum biomarker discovery study using liquid chromatography-tandem mass spectrometry in a set of pooled non-small cell lung cancer case sera and matched controls. Immunoaffinity subtraction was used to deplete the top most abundant serum proteins; the remaining serum proteins were subjected to trypsin digestion and analyzed in triplicate by liquid chromatography-tandem mass spectrometry. The tandem mass spectrum data were searched against the human proteome database, and the resultant spectral counting data were used to estimate the relative abundance of proteins across the case/control serum pools. The spectral counting-derived abundances of some candidate biomarker proteins were confirmed with multiple reaction monitoring mass spectrometry assays. Results: A list of 49 differentially abundant candidate proteins was compiled by applying a negative binomial regression model to the spectral counting data (p < 0.01). Functional analysis with Ingenuity Pathway Analysis tools showed significant enrichment of inflammatory response proteins, key molecules in cell-cell signaling and interaction network, and differential physiological responses for the two common non-small cell lung cancer subtypes. Conclusions: We identified a set of candidate serum biomarkers with statistically significant differential abundance across the lung cancer case/control pools, which, when validated, could improve lung cancer early detection.

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.

Segmentation conditional random fields (SCRFs): a new approach for protein fold recognition

Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e. segmentation conditional random fields (SCRFs), is proposed to solve the problem. In contrast to traditional graphical models such as hidden markov model (HMM), SCRFs follow a discriminative approach. It has the flexibility to include overlapping or long-range interaction features over the whole sequence, as well as global optimally solutions for the parameters. On the other hand, the segmentation setting in SCRFs makes its graphical structures intuitively similar to the protein 3-D structures and more importantly, provides a framework to model the long-range interactions directly. Our model is applied to predict the parallel β-helix fold, an important fold in bacterial infection of plants and binding of antigens. The cross-family validation shows that SCRFs not only can score all known β-helices higher than non β-helices in Protein Data Bank, but also demonstrate more success in locating each rung in the known β-helix proteins than BetaWrap, a state-of-the-art algorithm for predicting β-helix fold, and HMMER, a general motif detection algorithm based on HMM. Applying our prediction model to Uniprot database, we hypothesize previously unknown β-helices.

Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)

Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation conditional random fields (SCRFs), is proposed as an effective solution to this problem. In contrast to traditional graphical models, such as the hidden Markov model (HMM), SCRFs follow a discriminative approach. Therefore, it is flexible to include any features in the model, such as overlapping or long-range interaction features over the whole sequence. The model also employs a convex optimization function, which results in globally optimal solutions to the model parameters. On the other hand, the segmentation setting in SCRFs makes their graphical structures intuitively similar to the protein 3-D structures and more importantly provides a framework to model the long-range interactions between secondary structures directly. Our model is applied to predict the parallel beta-helix fold, an important fold in bacterial pathogenesis and carbohydrate binding/cleavage. The cross-family validation shows that SCRFs not only can score all known beta-helices higher than non-beta-helices in the Protein Data Bank (PDB), but also accurately locates rungs in known beta-helix proteins. Our method outperforms BetaWrap, a state-of-the-art algorithm for predicting beta-helix folds, and HMMER, a general motif detection algorithm based on HMM, and has the additional advantage of general application to other protein folds. Applying our prediction model to the Uniprot Database, we identify previously unknown potential beta-helices.

Automatic annotation of protein motif function with Gene Ontology terms

BackgroundConserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, amuch needed and importanttask is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base.ResultsThis paperpresents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifsis viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association isfound to be a very useful feature. We take advantageof the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correctassociation. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria.ConclusionsIn this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about thefunctions of newly discovered candidate protein motifs.