Discovery proteomics and nonparametric modeling pipeline in the development of a candidate biomarker panel for dengue hemorrhagic fever

Airway fibrin formation cascade in allergic asthma exacerbation: implications for inflammation and remodeling

BACKGROUND

Airway remodeling in patients with asthma, which leads to a decline in pulmonary function, is likely the result of repeated exacerbations often provoked by aeroallergen exposures. Aeroallegen exposure triggers a stereotypic response orchestrated by growth factor cytokines and other protein mediators. This results in a late-phase allergic reaction characterized by vascular permeability, recruitment of activated leukocytes, and activation of structural cells of the airway. The spectrum of protein mediators and their functions are incompletely understood.

METHODS

Bronchoalveolar lavage fluid (BALF) samples were obtained from 12 volunteers who exhibited robust eosinophilic recruitment following segmental bronchial provocation with allergen (SBP-Ag). We systematically identified and quantified proteins in BALF using high-performance liquid chromatography-high-resolution mass spectrometry (LC-MS/MS) followed by pathway analysis and correlations with airway physiology.

RESULTS

Pairwise analysis of protein abundance in BALF pre- vs post-SBP-Ag revealed that 55 proteins were upregulated and 103 proteins were downregulated. We observed enrichment of groups of proteins mapping to hemostasis/fibrin clot, platelet activation, lipoprotein assembly, neutrophil degranulation proteins, and acute-phase inflammation-airway remodeling pathways. The abundances of F2 and Fibrinogen γ (FGG) correlated with eosinophil numbers, whereas SERPINA3 negatively correlated with change in FeNO. The coagulation proteins F2 and KNG negatively correlated with FN1 an index of airway remodeling. Interestingly, patients with lower FEV1 showed distinct allergen-induced patterns of 8 BALF proteins, including MUC1, alarmins (HSPB1), and actin polymerization factors.

CONCLUSIONS

Protein abundance of the fibrin formation cascade, platelet activation and remodeling are associated with late-phase leukocyte numbers and markers of remodeling. Patients with lower FEV1 have distinct dynamic responses to allergen.

Role of cytokines produced by T helper immune-modulators in dengue pathogenesis: A systematic review and meta-analysis

BACKGROUND AND OBJECTIVES

Modulation of the immune reaction is essential in the development of various diseases, including dengue's "Cytokine Tsunami", an increase in vascular permeability with concomitant severe vascular leakage. We aim to identify the role of T-helper (Th) cells, Th2 and Th7, with their related cytokines in dengue pathogenesis.

MATERIAL AND METHODS

Nine electronic databases and manual search were applied to detect available publications. A meta-analysis using a fixed- or random-effect model was performed to measure standardized mean difference (SMD) with 95% confidence interval (CI). The National Institute of Health (NIH) tools for observational cohort, cross-sectional, and case-control studies were used to examine the risk of bias. The protocol was recorded in PROSPERO with CRD42017060230.

RESULTS

A total of 38 articles were found including 19 case-control, 11 cross-sectional and 8 prospective cohort studies. We indicated that Th2 cytokines (IL-4, IL-6, IL-8) and Th17 cytokine (IL-17) in dengue patients were notably higher than in a healthy control group in acute phase (SMD = 1.59, 95% CI [0.68, 2.51], p = 0.001; SMD = 1.24, 95% CI [0.41, 2.06], p = 0.003; SMD = 1.13, 95% CI [0.61, 1.66], p<0.0001; SMD = 1.74, 95% CI [0.87, 2.61], p<0.0001), respectively.

CONCLUSIONS

This study provides evidence of the significant roles of IL-4, IL-6, IL-8, IL-10 and IL-17 in the pathogenesis of developing a severe reaction in dengue fever. However, to fully determine the association of Th cytokines with dengue, it is necessary to perform further studies to assess kinetic levels during the duration of the illness.

Proteomics of Select Neglected Tropical Diseases

Technological advances in mass spectrometry have enabled the extensive identification, characterization, and quantification of proteins in any biological system. In disease processes proteins are often altered in response to external stimuli; therefore, proteomics, the large-scale study of proteins and their functions, represents an invaluable tool for understanding the molecular basis of disease. This review highlights the use of mass spectrometry-based proteomics to study the pathogenesis, etiology, and pathology of several neglected tropical diseases (NTDs), a diverse group of disabling diseases primarily associated with poverty in tropical and subtropical regions of the world. While numerous NTDs have been the subject of proteomic studies, this review focuses on Buruli ulcer, dengue, leishmaniasis, and snakebite envenoming. The proteomic studies highlighted provide substantial information on the pathogenic mechanisms driving these diseases; they also identify molecular targets for drug discovery and development and uncover promising biomarkers that can assist in early diagnosis.

Proteins Differentially Expressed in the Pancreas of Hepatic Alcohol Dehydrogenase-Deficient Deer Mice Fed Ethanol For 3 Months

OBJECTIVES

The aim of this study was to identify differentially expressed proteins in the pancreatic tissue of hepatic alcohol dehydrogenase-deficient deer mice fed ethanol to understand metabolic basis and mechanism of alcoholic chronic pancreatitis.

METHODS

Mice were fed liquid diet containing 3.5 g% ethanol daily for 3 months, and differentially expressed pancreatic proteins were identified by protein separation using 2-dimensional gel electrophoresis and identification by mass spectrometry.

RESULTS

Nineteen differentially expressed proteins were identified by applying criteria established for protein identification in proteomics. An increased abundance was found for ribosome-binding protein 1, 60S ribosomal protein L31-like isoform 1, histone 4, calcium, and adenosine triphosphate (ATP) binding proteins and the proteins involved in antiapoptotic processes and endoplasmic reticulum function, stress, and/or homeostasis. Low abundance was found for endoA cytokeratin, 40S ribosomal protein SA, amylase 2b isoform precursor, serum albumin, and ATP synthase subunit β and the proteins involved in cell motility, structure, and conformation.

CONCLUSIONS

Chronic ethanol feeding in alcohol dehydrogenase-deficient deer mice differentially expresses pancreatic functional and structural proteins, which can be used to develop biomarker(s) of alcoholic chronic pancreatitis, particularly amylase 2b precursor, and 60 kDa heat shock protein and those involved in ATP synthesis and blood osmotic pressure.

S-Nitrosylation Proteome Profile of Peripheral Blood Mononuclear Cells in Human Heart Failure

Nitric oxide (NO) protects the heart against ischemic injury; however, NO- and superoxide-dependent S-nitrosylation (S-NO) of cysteines can affect function of target proteins and play a role in disease outcome. We employed 2D-GE with thiol-labeling FL-maleimide dye and MALDI-TOF MS/MS to capture the quantitative changes in abundance and S-NO proteome of HF patients (versus healthy controls, n = 30/group). We identified 93 differentially abundant (59-increased/34-decreased) and 111 S-NO-modified (63-increased/48-decreased) protein spots, respectively, in HF subjects (versus controls, fold-change | ≥1.5|, p ≤ 0.05). Ingenuity pathway analysis of proteome datasets suggested that the pathways involved in phagocytes' migration, free radical production, and cell death were activated and fatty acid metabolism was decreased in HF subjects. Multivariate adaptive regression splines modeling of datasets identified a panel of proteins that will provide >90% prediction success in classifying HF subjects. Proteomic profiling identified ATP-synthase, thrombospondin-1 (THBS1), and vinculin (VCL) as top differentially abundant and S-NO-modified proteins, and these proteins were verified by Western blotting and ELISA in different set of HF subjects. We conclude that differential abundance and S-NO modification of proteins serve as a mechanism in regulating cell viability and free radical production, and THBS1 and VCL evaluation will potentially be useful in the prediction of heart failure.

The Non-Linear Child: Ontogeny, Isoniazid Concentration, and NAT2 Genotype Modulate Enzyme Reaction Kinetics and Metabolism

N-acetyltransferase 2 (NAT2) catalyzes the acetylation of isoniazid to N-acetylisoniazid. NAT2 polymorphism explains 88% of isoniazid clearance variability in adults. We examined the effects of clinical and genetic factors on Michaelis-Menten reaction kinetic constants of maximum velocity (V_max) and affinity (K_m) in children 0–10 years old. We measured the rates of isoniazid elimination and N-acetylisoniazid production in the blood of 30 children. Since maturation effects could be non-linear, we utilized a pharmacometric approach and the artificial intelligence method, multivariate adaptive regression splines (MARS), to identify factors predicting NAT2 V_max and K_m by examining clinical, genetic, and laboratory factors in toto. Isoniazid concentration predicted both V_max and K_m and superseded the contribution of NAT2 genotype. Age non-linearly modified the NAT2 genotype contribution until maturation at ≥ 5.3 years. Thus, enzyme efficiency was constrained by substrate concentration, genes, and age. Since MARS output is in the form of basis functions and equations, it allows multiscale systems modeling from the level of cellular chemical reactions to whole body physiological parameters, by automatic selection of significant predictors by the algorithm.

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We identified the NAT2 K_m and V_max in children treated with isoniazid.

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Artificial intelligence (AI) algorithms were used to find predictors of K_m and V_max.

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Isoniazid concentration affected V_max and K_m, and superseded NAT2 genotype.

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Age non-linearly modified NAT2 genotype contribution until maturation at ≥ 5.3 years.

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AI output is in the form of equations that allow multiscale systems modeling.

The effects of maturation on drug metabolism have not been studied for the type phase II enzymes such as NAT2, which metabolizes the drug isoniazid. Genes have been found to control speed of isoniazid metabolism. Studies to characterize affinity and maximum velocity for isoniazid metabolism in people were last performed in two individuals' livers in the 1960s. We identified NAT2 affinity and maximum velocity in 30 tuberculosis children treated with isoniazid. Artificial intelligence methods found that metabolism was affected by the drug's concentration more than by genes, which were affected by age up to 5.3 years.

Genomic approaches for understanding dengue: insights from the virus, vector, and host

The incidence and geographic range of dengue have increased dramatically in recent decades. Climate change, rapid urbanization and increased global travel have facilitated the spread of both efficient mosquito vectors and the four dengue virus serotypes between population centers. At the same time, significant advances in genomics approaches have provided insights into host–pathogen interactions, immunogenetics, and viral evolution in both humans and mosquitoes. Here, we review these advances and the innovative treatment and control strategies that they are inspiring.

Metabolomics-Based Discovery of Small Molecule Biomarkers in Serum Associated with Dengue Virus Infections and Disease Outcomes

BACKGROUND

Epidemic dengue fever (DF) and dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS) are overwhelming public health capacity for diagnosis and clinical care of dengue patients throughout the tropical and subtropical world. The ability to predict severe dengue disease outcomes (DHF/DSS) using acute phase clinical specimens would be of enormous value to physicians and health care workers for appropriate triaging of patients for clinical management. Advances in the field of metabolomics and analytic software provide new opportunities to identify host small molecule biomarkers (SMBs) in acute phase clinical specimens that differentiate dengue disease outcomes.

METHODOLOGY/PRINCIPAL FINDINGS

Exploratory metabolomic studies were conducted to characterize the serum metabolome of patients who experienced different dengue disease outcomes. Serum samples from dengue patients from Nicaragua and Mexico were retrospectively obtained, and hydrophilic interaction liquid chromatography (HILIC)-mass spectrometry (MS) identified small molecule metabolites that were associated with and statistically differentiated DHF/DSS, DF, and non-dengue (ND) diagnosis groups. In the Nicaraguan samples, 191 metabolites differentiated DF from ND outcomes and 83 differentiated DHF/DSS and DF outcomes. In the Mexican samples, 306 metabolites differentiated DF from ND and 37 differentiated DHF/DSS and DF outcomes. The structural identities of 13 metabolites were confirmed using tandem mass spectrometry (MS/MS). Metabolomic analysis of serum samples from patients diagnosed as DF who progressed to DHF/DSS identified 65 metabolites that predicted dengue disease outcomes. Differential perturbation of the serum metabolome was demonstrated following infection with different DENV serotypes and following primary and secondary DENV infections.

CONCLUSIONS/SIGNIFICANCE

These results provide proof-of-concept that a metabolomics approach can be used to identify metabolites or SMBs in serum specimens that are associated with distinct DENV infections and disease outcomes. The differentiating metabolites also provide insights into metabolic pathways and pathogenic and immunologic mechanisms associated with dengue disease severity.

Changes in Proteome Profile of Peripheral Blood Mononuclear Cells in Chronic Chagas Disease

Trypanosoma cruzi (Tc) infection causes chagasic cardiomyopathy; however, why 30-40% of the patients develop clinical disease is not known. To discover the pathomechanisms in disease progression, we obtained the proteome signature of peripheral blood mononuclear cells (PBMCs) of normal healthy controls (N/H, n = 30) and subjects that were seropositive for Tc-specific antibodies, but were clinically asymptomatic (C/A, n = 25) or clinically symptomatic (C/S, n = 28) with cardiac involvement and left ventricular dysfunction. Protein samples were labeled with BODIPY FL-maleimide (dynamic range: > 4 orders of magnitude, detection limit: 5 f-mol) and resolved by two-dimensional gel electrophoresis (2D-GE). After normalizing the gel images, protein spots that exhibited differential abundance in any of the two groups were analyzed by mass spectrometry, and searched against UniProt human database for protein identification. We found 213 and 199 protein spots (fold change: |≥ 1.5|, p< 0.05) were differentially abundant in C/A and C/S individuals, respectively, with respect to N/H controls. Ingenuity Pathway Analysis (IPA) of PBMCs proteome dataset identified an increase in disorganization of cytoskeletal assembly and recruitment/activation and migration of immune cells in all chagasic subjects, though the invasion capacity of cells was decreased in C/S individuals. IPA predicted with high probability a decline in cell survival and free radical scavenging capacity in C/S (but not C/A) subjects. The MYC/SP1 transcription factors that regulate hypoxia and oxidative/inflammatory stress were predicted to be key targets in the context of control of Chagas disease severity. Further, MARS-modeling identified a panel of proteins that had >93% prediction success in classifying infected individuals with no disease and those with cardiac involvement and LV dysfunction. In conclusion, we have identified molecular pathways and a panel of proteins that could aid in detecting seropositive individuals at risk of developing cardiomyopathy.

Discovery of Candidate Biomarkers

Properly performed, biomarker discovery can lead to effective candidates that can ultimately serve as predictors of disease, medical condition, define therapeutic parameters, and many other applications in medicine. Preferably, biomarkers comprise a panel of indicators, e.g. proteins and/or peptides that can be predictive or diagnostic of the medical condition of interest. Emphasis here is placed on "panel," as single candidates are rarely sufficient to provide the necessary sensitivity and specificity. To develop an effective panel that survives the development process described in Chap. 19 , proper experimental design and attention to important statistical parameters are critical to ensure success. Errors in discovery can lead to an inefficient use of expensive resources, as these may not be uncovered until the latter stages in biomarker development. Hence, accuracy, precision, and an estimate of the power of the proposed analyses are critical in the discovery of the panel of candidate biomarkers by proteomic methods, as is the selection of statistical approaches to refine and appropriately reduce the dataset for subsequent confirmatory assays.

Improved Detection of Invasive Pulmonary Aspergillosis Arising during Leukemia Treatment Using a Panel of Host Response Proteins and Fungal Antigens

Invasive pulmonary aspergillosis (IPA) is an opportunistic fungal infection in patients undergoing chemotherapy for hematological malignancy, hematopoietic stem cell transplant, or other forms of immunosuppression. In this group, Aspergillus infections account for the majority of deaths due to mold pathogens. Although early detection is associated with improved outcomes, current diagnostic regimens lack sensitivity and specificity. Patients undergoing chemotherapy, stem cell transplantation and lung transplantation were enrolled in a multi-site prospective observational trial. Proven and probable IPA cases and matched controls were subjected to discovery proteomics analyses using a biofluid analysis platform, fractionating plasma into reproducible protein and peptide pools. From 556 spots identified by 2D gel electrophoresis, 66 differentially expressed post-translationally modified plasma proteins were identified in the leukemic subgroup only. This protein group was rich in complement components, acute-phase reactants and coagulation factors. Low molecular weight peptides corresponding to abundant plasma proteins were identified. A candidate marker panel of host response (9 plasma proteins, 4 peptides), fungal polysaccharides (galactomannan), and cell wall components (β-D glucan) were selected by statistical filtering for patients with leukemia as a primary underlying diagnosis. Quantitative measurements were developed to qualify the differential expression of the candidate host response proteins using selective reaction monitoring mass spectrometry assays, and then applied to a separate cohort of 57 patients with leukemia. In this verification cohort, a machine learning ensemble-based algorithm, generalized pathseeker (GPS) produced a greater case classification accuracy than galactomannan (GM) or host proteins alone. In conclusion, Integration of host response proteins with GM improves the diagnostic detection of probable IPA in patients undergoing treatment for hematologic malignancy. Upon further validation, early detection of probable IPA in leukemia treatment will provide opportunities for earlier interventions and interventional clinical trials.

Differential proteomic analysis of virus-enriched fractions obtained from plasma pools of patients with dengue fever or severe dengue

Background

Dengue is the most widespread mosquito-borne viral disease of public health concern. In some patients, endothelial cell and platelet dysfunction lead to life-threatening hemorrhagic dengue fever or dengue shock syndrome. Prognostication of disease severity is urgently required to improve patient management. The pathogenesis of severe dengue has not been fully elucidated, and the role of host proteins associated with viral particles has received little exploration.

Methods

The proteomes of virion-enriched fractions purified from plasma pools of patients with dengue fever or severe dengue were compared. Virions were purified by ultracentrifugation combined with a water-insoluble polyelectrolyte-based technique. Following in-gel hydrolysis, peptides were analyzed by nano-liquid chromatography coupled to ion trap mass spectrometry and identified using data libraries.

Results

Both dengue fever and severe dengue viral-enriched fractions contained identifiable viral envelope proteins and host cellular proteins. Canonical pathway analysis revealed the identified host proteins are mainly involved in the coagulation cascade, complement pathway or acute phase response signaling pathway. Some host proteins were over- or under-represented in plasma from patients with severe dengue compared to patients with dengue fever. ELISAs were used to validate differential expression of a selection of identified host proteins in individual plasma samples of patients with dengue fever compared to patients with severe dengue. Among 22 host proteins tested, two could differentiate between dengue fever and severe dengue in two independent cohorts (olfactomedin-4: area under the curve (AUC), 0.958; and platelet factor-4: AUC, 0.836).

Conclusion

A novel technique of virion-enrichment from plasma has allowed to identify two host proteins that have prognostic value for classifying patients with acute dengue who are more likely to develop a severe dengue. The impact of these host proteins on pathogenicity and disease outcome are discussed.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-1271-7) contains supplementary material, which is available to authorized users.

Dengue laboratory diagnosis: still some room for improvement

Dengue is the most important and widely distributed arthropod-borne viral disease affecting humans. The number of dengue virus infections has steadily grown and more than 100 countries survey dengue incidence every year. Due to the lack of an approved antiviral treatment or licensed preventative vaccine, accurate and opportune diagnosis is commended for efficient dengue epidemiological surveillance, to propose control measures in order to curtail outbreaks timely and treat patients satisfactorily. In this review, the basis, application and indications for different diagnostic tests are described, and their advantages and limitations considered. At the end of this piece, we speculate what the future may hold for the diagnosis of dengue infections.

Molecular classification of outcomes from dengue virus -3 infections

OBJECTIVES

Dengue virus (DENV) infection is a significant risk to over a third of the human population that causes a wide spectrum of illness, ranging from sub-clinical disease to intermediate syndrome of vascular complications called dengue fever complicated (DFC) and severe, dengue hemorrhagic fever (DHF). Methods for discriminating outcomes will impact clinical trials and understanding disease pathophysiology.

STUDY DESIGN

We integrated a proteomics discovery pipeline with a heuristics approach to develop a molecular classifier to identify an intermediate phenotype of DENV-3 infectious outcome.

RESULTS

121 differentially expressed proteins were identified in plasma from DHF vs dengue fever (DF), and informative candidates were selected using nonparametric statistics. These were combined with markers that measure complement activation, acute phase response, cellular leak, granulocyte differentiation and viral load. From this, we applied quantitative proteomics to select a 15 member panel of proteins that accurately predicted DF, DHF, and DFC using a random forest classifier. The classifier primarily relied on acute phase (A2M), complement (CFD), platelet counts and cellular leak (TPM4) to produce an 86% accuracy of prediction with an area under the receiver operating curve of >0.9 for DHF and DFC vs DF.

CONCLUSIONS

Integrating discovery and heuristic approaches to sample distinct pathophysiological processes is a powerful approach in infectious disease. Early detection of intermediate outcomes of DENV-3 will speed clinical trials evaluating vaccines or drug interventions.

Discovery and characterization of potential prognostic biomarkers for dengue hemorrhagic fever

Half a million patients are hospitalized with severe dengue every year, many of whom would die without timely, appropriate clinical intervention. The majority of dengue cases are uncomplicated; however, 2-5% progress to severe dengue. Severe dengue cases have been reported with increasing frequency over the last 30 years. To discover biomarkers for severe dengue, we used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to analyze dengue virus positive serum samples from the acute phase of infection. Using this method, 16 proteins were identified as candidate biomarkers for severe dengue. From these 16 biomarkers, three candidates were selected for confirmation by enzyme-linked immunosorbent assay and Western blot: vitronectin (Vtn, 55.1 kDa), hemopexin (Hx, 52.4 kDa), and serotransferrin (Tf, 79.2 kDa). Vitronectin, Hx, and Tf best differentiated between dengue and severe dengue.

Analysis and predictive modeling of asthma phenotypes

Molecular classification using robust biochemical measurements provides a level of diagnostic precision that is unattainable using indirect phenotypic measurements. Multidimensional measurements of proteins, genes, or metabolites (analytes) can identify subtle differences in the pathophysiology of patients with asthma in a way that is not otherwise possible using physiological or clinical assessments. We overview a method for relating biochemical analyte measurements to generate predictive models of discrete (categorical) clinical outcomes, a process referred to as "supervised classification." We consider problems inherent in wide (small n and large p) high-dimensional data, including the curse of dimensionality, collinearity and lack of information content. We suggest methods for reducing the data to the most informative features. We describe different approaches for phenotypic modeling, using logistic regression, classification and regression trees, random forest and nonparametric regression spline modeling. We provide guidance on post hoc model evaluation and methods to evaluate model performance using ROC curves and generalized additive models. The application of validated predictive models for outcome prediction will significantly impact the clinical management of asthma.

Proteomic analysis of the asthmatic airway

Proteomic investigations in general utilize varied technologies for sample preparation, separations, quantification, protein identification, and biological rationalization. Their applications range from pure discovery and mechanistic studies to biomarker discovery/verification/validation. In each specific case, the analytical strategy to be implemented is tailored to the type of sample that serves as the target of the investigations. Proteomic investigations take into consideration sample complexity, the cellular heterogeneity (particularly from tissues), the potential dynamic range of the protein and peptide abundance within the sample, the likelihood of posttranslational modifications (PTM), and other important factors that might influence the final output of the study. We describe the sample types typically used for proteomic investigations into the biology of asthma and review the most recent related publications with special attention to those that deal with the unique airway samples such as bronchoalveolar lavage fluids (BALF), epithelial lining fluid and cells (ELF), induced sputum (IS), and exhaled breath condensate (EBC). Finally, we describe the newest proteomics approaches to sample preparation of the unique airway samples, BALF and IS.

Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth

OBJECTIVE

To develop classification models of demographic/clinical factors and biomarker data from spontaneous preterm birth in African Americans and Caucasians.

DESIGN

Secondary analysis of biomarker data using multivariate adaptive regression splines (MARS), a supervised machine learning algorithm method.

SETTING

Analysis of data on 36 biomarkers from 191 women was reduced by MARS to develop predictive models for preterm birth in African Americans and Caucasians.

SAMPLES

Maternal plasma, cord plasma collected at admission for preterm or term labor and amniotic fluid at delivery.

METHODS

Data were partitioned into training and testing sets. Variable importance, a relative indicator (0-100%) and area under the receiver operating characteristic curve (AUC) characterized results.

RESULTS

Multivariate adaptive regression splines generated models for combined and racially stratified biomarker data. Clinical and demographic data did not contribute to the model. Racial stratification of data produced distinct models in all three compartments. In African Americans maternal plasma samples IL-1RA, TNF-α, angiopoietin 2, TNFRI, IL-5, MIP1α, IL-1β and TGF-α modeled preterm birth (AUC train: 0.98, AUC test: 0.86). In Caucasians TNFR1, ICAM-1 and IL-1RA contributed to the model (AUC train: 0.84, AUC test: 0.68). African Americans cord plasma samples produced IL-12P70, IL-8 (AUC train: 0.82, AUC test: 0.66). Cord plasma in Caucasians modeled IGFII, PDGFBB, TGF-β1 , IL-12P70, and TIMP1 (AUC train: 0.99, AUC test: 0.82). Amniotic fluid in African Americans modeled FasL, TNFRII, RANTES, KGF, IGFI (AUC train: 0.95, AUC test: 0.89) and in Caucasians, TNF-α, MCP3, TGF-β3 , TNFR1 and angiopoietin 2 (AUC train: 0.94 AUC test: 0.79).

CONCLUSIONS

Multivariate adaptive regression splines models multiple biomarkers associated with preterm birth and demonstrated racial disparity.

High-throughput quantitative proteomic analysis of dengue virus type 2 infected A549 cells

Disease caused by dengue virus is a global health concern with up to 390 million individuals infected annually worldwide. There are no vaccines or antiviral compounds available to either prevent or treat dengue disease which may be fatal. To increase our understanding of the interaction of dengue virus with the host cell, we analyzed changes in the proteome of human A549 cells in response to dengue virus type 2 infection using stable isotope labelling in cell culture (SILAC) in combination with high-throughput mass spectrometry (MS). Mock and infected A549 cells were fractionated into nuclear and cytoplasmic extracts before analysis to identify proteins that redistribute between cellular compartments during infection and reduce the complexity of the analysis. We identified and quantified 3098 and 2115 proteins in the cytoplasmic and nuclear fractions respectively. Proteins that showed a significant alteration in amount during infection were examined using gene enrichment, pathway and network analysis tools. The analyses revealed that dengue virus infection modulated the amounts of proteins involved in the interferon and unfolded protein responses, lipid metabolism and the cell cycle. The SILAC-MS results were validated for a select number of proteins over a time course of infection by Western blotting and immunofluorescence microscopy. Our study demonstrates for the first time the power of SILAC-MS for identifying and quantifying novel changes in cellular protein amounts in response to dengue virus infection.

Measurement of the innate immune response in the airway

Asthma is an idiopathic disease associated with episodic inflammation and reversible airway obstruction that is triggered by environmental agents. Allergic and infectious agents trigger asthmatic exacerbations through the innate immune response (IIR). The IIR is activated by sentinel cells in the airways to elaborate inflammatory cytokines and protective mucosal interferons whose actions are designed to limit the spread of the organism, as well as to activate the adaptive immune response. We address the structure of the IIR pathway in sentinel cells of the airway and describe observations on its dysregulation. The IIR is triggered in a cell-type specific manner by germline-encoded pathogen recognition receptors (PPRs) including plasma membrane Toll-like receptors (TLRs) and the cytoplasmic Retinoic Acid-inducible Gene (RIG)-I-like RNA helicases, and protein kinase R (PKR). Although their mechanisms of intracellular signaling differ, both pathways converge on a small group of transcriptional effectors, nuclear factor-κB (NF-κB), IFN regulatory factor (IRF), and signal transducer and activator of transcription (STAT). We describe several distinct techniques to quantitate the IIR including assays based on quantitative real-time PCR (Q-RT-PCR) of NF-κB and IRF3-regulated genes, multiplex bead-based analysis of secreted proteins/cytokines and more recent developments in targeted, quantitative selected reaction monitoring (SRM)-mass spectrometry (MS). Application of these methods for quantitation of the IIR will further our understanding of the role of the IIR in asthma and its contribution to disease heterogeneity.

Variable selection methods for developing a biomarker panel for prediction of dengue hemorrhagic fever

BACKGROUND

The choice of selection methods to identify important variables for binary classification modeling is critical to produce stable models that are interpretable, that generate accurate predictions and have minimum bias. This work is motivated by data on clinical and laboratory features of severe dengue infections (dengue hemorrhagic fever, DHF) obtained from 51 individuals enrolled in a prospective observational study of acute human dengue infections.

RESULTS

We carry out a comprehensive performance comparison using several classification models for DHF over the dengue data set. We compared variable selection results by Multivariate Adaptive Regression Splines, Learning Ensemble, Random Forest, Bayesian Moving Averaging, Stochastic Search Variable Selection, and Generalized Regularized Logistics Regression. Model averaging methods (bagging, boosting and ensemble learners) have higher accuracy, but the generalized regularized regression model has the highest predictive power because the linearity assumptions of candidate predictors are strongly satisfied via deviance chi-square testing procedures. Bootstrapping applications for evaluating predictive regression coefficients in regularized regression model are performed.

CONCLUSIONS

Feature reduction methods introduce inherent biases and therefore are data-type dependent. We propose that these limitations can be overcome using an exhaustive approach for searching feature space. Using this approach, our results suggest that IL-10, platelet and lymphocyte counts are the major features for predicting dengue DHF on the basis of blood chemistries and cytokine measurements.

Sequential waves of gene expression in patients with clinically defined dengue illnesses reveal subtle disease phases and predict disease severity

Background

Dengue virus (DENV) infection can range in severity from mild dengue fever (DF) to severe dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS). Changes in host gene expression, temporally through the progression of DENV infection, especially during the early days, remains poorly characterized. Early diagnostic markers for DHF are also lacking.

Methodology/Principal Findings

In this study, we investigated host gene expression in a cohort of DENV-infected subjects clinically diagnosed as DF (n = 51) and DHF (n = 13) from Maracay, Venezuela. Blood specimens were collected daily from these subjects from enrollment to early defervescence and at one convalescent time-point. Using convalescent expression levels as baseline, two distinct groups of genes were identified: the “early” group, which included genes associated with innate immunity, type I interferon, cytokine-mediated signaling, chemotaxis, and complement activity peaked at day 0–1 and declined on day 3–4; the second “late” group, comprised of genes associated with cell cycle, emerged from day 4 and peaked at day 5–6. The up-regulation of innate immune response genes coincided with the down-regulation of genes associated with viral replication during day 0–3. Furthermore, DHF patients had lower expression of genes associated with antigen processing and presentation, MHC class II receptor, NK and T cell activities, compared to that of DF patients. These results suggested that the innate and adaptive immunity during the early days of the disease are vital in suppressing DENV replication and in affecting outcome of disease severity. Gene signatures of DHF were identified as early as day 1.

Conclusions/Significance

Our study reveals a broad and dynamic picture of host responses in DENV infected subjects. Host response to DENV infection can now be understood as two distinct phases with unique transcriptional markers. The DHF signatures identified during day 1–3 may have applications in developing early molecular diagnostics for DHF.

The clinical outcome of DENV infection in humans can be DF or the more severe DHF and DSS. The individual's previous DENV exposure history, infecting serotypes, and host genetics are thought to be contributing factors to dengue disease severity. Our study contributed to the current dengue research field in the following ways: 1) Our study reveals the dynamics of host gene expression over each day post onset of symptoms. The gene transcription patterns enabled classification of dengue disease into 2 subtle phases: early acute and late acute. 2) The study identified gene markers differentiating severe dengue cases from non-severe cases with >90% accuracy. Taken together, our study offers insight into host responses in DENV-infected subjects and these results may be valuable for the future development of diagnostic tools for disease severity.

A structured approach to predictive modeling of a two-class problem using multidimensional data sets

Biological experiments in the post-genome era can generate a staggering amount of complex data that challenges experimentalists to extract meaningful information. Increasingly, the success of an appropriately controlled experiment relies on a robust data analysis pipeline. In this paper, we present a structured approach to the analysis of multidimensional data that relies on a close, two-way communication between the bioinformatician and experimentalist. A sequential approach employing data exploration (visualization, graphical and analytical study), pre-processing, feature reduction and supervised classification using machine learning is presented. This standardized approach is illustrated by an example from a proteomic data analysis that has been used to predict the risk of infectious disease outcome. Strategies for model selection and post hoc model diagnostics are presented and applied to the case illustration. We discuss some of the practical lessons we have learned applying supervised classification to multidimensional data sets, one of which is the importance of feature reduction in achieving optimal modeling performance.

Liver proteomics in progressive alcoholic steatosis

Fatty liver is an early stage of alcoholic and nonalcoholic liver disease (ALD and NALD) that progresses to steatohepatitis and other irreversible conditions. In this study, we identified proteins that were differentially expressed in the livers of rats fed 5% ethanol in a Lieber-DeCarli diet daily for 1 and 3 months by discovery proteomics (two-dimensional gel electrophoresis and mass spectrometry) and non-parametric modeling (Multivariate Adaptive Regression Splines). Hepatic fatty infiltration was significantly higher in ethanol-fed animals as compared to controls, and more pronounced at 3 months of ethanol feeding. Discovery proteomics identified changes in the expression of proteins involved in alcohol, lipid, and amino acid metabolism after ethanol feeding. At 1 and 3 months, 12 and 15 different proteins were differentially expressed. Of the identified proteins, down regulation of alcohol dehydrogenase (-1.6) at 1 month and up regulation of aldehyde dehydrogenase (2.1) at 3 months could be a protective/adaptive mechanism against ethanol toxicity. In addition, betaine-homocysteine S-methyltransferase 2 a protein responsible for methionine metabolism and previously implicated in fatty liver development was significantly up regulated (1.4) at ethanol-induced fatty liver stage (1 month) while peroxiredoxin-1 was down regulated (-1.5) at late fatty liver stage (3 months). Nonparametric analysis of the protein spots yielded fewer proteins and narrowed the list of possible markers and identified d-dopachrome tautomerase (-1.7, at 3 months) as a possible marker for ethanol-induced early steatohepatitis. The observed differential regulation of proteins have potential to serve as biomarker signature for the detection of steatosis and its progression to steatohepatitis once validated in plasma/serum.

Serum proteome and cytokine analysis in a longitudinal cohort of adults with primary dengue infection reveals predictive markers of DHF

Background

Infections caused by dengue virus are a major cause of morbidity and mortality in tropical and subtropical regions of the world. Factors that control transition from mild forms of disease such as dengue fever (DF) to more life-threatening forms such as dengue hemorrhagic fever (DHF) are poorly understood. Consequently, there are no reliable methods currently available for early triage of DHF patients resulting in significant over-hospitalization.

Methodology/Principal Findings

We have systematically examined the proteome, cytokines and inflammatory markers in sera from 62 adult dengue patients (44 DF; 18 DHF) with primary DENV infection, at three different times of infection representing the early febrile, defervescence and convalescent stages. Using fluorescent bioplex assays, we measured 27 cytokines in these serum samples. Additionally, we used multiple mass spectrometry methods for iTRAQ-based comparative analysis of serum proteome as well as measurements of protein adducts- 3-nitrotyrosine and 3-chlorotyrosine as surrogate measures of free radical activity. Using multiple methods such as OPLS, MRMR and MSVM-RFE for multivariate feature selection and classification, we report molecular markers that allow prediction of primary DHF with sensitivity and specificity of >80%.

Conclusions/Significance

This report constitutes a comprehensive analysis of molecular signatures of dengue disease progression and will help unravel mechanisms of dengue disease progression. Our analysis resulted in the identification of markers that may be useful for early prediction of DHF during the febrile phase. The combination of highly sensitive analytical methods and novel statistical approaches described here forms a robust platform for biomarker discovery.

While the majority of patients who exhibit febrile dengue infection recover within a week, a small proportion of the patients progress to develop severe symptoms that can be life-threatening if not managed in a hospital setting. Because there is no method to accurately identify this subgroup of patients, many dengue patients are hospitalized unnecessarily, which causes significant burden to the healthcare system. In our study, we have systematically measured a large number of molecules including cytokines and serum proteins in blood samples from a dengue patient cohort using highly sensitive mass spectrometry-based methods. We have further developed novel statistical methods that allow us to identify small panels of measureable blood markers, which can distinguish dengue patients that develop milder, self-limiting form of the disease from those that progress to develop severe symptoms. Because these markers can be applied within 48–72 hours of onset of febrile symptoms, we expect them to be useful for early classification of severe dengue disease.

Isotope Coded Protein Labeling analysis of plasma specimens from acute severe dengue fever patients

Background

Dengue fever is the most important arthropod born viral disease of public health significance. Although most patients suffer only from flu-like symptoms, a small group of patient experiences more severe forms of the disease. To contribute to a better understanding of its pathogenesis this study aims to identify proteins differentially expressed in a pool of five viremic plasma from severe dengue patients relative to a pool of five non-severe dengue patients.

Results

The use of Isotope Coded Protein Labeling (ICPL^TM) to analyze plasma depleted of twenty high-abundance proteins allowed for the identification of 51 differentially expressed proteins, which were characterized by mass spectrometry. Using quantitative ELISA, three of these proteins (Leucine-rich glycoprotein 1, Vitamin D binding-protein and Ferritin) were confirmed as having an increased expression in a panel of severe dengue plasma. The proteins identified as overexpressed by ICPL^TM in severe dengue plasma involve in clear up action after cell injury, tissue coherence and immune defense.

Conclusion

This ICPL^TM study evaluating differences between acute severe dengue plasmas and acute non-severe dengue plasmas suggests that the three proteins identified are overexpressed early in the course of the disease. Their possible use as biomarkers for the prognostic of disease severity is discussed.