Identification of Novel Biomarkers for Sepsis Prognosis via Urinary Proteomic Analysis Using iTRAQ Labeling and 2D-LC-MS/MS

Objectives

Sepsis is the major cause of death for critically ill patients. Recent progress in proteomics permits a thorough characterization of the mechanisms associated with critical illness. The purpose of this study was to screen potential biomarkers for early prognostic assessment of patients with sepsis.

Methods

For the discovery stage, 30 sepsis patients with different prognoses were selected. Urinary proteins were identified using isobaric tags for relative and absolute quantitation (iTRAQ) coupled with LC-MS/MS. Mass spec instrument analysis were performed with Mascot software and the International Protein Index (IPI); bioinformatic analyses were used by the algorithm of set and the Gene Ontology (GO) Database. For the verification stage, the study involved another 54 sepsis-hospitalized patients, with equal numbers of patients in survivor and non-survivor groups based on 28-day survival. Differentially expressed proteins were verified by Western Blot.

Results

A total of 232 unique proteins were identified. Proteins that were differentially expressed were further analyzed based on the pathophysiology of sepsis and biomathematics. For sepsis prognosis, five proteins were significantly up-regulated: selenium binding protein-1, heparan sulfate proteoglycan-2, alpha-1-B glycoprotein, haptoglobin, and lipocalin; two proteins were significantly down-regulated: lysosome-associated membrane proteins-1 and dipeptidyl peptidase-4. Based on gene ontology clustering, these proteins were associated with the biological processes of lipid homeostasis, cartilage development, iron ion transport, and certain metabolic processes. Urinary LAMP-1 was down-regulated, consistent with the Western Blot validation.

Conclusion

This study provides the proteomic analysis of urine to identify prognostic biomarkers of sepsis. The seven identified proteins provide insight into the mechanism of sepsis. Low urinary LAMP-1 levels may be useful for early prognostic assessment of sepsis.

Trial Registration

ClinicalTrial.gov {"type":"clinical-trial","attrs":{"text":"NCT01493492","term_id":"NCT01493492"}}NCT01493492     

motifeR: An Integrated Web Software for Identification and Visualization of Protein Posttranslational Modification Motifs

With an exponential growth in applications identifying protein post‐translational modifications via mass spectrometry, discovery and presentation of motifs surrounding those modification sites have become increasingly desirable. Despite a few tools being designed, there is still a scarcity of effective and polyfunctional software for such analysis and illustrations. In this study, a versatile and user‐friendly web tool is developed, motifeR, for extracting and visualizing statistically significant motifs from large datasets. Particularly, several functions are also integrated for processing multi‐modification sites enrichment. Public datasets are applied to test their usability, indicating that some concurrent modification sites may form motifs and that peptides with low location probability may be not identified randomly and can be included to support motif discovery. In addition, for human phosphoproteomics datasets, the characterization of differential kinase signaling networks can be estimated and modeled by combining kinase‐substrate relations based on the NetworKIN database as an optional feature for users. The motifeR toolkit can be conveniently operated by any scientific community or individuals, even those without any bioinformatics background and is freely available at https://www.omicsolution.org/wukong/motifeR .     

An In Silico Based Approach Towards the Characterization with Feature Identification and Analogy Modeling of Human Osteoclast Protein

International Journal of Peptide Research and Therapeutics - The osteoclast is a kind of bone cell that splits down the bone tissue. The osteoclast is responsible for bone disease. So, the...     

Identification of Circulating Biomarker Candidates for Hepatocellular Carcinoma (HCC): An Integrated Prioritization Approach

Hepatocellular carcinoma (HCC) is the world’s third most widespread cancer. Currently available circulating biomarkers for this silently progressing malignancy are not sufficiently specific and sensitive to meet all clinical needs. There is an imminent and pressing need for the identification of novel circulating biomarkers to increase disease-free survival rate. In order to facilitate the selection of the most promising circulating protein biomarkers, we attempted to define an objective method likely to have a significant impact on the analysis of vast data generated from cutting-edge technologies. Current study exploits data available in seven publicly accessible gene and protein databases, unveiling 731 liver-specific proteins through initial enrichment analysis. Verification of expression profiles followed by integration of proteomic datasets, enriched for the cancer secretome, filtered out 20 proteins including 6 previously characterized circulating HCC biomarkers. Finally, interactome analysis of these proteins with midkine (MDK), dickkopf-1 (DKK-1), current standard HCC biomarker alpha-fetoprotein (AFP), its interacting partners in conjunction with HCC-specific circulating and liver deregulated miRNAs target filtration highlighted seven novel statistically significant putative biomarkers including complement component 8, alpha (C8A), mannose binding lectin (MBL2), antithrombin III (SERPINC1), 11β-hydroxysteroid dehydrogenase type 1 (HSD11B1), alcohol dehydrogenase 6 (ADH6), beta-ureidopropionase (UPB1) and cytochrome P450, family 2, subfamily A, polypeptide 6 (CYP2A6). Our proposed methodology provides a swift assortment process for biomarker prioritization that eventually reduces the economic burden of experimental evaluation. Further dedicated validation studies of potential putative biomarkers on HCC patient blood samples are warranted. We hope that the use of such integrative secretome, interactome and miRNAs target filtration approach will accelerate the selection of high-priority biomarkers for other diseases as well, that are more amenable to downstream clinical validation experiments.     

An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation

Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.     

miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets

Background

Many prediction tools for microRNA (miRNA) targets have been developed, but inconsistent predictions were observed across multiple algorithms, which can make further analysis difficult. Moreover, the nomenclature of human miRNAs changes rapidly. To address these issues, we developed a web-based system, miRSystem, for converting queried miRNAs to the latest annotation and predicting the function of miRNA by integrating miRNA target gene prediction and function/pathway analyses.

Results

First, queried miRNA IDs were converted to the latest annotated version to prevent potential conflicts resulting from multiple aliases. Next, by combining seven algorithms and two validated databases, potential gene targets of miRNAs and their functions were predicted based on the consistency across independent algorithms and observed/expected ratios. Lastly, five pathway databases were included to characterize the enriched pathways of target genes through bootstrap approaches. Based on the enriched pathways of target genes, the functions of queried miRNAs could be predicted.

Conclusions

MiRSystem is a user-friendly tool for predicting the target genes and their associated pathways for many miRNAs simultaneously. The web server and the documentation are freely available at http://mirsystem.cgm.ntu.edu.tw/.     

Aminopeptidase Fingerprints, an Integrated Approach for Identification of Good Substrates and Optimal Inhibitors

Aminopeptidases process the N-terminal amino acids of target substrates by sequential cleavage of one residue at a time. They are found in all cell compartments of prokaryotes and eukaryotes, being implicated in the major proteolytic events of cell survival, defense, growth, and development. We present a new approach for the fast and reliable evaluation of the substrate specificity of individual aminopeptidases. Using solid phase chemistry with the 7-amino-4-carbamoylmethylcoumarin fluorophore, we have synthesized a library of 61 individual natural and unnatural amino acids substrates, chosen to cover a broad spectrum of the possible interactions in the S1 pocket of this type of protease. As proof of concept, we determined the substrate specificity of human, pig, and rat orthologs of aminopeptidase N (CD13), a highly conserved cell surface protease that inactivates enkephalins and other bioactive peptides. Our data reveal a large and hydrophobic character for the S1 pocket of aminopeptidase N that is conserved with aminopeptidase Ns. Our approach, which can be applied in principle to all aminopeptidases, yields useful information for the design of specific inhibitors, and more importantly, reveals a relationship between the kinetics of substrate hydrolysis and the kinetics of enzyme inhibition.     

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data

Coronary artery disease(CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism(SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium(WTCCC) SNP datasets of CAD and control samples were used to assess the jointeffect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction(PPI)knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease.These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.     

Integrative Pathway-Based Approach for Genome-Wide Association Studies: Identification of New Pathways for Rheumatoid Arthritis and Type 1 Diabetes

Genome-wide association studies (GWAS) led to the identification of numerous novel loci for a number of complex diseases. Pathway-based approaches using genotypic data provide tangible leads which cannot be identified by single marker approaches as implemented in GWAS. The available pathway analysis approaches mainly differ in the employed databases and in the applied statistics for determining the significance of the associated disease markers.So far, pathway-based approaches using GWAS data failed to consider the overlapping of genes among different pathways or the influence of protein–interactions. We performed a multistage integrative pathway (MIP) analysis on three common diseases - Crohn''s disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) - incorporating genotypic, pathway, protein- and domain-interaction data to identify novel associations between these diseases and pathways. Additionally, we assessed the sensitivity of our method by studying the influence of the most significant SNPs on the pathway analysis by removing those and comparing the corresponding pathway analysis results. Apart from confirming many previously published associations between pathways and RA, CD and T1D, our MIP approach was able to identify three new associations between disease phenotypes and pathways. This includes a relation between the influenza-A pathway and RA, as well as a relation between T1D and the phagosome and toxoplasmosis pathways. These results provide new leads to understand the molecular underpinnings of these diseases.The developed software herein used is available at http://www.cogsys.cs.uni-tuebingen.de/software/GWASPathwayIdentifier/index.htm.     

An Integrated Lab-on-Chip for Rapid Identification and Simultaneous Differentiation of Tropical Pathogens

Tropical pathogens often cause febrile illnesses in humans and are responsible for considerable morbidity and mortality. The similarities in clinical symptoms provoked by these pathogens make diagnosis difficult. Thus, early, rapid and accurate diagnosis will be crucial in patient management and in the control of these diseases. In this study, a microfluidic lab-on-chip integrating multiplex molecular amplification and DNA microarray hybridization was developed for simultaneous detection and species differentiation of 26 globally important tropical pathogens. The analytical performance of the lab-on-chip for each pathogen ranged from 10² to 10³ DNA or RNA copies. Assay performance was further verified with human whole blood spiked with Plasmodium falciparum and Chikungunya virus that yielded a range of detection from 200 to 4×10⁵ parasites, and from 250 to 4×10⁷ PFU respectively. This lab-on-chip was subsequently assessed and evaluated using 170 retrospective patient specimens in Singapore and Thailand. The lab-on-chip had a detection sensitivity of 83.1% and a specificity of 100% for P. falciparum; a sensitivity of 91.3% and a specificity of 99.3% for P. vivax; a positive 90.0% agreement and a specificity of 100% for Chikungunya virus; and a positive 85.0% agreement and a specificity of 100% for Dengue virus serotype 3 with reference methods conducted on the samples. Results suggested the practicality of an amplification microarray-based approach in a field setting for high-throughput detection and identification of tropical pathogens.     

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry

The introduced protocol provides a tool for the analysis of multiprotein complexes in the thylakoid membrane, by revealing insights into complex composition under different conditions. In this protocol the approach is demonstrated by comparing the composition of the protein complex responsible for cyclic electron flow (CEF) in Chlamydomonas reinhardtii, isolated from genetically different strains. The procedure comprises the isolation of thylakoid membranes, followed by their separation into multiprotein complexes by sucrose density gradient centrifugation, SDS-PAGE, immunodetection and comparative, quantitative mass spectrometry (MS) based on differential metabolic labeling (¹⁴N/¹⁵N) of the analyzed strains. Detergent solubilized thylakoid membranes are loaded on sucrose density gradients at equal chlorophyll concentration. After ultracentrifugation, the gradients are separated into fractions, which are analyzed by mass-spectrometry based on equal volume. This approach allows the investigation of the composition within the gradient fractions and moreover to analyze the migration behavior of different proteins, especially focusing on ANR1, CAS, and PGRL1. Furthermore, this method is demonstrated by confirming the results with immunoblotting and additionally by supporting the findings from previous studies (the identification and PSI-dependent migration of proteins that were previously described to be part of the CEF-supercomplex such as PGRL1, FNR, and cyt f). Notably, this approach is applicable to address a broad range of questions for which this protocol can be adopted and e.g. used for comparative analyses of multiprotein complex composition isolated from distinct environmental conditions.     

An Automated and Multiplexed Method for High Throughput Peptide Immunoaffinity Enrichment and Multiple Reaction Monitoring Mass Spectrometry-based Quantification of Protein Biomarkers

There is an urgent need for quantitative assays in verifying and validating the large numbers of protein biomarker candidates produced in modern “-omics” experiments. Stable isotope standards with capture by anti-peptide antibodies (SISCAPA) has shown tremendous potential to meet this need by combining peptide immunoaffinity enrichment with quantitative mass spectrometry. In this study, we describe three significant advances to the SISCAPA technique. First, we develop a method for an automated magnetic bead-based platform capable of high throughput processing. Second, we implement the automated method in a multiplexed SISCAPA assay (nine targets in one assay) and assess the performance characteristics of the multiplexed assay. Using the automated, multiplexed platform, we demonstrate detection limits in the physiologically relevant ng/ml range (from 10 μl of plasma) with sufficient precision (median coefficient of variation, 12.6%) for quantifying biomarkers. Third, we demonstrate that enrichment of peptides from larger volumes of plasma (1 ml) can extend the limits of detection to the low pg/ml range of protein concentration. The method is generally applicable to any protein or biological specimen of interest and holds great promise for analyzing large numbers of biomarker candidates.The current gold standard for quantifying protein biomarkers is the ELISA. A well functioning ELISA can be run at high throughput and has excellent sensitivity; however, the cost associated with development is very high, the lead time is very long, and the failure rate can be high. In addition, sandwich immunoassays are subject to potential interference from endogenous antibodies (1). Unfortunately, there are no quantitative assays available for the majority of biomarker candidates, and a considerable investment is required to generate assays de novo, creating a bottleneck in the biomarker pipeline (2, 3).A technique that has shown potential for bridging the gap between discovery and validation of biomarkers is stable isotope standards with capture by anti-peptide antibodies (SISCAPA)¹ (4) coupled to multiple reaction monitoring (MRM) MS. SISCAPA has several advantages over other immunoassays in that the mass spectrometer provides excellent specificity for the analyte of interest; the sample (including endogenous immunoglobulins) is digested to peptides, avoiding potential interference from endogenous antibodies; and precise, relative quantification is possible via the use of an internal standard. Additionally, although it is very difficult to combine multiple analytes into one assay (i.e. multiplex) using ELISAs, SISCAPA assays can in theory be highly multiplexed as many analytes can be measured from a single enrichment step. To date, individual SISCAPA assays have been successfully configured to a number of analytes (4–9), and up to three peptides have been enriched simultaneously (7, 8). In this study, we sought to advance the utility of SISCAPA for testing large numbers of biomarker candidates in large numbers of patient samples by automating the method to improve throughput and performance, testing the performance of multiplexing analytes, and improving sensitivity.     

Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome: An Alternative Approach

Summary Recently, Cheng (2009 , Biometrics 65, 96–103) proposed a model for the causal effect of receiving treatment when there is all‐or‐none compliance in one randomization group, with maximum likelihood estimation based on convex programming. We discuss an alternative approach that involves a model for all‐or‐none compliance in two randomization groups and estimation via a perfect fit or an expectation–maximization algorithm for count data. We believe this approach is easier to implement, which would facilitate the reproduction of calculations.     

An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.     

Integrated Bioinformatics Analysis for the Identification of Key Molecules and Pathways in the Hippocampus of Rats After Traumatic Brain Injury

Neurochemical Research - High-throughput and bioinformatics technology have been broadly applied to demonstrate the key molecules involved in traumatic brain injury (TBI), while no study has...     

An Approach to the Current-Voltage Characteristics of Nerve Membranbs Based on Adsorption Phenomena

Using Stern's double-layer adsorption model for the density of cations in the membrane pores, a quantitative approach to the stationary current-voltage characteristic of nerve membranes is developed. The interaction of mobile cations with the negative fixed charges, located inside the membrane, constitutes a resistance for the current through the membrane. The stepwise increase in the resistance for the hyperpolarization is ascribed to a stronger interaction accompanying a depletion of the adsorbed cations from the interior. Thermodynamic treatment of flows and forces is adapted to the situation, to give a current voltage relation amenable to experimental check. The value of the resting potential thus obtained gives a deviation from Nernst equation applied to the ion for which the membrane is mainly permeable. The effect of the membrane double-layer potential on the potential range in which the transition from low to high resistance takes place, is explicitly incorporated. Finally, a comparison of the theory with the experimental results for the squid axon and frog nerve fibers is made.     

An Integrated Experimental and Computational Approach for Characterizing the Kinetics and Mechanism of Triadimefon Racemization

Enantiomers of chiral molecules commonly exhibit differing pharmacokinetics and toxicities, which can introduce significant uncertainty when evaluating biological and environmental fates and potential risks to humans and the environment. However, racemization (the irreversible transformation of one enantiomer into the racemic mixture) and enantiomerization (the reversible conversion of one enantiomer into the other) are poorly understood. To better understand these processes, we investigated the chiral fungicide, triadimefon, which undergoes racemization in soils, water, and organic solvents. Nuclear magnetic resonance (NMR) and gas chromatography / mass spectrometry (GC/MS) techniques were used to measure the rates of enantiomerization and racemization, deuterium isotope effects, and activation energies for triadimefon in H₂O and D₂O. From these results we were able to determine that: 1) the alpha‐carbonyl carbon of triadimefon is the reaction site; 2) cleavage of the C‐H (C‐D) bond is the rate‐determining step; 3) the reaction is base‐catalyzed; and 4) the reaction likely involves a symmetrical intermediate. The B3LYP/6–311 + G** level of theory was used to compute optimized geometries, harmonic vibrational frequencies, nature population analysis, and intrinsic reaction coordinates for triadimefon in water and three racemization pathways were hypothesized. This work provides an initial step in developing predictive, structure‐based models that are needed to identify compounds of concern that may undergo racemization. Chirality 28:633–641, 2016. © 2016 Wiley Periodicals, Inc.