ProCAT: a data analysis approach for protein microarrays

Pro-MAP: a robust pipeline for the pre-processing of single channel protein microarray data

BACKGROUND

The central role of proteins in diseases has made them increasingly attractive as therapeutic targets and indicators of cellular processes. Protein microarrays are emerging as an important means of characterising protein activity. Their accurate downstream analysis to produce biologically significant conclusions is largely dependent on proper pre-processing of extracted signal intensities. However, existing computational tools are not specifically tailored to the nature of these data and lack unanimity.

RESULTS

Here, we present the single-channel Protein Microarray Analysis Pipeline, a tailored computational tool for analysis of single-channel protein microarrays enabling biomarker identification, implemented in R, and as an interactive web application. We compared four existing background correction and normalization methods as well as three array filtering techniques, applied to four real datasets with two microarray designs, extracted using two software programs. The normexp, cyclic loess, and array weighting methods were most effective for background correction, normalization, and filtering respectively.

CONCLUSIONS

Thus, here we provided a versatile and effective pre-processing and differential analysis workflow for single-channel protein microarray data in form of an R script and web application ( https://metaomics.uct.ac.za/shinyapps/Pro-MAP/ .) for those not well versed in the R programming language.

A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.

Summit: Automated Analysis of Arrayed Single-Cell Gel Electrophoresis

New pipelines are required to automate the quantitation of emerging high-throughput electrophoretic (EP) assessment of DNA damage, or proteoform expression in single cells. EP cytometry consists of thousands of Western blots performed on a microscope slide-sized gel microwell array for single cells. Thus, EP cytometry images pose an analysis challenge that blends requirements for accurate and reproducible analysis encountered for both standard Western blots and protein microarrays. Here, we introduce the Summit algorithm to automate array segmentation, peak background subtraction, and Gaussian fitting for EP cytometry. The data structure storage of parameters allows users to perform quality control on identically processed data, yielding a ~6.5% difference in coefficient of quartile variation (CQV) of protein peak area under the curve (AUC) distributions measured by four users. Further, inspired by investigations of background subtraction methods to reduce technical variation in protein microarray measurements, we aimed to understand the trade-offs between EP cytometry analysis throughput and variation. We found an 11%-50% increase in protein peaks that passed quality control with a subtraction method similar to microarray "average on-boundary" versus an axial subtraction method. The background subtraction method only mildly influences AUC CQV, which varies between 1% and 4.5%. Finally, we determined that the narrow confidence interval for peak location and peak width parameters from Gaussian fitting yield minimal uncertainty in protein sizing. The AUC CQV differed by only ~1%-2% when summed over the peak width bounds versus the 95% peak width confidence interval. We expect Summit to be broadly applicable to other arrayed EP separations, or traditional Western blot analysis.

protGear: A protein microarray data pre-processing suite

Protein microarrays are versatile tools for high throughput study of the human proteome, but systematic and non-systematic sources of bias constrain optimal interpretation and the ultimate utility of the data. Published guidelines to limit technical variability whilst maintaining important biological variation favour DNA-based microarrays that often differ fundamentally in their experimental design. Rigorous tools to guide background correction, the quantification of within-sample variation, normalisation, and batch correction specifically for protein microarrays are limited, require extensive investigation and are not centrally accessible.

Here, we develop a generic one-stop-shop pre-processing suite for protein microarrays that is compatible with data from the major protein microarray scanners. Our graphical and tabular interfaces facilitate a detailed inspection of data and are coupled with supporting guidelines that enable users to select the most appropriate algorithms to systematically address bias arising in customized experiments. The localization and distribution of background signal intensities determine the optimal correction strategy. A novel function overcomes the limitations in the interpretation of the coefficient of variation when signal intensities are at the lower end of the detection threshold. We demonstrate essential considerations in the experimental design and their impact on a range of algorithms for normalization and minimization of batch effects.

Our user-friendly interactive web-based platform eliminates the need for prowess in programming. The open-source R interface includes illustrative examples, generates an auditable record, enables reproducibility, and can incorporate additional custom scripts through its online repository. This versatility will enhance its broad uptake in the infectious disease and vaccine development community.

Identification of MltG as a Prc Protease Substrate Whose Dysregulation Contributes to the Conditional Growth Defect of Prc-Deficient Escherichia coli

Microbial proteases play pivotal roles in many aspects of bacterial physiological processes. Because a protease exerts its biological function by proteolytically regulating its substrates, the identification and characterization of the physiological substrates of a protease advance our understanding of the biological roles of the protease. Prc (also named Tsp) is an Escherichia coli periplasmic protease thought to be indispensable for E. coli to survive under low osmolality at 42°C. The accumulation of the Prc substrate MepS due to Prc deficiency contributes to the conditional growth defect. Because preventing MepS accumulation only partially restored the growth of Prc-deficient E. coli, we hypothesized that other unidentified Prc substrates intracellularly accumulate due to Prc deficiency and contribute to the conditional growth defect. To identify previously undiscovered substrates, 85 E. coli proteins able to physically interact with Prc were identified using E. coli proteome arrays. Ten proteins were shown to be cleavable by Prc in vitro. Among these candidates, MltG was able to interact with Prc in E. coli. Prc regulated the intracellular level of MltG, indicating that MltG is a physiological substrate of Prc. Prc deficiency induced the accumulation of MltG in the bacteria. Blocking MltG accumulation by deleting mltG partially restored the growth of Prc-deficient E. coli. In addition, Prc-deficient E. coli with blocked MltG and MepS expression exhibited higher growth levels than those with only the MltG or MepS expression blocked under low osmolality at 42°C, suggesting that these accumulated substrates additively contributed to the conditional growth defect. MltG is a lytic transglycosylase involved in the biogenesis of peptidoglycan (PG). In addition to MltG, the previously identified physiological Prc substrates MepS and PBP3 are involved in PG biogenesis, suggesting a potential role of Prc in regulating PG biogenesis.

Peptides of neuron specific enolase as potential ASD biomarkers: From discovery to epitope mapping

Autism spectrum disorder (ASD) is an important health issue and affects 1 in 59 children in the US. Prior studies determined that maternal autoantibody related (MAR) autism is thought to be associated with ~23% of ASD cases. We previously identified seven MAR-specific autoantigens including CRMP1, CRMP2, GDA, LDHA, LDHB, STIP1, and YBX1. We subsequently described the epitope peptide sequences recognized by maternal autoantibodies for each of the seven ASD-specific autoantigens. The aim of the current study was to expand upon our previous work and identify additional antigens recognized by the ASD-specific maternal autoantibodies, as well as to map the unique ASD-specific epitopes using microarray technology. Fetal Rhesus macaque brain tissues were separated by molecular weight and a fraction containing bands between 37 and 45 kDa was analyzed using 2-D gel electrophoresis, followed by peptide mass mapping using MALDI-TOF MS and TOF/TOF tandem MS/MS. Using this methodology, Neuron specific enolase (NSE) was identified as a target autoantigen and selected for epitope mapping. The full NSE sequence was translated into 15-mer peptides with an overlap of 14 amino acids onto microarray slides and probed with maternal plasma from mothers with an ASD child and from mothers with a Typically Developing child (TD) (ASD = 27 and TD = 21). The resulting data were analyzed by T-test. We found 16 ASD-specific NSE-peptide sequences for which four sequences were statistically significant (p < 0.05) using both the t-test and SAM t-test: DVAASEFYRDGKYDL (p = 0.047; SAM score 1.49), IEDPFDQDDWAAWSK (p = 0.049; SAM score 1.49), ERLAKYNQLMRIEEE (p = 0.045; SAM score 1.57), and RLAKYNQLMRIEEEL (p = 0.017; SAM score 1.82). We further identified 5 sequences that were recognized by both ASD and TD antibodies suggesting a large immunodominant epitope (DYPVVSIEDPFDQDDWAAW). While maternal autoantibodies against the NSE protein are present both in mothers with ASD and mothers of TD children, there are several ASD-specific epitopes that can potentially be used as MAR ASD biomarkers. Further, studies including analysis of NSE as a target protein in combination with the previously identified MAR ASD autoantigens are currently underway.

PMA: Protein Microarray Analyser, a user-friendly tool for data processing and normalization

Objective

Protein microarrays provide a high-throughput platform to measure protein interactions and associated functions, and can aid in the discovery of cancer biomarkers. The resulting protein microarray data can however be subject to systematic bias and noise, thus requiring a robust data processing, normalization and analysis pipeline to ensure high quality and robust results. To date, a comprehensive data processing pipeline is yet to be developed. Furthermore, a lack of analysis consistency is evident amongst different research groups, thereby impeding collaborative data consolidation and comparison. Thus, we sought to develop an accessible data processing tool using methods that are generalizable to the protein microarray field and which can be adapted to individual array layouts with minimal software engineering expertise.

Results

We developed an improved version of a previously developed pipeline of protein microarray data processing and implemented it as an open source software tool, with particular focus on widening its use and applicability. The Protein Microarray Analyser software presented here includes the following tools: (1) neighbourhood background correction, (2) net intensity correction, (3) user-defined noise threshold, (4) user-defined CV threshold amongst replicates and (5) assay controls, (6) composite ‘pin-to-pin’ normalization amongst sub-arrays, and (7) ‘array-to-array’ normalization amongst whole arrays.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3266-0) contains supplementary material, which is available to authorized users.

Escherichia coli Proteome Microarrays Identified the Substrates of ClpYQ Protease

Proteolysis is a vital mechanism to regulate the cellular proteome in all kingdoms of life, and ATP-dependent proteases play a crucial role within this process. In Escherichia coli, ClpYQ is one of the primary ATP-dependent proteases. In addition to function with removals of abnormal peptides in the cells, ClpYQ degrades regulatory proteins if necessary and thus let cells adjust to various environmental conditions. In E. coli, SulA, RcsA, RpoH and TraJ as well as RNase R, have been identified as natural protein substrates of ClpYQ. ClpYQ contains ClpY and ClpQ. The ATPase ClpY is responsible for protein recognition, unfolding, and translocation into the catalytic core of ClpQ. In this study, we use an indirect identification strategy to screen possible ClpY targets with E. coli K12 proteome chips. The chip assay results showed that YbaB strongly bound to ClpY. We used yeast two-hybrid assay to confirm the interactions between ClpY and YbaB protein and determined the K_d between ClpY and YbaB by quartz crystal microbalance. Furthermore, we validated that YbaB was successfully degraded by ClpYQ protease activity using ClpYQ in vitro and in vivo degradation assay. These findings demonstrated the YbaB is a novel substrate of ClpYQ protease. This work also successfully demonstrated that with the use of recognition element of a protease can successfully screen its substrates by indirect proteome chip screening assay.

Systematic protein interactome analysis of glycosaminoglycans revealed YcbS as a novel bacterial virulence factor

Microbial pathogens have evolved several strategies for interacting with host cell components, such as glycosaminoglycans (GAGs). Some microbial proteins involved in host-GAG binding have been described; however, a systematic study on microbial proteome-mammalian GAG interactions has not been conducted. Here, we used Escherichia coli proteome chips to probe four typical mammalian GAGs, heparin, heparan sulphate (HS), chondroitin sulphate B (CSB), and chondroitin sulphate C (CSC), and identified 185 heparin-, 62 HS-, 98 CSB-, and 101 CSC-interacting proteins. Bioinformatics analyses revealed the unique functions of heparin- and HS-specific interacting proteins in glycine, serine, and threonine metabolism. Among all the GAG-interacting proteins, three were outer membrane proteins (MbhA, YcbS, and YmgH). Invasion assays confirmed that mutant E. coli lacking ycbS could not invade the epithelial cells. Introducing plasmid carrying ycbS complemented the invading defects at ycbS lacking E. coli mutant, that can be further improved by overexpressing ycbS. Preblocking epithelial cells with YcbS reduced the percentage of E. coli invasions. Moreover, we observed that whole components of the ycb operon were crucial for invasion. The displacement assay revealed that YcbS binds to the laminin-binding site of heparin and might affect the host extracellular matrix structure by displacing heparin from laminin.

Systematic Analysis of Intracellular-targeting Antimicrobial Peptides, Bactenecin 7, Hybrid of Pleurocidin and Dermaseptin, Proline-Arginine-rich Peptide, and Lactoferricin B, by Using Escherichia coli Proteome Microarrays

Antimicrobial peptides (AMPs) act either through membrane lysis or by attacking intracellular targets. Intracellular targeting AMPs are a resource for antimicrobial agent development. Several AMPs have been identified as intracellular targeting peptides; however, the intracellular targets of many of these peptides remain unknown. In the present study, we used an Escherichia coli proteome microarray to systematically identify the protein targets of three intracellular targeting AMPs: bactenecin 7 (Bac7), a hybrid of pleurocidin and dermaseptin (P-Der), and proline-arginine-rich peptide (PR-39). In addition, we also included the data of lactoferricin B (LfcinB) from our previous study for a more comprehensive analysis. We analyzed the unique protein hits of each AMP in the Kyoto Encyclopedia of Genes and Genomes. The results indicated that Bac7 targets purine metabolism and histidine kinase, LfcinB attacks the transcription-related activities and several cellular carbohydrate biosynthetic processes, P-Der affects several catabolic processes of small molecules, and PR-39 preferentially recognizes proteins involved in RNA- and folate-metabolism-related cellular processes. Moreover, both Bac7 and LfcinB target purine metabolism, whereas LfcinB and PR-39 target lipopolysaccharide biosynthesis. This suggested that LfcinB and Bac7 as well as LfcinB and PR-39 have a synergistic effect on antimicrobial activity, which was validated through antimicrobial assays. Furthermore, common hits of all four AMPs indicated that all of them target arginine decarboxylase, which is a crucial enzyme for Escherichia coli survival in extremely acidic environments. Thus, these AMPs may display greater inhibition to bacterial growth in extremely acidic environments. We have also confirmed this finding in bacterial growth inhibition assays. In conclusion, this comprehensive identification and systematic analysis of intracellular targeting AMPs reveals crucial insights into the intracellular mechanisms of the action of AMPs.

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

High-density functional protein microarrays containing ~4,200 recombinant yeast proteins are examined for kinase protein-protein interactions using an affinity purified yeast kinase fusion protein containing a V5-epitope tag for read-out. Purified kinase is obtained through culture of a yeast strain optimized for high copy protein production harboring a plasmid containing a Kinase-V5 fusion construct under a GAL inducible promoter. The yeast is grown in restrictive media with a neutral carbon source for 6 hr followed by induction with 2% galactose. Next, the culture is harvested and kinase is purified using standard affinity chromatographic techniques to obtain a highly purified protein kinase for use in the assay. The purified kinase is diluted with kinase buffer to an appropriate range for the assay and the protein microarrays are blocked prior to hybridization with the protein microarray. After the hybridization, the arrays are probed with monoclonal V5 antibody to identify proteins bound by the kinase-V5 protein. Finally, the arrays are scanned using a standard microarray scanner, and data is extracted for downstream informatics analysis to determine a high confidence set of protein interactions for downstream validation in vivo.

Antibody profiling of bipolar disorder using Escherichia coli proteome microarrays

To profile plasma antibodies of patients with bipolar disorder (BD), an E. coli proteome microarray comprising ca. 4200 proteins was used to analyze antibody differences between BD patients and mentally healthy controls (HCs). The plasmas of HCs and patients aged 18-45 years with bipolar I disorder (DSM-IV) in acute mania (BD-A) along with remission (BD-R) were collected. The initial samples consisting of 19 BD-A, 20 BD-R, and 20 HCs were probed with the microarrays. After selecting protein hits that recognized the antibody differences between BD and HC, the proteins were purified to construct BD focus arrays for training diagnosis committees and validation. Additional six BD-A, six BD-R, six HCs, and nine schizophrenic disorder (SZ, as another psychiatric control) samples were individually probed with the BD focus arrays. The trained diagnosis committee in BD-A versus HC combined top six proteins, including rpoA, thrA, flhB, yfcI, ycdU, and ydjL. However, the optimized committees in BD-R versus HC and BD-A versus BD-R were of low accuracy (< 0.6). In the single blind test using another four BD-A, four HC, and four SZ samples, the committee of BD-A versus HC was able to classify BD-A versus HC and SZ with 75% sensitivity and 80% specificity that both HC and SZ were regarded as negative controls. The consensus motif of the six proteins, which form the committee of BD-A versus HC, is [KE]DIL[AG]L[LV]I[NL][IC][SVKH]G[LV][VN][LV] by Gapped Local Alignment of Motifs. We demonstrated that the E. coli proteome microarray is capable of screening BD plasma antibody differences and the selected proteins committee was successfully used for BD diagnosis with 79% accuracy.

Exploiting fluorescence for multiplex immunoassays on protein microarrays

Protein microarray technology is becoming the method of choice for identifying protein interaction partners, detecting specific proteins, carbohydrates and lipids, or for characterizing protein interactions and serum antibodies in a massively parallel manner. Availability of the well-established instrumentation of DNA arrays and development of new fluorescent detection instruments promoted the spread of this technique. Fluorescent detection has the advantage of high sensitivity, specificity, simplicity and wide dynamic range required by most measurements. Fluorescence through specifically designed probes and an increasing variety of detection modes offers an excellent tool for such microarray platforms. Measuring for example the level of antibodies, their isotypes and/or antigen specificity simultaneously can offer more complex and comprehensive information about the investigated biological phenomenon, especially if we take into consideration that hundreds of samples can be measured in a single assay. Not only body fluids, but also cell lysates, extracted cellular components, and intact living cells can be analyzed on protein arrays for monitoring functional responses to printed samples on the surface. As a rapidly evolving area, protein microarray technology offers a great bulk of information and new depth of knowledge. These are the features that endow protein arrays with wide applicability and robust sample analyzing capability. On the whole, protein arrays are emerging new tools not just in proteomics, but glycomics, lipidomics, and are also important for immunological research. In this review we attempt to summarize the technical aspects of planar fluorescent microarray technology along with the description of its main immunological applications.

Experimental and analytical approaches to characterize plant kinases using protein microarrays

Comprehensive analysis of protein kinases and cellular signaling pathways requires the identification of kinase substrates and interaction partners using large-scale amenable approaches. Here, we describe our methods for producing plant protein microarrays (PMAs) and discuss various parameters critical to the quality of PMAs. Next, we describe methods for detecting protein-protein interactions and kinase activity including auto-phosphorylation and substrate phosphorylation. We have provided a short video demonstrating how to conduct an interaction assay and how to properly handle a protein microarray. Finally, a set of analytical methods are presented as a bioinformatics pipeline for the acquisition of PMA data and for selecting PMA candidates using statistical testing. The experimental and analytical protocols described here outline the steps to produce and utilize PMAs to analyze signaling networks.

Serum profiling using protein microarrays to identify disease related antigens

Disease related antigens are of great importance in the clinic. They are used as markers to screen patients for various forms of cancer, to monitor response to therapy, or to serve as therapeutic targets (Chapman et al., Ann Oncol 18(5):868-873, 2007; Soussi et al., Cancer Res 60:1777-1788, 2000; Anderson and LaBaer, J Proteome Res 4:1123-1133, 2005; Levenson, Biochim Biophy Acta 1770:847-856, 2007). In cancer endogenous levels of protein expression may be disrupted or proteins may be expressed in an aberrant fashion resulting in an immune response that bypasses self tolerance (Soussi et al., Cancer Res 60:1777-1788, 2000; Disis et al., J Clin Oncol 15(11):3363-3367, 1997; Molina et al., Breast Cancer Res Treat 51:109-119, 1998). Protein microarrays, which represent a large fraction of the human proteome, have been used to identify antigens in multiple diseases including cancer (Anderson and LaBaer, J Proteome Res 4:1123-1133, 2005; Disis et al., J Clin Oncol 15(11):3363-3367, 1997; Hudson et al., Proc Natl Acad Sci U S A 104(44):17494-17499, 2007; Beyer et al., J Neuroimmunol 242:26-32, 2012). Typically, arrays are probed with immunoglobulin (Ig) samples from patients as well as healthy controls, then compared to determine which antigens (Ag's) are more reactive within the patient group (Hudson et al., Proc Natl Acad Sci U S A 104(44):17494-17499).

Specific plasma autoantibody reactivity in myelodysplastic syndromes

Increased autoantibody reactivity in plasma from Myelodysplastic Syndromes (MDS) patients may provide novel disease signatures, and possible early detection. In a two-stage study we investigated Immunoglobulin G reactivity in plasma from MDS, Acute Myeloid Leukemia post MDS patients, and a healthy cohort. In exploratory Stage I we utilized high-throughput protein arrays to identify 35 high-interest proteins showing increased reactivity in patient subgroups compared to healthy controls. In validation Stage II we designed new arrays focusing on 25 of the proteins identified in Stage I and expanded the initial cohort. We validated increased antibody reactivity against AKT3, FCGR3A and ARL8B in patients, which enabled sample classification into stable MDS and healthy individuals. We also detected elevated AKT3 protein levels in MDS patient plasma. The discovery of increased specific autoantibody reactivity in MDS patients, provides molecular signatures for classification, supplementing existing risk categorizations, and may enhance diagnostic and prognostic capabilities for MDS.

Preparation of recombinant protein spotted arrays for proteome-wide identification of kinase targets

Protein microarrays allow unique approaches for interrogating global protein interaction networks. Protein arrays can be divided into two categories: antibody arrays and functional protein arrays. Antibody arrays consist of various antibodies and are appropriate for profiling protein abundance and modifications. Functional full-length protein arrays employ full-length proteins with various post-translational modifications. A key advantage of the latter is rapid parallel processing of large number of proteins for studying highly controlled biochemical activities, protein-protein interactions, protein-nucleic acid interactions, and protein-small molecule interactions. This unit presents a protocol for constructing functional yeast protein microarrays for global kinase substrate identification. This approach enables the rapid determination of protein interaction networks in yeast on a proteome-wide level. The same methodology can be readily applied to higher eukaryotic systems with careful consideration of overexpression strategy.

Semantically predicting protein functions based on protein functional connectivity

BACKGROUND

The current availability of public protein-protein interaction (PPI) databases which are usually modelled as PPI networks has led to the rapid development of protein function prediction approaches. The existing network-based prediction approaches mainly focus on the topological similarities between immediately interacting proteins, neglecting the protein functional connectivity which is the functional tightness between proteins. In this paper, we attempt to predict the functions of unannotated proteins based on PPI networks by incorporating the protein functional connectivity, as well as the similarity of protein functions, into the prediction procedure.

RESULTS

An approach named Semantic protein function Prediction based on protein Functional Connectivity (SPFC) is proposed to achieve a higher accuracy in predicting functions of unannotated protein. We define the functional connectivity and function addition for each protein, and incorporate them into the prediction. We evaluated the SPFC on real PPI datasets and the experiment results show that the SPFC method is more effective in function prediction than other network-based approaches.

CONCLUSION

Incorporating the functional connectivity of each protein into the function prediction can significantly improve the accuracy of protein prediction.

Identification of new autoimmune hepatitis-specific autoantigens by using protein microarray technology

Autoimmune hepatitis (AIH) is a chronic necroinflammatory disease of the liver with a poorly understood etiology. Detection of non-organ-specific and liver-related autoantibodies using immunoserological approaches has been widely used for diagnosis and prognosis. However, unambiguous and accurate detection of the disease requires the identification and characterization of disease-specific autoantigens. In the present study, we have used protein microarray technology to profile the autoantigen repertoire of patients with AIH versus those with other liver diseases, identifying and validating novel and highly specific biomarkers for AIH. Using two algorithms developed in the current study, we were able to demonstrate that a panel of six autoantigens could be used in combination to clearly diagnose AIH-positive serum samples.

A cancer/testis antigen microarray to screen autoantibody biomarkers of non-small cell lung cancer

Cancer/testis antigens (CTAs) are highly immunogenic in many tumors, especially in non-small cell lung cancer (NSCLC). A low-density protein microarray, which consisted of 72 CTAs and six non-CTAs, was used to screen for lung cancer-related autoantibodies. The CTA panel of NY-ESO-1, XAGE-1, ADAM29 and MAGEC1, had sensitivity and specificity values of 33% and 96%, respectively. When examined in a test set, this panel of markers had sensitivity and specificity values of 36% and 89%, respectively. This array of markers preferentially detected NSCLC, but did not detect breast cancer, and non-cancer lung disease.

Personal omics profiling reveals dynamic molecular and medical phenotypes

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.

Planar protein arrays in microtiter plates: development of a new format towards accurate, automation-friendly and affordable (A(3)) diagnostics

A technology for protein microarrays in 96 well microplates has been developed as a widely adoptable platform for multiplexed protein based diagnostics. Procedures for protein microarray manufacturing, immobilization, assay optimization and optical detection methods were developed. Using a clever combination of state of the art technologies, a versatile platform that works with peptides, proteins and antibodies as capture agents in a scalable format for planar arrays is described.

Identification of lactoferricin B intracellular targets using an Escherichia coli proteome chip

Lactoferricin B (LfcinB) is a well-known antimicrobial peptide. Several studies have indicated that it can inhibit bacteria by affecting intracellular activities, but the intracellular targets of this antimicrobial peptide have not been identified. Therefore, we used E. coli proteome chips to identify the intracellular target proteins of LfcinB in a high-throughput manner. We probed LfcinB with E. coli proteome chips and further conducted normalization and Gene Ontology (GO) analyses. The results of the GO analyses showed that the identified proteins were associated with metabolic processes. Moreover, we validated the interactions between LfcinB and chip assay-identified proteins with fluorescence polarization (FP) assays. Sixteen proteins were identified, and an E. coli interaction database (EcID) analysis revealed that the majority of the proteins that interact with these 16 proteins affected the tricarboxylic acid (TCA) cycle. Knockout assays were conducted to further validate the FP assay results. These results showed that phosphoenolpyruvate carboxylase was a target of LfcinB, indicating that one of its mechanisms of action may be associated with pyruvate metabolism. Thus, we used pyruvate assays to conduct an in vivo validation of the relationship between LfcinB and pyruvate level in E. coli. These results showed that E. coli exposed to LfcinB had abnormal pyruvate amounts, indicating that LfcinB caused an accumulation of pyruvate. In conclusion, this study successfully revealed the intracellular targets of LfcinB using an E. coli proteome chip approach.

Lactoferricin B inhibits the phosphorylation of the two-component system response regulators BasR and CreB

Natural antimicrobial peptides provide fundamental protection for multicellular organisms from microbes, such as Lactoferricin B (Lfcin B). Many studies have shown that Lfcin B penetrates the cell membrane and has intracellular activities. To elucidate the intracellular behavior of Lfcin B, we first used Escherichia coli K12 proteome chips to identify the intracellular targets of Lfcin B. The results showed that Lfcin B binds to two response regulators, BasR and CreB, of the two-component system. For further analysis, we conducted several in vitro and in vivo experiments and utilized bioinformatics methods. The electrophoretic mobility shift assays and kinase assays indicate that Lfcin B inhibits the phosphorylation of the response regulators (BasR and CreB) and their cognate sensor kinases (BasS and CreC). Antibacterial assays showed that Lfcin B reduced E. coli's tolerance to environmental stimuli, such as excessive ferric ions and minimal medium conditions. This is the first study to show that an antimicrobial peptide inhibits the growth of bacteria by influencing the phosphorylation of a two-component system directly.

Nucleic Acid programmable protein arrays: versatile tools for array-based functional protein studies

Protein microarrays offer a global perspective on the function of expressed gene products. However, technical issues related to the stability and dynamic range of microarrays printed with purified protein have hampered their widespread adoption. Taking an alternate approach, the Nucleic Acid Programmable Protein Array (NAPPA) is constructed by spotting protein-encoding plasmid DNA at high density, in addressable fashion, on an array surface. Proteins are subsequently generated in situ just prior to experimentation using cell-free expression systems. As such, the NAPPA platform offers a unique and viable alternative that circumvents many of the inherent limitations of spotted protein arrays, enabling diverse functional protein studies including protein-small molecule, protein-protein, antigen-antibody, and protein-nucleic acid interactions. It further offers a versatile and adaptable platform amenable to a variety of capture modalities and expression systems, and, most importantly, construction of the array is accessible to any lab with an array printer and laser slide scanner. This unit is intended to provide a reference for investigators wishing to generate arrays based on this platform, and details (1) the basic construction of cDNA-based protein microarrays from DNA isolation to printing and development, (2) quality-control efforts taken to ensure the usefulness and integrity of microarray data, and (3) a particular example of the application of self-assembling protein arrays to screen for blood-borne antibody biomarkers.

Kinase substrate interactions

Kinases have become popular therapeutic targets primarily due to their integral role in cell cycle and tumor progression. The efficacy of high-throughput screening efforts is dependent on the development of high quality multiplex tools capable of replacing lower-throughput technologies such as mass spectroscopy or solution-based assays for the study of kinase-substrate interactions. Functional protein microarrays are comprised of thousands of immobilized proteins on glass slides that have been used successfully to identify protein-protein interactions. Here, we describe the application of functional protein microarrays for the identification of the phosphorylation targets of individual protein kinases using highly sensitive radioactive detection and robust informatics algorithms.

Identification of a novel autoantigen in inflammatory bowel disease by protein microarray

BACKGROUND

Patients with inflammatory bowel disease (IBD) display immunoreactivity to self-antigens and microbial antigens. We used a protein microarray approach to identify novel autoantigens in IBD.

METHODS

ProtoArray Human Protein Microarray v4.0 containing 8268 human proteins from Invitrogen (La Jolla, CA) was used.

RESULTS

Twenty-five IBD patients and five healthy controls were screened for candidate autoantigens. For 256 antigens, IBD patients had a higher seroreactivity than controls. Twenty antigens were selected for further evaluation in a larger cohort (60 ulcerative colitis [UC] patients, 60 Crohn's disease [CD] patients, 60 healthy controls, and 60 gastrointestinal-diseased controls) by means of a customized protein microarray. Out of these 20 antigens, one antigen, family with sequence similarity 84 member A (FAM84A), was identified as a target antigen in IBD. Antibodies to FAM84A were significantly more prevalent in IBD patients (19%) than in gastrointestinal-diseased controls (1.7%) (P = 0.0008) and healthy controls (5%) (P = 0.01). Anti-FAM84A antibodies were found in 26.6% of UC patients and in 11.7% of CD patients. FAM84A was confirmed as target antigen in IBD by means of Western blotting in a large independent cohort (100 UC patients, 106 CD patients, 102 healthy controls, and 100 gastrointestinal-diseased controls). Antibodies to FAM84A were significantly more prevalent in IBD patients (20%) than in gastrointestinal-diseased controls (5%) (P = 0.0004) and healthy controls (0%) (P < 0.0001). Anti-FAM84A antibodies were found in 18% of UC patients and in 22% of CD patients.

CONCLUSIONS

We identified FAM84A as a novel autoantigen in IBD. (Inflamm Bowel Dis 2011;).

Diverse protein kinase interactions identified by protein microarrays reveal novel connections between cellular processes

Protein kinases are key regulators of cellular processes. In spite of considerable effort, a full understanding of the pathways they participate in remains elusive. We globally investigated the proteins that interact with the majority of yeast protein kinases using protein microarrays. Eighty-five kinases were purified and used to probe yeast proteome microarrays. One-thousand-twenty-three interactions were identified, and the vast majority were novel. Coimmunoprecipitation experiments indicate that many of these interactions occurred in vivo. Many novel links of kinases to previously distinct cellular pathways were discovered. For example, the well-studied Kss1 filamentous pathway was found to bind components of diverse cellular pathways, such as those of the stress response pathway and the Ccr4-Not transcriptional/translational regulatory complex; genetic tests revealed that these different components operate in the filamentation pathway in vivo. Overall, our results indicate that kinases operate in a highly interconnected network that coordinates many activities of the proteome. Our results further demonstrate that protein microarrays uncover a diverse set of interactions not observed previously.

Characterization of protein-DNA interactions using protein microarrays

INTRODUCTION

Protein-DNA interactions (PDIs) are critical for many cellular processes. We present here a protocol for the identification of PDIs in vitro using protein microarray technology. The procedure involves double-stranding synthesized DNA oligonucleotides with a fluorescent-labeled primer, binding the labeled double-stranded DNA directly to the protein microarray, and analyzing binding of the resulting PDIs. This approach provides simultaneous identification of PDIs for thousands of proteins, and multiple carefully designed DNA probes can be tested in parallel, which enables a rapid mapping of PDIs on a proteome-wide scale.

Data processing and analysis for protein microarrays

Protein microarrays are a high-throughput technology capable of generating large quantities of proteomics data. They can be used for general research or for clinical diagnostics. Bioinformatics and statistical analysis techniques are required for interpretation and reaching biologically relevant conclusions from raw data. We describe essential algorithms for processing protein microarray data, including spot-finding on slide images, Z score, and significance analysis of microarrays (SAM) calculations, as well as the concentration dependent analysis (CDA). We also describe available tools for protein microarray analysis, and provide a template for a step-by-step approach to performing an analysis centered on the CDA method. We conclude with a discussion of fundamental and practical issues and considerations.

Defining the humoral immune response to infectious agents using high-density protein microarrays

A major component of the adaptive immune response to infection is the generation of protective and long-lasting humoral immunity. Traditional approaches to understanding the host's humoral immune response are unable to provide an integrated understanding of the antibody repertoire generated in response to infection. By studying multiple antigenic responses in parallel, we can learn more about the breadth and dynamics of the antibody response to infection. Measurement of antibody production following vaccination is also a gauge for efficacy, as generation of antibodies can protect from future infections and limit disease. Protein microarrays are well suited to identify, quantify and compare individual antigenic responses following exposure to infectious agents. This technology can be applied to the development of improved serodiagnostic tests, discovery of subunit vaccine antigen candidates, epidemiologic research and vaccine development, as well as providing novel insights into infectious disease and the immune system. In this review, we will discuss the use of protein microarrays as a powerful tool to define the humoral immune response to bacteria and viruses.

Global identification of protein kinase substrates by protein microarray analysis

Herein, we describe a protocol for the global identification of in vitro substrates targeted by protein kinases using protein microarray technology. Large numbers of fusion proteins tagged at their carboxy-termini are purified in 96-well format and spotted in duplicate onto amino-silane-coated slides in a spatially addressable manner. These arrays are incubated in the presence of purified kinase and radiolabeled ATP, and then washed, dried and analyzed by autoradiography. The extent of phosphorylation of each spot is quantified and normalized, and proteins that are reproducibly phosphorylated in the presence of the active kinase relative to control slides are scored as positive substrates. This approach enables the rapid determination of kinase-substrate relationship on a proteome-wide scale, and although developed using yeast, has since been adapted to higher eukaryotic systems. Expression, purification and printing of the yeast proteome require about 3 weeks. Afterwards, each kinase assay takes approximately 3 h to perform.

Global analysis of the glycoproteome in Saccharomyces cerevisiae reveals new roles for protein glycosylation in eukaryotes

To further understand the roles of protein glycosylation in eukaryotes, we globally identified glycan-containing proteins in yeast. A fluorescent lectin binding assay was developed and used to screen protein microarrays containing over 5000 proteins purified from yeast. A total of 534 yeast proteins were identified that bound either Concanavalin A (ConA) or Wheat-Germ Agglutinin (WGA); 406 of them were novel. Among the novel glycoproteins, 45 were validated by mobility shift upon treatment with EndoH and PNGase F, thereby extending the number of validated yeast glycoproteins to 350. In addition to many components of the secretory pathway, we identified other types of proteins, such as transcription factors and mitochondrial proteins. To further explore the role of glycosylation in mitochondrial function, the localization of four mitochondrial proteins was examined in the presence and absence of tunicamycin, an inhibitor of N-linked protein glycosylation. For two proteins, localization to the mitochondria is diminished upon tunicamycin treatment, indicating that protein glycosylation is important for protein function. Overall, our studies greatly extend our understanding of protein glycosylation in eukaryotes through the cataloguing of glycoproteins, and describe a novel role for protein glycosylation in mitochondrial protein function and localization.

Identification of a new rice blast resistance gene, Pid3, by genomewide comparison of paired nucleotide-binding site--leucine-rich repeat genes and their pseudogene alleles between the two sequenced rice genomes

Rice blast, caused by Magnaporthe oryzae, is one of the most devastating diseases. The two major subspecies of Asian cultivated rice (Oryza sativa L.), indica and japonica, have shown obvious differences in rice blast resistance, but the genomic basis that underlies the difference is not clear. We performed a genomewide comparison of the major class of resistant gene family, the nucleotide-binding site-leucine-rich repeat (NBS-LRR) gene family, between 93-11 (indica) and Nipponbare (japonica) with a focus on their pseudogene members. We found great differences in either constitution or distribution of pseudogenes between the two genomes. According to this comparison, we designed the PCR-based molecular markers specific to the Nipponbare NBS-LRR pseudogene alleles and used them as cosegregation markers for blast susceptibility in a segregation population from a cross between a rice blast-resistant indica variety and a susceptible japonica variety. Through this approach, we identified a new blast resistance gene, Pid3, in the indica variety, Digu. The allelic Pid3 loci in most of the tested japonica varieties were identified as pseudogenes due to a nonsense mutation at the nucleotide position 2208 starting from the translation initiation site. However, this mutation was not found in any of the tested indica varieties, African cultivated rice varieties, or AA genome-containing wild rice species. These results suggest that the pseudogenization of Pid3 in japonica occurred after the divergence of indica and japonica.

Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and "antibodyome" measures

We have conducted an integrative genomics analysis of serological responses to non-HLA targets after renal transplantation, with the aim of identifying the tissue specificity and types of immunogenic non-HLA antigenic targets after transplantation. Posttransplant antibody responses were measured by paired comparative analysis of pretransplant and posttransplant serum samples from 18 pediatric renal transplant recipients, measured against 5,056 unique protein targets on the ProtoArray platform. The specificity of antibody responses were measured against gene expression levels specific to the kidney, and 2 other randomly selected organs (heart and pancreas), by integrated genomics, employing the mapping of transcription and ProtoArray platform measures, using AILUN. The likelihood of posttransplant non-HLA targets being recognized preferentially in any of 7 microdissected kidney compartments was also examined. In addition to HLA targets, non-HLA immune responses, including anti-MICA antibodies, were detected against kidney compartment-specific antigens, with highest posttransplant recognition for renal pelvis and cortex specific antigens. The compartment specificity of selected antibodies was confirmed by IHC. In conclusion, this study provides an immunogenic and anatomic roadmap of the most likely non-HLA antigens that can generate serological responses after renal transplantation. Correlation of the most significant non-HLA antibody responses with transplant health and dysfunction are currently underway.

Nanomedicine--challenge and perspectives

The application of nanotechnology concepts to medicine joins two large cross-disciplinary fields with an unprecedented societal and economical potential arising from the natural combination of specific achievements in the respective fields. The common basis evolves from the molecular-scale properties relevant to the two fields. Local probes and molecular imaging techniques allow surface and interface properties to be characterized on a nanometer scale at predefined locations, while chemical approaches offer the opportunity to elaborate and address surfaces, for example, for targeted drug delivery, enhanced biocompatibility, and neuroprosthetic purposes. However, concerns arise in this cross-disciplinary area about toxicological aspects and ethical implications. This Review gives an overview of selected recent developments and applications of nanomedicine.

Cell adhesion through alphaV-containing integrins is required for efficient HIV-1 infection in macrophages

Monocytes and macrophages are an important reservoir of human immunodeficiency virus (HIV) and may represent the largest reservoir of this virus in tissues. Differentiation of monocytes into macrophages leads to cell attachment and susceptibility to infection and replication of HIV. Among other cell-surface molecules, integrins are overexpressed during monocyte-macrophage differentiation and may play a role in the replication cycle of envelope viruses including HIV. Here, we show that inhibition of alphaV integrin in monocyte-derived macrophages, by RNA interference or their inhibition by a selective small heterocyclic RGD-mimetic nonpeptide compound, inhibited the replication of HIV in the absence of cytotoxicity. Interference or inhibition of alphaV integrins triggered a signal transduction pathway, leading to down-regulation of nuclear factor-kappaB-dependent HIV-1 transcription. Such inhibition was mediated by a MAP-kinase signaling cascade, probably involving ERK1/2, p38-mitogen-activated protein kinases, and HSP27. In conclusion, our results reveal a significant role of integrin alphaV-mediated adhesion in HIV-1 infection of macrophages.

A concentration-dependent analysis method for high density protein microarrays

Protein microarray technology is rapidly growing and has the potential to accelerate the discovery of targets of serum antibody responses in cancer, autoimmunity and infectious disease. Analytical tools for interpreting this high-throughput array data, however, are not well-established. We developed a concentration-dependent analysis (CDA) method which normalizes protein microarray data based on the concentration of spotted probes. We show that this analysis samples a data space that is complementary to other commonly employed analyses, and demonstrate experimental validation of 92% of hits identified by the intersection of CDA with other tools. These data support the use of CDA either as a preprocessing step for a more complete proteomic microarray data analysis or as a stand-alone analysis method.

Identification of differentially expressed proteins in ovarian cancer using high-density protein microarrays

Ovarian cancer is a leading cause of deaths, yet many aspects of the biology of the disease and a routine means of its detection are lacking. We have used protein microarrays and autoantibodies from cancer patients to identify proteins that are aberrantly expressed in ovarian tissue. Sera from 30 cancer patients and 30 healthy individuals were used to probe microarrays containing 5,005 human proteins. Ninety-four antigens were identified that exhibited enhanced reactivity from sera in cancer patients relative to control sera. The differential reactivity of four antigens was tested by using immunoblot analysis and tissue microarrays. Lamin A/C, SSRP1, and RALBP1 were found to exhibit increased expression in the cancer tissue relative to controls. The combined signals from multiple antigens proved to be a robust test to identify cancerous ovarian tissue. These antigens were also reactive with tissue from other types of cancer and thus are not specific to ovarian cancer. Overall our studies identified candidate tissue marker proteins for ovarian cancer and demonstrate that protein microarrays provide a powerful approach to identify proteins aberrantly expressed in disease states.