Implementation of a Multiplex and Quantitative Proteomics Platform for Assessing Protein Lysates Using DNA-Barcoded Antibodies.

Molecular analysis of tumors forms the basis for personalized cancer medicine and increasingly guides patient selection for targeted therapy. Future opportunities for personalized medicine are highlighted by the measurement of protein expression levels via immunohistochemistry, protein arrays, and other approaches; however, sample type, sample quantity, batch effects, and "time to result" are limiting factors for clinical application. Here, we present a development pipeline for a novel multiplexed DNA-labeled antibody platform which digitally quantifies protein expression from lysate samples. We implemented a rigorous validation process for each antibody and show that the platform is amenable to multiple protocols covering nitrocellulose and plate-based methods. Results are highly reproducible across technical and biological replicates, and there are no observed "batch effects" which are common for most multiplex molecular assays. Tests from basal and perturbed cancer cell lines indicate that this platform is comparable to orthogonal proteomic assays such as Reverse-Phase Protein Array, and applicable to measuring the pharmacodynamic effects of clinically-relevant cancer therapeutics. Furthermore, we demonstrate the potential clinical utility of the platform with protein profiling from breast cancer patient samples to identify molecular subtypes. Together, these findings highlight the potential of this platform for enhancing our understanding of cancer biology in a clinical translation setting.

Automatic Identification and Quantification of Extra-Well Fluorescence in Microarray Images.

In recent studies involving NAPPA microarrays, extra-well fluorescence is used as a key measure for identifying disease biomarkers because there is evidence to support that it is better correlated with strong antibody responses than statistical analysis involving intraspot intensity. Because this feature is not well quantified by traditional image analysis software, identification and quantification of extra-well fluorescence is performed manually, which is both time-consuming and highly susceptible to variation between raters. A system that could automate this task efficiently and effectively would greatly improve the process of data acquisition in microarray studies, thereby accelerating the discovery of disease biomarkers. In this study, we experimented with different machine learning methods, as well as novel heuristics, for identifying spots exhibiting extra-well fluorescence (rings) in microarray images and assigning each ring a grade of 1-5 based on its intensity and morphology. The sensitivity of our final system for identifying rings was found to be 72% at 99% specificity and 98% at 92% specificity. Our system performs this task significantly faster than a human, while maintaining high performance, and therefore represents a valuable tool for microarray image analysis.

Identification of Antibody Against SNRPB, Small Nuclear Ribonucleoprotein-Associated Proteins B and B', as an Autoantibody Marker in Crohn's Disease using an Immunoproteomics Approach.

BACKGROUND: Current non-invasive biomarkers for Crohn's disease are limited in their utility. Progress in identifying individual autoantigens and autoantibodies in Crohn's disease has been challenging due to limitations of available immunoassays. AIMS: Our aim was to identify autoantibodies associated with Crohn's disease that may be useful in diagnosis and management using an innovative protein array technology, namely nucleic acid programmable protein arrays [NAPPA]. METHODS: Serum samples of 96 patients with established Crohn's disease and 96 healthy controls were included and evenly split into discovery and validation sets randomly. Autoantibodies of both IgG and IgA classes were profiled against ~1900 human proteins in the discovery set on NAPPA. Autoantibodies discovered to be Crohn's disease-specific were further validated in the independent validation set by enzyme-linked immunosorbent assay. RESULTS: Overall, reactivity of IgG autoantibodies was stronger than that of IgA autoantibodies; however, IgA autoantibodies showed greater differential reactivity between cases and controls. Four IgA autoantibodies against SNRPB, PRPH, PTTG1 and SNAI1 were newly identified with sensitivities above 15% at 95% specificity, among which anti-SNRPB-IgA had the highest sensitivity of 24.0%. Autoantibodies associated with specific disease subtypes were also found. CONCLUSIONS: As one of the first studies to use immunoproteomics for the identification of autoantibodies in Crohn's disease, our results support the utility of NAPPA in implementing future expanded studies with better coverage of the human proteome and microbial proteomes relevant to Crohn's disease and identifying antibody markers that may have clinical impact in diagnosis and management.

Plasma Autoantibodies Associated with Basal-like Breast Cancers.

BACKGROUND: Basal-like breast cancer (BLBC) is a rare aggressive subtype that is less likely to be detected through mammographic screening. Identification of circulating markers associated with BLBC could have promise in detecting and managing this deadly disease. METHODS: Using samples from the Polish Breast Cancer study, a high-quality population-based case-control study of breast cancer, we screened 10,000 antigens on protein arrays using 45 BLBC patients and 45 controls, and identified 748 promising plasma autoantibodies (AAbs) associated with BLBC. ELISA assays of promising markers were performed on a total of 145 BLBC cases and 145 age-matched controls. Sensitivities at 98% specificity were calculated and a BLBC classifier was constructed. RESULTS: We identified 13 AAbs (CTAG1B, CTAG2, TP53, RNF216, PPHLN1, PIP4K2C, ZBTB16, TAS2R8, WBP2NL, DOK2, PSRC1, MN1, TRIM21) that distinguished BLBC from controls with 33% sensitivity and 98% specificity. We also discovered a strong association of TP53 AAb with its protein expression (P = 0.009) in BLBC patients. In addition, MN1 and TP53 AAbs were associated with worse survival [MN1 AAb marker HR = 2.25, 95% confidence interval (CI), 1.03-4.91; P = 0.04; TP53, HR = 2.02, 95% CI, 1.06-3.85; P = 0.03]. We found limited evidence that AAb levels differed by demographic characteristics. CONCLUSIONS: These AAbs warrant further investigation in clinical studies to determine their value for further understanding the biology of BLBC and possible detection. IMPACT: Our study identifies 13 AAb markers associated specifically with BLBC and may improve detection or management of this deadly disease.

Copper-catalyzed azide-alkyne cycloaddition (click chemistry)-based detection of global pathogen-host AMPylation on self-assembled human protein microarrays.

AMPylation (adenylylation) is a recently discovered mechanism employed by infectious bacteria to regulate host cell signaling. However, despite significant effort, only a few host targets have been identified, limiting our understanding of how these pathogens exploit this mechanism to control host cells. Accordingly, we developed a novel nonradioactive AMPylation screening platform using high-density cell-free protein microarrays displaying human proteins produced by human translational machinery. We screened 10,000 unique human proteins with Vibrio parahaemolyticus VopS and Histophilus somni IbpAFic2, and identified many new AMPylation substrates. Two of these, Rac2, and Rac3, were confirmed in vivo as bona fide substrates during infection with Vibrio parahaemolyticus. We also mapped the site of AMPylation of a non-GTPase substrate, LyGDI, to threonine 51, in a region regulated by Src kinase, and demonstrated that AMPylation prevented its phosphorylation by Src. Our results greatly expanded the repertoire of potential host substrates for bacterial AMPylators, determined their recognition motif, and revealed the first pathogen-host interaction AMPylation network. This approach can be extended to identify novel substrates of AMPylators with different domains or in different species and readily adapted for other post-translational modifications.

Hepatic signaling by the mechanistic target of rapamycin complex 2 (mTORC2).

The mechanistic target of rapamycin (mTOR) exists in two complexes that regulate diverse cellular processes. mTOR complex 1 (mTORC1), the canonical target of rapamycin, has been well studied, whereas the physiological role of mTORC2 remains relatively uncharacterized. In mice in which the mTORC2 component Rictor is deleted in liver [Rictor-knockout (RKO) mice], we used genomic and phosphoproteomic analyses to characterize the role of hepatic mTORC2 in vivo. Overnight food withdrawal followed by refeeding was used to activate mTOR signaling. Rapamycin was administered before refeeding to specify mTORC2-mediated events. Hepatic mTORC2 regulated a complex gene expression and post-translational network that affects intermediary metabolism, ribosomal biogenesis, and proteasomal biogenesis. Nearly all changes in genes related to intermediary metabolic regulation were replicated in cultured fetal hepatocytes, indicating a cell-autonomous effect of mTORC2 signaling. Phosphoproteomic profiling identified mTORC2-related signaling to 144 proteins, among which were metabolic enzymes and regulators. A reduction of p38 MAPK signaling in the RKO mice represents a link between our phosphoproteomic and gene expression results. We conclude that hepatic mTORC2 exerts a broad spectrum of biological effects under physiological conditions. Our findings provide a context for the development of targeted therapies to modulate mTORC2 signaling.

Phosphoproteomic analysis of liver homogenates.

Regulation of protein function via reversible phosphorylation is an essential component of cell signaling. Our ability to understand complex phosphorylation networks in the physiological context of a whole organism or tissue remains limited. This is largely due to the technical challenge of isolating serine/threonine phosphorylated peptides from a tissue sample. In the present study, we developed a phosphoproteomic strategy to purify and identify phosphopeptides from a tissue sample by employing protein gel filtration, protein strong anion exchange and strong cation exchange (SCX) chromatography, peptide SCX chromatography, and TiO(2) affinity purification. By applying this strategy to the mass spectrometry-based analysis of rat liver homogenates, we were able to identify with high confidence and quantify over 4,000 unique phosphopeptides. Finally, the reproducibility of our methodology was demonstrated by its application to analysis of the mammalian Target of Rapamycin (mTOR) signaling pathways in liver samples obtained from rats in which hepatic mTOR was activated by refeeding following a period of fasting.

Phosphoproteomic profiling of in vivo signaling in liver by the mammalian target of rapamycin complex 1 (mTORC1).

BACKGROUND: Our understanding of signal transduction networks in the physiological context of an organism remains limited, partly due to the technical challenge of identifying serine/threonine phosphorylated peptides from complex tissue samples. In the present study, we focused on signaling through the mammalian target of rapamycin (mTOR) complex 1 (mTORC1), which is at the center of a nutrient- and growth factor-responsive cell signaling network. Though studied extensively, the mechanisms involved in many mTORC1 biological functions remain poorly understood. METHODOLOGY/PRINCIPAL FINDINGS: We developed a phosphoproteomic strategy to purify, enrich and identify phosphopeptides from rat liver homogenates. Using the anticancer drug rapamycin, the only known target of which is mTORC1, we characterized signaling in liver from rats in which the complex was maximally activated by refeeding following 48 hr of starvation. Using protein and peptide fractionation methods, TiO(2) affinity purification of phosphopeptides and mass spectrometry, we reproducibly identified and quantified over four thousand phosphopeptides. Along with 5 known rapamycin-sensitive phosphorylation events, we identified 62 new rapamycin-responsive candidate phosphorylation sites. Among these were PRAS40, gephyrin, and AMP kinase 2. We observed similar proportions of increased and reduced phosphorylation in response to rapamycin. Gene ontology analysis revealed over-representation of mTOR pathway components among rapamycin-sensitive phosphopeptide candidates. CONCLUSIONS/SIGNIFICANCE: In addition to identifying potential new mTORC1-mediated phosphorylation events, and providing information relevant to the biology of this signaling network, our experimental and analytical approaches indicate the feasibility of large-scale phosphoproteomic profiling of tissue samples to study physiological signaling events in vivo.

Next-generation high-density self-assembling functional protein arrays.

We developed a high-density self-assembling protein microarray, based on the nucleic acid programmable protein array (NAPPA) concept, to display thousands of proteins that are produced and captured in situ from immobilized cDNA templates. We arrayed up to 1,000 unique human cDNAs and obtained high yields of protein expression and capture with minimal variation and good reproducibility. This method will enable various experimental approaches to study protein function in high throughput.

Application of protein microarrays for multiplexed detection of antibodies to tumor antigens in breast cancer.

There is strong preclinical evidence that cancer, including breast cancer, undergoes immune surveillance. This continual monitoring, by both the innate and the adaptive immune systems, recognizes changes in protein expression, mutation, folding, glycosylation, and degradation. Local immune responses to tumor antigens are amplified in draining lymph nodes, and then enter the systemic circulation. The antibody response to tumor antigens, such as p53 protein, are robust, stable, and easily detected in serum; may exist in greater concentrations than their cognate antigens; and are potential highly specific biomarkers for cancer. However, antibodies have limited sensitivities as single analytes, and differences in protein purification and assay characteristics have limited their clinical application. For example, p53 autoantibodies in the sera are highly specific for cancer patients, but are only detected in the sera of 10-20% of patients with breast cancer. Detection of p53 autoantibodies is dependent on tumor burden, p53 mutation, rapidly decreases with effective therapy, but is relatively independent of breast cancer subtype. Although antibodies to hundreds of other tumor antigens have been identified in the sera of breast cancer patients, very little is known about the specificity and clinical impact of the antibody immune repertoire to breast cancer. Recent advances in proteomic technologies have the potential for rapid identification of immune response signatures for breast cancer diagnosis and monitoring. We have adapted programmable protein microarrays for the specific detection of autoantibodies in breast cancer. Here, we present the first demonstration of the application of programmable protein microarray ELISAs for the rapid identification of breast cancer autoantibodies.

On-chip protein synthesis for making microarrays.

Protein microarrays are a miniaturized format for displaying in close spatial density hundreds or thousands of purified proteins that provide a powerful platform for the high-throughput assay of protein function. The traditional method of producing them requires the high-throughput production and printing of proteins, a laborious method that raises concerns about the stability of the proteins and the shelf life of the arrays. A novel method of producing protein microarrays, called nucleic acid programmable protein array (NAPPA), overcomes these limitations by synthesizing proteins in situ. NAPPA entails spotting plasmid DNA encoding the relevant proteins, which are then simultaneously transcribed and translated by a cell-free system. The expressed proteins are captured and oriented at the site of expression by a capture reagent that targets a fusion protein on either the N- or C-terminus of the protein. Using a mammalian extract, NAPPA expresses and captures 1000-fold more protein per feature than conventional protein-printing arrays. Moreover, this approach minimizes concerns about protein stability and integrity, because proteins are produced just in time for assaying. NAPPA has already proven to be a robust tool for protein functional assays.