Performance evaluation of machine-assisted interpretation of Gram stains from positive blood cultures.

Manual microscopy of Gram stains from positive blood cultures (PBCs) is crucial for diagnosing bloodstream infections but remains labor intensive, time consuming, and subjective. This study aimed to evaluate a scan and analysis system that combines fully automated digital microscopy with deep convolutional neural networks (CNNs) to assist the interpretation of Gram stains from PBCs for routine laboratory use. The CNN was trained to classify images of Gram stains based on staining and morphology into seven different classes: background/false-positive, Gram-positive cocci in clusters (GPCCL), Gram-positive cocci in pairs (GPCP), Gram-positive cocci in chains (GPCC), rod-shaped bacilli (RSB), yeasts, and polymicrobial specimens. A total of 1,555 Gram-stained slides of PBCs were scanned, pre-classified, and reviewed by medical professionals. The results of assisted Gram stain interpretation were compared to those of manual microscopy and cultural species identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The comparison of assisted Gram stain interpretation and manual microscopy yielded positive/negative percent agreement values of 95.8%/98.0% (GPCCL), 87.6%/99.3% (GPCP/GPCC), 97.4%/97.8% (RSB), 83.3%/99.3% (yeasts), and 87.0%/98.5% (negative/false positive). The assisted Gram stain interpretation, when compared to MALDI-TOF MS species identification, also yielded similar results. During the analytical performance study, assisted interpretation showed excellent reproducibility and repeatability. Any microorganism in PBCs should be detectable at the determined limit of detection of 105 CFU/mL. Although the CNN-based interpretation of Gram stains from PBCs is not yet ready for clinical implementation, it has potential for future integration and advancement.

Coordinated regulation of WNT/ß-catenin, c-Met, and integrin signalling pathways by miR-193b controls triple negative breast cancer metastatic traits.

BACKGROUND: Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC). Treatment options for TNBC patients are limited and further insights into disease aetiology are needed to develop better therapeutic approaches. microRNAs' ability to regulate multiple targets could hold a promising discovery approach to pathways relevant for TNBC aggressiveness. Thus, we address the role of miRNAs in controlling three signalling pathways relevant to the biology of TNBC, and their downstream phenotypes. METHODS: To identify miRNAs regulating WNT/ß-catenin, c-Met, and integrin signalling pathways, we performed a high-throughput targeted proteomic approach, investigating the effect of 800 miRNAs on the expression of 62 proteins in the MDA-MB-231 TNBC cell line. We then developed a novel network analysis, Pathway Coregulatory (PC) score, to detect miRNAs regulating these three pathways. Using in vitro assays for cell growth, migration, apoptosis, and stem-cell content, we validated the function of candidate miRNAs. Bioinformatic analyses using BC patients' datasets were employed to assess expression of miRNAs as well as their pathological relevance in TNBC patients. RESULTS: We identified six candidate miRNAs coordinately regulating the three signalling pathways. Quantifying cell growth of three TNBC cell lines upon miRNA gain-of-function experiments, we characterised miR-193b as a strong and consistent repressor of proliferation. Importantly, the effects of miR-193b were stronger than chemical inhibition of the individual pathways. We further demonstrated that miR-193b induced apoptosis, repressed migration, and regulated stem-cell markers in MDA-MB-231 cells. Furthermore, miR-193b expression was the lowest in patients classified as TNBC or Basal compared to other subtypes. Gene Set Enrichment Analysis showed that miR-193b expression was significantly associated with reduced activity of WNT/ß-catenin and c-Met signalling pathways in TNBC patients. CONCLUSIONS: Integrating miRNA-mediated effects and protein functions on networks, we show that miRNAs predominantly act in a coordinated fashion to activate or repress connected signalling pathways responsible for metastatic traits in TNBC. We further demonstrate that our top candidate, miR-193b, regulates these phenotypes to an extent stronger than individual pathway inhibition, thus emphasizing that its effect on TNBC aggressiveness is mediated by the coordinated repression of these functionally interconnected pathways.

Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.

Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.

A pro-inflammatory signalome is constitutively activated by C33Y mutant TNF receptor 1 in TNF receptor-associated periodic syndrome (TRAPS).

Mutations in TNFRSF1A encoding TNF receptor 1 (TNFR1) cause the autosomal dominant TNF receptor-associated periodic syndrome (TRAPS): a systemic autoinflammatory disorder. Misfolding, intracellular aggregation, and ligand-independent signaling by mutant TNFR1 are central to disease pathophysiology. Our aim was to understand the extent of signaling pathway perturbation in TRAPS. A prototypic mutant TNFR1 (C33Y), and wild-type TNFR1 (WT), were expressed at near physiological levels in an SK-Hep-1 cell model. TNFR1-associated signaling pathway intermediates were examined in this model, and in PBMCs from C33Y TRAPS patients and healthy controls. In C33Y-TNFR1-expressing SK-Hep-1 cells and TRAPS patients' PBMCs, a subtle, constitutive upregulation of a wide spectrum of signaling intermediates and their phosphorylated forms was observed; these were associated with a proinflammatory/antiapoptotic phenotype. In TRAPS patients' PBMCs, this upregulation of proinflammatory signaling pathways was observed irrespective of concurrent treatment with glucocorticoids, anakinra or etanercept, and the absence of overt clinical symptoms at the time that the blood samples were taken. This study reveals the pleiotropic effect of a TRAPS-associated mutant form of TNFR1 on inflammatory signaling pathways (a proinflammatory signalome), which is consistent with the variable and limited efficacy of cytokine-blocking therapies in TRAPS. It highlights new potential target pathways for therapeutic intervention.

Evaluation of reverse phase protein array (RPPA)-based pathway-activation profiling in 84 non-small cell lung cancer (NSCLC) cell lines as platform for cancer proteomics and biomarker discovery.

The reverse phase protein array (RPPA) approach was employed for a quantitative analysis of 71 cancer-relevant proteins and phosphoproteins in 84 non-small cell lung cancer (NSCLC) cell lines and by monitoring the activation state of selected receptor tyrosine kinases, PI3K/AKT and MEK/ERK1/2 signaling, cell cycle control, apoptosis, and DNA damage. Additional information on NSCLC cell lines such as that of transcriptomic data, genomic aberrations, and drug sensitivity was analyzed in the context of proteomic data using supervised and non-supervised approaches for data analysis. First, the unsupervised analysis of proteomic data indicated that proteins clustering closely together reflect well-known signaling modules, e.g. PI3K/AKT- and RAS/RAF/ERK-signaling, cell cycle regulation, and apoptosis. However, mutations of EGFR, ERBB2, RAF, RAS, TP53, and PI3K were found dispersed across different signaling pathway clusters. Merely cell lines with an amplification of EGFR and/or ERBB2 clustered closely together on the proteomic, but not on the transcriptomic level. Secondly, supervised data analysis revealed that sensitivity towards anti-EGFR drugs generally correlated better with high level EGFR phosphorylation than with EGFR abundance itself. High level phosphorylation of RB and high abundance of AURKA were identified as candidates that can potentially predict sensitivity towards the aurora kinase inhibitor VX680. Examples shown demonstrate that the RPPA approach presents a useful platform for targeted proteomics with high potential for biomarker discovery. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Triplex DNA-binding proteins are associated with clinical outcomes revealed by proteomic measurements in patients with colorectal cancer.

BACKGROUND: Tri- and tetra-nucleotide repeats in mammalian genomes can induce formation of alternative non-B DNA structures such as triplexes and guanine (G)-quadruplexes. These structures can induce mutagenesis, chromosomal translocations and genomic instability. We wanted to determine if proteins that bind triplex DNA structures are quantitatively or qualitatively different between colorectal tumor and adjacent normal tissue and if this binding activity correlates with patient clinical characteristics. METHODS: Extracts from 63 human colorectal tumor and adjacent normal tissues were examined by gel shifts (EMSA) for triplex DNA-binding proteins, which were correlated with clinicopathological tumor characteristics using the Mann-Whitney U, Spearman's rho, Kaplan-Meier and Mantel-Cox log-rank tests. Biotinylated triplex DNA and streptavidin agarose affinity binding were used to purify triplex-binding proteins in RKO cells. Western blotting and reverse-phase protein array were used to measure protein expression in tissue extracts. RESULTS: Increased triplex DNA-binding activity in tumor extracts correlated significantly with lymphatic disease, metastasis, and reduced overall survival. We identified three multifunctional splicing factors with biotinylated triplex DNA affinity: U2AF65 in cytoplasmic extracts, and PSF and p54nrb in nuclear extracts. Super-shift EMSA with anti-U2AF65 antibodies produced a shifted band of the major EMSA H3 complex, identifying U2AF65 as the protein present in the major EMSA band. U2AF65 expression correlated significantly with EMSA H3 values in all extracts and was higher in extracts from Stage III/IV vs. Stage I/II colon tumors (p=0.024). EMSA H3 values and U2AF65 expression also correlated significantly with GSK3 beta, beta-catenin, and NF- B p65 expression, whereas p54nrb and PSF expression correlated with c-Myc, cyclin D1, and CDK4. EMSA values and expression of all three splicing factors correlated with ErbB1, mTOR, PTEN, and Stat5. Western blots confirmed that full-length and truncated beta-catenin expression correlated with U2AF65 expression in tumor extracts. CONCLUSIONS: Increased triplex DNA-binding activity in vitro correlates with lymph node disease, metastasis, and reduced overall survival in colorectal cancer, and increased U2AF65 expression is associated with total and truncated beta-catenin expression in high-stage colorectal tumors.

uPA and PAI-1-Related Signaling Pathways Differ between Primary Breast Cancers and Lymph Node Metastases.

The supporting role of urokinase-type plasminogen activator (uPA) and its inhibitor plasminogen activator inhibitor 1 (PAI-1) in migration and invasion is well known. In addition, both factors are key components in cancer cell-related signaling. However, little information is available for uPA and PAI-1-associated signaling pathways in primary cancers and corresponding lymph node metastases. The aim of this study was to compare the expression of uPA and PAI-1-associated signaling proteins in 52 primary breast cancers and corresponding metastases. Proteins were extracted from formalin-fixed paraffin-embedded tissue samples of the primary tumors and metastases. Protein lysates were subsequently analyzed by reverse phase protein array for the expression of members of the PI3K/AKT (FAK, GSK3-ß, ILK, pGSK3-ß, PI3K, and ROCK) and the MAPK pathways (pp38, pSTAT3, and p38). A solid correlation of uPA expression existed between primary tumors and metastases, whereas PAI-1 expression did not significantly correlate between them. The correlations of uPA and PAI-1 with signaling pathways found in primary tumors did not persist in metastases. Analysis of single molecules revealed that some correlated well between tumors and metastases (FAK, pGSK3-ß, ILK, Met, PI3K, ROCK, uPA, p38, and pp38), whereas others did not (PAI-1 and GSK3-ß). Whether the expression of a protein correlated between tumor and metastasis or not was independent of the pathway the protein is related to. These findings hint at a complete deregulation of uPA and PAI-1-related signaling in metastases, which might be the reason why uPA and PAI-1 reached clinical relevance only for lymph node-negative breast cancer tissues.

Peroxiredoxins 3 and 4 are overexpressed in prostate cancer tissue and affect the proliferation of prostate cancer cells in vitro.

The present study aimed to investigate the proteome profiling of surgically treated prostate cancers. Hereto, 2D-DIGE and mass spectrometry were performed for protein identification, and data validation for peroxiredoxin 3 and 4 (PRDX3 and PRDX4) was accomplished by reverse phase protein arrays (RPPA). The Formal Concept Analysis (FCA) method was applied to assess whether the TMPRSS2-ERG gene fusion could influence the degree of overexpression of PRDX3 and PRDX4 in prostate cancer. Lastly, we performed an in vitro functional characterization of both PRDX3 and PRDX4 using the classical human prostate cancer cell lines DU145 and LNCaP. Reverse phase protein arrays verified that the overexpression of both PRDX3 and PRDX4 in tumor samples is negatively correlated with the presence of the TMPRSS2-ERG gene fusion. Functional characterization of PRDX3 and PRDX4 activity in PCa cell lines suggests a role of these members of the peroxiredoxin family in the pathophysiology of this tumor entity.

Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer.

The EGFR-driven cell-cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3'-UTR of target genes. Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR-124, miR-147 and miR-193a-3p) as novel tumor suppressors that co-target EGFR-driven cell-cycle network proteins and inhibit cell-cycle progression and proliferation in breast cancer.

TMPRSS2-ERG -specific transcriptional modulation is associated with prostate cancer biomarkers and TGF-ß signaling.

BACKGROUND: TMPRSS2-ERG gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although TMPRSS2-ERG fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear. METHODS: We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays. RESULTS: Comparison of gene expression levels among TMPRSS2-ERG fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like CRISP3 were found to be associated with the gene fusion status. WNT and TGF-ß/BMP signaling pathways were significantly associated with genes upregulated in TMPRSS2-ERG fusion-positive tumors. CONCLUSIONS: The TMPRSS2-ERG gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.

Utilization of RNAi to validate antibodies for reverse phase protein arrays.

Reverse phase protein arrays (RPPAs) emerged as a very useful tool for high-throughput screening of protein expression in large numbers of small specimen. Similar to other protein chemistry methods, antibody specificity is also a major concern for RPPA. Currently, testing antibodies on Western blot for specificity and applying serial dilution curves to determine signal/concentration linearity of RPPA signals are most commonly employed to validate antibodies for RPPA applications. However, even the detection antibodies fulfilling both requirements do not always give the expected result. Chemically synthesized small interfering RNAs (siRNAs) are one of the most promising and time-efficient tools for loss-of-function studies by specifically targeting the gene of interest resulting in a reduction at the protein expression level, and are therefore used to dissect biological processes. Here, we report the utilization of siRNA-treated sample lysates for the quantification of a protein of interest as a useful and reliable tool to validate antibody specificity for RPPAs. As our results indicate, we recommend the use of antibodies which give the highest dynamic range between the control siRNA-treated samples and the target protein (here: EGFR) siRNA-treated ones on RPPAs, to be able to quantify even small differences of protein abundance with high confidence.

Antibody-mediated signal amplification for reverse phase protein array-based protein quantification.

Reverse phase protein array (RPPA) techniques allow the quantitative analysis of signal transduction events in a high-throughput format. Sensitivity is important for RPPA-based detection approaches, since numerous signaling proteins or posttranslational modifications are present at low levels. Especially, the proteomic analysis of clinical samples exposes its own challenges with respect to sensitivity. Antibody-mediated signal amplification (AMSA) is a novel strategy relying on sequential incubation steps with fluorescently labeled secondary antibodies reactive against each other. AMSA is a simple extension of the standard quantification in the near-infrared range and is highly specific and robust. In this chapter, we present the amplification protocol and application examples for the time-resolved analysis of signaling pathways as well as protein profiling of clinical samples.

Microspot immunoassay-based analysis of plasma protein profiles for biomarker discovery strategies.

To expedite the development of personalized medicine, new and reliable biomarkers are required to facilitate early diagnosis, to determine prognosis, predict response or resistance to different therapies, and to monitor disease progression or recurrence. Human body fluids, such as blood, present a promising resource for biomarker discovery, in every sense. Microspot immunoassays allow the simultaneous quantification of multiple analytes from a minute amount of samples in a single measurement. The experimental design of microspot immunoassays is based on antibody pairs recognizing different epitopes of the analyte. The first antibody is used to capture the analyte from the complex sample, and the second antibody is used for detection. As with traditional enzyme-linked immunosorbent assays, highly reliable and reproducible results are obtained.

RNAi-based validation of antibodies for reverse phase protein arrays.

BACKGROUND: Reverse phase protein arrays (RPPA) have been demonstrated to be a useful experimental platform for quantitative protein profiling in a high-throughput format. Target protein detection relies on the readout obtained from a single detection antibody. For this reason, antibody specificity is a key factor for RPPA. RNAi allows the specific knockdown of a target protein in complex samples and was therefore examined for its utility to assess antibody performance for RPPA applications. RESULTS: To proof the feasibility of our strategy, two different anti-EGFR antibodies were compared by RPPA. Both detected the knockdown of EGFR but at a different rate. Western blot data were used to identify the most reliable antibody. The RNAi approach was also used to characterize commercial anti-STAT3 antibodies. Out of ten tested anti-STAT3 antibodies, four antibodies detected the STAT3-knockdown at 80-85%, and the most sensitive anti-STAT3 antibody was identified by comparing detection limits. Thus, the use of RNAi for RPPA antibody validation was demonstrated to be a stringent approach to identify highly specific and highly sensitive antibodies. Furthermore, the RNAi/RPPA strategy is also useful for the validation of isoform-specific antibodies as shown for the identification of AKT1/AKT2 and CCND1/CCND3-specific antibodies. CONCLUSIONS: RNAi is a valuable tool for the identification of very specific and highly sensitive antibodies, and is therefore especially useful for the validation of RPPA-suitable detection antibodies. On the other hand, when a set of well-characterized RPPA-antibodies is available, large-scale RNAi experiments analyzed by RPPA might deliver useful information for network reconstruction.

RPPanalyzer: Analysis of reverse-phase protein array data.

SUMMARY: RPPanalyzer is a statistical tool developed to read reverse-phase protein array data, to perform the basic data analysis and to visualize the resulting biological information. The R-package provides different functions to compare protein expression levels of different samples and to normalize the data. Implemented plotting functions permit a quality control by monitoring data distribution and signal validity. Finally, the data can be visualized in heatmaps, boxplots, time course plots and correlation plots. RPPanalyzer is a flexible tool and tolerates a huge variety of different experimental designs. AVAILABILITY: The RPPAanalyzer is open source and freely available as an R-Package on the CRAN platform http://cran.r-project.org/.

Increasing the sensitivity of reverse phase protein arrays by antibody-mediated signal amplification.

BACKGROUND: Reverse phase protein arrays (RPPA) emerged as a useful experimental platform to analyze biological samples in a high-throughput format. Different signal detection methods have been described to generate a quantitative readout on RPPA including the use of fluorescently labeled antibodies. Increasing the sensitivity of RPPA approaches is important since many signaling proteins or posttranslational modifications are present at a low level. RESULTS: A new antibody-mediated signal amplification (AMSA) strategy relying on sequential incubation steps with fluorescently-labeled secondary antibodies reactive against each other is introduced here. The signal quantification is performed in the near-infrared range. The RPPA-based analysis of 14 endogenous proteins in seven different cell lines demonstrated a strong correlation (r = 0.89) between AMSA and standard NIR detection. Probing serial dilutions of human cancer cell lines with different primary antibodies demonstrated that the new amplification approach improved the limit of detection especially for low abundant target proteins. CONCLUSIONS: Antibody-mediated signal amplification is a convenient and cost-effective approach for the robust and specific quantification of low abundant proteins on RPPAs. Contrasting other amplification approaches it allows target protein detection over a large linear range.

Antibody microarrays as an experimental platform for the analysis of signal transduction networks.

A significant bottleneck for the time-resolved and quantitative description of signaling networks is the limited sample capacity and sensitivity of existing methods. Recently, antibody microarrays have emerged as a promising experimental platform for the quantitative and comprehensive determination of protein abundance and protein phosphorylation. This review summarizes the development of microarray applications involving antibody-based capture of target proteins with a focus on quantitative applications. Technical aspects regarding the production of antibody microarrays, identification of suitable detection and capture antibody pairs, signal detection methods, detection limit, and data analysis are discussed in detail.