[Inherent Instability: The Role of the Output Gap in the Stability and Growth Pact]. / Inhärente Instabilität: zur Rolle der Output-Lücke im Stabilitäts- und Wachstumspakt.

Fiscal rules in general and the Stability and Growth Pact in particular are repeatedly used to effectively limit public debt and achieve fiscal sustainability. In this context, the concept of the unobservable output gap deserves special attention.

Biophysical and Sequence-Based Methods for Identifying Monovalent and Bivalent Antibodies with High Colloidal Stability.

In vitro antibody discovery and/or affinity maturation are often performed using antibody fragments (Fabs), but most monovalent Fabs are reformatted as bivalent IgGs (monoclonal antibodies, mAbs) for therapeutic applications. One problem related to reformatting antibodies is that the bivalency of mAbs can lead to increased antibody self-association and poor biophysical properties (e.g., reduced antibody solubility and increased viscosity). Therefore, it is important to identify monovalent Fabs early in the discovery and/or optimization process that will display favorable biophysical properties when reformatted as bivalent mAbs. Here we demonstrate a facile approach for evaluating Fab self-association in a multivalent assay format that is capable of identifying antibodies with low self-association and favorable colloidal properties when reformatted as bivalent mAbs. Our approach (self-interaction nanoparticle spectroscopy, SINS) involves immobilizing Fabs on gold nanoparticles in a multivalent format (multiple Fabs per nanoparticle) and evaluating their self-association behavior via shifts in the plasmon wavelength or changes in the absorbance values. Importantly, we find that SINS measurements of Fab self-association are correlated with self-interaction measurements of bivalent mAbs and are useful for identifying antibodies with favorable biophysical properties. Moreover, the significant differences in the levels of self-association detected for Fabs and mAbs with similar frameworks can be largely explained by the physicochemical properties of the complementarity-determining regions (CDRs). Comparison of the properties of the CDRs in this study relative to those of approved therapeutic antibodies reveals several key factors (net charge, fraction of charged residues, and presence of self-interaction motifs) that strongly influence antibody self-association behavior. Increased positive charge in the CDRs was observed to correlate with increased risk of high self-association for the mAbs in this study and clinical-stage antibodies. We expect that these findings will be useful for improving the development of therapeutic antibodies that are well suited for high concentration applications.

MicroRNA-31 sensitizes human breast cells to apoptosis by direct targeting of protein kinase C epsilon (PKCepsilon).

MicroRNAs post-transcriptionally regulate gene expression and thereby contribute to the modulation of numerous complex and disease-relevant cellular phenotypes, including cell proliferation, cell motility, apoptosis, and stress response. In breast cancer cell systems, miR-31 has been shown to inhibit cell migration, invasion, and metastasis. Here, we link enhanced expression of miR-31 to the inhibition of the oncogenic NF-κB pathway, thus supporting the tumor-suppressive function of this microRNA. We identified protein kinase C epsilon (PKCε encoded by the PRKCE gene) as a novel direct target of miR-31 and show that down-regulation of PKCε results in impaired NF-κB signaling, enhanced apoptosis, and increased sensitivity of MCF10A breast epithelial and MDA-MB-231 triple-negative breast cancer cells toward ionizing radiation as well as treatment with chemotherapeutics. Mechanistically, we attribute this sensitization to anti-cancer treatments to the PRKCE-mediated down-regulation of the anti-apoptotic factor BCL2. In clinical breast cancer samples, high BCL2 expression was associated with poor prognosis. Furthermore, we found an inverse correlation between miR-31 and BCL2 expression, highlighting the functional relevance of the indirect down-regulation of BCL2 via direct targeting of PRKCE by miR-31.

Immunomic, genomic and transcriptomic characterization of CT26 colorectal carcinoma.

BACKGROUND: Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. Here, we present an integrated map of the genome, transcriptome and immunome of an epithelial mouse tumor, the CT26 colon carcinoma cell line. RESULTS: We found that Kras is homozygously mutated at p.G12D, Apc and Tp53 are not mutated, and Cdkn2a is homozygously deleted. Proliferation and stem-cell markers, including Top2a, Birc5 (Survivin), Cldn6 and Mki67, are highly expressed while differentiation and top-crypt markers Muc2, Ms4a8a (MS4A8B) and Epcam are not. Myc, Trp53 (tp53), Mdm2, Hif1a, and Nras are highly expressed while Egfr and Flt1 are not. MHC class I but not MHC class II is expressed. Several known cancer-testis antigens are expressed, including Atad2, Cep55, and Pbk. The highest expressed gene is a mutated form of the mouse tumor antigen gp70. Of the 1,688 non-synonymous point variations, 154 are both in expressed genes and in peptides predicted to bind MHC and thus potential targets for immunotherapy development. Based on its molecular signature, we predicted that CT26 is refractory to anti-EGFR mAbs and sensitive to MEK and MET inhibitors, as have been previously reported. CONCLUSIONS: CT26 cells share molecular features with aggressive, undifferentiated, refractory human colorectal carcinoma cells. As CT26 is one of the most extensively used syngeneic mouse tumor models, our data provide a map for the rationale design of mode-of-action studies for pre-clinical evaluation of targeted- and immunotherapies.

Environmental and genetic predictors of human cardiovascular ageing.

Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we use machine learning approaches to quantify cardiovascular age from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identify prescribed medications that are potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.

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.

Evaluation of In Vitro Tools to Predict the In Vivo Absorption of Biopharmaceuticals Following Subcutaneous Administration.

PURPOSE: For injectable biopharmaceuticals, the subcutaneous route of administration is increasingly preferred over intravenous administration. However, one of the challenges in the development of subcutaneously administered biopharmaceuticals is a reduced bioavailability, which is difficult to predict. Since animal models do not reliably reflect bioavailability in patients, in vitro models could help to develop drug candidates. The purpose of this study was to evaluate a versatile set of in vitro tools for their suitability to predict bioavailability of biopharmaceuticals after subcutaneous administration. METHODS: We examined seven commercially available biopharmaceuticals using three instruments, i.e., the Subcutaneous Injection Site Simulator (Scissor), the Osmomat 050, and a dialysis system using three artificial extracellular matrices, two dissolution apparatuses, i.e., the USP4 and the USP7, and two evaluation tools, i.e., the affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) and the Developability Index (DI). Results were evaluated for their usefulness to predict the bioavailability and other pharmacokinetic parameters in humans using the Pearson correlation. RESULTS: None of the tested instruments and methods could reliably approximate bioavailability. Only pressure values derived with the Osmomat 050 instrument correlated with Cmax with a Pearson correlation coefficient greater than 0.8. CONCLUSION: No single in vitro method confidently predicted the bioavailability in humans. We only found a correlation to maximum plasma concentration values for one of the tested approaches. However, a more focused evaluation would be necessary to confirm our findings and test combinations of orthogonal methods that may improve the confidence of such a prediction.

Genetic and environmental determinants of diastolic heart function.

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets.

Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'.

BACKGROUND: Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks. RESULTS: We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition) and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property. CONCLUSIONS: The package 'ddepn' is freely available on R-Forge and CRAN http://ddepn.r-forge.r-project.org, http://cran.r-project.org. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.

Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data.

MOTIVATION: Network modelling in systems biology has become an important tool to study molecular interactions in cancer research, because understanding the interplay of proteins is necessary for developing novel drugs and therapies. De novo reconstruction of signalling pathways from data allows to unravel interactions between proteins and make qualitative statements on possible aberrations of the cellular regulatory program. We present a new method for reconstructing signalling networks from time course experiments after external perturbation and show an application of the method to data measuring abundance of phosphorylated proteins in a human breast cancer cell line, generated on reverse phase protein arrays. RESULTS: Signalling dynamics is modelled using active and passive states for each protein at each timepoint. A fixed signal propagation scheme generates a set of possible state transitions on a discrete timescale for a given network hypothesis, reducing the number of theoretically reachable states. A likelihood score is proposed, describing the probability of measurements given the states of the proteins over time. The optimal sequence of state transitions is found via a hidden Markov model and network structure search is performed using a genetic algorithm that optimizes the overall likelihood of a population of candidate networks. Our method shows increased performance compared with two different dynamical Bayesian network approaches. For our real data, we were able to find several known signalling cascades from the ERBB signalling pathway. AVAILABILITY: Dynamic deterministic effects propagation networks is implemented in the R programming language and available at http://www.dkfz.de/mga2/ddepn/.

Loss of aquaporin-4 expression and putative function in non-small cell lung cancer.

BACKGROUND: Aquaporins (AQPs) have been recognized to promote tumor progression, invasion, and metastasis and are therefore recognized as promising targets for novel anti-cancer therapies. Potentially relevant AQPs in distinct cancer entities can be determined by a comprehensive expression analysis of the 13 human AQPs. METHODS: We analyzed the presence of all AQP transcripts in 576 different normal lung and non-small cell lung cancer (NSCLC) samples using microarray data and validated our findings by qRT-PCR and immunohistochemistry. RESULTS: Variable expression of several AQPs (AQP1, -3, -4, and -5) was found in NSCLC and normal lung tissues. Furthermore, we identified remarkable differences between NSCLC subtypes in regard to AQP1, -3 and -4 expression. Higher transcript and protein levels of AQP4 in well-differentiated lung adenocarcinomas suggested an association with a more favourable prognosis. Beyond water transport, data mining of co-expressed genes indicated an involvement of AQP4 in cell-cell signalling, cellular movement and lipid metabolism, and underlined the association of AQP4 to important physiological functions in benign lung tissue. CONCLUSIONS: Our findings accentuate the need to identify functional differences and redundancies of active AQPs in normal and tumor cells in order to assess their value as promising drug targets.

Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions.

BACKGROUND: Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems. RESULTS: In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the ERBB signaling network in de novo trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi. CONCLUSION: DEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.

A Robust and All-Inclusive Pipeline for Shuffling of Adeno-Associated Viruses.

Adeno-associated viruses (AAV) are attractive templates for engineering of synthetic gene delivery vectors. A particularly powerful technology for breeding of novel vectors with improved properties is DNA family shuffling, i.e., generation of chimeric capsids by homology-driven DNA recombination. Here, to make AAV DNA shuffling available to a wider community, we present a robust experimental and bioinformatical pipeline comprising: (i) standardized and partially codon-optimized plasmids carrying 12 different AAV capsid genes; (ii) a scalable protocol including troubleshooting guide for viral library production; and (iii) the freely available software SALANTO for comprehensive analysis of chimeric AAV DNA and protein sequences. Moreover, we describe a set of 12 premade and ready-to-use AAV libraries. Finally, we demonstrate the usefulness of DNA barcoding technology to trace AAV capsid libraries within a complex mixture. Our protocols and resources facilitate the implementation and tailoring of AAV evolution technology in any laboratory interested in customized viral gene transfer.

Implementation of Plate Imaging for Demonstration of Monoclonality in Biologics Manufacturing Development.

Monoclonality of mammalian cell lines used for production of biologics is a regulatory expectation and one of the attributes assessed as part of a larger process to ensure consistent quality of the biologic. Historically, monoclonality has been demonstrated through statistics generated from limiting dilution cloning or through verified flow cytometry methods. A variety of new technologies are now on the market with the potential to offer more efficient and robust approaches to generating and documenting a clonal cell line.Here we present an industry perspective on approaches for the application of imaging and integration of that information into a regulatory submission to support a monoclonality claim. These approaches represent the views of a consortium of companies within the BioPhorum Development Group and include case studies utilising imaging technology that apply scientifically sound approaches and efforts in demonstrating monoclonality. By highlighting both the utility of these alternative approaches and the advantages they bring over the traditional methods, as well as their adoption by industry leaders, we hope to encourage acceptance of their use within the biologics cell line development space and provide guidance for regulatory submission using these alternative approaches.LAY ABSTRACT: In the manufacture of biologics produced in mammalian cells, one recommendation by regulatory agencies to help ensure product consistency, safety, and efficacy is to produce the material from a monoclonal cell line derived from a single, progenitor cell. The process by which monoclonality is assured can be supplemented with single-well plate images of the progenitor cell. Here we highlight the utility of that imaging technology, describe approaches to verify the validity of those images, and discuss how to analyze that information to support a biologic filing application. This approach serves as an industry perspective to increased regulatory interest within the scope of monoclonality for mammalian cell culture-derived biologics.

Reconstruction of Protein Networks Using Reverse-Phase Protein Array Data.

In this chapter, we describe an approach to reconstruct cellular signaling networks based on measurements of protein activation after different stimulation experiments. As experimental platform reverse-phase protein arrays (RPPA) are used. RPPA allow the measurement of proteins and phosphoproteins across many samples in parallel with minimal sample consumption using a panel of highly target protein-specific antibodies. Functional interactions of proteins are modeled using a Boolean network. We describe the Boolean network reconstruction approach ddepn (dynamic deterministic effects propagation networks), which uses time course data to derive protein interactions based on perturbation experiments. We explain how the method works, give a practical application example, and describe how the results can be interpreted. Furthermore prior knowledge on signaling pathways is essential for network reconstruction. Here we describe the use of our software rBiopaxParser to integrate prior knowledge on protein signaling available in public databases. All applied methods are freely available as open-source R software packages. We describe the preparation of RPPA data as well as all relevant programming steps to format the RPPA data, to infer the prior knowledge, and to reconstruct and analyze the protein signaling networks.

CXorf61 is a target for T cell based immunotherapy of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a high medical need disease with limited treatment options. CD8+ T cell-mediated immunotherapy may represent an attractive approach to address TNBC. The objectives of this study were to assess the expression of CXorf61 in TNBCs and healthy tissues and to evaluate its capability to induce T cell responses. We show by transcriptional profiling of a broad comprehensive set of normal human tissue that CXorf61 expression is strictly restricted to testis. 53% of TNBC patients express this antigen in at least 30% of their tumor cells. In CXorf61-negative breast cancer cell lines CXorf61 expression is activated by treatment with the hypomethylating agent 5-aza-2'-deoxycytidine. By vaccination of HLA-A*02-transgenic mice with CXorf61 encoding RNA we obtained high frequencies of CXorf61-specific T cells. Cloning and characterization of T cell receptors (TCRs) from responding T cells resulted in the identification of the two HLA-A*0201-restricted T cell epitopes CXorf6166-74 and CXorf6179-87. Furthermore, by in vitro priming of human CD8+ T cells derived from a healthy donor recognizing CXorf6166-74 we were able to induce a strong antigen-specific immune response and clone a human TCR recognizing this epitope. In summary, our data confirms this antigen as promising target for T cell based therapies.

Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines.

BACKGROUND: Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging. The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitor erlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated protein signalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds in case of overexpression or mutations. Dimerisation with other receptors allows to bypass pathway blockades. Our intention is to reconstruct the ErbB network to reveal resistance mechanisms. We used longitudinal proteomic data of ErbB receptors and downstream targets in the ErbB-2 amplified breast cancer cell lines BT474, SKBR3 and HCC1954 treated with erlotinib, trastuzumab or pertuzumab, alone or combined, up to 60 minutes and 30 hours, respectively. In a Boolean modelling approach, signalling networks were reconstructed based on these data in a cell line and time course specific manner, including prior literature knowledge. Finally, we simulated network response to inhibitor combinations to detect signalling nodes reflecting growth inhibition. RESULTS: The networks pointed to cell line specific activation patterns of the MAPK and PI3K pathway. In BT474, the PI3K signal route was favoured, while in SKBR3, novel edges highlighted MAPK signalling. In HCC1954, the inferred edges stimulated both pathways. For example, we uncovered feedback loops amplifying PI3K signalling, in line with the known trastuzumab resistance of this cell line. In the perturbation simulations on the short-term networks, we analysed ERK1/2, AKT and p70S6K. The results indicated a pathway specific drug response, driven by the type of growth factor stimulus. HCC1954 revealed an edgetic type of PIK3CA-mutation, contributing to trastuzumab inefficacy. Drug impact on the AKT and ERK1/2 signalling axes is mirrored by effects on RB and RPS6, relating to phenotypic events like cell growth or proliferation. Therefore, we additionally analysed RB and RPS6 in the long-term networks. CONCLUSIONS: We derived protein interaction models for three breast cancer cell lines. Changes compared to the common reference network hint towards individual characteristics and potential drug resistance mechanisms. Simulation of perturbations were consistent with the experimental data, confirming our combined reverse and forward engineering approach as valuable for drug discovery and personalised medicine.

Activation of AMP-activated protein kinase sensitizes lung cancer cells and H1299 xenografts to erlotinib.

OBJECTIVES: The therapeutic scheme for non-small cell lung cancer (NSCLC) patients can be improved if adapted to the individual response. For example, 60-70% of adenocarcinoma patients show response to EGFR-tyrosine kinase inhibitors in the presence of mutated EGFR. We searched for additional target molecules involved in the action of the EGFR-tyrosine kinase inhibitor erlotinib in the absence of EGFR mutations, which might be suitable for combinatorial therapy approaches. MATERIALS AND METHODS: Erlotinib-response associated proteins were investigated in patient-derived NSCLC mouse xenografts by reverse-phase protein array technology (RPPA) and Western blotting. A combinatorial treatment approach was carried out in NSCLC cell lines and H1299 mouse xenografts, and subsequently analyzed for consequences in cell growth and signal transduction. RESULTS: AMP-activated protein kinase (AMPK) expression was increased in erlotinib responders before and after treatment. In a combinatorial approach, activation of AMPK by A-769662 and erlotinib treatment showed a synergistic effect in cell growth reduction and apoptosis activation in H1299 cells compared to the single drugs. AMPK pathway analyses revealed an effective inhibition of mTOR signaling by drug combination. In H1299 xenografts, the tumor size was significantly decreased after combinatorial treatment. CONCLUSION: Our results suggest that AMPK activation status affects response to erlotinib in distinct lung tumor models.

RPPanalyzer toolbox: an improved R package for analysis of reverse phase protein array data.

Analysis of large-scale proteomic data sets requires specialized software tools, tailored toward the requirements of individual approaches. Here we introduce an extension of an open-source software solution for analyzing reverse phase protein array (RPPA) data. The R package RPPanalyzer was designed for data preprocessing followed by basic statistical analyses and proteomic data visualization. In this update, we merged relevant data preprocessing steps into a single user-friendly function and included a new method for background noise correction as well as new methods for noise estimation and averaging of replicates to transform data in such a way that they can be used as input for a new time course plotting function. We demonstrate the robustness of our enhanced RPPanalyzer platform by analyzing longitudinal RPPA data of MET receptor signaling upon stimulation with different hepatocyte growth factor concentrations.

Molecular dissection of human Argonaute proteins by DNA shuffling.

A paramount task in RNA interference research is to decipher the complex biology of cellular effectors, exemplified in humans by four pleiotropic Argonaute proteins (Ago1-Ago4). Here, we exploited DNA family shuffling, a molecular evolution technology, to generate chimeric Ago protein libraries for dissection of intricate phenotypes independently of prior structural knowledge. Through shuffling of human Ago2 and Ago3, we discovered two N-terminal motifs that govern RNA cleavage in concert with the PIWI domain. Structural modeling predicts an impact on protein rigidity and/or RNA-PIWI alignment, suggesting new mechanistic explanations for Ago3's slicing deficiency. Characterization of hybrids including Ago1 and Ago4 solidifies that slicing requires the juxtaposition and combined action of multiple disseminated modules. We also present a Gateway library of codon-optimized chimeras of human Ago1-Ago4 and molecular evolution analysis software as resources for future investigations into RNA interference sequence-structure-function relationships.