Combinatorial optimization for affinity proteomics

Biochemical test development can significantly benefit from combinatorial optimization. Multiplex assays do require complex planning decisions during implementation and subsequent validation. Due to the increasing complexity of setups and the limited resources, the need to work efficiently is a key element for the success of biochemical research and test development. The first approached problem was to systemically pool samples in order to create a multi-positive control sample. We could show that pooled samples exhibit a predictable serological profile and by using this prediction a pooled sample with the desired property. For serological assay validation it must be shown that the low, medium, and high levels can be reliably measured. It is shown how to optimally choose a few samples to achieve this requirements. Finally the latter methods were merged to validate multiplexed assays using a set of pooled samples. A novel algorithm combining fast enumeration and a set cover formulation has been introduced. The major part of the thesis deals with optimization and data analysis for Triple X Proteomics - immunoaffinity assays using antibodies binding short linear, terminal epitopes of peptides. It has been shown that the problem of choosing a minimal set of epitopes for TXP setups, which combine mass spectrometry with immunoaffinity enrichment, is equivalent to the well-known set cover problem. TXP Sandwich immunoassays capture and detect peptides by combining the C-terminal and N-terminal binders. A greedy heuristic and a meta-heuristic using local search is presented, which proves to be more efficient than pure ILP formulations. All models were implemented in the novel Java framework SCPSolver, which is applicable to many problems that can be formulated as integer programs. While the main design goal of the software was usability, it also provides a basic modelling language, easy deployment and platform independence. One question arising when analyzing TXP data was: How likely is it to observe multiple peptides sharing the same terminus? The algorithms TXP-TEA and MATERICS were able to identify binding characteristics of TXP antibodies from data obtained in immunoaffinity MS experiments, reducing the cost of such analyses. A multinomial statistical model explains the distributions of short sequences observed in protein databases. This allows deducing the average optimal length of the targeted epitope. Further a closed-from scoring function for epitope enrichment in sequence lists is derived

Proteomic analysis of hepatic effects of phenobarbital in mice with humanized liver

Activation of the constitutive androstane receptor (CAR) may induce adaptive but also adverse effects in rodent liver, including the induction of drug-metabolizing enzymes, transient hepatocellular proliferation, and promotion of liver tumor growth. Human relevance of CAR-related adverse hepatic effects is controversially debated. Here, we used the chimeric FRG-KO mouse model with livers largely repopulated by human hepatocytes, in order to study human hepatocytes and their response to treatment with the model CAR activator phenobarbital (PB) in vivo. Mice received an intraperitoneal injection with 50 mg/kg body weight PB or saline, and were sacrificed after 72–144 h. Non-repopulated FRG-KO mice were used as additional control. Comprehensive proteomics datasets were generated by merging data obtained by targeted as well as non-targeted proteomics approaches. For the first time, a novel proteomics workflow was established to comparatively analyze the effects of PB on human and murine proteins within one sample. Analysis of merged proteome data sets and bioinformatics data mining revealed comparable responses in murine and human hepatocytes with respect to nuclear receptor activation and induction of xenobiotic metabolism. By contrast, activation of MYC, a key regulator of proliferation, was predicted only for mouse but not human hepatocytes. Analyses of 5-bromo-2′-deoxyuridine incorporation confirmed this finding. In summary, this study for the first time presents a comprehensive proteomic analysis of CAR-dependent effects in human and mouse hepatocytes from humanized FRG-KO mice. The data support the hypothesis that PB does induce adaptive metabolic responses, but not hepatocellular proliferation in human hepatocytes in vivo.publishedVersio

Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies

<p>Abstract</p> <p>Background</p> <p>To understand the dynamic behavior of cellular systems, mathematical modeling is often necessary and comprises three steps: (1) experimental measurement of participating molecules, (2) assignment of rate laws to each reaction, and (3) parameter calibration with respect to the measurements. In each of these steps the modeler is confronted with a plethora of alternative approaches, e. g., the selection of approximative rate laws in step two as specific equations are often unknown, or the choice of an estimation procedure with its specific settings in step three. This overall process with its numerous choices and the mutual influence between them makes it hard to single out the best modeling approach for a given problem.</p> <p>Results</p> <p>We investigate the modeling process using multiple kinetic equations together with various parameter optimization methods for a well-characterized example network, the biosynthesis of valine and leucine in <it>C. glutamicum</it>. For this purpose, we derive seven dynamic models based on generalized mass action, Michaelis-Menten and convenience kinetics as well as the stochastic Langevin equation. In addition, we introduce two modeling approaches for feedback inhibition to the mass action kinetics. The parameters of each model are estimated using eight optimization strategies. To determine the most promising modeling approaches together with the best optimization algorithms, we carry out a two-step benchmark: (1) coarse-grained comparison of the algorithms on all models and (2) fine-grained tuning of the best optimization algorithms and models. To analyze the space of the best parameters found for each model, we apply clustering, variance, and correlation analysis.</p> <p>Conclusion</p> <p>A mixed model based on the convenience rate law and the Michaelis-Menten equation, in which all reactions are assumed to be reversible, is the most suitable deterministic modeling approach followed by a reversible generalized mass action kinetics model. A Langevin model is advisable to take stochastic effects into account. To estimate the model parameters, three algorithms are particularly useful: For first attempts the settings-free Tribes algorithm yields valuable results. Particle swarm optimization and differential evolution provide significantly better results with appropriate settings.</p

Performance of a Multiplex Serological Helicobacter pylori Assay on a Novel Microfluidic Assay Platform

International audienceInfection with Helicobacter pylori (H. pylori) occurs in 50% of the world population, and is associated with the development of ulcer and gastric cancer. Serological diagnostic tests indicate an H. pylori infection by detecting antibodies directed against H. pylori proteins. In addition to line blots, multiplex assay platforms provide smart solutions for the simultaneous analysis of antibody responses towards several H. pylori proteins. We used seven H. pylori proteins (FliD, gGT, GroEL, HpaA, CagA, VacA, and HP0231) and an H. pylori lysate for the development of a multiplex serological assay on a novel microfluidic platform. The reaction limited binding regime in the microfluidic channels allows for a short incubation time of 35 min. The developed assay showed very high sensitivity (99%) and specificity (100%). Besides sensitivity and specificity, the technical validation (intra-assay CV = 3.7 ± 1.2% and inter-assay CV = 5.5 ± 1.2%) demonstrates that our assay is also a robust tool for the analysis of the H. pylori-specific antibody response. The integration of the virulence factors CagA and VacA allow for the assessment of the risk for gastric cancer development. The short assay time and the performance of the platform shows the potential for implementation of such assays in a clinical setting

RNA-protein correlation of liver toxicity markers in HepaRG cells

The liver is a main target organ for the toxicity of many different compounds. While in general, in vivo testing is still routinely used for assessing the hepatotoxic potential of test chemicals, the use of in vitro models offers advantages with regard to throughput, consumption of resources, and animal welfare aspects. Using the human hepatoma cell line HepaRG, we performed a comparative evaluation of a panel of hepatotoxicity marker mRNAs and proteins after exposure of the cells to 30 different pesticidal active compounds comprising herbizides, fungicides, insecticides, and others. The panel of hepatotoxicity markers included nuclear receptor target genes, key players of fatty acid and bile acid metabolism-related pathways, as well as recently identified biomarkers of drug-induced liver injury. Moreover, marker genes and proteins were identified, for example, S100P, ANXA10, CYP1A1, and CYP7A1. These markers respond with high sensitivity to stimulation with chemically diverse test compounds already at non-cytotoxic concentrations. The potency of the test compounds, determined as an overall parameter of their ability to deregulate marker expression in vitro, was very similar between the mRNA and protein levels. Thus, this study does not only characterize the response of human liver cells to 30 different pesticides but also demonstrates that hepatotoxicity testing in human HepaRG cells yields well comparable results at the mRNA and protein levels. Furthermore, robust hepatotoxicity marker genes and proteins were identified in HepaRG cells

Indirect protein quantification of drug-transforming enzymes using peptide group-specific immunoaffinity enrichment and mass spectrometry

Immunoaffinity enrichment of proteotypic peptides, coupled with selected reaction monitoring, enables indirect protein quantification. However the lack of suitable antibodies limits its widespread application. We developed a method in which multi-specific antibodies are used to enrich groups of peptides, thus facilitating multiplexed quantitative protein assays. We tested this strategy in a pharmacokinetic experiment by targeting a group of homologous drug transforming proteins in human hepatocytes. Our results indicate the generic applicability of this method to any biological system

Indirect protein quantification of drug-transforming enzymes using peptide group-specific immunoaffinity enrichment and mass spectrometry

Immunoaffinity enrichment of proteotypic peptides, coupled with selected reaction monitoring, enables indirect protein quantification. However the lack of suitable antibodies limits its widespread application. We developed a method in which multi-specific antibodies are used to enrich groups of peptides, thus facilitating multiplexed quantitative protein assays. We tested this strategy in a pharmacokinetic experiment by targeting a group of homologous drug transforming proteins in human hepatocytes. Our results indicate the generic applicability of this method to any biological system

The systems biology simulation core algorithm

Background: With the increasing availability of high dimensional time course data for metabolites, genes, and fluxes, the mathematical description of dynamical systems has become an essential aspect of research in systems biology. Models are often encoded in formats such as SBML, whose structure is very complex and difficult to evaluate due to many special cases. Results: This article describes an efficient algorithm to solve SBML models that are interpreted in terms of ordinary differential equations. We begin our consideration with a formal representation of the mathematical form of the models and explain all parts of the algorithm in detail, including several preprocessing steps. We provide a flexible reference implementation as part of the Systems Biology Simulation Core Library, a community-driven project providing a large collection of numerical solvers and a sophisticated interface hierarchy for the definition of custom differential equation systems. To demonstrate the capabilities of the new algorithm, it has been tested with the entire SBML Test Suite and all models of BioModels Database. Conclusions: The formal description of the mathematics behind the SBML format facilitates the implementation of the algorithm within specifically tailored programs. The reference implementation can be used as a simulation backend for Java™-based programs. Source code, binaries, and documentation can be freely obtained under the terms of the LGPL version 3 from http://simulation-core.sourceforge.net. Feature requests, bug reports, contributions, or any further discussion can be directed to the mailing list [email protected]

Optimal selection of epitopes for TXP-immunoaffinity mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry (MS) based protein profiling has become one of the key technologies in biomedical research and biomarker discovery. One bottleneck in MS-based protein analysis is sample preparation and an efficient fractionation step to reduce the complexity of the biological samples, which are too complex to be analyzed directly with MS. Sample preparation strategies that reduce the complexity of tryptic digests by using immunoaffinity based methods have shown to lead to a substantial increase in throughput and sensitivity in the proteomic mass spectrometry approach. The limitation of using such immunoaffinity-based approaches is the availability of the appropriate peptide specific capture antibodies. Recent developments in these approaches, where subsets of peptides with short identical terminal sequences can be enriched using antibodies directed against short terminal epitopes, promise a significant gain in efficiency.</p> <p>Results</p> <p>We show that the minimal set of terminal epitopes for the coverage of a target protein list can be found by the formulation as a set cover problem, preceded by a filtering pipeline for the exclusion of peptides and target epitopes with undesirable properties.</p> <p>Conclusions</p> <p>For small datasets (a few hundred proteins) it is possible to solve the problem to optimality with moderate computational effort using commercial or free solvers. Larger datasets, like full proteomes require the use of heuristics.</p