Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Online Peptide Fractionation Using a Multiphasic Microfluidic Liquid Chromatography Chip Improves Reproducibility and Detection Limits for Quantitation in Discovery and Targeted Proteomics

Comprehensive proteomic profiling of biological specimens usually requires multidimensional chromatographic peptide fractionation prior to mass spectrometry. However, this approach can suffer from poor reproducibility because of the lack of standardization and automation of the entire workflow, thus compromising performance of quantitative proteomic investigations. To address these variables we developed an online peptide fractionation system comprising a multiphasic liquid chromatography (LC) chip that integrates reversed phase and strong cation exchange chromatography upstream of the mass spectrometer (MS). We showed superiority of this system for standardizing discovery and targeted proteomic workflows using cancer cell lysates and nondepleted human plasma. Five-step multiphase chip LC MS/MS acquisition showed clear advantages over analyses of unfractionated samples by identifying more peptides, consuming less sample and often improving the lower limits of quantitation, all in highly reproducible, automated, online configuration. We further showed that multiphase chip LC fractionation provided a facile means to detect many N- and C-terminal peptides (including acetylated N terminus) that are challenging to identify in complex tryptic peptide matrices because of less favorable ionization characteristics. Given as much as 95% of peptides were detected in only a single salt fraction from cell lysates we exploited this high reproducibility and coupled it with multiple reaction monitoring on a high-resolution MS instrument (MRM-HR). This approach increased target analyte peak area and improved lower limits of quantitation without negatively influencing variance or bias. Further, we showed a strategy to use multiphase LC chip fractionation LC-MS/MS for ion library generation to integrate with SWATH^TM data-independent acquisition quantitative workflows. All MS data are available via ProteomeXchange with identifier PXD001464.Mass spectrometry based proteomic quantitation is an essential technique used for contemporary, integrative biological studies. Whether used in discovery experiments or for targeted biomarker applications, quantitative proteomic studies require high reproducibility at many levels. It requires reproducible run-to-run peptide detection, reproducible peptide quantitation, reproducible depth of proteome coverage, and ideally, a high degree of cross-laboratory analytical reproducibility. Mass spectrometry centered proteomics has evolved steadily over the past decade, now mature enough to derive extensive draft maps of the human proteome (1, 2). Nonetheless, a key requirement yet to be realized is to ensure that quantitative proteomics can be carried out in a timely manner while satisfying the aforementioned challenges associated with reproducibility. This is especially important for recent developments using data independent MS quantitation and multiple reaction monitoring on high-resolution MS (MRM-HR)¹ as they are both highly dependent on LC peptide retention time reproducibility and precursor detectability, while attempting to maximize proteome coverage (3). Strategies usually employed to increase the depth of proteome coverage utilize various sample fractionation methods including gel-based separation, affinity enrichment or depletion, protein or peptide chemical modification-based enrichment, and various peptide chromatography methods, particularly ion exchange chromatography (4–10). In comparison to an unfractionated “naive” sample, the trade-off in using these enrichments/fractionation approaches are higher risk of sample losses, introduction of undesired chemical modifications (e.g. oxidation, deamidation, N-terminal lactam formation), and the potential for result skewing and bias, as well as numerous time and human resources required to perform the sample preparation tasks. Online-coupled approaches aim to minimize those risks and address resource constraints. A widely practiced example of the benefits of online sample fractionation has been the decade long use of combining strong cation exchange chromatography (SCX) with C18 reversed-phase (RP) for peptide fractionation (known as MudPIT – multidimensional protein identification technology), where SCX and RP is performed under the same buffer conditions and the SCX elution performed with volatile organic cations compatible with reversed phase separation (11). This approach greatly increases analyte detection while avoiding sample handling losses. The MudPIT approach has been widely used for discovery proteomics (12–14), and we have previously shown that multiphasic separations also have utility for targeted proteomics when configured for selected reaction monitoring MS (SRM-MS). We showed substantial advantages of MudPIT-SRM-MS with reduced ion suppression, increased peak areas and lower limits of detection (LLOD) compared with conventional RP-SRM-MS (15).To improve the reproducibility of proteomic workflows, increase throughput and minimize sample loss, numerous microfluidic devices have been developed and integrated for proteomic applications (16, 17). These devices can broadly be classified into two groups: (1) microfluidic chips for peptide separation (18–25) and; (2) proteome reactors that combine enzymatic processing with peptide based fractionation (26–30). Because of the small dimension of these devices, they are readily able to integrate into nanoLC workflows. Various applications have been described including increasing proteome coverage (22, 27, 28) and targeting of phosphopeptides (24, 31, 32), glycopeptides and released glycans (29, 33, 34).In this work, we set out to take advantage of the benefits of multiphasic peptide separations and address the reproducibility needs required for high-throughput comparative proteomics using a variety of workflows. We integrated a multiphasic SCX and RP column in a “plug-and-play” microfluidic chip format for online fractionation, eliminating the need for users to make minimal dead volume connections between traps and columns. We show the flexibility of this format to provide robust peptide separation and reproducibility using conventional and topical mass spectrometry workflows. This was undertaken by coupling the multiphase liquid chromatography (LC) chip to a fast scanning Q-ToF mass spectrometer for data dependent MS/MS, data independent MS (SWATH) and for targeted proteomics using MRM-HR, showing clear advantages for repeatable analyses compared with conventional proteomic workflows.     

Coomassie Blue as a Near-infrared Fluorescent Stain: A Systematic Comparison With Sypro Ruby for In-gel Protein Detection

Quantitative proteome analyses suggest that the well-established stain colloidal Coomassie Blue, when used as an infrared dye, may provide sensitive, post-electrophoretic in-gel protein detection that can rival even Sypro Ruby. Considering the central role of two-dimensional gel electrophoresis in top-down proteomic analyses, a more cost effective alternative such as Coomassie Blue could prove an important tool in ongoing refinements of this important analytical technique. To date, no systematic characterization of Coomassie Blue infrared fluorescence detection relative to detection with SR has been reported. Here, seven commercial Coomassie stain reagents and seven stain formulations described in the literature were systematically compared. The selectivity, threshold sensitivity, inter-protein variability, and linear-dynamic range of Coomassie Blue infrared fluorescence detection were assessed in parallel with Sypro Ruby. Notably, several of the Coomassie stain formulations provided infrared fluorescence detection sensitivity to <1 ng of protein in-gel, slightly exceeding the performance of Sypro Ruby. The linear dynamic range of Coomassie Blue infrared fluorescence detection was found to significantly exceed that of Sypro Ruby. However, in two-dimensional gel analyses, because of a blunted fluorescence response, Sypro Ruby was able to detect a few additional protein spots, amounting to 0.6% of the detected proteome. Thus, although both detection methods have their advantages and disadvantages, differences between the two appear to be small. Coomassie Blue infrared fluorescence detection is thus a viable alternative for gel-based proteomics, offering detection comparable to Sypro Ruby, and more reliable quantitative assessments, but at a fraction of the cost.Gel electrophoresis is an accessible, widely applicable and mature protein resolving technology. As the original top-down approach to proteomic analyses, among its many attributes the high resolution achievable by two dimensional gel-electrophoresis (2DE)¹ ensures that it remains an effective analytical technology despite the appearance of alternatives. However, in-gel detection remains a limiting factor for gel-based analyses; available technology generally permits the detection and quantification of only relatively abundant proteins (35). Many critical components in normal physiology and also disease may be several orders of magnitude less abundant and thus below the detection threshold of in-gel stains, or indeed most techniques. Pre- and post-fractionation technologies have been developed to address this central issue in proteomics but these are not without limitations (1–5). Thus improved detection methods for gel-based proteomics continue to be a high priority, and the literature is rich with different in-gel detection methods and innovative improvements (6–34). This history of iterative refinement presents a wealth of choices when selecting a detection strategy for a gel-based proteomic analysis (35).Perhaps the best known in-gel detection method is the ubiquitous Coomassie Blue (CB) stain; CB has served as a gel stain and protein quantification reagent for over 40 years. Though affordable, robust, easy to use, and compatible with mass spectrometry (MS), CB staining is relatively insensitive. In traditional organic solvent formulations, CB detects ∼ 10 ng of protein in-gel, and some reports suggest poorer sensitivity (27, 29, 36, 37). Sensitivity is hampered by relatively high background staining because of nonspecific retention of dye within the gel matrix (32, 36, 38, 39). The development of colloidal CB (CCB) formulations largely addressed these limitations (12); the concentration of soluble CB was carefully controlled by sequestering the majority of the dye into colloidal particles, mediated by pH, solvent, and the ionic strength of the solution. Minimizing soluble dye concentration and penetration of the gel matrix mitigated background staining, and the introduction of phosphoric acid into the staining reagent enhanced dye-protein interactions (8, 12, 40), contributing to an in-gel staining sensitivity of 5–10 ng protein, with some formulations reportedly yielding sensitivities of 0.1–1 ng (8, 12, 22, 39, 41, 42). Thus CCB achieved higher sensitivity than traditional CB staining, yet maintained all the advantages of the latter, including low cost and compatibility with existing densitometric detection instruments and MS. Although surpassed by newer methods, the practical advantages of CCB ensure that it remains one of the most common gel stains in use.Fluorescent stains have become the routine and sensitive alternative to visible dyes. Among these, the ruthenium-organometallic family of dyes have been widely applied and the most commercially well-known is Sypro Ruby (SR), which is purported to interact noncovalently with primary amines in proteins (15, 18, 19, 43). Chief among the attributes of these dyes is their high sensitivity. In-gel detection limits of < 1 ng for some proteins have been reported for SR (6, 9, 14, 44, 45). Moreover, SR staining has been reported to yield a greater linear dynamic range (LDR), and reduced interprotein variability (IPV) compared with CCB and silver stains (15, 19, 46–49). SR is easy to use, fully MS compatible, and relatively forgiving of variations in initial conditions (6, 15). The chief consequence of these advances remains high cost; SR and related stains are notoriously expensive, and beyond the budget of many laboratories. Furthermore, despite some small cost advantage relative to SR, none of the available alternatives has been consistently and quantitatively demonstrated to substantially improve on the performance of SR under practical conditions (9, 50).Notably, there is evidence to suggest that CCB staining is not fundamentally insensitive, but rather that its sensitivity has been limited by traditional densitometric detection (50, 51). When excited in the near IR at ∼650 nm, protein-bound CB in-gel emits light in the range of 700–800 nm. Until recently, the lack of low-cost, widely available and sufficiently sensitive infrared (IR)-capable imaging instruments prevented mainstream adoption of in-gel CB infrared fluorescence detection (IRFD); advances in imaging technology are now making such instruments far more accessible. Initial reports suggested that IRFD of CB-stained gels provided greater sensitivity than traditional densitometric detection (50, 51). Using CB R250, in-gel IRFD was reported to detect as little as 2 ng of protein in-gel, with a LDR of about an order of magnitude (2 to 20 ng, or 10 to 100 ng in separate gels), beyond which the fluorescent response saturated into the μg range (51). Using the G250 dye variant, it was determined that CB-IRFD of 2D gels detected ∼3 times as many proteins as densitometric imaging, and a comparable number of proteins as seen by SR (50). This study also concluded that CB-IRFD yielded a significantly higher signal to background ratio (S/BG) than SR, providing initial evidence that CB-IRFD may be superior to SR in some aspects of stain performance (50).Despite this initial evidence of the viability of CB-IRF as an in-gel protein detection method, a detailed characterization of this technology has not yet been reported. Here a more thorough, quantitative characterization of CB-IRFD is described, establishing its lowest limit of detection (LLD), IPV, and LDR in comparison to SR. Finally a wealth of modifications and enhancements of CCB formulations have been reported (8, 12, 21, 24, 26, 29, 40, 41, 52–54), and likewise there are many commercially available CCB stain formulations. To date, none of these formulations have been compared quantitatively in terms of their relative performance when detected using IRF. As a general detection method for gel-based proteomics, CB-IRFD was found to provide comparable or even slightly superior performance to SR according to most criteria, including sensitivity and selectivity (50). Furthermore, in terms of LDR, CB-IRFD showed distinct advantages over SR. However, assessing proteomes resolved by 2DE revealed critical distinctions between CB-IRFD and SR in terms of protein quantification versus threshold detection: neither stain could be considered unequivocally superior to the other by all criteria. Nonetheless, IRFD proved the most sensitive method of detecting CB-stained protein in-gel, enabling high sensitivity detection without the need for expensive reagents or even commercial formulations. Overall, CB-IRFD is a viable alternative to SR and other mainstream fluorescent stains, mitigating the high cost of large-scale gel-based proteomic analyses, making high sensitivity gel-based proteomics accessible to all labs. With improvements to CB formulations and/or image acquisition instruments, the performance of this detection technology may be further enhanced.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

Quantitative Proteomics Reveals that Plasma Membrane Microdomains From Poplar Cell Suspension Cultures Are Enriched in Markers of Signal Transduction,Molecular Transport,and Callose Biosynthesis

The plasma membrane (PM) is a highly dynamic interface that contains detergent-resistant microdomains (DRMs). The aim of this work was to determine the main functions of such microdomains in poplar through a proteomic analysis using gel-based and solution (iTRAQ) approaches. A total of 80 proteins from a limited number of functional classes were found to be significantly enriched in DRM relative to PM. The enriched proteins are markers of signal transduction, molecular transport at the PM, or cell wall biosynthesis. Their intrinsic properties are presented and discussed together with the biological significance of their enrichment in DRM. Of particular importance is the significant and specific enrichment of several callose [(1→3)-β-glucan] synthase isoforms, whose catalytic activity represents a final response to stress, leading to the deposition of callose plugs at the surface of the PM. An integrated functional model that connects all DRM-enriched proteins identified is proposed. This report is the only quantitative analysis available to date of the protein composition of membrane microdomains from a tree species.The plasma membrane (PM)¹ is considered as one of the most interactive and dynamic supramolecular structures of the cell (1, 2). It forms a physical interface between the cytoplasm and the extracellular environment and is involved in many biological processes such as metabolite and ion transport, gaseous exchanges, endocytosis, cell differentiation and proliferation, defense against pathogens, etc. (3). Various combinations of biochemical and analytical approaches have been used to characterize the PM proteome in different organisms such as yeast, plants, and animals (4–8). Typically, PM proteins are either embedded in the phospholipid bilayer through transmembrane helices or less tightly bound to the membrane through reversible or irreversible surface interactions. In eukaryotic cells, some PM proteins are enriched in lateral lipid patches that form microdomains within the membrane (9, 10). These microdomains are considered to act as functional units that support and regulate specific biological processes associated with the PM (9, 10). Often referred to as “membrane (lipid) rafts” in animals and other organisms, they are typically described as being enriched in sphingolipids, sterols, and phospholipids that contain essentially saturated fatty acids (9–11). Early work on PM microdomains has suggested that their specific lipid composition confers resistance to certain concentrations of nonionic detergents, such as Triton X-100 and Nonidet P-40 (10, 11). Although this property has been exploited experimentally to isolate so-called detergent-resistant microdomains (DRMs), the relationship between DRMs and membrane rafts remains controversial (12). Indeed, the relation between the two is much debated, essentially because the use of Triton X-100 at 4 °C to prepare DRMs has been proposed to potentially induce the artificial formation of detergent-resistant structures whose composition may not fully reflect that of physiological membrane rafts (12). Nonetheless, DRM preparations represent an excellent system for the isolation and identification of groups of proteins—eventually associated in complexes—that tend to naturally interact with specific sets of lipids, thereby forming specialized functional units. Their biochemical characterization is therefore most useful in attempts to better understand the mode of interaction of specific proteins with sterols and sphingolipids and to gain insight into the protein composition and biological activity of subdomains from the PM.Plant DRMs have been understudied relative to their animal counterparts. Indeed, proteomic studies have been undertaken on DRM preparations from only a limited number of plant species. These include tobacco (13–15), Arabidopsis (16), barrel clover (Medicago truncatula) (17), rice (18), oat, and rye (19). These studies, essentially based on qualitative or semi-quantitative proteomics, led to the identification of hundreds of proteins involved in a large range of mechanisms, functions, and biochemical activities (15–19). Depending on the report considered, a variable proportion of the identified proteins can be intuitively linked to DRMs and potentially to PM microdomains. However, many proteins that are clearly not related to the PM and its microdomains co-purify with DRM. These include, for instance, soluble proteins from cytoplasmic metabolic pathways; histones; and ribosomal, chloroplastic, and mitochondrial proteins (15–19). Thus, there is a need to obtain a more restricted list of proteins that are specifically enriched in DRMs and that define specialized functional structures. One way to tackle this problem is through quantitative proteomics, eventually in combination with complementary biochemical approaches. Although quantitative techniques have been increasingly applied to the proteomic analysis of complex mixtures of soluble proteins, their exploitation for the characterization of membrane samples remains challenging. As a result, very few studies of plant DRMs have been based on truly quantitative methods. For instance, stable isotope labeling combined with the selective disruption of sterol-rich membrane domains by methylcyclodextrin was performed in Arabidopsis cell cultures (20). A similar approach was used to study compositional changes of tobacco DRMs upon cell treatment with the signaling elicitor cryptogenin (21). In another study, 64 Arabidopsis proteins were shown to be significantly enriched in DRMs in response to a pathogen-associated molecular pattern protein (22). Together, these few quantitative proteomics analyses suggest a role of plant membrane microdomains in signal transduction, as in mammalian cells.Although several reports describe the partial characterization of DRMs from higher plants (13–23), there are no data available to date on the protein composition of DRMs from a tree species. We have therefore employed a quantitative proteomic approach for the characterization of DRMs from cell suspension cultures of Populus trichocarpa. In addition, earlier work in our laboratory based on biochemical activity assays revealed the presence of cell wall polysaccharide synthases in DRMs from poplar (23), which suggests the existence of DRM populations specialized in cell wall biosynthesis. This concept was further supported by similar investigations performed on DRMs isolated from the oomycete Saprolegnia monoica (24). The comprehensive quantitative proteomic analysis performed here revealed enrichment in the poplar DRMs of specific carbohydrate synthases involved in callose polymerization. Consistent with the role of callose in plant defense mechanisms, additional proteins related to stress responses and signal transduction were found to be specifically enriched in the poplar DRMs, together with proteins involved in molecular transport. To date, our report is the only analysis available of the DRM proteome of a tree species based on quantitative proteomics. The specific biochemical properties of the 80 proteins significantly enriched in DRMs are described and examined in relation to their localization in membrane microdomains. The relationship between poplar DRMs and molecular transport, signal transduction, stress responses, and callose biosynthesis is discussed, with support from a hypothetical model that integrates the corresponding enriched proteins.     

Molecular Responses of Mouse Macrophages to Copper and Copper Oxide Nanoparticles Inferred from Proteomic Analyses

The molecular responses of macrophages to copper-based nanoparticles have been investigated via a combination of proteomic and biochemical approaches, using the RAW264.7 cell line as a model. Both metallic copper and copper oxide nanoparticles have been tested, with copper ion and zirconium oxide nanoparticles used as controls. Proteomic analysis highlighted changes in proteins implicated in oxidative stress responses (superoxide dismutases and peroxiredoxins), glutathione biosynthesis, the actomyosin cytoskeleton, and mitochondrial proteins (especially oxidative phosphorylation complex subunits). Validation studies employing functional analyses showed that the increases in glutathione biosynthesis and in mitochondrial complexes observed in the proteomic screen were critical to cell survival upon stress with copper-based nanoparticles; pharmacological inhibition of these two pathways enhanced cell vulnerability to copper-based nanoparticles, but not to copper ions. Furthermore, functional analyses using primary macrophages derived from bone marrow showed a decrease in reduced glutathione levels, a decrease in the mitochondrial transmembrane potential, and inhibition of phagocytosis and of lipopolysaccharide-induced nitric oxide production. However, only a fraction of these effects could be obtained with copper ions. In conclusion, this study showed that macrophage functions are significantly altered by copper-based nanoparticles. Also highlighted are the cellular pathways modulated by cells for survival and the exemplified cross-toxicities that can occur between copper-based nanoparticles and pharmacological agents.Manufactured nanoparticles are more and more widely used in more and more consumer products, ranging from personal care products to tires and concrete. Among the nanoparticles, metals and metal oxides represent an important part of the total production and are used in water treatment, as antibacterials, in antifouling paints, and in microelectronics. These varied uses in turn pose the problem of the toxicological evaluation of these nanoparticles (1, 2), and especially of the long-term effects that often come not from simple cell mortality but from altered cellular functions.Macrophages are one of the cell types that deserve special attention in toxicology, because of the variety of their functions. Altered cytokine production can lead to adverse long-term effects, as documented, for example, in the case of asbestos (3). Other dysfunctions of the innate immune system can lead to deregulation of the immune responses and to severe adverse effects, such as a higher incidence of tumors (4).It is therefore not surprising that the immunotoxicology of nanoparticles is a developing field (5–7), and several studies have been devoted to macrophages'' response to nanoparticles. However, most of these studies have been limited to the effect of nanoparticles on cell viability and on cytokine production (e.g. 8–11), although some also studied oxidative stress (12–14) and sometimes other functional parameters (15–17). Very few studies have used the analytical power of proteomics to go deeper into the mechanisms of the response to nanoparticles or metals (reviewed in Ref. 18). A few exceptions are studies on, for example, carbon-based nanoparticles (19) and titanium dioxide (20, 21).Most of the toxicological studies in this field have been focused on a few nanoparticles used either as health products, such as iron oxide (15, 17, 22), or in a variety of consumer products, such as silver (13, 14), silica (9, 12), and titanium dioxide (11, 16, 20, 21).However, many other nanoparticles are being used more and more in industrial applications without extensive toxicological testing. Good examples are indium-tin oxide, used in electronic screens, which appears to be toxic (23), and the copper-based nanoparticles used in high-performance batteries (24), in water depollution (25), and as bactericides as a replacement for nano-silver. Copper and copper oxide induce a strong toxicity (26, 27), coupled with inflammation (28), oxidative stress (29), and genotoxicity (30), at least in epithelial cells.In light of these various effects, we decided to use a combination of a proteomics approach and targeted approaches to address in more molecular detail the responses of macrophages to copper-based nanoparticles (i.e. both metallic copper and copper II oxide).