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Epidemiology and experimental studies provide an overwhelming support of the notion that diets high in red or processed meat accompany an elevated risk of developing pre-neoplastic colorectal adenoma and frank colorectal carcinoma (CRC). The underlying mechanisms are disputed; thus several hypotheses have been proposed. A large body of reports converges, however, on haem and nitrosyl haem as major contributors to the CRC development, presumably acting through various mechanisms. Apart from a potentially higher intestinal mutagenic load among consumers on a diet rich in red/processed meat, other mechanisms involving subtle interference with colorectal stem/progenitor cell survival or maturation are likewise at play. From an overarching perspective, suggested candidate mechanisms for red/processed meat-induced CRC appear as three partly overlapping tenets: (i) increased N-nitrosation/oxidative load leading to DNA adducts and lipid peroxidation in the intestinal epithelium, (ii) proliferative stimulation of the epithelium through haem or food-derived metabolites that either act directly or subsequent to conversion, and (iii) higher inflammatory response, which may trigger a wide cascade of pro-malignant processes. In this review, we summarize and discuss major findings of the area in the context of potentially pertinent mechanisms underlying the above-mentioned association between consumption of red/processed meat and increased risk of developing CRC.

Analysis of vesicle formation and degradation is a central issue in autophagy research and microscopy imaging is revolutionizing the study of such dynamic events inside living cells. A limiting factor is the need for labeling techniques that are labor intensive, expensive, and not always completely reliable. To enable label-free analyses we introduced a generic computational algorithm, the label-free vesicle detector (LFVD), which relies on a matched filter designed to identify circular vesicles within cells using only phase-contrast microscopy images. First, the usefulness of the LFVD is illustrated by presenting successful detections of autophagy modulating drugs found by analyzing the human colorectal carcinoma cell line HCT116 exposed to each substance among 1266 pharmacologically active compounds. Some top hits were characterized with respect to their activity as autophagy modulators using independent in vitro labeling of acidic organelles, detection of LC3-II protein, and analysis of the autophagic flux. Selected detection results for 2 additional cell lines (DLD1 and RKO) demonstrate the generality of the method. In a second experiment, label-free monitoring of dose-dependent vesicle formation kinetics is demonstrated by recorded detection of vesicles over time at different drug concentrations. In conclusion, label-free detection and dynamic monitoring of vesicle formation during autophagy is enabled using the LFVD approach introduced.

For decades, the standard procedure when screening for candidate anticancer drug combinations has been to search for synergy, defined as any positive deviation from trivial cases like when the drugs are regarded as diluted versions of each other (Loewe additivity), independent actions (Bliss independence), or no interaction terms in a response surface model (no interaction). Here, we show that this kind of conventional synergy analysis may be completely misleading when the goal is to detect if there is a promising in vitro therapeutic window. Motivated by this result, and the fact that a drug combination offering a promising therapeutic window seldom is interesting if one of its constituent drugs can provide the same window alone, the largely overlooked concept of therapeutic synergy (TS) is reintroduced. In vitro TS is said to occur when the largest therapeutic window obtained by the best drug combination cannot be achieved by any single drug within the concentration range studied. Using this definition of TS, we introduce a procedure that enables its use in modern massively parallel experiments supported by a statistical omnibus test for TS designed to avoid the multiple testing problem. Finally, we suggest how one may perform TS analysis, via computational predictions of the reference cell responses, when only the target cell responses are available. In conclusion, the conventional error-prone search for promising drug combinations may be improved by replacing conventional (toxicology-rooted) synergy analysis with an analysis focused on (clinically motivated) TS.

Although computational multivariate data analysis (MDA) already has been employed in the dietary survey area, the results reported are based mainly on classical exploratory (descriptive) techniques. Therefore, data of a Swedish and a Danish dietary survey on young consumers (4 to 5 years of age) were subjected not only to modern exploratory MDA, but also modern predictive MDA that via supervised learning yielded predictive classification models. The exploratory part, also encompassing Swedish 8 or 11-year old Swedish consumers, included new innovative forms of hierarchical clustering and bi-clustering. This resulted in several interesting multi-dimensional dietary patterns (dietary prototypes), including striking difference between those of the age-matched Danish and Swedish children. The predictive MDA disclosed additional multi-dimensional food consumption relationships. For instance, the consumption patterns associated with each of several key foods like bread, milk, potato and sweetened beverages, were found to differ markedly between the Danish and Swedish consumers. In conclusion, the joint application of modern descriptive and predictive MDA to dietary surveys may enable new levels of diet quality evaluation and perhaps also prototype-based toxicology risk assessment.

Drug induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances as well as to support drug repurposing, i.e. identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the “Connectivity Map” (CMap). The potential of similar exercises at the metabolite level is, however, still largely unexplored. Only recently the first high throughput metabolomic assay pilot study was published, involving screening of metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here we report results from a separately developed metabolic profiling assay, which leverages 1H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of most tested drugs according to their respective pharmacological class. A more advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (three metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this kind of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement.

A major objective of Computational Toxicology is to provide reliable and useful estimates in silico of (potentially) harmful actions of chemicals in humans. Predictive models are commonly based on in vitro and in vivo data, and aims at supporting risk assessment in various areas, including the environmental protection, food, and pharmaceutical sectors. The field is however hampered by the lack of standards, access to high quality data, validated predictive models, as well as means to connect toxicity data to genomics data. We present a framework and roadmap for a novel public infrastructure for predictive computational toxicology and chemical safety assessment, consisting of: (1) a repository capable of aggregating high quality toxicity data with gene expression data, (2) a repository where scientists can share and download predictive models for chemical safety, and (3) a user-friendly platform which makes the services and resources accessible for the scientific community. Databases under the framework will adhere to open standards and use standardized open exchange formats in order to interoperate with emerging international initiatives, such as the FP7-funded OpenTox and ToxBank projects. The infrastructure will strengthen and facilitate already ongoing activities within in silico toxicology, open up new possibilities for incorporating genomics data in chemicals safety modeling (toxicogenomics), as well as deepen the exploitation of signal transduction networks. The initiative will lay the foundation needed to boost decision support in risk assessment in a wide range of fields, including drug discovery, food safety, as well as agricultural and ecological safety assessment.

Structurally different chemical substances may cause similar systemic effects in mammalian cells. It is therefore necessary to go beyond structural comparisons to quantify similarity in terms of their bioactivities. In this work, we introduce a generic methodology to achieve this on the basis of Network Biology principles and using publicly available molecular network topology information. An implementation of this method, denoted QuantMap, is outlined and applied to antidiabetic drugs, NSAIDs, 17 beta-estradiol, and 12 substances known to disrupt estrogenic pathways. The similarity of any pair of compounds is derived from topological comparison of intracellular protein networks, directly and indirectly associated with the respective query chemicals, via a straightforward pairwise comparison of ranked proteins. Although output derived from straightforward chemical/structural similarity analysis provided some guidance on bioactivity, QuantMap produced substance interrelationships that align well with reports on their respective perturbation properties. We believe that QuantMap has potential to provide substantial assistance to drug repositioning, pharmacology evaluation, and toxicology risk assessment.

The pathological process of allergies generally involves an initial activation of certain immune cells, tied to an ensuing inflammatory reaction on renewed contact with the allergen. In IgE-mediated hypersensitivity, this typically occurs in response to otherwise harmless food-or air-borne proteins. As some members of certain protein families carry special properties that make them allergenic, exploring protein allergens at the molecular level is instrumental to an improved understanding of the disease mechanisms, including the identification of relevant antigen features. For this purpose, we inspected a previously identified set of allergen representative peptides (ARPs) to scrutinize protein intrinsic disorder. The resulting study presented here focused on the association between these ARPs and protein intrinsic disorder. In addition, the connection between the disorder-enriched ARPs and UniProt functional keywords was considered. Our analysis revealed that similar to 20% of the allergen peptides are highly disordered, and that similar to 77% of ARPs are either located within disordered regions of corresponding allergenic proteins or show more disorder/flexibility than their neighbor regions. Furthermore, among the subset of allergenic proteins, similar to 70% of the predicted molecular recognition features (MoRFs that consist of short interactive disordered regions undergoing disorder-to-order transitions at interaction with binding partners) were identified as ARPs. These results suggest that intrinsic disorder and MoRFs may play functional roles in IgE-mediated allergy.

Over the last 15 years, an expanding number of databases with information on noxious effects of substances on mammalian organisms and the environment have been made available on the Internet. This set of databases is a key source of information for risk assessment within several areas of toxicology. Here we present features and relationships across a relatively wide set of publicly accessible databases broadly within toxicology, in part by clustering multi-score representations of such repositories, to support risk assessment within food toxicology. For this purpose 36 databases were each scrutinized, using 18 test substances from six different categories as probes. Results have been analyzed by means of various uni- and multi-variate statistical operations. The former included a special index devised to afford context-specific rating of databases across a highly heterogeneous data matrix, whereas the latter involved cluster analysis, enabling the identification of database assemblies with overall shared characteristics. One database – HSDB – was outstanding due to rich and qualified information for most test substances, but an appreciable fraction of the interrogated repositories showed good to decent scoring. Among the six chosen substance groups, Food contact materials had the most comprehensive toxicological information, followed by the Pesticides category.

Genetically modified (GM) plants aimed at producing food/feed are part of regular agriculture in many areas of the World. Commodity plants have also found application as bioreactors, designated non-food/non-feed GM (NFGM) plants, thereby making raw material for further refinement to industrial, diagnostic or pharmaceutical preparations. Many among them may pose health challenge to consumers or livestock animals, if occurring in food/feed. NFGM plants are typically released into the environment, but are grown under special oversight and any among several containment practices, none of which provide full protection against accidental dispersal. Adventitious admixture with food or feed can occur either through distributional mismanagement or as a consequence of gene flow to plant relatives. To facilitate NFGM surveillance we propose a new mandatory tagging of essentially all such plants, prior to cultivation or marketing in the European Union. The suggested tag--Plant-Made Industrial or Pharmaceutical Products Tag (PMIP-T)--is envisaged to occur as a transgenic silent DNA identifier in host plants and designed to enable technically simple identification and characterisation of any NFGM. Implementation of PMIP-T would permit inexpensive, reliable and high-throughput screening for NFGM specifically. The paper outlines key NFGM prospects and challenges as well as the PMIP-T concept.

Historically, different approaches have been adopted for comparing and characterizing hazards that can be found in the very complex mixture of substances present in food. In this review a variety of prominent risk assessment models are evaluated in the context of food safety. In their current state of refinement, though, they show limited applicability for comparative hazard characterization and impact magnitude scoring of adverse effects of substances in food. Nonetheless, some existing models hold building blocks and modelling concepts that appear promising for further development and integration. Thus, a new, dedicated, and generally accepted model is needed that is capable of generating relevant "Impact Magnitude Score" (IMS) values for comparing potentially toxic substances in food. A brief outline of requirements for a model (Guided Toxicology-assessment of Health Impact; GTHI) is presented that considers "severity" (S), "duration" (D), and "proportion of population affected" (P). An important demand on such a model is to provide significantly improved food safety evaluation amenable to regulatory agencies and consumers. This review is based on a project entitled "Promoting food safety through a new integrated risk analysis approach for foods" (acronym: "SAFE FOODS") that is under the subsidy of the European Commission.

Bioinformatics testing approaches for protein allergenicity, involving amino acid sequence comparisons, have evolved appreciably over the last several years to increased sophistication and performance. EVALLER, the web server presented in this article is based on our recently published 'Detection based on Filtered Length-adjusted Allergen Peptides' (DFLAP) algorithm, which affords in silico determination of potential protein allergenicity of high sensitivity and excellent specificity. To strengthen bioinformatics risk assessment in allergology EVALLER provides a comprehensive outline of its judgment on a query protein's potential allergenicity. Each such textual output incorporates a scoring figure, a confidence numeral of the assignment and information on high- or low-scoring matches to identified allergen-related motifs, including their respective location in accordingly derived allergens. The interface, built on a modified Perl Open Source package, enables dynamic and color-coded graphic representation of key parts of the output. Moreover, pertinent details can be examined in great detail through zoomed views. The server can be accessed at http://bioinformatics.bmc.uu.se/evaller.html.

The placing of novel or new-in-the-context proteins on the market, appearing in genetically modified foods, certain bio-pharmaceuticals and some household products leads to human exposure to proteins that may elicit allergic responses. Accurate methods to detect allergens are therefore necessary to ensure consumer/patient safety. We demonstrate that it is possible to reach a new level of accuracy in computational detection of allergenic proteins by presenting a novel detector, Detection based on Filtered Length-adjusted Allergen Peptides (DFLAP). The DFLAP algorithm extracts variable length allergen sequence fragments and employs modern machine learning techniques in the form of a support vector machine. In particular, this new detector shows hitherto unmatched specificity when challenged to the Swiss-Prot repository without appreciable loss of sensitivity. DFLAP is also the first reported detector that successfully discriminates between allergens and non-allergens occurring in protein families known to hold both categories. Allergenicity assessment for specific protein sequences of interest using DFLAP is possible via ulfh@slv.se.

Key issues in protein science and computational biology are design and evaluation of algorithms aimed at detection of proteins that belong to a specific family, as defined by structural, evolutionary, or functional criteria. In this context, several validation techniques are often used to compare different parameter settings of the detector, and to subsequently select the setting that yields the smallest error rate estimate. A frequently overlooked problem associated with this approach is that this smallest error rate estimate may have a large optimistic bias. Based on computer simulations, we show that a detector's error rate estimate can be overly optimistic and propose a method to obtain unbiased performance estimates of a detector design procedure. The method is founded on an external 10-fold cross-validation (CV) loop that embeds an internal validation procedure used for parameter selection in detector design. The designed detector generated in each of the 10 iterations are evaluated on held-out examples exclusively available in the external CV iterations. Notably, the average of these 10 performance estimates is not associated with a final detector, but rather with the average performance of the design procedure used. We apply the external CV loop to the particular problem of detecting potentially allergenic proteins, using a previously reported design procedure. Unbiased performance estimates of the allergen detector design procedure are presented together with information about which algorithms and parameter settings that are most frequently selected.

SH-SY5Y human neuroblastoma cells can be induced to differentiate into a neuronal phenotype by treatment with 12-O-tetradecanoylphorbol-13-acetate (TPA). In other cell systems, TPA treatment frequently leads to down-regulation of protein kinase C (PKC). However, we now report that TPA-treated and non-treated SH-SY5Y cells express PKC-alpha, but not PKC-beta and PKC-gamma, mRNA. Furthermore, only a slight down-regulation of the PKC-alpha protein could be seen during prolonged treatment with 16 nM TPA, the concentration giving optimal differentiation. In contrast, a higher concentration of TPA (1.6 microM) results in a poor neuronal differentiation and a complete down-regulation of PKC-alpha. PKC-alpha was rapidly translocated to the particulate fraction and remained membrane bound for at least 4 days during treatment with 16 nM TPA. In such cells a sustained increased level of the phosphorylated form of a 80,000 Dalton PKC-substrate was found. In addition to this sustained augmented phosphorylation, administration of fresh TPA at day 4 caused a small but reproducible further increased level of phosphorylated substrate. When the PKC activity was measured by the histone phosphorylation assay a substantial fraction of the initial enzyme activity could still be detected after 4 days of TPA treatment. Taken together, the data demonstrate that PKC remains functionally active during TPA induced differentiation of SH-SY5Y cells, which may suggest a continuous role for the enzyme during the differentiation process.