Consumption of Red/Processed Meat and Colorectal Carcinoma: Possible Mechanisms Underlying the Significant Association.

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.

Label free high throughput screening for apoptosis inducing chemicals using time-lapse microscopy signal processing.

Label free time-lapse microscopy has opened a new avenue to the study of time evolving events in living cells. When combined with automated image analysis it provides a powerful tool that enables automated large-scale spatiotemporal quantification at the cell population level. Very few attempts, however, have been reported regarding the design of image analysis algorithms dedicated to the detection of apoptotic cells in such time-lapse microscopy images. In particular, none of the reported attempts is based on sufficiently fast signal processing algorithms to enable large-scale detection of apoptosis within hours/days without access to high-end computers. Here we show that it is indeed possible to successfully detect chemically induced apoptosis by applying a two-dimensional linear matched filter tailored to the detection of objects with the typical features of an apoptotic cell in phase-contrast images. First a set of recorded computational detections of apoptosis was validated by comparison with apoptosis specific caspase activity readouts obtained via a fluorescence based assay. Then a large screen encompassing 2,866 drug like compounds was performed using the human colorectal carcinoma cell line HCT116. In addition to many well known inducers (positive controls) the screening resulted in the detection of two compounds here reported for the first time to induce apoptosis.

Automated QuantMap for rapid quantitative molecular network topology analysis.

SUMMARY: The previously disclosed QuantMap method for grouping chemicals by biological activity used online services for much of the data gathering and some of the numerical analysis. The present work attempts to streamline this process by using local copies of the databases and in-house analysis. Using computational methods similar or identical to those used in the previous work, a qualitatively equivalent result was found in just a few seconds on the same dataset (collection of 18 drugs). We use the user-friendly Galaxy framework to enable users to analyze their own datasets. Hopefully, this will make the QuantMap method more practical and accessible and help achieve its goals to provide substantial assistance to drug repositioning, pharmacology evaluation and toxicology risk assessment. AVAILABILITY: http://galaxy.predpharmtox.org CONTACT: mats.gustafsson@medsci.uu.se or ola.spjuth@farmbio.uu.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NMR spectroscopy-based metabolic profiling of drug-induced changes in vitro can discriminate between pharmacological classes.

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). However, the potential of similar exercises at the metabolite level is still largely unexplored. Only recently, the first high-throughput metabolomic assay pilot study was published, which involved screening the 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 (1)H 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 the 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 (3 metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this type 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.

Exhaustive in vitro evaluation of the 9-drug cocktail CUSP9 for treatment of glioblastoma.

The CUSP9 protocol is a polypharmaceutical strategy aiming at addressing the complexity of glioblastoma by targeting multiple pathways. Although the rationale for this 9-drug cocktail is well-supported by theoretical and in vitro data, its effectiveness compared to its 511 possible subsets has not been comprehensively evaluated. Such an analysis could reveal if fewer drugs could achieve similar or better outcomes. We conducted an exhaustive in vitro evaluation of the CUSP9 protocol using COMBImageDL, our specialized framework for testing higher-order drug combinations. This study assessed all 511 subsets of the CUSP9v3 protocol, in combination with temozolomide, on two clonal cultures of glioma-initiating cells derived from patient samples. The drugs were used at fixed, clinically relevant concentrations, and the experiment was performed in quadruplicate with endpoint cell viability and live-cell imaging readouts. Our results showed that several lower-order drug combinations produced effects equivalent to the full CUSP9 cocktail, indicating potential for simplified regimens in personalized therapy. Further validation through in vivo and precision medicine testing is required. Notably, a subset of four drugs (auranofin, disulfiram, itraconazole, sertraline) was particularly effective, reducing cell growth, altering cell morphology, increasing apoptotic-like cells within 4-28 h, and significantly decreasing cell viability after 68 h compared to untreated cells. This study underscores the importance and feasibility of comprehensive in vitro evaluations of complex drug combinations on patient-derived tumor cells, serving as a critical step toward (pre-)clinical development.

Assessing relative bioactivity of chemical substances using quantitative molecular network topology analysis.

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, 17ß-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.

Abundance and functional roles of intrinsic disorder in allergenic proteins and allergen representative peptides.

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 ∼ 20% of the allergen peptides are highly disordered, and that ∼ 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, ∼ 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.

Comparative hazard characterization in food toxicology.

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.

EVALLER: a web server for in silico assessment of potential protein allergenicity.

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.

Multivariate statistical analysis of large-scale IgE antibody measurements reveals allergen extract relationships in sensitized individuals.

BACKGROUND: Many allergenic sources are reportedly cross-reactive because of protein structural similarities. Although several aggregations are well characterized, no holistic mapping of IgE reactivity has hitherto been reported. OBJECTIVE: The aim of this study was to disclose relevant associations within a large set of allergen preparations, as revealed by specific IgE antibody levels in blood sera of multireactive human donors. METHODS: A dataset of recorded IgE antibody serum concentrations of 1011 nonidentifiable multireactive individuals (devoid of clinical records) to 89 allergen extracts was compiled for in silico analysis. Various algorithms were used to identify specific multivariate dependencies between the IgE antibody levels. RESULTS: Exhaustive cluster analysis demonstrates that IgE antibody responses to the 89 extracts can be aggregated into 12 stable formations. These clusters hold both well-known relationships, unexpected patterns, and unknown patterns, the latter categories being exemplified by the coclustering of wasp and certain seafood and a clear differentiation among pollen allergens. CONCLUSION: Identified relationships within several well-known groups of cross-reactive allergen extracts confirm the applicability of dedicated multivariate data analysis within the allergology field. Moreover, some of the unexpected IgE reactivity associations in sensitized human subjects might help in identifying new relationships with potential importance to allergy. CLINICAL IMPLICATIONS: Although clinical implications from this study should be validated in subsequent investigations with documentation on symptoms included, we believe this seminal approach is a key step toward the development of new analysis tools for interpretation of allergy data generated by using high-throughput recording systems.

Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions.

INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC-MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols. OBJECTIVES: This study illustrates some key pitfalls in LC-MS based metabolomics and introduces an automated computational procedure to compensate for them. METHOD: Non-cancerous mammary gland derived cells were exposed to 27 chemicals from four pharmacological classes plus a set of six pesticides. Changes in the metabolome of cell lysates were assessed after 24 h using LC-MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling. RESULT: The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC-MS spectra hold drug class specific information. CONCLUSION: LC-MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing.

External cross-validation for unbiased evaluation of protein family detectors: application to allergens.

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.

Detection of cell aggregation and altered cell viability by automated label-free video microscopy: a promising alternative to endpoint viability assays in high-throughput screening.

Automated phase-contrast video microscopy now makes it feasible to monitor a high-throughput (HT) screening experiment in a 384-well microtiter plate format by collecting one time-lapse video per well. Being a very cost-effective and label-free monitoring method, its potential as an alternative to cell viability assays was evaluated. Three simple morphology feature extraction and comparison algorithms were developed and implemented for analysis of differentially time-evolving morphologies (DTEMs) monitored in phase-contrast microscopy videos. The most promising layout, pixel histogram hierarchy comparison (PHHC), was able to detect several compounds that did not induce any significant change in cell viability, but made the cell population appear as spheroidal cell aggregates. According to recent reports, all these compounds seem to be involved in inhibition of platelet-derived growth factor receptor (PDGFR) signaling. Thus, automated quantification of DTEM (AQDTEM) holds strong promise as an alternative or complement to viability assays in HT in vitro screening of chemical compounds.

Label-free detection and dynamic monitoring of drug-induced intracellular vesicle formation enabled using a 2-dimensional matched filter.

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.

A pragmatic definition of therapeutic synergy suitable for clinically relevant in vitro multicompound analyses.

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.

Supervised identification of allergen-representative peptides for in silico detection of potentially allergenic proteins.

MOTIVATION: Identification of potentially allergenic proteins is needed for the safety assessment of genetically modified foods, certain pharmaceuticals and various other products on the consumer market. Current methods in bioinformatic allergology exploit common features among allergens for the detection of amino acid sequences of potentially allergenic proteins. Features for identification still unexplored include the motifs occurring commonly in allergens, but rarely in ordinary proteins. In this paper, we present an algorithm for the identification of such motifs with the purpose of biocomputational detection of amino acid sequences of potential allergens. RESULTS: Identification of allergen-representative peptides (ARPs) with low or no occurrence in proteins lacking allergenic properties is the essential component of our new method, designated DASARP (Detection based on Automated Selection of Allergen-Representative Peptide). This approach consistently outperforms the criterion based on identical peptide match for predicting allergenicity recommended by ILSI/IFBC and FAO/WHO and shows results comparable to the alignment-based criterion as outlined by FAO/WHO. AVAILABILITY: The detection software and the ARP set needed for the analysis of a query protein reported here are properties of the Swedish National Food Agency and are available upon request. The protein sequence sets used in this work are publicly available on http://www.slv.se/templatesSLV/SLV_Page____9343.asp. Allergenicity assessment for specific protein sequences of interest is also possible via ulfh@slv.se

Prediction of food protein allergenicity: a bioinformatic learning systems approach.

Food hypersensitivity is constantly increasing in Western societies with a prevalence of about 1-2% in Europe and in the USA. Among children, the incidence is even higher. Because of the introduction of foods derived from genetically modified crops on the marketplace, the scientific community, regulatory bodies and international associations have intensified discussions on risk assessment procedures to identify potential food allergenicity of the newly introduced proteins. In this work, we present a novel biocomputational methodology for the classification of amino acid sequences with regard to food allergenicity and non-allergenicity. This method relies on a computerised learning system trained using selected excerpts of amino acid sequences. One example of such a successful learning system is presented which consists of feature extraction from sequence alignments performed with the FASTA3 algorithm (employing the BLOSUM50 substitution matrix) combined with the k-Nearest-Neighbour (kNN) classification algorithm. Briefly, the two features extracted are the alignment score and the alignment length and the kNN algorithm assigns the pair of extracted features from an unknown sequence to the prevalent class among its k nearest neighbours in the training (prototype) set available. 91 food allergens from several specialised public repositories of food allergy and the SWALL database were identified, pre-processed, and stored, yielding one of the most extensively characterised repositories of allergenic sequences known today. All allergenic sequences were classified using a standard one-leave-out cross validation procedure yielding about 81% correctly classified allergens and the classification of 367 non-allergens in an independent test set resulted in about 98% correct classifications. The biocomputational approach presented should be regarded as a significant extension and refinement of earlier attempts suggested for in silico food safety assessment. Our results show that the framework described here is powerful enough to become useful as part of a multiple-procedure test scheme that also depicts other evaluation approaches such as solid phase immunoassay and tests for stability to digestions.