Evaluation of the zucker diabetic fatty (ZDF) rat as a model for human disease based on urinary peptidomic profiles

Representative animal models for diabetes-associated vascular complications are extremely relevant in assessing potential therapeutic drugs. While several rodent models for type 2 diabetes (T2D) are available, their relevance in recapitulating renal and cardiovascular features of diabetes in man is not entirely clear. Here we evaluate at the molecular level the similarity between Zucker diabetic fatty (ZDF) rats, as a model of T2D-associated vascular complications, and human disease by urinary proteome analysis. Urine analysis of ZDF rats at early and late stages of disease compared to age- matched LEAN rats identified 180 peptides as potentially associated with diabetes complications. Overlaps with human chronic kidney disease (CKD) and cardiovascular disease (CVD) biomarkers were observed, corresponding to proteins marking kidney damage (eg albumin, alpha-1 antitrypsin) or related to disease development (collagen). Concordance in regulation of these peptides in rats versus humans was more pronounced in the CVD compared to the CKD panels. In addition, disease-associated predicted protease activities in ZDF rats showed higher similarities to the predicted activities in human CVD. Based on urinary peptidomic analysis, the ZDF rat model displays similarity to human CVD but might not be the most appropriate model to display human CKD on a molecular level

Self-organizing ontology of biochemically relevant small molecules

<p>Abstract</p> <p>Background</p> <p>The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest.</p> <p>Results</p> <p>To address this, we present a formal framework based on Semantic Web technologies for the automatic design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes. This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based classes with molecular feature information and demonstrate the utility of our framework for classification of functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical ontology development.</p> <p>Conclusions</p> <p>We conclude that the proposed methodology can ease the burden of chemical data annotators and dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will make chemical classification criteria more transparent to humans and machines alike and will thus facilitate predictive and integrative bioactivity model development.</p

Skin sensitization in silico protocol

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship of skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization

A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells

Human exposure to carcinogens occurs via a plethora of environmental sources, with 70–90% of cancers caused by extrinsic factors. Aberrant phenotypes induced by such carcinogenic agents may provide universal biomarkers for cancer causation. Both current in vitro genotoxicity tests and the animal-testing paradigm in human cancer risk assessment fail to accurately represent and predict whether a chemical causes human carcinogenesis. The study aimed to establish whether the integrated analysis of multiple cellular endpoints related to the Hallmarks of Cancer could advance in vitro carcinogenicity assessment. Human lymphoblastoid cells (TK6, MCL-5) were treated for either 4 or 23 h with 8 known in vivo carcinogens, with doses up to 50% Relative Population Doubling (maximum 66.6 mM). The adverse effects of carcinogens on wide-ranging aspects of cellular health were quantified using several approaches; these included chromosome damage, cell signalling, cell morphology, cell-cycle dynamics and bioenergetic perturbations. Cell morphology and gene expression alterations proved particularly sensitive for environmental carcinogen identification. Composite scores for the carcinogens’ adverse effects revealed that this approach could identify both DNA-reactive and non-DNA reactive carcinogens in vitro. The richer datasets generated proved that the holistic evaluation of integrated phenotypic alterations is valuable for effective in vitro risk assessment, while also supporting animal test replacement. Crucially, the study offers valuable insights into the mechanisms of human carcinogenesis resulting from exposure to chemicals that humans are likely to encounter in their environment. Such an understanding of cancer induction via environmental agents is essential for cancer prevention

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Scientific Guidance on the data required for the risk assessment of flavourings to be used in or on foods

Following a request from the European Commission, EFSA developed a new scientific guidance to assist applicants in the preparation of applications for the authorisation of flavourings to be used in or on foods. This guidance applies to applications for a new authorisation as well as for a modification of an existing authorisation of a food flavouring, submitted under Regulation (EC) No 1331/2008. It defines the scientific data required for the evaluation of those food flavourings for which an evaluation and approval is required according to Article 9 of Regulation (EC) No 1334/2008. This applies to flavouring substances, flavouring preparations, thermal process flavourings, flavour precursors, other flavourings and source materials, as defined in Article 3 of Regulation (EC) No 1334/2008. Information to be provided in all applications relates to: (a) the characterisation of the food flavouring, including the description of its identity, manufacturing process, chemical composition, specifications, stability and reaction and fate in foods; (b) the proposed uses and use levels and the assessment of the dietary exposure and (c) the safety data, including information on the genotoxic potential of the food flavouring, toxicological data other than genotoxicity and information on the safety for the environment. For the toxicological studies, a tiered approach is applied, for which the testing requirements, key issues and triggers are described. Applicants should generate the data requested in each section to support the safety assessment of the food flavouring. Based on the submitted data, EFSA will assess the safety of the food flavouring and conclude whether or not it presents risks to human health and to the environment, if applicable, under the proposed conditions of use

Restoration of Podocyte Structure and Improvement of Chronic Renal Disease in Transgenic Mice Overexpressing Renin

Proteinuria is a major marker of the decline of renal function and an important risk factor of coronary heart disease. Elevated proteinuria is associated to the disruption of slit-diaphragm and loss of podocyte foot processes, structural alterations that are considered irreversible. The objective of the present study was to investigate whether proteinuria can be reversed and to identify the structural modifications and the gene/protein regulation associated to this reversal.We used a novel transgenic strain of mouse (RenTg) that overexpresses renin at a constant high level. At the age of 12-month, RenTg mice showed established lesions typical of chronic renal disease such as peri-vascular and periglomerular inflammation, glomerular ischemia, glomerulosclerosis, mesangial expansion and tubular dilation. Ultrastructural analysis indicated abnormal heterogeneity of basement membrane thickness and disappearance of podocyte foot processes. These structural alterations were accompanied by decreased expressions of proteins specific of podocyte (nephrin, podocin), or tubular epithelial cell (E-cadherin and megalin) integrity. In addition, since TGFbeta is considered the major pro-fibrotic agent in renal disease and since exogenous administration of BMP7 is reported to antagonize the TGFbeta-induced phenotype changes in kidney, we have screened the expressions of several genes belonging in the TGFbeta/BMP superfamily. We found that the endogenous inhibitors of BMPs such as noggin and Usag-1 were several-fold activated inhibiting the action of BMPs and thus reinforcing the deleterious action of TGFbeta.Treatment with an AT1 receptor antagonist, at dose that did not decrease arterial pressure, gradually reduced albuminuria. This decrease was accompanied by re-expression of podocin, nephrin, E-cadherin and megalin, and reappearance of podocyte foot processes. In addition, expressions of noggin and Usag-1 were markedly decreased, permitting thus activation of the beneficial action of BMPs.These findings show that proteinuria and alterations in the expression of proteins involved in the integrity and function of glomerular and renal epithelial phenotype are reversible events when the local action of angiotensin II is blocked, and provide hope that chronic renal disease can be efficiently treated

Blocking TGF-β signaling pathway preserves mitochondrial proteostasis and reduces early activation of PDGFRβ+ pericytes in aristolochic acid induced acute kidney injury in wistar male rats

The platelet-derived growth factor receptor β (PDGFRβ)+ perivascular cell activation becomes increasingly recognized as a main source of scar-associated kidney myofibroblasts and recently emerged as a new cellular therapeutic target.In this regard, we first confirmed the presence of PDGFRβ+ perivascular cells in a human case of end-stage aristolochic acid nephropathy (AAN) and thereafter we focused on the early fibrosis events of transforming growth factor β (TGFβ) inhibition in a rat model of AAN.Neutralizing anti-TGFβ antibody (1D11) and its control isotype (13C4) were administered (5 mg/kg, i.p.) at Days -1, 0, 2 and 4; AA (15 mg/kg, sc) was injected daily.At Day 5, 1D11 significantly suppressed p-Smad2/3 signaling pathway improving renal function impairment, reduced the score of acute tubular necrosis, peritubular capillaritis, interstitial inflammation and neoangiogenesis. 1D11 markedly decreased interstitial edema, disruption of tubular basement membrane loss of brush border, cytoplasmic edema and organelle ultrastructure alterations (mitochondrial disruption and endoplasmic reticulum edema) in proximal tubular epithelial cells. Moreover, 1D11 significantly inhibited p-PERK activation and attenuated dysregulation of unfolded protein response (UPR) pathways, endoplasmic reticulum and mitochondrial proteostasis in vivo and in vitro.The early inhibition of p-Smad2/3 signaling pathway improved acute renal function impairment, partially prevented epithelial-endothelial axis activation by maintaining PTEC proteostasis and reduced early PDGFRβ+ pericytes-derived myofibroblasts accumulation

DPRESS: Localizing estimates of predictive uncertainty

<p>Abstract</p> <p>Background</p> <p>The need to have a quantitative estimate of the uncertainty of prediction for QSAR models is steadily increasing, in part because such predictions are being widely distributed as tabulated values disconnected from the models used to generate them. Classical statistical theory assumes that the error in the population being modeled is independent and identically distributed (IID), but this is often not actually the case. Such inhomogeneous error (heteroskedasticity) can be addressed by providing an individualized estimate of predictive uncertainty for each particular new object <it>u</it>: the standard error of prediction <it>s</it>_ucan be estimated as the non-cross-validated error <it>s</it>_t*for the closest object <it>t</it>* in the training set adjusted for its separation <it>d </it>from <it>u </it>in the descriptor space relative to the size of the training set.</p> <p><display-formula><graphic file="1758-2946-1-11-i1.gif"/></display-formula></p> <p>The predictive uncertainty factor <it>γ</it>_t*is obtained by distributing the internal predictive error sum of squares across objects in the training set based on the distances between them, hence the acronym: <it>D</it>istributed <it>PR</it>edictive <it>E</it>rror <it>S</it>um of <it>S</it>quares (DPRESS). Note that <it>s</it>_t*and <it>γ</it>_t*are characteristic of each training set compound contributing to the model of interest.</p> <p>Results</p> <p>The method was applied to partial least-squares models built using 2D (molecular hologram) or 3D (molecular field) descriptors applied to mid-sized training sets (<it>N </it>= 75) drawn from a large (<it>N </it>= 304), well-characterized pool of cyclooxygenase inhibitors. The observed variation in predictive error for the external 229 compound test sets was compared with the uncertainty estimates from DPRESS. Good qualitative and quantitative agreement was seen between the distributions of predictive error observed and those predicted using DPRESS. Inclusion of the distance-dependent term was essential to getting good agreement between the estimated uncertainties and the observed distributions of predictive error. The uncertainty estimates derived by DPRESS were conservative even when the training set was biased, but not excessively so.</p> <p>Conclusion</p> <p>DPRESS is a straightforward and powerful way to reliably estimate individual predictive uncertainties for compounds outside the training set based on their distance to the training set and the internal predictive uncertainty associated with its nearest neighbor in that set. It represents a sample-based, <it>a posteriori </it>approach to defining applicability domains in terms of localized uncertainty.</p