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Summary: Quantitative structure-activity relationship (QSAR) modelling is currently used in multiple fields to relate structural properties of compounds to their biological activities. This technique is also used for drug design purposes with the aim of predicting parameters that determine drug behaviour. To this end, a sophisticated process, involving various analytical steps concatenated in series, is employed to identify and fine-tune the optimal set of predictors from a large dataset of molecular descriptors (MDs). The search of the optimal model requires to optimize multiple objectives at the same time, as the aim is to obtain the minimal set of features that maximizes the goodness of fit and the applicability domain (AD). Hence, a multi-objective optimization strategy, improving multiple parameters in parallel, can be applied. Here we propose a new multi-niche multi-objective genetic algorithm that simultaneously enables stable feature selection as well as obtaining robust and validated regression models with maximized AD. We benchmarked our method on two simulated datasets. Moreover, we analyzed an aquatic acute toxicity dataset and compared the performances of single- and multi-objective fitness functions on different regression models. Our results show that our multi-objective algorithm is a valid alternative to classical QSAR modelling strategy, for continuous response values, since it automatically finds the model with the best compromise between statistical robustness, predictive performance, widest AD, and the smallest number of MDs., Availability and Implementation: The python implementation of MaNGA is available at https://github.com/Greco-Lab/MaNGA., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discovered relationships in the form of human readable associative rules. The application of such techniques to the modern plethora of collected cancer omics data can effectively boost our understanding of cancer-related mechanisms. In fact, the capability of these methods to extract a huge amount of human readable knowledge will eventually help to uncover unknown relationships between molecular attributes and the malignant phenotype. In this review, we describe applications and strategies for the usage of rule induction approaches in cancer omics data analysis. In particular, we explore the canonical applications and the future challenges and opportunities posed by multi-omics integration problems., Competing Interests: The authors declare no conflict of interest

After over a decade of nanosafety research, it is indisputable that the vast majority of nano-sized particles induce a plethora of adverse cellular responses - the severity of which is linked to the material's physicochemical properties. Differentiated THP-1 cells were previously exposed for 6 h and 24 h to silver, titanium dioxide, and zinc oxide nanoparticles at the maximum molar concentration at which no more than 15% cellular cytotoxicity was observed. All three nanoparticles differed in extent of induction of biological pathways corresponding to immune response signaling and metal ion homeostasis. In this study, we integrated gene and miRNA expression profiles from the same cells to propose miRNA biomarkers of adverse exposure to metal-based nanoparticles. We employed RNA sequencing together with a quantitative strategy that also enables analysis of the overlooked repertoire of length and sequence miRNA variants called isomiRs. Whilst only modest changes in expression were observed within the first 6 h of exposure, the miRNA/isomiR (miR) profiles of each nanoparticle were unique. Via canonical correlation and pathway enrichment analyses, we identified a co-regulated miR-mRNA cluster, predicted to be highly relevant for cellular response to metal ion homeostasis. These miRs were annotated to be canonical or variant isoforms of hsa-miR-142-5p, -342-3p, -5100, -6087, -6894-3p, and -7704. Hsa-miR-5100 was differentially expressed in response to each nanoparticle in both the 6 h and 24 h exposures. Taken together, this co-regulated miR-mRNA cluster could represent potential biomarkers of sub-toxic metal-based nanoparticle exposure.

Biological target (commonly genes or proteins) identification is still largely a manual process, where experts manually try to collect and combine information from hundreds of data sources, ranging from scientific publications to omics databases. Targeting the wrong gene or protein will lead to failure of the drug development process, as well as incur delays and costs. To improve this process, different software platforms are being developed. These platforms rely strongly on efficacy estimates based on target-disease association scores created by computational methods for drug target prioritization. Here novel computational methods are presented to more accurately evaluate the efficacy and safety of potential drug targets. The proposed efficacy scores utilize existing gene expression data and tissue/disease specific networks to improve the inference of target-disease associations. Conversely, safety scores enable the identification of genes that are essential, potentially susceptible to adverse effects or carcinogenic. Benchmark results demonstrate that our transcriptome-based methods for drug target prioritization can increase the true positive rate of target-disease associations. Additionally, the proposed safety evaluation system enables accurate predictions of targets of withdrawn drugs and targets of drug trials prematurely discontinued.

Background: Copper oxide (CuO) nanomaterials are used in a wide range of industrial and commercial applications. These materials can be hazardous, especially if they are inhaled. As a result, the pulmonary effects of CuO nanomaterials have been studied in healthy subjects but limited knowledge exists today about their effects on lungs with allergic airway inflammation (AAI). The objective of this study was to investigate how pristine CuO modulates allergic lung inflammation and whether surface modifications can influence its reactivity. CuO and its carboxylated (CuO COOH), methylaminated (CuO NH 3 ) and PEGylated (CuO PEG) derivatives were administered here on four consecutive days via oropharyngeal aspiration in a mouse model of AAI. Standard genome-wide gene expression profiling as well as conventional histopathological and immunological methods were used to investigate the modulatory effects of the nanomaterials on both healthy and compromised immune system., Results: Our data demonstrates that although CuO materials did not considerably influence hallmarks of allergic airway inflammation, the materials exacerbated the existing lung inflammation by eliciting dramatic pulmonary neutrophilia. Transcriptomic analysis showed that CuO, CuO COOH and CuO NH 3 commonly enriched neutrophil-related biological processes, especially in healthy mice. In sharp contrast, CuO PEG had a significantly lower potential in triggering changes in lungs of healthy and allergic mice revealing that surface PEGylation suppresses the effects triggered by the pristine material., Conclusions: CuO as well as its functionalized forms worsen allergic airway inflammation by causing neutrophilia in the lungs, however, our results also show that surface PEGylation can be a promising approach for inhibiting the effects of pristine CuO. Our study provides information for health and safety assessment of modified CuO materials, and it can be useful in the development of nanomedical applications.

Background: Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R language that have become standard for high-throughput analytics. However, application of these tools is technically challenging for generic users and require specific computational skills. There is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results., Results: We propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application., Conclusions: eUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods., Competing Interests: Not Applicable.Not Applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Engineered nanomaterials (ENMs) are widely present in our daily lives. Despite the efforts to characterize their mechanism of action in multiple species, their possible implications in human pathologies are still not fully understood. Here we performed an integrated analysis of the effects of ENMs on human health by contextualizing their transcriptional mechanism-of-action with respect to drugs, chemicals and diseases. We built a network of interactions of over 3,000 biological entities and developed a novel computational tool, INSIdE NANO, to infer new knowledge about ENM behavior. We highlight striking association of metal and metal-oxide nanoparticles and major neurodegenerative disorders. Our novel strategy opens possibilities to achieve fast and accurate read-across evaluation of ENMs and other chemicals based on their biosignatures.

New strategies to characterize the effects of engineered nanomaterials (ENMs) based on omics technologies are emerging. However, given the intricate interplay of multiple regulatory layers, the study of a single molecular species in exposed biological systems might not allow the needed granularity to successfully identify the pathways of toxicity (PoT) and, hence, portraying adverse outcome pathways (AOPs). Moreover, the intrinsic diversity of different cell types composing the exposed organs and tissues in living organisms poses a problem when transferring in vivo experimentation into cell-based in vitro systems. To overcome these limitations, we have profiled genome-wide DNA methylation, mRNA and microRNA expression in three human cell lines representative of relevant cell types of the respiratory system, A549, BEAS-2B and THP-1, exposed to a low dose of ten carbon nanomaterials (CNMs) for 48 h. We applied advanced data integration and modelling techniques in order to build comprehensive regulatory and functional maps of the CNM effects in each cell type. We observed that different cell types respond differently to the same CNM exposure even at concentrations exerting similar phenotypic effects. Furthermore, we linked patterns of genomic and epigenomic regulation to intrinsic properties of CNM. Interestingly, DNA methylation and microRNA expression only partially explain the mechanism of action (MOA) of CNMs. Taken together, our results strongly support the implementation of approaches based on multi-omics screenings on multiple tissues/cell types, along with systems biology-based multi-variate data modelling, in order to build more accurate AOPs., (© 2018 The Authors.)

Summary: Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps., Availability and Implementation: INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM., Supplementary Information: Supplementary data are available at Bioinformatics online.

We present data derived from an exposure experiment in which three cell-lines representative of cell types of the respiratory tissue (epithelial type-I A549, epithelial type-II BEAS-2B, and macrophage THP-1) have been exposed to ten different carbon-based nanomaterials for 48 h. In particular, we provide: genome-wide mRNA and miRNA expression, and DNA methylation; gene tables, containing information on the aberrations induced in these three genomic data layers at the gene level; mechanism of action (MOA) maps representing the comparative functional alteration induced in each cell line and each exposure.

Nano-sized metal oxides are currently the most manufactured nanomaterials (NMs), and are increasingly used in consumer products. Recent exposure data reveal a genuine potential for adverse health outcomes for a vast array of NMs, however the underlying mechanisms are not fully understood. To elucidate size-related molecular effects, differentiated THP-1 cells were exposed to nano-sized materials (n-TiO 2, n-ZnO and n-Ag), or their bulk-sized (b-ZnO and b-TiO 2 ) or ionic (i-Ag) counterparts, and genome-wide gene expression changes were studied at low-toxic concentrations (<15% cytotoxicity). TiO 2 materials were nontoxic in MTT assay, inducing only minor transcriptional changes. ZnO and Ag elicited dose-dependent cytotoxicity, wherein ionic and particulate effects were synergistic with respect to n-ZnO-induced cytotoxicity. In gene expression analyzes, 6 h and 24 h samples formed two separate hierarchical clusters. N-ZnO and n-Ag shared only 3.1% and 24.6% differentially expressed genes (DEGs) when compared to corresponding control. All particles, except TiO 2 , activated various metallothioneins. At 6 h, n-Zn, b-Zn and n-Ag induced various immunity related genes associating to pattern recognition (including toll-like receptor), macrophage maturation, inflammatory response (TNF and IL-1beta), chemotaxis (CXCL8) and leucocyte migration (CXCL2-3 and CXCL14). After 24 h exposure, especially n-Ag induced the expression of genes related to virus recognition and type I interferon responses. These results strongly suggest that in addition to ionic effects mediated by metallothioneins, n-Zn and n-Ag induce expression of genes involved in several innate and adaptive immunity associated pathways, which are known to play crucial role in immuno-regulation. This raises the concern of safe use of metal oxide and metal nanoparticle products, and their biological effects.

Understanding the complex molecular alterations related to engineered nanomaterial (ENM) exposure is essential for carrying out toxicity assessment. Current experimental paradigms rely on both in vitro and in vivo exposure setups that often are difficult to compare, resulting in questioning the real efficacy of cell models to mimic more complex exposure scenarios at the organism level. Here, we have systematically investigated transcriptomic responses of the THP-1 macrophage cell line and lung tissues of mice, after exposure to several carbon nanomaterials (CNMs). Under the assumption that the CNM exposure related molecular alterations are mixtures of signals related to their intrinsic properties, we inferred networks of responding genes, whose expression levels are coordinately altered in response to specific CNM intrinsic properties. We observed only a minute overlap between the sets of intrinsic property-correlated genes at different exposure scenarios, suggesting specific transcriptional programs working in different exposure scenarios. However, when the effects of the CNM were investigated at the level of significantly altered molecular functions, a broader picture of substantial commonality emerged. Our results imply that in vitro exposures can efficiently recapitulate the complex molecular functions altered in vivo. In this study, altered molecular pathways in response to specific CNM intrinsic properties have been systematically characterized from transcriptomic data generated from multiple exposure setups. Our computational approach to the analysis of network response modules further revealed similarities between in vitro and in vivo exposures that could not be detected by traditional analysis of transcriptomics data. Our analytical strategy also opens a possibility to look for pathways of toxicity and understanding the molecular and cellular responses identified across predefined biological themes.

Carbon nanotubes (CNTs) have the potential to impact technological and industrial progress, but their production and use may, in some cases, cause serious health problems. Certain rod-shaped multiwalled CNTs (rCNTs) can, in fact, induce severe asbestos-like pathogenicity in mice, including granuloma formation, fibrosis, and even cancer. Evaluating the comparability between alternative hazard assessment methods is needed to ensure fast and reliable evaluation of the potentially adverse effects of these materials. To compare two alternative airway exposure methods, C57BL/6 mice were exposed to rCNTs by a state-of-the-art but laborious and expensive inhalation method (6.2-8.2 mg/m 3 , 4 h/day for 4 days) or by oropharyngeal aspiration (10 or 40 μg/day for 4 days), which is cheaper and easier to perform. In addition to histological and cytological studies, transcriptome analysis was also carried out on the lung tissue samples. Both inhalation and low-dose (10 μg/day) aspiration exposure to rCNTs promoted strong accumulation of eosinophils in the lungs and recruited also a few neutrophils and lymphocytes. In contrast, the aspiration of a high-dose (40 μg/day) rCNT caused only a mild pulmonary eosinophilia but enhanced accumulation of neutrophils in the airways. Inhalation and low-dose aspiration exposure promoted comparable giant cell formation, mucus production, and IL-13 expression in the lungs. Both exposure methods also exacerbated similar expression alterations with 154 (56.4%) differentially expressed, overlapping genes in microarray analyses. Of all differentially expressed genes, up to 80% of the activated biological functions were shared according to pathway enrichment analyses. Inhalation and low-dose aspiration elicited very similar pulmonary inflammation providing evidence that oropharyngeal aspiration is a valid approach and a convenient alternative to the inhalation exposure for the hazard assessment of nanomaterials.

The use of high-throughput RNA sequencing to predict dynamic operon structures in prokaryotic genomes has recently gained popularity in bioinformatics. We provide the R implementation of a novel method that uses transcriptomic features extracted from RNA-seq transcriptome profiles to develop ensemble classifiers for condition-dependent operon predictions. The CONDOP package provides a deeper insight into RNA-seq data analysis and allows scientists to highlight the operon organization in the context of transcriptional regulation with a few lines of code., Availability and Implementation: CONDOP is implemented in R and is freely available at CRAN., Contact: vittorio.fortino@helsinki.fiSupplementary information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Idiopathic pulmonary fibrosis (IPF) is characterized by activation and injury of epithelial cells, the accumulation of connective tissue and changes in the inflammatory microenvironment. The bone morphogenetic protein (BMP) inhibitor protein gremlin-1 is associated with the progression of fibrosis both in human and mouse lung. We generated a transgenic mouse model expressing gremlin-1 in type II lung epithelial cells using the surfactant protein C (SPC) promoter and the Cre-LoxP system. Gremlin-1 protein expression was detected specifically in the lung after birth and did not result in any signs of respiratory insufficiency. Exposure to silicon dioxide resulted in reduced amounts of lymphocyte aggregates in transgenic lungs while no alteration in the fibrotic response was observed. Microarray gene expression profiling and analyses of bronchoalveolar lavage fluid cytokines indicated a reduced lymphocytic response and a downregulation of interferon-induced gene program. Consistent with reduced Th1 response, there was a downregulation of the mRNA and protein expression of the anti-fibrotic chemokine CXCL10, which has been linked to IPF. In human IPF patient samples we also established a strong negative correlation in the mRNA expression levels of gremlin-1 and CXCL10. Our results suggest that in addition to regulation of epithelial-mesenchymal crosstalk during tissue injury, gremlin-1 modulates inflammatory cell recruitment and anti-fibrotic chemokine production in the lung.

In oncolytic virotherapy, the ability of the virus to activate the immune system is a key attribute with regard to long-term antitumor effects. Vaccinia viruses bear one of the strongest oncolytic activities among all oncolytic viruses. However, its capacity for stimulation of antitumor immunity is not optimal, mainly due to its immunosuppressive nature. To overcome this problem, we developed an oncolytic VV that expresses intracellular pattern recognition receptor DNA-dependent activator of IFN-regulatory factors (DAI) to boost the innate immune system and to activate adaptive immune cells in the tumor. We showed that infection with DAI-expressing VV increases expression of several genes related to important immunological pathways. Treatment with DAI-armed VV resulted in significant reduction in the size of syngeneic melanoma tumors in mice. When the mice were rechallenged with the same tumor, DAI-VV-treated mice completely rejected growth of the new tumor, which indicates immunity established against the tumor. We also showed enhanced control of growth of human melanoma tumors and elevated levels of human T-cells in DAI-VV-treated mice humanized with human peripheral blood mononuclear cells. We conclude that expression of DAI by an oncolytic VV is a promising way to amplify the vaccine potency of an oncolytic vaccinia virus to trigger the innate-and eventually the long-lasting adaptive immunity against cancer., Competing Interests: A.H. is employee and shareholder in TILT Biotherapeutics Ltd. and shareholder in Oncos Therapeutics, Ltd. The other authors declare no potential conflict of interest.

Background: Multiple high-throughput molecular profiling by omics technologies can be collected for the same individuals. Combining these data, rather than exploiting them separately, can significantly increase the power of clinically relevant patients subclassifications., Results: We propose a multi-view approach in which the information from different data layers (views) is integrated at the levels of the results of each single view clustering iterations. It works by factorizing the membership matrices in a late integration manner. We evaluated the effectiveness and the performance of our method on six multi-view cancer datasets. In all the cases, we found patient sub-classes with statistical significance, identifying novel sub-groups previously not emphasized in literature. Our method performed better as compared to other multi-view clustering algorithms and, unlike other existing methods, it is able to quantify the contribution of single views on the final results., Conclusion: Our observations suggest that integration of prior information with genomic features in the subtyping analysis is an effective strategy in identifying disease subgroups. The methodology is implemented in R and the source code is available online at http://neuronelab.unisa.it/a-multi-view-genomic-data-integration-methodology/ .

Background: OMICs technologies allow to assay the state of a large number of different features (e.g., mRNA expression, miRNA expression, copy number variation, DNA methylation, etc.) from the same samples. The objective of these experiments is usually to find a reduced set of significant features, which can be used to differentiate the conditions assayed. In terms of development of novel feature selection computational methods, this task is challenging for the lack of fully annotated biological datasets to be used for benchmarking. A possible way to tackle this problem is generating appropriate synthetic datasets, whose composition and behaviour are fully controlled and known a priori., Results: Here we propose a novel method centred on the generation of networks of interactions among different biological molecules, especially involved in regulating gene expression. Synthetic datasets are obtained from ordinary differential equations based models with known parameters. Our results show that the generated datasets are well mimicking the behaviour of real data, for popular data analysis methods are able to selectively identify existing interactions., Conclusions: The proposed method can be used in conjunction to real biological datasets in the assessment of data mining techniques. The main strength of this method consists in the full control on the simulated data while retaining coherence with the real biological processes. The R package MVBioDataSim is freely available to the scientific community at http://neuronelab.unisa.it/?p=1722.

Background: Age-related changes in DNA methylation occurring in blood leukocytes during early childhood may reflect epigenetic maturation. We hypothesized that some of these changes involve gene networks of critical relevance in leukocyte biology and conducted a prospective study to elucidate the dynamics of DNA methylation. Serial blood samples were collected at 3, 6, 12, 24, 36, 48 and 60 months after birth in ten healthy girls born in Finland and participating in the Type 1 Diabetes Prediction and Prevention Study. DNA methylation was measured using the HumanMethylation450 BeadChip., Results: After filtering for the presence of polymorphisms and cell-lineage-specific signatures, 794 CpG sites showed significant DNA methylation differences as a function of age in all children (41.6% age-methylated and 58.4% age-demethylated, Bonferroni-corrected P value <0.01). Age-methylated CpGs were more frequently located in gene bodies and within +5 to +50 kilobases (kb) of transcription start sites (TSS) and enriched in developmental, neuronal and plasma membrane genes. Age-demethylated CpGs were associated to promoters and DNAse-I hypersensitivity sites, located within -5 to +5 kb of the nearest TSS and enriched in genes related to immunity, antigen presentation, the polycomb-group protein complex and cytoplasm., Conclusions: This study reveals that susceptibility loci for complex inflammatory diseases (for example, IRF5, NOD2, and PTGER4) and genes encoding histone modifiers and chromatin remodeling factors (for example, HDAC4, KDM2A, KDM2B, JARID2, ARID3A, and SMARCD3) undergo DNA methylation changes in leukocytes during early childhood. These results open new perspectives to understand leukocyte maturation and provide a catalogue of CpG sites that may need to be corrected for age effects when performing DNA methylation studies in children.

Background: DAVID is the most popular tool for interpreting large lists of gene/proteins classically produced in high-throughput experiments. However, the use of DAVID website becomes difficult when analyzing multiple gene lists, for it does not provide an adequate visualization tool to show/compare multiple enrichment results in a concise and informative manner., Result: We implemented a new R-based graphical tool, BACA (Bubble chArt to Compare Annotations), which uses the DAVID web service for cross-comparing enrichment analysis results derived from multiple large gene lists. BACA is implemented in R and is freely available at the CRAN repository ( http://cran.r-project.org/web/packages/BACA/ )., Conclusion: The package BACA allows R users to combine multiple annotation charts into one output graph by passing DAVID website.

Background: Carbon nanotubes (CNT) represent a great promise for technological and industrial development but serious concerns on their health effects have also emerged. Rod-shaped CNT are, in fact, able to induce asbestos-like pathogenicity in mice including granuloma formation in abdominal cavity and sub-pleural fibrosis. Exposure to CNT, especially in the occupational context, happens mainly by inhalation. However, little is known about the possible effects of CNT on pulmonary allergic diseases, such as asthma., Methods: We exposed mice by inhalation to two types of multi-walled CNT, rigid rod-like and flexible tangled CNT, for four hours a day once or on four consecutive days. Early events were monitored immediately and 24 hours after the single inhalation exposure and the four day exposure mimicked an occupational work week. Mast cell deficient mice were used to evaluate the role of mast cells in the occurring inflammation., Results: Here we show that even a short-term inhalation of the rod-like CNT induces novel innate immunity-mediated allergic-like airway inflammation in healthy mice. Marked eosinophilia was accompanied by mucus hypersecretion, AHR and the expression of Th2-type cytokines. Exploration of the early events by transcriptomics analysis reveals that a single 4-h exposure to rod-shaped CNT, but not to tangled CNT, causes a radical up-regulation of genes involved in innate immunity and cytokine/chemokine pathways. Mast cells were found to partially regulate the inflammation caused by rod-like CNT, but also alveaolar macrophages play an important role in the early stages., Conclusions: These observations emphasize the diverse abilities of CNT to impact the immune system, and they should be taken into account for hazard assessment.

Selecting relevant features is a common task in most OMICs data analysis, where the aim is to identify a small set of key features to be used as biomarkers. To this end, two alternative but equally valid methods are mainly available, namely the univariate (filter) or the multivariate (wrapper) approach. The stability of the selected lists of features is an often neglected but very important requirement. If the same features are selected in multiple independent iterations, they more likely are reliable biomarkers. In this study, we developed and evaluated the performance of a novel method for feature selection and prioritization, aiming at generating robust and stable sets of features with high predictive power. The proposed method uses the fuzzy logic for a first unbiased feature selection and a Random Forest built from conditional inference trees to prioritize the candidate discriminant features. Analyzing several multi-class gene expression microarray data sets, we demonstrate that our technique provides equal or better classification performance and a greater stability as compared to other Random Forest-based feature selection methods.

Background: Inferring operon maps is crucial to understanding the regulatory networks of prokaryotic genomes. Recently, RNA-seq based transcriptome studies revealed that in many bacterial species the operon structure vary with the change of environmental conditions. Therefore, new computational solutions that use both static and dynamic data are necessary to create condition specific operon predictions., Results: In this work, we propose a novel classification method that integrates RNA-seq based transcriptome profiles with genomic sequence features to accurately identify the operons that are expressed under a measured condition. The classifiers are trained on a small set of confirmed operons and then used to classify the remaining gene pairs of the organism studied. Finally, by linking consecutive gene pairs classified as operons, our computational approach produces condition-dependent operon maps. We evaluated our approach on various RNA-seq expression profiles of the bacteria Haemophilus somni, Porphyromonas gingivalis, Escherichia coli and Salmonella enterica. Our results demonstrate that, using features depending on both transcriptome dynamics and genome sequence characteristics, we can identify operon pairs with high accuracy. Moreover, the combination of DNA sequence and expression data results in more accurate predictions than each one alone., Conclusion: We present a computational strategy for the comprehensive analysis of condition-dependent operon maps in prokaryotes. Our method can be used to generate condition specific operon maps of many bacterial organisms for which high-resolution transcriptome data is available.

Chionodraco hamatus and Trematomus bernacchii are perciforms, members of the fish suborder Notothenioidei that live in the Antarctic Ocean and experience very cold and persistent environmental temperature. These fish have biochemical and molecular features that allow them to live at these extreme cold temperatures. Fine tuning of the level of unsaturated fatty acids content in membrane is a key mechanism of living organisms to adapt to cold and high temperatures. Desaturases are key enzymes that synthesize unsaturated fatty acyl-CoAs from saturated fatty acids. We cloned and sequenced a Δ(9)-desaturase gene and its cDNA of C. hamatus, and the cDNA of T. bernacchii. The coded proteins are virtually identical and share homology to other Δ(9)-desaturase fish sequences. These proteins contain, in the first trans-membrane domain, two cysteine residues that may form a disulfur bond present in the corresponding membrane region of Δ(9)-desaturase proteins of other Antarctic fish but not in Eleginops maclovinus that experiences higher environmental temperatures and in all other Δ(9)-desaturase genes of mammals present in data bases. C. hamatus Δ(9)-desaturase gene complements a Saccharomyces cerevisiae mutant lacking Δ(9)-desaturase (Ole1) gene. Analysis of sequence homology of the trans-membrane domains of Δ(9)-desaturase and the cytoplasmic region of the same proteins of Antarctic fish, non-Antarctic fish and mammals suggest that the significant differences found in the homologous sequences of the first trans-membrane domain may be due to the specific lipid content of their membrane.

Rottlerin is a natural product isolated from Mallotus philippinensis. This polyphenolic compound, originally described as a selective inhibitor of PKCδ, can inhibit many other PKC-unrelated kinases and has a number of biological actions, including mitochondrial uncoupling effects. We recently found that Rottlerin inhibits the transcription factor nuclear factor κB in different cell types, causing downregulation of cyclin D1 and growth arrest. The present study was carried out to clarify the surprising lack of effect of Rottlerin on MCF-7 cell viability, assessed by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) test. We found that Rottlerin causes overestimation of the MTT test, leading to inconsistent results between cell number and cell viability. Rottlerin, however, strongly differs from other antioxidant polyphenols, which directly reduce tetrazolium salts, since it does not exhibit any reactivity toward the tetrazolium salts in vitro nor does it modulate lactate dehydrogenase activity. The interference in the MTT assay occurred only in cultured cells, concomitantly with a decrease in the energy charge. Because the same MTT overestimation was observed in the presence of uncoupling agents, we conclude that the Rottlerin artifact is linked to its uncoupling action that, by accelerating oxidative chain, accidentally results in enhanced MTT reduction. These results suggest caution in the use of the MTT assay in the presence of Rottlerin and uncouplers in general.

In this study we showed that Rottlerin (also called Kamala or Mallotoxin), a natural product purified from Mallotus phillippinensis, is a potent suppressor of human keratinocytes (HaCaT cell line) proliferation. Following Rottlerin treatment, Thymidine incorporation into DNA and re-epithelialisation in a scratch wound model was decreased. At the molecular level, Rottlerin hampered the NFkB activation process, causing loss of cyclin D1 and promoting, in a PKCdelta-dependent pathway, ERK activation, which, in turn induced the cell cycle inhibitor p21 Cip1/Kip1. The NFkB-dependent drop in cyclin D1, along with the PKCdelta/ERK-dependent induction of p21 Cip1/Kip1, is responsible for growth arrest. These results open the way to further investigation on the Rottlerin therapeutic potential against keratinocyte hyper-proliferative disorders.

Rottlerin, a polyphenol isolated from Mallotus Philippinensis, has been recently used as a selective inhibitor of PKC delta, although it can inhibit many kinases and has several biological effects. Among them, we recently found that Rottlerin inhibits the Nuclear Factor kappaB (NFkappaB), activated by either phorbol esters or H(2)O(2). Because of the redox sensitivity of NFkappaB and on the basis of Rottlerin antioxidant property, we hypothesized that Rottlerin could prevent NFkappaB activation acting as a free radicals scavenger, as other natural polyphenols. The current study confirms the antioxidant property of Rottlerin against the 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) in vitro and against oxidative stress induced by H(2)O(2) and by menadione in culture cells. We also demonstrate that Rottlerin prevents TNFalpha-dependent NFkappaB activation in MCF-7 cells and in HT-29 cells transfected with the NFkappaB-driven plasmid pBIIX-LUC, suggesting that Rottlerin can inhibit NFkappaB via several pathways and in several cell types.

Skin is frequently exposed to pro-oxidative insults such as UV light, ozone (O(3)) and cigarette smoke (CS), which are able to deplete antioxidants and induce oxidation products affecting skin pathophysiology. Skin turnover and regeneration are largely dependent on extracellular matrix metabolism, which is under the control of matrix metalloproteinases, MMPs. The present study evaluated cutaneous MMPs activity upon environmental pollutants exposure and analyzed the response of old and young animals. For this purpose, SKH-1 hairless mice (8 weeks and 18 months old) were exposed for 6h/day to 0.25ppm of O(3) or to UV radiation (0.3 MED) or to CS for 4 days. Gelatin zymography revealed an increase of MMP-2 in both young and old animals, after exposure to pollutants, while MMP-9, undetectable in unexposed subjects, was strongly induced only in old mice. Casein zymography and Western blot analysis showed an increase of MMP-12 in the aged group after environmental stressors exposure. TIMP-1 and -2 expression levels did not change. The current study demonstrates the ability of certain environmental pollutants to affect the ECM turnover through modulation of specific MMPs, and confirms the higher susceptibility of old subjects to exogenous pro-oxidant insults.

In this paper the effect of N-terminal parathyroid hormone-related protein (PTHrp) and PTHrp-engaged pathways on MCF-7 breast cancer cell migration/invasivity and matrix metalloproteinases (MMPs) production were investigated. We found that: a) migration is not affected by PTHrp and Forskolin (FK)-activated PKA, while Phorbol Myristate Acetate (PMA)-activated PKC strongly stimulates MCF-7 cells motility. b) MMPs production was unaffected by PTHrp, but FK reduced membrane-type (MT)-1 MMP expression. Conversely, PMA induced a marked increase of MT1-MMP and MMP-9. c) Chemical activation of PKC is not sufficient, by itself, to confer invasive ability to MCF-7 cells, unless they were provided with additional factors, supplied by fibroblasts. d) Matrix invasion likely occurs through an activation cascade, involving at least three components: pro-MMP-9 and MT-1 MMP (supplied by PMA-stimulated MCF-7 cells) and pro MMP-2 (supplied by fibroblasts). e) The selective chemical inhibition of the adenylylciclase (AC)/PKA and phospholipase C (PLC)/PKC pathways confirmed that MCF-7 cells invasivity is not affected by exogenous PTHrp, which can only modulate their growth. However, the PTHrp responsibility in breast cancer invasion cannot be completely excluded. Indeed, fibroblasts are known to respond to PTHrp (which is a normal product of MCF-7 as well as other breast cancer cells) with enhanced release of MMP-2. On the basis of the documented requirement of fibroblast-derived MMP-2 for MCF-7 cell invasivity, a novel humoral fibroblast-breast cancer cell interaction, mediated by PTHrp, can be recognised.

Preeclampsia is a disorder associated with pregnancy that affects both the mother and the fetus. Typical features of the disease are maternal hypertension, proteinuria, and edema as well as fetal growth retardation. Although the etiological details are still being debated, a consensus exists that the starting point is deficient placentation in the first half of pregnancy. The crucial early steps are reduced trophoblast invasiveness and enhanced apoptotic death. In the present review, we demonstrate that parathyroid hormone-related protein is involved not only in the maternal and fetal failures but also in the etiological aspects of the disease. We hypothesize that reduced local production of the peptide is a major causative event.

Various studies on hepatic fibrosis occurring in iron overload suggest that excess of tissue iron may be involved in the stimulation of collagen synthesis. Anyway, up to date, direct evidence on the role of iron in hepatic fibrosis is lacking. Moreover, it is not clear whether iron acts as direct initiator of fibrogenesis or as mediator of hepatocellular necrosis. In the present study, we investigated the effect of nontoxic doses of iron on collagen metabolism and proliferation, key features of liver fibrosis, by means of cultures of hepatic stellate cells, the liver cells responsible for collagen production. Iron treatment increased collagen synthesis without affecting noncollagen proteins. The maximum effect was observed at 5 microM iron (+132%). At this dose, no cell damage or proliferation was detected. Conversely, higher doses of iron (10 and 25 microM) induced cell proliferation and a lower increase in collagen synthesis, suggesting the prevalence of proliferative effect on the synthetic one. These effects occurred without the intervention of serum factors and were not mediated by lipid peroxidation. Our results strongly support the hypothesis that iron "per sé" may act as a profibrogenic agent. Finally, we provide evidence that iron plays a role also in matrix degradation, by stimulating some metalloprotease activities. Iron treatment increased metalloprotease-2 activity in hepatic stellate cells, while no changes were observed for interstitial collagenase activity suggesting that, in these conditions, a pathological accumulation of hepatic extracellular matrix may occur.

The parathyroid hormone-related protein (PTHrp), structurally similar to the parathyroid hormone (PTH) in its NH(2)-terminal part, was first identified as a tumour-derived peptide responsible for a paraneoplastic syndrome known as humoral hypercalcemia of malignancy. The PTHrp gene is expressed not only in cancer but also in normal tissues during adult and/or fetal life, where it plays predominantly paracrine and/or autocrine roles. In the skin PTHrp produced by keratinocytes acts on fibroblasts by complex cooperative circuits involving cytokines and growth factors. In this report, we studied the direct effects of synthetic PTHrp 1-40 on proliferation and collagen synthesis and matrix metalloproteinase-2 (MMP-2) activity in cultures of fibroblasts isolated from normal human skin. Fibroblasts exposure to varying doses of PTHrp for 48 h, significantly and dose-dependently inhibited proliferation evaluated by [(3)H]-thymidine incorporation into DNA. A dose-dependent stimulation of cAMP released into the medium was concomitantly observed. In contrast, PTHrp had no effect on collagen synthesis evaluated either by [(3)H]-proline incorporation or by radioimmunoassay (RIA) of the carboxyterminal fragment of type I procollagen (PICP). MMP-2 activity, evaluated by quantitative zymographic analysis, was significantly increased by PTHrp treatment at doses of 160 and 320 nM. These findings indicate that PTHrp may play a role in normal dermal physiology by controlling both fibroblast proliferation and extracellular matrix degradation.

Problem: Type-I interferon (IFN) is the protein recognizing pregnancy in ruminants. Although IFN is secreted in early pregnancy, its role is not still clear in other species. Like other cytokines, IFN exerts its biological functions through specific membrane receptors. We have investigated the potential action of IFN in human pregnancy by studying the distribution of the receptors in the human placenta., Method: Reactivity to monoclonal antibodies (mAbs) to the type-I IFN-receptor (R) was analyzed by immunohistochemistry in human placental tissues and in cytospins of first trimester trophoblast cells., Results: Type-I IFN-R immunoreactivity was observed mostly in first trimester villous cytotrophoblasts and in the cytotrophoblast cell columns. Trophoblast in the decidua, the epithelium of the uterine glands, and most of the isolated trophoblast cells were also immunoreactive., Conclusion: The expression of type-I IFN-R in the highly proliferating and migrating trophoblast suggests that this cytokine has a role in trophoblast growth and invasion.