The toxicogenomics field aims to understand and predict toxicity by using ‘omics’ data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and ‘omics’ data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

It is more effective to maintain good health than to regain it after losing it. This work focuses on the biochemical defense mechanisms against free radicals and their role in building and maintaining antioxidant shields, aiming to show how to balance, as much as possible, the situations in which we are exposed to free radicals. To achieve this aim, foods, fruits, and marine algae with a high antioxidant content should constitute the basis of nutritional elements, since natural products are known to have significantly greater assimilation efficiency. This review also gives the perspective in which the use of antioxidants can extend the life of food products, by protecting them from damage caused by oxidation as well as their use as food additives.

Mass spectrometry (MS) is an essential analytical technology on which the emerging omics domains; such as genomics; transcriptomics; proteomics and metabolomics; are based. This quantifiable technique allows for the identification of thousands of proteins from cell culture; bodily fluids or tissue using either global or targeted strategies; or detection of biologically active metabolites in ultra amounts. The routine performance of MS technology in the oncological field provides a better understanding of human diseases in terms of pathophysiology; prevention; diagnosis and treatment; as well as development of new biomarkers; drugs targets and therapies. In this review; we argue that the recent; successful advances in MS technologies towards cancer omics studies provides a strong rationale for its implementation in biomedicine as a whole.

Pathway analysis is an informative method for comparing and contrasting drug-induced gene expression in cellular systems. Here, we define the effects of the marine natural product fucoxanthin, separately and in combination with the prototypic phosphatidylinositol 3-kinase (PI3K) inhibitor LY-294002, on gene expression in a well-established human glioblastoma cell system, U87MG. Under conditions which inhibit cell proliferation, LY-294002 and fucoxanthin modulate many pathways in common, including the retinoblastoma, DNA damage, DNA replication and cell cycle pathways. In sharp contrast, we see profound differences in the expression of genes characteristic of pathways such as apoptosis and lipid metabolism, contributing to the development of a differentiated and distinctive drug-induced gene expression signature for each compound. Furthermore, in combination, fucoxanthin synergizes with LY-294002 in inhibiting the growth of U87MG cells, suggesting complementarity in their molecular modes of action and pointing to further treatment combinations. The synergy we observe between the dietary nutraceutical fucoxanthin and the synthetic chemical LY-294002 in producing growth arrest in glioblastoma, illustrates the potential of nutri-pharmaceutical combinations in targeting this challenging disease.

Background: Food and feed supplements containing microorganisms with probiotic potential are of increasing interest due to their healthy promoting effect on human and animals. Their mechanism of action is still unknown. Using a microarray approach, the aim of this study was to investigate the differences in genome-wide gene expression induced by a mixture of three Lactobacillus strains (L. rhamnosus, L. plantarum, and L. paracasei) in intestinal porcine epithelial cells (IPEC-1) and to identify the genes and pathways involved in intestinal barrier functions. Methods: Undifferentiated IPEC-1 cells seeded at a density of 2.0 × 10⁵/mL in 24-wells culture plates were cultivated at 37 °C and 5% CO₂ until they reached confluence (2–3 days). Confluent cells monolayer were then cultivated with 1 mL of fresh lactobacilli (LB) mixture suspension prepared for a concentration of approximately 3.3 × 10⁷ CFU/mL for each strain (1 × 10⁸ CFU/mL in total) for 3 h and analyzed by microarray using Gene Spring GX v.11.5. Results: The functional analysis showed that 1811 of the genes modulated by LB treatment are involved in signaling (95% up-regulation, 121 genes with a fold change higher than 10). The most enhanced expression was registered for AXIN2 (axis inhibition protein 2-AXIN2) gene (13.93 Fc, p = 0.043), a negative regulator of β-catenin with a key role in human cancer. LB affected the cellular proliferation by increasing 10 times (Fc) the NF1 gene encoding for the neurofibromin protein, a tumor suppressor that prevent cells from uncontrolled proliferation. The induction of genes like serpin peptidase inhibitor, clade A member 3 (SERPINA 3), interleukin-20 (IL-20), oncostatin M (OSM), granulocyte-macrophage colony-stimulating factor (GM-CSF), and the suppression of chemokine (C-X-C motif) ligand 2/macrophage inflammatory protein 2-alpha (CXCL-2/MIP-2), regulator of G-protein signaling 2 (RGS2), and of pro-inflammatory interleukin-18 (IL-18) genes highlights the protective role of lactobacilli in epithelial barrier function against inflammation and in the activation of immune response. Conclusion: Gene overexpression was the predominant effect produced by lactobacilli treatment in IPEC-1 cells, genes related to signaling pathways being the most affected. The protective role of lactobacilli in epithelial barrier function against inflammation and in the activation of immune response was also noticed.

Simple and multiple linear regression analyses are statistical methods used to investigate the link between activity/property of active compounds and the structural chemical features. One assumption of the linear regression is that the errors follow a normal distribution. This paper introduced a new approach to solving the simple linear regression in which no assumptions about the distribution of the errors are made. The proposed approach maximizes the probability of observing the event according to the random error. The use of the proposed approach is illustrated in ten classes of compounds with different activities or properties. The proposed method proved reliable and was showed to fit properly the observed data compared to the convenient approach of normal distribution of the errors.