Biomonitoring of Microcystin and Aflatoxin Co-Occurrence in Aquaculture Using Immunohistochemistry and Genotoxicity Assays

This work investigated the effects of co-occurring aflatoxin B-1 (AFB(1)) and microcystin (MC) in aquaculture, using immunohistochemistry and genotoxicity methods. Tilapia (Oreochromis niloticus) were exposed to AFB(1) by intraperitoneal and MC (cell extract of Microcystis aeruginosa) by intraperitoneal and immersion routes. The interaction of MC-AFB(1) was evaluated co-exposing the intraperitoneal doses. Blood samples were collected after 8, 24, and 48h to analyze the micronucleus frequency and comet score. The interaction of MC-AFB(1) showed a synergic mutagenic response by higher micronucleus frequency of co-exposed group. A slight genotoxic synergism was also observed in the comet score. Immunohistochemistry detected MC in al lthe fish liver tissues exposed to MC by intraperitoneal route, and only the immersed group with the highest dose of MC showed a positive response. Although MC was non-detectable in the edible muscle, the combination of immunohistochemistry with genotoxicity assay was an attractive biomonitoring tool in aquaculture, where the animals were frequently exposed to co-occurring synergic hazards.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.