Exploring the effects of habitat management on grassland biodiversity: A case study from northern Serbia.

Grasslands represent a biodiversity hotspot in the European agricultural landscape, their restoration is necessary and offers a great opportunity to mitigate or halt harmful processes. These measures require a comprehensive knowledge of historical landscape changes, but also adequate management strategies. The required data was gathered from the sand grasslands of northern Serbia, as this habitat is of high conservation priority. This area also has a long history of different habitat management approaches (grazing and mowing versus unmanaged), which has been documented over of the last two decades. This dataset enabled us to quantify the effects of different measures across multiple taxa (plants, insect pollinators, and birds). We linked the gathered data on plants, pollinators, and birds with habitat management measures. Our results show that, at the taxon level, the adopted management strategies were beneficial for species richness, abundance, and composition, as the highest diversity of plant, insect pollinator, and bird species was found in managed areas. Thus, an innovative modelling approach was adopted in this work to identify and explain the effects of management practices on changes in habitat communities. The findings yielded can be used in the decision making as well as development of new management programmes. We thus posit that, when restoring and establishing particular communities, priority needs to be given to species with a broad ecological response. We recommend using the decision tree as a suitable machine learning model for this purpose.

Differential eigengene network analysis reveals benzo[a]pyrene and 2,3,7,8-tetrachlorodibenzo-p-dioxin consensus regulatory network in human liver cell line HepG2.

Aryl hydrocarbon receptor (AHR) is one of the main mediators of the toxic effects of benzo[a]pyrene (BaP) and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). However, a vast number of BaP- and TCDD-affected genes may suggest a more complex transcriptional regulatory network driving common adverse effects of these two chemicals. Unlike TCDD, BaP is rapidly metabolized in the liver, yielding products with a questionable ability to bind and activate AHR. In this study, we used transcriptomics data from the BaP- and TCCD-exposed human liver cell line HepG2, and performed differential eigengene network analysis to understand the correlation among genes and to untangle the common regulatory mechanism in the action of BaP and TCDD. The genes were grouped into 11 meta-modules with an overall preservation of 0.72 and were also segregated into three consensus time clusters: 12, 24, and 48 h. The analysis showed that the consensus genes in each time cluster were either directly regulated by the AHR or the AHR-TF interactions. Some TFs form a direct physical interaction with AHR such as ESR1, FOXA1, and E2F1, whereas others, including CTCF, RXRA, FOXO1, CEBPA, CEBPB, and TP53 show an indirect interaction with AHR. The analysis of biological processes (BPs) identified unique and common BPs in BaP and TCDD samples, with DNA damage response detected in all three time points. In summary, we identified a consensus transcriptional regulatory network common for BaP and TCDD consisting of direct AHR targets and AHR-TF targets. This analysis sheds new light on the common mechanism of action of a genotoxic (BaP) and non-genotoxic (TCDD) chemical in liver cells.

Predicting chemicals' toxicity pathway of female reproductive disorders using AOP7 and deep neural networks.

Experimental evidence shows that certain chemicals, particularly endocrine disrupting chemicals, may negatively affect the female reproductive system, thereby lowering women's fertility. However, humans are constantly exposed to a number of different chemicals with limited or no experimental data regarding their effect and the mechanism of action in the female reproductive system. To predict chemical hazards to the female reproductive system, we used a previously defined adverse outcome pathway (AOP) that links activation of the peroxisome proliferator-activated receptor γ to the reproductive toxicity in adult females (AOP7) and the Convolutional Deep Neural Network models that produce meaningful predictions when trained on a significant amount of data. The models trained using CompTox assays with intended molecular and biological targets corresponding to AOP7 achieved high performance (over 90% validation accuracy). The integration of AOP7 and Deep Neural Network identified chemicals that could negatively affect female reproduction through the mechanism described in AOP7. We provide a solution to quickly analyze the data and produce machine learning models to identify potentially active chemicals in the female reproductive system. Although we focused on the female reproductive system, this approach could be valid for a number of other chemicals and AOPs if the right data exist.

An in silico toxicogenomic approach in constructing the aflatoxin B1-mediated regulatory network of hub genes in hepatocellular carcinoma.

Aflatoxin B1 (AFB1) can cause hepatocellular carcinoma (HCC) through a mutagenic mode of action but can also lead to global changes in gene expression; however, the AFB1 network of molecular pathways involved in HCC is not known. Here, we used toxicogenomic data from human liver cells exposed to AFB1 to infer the network of AFB1-responsive molecular pathways involved in HCC. The following computational tools: STRING, MCODE, cytoHubba, iRegulon, kinase enrichment tool KEA3, and DAVID were used to identify protein-protein interaction network, hub genes, transcription factors (TFs), upstream kinases, and biological processes (BPs). Predicted molecular events were validated with an external dataset, whereas the hub genes in HCC were validated using the UALCAN database. The results revealed an association between AFB1 and the hub genes involved in the cell cycle. We identified TFs that regulate the hub genes and linked them with upstream kinases including cyclin-dependent kinases, mitogen-activated protein kinase 1, and AKT. This approach enabled the construction of the AFB1-mediated regulatory network consisting of upstream kinases, TFs, hub genes, and BPs, thus revealing the signaling hierarchy and information flow that may contribute to AFB1-induced HCC. This could be a useful tool in predicting the molecular mechanisms involved in chemical-induced diseases when available toxicogenomic data exist.