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.

Discharge of landfill leachate to streambed sediments impacts the mineralization potential of phenoxy acid herbicides depending on the initial abundance of tfdA gene classes.

To understand the role of abundance of tfdA gene classes belonging to ß- and γ-proteobacteria on phenoxy acid herbicide degradation, streambed sediments were sampled around three seepage meters (SMs) installed in a landfill-impacted groundwater-surface water interface. Highest herbicide mass discharge to SM3, and lower herbicide mass discharges to SM1 and SM2 were determined due to groundwater discharge rates and herbicide concentrations. SM1-sediment with the lowest abundance of tfdA gene classes had the slowest mineralization, whereas SM2- and SM3-sediments with more abundant tfdA genes had faster mineralization. The observed difference in mineralization rates between discharge zones was simulated by a Monod-based kinetic model, which confirmed the role of abundance of tfdA gene classes. This study suggests presence of specific degraders adapted to slow growth rate and high yield strategy due to long-term herbicide exposure; and thus groundwater-surface water interface could act as a natural biological filter and protect stream water quality.

Application of a contaminant mass balance method at an old landfill to assess the impact on water resources.

Old and unlined landfill sites pose a risk to groundwater and surface water resources. While landfill leachate plumes in sandy aquifers have been studied, landfills in clay till settings and their impact on receiving water bodies are not well understood. In addition, methods for quantitatively linking soil and groundwater contamination to surface water pollution are required. This paper presents a method which provides an estimate of the contaminant mass discharge, using a combination of a historical investigation and contaminant mass balance approach. The method works at the screening level and could be part of a risk assessment. The study site was Risby Landfill, an old unlined landfill located in a clay till setting on central Zealand, Denmark. The contaminant mass discharge was determined for three common leachate indicators: chloride, dissolved organic carbon and ammonium. For instance, the mass discharge of chloride from the landfill was 9.4 ton/year and the mass discharge of chloride to the deep limestone aquifer was 1.4 ton/year. This resulted in elevated concentrations of leachate indicators (chloride, dissolved organic carbon and ammonium) in the groundwater. The mass discharge of chloride to the small Risby Stream down gradient of the landfill was approximately 31 kg/year. The contaminant mass balance method worked well for chloride and dissolved organic carbon, but the uncertainties were elevated for ammonium due to substantial spatial variability in the source composition and attenuation processes in the underlying clay till.

Development of the bacterial compartment along the Danube River: a continuum despite local influences.

Microbial food webs dominate heterotrophic food webs in large rivers with bacterial metabolism being a key component of carbon processing. Thus, analysis of bacterial population dynamics is critical to understanding patterns and mechanisms of material cycling and energy fluxes in large rivers. Within the frame of the Joint Danube Survey (JDS) 2007, the longitudinal development of the natural bacterial community in the Danube in terms of bacterial numbers, morphotype composition, and heterotrophic production of the suspended and particle-attached fractions was followed at a fine spatial resolution of approximately 30 km for the first time in such a large river along a 2,600-km stretch. Twenty-one major tributaries and branches were also included. This allowed us to investigate whether bacterial standing stock and production undergo continuous, linear changes or whether discontinuities and local processes like the merging of tributaries or the potential impact of sewage input drive the bacterial population in the Danube. The presented investigation revealed surprising continuous patterns of changes of bacterial parameters along the Danube River. Despite the presence of impoundments or hydropower plants, large municipalities, and the discharge of large tributaries, most bacterial parameters (standing stock, morphotype succession, and attached bacterial production) developed gradually, indicating that mainly broad-scale drivers and not local conditions shape and control the bacterial community in the midstream of this large river. As most important broad-scale drivers, nutrients (inorganic and organic) and changes in particle concentrations were identified. These data are also in remarkable accordance with the patterns of changes of the genetic bacterial community composition, observed during the first JDS (2001) 6 years before. In contrast, bacterial activity did not follow a continuous trend and was mainly controlled by the input of sewage from large cities in the middle section, leading to a bloom of phytoplankton. The observed patterns and the comparison between the Danube, its tributaries and other large rivers worldwide indicate that the bacterial community in rivers has a powerful indicator function for estimating the ecological status of large river ecosystems once enough information has been collected at various temporal and spatial scales.