Transcriptional Responses as Biomarkers of General Toxicity: A Systematic Review and Meta-analysis on Metal-Exposed Bivalves.

Through a systematic review and a series of meta-analyses, we evaluated the general responsiveness of putative transcriptional biomarkers of general toxicity and chemical stress. We targeted metal exposures performed on bivalves under controlled laboratory conditions and selected six transcripts associated with general toxicity for evaluation: catalase, glutathione-S-transferase, heat shock proteins 70 and 90, metallothionein, and superoxide dismutase. Transcriptional responses (n = 396) were extracted from published scientific articles (k = 22) and converted to log response ratios (lnRRs). By estimating toxic units, we normalized different metal exposures to a common scale, as a proxy of concentration. Using Bayesian hierarchical random effect models, we then tested the effects of metal exposure on lnRR, both for metal exposure in general and in meta-regressions using toxic unit and exposure time as independent variables. Corresponding analyses were also repeated with transcript and tissue as additional moderators. Observed patterns were similar for general and for transcript- and tissue-specific responses. The expected overall response to arbitrary metal exposure was an lnRR of 0.50, corresponding to a 65% increase relative to a nonexposed control. However, when accounting for publication bias, the estimated "true" response showed no such effect. Furthermore, expected response magnitude increased slightly with exposure time, but there was little support for general monotonic concentration dependence with regard to toxic unit. Altogether, the present study reveals potential limitations that need consideration prior to applying the selected transcripts as biomarkers in environmental risk assessment. Environ Toxicol Chem 2023;42:628-641. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Molecular biomarker responses in the freshwater mussel Anodonta anatina exposed to an industrial wastewater effluent.

Using a selection of molecular biomarkers, we evaluated responses in freshwater mussels (Anodonta anatina) exposed to effluent from an industrial wastewater treatment facility. The aims of this work were to (1) assess biomarkers of general toxicity under sublethal exposure to an anthropogenic mixture of chemicals, represented by an arbitrary effluent, and (2) evaluate the potential of A. anatina as a bioindicator of pollution. Adult mussels (n = in total 32; 24 males and 8 females) were exposed (96 h) in the laboratory to a fixed dilution of effluent or to a control treatment of standardized freshwater. Metal concentrations were in general higher in the effluent, by an order of magnitude or more, compared to the control. Toxic unit estimates were used as proxies of chemical stress, and Cu, Ni, and Zn were identified as potential major contributors (Cu> Ni > Zn). Six transcriptional (cat, gst, hsp70, hsp90, mt, sod) and two biochemical (AChE, GST) biomarkers were analyzed in two tissues, gills, and digestive glands. Out of the 16 responses (eight biomarkers × two tissues), 14 effect sizes were small (within ± 28 % of control) and differences non-significant (p > 0.05). Results did however show that (1) AChE activity increased by 40% in gills of exposed mussels compared to control, (2) hsp90 expression was 100% higher in exposed female gills compared to control, and (3) three marker signals (AChE in both tissues, and hsp70 in gills) differed between sexes, independent of treatment. Results highlight a need for further investigation of molecular biomarker variability and robustness in A. anatina.

Evaluation of transcriptional biomarkers using a high-resolution regression approach: Concentration-dependence of selected transcripts in copper-exposed freshwater mussels (Anodonta anatina).

We tested concentration-dependence of selected gene transcripts (cat, gst, hsp70, hsp90, mt and sod) for evaluation as biomarkers of chemical stress. Contrary to the common approach of factorial designs and few exposure concentrations, we used regression across a high-resolution concentration series. Specifically, freshwater mussels (Anodonta anatina) were acutely (96 h) exposed to Cu (13 nominal concentrations, measuring 0.13-1 600 µg/L), and transcripts were measured by RT-qPCR. In digestive glands, cat, hsp90 and mt decreased with water Cu (p < 0.05), but response magnitudes saturated at < 2-fold decreases. In gills, gst, hsp70, hsp90 and mt increased with water Cu (p < 0.05). While hsp70, hsp90 and mt exceeded 2-fold increases within the exposure range, high Cu concentrations were required (38-160 µg/L). Although gill responses were generally more robust compared to digestive glands, overall small response magnitudes and moderate sensitivity may set limit for potential application as general biomarkers of chemical stress.

Transcriptional and biochemical biomarker responses in a freshwater mussel (Anodonta anatina) under environmentally relevant Cu exposure.

Molecular biomarkers, like gene transcripts or enzyme activities, are potentially powerful tools for early warning assessment of pollution. However, a thorough understanding of response and baseline variation is required to distinguish actual effects from pollution. Here, we assess the freshwater mussel Anodonta anatina as a biomarker model species for freshwater ecosystems, by testing responses of six transcriptional (cat, gst, hsp70, hsp90, mt, and sod) and two biochemical (AChE and GST) biomarkers to environmentally relevant Cu water concentrations. Mussels (n = 20), collected from a stream free from point source pollution, were exposed in the laboratory, for 96 h, to Cu treatments (< 0.2 µg/L, 0.77 ± 0.87 µg/L, and 6.3 ± 5.4 µg/L). Gills and digestive glands were extracted and analyzed for transcriptional and biochemical responses. Biological and statistical effect sizes from Cu treatments were in general small (mean log2 fold-change ≤ 0.80 and Cohen's f ≤ 0.69, respectively), and no significant treatment effects were observed. In contrast, four out of eight biomarkers (cat, gst, hsp70, and GST) showed a significant sex:tissue interaction, and additionally one (sod) showed significant overall effects from sex. Specifically, three markers in gills (cat, mt, GST) and one in digestive gland (AChE) displayed significant sex differences, independent of treatment. Results suggest that sex or tissue effects might obscure low-magnitude biomarker responses and potential early warnings. Thus, variation in biomarker baselines and response patterns needs to be further addressed for the future use of A. anatina as a biomarker model species.

Route optimization as an instrument to improve animal welfare and economics in pre-slaughter logistics.

Each year, more than three million animals are transported from farms to abattoirs in Sweden. Animal transport is related to economic and environmental costs and a negative impact on animal welfare. Time and the number of pick-up stops between farms and abattoirs are two key parameters for animal welfare. Both are highly dependent on efficient and qualitative transportation planning, which may be difficult if done manually. We have examined the benefits of using route optimization in cattle transportation planning. To simulate the effects of various planning time windows and transportation time regulations and number of pick-up stops along each route, we have used data that represent one year of cattle transport. Our optimization model is a development of a model used in forestry transport that solves a general pick-up and delivery vehicle routing problem. The objective is to minimize transportation costs. We have shown that the length of the planning time window has a significant impact on the animal transport time, the total driving time and the total distance driven; these parameters that will not only affect animal welfare but also affect the economy and environment in the pre-slaughter logistic chain. In addition, we have shown that changes in animal transportation regulations, such as minimizing the number of allowed pick-up stops on each route or minimizing animal transportation time, will have positive effects on animal welfare measured in transportation hours and number of pick-up stops. However, this leads to an increase in working time and driven distances, leading to higher transportation costs for the transport and negative environmental impact.

SpecNet: a spatial network algorithm that generates a wide range of specific structures.

Network measures are used to predict the behavior of different systems. To be able to investigate how various structures behave and interact we need a wide range of theoretical networks to explore. Both spatial and non-spatial methods exist for generating networks but they are limited in the ability of producing wide range of network structures. We extend an earlier version of a spatial spectral network algorithm to generate a large variety of networks across almost all the theoretical spectra of the following network measures: average clustering coefficient, degree assortativity, fragmentation index, and mean degree. We compare this extended spatial spectral network-generating algorithm with a non-spatial algorithm regarding their ability to create networks with different structures and network measures. The spatial spectral network-generating algorithm can generate networks over a much broader scale than the non-spatial and other known network algorithms. To exemplify the ability to regenerate real networks, we regenerate networks with structures similar to two real Swedish swine transport networks. Results show that the spatial algorithm is an appropriate model with correlation coefficients at 0.99. This novel algorithm can even create negative assortativity and managed to achieve assortativity values that spans over almost the entire theoretical range.

Network analysis of cattle and pig movements in Sweden: measures relevant for disease control and risk based surveillance.

Registration of cattle and pig movements is mandatory in Sweden and all registered movements between farms in the years 2006-2008 were investigated using network analysis. The networks were analysed as monthly and yearly networks, separately per species and with the two species together. Measures that have been previously discussed in relation to outbreaks and disease control were calculated; moreover a measure of the ingoing infection chain was constructed. The ingoing infection chain captures ingoing contacts through other holdings, taking the temporal aspect and sequence of the movements into account. The distribution of the contacts among the holdings was skewed. Many farms had few or no contacts, while others had many, a pattern which has also been described from other countries. The cattle network and the combined network showed a recurring seasonal pattern, while this was not seen in the pig network. The in-degree was not equivalent to the ingoing infection chain; there were holdings with limited direct contacts, but a large number of indirect contacts. The ingoing infection chain could be a useful measure when setting up strategies for disease control and for risk based surveillance as it identifies holdings with many contacts through live animal movements and thus at potentially higher risk for introduction of contagious diseases.

Estimation of distance related probability of animal movements between holdings and implications for disease spread modeling.

Between holding contacts are more common over short distances and this may have implications for the dynamics of disease spread through these contacts. A reliable estimation of how contacts depend on distance is therefore important when modeling livestock diseases. In this study, we have developed a method for analyzing distant dependent contacts and applied it to animal movement data from Sweden. The data were analyzed with two competing models. The first model assumes that contacts arise from a purely distance dependent process. The second is a mixture model and assumes that, in addition, some contacts arise independent of distance. Parameters were estimated with a Bayesian Markov Chain Monte Carlo (MCMC) approach and the model probabilities were compared. We also investigated possible between model differences in predicted contact structures, using a collection of network measures. We found that the mixture model was a much better model for the data analyzed. Also, the network measures showed that the models differed considerably in predictions of contact structures, which is expected to be important for disease spread dynamics. We conclude that a model with contacts being both dependent on, and independent of, distance was preferred for modeling the example animal movement contact data.

Food web structure and interaction strength pave the way for vulnerability to extinction.

This paper focuses on how food web structure and interactions among species affects the vulnerability, due to environmental variability, to extinction of species at different positions in model food webs. Vulnerability is here not measured by a traditional extinction threshold but is instead inspired by the IUCN criteria for endangered species: an observed rapid decline in population abundance. Using model webs influenced by stochasticity with zero autocorrelation, we investigate the ecological determinants of species vulnerability, i.e. the trophic interactions between species and food web structure and how these interact with the risk of sudden drops in abundance of species. We find that (i) producers fulfil the criterion of vulnerable species more frequently than other species, (ii) food web structure is related to vulnerability, and (iii) the vulnerability of species is greater when involved in a strong trophic interaction than when not. We note that our result on the relationship between extinction risk and trophic position of species contradict previous suggestions and argue that the main reason for the discrepancy probably is due to the fact that we study the vulnerability to environmental stochasticity and not extinction risk due to overexploitation, habitat destruction or interactions with introduced species. Thus, we suggest that the vulnerability of species to environmental stochasticity may be differently related to trophic position than the vulnerability of species to other factors. Earlier research on species extinctions has looked for intrinsic traits of species that correlate with increased vulnerability to extinction. However, to fully understand the extinction process we must also consider that species interactions may affect vulnerability and that not all extinctions are the result of long, gradual reductions in species abundances. Under environmental stochasticity (which importance frequently is assumed to increase as a result of climate change) and direct and indirect interactions with other species some extinctions may occur rapidly and apparently unexpectedly. To identify the first declines of population abundances that may escalate and lead to extinctions as early as possible, we need to recognize which species are at greatest risk of entering such dangerous routes and under what circumstances. This new perspective may contribute to our understanding of the processes leading to extinction of populations and eventually species. This is especially urgent in the light of the current biodiversity crisis where a large fraction of the world's biodiversity is threatened.