Multiple stressors in a top predator seabird: potential ecological consequences of environmental contaminants, population health and breeding conditions

Environmental contaminants may have impacts on reproduction and survival in wildlife populations suffering from multiple stressors. This study examined whether adverse effects of persistent organic pollutants (POPs) increased with poor population health and breeding conditions in three colonies (60–74°N) of great skua (Stercorarius skua) in the north-eastern Atlantic (Shetland, Iceland and Bjørnøya [Bear Island]). POPs (organochlorines [OCs] and polybrominated diphenyl ethers [BDEs]) were measured in plasma of incubating birds (n = 222), concentrations differing nearly tenfold among colonies: Bjørnøya (2009) > Bjørnøya (2010) > Iceland (2009) > Shetland (2009). Reproductive success (hatching success and chick survival) showed that breeding conditions were favourable in Shetland and at Bjørnøya (2010), but were very poor in Iceland and at Bjørnøya (2009). Biomarkers indicated that health was poor in the Shetland population compared to the other populations. Females whose chicks hatched late had high POP concentrations in all colonies except at Bjørnøya (2010), and females losing their eggs at Bjørnøya (2009) tended to have higher concentrations than those hatching. Moreover, there was a negative relationship between female POP concentrations and chick body condition at hatching in Iceland and at Bjørnøya (2010). Supplementary feeding experiments were conducted, and in Iceland where feeding conditions were poor, significant negative relationships were found between female POP concentrations and daily growth-rate in first-hatched chicks of control nests, but not in food supplemented nests. This suggests that negative impacts of POPs were mitigated by improved feeding conditions. For second-chicks, there was a strong negative relationship between the female POP concentrations and growth-rate, but no effects of supplementary feeding. Lowered adult return-rate between breeding seasons with increasing POP loads were found both at Bjørnøya (2009) and in Shetland, especially related to BDEs. This indicates stronger fitness consequences of POPs following seasons with very poor breeding conditions and/or high reproductive effort. This study suggests that the impacts of POPs may differ depending on population health and breeding conditions, and that even low concentrations of POPs could have ecological consequences during adverse circumstances. This is important with regard to risk assessment of biomagnifying contaminants in marine ecosystems

Individual variation in biomarkers of health: influence of persistent organic pollutants in Great Skuas (Stercorarius Skua) breeding at different geographical locations

Persistent organic pollutants (POPs) have been shown to cause adverse effects on a number of biomarkers of health in birds. POPs may impair immune function and alter the stress response, defined as a suite of behavioral and physiological responses to environmental perturbations. Recent studies have also proposed that POPs can induce oxidative stress. Nevertheless, there is a lack of studies simultaneously assessing the potential damaging effects of POPs on the latter biomarkers. In this study, we examined the contribution of legacy (organochlorines; (OCs)) and emerging (flame retardants; PBDEs) POPs to individual variations in stress levels (feather corticosterone), humoral immunity (plasma immunoglobulin Y levels) and oxidative stress occurring in three breeding colonies of a top predator seabird, the Great skua (Stercorarius skua), distributed from temperate regions to the high Arctic: Shetland (60°N), Iceland (63°N) and Bjørnøya (74°N). Our results demonstrated that plasma concentrations of OCs in Great skuas from Bjørnøya are among the highest in North Atlantic seabirds, with up to 7900 μg/kg (ww) ∑OCs. Yet, a latitudinal gradient in POP levels was observed with all compounds being significantly higher in Bjørnøya than in Iceland and Shetland (on average 4-7 fold higher for OCs and 2.5-4.5 for PBDEs, respectively). Contrary to our predictions, skuas breeding at the least contaminated site (i.e., Shetland) experienced the poorest physiological condition; i.e., the highest levels of stress hormones (25% higher) and oxidative stress (50% higher) and the lowest immunoglobulin levels (15% lower) compared to the two other colonies. Finally, our results failed to point out consistent within-colony relationships between biomarkers of health and POPs. Overall, it is suggested that other ecological factors such as food availability could constrain physiological indicators more than anthropogenic contaminants

Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3. 0

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods.status: publishe

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods

[Scheda bibliografica]: Popoli dell'Africa mediterranea in et\ue0 romana

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.This study was funded by the National Centre of Excellence in Research (NCER) on Parkinson’s disease, the U.S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant no. DE-SC0010429. This project also received funding from the European Union’s HORIZON 2020 Research and Innovation Programme under grant agreement no. 668738 and the Luxembourg National Research Fund (FNR) ATTRACT program (FNR/A12/01) and OPEN (FNR/O16/11402054) grants. N.E.L. was supported by NIGMS (R35 GM119850) and the Novo Nordisk Foundation (NNF10CC1016517). M.A.P.O. was supported by the Luxembourg National Research Fund (FNR) grant AFR/6669348. A.R. was supported by the Lilly Innovation Fellows Award. F.J.P. was supported by the Minister of Economy and Competitiveness of Spain (BIO2016-77998-R) and the ELKARTEK Programme of the Basque Government (KK-2016/00026). I.A. was supported by a Basque Government predoctoral grant (PRE_2016_2_0044). B.Ø.P. was supported by the Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517)