Estuarine ecosystems play an essential role as nurseries for several aquatic species. However, increasing anthropogenic influences compromise habitat quality, species recruitment and nursery value in these systems. From this perspective, the study examined estuarine-coastal connectivity of the Mugil curema species in four tropical estuaries with different levels of anthropogenic impact: Camaratuba, Mamanguape, and Paraíba and Gramame river estuaries, located in the northeastern Brazilian state of Paraíba. Based on the chemical analysis of the otolith cores, the elemental signatures of individuals from the same cohort, i.e. juveniles collected in 2022 (age 0+) and subadults (age 1+) collected in 2023, were retrospectively evaluated and compared, aiming to identify recruitment patterns and relative contribution of each estuary to coastal populations. One-Way Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA) and Linear Discriminant Function Analysis (LDFA) revealed partial separation of multielement signatures spatially, with variations associated with local environmental conditions and the intensity of anthropogenic impacts. Elemental ratios such as Li:Ca and Ba:Ca stood out as important markers for spatial differentiation. The Paraíba River estuary stood out as the main nursery area for Mugil curema, presenting a high rate of self-recruitment through Multinomial Logistic Regression (MLR) and contributing to other estuarine and coastal areas. Despite being the most impacted estuary, especially due to the high concentration of lead (Pb:Ca), this system still plays an essential role in maintaining coastal stocks of the species, evidencing its ecological importance and the need for conservation policies. These results indicate that connectivity between estuaries and the coastal zone is shaped by local environmental and anthropogenic factors, and the nursery value of estuaries can vary considerably. The identification of key areas for recruitment and connectivity, such as the Paraíba estuary, provides important insights for fisheries management and conservation, especially in tropical regions under increasing anthropogenic pressure.

The Seybouse River (Oued Seybouse) is one of the largest rivers in Northeastern Algeria. It rises in the Guelma region and flows through several urban and industrial areas before reaching the Mediterranean Sea near Annaba. Along its course, the river receives domestic, industrial, agricultural, and hospital effluents, which significantly affect its water quality.

This study aimed to evaluate the physicochemical and microbiological quality of the Seybouse River and to assess the extent of pollution caused by human activities. Monthly sampling was conducted over a full year at different stations along the river. The physicochemical analyses included measurements of temperature, pH, electrical conductivity, major cations (Ca²⁺, Mg²⁺, K⁺, Na⁺), anions (Cl⁻, SO₄²⁻, NO₃⁻, NO₂⁻, NH₄⁺, HCO₃⁻), and organic pollution indicators such as BOD₅, COD, and suspended solids.

The microbiological assessment focused on fecal contamination, pathogenic bacteria, and yeasts, summarized through a microbiological contamination index.

Results showed clear spatial and temporal variations, identifying high mineral, organic, and fecal loads, especially downstream. These findings reveal that the Seybouse River is heavily polluted and that its water presents serious environmental and health risks, making it unsuitable for irrigation or any direct use.

The spatiotemporal variations of many physicochemical and microbiological parameters indicated intensive pollution of the Seybouse River. This pollution was reflected by a significant mineral, organic and faecal load downstream of the river. Our study indicates that the water of the Seybouse River is highly degraded and presents a real threat to aquatic life. The Seybouse River is heavily polluted, and its water presents serious environmental and health risks. Our study also suggests that the use of river water for crop irrigation should be avoided.

Understanding the impact of single-nucleotide polymorphisms (SNPs) on protein structure is a critical challenge in functional genomics. We developed a scalable computational pipeline to evaluate SNP effects on protein stability, flexibility, and dynamics, using genomic data from 1,467 adult male honey bee drones (Apis mellifera) across 25 countries and 18 subspecies, sourced from the MEDIBEES project. High-stringency variant calling identified SNPs for analysis.

Wild-type structures were predicted using AlphaFold2, SWISS-MODEL, and AlphaFold Protein Structure Database models, ranked by confidence metrics (pLDDT, DOPE, TM-score). AlphaFold2 models showed high internal consistency (average TM-score 0.9975 ± 0.0015) and similarity to homology-based models (average TM-score 0.886 ± 0.076) and experimental structures (average TM-score 0.837 ± 0.001). SNP variants were generated via in silico mutagenesis using mutation-aware AlphaFold2, MODELLER (for homology modelling), and SWISS-MODEL. All structures underwent energy minimization to resolve clashes.

Molecular dynamics simulations (AMBER25, 100 ns each) under physiological conditions analyzed trajectories for solvent-accessible surface area (SASA), root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), hydrogen bonds (H-bonds), and dihedral angles (phi/φ, psi/ψ). These metrics revealed SNP-induced changes: in AlphaFold2 models, mean RMSD decreased from 1.81 Å (wild-type) to 1.58 ± 0.12 Å (mutants), suggesting improved stability; RMSF increased from 0.82 to 0.85 ± 0.05 Å, indicating higher flexibility; Rg decreased from 38.78 to 38.66 ± 0.09 Å, reflecting greater compactness; H-bonds reduced from 270.5 to 268.7 ± 3.6; and SASA declined from 54,139 to 52,946 ± 977 Ų, implying reduced solvent exposure. In contrast, homology-based models (e.g., trRosetta) showed smaller perturbations (e.g., RMSD from 2.25 to 2.22 Å).

AlphaFold2 and AlphaFold Protein Structure Database wild-type models demonstrated the greatest sensitivity to SNP perturbations compared to homology models, with mutant structures maintaining high congruence to mutation-aware predictions (TM-scores >0.99), underscoring the method's robustness for SNP effect modeling.

Assessing the impact of pesticide exposure on honey bees (Apis mellifera) is crucial for understanding the drivers of pollinator decline and the mechanisms underlying environmental adaptation. Honey bees play a key role in ecosystem functioning and agricultural productivity, but they are increasingly threatened by chemical stressors that can affect their health, behaviour, and genetic resilience.

Directly measuring exposure across large geographic scales is often logistically and financially challenging; therefore, environmental proxies—such as modelled pesticide use grids and land-use data—could facilitate this type of analysis by providing spatially explicit estimates of potential stressors. These proxies allow researchers to approximate exposure across landscapes, integrate multiple sources of environmental information, and combine them with genomic data to identify genetic variants associated with environmental pressures, even when direct measurements are limited.

To quantify pesticide exposure, we used two complementary approaches. The first measured direct pesticide exposure using INSIGNIA-EU’s APIStrips (absorb pesticide in-hive strip) to quantify real-world pesticide residues. The second estimated the landscape-scale exposure integrating modelled pesticides-exposure grids from PEST-CHEMGRIDS to quantify the proportion of agricultural fields within 3km radius of each colony using Corine Land Cover.

Leveraging these measured and proxy-based pesticide exposures, we analysed 102 whole-genome sequences across 33 European countries as part of the Better-B project. To identify candidate single-nucleotide polymorphisms (SNPs), three complementary Genomic–Environment Association (GEA) approaches were applied: SAMBADA (a spatial analysis tool), LFMM (latent factor mixed models), and Redundancy Analysis (RDA).

Even though the APIStrip measurements yielded a greater number of SNPs, both approaches converged on key detoxification and stress-response genes, including a transcript variant of sushi von Willebrand factor type gene. This overlap highlights the biological relevance of proxy-based signals, even with reduced statistical power, and supports their utility for large-scale genomic studies where direct sampling is infeasible.

How complex life-history traits evolve under rapid climatic drying is a central question in evolutionary biology. Indigenous sheep maintained under pastoral systems experience strong, spatially structured selection on reproductive timing and thermotolerance, yet the genomic architecture and repeatability of these responses remain unclear. We sampled multiple native sheep populations spanning replicated aridity and heat-load gradients. Animals were phenotyped for puberty onset, litter size, lamb survival, and field thermophysiology (rectal temperature, respiration rate, body condition). Genomes were assayed using high-density SNP genotyping complemented by RNA-seq from peri-pubertal granulosa cells. After modeling structure and relatedness, we combined genotype–environment association tests (LFMM, redundancy analysis) with selection scans (iHS, XP-EHH) and fitted reaction-norm animal models to quantify G×E. Expression QTL colocalization linked variants to gene regulation; GO and network enrichment interpreted pathways. Dozens of climate-associated loci clustered in modules tied to KNDy–GnRH signaling (e.g., KISS1/KISS1R, TAC3), ovarian steroidogenesis (STAR, CYP19A1), and cellular stress responses (heat-shock and vasoregulatory genes). Signals were dominated by soft sweeps and allele-frequency clines repeated across eco-clines, consistent with polygenic and convergent adaptation rather than single large-effect mutations. Reaction-norms revealed significant G×E for reproductive output, and polygenic indices derived from climate-associated SNPs predicted between-population variation in puberty timing and lamb survival under heat. Colocalization with granulosa-cell eQTLs implicated cis-regulatory shifts as a common mechanism. Reproductive timing and heat resilience in indigenous sheep evolve via repeatable, polygenic routes channelled through endocrine and stress-response networks. These results illuminate how selection structures complex traits in heterogeneous environments and provide evolution-informed targets for balanced improvement under warming, drying climates.

Introduction: Mosquito cell lines such as SuaE1 (An. gambiae), MSQ43 (An. stephensi), and 4a-2 (An. coluzzii) differ from mammalian cell models in that they achieve immortality through spontaneous immortalization rather than deliberate genetic transformation. The establishment of cell lines benefit from a regimented consistent approach which should allow for consistent repeatable results over time. While this makes them valuable and accessible tools, it may also predispose them to variability that complicates reproducibility. In this project we will be investigating the key corecomponents seen across different cell culture media for mosquito cell lines, and determining which components increase or may decrease the variability and potential dedifferentiation of these cells.

Methods: To investigate this, we continuously cultured these three mosquito cell lines and documented their phenotypes across passages. We imaged each cell line with brightfield microscopy in triplicate over several months. We further evaluated culture media and the lab environment for effects that may have altered cell differentiation in our cultured cell species.

Results: We observed significant, fully penetrant morphological changes in two of our three cell lines (SuaE1: high severity; 4a-2: moderate severity), including stratification, size and shape changes, differences in flask adherence, and cyst-like division. These phenotypes were absent in one line (MSQ-43).

Conclusion/Discussion: These findings suggest that spontaneous immortalization contributes to phenotypic instability between mosquito cell lines, raising important concerns for reproducibility in downstream applications such as transcriptomics, proteomics, and pathogen interaction studies. We also found that different media cell conditions can lead to insufficient supplementation of requirements for cell growth for initial establishment after thawing versus subsequent long-term maintenance growth.