Environmental,geographical and time-related impacts on avian malaria infections in native and introduced populations of house sparrows (Passer domesticus), a globally invasive species

Aim

The increasing spread of vector-borne diseases has resulted in severe health concerns for humans, domestic animals and wildlife, with changes in land use and the introduction of invasive species being among the main possible causes for this increase. We explored several ecological drivers potentially affecting the local prevalence and richness of avian malaria parasite lineages in native and introduced house sparrows (Passer domesticus) populations.

Location

Global.

Time period

2002–2019.

Major taxa studied

Avian Plasmodium parasites in house sparrows.

Methods

We analysed data from 2,220 samples from 69 localities across all continents, except Antarctica. The influence of environment (urbanization index and human density), geography (altitude, latitude, hemisphere) and time (bird breeding season and years since introduction) were analysed using generalized additive mixed models (GAMMs) and random forests.

Results

Overall, 670 sparrows (30.2%) were infected with 22 Plasmodium lineages. In native populations, parasite prevalence was positively related to urbanization index, with the highest prevalence values in areas with intermediate urbanization levels. Likewise, in introduced populations, prevalence was positively associated with urbanization index; however, higher infection occurred in areas with either extreme high or low levels of urbanization. In introduced populations, the number of parasite lineages increased with altitude and with the years elapsed since the establishment of sparrows in a new locality. Here, after a decline in the number of parasite lineages in the first 30 years, an increase from 40 years onwards was detected.

Main conclusions

Urbanization was related to parasite prevalence in both native and introduced bird populations. In invaded areas, altitude and time since bird introduction were related to the number of Plasmodium lineages found to be infecting sparrows.     

Corticotropin-Releasing Hormone Affects Short Immobilization Stress-Induced Changes in Lung Cytosolic and Membrane Glucocorticoid Binding Sites

Glucocorticoids act via glucocorticoid receptors (GR), typically localized in the cytosol (cGR). Rapid action is probably mediated via membrane receptors (mGR). In corticotropin-releasing hormone knockouts (CRH-KO), basal plasma glucocorticoid levels do differ from wild type levels (WT), but are approximately ten times lower during exposure to immobilization stress (IMMO) in comparison to WT. We tested the following hypotheses: (1) the mice lung tissue GR basal numbers would not be changed in CRH-KO (because of similar glucocorticoid levels), (2) the number of GR would be changed in WT but not in KO during short (30, 90, and 120 min) IMMO (because of higher increase of glucocorticoid levels in WT). The basal levels of cGR were not changed in CRH-KO (compared to WT), while mGR were significantly lower (62 %) in CRH-KO. In WT, there was the only decrease (to 32 %) in cGR after 120 min when we also found an increase in mGR in WT (to 201 %). In CRH-KO, IMMO caused gradual decrease in cGR (to 52 % after 30 min, to 46 % after 90 min, and to 32 % after 120 min). In CRH-KO, the only increase in mGR appeared already at 30 min of IMMO. These data suggest, on the contrary to our hypotheses, that CRH-KO are more susceptible to GR changes in early phases of stress.     

A study on the minimum number of loci required for genetic evaluation using a finite locus model

For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.     

Compromised root development constrains the establishment potential of native plants in unamended alkaline post-mining substrates

Plant and Soil - In water-limited areas, shrubs influence biological soil crust (biocrust) composition and diversity via soil microenvironment alterations and through modifying biotic interactions...     

Novel Neuroprotective Function of Apical-Basal Polarity Gene Crumbs in Amyloid Beta 42 (Aβ42) Mediated Neurodegeneration

Alzheimer''s disease (AD, OMIM: 104300), a progressive neurodegenerative disorder with no cure to date, is caused by the generation of amyloid-beta-42 (Aβ42) aggregates that trigger neuronal cell death by unknown mechanism(s). We have developed a transgenic Drosophila eye model where misexpression of human Aβ42 results in AD-like neuropathology in the neural retina. We have identified an apical-basal polarity gene crumbs (crb) as a genetic modifier of Aβ42-mediated-neuropathology. Misexpression of Aβ42 caused upregulation of Crb expression, whereas downregulation of Crb either by RNAi or null allele approach rescued the Aβ42-mediated-neurodegeneration. Co-expression of full length Crb with Aβ42 increased severity of Aβ42-mediated-neurodegeneration, due to three fold induction of cell death in comparison to the wild type. Higher Crb levels affect axonal targeting from the retina to the brain. The structure function analysis identified intracellular domain of Crb to be required for Aβ42-mediated-neurodegeneration. We demonstrate a novel neuroprotective role of Crb in Aβ42-mediated-neurodegeneration.     

Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction (G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations. However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the performance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage disequilibrium (LD) between markers and QTL, the prediction R-squared (R²) of G-BLUP reaches trait-heritability, asymptotically. However, under imperfect LD between markers and QTL, prediction R² based on G-BLUP has a much lower upper bound. We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by (1−b)², where b is the regression of marker-derived genomic relationships on those realized at causal loci. For pairs of related individuals, due to within-family disequilibrium, the patterns of realized genomic similarity are similar across the genome; therefore b is close to one inducing small decrease in R². However, with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R². Our simulations suggest that for the analysis of data from unrelated individuals, the asymptotic upper bound on R² may be of the order of 20% of the trait heritability. We show how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects.