Hawkmoth and bee pollinators impact pollen dispersal at the landscape but not local scales in two species of Oenothera

Premise

Animal pollinators play an important role in pollen dispersal. Here, we assessed differences in pollen and seed dispersal and the role of pollinator functional groups with different foraging behaviors in generating patterns of genetic diversity over similar geographic ranges for two closely related taxa. We focused on two members of Oenothera section Calylophus (Onagraceae) that co-occur on gypsum outcrops throughout the northern Chihuahuan Desert but differ in floral phenotype and primary pollinator: Oenothera gayleana (bee) and O. hartwegii subsp. filifolia (hawkmoth).

Methods

We measured breeding system and floral traits and studied gene flow and population differentiation at the local (<13 km; four populations) and landscape (60–440 km; five populations) scales using 10–11 nuclear (pollen dispersal) and three plastid (seed dispersal) microsatellite markers.

Results

Both taxa were self-incompatible and floral traits were consistent with expectations for different pollinators. Seed and pollen dispersal patterns were distinctly different for both species. We found no evidence of genetic structure at the local scale but did at the landscape scale; O. gayleana showed greater differentiation and significant isolation by distance than in O. hartwegii subsp. filifolia. The plastid data were consistent with gravity dispersal of seeds and suggest that pollen dispersal is the principal driver of genetic structure in both species.

Conclusions

We demonstrated that pollinator functional groups can impact genetic differentiation in different and predictable ways. Hawkmoths, with larger foraging distances, can maintain gene flow across greater spatial scales than bees.

     

Metabolomics of urinary organic acids in respiratory chain deficiencies in children

Metabolomic analysis of the urinary organic acids from 39 selected children with defined respiratory chain deficiencies (RCDs) was performed using untargeted gas chromatography–mass spectrometry, revealing the presence of 255 endogenous and 46 exogenous substances. Variable reduction identified 92 variables from the endogenous substances, which could be analysed by univariate and multivariate statistical methods. Using these methods, no characteristic organic acid biomarker profile could be defined of practical value for diagnostic purposes for complex I (CI), complex III (CIII) and multiple complex (CM) deficiencies. The statistical procedures used did, however, disclose 24 metabolites that were practical highly (d > 0.75) and statistically (P < 0.05) significant for the combined and clinically closely related group of RCDs. Several of these metabolites occur in single enzyme inherited metabolic diseases, but most were not previously reported to be linked to the metabolic perturbations that are due to RCDs. Ultimately, we constructed a global metabolic profile of carbohydrate, amino acid and fatty acid catabolism, illuminating the diverse and complex biochemical consequences of these disorders. This metabolomics investigation disclosed a metabolite profile that has the potential to define an extended and characteristic biosignature for RCDs and the development of a non-invasive screening procedure for these disorders.     

Selection strategies for linkage studies using twins.

Genetic linkage analysis for complex diseases offers a major challenge to geneticists. In these complex diseases multiple genetic loci are responsible for the disease and they may vary in the size of their contribution; the effect of any single one of them is likely to be small. In many situations, like in extensive twin registries, trait values have been recorded for a large number of individuals, and preliminary studies have revealed summary measures for those traits, like mean, variance and components of variance, including heritability. Given the small effect size, a random sample of twins will require a prohibitively large sample size. It is well known that selective sampling is far more efficient in terms of genotyping effort. In this paper we derive easy expressions for the information contributed by sib pairs for the detection of linkage to a quantitative trait locus (QTL). We consider random samples as well as samples of sib pairs selected on the basis of their trait values. These expressions can be rapidly computed and do not involve simulation. We extend our results for quantitative traits to dichotomous traits using the concept of a liability threshold model. We present tables with required sample sizes for height, insulin levels and migraine, three of the traits studied in the GenomEUtwin project.     

Bioluminescence-Based Tumor Quantification Method for Monitoring Tumor Progression and Treatment Effects in Mouse Lymphoma Models

Although bioluminescence imaging (BLI) shows promise for monitoring tumor burden in animal models of cancer, these analyses remain mostly qualitative. Here we describe a method for bioluminescence imaging to obtain a semi-quantitative analysis of tumor burden and treatment response. This method is based on the calculation of a luminoscore, a value that allows comparisons of two animals from the same or different experiments. Current BLI instruments enable the calculation of this luminoscore, which relies mainly on the acquisition conditions (back and front acquisitions) and the drawing of the region of interest (manual markup around the mouse). Using two previously described mouse lymphoma models based on cell engraftment, we show that the luminoscore method can serve as a noninvasive way to verify successful tumor cell inoculation, monitor tumor burden, and evaluate the effects of in situ cancer treatment (CpG-DNA). Finally, we show that this method suits different experimental designs. We suggest that this method be used for early estimates of treatment response in preclinical small-animal studies.     

Quantifying the Diversification of Hepatitis C Virus (HCV) during Primary Infection: Estimates of the In Vivo Mutation Rate

Hepatitis C virus (HCV) is present in the host with multiple variants generated by its error prone RNA-dependent RNA polymerase. Little is known about the initial viral diversification and the viral life cycle processes that influence diversity. We studied the diversification of HCV during acute infection in 17 plasma donors, with frequent sampling early in infection. To analyze these data, we developed a new stochastic model of the HCV life cycle. We found that the accumulation of mutations is surprisingly slow: at 30 days, the viral population on average is still 46% identical to its transmitted viral genome. Fitting the model to the sequence data, we estimate the median in vivo viral mutation rate is 2.5×10⁻⁵ mutations per nucleotide per genome replication (range 1.6–6.2×10⁻⁵), about 5-fold lower than previous estimates. To confirm these results we analyzed the frequency of stop codons (N = 10) among all possible non-sense mutation targets (M = 898,335), and found a mutation rate of 2.8–3.2×10⁻⁵, consistent with the estimate from the dynamical model. The slow accumulation of mutations is consistent with slow turnover of infected cells and replication complexes within infected cells. This slow turnover is also inferred from the viral load kinetics. Our estimated mutation rate, which is similar to that of other RNA viruses (e.g., HIV and influenza), is also compatible with the accumulation of substitutions seen in HCV at the population level. Our model identifies the relevant processes (long-lived cells and slow turnover of replication complexes) and parameters involved in determining the rate of HCV diversification.     

Variation in overall fitness due to seed source: projections for predictive provenancing

Seed sourcing decisions affect short- and long-term restoration outcomes. Seeds sourced closer to restoration sites are likely to be better adapted to local conditions and therefore may perform better than those sourced farther away, following assumptions of local adaptation. However, plants may not be adapted to future conditions under climate change; hence, managers are considering a predictive provenancing approach, where plant materials adapted to predicted conditions are used at a site. Currently, there is little empirical evidence available to inform this approach. To address this, we evaluate predictive provenancing using three species of forbs used in tallgrass prairie restorations (Allium cernuum, Chamaecrista fasciculata, and Rudbeckia hirta) in a common garden experiment in northeastern Illinois, U.S.A. We compared the fitness in plants sourced from three regional zones across a latitudinal gradient that represents different climate projections, relative to the planting site. Data were analyzed using Aster life-history models and generalized linear models. We found that source affected overall fitness in all three species, but no climate proxy had the highest fitness across all species. The performance at each life stage had different effects on overall fitness, which varied by source. We observed later reproductive phenology in southern-sourced plants for all three species, possibly due to adaptation to longer growing seasons. The mixed results of this study suggest that climate proxy alone would not be sufficient to determine an effective and accurate predictive provenancing strategy. Long-term tests are needed to pursue such a strategy for high-priority species.