Functional role of phenylacetic acid from metapleural gland secretions in controlling fungal pathogens in evolutionarily derived leaf-cutting ants

Fungus-farming ant colonies vary four to five orders of magnitude in size. They employ compounds from actinomycete bacteria and exocrine glands as antimicrobial agents. Atta colonies have millions of ants and are particularly relevant for understanding hygienic strategies as they have abandoned their ancestors'' prime dependence on antibiotic-based biological control in favour of using metapleural gland (MG) chemical secretions. Atta MGs are unique in synthesizing large quantities of phenylacetic acid (PAA), a known but little investigated antimicrobial agent. We show that particularly the smallest workers greatly reduce germination rates of Escovopsis and Metarhizium spores after actively applying PAA to experimental infection targets in garden fragments and transferring the spores to the ants'' infrabuccal cavities. In vitro assays further indicated that Escovopsis strains isolated from evolutionarily derived leaf-cutting ants are less sensitive to PAA than strains from phylogenetically more basal fungus-farming ants, consistent with the dynamics of an evolutionary arms race between virulence and control for Escovopsis, but not Metarhizium. Atta ants form larger colonies with more extreme caste differentiation relative to other attines, in societies characterized by an almost complete absence of reproductive conflicts. We hypothesize that these changes are associated with unique evolutionary innovations in chemical pest management that appear robust against selection pressure for resistance by specialized mycopathogens.     

Effects of Genetic and Environmental Factors on Trait Network Predictions From Quantitative Trait Locus Data

The use of high-throughput genomic techniques to map gene expression quantitative trait loci has spurred the development of path analysis approaches for predicting functional networks linking genes and natural trait variation. The goal of this study was to test whether potentially confounding factors, including effects of common environment and genes not included in path models, affect predictions of cause–effect relationships among traits generated by QTL path analyses. Structural equation modeling (SEM) was used to test simple QTL-trait networks under different regulatory scenarios involving direct and indirect effects. SEM identified the correct models under simple scenarios, but when common-environment effects were simulated in conjunction with direct QTL effects on traits, they were poorly distinguished from indirect effects, leading to false support for indirect models. Application of SEM to loblolly pine QTL data provided support for biologically plausible a priori hypotheses of QTL mechanisms affecting height and diameter growth. However, some biologically implausible models were also well supported. The results emphasize the need to include any available functional information, including predictions for genetic and environmental correlations, to develop plausible models if biologically useful trait network predictions are to be made.     

Systematics within Gyps vultures: a clade at risk

Background  

Populations of the Oriental White-backed Vulture (Gyps bengalensis) have declined by over 95% within the past decade. This decline is largely due to incidental consumption of the non-steroidal anti-inflammatory veterinary pharmaceutical diclofenac, commonly used to treat domestic livestock. The conservation status of other Gyps vultures in southern Asia is also of immediate concern, given the lack of knowledge regarding status of their populations and the continuing existence of taxonomic uncertainties. In this study, we assess phylogenetic relationships for all recognized species and the majority of subspecies within the genus Gyps. The continuing veterinary use of diclofenac is an unknown but potential risk to related species with similar feeding habits to Gyps bengalensis. Therefore, an accurate assessment of the phylogenetic relationships among Gyps vultures should aid in their conservation by clarifying taxonomic uncertainties, and enabling inference of their respective relatedness to susceptible G. bengalensis.     

Phenotype Classification of Zebrafish Embryos by Supervised Learning

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.