Impact of Apple Rust Mite (Acari: Eriophyiidae) Feeding on Apple Leaf Gas Exchange and Leaf Color Associated with Changes in Leaf Tissue

The impact of the apple rust mite, Aculus schlechtendali (Nalepa), on net CO2 exchange, transpiration rate, and leaf color of field-grown Jonagold and Golden Delicious apples was investigated. Apple rust mite feeding causes leaf browning. Changes in leaf color were measured to assess the cumulative leaf damage. Significant negative relationships were found between cumulative leaf damage and single-leaf net CO2 exchange as well as transpiration rate. The same trends were observed on both varieties, but the effect of apple rust mite feeding was more severe on Jonagold than on Golden Delicious. Leaf tissue injury was analyzed by cryoscanning electron microscopy and light microscopy. The pictures show that apple rust mites penetrate epidermal cells with their stylets, causing multiple puncture wounds. On heavily infested leaves, apple rust mite feeding causes desiccation of the epidermis and the spongy parenchyma. The resulting malfunction of the stomata and problems in gas exchange within the spongy parenchyma are likely to be the main reason for the reduction of gas exchang

Schädlingsprognose mit SOPRA als Entscheidungshilfe im Bioobstbau

The forecasting tool SOPRA has been developed with the objective to optimize timing of monitoring and control measures of insect pests in fruit orchards. Phenologymodels were developed driven by solar radiation, air temperature and soil temperature on hourly basis including simulation of characteristic habitat conditions for Dysaphis plantaginea, Hoplocampa testudinea, Cydia pomonella, Grapholita lobarzewskii, Cacopsylla pyri, Rhagoletis cerasi, Anthonomus pomorum and Adoxophyes orana. On base of local weather data, age structure of pest populations is simulated and crucial events for management in organic and integrated production are announced. Through a web-interface, the simulation results are made available to consultants and growers (www.sopra.info). As timing of monitoring and management are especially important in organic growing, SOPRA is applied as decision support system (DSS) for major insect pests of organic fruit orchards in Switzerland

Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models

<p>Abstract</p> <p>Background</p> <p>Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH) and its receptor type 1 (CRHR1) are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established <it>in vitro </it>model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1-dependent signaling networks, we combined supervised and unsupervised algorithms.</p> <p>Results</p> <p>We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD) discriminant function within GALGO, an R package based on a genetic algorithm (GA), was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size) in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic algorithm.</p> <p>With two unsupervised algorithms, principal component analysis and graphical Gaussian models, putative interactions of the candidate genes were determined and reconstructed by literature mining. Differential regulation of six candidate genes was validated by qRT-PCR.</p> <p>Conclusions</p> <p>The combination of supervised and unsupervised algorithms in this study allowed extracting a small subset of meaningful candidate genes from the genome-wide expression data set. Thereby, variable selection using different optimization algorithms based on linear classifiers as well as the nonlinear random forest method resulted in congruent candidate genes. The calculated interacting network connecting these new target genes was bioinformatically mapped to known CRHR1-dependent signaling pathways. Additionally, the differential expression of the identified target genes was confirmed experimentally.</p

A Hypomorphic Vasopressin Allele Prevents Anxiety-Related Behavior

To investigate neurobiological correlates of trait anxiety, CD1 mice were selectively bred for extremes in anxiety-related behavior, with high (HAB) and low (LAB) anxiety-related behavior mice additionally differing in behavioral tests reflecting depression-like behavior. promoter deletion to anxiety-related behavior. gene promoter explains gene expression differences in association with the observed phenotype, thus further strengthening the concept of the critical involvement of centrally released AVP in trait anxiety

A hypomorphic vasopressin allele prevents anxiety-related behavior

In this study, microarray analysis, in situ hybridization, quantitative real-time PCR and immunohistochemistry revealed decreased expression of the vasopressin gene (Avp) in the hypothalamic paraventricular (PVN) and supraoptic (SON) nuclei of adult LAB mice compared to HAB, NAB (normal anxiety-related behavior) and HABxLAB F1 intercross controls, without detecting differences in receptor expression or density. By sequencing the regions 2.5 kbp up- and downstream of the Avp gene locus, we could identify several polymorphic loci, differing between the HAB and LAB lines. In the gene promoter, a deletion of twelve bp Δ(−2180–2191) is particularly likely to contribute to the reduced Avp expression detected in LAB animals under basal conditions. Indeed, allele-specific transcription analysis of F1 animals revealed a hypomorphic LAB-specific Avp allele with a reduced transcription rate by 75% compared to the HAB-specific allele, thus explaining line-specific Avp expression profiles and phenotypic features. Accordingly, intra-PVN Avp mRNA levels were found to correlate with anxiety-related and depression-like behaviors. In addition to this correlative evidence, a significant, though moderate, genotype/phenotype association was demonstrated in 258 male mice of a freely-segregating F2 panel, suggesting a causal contribution of the Avp promoter deletion to anxiety-related behavior

Profiling Trait Anxiety: Transcriptome Analysis Reveals Cathepsin B (Ctsb) as a Novel Candidate Gene for Emotionality in Mice

Behavioral endophenotypes are determined by a multitude of counteracting but precisely balanced molecular and physiological mechanisms. In this study, we aim to identify potential novel molecular targets that contribute to the multigenic trait “anxiety”. We used microarrays to investigate the gene expression profiles of different brain regions within the limbic system of mice which were selectively bred for either high (HAB) or low (LAB) anxiety-related behavior, and also show signs of comorbid depression-like behavior