Olfaction in mosquitoes

Female mosquitoes are vectors of diseases, affecting both livestock and humans. The host-seeking and identification behaviors of mosquitoes are mediated mainly by olfactory cues. The peripheral olfactory organs of mosquitoes which perceive olfactory cues are the antennae and maxillary palps. These appendages bear numerous hair shaped structures, sensilla, in which olfactory receptor neurons (ORNs) are housed. The ORNs detect and discriminate various odorant molecules and send information regarding odor quality, quantity and spatio-temporal patterns to the central olfactory system in the brain for further analysis. The first goal of this study was to investigate the neuroanatomy of the mosquito central olfactory system. Using different staining techniques, the neuronal architecture of the deutocerebrum as well as 3D reconstructions of antennal lobe (AL) glomeruli were depicted for both sexes of the Afrcian malaria mosquito, Anopheles gambiae and the yellow fever mosquito, Aedes aegypti. To study how mosquitoes detect olfactory cues, single sensillum recordings (SSRs) were performed, which allowed me to investigate electrophysiological properties of individual ORNs housed in four morphological types of the most abundant olfactory sensilla, s. trichodea. I was able to identify 11 functional types which their ORNs displayed distinct responses to a set of compounds. As part of this study, axons of functionally defined ORNs were traced by neurobiotin to indicate which glomeruli they targeted. This resulted in a functional map of AL glomeruli. The map indicated that different functional types of ORNs converged onto different spatially fixed glomeruli. My next step was to identify novel biologically active compounds for the ORNs using gas chromatography coupled SSRs (GC-SSRs). Headspace odors from different human body parts, i.e. armpit, feet and trunk regions as well as from a plant used as a mosquito repellent (Nepeta faassenii) were collected, extracted and eventually injected onto the GC-column. I found that some of the extract components elicited responses in previously defined ORNs as well as in ORNs of the intermediate sensilla. Some of the compounds, which were subsequently identified by using GC-mass spectrometry (GC-MS) were heptanal, octanal, nonanal and decanal

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Allo-network drugs: Extension of the allosteric drug concept to protein-protein interaction and signaling networks

Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design