An integrated modelling framework for neural circuits with multiple neuromodulators

Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours. Specifically, we propose a novel dynamical neural circuit model that integrates the effective neuromodulator-induced currents based on various experimental data (e.g. electrophysiology, neuropharmacology and voltammetry). The model can incorporate multiple interacting brain regions, including neuromodulator sources, simulate efficiently and easily extendable to largescale brain models, e.g. for neuroimaging purposes. As an example, we model a network of mutually interacting neural populations in the lateral hypothalamus, dorsal raphe nucleus and locus coeruleus, which are major sources of neuromodulator orexin/hypocretin, serotonin and norepinephrine/noradrenaline, respectively, and which play significant roles in regulating many physiological functions. We demonstrate that such a model can provide predictions of systemic drug effects of the popular antidepressants (e.g. reuptake inhibitors), neuromodulator antagonists or their combinations. Finally, we developed user-friendly graphical user interface software for model simulation and visualization for both fundamental sciences and pharmacological studies

Prevalence of groundnut dry root rot (Macrophomina phaseolina (Tassi) Goid.) and its pathogenic variability in Southern India

Macrophomina phaseolina is the most devastating and emerging threat to groundnut production in India. An increase in average temperature and inconsistent rainfalls resulting from changing climatic conditions are strongly believed to aggravate the disease and cause severe yield losses. The present study aims to conduct a holistic survey to assess the prevalence and incidence of dry root rot of groundnut in major groundnut growing regions of Southern India, viz., Andhra Pradesh, Telangana, Karnataka, and Tamil Nadu. Furthermore, the pathogenic variability was determined using different assays such as morphological, cultural, pathogenic, and molecular assays. Results indicate that disease incidence in surveyed locations ranged from 8.06 to 20.61%. Both temperature and rainfall played a major role in increasing the disease incidence. The pathogenic variability of M. phaseolina isolates differed significantly, based on the percent disease incidence induced on cultivars of JL-24 groundnut and K-6 groundnut. Morphological variations in terms of growth pattern, culture color, sclerotia number, and sclerotia size were observed. The molecular characterization of M. phaseolina isolates done by ITS rDNA region using ITS1 and ITS4 primers yielded approximately 600 bp PCR amplicons, sequenced and deposited in GenBank (NCBI). Molecular variability analysis using SSR primers indicated the genetic variation among the isolates collected from different states. The present investigation revealed significant variations in pathogenic variability among isolates of M. phaseolina and these may be considered important in disease management and the development of resistant cultivars against groundnut dry root rot disease

Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways

BACKGROUND: Interpreting large-scale studies from microarrays or next-generation sequencing for further experimental testing remains one of the major challenges in quantitative biology. Combining expression with physical or genetic interaction data has already been successfully applied to enhance knowledge from all types of high-throughput studies. Yet, toolboxes for navigating and understanding even small gene or protein networks are poorly developed. RESULTS: We introduce two Cytoscape plug-ins, which support the generation and interpretation of experiment-based interaction networks. The virtual pathway explorer viPEr creates so-called focus networks by joining a list of experimentally determined genes with the interactome of a specific organism. viPEr calculates all paths between two or more user-selected nodes, or explores the neighborhood of a single selected node. Numerical values from expression studies assigned to the nodes serve to score identified paths. The pathway enrichment analysis tool PEANuT annotates networks with pathway information from various sources and calculates enriched pathways between a focus and a background network. Using time series expression data of atorvastatin treated primary hepatocytes from six patients, we demonstrate the handling and applicability of viPEr and PEANuT. Based on our investigations using viPEr and PEANuT, we suggest a role of the FoxA1/A2/A3 transcriptional network in the cellular response to atorvastatin treatment. Moreover, we find an enrichment of metabolic and cancer pathways in the Fox transcriptional network and demonstrate a patient-specific reaction to the drug. CONCLUSIONS: The Cytoscape plug-in viPEr integrates –omics data with interactome data. It supports the interpretation and navigation of large-scale datasets by creating focus networks, facilitating mechanistic predictions from –omics studies. PEANuT provides an up-front method to identify underlying biological principles by calculating enriched pathways in focus networks. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-015-2017-z) contains supplementary material, which is available to authorized users

Prevalence of groundnut dry root rot (Macrophomina phaseolina (Tassi) Goid.) and its pathogenic variability in Southern India

Macrophomina phaseolina is the most devastating and emerging threat to groundnut production in India. An increase in average temperature and inconsistent rainfalls resulting from changing climatic conditions are strongly believed to aggravate the disease and cause severe yield losses. The present study aims to conduct a holistic survey to assess the prevalence and incidence of dry root rot of groundnut in major groundnut growing regions of Southern India, viz., Andhra Pradesh, Telangana, Karnataka, and Tamil Nadu. Furthermore, the pathogenic variability was determined using different assays such as morphological, cultural, pathogenic, and molecular assays. Results indicate that disease incidence in surveyed locations ranged from 8.06 to 20.61%. Both temperature and rainfall played a major role in increasing the disease incidence. The pathogenic variability of M. phaseolina isolates differed significantly, based on the percent disease incidence induced on cultivars of JL-24 groundnut and K-6 groundnut. Morphological variations in terms of growth pattern, culture color, sclerotia number, and sclerotia size were observed. The molecular characterization of M. phaseolina isolates done by ITS rDNA region using ITS1 and ITS4 primers yielded approximately 600 bp PCR amplicons, sequenced and deposited in GenBank (NCBI). Molecular variability analysis using SSR primers indicated the genetic variation among the isolates collected from different states. The present investigation revealed significant variations in pathogenic variability among isolates of M. phaseolina and these may be considered important in disease management and the development of resistant cultivars against groundnut dry root rot disease

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