All organisms react to noxious and mechanical stimuli but we still lack a complete understanding of cellular and molecular mechanisms by which somatosensory information is transformed into appropriate motor outputs. The small number of neurons and excellent genetic tools make Drosophila larva an especially tractable model system in which to address this problem. We developed high throughput assays with which we can simultaneously expose more than 1,000 larvae per man-hour to precisely timed noxious heat, vibration, air current, or optogenetic stimuli. Using this hardware in combination with custom software we characterized larval reactions to somatosensory stimuli in far greater detail than possible previously. Each stimulus evoked a distinctive escape strategy that consisted of multiple actions. The escape strategy was context-dependent. Using our system we confirmed that the nociceptive class IV multidendritic neurons were involved in the reactions to noxious heat. Chordotonal (ch) neurons were necessary for normal modulation of head casting, crawling and hunching, in response to mechanical stimuli. Consistent with this we observed increases in calcium transients in response to vibration in ch neurons. Optogenetic activation of ch neurons was sufficient to evoke head casting and crawling. These studies significantly increase our understanding of the functional roles of larval ch neurons. More generally, our system and the detailed description of wild type reactions to somatosensory stimuli provide a basis for systematic identification of neurons and genes underlying these behaviors.
Novel high-throughput sequencing technologies pose new algorithmic challenges in handling massive amounts of short-read, high-coverage data. A robust and versatile consensus tool is of particular interest for such data since a sound multi-read alignment is a prerequisite for variation analyses, accurate genome assemblies and insert sequencing.
With a genome size of approximately 580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.
Access to antiretroviral treatment in resource-limited-settings is inevitably paralleled by the emergence of HIV drug resistance. Monitoring treatment efficacy and HIV drugs resistance testing are therefore of increasing importance in resource-limited settings. Yet low-cost technologies and procedures suited to the particular context and constraints of such settings are still lacking. The ART-A (Affordable Resistance Testing for Africa) consortium brought together public and private partners to address this issue.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.