Activation of GABAA receptors modulates all stages of mechanoreception in spider mechanosensory neurons

The effects of SLICE®- and ivermectin-contaminated sediment on avoidance behaviour and oxygen consumption in marine benthic invertebrates

Pest management strategies to reduce sea lice infestations in the salmon aquaculture industry include in-feed treatments with ivermectin (IVM) and SLICE® (active ingredient [AI] emamectin benzoate [EMB]), which can result in local contamination of the environment. These compounds partition to sediments, have moderate persistence, and may pose a risk to non-target benthic organisms. The sub-lethal effects of EMB, IVM and a combination of both (EMB/IVM) on the benthic amphipod Eohaustorius estuarius and polychaete Nereis virens at environmentally relevant sediment concentrations were examined in subchronic exposures (28-30-d). E. estuarius avoided sediment containing >50 μg/kg IVM alone and in combination with EMB. N. virens avoided sediment with >50 μg/kg IVM and >0.5 μg/kg EMB/IVM and exhibited impaired burrowing and locomotory behaviour with both treatments. Oxygen consumption was significantly decreased in E. estuarius (up to 50% compared to controls) and increased in N. virens (by ∼ 200%) when exposed to EMB, IVM and EMB/IVM at concentrations <5 μg/kg. IVM, SLICE® and combination exposures at environmentally relevant concentrations caused adverse effects in E. estuarius and N. virens which could significantly alter organism fitness near salmon aquaculture operations.

Tyrosine hydroxylase immunolabeling reveals the distribution of catecholaminergic neurons in the central nervous systems of the spiders Hogna lenta (Araneae: Lycosidae) and Phidippus regius (Araneae: Salticidae)

With over 48,000 species currently described, spiders (Arthropoda: Chelicerata: Araneae) comprise one of the most diverse groups of animals on our planet, and exhibit an equally wide array of fascinating behaviors. Studies of central nervous systems (CNSs) in spiders, however, are relatively sparse, and no reports have yet characterized catecholaminergic (dopamine [DA]- or norepinephrine-synthesizing) neurons in any spider species. Because these neuromodulators are especially important for sensory and motor processing across animal taxa, we embarked on a study to identify catecholaminergic neurons in the CNS of the wolf spider Hogna lenta (Lycosidae) and the jumping spider Phidippus regius (Salticidae). These neurons were most effectively labeled with an antiserum raised against tyrosine hydroxylase (TH), the rate-limiting enzyme in catecholamine synthesis. We found extensive catecholamine-rich neuronal fibers in the first- and second-order optic neuropils of the supraesophageal mass (brain), as well as in the arcuate body, a region of the brain thought to receive visual input and which may be involved in higher order sensorimotor integration. This structure likely shares evolutionary origins with the DA-enriched central complex of the Mandibulata. In the subesophageal mass, we detected an extensive filigree of TH-immunoreactive (TH-ir) arborizations in the appendage neuromeres, as well as three prominent plurisegmental fiber tracts. A vast abundance of TH-ir somata were located in the opisthosomal neuromeres, the largest of which appeared to project to the brain and decorate the appendage neuromeres. Our study underscores the important roles that the catecholamines likely play in modulating spider vision, higher order sensorimotor processing, and motor patterning.

Static and Dynamic Adaptation of Insect Photoreceptor Responses to Naturalistic Stimuli

We describe a new nonlinear dynamic model of insect phototransduction using a NLN (nonlinear, linear, nonlinear) block structure. The first nonlinear stage provides a single exponential decline in gain and mean following the start of light stimulation. The linear stage uses a two-parameter log-normal convolution model previously applied alone to insect photoreceptors. The final stage is a static quadratic function. The model fitted current and voltage responses of isolated single photoreceptors from three different insect species with reasonable fidelity when they were stimulated by naturalistic time series having wide bandwidth and contrast, over a light intensity range of >1:10⁴. Mean squared error values for receptor current and receptor potential varied over ~2–60%, with many values below 10%. Linear log-normal filter parameters did not vary strongly with species or light intensity. Initial gain reduction was only large for the highest light levels, while the time constant of gain and mean reduction decreased with light intensity. The final nonlinearity changed from positively to negatively quadratic with increasing light intensity, indicating a change from threshold, or expansion to saturating compression with greater signal strength. Photoreceptor information transmission was estimated by linear information capacity and signal entropy measurements of both experimental data and predicted outputs of the model for identical stimuli at each light level. Comparison of actual and predicted data indicated significant added noise during phototransduction, with information being progressively lost by nonlinear behavior with increasing light intensity.

Naturalistic stimulation changes the dynamic response of action potential encoding in a mechanoreceptor

Naturalistic signals were created from vibrations made by locusts walking on a Sansevieria plant. Both naturalistic and Gaussian noise signals were used to mechanically stimulate VS-3 slit-sense mechanoreceptor neurons of the spider, Cupiennius salei, with stimulus amplitudes adjusted to give similar firing rates for either stimulus. Intracellular microelectrodes recorded action potentials, receptor potential, and receptor current, using current clamp and voltage clamp. Frequency response analysis showed that naturalistic stimulation contained relatively more power at low frequencies, and caused increased neuronal sensitivity to higher frequencies. In contrast, varying the amplitude of Gaussian stimulation did not change neuronal dynamics. Naturalistic stimulation contained less entropy than Gaussian, but signal entropy was higher than stimulus in the resultant receptor current, indicating addition of uncorrelated noise during transduction. The presence of added noise was supported by measuring linear information capacity in the receptor current. Total entropy and information capacity in action potentials produced by either stimulus were much lower than in earlier stages, and limited to the maximum entropy of binary signals. We conclude that the dynamics of action potential encoding in VS-3 neurons are sensitive to the form of stimulation, but entropy and information capacity of action potentials are limited by firing rate.

Low doses of ivermectin cause sensory and locomotor disorders in dung beetles

Ivermectin is a veterinary pharmaceutical generally used to control the ecto- and endoparasites of livestock, but its use has resulted in adverse effects on coprophilous insects, causing population decline and biodiversity loss. There is currently no information regarding the direct effects of ivermectin on dung beetle physiology and behaviour. Here, based on electroantennography and spontaneous muscle force tests, we show sub-lethal disorders caused by ivermectin in sensory and locomotor systems of Scarabaeus cicatricosus, a key dung beetle species in Mediterranean ecosystems. Our findings show that ivermectin decreases the olfactory and locomotor capacity of dung beetles, preventing them from performing basic biological activities. These effects are observed at concentrations lower than those usually measured in the dung of treated livestock. Taking into account that ivermectin acts on both glutamate-gated and GABA-gated chloride ion channels of nerve and muscle cells, we predict that ivermectin’s effects at the physiological level could influence many members of the dung pat community. The results indicate that the decline of dung beetle populations could be related to the harmful effects of chemical contamination in the dung.

Transcriptome walking: a laboratory-oriented GUI-based approach to mRNA identification from deep-sequenced data

Background

Deep sequencing technology provides efficient and economical production of large numbers of randomly positioned, relatively short, estimates of base identities in DNA molecules. Application of this technology to mRNA samples allows rapid examination of the molecular genetic environment in individual cells or tissues, the transcriptome. However, assembly of such short sequences into complete mRNA creates a challenge that limits the usefulness of the technology, particularly when no, or limited, genomic data is available. Several approaches to this problem have been developed, but there is still no general method to rapidly obtain an mRNA sequence from deep sequence data when a specific molecule, or family of molecules, are of interest. A frequent requirement is to identify specific mRNA molecules from tissues that are being investigated by methods such as electrophysiology, immunocytology and pharmacology. To be widely useful, any approach must be relatively simple to use in the laboratory by operators without extensive statistical or bioinformatics knowledge, and with readily available hardware.

Findings

An approach was developed that allows de novo assembly of individual mRNA sequences in two linked stages: sequence discovery and sequence completion. Both stages rely on computer assisted, Graphical User Interface (GUI)-guided, user interaction with the data, but proceed relatively efficiently once discovery is complete. The method grows a discovered sequence by repeated passes through the complete raw data in a series of steps, and is hence termed ‘transcriptome walking’. All of the operations required for transcriptome analysis are combined in one program that presents a relatively simple user interface and runs on a standard desktop, or laptop computer, but takes advantage of multi-core processors, when available. Complete mRNA sequence identifications usually require less than 24 hours. This approach has already identified previously unknown mRNA sequences in two animal species that currently lack any significant genome or transcriptome data.

Conclusions

As deep sequencing data becomes more widely available, accessible methods for extracting useful sequence information in the biological or medical laboratory will be of increasing importance. The approach described here does not rely on detailed knowledge of bioinformatic algorithms, and allows users with basic knowledge of molecular biology and standard laboratory computing equipment, but limited software or bioinformatics experience, to extract complete gene sequences from deep-sequencing data.