Nonreciprocal homeostatic compensation in Drosophila potassium channel mutants.

Homeostatic control of intrinsic excitability is important for long-term regulation of neuronal activity. In conjunction with many other forms of plasticity, intrinsic homeostasis helps neurons maintain stable activity regimes in the face of external input variability and destabilizing genetic mutations. In this study, we report a mechanism by which Drosophila melanogaster larval motor neurons stabilize hyperactivity induced by the loss of the delayed rectifying K+ channel Shaker cognate B (Shab), by upregulating the Ca2+-dependent K+ channel encoded by the slowpoke (slo) gene. We also show that loss of SLO does not trigger a reciprocal compensatory upregulation of SHAB, implying that homeostatic signaling pathways utilize compensatory pathways unique to the channel that was mutated. SLO upregulation due to loss of SHAB involves nuclear Ca2+ signaling and dCREB, suggesting that the slo homeostatic response is transcriptionally mediated. Examination of the changes in gene expression induced by these mutations suggests that there is not a generic transcriptional response to increased excitability in motor neurons, but that homeostatic compensations are influenced by the identity of the lost conductance.NEW & NOTEWORTHY The idea that activity-dependent homeostatic plasticity is driven solely by firing has wide credence. In this report we show that homeostatic compensation after loss of an ion channel conductance is tailored to identity of the channel lost, not its properties.

The domain structure and distribution of Alu elements in long noncoding RNAs and mRNAs.

Approximately 75% of the human genome is transcribed and many of these spliced transcripts contain primate-specific Alu elements, the most abundant mobile element in the human genome. The majority of exonized Alu elements are located in long noncoding RNAs (lncRNAs) and the untranslated regions of mRNA, with some performing molecular functions. To further assess the potential for Alu elements to be repurposed as functional RNA domains, we investigated the distribution and evolution of Alu elements in spliced transcripts. Our analysis revealed that Alu elements are underrepresented in mRNAs and lncRNAs, suggesting that most exonized Alu elements arising in the population are rare or deleterious to RNA function. When mRNAs and lncRNAs retain exonized Alu elements, they have a clear preference for Alu dimers, left monomers, and right monomers. mRNAs often acquire Alu elements when their genes are duplicated within Alu-rich regions. In lncRNAs, reverse-oriented Alu elements are significantly enriched and are not restricted to the 3' and 5' ends. Both lncRNAs and mRNAs primarily contain the Alu J and S subfamilies that were amplified relatively early in primate evolution. Alu J subfamilies are typically overrepresented in lncRNAs, whereas the Alu S dimer is overrepresented in mRNAs. The sequences of Alu dimers tend to be constrained in both lncRNAs and mRNAs, whereas the left and right monomers are constrained within particular Alu subfamilies and classes of RNA. Collectively, these findings suggest that Alu-containing RNAs are capable of forming stable structures and that some of these Alu domains might have novel biological functions.

High-resolution positional tracking for long-term analysis of Drosophila sleep and locomotion using the "tracker" program.

Drosophila melanogaster has been used for decades in the study of circadian behavior, and more recently has become a popular model for the study of sleep. The classic method for monitoring fly activity involves counting the number of infrared beam crosses in individual small glass tubes. Incident recording methods such as this can measure gross locomotor activity, but they are unable to provide details about where the fly is located in space and do not detect small movements (i.e. anything less than half the enclosure size), which could lead to an overestimation of sleep and an inaccurate report of the behavior of the fly. This is especially problematic if the fly is awake, but is not moving distances that span the enclosure. Similarly, locomotor deficiencies could be incorrectly classified as sleep phenotypes. To address these issues, we have developed a locomotor tracking technique (the "Tracker" program) that records the exact location of a fly in real time. This allows for the detection of very small movements at any location within the tube. In addition to circadian locomotor activity, we are able to collect other information, such as distance, speed, food proximity, place preference, and multiple additional parameters that relate to sleep structure. Direct comparisons of incident recording and our motion tracking application using wild type and locomotor-deficient (CASK-ß null) flies show that the increased temporal resolution in the data from the Tracker program can greatly affect the interpretation of the state of the fly. This is especially evident when a particular condition or genotype has strong effects on the behavior, and can provide a wealth of information previously unavailable to the investigator. The interaction of sleep with other behaviors can also be assessed directly in many cases with this method.

High-resolution video tracking of locomotion in adult Drosophila melanogaster.

Flies provide an important model for studying complex behavior due to the plethora of genetic tools available to researchers in this field. Studying locomotor behavior in Drosophila melanogaster relies on the ability to be able to quantify changes in motion during or in response to a given task. For this reason, a high-resolution video tracking system, such as the one we describe in this paper, is a valuable tool for measuring locomotion in real-time. Our protocol involves the use of an initial air pulse to break the flies momentum, followed by a thirty second filming period in a square chamber. A tracking program is then used to calculate the instantaneous speed of each fly within the chamber in 10 msec increments. Analysis software then compiles this data, and outputs a variety of parameters such as average speed, max speed, time spent in motion, acceleration, etc. This protocol will discuss proper feeding and management of flies for behavioral tasks, handling flies without anesthetization or immobilization, setting up a controlled environment, and running the assay from start to finish.