BACKGROUND: In a series of landmark papers, Kyriacou, Hall, and colleagues reported that the average inter-pulse interval of Drosophila melanogaster male courtship song varies rhythmically (KH cycles), that the period gene controls this rhythm, and that evolution of the period gene determines species differences in the rhythm's frequency. Several groups failed to recover KH cycles, but this may have resulted from differences in recording chamber size.

RESULTS: Here, using recording chambers of the same dimensions as used by Kyriacou and Hall, I found no compelling evidence for KH cycles at any frequency. By replicating the data analysis procedures employed by Kyriacou and Hall, I found that two factors--data binned into 10-second intervals and short recordings--imposed non-significant periodicity in the frequency range reported for KH cycles. Randomized data showed similar patterns.

CONCLUSIONS: All of the results related to KH cycles are likely to be artifacts of binning data from short songs. Reported genotypic differences in KH cycles cannot be explained by this artifact and may have resulted from the use of small sample sizes and/or from the exclusion of samples that did not exhibit song rhythms.

In cap-dependent translation initiation, the open reading frame (ORF) of mRNA is established by the placement of the AUG start codon and initiator tRNA in the ribosomal peptidyl (P) site. Internal ribosome entry sites (IRESs) promote translation of mRNAs in a cap-independent manner. We report two structures of the ribosome-bound Taura syndrome virus (TSV) IRES belonging to the family of Dicistroviridae intergenic IRESs. Intersubunit rotational states differ in these structures, suggesting that ribosome dynamics play a role in IRES translocation. Pseudoknot I of the IRES occupies the ribosomal decoding center at the aminoacyl (A) site in a manner resembling that of the tRNA anticodon-mRNA codon. The structures reveal that the TSV IRES initiates translation by a previously unseen mechanism, which is conceptually distinct from initiator tRNA-dependent mechanisms. Specifically, the ORF of the IRES-driven mRNA is established by the placement of the preceding tRNA-mRNA-like structure in the A site, whereas the 40S P site remains unoccupied during this initial step.

The patch-clamp technique and the whole-cell measurements derived from it have greatly advanced our understanding of the coding properties of individual neurons by allowing for a detailed analysis of their excitatory/inhibitory synaptic inputs, intrinsic electrical properties, and morphology. Because such measurements require a high level of mechanical stability they have for a long time been limited to in vitro and anesthetized preparations. Recently, however, a considerable amount of effort has been devoted to extending these techniques to awake restrained/head-fixed preparations allowing for the study of the input-output functions of neurons during behavior. In this chapter we describe a technique extending patch-clamp recordings to awake animals free to explore their environments.

Modern biological research relies heavily on microscopic imaging. The advanced genetic toolkit of Drosophila makes it possible to label molecular and cellular components with unprecedented level of specificity necessitating the application of the most sophisticated imaging technologies. Imaging in Drosophila spans all scales from single molecules to the entire populations of adult organisms, from electron microscopy to live imaging of developmental processes. As the imaging approaches become more complex and ambitious, there is an increasing need for quantitative, computer-mediated image processing and analysis to make sense of the imagery. Bioimage Informatics is an emerging research field that covers all aspects of biological image analysis from data handling, through processing, to quantitative measurements, analysis and data presentation. Some of the most advanced, large scale projects, combining cutting edge imaging with complex bioimage informatics pipelines, are realized in the Drosophila research community. In this review, we discuss the current research in biological image analysis specifically relevant to the type of systems level image datasets that are uniquely available for the Drosophila model system. We focus on how state-of-the-art computer vision algorithms are impacting the ability of Drosophila researchers to analyze biological systems in space and time. We pay particular attention to how these algorithmic advances from computer science are made usable to practicing biologists through open source platforms and how biologists can themselves participate in their further development.

Gene regulation relies on transcription factors (TFs) exploring the nucleus searching their targets. So far, most studies have focused on how fast TFs diffuse, underestimating the role of nuclear architecture. We implemented a single-molecule tracking assay to determine TFs dynamics. We found that c-Myc is a global explorer of the nucleus. In contrast, the positive transcription elongation factor P-TEFb is a local explorer that oversamples its environment. Consequently, each c-Myc molecule is equally available for all nuclear sites while P-TEFb reaches its targets in a position-dependent manner. Our observations are consistent with a model in which the exploration geometry of TFs is restrained by their interactions with nuclear structures and not by exclusion. The geometry-controlled kinetics of TFs target-search illustrates the influence of nuclear architecture on gene regulation, and has strong implications on how proteins react in the nucleus and how their function can be regulated in space and time.

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (<20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

This paper proposes a novel agglomerative framework for Electron Microscopy (EM) image (or volume) segmentation. For the overall segmentation methodology, we propose a context-aware algorithm that clusters the over-segmented regions of different sub-classes (representing different biological entities) in different stages. Furthermore, a delayed scheme for agglomerative clustering, which postpones the merge of newly formed bodies, is also proposed to generate a more confident boundary prediction. We report significant improvements in both segmentation accuracy and speed attained by the proposed approaches over existing standard methods on both 2D and 3D datasets.

The self-assembly of proteins into highly ordered nanoscale architectures is a hallmark of biological systems. The sophisticated functions of these molecular machines have inspired the development of methods to engineer self-assembling protein nanostructures; however, the design of multi-component protein nanomaterials with high accuracy remains an outstanding challenge. Here we report a computational method for designing protein nanomaterials in which multiple copies of two distinct subunits co-assemble into a specific architecture. We use the method to design five 24-subunit cage-like protein nanomaterials in two distinct symmetric architectures and experimentally demonstrate that their structures are in close agreement with the computational design models. The accuracy of the method and the number and variety of two-component materials that it makes accessible suggest a route to the construction of functional protein nanomaterials tailored to specific applications.

Acetylation of α-tubulin Lys40 by tubulin acetyltransferase (TAT) is the only known posttranslational modification in the microtubule lumen. It marks stable microtubules and is required for polarity establishment and directional migration. Here, we elucidate the mechanistic underpinnings for TAT activity and its preference for microtubules with slow turnover. 1.35 Å TAT cocrystal structures with bisubstrate analogs constrain TAT action to the microtubule lumen and reveal Lys40 engaged in a suboptimal active site. Assays with diverse tubulin polymers show that TAT is stimulated by microtubule interprotofilament contacts. Unexpectedly, despite the confined intraluminal location of Lys40, TAT efficiently scans the microtubule bidirectionally and acetylates stochastically without preference for ends. First-principles modeling and single-molecule measurements demonstrate that TAT catalytic activity, not constrained luminal diffusion, is rate limiting for acetylation. Thus, because of its preference for microtubules over free tubulin and its modest catalytic rate, TAT can function as a slow clock for microtubule lifetimes.

A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.