HMMER is a software suite for protein sequence similarity searches using probabilistic methods. Previously, HMMER has mainly been available only as a computationally intensive UNIX command-line tool, restricting its use. Recent advances in the software, HMMER3, have resulted in a 100-fold speed gain relative to previous versions. It is now feasible to make efficient profile hidden Markov model (profile HMM) searches via the web. A HMMER web server (http://hmmer.janelia.org) has been designed and implemented such that most protein database searches return within a few seconds. Methods are available for searching either a single protein sequence, multiple protein sequence alignment or profile HMM against a target sequence database, and for searching a protein sequence against Pfam. The web server is designed to cater to a range of different user expertise and accepts batch uploading of multiple queries at once. All search methods are also available as RESTful web services, thereby allowing them to be readily integrated as remotely executed tasks in locally scripted workflows. We have focused on minimizing search times and the ability to rapidly display tabular results, regardless of the number of matches found, developing graphical summaries of the search results to provide quick, intuitive appraisement of them.

The brain generates diverse neuron types which express unique homeodomain transcription factors (TFs) and assemble into precise neural circuits. Yet a mechanistic framework is lacking for how homeodomain TFs specify both neuronal fate and synaptic connectivity. We use Drosophila lamina neurons (L1-L5) to show the homeodomain TF Brain-specific homeobox (Bsh) is initiated in lamina precursor cells (LPCs) where it specifies L4/L5 fate and suppresses homeodomain TF Zfh1 to prevent L1/L3 fate. Subsequently, Bsh activates the homeodomain TF Apterous (Ap) in L4 in a feedforward loop to express the synapse recognition molecule DIP-β, in part by Bsh direct binding a DIP-β intron. Thus, homeodomain TFs function hierarchically: primary homeodomain TF (Bsh) first specifies neuronal fate, and subsequently acts with secondary homeodomain TF (Ap) to activate DIP-β, thereby generating precise synaptic connectivity. We speculate that hierarchical homeodomain TF function may represent a general principle for coordinating neuronal fate specification and circuit assembly.

While we think of neurons as having a fixed identity, many show spectacular plasticity. Metamorphosis drives massive changes in the fly brain; neurons that persist into adulthood often change in response to the steroid hormone ecdysone. Besides driving remodeling, ecdysone signaling can also alter the differentiation status of neurons. The three sequentially born subtypes of mushroom body (MB) Kenyon cells (γ, followed by α'/β', and finally α/β) serve as a model of temporal fating. γ neurons are also used as a model of remodeling during metamorphosis. As γ neurons are the only functional Kenyon cells in the larval brain, they serve the function of all three adult subtypes. Correspondingly, larval γ neurons have a similar morphology to α'/β' and α/β neurons-their axons project dorsally and medially. During metamorphosis, γ neurons remodel to form a single medial projection. Both temporal fate changes and defects in remodeling therefore alter γ-neuron morphology in similar ways. Mamo, a broad-complex, tramtrack, and bric-à-brac/poxvirus and zinc finger (BTB/POZ) transcription factor critical for temporal specification of α'/β' neurons, was recently described as essential for γ remodeling. In a previous study, we noticed a change in the number of adult Kenyon cells expressing γ-specific markers when mamo was manipulated. These data implied a role for Mamo in γ-neuron fate specification, yet mamo is not expressed in γ neurons until pupariation, well past γ specification. This indicates that mamo has a later role in ensuring that γ neurons express the correct Kenyon cell subtype-specific genes in the adult brain.

HortaCloud is a cloud-based, open-source platform designed to facilitate the collaborative reconstruction of long-range projection neurons from whole-brain light microscopy data. By providing virtual environments directly within the cloud, it eliminates the need for costly and time-consuming data downloads, allowing researchers to work efficiently with terabyte- scale volumetric datasets. Standardization of computational resources in the cloud make deployment easier and more predictable. The pay-as-you-go cloud model reduces adoption barriers by eliminating upfront investments in expensive hardware. Finally, HortaCloud’s decentralized architecture enables global collaboration between researchers and between institutions.

Most mammalian cells prevent viral infection and proliferation by expressing various restriction factors and sensors that activate the immune system. While anti-human immunodeficiency virus type 1 (HIV-1) host restriction factors have been identified, most of them are antagonized by viral proteins. This has severely hindered their development in anti-HIV-1 therapy. Here, we describe CCHC-type zinc-finger-containing protein 3 (ZCCHC3) as a novel anti-HIV-1 factor that is not antagonized by viral proteins. ZCCHC3 suppresses production of HIV-1 and other retroviruses. We show that ZCCHC3 acts by binding to Gag nucleocapsid protein via zinc-finger motifs. This prevents interaction between the Gag nucleocapsid protein and viral genome and results in production of genome-deficient virions. ZCCHC3 also binds to the long terminal repeat on the viral genome via the middle-folded domain, sequestering the viral genome to P-bodies, which leads to decreased viral replication and production. Such a dual antiviral mechanism is distinct from that of any other known host restriction factors. Therefore, ZCCHC3 is a novel potential target in anti-HIV-1 therapy.

Machine learning models are only as good as the data to which they are fit. As such, it is always preferable to use as much data as possible in training models. What data can be used for fitting a model depends a lot on the formulation of the task. We introduce Hot-Distance, a novel segmentation target that incorporates the strength of signed boundary distance prediction with the flexibility of one-hot encoding, to increase the amount of usable training data for segmentation of subcellular structures in focused ion beam scanning electron microscopy (FIB-SEM).

In insects juvenile hormone (JH) regulates both metamorphosis and reproduction. This lecture focuses on our current understanding of JH action at the molecular level in both of these processes based primarily on studies in the tobacco hornworm Manduca sexta, the flour beetle Tribolium castaneum, the mosquito Aedes aegypti, and the fruit fly Drosophila melanogaster. The roles of the JH receptor complex and the transcription factors that it regulates during larval molting and metamorphosis are summarized. Also highlighted are the intriguing interactions of the JH and insulin signaling pathways in both imaginal disc development and vitellogenesis. Critical actions of JH and its receptor in the timing of maturation of the adult optic lobe and of female receptivity in Drosophila are also discussed.

The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the population’s mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of an evolving population subject to a standard serial dilution protocol. We find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but has a negligible effect when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape.