Sequence database searches require accurate estimation of the statistical significance of scores. Optimal local sequence alignment scores follow Gumbel distributions, but determining an important parameter of the distribution (lambda) requires time-consuming computational simulation. Moreover, optimal alignment scores are less powerful than probabilistic scores that integrate over alignment uncertainty ("Forward" scores), but the expected distribution of Forward scores remains unknown. Here, I conjecture that both expected score distributions have simple, predictable forms when full probabilistic modeling methods are used. For a probabilistic model of local sequence alignment, optimal alignment bit scores ("Viterbi" scores) are Gumbel-distributed with constant lambda = log 2, and the high scoring tail of Forward scores is exponential with the same constant lambda. Simulation studies support these conjectures over a wide range of profile/sequence comparisons, using 9,318 profile-hidden Markov models from the Pfam database. This enables efficient and accurate determination of expectation values (E-values) for both Viterbi and Forward scores for probabilistic local alignments.

We present CLADES (cell lineage access driven by an edition sequence), a technology for cell lineage studies based on CRISPR-Cas9 techniques. CLADES relies on a system of genetic switches to activate and inactivate reporter genes in a predetermined order. Targeting CLADES to progenitor cells allows the progeny to inherit a sequential cascade of reporters, thereby coupling birth order to reporter expression. This system, which can also be temporally induced by heat shock, enables the temporal resolution of lineage development and can therefore be used to deconstruct an extended cell lineage by tracking the reporters expressed in the progeny. When targeted to the germ line, the same cascade progresses across animal generations, predominantly marking each generation with the corresponding combination of reporters. CLADES therefore offers an innovative strategy for making programmable cascades of genes that can be used for genetic manipulation or to record serial biological events.

Many breast cancer (BC) patients suffer from complications of metastatic disease. To form metastases, cancer cells must become migratory and coordinate both invasive and proliferative programs at distant organs. Here, we identify srGAP1 as a regulator of a proliferative-to-invasive switch in BC cells. High-resolution light-sheet microscopy demonstrates that BC cells can form actin-rich protrusions during extravasation. srGAP1 cells display a motile and invasive phenotype that facilitates their extravasation from blood vessels, as shown in zebrafish and mouse models, while attenuating tumor growth. Interestingly, a population of srGAP1 cells remain as solitary disseminated tumor cells in the lungs of mice bearing BC tumors. Overall, srGAP1 cells have increased Smad2 activation and TGF-β2 secretion, resulting in increased invasion and p27 levels to sustain quiescence. These findings identify srGAP1 as a mediator of a proliferative to invasive phenotypic switch in BC cells in vivo through a TGF-β2-mediated signaling axis.

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is critical, however, for both basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brainwide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open-access data repository; compatibility with existing resources; and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.

In this Scientific Perspectives we first review the recent advances in our understanding of the functional architecture of basal ganglia circuits. Then we argue that these data can best be explained by a model in which basal ganglia act to control the gain of movement kinematics to shape performance based on prior experience, which we refer to as a history-dependent gain computation. Finally, we discuss how insights from the history-dependent gain model might translate from the bench to the bedside, primarily the implications for the design of adaptive deep brain stimulation. Thus, we explicate the key empirical and conceptual support for a normative, computational model with substantial explanatory power for the broad role of basal ganglia circuits in health and disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

The fruit fly Drosophila melanogaster performs many behaviors, from simple motor actions to complex social interactions, that are of interest to neurobiologists studying how the brain controls behavior. Here, an undergraduate laboratory exercise uses cutting-edge methods to activate sets of neurons thermogenetically, triggering 60 different behaviors. Students learn how to recognize this large repertoire of behaviors from 16 fly strains that are publicly available, and from a large set of training videos provided here. A full protocol, timeline and handouts are included. Instructors need not have any experience working with flies. Student feedback is reported; in our experience, students are fascinated and highly engaged by watching animals perform such a broad array of behaviors. This exercise teaches fly husbandry and crossing, careful scientific observation, and principles of behavioral screening.

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

The endoplasmic reticulum (ER) is an important regulator of Ca2+ in cells and dysregulation of ER calcium homeostasis can lead to numerous pathologies. Understanding how various pharmacological and genetic perturbations of ER Ca2+ homeostasis impacts cellular physiology would likely be facilitated by more quantitative measurements of ER Ca2+ levels that allow easier comparisons across conditions. Here, we developed a ratiometric version of our original ER-GCaMP probe that allows for more quantitative comparisons of the concentration of Ca2+ in the ER across cell types and sub-cellular compartments. Using this approach we show that the resting concentration of ER Ca2+ in primary dissociated neurons is substantially lower than that in measured in embryonic fibroblasts.

The thalamus is the central communication hub of the forebrain and provides the cerebral cortex with inputs from sensory organs, subcortical systems and the cortex itself. Multiple thalamic regions send convergent information to each cortical region, but the organizational logic of thalamic projections has remained elusive. Through comprehensive transcriptional analyses of retrogradely labeled thalamic neurons in adult mice, we identify three major profiles of thalamic pathways. These profiles exist along a continuum that is repeated across all major projection systems, such as those for vision, motor control and cognition. The largest component of gene expression variation in the mouse thalamus is topographically organized, with features conserved in humans. Transcriptional differences between these thalamic neuronal identities are tied to cellular features that are critical for function, such as axonal morphology and membrane properties. Molecular profiling therefore reveals covariation in the properties of thalamic pathways serving all major input modalities and output targets, thus establishing a molecular framework for understanding the thalamus.

Here, we describe the embryonic central nervous system expression of 5,000 GAL4 lines made using molecularly defined cis-regulatory DNA inserted into a single attP genomic location. We document and annotate the patterns in early embryos when neurogenesis is at its peak, and in older embryos where there is maximal neuronal diversity and the first neural circuits are established. We note expression in other tissues, such as the lateral body wall (muscle, sensory neurons, and trachea) and viscera. Companion papers report on the adult brain and larval imaginal discs, and the integrated data sets are available online (http://www.janelia.org/gal4-gen1). This collection of embryonically expressed GAL4 lines will be valuable for determining neuronal morphology and function. The 1,862 lines expressed in small subsets of neurons (<20/segment) will be especially valuable for characterizing interneuronal diversity and function, because although interneurons comprise the majority of all central nervous system neurons, their gene expression profile and function remain virtually unexplored.