Persistent changes in spine shape are coupled to long-lasting synaptic plasticity in hippocampus. The molecules that coordinate such persistent structural and functional plasticity are unknown. Here, we generated mice in which the cell adhesion molecule N-cadherin was conditionally ablated from postnatal, excitatory synapses in hippocampus. We applied to adult mice of either sex a combination of whole-cell recording, two-photon microscopy, and spine morphometric analysis to show that postnatal ablation of N-cadherin has profound effects on the stability of coordinated spine enlargement and long-term potentiation (LTP) at mature CA1 synapses, with no effects on baseline spine density or morphology, baseline properties of synaptic neurotransmission, or long-term depression. Thus, N-cadherin couples persistent spine structural modifications with long-lasting synaptic functional modifications associated selectively with LTP, revealing unexpectedly distinct roles at mature synapses in comparison with earlier, broader functions in synapse and spine development.

Recurrent connections are thought to be a common feature of the neural circuits that encode memories, but how memories are laid down in such circuits is not fully understood. Here we present evidence that courtship memory in Drosophila relies on the recurrent circuit between mushroom body gamma (MBg), M6 output, and aSP13 dopaminergic neurons. We demonstrate persistent neuronal activity of aSP13 neurons and show that it transiently potentiates synaptic transmission from MBγ>M6 neurons. M6 neurons in turn provide input to aSP13 neurons, prolonging potentiation of MBγ>M6 synapses over time periods that match short-term memory. These data support a model in which persistent aSP13 activity within a recurrent circuit lays the foundation for a short-term memory.

The mammalian hippocampus forms a cognitive map using neurons that fire according to an animal's position ("place cells") and many other behavioral and cognitive variables. The responses of these neurons are shaped by their presynaptic inputs and the nature of their postsynaptic integration. In CA1 pyramidal neurons, spatial responses in vivo exhibit a strikingly supralinear dependence on baseline membrane potential. The biophysical mechanisms underlying this nonlinear cellular computation are unknown. Here, through a combination of in vitro, in vivo, and in silico approaches, we show that persistent sodium current mediates the strong membrane potential dependence of place cell activity. This current operates at membrane potentials below the action potential threshold and over seconds-long timescales, mediating a powerful and rapidly reversible amplification of synaptic responses, which drives place cell firing. Thus, we identify a biophysical mechanism that shapes the coding properties of neurons composing the hippocampal cognitive map.

Auditory feedback is critical for the development and maintenance of speech in humans. In contrast, studies of nonhuman primate vocal production generally report that subjects show little reliance on auditory input. We examined the extent to which cotton-top tamarin (Saguinus oedipus) vocal production is sensitive to perturbation of auditory feedback by manipulating the predictability of presentation of a 1 s burst of white noise during the production of the species-specific contact call, the combination long call (CLC). We used three experimental conditions: the Begin condition, in which white noise was presented only during the first half of a recording session, the End condition, in which white noise was presented only in the last half, and the Random condition, in which each call had a 50% probability of receiving white noise playback throughout the recording session, making the auditory feedback unpredictable. In addition we recorded calls before and after the experimental series (Baseline condition) to determine whether any changes induced by modification of auditory feedback persisted. Results showed that playback of white noise during the production of the CLC produced changes in the temporal structure of the CLC: calls were shorter and had fewer pulses, indicating that modification of auditory feedback can interrupt vocal production. In addition, calls that received modified feedback were louder and had longer inter-pulse intervals than those that did not, consistent with an adaptive response to the masking effect of white noise playback. The magnitude of this compensatory effect and the interruption rate were both sensitive to whether the feedback modification occurred at the beginning or end of the experimental session: early feedback produced less interruption and more compensation. Finally, when auditory feedback modification was unpredictable, adaptive changes were observed in both calls that received modified feedback and those that received normal feedback, suggesting that tamarins can generate an expectation of noise playback and increase vocal amplitude in anticipation of masking.

Fold-switching proteins challenge the one-sequence-one-structure paradigm by adopting multiple stable folds. Nevertheless, it is uncertain whether fold switchers are naturally pervasive or rare exceptions to the well-established rule. To address this question, we developed a predictive method and applied it to the NusG superfamily of >15,000 transcription factors. We predicted that a substantial population (25%) of the proteins in this family switch folds. Circular dichroism and nuclear magnetic resonance spectroscopies of 10 sequence-diverse variants confirmed our predictions. Thus, we leveraged family-wide predictions to determine both conserved contacts and taxonomic distributions of fold-switching proteins. Our results indicate that fold switching is pervasive in the NusG superfamily and that the single-fold paradigm significantly biases structure-prediction strategies.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

Pfam, available via servers in the UK (http://pfam.sanger.ac.uk/) and the USA (http://pfam.janelia.org/), is a widely used database of protein families, containing 14 831 manually curated entries in the current release, version 27.0. Since the last update article 2 years ago, we have generated 1182 new families and maintained sequence coverage of the UniProt Knowledgebase (UniProtKB) at nearly 80%, despite a 50% increase in the size of the underlying sequence database. Since our 2012 article describing Pfam, we have also undertaken a comprehensive review of the features that are provided by Pfam over and above the basic family data. For each feature, we determined the relevance, computational burden, usage statistics and the functionality of the feature in a website context. As a consequence of this review, we have removed some features, enhanced others and developed new ones to meet the changing demands of computational biology. Here, we describe the changes to Pfam content. Notably, we now provide family alignments based on four different representative proteome sequence data sets and a new interactive DNA search interface. We also discuss the mapping between Pfam and known 3D structures.

Fluorescence microscopy is an invaluable tool in biology, yet its performance is compromised when the wavefront of light is distorted due to optical imperfections or the refractile nature of the sample. Such optical aberrations can dramatically lower the information content of images by degrading image contrast, resolution, and signal. Adaptive optics (AO) methods can sense and subsequently cancel the aberrated wavefront, but are too complex, inefficient, slow, or expensive for routine adoption by most labs. Here we introduce a rapid, sensitive, and robust wavefront sensing scheme based on phase diversity, a method successfully deployed in astronomy but underused in microscopy. Our method enables accurate wavefront sensing to less than λ/35 root mean square (RMS) error with few measurements, and AO with no additional hardware besides a corrective element. After validating the method with simulations, we demonstrate calibration of a deformable mirror > 100-fold faster than comparable methods (corresponding to wavefront sensing on the ~100 ms scale), and sensing and subsequent correction of severe aberrations (RMS wavefront distortion exceeding λ/2), restoring diffraction-limited imaging on extended biological samples.

We show through experiments and simulations that parallel phase modulation, a technique developed in the field of adaptive optics, can be employed to quickly determine the spectral phase profile of ultrafast laser pulses and to perform phase compensation as well as pulse shaping. Different from many existing ultrafast pulse measurement methods, the technique reported here requires no spectrum measurements of nonlinear signals. Instead, the power of nonlinear signals is used directly to quickly measure the spectral phase, a convenient feature for applications such as two-photon fluorescence microscopy. The method is found to work with both smooth and even completely random distortions. The experimental results are verified with MIIPS measurements.