We recently identified ten novel SLE susceptibility loci in Asians and uncovered several additional suggestive loci requiring further validation. This study aimed to replicate five of these suggestive loci in a Han Chinese cohort from Hong Kong, followed by meta-analysis (11,656 cases and 23,968 controls) on previously reported Asian and European populations, and to perform bioinformatic analyses on all 82 reported SLE loci to identify shared regulatory signatures. We performed a battery of analyses for these five loci, as well as joint analyses on all 82 SLE loci. All five loci passed genome-wide significance: MYNN (rs10936599, Pmeta = 1.92 × 10-13, OR = 1.14), ATG16L2 (rs11235604, Pmeta = 8.87 × 10 -12, OR = 0.78), CCL22 (rs223881, Pmeta = 5.87 × 10-16, OR = 0.87), ANKS1A (rs2762340, Pmeta = 4.93 × 10-15, OR = 0.87) and RNASEH2C (rs1308020, Pmeta = 2.96 × 10-19, OR = 0.84) and co-located with annotated gene regulatory elements. The novel loci share genetic signatures with other reported SLE loci, including effects on gene expression, transcription factor binding, and epigenetic characteristics. Most (56%) of the correlated (r2 > 0.8) SNPs from the 82 SLE loci were implicated in differential expression (9.81 × 10-198 < P < 5 × 10-3) of cis-genes. Transcription factor binding sites for p53, MEF2A and E2F1 were significantly (P < 0.05) over-represented in SLE loci, consistent with apoptosis playing a critical role in SLE. Enrichment analysis revealed common pathways, gene ontology, protein domains, and cell type-specific expression. In summary, we provide evidence of five novel SLE susceptibility loci. Integrated bioinformatics using all 82 loci revealed that SLE susceptibility loci share many gene regulatory features, suggestive of conserved mechanisms of SLE etiopathogenesis.

The nanoscale connectomics community has recently generated automated and semi-automated "wiring diagrams" of brain subregions from terabytes and petabytes of dense 3D neuroimagery. This process involves many challenging and imperfect technical steps, including dense 3D image segmentation, anisotropic nonrigid image alignment and coregistration, and pixel classification of each neuron and their individual synaptic connections. As data volumes continue to grow in size, and connectome generation becomes increasingly commonplace, it is important that the scientific community is able to rapidly assess the quality and accuracy of a connectome product to promote dataset analysis and reuse. In this work, we share our scalable toolkit for assessing the quality of a connectome reconstruction via targeted inquiry and large-scale graph analysis, and to provide insights into how such connectome proofreading processes may be improved and optimized in the future. We illustrate the applications and ecosystem on a recent reference dataset.Clinical relevance- Large-scale electron microscopy (EM) data offers a novel opportunity to characterize etiologies and neurological diseases and conditions at an unprecedented scale. EM is useful for low-level analyses such as biopsies; this increased scale offers new possibilities for research into areas such as neural networks if certain bottlenecks and problems are overcome.

HIV-1 envelope spike [Env; trimeric (gp160)3, cleaved to (gp120/gp41)3] induces membrane fusion, leading to viral entry. It is also the viral component targeted by neutralizing antibodies. Vaccine development requires production, in quantities suitable for clinical studies, of a recombinant form that resembles functional Env. HIV-1 gp140 trimers - the uncleaved ectodomains of (gp160)3 - from a few selected viral isolates adopt a compact conformation with many antigenic properties of native Env spikes. One is currently being evaluated in a clinical trial. We report here low-resolution (20Å) cryoEM (electron cryomicroscopy) structures of this gp140 trimer, which adopts two principal conformations, one closed and the other slightly open. The former is indistinguishable at this resolution from those adopted by a stabilized, cleaved trimer (SOSIP) or by a membrane-bound Env trimer with truncated cytoplasmic tail (EnvΔCT). The latter conformation is closer to a partially open Env trimer than to the fully open conformation induced by CD4. These results show that a stable, uncleaved HIV-1 gp140 trimer has a compact structure close to that of native Env.IMPORTANCE Development of any HIV vaccine with a protein component (either prime or boost) requires production of a recombinant form to mimic the trimeric, functional HIV-1 envelope spike, in quantities suitable for clinical studies. Our understanding of the envelope structure has depended in part on a cleaved, soluble trimer, known as SOSIP.664, stabilized by several modifications including an engineered disulfide. This construct, difficult to produce in large quantities, has yet to induce better antibody responses than other envelope-based immunogens, even in animal models. The uncleaved ectodomain of the envelope protein, called gp140, has also been made as a soluble form to mimic the native Env present on the virion surface. Most HIV-1 gp140 preparations are not stable, however, and of inhomogeneous conformation. The results presented here show that gp140 preparations from suitable isolates can adopt a compact, native-like structure, supporting its use as a vaccine candidate.

The interplay between two major forebrain structures - cortex and subcortical striatum - is critical for flexible, goal-directed action. Traditionally, it has been proposed that striatum is critical for selecting what type of action is initiated while the primary motor cortex is involved in the online control of movement execution. Recent data indicates that striatum may also be critical for specifying movement execution. These alternatives have been difficult to reconcile because when comparing very distinct actions, as in the vast majority of work to date, they make essentially indistinguishable predictions. Here, we develop quantitative models to reveal a somewhat paradoxical insight: only comparing neural activity during similar actions makes strongly distinguishing predictions. We thus developed a novel reach-to-pull task in which mice reliably selected between two similar, but distinct reach targets and pull forces. Simultaneous cortical and subcortical recordings were uniquely consistent with a model in which cortex and striatum jointly specify flexible parameters of action during movement execution.

Feature-selective firing allows networks to produce representations of the external and internal environments. Despite its importance, the mechanisms generating neuronal feature selectivity are incompletely understood. In many cortical microcircuits the integration of two functionally distinct inputs occurs nonlinearly through generation of active dendritic signals that drive burst firing and robust plasticity. To examine the role of this processing in feature selectivity, we recorded CA1 pyramidal neuron membrane potential and local field potential in mice running on a linear treadmill. We found that dendritic plateau potentials were produced by an interaction between properly timed input from entorhinal cortex and hippocampal CA3. These conjunctive signals positively modulated the firing of previously established place fields and rapidly induced new place field formation to produce feature selectivity in CA1 that is a function of both entorhinal cortex and CA3 input. Such selectivity could allow mixed network level representations that support context-dependent spatial maps.

Brains are optimized for processing ethologically relevant sensory signals. However, few studies have characterized the neural coding mechanisms that underlie the transformation from natural sensory information to behavior. Here, we focus on acoustic communication in Drosophila melanogaster and use computational modeling to link natural courtship song, neuronal codes, and female behavioral responses to song. We show that melanogaster females are sensitive to long timescale song structure (on the order of tens of seconds). From intracellular recordings, we generate models that recapitulate neural responses to acoustic stimuli. We link these neural codes with female behavior by generating model neural responses to natural courtship song. Using a simple decoder, we predict female behavioral responses to the same song stimuli with high accuracy. Our modeling approach reveals how long timescale song features are represented by the Drosophila brain and how neural representations can be decoded to generate behavioral selectivity for acoustic communication signals.

Neural representations of information are shaped by local network interactions. Previous studies linking neural coding and cortical connectivity focused on stimulus selectivity in the sensory cortex 1–4. Here we study neural activity in the motor cortex during naturalistic behavior in which mice gathered rewards with multidirectional tongue reaching. This behavior does not require training and thus allowed us to probe neural coding and connectivity in motor cortex before its activity is shaped by learning a specific task. Neurons typically responded during and after reaching movements and exhibited conjunctive tuning to target location and reward outcome. We used an all-optical 5,4,6,7 method for large-scale causal functional connectivity mapping in vivo. Mapping connectivity between > 20,000,000 excitatory neuronal pairs revealed fine-scale columnar architecture in layer 2/3 of the motor cortex. Neurons displayed local (< 100 µm) like-to-like connectivity according to target-location tuning, and inhibition over longer spatial scales. Connectivity patterns comprised a continuum, with abundant weakly connected neurons and sparse strongly connected neurons that function as network hubs. Hub neurons were weakly tuned to target-location and reward-outcome but strongly influenced neighboring neurons. This network of neurons, encoding location and outcome of movements to different motor goals, may be a general substrate for rapid learning of complex, goal-directed behaviors.

Recent powerful tools for reconstructing connectomes using electron microscopy (EM) have made outstanding contributions to the field of neuroscience. As a prime example, the detection of visual motion is a classic problem of neural computation, yet our understanding of the exact mechanism has been frustrated by our incomplete knowledge of the relevant neurons and synapses. Recent connectomic studies have successfully identified the concrete neuronal circuit in the fly's visual system that computes the motion signals. This identification was greatly aided by the comprehensiveness of the EM reconstruction. Compared with light microscopy, which gives estimated connections from arbor overlap, EM gives unequivocal connections with precise synaptic counts. This paper reviews the recent study of connectomics in a brain of the fruit fly Drosophila and highlights how connectomes can provide a foundation for understanding the mechanism of neuronal functions by identifying the underlying neural circuits.

The brain is a network of neurons, one that generates behaviour, and knowing the former is crucial to understanding the latter. Identifying the exact network of synaptic connections, or connectome, of the fly's central nervous system is now a major objective in Drosophila neurobiology, one that has been initiated in several laboratories, especially the Janelia Research Campus of the Howard Hughes Medical Institute. Progress is most advanced in the optic neuropiles of the visual system. The effort to derive a connectome from these and other neuropile regions is proceeding by various methods of electron microscopy, especially focused-ion beam milling scanning electron microscopy, and relies upon - but is to be carefully distinguished from - published light microscopic methods that reveal the projections of genetically labelled cell types. The latter reveal those neurons that come into close proximity and are therefore candidate synaptic partners. Synaptic partnerships are not in fact reliably revealed by such candidate pairs, anatomical connections often revealing unexpected pathways. Synaptic partnerships identified from ultrastructural features provide a strong heuristic basis to interpret not only functional interactions between identified neurons, but also a powerful means to predict such interactions, and suggest functional pathways not readily predicted from existing experimental evidence. The analysis of circuit function may proceed cell by cell, by examining the behavioural outcome of either interrupting or restoring function to any one element in an anatomically defined circuit, but can be foiled by degeneracy in pathway elements. Circuit information can also be used to identify and analyse circuit motifs, and their role in higher-order network properties. These attempts in Drosophila anticipate parallel attempts in other systems, notably the inner plexiform layer of the vertebrate retina, and augment the one complete connectome already available to us, that available for 30 years in the nematode Caenorhabditis elegans.

We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question. Here we show that with only measurements of the connectivity of a biological neural network, we can predict the neural activity underlying neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single neuron and single synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion. Our mechanistic model makes detailed experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 24 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected—a universally observed feature of biological neural networks across species and brain regions.