Hundreds of millions of structured proteins sustain life through chemical interactions and catalytic reactions1. Though dynamic, these proteins are assumed to be built upon fixed scaffolds of secondary structure, α-helices and β-sheets. Experimentally determined structures of over >58,000 non-redundant proteins support this assumption, though it has recently been challenged by ∼100 fold-switching proteins2. These “metamorphic3” proteins, though ostensibly rare, raise the question of how many uncharacterized proteins have shapeshifting–rather than fixed–secondary structures. To address this question, we developed a comparative sequence-based approach that predicts fold-switching proteins from differences in secondary structure propensity. We applied this approach to the universally conserved NusG transcription factor family of ∼15,000 proteins, one of which has a 50-residue regulatory subunit experimentally shown to switch between α-helical and β-sheet folds4. Our approach predicted that 25% of the sequences in this family undergo similar α-helix ⇌ β-sheet transitions, a frequency two orders of magnitude larger than previously observed. Our predictions evade state-of-the-art computational methods but were confirmed experimentally by circular dichroism and nuclear magnetic resonance spectroscopy for all 10 assiduously chosen dissimilar variants. These results suggest that fold switching is a pervasive mechanism of transcriptional regulation in all kingdoms of life and imply that numerous uncharacterized proteins may also switch folds.

Synchronised rhythmic activity of the brain is thought to arise from neuronal network behaviours that rely on synaptic signalling between individual cells. This notion has been a basis to explain periodic epileptiform discharges that are driven by interneuronal networks. However, interneuronal discharges not only engage cell-cell GABAergic transmission but also control the extracellular GABA concentration ([GABA]e) and thus tonic GABAA receptor conductance (Gtonic) across the cell population. At the same time, the firing activity of interneurons shows a bell-shaped dependence on Gtonic, suggesting an innate susceptibility to self-sustained oscillations. Here, we employ patch-clamp GABA ‘sniffer’ and fast two-photon excitation imaging of GABA sensor to show that periodic epileptiform discharges are preceded by a region-wide, rising wave of extracellular GABA. Neural network simulations based on such observations reveal that it is the volume-transmitted, extrasynaptic actions of GABA targeting multiple off-target cells that drives synchronised interneuronal spiking prompting periodic epileptiform bursts. We validate this hypothesis using simultaneous patch-clamp recordings from multiple nerve cells, selective optogenetic stimulation of fast-spiking interneurons, and by revealing the role of GABA uptake. Our findings thus unveil a key role of extrasynaptic, volume-transmitted GABA actions in enabling and pacing regenerative rhythmic activity in brain networks.

The fluorescent glutamate indicator iGluSnFR enables imaging of neurotransmission with genetic and molecular specificity. However, existing iGluSnFR variants exhibit saturating activation kinetics and are excluded from post-synaptic densities, limiting their ability to distinguish synaptic from extrasynaptic glutamate. Using a multi-assay screen in bacteria, soluble protein, and cultured neurons, we generated novel variants with improved kinetics and signal-to-noise ratios. We also developed surface display constructs that improve iGluSnFR’s nanoscopic localization to post-synapses. The resulting indicator, iGluSnFR3, exhibits rapid non-saturating activation kinetics and reports synaptic glutamate release with improved linearity and increased specificity versus extrasynaptic signals in cultured neurons. In mouse visual cortex, imaging of iGluSnFR3 at individual boutons reported single electrophysiologically-observed action potentials with high specificity versus non-synaptic transients. In vibrissal sensory cortex Layer 4, we used iGluSnFR3 to characterize distinct patterns of touch-evoked feedforward input from thalamocortical boutons and both feedforward and recurrent input onto L4 cortical neuron dendritic spines.

Nicotinic partial agonists provide an accepted aid for smoking cessation and thus contribute to decreasing tobacco-related disease. Improved drugs constitute a continued area of study. However, there remains no reductionist method to examine the cellular and subcellular pharmacokinetic properties of these compounds in living cells. Here, we developed new intensity-based drug sensing fluorescent reporters ('iDrugSnFRs') for the nicotinic partial agonists dianicline, cytisine, and two cytisine derivatives - 10-fluorocytisine and 9-bromo-10-ethylcytisine. We report the first atomic-scale structures of liganded periplasmic binding protein-based biosensors, accelerating development of iDrugSnFRs and also explaining the activation mechanism. The nicotinic iDrugSnFRs detect their drug partners in solution, as well as at the plasma membrane (PM) and in the endoplasmic reticulum (ER) of cell lines and mouse hippocampal neurons. At the PM, the speed of solution changes limits the growth and decay rates of the fluorescence response in almost all cases. In contrast, we found that rates of membrane crossing differ among these nicotinic drugs by > 30 fold. The new nicotinic iDrugSnFRs provide insight into the real-time pharmacokinetic properties of nicotinic agonists and provide a methodology whereby iDrugSnFRs can inform both pharmaceutical neuroscience and addiction neuroscience.

Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.

OBJECTIVE: A recent genome-wide association study (GWAS) reported a significant genetic association between rs34330 of cyclin-dependent kinase inhibitor 1B (CDKN1B) and risk of systemic lupus erythematosus (SLE) in Han Chinese. This study aims to validate the reported association and elucidate the biochemical mechanisms underlying the variant's effect.

METHODS: We performed allelic association with SLE followed by meta-analysis across 11 independent cohorts (n=28,872). We applied in silico bioinformatics and experimental validation in SLE-relevant cell lines to determine the functional consequences of rs34330.

RESULTS: We replicated genetic association between SLE and rs34330 (P =5.29x10 , OR (95% CI)=0.84 (0.81-0.87)). Follow-up bioinformatics and eQTL analysis suggest that rs34330 is located in active chromatin and potentially regulates several target genes. Using luciferase and ChIP-qPCR, we demonstrated substantial allele-specific promoter and enhancer activity, and allele-specific binding of three histone marks (H3K27ac, H3K4me3, H3K4me1), RNA pol II, CTCF, and a critical immune transcription factor (IRF-1). Chromosome conformation capture (3C) detected long-range chromatin interactions between rs34330 and the promoters of neighboring genes APOLD1 and DDX47, and effects on CDKN1B and the other target genes were directly validated by CRISPR-based genome editing. Finally, CRISPR-dCas9-based epigenetic activation/silencing confirmed these results. Gene-edited cell lines also showed higher levels of proliferation and apoptosis.

CONCLUSION: Collectively, these findings suggest a mechanism whereby the rs34330 risk allele (C) influences the presence of histone marks, RNA pol II, and the IRF-1 transcription factor to regulate expression of several target genes linked to proliferation and apoptosis, which potentially underlie the association of rs34330 with SLE.

Although most experimentally characterized proteins with similar sequences assume the same folds and perform similar functions, an increasing number of exceptions is emerging. One class of exceptions comprises sequence-similar fold switchers, whose secondary structures shift from α-helix <-> β-sheet through a small number of mutations, a sequence insertion, or a deletion. Predictive methods for identifying sequence-similar fold switchers are desirable because some are associated with disease and/or can perform different functions in cells. Here, we use homology-based secondary structure predictions to identify sequence-similar fold switchers from their amino acid sequences alone. To do this, we predicted the secondary structures of sequence-similar fold switchers using three different homology-based secondary structure predictors: PSIPRED, JPred4, and SPIDER3. We found that α-helix <-> β-strand prediction discrepancies from JPred4 discriminated between the different conformations of sequence-similar fold switchers with high statistical significance (P < 1.8*10 ). Thus, we used these discrepancies as a classifier and found that they can often robustly discriminate between sequence-similar fold switchers and sequence-similar proteins that maintain the same folds (Matthews Correlation Coefficient of 0.82). We found that JPred4 is a more robust predictor of sequence-similar fold switchers because of (a) the curated sequence database it uses to produce multiple sequence alignments and (b) its use of sequence profiles based on Hidden Markov Models. Our results indicate that inconsistencies between JPred4 secondary structure predictions can be used to identify some sequence-similar fold switchers from their sequences alone. Thus, the negative information from inconsistent secondary structure predictions can potentially be leveraged to identify sequence-similar fold switchers from the broad base of genomic sequences.

Extant fold-switching proteins remodel their secondary structures and change their functions in response to cellular stimuli, regulating biological processes and affecting human health. In spite of their biological importance, these proteins remain understudied. Few representative examples of fold switchers are available in the Protein Data Bank, and they are difficult to predict. In fact, all 96 experimentally validated examples of extant fold switchers were stumbled upon by chance. Thus, predictive methods are needed to expedite the process of discovering and characterizing more of these shapeshifting proteins. Previous approaches require a solved structure or all-atom simulations, greatly constraining their use. Here, we propose a high-throughput sequence-based method for predicting extant fold switchers that transition from α-helix in one conformation to β-strand in the other. This method leverages two previous observations: (1) α-helix <-> β-strand prediction discrepancies from JPred4 are a robust predictor of fold switching, and (2) the fold-switching regions (FSRs) of some extant fold switchers have different secondary structure propensities when expressed in isolation (isolated FSRs) than when expressed within the context of their parent protein (contextualized FSRs). Combining these two observations, we ran JPred4 on the sequences of isolated and contextualized FSRs from 14 known extant fold switchers and found α-helix <->β-strand prediction discrepancies in every case. To test the overall robustness of this finding, we randomly selected regions of proteins not expected to switch folds (single-fold proteins) and found significantly fewer α-helix <-> β-strand prediction discrepancies (p < 4.2*10−20, Kolmogorov-Smirnov test). Combining these discrepancies with the overall percentage of predicted secondary structure, we developed a classifier that often robustly identifies extant fold switchers (Matthews Correlation Coefficient of 0.70). Although this classifier had a high false negative rate (6/14), its false positive rate was very low (1/211), suggesting that it can be used to predict a subset of extant fold switchers from billions of available genomic sequences.

Recent advances in super-resolution microscopy have pushed the resolution limit of light microscopy closer to that of electron microscopy. However, as they invariably rely on fluorescence, light microscopy techniques only visualize whatever gets labeled. On the other hand, while electron microscopy reveals cellular structures at the highest resolution, it offers no specificity. The information gap between the two imaging modalities can only be bridged by correlative light and electron microscopy (CLEM). Previously we have developed a probe (mEos4) whose fluorescence and photoconversion survive 0.5-1% OsO4 fixation, allowing super-resolution visualization of organelles and fused proteins in the context of resinembedded ultrastructure in both transmission EM (TEM) and scanning EM (SEM) [1,2].

Correlative light and electron microscopy (CLEM) combines the power of electron microscopy, with its excellent resolution and contrast, with that of fluorescence imaging, which allows the staining of specific molecules, organelles, and cell populations. Fluorescence imaging is also readily compatible with live cells and behaving animals, facilitating real-time visualization of cellular processes, potentially followed by electron microscopic reconstruction. Super-resolution single-molecule localization microscopy is a relatively new modality that harnesses the ability of some fluorophores to photoconvert, through which localization precision better than Abbe’s diffraction limit is achieved through iterative high-resolution localization of single-molecule emitters. Here we describe our lab’s recent progress in the development of reagents and techniques for super-resolution single-molecule localization CLEM and their applications to biological problems.