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There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations. One major challenge is the quality of data from TR-SFX measurements, which often faces issues like data sparsity, partial recording of Bragg reflections, timing errors, and pixel noise. To overcome these difficulties, conventionally, large volumes of data are collected and grouped into a few temporal bins. The data in each bin are then averaged and paired with the mean of their corresponding jittered timestamps. This procedure provides one structure per bin, resulting in a limited number of averaged structures for the entire time interval spanned by the experiment. Therefore, the information on ultrafast structural dynamics at high temporal resolution is lost. This has initiated research for advanced methods of analyzing experimental TR-SFX data beyond the standard binning and averaging method. To address this problem, we use a machine learning algorithm called Nonlinear Laplacian Spectral Analysis (NLSA), which has emerged as a promising technique for studying the dynamics of complex systems. In this work, we demonstrate the power of this algorithm using synthetic x-ray diffraction snapshots from a protein with significant data incompleteness, timing uncertainties, and noise. Our study confirms that NLSA is a suitable approach that effectively mitigates the effects of these artifacts in TR-SFX data and recovers accurate structural dynamics information hidden in such data.

Observing and quantifying the interactions of proteins as they perform their biological functions in living cells is of high importance in modern biophysical research. Serial femtosecond crystallography (SFX) and time-resolved serial femtosecond crystallography (TR-SFX) have been successful in obtaining protein structures at near-atomic spatial resolutions and ultrafast temporal resolutions. However, this method of obtaining protein structures presents data analysis challenges. In typical SFX experiments, the microcrystals are streamed through the X-ray exposure path, and each X-ray pulse can only interact with a crystal for a short duration of time before it is destroyed. Hence, each collected diffraction pattern is only a small portion of the full diffraction information needed to fully characterize the structure of the proteins in the crystal. Therefore, the collected data is highly incomplete. Further, the data may also suffer from issues like noise and timing uncertainty. Classically, SFX data has been analyzed by merging all the collected data to obtain a single structure from the whole dataset. Time-resolved datasets have, on the other hand, been analyzed by first grouping data into temporal bins before averaging the data in each bin to obtain a time series of distinct structures. While this method has yielded structural information for many proteins, it loses finer resolution information. In this work, we employ a machine learning algorithm known as nonlinear Laplacian spectral analysis, or NLSA, to fill the data analysis gaps left by simpler averaging methods. To test the effectiveness of NLSA, we simulated sets of diffraction data for photoactive yellow protein (PYP) suffering from noise, incompleteness and timing uncertainty. With this, we demonstrate that NLSA is an effective algorithm for overcoming noise and timing uncertainty and can recover useful structural and dynamical information from TR-SFX experiments. Other ways of studying proteins have also proven very fruitful, specifically when studying phenomena like protein-protein interactions. One such method is the use of fluorescence microscopy paired with Förster resonance energy transfer, or FRET. FRET is the non-radiative transfer of energy from an excited donor fluorescent molecule to a nearby acceptor. Since FRET is highly sensitive to the separation distance between donor and acceptor molecules, it is a natural choice in quantifying protein-protein interactions in cells. In biological studies, fluorescent proteins are commonly used, which serve as donors and acceptors, and can be tagged to other proteins of interest to quantify interaction. The practice of FRET spectrometry has revealed geometrical properties such as the quaternary structure of proteins but has been limited to using spectrally resolved instruments. In this work, we implement an alternative method of processing data from time-resolved fluorescence decay signal, as would be obtained in fluorescence lifetime imaging microscopy (FLIM) studies. This new approach, dubbed tiFRET, involves integration of the fluorescence decay signal instead of fitting with exponential functions. This methodology may allow users of FLIM to perform FRET spectrometry, which may broaden the capabilities of FLIM practitioners.

Förster resonance energy transfer (FRET) spectrometry is a method for determining the quaternary structure of protein oligomers from distributions of FRET efficiencies that are drawn from pixels of fluorescence images of cells expressing the proteins of interest. FRET spectrometry protocols currently rely on obtaining spectrally resolved fluorescence data from intensity-based experiments. Another imaging method, fluorescence lifetime imaging microscopy (FLIM), is a widely used alternative to compute FRET efficiencies for each pixel in an image from the reduction of the fluorescence lifetime of the donors caused by FRET. In FLIM studies of oligomers with different proportions of donors and acceptors, the donor lifetimes may be obtained by fitting the temporally resolved fluorescence decay data with a predetermined number of exponential decay curves. However, this requires knowledge of the number and the relative arrangement of the fluorescent proteins in the sample, which is precisely the goal of FRET spectrometry, thus creating a conundrum that has prevented users of FLIM instruments from performing FRET spectrometry. Here, we describe an attempt to implement FRET spectrometry on temporally resolved fluorescence microscopes by using an integration-based method of computing the FRET efficiency from fluorescence decay curves. This method, which we dubbed time-integrated FRET (or tiFRET), was tested on oligomeric fluorescent protein constructs expressed in the cytoplasm of living cells. The present results show that tiFRET is a promising way of implementing FRET spectrometry and suggest potential instrument adjustments for increasing accuracy and resolution in this kind of study.

The emergence of an epidemic evokes the need to monitor its spread and assess and validate any mitigation measures enacted by governments and administrative bodies in real time. We present here a method to observe and quantify this spread and the response of affected populations and governing bodies and apply it to COVID-19 as a case study. This method provides means to simultaneously track in real time quantities such as the mortality and the recovery rates as well as the number of new infections caused by an infected person. With sufficient data, this method enables thorough monitoring and assessment of an epidemic without assumptions regarding the evolution of the pandemic in the future.

Prenatal alcohol exposure is the foremost preventable etiology of intellectual disability and leads to a collection of diagnoses known as Fetal Alcohol Spectrum Disorders (FASD). Alcohol (EtOH) impacts diverse neural cell types and activity, but the precise functional pathophysiological effects on the human fetal cerebral cortex are unclear. Here, we used human cortical organoids to study the effects of EtOH on neurogenesis and validated our findings in primary human fetal neurons. EtOH exposure produced temporally dependent cellular effects on proliferation, cell cycle, and apoptosis. In addition, we identified EtOH-induced alterations in post-translational histone modifications and chromatin accessibility, leading to impairment of cAMP and calcium signaling, glutamatergic synaptic development, and astrocytic function. Proteomic spatial profiling of cortical organoids showed region-specific, EtOH-induced alterations linked to changes in cytoskeleton, gliogenesis, and impaired synaptogenesis. Finally, multi-electrode array electrophysiology recordings confirmed the deleterious impact of EtOH on neural network formation and activity in cortical organoids, which was validated in primary human fetal tissues. Our findings demonstrate progress in defining the human molecular and cellular phenotypic signatures of prenatal alcohol exposure on functional neurodevelopment, increasing our knowledge for potential therapeutic interventions targeting FASD symptoms.

Cyclic GMP-AMP synthase (cGAS) initiates the innate immune system in response to cytosolic dsDNA. After binding and activation from dsDNA, cGAS uses ATP and GTP to synthesize 2', 3' -cGAMP (cGAMP), a cyclic dinucleotide second messenger with mixed 2'-5' and 3'-5' phosphodiester bonds. Inappropriate stimulation of cGAS has been implicated in autoimmune disease such as systemic lupus erythematosus, thus inhibition of cGAS may be of therapeutic benefit in some diseases; however, the size and polarity of the cGAS active site makes it a challenging target for the development of conventional substrate-competitive inhibitors. We report here the development of a high affinity (KD = 200 nM) inhibitor from a low affinity fragment hit with supporting biochemical and structural data showing these molecules bind to the cGAS active site. We also report a new high throughput cGAS fluorescence polarization (FP)-based assay to enable the rapid identification and optimization of cGAS inhibitors. This FP assay uses Cy5-labelled cGAMP in combination with a novel high affinity monoclonal antibody that specifically recognizes cGAMP with no cross reactivity to cAMP, cGMP, ATP, or GTP. Given its role in the innate immune response, cGAS is a promising therapeutic target for autoinflammatory disease. Our results demonstrate its druggability, provide a high affinity tool compound, and establish a high throughput assay for the identification of next generation cGAS inhibitors.

Crosstalk between posttranslational modifications, such as ubiquitination and phosphorylation, play key roles in controlling the duration and intensity of signaling events to ensure cellular homeostasis. However, the molecular mechanisms underlying the regulation of negative feedback loops remain poorly understood. Here, we uncover a pathway in Arabidopsis thaliana by which a negative feedback loop involving the E3 ubiquitin ligase PUB22 that dampens the immune response is triggered by MITOGEN-ACTIVATED PROTEIN KINASE3 (MPK3), best known for its function in the activation of signaling. PUB22’s stability is controlled by MPK3-mediated phosphorylation of residues localized in and adjacent to the E2 docking domain. We show that phosphorylation is critical for stabilization by inhibiting PUB22 oligomerization and, thus, autoubiquitination. The activity switch allows PUB22 to dampen the immune response. This regulatory mechanism also suggests that autoubiquitination, which is inherent to most single unit E3s in vitro, can function as a self-regulatory mechanism in vivo.

Peptidylarginine deiminases (PAD1-4) are calcium dependent enzymes responsible for protein citrullination, a post-translational modification converting arginine residues to citrulline. Elevated levels of citrullinated proteins have been associated with rheumatoid arthritis, neurodegenerative diseases, and cancers. Though highly selective PAD4 inhibitors have been described, inhibitors to the broader family currently are limited to covalent substrate analogs. Herein, we describe an allosteric binding pocket common to PAD1-4 suitable for the identification of potent, non-covalent enzyme inhibitors. A ligand-based virtual screen is utilized to identify a PAD4 inhibitor for which surface plasmon resonance confirms target binding but non-competitively with a known PAD4 ligand. We further show through co-crystal structure analysis that the ligand binds PAD4 at an allosteric pocket resulting in stabilization of a catalytically inactive, calcium-deficient enzyme conformation. A ligand designed based on this site potently inhibits all four PAD isozymes and prevents protein citrullination in neutrophils with a broader protein repertoire than observed with a PAD4-selective inhibitor. Protein citrullination regulates the physiological function of proteins and is linked to various autoimmune disorders. Here, the authors report on the allosteric targeting of PAD1-4 leading to broad inhibition of protein citrullination in neutrophils.

Early-onset epileptic encephalopathies are severe disorders often associated with specific genetic mutations. In this context, the CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by early-onset seizures, intellectual delay, and motor dysfunction. Although crucial for proper brain development, the precise targets of CDKL5 and its relation to patients’ symptoms are still unknown. Here, induced pluripotent stem cells derived from individuals deficient in CDKL5 protein were used to generate neural cells. Proteomic and phosphoproteomic approaches revealed disruption of several pathways, including microtubule-based processes and cytoskeleton organization. While CDD-derived neural progenitor cells have proliferation defects, neurons showed morphological alterations and compromised glutamatergic synaptogenesis. Moreover, the electrical activity of CDD cortical neurons revealed hyperexcitability during development, leading to an overly synchronized network. Many parameters of this hyperactive network were rescued by lead compounds selected from a human high-throughput drug screening platform. Our results enlighten cellular, molecular, and neural network mechanisms of genetic epilepsy that could ultimately promote novel therapeutic opportunities for patients.

OBJECTIVE. This study examined (1) the relationship of fear of falling to depression, anxiety, activity level, and activity restriction and (2) whether depression or anxiety predicted fear of falling, activity level, activity restriction, or changes in activity level. METHOD. We administered the Survey of Activities and Fear of Falling in the Elderly; the Geriatric Depression Scale–30; and the Hamilton Anxiety Scale, IVR Version, during a one-time visit to 99 community-dwelling adults ≥55 yr old. RESULTS. We found significant relationships between (1) fear of falling and depression, anxiety, and activity level; (2) depression and anxiety; and (3) activity restriction and depression. Activity level was negatively correlated with activity restriction, fear of falling, depression, and anxiety. Anxiety predicted both fear of falling and activity level. Both anxiety and depression predicted activity restriction because of fear of falling and for other reasons. CONCLUSION. Occupational therapy practitioners should consider screening their older adult clientele for fear of falling, anxiety, and depression because these states may lead to fall risk and activity restriction.