Induction of Superior Systemic and Mucosal Protective Immunity to SARS-CoV-2 by Nasal Administration of a VSV-ΔG-Spike Vaccine.

The emergence of rapidly spreading variants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) poses a major challenge to vaccines' protective efficacy. Intramuscular (IM) vaccine administration induces short-lived immunity but does not prevent infection and transmission. New vaccination strategies are needed to extend the longevity of vaccine protection, induce mucosal and systemic immunity and prevent viral transmission. The intranasal (IN) administration of the VSV-ΔG-spike vaccine candidate directly to mucosal surfaces yielded superior mucosal and systemic immunity at lower vaccine doses. Compared to IM vaccination in the K18-hACE2 model, IN vaccination preferentially induced mucosal IgA and T-cells, reduced the viral load at the site of infection, and ameliorated disease-associated brain gene expression. IN vaccination was protective even one year after administration. As most of the world population has been vaccinated by IM injection, we demonstrate the potential of a heterologous IM + IN vaccination regimen to induce mucosal immunity while maintaining systemic immunity. Furthermore, the IM + IN regimen prevented virus transmission in a golden Syrian hamster co-caging model. Taken together, we show that IN vaccination with VSV-ΔG-spike, either as a homologous IN + IN regimen or as a boost following IM vaccination, has a favorable potential over IM vaccination in inducing efficient mucosal immunity, long-term protection and preventing virus transmission.

Short- and long-term outcomes of pulmonary exposure to a sublethal dose of ricin in mice.

Ricin, an extremely potent toxin produced from the seeds of castor plant, Ricinus communis, is ribosome-inactivating protein that blocks cell-protein synthesis. It is considered a biological threat due to worldwide availability of castor beans, massive quantities as a by-product of castor oil production, high stability and ease of production. The consequence of exposure to lethal dose of ricin was extensively described in various animal models. However, it is assumed that in case of aerosolized ricin bioterror attack, the majority of individuals would be exposed to sublethal doses rather than to lethal ones. Therefore, the purpose of current study was to assess short- and long-term effects on physiological parameters and function following sublethal pulmonary exposure. We show that in the short-term, sublethal exposure of mice to ricin resulted in acute lung injury, including interstitial pneumonia, cytokine storm, neutrophil influx, edema and cellular death. This damage was manifested in reduced lung performance and physiological function. Interestingly, although in the long-term, mice recovered from acute lung damage and restored pulmonary and physiological functionality, the reparative process was associated with lasting fibrotic lesions. Therefore, restriction of short-term acute phase of the disease and management of long-term pulmonary fibrosis by medical countermeasures is expected to facilitate the quality of life of exposed survivors.

Decreased brain iron deposition based on quantitative susceptibility mapping correlates with reduced neurodevelopmental status in children with autism spectrum disorder.

To investigate potential correlations between the susceptibility values of certain brain regions and the severity of disease or neurodevelopmental status in children with autism spectrum disorder (ASD), 18 ASD children and 15 healthy controls (HCs) were recruited. The neurodevelopmental status was assessed by the Gesell Developmental Schedules (GDS) and the severity of the disease was evaluated by the Autism Behavior Checklist (ABC). Eleven brain regions were selected as regions of interest and the susceptibility values were measured by quantitative susceptibility mapping. To evaluate the diagnostic capacity of susceptibility values in distinguishing ASD and HC, the receiver operating characteristic (ROC) curve was computed. Pearson and Spearman partial correlation analysis were used to depict the correlations between the susceptibility values, the ABC scores, and the GDS scores in the ASD group. ROC curves showed that the susceptibility values of the left and right frontal white matter had a larger area under the curve in the ASD group. The susceptibility value of the right globus pallidus was positively correlated with the GDS-fine motor scale score. These findings indicated that the susceptibility value of the right globus pallidus might be a viable imaging biomarker for evaluating the neurodevelopmental status of ASD children.

Doxycycline, levofloxacin, and moxifloxacin are superior to ciprofloxacin in treating anthrax meningitis in rabbits and NHP.

Efficient treatment of anthrax-related meningitis in patients poses a significant therapeutic challenge. Previously, we demonstrated in our anthrax meningitis rabbit model that ciprofloxacin treatment is ineffective with most of the treated animals succumbing to the infection. Herein we tested the efficacy of doxycycline in our rabbit model and found it highly effective. Since all of our findings are based on a rabbit model, we test the efficacy of ciprofloxacin or doxycycline in a specific central nervous system (CNS) model developed in non-human primates (NHPs). Similar to rabbits, ciprofloxacin treatment was ineffective, while doxycycline protected the infected rhesus macaques (n = 2) from the lethal CNS Bacillus anthracis infection. To test whether the low efficacy of Ciprofloxacin is an example of low efficacy of all fluoroquinolones or only this substance, we treated rabbits that were inoculated intracisterna magna (ICM) with levofloxacin or moxifloxacin. We found that in contrast to ciprofloxacin, levofloxacin and moxifloxacin were highly efficacious in treating lethal anthrax-related meningitis in rabbits and NHP (levofloxacin). We demonstrated (in naïve rabbits) that this difference probably results from variances in blood-brain-barrier penetration of the different fluoroquinolones. The combined treatment of doxycycline and any one of the tested fluoroquinolones was highly effective in the rabbit CNS infection model. The combined treatment of doxycycline and levofloxacin was effective in an inhalation rabbit model, as good as the doxycycline mono-therapy. These findings imply that while ciprofloxacin is highly effective as a post-exposure prophylactic drug, using this drug to treat symptomatic patients should be reconsidered.

Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study.

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme.

BACKGROUND: Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients. METHODS: We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model. RESULTS: We identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan-Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination. CONCLUSIONS: We identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.

Diffusion tensor imaging of optic neuropathies: a narrative review.

Background and Objective: Diffusion tensor imaging (DTI) has been implemented in a breadth of scientific investigations of optic neuropathies, though it has yet to be fully adopted for diagnosis or prognosis. This is potentially due to a lack of standardization and weak replication of results. The aim of this investigation was to review DTI results from studies specific to three distinct optic neuropathies in order to probe its current clinical utility. Methods: We reviewed the DTI literature specific to primary open-angle glaucoma (POAG), optic neuritis (ON), and traumatic optic neuropathy (TON) by systematically searching the PubMed database on March 1st, 2023. Four distinct DTI metrics are considered: fractional anisotropy (FA), along with mean diffusivity (MD, axial diffusivity (AD), and radial diffusivity (RD). Results from within-group, between-group, and correlational studies were thoroughly assessed. Key Content and Findings: POAG studies most consistently report a decrease in FA, especially in the optic radiations, followed in prevalence by an increase in RD and then MD, whilst AD yields conflicting results between studies. It is notable that there is not an equal distribution of investigated DTI metrics, with FA utilized the most, followed by MD, RD, and AD. Studies of ON are similar in that the most consistent findings are specific to FA, RD, and MD. These results are specific to the optic nerve and radiation since only one study measured the intermediary regions. More studies are needed to assess the effect that ON has on the tracts of the visual system. Finally, only three studies assessing DTI of TON have been performed to date, displaying low to moderate replicability of results. To improve the level of agreement between studies assessing each optic neuropathy, an increased level of standardization is recommended. Conclusions: Both POAG and ON studies have yielded some prevalent DTI findings, both for contrast and correlation-based assessments. Although the clinical need is high for TON, considering the limitations of the current diagnostic tools, too few studies exist to make confident conclusions. Future use of standardized and longitudinal DTI, along with the foreseen methodological and technical improvements, is warranted to effectively study optic neuropathies.

Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme.

Objective: To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients. Methods: 131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index. Results: After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560). Conclusion: This study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors.

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images.

Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecting these changes depends on the MRI data quality. Unfortunately, image artifacts frequently compromise the MRI utility, making it critical to screen the data. Currently, quality assessment requires visual inspection, a time-consuming process that suffers from inter-rater variability. Automated methods to detect MRI artifacts could improve the efficiency of the process. Such automated methods have achieved high accuracy using small datasets, with balanced proportions of MRI data with and without artifacts. With the current trend towards big data in neuroimaging, there is a need for automated methods that achieve accurate detection in large and imbalanced datasets. Deep learning (DL) is the ideal MRI artifact detection algorithm for large neuroimaging databases. However, the inference generated by DL does not commonly include a measure of uncertainty. Here, we present the first stochastic DL algorithm to generate automated, high-performing MRI artifact detection implemented on a large and imbalanced neuroimaging database. We implemented Monte Carlo dropout in a 3D AlexNet to generate probabilities and epistemic uncertainties. We then developed a method to handle class imbalance, namely data-ramping to transfer the learning by extending the dataset size and the proportion of the artifact-free data instances. We used a 34,800 scans (98% clean) dataset. At baseline, we obtained 89.3% testing accuracy (F1 = 0.230). Following the transfer learning (with data-ramping), we obtained 94.9% testing accuracy (F1 = 0.357) outperforming focal cross-entropy (92.9% testing accuracy, F1 = 0.304) incorporated for comparison at handling class imbalance. By implementing epistemic uncertainties, we improved the testing accuracy to 99.5% (F1 = 0.834), outperforming the results obtained in previous comparable studies. In addition, we estimated aleatoric uncertainties by incorporating random flips to the MRI volumes, and demonstrated that aleatoric uncertainty can be implemented as part of the pipeline. The methods we introduce enhance the efficiency of managing large databases and the exclusion of artifact images from big data analyses.

Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD.

OBJECTIVE: The objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition. METHODS: Twenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. We applied the QQ-CCTV algorithm which combines quantitative susceptibility mapping and quantitative blood oxygen level-dependent models (QQ) for OEF calculation. CBF map was computed from arterial spin labeling and CMRO2 was generated based on Fick's principle. Whole-brain and regional OEF, CBF, and CMRO2 analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed. RESULTS: Whole brain voxel-wise analysis showed that CBF and CMRO2 values significantly decreased in AD predominantly in the bilateral angular gyrus, precuneus gyrus and parieto-temporal regions. Regional analysis showed that CBF value decreased in the bilateral caudal hippocampus and left rostral hippocampus and CMRO2 value decreased in left caudal and rostral hippocampus in AD patients. Considering all subjects in the AD and HC groups combined, the mean CBF and CMRO2 values in the bilateral hippocampus positively correlated with the MMSE score. CONCLUSION: CMRO2 mapping with the QQ-CCTV method - which is readily available in MR systems for clinical practice - can be a potential biomarker for AD. In addition, CMRO2 in the hippocampus may be a useful tool for monitoring cognitive impairment.

versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database.

The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.

A framework and resource for global collaboration in non-human primate neuroscience.

As science and technology evolve, there is an increasing need for promotion of international scientific exchange. Collaborations, while offering substantial opportunities for scientists and benefit to society, also present challenges for those working with animal models, such as non-human primates (NHPs). Diversity in regulation of animal research is sometimes mistaken for the absence of common international welfare standards. Here, the ethical and regulatory protocols for 13 countries that have guidelines in place for biomedical research involving NHPs were assessed with a focus on neuroscience. Review of the variability and similarity in trans-national NHP welfare regulations extended to countries in Asia, Europe and North America. A tabulated resource was established to advance solution-oriented discussions and scientific collaborations across borders. Our aim is to better inform the public and other stakeholders. Through cooperative efforts to identify and analyze information with reference to evidence-based discussion, the proposed key ingredients may help to shape and support a more informed, open framework. This framework and resource can be expanded further for biomedical research in other countries.

Effects of repetitive transcranial magnetic stimulation and their underlying neural mechanisms evaluated with magnetic resonance imaging-based brain connectivity network analyses.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain modulation and rehabilitation technique used in patients with neuropsychiatric diseases. rTMS can structurally remodel or functionally induce activities of specific cortical regions and has developed to an important therapeutic method in such patients. Magnetic resonance imaging (MRI) provides brain data that can be used as an explanation tool for the neural mechanisms underlying rTMS effects; brain alterations related to different functions or structures may be reflected in changes in the interaction and influence of brain connections within intrinsic specific networks. In this review, we discuss the technical details of rTMS and the biological interpretation of brain networks identified with MRI analyses, comprehensively summarize the neurobiological effects in rTMS-modulated individuals, and elaborate on changes in the brain network in patients with various neuropsychiatric diseases receiving rehabilitation treatment with rTMS. We conclude that brain connectivity network analysis based on MRI can reflect alterations in functional and structural connectivity networks comprising adjacent and separated brain regions related to stimulation sites, thus reflecting the occurrence of intrinsic functional integration and neuroplasticity. Therefore, MRI is a valuable tool for understanding the neural mechanisms of rTMS and practically tailoring treatment plans for patients with neuropsychiatric diseases.

Altered coupling of resting-state cerebral blood flow and functional connectivity in Meige syndrome.

Introduction: Meige syndrome (MS) is an adult-onset segmental dystonia disease, mainly manifested as blepharospasm and involuntary movement caused by dystonic dysfunction of the oromandibular muscles. The changes of brain activity, perfusion and neurovascular coupling in patients with Meige syndrome are hitherto unknown. Methods: Twenty-five MS patients and thirty age- and sex-matched healthy controls (HC) were prospectively recruited in this study. All the participants underwent resting-state arterial spin labeling and blood oxygen level-dependent examinations on a 3.0 T MR scanner. The measurement of neurovascular coupling was calculated using cerebral blood flow (CBF)-functional connectivity strength (FCS) correlations across the voxels of whole gray matter. Also, voxel-wised analyses of CBF, FCS, and CBF/FCS ratio images between MS and HC were conducted. Additionally, CBF and FCS values were compared between these two groups in selected motion-related brain regions. Results: MS patients showed increased whole gray matter CBF-FCS coupling relative to HC (t = 2.262, p = 0.028). In addition, MS patients showed significantly increased CBF value in middle frontal gyrus and bilateral precentral gyrus. Conclusion: The abnormal elevated neurovascular coupling of MS may indicate a compensated blood perfusion in motor-related brain regions and reorganized the balance between neuronal activity and brain blood supply. Our results provide a new insight into the neural mechanism underlying MS from the perspective of neurovascular coupling and cerebral perfusion.

In Vitro and In Vivo Therapeutic Potential of 6,6'-Dihydroxythiobinupharidine (DTBN) from Nuphar lutea on Cells and K18-hACE2 Mice Infected with SARS-CoV-2.

We have previously published research on the anti-viral properties of an alkaloid mixture extracted from Nuphar lutea, the major components of the partially purified mixture found by NMR analysis. These are mostly dimeric sesquiterpene thioalkaloids called thiobinupharidines and thiobinuphlutidines against the negative strand RNA measles virus (MV). We have previously reported that this extract inhibits the MV as well as its ability to downregulate several MV proteins in persistently MV-infected cells, especially the P (phospho)-protein. Based on our observation that the Nuphar extract is effective in vitro against the MV, and the immediate need that the coronavirus disease 2019 (COVID-19) pandemic created, we tested here the ability of 6,6'-dihydroxythiobinupharidine DTBN, an active small molecule, isolated from the Nuphar lutea extract, on COVID-19. As shown here, DTBN effectively inhibits SARS-CoV-2 production in Vero E6 cells at non-cytotoxic concentrations. The short-term daily administration of DTBN to infected mice delayed the occurrence of severe clinical outcomes, lowered virus levels in the lungs and improved survival with minimal changes in lung histology. The viral load on lungs was significantly reduced in the treated mice. DTBN is a pleiotropic small molecule with multiple targets. Its anti-inflammatory properties affect a variety of pathogens including SARS-CoV-2 as shown here. Its activity appears to target both pathogen specific (as suggested by docking analysis) as well as cellular proteins, such as NF-κB, PKCs, cathepsins and topoisomerase 2, that we have previously identified in our work. Thus, this combined double action of virus inhibition and anti-inflammatory activity may enhance the overall effectivity of DTBN. The promising results from this proof-of-concept in vitro and in vivo preclinical study should encourage future studies to optimize the use of DTBN and/or its molecular derivatives against this and other related viruses.

Turning high-throughput structural biology into predictive inhibitor design.

A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein-ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein-ligand complexes and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high-throughput crystallography campaign against the SARS-CoV-2 main protease (MPro), obtaining parallel measurements of over 200 protein-ligand complexes and their binding activities. This allows us to design one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a noncovalent and nonpeptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry.

Alternate atxA and acpA dependent response of Bacillus anthracis to serum, HCO3- and CO2.

Bacillus anthracis overcomes host immune responses by producing capsule and secreting toxins. Production of these virulence factors in response to entering the host environment was shown to be regulated by atxA, the major virulence regulator, known to be activated by HCO3- and CO2. While toxin production is regulated directly by atxA, capsule production is independently mediated by two regulators; acpA and acpB. In addition, it was demonstrated that acpA has at least two promotors, one of them shared with atxA. We used a genetic approach to study capsule and toxin production under different conditions. Unlike previous works utilizing NBY, CA or R-HCO3- medium under CO2 enriched conditions, we used a sDMEM-based medium. Thus, toxin and capsule production can be induced in ambient or CO2 enriched atmosphere. Using this system, we could differentiate between induction by 10% NRS, 10% CO2 or 0.75% HCO3-. In response to high CO2, capsule production is induced by acpA based response in an atxA-independent manner, with little to no toxin (protective antigen PA) production. atxA based response is activated in response to serum independently of CO2, inducing toxin and capsule production in an acpA or acpB dependent manner. HCO3- was also found to activate atxA based response, but in non-physiological concentrations. Our findings may help explain the first stages of inhalational infection, in which spores germinating in dendritic cells require protection (by encapsulation) without affecting cell migration to the draining lymph-node by toxin secretion.

Resting-state functional MRI of the visual system for characterization of optic neuropathy.

Optic neuropathy refers to disease of the optic nerve and can result in loss of visual acuity and/or visual field defects. Combining findings from multiple fMRI modalities can offer valuable information for characterizing and managing optic neuropathies. In this article, we review a subset of resting-state functional magnetic resonance imaging (RS-fMRI) studies of optic neuropathies. We consider glaucoma, acute optic neuritis (ON), discuss traumatic optic neuropathy (TON), and explore consistency between findings from RS and visually driven fMRI studies. Consistent with visually driven studies, glaucoma studies at rest also indicated reduced activation in the visual cortex and dorsal visual stream. RS-fMRI further reported varying levels of functional connectivity in the ventral stream depending on disease severity. ON patients show alterations within the visual cortex in both fMRI techniques. Particularly, higher-than-normal RS activity is observed in the acute phase and decreases as the disease progresses. A similar pattern is observed in the visual cortex of TON-like, open globe injury (OGI), patients. Additionally, visually driven and RS-fMRI studies of ON patients show recovery of brain activity in the visual cortex. RS-fMRI suggests recovery of signals in higher-tier visual areas MT and LOC as well. Finally, RS-fMRI has not yet been applied to TON, although reviewing OGI studies suggests that it is feasible. Future RS-fMRI studies of optic neuropathies could prioritize studying the fine scale RS activity of brain areas that visually driven studies have identified. We suggest that a more systematic longitudinal comparison of optic neuropathies with advanced fMRI would provide improved diagnostic and prognostic information.

Application of pseudocontinuous arterial spin labeling perfusion imaging in children with autism spectrum disorders.

Introduction: Pseudocontinuous Arterial Spin Labeling (pCASL) perfusion imaging allows non-invasive quantification of regional cerebral blood flow (CBF) as part of a multimodal magnetic resonance imaging (MRI) protocol. This study aimed to compare regional CBF in autism spectrum disorders (ASD) individuals with their age-matched typically developing (TD) children using pCASL perfusion imaging. Materials and methods: This cross-sectional study enrolled 17 individuals with ASD and 13 TD children. All participants underwent pCASL examination on a 3.0 T MRI scanner. Children in two groups were assessed for clinical characteristics and developmental profiles using Autism Behavior Checklist (ABC) and Gesell development diagnosis scale (GDDS), respectively. We compared CBF in different cerebral regions of ASD and TD children. We also assessed the association between CBF and clinical characteristics/developmental profile. Results: Compared with TD children, individuals with ASD demonstrated a reduction in CBF in the left frontal lobe, the bilateral parietal lobes, and the bilateral temporal lobes. Within the ASD group, CBF was significantly higher in the right parietal lobe than in the left side. Correlation analysis of behavior characteristics and CBF in different regions showed a positive correlation between body and object domain scores on the ABC and CBF of the bilateral occipital lobes, and separately, between language domain scores and CBF of the left frontal lobe. The score of the social and self-help domain was negatively correlated with the CBF of the left frontal lobe, the left parietal lobe, and the left temporal lobe. Conclusion: Cerebral blood flow was found to be negatively correlated with scores in the social and self-help domain, and positively correlated with those in the body and object domain, indicating that CBF values are a potential MRI-based biomarker of disease severity in ASD patients. The findings may provide novel insight into the pathophysiological mechanisms of ASD.