Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Quantitative diffusion imaging and genotype-by-sex interactions in a rat model of Alexander disease.

PURPOSE: The clinical diagnosis and classification of Alexander disease (AxD) relies in part on qualitative neuroimaging biomarkers; however, these biomarkers fail to distinguish and discriminate different subtypes of AxD, especially in the presence of overlap in clinical symptoms. To address this gap in knowledge, we applied neurite orientation dispersion and density imaging (NODDI) to an innovative CRISPR-Cas9 rat genetic model of AxD to gain quantitative insights into the neural substrates and brain microstructural changes seen in AxD and to potentially identify novel quantitative NODDI biomarkers of AxD. METHODS: Multi-shell DWI of age- and sex-matched AxD and wild-type Sprague Dawley rats (n = 6 per sex per genotype) was performed and DTI and NODDI measures calculated. A 3 × 2 × 2 analysis of variance model was used to determine the effect of genotype, biological sex, and laterality on quantitative measures of DTI and NODDI across regions of interest implicated in AxD. RESULTS: There is a significant effect of genotype in the amygdala, hippocampus, neocortex, and thalamus in measures of both DTI and NODDI brain microstructure. A genotype by biological sex interaction was identified in DTI and NODDI measures in the corpus callosum, hippocampus, and neocortex. CONCLUSION: We present the first application of NODDI to the study of AxD using a rat genetic model of AxD. Our analysis identifies alterations in NODDI and DTI measures to large white matter tracts and subcortical gray nuclei. We further identified genotype by sex interactions, suggesting a possible role for biological sex in the neuropathogenesis of AxD.

Prevalence and Impact of Workplace Sexual Harassment Experienced by Medical Sonographers.

OBJECTIVE: To assess the prevalence and impact of sexual harassment among a nationwide sample of medical sonographers. METHODS: A survey was distributed anonymously to a convenience sample of medical sonographers via email contacts and sonographer-specific social media pages. Data were analyzed to determine respondent demographics, the prevalence of sexual harassment in the last 2 years, the type and severity of harassment experienced, demographics of perpetrators, personal and institutional responses to such experiences, and the impact of sexual harassment on sonographer physical and mental health and job satisfaction. RESULTS: Of the 220 sonographers (83% female) most (45%) were between 18 and 34 years and identified as white (81%). A total of 192 (87%) reported experiencing at least 1 incident of harassment within the last 2 years. Female respondents experienced higher harassment rates (76%) compared to males (50%, P = .02). The most common forms of harassment were verbal, including suggestive or sexist jokes (69%) and offensive sexist remarks (61%). Perpetrators were predominantly male (78%) and most commonly patients (89%) or their friends/family members (46%). The majority of respondents either ignored the harassing behavior (70%) or treated it like a joke (50%), with only a minority (12%) officially reporting incidents. Of those who reported, 44% were unsatisfied with their institution's response. Among respondents, 34% reported negative impacts of workplace sexual harassment, such as anxiety, depression, sleep loss, or adverse workplace consequences. DISCUSSION: Workplace sexual harassment is a common occurrence for sonographers and often leads to negative health and career outcomes. Further institutional policies to prevent harassment and mitigate its effects are needed.

Understanding the Effects of News-Finds-Me Perception on Health Knowledge and Information Seeking During Public Health Crises.

News-finds-me (NFM) perception is a belief that, in the era of social media, individuals can remain adequately well-informed about current events even if they do not actively seek news. While it has been examined in the context of general and political news, NFM perception has not been explored in the context of other genres of news. Through an online survey involving 1,001 Singaporeans, with the Planned Risk Information Seeking Model, this study examines how NFM perception is related to information seeking and COVID-19 knowledge. An issue-specific NFM perception was also proposed and tested in order to determine whether NFM perception and its associated effects differ when operationalized as general news exposure or issue-specific news relating to COVID-19. The negative relationship between general NFM perception and knowledge and the mediating role of information seeking on social media in this relationship are detected. It is also found that when the NFM perception is issue-specific (i.e. COVID-NFM perception), information insufficiency and intentions of information seeking on social media fully mediated the relationship between NFM perception and knowledge. Theoretical and practical implications are discussed.

Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions.

Computational prediction of drug-target interactions (DTIs) is of particular importance in the process of drug repositioning because of its efficiency in selecting potential candidates for DTIs. A variety of computational methods for predicting DTIs have been proposed over the past decade. Our interest is which methods or techniques are the most advantageous for increasing prediction accuracy. This article provides a comprehensive overview of network-based, machine learning, and integrated DTI prediction methods. The network-based methods handle a DTI network along with drug and target similarities in a matrix form and apply graph-theoretic algorithms to identify new DTIs. Machine learning methods use known DTIs and the features of drugs and target proteins as training data to build a predictive model. Integrated methods combine these two techniques. We assessed the prediction performance of the selected state-of-the-art methods using two different benchmark datasets. Our experimental results demonstrate that the integrated methods outperform the others in general. Some previous methods showed low accuracy on predicting interactions of unknown drugs which do not exist in the training dataset. Combining similarity matrices from multiple features by data fusion was not beneficial in increasing prediction accuracy. Finally, we analyzed future directions for further improvements in DTI predictions.

Increased expression of SLC25A1/CIC causes an autistic-like phenotype with altered neuron morphology.

N ε-lysine acetylation within the lumen of the endoplasmic reticulum is a recently characterized protein quality control system that positively selects properly folded glycoproteins in the early secretory pathway. Overexpression of the endoplasmic reticulum acetyl-CoA transporter AT-1 in mouse forebrain neurons results in increased dendritic branching, spine formation and an autistic-like phenotype that is attributed to altered glycoprotein flux through the secretory pathway. AT-1 overexpressing neurons maintain the cytosolic pool of acetyl-CoA by upregulation of SLC25A1, the mitochondrial citrate/malate antiporter and ATP citrate lyase, which converts cytosolic citrate into acetyl-CoA. All three genes have been associated with autism spectrum disorder, suggesting that aberrant cytosolic-to-endoplasmic reticulum flux of acetyl-CoA can be a mechanistic driver for the development of autism spectrum disorder. We therefore generated a SLC25A1 neuron transgenic mouse with overexpression specifically in the forebrain neurons. The mice displayed autistic-like behaviours with a jumping stereotypy. They exhibited increased steady-state levels of citrate and acetyl-CoA, disrupted white matter integrity with activated microglia and altered synaptic plasticity and morphology. Finally, quantitative proteomic and acetyl-proteomic analyses revealed differential adaptations in the hippocampus and cortex. Overall, our study reinforces the connection between aberrant cytosolic-to-endoplasmic reticulum acetyl-CoA flux and the development of an autistic-like phenotype.

Clinical translational neuroimaging of the antioxidant effect of N-acetylcysteine on neural microstructure.

PURPOSE: Oxidative stress and downstream effectors have emerged as important pathological processes that drive psychiatric illness, suggesting that antioxidants may have a therapeutic role in psychiatric disease. However, no imaging biomarkers are currently available to track therapeutic response. The purpose of this study was to examine whether advanced DWI techniques are able to sensitively detect the potential therapeutic effects of the antioxidant N-acetylcysteine (NAC) in a Disc1 svΔ2 preclinical rat model of psychiatric illness. METHODS: Male and female Disc1 svΔ2 rats and age-matched, sex-matched Sprague-Dawley wild-type controls were treated with a saline vehicle or NAC before ex vivo MRI acquisition at P50. Imaging data were fit to DTI and neurite orientation dispersion and density imaging models and analyzed for region-specific changes in quantitative diffusion metrics. Brains were further processed for cellular quantification of microglial density and morphology. All experiments were repeated for Disc1 svΔ2 rats exposed to chronic early-life stress to test how gene-environment interactions might alter effectiveness of NAC therapy. RESULTS: The DTI and neurite orientation dispersion and density imaging analyses demonstrated amelioration of early-life, sex-specific neural microstructural deficits with concomitant differences in microglial morphology across multiple brain regions relevant to neuropsychiatric illness with NAC treatment, but only in male Disc1 svΔ2 rats. Addition of chronic early-life stress reduced the ability of NAC to restore microstructural deficits. CONCLUSION: These findings provide evidence for a treatment pathway targeting endogenous antioxidant capacity, and the clinical translational utility of neurite orientation dispersion and density imaging microstructural imaging to sensitively detect microstructural alterations resulting from antioxidant treatment.

Designed proteins induce the formation of nanocage-containing extracellular vesicles.

Complex biological processes are often performed by self-organizing nanostructures comprising multiple classes of macromolecules, such as ribosomes (proteins and RNA) or enveloped viruses (proteins, nucleic acids and lipids). Approaches have been developed for designing self-assembling structures consisting of either nucleic acids or proteins, but strategies for engineering hybrid biological materials are only beginning to emerge. Here we describe the design of self-assembling protein nanocages that direct their own release from human cells inside small vesicles in a manner that resembles some viruses. We refer to these hybrid biomaterials as 'enveloped protein nanocages' (EPNs). Robust EPN biogenesis requires protein sequence elements that encode three distinct functions: membrane binding, self-assembly, and recruitment of the endosomal sorting complexes required for transport (ESCRT) machinery. A variety of synthetic proteins with these functional elements induce EPN biogenesis, highlighting the modularity and generality of the design strategy. Biochemical analyses and cryo-electron microscopy reveal that one design, EPN-01, comprises small (~100 nm) vesicles containing multiple protein nanocages that closely match the structure of the designed 60-subunit self-assembling scaffold. EPNs that incorporate the vesicular stomatitis viral glycoprotein can fuse with target cells and deliver their contents, thereby transferring cargoes from one cell to another. These results show how proteins can be programmed to direct the formation of hybrid biological materials that perform complex tasks, and establish EPNs as a class of designed, modular, genetically-encoded nanomaterials that can transfer molecules between cells.

Design of a hyperstable 60-subunit protein dodecahedron. [corrected].

The dodecahedron [corrected] is the largest of the Platonic solids, and icosahedral protein structures are widely used in biological systems for packaging and transport. There has been considerable interest in repurposing such structures for applications ranging from targeted delivery to multivalent immunogen presentation. The ability to design proteins that self-assemble into precisely specified, highly ordered icosahedral structures would open the door to a new generation of protein containers with properties custom-tailored to specific applications. Here we describe the computational design of a 25-nanometre icosahedral nanocage that self-assembles from trimeric protein building blocks. The designed protein was produced in Escherichia coli, and found by electron microscopy to assemble into a homogenous population of icosahedral particles nearly identical to the design model. The particles are stable in 6.7 molar guanidine hydrochloride at up to 80 degrees Celsius, and undergo extremely abrupt, but reversible, disassembly between 2 molar and 2.25 molar guanidinium thiocyanate. The dodecahedron [corrected] is robust to genetic fusions: one or two copies of green fluorescent protein (GFP) can be fused to each of the 60 subunits to create highly fluorescent 'standard candles' for use in light microscopy, and a designed protein pentamer can be placed in the centre of each of the 20 pentameric faces to modulate the size of the entrance/exit channels of the cage. Such robust and customizable nanocages should have considerable utility in targeted drug delivery, vaccine design and synthetic biology.

Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins.

Functional modules can be predicted using genome-wide protein-protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

Plasma signature of neurological disease in the monogenetic disorder Niemann-Pick Type C.

Early diagnosis of neurological disorders would greatly improve their management and treatment. A major hurdle is that inflammatory products of cerebral disease are not easily detected in blood. Inflammation in multiple organs and heterogeneity in disease present additional challenges in distinguishing the extent to which a blood-based marker reflects disease in brain or other afflicted organs. Murine models of the monogenetic disorder Niemann-Pick Type C present aggressive forms of cerebral and liver inflammatory disease. Microarray analyses previously revealed age-dependent changes in innate immunity transcripts in the mouse brain. We have now validated four putative secretory inflammatory markers that are also elevated in mouse liver. We include limited, first time analysis of human Niemann-Pick Type C liver and cerebellum. Furthermore, we utilized 2-hydroxypropyl-ß-cyclodextrin (HPßCD, an emerging therapeutic) administered intraperitoneally in mice, which abrogates inflammatory pathology in the liver but has limited effect on the brain. By analyzing the corresponding effects on inflammatory plasma proteins, we identified cathepsin S as a lead indicator of liver disease. In contrast, lysozyme was a marker of both brain and liver disease. 2-Hydroxypropyl-ß-cyclodextrin had no effect on transcripts of neuron-specific 24-hydroxylase, and its product 24(S)-hydroxycholesterol was not a useful indicator in mouse plasma. Our data suggest that dual analysis of levels of the inflammatory markers lysozyme and cathepsin S may enable detection of multiple distinct states of neurodegeneration in plasma.

Patient, Provider, and Practice Characteristics Predicting Use of Diagnostic Imaging in Primary Care: Cross-Sectional Data From the National Ambulatory Medical Care Survey.

OBJECTIVE: To determine imaging utilization rates in outpatient primary care visits and factors influencing likelihood of imaging use. METHODS: We used 2013 to 2018 National Ambulatory Medical Care Survey cross-sectional data. All visits to primary care clinics during the study period were included in the sample. Descriptive statistics on visit characteristics including imaging utilization were calculated. Logistic regression analyses evaluated the influence of a variety of patient-, provider-, and practice-level variables on the odds of obtaining diagnostic imaging, further subdivided by modality (radiographs, CT, MRI, and ultrasound). The data's survey weighting was accounted for to produce valid national-level estimates of imaging use for US office-based primary care visits. RESULTS: Using survey weights, approximately 2.8 billion patient visits were included. Diagnostic imaging was ordered at 12.5% of visits with radiographs the most common (4.3%) and MRI the least common (0.8%). Imaging utilization was similar or greater among minority patients compared with White, non-Hispanic patients. Physician assistants used imaging at higher rates than physicians, in particular CT at 6.5% of visits compared with 0.7% for doctors of medicine and doctors of osteopathic medicine (odds ratio 5.67, 95% confidence interval 4.07-7.88). CONCLUSION: Disparities in rates of imaging utilization for minorities seen in other health care settings were not present in this sample of primary care visits, supporting that access to primary care is a path to promote health equity. Higher rates of imaging utilization among advanced-level practitioners highlight an opportunity to evaluate imaging appropriateness and promote equitable, high-value imaging among all practitioners.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.

The design of novel protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. A new generation of deep learning methods promises to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Well-being and relative deprivation in a digital era.

Social unrest is a feature of the early 21st century, yet relatively little research binds theoretical aspects with empirical validation of the drivers of protests and revolutions. This study aims to empirically validate the Davies J-Curve considering the digital era, with economic, social, and political factors. Using big data techniques, network analysis, and theoretical analysis, we analyzed countries' similarities by analyzing Human Development Index (HDI) and Worldwide Governance Indicator (WGI) as proxies of social well-being. Results established the existence of a J-Curve during social crises in countries prior to an occurrence of large-scale social unrest. In addition, our results suggest that HDI was not a sufficient indicator regarding countries' experienced well-being, likely because it is missing the highly granular aspects of daily life. We further recommended that other indicators from political and psychological areas should be considered and treated in the data preparation phase for future society-wide well-being research for a more realistic baseline.

Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning.

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug-disease associations. This review reveals existing network-based approaches for predicting drug-disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug-drug similarities based on chemical structures and ATC codes, ontology-based disease-disease similarities, and drug-disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug-drug similarity measurement are crucial for improving disease-side prediction.

18F-SynVesT-1 PET/MR Imaging of the Effect of Gut Microbiota on Synaptic Density and Neurite Microstructure: A Preclinical Pilot Study.

The gut microbiome profoundly influences brain structure and function. The gut microbiome is hypothesized to play a key role in the etiopathogenesis of neuropsychiatric and neurodegenerative illness; however, the contribution of an intact gut microbiome to quantitative neuroimaging parameters of brain microstructure and function remains unknown. Herein, we report the broad and significant influence of a functional gut microbiome on commonly employed neuroimaging measures of diffusion tensor imaging (DTI), neurite orientation dispersion and density (NODDI) imaging, and SV2A 18F-SynVesT-1 synaptic density PET imaging when compared to germ-free animals. In this pilot study, we demonstrate that mice, in the presence of a functional gut microbiome, possess higher neurite density and orientation dispersion and decreased synaptic density when compared to age- and sex-matched germ-free mice. Our results reveal the region-specific structural influences and synaptic changes in the brain arising from the presence of intestinal microbiota. Further, our study highlights important considerations for the development of quantitative neuroimaging biomarkers for precision imaging in neurologic and psychiatric illness.

SLC13A5/sodium-citrate co-transporter overexpression causes disrupted white matter integrity and an autistic-like phenotype.

Endoplasmic reticulum-based N ɛ-lysine acetylation serves as an important protein quality control system for the secretory pathway. Dysfunctional endoplasmic reticulum-based acetylation, as caused by overexpression of the acetyl coenzyme A transporter AT-1 in the mouse, results in altered glycoprotein flux through the secretory pathway and an autistic-like phenotype. AT-1 works in concert with SLC25A1, the citrate/malate antiporter in the mitochondria, SLC13A5, the plasma membrane sodium/citrate symporter and ATP citrate lyase, the cytosolic enzyme that converts citrate into acetyl coenzyme A. Here, we report that mice with neuron-specific overexpression of SLC13A5 exhibit autistic-like behaviours with a jumping stereotypy. The mice displayed disrupted white matter integrity and altered synaptic structure and function. Analysis of both the proteome and acetyl-proteome revealed unique adaptations in the hippocampus and cortex, highlighting a metabolic response that likely plays an important role in the SLC13A5 neuron transgenic phenotype. Overall, our results support a mechanistic link between aberrant intracellular citrate/acetyl coenzyme A flux and the development of an autistic-like phenotype.

Mapping Sex-Specific Neurodevelopmental Alterations in Neurite Density and Morphology in a Rat Genetic Model of Psychiatric Illness.

Neurite orientation dispersion and density imaging (NODDI) is an emerging magnetic resonance (MR) diffusion-weighted imaging (DWI) technique that permits non-invasive quantitative assessment of neurite density and morphology. NODDI has improved our ability to image neuronal microstructure over conventional techniques such as diffusion tensor imaging (DTI) and is particularly suited for studies of the developing brain as it can measure and characterize the dynamic changes occurring in dendrite cytoarchitecture that are critical to early brain development. Neurodevelopmental alterations to the diffusion tensor have been reported in psychiatric illness, but it remains unknown whether advanced DWI techniques such as NODDI are able to sensitively and specifically detect neurodevelopmental changes in brain microstructure beyond those provided by DTI. We show, in an extension of our previous work with a Disc1 svΔ2 rat genetic model of psychiatric illness, the enhanced sensitivity and specificity of NODDI to identify neurodevelopmental and sex-specific changes in brain microstructure that are otherwise difficult to observe with DTI and further corroborate observed changes in brain microstructure to differences in sex-specific systems-level animal behavior. Together, these findings inform the potential application and clinical translational utility of NODDI in studies of brain microstructure in psychiatric illness throughout neurodevelopment and further, the ability of advanced DWI methods such as NODDI to examine the role of biological sex and its influence on brain microstructure in psychiatric illness.

Design and structure of two new protein cages illustrate successes and ongoing challenges in protein engineering.

In recent years, new protein engineering methods have produced more than a dozen symmetric, self-assembling protein cages whose structures have been validated to match their design models with near-atomic accuracy. However, many protein cage designs that are tested in the lab do not form the desired assembly, and improving the success rate of design has been a point of recent emphasis. Here we present two protein structures solved by X-ray crystallography of designed protein oligomers that form two-component cages with tetrahedral symmetry. To improve on the past tendency toward poorly soluble protein, we used a computational protocol that favors the formation of hydrogen-bonding networks over exclusively hydrophobic interactions to stabilize the designed protein-protein interfaces. Preliminary characterization showed highly soluble expression, and solution studies indicated successful cage formation by both designed proteins. For one of the designs, a crystal structure confirmed at high resolution that the intended tetrahedral cage was formed, though several flipped amino acid side chain rotamers resulted in an interface that deviates from the precise hydrogen-bonding pattern that was intended. A structure of the other designed cage showed that, under the conditions where crystals were obtained, a noncage structure was formed wherein a porous 3D protein network in space group I21 3 is generated by an off-target twofold homomeric interface. These results illustrate some of the ongoing challenges of developing computational methods for polar interface design, and add two potentially valuable new entries to the growing list of engineered protein materials for downstream applications.