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Abstract Adaptation to hypothermia is important for skeletal muscle cells under physiological stress and is used for therapeutic hypothermia (mild hypothermia at 32 °C). We show that hypothermic preconditioning at 32 °C for 72 hours improves the differentiation of skeletal muscle myoblasts using both C2C12 and primary myoblasts isolated from 3 month and 18-month-old mice. We analyzed the cold-shock proteome of myoblasts exposed to hypothermia (32 °C for 6 and 48 h) and identified significant changes in pathways related to RNA processing and central carbon, fatty acid, and redox metabolism. The analysis revealed that levels of the cold-shock protein RBM3, an RNA-binding protein, increases with both acute and chronic exposure to hypothermic stress, and is necessary for the enhanced differentiation and maintenance of mitochondrial metabolism. We also show that overexpression of RBM3 at 37 °C is sufficient to promote mitochondrial metabolism, cellular proliferation, and differentiation of C2C12 and primary myoblasts. Proteomic analysis of C2C12 myoblasts overexpressing RBM3 show significant enrichment of pathways involved in fatty acid metabolism, RNA metabolism and the electron transport chain. Overall, we show that the cold-shock protein RBM3 is a critical factor that can be used for controlling the metabolic network of myoblasts.

Senescent cells are characterized by multiple features such as increased expression of senescence-associated β-galactosidase activity (SA β-gal) and cell cycle inhibitors such as p21 or p16. They accumulate with tissue damage and dysregulate tissue homeostasis. In the context of skeletal muscle, it is known that agents used for chemotherapy such as Doxorubicin (Doxo) cause buildup of senescent cells, leading to the inhibition of tissue regeneration. Senescent cells influence the neighboring cells via numerous secreted factors which form the senescence-associated secreted phenotype (SASP). Lipids are emerging as a key component of SASP that can control tissue homeostasis. Arachidonic acid-derived lipids have been shown to accumulate within senescent cells, specifically 15d-PGJ2, which is an electrophilic lipid produced by the non-enzymatic dehydration of the prostaglandin PGD2. This study shows that 15d-PGJ2 is also released by Doxo-induced senescent cells as an SASP factor. Treatment of skeletal muscle myoblasts with the conditioned medium from these senescent cells inhibits myoblast fusion during differentiation. Inhibition of L-PTGDS, the enzyme that synthesizes PGD2, diminishes the release of 15d-PGJ2 by senescent cells and restores muscle differentiation. We further show that this lipid post-translationally modifies Cys184 of HRas in C2C12 mouse skeletal myoblasts, causing a reduction in the localization of HRas to the Golgi, increased HRas binding to Ras Binding Domain (RBD) of RAF Kinase (RAF-RBD), and activation of cellular Mitogen Activated Protein (MAP) kinase–Extracellular Signal Regulated Kinase (Erk) signaling (but not the Akt signaling). Mutating C184 of HRas prevents the ability of 15d-PGJ2 to inhibit the differentiation of muscle cells and control the activity of HRas. This work shows that 15d-PGJ2 released from senescent cells could be targeted to restore muscle homeostasis after chemotherapy.

Summary: Coronavirus nucleocapsid protein (NP) of SARS-CoV-2 plays a central role in many functions important for virus proliferation including packaging and protecting genomic RNA. The protein shares sequence, structure, and architecture with nucleocapsid proteins from betacoronaviruses. The N-terminal domain (NPRBD) binds RNA and the C-terminal domain is responsible for dimerization. After infection, NP is highly expressed and triggers robust host immune response. The anti-NP antibodies are not protective and not neutralizing but can effectively detect viral proliferation soon after infection. Two structures of SARS-CoV-2 NPRBD were determined providing a continuous model from residue 48 to 173, including RNA binding region and key epitopes. Five structures of NPRBD complexes with human mAbs were isolated using an antigen-bait sorting. Complexes revealed a distinct complement-determining regions and unique sets of epitope recognition. This may assist in the early detection of pathogens and designing peptide-based vaccines. Mutations that significantly increase viral load were mapped on developed, full length NP model, likely impacting interactions with host proteins and viral RNA.

Abstract The dynamics and structure of mixed phases in a complex fluid can significantly impact its material properties, such as viscoelasticity. Small-angle X-ray Photon Correlation Spectroscopy (SA-XPCS) can probe the spontaneous spatial fluctuations of the mixed phases under various in situ environments over wide spatiotemporal ranges (10−6–103 s /10−10–10−6 m). Tailored material design, however, requires searching through a massive number of sample compositions and experimental parameters, which is beyond the bandwidth of the current coherent X-ray beamline. Using 3.7-μs-resolved XPCS synchronized with the clock frequency at the Advanced Photon Source, we demonstrated the consistency between the Brownian dynamics of ~100 nm diameter colloidal silica nanoparticles measured from an enclosed pendant drop and a sealed capillary. The electronic pipette can also be mounted on a robotic arm to access different stock solutions and create complex fluids with highly-repeatable and precisely controlled composition profiles. This closed-loop, AI-executable protocol is applicable to light scattering techniques regardless of the light wavelength and optical coherence, and is a first step towards high-throughput, autonomous material discovery.

Abstract Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

Abstract Background Vascular endothelial growth factor A (VEGF-A) plays an integral role in angiogenesis by contributing to growth, development, and metastasis of solid tumors. Recently, a single-nucleotide polymorphism +936C/T located in the VEGF-A 3′ untranslated region (UTR) facilitated the susceptibility of colorectal cancer. The association between VEGF-A gene polymorphism +936C/T and colorectal cancer risk has been widely studied in the last decade, but presently, the results furnished remain enigmatic. Hence, the study aimed to investigate the association between VEGF-A +936C/T miRNA binding site polymorphism and the risk of developing colorectal cancer. Methods This meta-analysis included 13 published case-control studies covering 3465 cases (colorectal cancer) and 3476 healthy controls. Publication bias was examined by means of Begg’s funnel plots and Egger’s regression tests. The quality of the studies included was evaluated using Newcastle-Ottawa scale. Subgroup analyses were performed in accordance to the various ethnicities of the study subjects and the study quality. Results From the data obtained, it is implied that VEGF-A +936C/T polymorphism did not correlate with elevated colorectal cancer risk in all genetic models. But the results acquired from the subgroup analysis in over dominant model (CT vs. CC + TT: OR = 1.5047, 95% CI = 1.19–1.90) suggest that VEGF-A +936C/T polymorphism leads to the raise in the risk of developing CRC among the East Asian population. No association was observed in Caucasian and South Asian population. Conclusions Our results indicate that VEGF-A +936C/T polymorphism is not a risk factor for developing CRC in Caucasian and South Asian population. However, the East Asian population was related to an increased risk of developing colorectal cancer due to the presence of the minor allele.

Abstract A systematic treatment plan of controlling chronic sinusitis, optimizing systemic health, and appropriate selection of surgical technique are essential requirements for successful closure of oroantral fistula.

Presurgical planning forms an important aspect of craniofacial surgery which involves repositioning and reconstruction of facial and skull bones. Digital technology is being increasingly used for planning surgical procedures. Virtual surgical planning, Computer-aided design and Computer-aided modeling greatly improve the speed, scope and accuracy of surgery thereby providing better patient outcomes and satisfaction. In cases of craniofacial anomalies, cutting and positioning guides can be fabricated. For orthognathic surgery, realistic predictions of outcomes can be provided, in addition to improved presurgical planning. In maxillofacial trauma, facial height, width and projection can be better restored. TMJ/Skull base resection and reconstruction can be performed as single-stage operation. Intraoperative and graft ischemic time is reduced during oncologic resection and reconstruction. Potential limitation is added cost but this is offset by benefits like significantly reduced operating time, better dental alignment and improved esthetic outcome. In this review we attempt to briefly describe the historical developments, detail the application and assess the impact of technology for surgical planning and outcome.

Background: Though factors such as diet, oral hygiene, salivary flow and surface characteristics of the tooth enamel play a role in the causation of dental caries, genetic factors also contribute significantly in influencing the susceptibility or resistanance to the occurrence of caries. Aims and Objectives: To analyse the relationship between a single nucleotide polymorphism in the vitamin D receptor gene TaqI locus and associate its presence with dental caries in children. Materials and Methods: A case control study was conducted among 120 children in the age group of 6-12 years. 60 cases with a DMFT of >3 and 60 controls with a DMFT of 0 were selected. Genomic DNA was extracted from salivary samples collected from the patients and presence of polymorphism was analyzed by PCR-RFLP. Results: Pearson chi square test and Fisher exact tests were used for comparison of differences in genotypes or allele frequencies between groups. No significant difference between the cases and control was observed among the different genotypes and the alleles. However, there was an inclination in the incidence of caries with the genotype 'tt' and 'Tt' when compared with the genotype'TT'. Conclusion: In this study done to analyse the SNP in vitamin D receptor gene and its association with dental caries in children indicates a higher caries risk for a patient with't' allele and 'tt' genotype.

Intraoral squamous papilloma is an uncommon lesion which presents as a verrucous, exophytic or papillary growth of the oral mucosa. Human Papilloma Virus (HPV) is often detected associated with this condition but HPV may be an incidental finding. In this report we present the surgical management of squamous papilloma of hard palate and discuss its association with HPV.

Abstract Background Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by larger and larger data sets. This vastly increased computational demand challenges the feasibility of conducting cutting-edge research. One solution is to distribute the vast computational workload across multiple computing cluster nodes with data parallelism algorithms. In this study, we used a High-Performance Computing environment and implemented the Downpour Stochastic Gradient Descent algorithm for data parallelism to train a Convolutional Neural Network (CNN) for the natural language processing task of information extraction from a massive dataset of cancer pathology reports. We evaluated the scalability improvements using data parallelism training and the Titan supercomputer at Oak Ridge Leadership Computing Facility. To evaluate scalability, we used different numbers of worker nodes and performed a set of experiments comparing the effects of different training batch sizes and optimizer functions. Results We found that Adadelta would consistently converge at a lower validation loss, though requiring over twice as many training epochs as the fastest converging optimizer, RMSProp. The Adam optimizer consistently achieved a close 2nd place minimum validation loss significantly faster; using a batch size of 16 and 32 allowed the network to converge in only 4.5 training epochs. Conclusions We demonstrated that the networked training process is scalable across multiple compute nodes communicating with message passing interface while achieving higher classification accuracy compared to a traditional machine learning algorithm.

Abstract Background We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. Results We use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely Fs-peptide (14 μs aggregate sampling), villin head piece (single trajectory of 125 μs) and β- β- α (BBA) protein (223 + 102 μs sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts, on average, nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. Conclusions Together, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks-site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

Summary: Vitamin D has multiple roles, including the regulation of bone and calcium homeostasis. Deficiency of 25-hydroxyvitamin D, the major circulating form of vitamin D, is associated with an increased risk of age-related chronic diseases, including Alzheimer’s disease, Parkinson’s disease, cognitive impairment, and cancer. In this study, we utilized Caenorhabditis elegans to examine the mechanism by which vitamin D influences aging. We found that vitamin-D3-induced lifespan extension requires the stress response pathway genes skn-1, ire-1, and xbp-1. Vitamin D3 (D3) induced expression of SKN-1 target genes but not canonical targets of XBP-1. D3 suppressed an important molecular pathology of aging, that of widespread protein insolubility, and prevented toxicity caused by human β-amyloid. Our observation that D3 improves protein homeostasis and slows aging highlights the importance of maintaining appropriate vitamin D serum levels and may explain why such a wide variety of human age-related diseases are associated with vitamin D deficiency. : Maintenance of protein homeostasis is crucial to cellular health and contributes significantly to the lifespan of organisms. Mark et al. demonstrate that vitamin D supplementation promotes protein homeostasis and slows aging in the nematode, C. elegans. These findings identify a mechanism by which vitamin D influences aging. Keywords: Ceanorhabditis elegans, vitamin D, lifespan. aging, insoluble protein, SKN-1, XBP-1, IRE-1, proteostasis, protein aggregation, Alzheimer’s disease

Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) play important roles in many aspects of normal cell physiology, such as signal transduction and transcription, as well as pathological states, including Alzheimer's, Parkinson's, and Huntington's disease. Unlike their globular counterparts that are defined by a few structures and free energy minima, IDP/IDR comprise a large ensemble of rapidly interconverting structures and a corresponding free energy landscape characterized by multiple minima. This aspect has precluded the use of structural biological techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR) for resolving their structures. Instead, low-resolution techniques, such as small-angle X-ray or neutron scattering (SAXS/SANS), have become a mainstay in characterizing coarse features of the ensemble of structures. These are typically complemented with NMR data if possible or computational techniques, such as atomistic molecular dynamics, to further resolve the underlying ensemble of structures. However, over the past 10–15 years, it has become evident that the classical, pairwise-additive force fields that have enjoyed a high degree of success for globular proteins have been somewhat limited in modeling IDP/IDR structures that agree with experiment. There has thus been a significant effort to rehabilitate these models to obtain better agreement with experiment, typically done by optimizing parameters in a piecewise fashion. In this work, we take a different approach by optimizing a set of force field parameters simultaneously, using machine learning to adapt force field parameters to experimental SAXS scattering profiles. We demonstrate our approach in modeling three biologically IDP ensembles based on experimental SAXS profiles and show that our optimization approach significantly improve force field parameters that generate ensembles in better agreement with experiment.

Viral BCL2 proteins (vBCL2s) help to sustain chronic infection of host proteins to inhibit apoptosis and autophagy. However, details of conformational changes in vBCL2s that enable binding to BH3Ds remain unknown. Using all-atom, multiple microsecond-long molecular dynamic simulations (totaling 17 μs) of the murine γ-herpesvirus 68 vBCL2 (M11), and statistical inference techniques, we show that regions of M11 transiently unfold and refold upon binding of the BH3D. Further, we show that this partial unfolding/refolding within M11 is mediated by a network of hydrophobic interactions, which includes residues that are 10 Å away from the BH3D binding cleft. We experimentally validate the role of these hydrophobic interactions by quantifying the impact of mutating these residues on binding to the Beclin1/BECN1 BH3D, demonstrating that these mutations adversely affect both protein stability and binding. To our knowledge, this is the first study detailing the binding-associated conformational changes and presence of long-range interactions within vBCL2s.

Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnostics data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. Calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.

Ectopic calcification is a driving force for a variety of diseases, including kidney stones and atherosclerosis, but initiating factors remain largely unknown. Given its importance in seemingly divergent disease processes, identifying fundamental principal actors for ectopic calcification may have broad translational significance. Here we establish a Drosophila melanogaster model for ectopic calcification by inhibiting xanthine dehydrogenase whose deficiency leads to kidney stones in humans and dogs. Micro X-ray absorption near edge spectroscopy (μXANES) synchrotron analyses revealed high enrichment of zinc in the Drosophila equivalent of kidney stones, which was also observed in human kidney stones and Randall's plaques (early calcifications seen in human kidneys thought to be the precursor for renal stones). To further test the role of zinc in driving mineralization, we inhibited zinc transporter genes in the ZnT family and observed suppression of Drosophila stone formation. Taken together, genetic, dietary, and pharmacologic interventions to lower zinc confirm a critical role for zinc in driving the process of heterogeneous nucleation that eventually leads to stone formation. Our findings open a novel perspective on the etiology of urinary stones and related diseases, which may lead to the identification of new preventive and therapeutic approaches.

In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain unclear. Here, we use non-negative matrix factorization (NMF)--a dimensionality reduction technique--to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor dimensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner. We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures.

Proteins are intrinsically flexible molecules. The role of internal motions in a protein's designated function is widely debated. The role of protein structure in enzyme catalysis is well established, and conservation of structural features provides vital clues to their role in function. Recently, it has been proposed that the protein function may involve multiple conformations: the observed deviations are not random thermodynamic fluctuations; rather, flexibility may be closely linked to protein function, including enzyme catalysis. We hypothesize that the argument of conservation of important structural features can also be extended to identification of protein flexibility in interconnection with enzyme function. Three classes of enzymes (prolyl-peptidyl isomerase, oxidoreductase, and nuclease) that catalyze diverse chemical reactions have been examined using detailed computational modeling. For each class, the identification and characterization of the internal protein motions coupled to the chemical step in enzyme mechanisms in multiple species show identical enzyme conformational fluctuations. In addition to the active-site residues, motions of protein surface loop regions (>10 Å away) are observed to be identical across species, and networks of conserved interactions/residues connect these highly flexible surface regions to the active-site residues that make direct contact with substrates. More interestingly, examination of reaction-coupled motions in non-homologous enzyme systems (with no structural or sequence similarity) that catalyze the same biochemical reaction shows motions that induce remarkably similar changes in the enzyme-substrate interactions during catalysis. The results indicate that the reaction-coupled flexibility is a conserved aspect of the enzyme molecular architecture. Protein motions in distal areas of homologous and non-homologous enzyme systems mediate similar changes in the active-site enzyme-substrate interactions, thereby impacting the mechanism of catalyzed chemistry. These results have implications for understanding the mechanism of allostery, and for protein engineering and drug design.

Internal motions enable proteins to explore a range of conformations, even in the vicinity of native state. The role of conformational fluctuations in the designated function of a protein is widely debated. Emerging evidence suggests that sub-groups within the range of conformations (or sub-states) contain properties that may be functionally relevant. However, low populations in these sub-states and the transient nature of conformational transitions between these sub-states present significant challenges for their identification and characterization.To overcome these challenges we have developed a new computational technique, quasi-anharmonic analysis (QAA). QAA utilizes higher-order statistics of protein motions to identify sub-states in the conformational landscape. Further, the focus on anharmonicity allows identification of conformational fluctuations that enable transitions between sub-states. QAA applied to equilibrium simulations of human ubiquitin and T4 lysozyme reveals functionally relevant sub-states and protein motions involved in molecular recognition. In combination with a reaction pathway sampling method, QAA characterizes conformational sub-states associated with cis/trans peptidyl-prolyl isomerization catalyzed by the enzyme cyclophilin A. In these three proteins, QAA allows identification of conformational sub-states, with critical structural and dynamical features relevant to protein function.Overall, QAA provides a novel framework to intuitively understand the biophysical basis of conformational diversity and its relevance to protein function.