Search

HRas is an important node that controls cellular signaling, proliferation, and differentiation. Mutants of HRas (e.g., the constitutively active HRas V12) can be oncogenic, and can also inhibit myoblast differentiation. The C-terminal cysteines of HRas (Cys181 and Cys184) serve as substrates for intra-cellular reversible palmitoylation and de-palmitoylation reactions, which control its subcellular distribution. The relationship between the C-terminal cysteines of HRas, its intracellular distribution, and its cellular activity has remained unclear. Understanding this relationship has important implications for targeting HRas in pathogenic states where it is activated. In this study, we show that a mutation in the C-terminal of HRas, C181S, is sufficient to cause increased levels of HRas V12 in the Golgi, decreased HRas V12-driven Akt and Erk signaling and reverse the ability of HRas V12 to inhibit myoblast differentiation. This demonstrates the importance of C-terminal cysteines in controlling HRas V12. It has been previously shown that Cys184 can also be irreversibly modified by an electrophilic prostaglandin lipid 15d-PGJ2. This lipid is released by senescent cells as a part of senescence-associated secretory phenotype (SASP). In this study, we show that 15d-PGJ2is secreted by senescent myoblasts formed by treatment with Doxorubicin. We also show that 15d-PGJ2causes decreased levels of HRas within Golgi, activates Erk signaling (but not Akt signaling), and inhibits differentiation of C2C12 myoblasts in an HRas Cys184-dependent fashion. Chemotherapeutics such as Doxorubicin drive senescence and loss of skeletal muscle homeostasis in cancer patients. This study suggests that targeting the senescence-derived synthesis of 15-PGJ2might be a target to promote muscle homeostasis after chemotherapy.

Adaptation to hypothermic stress is important for skeletal muscle cells, but a comprehensive knowledge of molecular mediators is lacking. We show that adaptation to mild hypothermia (320C) improves the ability of skeletal muscle myoblasts to differentiate into myotubesin vitro. We performed proteomic analysis of mouse myoblasts exposed to mild hypothermia for various time points and identified dynamic changes in mitochondrial metabolism and proteostasis. This revealed that RBM3, an RNA-binding protein, increases progressively with acute and chronic exposure to hypothermic stress, and is necessary for the enhanced differentiation upon hypothermic adaptation. We also demonstrate that overexpression of RBM3 at physiological temperatures is sufficient to (i) enhance mitochondrial metabolism as judged by a decrease in the AMPK energy-sensing pathway, (ii) increase levels of proteins associated with translation and increase levels of 4E-BP1 phosphorylation, (iii) increase stem cell markers (MyoD1, PAX7), and improve differentiation of myoblasts from both young and aged mice.

We seek to transform how new and emergent variants of pandemiccausing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pretraining on over 110 million prokaryotic gene sequences and finetuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data.

BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an RNA virus encapsulated by a spike (S) glycoprotein envelope, binds with high affinity to angiotensin converting enzyme 2 (ACE2) during cell entry of a susceptible host. Recent studies suggest nicotinic acetylcholine receptors (nAChRs) play a role in functional ACE2 regulation and nicotine may contribute to the progression of coronavirus disease 2019 (COVID-19). Here, we present evidence for coupling between ACE2 and nAChR through bioinformatic analysis and cell culture experiments. Following molecular and structural protein comparison of over 250 ACE2 vertebrate orthologues, a region of human ACE2 at positions C542-L554 was identified to have sequence similarity to nAChR-binding neurotoxin and rabies virus glycoproteins (RBVG). Furthermore, experiments conducted in PC12 cells indicate a potential for physical interaction between ACE2 and α7 nAChR proteins. Our findings support a model of nAChR involvement in in COVID-19.

This article is a preprint and has not been certified by peer review. It was eventually published online on 14 June 2021 by American Chemical Society (ACS Publications) as: Cho, E. et al. (2021) 'Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites', Journal of Chemical Information and Modeling, 61 (6), pp. 3058 - 3073. doi: 10.1021/acs.jcim.1c00449. Data Availability Statement: The trajectories of Mpro simulations and models of the metastable states can be obtained from the corresponding author. Jupyter-notebooks to generate Markov State Models can be downloaded from 10.6084/m9.figshare.14343725 bioRxiv posts many COVID19-related papers. A reminder: they have not been formally peer-reviewed and should not guide health-related behavior or be reported in the press as conclusive. β-coronaviruses alone have been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a back-up against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensible role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all non-redundant ligand-binding sites available for SARS-CoV2, SARS-CoV and MERS-CoV Mpro. Extensive adaptive sampling has been used to explore conformational dynamics employing convolutional variational auto encoder-based deep learning, and investigates structural conservation of the ligand binding sites using Markov state models across β-coronavirus homologs. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-coronavirus homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor. U.S. Department of Energy, Office of Science, through the Advanced Scientific Computing Research (ASCR), under contract number DEAC05-00OR22725 and the Exascale Computing Project (ECP) (17-SC-20-SC). BI would like to acknowledge the COVID-19 pump-priming grant from the University of Huddersfield for funding computing resources for analysis.

Aging induces progressive organ degeneration and worsening of tissue homeostasis leading to multiple pathologies. Yet, little is known about the mechanisms and molecular intermediates involved. Here, we report that aged aryl hydrocarbon receptor-null mice (AhR-/-) had exacerbated senescence and larger numbers of liver progenitor cells. Senescence-associated markers β-galactosidase (SA-β-Gal), p16Ink4aand p21Cip1and genes of the senescence-associated secretory phenotype (SASP) TNF and IL1 were overexpressed in agedAhR-/-livers. AhR binding to the promoter of those genes, as shown by chromatin immunoprecipitation, likely had a repressive effect maintaining their physiological levels inAhR+/+livers. Furthermore, factors secreted by senescent cells MCP-2, MMP12 and FGF were also produced at higher levels in aged AhR-null livers. Supporting the linkage between senescence and stemness, liver progenitor cells were more abundant inAhR-/-mice, which could probably contribute to their increased hepatocarcinoma burden. These roles of AhR are not liver-specific since adult and embryonic AhR-null fibroblasts acquired cellular senescence upon culturing with overexpression of SA-β-Gal, p16Ink4aand p21Cip1. Notably, depletion of senescent cells with the senolytic agent navitoclax restored basal expression of senescent markers inAhR-/-fibroblasts. Oppositely, senescence promoter palbociclib induced an AhR-null like phenotype inAhR+/+fibroblasts. Moreover, doxycycline-induced senescence reduced AhR levels while depletion of p16Ink4a-expressing senescent cells restored basal AhR levels in mouse lungs. Thus, AhR is needed to restrict age-induced senescence, and such activity seems to correlate with a more differentiated phenotype and with increased resistance to liver tumorigenesis.

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.ACM Reference FormatLorenzo Casalino1†, Abigail Dommer1†, Zied Gaieb1†, Emilia P. Barros1, Terra Sztain1, Surl-Hee Ahn1, Anda Trifan2,3, Alexander Brace2, Anthony Bogetti4, Heng Ma2, Hyungro Lee5, Matteo Turilli5, Syma Khalid6, Lillian Chong4, Carlos Simmerling7, David J. Hardy3, Julio D. C. Maia3, James C. Phillips3, Thorsten Kurth8, Abraham Stern8, Lei Huang9, John McCalpin9, Mahidhar Tatineni10, Tom Gibbs8, John E. Stone3, Shantenu Jha5, Arvind Ramanathan2∗, Rommie E. Amaro1∗. 2020. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics. In Supercomputing ’20: International Conference for High Performance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI

We present a novel method to incorporate structural results from surface-sensitive scattering, such as X-ray or neutron reflectometry, into molecular dynamics simulations. While reflectometry techniques generally provide a means to determine the molecular-scale structures of organized interfacial films, they were recently shown to offer the capability to characterize the structures of protein-membrane complexes supported by a solid substrate. One-dimensional information inherent in the experimental results is used in the form of component volume occupancy (CVO) profiles, which describe the distribution of molecular components within an interfacial architecture, to construct real-space constraints in the form of a biasing potential for the simulation that vanishes when the simulated and experimental profiles agree. This approach improves the correspondence between simulation and experiment, as shown in the re-evaluation of an neutron-reflection-derived structure which was approximated by an independent molecular dynamics simulation in earlier work, and it also leads to faster equilibration of ensemble structures. We further show that time averaging the CVO profile that develops in the simulation while biasing with this approach permits fluctuations about the average that are necessary for conformational exploration of the system. This method is particularly valuable for studies of proteins at interfaces that contain disordered regions since the conformation of such regions is difficult to judge from the analysis of one-dimensional experimental profiles and may take prohibitively long to equilibrate in simulations.

Neurofibromin is the protein product of the NF1 gene which is mutated in the Rasopathy disease Neurofibromatosis Type I. Defects in NF1 lead to aberrant signaling through the RAS-MAPK pathway due to disruption of the Neurofibromin GTPase-activating function on RAS family small GTPases. Very little is known about the function of the majority of Neurofibromin—to date, biochemical and structural data exist only for the GAP domain and the region containing a Sec-PH motif. To better understand the role of this large protein, we carried out a series of biochemical and biophysical studies which demonstrate that full length Neurofibromin forms a high-affinity dimer. Neurofibromin dimerization also occurs in cells, and likely has biological and clinical implications. Analysis of purified full-length and truncated variants of Neurofibromin by negative stain electron microscopy reveals the overall architecture of the dimer and predicts the potential interactions which contribute to the dimer interface. Structures resembling high-affinity full-length dimers could be reconstituted by mixing N- and C-terminal protein domains in vitro. Taken together these data suggest how Neurofibromin dimers might form and be stabilized within the cell.

We have constructed NheI and XhoI optical maps ofEscherichia coli O157:H7 solely from genomic DNA molecules to provide a uniquely valuable scaffold for contig closure and sequence validation. E. coli O157:H7 is a common pathogen found in contaminated food and water. Our approach obviated the need for the analysis of clones, PCR products, and hybridizations, because maps were constructed from ensembles of single DNA molecules. Shotgun sequencing of bacterial genomes remains labor-intensive, despite advances in sequencing technology. This is partly due to manual intervention required during the last stages of finishing. The applicability of optical mapping to this problem was enhanced by advances in machine vision techniques that improved mapping throughput and created a path to full automation of mapping. Comparisons were made between maps and sequence data that characterized sequence gaps and guided nascent assemblies.