The Mechanism of Cu,Zn Superoxide Dismutase Aggregation in Familial Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a degenerative disease of the motor neurons characterized by the progressive loss of muscle strength and eventual death due to selective killing of motor neurons in the brain stem and spinal cord. ALS consists of both sporadic and familial subtypes that share the same clinical progression of symptoms. Of the 10% of ALS cases considered familial ALS (FALS), 1 in 5 is the result of a mutation in the enzyme Cu,Zn superoxide dismutase (SOD1). Over 100 mutations have been identified, and though they are distributed evenly throughout the homodimeric structure of SOD1, the mutations have the general property of inducing SOD1 aggregation and toxicity in motor neurons and surrounding glial cells. In recent years, a shift has occurred in ALS research and the broader field of protein aggregation diseases toward the hypothesis that soluble oligomers, rather than the end products of aggregation, are the species responsible for the patterns of toxicity observed in these diseases. Previous studies of SOD1 oligomerization have thus far focused on large-scale oligomers and ignored the earliest stages of oligomerization during which the transition from the native state of SOD1 occurs. Knowledge of structural transformations that initiate SOD1 aggregation, as well as the structure of early oligomeric intermediates, is essential for the design of strategies to prevent the aggregation of SOD1 in FALS. The following chapters contain a multifaceted description of the initiation of SOD1 oligomerization including "first-principles" computational approaches for modeling the formation of aberrant SOD1 dimers, in vitro mechanistic studies of SOD1 oligomerization, as well as the characterization of the in vivo modification state of SOD1. By calling attention to the fact that SOD1 is highly post-translationally modified in-vivo and showing that mutations allow SOD1 to access altogether different oligomeric intermediates than wild type, we lay the groundwork for significant advances in understanding the structural basis of SOD1 oligomerization in ALS

From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows.

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Vance, T. C., Wengren, M., Burger, E., Hernandez, D., Kearns, T., Medina-Lopez, E., Merati, N., O'Brien, K., O'Neil, J., Potemrag, J. T., Signell, R. P., & Wilcox, K. From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows. Frontiers in Marine Science, 6(211), (2019), doi:10.3389/fmars.2019.00211.Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.This is PMEL contribution 4873

Glutathionylation at Cys-111 Induces Dissociation of Wild Type and FALS Mutant SOD1 Dimers

Mutation of the ubiquitous cytosolic enzyme Cu/Zn superoxide dismutase (SOD1) is hypothesized to cause familial amyotrophic lateral sclerosis (FALS) through structural destabilization leading to misfolding and aggregation. Considering the late onset of symptoms as well as the phenotypic variability among patients with identical SOD1 mutations, it is clear that nongenetic factor(s) impact ALS etiology and disease progression. Here we examine the effect of Cys-111 glutathionylation, a physiologically prevalent post-translational oxidative modification, on the stabilities of wild type SOD1 and two phenotypically diverse FALS mutants, A4V and I112T. Glutathionylation results in profound destabilization of SOD1WT dimers, increasing the equilibrium dissociation constant Kd to ~10−20 μM, comparable to that of the aggressive A4V mutant. SOD1A4V is further destabilized by glutathionylation, experiencing an ~30-fold increase in Kd. Dissociation kinetics of glutathionylated SOD1WT and SOD1A4V are unchanged, as measured by surface plasmon resonance, indicating that glutathionylation destabilizes these variants by decreasing association rate. In contrast, SOD1I112T has a modestly increased dissociation rate but no change in Kd when glutathionylated. Using computational structural modeling, we show that the distinct effects of glutathionylation on different SOD1 variants correspond to changes in composition of the dimer interface. Our experimental and computational results show that Cys-111 glutathionylation induces structural rearrangements that modulate stability of both wild type and FALS mutant SOD1. The distinct sensitivities of SOD1 variants to glutathionylation, a modification that acts in part as a coping mechanism for oxidative stress, suggest a novel mode by which redox regulation and aggregation propensity interact in ALS

Protein folding: Then and now

Over the past three decades the protein folding field has undergone monumental changes. Originally a purely academic question, how a protein folds has now become vital in understanding diseases and our abilities to rationally manipulate cellular life by engineering protein folding pathways. We review and contrast past and recent developments in the protein folding field. Specifically, we discuss the progress in our understanding of protein folding thermodynamics and kinetics, the properties of evasive intermediates, and unfolded states. We also discuss how some abnormalities in protein folding lead to protein aggregation and human diseases

Religious Identity, Religious Attendance, and Parental Control

Using a national sample of adolescents aged 10–18 years and their parents (N = 5,117), this article examines whether parental religious identity and religious participation are associated with the ways in which parents control their children. We hypothesize that both religious orthodoxy and weekly religious attendance are related to heightened levels of three elements of parental control: monitoring activities, normative regulations, and network closure. Results indicate that an orthodox religious identity for Catholic and Protestant parents and higher levels of religious attendance for parents as a whole are associated with increases in monitoring activities and normative regulations of American adolescents

Accurate detection of spontaneous seizures using a generalized linear model with external validation

Objective Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes. Methods We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures. Results From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures. Significance This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high-throughput analysis of large number of seizures

Challenges in planning and initiating a randomized clinical study of sphincter of Oddi dysfunction

Sphincter of Oddi dysfunction (SOD) is a controversial topic, especially in patients with no objective findings on laboratory or imaging studies (SOD type III). The value of ERCP manometry with sphincterotomy is unproven and carries significant risks

Can patient and pain characteristics predict manometric sphincter of Oddi dysfunction in patients with clinically suspected sphincter of Oddi dysfunction?

Biliopancreatic-type postcholecystectomy pain, without significant abnormalities on imaging and laboratory test results, has been categorized as “suspected” sphincter of Oddi dysfunction (SOD) type III. Clinical predictors of “manometric” SOD are important to avoid unnecessary ERCP, but are unknown

In-Datacenter Performance Analysis of a Tensor Processing Unit

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.Comment: 17 pages, 11 figures, 8 tables. To appear at the 44th International Symposium on Computer Architecture (ISCA), Toronto, Canada, June 24-28, 201