Regulation of calcium entry by cyclic GMP signaling in Toxoplasma gondii.

Ca2+ signaling impacts almost every aspect of cellular life. Ca2+ signals are generated through the opening of ion channels that permit the flow of Ca2+ down an electrochemical gradient. Cytosolic Ca2+ fluctuations can be generated through Ca2+ entry from the extracellular milieu or release from intracellular stores. In Toxoplasma gondii, Ca2+ ions play critical roles in several essential functions for the parasite, like invasion of host cells, motility, and egress. Plasma membrane Ca2+ entry in T. gondii was previously shown to be activated by cytosolic calcium and inhibited by the voltage-operated Ca2+ channel blocker nifedipine. However, Ca2+ entry in T. gondii did not show the classical characteristics of store regulation. In this work, we characterized the mechanism by which cytosolic Ca2+ regulates plasma membrane Ca2+ entry in extracellular T. gondii tachyzoites loaded with the Ca2+ indicator Fura-2. We compared the inhibition by nifedipine with the effect of the broad spectrum TRP channel inhibitor, anthranilic acid or ACA, and we find that both inhibitors act on different Ca2+ entry activities. We demonstrate, using pharmacological and genetic tools, that an intracellular signaling pathway engaging cyclic GMP, protein kinase G, Ca2+, and the phosphatidyl inositol phospholipase C affects Ca2+ entry and we present a model for crosstalk between cyclic GMP and cytosolic Ca2+ for the activation of T. gondii's lytic cycle traits.

Are we there yet? A machine learning architecture to predict organotropic metastases.

BACKGROUND & AIMS: Cancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage of the disease. To this end, we aimed to utilize transcript expression profiling features to predict the site-specific metastases of primary tumors and second, to identify the determinants of tissue specific progression. METHODS: We used statistical machine learning for transcript feature selection to optimize classification and built tree-based classifiers to predict tissue specific sites of metastatic progression. RESULTS: We developed a novel machine learning architecture that analyzes 33 types of RNA transcriptome profiles from The Cancer Genome Atlas (TCGA) database. Our classifier identifies the tumor type, derives synthetic instances of primary tumors metastasizing to distant organs and classifies the site-specific metastases in 16 types of cancers metastasizing to 12 locations. CONCLUSIONS: We have demonstrated that site specific metastatic progression is predictable using transcriptomic profiling data from primary tumors and that the overrepresented biological processes in tumors metastasizing to congruent distant loci are highly overlapping. These results indicate site-specific progression was organotropic and core features of biological signaling pathways are identifiable that may describe proliferative plasticity in distant soils.

Ca2+ entry at the plasma membrane and uptake by acidic stores is regulated by the activity of the V-H+ -ATPase in Toxoplasma gondii.

Ca2+ is a universal intracellular signal that regulates many cellular functions. In Toxoplasma gondii, the controlled influx of extracellular and intracellular Ca2+ into the cytosol initiates a signaling cascade that promotes pathogenic processes like tissue destruction and dissemination. In this work, we studied the role of proton transport in cytosolic Ca2+ homeostasis and the initiation of Ca2+ signaling. We used a T. gondii mutant of the V-H+ -ATPase, a pump previously shown to transport protons to the extracellular medium, and to control intracellular pH and membrane potential and we show that proton gradients are important for maintaining resting cytosolic Ca2+ at physiological levels and for Ca2+ influx. Proton transport was also important for Ca2+ storage by acidic stores and, unexpectedly, the endoplasmic reticulum. Proton transport impacted the amount of polyphosphate (polyP), a phosphate polymer that binds Ca2+ and concentrates in acidocalcisomes. This was supported by the co-localization of the vacuolar transporter chaperone 4 (VTC4), the catalytic subunit of the VTC complex that synthesizes polyP, with the V-ATPase in acidocalcisomes. Our work shows that proton transport regulates plasma membrane Ca2+ transport and control acidocalcisome polyP and Ca2+ content, impacting Ca2+ signaling and downstream stimulation of motility and egress in T. gondii.

A Distributed Computing Platform for fMRI Big Data Analytics.

Since the BRAIN Initiative and Human Brain Project began, a few efforts have been made to address the computational challenges of neuroscience Big Data. The promises of these two projects were to model the complex interaction of brain and behavior and to understand and diagnose brain diseases by collecting and analyzing large quanitites of data. Archiving, analyzing, and sharing the growing neuroimaging datasets posed major challenges. New computational methods and technologies have emerged in the domain of Big Data but have not been fully adapted for use in neuroimaging. In this work, we introduce the current challenges of neuroimaging in a big data context. We review our efforts toward creating a data management system to organize the large-scale fMRI datasets, and present our novel algorithms/methods for the distributed fMRI data processing that employs Hadoop and Spark. Finally, we demonstrate the significant performance gains of our algorithms/methods to perform distributed dictionary learning.

Predicting risks from down-the-drain chemicals in a developing country: Mexico and linear alkylbenzene sulfonate as a case study.

It is recognized that the amount of natural dilution available can make a significant difference in the exposure and risk assessment of chemicals that emanate from wastewater treatment plants (WWTPs). However, data availability is a common limiting factor in exposure assessments for emerging markets. In the present study, we used a novel approach to derive dilution factors for the receiving waters within 5 km of wastewater discharge points in Mexico by combining locally measured river volumes, ecoregion categorization, data on WWTP capacity, and global river network models. Distributions of wastewater effluent into receiving stream dilution factors were developed for the entire country and organized by ecoregion type to explore spatial differences. The distribution of dilution factors in Mexico ranged from >1000 in tropical and temperate ecoregions to 1 in desert ecoregions. To demonstrate its utility, dilution factors were used to develop a probabilistic model to explore the potential ecological risks of the high-volume surfactant linear alkylbenzene sulfonate (LAS), commonly used in down-the-drain cleaning products. The predicted LAS river exposure values were below the predicted no-effect concentration in all regions. The methodology developed for Mexico can be used to derive refined exposure assessments in other countries with emerging markets throughout the world, resulting in more realistic risk assessments. Environ Toxicol Chem 2018;37:2475-2486. © 2018 SETAC.

P2Y6 Receptor Antagonist MRS2578 Inhibits Neutrophil Activation and Aggregated Neutrophil Extracellular Trap Formation Induced by Gout-Associated Monosodium Urate Crystals.

Human neutrophils (polymorphonuclear leukocytes [PMNs]) generate inflammatory responses within the joints of gout patients upon encountering monosodium urate (MSU) crystals. Neutrophil extracellular traps (NETs) are found abundantly in the synovial fluid of gout patients. The detailed mechanism of MSU crystal-induced NET formation remains unknown. Our goal was to shed light on possible roles of purinergic signaling and neutrophil migration in mediating NET formation induced by MSU crystals. Interaction of human neutrophils with MSU crystals was evaluated by high-throughput live imaging using confocal microscopy. We quantitated NET levels in gout synovial fluid supernatants and detected enzymatically active neutrophil primary granule enzymes, myeloperoxidase, and human neutrophil elastase. Suramin and PPADS, general P2Y receptor blockers, and MRS2578, an inhibitor of the purinergic P2Y6 receptor, blocked NET formation triggered by MSU crystals. AR-C25118925XX (P2Y2 antagonist) did not inhibit MSU crystal-stimulated NET release. Live imaging of PMNs showed that MRS2578 represses neutrophil migration and blocked characteristic formation of MSU crystal-NET aggregates called aggregated NETs. Interestingly, the store-operated calcium entry channel inhibitor (SK&F96365) also reduced MSU crystal-induced NET release. Our results indicate that the P2Y6/store-operated calcium entry/IL-8 axis is involved in MSU crystal-induced aggregated NET formation, but MRS2578 could have additional effects affecting PMN migration. The work presented in the present study could lead to a better understanding of gouty joint inflammation and help improve the treatment and care of gout patients.

ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics.

BACKGROUND: The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact. METHODS: In this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks. RESULTS: We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences. CONCLUSIONS: These quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance.

Automated identification of abnormal respiratory ciliary motion in nasal biopsies.

Motile cilia lining the nasal and bronchial passages beat synchronously to clear mucus and foreign matter from the respiratory tract. This mucociliary defense mechanism is essential for pulmonary health, because respiratory ciliary motion defects, such as those in patients with primary ciliary dyskinesia (PCD) or congenital heart disease, can cause severe sinopulmonary disease necessitating organ transplant. The visual examination of nasal or bronchial biopsies is critical for the diagnosis of ciliary motion defects, but these analyses are highly subjective and error-prone. Although ciliary beat frequency can be computed, this metric cannot sensitively characterize ciliary motion defects. Furthermore, PCD can present without any ultrastructural defects, limiting the use of other detection methods, such as electron microscopy. Therefore, an unbiased, computational method for analyzing ciliary motion is clinically compelling. We present a computational pipeline using algorithms from computer vision and machine learning to decompose ciliary motion into quantitative elemental components. Using this framework, we constructed digital signatures for ciliary motion recognition and quantified specific properties of the ciliary motion that allowed high-throughput classification of ciliary motion as normal or abnormal. We achieved >90% classification accuracy in two independent data cohorts composed of patients with congenital heart disease, PCD, or heterotaxy, as well as healthy controls. Clinicians without specialized knowledge in machine learning or computer vision can operate this pipeline as a "black box" toolkit to evaluate ciliary motion.

Discovering Multi-Scale Co-Occurrence Patterns of Asthma and Influenza with Oak Ridge Bio-Surveillance Toolkit.

We describe a data-driven unsupervised machine learning approach to extract geo-temporal co-occurrence patterns of asthma and the flu from large-scale electronic healthcare reimbursement claims (eHRC) datasets. Specifically, we examine the eHRC data from 2009 to 2010 pandemic H1N1 influenza season and analyze whether different geographic regions within the United States (US) showed an increase in co-occurrence patterns of the flu and asthma. Our analyses reveal that the temporal patterns extracted from the eHRC data show a distinct lag time between the peak incidence of the asthma and the flu. While the increased occurrence of asthma contributed to increased flu incidence during the pandemic, this co-occurrence is predominant for female patients. The geo-temporal patterns reveal that the co-occurrence of the flu and asthma are typically concentrated within the south-east US. Further, in agreement with previous studies, large urban areas (such as New York, Miami, and Los Angeles) exhibit co-occurrence patterns that suggest a peak incidence of asthma and flu significantly early in the spring and winter seasons. Together, our data-analytic approach, integrated within the Oak Ridge Bio-surveillance Toolkit platform, demonstrates how eHRC data can provide novel insights into co-occurring disease patterns.

Automated image analysis for high-content screening and analysis.

The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.