An integrative analysis of cancer gene expression studies using Bayesian latent factor modeling

We present an applied study in cancer genomics for integrating data and inferences from laboratory experiments on cancer cell lines with observational data obtained from human breast cancer studies. The biological focus is on improving understanding of transcriptional responses of tumors to changes in the pH level of the cellular microenvironment. The statistical focus is on connecting experimentally defined biomarkers of such responses to clinical outcome in observational studies of breast cancer patients. Our analysis exemplifies a general strategy for accomplishing this kind of integration across contexts. The statistical methodologies employed here draw heavily on Bayesian sparse factor models for identifying, modularizing and correlating with clinical outcome these signatures of aggregate changes in gene expression. By projecting patterns of biological response linked to specific experimental interventions into observational studies where such responses may be evidenced via variation in gene expression across samples, we are able to define biomarkers of clinically relevant physiological states and outcomes that are rooted in the biology of the original experiment. Through this approach we identify microenvironment-related prognostic factors capable of predicting long term survival in two independent breast cancer datasets. These results suggest possible directions for future laboratory studies, as well as indicate the potential for therapeutic advances though targeted disruption of specific pathway components.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS261 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

amei: An R Package for the Adaptive Management of Epidemiological Interventions

The amei package for R is a tool that provides a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic. Uncertainty regarding the underlying disease parameters is propagated through to the decision process via Bayesian posterior inference. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. This document briefly covers the background and methodology underpinning the implementation provided by the package and contains extensive examples showing the functions and methods in action.

amei: An R Package for the Adaptive Management of Epidemiological Interventions

The <b>amei</b> package for <b>R</b> is a tool that provides a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic. Uncertainty regarding the underlying disease parameters is propagated through to the decision process via Bayesian posterior inference. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. This document briefly covers the background and methodology underpinning the implementation provided by the package and contains extensive examples showing the functions and methods in action

A Statistical Framework for the Adaptive Management of Epidemiological Interventions

Background: Epidemiological interventions aim to control the spread of infectious disease through various mechanisms, each carrying a different associated cost. Methodology: We describe a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic while simultaneously propagating uncertainty regarding the underlying disease model parameters through to the decision process. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. Conclusions: Using simulation studies based on a classic influenza outbreak, we demonstrate the advantages of adaptiv

Collagen VI regulates motor circuit plasticity and motor performance by cannabinoid modulation

Collagen VI is a key component of muscle basement membranes, and genetic variants can cause monogenic muscular dystrophies. Conversely, human genetic studies recently implicated collagen VI in central nervous system function, with variants causing the movement disorder dystonia. To elucidate the neurophysiological role of collagen VI, we generated mice with a truncation of the dystonia-related collagen alpha 3 (VI) (COL6A3) C-terminal domain (CTD). These Col6a3(CTT) mice showed a recessive dystonia-like phenotype in both sexes. We found that COL6A3 interacts with the cannabinoid receptor 1 (CB1R) complex in a CTD-dependent manner. Col6a3(CTT) mice of both sexes have impaired homeostasis of excitatory input to the basal pontine nuclei (BPN), a motor control hub with dense COL6A3 expression, consistent with deficient endocannabinoid signaling. Aberrant synaptic input in the BPN was normalized by a CB1R agonist, and motor performance in Col6a3(CTT) mice of both sexes was improved by CB1R agonist treatment. Our findings identify a readily therapeutically addressable synaptic mechanism for motor control

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements

The Experiment for Space Radiation Analysis: Probing the Earth\u27s Radiation Belts Using a CubeSat Platform

The Experiment for Space Radiation Analysis (ESRA) is the latest of a series of Demonstration and Validation missions built by the Los Alamos National Laboratory, with the focus on testing a new generation of plasma and energetic particle sensors. The primary motivation for the ESRA payloads is to minimize size, weight, power, and cost while still providing necessary mission data. These new instruments will be demonstrated by ESRA through testing and on-orbit operations to increase their technology readiness level such that they can support the evolution of technology and mission objectives. This project will leverage a commercial off-the-shelf CubeSat avionics bus and commercial satellite ground networks to reduce the cost and timeline associated with traditional DemVal missions. The system will launch as a ride share with the DoD Space Test Program to be inserted in Geosynchronous Transfer Orbit (GTO) and allow observations of the Earth’s radiation belts. The ESRA CubeSat consists of two science payloads and several subsystems: the Wide-field-of-view Plasma Spectrometer, the Energetic Charged Particle telescope, high voltage power supply, payload processor, flight software architecture, and distributed processor module. The ESRA CubeSat will provide measurements of the plasma and energetic charged particle populations in the GTO environment for ions ranging from ~100 eV to ~1000 MeV and electrons with energy ranging from 100 keV to 20 MeV. ESRA will utilize a commercial 12U bus and demonstrate a low-cost, rapidly deployable spaceflight platform with sufficient SWAP to enable efficient measurements of the energetic particle populations in the dynamic radiation belts

Prototype Testing Results of Charged Particle Detectors and Critical Subsystems for the ESRA Mission to GTO

The Experiment for Space Radiation Analysis (ESRA) is the latest of a series of Demonstration and Validation (DemVal) missions built by the Los Alamos National Laboratory, with the focus on testing a new generation of plasma and energetic paritcle sensors along with critical subsystems. The primary motivation for the ESRA payloads is to minimize size, weight, power, and cost while still providing necessary mission data. These new instruments will be demonstrated by ESRA through ground-based testing and on-orbit operations to increase their technology readiness level such that they can support the evolution of technology and mission objectives. This project will leverage a commercial off-the-shelf CubeSat avionics bus and commercial satellite ground networks to reduce the cost and timeline associated with traditional DemVal missions. The system will launch as a ride share with the DoD Space Test Program to be inserted in Geosynchronous Transfer Orbit (GTO) and allow observations of the Earth\u27s radiation belts. The ESRA CubeSat consists of two science payloads and several subsystems: the Wide field-of-view Plasma Spectrometer, the Energetic Charged Particle telescope, high voltage power supply, payload processor, flight software architecture, and distributed processor module. The ESRA CubeSat will provide measurements of the plasma and energetic charged particle populations in the GTO environment for ions ranging from ~100 eV to ~1000 MeV and electrons with energy ranging from 100 keV to 20 MeV. ESRA will utilize a commercial 12U bus and demonstrate a low-cost, rapidly deployable spaceflight platform with sufficient SWAP to enable efficient measurements of the charged particle populations in the dynamic radiation belts

Lactic Acidosis Triggers Starvation Response with Paradoxical Induction of TXNIP through MondoA

Although lactic acidosis is a prominent feature of solid tumors, we still have limited understanding of the mechanisms by which lactic acidosis influences metabolic phenotypes of cancer cells. We compared global transcriptional responses of breast cancer cells in response to three distinct tumor microenvironmental stresses: lactic acidosis, glucose deprivation, and hypoxia. We found that lactic acidosis and glucose deprivation trigger highly similar transcriptional responses, each inducing features of starvation response. In contrast to their comparable effects on gene expression, lactic acidosis and glucose deprivation have opposing effects on glucose uptake. This divergence of metabolic responses in the context of highly similar transcriptional responses allows the identification of a small subset of genes that are regulated in opposite directions by these two conditions. Among these selected genes, TXNIP and its paralogue ARRDC4 are both induced under lactic acidosis and repressed with glucose deprivation. This induction of TXNIP under lactic acidosis is caused by the activation of the glucose-sensing helix-loop-helix transcriptional complex MondoA:Mlx, which is usually triggered upon glucose exposure. Therefore, the upregulation of TXNIP significantly contributes to inhibition of tumor glycolytic phenotypes under lactic acidosis. Expression levels of TXNIP and ARRDC4 in human cancers are also highly correlated with predicted lactic acidosis pathway activities and associated with favorable clinical outcomes. Lactic acidosis triggers features of starvation response while activating the glucose-sensing MondoA-TXNIP pathways and contributing to the “anti-Warburg” metabolic effects and anti-tumor properties of cancer cells. These results stem from integrative analysis of transcriptome and metabolic response data under various tumor microenvironmental stresses and open new paths to explore how these stresses influence phenotypic and metabolic adaptations in human cancers