Overview of NSTX Upgrade initial results and modelling highlights

The National Spherical Torus Experiment (NSTX) has undergone a major upgrade, and the NSTX Upgrade (NSTX-U) Project was completed in the summer of 2015. NSTX-U first plasma was subsequently achieved, diagnostic and control systems have been commissioned, the H-mode accessed, magnetic error fields identified and mitigated, and the first physics research campaign carried out. During ten run weeks of operation, NSTX-U surpassed NSTX record pulse-durations and toroidal fields (TF), and high-performance similar to 1 MA H-mode plasmas comparable to the best of NSTX have been sustained near and slightly above the n = 1 no-wall stability limit and with H-mode confinement multiplier H-98y,H-2 above 1. Transport and turbulence studies in L-mode plasmas have identified the coexistence of at least two ion-gyro-scale turbulent micro-instabilities near the same radial location but propagating in opposite (i.e. ion and electron diamagnetic) directions. These modes have the characteristics of ion-temperature gradient and micro-tearing modes, respectively, and the role of these modes in contributing to thermal transport is under active investigation. The new second more tangential neutral beam injection was observed to significantly modify the stability of two types of Alfven eigenmodes. Improvements in offline disruption forecasting were made in the areas of identification of rotating MHD modes and other macroscopic instabilities using the disruption event characterization and forecasting code. Lastly, the materials analysis and particle probe was utilized on NSTX-U for the first time and enabled assessments of the correlation between boronized wall conditions and plasma performance. These and other highlights from the first run campaign of NSTX-U are described

Cell autonomous expression of inflammatory genes in biologically aged fibroblasts associated with elevated NF-kappaB activity

<p>Abstract</p> <p>Background</p> <p>Chronic inflammation is a well-known corollary of the aging process and is believed to significantly contribute to morbidity and mortality of many age-associated chronic diseases. However, the mechanisms that cause age-associated inflammatory changes are not well understood. Particularly, the contribution of cell stress responses to age-associated inflammation in 'non-inflammatory' cells remains poorly defined. The present cross-sectional study focused on differences in molecular signatures indicative of inflammatory states associated with biological aging of human fibroblasts from donors aged 22 to 92 years.</p> <p>Results</p> <p>Gene expression profiling revealed elevated steady-state transcript levels consistent with a chronic inflammatory state in fibroblast cell-strains obtained from older donors. We also observed enhanced NF-κB DNA binding activity in a subset of strains, and the NF-κB profile correlated with mRNA expression levels characteristic of inflammatory processes, which include transcripts coding for cytokines, chemokines, components of the complement cascade and MHC molecules. This intrinsic low-grade inflammatory state, as it relates to aging, occurs in cultured cells irrespective of the presence of other cell types or the <it>in vivo </it>context.</p> <p>Conclusion</p> <p>Our results are consistent with the view that constitutive activation of inflammatory pathways is a phenomenon prevalent in aged fibroblasts. It is possibly part of a cellular survival process in response to compromised mitochondrial function. Importantly, the inflammatory gene expression signature described here is cell autonomous, i.e. occurs in the absence of prototypical immune or pro-inflammatory cells, growth factors, or other inflammatory mediators.</p

Rule-Based Cell Systems Model of Aging using Feedback Loop Motifs Mediated by Stress Responses

Investigating the complex systems dynamics of the aging process requires integration of a broad range of cellular processes describing damage and functional decline co-existing with adaptive and protective regulatory mechanisms. We evolve an integrated generic cell network to represent the connectivity of key cellular mechanisms structured into positive and negative feedback loop motifs centrally important for aging. The conceptual network is casted into a fuzzy-logic, hybrid-intelligent framework based on interaction rules assembled from a priori knowledge. Based upon a classical homeostatic representation of cellular energy metabolism, we first demonstrate how positive-feedback loops accelerate damage and decline consistent with a vicious cycle. This model is iteratively extended towards an adaptive response model by incorporating protective negative-feedback loop circuits. Time-lapse simulations of the adaptive response model uncover how transcriptional and translational changes, mediated by stress sensors NF-κB and mTOR, counteract accumulating damage and dysfunction by modulating mitochondrial respiration, metabolic fluxes, biosynthesis, and autophagy, crucial for cellular survival. The model allows consideration of lifespan optimization scenarios with respect to fitness criteria using a sensitivity analysis. Our work establishes a novel extendable and scalable computational approach capable to connect tractable molecular mechanisms with cellular network dynamics underlying the emerging aging phenotype

Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

<p>Abstract</p> <p>Background</p> <p>For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or <it>in-silico </it>deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.</p> <p>Results</p> <p>Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.</p> <p>Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.</p> <p>Conclusions</p> <p>The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.</p

Disease-Aging Network Reveals Significant Roles of Aging Genes in Connecting Genetic Diseases

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system–based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases

Delayed and Accelerated Aging Share Common Longevity Assurance Mechanisms

Mutant dwarf and calorie-restricted mice benefit from healthy aging and unusually long lifespan. In contrast, mouse models for DNA repair-deficient progeroid syndromes age and die prematurely. To identify mechanisms that regulate mammalian longevity, we quantified the parallels between the genome-wide liver expression profiles of mice with those two extremes of lifespan. Contrary to expectation, we find significant, genome-wide expression associations between the progeroid and long-lived mice. Subsequent analysis of significantly over-represented biological processes revealed suppression of the endocrine and energy pathways with increased stress responses in both delayed and premature aging. To test the relevance of these processes in natural aging, we compared the transcriptomes of liver, lung, kidney, and spleen over the entire murine adult lifespan and subsequently confirmed these findings on an independent aging cohort. The majority of genes showed similar expression changes in all four organs, indicating a systemic transcriptional response with aging. This systemic response included the same biological processes that are triggered in progeroid and long-lived mice. However, on a genome-wide scale, transcriptomes of naturally aged mice showed a strong association to progeroid but not to long-lived mice. Thus, endocrine and metabolic changes are indicative of “survival” responses to genotoxic stress or starvation, whereas genome-wide associations in gene expression with natural aging are indicative of biological age, which may thus delineate pro- and anti-aging effects of treatments aimed at health-span extension

Nanoparticles for Applications in Cellular Imaging

In the following review we discuss several types of nanoparticles (such as TiO2, quantum dots, and gold nanoparticles) and their impact on the ability to image biological components in fixed cells. The review also discusses factors influencing nanoparticle imaging and uptake in live cells in vitro. Due to their unique size-dependent properties nanoparticles offer numerous advantages over traditional dyes and proteins. For example, the photostability, narrow emission peak, and ability to rationally modify both the size and surface chemistry of Quantum Dots allow for simultaneous analyses of multiple targets within the same cell. On the other hand, the surface characteristics of nanometer sized TiO2allow efficient conjugation to nucleic acids which enables their retention in specific subcellular compartments. We discuss cellular uptake mechanisms for the internalization of nanoparticles and studies showing the influence of nanoparticle size and charge and the cell type targeted on nanoparticle uptake. The predominant nanoparticle uptake mechanisms include clathrin-dependent mechanisms, macropinocytosis, and phagocytosis

Human serum-derived hydroxy long-chain fatty acids exhibit anti-inflammatory and anti-proliferative activity

<p>Abstract</p> <p>Background</p> <p>Circulating levels of novel long-chain hydroxy fatty acids (called GTAs) were recently discovered in the serum of healthy subjects which were shown to be reduced in subjects with colorectal cancer (CRC), independent of tumor burden or disease stage. The levels of GTAs were subsequently observed to exhibit an inverse association with age in the general population. The current work investigates the biological activity of these fatty acids by evaluating the effects of enriched human serum extracts on cell growth and inflammation.</p> <p>Methods</p> <p>GTAs were extracted from commercially available bulk human serum and then chromatographically separated into enriched (GTA-positive) and depleted (GTA-negative) fractions. SW620, MCF7 and LPS stimulated RAW264.7 cells were treated with various concentrations of the GTA-positive and GTA-negative extracts, and the effects on cell growth and inflammation determined.</p> <p>Results</p> <p>Enriched fractions resulted in poly-ADP ribose polymerase (PARP) cleavage, suppression of NFκB, induction of IκBα, and reduction in NOS2 mRNA transcript levels. In RAW264.7 mouse macrophage cells, incubation with enriched fractions prior to treatment with LPS blocked the induction of several pro-inflammatory markers including nitric oxide, TNFα, IL-1β, NOS2 and COX2.</p> <p>Conclusions</p> <p>Our results show that human serum extracts enriched with endogenous long-chain hydroxy fatty acids possess anti-inflammatory and anti-proliferative activity. These findings support a hypothesis that the reduction of these metabolites with age may result in a compromised ability to defend against uncontrolled cell growth and inflammation, and could therefore represent a significant risk for the development of CRC.</p