Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

<p>Abstract</p> <p>Background</p> <p>Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.</p> <p>Results</p> <p>In this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say <it>C</it>₁and <it>C</it>₂). We model the expression at <it>C</it>₁using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from <it>C</it>₂is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains.</p> <p>Conclusion</p> <p>In both cases, the proposed method identified biologically significant genes.</p

High-Resolution Quantification of Focal Adhesion Spatiotemporal Dynamics in Living Cells

Focal adhesions (FAs) are macromolecular complexes that provide a linkage between the cell and its external environment. In a motile cell, focal adhesions change size and position to govern cell migration, through the dynamic processes of assembly and disassembly. To better understand the dynamic regulation of focal adhesions, we have developed an analysis system for the automated detection, tracking, and data extraction of these structures in living cells. This analysis system was used to quantify the dynamics of fluorescently tagged Paxillin and FAK in NIH 3T3 fibroblasts followed via Total Internal Reflection Fluorescence Microscopy (TIRF). High content time series included the size, shape, intensity, and position of every adhesion present in a living cell. These properties were followed over time, revealing adhesion lifetime and turnover rates, and segregation of properties into distinct zones. As a proof-of-concept, we show how a single point mutation in Paxillin at the Jun-kinase phosphorylation site Serine 178 changes FA size, distribution, and rate of assembly. This study provides a detailed, quantitative picture of FA spatiotemporal dynamics as well as a set of tools and methodologies for advancing our understanding of how focal adhesions are dynamically regulated in living cells. A full, open-source software implementation of this pipeline is provided at http://gomezlab.bme.unc.edu/tools

Frequency Domain Analysis Reveals External Periodic Fluctuations Can Generate Sustained p53 Oscillation

p53 is a well-known tumor suppressor protein that regulates many pathways, such as ones involved in cell cycle and apoptosis. The p53 levels are known to oscillate without damping after DNA damage, which has been a focus of many recent studies. A negative feedback loop involving p53 and MDM2 has been reported to be responsible for this oscillatory behavior, but questions remain as how the dynamics of this loop alter in order to initiate and maintain the sustained or undamped p53 oscillation. Our frequency domain analysis suggests that the sustained p53 oscillation is not completely dictated by the negative feedback loop; instead, it is likely to be also modulated by periodic DNA repair-related fluctuations that are triggered by DNA damage. According to our analysis, the p53-MDM2 feedback mechanism exhibits adaptability in different cellular contexts. It normally filters noise and fluctuations exerted on p53, but upon DNA damage, it stops performing the filtering function so that DNA repair-related oscillatory signals can modulate the p53 oscillation. Furthermore, it is shown that the p53-MDM2 feedback loop increases its damping ratio allowing p53 to oscillate at a frequency more synchronized with the other cellular efforts to repair the damaged DNA, while suppressing its inherent oscillation-generating capability. Our analysis suggests that the overexpression of MDM2, observed in many types of cancer, can disrupt the operation of this adaptive mechanism by making it less responsive to the modulating signals after DNA damage occurs

U.S. academic libraries: understanding their web presence and their relationship with economic indicators

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-013-1001-0The main goal of this research is to analyze the web structure and performance of units and services belonging to U.S. academic libraries in order to check their suitability for webometric studies. Our objectives include studying their possible correlation with economic data and assessing their use for complementary evaluation purposes. We conducted a survey of library homepages, institutional repositories, digital collections, and online catalogs (a total of 374 URLs) belonging to the 100 U.S. universities with the highest total expenditures in academic libraries according to data provided by the National Center for Education Statistics. Several data points were taken and analyzed, including web variables (page count, external links, and visits) and economic variables (total expenditures, expenditures on printed and electronic books, and physical visits). The results indicate that the variety of URL syntaxes is wide, diverse and complex, which produces a misrepresentation of academic libraries’ web resources and reduces the accuracy of web analysis. On the other hand, institutional and web data indicators are not highly correlated. Better results are obtained by correlating total library expenditures with URL mentions measured by Google (r = 0.546) and visits measured by Compete (r = 0.573), respectively. Because correlation values obtained are not highly significant, we estimate such correlations will increase if users can avoid linkage problems (due to the complexity of URLs) and gain direct access to log files (for more accurate data about visits).Orduña Malea, E.; Regazzi, JJ. (2014). U.S. academic libraries: understanding their web presence and their relationship with economic indicators. Scientometrics. 98(1):315-336. doi:10.1007/s11192-013-1001-0S315336981Adecannby, J. (2011). 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Substrate Adhesion Regulates Sealing Zone Architecture and Dynamics in Cultured Osteoclasts

The bone-degrading activity of osteoclasts depends on the formation of a cytoskeletal-adhesive super-structure known as the sealing zone (SZ). The SZ is a dynamic structure, consisting of a condensed array of podosomes, the elementary adhesion-mediating structures of osteoclasts, interconnected by F-actin filaments. The molecular composition and structure of the SZ were extensively investigated, yet despite its major importance for bone formation and remodelling, the mechanisms underlying its assembly and dynamics are still poorly understood. Here we determine the relations between matrix adhesiveness and the formation, stability and expansion of the SZ. By growing differentiated osteoclasts on micro-patterned glass substrates, where adhesive areas are separated by non-adhesive PLL-g-PEG barriers, we show that SZ growth and fusion strictly depend on the continuity of substrate adhesiveness, at the micrometer scale. We present a possible model for the role of mechanical forces in SZ formation and reorganization, inspired by the current data

The Involvement of IL-17A in the Murine Response to Sub-Lethal Inhalational Infection with Francisella tularensis

Background: Francisella tularensis is an intercellular bacterium often causing fatal disease when inhaled. Previous reports have underlined the role of cell-mediated immunity and IFNc in the host response to Francisella tularensis infection. Methodology/Principal Findings: Here we provide evidence for the involvement of IL-17A in host defense to inhalational tularemia, using a mouse model of intranasal infection with the Live Vaccine Strain (LVS). We demonstrate the kinetics of IL-17A production in lavage fluids of infected lungs and identify the IL-17A-producing lymphocytes as pulmonary cd and Th17 cells. The peak of IL-17A production appears early during sub-lethal infection, it precedes the peak of immune activation and the nadir of the disease, and then subsides subsequently. Exogenous airway administration of IL-17A or of IL-23 had a limited yet consistent effect of delaying the onset of death from a lethal dose of LVS, implying that IL-17A may be involved in restraining the infection. The protective role for IL-17A was directly demonstrated by in vivo neutralization of IL-17A. Administration of anti IL-17A antibodies concomitantly to a sub-lethal airway infection with 0.16LD50 resulted in a fatal disease. Conclusion: In summary, these data characterize the involvement and underline the protective key role of the IL-17A axis in the lungs from inhalational tularemia

MicroRNA profiling of rhesus macaque embryonic stem cells

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) play important roles in embryonic stem cell (ESC) self-renewal and pluripotency. Numerous studies have revealed human and mouse ESC miRNA profiles. As a model for human-related study, the rhesus macaque is ideal for delineating the regulatory mechanisms of miRNAs in ESCs. However, studies on rhesus macaque (r)ESCs are lacking due to limited rESC availability and a need for systematic analyses of fundamental rESC characteristics.</p> <p>Results</p> <p>We established three rESC lines and profiled microRNA using Solexa sequencing resulting in 304 known and 66 novel miRNAs. MiRNA profiles were highly conserved between rESC lines and predicted target genes were significantly enriched in differentiation pathways. Further analysis of the miRNA-target network indicated that gene expression regulated by miRNAs was negatively correlated to their evolutionary rate in rESCs. Moreover, a cross-species comparison revealed an overall conservation of miRNA expression patterns between human, mouse and rhesus macaque ESCs. However, we identified three miRNA clusters (miR-467, the miRNA cluster in the imprinted Dlk1-Dio3 region and C19MC) that showed clear interspecies differences.</p> <p>Conclusions</p> <p>rESCs share a unique miRNA set that may play critical roles in self-renewal and pluripotency. MiRNA expression patterns are generally conserved between species. However, species and/or lineage specific miRNA regulation changed during evolution.</p

Multivariate curve resolution of time course microarray data

BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements