Integrator restrains paraspeckles assembly by promoting isoform switching of the lncRNA NEAT1

RNA 3' end processing provides a source of transcriptome diversification which affects various (patho)-physiological processes. A prime example is the transcript isoform switch that leads to the read-through expression of the long non-coding RNA NEAT1_2, at the expense of the shorter polyadenylated transcript NEAT1_1. NEAT1_2 is required for assembly of paraspeckles (PS), nuclear bodies that protect cancer cells from oncogene-induced replication stress and chemotherapy. Searching for proteins that modulate this event, we identified factors involved in the 3' end processing of polyadenylated RNA and components of the Integrator complex. Perturbation experiments established that, by promoting the cleavage of NEAT1_2, Integrator forces NEAT1_2 to NEAT1_1 isoform switching and, thereby, restrains PS assembly. Consistently, low levels of Integrator subunits correlated with poorer prognosis of cancer patients exposed to chemotherapeutics. Our study establishes that Integrator regulates PS biogenesis and a link between Integrator, cancer biology, and chemosensitivity, which may be exploited therapeutically

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

Characterization of the measurement error structure in 1D 1H NMR data for metabolomics studies

NMR-based metabolomics is characterized by high throughput measurements of the signal intensities of complex mixtures of metabolites in biological samples by assaying, typically, bio-fluids or tissue homogenates. The ultimate goal is to obtain relevant biological information regarding the dissimilarity in patho-physiological conditions that the samples experience. For a long time now, this information has been obtained through the analysis of measured NMR signals via multivariate statistics. NMR data are quite complex and the use of such multivariate statistical methods as principal components analysis (PCA) for their analysis assumes that the data are multivariate normal with errors that are identical, independent and normally distributed (i.e. iid normal). There is a consensus that these assumptions are not always true for these data and, thus, several methods have been devised to transform the data or weight them prior to analysis by PCA. The structure of NMR measurement noise, or the extent to which violations of error homoscedasticity affect PCA results have neither been characterized nor investigated. A comprehensive characterization of measurement uncertainties in NMR based metabolomics was achieved in this work using an experiment designed to capture contributions of several sources of error to the total variance in the measurements. The noise structure was found to be heteroscedastic and highly correlated with spectral characteristics that are similar to the mean of the spectra and their standard deviation. A model was subsequently developed that potentially allows errors in NMR measurements to be accurately estimated without the need for extensive replication.Peer reviewed: YesNRC publication: Ye

An introduction to DNA microarrays for gene expression analysis

This tutorial presents a basic introduction to DNA microarrays as employed for gene expression analysis, approaching the subject from a chemometrics perspective. The emphasis is on describing the nature of the measurement process, from the platforms used to a few of the standard higher-level data analysis tools employed. Topics include experimental design, detection, image processing, measurement errors, ratio calculation, background correction, normalization, and higher-level data processing. The objective is to present the chemometrician with as clear a picture as possible of an evolving technology so that the strengths and limitations of DNA microarrays are appreciated. Although the focus is primarily on spotted, two-color microarrays, a signi\ufb01cant discussion of single-channel, lithographic arrays is also included.Peer reviewed: YesNRC publication: Ye

Real-time monitoring, diagnosis, and time-course analysis of microalgae Scenedesmus AMDD cultivation using dual excitation wavelength fluorometry

Dual excitation wavelength fluorometry was used for real-time monitoring and diagnosis of the growth of the microalgae Scenedesmus AMDD in a 300-L continuous photobioreactor (PBR). Emission spectra were acquired at 1-min intervals using excitation lights at 365 and 540 nm. Real-time dry weight estimations were achieved using linear regression with the chlorophyll peak, while protein estimations required a more complex Principal Component Regression model, which takes advantage of the full emission spectrum. The resulting regression coefficients were 0.95 and 0.80, respectively. Furthermore, the spectra were analyzed using multivariate curve resolution technique. The proposed approach for fluorescence-based, real-time measurements of key algae cultivation parameters and culture state diagnosis was successfully demonstrated in a 42-day PBR validation test.Peer reviewed: YesNRC publication: Ye

Normalization Methods for Time-Course DNA Microarray Data

<p>Normalization is an important step in any DNA microarray analysis, with the goal of removing sources of bias that may interfere with the identification of truly differentially expressed genes. Many different normalization methods have been proposed in the literature, however, the majority are only applicable to experiments wherein two samples are compared to one another (comparator experiments).  Time-course DNA microarray experiments (and other serial types of experiments), wherein the arrays form a pseudo-continuum of expression, have become more common in recent years, with no concomitant discussion or development of appropriate normalization methods. We have developed a conceptually simple technique for normalizing DNA time-course data that we refer to as sequential normalization.</p