Bivariate microarray analysis: statistical interpretation of two-channel functional genomics data

Conventional statistical methods for interpreting microarray data require large numbers of replicates in order to provide sufficient levels of sensitivity. We recently described a method for identifying differentially-expressed genes in one-channel microarray data 1. Based on the idea that the variance structure of microarray data can itself be a reliable measure of noise, this method allows statistically sound interpretation of as few as two replicates per treatment condition. Unlike the one-channel array, the two-channel platform simultaneously compares gene expression in two RNA samples. This leads to covariation of the measured signals. Hence, by accounting for covariation in the variance model, we can significantly increase the power of the statistical test. We believe that this approach has the potential to overcome limitations of existing methods. We present here a novel approach for the analysis of microarray data that involves modeling the variance structure of paired expression data in the context of a Bayesian framework. We also describe a novel statistical test that can be used to identify differentially-expressed genes. This method, bivariate microarray analysis (BMA), demonstrates dramatically improved sensitivity over existing approaches. We show that with only two array replicates, it is possible to detect gene expression changes that are at best detected with six array replicates by other methods. Further, we show that combining results from BMA with Gene Ontology annotation yields biologically significant results in a ligand-treated macrophage cell system.     

Inferring gene expression dynamics via functional regression analysis

Background

The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.

Results

Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms.

Conclusion

We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.     

Live imaging and single-cell analysis reveal differential dynamics of autophagy and apoptosis

Autophagy is induced by many cytotoxic stimuli but it is often unclear whether, under specific conditions, autophagy plays a prosurvival or a prodeath role. To answer this critical question we developed a novel methodology that employs automated live microscopy and image analysis to measure autophagy and apoptosis simultaneously in single cells. We used this approach to perform a systems-level analysis of pathway dynamics for both autophagy and apoptosis. We found that induction of autophagy in response to different stimuli is uniformly unimodal; in contrast, cells induce apoptosis in an all-or-none bimodal fashion. By tracking the fate of single cells we found that autophagy precedes apoptosis, and that within the same population apoptosis is delayed in cells that mount a stronger autophagy response. Inhibition of autophagy by knocking down ATG5 promoted apoptosis, thus confirming that autophagy plays a protective role. We anticipate that our single-cell approach will be a powerful tool for gaining a quantitative understanding of the complex regulation of autophagy, its influence on cell fate decisions and its relationship with other cellular pathways.     

Proteomics of breast cancer: From differential to functional analysis

From differential analysis to identify biomarkers, to functional analysis for finding new therapeutic targets, proteomics bring new comprehensive information for a better understanding of the molecular basis of oncology and new perspectives for the clinic. However the major limitation of proteomic investigations, more generally of post-genomic approaches, remains the molecular and cellular complexity of the mammary gland that is still a major challenge.     

Characterization of functional transposable element enhancers in acute myeloid leukemia

Transposable elements(TEs) have been shown to have important gene regulatory functions and their alteration could lead to disease phenotypes. Acute myeloid leukemia(AML) develops as a consequence of a series of genetic changes in hematopoietic precursor cells, including mutations in epigenetic factors. Here, we set out to study the gene regulatory role of TEs in AML. We first explored the epigenetic landscape of TEs in AML patients using ATAC-seq data. We show that a large number of TEs in general, and more specifically mammalian-wide interspersed repeats(MIRs), are more enriched in AML cells than in normal blood cells. We obtained a similar finding when analyzing histone modification data in AML patients. Gene Ontology enrichment analysis showed that genes near MIRs in open chromatin regions are involved in leukemogenesis. To functionally validate their regulatory role, we selected 19 MIR regions in AML cells, and tested them for enhancer activity in an AML cell line(Kasumi-1) and a chronic myeloid leukemia(CML) cell line(K562); the results revealed several MIRs to be functional enhancers. Taken together, our results suggest that TEs are potentially involved in myeloid leukemogenesis and highlight these sequences as potential candidates harboring AML-associated variation.     

Nucleosome structural dynamics and super spiral linking number paradox

The minireview presents recent results obtained in the experiments with DNA minicircles containing reconstituted nucleosomes. This system allows one to register and to characterize conformational dynamics of nucleosomes and of subnucleosomal particles containing histone tetramer (H3-H4)2. In particular, it has revealed an important role of the histone N-terminal tails in this dynamics. Solution of the linking number paradox and relevance of the results obtained to chromatin structural dynamics are discussed.     

Fast dynamics perturbation analysis for prediction of protein functional sites

Background  

We present a fast version of the dynamics perturbation analysis (DPA) algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy D _x . Such regions are associated with functional sites.     

Octopamine boosts snail locomotion: behavioural and cellular analysis

We measured the reduction in locomotion of unrestrained pond snails, Lymnaea stagnalis, subsequent to transdermal application of two selective octopamine antagonists, epinastine and phentolamine. After 3 h in fresh standard snail water following treatment with 4 mM epinastine or 3.5 mM phentolamine, the snails’ speed was reduced to 25 and 56% of the controls (P < 0.001 and P = 0.02, respectively). The snails’ speed decreased as the drug concentration increased. In the isolated CNS, 0.5 mM octopamine increased the firing rate of the pedal A cluster motoneurons, which innervate the cilia of the foot. In normal saline the increase was 26% and in a high magnesium/low calcium saline 22% (P < 0.05 and 0.01, respectively). We conclude that octopamine is likely to modulate snail locomotion, partially through effects on pedal motoneurons.     

Consensus clustering and functional interpretation of gene-expression data

Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFκB and the unfolded protein response in certain B-cell lymphomas.     

A functional analysis of vegetation dynamics in abandoned and restored limestone grasslands

Question: How do various plant functional groups and types are related to regeneration of limestone grasslands and to temporal changes in cover of trees and shrubs? Which mechanisms are responsible for changes in differently treated grasslands? Location: Southern Poland, 50° 01'N, 19° 50'E. Methods: A 12‐year restoration experiment after the clearing of a 35‐yr‐old secondary pine wood developed on limestone grassland, and a parallel observation of succession in neighbouring abandoned grassland were carried out. Changes in cover of species with different functional traits and established strategies in relation to time and cover of shrub layer were analysed using multiple linear regression. Results: Over 12 years the composition and cover of species changed in the studied grasslands, but significant differences still occurred between the old grassland and the grassland restored in former wood gaps and that developed in former closed wood. Despite the very close proximity of the old grassland, the composition of restored grasslands was still much determined by initial conditions. In these grasslands, the cover of Festuco‐Brometea species was significantly lower than in the old grassland. In all sites, the cover of species with large leaves and with leaves distributed regularly along the stem, and the cover of CS strategists increased. With increasing shrub cover in restored grasslands, the cover of annuals and biennials, tall species, and R and CS strategists decreased. Conclusions: Limited availability of seeds seems the principal reason for the weak regeneration of xerothermic limestone grasslands. Trees and shrubs inhibit the developed of both early successional species and perennials occurring in established communities. They significantly hinder regeneration of grasslands and should be cut every five or six years.     

CAT vectors for analysis of eukaryotic promoters and enhancers

We have constructed two sets of plasmids for analysis of factors affecting mammalian gene expression. The pOCAT series contains a bacterial chloramphenicol-resistance expression unit (cat) and no eukaryotic promoter. The pUTKAT series contains the same cat unit under the control of the thymidine-kinase promoter of Herpes simplex virus. These plasmids are designed for testing effects of inserted regulatory elements on cat expression after transient transfection of mammalian cells in culture. We demonstrate here that the pOCAT series is useful for studying activities of inserted eukaryotic promoters, and the pUTKAT series is useful for studying activities of inserted eukaryotic enhancers.     

Noise and functional protein dynamics

The magnetic field dependence of the proton-spin-lattice relaxation rate in rotationally immobilized proteins shows that the one-dimensional character of the protein primary structure causes a dramatic increase in the population of low-frequency motions from 10 kHz to 20 MHz. As a consequence, the probability and rate at which functionally critical conformational states are thermally sampled in a protein are dramatically increased as well, when compared with a three-dimensional lattice structure. Studies of protein dynamics often focus on time periods far shorter than those associated with catalytic function, but we show here that the magnetic field dependence of the proton nuclear spin-lattice relaxation rate in rotationally immobilized proteins reports unambiguously the structural fluctuations in the frequency range from 10 kHz to 20 MHz. This relaxation rate decreases with increasing Larmor frequency according to a power law that derives from the distribution of dynamical states, the localization of the structural disturbances, and the spatial distribution of hydrogen atoms in the structure. The robust theoretical foundation for the spin-relaxation process, loosely characterized as a direct spin-phonon coupling, shows that the disturbances propagate in a space of reduced dimensionality, essentially along the stiff connections of the polypeptide chain. The reduced dimensionality traps the disturbance and changes the efficiency for energy redistribution in the protein and the processes that drive nuclear spin relaxation. We also show that the Larmor frequency dependence of the protein-proton-spin-lattice relaxation rate constant is related to the frequency dependence of force constants and mean-square displacement commonly observed or calculated for proteins. We believe that these approaches give additional physical insight into the character of the extremely low-frequency protein dynamics.