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

Defining Developmental Potency and Cell Lineage Trajectories by Expression Profiling of Differentiating Mouse Embryonic Stem Cells

Biologists rely on morphology, function and specific markers to define the differentiation status of cells. Transcript profiling has expanded the repertoire of these markers by providing the snapshot of cellular status that reflects the activity of all genes. However, such data have been used only to assess relative similarities and differences of these cells. Here we show that principal component analysis of global gene expression profiles map cells in multidimensional transcript profile space and the positions of differentiating cells progress in a stepwise manner along trajectories starting from undifferentiated embryonic stem (ES) cells located in the apex. We present three ‘cell lineage trajectories’, which represent the differentiation of ES cells into the first three lineages in mammalian development: primitive endoderm, trophoblast and primitive ectoderm/neural ectoderm. The positions of the cells along these trajectories seem to reflect the developmental potency of cells and can be used as a scale for the potential of cells. Indeed, we show that embryonic germ cells and induced pluripotent cells are mapped near the origin of the trajectories, whereas mouse embryo fibroblast and fibroblast cell lines are mapped near the far end of the trajectories. We suggest that this method can be used as the non-operational semi-quantitative definition of cell differentiation status and developmental potency. Furthermore, the global expression profiles of cell lineages provide a framework for the future study of in vitro and in vivo cell differentiation

Array-based DNA methylation profiling of primary lymphomas of the central nervous system

<p>Abstract</p> <p>Background</p> <p>Although primary lymphomas of the central nervous system (PCNSL) and extracerebral diffuse large B-cell lymphoma (DLBCL) cannot be distinguished histologically, it is still a matter of debate whether PCNSL differ from systemic DLBCL with respect to their molecular features and pathogenesis. Analysis of the DNA methylation pattern might provide further data distinguishing these entities at a molecular level.</p> <p>Methods</p> <p>Using an array-based technology we have assessed the DNA methylation status of 1,505 individual CpG loci in five PCNSL and compared the results to DNA methylation profiles of 49 DLBCL and ten hematopoietic controls.</p> <p>Results</p> <p>We identified 194 genes differentially methylated between PCNSL and normal controls. Interestingly, Polycomb target genes and genes with promoters showing a high CpG content were significantly enriched in the group of genes hypermethylated in PCNSL. However, PCNSL and systemic DLBCL did not differ in their methylation pattern.</p> <p>Conclusions</p> <p>Based on the data presented here, PCNSL and DLBCL do not differ in their DNA methylation pattern. Thus, DNA methylation analysis does not support a separation of PCNSL and DLBCL into individual entities. However, PCNSL and DLBCL differ in their DNA methylation pattern from non- malignant controls.</p

Independent component analysis of Alzheimer's DNA microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, high-dimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA) and independent component analysis (ICA) have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics.</p> <p>Results</p> <p>ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD) hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In comparison to the PCA and support vector machine recursive feature elimination (SVM-RFE) methods, which are widely used in microarray data analysis, ICA can identify more AD-related genes. Furthermore, we have validated and identified many genes that are associated with AD pathogenesis.</p> <p>Conclusion</p> <p>We demonstrated that ICA exploits higher-order statistics to identify gene expression profiles as linear combinations of elementary expression patterns that lead to the construction of potential AD-related pathogenic pathways. Our computing results also validated that the ICA model outperformed PCA and the SVM-RFE method. This report shows that ICA as a microarray data analysis tool can help us to elucidate the molecular taxonomy of AD and other multifactorial and polygenic complex diseases.</p

Analysis of the heat shock response in mouse liver reveals transcriptional dependence on the nuclear receptor peroxisome proliferator-activated receptor α (PPARα)

<p>Abstract</p> <p>Background</p> <p>The nuclear receptor peroxisome proliferator-activated receptor alpha (PPARα) regulates responses to chemical or physical stress in part by altering expression of genes involved in proteome maintenance. Many of these genes are also transcriptionally regulated by heat shock (HS) through activation by HS factor-1 (HSF1). We hypothesized that there are interactions on a genetic level between PPARα and the HS response mediated by HSF1.</p> <p>Results</p> <p>Wild-type and PPARα-null mice were exposed to HS, the PPARα agonist WY-14,643 (WY), or both; gene and protein expression was examined in the livers of the mice 4 or 24 hrs after HS. Gene expression profiling identified a number of <it>Hsp </it>family members that were altered similarly in both mouse strains. However, most of the targets of HS did not overlap between strains. A subset of genes was shown by microarray and RT-PCR to be regulated by HS in a PPARα-dependent manner. HS also down-regulated a large set of mitochondrial genes specifically in PPARα-null mice that are known targets of PPARγ co-activator-1 (PGC-1) family members. Pretreatment of PPARα-null mice with WY increased expression of PGC-1β and target genes and prevented the down-regulation of the mitochondrial genes by HS. A comparison of HS genes regulated in our dataset with those identified in wild-type and HSF1-null mouse embryonic fibroblasts indicated that although many HS genes are regulated independently of both PPARα and HSF1, a number require both factors for HS responsiveness.</p> <p>Conclusions</p> <p>These findings demonstrate that the PPARα genotype has a dramatic effect on the transcriptional targets of HS and support an expanded role for PPARα in the regulation of proteome maintenance genes after exposure to diverse forms of environmental stress including HS.</p

Osteoarthritis: pathogenesis and therapeutic interventions for a whole joint disease

__Abstract__ Osteoarthritis (OA) is an invalidating disease characterized by progressive cartilage degradation. OA is the most prevalent arthritic disease and leading cause of disability that effects approximately 34% of the population in the United states over age 65. Also in the Netherlands, approximately 30% of persons aged 65 and older are affected in either the hip or knee joint by this severely disabling disease. Due to the obvious cartilage pathology, research has much focused on articular cartilage and chondrocyte pathobiology. Over the years more knowledge has been gained on complex biochemical and biomechanical influences of chondrocyte behavior. During the past decade, however, pathologic cellular and structural changes in subchondral and trabecular bone, ligaments, synovium, supporting musculature, fibrocartilagenous structures such as the meniscus, and intra-articular fat tissue support the idea that osteoarthritis is not just a cartilage problem. In the current dogma, OA is explained as ‘a whole joint disease’ that involves a degenerative continuum between multiple joint tissues and cell types

Evaluation of cellular processes and identification of candidate genes critical to corneal epithelial development

The overall aim of this study was to determine factors and mechanisms that underlie the regulation of epithelial patterning and homeostasis during corneal development. Histological staining was performed in chick corneas, embryonic day (ED) 4 to 21, to evaluate changes in the overall epithelial cell morphology, in particular cell shape, cell size and the number of epithelial cell layers. Epithelial differentiation patterns were identified in frozen sections of chicken corneas after immunolocalisation of pan-cytokeratins (Pan-CK) and cytokeratin 3 (CK3). Proliferating Cell Nuclear Antigen (PCNA) and caspase 3 (active) immunolocalisation studies, as well as, TUNEL-labelling (Terminal deoxynucleotidyl transferase dUTP-biotin nick-end labelling) were performed to assess temporal and spatial localisation of cell proliferation and death in the developing corneal epithelium respectively. The expression of PCNA and CK3 were later confirmed by immunoblotting. Total RNA was isolated from epithelia at selected developmental time points and collected for microarray analysis. Gene expression profiles were analysed by appropriate mathematical methods. The sensitivity of arrays in producing data trends was validated by quantitative RT-PCR. Histological findings included changes in stratification an increase in the number of cell layers, change in cell morphology. In this study it was demonstrated that after becoming two layered by ED4, the epithelium underwent further stratification to form intermediate cell layers at about EDM. These changes were accompanied by changes in cell shape commencing at ED10. Cell proliferation appeared high throughout corneal development, with peak proliferation between ED12 and ED14 in the limbal, peripheral and central epithelium, respectively, thereafter the level of proliferation decreased. The above coincided with changes in epithelial morphology (stratification) and changes in expression of cytokeratin (CK) epithelial markers. The appearance of pan-CK labelling was first observed at ED10 and the presence of CK3 immunolabelling appeared in epithelial cells at ED12. TUNEL-labelling and caspase 3 (active) immunolocalisation demonstrated only few TUNEL-positive cells, mostly restricted in the limbal region of the corneal epithelium, in the mid and later developmental stages. Microarray analyses identified gene families and their members (including these involved in stem cell biology) likely to be relevant in the regulation of homeostasis during corneal epithelial development, as well as, differentially expressed genes that reveal changes in biological processes due to the change in time. RT-qPCR confirmed the differential expression patterns of seven genes of interest following analysis of microarray data. Patterns of cell proliferation and differentiation showed changes during the development of the corneal epithelium that reflect the interaction of a complex network of mitogenic, apoptotic and differentiation agents. The changes in gene expression profiles, detected by the microarray analyses, were consistent with the phenotypic changes in the developing chick corneal epithelium. The microarray data provided the first study to present a good overall picture of genes expression in the developing chick corneal epithelium