Profiling gene expression in whole blood samples following an in-vitro challenge.

Genomics tools (gene- and protein-expression studies) can be used to find possible target genes involved in a quantifiable trait or disease state. However in many instances, cells and tissues directly involved in the trait's expression, for example, brain tissue, are not amenable for gene expression analysis. Whole blood cells share a molecular make-up for cellular communication and gene regulation systems with many other cell types, for example, neuronal cells, and have the advantage of being very accessible for gene profiling. We investigated the feasibility of nationwide blood sample collection for lymphocyte RNA isolation and real-time PCR analysis to quantify genomic responses. We tested several designs for blood collection and storage: blood sampling in PAXgene blood collection tubes and storage at -20 degrees C, blood sampling in heparin tubes and decanting the samples (with or without in-vitro stimulus) into either PAXgene blood collection tubes and storage at -20 degrees C, or polypropylene tubes followed by snap-freezing and storage at -80 degrees C. The latter procedure is the best cost-wise when only small amounts of total RNA are needed for downstream applications. Lymphocyte gene expression studies are most likely hampered by the quality of isolated RNA rather than the sampling method. We show that large-scale nationwide sample collections did not alter RNA quality or gene expression levels when compared to sampling and processing in a more controlled way. To this end, we present an optimized protocol for easy and standardized isolation of high quality RNA using the PAXgene isolation kit. Based on these results, we suggest that whole blood genomic data can be used as a genomic probe in experimental and clinical research.     

Separation of samples into their constituents using gene expression data

Gene expression measurements are a powerful tool in molecular biology, but when applied to heterogeneous samples containing more than one cellular type the results are difficult to interpret. We present here a new approach to this problem allowing to deduce the gene expression profile of the various cellular types contained in a set of samples directly from the measurements taken on the whole sample.     

Computational exploration of the activated pathways associated with DNA damage response in breast cancer

Molecular signaling events regulate cellular activity. Cancer stimulating signals trigger cellular responses that evade the regulatory control of cell development. To understand the mechanism of signaling regulation in cancer, it is necessary to identify the activated pathways in cancer. We have developed RepairPATH, a computational algorithm that explores the activated signaling pathways in cancer. The RepairPATH integrates RepairNET, an assembled protein interaction network associated with DNA damage response, with the gene expression profiles derived from the microarray data. Based on the observation that cofunctional proteins often exhibit correlated gene expression profiles, it identifies the activated signaling pathways in cancer by systematically searching the RepairNET for proteins with significantly correlated gene expression profiles. Analyzing the gene expression profiles of breast cancer, we found distinct similarities and differences in the activated signaling pathways between the samples from the patients who developed metastases and the samples from the patients who were disease free within 5 years. The cellular pathways associated with the various DNA repair mechanisms and the cell-cycle checkpoint controls are found to be activated in both sample groups. One of the most intriguing findings is that the pathways associated with different cellular processes are functionally coordinated through BRCA1 in the disease-free sample group, whereas such functional coordination is absent in the samples from patients who developed metastases. Our analysis revealed the potential cellular pathways that regulate the signaling events in breast cancer.     

Affymetrix GeneChip microarray preprocessing for multivariate analyses

Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.     

Optimized analysis of DNA methylation and gene expression from small, anatomically-defined areas of the brain

Exposure to diet, drugs and early life adversity during sensitive windows of life can lead to lasting changes in gene expression that contribute to the display of physiological and behavioural phenotypes. Such environmental programming is likely to increase the susceptibility to metabolic, cardiovascular and mental diseases. DNA methylation and histone modifications are considered key processes in the mediation of the gene-environment dialogue and appear also to underlay environmental programming. In mammals, DNA methylation typically comprises the covalent addition of a methyl group at the 5-position of cytosine within the context of CpG dinucleotides. CpG methylation occurs in a highly tissue- and cell-specific manner making it a challenge to study discrete, small regions of the brain where cellular heterogeneity is high and tissue quantity limited. Moreover, because gene expression and methylation are closely linked events, increased value can be gained by comparing both parameters in the same sample. Here, a step-by-step protocol (Figure 1) for the investigation of epigenetic programming in the brain is presented using the 'maternal separation' paradigm of early life adversity for illustrative purposes. The protocol describes the preparation of micropunches from differentially-aged mouse brains from which DNA and RNA can be simultaneously isolated, thus allowing DNA methylation and gene expression analyses in the same sample.     

Altered gene expression in lymphoblastoid cell lines after subculture

Lymphoblastoid cell lines (LCLs) are nearly immortalized B lymphocytes that are used as long-lasting supply of human cells for studies on gene expression analyses. However, studies on the stability of the cellular features of LCLs are scarce. To address this issue, we measured gene expression in LCLs with different passage numbers and observed that gene expression substantially changed within 10 passages. In particular, the expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a well-known housekeeping gene, varied considerably during subculture; thus, the use GAPDH as an internal control may be unsuitable. In conclusion, this study highlights the need for exercising caution during determination of gene expression in LCLs.     

Microfluidic single-cell real-time PCR for comparative analysis of gene expression patterns

Single-cell quantitative real-time PCR (qRT-PCR) combined with high-throughput arrays allows the analysis of gene expression profiles at a molecular level in approximately 11 h after cell sample collection. We present here a high-content microfluidic real-time platform as a powerful tool for comparatively investigating the regulation of developmental processes in single cells. This approach overcomes the limitations involving heterogeneous cell populations and sample amounts, and may shed light on differential regulation of gene expression in normal versus disease-related contexts. Furthermore, high-throughput single-cell qRT-PCR provides a standardized, comparative assay for in-depth analysis of the mechanisms underlying human pluripotent stem cell self-renewal and differentiation.     

Gene expression during Drosophila wing morphogenesis and differentiation

The simple cellular composition and array of distally pointing hairs has made the Drosophila wing a favored system for studying planar polarity and the coordination of cellular and tissue level morphogenesis. We carried out a gene expression screen to identify candidate genes that functioned in wing and wing hair morphogenesis. Pupal wing RNA was isolated from tissue prior to, during, and after hair growth and used to probe Affymetrix Drosophila gene chips. We identified 435 genes whose expression changed at least fivefold during this period and 1335 whose expression changed at least twofold. As a functional validation we chose 10 genes where genetic reagents existed but where there was little or no evidence for a wing phenotype. New phenotypes were found for 9 of these genes, providing functional validation for the collection of identified genes. Among the phenotypes seen were a delay in hair initiation, defects in hair maturation, defects in cuticle formation and pigmentation, and abnormal wing hair polarity. The collection of identified genes should be a valuable data set for future studies on hair and bristle morphogenesis, cuticle synthesis, and planar polarity.     

The Pectin Lyases in Arabidopsis thaliana: Evolution,Selection and Expression Profiles

Pectin lyases are a group of enzymes that are thought to contribute to many biological processes, such as the degradation of pectin. However, until this study, no comprehensive study incorporating phylogeny, chromosomal location, gene duplication, gene organization, functional divergence, adaptive evolution, expression profiling and functional networks has been reported for Arabidopsis. Sixty-seven pectin lyase genes have been identified, and most of them possess signal sequences targeting the secretory pathway. Phylogenetic analyses identified five gene groups with considerable conservation among groups. Pectin lyase genes were non-randomly distributed across chromosomes and clustering was evident. Functional divergence and adaptive evolution analyses suggested that purifying selection was the main force driving pectin lyase evolution, although some critical sites responsible for functional divergence might be the consequence of positive selection. A stigma- and receptacle-specific expression promoter was identified, and it had increased expression in response to wounding. Two hundred and eighty-eight interactions were identified by functional network analyses, and most of these were involved in cellular metabolism, cellular transport and localization, and stimulus responses. This investigation contributes to an improved understanding of the complexity of the Arabidopsis pectin lyase gene family.     

From genomes to in silico cells via metabolic networks

Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis of phenotypes from phenotypic arrays and in gene-deletion studies. They have also been used for gene expression analysis guided by metabolic network structure, leading to the identification of commonly regulated genes. Thus, genome-scale metabolic modeling currently stands out as one of the most promising approaches to obtain an in silico prediction of cellular function based on the interaction of all of the cellular components.     

A Systems Biology-Based Gene Expression Classifier of Glioblastoma Predicts Survival with Solid Tumors

Accurate prediction of survival of cancer patients is still a key open problem in clinical research. Recently, many large-scale gene expression clusterings have identified sets of genes reportedly predictive of prognosis; however, those gene sets shared few genes in common and were poorly validated using independent data. We have developed a systems biology-based approach by using either combined gene sets and the protein interaction network (Method A) or the protein network alone (Method B) to identify common prognostic genes based on microarray gene expression data of glioblastoma multiforme and compared with differential gene expression clustering (Method C). Validations of prediction performance show that the 23-prognostic gene classifier identified by Method A outperforms other gene classifiers identified by Methods B and C or previously reported for gliomas on 17 of 20 independent sample cohorts across five tumor types. We also find that among the 23 genes are 21 related to cellular proliferation and two related to response to stress/immune response. We further find that the increased expression of the 21 genes and the decreased expression of the other two genes are associated with poorer survival, which is supportive with the notion that cellular proliferation and immune response contribute to a significant portion of predictive power of prognostic classifiers. Our results demonstrate that the systems biology-based approach enables to identify common survival-associated genes.     

UV-C response of the ribonucleotide reductase large subunit involves both E2F-mediated gene transcriptional regulation and protein subcellular relocalization in tobacco cells

E2F factors are implicated in various cellular processes including specific gene induction at the G1/S transition of the cell cycle. We present in this study a novel regulatory aspect for the tobacco large subunit of ribonucleotide reductase (R1a) and its encoding gene (RNR1a) in the UV-C response. By structural analyses, two E2F sites were identified on the promoter of this gene. Functional analysis showed that, in addition to their role in the specific G1/S induction of the RNR1a gene, both E2F sites were important for regulating specific RNR1a gene expression in response to UV-C irradiation in non-synchronized and synchronized cells. Concomitantly, western blot and cellular analyses showed an increase of a 60 kDa E2F factor and a transient translocation of a GFP-R1a protein fusion from cytoplasm to nucleus in response to UV irradiation.     

The filamentous fungal gene expression database (FFGED)

Filamentous fungal gene expression assays provide essential information for understanding systemic cellular regulation. To aid research on fungal gene expression, we constructed a novel, comprehensive, free database, the filamentous fungal gene expression database (FFGED), available at http://bioinfo.townsend.yale.edu. FFGED features user-friendly management of gene expression data, which are assorted into experimental metadata, experimental design, raw data, normalized details, and analysis results. Data may be submitted in the process of an experiment, and any user can submit multiple experiments, thus classifying the FFGED as an “active experiment” database. Most importantly, FFGED functions as a collective and collaborative platform, by connecting each experiment with similar related experiments made public by other users, maximizing data sharing among different users, and correlating diverse gene expression levels under multiple experimental designs within different experiments. A clear and efficient web interface is provided with enhancement by AJAX (Asynchronous JavaScript and XML) and through a collection of tools to effectively facilitate data submission, sharing, retrieval and visualization.