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PubMed2002

Reproducibility of oligonucleotide microarray transcriptome analyses. An interlaboratory comparison using chemostat cultures of Saccharomyces cerevisiae.

Assessment of reproducibility of DNA-microarray analysis from published data sets is complicated by the use of different microbial strains, cultivation techniques, and analytical procedures. Because intra- and interlaboratory reproducibility is highly relevant for application of DNA-microarray analysis in functional genomics and metabolic engineering, we designed a set of experiments to specifically address this issue. Saccharomyces cerevisiae CEN.PK113-7D was grown under defined conditions in glucose-limited chemostats, followed by transcriptome analysis with Affymetrix GeneChip arrays. In each of the laboratories, three independent replicate cultures were grown aerobically as well as anaerobically. Although variations introduced by in vitro handling steps were small and unbiased, greater variation from replicate cultures underscored that, to obtain reliable information, experimental replication is essential. Under aerobic conditions, 86% of the most highly expressed yeast genes showed an average intralaboratory coefficient of variation of 0.23. This is significantly lower than previously reported for shake-flask-culture transcriptome analyses and probably reflects the strict control of growth conditions in chemostats. Using the triplicate data sets and appropriate statistical analysis, the change calls from anaerobic versus aerobic comparisons yielded an over 95% agreement between the laboratories for transcripts that changed by over 2-fold, leaving only a small fraction of genes that exhibited laboratory bias.

Aerobiosis↗

PubMed2004

Two-dimensional transcriptome analysis in chemostat cultures. Combinatorial effects of oxygen availability and macronutrient limitation in Saccharomyces cerevisiae.

Genome-wide analysis of transcriptional regulation is generally studied by determining sets of "signature transcripts" that are up- or down-regulated relative to a reference situation when a single culture parameter or genetic modification is changed. This approach is especially relevant for defining small subsets of transcripts for use in high throughput, cost-effective diagnostic analyses. However, this approach may overlook the simultaneous control of transcription by more than one environmental parameter. This study represents the first quantitative assessment of the impact of transcriptional cross-regulation by different environmental parameters. As a model, we compared the response of aerobic as well as anaerobic chemostat cultures of the yeast Saccharomyces cerevisiae to growth limitation by four different macronutrients (carbon, nitrogen, phosphorus, and sulfur). The identity of the growth-limiting nutrient was shown to have a strong impact on the sets of transcripts that responded to oxygen availability and vice versa. We concluded that identification of reliable signature transcripts for specific environmental parameters can be obtained only by combining transcriptome data sets obtained under several sets of reference conditions. Furthermore, the two-dimensional approach to transcriptome analysis is a valuable new tool to study the interaction of different transcriptional regulation systems.

Aerobiosis↗

PubMed2005

Induction of apoptosis in mouse liver by microcystin-LR: a combined transcriptomic, proteomic, and simulation strategy.

Microcystins (MCs) are a family of cyclic heptapeptide hepatotoxins produced by freshwater species of cyanobacteria that have been implicated in the development of liver cancer, necrosis, and even deadly intrahepatic bleeding. MC-LR, the most toxic MC variant, is also the most commonly encountered in a contaminated aquatic system. This study presents the first data in the toxicological research of MCs that combines the use of standard apoptotic assays with transcriptomics, proteomic technologies, and computer simulations. By using histochemistry, DNA fragmentation assays, and flow cytometry analysis, we determined that MC-LR causes rapid, dose-dependent apoptosis in mouse liver when BALB/c mice are treated with MC-LR for 24 h at doses of either 50, 60, or 70 microg/kg of body weight. We then used gene expression profiling to demonstrate differential expressions (>2-fold) of 61 apoptosis-related genes in cells treated with MC-LR. Further proteomic analysis identified a total of 383 proteins of which 35 proteins were up-regulated and 30 proteins were down-regulated more than 2.5-fold when compared with controls. Combining computer simulations with the transcriptomic and proteomic data, we found that low doses (50 microg/kg) of MC-LR lead to apoptosis primarily through the BID-BAX-BCL-2 pathway, whereas high doses of MC-LR (70 microg/kg) caused apoptosis via a reactive oxygen species pathway. These results indicated that MC-LR exposure can cause apoptosis in mouse liver and revealed two independent pathways playing a major regulatory role in MC-LR-induced apoptosis, thereby contributing to a better understanding of the hepatotoxicity and the tumor-promoting mechanisms of MCs.

Animals↗

PubMed2003

Use of transcriptome data to unravel the fine structure of genes involved in sepsis.

The sequence of the human genome is providing researchers with the scaffold upon which genes are built. The definition of the boundaries of the genes themselves and of their complex architecture requires a mapping of the transcriptome to the genome. A methodology was developed for generating a detailed transcriptome map and for reconstituting transcripts by using the genome as a template. As a demonstration of the potential of this method, the structure of the human Toll-like receptor (TLR) genes was reevaluated. For all TLR genes for which a genomic sequence was available (i.e., all except TLR10), novel features of the gene structure were discovered. These features include multiple alternative polyadenylation sites, additional exons or splice variants, and overlaps with other genes. These findings have implications for the analysis of TLR gene expression and for the diversity of the proteins encoded by these genes.

Gene Expression Profiling↗

PubMed2005

In vivo transcriptome of Plasmodium falciparum reveals overexpression of transcripts that encode surface proteins.

Infections with the human parasite Plasmodium falciparum continue to present a great challenge to global health. Fundamental questions regarding the molecular basis of virulence and immune evasion in P. falciparum have been only partially answered. Because of the parasite's intracellular location and complex life cycle, standard genetic approaches to the study of the pathogenesis of malaria have been limited. The present study presents a novel approach to the identification of the biological processes involved in host-pathogen interactions, one that is based on the analysis of in vivo P. falciparum transcripts. We demonstrate that a sufficient quantity of P. falciparum RNA transcripts can be derived from a small blood sample from infected patients for whole-genome microarray analysis. Overall, excellent correlation was observed between the transcriptomes derived from in vivo samples and in vitro samples with ring-stage P. falciparum 3D7 reference strain. However, gene families that encode surface proteins are overexpressed in vivo. Moreover, this analysis has identified a new family of hypothetical genes that may encode surface variant antigens. Comparative studies of the transcriptomes derived from in vivo samples and in vitro 3D7 samples may identify important strategies used by the pathogen for survival in the human host and highlight, for vaccine development, new candidate antigens that were not previously identified through the use of in vitro cultures.

Adaptation, Physiological↗

PubMed2004

Derivation and comparative assessment of retinal pigment epithelium from human embryonic stem cells using transcriptomics.

Human stem-cell derivatives are likely to play an important role in the future of regenerative medicine. Evaluation and comparison to their in vivo counterparts is critical for assessment of their therapeutic potential. Transcriptomics was used to compare a new differentiation derivative of human embryonic stem (hES) cells--retinal pigment epithelium (RPE)--to human fetal RPE. Several hES cell lines were differentiated into putative RPE, which expressed RPEspecific molecular markers and was capable of phagocytosis, an important RPE function. Isolated hES cell-derived RPE was able to transdifferentiate into cells of neuronal lineage and redifferentiate into RPE-like cells through multiple passages (>30 Population doublings). Gene expression profiling demonstrated their higher similarity to primary RPE tissue than of existing human RPE cell lines D407 and ARPE-19, which has been shown to attenuate loss of visual function in animals. This is the first report of the isolation and characterization of putative RPE cells from hES cells, as well as the first application of transcriptomics to assess embryonic stem-cell derivatives and their in vivo counterparts--a "differentiomics" outlook. We describe for the first time, a differentiation system that does not require coculture with animal cells or factors, thus allowing the production of zoonoses-free RPE cells suitable for subretinal transplantation in patients with retinal degenerative diseases. With the further development of therapeutic cloning, or the creation of the banks of homozygous human leucocyte antigen (HLA) hES cells using parthenogenesis, RPE lines could be generated to overcome the problem of immune rejection and could be one of the nearest term applications of stem-cell technology.

Cell Differentiation↗

PubMed2005

Clusters of adjacent and similarly expressed genes across normal human tissues complicate comparative transcriptomic discovery.

Transcriptomic techniques are valuable tools with which to validate genetic and biological hypotheses and are now widely available for research. However, with the exception of tumor biology, comparative genomics analyses have been difficult to use as discovery engines to describe biologically relevant expression changes. We propose that physical proximity of human genes correlates with similar mRNA expression, so that increased expression might include a disease-relevant gene and many other genes in the adjacent region. To increase the efficiency of combining susceptibility gene mapping and interpretation of transcriptomics, we developed a method to identify clusters of adjacent and similarly expressed genes. Gene expression profiles for 28,945 genes across 101 normal human tissues were obtained from the Gene Logic BioExpress system. The expression similarity for genes in sliding-windows was measured using average pair-wise Pearson correlation coefficients. We identified 187 clusters (p < 10e-4) of co-regulated genes, including 2648 genes, or 9.1% of all genes considered and termed these "clusters of adjacent and similarly expressed genes" (CASEGs). Genes in 15 (8.2%) of these clusters demonstrate a significant co-expression enrichment (p < 10e-10). This study demonstrates the coordinate expression of neighboring genes and provides a comprehensive view of expression-based compartmentalization of the human genome, which can be overlaid on genetic susceptibility gene maps.

Gene Expression Profiling↗

PubMed2006

High-protein and high-carbohydrate breakfasts differentially change the transcriptome of human blood cells.

BACKGROUND: Application of transcriptomics technology in human nutrition intervention studies would allow for genome-wide screening of the effects of specific diets or nutrients and result in biomarker profiles. OBJECTIVE: The aim was to evaluate the potential of gene expression profiling in blood cells collected in a human intervention study that investigated the effect of a high-carbohydrate (HC) or a high-protein (HP) breakfast on satiety. DESIGN: Blood samples were taken from 8 healthy men before and 2 h after consumption of an HP or an HC breakfast. Both breakfasts contained acetaminophen for measuring the gastric emptying rate. Analysis of the transcriptome data focused on the effects of the HP or HC breakfast and of acetaminophen on blood leukocyte gene expression profiles. RESULTS: Breakfast consumption resulted in differentially expressed genes, 317 for the HC breakfast and 919 for the HP breakfast. Immune response and signal transduction, specifically T cell receptor signaling and nuclear transcription factor kappaB signaling, were the overrepresented functional groups in the set of 141 genes that were differentially expressed in response to both breakfasts. Consumption of the HC breakfast resulted in differential expression of glycogen metabolism genes, and consumption of the HP breakfast resulted in differential expression of genes involved in protein biosynthesis. CONCLUSIONS: Gene expression changes in blood leukocytes corresponded with and may be related to the difference in macronutrient content of the breakfast, meal consumption as such, and acetaminophen exposure. This study illustrates the potential of gene expression profiling in blood to study the effects of dietary exposure in human intervention studies.

Acetaminophen↗

PubMed2003

Transcriptomics and proteomics of human skin.

The epidermis protects the organism against physical, chemical and biological challenges, and it acts as a signalling interface between the environment and the body. In order to perform these functions, the epidermal keratinocytes express a wide range of genes, several of which have been characterised previously. Recently, significant progress has been made in the large-scale analysis of keratinocyte gene expression, enabling a more profound insight into keratinocyte biology and human skin diseases. Transcriptome analysis--serial analysis of gene expression (SAGE) and microarrays--and proteome analysis have been performed on intact human epidermis and on keratinocytes cultured in model systems that mimic normal and diseased human epidermis. Here, we review the current state of large-scale gene expression analysis of human skin, with an emphasis on SAGE and complementary DNA microarrays. The merits and limitations of various approaches (transcriptomics versus proteomics) are discussed and the practical issues such as sample preparation from skin biopsies, and the use of in vitro models are briefly addressed.

Genomics↗

PubMed2006

Transcriptomics, proteomics and interactomics: unique approaches to track the insights of bioremediation.

Microbial mediated bioremediation has a great potential to effectively restore contaminated environment, but the lack of information about factors regulating the growth and metabolism of various microbial communities in polluted environment often limits its implementation. Newly seeded techniques such as transcriptomics, proteomics and interactomics offer remarkable promise as tools to address longstanding questions regarding the molecular mechanisms involved in the control of mineralization pathways. During mineralization, transcript structures and their expression have been studied using high-throughput transcriptomic techniques with microarrays. Generally however, transcripts have no ability to operate any physiological response; rather, they must be translated into proteins with significant functional impact. These proteins can be identified by proteomic techniques using powerful two-dimensional polyacrylamide gel electrophoresis (2-DE). Towards the establishment of functional proteomics, the current advances in mass spectrometry (MS) and protein microarrays play a central role in the proteomics approach. Exploring the differential expression of a wide variety of proteins and screening of the entire genome for proteins that interact with particular mineralization regulatory factors would help us to gain insights into bioremediation.

Biodegradation, Environmental↗

PubMed2026

jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.

MOTIVATION: Spatial transcriptomics technologies record genome-wide measurements of gene expression with high spatial resolution. These technologies generate large and high-dimensional datasets requiring efficient automated methods for their analysis. We introduce joint spatial PCA (jsPCA), a novel, fast, scalable and interpretable method for the automatic identification of spatial domains and variable genes in multi-slice and multi-sample spatial transcriptomics data. RESULTS: jsPCA relies on a simple mathematical formulation of a spatial covariance defined as the product of the gene expression covariance with the spatial autocorrelation. The principal components of this spatial covariance yield a biologically meaningful low-dimensional representation. From this representation, spatial domains are derived by simple clustering and spatially variable genes are identified directly from the principal component coefficients. A joint representation of multiple slices and samples without spatial alignment is obtained by computing common principal components via joint diagonalization. By leveraging data sparsity and non-convex manifold optimization, jsPCA leads to computing time in the order of seconds to minutes, substantially outperforming state-of-the-art approaches. We benchmarked jsPCA against 10 state-of-the-art methods on two reference databases. Our approach demonstrated excellent performance, comparable or better than state-of-the-art methods, while being much faster, interpretable, and scalable to very large datasets.

Journal Article↗

PubMed2003

Can transcriptome size be estimated from SAGE catalogs?

MOTIVATION: SAGE (Serial Analysis of Gene Expression) can be used to estimate the number of unique transcripts in a transcriptome. A simple estimator that corrects for sequencing and sampling errors was applied to a SAGE library (137 832 tags) obtained from mouse embryonic stem cells, and also to Monte Carlo simulated libraries generated using assumed distributions of 'true' expression levels consistent with the data. RESULTS: When the corrected data themselves were taken as the underlying model of 'ground truth', the estimator converged to the 'true' value (53 535) only after counting 300 000 simulated tags, more than twice the number in the experiment. The SAGE data could also be well fit by a Monte Carlo model based on a truncated inverse-square distribution of expression levels, with 130 000 'true' transcripts and 10(6) samples needed for convergence. We conclude that the size of a transcriptome is ill-determined from SAGE libraries of even moderately large size. In order to obtain a valid estimate, one must sample a number of tags inversely proportional to the lowest abundance level, which is not known a priori. This constrains the design of SAGE experiments intended to determine biological complexity. AVAILABILITY: The 'homemade' software used for this analysis was not designed for general or 'production' use, but the authors will be happy to share Fortran sourcecode with interested parties. CONTACT: sternm@grc.nia.nih.gov

Algorithms↗

PubMed2004

A rapid method for computationally inferring transcriptome coverage and microarray sensitivity.

MOTIVATION: There are many different gene expression technologies, including cDNA and oligo-based microarrays, SAGE and MPSS. For each organism of interest, coverage of the transcriptome and the genome will be different. We address the question of what level of coverage is required to exploit the sensitivity of the different technologies, and what is the sensitivity of the different approaches in the experimental study. RESULTS: We estimate the transcriptome coverage by randomly sampling transcripts from a pre-defined tag-to-gene mapping function. For a given microarray experiment, we locate the thresholds in intensities that define the distribution of transcript abundance. These values are compared against the distribution obtained by applying the same thresholds to the intensities from differentially expressed genes. The ratio of these two distributions meets at the equilibrium defining sensitivity. We conclude that a collection of approximately 340,000 sequences is adequate for microarrays, but not large enough for maximum utilization of tag-based technologies. In the absence of large-scale sequencing, the majority of the tags detected by the latter approaches will remain unidentified until the genome sequence is available.

Algorithms↗

PubMed2004

Blood T-cell receptor beta chain transcriptome in multiple sclerosis. Characterization of the T cells with altered CDR3 length distribution.

Multiple sclerosis is an inflammatory demyelinating disease of the CNS associated with T cells autoreactive for myelin components. In this study, we analysed the T-cell receptor (TCR) usage of the variable beta (Vbeta) chain transcriptome in the blood of multiple sclerosis patients at various stages of the disease using a global and quantitative comparison of the complementarity-determining region 3 length distribution (CDR3-LD) of transcripts of the 26 Vbeta genes. We investigated 35 patients: 12 with a high risk of multiple sclerosis, 10 with clinically definite multiple sclerosis, 13 with a relapsing-remitting worsening and active multiple sclerosis and 13 healthy individuals. Cells bearing the TCR transcripts with altered CDR3-LD were sorted and studied for CD4 or CD8 phenotype, cytokine transcript accumulation and response to human myelin basic protein (MBP). We show that patients from all the groups have a significantly skewed blood T-cell repertoire. Vbeta transcriptome patterns were more altered in patients from the clinically definite multiple sclerosis group and the worsening and active multiple sclerosis group than in the high risk group. The T cells sorted from Vbeta families with altered CDR3-LD concerned both CD4 and CD8 T cells, with a more pronounced skewing in the CD8 compartment. These cells displayed a significantly increased level of interferon-gamma, interleukin-2 and tumour necrosis factor-alpha transcripts compared with their counterparts from the healthy individual group. Furthermore, using interferon-gamma enzyme-linked immunospot (ELISPOT) assays, T cells from four out of seven altered Vbeta families tested from multiple sclerosis patients responded to human MBP, whereas no response was observed with human albumin or with altered Vbeta families from healthy individuals. Our data support the concept of an early autoimmune component in the disease and emphasize the possible involvement of CD8-positive T cells in multiple sclerosis.

Adult↗

PubMed2002

Transcriptome analysis of human autosomal trisomy.

We present transcriptome analyses of primary cultures of human fetal cells from pregnancies affected with trisomy 21 (t21) and trisomy 13 (t13). Pooled mRNA samples from t21 and t13 cases were used for comparative hybridizations to cDNA arrays with pooled mRNA from normal cells. When the array cDNAs were grouped by chromosomal location the relevant trisomic chromosome could be clearly identified as showing the most significant misregulation. The average level of transcription on the trisomic chromosome was increased only approximately 1.1-fold compared to normal cells on array analysis. Since the karyotype could be accurately predicted by the transcriptome this could provide a novel method of detecting aneusomy of unknown position. Subsequent analysis of individuals cases demonstrated that variation in transcriptional profiles between samples within each class made transcriptional karyotyping difficult without pooling or the use of arrays with a higher proportion of all human cDNAs. Interestingly, consistent differences in the relative expression levels between chromosomes were detected suggesting that genomic control mechanisms may act over larger distances than previously thought. Most (>95%) >+/-2 SD misregulated genes did not map to the trisomic chromosome and significant misregulation was more common in t13 than t21. These data support a model of a subtle primary upregulation of genes on the trisomic chromosome resulting in a secondary, generalized and more extreme transcriptional misregulation. It seems likely that the degree of this misregulation determines the severity of the phenotype in most aneuploidy.

Chromosomes, Human, Pair 13↗

PubMed2006

ITTACA: a new database for integrated tumor transcriptome array and clinical data analysis.

Transcriptome microarrays have become one of the tools of choice for investigating the genes involved in tumorigenesis and tumor progression, as well as finding new biomarkers and gene expression signatures for the diagnosis and prognosis of cancer. Here, we describe a new database for Integrated Tumor Transcriptome Array and Clinical data Analysis (ITTACA). ITTACA centralizes public datasets containing both gene expression and clinical data. ITTACA currently focuses on the types of cancer that are of particular interest to research teams at Institut Curie: breast carcinoma, bladder carcinoma and uveal melanoma. A web interface allows users to carry out different class comparison analyses, including the comparison of expression distribution profiles, tests for differential expression and patient survival analyses. ITTACA is complementary to other databases, such as GEO and SMD, because it offers a better integration of clinical data and different functionalities. It also offers more options for class comparison analyses when compared with similar projects such as Oncomine. For example, users can define their own patient groups according to clinical data or gene expression levels. This added flexibility and the user-friendly web interface makes ITTACA especially useful for comparing personal results with the results in the existing literature. ITTACA is accessible online at http://bioinfo.curie.fr/ittaca.

Breast Neoplasms↗

PubMed2006

Comparative whole genome transcriptome analysis of three Plasmodium falciparum strains.

Gene expression patterns have been demonstrated to be highly variable between similar cell types, for example lab strains and wild strains of Saccharomyces cerevisiae cultured under identical growth conditions exhibit a wide range of expression differences. We have used a genome-wide approach to characterize transcriptional differences between strains of Plasmodium falciparum by characterizing the transcriptome of the 48 h intraerythrocytic developmental cycle (IDC) for two strains, 3D7 and Dd2 and compared these results to our prior work using the HB3 strain. These three strains originate from geographically diverse locations and possess distinct drug sensitivity phenotypes. Our goal was to identify transcriptional differences related to phenotypic properties of these strains including immune evasion and drug sensitivity. We find that the highly streamlined transcriptome is remarkably well conserved among all three strains, and differences in gene expression occur mainly in genes coding for surface antigens involved in parasite-host interactions. Our analysis also detects several transcripts that are unique to individual strains as well as identifying large chromosomal deletions and highly polymorphic regions across strains. The majority of these genes are uncharacterized and have no homology to other species. These tractable transcriptional differences provide important phenotypes for these otherwise highly related strains of Plasmodium.

Animals↗

PubMed2006

TACT: Transcriptome Auto-annotation Conducting Tool of H-InvDB.

Transcriptome Auto-annotation Conducting Tool (TACT) is a newly developed web-based automated tool for conducting functional annotation of transcripts by the integration of sequence similarity searches and functional motif predictions. We developed the TACT system by integrating two kinds of similarity searches, FASTY and BLASTX, against protein sequence databases, UniProtKB (Swiss-Prot/TrEMBL) and RefSeq, and a unified motif prediction program, InterProScan, into the ORF-prediction pipeline originally designed for the 'H-Invitational' human transcriptome annotation project. This system successively applies these constituent programs to an mRNA sequence in order to predict the most plausible ORF and the function of the protein encoded. In this study, we applied the TACT system to 19 574 non-redundant human transcripts registered in H-InvDB and evaluated its predictive power by the degree of agreement with human-curated functional annotation in H-InvDB. As a result, the TACT system could assign functional description to 12 559 transcripts (64.2%), the remainder being hypothetical proteins. Furthermore, the overall agreement of functional annotation with H-InvDB, including those transcripts annotated as hypothetical proteins, was 83.9% (16 432/19 574). These results show that the TACT system is useful for functional annotation and that the prediction of ORFs and protein functions is highly accurate and close to the results of human curation. TACT is freely available at http://www.jbirc.aist.go.jp/tact/.

Amino Acid Motifs↗