Comparison and consolidation of microarray data sets of human tissue expression

PINTology: A short history of the lncRNA LINC-PINT in different diseases

LINC-PINT is a p53-induced long intergenic noncoding transcript that plays a crucial role in many diseases, especially cancer. This long noncoding RNA (lncRNA) gene produces in total 102 (LNCipedia) alternatively spliced variants (LINC-PINT:1 to LINC-PINT:102). The functions of known variants include RNA transcripts, host transcripts for circular RNA (circRNA) generation and as sources for the translation of short peptides. In most human tumors, LINC-PINT is down-regulated where it serves as a tumor suppressor. However, the diversity of its functions in other maladies signifies its general clinical importance. Current LINC-PINT molecular functions include RNA-protein interactions, miRNA sponging and epigenetic modulation with these mechanisms operating in different cellular contexts to exert effects on biological processes ranging from DNA damage responses, cell cycle and growth arrest, senescence, cell migration and invasion, and apoptosis. Genetic polymorphisms in LINC-PINT have also been functionally associated with cancer and other pathologies including the autoimmune diseases pemphigus foliaceus and arthritis. Hence, LINC-PINT shows great potential as a clinical biomarker, especially for the diagnosis and prognosis of cancer. In this review, we explore the current knowledge highlighting the distinctive molecular functions of LINC-PINT in specific cancers and other disease states. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Genome-wide identification and characterization of Fur-binding sites in the cyanobacteria Synechocystis sp. PCC 6803 and PCC 6714

The Ferric uptake regulator (Fur) is crucial to both pathogenic and non-pathogenic bacteria for the maintenance of iron homeostasis as well as the defence against reactive oxygen species. Based on datasets from the genome-wide mapping of transcriptional start sites and transcriptome data, we identified a high confidence regulon controlled by Fur for the model cyanobacterium Synechocystis sp. PCC 6803 and its close relative, strain 6714, based on the conserved strong iron starvation response and Fur-binding site occurrence. This regulon comprises 33 protein-coding genes and the sRNA IsaR1 that are under the control of 16 or 14 individual promoters in strains 6803 and 6714, respectively. The associated gene functions are mostly restricted to transporters and enzymes involved in the uptake and storage of iron ions, with few exceptions or unknown functional relevance. Within the isiABC operon, we identified a previously neglected gene encoding a small cysteine-rich protein, which we suggest calling, IsiE. The regulation of iron uptake, storage, and utilization ultimately results from the interplay between the Fur regulon, several other transcription factors, the FtsH3 protease, and the sRNA IsaR1.

Chemogenetics: drug-controlled gene therapies for neural circuit disorders

Many patients with nervous system disorders have considerable unmet clinical needs or suffer debilitating drug side effects. A major limitation of exiting treatment approaches is that traditional small molecule pharmacotherapy lacks sufficient specificity to effectively treat many neurological diseases. Chemogenetics is a new gene therapy technology that targets an engineered receptor to cell types involved in nervous system dysfunction, enabling highly selective drug-controlled neuromodulation. Here, we discuss chemogenetic platforms and considerations for their potential application as human nervous system therapies.

Intron exon boundary junctions in human genome have in-built unique structural and energetic signals

Precise identification of correct exon–intron boundaries is a prerequisite to analyze the location and structure of genes. The existing framework for genomic signals, delineating exon and introns in a genomic segment, seems insufficient, predominantly due to poor sequence consensus as well as limitations of training on available experimental data sets. We present here a novel concept for characterizing exon–intron boundaries in genomic segments on the basis of structural and energetic properties. We analyzed boundary junctions on both sides of all the exons (3 28 368) of protein coding genes from human genome (GENCODE database) using 28 structural and three energy parameters. Study of sequence conservation at these sites shows very poor consensus. It is observed that DNA adopts a unique structural and energy state at the boundary junctions. Also, signals are somewhat different for housekeeping and tissue specific genes. Clustering of 31 parameters into four derived vectors gives some additional insights into the physical mechanisms involved in this biological process. Sites of structural and energy signals correlate well to the positions playing important roles in pre-mRNA splicing.

Reliability of microarray analysis for studying periodontitis: low consistency in 2 periodontitis cohort data sets from different platforms and an integrative meta-analysis

PURPOSE

The aim of this study was to compare the characteristic expression patterns of advanced periodontitis in 2 cohort data sets analyzed using different microarray platforms, and to identify differentially expressed genes (DEGs) through a meta-analysis of both data sets.

METHODS

Twenty-two patients for cohort 1 and 40 patients for cohort 2 were recruited with the same inclusion criteria. The 2 cohort groups were analyzed using different platforms: Illumina and Agilent. A meta-analysis was performed to increase reliability by removing statistical differences between platforms. An integrative meta-analysis based on an empirical Bayesian methodology (ComBat) was conducted. DEGs for the integrated data sets were identified using the limma package to adjust for age, sex, and platform and compared with the results for cohorts 1 and 2. Clustering and pathway analyses were also performed.

RESULTS

This study detected 557 and 246 DEGs in cohorts 1 and 2, respectively, with 146 and 42 significantly enriched gene ontology (GO) terms. Overlapping between cohorts 1 and 2 was present in 59 DEGs and 18 GO terms. However, only 6 genes from the top 30 enriched DEGs overlapped, and there were no overlapping GO terms in the top 30 enriched pathways. The integrative meta-analysis detected 34 DEGs, of which 10 overlapped in all the integrated data sets of cohorts 1 and 2.

CONCLUSIONS

The characteristic expression pattern differed between periodontitis and the healthy periodontium, but the consistency between the data sets from different cohorts and metadata was too low to suggest specific biomarkers for identifying periodontitis.

lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,199 human lncRNA (224,286 transcripts). The user-friendly knowledgebase covers a comprehensive breadth and depth of lncRNA annotation. lncRNAKB is a compendium of expression patterns, derived from analysis of RNA-seq data in thousands of samples across 31 solid human normal tissues (GTEx). Thousands of co-expression modules identified via network analysis and pathway enrichment to delineate lncRNA function are also accessible. Millions of expression quantitative trait loci (cis-eQTL) computed using whole genome sequence genotype data (GTEx) can be downloaded at lncRNAKB that also includes tissue-specificity, phylogenetic conservation and coding potential scores. Tissue-specific lncRNA-trait associations encompassing 323 GWAS (UK Biobank) are also provided. LncRNAKB is accessible at http://www.lncrnakb.org/ , and the data are freely available through Open Science Framework ( https://doi.org/10.17605/OSF.IO/RU4D2 ).

Comparison of multiple transcriptomes exposes unified and divergent features of quiescent and activated skeletal muscle stem cells

BACKGROUND

Skeletal muscle satellite (stem) cells are quiescent in adult mice and can undergo multiple rounds of proliferation and self-renewal following muscle injury. Several labs have profiled transcripts of myogenic cells during the developmental and adult myogenesis with the aim of identifying quiescent markers. Here, we focused on the quiescent cell state and generated new transcriptome profiles that include subfractionations of adult satellite cell populations, and an artificially induced prenatal quiescent state, to identify core signatures for quiescent and proliferating.

METHODS

Comparison of available data offered challenges related to the inherent diversity of datasets and biological conditions. We developed a standardized workflow to homogenize the normalization, filtering, and quality control steps for the analysis of gene expression profiles allowing the identification up- and down-regulated genes and the subsequent gene set enrichment analysis. To share the analytical pipeline of this work, we developed Sherpa, an interactive Shiny server that allows multi-scale comparisons for extraction of desired gene sets from the analyzed datasets. This tool is adaptable to cell populations in other contexts and tissues.

RESULTS

A multi-scale analysis comprising eight datasets of quiescent satellite cells had 207 and 542 genes commonly up- and down-regulated, respectively. Shared up-regulated gene sets include an over-representation of the TNFα pathway via NFKβ signaling, Il6-Jak-Stat3 signaling, and the apical surface processes, while shared down-regulated gene sets exhibited an over-representation of Myc and E2F targets and genes associated to the G2M checkpoint and oxidative phosphorylation. However, virtually all datasets contained genes that are associated with activation or cell cycle entry, such as the immediate early stress response genes Fos and Jun. An empirical examination of fixed and isolated satellite cells showed that these and other genes were absent in vivo, but activated during procedural isolation of cells.

CONCLUSIONS

Through the systematic comparison and individual analysis of diverse transcriptomic profiles, we identified genes that were consistently differentially expressed among the different datasets and shared underlying biological processes key to the quiescent cell state. Our findings provide impetus to define and distinguish transcripts associated with true in vivo quiescence from those that are first responding genes due to disruption of the stem cell niche.

A benchmark of gene expression tissue-specificity metrics

One of the major properties of genes is their expression pattern. Notably, genes are often classified as tissue specific or housekeeping. This property is of interest to molecular evolution as an explanatory factor of, e.g. evolutionary rate, as well as a functional feature which may in itself evolve. While many different methods of measuring tissue specificity have been proposed and used for such studies, there has been no comparison or benchmarking of these methods to our knowledge, and little justification of their use. In this study, we compare nine measures of tissue specificity. Most methods were established for ESTs and microarrays, and several were later adapted to RNA-seq. We analyse their capacity to distinguish gene categories, their robustness to the choice and number of tissues used and their capture of evolutionary conservation signal.

HDNetDB: A Molecular Interaction Database for Network-Oriented Investigations into Huntington's Disease

Huntington’s disease (HD) is a progressive and fatal neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin gene. Although HD is monogenic, its molecular manifestation appears highly complex and involves multiple cellular processes. The recent application of high throughput platforms such as microarrays and mass-spectrometry has indicated multiple pathogenic routes. The massive data generated by these techniques together with the complexity of the pathogenesis, however, pose considerable challenges to researchers. Network-based methods can provide valuable tools to consolidate newly generated data with existing knowledge, and to decipher the interwoven molecular mechanisms underlying HD. To facilitate research on HD in a network-oriented manner, we have developed HDNetDB, a database that integrates molecular interactions with many HD-relevant datasets. It allows users to obtain, visualize and prioritize molecular interaction networks using HD-relevant gene expression, phenotypic and other types of data obtained from human samples or model organisms. We illustrated several HDNetDB functionalities through a case study and identified proteins that constitute potential cross-talk between HD and the unfolded protein response (UPR). HDNetDB is publicly accessible at http://hdnetdb.sysbiolab.eu.

PPARγ targeted oral cancer treatment and additional utility of genomics analytic techniques

OBJECTIVE

Peroxisome proliferator-activated receptor γ (PPARγ) agonists have been shown to have anti-proliferative, anti-angiogenic, and proapoptotic effects, leading to interest in their use as cancer therapeutics. Pioglitazone, a U.S. Food and Drug Administration-approved type II diabetes medication and PPARγ agonist, may have a role in adjuvant head-and-neck squamous cell carcinoma treatment or prevention. Therefore, the purpose of this study was: 1) to treat oral cavity cancer cells with the PPARγ activator, pioglitazone, to analyze gene expression changes; and 2) to compare those changes with our preexisting genomic data for development of hypothesis-driven additional basic and clinical studies.

STUDY DESIGN

Prospective in vitro.

METHODS

We utilized microarray technology, as well as OCPlus (Bioconductor open source software) and Ingenuity Pathway Analysis (Qiagen, Redwood City, CA), to analyze differential gene expression in tumor and pioglitazone-treated tumor cells on a genome-wide level to demonstrate the feasibility of such an approach and determine appropriate sample size for future investigations.

RESULTS

We found that approximately 35 samples are required to adequately power future studies. We next discovered that pioglitazone significantly affects Inducible T-Cell Costimulator (iCOS)-Ligand for the T-cell-specific cell surface receptor ICOS (iCOSL) and type II diabetes mellitus pathways as a putative anti-cancer mechanism.

CONCLUSION

Genome-wide analysis is possible for the exploration of differential pathway modulation and rapid hypothesis generation. Both inflammation and type II diabetes pathways were significantly altered and therefore might provide unique hypothesis-driven pharmacodynamic parameters for future in vitro or in vivo studies utilizing thiazolidinediones. These techniques could be applied to microarray or other high throughput data from a variety of hypothesis-generating research scenarios in otolaryngology (e.g., middle ear proteomics, sinus microbiome studies).

LEVEL OF EVIDENCE

NA. Laryngoscope, 127:E124-E131, 2017.

Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson's disease: a comparison of 33 human and animal studies

BACKGROUND

As the popularity of transcriptomic analysis has grown, the reported lack of concordance between different studies of the same condition has become a growing concern, raising questions as to the representativeness of different study types, such as non-human disease models or studies of surrogate tissues, to gene expression in the human condition.

METHODS

In a comparison of 33 microarray studies of Parkinson's disease, correlation and clustering analyses were used to determine the factors influencing concordance between studies, including agreement between different tissue types, different microarray platforms, and between neurotoxic and genetic disease models and human Parkinson's disease.

RESULTS

Concordance over all studies is low, with correlation of only 0.05 between differential gene expression signatures on average, but increases within human patients and studies of the same tissue type, rising to 0.38 for studies of human substantia nigra. Agreement of animal models, however, is dependent on model type. Studies of brain tissue from Parkinson's disease patients (specifically the substantia nigra) form a distinct group, showing patterns of differential gene expression noticeably different from that in non-brain tissues and animal models of Parkinson's disease; while comparison with other brain diseases (Alzheimer's disease and brain cancer) suggests that the mixed study types display a general signal of neurodegenerative disease. A meta-analysis of these 33 microarray studies demonstrates the greater ability of studies in humans and highly-affected tissues to identify genes previously known to be associated with Parkinson's disease.

CONCLUSIONS

The observed clustering and concordance results suggest the existence of a 'characteristic' signal of Parkinson's disease found in significantly affected human tissues in humans. These results help to account for the consistency (or lack thereof) so far observed in microarray studies of Parkinson's disease, and act as a guide to the selection of transcriptomic studies most representative of the underlying gene expression changes in the human disease.

The Transcriptional Landscape of the Photosynthetic Model Cyanobacterium Synechocystis sp. PCC6803

Cyanobacteria exhibit a great capacity to adapt to different environmental conditions through changes in gene expression. Although this plasticity has been extensively studied in the model cyanobacterium Synechocystis sp. PCC 6803, a detailed analysis of the coordinated transcriptional adaption across varying conditions is lacking. Here, we report a meta-analysis of 756 individual microarray measurements conducted in 37 independent studies-the most comprehensive study of the Synechocystis transcriptome to date. Using stringent statistical evaluation, we characterized the coordinated adaptation of Synechocystis' gene expression on systems level. Evaluation of the data revealed that the photosynthetic apparatus is subjected to greater changes in expression than other cellular components. Nevertheless, network analyses indicated a significant degree of transcriptional coordination of photosynthesis and various metabolic processes, and revealed the tight co-regulation of components of photosystems I, II and phycobilisomes. Detailed inspection of the integrated data led to the discovery a variety of regulatory patterns and novel putative photosynthetic genes. Intriguingly, global clustering analyses suggested contrasting transcriptional response of metabolic and regulatory genes stress to conditions. The integrated Synechocystis transcriptome can be accessed and interactively analyzed via the CyanoEXpress website (http://cyanoexpress.sysbiolab.eu).

Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis

BACKGROUND

Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease.

METHODS

We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n = 116 = 44 + 15 + 30 + 27) were treated with infliximab, and their response assessed 14-16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression.

RESULTS

The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment.

CONCLUSIONS

We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.

TRIB2 as a biomarker for diagnosis and progression of melanoma

Malignant melanoma is the most deadly form of skin cancer. There is a critical need to identify the patients that could be successfully treated by surgery alone and those that require adjuvant treatment. In this study, we demonstrate that the expression of tribbles2 (TRIB2) strongly correlates with both the presence and progression of melanocyte-derived malignancies. We examined the expression of TRIB2 in addition to 12 previously described melanoma biomarkers across three independent full genome microarray studies. TRIB2 expression was consistently and significantly increased in benign nevi and melanoma, and was highest in samples from patients with metastatic melanoma. The expression profiles for the 12 biomarkers were poorly conserved throughout these studies with only TYR, S100B and SPP1 showing consistently elevated expression in metastatic melanoma versus normal skin. Strikingly we confirmed these findings in 20 freshly obtained primary melanoma tissue samples from metastatic lesions where the expression of these biomarkers were evaluated revealing that TRIB2 expression correlated with disease stage and clinical prognosis. Our results suggest that TRIB2 is a meaningful biomarker reflecting diagnosis and progression of melanoma, as well as predicting clinical response to chemotherapy.

Sex-related gene expression profiles in the adrenal cortex in the mature rat: microarray analysis with emphasis on genes involved in steroidogenesis

Notable sex-related differences exist in mammalian adrenal cortex structure and function. In adult rats, the adrenal weight and the average volume of zona fasciculata cells of females are larger and secrete greater amounts of corticosterone than those of males. The molecular bases of these sex-related differences are poorly understood. In this study, to explore the molecular background of these differences, we defined zone- and sex-specific transcripts in adult male and female (estrous cycle phase) rats. Twelve-week-old rats of both genders were used and samples were taken from the zona glomerulosa (ZG) and zona fasciculata/reticularis (ZF/R) zones. Transcriptome identification was carried out using the Affymetrix^® Rat Gene 1.1 ST Array. The microarray data were compared by fold change with significance according to moderated t-statistics. Subsequently, we performed functional annotation clustering using the Gene Ontology (GO) and Database for Annotation, Visualization and Integrated Discovery (DAVID). In the first step, we explored differentially expressed transcripts in the adrenal ZG and ZF/R. The number of differentially expressed transcripts was notably higher in the female than in the male rats (702 vs. 571). The differentially expressed genes which were significantly enriched included genes involved in steroid hormone metabolism, and their expression levels in the ZF/R of adult female rats were significantly higher compared with those in the male rats. In the female ZF/R, when compared with that of the males, prevailing numbers of genes linked to cell fraction, oxidation/reduction processes, response to nutrients and to extracellular stimuli or steroid hormone stimuli were downregulated. The microarray data for key genes involved directly in steroidogenesis were confirmed by qPCR. Thus, when compared with that of the males, in the female ZF/R, higher expression levels of genes involved directly in steroid hormone synthesis were accompanied by lower expression levels of genes regulating basal cell functions.

Toward computational cumulative biology by combining models of biological datasets

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

UniHI 7: an enhanced database for retrieval and interactive analysis of human molecular interaction networks

Unified Human Interactome (UniHI) (http://www.unihi.org) is a database for retrieval, analysis and visualization of human molecular interaction networks. Its primary aim is to provide a comprehensive and easy-to-use platform for network-based investigations to a wide community of researchers in biology and medicine. Here, we describe a major update (version 7) of the database previously featured in NAR Database Issue. UniHI 7 currently includes almost 350 000 molecular interactions between genes, proteins and drugs, as well as numerous other types of data such as gene expression and functional annotation. Multiple options for interactive filtering and highlighting of proteins can be employed to obtain more reliable and specific network structures. Expression and other genomic data can be uploaded by the user to examine local network structures. Additional built-in tools enable ready identification of known drug targets, as well as of biological processes, phenotypes and pathways enriched with network proteins. A distinctive feature of UniHI 7 is its user-friendly interface designed to be utilized in an intuitive manner, enabling researchers less acquainted with network analysis to perform state-of-the-art network-based investigations.

The use of transcriptomics to unveil the role of nutrients in Mammalian liver

Liver is the organ primarily responding to diet, and it is crucial in determining plasma carbohydrate, protein, and lipid levels. In addition, it is mainly responsible for transformation of xenobiotics. For these reasons, it has been a target of transcriptomic analyses. In this review, we have covered the works dealing with the response of mammalian liver to different nutritional stimuli such as fasting/feeding, caloric restriction, dietary carbohydrate, cholesterol, fat, protein, bile acid, salt, vitamin, and oligoelement contents. Quality of fats or proteins has been equally addressed, and has the influence of minor dietary components. Other compounds, not purely nutritional as those represented by alcohol and food additives, have been included due to their relevance in processed food. The influence has been studied not only on mRNA but also on miRNA. The wide scope of the technology clearly reflects that any simple intervention has profound changes in many metabolic parameters and that there is a synergy in response when more compounds are included in the intervention. Standardized arrays to systematically test the same genes in all studies and analyzing data to establish patterns of response are required, particularly for RNA sequencing. Moreover, RNA is a valuable, easy-screening ally but always requires further confirmation.

A novel gene expression index (GEI) with software support for comparing microarray gene signatures

This study was aimed to examine the validity of commonly used statistical tests for comparison of expression data from simulated and real gene signatures as well as pathway-characterized gene sets. A novel algorithm based on 10 sub-gradations (5 for up- and 5 for down-regulation) of fold-changes has been designed and testified using an Excel add-in software support. Our findings showed the limitations of conventional statistics for comparing the microarray gene expression data. However, the newly introduced Gene Expression Index (GEI) appeared to be more robust and straightforward for two-group comparison of normalized data. The software automation simplifies the task and the results are displayed in a comprehensive format including a color-coded bar showing the intensity of cumulative gene expression.

The use of EST expression matrixes for the quality control of gene expression data

EST expression profiling provides an attractive tool for studying differential gene expression, but cDNA libraries' origins and EST data quality are not always known or reported. Libraries may originate from pooled or mixed tissues; EST clustering, EST counts, library annotations and analysis algorithms may contain errors. Traditional data analysis methods, including research into tissue-specific gene expression, assume EST counts to be correct and libraries to be correctly annotated, which is not always the case. Therefore, a method capable of assessing the quality of expression data based on that data alone would be invaluable for assessing the quality of EST data and determining their suitability for mRNA expression analysis. Here we report an approach to the selection of a small generic subset of 244 UniGene clusters suitable for identification of the tissue of origin for EST libraries and quality control of the expression data using EST expression information alone. We created a small expression matrix of UniGene IDs using two rounds of selection followed by two rounds of optimisation. Our selection procedures differ from traditional approaches to finding "tissue-specific" genes and our matrix yields consistency high positive correlation values for libraries with confirmed tissues of origin and can be applied for tissue typing and quality control of libraries as small as just a few hundred total ESTs. Furthermore, we can pick up tissue correlations between related tissues e.g. brain and peripheral nervous tissue, heart and muscle tissues and identify tissue origins for a few libraries of uncharacterised tissue identity. It was possible to confirm tissue identity for some libraries which have been derived from cancer tissues or have been normalised. Tissue matching is affected strongly by cancer progression or library normalisation and our approach may potentially be applied for elucidating the stage of normalisation in normalised libraries or for cancer staging.

Evaluation of candidate genes from orphan FEB and GEFS+ loci by analysis of human brain gene expression atlases

Febrile seizures, or febrile convulsions (FEB), represent the most common form of childhood seizures and are believed to be influenced by variations in several susceptibility genes. Most of the associated loci, however, remain ‘orphan’, i.e. the susceptibility genes they contain still remain to be identified. Further orphan loci have been mapped for a related disorder, genetic (generalized) epilepsy with febrile seizures plus (GEFS+).

We show that both spatially mapped and ‘traditional’ gene expression data from the human brain can be successfully employed to predict the most promising candidate genes for FEB and GEFS+, apply our prediction method to the remaining orphan loci and discuss the validity of the predictions. For several of the orphan FEB/GEFS+ loci we propose excellent, and not always obvious, candidates for mutation screening in order to aid in gaining a better understanding of the genetic origin of the susceptibility to seizures.