The Genexpress IMAGE Knowledge Base of the Human Muscle Transcriptome: A Resource of Structural, Functional, and Positional Candidate Genes for Muscle Physiology and Pathologies

Integration of transcriptomics, proteomics and metabolomics identifies biomarkers for pulmonary injury by polyhexamethylene guanidine phosphate (PHMG-p), a humidifier disinfectant, in rats

Polyhexamethylene guanidine phosphate (PHMG-p) was used as a humidifier disinfectant in Korea. PHMG induced severe pulmonary fibrosis in Koreans. The objective of this study was to elucidate mechanism of pulmonary toxicity caused by PHMG-p in rats using multi-omics analysis. Wistar rats were intratracheally instilled with PHMG-p by single (1.5 mg/kg) administration or 4-week (0.1 mg/kg, 2 times/week) repeated administration. Histopathologic examination was performed with hematoxylin and eosin staining. Alveolar macrophage aggregation and granulomatous inflammation were observed in rats treated with single dose of PHMG-p. Pulmonary fibrosis, chronic inflammation, bronchiol-alveolar fibrosis, and metaplasia of squamous cell were observed in repeated dose group. Next generation sequencing (NGS) was performed for transcriptome profiling after mRNA isolation from bronchiol-alveoli. Bronchiol-alveoli proteomic profiling was performed using an Orbitrap Q-exactive mass spectrometer. Serum and urinary metabolites were determined using 1H-NMR. Among 418 differentially expressed genes (DEGs) and 67 differentially expressed proteins (DEPs), changes of 16 mRNA levels were significantly correlated with changes of their protein levels in both single and repeated dose groups. Remarkable biological processes represented by both DEGs and DEPs were defense response, inflammatory response, response to stress, and immune response. Arginase 1 (Arg1) and lipocalin 2 (Lcn2) were identified to be major regulators for PHMG-p-induced pulmonary toxicity based on merged analysis using DEGs and DEPs. In metabolomics study, 52 metabolites (VIP > 0.5) were determined in serum and urine of single and repeated-dose groups. Glutamate and choline were selected as major metabolites. They were found to be major factors affecting inflammatory response in association with DEGs and DEPs. Arg1 and Lcn2 were suggested to be major gene and protein related to pulmonary damage by PHMG-p while serum or urinary glutamate and choline were endogenous metabolites related to pulmonary damage by PHMG-p.

Human cellular models of medium spiny neuron development and Huntington disease

The loss of gamma-aminobutyric acid (GABA)-ergic medium spiny neurons (MSNs) in the striatum is the hallmark of Huntington disease (HD), an incurable neurodegenerative disorder characterized by progressive motor, psychiatric, and cognitive symptoms. Transplantation of MSNs or their precursors represents a promising treatment strategy for HD. In initial clinical trials in which HD patients received fetal neurografts directly into the striatum without a pretransplant cell-differentiation step, some patients exhibited temporary benefits. Meanwhile, major challenges related to graft overgrowth, insufficient survival of grafted cells, and limited availability of donated fetal tissue remain. Thus, the development of approaches that allow modeling of MSN differentiation and HD development in cell culture platforms may improve our understanding of HD and translate, ultimately, into HD treatment options. Here, recent advances in the in vitro differentiation of MSNs derived from fetal neural stem cells/progenitor cells (NSCs/NPCs), embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and induced NSCs (iNSCs) as well as advances in direct transdifferentiation are reviewed. Progress in non-allele specific and allele specific gene editing of HTT is presented as well. Cell characterization approaches involving phenotyping as well as in vitro and in vivo functional assays are also discussed.

Transcriptomics technologies

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst noncoding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. The first attempts to study the whole transcriptome began in the early 1990s, and technological advances since the late 1990s have made transcriptomics a widespread discipline. Transcriptomics has been defined by repeated technological innovations that transform the field. There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA sequencing (RNA-Seq), which uses high-throughput sequencing to capture all sequences. Measuring the expression of an organism's genes in different tissues, conditions, or time points gives information on how genes are regulated and reveals details of an organism's biology. It can also help to infer the functions of previously unannotated genes. Transcriptomic analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human disease. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays.

Transcriptomics in the RNA-seq era

The transcriptomics field has developed rapidly with the advent of next-generation sequencing technologies. RNA-seq has now displaced microarrays as the preferred method for gene expression profiling. The comprehensive nature of the data generated has been a boon in terms of transcript identification but analysis challenges remain. Key among these problems is the development of suitable expression metrics for expression level comparisons and methods for identification of differentially expressed genes (and exons). Several approaches have been developed but as yet no consensus exists on the best pipeline to use. De novo transcriptome approaches are increasingly viable for organisms lacking a sequenced genome. The reduction in starting RNA required has enabled the development of new applications such as single cell transcriptomics. The emerging picture of mammalian transcription is complex with further refinement expected with the integration of epigenomic data generated by projects such as ENCODE.

Fem1a is a mitochondrial protein up-regulated upon ischemia-reperfusion injury

Various expression studies have shown a preferential muscle expression of the mouse Fem1a gene, but no data is available on the subcellular localization of the corresponding protein. Here, using a specific antibody, we show that Fem1a is expressed preferentially in cardiac muscle, brain and liver. Moreover, using immunofluorescence and electron microscopy, as well as biochemical assays, we demonstrate that Fem1a is localized within mitochondria of C2C12 myoblasts and cardiac muscle cells. Finally, we show that the expression of Fem1a, which is a cellular partner of the EP4 receptor for prostaglandin E(2), is increased in mouse hearts after myocardial infarction.

Differentially transcribed genes in skeletal muscle of Duroc and Taoyuan pigs

The objective of this study was to compare gene transcription profiles of LM between two pig breeds, Duroc and Taoyuan, which display dramatically different postnatal muscle growth. We isolated LM from neonatal pigs, and the Duroc muscle length and mass were greater (P < 0.01) than for Taoyuan pigs; however, insignificant differences in the muscle fiber area and the percentage of fiber types were found. A human high-density complementary DNA (cDNA) microarray consisting of 9,182 probes was used to compare gene transcription profiles of LM between the two breeds. The results showed that the transcription level of 73 genes and 44 genes in Duroc LM were upregulated and down-regulated by at least 1.75-fold (P < 0.05) compared with Taoyuan, respectively. The strongly upregulated genes in Duroc pigs included those encoding the complex of myofibrillar proteins (e.g., myosin light and heavy chains, and troponin), ribosomal proteins, transcription regulatory proteins (e.g., skeletal muscle LIM protein 1 [SLIM1] and high-mobility group proteins), and energy metabolic enzymes (e.g., electron-transferring flavo-protein dehydrogenase, NADH dehydrogenase, malate dehydrogenase, and ATP synthases). The highly transcribed genes that encode energy metabolic enzymes indicate a more glycolytic metabolism in Duroc LM, thereby favoring carbohydrates rather than lipids for use as energy substrates in this tissue. The over-transcribed genes that encode skeletal muscle-predominant proteins or transcription regulators that control myogenesis and/or muscle growth suggest a general mechanism for the observed higher rate of postnatal muscle growth in Duroc pigs. The transcription of one such gene, SLIM1, was more highly transcribed (P < 0.01) in Duroc LM at birth and at postnatal d 7 than in Taoyuan. The transcription of SLIM1 increased (P < 0.05) in Duroc LM from neonate through 7 d of age, whereas its transcription remained essentially constant in Taoyuan during this period. These results suggest that SLIM1 may be useful for the development of markers associated with the postnatal muscle growth of pigs.

One hundred years of high-throughput Drosophila research

From the beginning, Drosophila was a high-throughput model organism. Unbiased and genome-wide efforts ranging from Morgan's search for spontaneous mutations and subsequent saturating loss-of-function and gain-of-function screens up to more recent techniques such as microarrays, proteomics and cellular assays have been and will continue to be the backbone of Drosophila research. Integrating these large datasets is one of the next challenges. However, once achieved, a plethora of information far exceeding the information content of the singular experiments will be revealed. Several high-throughput techniques and experimental strategies highlighting the unbiased and integrative nature of Drosophila research during the last century will be discussed.

Analysis of microarray experiments of gene expression profiling

The study of gene expression profiling of cells and tissue has become a major tool for discovery in medicine. Microarray experiments allow description of genome-wide expression changes in health and disease. The results of such experiments are expected to change the methods employed in the diagnosis and prognosis of disease in obstetrics and gynecology. Moreover, an unbiased and systematic study of gene expression profiling should allow the establishment of a new taxonomy of disease for obstetric and gynecologic syndromes. Thus, a new era is emerging in which reproductive processes and disorders could be characterized using molecular tools and fingerprinting. The design, analysis, and interpretation of microarray experiments require specialized knowledge that is not part of the standard curriculum of our discipline. This article describes the types of studies that can be conducted with microarray experiments (class comparison, class prediction, class discovery). We discuss key issues pertaining to experimental design, data preprocessing, and gene selection methods. Common types of data representation are illustrated. Potential pitfalls in the interpretation of microarray experiments, as well as the strengths and limitations of this technology, are highlighted. This article is intended to assist clinicians in appraising the quality of the scientific evidence now reported in the obstetric and gynecologic literature.

'The 39 steps' in gene expression profiling: critical issues and proposed best practices for microarray experiments

Gene expression microarrays have been used widely to address increasingly complex biological questions and to produce an unprecedented amount of data, but have yet to realize their full potential. The interpretation of microarray data remains a major challenge because of the complexity of the underlying biological networks. To gather meaningful expression data, it is crucial to develop standardized approaches for vigilant study design, controlled annotation of resources, careful quality control of experiments, robust statistics, and data registration and storage. This article reviews the steps needed in the design and execution of valid microarray experiments so that global gene expression data can play a major role in the pursuit of future biological discoveries that will impact drug development.

Homeostatic Signature of Anabolic Steroids in Cattle Using1H−13C HMBC NMR Metabonomics

We used metabonomics to discriminate the urinary signature of different anabolic steroid treatments in cattle having different physiological backgrounds (age, sex, and race). (1)H-(13)C heteronuclear multiple bonding connectivity NMR spectroscopy and multivariate statistical methods reveal that metabolites such as trimethylamine-N-oxide, dimethylamine, hippurate, creatine, creatinine, and citrate characterize the biological fingerprint of anabolic treatment. These urinary biomarkers suggest an overall homeostatic adaptation in nitrogen and energy metabolism. From results obtained in this study, it is now possible to consider metabonomics as a complementary method usable to improve doping control strategies to detect fraudulent anabolic treatment in cattle since the oriented global metabolic response provides helpful discrimination.

A collection of bovine cDNA probes for gene expression profiling in muscle

Array technology has been increasingly used to monitor global gene expression patterns in various tissues and cell types. However, applications to muscle development and pathology as well as meat production in livestock species have been hampered by the lack of appropriate cDNA collections. To overcome this problem, a directed cDNA library was constructed starting from 23 muscles of meat-producing bovines to derive a collection of 3573 clones. A preliminary sequence characterization of this collection indicated that the most abundant transcripts correspond to genes encoding proteins involved in energy metabolism (COX and NADH dehydrogenase subunits) and belonging to the contractile apparatus (myosin chains and troponin isoforms). From this cDNA library, we selected a set of 435 clones representing 340 unique genes, of which 24 were novel. This collection was subsequently completed with 75 specific cDNA probes for genes of interest already studied in our laboratory. The bovine 'muscle' cDNA repertoire thus designed was spotted onto a nylon membrane (macroarray) in order to test its utility to further investigate the transcriptome of bovine muscles in relation to meat quality traits. It is also anticipated that this type of collection might be useful for the study of chronic myologic diseases in other mammalian species, including humans.

The topoisomerase 1-interacting protein BTBD1 is essential for muscle cell differentiation

DNA topoisomerase I (Topo1) contributes to vital biological functions, but its regulation is not clearly understood. The BTBD1 protein was recently cloned on the basis of its interaction with the core domain of Topo1 and is expressed particularly in skeletal muscle. To determine BTBD1 functions in this tissue, the in vitro model used was the C2C12 mouse muscle cell line, which expresses BTBD1 mainly after myotube differentiation. We studied the effects of a stably overexpressed BTBD1 protein truncated of the 108 N-terminal amino-acid residues and harbouring a C-terminal FLAG tag (Delta-BTBD1). The proliferation speed of Delta-BTBD1 C2C12 cells was significantly decreased and no myogenic differentiation was observed, although these cells maintained their capacity to enter adipocyte differentiation. These alterations could be related to Topo1 deregulation. This hypothesis is further supported by the decrease in nuclear Topo1 content in Delta-BTBTD1 proliferative C2C12 cells and the switch from the main peripheral nuclear localization of Topo1 to a mainly nuclear diffuse localization in Delta-BTBTD1 C2C12 cells. Finally, this study demonstrated that BTBD1 is essential for myogenic differentiation.

Integrative annotation of 21,037 human genes validated by full-length cDNA clones

The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/). It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs), identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly) may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci) did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA genes. In addition, among 72,027 uniquely mapped SNPs and insertions/deletions localized within human genes, 13,215 nonsynonymous SNPs, 315 nonsense SNPs, and 452 indels occurred in coding regions. Together with 25 polymorphic microsatellite repeats present in coding regions, they may alter protein structure, causing phenotypic effects or resulting in disease. The H-InvDB platform represents a substantial contribution to resources needed for the exploration of human biology and pathology.

Gene expression profiling of human satellite cells during muscular aging using cDNA arrays

It is well established that biological aging is associated with functional deficits at the cellular, tissue, organ and system levels, but the molecular mechanisms that control lifespan and age-related phenotypes are still not well understood. In order to investigate the molecular mechanisms underlying myoblast aging, we have used quantitative hybridization of a cDNA array of 2016 clones from a human skeletal muscle 3'-end cDNA library to monitor gene expression patterns of myoblasts of individuals with different ages (5 days old, 52 years old and 79 years old) and at different stages of proliferation (early, presenescent and senescent). We have shown that expression profiles in satellite cells vary with donor age, with an up-regulation of genes involved in muscle structure, muscle differentiation and in metabolism in the newborn, and a down-regulation of genes involved in protein renewal in adults. We have also observed that myoblasts isolated from subjects of different ages have typical expression profiles at the beginning of their proliferative lifespan. However, this phenomenon progressively disappears as the cells approach senescence. In addition, even though some of the modifications are similar to those observed in other cell types, we have observed that many changes in gene expression are characteristic of the myoblasts, confirming the hypothesis that the program of replicative senescence is specific for each cell type. Finally, we have identified four potential new markers of presenescence for human myoblasts, which could be useful in developing therapeutic strategies.

Global functional profiling of gene expression

The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independent of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function, and chromosome location. Statistical significance values are calculated for each category. We demonstrate the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer datasets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms.

Microarray profiling of human skeletal muscle reveals that insulin regulates approximately 800 genes during a hyperinsulinemic clamp

Insulin action in target tissues involved precise regulation of gene expression. To define the set of insulin-regulated genes in human skeletal muscle, we analyzed the global changes in mRNA levels during a 3-h hyperinsulinemic euglycemic clamp in vastus lateralis muscle of six healthy subjects. Using 29,308 cDNA element microarrays, we found that the mRNA expression of 762 genes, including 353 expressed sequence tags, was significantly modified during insulin infusion. 478 were up-regulated and 284 down-regulated. Most of the genes with known function are novel targets of insulin. They are involved in the transcriptional and translational regulation (29%), intermediary and energy metabolisms (14%), intracellular signaling (12%), and cytoskeleton and vesicle traffic (9%). Other categories consisted of genes coding for receptors, carriers, and transporters (8%), components of the ubiquitin/proteasome pathways (7%) and elements of the immune response (5.5%). These results thus define a transcriptional signature of insulin action in human skeletal muscle. They will help to better define the mechanisms involved in the reduction of insulin effectiveness in pathologies such as type 2 diabetes mellitus, a disease characterized by defective regulation of gene expression in response to insulin.

Development of a porcine skeletal muscle cDNA microarray: analysis of differential transcript expression in phenotypically distinct muscles

BACKGROUND

Microarray profiling has the potential to illuminate the molecular processes that govern the phenotypic characteristics of porcine skeletal muscles, such as hypertrophy or atrophy, and the expression of specific fibre types. This information is not only important for understanding basic muscle biology but also provides underpinning knowledge for enhancing the efficiency of livestock production.

RESULTS

We report on the de novo development of a composite skeletal muscle cDNA microarray, comprising 5500 clones from two developmentally distinct cDNA libraries (longissimus dorsi of a 50-day porcine foetus and the gastrocnemius of a 3-day-old pig). Clones selected for the microarray assembly were of low to moderate abundance, as indicated by colony hybridisation. We profiled the differential expression of genes between the psoas (red muscle) and the longissimus dorsi (white muscle), by co-hybridisation of Cy3 and Cy5 labelled cDNA derived from these two muscles. Results from seven microarray slides (replicates) correctly identified genes that were expected to be differentially expressed, as well as a number of novel candidate regulatory genes. Quantitative real-time RT-PCR on selected genes was used to confirm the results from the microarray.

CONCLUSION

We have developed a porcine skeletal muscle cDNA microarray and have identified a number of candidate genes that could be involved in muscle phenotype determination, including several members of the casein kinase 2 signalling pathway.

Transcriptomal analysis of failing and nonfailing human hearts

Heart failure is a multifactorial disease that may result from different initiating events. To contribute to an improved comprehension of normal cardiac function and the molecular events leading to heart failure, we performed large-scale gene expression analysis of failing and nonfailing human ventricle. Our aim was to define and compare expression profiles of 4 specific pathophysiological cardiac situations: 1) left ventricle (LV) from nonfailing heart; 2) LV from failing hearts affected by dilated cardiomyopathy (DCM); 3) LV from failing hearts affected by ischemic CM (ICM); 4) right ventricle (RV) from failing hearts affected by DCM or ICM. We used oligonucleotide arrays representing approximately 12,000 human genes. After stringent numerical analyses using several statistical tests, we identified 1,306 genes with a similar expression profile in all 4 cardiac situations, therefore representative of part of the human cardiac expression profile. A total of 95 genes displayed differential expression between failing and nonfailing heart samples, reflecting a reversal to developmental gene expression, dedifferentiation of failing cardiomyocytes, and involvement of apoptosis. Twenty genes were differentially expressed between failing LV and failing RV, identifying possible candidates for different functioning of both ventricles. Finally, no genes were found to be significantly differentially expressed between failing DCM and failing ICM LV, emphasizing that transcriptomal analysis of explanted hearts results mainly in identification of expression profiles of end-stage heart failure and less in determination of expression profiles of the underlying etiology. Taken together, our data resulted in identification of putative transcriptomal landmarks for normal and disturbed cardiac function.

Complementary DNA arrays identify CD63 tetraspanin and alpha3 integrin chain as differentially expressed in low and high metastatic human colon carcinoma cells

SUMMARY

Malignant tumor cell invasion is determinant for metastasis to occur. E2 and C5 colon carcinoma cells that were derived from the parental Lovo line and that differ experimentally in spontaneous metastatic ability have been monitored for gene expression by cDNA arrays. Among genes found differentially expressed, the CD63 tetraspanin, not previously recognized in colon cancer progression, and the alpha3 integrin chain were both up-regulated in low metastatic E2 cells and were analyzed for their functional role using adhesion, migration, and invasion assays. Cell surface expression of CD63 and alpha3 integrin was about 2-fold higher in E2 than in C5 cells and confocal microscopy showed that CD63 and alpha3 integrin colocalized evenly on C5 cells whereas they concentrated at elongated tips of the low-metastatic more substrate-adhesive E2 cells. Antibody-interference experiments identified laminin-5 (LN-5) as a ligand interacting with the alpha3beta1/CD63 complex. Substrate-immobilized anti-CD63 antibodies enhanced tumor cell migration and invasion and induced prominent cell surface protrusions that were repressed by the PI3-kinase LY294002 inhibitor. Our results suggest that changes in the expression of surface CD63 and alpha3beta1 integrin interacting with LN-5 could affect migratory signals and the progression of the metastatic disease.

Matching gene expression with hypometabolism after cerebral ischemia in the nonhuman primate

To correlate brain metabolic status with the molecular events during cerebral ischemia, a cDNA array was performed after positron emission tomography scanning in a model of focal cerebral ischemia in baboons. Cluster analysis for the expression of 74 genes allowed the identification of 4 groups of genes. In each of the distinct groups, the authors observed a marked inflection in the pattern of gene expression when the CMRo was reduced by 48% to 66%. These patterns of coordinated modifications in gene expression could define molecular checkpoints for the development of an ischemic infarct and a molecular definition of the penumbra.

Statistical intelligence: effective analysis of high-density microarray data

Microarrays enable researchers to interrogate thousands of genes simultaneously. A crucial step in data analysis is the selection of subsets of interesting genes from the initial set of genes. In many cases, especially when comparing genes expressed in a specific condition to a reference condition, the genes of interest are those which are differentially regulated. This review focuses on the methods currently available for the selection of such genes. Fold change, unusual ratio, univariate testing with correction for multiple experiments, ANOVA and noise sampling methods are reviewed and compared.

Statistical issues with microarrays: processing and analysis

The study of gene expression with printed arrays and prefabricated chips is evolving from a qualitative to a quantitative science. Statistical procedures for determining quality control, differential expression, and reproducibility of findings are a natural consequence of this evolution. However, problems inherent to the technologies have raised important issues of how to apply adequate statistical tests. As a consequence, statistical approaches to microarray research are not yet as routine as they are in other sciences. Statistical methods, tailored to microarrays, continue to be adapted and developed. We present an overview of these methods and of outstanding issues in their use and validation.

Characterization of tissue expression and full-length coding sequence of a novel human gene mapping at 3q12.1 and transcribed in oligodendrocytes

Macro-array differential hybridization of a collection of 5058 human gene transcripts represented in an IMAGE infant brain cDNA library has led to the identification of transcripts displaying preferential or specific expression in brain (Genome Res. 9 (1999) 195; http://idefix.upr420.vjf.cnrs.fr/IMAGE). Most of these genes correspond to as yet undescribed functions. Detailed characterization of the expression, sequence, and genome assignment of one of these genes named C3orf4, is reported here. The full-length sequence of the transcript was obtained by 5' extension RT-PCR. The gene transcript (2.8 kb) encodes a 253 amino acid long protein, with four transmembrane domains. The position of the C3orf4 gene was determined at 3q12.1 thanks to the draft sequence of the human genome. It is composed of five exons spanning more than 7 kb. No TATAA box but a CpG island was found upstream of the beginning of the gene. Northern blot analysis and in situ hybridization revealed a predominant expression in myelinated structures such as corpus callosum and spinal cord. RT-PCR showed expression of the C3orf4 gene in rat optic nerve and cultured oligodendrocytes, the myelinating cells of the central nervous system, but not in astrocytes. This work supports further investigations aimed at determining the role of the C3orf4 gene in myelinating cells.

In vivo regulation of human skeletal muscle gene expression by thyroid hormone

Thyroid hormones are key regulators of metabolism that modulate transcription via nuclear receptors. Hyperthyroidism is associated with increased metabolic rate, protein breakdown, and weight loss. Although the molecular actions of thyroid hormones have been studied thoroughly, their pleiotropic effects are mediated by complex changes in expression of an unknown number of target genes. Here, we measured patterns of skeletal muscle gene expression in five healthy men treated for 14 days with 75 microg of triiodothyronine, using 24,000 cDNA element microarrays. To analyze the data, we used a new statistical method that identifies significant changes in expression and estimates the false discovery rate. The 381 up-regulated genes were involved in a wide range of cellular functions including transcriptional control, mRNA maturation, protein turnover, signal transduction, cellular trafficking, and energy metabolism. Only two genes were down-regulated. Most of the genes are novel targets of thyroid hormone. Cluster analysis of triiodothyronine-regulated gene expression among 19 different human tissues or cell lines revealed sets of coregulated genes that serve similar biologic functions. These results define molecular signatures that help to understand the physiology and pathophysiology of thyroid hormone action.

Efficient isolation of regulatory sequences from human genome and BAC DNA

Isolation of regulatory DNA fragments is the basis of the identification of DNA binding proteins and the study of the regulation of gene expression. Presently there is a lack of efficient methods to broadly isolate and identify DNA regulatory fragments. We developed an efficient method to isolate regulatory DNA sequences from both genome and bacterial artificial chromosome (BAC) based on electrophoretic mobility shift assay and PCR techniques without purified transcription factors. Twenty-nine DNA fragments were isolated from human genome and 24 from BAC DNA containing human apolipoprotein AI gene cluster. Transient transfection assay showed that some fragments could enhance the transcription of reporter gene.

Cloning of tissue-specific genes using serial analysis of gene expression and a novel computational substraction approach

A paradigm of molecular medicine is the identification of functionally specialized genes in the search of defects responsible for human disease. To identify novel genes relevant for thyroid physiology, we applied serial analysis of gene expression (SAGE) and identified 4260 tag sequences that did not match any known gene present in the GenBank database ("no-match" tags). These no-match tags represent still uncharacterized transcripts. Most of them are expected to correspond to housekeeping genes and only a few to genes with a tissue-restricted pattern of expression. To pinpoint the best candidates for tissue-specificity in a large series of tags, we used a computer-based approach. We compared the relative abundance of 80 no match tags in our thyroid SAGE library with the expression level in 14 other SAGE libraries derived from 9 different human tissues. Based on the expression data, we developed the "tissue preferential expression" (TPE) algorithm to discriminate tags expressed specifically in the thyroid. We then selected four tags as preferentially expressed in thyroid. Results were validated by RT-PCR and northern blot on multiple-tissue RNA samples. Finally, the screening of a thyroid cDNA library with expressed sequence tag (EST) sequences related to the selected tags allowed the isolation of four novel thyroid-specific cDNAs. We demonstrate that the computational substraction of SAGE tags by the proposed TPE algorithm is a rapid and reliable way to expedite the cloning of tissue-specific genes through the combined use of SAGE and EST databases.

Applied neurogenomics

Neurogenomics, the study of the genes of the nervous system, has applications in basic research, in the pharmaceutical industry and in the management of neurological disorders. Basic research applications include molecular neuropathology, the detection of genes for neurological disorders, the study of gene expression in the CNS and creation of transgenic models of neurological disorders. Pharmaceutical applications may be in the areas of molecular neuropharmacology, the discovery of new drugs for neurological disorders, gene therapy and the development of personalised medicines based on pharmacogenomics. Clinical applications in neurology include the redefinition and reclassification of diseases, molecular diagnostics and the integration of diagnostics with therapeutics. Various methods for the study of genes and gene expression are described. Genes have been identified for only a limited number of neurological disorders so far. The discovery of genes defective in neurological disorders would facilitate drug discovery, molecular diagnostics and gene therapy diseases. There is a trend towards the integration of diagnosis, genetic screening, prevention, treatment and monitoring of therapy of neurological disorders, which will be facilitated by neurogenomics. Pharmacogenomics-based personalised medicines are anticipated to be part of medical practice by the end of the first decade of the 21st century, and neurogenomics will contribute to the development of personalised medicines for diseases of the CNS.

Identification of altered gene expression in skeletal muscles from Duchenne muscular dystrophy patients

Mutations in the dystrophin gene lead to dystrophin deficiency, which is the cause of Duchenne muscular dystrophy (DMD). This important discovery more than 10 years ago opened a new field for very productive investigations. However, the exact functions of dystrophin are still not fully understood and the complex process leading to subsequent muscle fiber necrosis has not been clearly described; hence there has not yet been any marked improvement in patient treatment. To decipher the molecular mechanisms induced by a lack of dystrophin, we started identifying genes whose expression is altered in DMD skeletal muscles. The approach was based on differential screening of a human muscle cDNA array. Nine genes were found to be up- or downregulated. Our results indicate expression alterations in mitochondrial genes, titin, a muscle transcription factor and three novel genes. First characterizations of these novel genes indicated that two of them have striated muscle tissue specificity.

Identification of differentially expressed genes in the visual structures of brain using high-density cDNA grids

The hybridization patterns of 18,371 high-density-grid-arrayed non-redundant complementary DNA (cDNA) clones were examined using three different sources of cDNA probes. The first set of probes was synthesized from mRNA isolated from visual brain areas MT and V4 of Vervet monkey. The second set of probes was derived from cDNA libraries constructed from two micro dissected sets of layers of the monkey Lateral Geniculate Nucleus layers within the visual pathway, namely the magnocellular and parvocellular layers. The third set of cDNA probes was synthesized from the subtracted fractions of the cDNAs enriched for either the magnocellular or the parvocellular layers of the Lateral Geniculate Nucleus. Software, linked directly to the Genbank database, was developed to aid in the rapid identification of both expressed and differentially expressed genes. Our results indicate that both the cDNA probes synthesized from mRNA and cDNA libraries can identify similar fractions of expressed genes. However, the subtracted cDNA probes improve the efficiency of detection for those genes that are expressed at much lower abundance. Analyses of these results for the differential expression patterns of these genes were validated by semi-quantitative PCR on the DNA derived from the whole tissue cDNA libraries. A list of some known genes that are statistically differentially expressed within the magnocellular layers of the LGN and area MT in the primate visual areas is derived.

Recent developments and future directions in computational genomics

Computational genomics is a subfield of computational biology that deals with the analysis of entire genome sequences. Transcending the boundaries of classical sequence analysis, computational genomics exploits the inherent properties of entire genomes by modelling them as systems. We review recent developments in the field, discuss in some detail a number of novel approaches that take into account the genomic context and argue that progress will be made by novel knowledge representation and simulation technologies.

Regulatory elements and expression profiles

There has been steady progress in the computational analysis of transcription control regions, but current methods of predicting the gene regulatory features of noncoding sequences are still not accurate enough to be useful in automatic genome annotation. Therefore, detailed information on the expression patterns of newly sequenced genes is more likely to come from microarray-based high-throughput mRNA quantitation technologies, which have made revolutionary progress over the past few years and are now ready for genome-wide application. Future solutions to the regulatory element prediction problem may be found by the combined analysis of genome sequence and expression data.