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A thermodynamic model of transcriptome formation.

The genome supplies information on both the quality and quantity of the transcriptome. However, as it remains unknown how a cell determines transcript levels from the genome sequences, despite comprehensive knowledge of the cellular components involved, the quantity information held by the genome cannot as yet be derived from nucleotide sequences. The model presented here explains on a thermodynamic basis how the components decode the genome to form and maintain the transcriptome. The model describes the level of a transcript as a pseudo-equilibrium between velocities of synthesis and degradation, both of which are controlled by sequence-specific interactions between protein factors and nucleic acids. Each of the transcript levels can be described by a single equation expressing a function of the activity concentrations of the protein factors. Quantitative information in the genome can thus be transformed into constants determined from the nucleotide sequences. Using this model, the transcriptome can be traced back to the protein factors and the state of chromosome packaging. The total description of transcript levels allows the model to be verified through comparison of derived hypotheses with comprehensive measurements of the transcriptome. The hypotheses thus derived in the present study are well supported by experimental microarray data, confirming the appropriateness of the model.

Gene Expression Regulation↗

Endothelial cells preparing to die by apoptosis initiate a program of transcriptome and glycome regulation.

The protein-based changes that underlie the cell biology of apoptosis have been extensively studied. In contrast, mRNA- and polysaccharide-based changes have received relatively little attention. We have combined transcriptome and glycome analyses to show that apoptotic endothelial cell cultures undergo programmed changes to RNA transcript abundance and cell surface polysaccharide profiles. Although a few of the transcriptome changes were protective, most appeared to prepare cells for apoptosis by decreasing the reception and transduction of pro-survival signals, increasing pro-death signals, increasing abundance of apoptotic machinery, inhibiting cellular proliferation, recruiting phagocytes to regions of cell death, and promoting phagocytosis. Additional transcriptomal changes appeared to alter the synthesis and modification of cell surface glycosaminoglycans. The resultant reduced abundance of sulphated cell surface glycosaminoglycans may further promote cell death by inhibiting the presentation of extracellular matrix-tethered survival factors to their receptors on dying cells. We propose that the transcriptome and glycome regulation presented here synergize with previously described protein-based changes to guide the apoptotic program.

Apoptosis↗

Selection-driven transcriptome polymorphism in Escherichia coli/Shigella species.

To explore the role of transcriptome polymorphism in adaptation of organisms to their environment, we evaluated this parameter for the Escherichia coli/Shigella bacterial species, which is composed of well-characterized phylogenetic groups that exhibit characteristic life styles ranging from commensalism to intracellular pathogenicity. Both the genomic content and the transcriptome of 10 strains representative of the major E. coli/Shigella phylogenetic groups were evaluated using macroarrays displaying the 4290 K12-MG1655 open reading frames (ORFs). Although Shigella and enteroinvasive E. coli (EIEC) are not monophyletic, phylogenetic analysis of the binary coded (presence/absence) gene content data showed that these organisms group together due to similar patterns of undetectable K12-MG1655 genes. The variation in transcript abundance was then analyzed using a core genome of 2880 genes present in all strains, after adjusting RNA hybridization signals for DNA hybridization signals. Nonrandom changes in gene expression during the evolution of the E. coli/Shigella species were evidenced. Phylogenetic analysis of transcriptome data again showed that Shigella and EIEC strains group together in terms of gene expression, and this convergence involved groups of genes displaying biologically coherent patterns of functional divergence. Unlike the other E. coli strains evaluated, Shigella and EIEC are intracellular pathogens, and therefore face similar selective pressures. Thus, within the E. coli/Shigella species, strains exhibiting a particular life style have converged toward a specific gene expression pattern in a subset of genes common to the species, revealing the role of selection in shaping transcriptome polymorphism.

Caco-2 Cells↗

The maize root transcriptome by serial analysis of gene expression.

Serial Analysis of Gene Expression was used to define number and relative abundance of transcripts in the root tip of well-watered maize seedlings (Zea mays cv FR697). In total, 161,320 tags represented a minimum of 14,850 genes, based on at least two tags detected per transcript. The root transcriptome has been sampled to an estimated copy number of approximately five transcripts per cell. An extrapolation from the data and testing of single-tag identifiers by reverse transcription-PCR indicated that the maize root transcriptome should amount to at least 22,000 expressed genes. Frequency ranged from low copy number (2-5, 68.8%) to highly abundant transcripts (100-->1,200; 1%). Quantitative reverse transcription-PCR for selected transcripts indicated high correlation with tag frequency. Computational analysis compared this set with known maize transcripts and other root transcriptome models. Among the 14,850 tags, 7,010 (47%) were found for which no maize cDNA or gene model existed. Comparing the maize root transcriptome with that in other plants indicated that highly expressed transcripts differed substantially; less than 5% of the most abundant transcripts were shared between maize and Arabidopsis (Arabidopsis thaliana). Transcript categories highlight functions of the maize root tip. Significant variation in abundance characterizes transcripts derived from isoforms of individual enzymes in biochemical pathways.

Base Sequence↗

Gene family analysis of the Arabidopsis pollen transcriptome reveals biological implications for cell growth, division control, and gene expression regulation.

Upon germination, pollen forms a tube that elongates dramatically through female tissues to reach and fertilize ovules. While essential for the life cycle of higher plants, the genetic basis underlying most of the process is not well understood. We previously used a combination of flow cytometry sorting of viable hydrated pollen grains and GeneChip array analysis of one-third of the Arabidopsis (Arabidopsis thaliana) genome to define a first overview of the pollen transcriptome. We now extend that study to approximately 80% of the genome of Arabidopsis by using Affymetrix Arabidopsis ATH1 arrays and perform comparative analysis of gene family and gene ontology representation in the transcriptome of pollen and vegetative tissues. Pollen grains have a smaller and overall unique transcriptome (6,587 genes expressed) with greater proportions of selectively expressed (11%) and enriched (26%) genes than any vegetative tissue. Relative gene ontology category representations in pollen and vegetative tissues reveal a functional skew of the pollen transcriptome toward signaling, vesicle transport, and the cytoskeleton, suggestive of a commitment to germination and tube growth. Cell cycle analysis reveals an accumulation of G2/M-associated factors that may play a role in the first mitotic division of the zygote. Despite the relative underrepresentation of transcription-associated transcripts, nonclassical MADS box genes emerge as a class with putative unique roles in pollen. The singularity of gene expression control in mature pollen grains is further highlighted by the apparent absence of small RNA pathway components.

Arabidopsis↗

Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor.

The integration of metabolomics and transcriptomics can provide precise information on gene-to-metabolite networks for identifying the function of unknown genes unless there has been a post-transcriptional modification. Here, we report a comprehensive analysis of the metabolome and transcriptome of Arabidopsis thaliana over-expressing the PAP1 gene encoding an MYB transcription factor, for the identification of novel gene functions involved in flavonoid biosynthesis. For metabolome analysis, we performed flavonoid-targeted analysis by high-performance liquid chromatography-mass spectrometry and non-targeted analysis by Fourier-transform ion-cyclotron mass spectrometry with an ultrahigh-resolution capacity. This combined analysis revealed the specific accumulation of cyanidin and quercetin derivatives, and identified eight novel anthocyanins from an array of putative 1800 metabolites in PAP1 over-expressing plants. The transcriptome analysis of 22,810 genes on a DNA microarray revealed the induction of 38 genes by ectopic PAP1 over-expression. In addition to well-known genes involved in anthocyanin production, several genes with unidentified functions or annotated with putative functions, encoding putative glycosyltransferase, acyltransferase, glutathione S-transferase, sugar transporters and transcription factors, were induced by PAP1. Two putative glycosyltransferase genes (At5g17050 and At4g14090) induced by PAP1 expression were confirmed to encode flavonoid 3-O-glucosyltransferase and anthocyanin 5-O-glucosyltransferase, respectively, from the enzymatic activity of their recombinant proteins in vitro and results of the analysis of anthocyanins in the respective T-DNA-inserted mutants. The functional genomics approach through the integration of metabolomics and transcriptomics presented here provides an innovative means of identifying novel gene functions involved in plant metabolism.

Arabidopsis↗

Analysis of the root-hair morphogenesis transcriptome reveals the molecular identity of six genes with roles in root-hair development in Arabidopsis.

Root-hair morphogenesis is a model for studying the genetic regulation of plant cell development, and double-mutant analyses have revealed a complex genetic network underlying the development of this type of cell. Therefore, to increase knowledge of gene expression in root hairs and to identify new genes involved in root-hair morphogenesis, the transcriptomes of the root-hair differentiation zone of wild-type (WT) plants and a tip-growth defective root-hair mutant, rhd2-1, were compared using Affymetrix ATH1 GeneChips. A set of 606 genes with significantly greater expression in WT plants defines the 'root-hair morphogenesis transcriptome'. Compared with the whole genome, this set is highly enriched in genes known to be involved in root-hair morphogenesis. The additional gene families and functional groups enriched in the root-hair morphogenesis transcriptome are cell wall enzymes, hydroxyproline-rich glycoproteins (extensins) and arabinogalactan proteins, peroxidases, receptor-like kinases and proteins with predicted glycosylphosphatidylinositol (GPI) anchors. To discover new root-hair genes, 159 T-DNA insertion lines identified from the root-hair morphogenesis transcriptome were screened for defects in root-hair morphogenesis. This identified knockout mutations in six genes (RHM1-RHM6) that affected root-hair morphogenesis and that had not previously been identified at the molecular level: At2g03720 (similar to Escherichia coli universal stress protein); At3g54870 (armadillo-repeat containing kinesin-related protein); At4g18640 (leucine-rich repeat receptor-like kinase subfamily VI); At4g26690 (glycerophosphoryl diester phosphodiesterase-like GPI-anchored protein); At5g49270 (COBL9 GPI-anchored protein) and At5g65090 (inositol-1,4,5 triphosphate 5-phosphatase-like protein). The mutants were transcript null, their root-hair phenotypes were characterized and complementation testing with uncloned root-hair genes was performed. The results suggest a role for GPI-anchored proteins and lipid rafts in root-hair tip growth because two of these genes (At4g26690 and At5g49270) encode predicted GPI-anchored proteins likely to be associated with lipid rafts, and several other genes previously shown to be required for root-hair development also encode proteins associated with sterol-rich lipid rafts.

Arabidopsis↗

Analysis of environmental transcriptomes by DNA microarrays.

In this work we investigated the correlations between global gene expression patterns and environmental parameters in natural ecosystems. We studied the preferential gene expression of the iron oxidizer bacterium Leptospirillum ferrooxidans to adapt its physiology to changes in the physicochemical parameters in its natural medium. Transcriptome analysis by DNA microarrays can proportionate an instant picture about the preferential gene expression between two different environmental samples. However, this type of analysis is very difficult and complex in natural ecosystems, mainly because of the broad biodiversity and multiple environmental parameters that may affect gene expression. The necessity of high-quality RNA preparations as well as complicated data analysis are also technological limitations. The low prokaryotic diversity of the extremely acidic and iron-rich waters of the Tinto River (Spain) ecosystem, where L. ferrooxidans is abundant, allows the opportunity to achieve global gene expression studies and to associate gene function with environmental parameters. We applied a total RNA amplification protocol validated previously for the amplification of the environmental transcriptome (meta-transcriptome). The meta-transcriptome of two sites from the Tinto River mainly differing in the salt and oxygen contents were amplified and analysed by a L. ferrooxidans DNA microarray. The results showed a clear preferential induction of genes involved in certain physicochemical parameters like: high salinity (ectAB, otsAB), low oxygen concentration (cydAB), iron uptake (fecA-exbBD-tonB), oxidative stress (carotenoid synthesis, oxyR, recG), potassium (kdpBAC) or phosphate concentrations (pstSCAB), etc. We conclude that specific gene expression patterns can be useful indicators for the physiological conditions in a defined ecosystem. Also, the upregulation of certain genes and operons reveals information about the environmental conditions (nutrient limitations, stresses, etc.).

Bacteria↗

Cycloheximide treatment to identify components of the transitional transcriptome in PACAP-induced PC12 cell differentiation.

Pituitary adenylate cyclase-activating polypeptide (PACAP) promotes neurite outgrowth, reduces proliferation and inhibits apoptosis of PC12 cells. We have partially characterized the transcriptome changes induced by PACAP after 6 h of treatment, when commitment to differentiation has occurred. Here, we have investigated the effects of a 6-h treatment with PACAP (10(-7) m) in the presence of cycloheximide (5 microm) to identify, via superinduction, components of the transitional transcriptome initially induced by PACAP and potentially participating in the regulation of late-response genes required for differentiation. Approximately 100 new transcripts were identified in this screen, i.e. as many individual genes as make up the 6-h PACAP differentiation transcriptome itself. Six known transcripts in this cohort were then measured at several time points between 0 and 6 h by real-time PCR to determine whether these transcripts are induced early following PACAP treatment in the absence of cycloheximide, and therefore may be of functional importance in differentiation. Five out of the six transcripts were indeed induced by PACAP alone soon (between 30 min and 3 h) after cell treatment. beta-Cell translocation gene 2, antiproliferative (Btg2), serum/glucocorticoid-regulated kinase (Sgk), nuclear factor for the kappa chain of B-cells (NFkappaB), seven in absentia homologue 2 (Siah2) and FBJ osteosarcoma related oncogene (Fos) showed a 2.5-200-fold induction by PACAP between 15 min and 3 h, and mRNA levels returned either to baseline or near baseline after 6 h. This work provides new information concerning genes whose transient regulation early after PACAP exposure may contribute to the expression of the differentiated transcriptome in PC12 cells, and should help to elucidate the molecular mechanisms involved in the control of nerve cell survival and differentiation.

Animals↗

The complexity of the mammalian transcriptome.

A comprehensive understanding of protein and regulatory networks is strictly dependent on the complete description of the transcriptome of cells. After the determination of the genome sequence of several mammalian species, gene identification is based on in silico predictions followed by evidence of transcription. Conservative estimates suggest that there are about 20,000 protein-encoding genes in the mammalian genome. In the last few years the combination of full-length cDNA cloning, cap-analysis gene expression (CAGE) tag sequencing and tiling arrays experiments have unveiled unexpected additional complexities in the transcriptome. Here we describe the current view of the mammalian transcriptome focusing on transcripts diversity, the growing non-coding RNA world, the organization of transcriptional units in the genome and promoter structures. In-depth analysis of the brain transcriptome has been challenging due to the cellular complexity of this organ. Here we present a computational analysis of CAGE data from different regions of the central nervous system, suggesting distinctive mechanisms of brain-specific transcription.

Alternative Splicing↗

Adenovirus infection triggers a rapid, MyD88-regulated transcriptome response critical to acute-phase and adaptive immune responses in vivo.

Nearly 50 years ago, the discovery of interferon prompted the notion that host cells innately respond to viral invasion. Since that time, technological advances have allowed this response to be extensively characterized and dissected in vitro. However, these advances have only recently been applied to highly complex, in vivo biological systems. To this end, we exploited high-titer adenovirus (Ad) vectors to globally investigate the innate immune response to nonenveloped viral infection in vivo. Our results indicated a potent cellular transcriptome response shortly after infection, with global assessments revealing significant dysregulation in approximately 15% of the measured transcripts derived from Ad vector-transduced tissue. Bioinformatics-based transcriptome analysis revealed a complex innate response to Ad infection, with induction of proinflammatory responses (and suppression of metabolism and mitochondrial genes) akin to those observed when mice are challenged with lipopolysaccharide. Despite this commonality, there were many unique aspects of the Ad-dependent transcriptome response, including the upregulation of several RNA regulatory mechanisms and apoptosis-related pathways, accompanied by the suppression of lysosomal and endocytic genes. Our results also implicated the Toll-like receptors (TLRs) in these responses, prompting specific investigations into this pathway. By using MyD88KO mice, our results confirmed that Ad-induced dysregulation of five functionally related gene clusters are significantly dependent on this TLR adaptor gene. MyD88 deficiency also resulted in significantly diminished, although not abolished, adaptive and acute-phase immune responses to Ad, confirming the transcriptome data, as well as specifically identifying MyD88 as a significant Ad immunity amplifier and regulator in vivo.

Adenoviridae↗

Isolation, functional characterization, and transcriptome of Mastomys ileal enterochromaffin cells.

Although the enterochromaffin (EC) cell is one of the primary neuroendocrine regulatory cells of the small intestine, the lack of a purified cell system has precluded characterization of the cell and limited precise physiological evaluation. We developed methodology to obtain a pure population of Mastomys ileal EC cells, evaluated their functional regulation, and defined the transcriptome. Mastomys ilea were everted, end ligated, pronase-collagenase digested, and Nycodenz gradient centrifuged, and EC cells were collected by fluorescence-activated cell sorting (FACS) of acridine orange-labeled cells. Enrichment was confirmed by immunostaining of tryptophan hydroxylase and chromogranin A, specific EC cell markers, serotonin content, EC cell marker gene expression, and electron microscopy. Pituitary adenylate cyclase-activating polypeptide (PACAP), somatostatin, and gastrin receptor expression was determined by real-time RT-PCR. Live post-FACS-sorted cells were cultured, and the effects of forskolin, isoproterenol, acetylcholine, GABAA, PACAP-38, and gastrin on serotonin secretion were measured by ELISA. GeneChip Affymetrix profiling of FACS-sorted cells was undertaken to obtain the EC cell transcriptome. FACS produced a >70-fold enrichment of EC cells with a serotonin content of 240 +/- 22 ng/mg protein. Preparations were 99 +/- 0.7% pure by immunostaining for tryptophan hydroxylase. Vasoactive intestinal peptide/PACAP receptor 1 (VPAC1) and somatostatin receptor 2 were present, whereas PACAP receptor 1 (PAC1) and CCK2 receptors were undetectable. Forskolin, isoproterenol, and PACAP-38 stimulated serotonin secretion at EC50 values of 5 x 10(-10), 4.5 x 10(-10), and 1.2 x 10(-9) M, respectively. Isoproterenol stimulated cAMP levels by approximately 3.5 +/- 0.62-fold vs. unstimulated cells (EC50 of approximately 10(-9) M). Octreotide, acetylcholine, and GABAA inhibited serotonin secretion with IC50 values of 3 x 10(-11), 3 x 10(-10), and 2.9 x 10(-10) M, respectively. Gastrin had no effect on serotonin secretion. The naive EC cell transcriptome revealed highly expressed EC cell marker genes, the absence of marker genes for other small intestinal cell types, and a receptor profile that included cholinergic, adrenergic, dopaminergic, serotoninergic, GABAergic, and prostaglandin receptors. We were able to isolate homogeneous preparations (>99%) of live ileal EC cells and demonstrated regulation of serotonin secretion as well as established the normal EC cell transcriptome. Application of this methodology to normal and diseased human ileum will facilitate the elucidation of the pathophysiology of EC cells.

Acetylcholine↗

Visualizing chromosomes as transcriptome correlation maps: evidence of chromosomal domains containing co-expressed genes--a study of 130 invasive ductal breast carcinomas.

Completion of the working draft of the human genome has made it possible to analyze the expression of genes according to their position on the chromosomes. Here, we used a transcriptome data analysis approach involving for each gene the calculation of the correlation between its expression profile and those of its neighbors. We used the U133 Affymetrix transcriptome data set for a series of 130 invasive ductal breast carcinomas to construct chromosomal maps of gene expression correlation (transcriptome correlation map). This highlighted nonrandom clusters of genes along the genome with correlated expression in tumors. Some of the gene clusters identified by this method probably arose because of genetic alterations, as most of the chromosomes with the highest percentage of correlated genes (1q, 8p, 8q, 16p, 16q, 17q, and 20q) were also the most frequent sites of genomic alterations in breast cancer. Our analysis showed that several known breast tumor amplicons (at 8p11-p12, 11q13, and 17q12) are located within clusters of genes with correlated expression. Using hierarchical clustering on samples and a Treeview representation of whole chromosome arms, we observed a higher-order organization of correlated genes, sometimes involving very large chromosomal domains that could extend to a whole chromosome arm. Transcription correlation maps are a new way of visualizing transcriptome data. They will help to identify new genes involved in tumor progression and new mechanisms of gene regulation in tumors.

Breast Neoplasms↗

Salivary transcriptome diagnostics for oral cancer detection.

PURPOSE: Oral fluid (saliva) meets the demand for noninvasive, accessible, and highly efficient diagnostic medium. Recent discovery that a large panel of human RNA can be reliably detected in saliva gives rise to a novel clinical approach, salivary transcriptome diagnostics. The purpose of this study is to evaluate the diagnostic value of this new approach by using oral squamous cell carcinoma (OSCC) as the proof-of-principle disease. EXPERIMENTAL DESIGN: Unstimulated saliva was collected from patients (n = 32) with primary T1/T2 OSCC and normal subjects (n = 32) with matched age, gender, and smoking history. RNA isolation was done from the saliva supernatant, followed by two-round linear amplification with T7 RNA polymerase. Human Genome U133A microarrays were applied for profiling human salivary transcriptome. The different gene expression patterns were analyzed by combining a t test comparison and a fold-change analysis on 10 matched cancer patients and controls. Quantitative polymerase chain reaction (qPCR) was used to validate the selected genes that showed significant difference (P < 0.01) by microarray. The predictive power of these salivary mRNA biomarkers was analyzed by receiver operating characteristic curve and classification models. RESULTS: Microarray analysis showed there are 1,679 genes exhibited significantly different expression level in saliva between cancer patients and controls (P < 0.05). Seven cancer-related mRNA biomarkers that exhibited at least a 3.5-fold elevation in OSCC saliva (P < 0.01) were consistently validated by qPCR on saliva samples from OSCC patients (n = 32) and controls (n = 32). These potential salivary RNA biomarkers are transcripts of IL8, IL1B, DUSP1, HA3, OAZ1, S100P, and SAT. The combinations of these biomarkers yielded sensitivity (91%) and specificity (91%) in distinguishing OSCC from the controls. CONCLUSIONS: The utility of salivary transcriptome diagnostics is successfully demonstrated in this study for oral cancer detection. This novel clinical approach could be exploited to a robust, high-throughput, and reproducible tool for early cancer detection. Salivary transcriptome profiling can be applied to evaluate its usefulness for other major disease applications as well as for normal health surveillance.

Biomarkers, Tumor↗

GO-Diff: mining functional differentiation between EST-based transcriptomes.

BACKGROUND: Large-scale sequencing efforts produced millions of Expressed Sequence Tags (ESTs) collectively representing differentiated biochemical and functional states. Analysis of these EST libraries reveals differential gene expressions, and therefore EST data sets constitute valuable resources for comparative transcriptomics. To translate differentially expressed genes into a better understanding of the underlying biological phenomena, existing microarray analysis approaches usually involve the integration of gene expression with Gene Ontology (GO) databases to derive comparable functional profiles. However, methods are not available yet to process EST-derived transcription maps to enable GO-based global functional profiling for comparative transcriptomics in a high throughput manner. RESULTS: Here we present GO-Diff, a GO-based functional profiling approach towards high throughput EST-based gene expression analysis and comparative transcriptomics. Utilizing holistic gene expression information, the software converts EST frequencies into EST Coverage Ratios of GO Terms. The ratios are then tested for statistical significances to uncover differentially represented GO terms between the compared transcriptomes, and functional differences are thus inferred. We demonstrated the validity and the utility of this software by identifying differentially represented GO terms in three application cases: intra-species comparison; meta-analysis to test a specific hypothesis; inter-species comparison. GO-Diff findings were consistent with previous knowledge and provided new clues for further discoveries. A comprehensive test on the GO-Diff results using series of comparisons between EST libraries of human and mouse tissues showed acceptable levels of consistency: 61% for human-human; 69% for mouse-mouse; 47% for human-mouse. CONCLUSION: GO-Diff is the first software integrating EST profiles with GO knowledge databases to mine functional differentiation between biological systems, e.g. tissues of the same species or the same tissue cross species. With rapid accumulation of EST resources in the public domain and expanding sequencing effort in individual laboratories, GO-Diff is useful as a screening tool before undertaking serious expression studies.

Animals↗

Transcriptomes of human prostate cells.

BACKGROUND: The gene expression profiles of most human tissues have been studied by determining the transcriptome of whole tissue homogenates. Due to the solid composition of tissues it is difficult to study the transcriptomes of individual cell types that compose a tissue. To overcome the problem of heterogeneity we have developed a method to isolate individual cell types from whole tissue that are a source of RNA suitable for transcriptome profiling. RESULTS: Using monoclonal antibodies specific for basal (integrin beta4), luminal secretory (dipeptidyl peptidase IV), stromal fibromuscular (integrin alpha 1), and endothelial (PECAM-1) cells, respectively, we separated the cell types of the prostate with magnetic cell sorting (MACS). Gene expression of MACS-sorted cell populations was assessed with Affymetrix GeneChips. Analysis of the data provided insight into gene expression patterns at the level of individual cell populations in the prostate. CONCLUSION: In this study, we have determined the transcriptome profile of a solid tissue at the level of individual cell types. Our data will be useful for studying prostate development and cancer progression in the context of single cell populations within the organ.

Bone Morphogenetic Proteins↗

Ovulatory surges of human CG prevent hormone-induced granulosa cell tumor formation leading to the identification of tumor-associated changes in the transcriptome.

Granulosa cell tumors comprise approximately 10% of ovarian tumors and, although rare, are clinically important due to their potential for malignancy and recurrence. Although their morphological features have been carefully described, the global changes in gene expression associated with their formation remain undetermined. To initiate this characterization, we used a transgenic mouse model in which granulosa cell tumors occur with 100% penetrance in CF-1 mice that harbor a novel transgene encoding a chimeric LHbeta subunit. When this transgene is expressed in other strains of mice, including (C57BL/6 female symbol x CF-1 male symbol,Tg) F1 hybrids, luteomas develop even though levels of LH remain high. This dichotomous response permits a longitudinal comparison of global changes in transcriptomes uniquely associated with either granulosa cell tumors or luteomas. Herein we report numerous changes in the transcriptome, including a decrease in LH receptor mRNA and increases in several mRNAs that encode secreted proteins previously associated with granulosa cell tumors. Furthermore, we identified a constellation of mRNAs that encode proteins that may serve as new markers for this tumor phenotype. Additional experiments indicated that periodic treatment with human CG prevented formation of granulosa cell tumors in mice genetically predisposed to tumor development and, instead, led to the appearance of luteomas. More importantly, ovarian transcriptomes from the luteomas induced by ovulatory doses of human CG permitted refined confirmation of gene expression changes that were uniquely associated with either granulosa cell tumors in the permissive CF-1 genetic background or in luteomas in the F1 hybrids. Together, these dynamic changes in the ovarian transcriptome indict various signaling pathways potentially involved in mediating the actions of LH over time and, depending on genetic background, the formation of either a luteoma or a granulosa cell tumor.

Animals↗

Whole transcriptome sequencing analyses of islets reveal ncRNA regulatory networks underlying impaired insulin secretion and increased &#x3b2;-cell mass in high fat diet-induced diabetes mellitus.

AIM: Our study aims to identify novel non-coding RNA-mRNA regulatory networks associated with &#x3b2;-cell dysfunction and compensatory responses in obesity-related diabetes. METHODS: Glucose metabolism, islet architecture and secretion, and insulin sensitivity were characterized in C57BL/6J mice fed on a 60% high-fat diet (HFD) or control for 24 weeks. Islets were isolated for whole transcriptome sequencing to identify differentially expressed (DE) mRNAs, miRNAs, IncRNAs, and circRNAs. Regulatory networks involving miRNA-mRNA, lncRNA-mRNA, and lncRNA-miRNA-mRNA were constructed and functions were assessed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. RESULTS: Despite compensatory hyperinsulinemia and a significant increase in &#x3b2;-cell mass with a slow rate of proliferation, HFD mice exhibited impaired glucose tolerance. In isolated islets, insulin secretion in response to glucose and palmitic acid deteriorated after 24 weeks of HFD. Whole transcriptomic sequencing identified a total of 1324 DE mRNAs, 14 DE miRNAs, 179 DE lncRNAs, and 680 DE circRNAs. Our transcriptomic dataset unveiled several core regulatory axes involved in the impaired insulin secretion in HFD mice, such as miR-6948-5p/Cacna1c, miR-6964-3p/Cacna1b, miR-3572-5p/Hk2, miR-3572-5p/Cckar and miR-677-5p/Camk2d. Additionally, proliferative and apoptotic targets, including miR-216a-3p/FKBP5, miR-670-3p/Foxo3, miR-677-5p/RIPK1, miR-802-3p/Smad2 and ENSMUST00000176781/Caspase9 possibly contribute to the increased &#x3b2;-cell mass in HFD islets. Furthermore, competing endogenous RNAs (ceRNA) regulatory network involving 7 DE miRNAs, 15 DE lncRNAs and 38 DE mRNAs might also participate in the development of HFD-induced diabetes. CONCLUSIONS: The comprehensive whole transcriptomic sequencing revealed novel non-coding RNA-mRNA regulatory networks associated with impaired insulin secretion and increased &#x3b2;-cell mass in obesity-related diabetes.

Mice↗