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Transcriptional analysis of the mammalian heart with special reference to its endocrine function.

ABSTRACT: BACKGROUND: Pharmacological and gene ablation studies have demonstrated the crucial role of the endocrine function of the heart as mediated by the polypeptide hormones ANF and BNP in the maintenance of cardiovascular homeostasis. The importance of these studies lies on the fact that hypertension and chronic congestive heart failure are clinical entities that may be regarded as states of relative deficiency of ANF and BNP. These hormones are produced by the atrial muscle cells (cardiocytes), which display a dual secretory/muscle phenotype. In contrast, ventricular cardiocytes display mainly a muscle phenotype. Comparatively little information is available regarding the genetic background for this important phenotypic difference with particular reference to the endocrine function of the heart. We postulated that comparison of gene expression profiles between atrial and ventricular muscles would help identify gene transcripts that underlie the phenotypic differences associated with the endocrine function of the heart. RESULTS: Comparison of gene expression profiles in the rat heart revealed a total of 1415 differentially expressed genes between the atria and ventricles based on a 1.8 fold cut-off. The identification of numerous chamber specific transcripts, such as ANF for the atria and Irx4 for the ventricles among several others, support the soundness of the GeneChip data and demonstrates that the differences in gene expression profiles observed between the atrial and ventricular tissues were not spurious in nature. Pathway analysis revealed unique expression profiles in the atria for G protein signaling that included Galphao1, Ggamma2 and Ggamma3, AGS1, RGS2, and RGS6 and the related K+ channels GIRK1 and GIRK4. Transcripts involved in vesicle trafficking, hormone secretion as well as mechanosensors (e.g. the potassium channel TREK-1) were identified in relationship to the synthesis, storage and secretion of hormones. CONCLUSION: The data developed in this investigation describes for the first time data on gene expression particularly centred on the secretory function of the heart. This provides for a rational approach in the investigation of determinants of the endocrine of the heart in health and disease.

SUBMITTER: McGrath MF

PROVIDER: S-EPMC2694839 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Transcriptional analysis of the mammalian heart with special reference to its endocrine function.

McGrath Monica Forero MF de Bold Adolfo J AJ

BMC genomics 20090601

<h4>Background</h4>Pharmacological and gene ablation studies have demonstrated the crucial role of the endocrine function of the heart as mediated by the polypeptide hormones ANF and BNP in the maintenance of cardiovascular homeostasis. The importance of these studies lies on the fact that hypertension and chronic congestive heart failure are clinical entities that may be regarded as states of relative deficiency of ANF and BNP. These hormones are produced by the atrial muscle cells (cardiocytes ...[more]

PMID: 19486520

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Project description:BACKGROUND:The inability of the adult mammalian heart to regenerate following injury represents a major barrier in cardiovascular medicine. In contrast, the neonatal mammalian heart retains a transient capacity for regeneration, which is lost shortly after birth. Defining the molecular mechanisms that govern regenerative capacity in the neonatal period remains a central goal in cardiac biology. Here, we assemble a transcriptomic framework of multiple cardiac cell populations during postnatal development and following injury, which enables comparative analyses of the regenerative (neonatal) versus nonregenerative (adult) state for the first time. METHODS:Cardiomyocytes, fibroblasts, leukocytes, and endothelial cells from infarcted and noninfarcted neonatal (P1) and adult (P56) mouse hearts were isolated by enzymatic dissociation and fluorescence-activated cell sorting at day 3 following surgery. RNA sequencing was performed on these cell populations to generate the transcriptome of the major cardiac cell populations during cardiac development, repair, and regeneration. To complement our transcriptomic data, we also surveyed the epigenetic landscape of cardiomyocytes during postnatal maturation by performing deep sequencing of accessible chromatin regions by using the Assay for Transposase-Accessible Chromatin from purified mouse cardiomyocyte nuclei (P1, P14, and P56). RESULTS:Profiling of cardiomyocyte and nonmyocyte transcriptional programs uncovered several injury-responsive genes across regenerative and nonregenerative time points. However, the majority of transcriptional changes in all cardiac cell types resulted from developmental maturation from neonatal stages to adulthood rather than activation of a distinct regeneration-specific gene program. Furthermore, adult leukocytes and fibroblasts were characterized by the expression of a proliferative gene expression network following infarction, which mirrored the neonatal state. In contrast, cardiomyocytes failed to reactivate the neonatal proliferative network following infarction, which was associated with loss of chromatin accessibility around cell cycle genes during postnatal maturation. CONCLUSIONS:This work provides a comprehensive framework and transcriptional resource of multiple cardiac cell populations during cardiac development, repair, and regeneration. Our findings define a regulatory program underpinning the neonatal regenerative state and identify alterations in the chromatin landscape that could limit reinduction of the regenerative program in adult cardiomyocytes.

Project description:Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies.We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network.The advantage of our new algorithm, relative to the original NCA, is that our algorithm can identify the important TF-gene interactions. Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53. Also, our algorithm has been developed to overcome NCA's inability to analyze large networks where multiple TFs regulate a single gene. Thus, our algorithm extends the applicability of NCA to the realm of mammalian regulatory network analysis.

Project description:Background:Germline mutations BRCA1 and BRCA2 contribute almost equally in the causation of breast cancer (BC). The type of mutations in the Indian population that cause this condition is largely unknown. Purpose:In this cohort, 79 randomized BC patients were screened for various types of BRCA1 and BRCA2 mutations including frameshift, nonsense, missense, in-frame and splice site types. Materials and methods:The purified extracted DNA of each referral patient was subjected to Sanger gene sequencing using Codon Code Analyzer and Mutation Surveyor and next-generation sequencing (NGS) methods with Ion torrent software, after appropriate care. Results:The data revealed that 35 cases were positive for BRCA1 or BRCA2 (35/79: 44.3%). BRCA2 mutations were higher (52.4%) than BRCA1 mutations (47.6%). Five novel mutations detected in this study were p.pro163 frameshift, p.asn997 frameshift, p.ser148 frameshift and two splice site single-nucleotide polymorphisms (SNPs). Additionally, four nonsense and one in-frame deletion were identified, which all seemed to be pathogenic. Polymorphic SNPs contributed the highest percentage of mutations (72/82: 87.8%) and contributed to pathogenic, likely pathogenic, likely benign, benign and variant of unknown significance (VUS). Young age groups (20-60 years) had a high frequency of germline mutations (62/82;75.6%) in the Indian population. Conclusion:This study suggested that polymorphic SNPs contributed a high percentage of mutations along with five novel types. Younger age groups are prone to having BC with a higher mutational rate. Furthermore, the SNPs detected in exons 10, 11 and 16 of BRCA1 and BRCA2 were higher than those in other exons 2, 3 and 9 polymorphic sites in two germline genes. These may be contributory for BC although missense types are known to be susceptible for cancer depending on the type of amino acid replaced in the protein and associated with pathologic events. Accordingly, appropriate counseling and treatment may be suggested.

Dataset Information

Transcriptional analysis of the mammalian heart with special reference to its endocrine function.

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Transcriptional analysis of the mammalian heart with special reference to its endocrine function.

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