Protein synthesis and degradation during regression of thyroxine-induced cardiac hypertrophy

To characterize changes in rates of protein turnover during regression of thyroxine-induced left ventricular hypertrophy, New Zealand White rabbits received intravenous thyroxine (200 [mu]g/kg/d) for 9 days. Thyroxine was withheld, and in vivo protein turnover was evaluated on the 10th, 15th and 20th days. Animals not receiving thyroxine served as controls. Heart rate, blood pressure, and rate-pressure product were measured to correlate changes in cardiac work with protein turnover rates during the development and regression of hypertrophy. Thyroxine administration produced left ventricular hypertrophy by increasing the rate of protein synthesis (from 37.9 +/- 8.9 to 64.1 +/- 15.3 mg/day; P P < 0.05). Cessation of thyroxine administration resulted in an eventual return of left ventricular mass to that of normally growing control animals. The major observation noted during thyroxine withdrawal was a return of protein synthetic rates to normal. Absolute rates of protein degradation remained elevated, whereas fractional protein degradative rates (i.e. the fraction of total protein degraded per day) were unchanged by the administration and withdrawal of thyroxine. These results indicate that suppression of both physiological and hormone-induced growth following cessation of thyroxine resulted from a decrease in cardiac protein synthetic rates and an increased rate of flux through the protein degradative pathway(s), while fractional rates of protein degradation (and thus average protein half-life) remained unchanged. The development and regression of thyroxine-induced hypertrophy correlated with thyroxine-mediated alterations in cardiac work.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27795/1/0000195.pd

Direct gene transfer into cardiac myocytes in vivo

Recent studies have demonstrated that cardiac and skeletal myocytes share the ability to take up and stably express plasmid DNA injected directly into myocardium or skeletal muscle in vivo. Although this is a relatively inefficient process, with less than 1% of the myocytes expressing the injected recombinant DNA, expression in these cells is stable for periods of at least 6 months. The majority of the injected DNA is maintained in myocytes as an episome and apparently does not undergo DNA replication. The direct DNA injection approach has been used to map cardiac-specific transcriptional regulatory elements in cellular promoter/enhancers. Expression of recombinant proteins in the heart following direct DNA injection also holds promise for the treatment of a variety of acquired and inherited cardiovascular diseases.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29917/1/0000274.pd

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process

Identification of gene co-regulatory modules and associated cis-elements involved in degenerative heart disease

<p>Abstract</p> <p>Background</p> <p>Cardiomyopathies, degenerative diseases of cardiac muscle, are among the leading causes of death in the developed world. Microarray studies of cardiomyopathies have identified up to several hundred genes that significantly alter their expression patterns as the disease progresses. However, the regulatory mechanisms driving these changes, in particular the networks of transcription factors involved, remain poorly understood. Our goals are (A) to identify modules of co-regulated genes that undergo similar changes in expression in various types of cardiomyopathies, and (B) to reveal the specific pattern of transcription factor binding sites, <it>cis</it>-elements, in the proximal promoter region of genes comprising such modules.</p> <p>Methods</p> <p>We analyzed 149 microarray samples from human hypertrophic and dilated cardiomyopathies of various etiologies. Hierarchical clustering and Gene Ontology annotations were applied to identify modules enriched in genes with highly correlated expression and a similar physiological function. To discover motifs that may underly changes in expression, we used the promoter regions for genes in three of the most interesting modules as input to motif discovery algorithms. The resulting motifs were used to construct a probabilistic model predictive of changes in expression across different cardiomyopathies.</p> <p>Results</p> <p>We found that three modules with the highest degree of functional enrichment contain genes involved in myocardial contraction (n = 9), energy generation (n = 20), or protein translation (n = 20). Using motif discovery tools revealed that genes in the contractile module were found to contain a TATA-box followed by a CACC-box, and are depleted in other GC-rich motifs; whereas genes in the translation module contain a pyrimidine-rich initiator, Elk-1, SP-1, and a novel motif with a GCGC core. Using a naïve Bayes classifier revealed that patterns of motifs are statistically predictive of expression patterns, with odds ratios of 2.7 (contractile), 1.9 (energy generation), and 5.5 (protein translation).</p> <p>Conclusion</p> <p>We identified patterns comprised of putative <it>cis</it>-regulatory motifs enriched in the upstream promoter sequence of genes that undergo similar changes in expression secondary to cardiomyopathies of various etiologies. Our analysis is a first step towards understanding transcription factor networks that are active in regulating gene expression during degenerative heart disease.</p

Predicting tissue specific cis-regulatory modules in the human genome using pairs of co-occurring motifs

<p>Abstract</p> <p>Background</p> <p>Researchers seeking to unlock the genetic basis of human physiology and diseases have been studying gene transcription regulation. The temporal and spatial patterns of gene expression are controlled by mainly non-coding elements known as cis-regulatory modules (CRMs) and epigenetic factors. CRMs modulating related genes share the regulatory signature which consists of transcription factor (TF) binding sites (TFBSs). Identifying such CRMs is a challenging problem due to the prohibitive number of sequence sets that need to be analyzed.</p> <p>Results</p> <p>We formulated the challenge as a supervised classification problem even though experimentally validated CRMs were not required. Our efforts resulted in a software system named CrmMiner. The system mines for CRMs in the vicinity of related genes. CrmMiner requires two sets of sequences: a mixed set and a control set. Sequences in the vicinity of the related genes comprise the mixed set, whereas the control set includes random genomic sequences. CrmMiner assumes that a large percentage of the mixed set is made of background sequences that do not include CRMs. The system identifies pairs of closely located motifs representing vertebrate TFBSs that are enriched in the training mixed set consisting of 50% of the gene loci. In addition, CrmMiner selects a group of the enriched pairs to represent the tissue-specific regulatory signature. The mixed and the control sets are searched for candidate sequences that include any of the selected pairs. Next, an optimal Bayesian classifier is used to distinguish candidates found in the mixed set from their control counterparts. Our study proposes 62 tissue-specific regulatory signatures and putative CRMs for different human tissues and cell types. These signatures consist of assortments of ubiquitously expressed TFs and tissue-specific TFs. Under controlled settings, CrmMiner identified known CRMs in noisy sets up to 1:25 signal-to-noise ratio. CrmMiner was 21-75% more precise than a related CRM predictor. The sensitivity of the system to locate known human heart enhancers reached up to 83%. CrmMiner precision reached 82% while mining for CRMs specific to the human CD4⁺T cells. On several data sets, the system achieved 99% specificity.</p> <p>Conclusion</p> <p>These results suggest that CrmMiner predictions are accurate and likely to be tissue-specific CRMs. We expect that the predicted tissue-specific CRMs and the regulatory signatures broaden our knowledge of gene transcription regulation.</p

Synergistic Activation of Cardiac Genes by Myocardin and Tbx5

Myocardial differentiation is associated with the activation and expression of an array of cardiac specific genes. However, the transcriptional networks that control cardiac gene expression are not completely understood. Myocardin is a cardiac and smooth muscle-specific expressed transcriptional coactivator of Serum Response Factor (SRF) and is able to potently activate cardiac and smooth muscle gene expression during development. We hypothesize that myocardin discriminates between cardiac and smooth muscle specific genes by associating with distinct co-factors. Here, we show that myocardin directly interacts with Tbx5, a member of the T-box family of transcription factors involved in the Holt-Oram syndrome. Tbx5 synergizes with myocardin to activate expression of the cardiac specific genes atrial natriuretic factor (ANF) and alpha myosin heavy chain (α-MHC), but not that of smooth muscle specific genes SM22 or smooth muscle myosin heavy chain (SM-MHC). We found that this synergistic activation of shared target genes is dependent on the binding sites for Tbx5, T-box factor-Binding Elements (TBEs). Myocardin and Tbx5 physically interact and their interaction domains were mapped to the basic domain and the coil domain of myocardin and Tbx5, respectively. Our analysis demonstrates that the Tbx5G80R mutation, which leads to the Holt-Oram syndrome in humans, failed to synergize with myocardin to activate cardiac gene expression. These data uncover a key role for Tbx5 and myocardin in establishing the transcriptional foundation for cardiac gene activation and suggest that the interaction of myocardin and Tbx5 maybe involved in cardiac development and diseases

Heavy and light roles: myosin in the morphogenesis of the heart

Myosin is an essential component of cardiac muscle, from the onset of cardiogenesis through to the adult heart. Although traditionally known for its role in energy transduction and force development, recent studies suggest that both myosin heavy-chain and myosin lightchain proteins are required for a correctly formed heart. Myosins are structural proteins that are not only expressed from early stages of heart development, but when mutated in humans they may give rise to congenital heart defects. This review will discuss the roles of myosin, specifically with regards to the developing heart. The expression of each myosin protein will be described, and the effects that altering expression has on the heart in embryogenesis in different animal models will be discussed. The human molecular genetics of the myosins will also be reviewed