Acetyltransferase p300 regulates atrial fibroblast senescence and age-related atrial fibrosis through p53/Smad3 axis.

Atrial fibrosis induced by aging is one of the main causes of atrial fibrillation (AF), but the potential molecular mechanism is not clear. Acetyltransferase p300 participates in the cellular senescence and fibrosis, which might be involved in the age-related atrial fibrosis. Four microarray datasets generated from atrial tissue of AF patients and sinus rhythm (SR) controls were analyzed to find the possible relationship of p300 (EP300) with senescence and fibrosis. And then, biochemical assays and in vivo electrophysiological examination were performed on older AF patients, aging mice, and senescent atrial fibroblasts. The results showed that (1) the left atrial tissues of older AF patients, aging mouse, and senescence human atrial fibroblasts had more severe atrial fibrosis and higher protein expression levels of p300, p53/acetylated p53 (ac-p53)/p21, Smad3/p-Smads, and fibrosis-related factors. (2) p300 inhibitor curcumin and p300 knockdown treated aging mouse and senescence human atrial fibroblasts reduced the senescence ratio of atrial fibroblasts, ameliorated the atrial fibrosis, and decreased the AF inducibility. In contrast, over-expression of p300 can lead to the senescence of atrial fibroblasts and atrial fibrosis. (3) p53 knockdown decreased the expression of aging and fibrosis-related proteins. (4) Co-immunoprecipitation and immunofluorescence showed that p53 forms a complex with smad3 and directly regulates the expression of smad3 in atrial fibroblasts. Our findings suggest that the mechanism of atrial fibrosis induced by aging is, at least, partially dependent on the regulation of p300, which provides new sights into the AF treatment, especially for the elderly.

Connexin 43 participates in atrial electrical remodelling through colocalization with calcium channels in atrial myocytes.

Atrial fibrillation (AF) is associated with atrial conduction disturbances caused by electrical and/or structural remodelling. In the present study, we hypothesized that connexin might interact with the calcium channel through forming a protein complex and, then, participates in the pathogenesis of AF. Western blot and whole-cell patch clamp showed that protein levels of Cav1.2 and connexin 43 (Cx43) and basal ICa,L were decreased in AF subjects compared to sinus rhythm (SR) controls. In cultured atrium-derived myocytes (HL-1 cells), knocking-down of Cx43 or incubation with 30 mmol/L glycyrrhetinic acid significantly inhibited protein levels of Cav1.2 and Cav3.1 and the current density of ICa,L and ICa,T . Incubation with nifedipine or mibefradil decreased the protein level of Cx43 in HL-1 cells. Moreover, Cx43 was colocalized with Cav1.2 and Cav3.1 in atrial myocytes. Therefore, Cx43 might regulate the ICa,L and ICa,T through colocalization with calcium channel subunits in atrial myocytes, representing a potential pathogenic mechanism in AF.

Activation of PKCα participates in the reduction of Ikur in atrial myocytes induced by tumour necrosis factor-α.

The atrial-specific ultra-rapid delayed rectifier K+ current (Ikur) plays an important role in the progression of atrial fibrillation (AF). Because inflammation is known to lead to the onset of AF, we aimed to investigate whether tumour necrosis factor-α (TNF-α) played a role in regulating Ikur and the potential signalling pathways involved. Whole-cell patch-clamp and biochemical assays were used to study the regulation and expression of Ikur in myocytes and in tissues from left atrial appendages (LAAs) obtained from patients with sinus rhythm (SR) or AF, as well as in rat cardiomyocytes (H9c2 cells) and mouse atrial myocytes (HL-1 cells). Ikur current density was markedly reduced in atrial myocytes from AF patients compared with SR controls. Reduction of Kv1.5 protein levels was accompanied by increased expression of TNF-α and protein kinase C (PKC)α activation in AF patients. Treatment with TNF-α dose-dependently reduced Ikur and protein expression of Kv1.5 but not Kv3.1b in H9c2 cells and HL-1 cells. TNF-α also increased activity of PKCα. Specific PKCα inhibitor Gö6976 alleviated the reduction in Ikur induced by TNF-α, but not the reduction in Kv1.5 protein. TNF-α was involved in the electrical remodelling associated with AF, probably by depressing Ikur in atrial myocytes via activation of PKCα.

Inferring domain-domain interactions from protein-protein interactions in the complex network conformation.

BACKGROUND: As protein domains are functional and structural units of proteins, a large proportion of protein-protein interactions (PPIs) are achieved by domain-domain interactions (DDIs), many computational efforts have been made to identify DDIs from experimental PPIs since high throughput technologies have produced a large number of PPIs for different species. These methods can be separated into two categories: deterministic and probabilistic. In deterministic methods, parsimony assumption has been utilized. Parsimony principle has been widely used in computational biology as the evolution of the nature is considered as a continuous optimization process. In the context of identifying DDIs, parsimony methods try to find a minimal set of DDIs that can explain the observed PPIs. This category of methods are promising since they can be formulated and solved easily. Besides, researches have shown that they can detect specific DDIs, which is often hard for many probabilistic methods. We notice that existing methods just view PPI networks as simply assembled by single interactions, but there is now ample evidence that PPI networks should be considered in a global (systematic) point of view for it exhibits general properties of complex networks, such as 'scale-free' and 'small-world'. RESULTS: In this work, we integrate this global point of view into the parsimony-based model. Particularly, prior knowledge is extracted from these global properties by plausible reasoning and then taken as input. We investigate the role of the added information extensively through numerical experiments. Results show that the proposed method has improved performance, which confirms the biological meanings of the extracted prior knowledge. CONCLUSIONS: This work provides us some clues for using these properties of complex networks in computational models and to some extent reveals the biological meanings underlying these general network properties.