The regulated recruitment and differentiation of multipotent bone marrow-derived cells (BMDCs) to sites of injury are critical for efficient wound healing. Previously we demonstrated that sustained expression of HOXA3 both accelerated wound healing and promoted angiogenesis in diabetic mice. In this study, we have used green fluorescent protein-positive bone marrow chimeras to investigate the effect of HOXA3 expression on recruitment of BMDCs to wounds. We hypothesized that the enhanced neovascularization induced by HOXA3 is due to enhanced mobilization, recruitment, and/or differentiation of BMDCs. Here we show that diabetic mice treated with HOXA3 displayed a significant increase in both mobilization and recruitment of endothelial progenitor cells compared with control mice. Importantly, we also found that HOXA3-treated mice had significantly fewer inflammatory cells recruited to the wound compared with control mice. Microarray analyses of HOXA3-treated wounds revealed that indeed HOXA3 locally increased expression of genes that selectively promote stem/progenitor cell mobilization and recruitment while also suppressing expression of numerous members of the proinflammatory nuclear factor kappaB pathway, including myeloid differentiation primary response gene 88 and toll-interacting protein. Thus HOXA3 accelerates wound repair by mobilizing endothelial progenitor cells and attenuating the excessive inflammatory response of chronic wounds.

Aims

Animal studies show that transforming growth factor-β1 (TGF-β1) is an important mediator of atrial fibrosis and atrial fibrillation (AF). This study investigated the role of TGF-β1 in human AF and the mechanism of atrial-selective fibrosis.

Methods and results

Atrial specimens from 17 open heart surgery patients and left atrial and ventricular specimens from 17 explanted hearts were collected to assess the relationship between TGF-β1, AF, and differential atrial vs. ventricular TGF-β1 levels. A transgenic mouse model overexpressing active TGF-β1 was used to study the mechanisms underlying the resultant atrial-selective fibrosis. Higher right atrial total TGF-β1 levels (2.58 ± 0.16-fold, P < 0.0001) and active TGF-β1 (3.7 ± 0.7-fold, P = 0.013) were observed in those that developed post-operative AF. Although no ventricular differences were observed, 11 explanted heart failure hearts exhibited higher atrial TGF-β1 levels than 6 non-failing hearts (2.30 ± 0.87 fold higher, P < 0.001). In the transgenic mouse, TGF-β1 receptor-1 kinase blockade resulted in decreased atrial expression of fibrosis-related genes. By RNA microarray analyses in that model, 80 genes in the atria and only 2 genes in the ventricle were differentially expressed. Although these mice atria, but not the ventricles, exhibited increased expression of fibrosis-related genes and phosphorylation of Smad2, there were no differences in TGF-β1 receptor levels or Smads in the atria compared with the ventricles.

Conclusions

TGF-β1 mediates selective atrial fibrosis in AF that occurs via TGF-β Receptor 1/2 and the classical Smad pathway. The differential atrial vs. ventricular fibrotic response occurs at the level of TGF-β1 receptor binding or phosphorylation.

Accurate co-receptor tropism (CRT) determination is critical for making treatment decisions in HIV management. We created a genotypic tropism prediction tool by utilizing the case-based reasoning (CBR) technique that attempts to solve new problems through applying the solution from similar past problems. V3 loop sequences from 732 clinical samples with diverse characteristics were used to build a case library. Additional sequence and molecular properties of the V3 loop were examined and used for similarity assessment. A similarity metric was defined based on each attribute's frequency in the CXCR4-using viruses. We implemented three other genotype-based tropism predictors, support vector machines (SVM), position specific scoring matrices (PSSM), and the 11/25 rule, and evaluated their performance as the ability to predict CRT compared to Monogram's enhanced sensitivity Trofile(®) assay (ESTA). Overall concordance of the CBR based tropism prediction algorithm was 81%, as compared to ESTA. Sensitivity to detect CXCR4 usage was 90% and specificity was at 73%. In comparison, sensitivity of the SVM, PSSM, and the 11/25 rule were 85%, 81%, and 36% respectively while achieving a specificity of 90% by SVM, 75% by PSSM, and 97% by the 11/25 rule. When we evaluated these predictors in an unseen dataset, higher sensitivity was achieved by the CBR algorithm (87%), compared to SVM (82%), PSSM (76%), and the 11/25 rule (33%), while maintaining similar level of specificity. Overall this study suggests that CBR can be utilized as a genotypic tropism prediction tool, and can achieve improved performance in independent datasets compared to model or rule based methods.