Large-scale gene expression studies provide significant insight into genes differentially regulated in disease processes such as cancer. However, these investigations offer limited understanding of multisystem, multicellular diseases such as atherosclerosis. A systems biology approach that accounts for gene interactions, incorporates nontranscriptionally regulated genes, and integrates prior knowledge offers many advantages. We performed a comprehensive gene level assessment of coronary atherosclerosis using 51 coronary artery segments isolated from the explanted hearts of 22 cardiac transplant patients. After histological grading of vascular segments according to American Heart Association guidelines, isolated RNA was hybridized onto a customized 22-K oligonucleotide microarray, and significance analysis of microarrays and gene ontology analyses were performed to identify significant gene expression profiles. Our studies revealed that loss of differentiated smooth muscle cell gene expression is the primary expression signature of disease progression in atherosclerosis. Furthermore, we provide insight into the severe form of coronary artery disease associated with diabetes, reporting an overabundance of immune and inflammatory signals in diabetics. We present a novel approach to pathway development based on connectivity, determined by language parsing of the published literature, and ranking, determined by the significance of differentially regulated genes in the network. In doing this, we identify highly connected "nexus" genes that are attractive candidates for therapeutic targeting and followup studies. Our use of pathway techniques to study atherosclerosis as an integrated network of gene interactions expands on traditional microarray analysis methods and emphasizes the significant advantages of a systems-based approach to analyzing complex disease.

The propensity for developing atherosclerosis is dependent on underlying genetic risk and varies as a function of age and exposure to environmental risk factors. Employing three mouse models with different disease susceptibility, two diets, and a longitudinal experimental design, it was possible to manipulate each of these factors to focus analysis on genes most likely to have a specific disease-related function. To identify differences in longitudinal gene expression patterns of atherosclerosis, we have developed and employed a statistical algorithm that relies on generalized regression and permutation analysis. Comprehensive annotation of the array with ontology and pathway terms has allowed rigorous identification of molecular and biological processes that underlie disease pathophysiology. The repertoire of atherosclerosis-related immunomodulatory genes has been extended, and additional fundamental pathways have been identified. This highly disease-specific group of mouse genes was combined with an extensive human coronary artery data set to identify a shared group of genes differentially regulated among atherosclerotic tissues from different species and different vascular beds. A small core subset of these differentially regulated genes was sufficient to accurately classify various stages of the disease in mouse. The same gene subset was also found to accurately classify human coronary lesion severity. In addition, this classifier gene set was able to distinguish with high accuracy atherectomy specimens from native coronary artery disease vs. those collected from in-stent restenosis lesions, thus identifying molecular differences between these two processes. These studies significantly focus efforts aimed at identifying central gene regulatory pathways that mediate atherosclerotic disease, and the identification of classification gene sets offers unique insights into potential diagnostic and therapeutic strategies in atherosclerotic disease.

Background-Recent successes in the treatment of in-stent restenosis (ISR) by drug-eluting stents belie the challenges still faced in certain lesions and patient groups. We analyzed human coronary atheroma in de novo and restenotic disease to identify targets of therapy that might avoid these limitations. Methods and Results-We recruited 89 patients who underwent coronary atherectomy for de novo atherosclerosis (nϭ55) or in-stent restenosis (ISR) of a bare metal stent (nϭ34). Samples were fixed for histology, and gene expression was assessed with a dual-dye 22 000 oligonucleotide microarray. Histological analysis revealed significantly greater cellularity and significantly fewer inflammatory infiltrates and lipid pools in the ISR group. Gene ontology analysis demonstrated the prominence of cell proliferation programs in ISR and inflammation/immune programs in de novo restenosis. Network analysis, which combines semantic mining of the published literature with the expression signature of ISR, revealed gene expression modules suggested as candidates for selective inhibition of restenotic disease. Two modules are presented in more detail, the procollagen type 1 ␣2 gene and the ADAM17/tumor necrosis factor-␣ converting enzyme gene. We tested our contention that this method is capable of identifying successful targets of therapy by comparing mean significance scores for networks generated from subsets of the published literature containing the terms "sirolimus" or "paclitaxel." In addition, we generated 2 large networks with sirolimus and paclitaxel at their centers. Both analyses revealed higher mean values for sirolimus, suggesting that this agent has a broader suppressive action against ISR than paclitaxel. Conclusions-Comprehensive

BackgroundSome abnormalities in nailfold videocapillaroscopy (NVC), such as the presence of micro-haemorrhages (MHEs), micro-thromboses (MTs), giant capillaries (GCs) and reduction in the number of capillaries (nCs), suggest a disease activity (DA) phase in systemic sclerosis (SSc). In a previous paper, we showed that the number of micro-haemorrhages and micro-thromboses (the so-called NEMO score) was the NVC feature more closely associated with DA. The present study was aimed at validating the NEMO score as a measure of DA in patients with SSc.MethodsTwo cohorts of 122 and 97 patients with SSc who were referred to two different rheumatology units, one in Milan and one in Naples, respectively, constituted the validation cohorts. The NEMO score, the total number of GCs and the mean nCs per digit were the parameters defined in each patient by eight-finger NVC. An expert operator analysed the NVCs in each of the participating units. The European Scleroderma Study Group (ESSG) index was used to define the DA level in each patient at the time of NVC examination.ResultsThe NEMO score was the NVC parameter more strictly correlated with the ESSG score in both the Milan and Naples cohorts (p < 0.0001), and it was the only one among the NVC variables that gave a significant contribution in a logistic model where the ESSG score represented the dependent variable. ROC curve analysis confirmed that the NEMO score had the best performance in measuring DA. The AUC of the NEMO score was significantly greater than the AUCs obtained by plotting the sensitivity and specificity of the number of GCs and the mean nCs (p < 0.0001 in all cases). The NEMO score values that showed the best sensitivity-specificity balance in capturing patients with a relevant DA level were slightly higher in the Naples cohort than in the Milan cohort.ConclusionsThis study confirms that the presence of a certain number of MHEs and MTs in NVC may be considered a strong warning signal of a current phase of DA in patients with SSc.

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