Porphyromonas gingivalis gingipains cause defective macrophage migration towards apoptotic cells and inhibit phagocytosis of primary apoptotic neutrophils:gingipains, apoptotic cell removal & inflammation

Periodontal disease is a prevalent chronic inflammatory condition characterised by an aberrant host response to a pathogenic plaque biofilm resulting in local tissue damage and frustrated healing that can result in tooth loss. Cysteine proteases (gingipains) from the key periodontal pathogen Porphyromonas gingivalis have been implicated in periodontal disease pathogenesis by inhibiting inflammation resolution and are linked with systemic chronic inflammatory conditions such as rheumatoid arthritis. Efficient clearance of apoptotic cells is essential for the resolution of inflammation and tissue restoration. Here we sought to characterise the innate immune clearance of apoptotic cells and its modulation by gingipains. We examined the capacity of gingipain-treated macrophages to migrate towards and phagocytose apoptotic cells. Lysine gingipain treatment of macrophages impaired macrophage migration towards apoptotic neutrophils. Furthermore, lysine gingipain treatment reduced surface expression levels of CD14, a key macrophage receptor for apoptotic cells, which resulted in reduced macrophage interactions with apoptotic cells. Additionally, whilst apoptotic cells and their derived secretome were shown to inhibit TNF-α induced expression by P.gingivalis LPS, we demonstrated that gingipain preparations induced a rapid inflammatory response in macrophages that was resistant to the anti-inflammatory effects of apoptotic cells or their secretome. Taken together these data indicate that P.gingivalis may promote the chronic inflammation seen in periodontal disease patients by multiple mechanisms including rapid, potent gingipain-mediated inflammation coupled with receptor cleavage leading to defective clearance of apoptotic cells and reduced anti-inflammatory responses. Thus gingipains represent a potential therapeutic target for intervention in the management of chronic periodontal disease

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases