MiR-155 Induction by F. novicida but Not the Virulent F. tularensis Results in SHIP Down-Regulation and Enhanced Pro-Inflammatory Cytokine Response

The intracellular Gram-negative bacterium Francisella tularensis causes the disease tularemia and is known for its ability to subvert host immune responses. Previous work from our laboratory identified the PI3K/Akt pathway and SHIP as critical modulators of host resistance to Francisella. Here, we show that SHIP expression is strongly down-regulated in monocytes and macrophages following infection with F. tularensis novicida (F.n.). To account for this negative regulation we explored the possibility that microRNAs (miRs) that target SHIP may be induced during infection. There is one miR that is predicted to target SHIP, miR-155. We tested for induction and found that F.n. induced miR-155 both in primary monocytes/macrophages and in vivo. Using luciferase reporter assays we confirmed that miR-155 led to down-regulation of SHIP, showing that it specifically targets the SHIP 3′UTR. Further experiments showed that miR-155 and BIC, the gene that encodes miR-155, were induced as early as four hours post-infection in primary human monocytes. This expression was dependent on TLR2/MyD88 and did not require inflammasome activation. Importantly, miR-155 positively regulated pro-inflammatory cytokine release in human monocytes infected with Francisella. In sharp contrast, we found that the highly virulent type A SCHU S4 strain of Francisella tularensis (F.t.) led to a significantly lower miR-155 response than the less virulent F.n. Hence, F.n. induces miR-155 expression and leads to down-regulation of SHIP, resulting in enhanced pro-inflammatory responses. However, impaired miR-155 induction by SCHU S4 may help explain the lack of both SHIP down-regulation and pro-inflammatory response and may account for the virulence of Type A Francisella

Microarray Analysis of the Effect of Streptococcus equi subsp. zooepidemicus M-Like Protein in Infecting Porcine Pulmonary Alveolar Macrophage

Streptococcus equi subsp. zooepidemicus (S. zooepidemicus), which belongs to Lancefield group C streptococci, is an important pathogen of domesticated species, causing septicemia, meningitis and mammitis. M-like protein (SzP) is an important virulence factor of S. zooepidemicus and contributes to bacterial infection and antiphagocytosis. To increase our knowledge of the mechanism of SzP in infection, we profiled the response of porcine pulmonary alveolar macrophage (PAM) to infection with S. zooepidemicus ATCC35246 wild strain (WD) and SzP-knockout strain (KO) using the Roche NimbleGen Porcine Genome Expression Array. We found SzP contributed to differential expression of 446 genes, with upregulation of 134 genes and downregulation of 312 genes. Gene Ontology category and KEGG pathway were analyzed for relationships among differentially expressed genes. These genes were represented in a variety of functional categories, including genes involved in immune response, regulation of chemokine production, signal transduction and regulation of apoptosis. The reliability of the data obtained from the microarray was verified by performing quantitative real-time PCR on 12 representative genes. The data will contribute to understanding of SzP mediated mechanisms of S. zooepidemicus pathogenesis

Orientia tsutsugamushi Stimulates an Original Gene Expression Program in Monocytes: Relationship with Gene Expression in Patients with Scrub Typhus

Orientia tsutsugamushi is the causal agent of scrub typhus, a public health problem in the Asia-Pacific region and a life-threatening disease. O. tsutsugamushi is an obligate intracellular bacterium that mainly infects endothelial cells. We demonstrated here that O. tsutsugamushi also replicated in monocytes isolated from healthy donors. In addition, O. tsutsugamushi altered the expression of more than 4,500 genes, as demonstrated by microarray analysis. The expression of type I interferon, interferon-stimulated genes and genes associated with the M1 polarization of macrophages was significantly upregulated. O. tsutsugamushi also induced the expression of apoptosis-related genes and promoted cell death in a small percentage of monocytes. Live organisms were indispensable to the type I interferon response and apoptosis and enhanced the expression of M1-associated cytokines. These data were related to the transcriptional changes detected in mononuclear cells isolated from patients with scrub typhus. Here, the microarray analyses revealed the upregulation of 613 genes, which included interferon-related genes, and some features of M1 polarization were observed in these patients, similar to what was observed in O. tsutsugamushi-stimulated monocytes in vitro. This is the first report demonstrating that monocytes are clearly polarized in vitro and ex vivo following exposure to O. tsutsugamushi. These results would improve our understanding of the pathogenesis of scrub typhus, during which interferon-mediated activation of monocytes and their subsequent polarization into an M1 phenotype appear critical. This study may give us a clue of new tools for the diagnosis of patients with scrub typhus

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

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