The Stress-Response Factor SigH Modulates the Interaction between Mycobacterium tuberculosis and Host Phagocytes

The Mycobacterium tuberculosis stress response factor SigH plays a crucial role in modulating the pathogen's response to heat, oxidative-stress, envelope damage and hypoxia. We hypothesized that the lack of this key stress response factor would alter the interaction between the pathogen and its host cells. We compared the interaction of Mtb, Mtb:Δ-sigH and a strain where the mutation had been genetically complemented (Mtb: Δ-sigH:CO) with primary rhesus macaque bone marrow derived macrophages (Rh-BMDMs). The expression of numerous inducible and homeostatic (CCL) β-chemokines and several apoptotic markers was induced to higher levels in the cells infected with Mtb:Δ-sigH, relative to Mtb or the complemented strain. The differential expression of these genes manifested into functional differences in chemotaxis and apoptosis in cells infected with these two strains. The mutant strain also exhibited reduced late-stage survival in Rh-BMDMs. We hypothesize that the product of one or more SigH-dependent genes may modulate the innate interaction of Mtb with host cells, effectively reducing the chemokine-mediated recruitment of immune effector cells, apoptosis of infected monocytes and enhancing the long-term survival and replication of the pathogen in this milieu The significantly higher induction of Prostaglandin Synthetase 2 (PTGS2 or COX2) in Rh-BMDMs infected with Mtb relative to Mtb: Δ-sigH may explain reduced apoptosis in Mtb-infected cells, as PTGS2 is known to inhibit p53-dependent apoptosis.The SigH-regulon modulates the innate interaction of Mtb with host phagocytes, perhaps as part of a strategy to limit its clearance and prolong its survival. The SigH regulon appears to be required to modulate innate immune responses directed against Mtb

IRAK4 gene polymorphism and odontogenic maxillary sinusitis

Objectives This study aimed to evaluate whether a specific interleukin-1 receptor-associated kinase-4 (IRAK4) gene polymorphism had any influence on the development of changes in maxillary sinus, particularly in the presence of etiological factors of dental origin.Materials and methods The study population included 153 Portuguese Caucasians that were selected from a database of 504 retrospectively analysed computed tomography (CT) scans. A genetic test was performed, and a model was created through logistic analysis and regression coefficients. The statistical methodologies included were the independent Chi test, Fisher's exact test, binary logistic regression and the receiver operating characteristic (ROC) curve.Results The estimated prevalence of IRAK4 gene polymorphism found in a Portuguese Caucasian population was 26.8 % (CI 95 %) [20.1, 34.7 %]. A model to predict the inflammatory response in the maxillary sinus in the presence etiological factors of dental origin was constructed. This model had the following as variables: previously diagnosed sinusitis, sinus pressure symptoms, cortical bone loss observed on CT, positive genetic test result and radiographic examination that revealed the roots of the teeth communication with the maxillary sinus, which are interpreted as risk factors.Conclusions The constructed model should be considered an initial clinical tool. The area under the ROC curve found, AUC=0.91, revealed that the model correctly predicts the outcome in 91.1 % of cases.info:eu-repo/semantics/publishedVersio

Characterization of a Clp Protease Gene Regulator and the Reaeration Response in Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB) enters a non-replicating state when exposed to low oxygen tension, a condition the bacillus encounters in granulomas during infection. Determining how mycobacteria enter and maintain this state is a major focus of research. However, from a public health standpoint the importance of latent TB is its ability to reactivate. The mechanism by which mycobacteria return to a replicating state upon re-exposure to favorable conditions is not understood. In this study, we utilized reaeration from a defined hypoxia model to characterize the adaptive response of MTB following a return to favorable growth conditions. Global transcriptional analysis identified the ∼100 gene Reaeration Response, induced relative to both log-phase and hypoxic MTB. This response includes chaperones and proteases, as well as the transcription factor Rv2745c, which we characterize as a Clp protease gene regulator (ClgR) orthologue. During reaeration, genes repressed during hypoxia are also upregulated in a wave of transcription that includes genes crucial to transcription, translation and oxidative phosphorylation and culminates in bacterial replication. In sum, this study defines a new transcriptional response of MTB with potential relevance to disease, and implicates ClgR as a regulator involved in resumption of replication following hypoxia

Expression of FOXA1 and GATA-3 in breast cancer: the prognostic significance in hormone receptor-negative tumours

The expression of additional genes, other than oestrogen receptor (ER), may be important to the hormone-responsive phenotype of breast cancer. Microarray analyses have revealed that forkhead box A1 (FOXA1) and GATA binding protein 3 (GATA-3) are expressed in close association with ERalpha, both encoding for transcription factors with a potential involvement in the ERalpha-mediated action in breast cancer. The purpose of this study was to explore if the expression of FOXA1 and GATA-3 may provide an opportunity to stratify subsets of patients that could have better outcome, among the ERalpha-negative/poor prognosis breast cancer group.The present study was supported by a research grant (SFRH/BD/15316/ 2005 to AA) financed by the Portuguese Science and Technology Foundation (FCT). The authors thank Prof. Raquel Seruca ( coordinator from the Cancer Genetics group at IPATIMUP) for scientific assistance, Dr Jose Luis Costa (postdoctorate at IPATIMUP) for critically reading the manuscript before submission, and Dr Nuno Marcos ( PhD student at IPATIMUP) for artwork assistance

Data enhancement for co-morbidity measurement among patients referred for sleep diagnostic testing: an observational study

<p>Abstract</p> <p>Background</p> <p>Observational outcome studies of patients with obstructive sleep apnea (OSA) require adjustment for co-morbidity to produce valid results. The aim of this study was to evaluate whether the combination of administrative data and self-reported data provided a more complete estimate of co-morbidity among patients referred for sleep diagnostic testing.</p> <p>Methods</p> <p>A retrospective observational study of 2149 patients referred for sleep diagnostic testing in Calgary, Canada. Self-reported co-morbidity was obtained with a questionnaire; administrative data and validated algorithms (when available) were also used to define the presence of these co-morbid conditions within a two-year period prior to sleep testing.</p> <p>Results</p> <p>Patient self-report of co-morbid conditions had varying levels of agreement with those derived from administrative data, ranging from substantial agreement for diabetes (κ = 0.79) to poor agreement for cardiac arrhythmia (κ = 0.14). The enhanced measure of co-morbidity using either self-report or administrative data had face validity, and provided clinically meaningful trends in the prevalence of co-morbidity among this population.</p> <p>Conclusion</p> <p>An enhanced measure of co-morbidity using self-report and administrative data can provide a more complete measure of the co-morbidity among patients with OSA when agreement between the two sources is poor. This methodology will aid in the adjustment of these coexisting conditions in observational studies in this area.</p

PrognoScan: a new database for meta-analysis of the prognostic value of genes

<p>Abstract</p> <p>Background</p> <p>In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform.</p> <p>Description</p> <p>To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum <it>P</it>-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets.</p> <p>Conclusion</p> <p>PrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at <url>http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html</url>.</p

Therapeutic opportunities within the DNA damage response

The DNA damage response (DDR) is essential for maintaining the genomic integrity of the cell, and its disruption is one of the hallmarks of cancer. Classically, defects in the DDR have been exploited therapeutically in the treatment of cancer with radiation therapies or genotoxic chemotherapies. More recently, protein components of the DDR systems have been identified as promising avenues for targeted cancer therapeutics. Here, we present an in-depth analysis of the function, role in cancer and therapeutic potential of 450 expert-curated human DDR genes. We discuss the DDR drugs that have been approved by the US Food and Drug Administration (FDA) or that are under clinical investigation. We examine large-scale genomic and expression data for 15 cancers to identify deregulated components of the DDR, and we apply systematic computational analysis to identify DDR proteins that are amenable to modulation by small molecules, highlighting potential novel therapeutic targets

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering

<p>Abstract</p> <p>Background</p> <p>Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments.</p> <p>Results</p> <p>We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification.</p> <p>Conclusion</p> <p>We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich-data poor' paradox in Systems Biology.</p