Elucidating the molecular physiopathology of acute respiratory distress syndrome in severe acute respiratory syndrome patients.
ABSTRACT Acute respiratory distress syndrome (ARDS) is a severe form of acute lung injury. It is a response to various diseases of variable etiology, including SARS-CoV infection. To date, a comprehensive study of the genomic physiopathology of ARDS (and SARS) is lacking, primarily due to the difficulty of finding suitable materials to study the disease process at a tissue level (instead of blood, sputa or swaps). Hereby we attempt to provide such study by analyzing autopsy lung samples from patient who died of SARS and showed different degrees of severity of the pulmonary involvement. We performed real-time quantitative PCR analysis of 107 genes with functional roles in inflammation, coagulation, fibrosis and apoptosis; some key genes were confirmed at a protein expression level by immunohistochemistry and correlated to the degree of morphological severity present in the individual samples analyzed. Significant expression levels were identified for ANPEP (a receptor for CoV), as well as inhibition of the STAT1 pathway, IFNs production and CXCL10 (a T-cell recruiter). Other genes unassociated to date with ARDS/SARS include C1Qb, C5R1, CASP3, CASP9, CD14, CD68, FGF7, HLA-DRA, IGF1, IRF3, MALAT-1, MSR1, NFIL3, SLPI, USP33, CLC, GBP1 and TAC1. As a result, we proposed to therapeutically target some of these genes with compounds such as ANPEP inhibitors, SLPI and dexamethasone. Ultimately, this study may serve as a model for future, tissue-based analyses of fibroinflammatory conditions affecting the lung.
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ABSTRACT: Emerging respiratory coronaviruses such as the severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) pose potential biological threats to humans. SARS and MERS are manifested as severe atypical pneumonia associated with high morbidity and mortality in humans. The majority of studies carried out in SARS-CoV-infected humans and animals attribute a dysregulated/exuberant innate response as a leading contributor to SARS-CoV-mediated pathology. A decade after the 2002-2003 SARS epidemic, we do not have any approved preventive or therapeutic agents available in case of re-emergence of SARS-CoV or other related viruses. A strong neutralizing antibody response generated against the spike (S) glycoprotein of SARS-CoV is completely protective in the susceptible host. However, neutralizing antibody titers and the memory B cell response are short lived in SARS-recovered patients and the antibody will target primary homologous strain. Interestingly, the acute phase of SARS in humans is associated with a severe reduction in the number of T cells in the blood. Surprisingly, only a limited number of studies have explored the role of the T cell-mediated adaptive immune response in respiratory coronavirus pathogenesis. In this review, we discuss the role of anti-virus CD4 and CD8 T cells during respiratory coronavirus infections with a special emphasis on emerging coronaviruses.Immunologic research. 05/2014;
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ABSTRACT: The metastasis-associated lung adenocarcinoma transcript 1, MALAT1, is a long non-coding RNA (lncRNA) that has been discovered as a marker for lung cancer metastasis. It is highly abundant, its expression is strongly regulated in many tumor entities including lung adenocarcinoma and hepatocellular carcinoma as well as physiological processes, and it is associated with many RNA binding proteins and highly conserved throughout evolution. The nuclear transcript MALAT-1 has been functionally associated with gene regulation and alternative splicing and its regulation has been shown to impact proliferation, apoptosis, migration and invasion. Here, we have developed a human and a mouse knockout system to study the loss-of-function phenotypes of this important ncRNA. In human tumor cells, MALAT1 expression was abrogated using Zinc Finger Nucleases. Unexpectedly, the quantitative loss of MALAT1 did neither affect proliferation nor cell cycle progression nor nuclear architecture in human lung or liver cancer cells. Moreover, genetic loss of Malat1 in a knockout mouse model did not give rise to any obvious phenotype or histological abnormalities in Malat1-null compared with wild-type animals. Thus, loss of the abundant nuclear long ncRNA MALAT1 is compatible with cell viability and normal development.RNA biology 08/2012; 9(8). · 5.56 Impact Factor
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ABSTRACT: 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. 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. 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.PLoS ONE 01/2010; 5(8):e12262. · 3.53 Impact Factor