Inflammasomes are Differentially Expressed in Cardiovascular and other Tissues

Abstract

To determine the expression of components in Toll-like receptors (TLRs)/Nod-like receptors (NLRs)/ infiammasome/caspase-llinterleukin (IL-l)-P pathway, we examined the expression profiles of those genes by analyzing the data from expression sequence tag cDNA cloning and sequencing. We made several important findings: firstly, among 11 tissues examined, vascular tissues and heart express fewer types of TLRs and NLRs than immune and defense tissues including blood, lymph nodes, thymus and trachea; secondly, brain, lymph nodes and thymus do not express proinflammatory cytokines IL-IP and IL-18 constitutively, suggesting that these two cytokines need to be up regulated in the tissues; and thirdly, based on the expression data of three characterized inflammasomes (NALPl, NALP3 and IPAF inflammasome), the examined tissues can be classified into three tiers: the first tier tissues including brain, placenta, blood and thymus express inflammasome(s) in constitutive status; the second tier tissues have inflammasome(s) in nearly-ready expression status (with the requirement of upregulation of one component); the third tier tissues, like heart and bone marrow, require upregulation of at least two components in order to assemble functional inflammasomes. Our original model of three-tier expression of inflammasomes would suggest a new concept oftissue inflammation privilege, and provides an insight to the differences among tissues in initiating acute inflammation in response to stimuli.

Database Mining: Defining the Pathogenesis of Inflammatory and Immunological Diseases

“…In order to generate valid confidence intervals, sample sizes have to be estimated with statistical tools of sample size determination (Rosner, 2000) as we reported ; 5) Verifiable methods: Experimental methods are available to verify the data generated by the database mining ; and 6) A new working model/hypothesis: Through database mining, a new knowledge gap will be identified, and a new hypothesis will be proposed to test fewer, much more-focused genes in further experiments. The following sections will illustrate these principles in our own publications (Chen et al, 2010;Jan et al, 2010;Ng et al, 2004;Virtue, 2011;Yang et al, 2006a;Yang et al, 2006b;Yin et al, 2009). In our invited review, we pointed out that the identification and molecular characterization of self-antigens expressed by human malignancies, that are capable of elicitation of anti-tumor immune responses in patients, have been an active field in tumor immunology .…”

“…For example, our own research will examine mRNA transcript expressions of about 30 genes including all the reported toll-like receptors, NOD-like receptors, and inflammatory caspases in more than ten tissues. This scope allows us to obtain a panoramic view on the expressions of inflammatory pathways without focusing on a single gene in many tissues (Yin et al, 2009); 3) Suitable databases: Databases that are suitable for examining the hypothesis are available for online analytic search, which is also similar to the methods and reagents for experimental projects; 4) Sizable experimentally verified data for generating confidence intervals with statistical significance: To consolidate the results generated from database mining, the experimentally verified data are published by various laboratories, which can be used to generate statistically significant confidence intervals by using the same online analysis tools as we reported (Virtue, 2011). In this study, our analysis in the TargetScan yielded 524 microRNAs, which were predicted to participate in 1368 unique interactions with the 33 inflammatory gene mRNAs.…”

“…To address these issues, we hypothesized that a group of anti-inflammatory microRNAs may regulate the expressions of proatherogenic molecules (Virtue, 2011). We then developed a novel database mining approach using three types of databases including the online microRNA target prediction software TargetScan (http://www.targetscan.org/) Rosero et al, 2010;Vickers & Remaley, 2010), the Tarbase, an online database of experimentally verified microRNAs (http://diana.cslab.ece.ntua.gr/tarbase/) (Papadopoulos et al, 2009;Sethupathy et al, 2006), and the online microRNA.org expression database (http://www.microrna.org/ microrna/home.do) (Betel et al, 2008), in concert with a statistical analysis strategy established in our previous database mining publications (Chen et al, 2010;Ng et al, 2004;Shen et al, 2010;Yang et al, 2006b;Yin et al, 2009). Our unique research using database mining yielded several key findings.…”

“…NCBI-PubMed search and Google Scholar search; 2) experimentally screening cDNA libraries and various arrays (nucleic acid arrays, antibody arrays, protein arrays and metabolic arrays) (King et al, 2005;Loza et al, 2007;Pandey et al, 2004;Warner & Dieckgraefe, 2002); and 3) mining experimental databases (Chen et al, 2010;Jan et al, 2010;Ng et al, 2004;Yang et al, 2006a;Yang et al, 2006b;Yin et al, 2009). The screening analysis of microarray data often requires bioinformatic methods, algorithms, and expertise.…”

“…These progresses suggest that a data mining approach has gradually been accepted as mainstream practice in analyzing experimental data and generating new hypotheses for various projects (Salimi et al, 2010). Our lab has successfully pioneered major advances in database mining in the fields of adaptive immune reactions, innate immune responses, and inflammation (Chen et al, 2010;Jan et al, 2010;Ng et al, 2004;Virtue, 2011;Yang et al, 2006a;Yang et al, 2006b;Yin et al, 2009). In this chapter, we will summarize the general approaches, principles, and databases used and new working models proposed in our database mining research.…”