Cross-reactivity of GroEL antibodies with human heat shock protein 60 and quantification of pathogens in atherosclerosis

Background/aims: Chronic infections such as those caused by Chlamydia pneumoniae and periodontopathic bacteria such as Porphyromonas gingivalis have been associated with atherosclerosis, possibly due to cross-reactivity of the immune response to bacterial GroEL with human heat shock protein (hHSP) 60. Methods: We examined the cross-reactivity of anti-GroEL and anti-P. gingivalis antibodies with hHSP60 in atherosclerosis patients and quantified a panel of six pathogens in atheromas. Results: After absorption of plasma samples with hHSP60, there were variable reductions in the levels of anti-GroEL and anti-P. gingivalis antibodies, suggesting that these antibodies cross-reacted with hHSP60. All of the artery specimens were positive for P. gingivalis. Fusobacterium nucleatum, Tannerella forsythia, C. pneumoniae, Helicobacter pylori, and Haemophilus influenzae were found in 84%, 48%, 28%, 4%, and 4% of arteries, respectively. The prevalence of the three periodontopathic microorganisms, P. gingivalis, F. nucleatum and T. forsythia, was significantly higher than that of the remaining three microorganisms. Conclusions: These results support the hypothesis that in some patients, cross-reactivity of the immune response to bacterial HSPs including those of periodontal pathogens, with arterial endothelial cells expressing hHSP60 may be a possible mechanism for the association between atherosclerosis and periodontal infection.

Interfaces between cardiovascular and kidney disease among Aboriginal Australians

Rates of kidney disease among several indigenous groups have been shown to be substantially higher than corresponding non-indigenous groups. This excess has been clearly shown among Aboriginal Australians with respect to both end-stage kidney disease and early kidney disease. Rates of cardiovascular disease among Aboriginal Australians are also very high, as are rates of diabetes, smoking, and possibly overweight and obesity. These factors have been traditionally linked with cardiovascular and renal disease as part of a broader metabolic syndrome. However, the links and interfaces between cardiovascular and kidney disease in this environment extend beyond these traditional factors. The factors associated with atherosclerosis have expanded in recent years to include markers of inflammation, some infection, antioxidants, and other non-traditional risk factors. Given the high rates of acute infection and poor living conditions endured by many indigenous people, one might expect these non-traditional risk factors to be highly prevalent. In this review, we explore the relationships between markers of inflammation, some serological markers of infection, and other selected markers and both cardiovascular and renal disease. In doing so, we demonstrate links between kidney and cardiovascular disease at a number of levels, beyond the traditional cardiovascular/renal risk factors. Many of these factors are beyond the control of the individual or even community; addressing these issues a broader focus and biopsychosocial model. (C) 2005 by the National Kidney Foundation, Inc.

Detection and discrimination of herpes simplex viruses, Haemophilus ducreyi, Treponema pallidum, and Calymmatobacterium (Klebsiella) granulomatis from genital ulcers

Background. Genital ulcer disease (GUD) is commonly caused by pathogens for which suitable therapies exist, but clinical and laboratory diagnoses may be problematic. This collaborative project was undertaken to address the need for a rapid, economical, and sensitive approach to the detection and diagnosis of GUD using noninvasive techniques to sample genital ulcers. Methods. The genital ulcer disease multiplex polymerase chain reaction (GUMP) was developed as an inhouse nucleic acid amplification technique targeting serious causes of GUD, namely, herpes simplex viruses (HSVs), Haemophilus ducreyi, Treponema pallidum, and Klebsiella species. In addition, the GUMP assay included an endogenous internal control. Amplification products from GUMP were detected by enzyme linked amplicon hybridization assay (ELAHA). Results. GUMP-ELAHA was sensitive and specific in detecting a target microbe in 34.3% of specimens, including 1 detection of HSV-1, three detections of HSV-2, and 18 detections of T. pallidum. No H. ducreyi has been detected in Australia since 1998, and none was detected here. No Calymmatobacterium ( Klebsiella) granulomatis was detected in the study, but there were 3 detections during ongoing diagnostic use of GUMP-ELAHA in 2004 and 2005. The presence of C. granulomatis was confirmed by restriction enzyme digestion and nucleotide sequencing of the 16S rRNA gene for phylogenetic analysis. Conclusions. GUMP-ELAHA permitted comprehensive detection of common and rare causes of GUD and incorporated noninvasive sampling techniques. Data obtained by using GUMP-ELAHA will aid specific treatment of GUD and better define the prevalence of each microbe among at-risk populations with a view to the eradication of chancroid and donovanosis in Australia.

Real-Time Gene-Expression Extraction with Fast Classification (FC) Networks

Fast Classification (FC) networks were inspired by a biologically plausible mechanism for short term memory where learning occurs instantaneously. Both weights and the topology for an FC network are mapped directly from the training samples by using a prescriptive training scheme. Only two presentations of the training data are required to train an FC network. Compared with iterative learning algorithms such as Back-propagation (which may require many hundreds of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks may be suitable for applications where real-time classification is needed. In this paper, the FC networks are applied for the real-time extraction of gene expressions for Chlamydia microarray data. Both the classification performance and learning time of the FC networks are compared with the Multi-Layer Proceptron (MLP) networks and support-vector-machines (SVM) in the same classification task. The FC networks are shown to have extremely fast learning time and comparable classification accuracy.