Comprehensive analysis of genetic polymorphisms in the interleukin-10 promoter: implications for immune regulation in specific ethnic populations

The association of interleukin-10 (IL-10) promoter single-nucleotide polymorphisms (SNPs) as risk factors for certain inflammatory diseases, viral infections, cancers, and transplant rejection have been the subject of recent studies. The SNPs -1082 G --> A, -819 C --> T, and -592 C --> A, which have been associated with differential IL-10 production, are strongly linked with ethnicity. In this study, we determined the ethnic distribution of IL-10 promoter SNPs and their haplotype rates among Hispanics, African Americans, and Caucasians from Texas and Ashkenazi Jews from New York. Significant differences in prevalence rates of IL-10 SNPs (and their haplotype distribution) were found. African Americans and Hispanics have a lower rate of putative high-producer SNPs and a higher rate of low IL-10 producers when compared to Caucasians or Ashkenazi Jews. No statistically significant differences in allelic frequencies and haplotype rates were observed between Caucasians and Ashkenazi Jews. This study provides critical new information on the ethnic distribution of IL-10 promoter SNPs in a regional U. S. population and is the first to analyze the rate of SNPs in an unstudied ethnic population, Ashkenazi Jews. Knowledge of IL-10 promoter polymorphisms may prove useful in prediction of immunization responses, disease severity, and in the intelligent design of customized immunotherapy.     

Vesicular stomatitis virus glycoprotein: a transducing coat for SFV-based RNA vectors

BACKGROUND: Semliki Forest virus (SFV) vectors have a great potential for the induction of protective immunity in a large number of clinical conditions including cancer. Such a potential accounts for the huge efforts made to improve the in vivo expression from SFV vectors. It is noteworthy that efficient in vivo expression strongly relies on the ability to deliver high-titre vectors. To achieve this, the generation of recombinant SFV particles, using independent expression systems for structural SFV genes, has been proposed. However, despite several modifications in the production process, a risk of contamination with replication-competent, or partially recombined, virus has remained. METHODS: Here, we exploit the ability of the vesicular stomatitis virus glycoprotein (VSV-G), expressed in trans, to hijack full-length genomic SFV RNA into secreted virus-like particles (VLPs). To allow SFV vector mobilisation, we designed a CMV driven SFV vector in which the internal 26S promoter has been extensively mutated. With this vector, mobilisation events were monitored using the Green Fluorescent Protein (GFP). The production procedure involves a sequential transfection protocol, of plasmids expressing the VSV-G and the SFV vector respectively. RESULTS: We show that the VLPs are effective for cellular delivery of SFV vectors in a broad range of human and non-human cellular targets. Furthermore, production of VLPs is easy and allows, through concentration, the harvest of high-titre vector. CONCLUSIONS: The present paper describes a convenient process aimed at mobilising full length SFV vectors. A major issue to consider, while developing clinically relevant gene transfer vectors, is the risk of undesirable generation of replication competent by-products. Importantly, as the VSV-G gene shares no homology with the SFV genome, our VLPs offer a strong guarantee of biosafety.     

Case study: data management strategies in an integrated pathway tool

This paper describes the development strategies for an integrated tool to support scientists in the creative exploration of data relating to biochemical pathways. The multiple user groups, diverse functionalities, and many types and sources of data demanded a flexible yet coherent approach. This paper summarises the software requirements and the implied modules and functions, and focuses on the design decisions relevant to the representation, management and flow of data. Finally, several case studies in the use of the software are described and evaluated, and recommendations are made for future work.     

Target-biased scoring approaches and expert systems in structure-based virtual screening

Structure-based virtual screening followed by selection of a top fraction of the rank-ordered result list suffers from many false positives and false negatives because the general scoring functions are not accurate enough. Many approaches have emerged to address this problem by including knowledge about the specific target in the scoring and selection steps. This target bias can include requirements for critical interactions, use of pharmacophore patterns or interaction patterns found in known co-crystal structures, and similarity to known ligands. Such biases are implemented in methods that vary from filtering tools for pre- or post-processing, to expert systems, quantitative (re)scoring functions, and docking tools that generate target-biased poses.     

"Emerging" parasitic infections in arctic ungulates

Important drivers for emergence of infectious disease in wildlifeinclude changes in the environment, shrinking habitats or concentrationof wildlife, and movement of people, animals, pathogens, orvectors. In this paper we present three case-studies of emergingparasitic infections and diseases in ungulates in the Canadiannorth. First we discuss climate warming as an important driverfor the emergence of disease associated with Umingmakstrongyluspallikuukensis, a nematode lungworm of muskoxen. Then we examinehow Protostrongylus stilesi, the sheep lungworm, emerged (orre-emerged) in muskoxen after re-introduction of this host intoits historical range made it sympatric with Dall's sheep. Finally,we consider Teladorsagia boreoarcticus, a newly described andcommon abomasal nematode of muskoxen that is emerging as a disease-causingparasite and may be an important regulator for muskox populationson Banks Island, Northwest Territories. These and other arctichost-parasite systems are exquisitely tuned and constrainedby a harsh and highly seasonal environment. The dynamics ofthese systems will be impacted by climate change and other ecologicaldisruptions. Baseline knowledge of parasite biodiversity andparasite and host ecology, together with predictive models andlong-term monitoring programs, are essential for anticipatingand detecting altered patterns of host range, geographic distribution,and the emergence of parasitic infections and diseases.     

Efficient down-regulation of cyclin A-associated activity and expression in suspended primary keratinocytes requires p21(Cip1)

When suspended in methylcellulose, primary mouse keratinocytes cease proliferation and differentiate. Suspension also reduces the activity of the cyclin-dependent kinase cdk2, an important cell cycle regulatory enzyme. To determine how suspension modulates these events, we examined its effects on wild-type keratinocytes and keratinocytes nullizygous for the cdk2 inhibitor p21(Cip1). After suspension of cycling cells, amounts of cyclin A (a cdk2 partner), cyclin A mRNA, and cyclin A-associated activity decreased much more rapidly in the presence than in the absence of p21(Cip1). Neither suspension nor p21(Cip1) status affected the stability of cyclin A mRNA. Loss of p21(Cip1) reduced the capacity of suspended cells to growth arrest, differentiate, and accumulate p27(Kip1) (a second cdk2 inhibitor) and affected the composition of E2F DNA binding complexes. Cyclin A-cdk2 complexes in suspended p21(+/+) cells contained p21(Cip1) or p27(Kip1), whereas most of the cyclin A-cdk2 complexes in p21(-/-) cells lacked p27(Kip1). Ectopic expression of p21(Cip1) allowed p21(-/-) keratinocytes to efficiently down-regulate cyclin A and differentiate when placed in suspension. These findings show that p21(Cip1) mediates the effects of suspension on numerous processes in primary keratinocytes including cdk2 activity, cyclin A expression, cell cycle progression, and differentiation.     

Electroencephalographic brain dynamics following manually responded visual targets

Scalp-recorded electroencephalographic (EEG) signals produced by partial synchronization of cortical field activity mix locally synchronous electrical activities of many cortical areas. Analysis of event-related EEG signals typically assumes that poststimulus potentials emerge out of a flat baseline. Signals associated with a particular type of cognitive event are then assessed by averaging data from each scalp channel across trials, producing averaged event-related potentials (ERPs). ERP averaging, however, filters out much of the information about cortical dynamics available in the unaveraged data trials. Here, we studied the dynamics of cortical electrical activity while subjects detected and manually responded to visual targets, viewing signals retained in ERP averages not as responses of an otherwise silent system but as resulting from event-related alterations in ongoing EEG processes. We applied infomax independent component analysis to parse the dynamics of the unaveraged 31-channel EEG signals into maximally independent processes, then clustered the resulting processes across subjects by similarities in their scalp maps and activity power spectra, identifying nine classes of EEG processes with distinct spatial distributions and event-related dynamics. Coupled two-cycle postmotor theta bursts followed button presses in frontal midline and somatomotor clusters, while the broad postmotor "P300" positivity summed distinct contributions from several classes of frontal, parietal, and occipital processes. The observed event-related changes in local field activities, within and between cortical areas, may serve to modulate the strength of spike-based communication between cortical areas to update attention, expectancy, memory, and motor preparation during and after target recognition and speeded responding.     

Semi-supervised methods to predict patient survival from gene expression data

An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer.