PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-3 (3)
 

Clipboard (0)
None
Journals
Year of Publication
Document Types
1.  Genetic Analyses of Interferon Pathway-Related Genes Reveals Multiple New Loci Associated with Systemic Lupus Erythematosus (SLE) 
Arthritis and rheumatism  2011;63(7):2049-2057.
Objective
The overexpression of interferon (IFN)-inducible genes is a prominent feature of SLE, serves as a marker for active and more severe disease, and is also observed in other autoimmune and inflammatory conditions. The genetic variations responsible for sustained activation of IFN responsive genes are unknown.
Methods
We systematically evaluated association of SLE with a total of 1,754 IFN-pathway related genes, including IFN-inducible genes known to be differentially expressed in SLE patients and their direct regulators. We performed a three-stage design where two cohorts (total n=939 SLE cases, 3,398 controls) were analyzed independently and jointly for association with SLE, and the results were adjusted for the number of comparisons.
Results
A total of 16,137 SNPs passed all quality control filters of which 316 demonstrated replicated association with SLE in both cohorts. Nine variants were further genotyped for confirmation in an average of 1,316 independent SLE cases and 3,215 independent controls. Association with SLE was confirmed for several genes, including the transmembrane receptor CD44 (rs507230, P = 3.98×10−12), cytokine pleiotrophin (PTN) (rs919581, P = 5.38×10−04), the heat-shock DNAJA1 (rs10971259, P = 6.31×10−03), and the nuclear import protein karyopherin alpha 1 (KPNA1) (rs6810306, P = 4.91×10−02).
Conclusion
This study expands the number of candidate genes associated with SLE and highlights the potential of pathway-based approaches for gene discovery. Identification of the causal alleles will help elucidate the molecular mechanisms responsible for activation of the IFN system in SLE.
doi:10.1002/art.30356
PMCID: PMC3128183  PMID: 21437871
2.  Imaging Drug Delivery to Skin with Stimulated Raman Scattering Microscopy 
Molecular pharmaceutics  2011;8(3):969-975.
Efficient drug delivery to the skin is essential for the treatment of major dermatologic diseases, such as eczema, psoriasis and acne. However, many compounds penetrate the skin barrier poorly and require optimized formulations to ensure their bioavailability. Here, stimulated Raman scattering (SRS) microscopy, a recently-developed, label-free chemical imaging tool, is used to acquire high resolution images of multiple chemical components of a topical formulation as it penetrates into mammalian skin. This technique uniquely provides label-free, non-destructive, three-dimensional images with high spatiotemporal resolution. It reveals novel features of (trans)dermal drug delivery in the tissue environment: different rates of drug penetration via hair follicles as compared to the intercellular pathway across the stratum corneum are directly observed, and the precipitation of drug crystals on the skin surface is visualized after the percutaneous penetration of the co-solvent excipient in the formulation. The high speed three-dimensional imaging capability of SRS thus reveals features that cannot be seen with other techniques, providing both kinetic information and mechanistic insight into the (trans)dermal drug delivery process.
doi:10.1021/mp200122w
PMCID: PMC3109166  PMID: 21548600
Skin; topical drug delivery; stimulated Raman scattering microscopy; skin penetration pathways; dermatopharmacokinetics
3.  Comparative analysis of methods for detecting interacting loci 
BMC Genomics  2011;12:344.
Background
Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted.
Results
We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs.
Conclusion
This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list.
doi:10.1186/1471-2164-12-344
PMCID: PMC3161015  PMID: 21729295

Results 1-3 (3)