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The authors studied the possible association between the presence of a 32-base pair deletion allele in CC chemokine receptor 5 gene [3p21] (CCR5 Delta 32 allele) and the occurrence of MS. The presence of CCR5 Delta 32 homozygotes among patients with MS indicates that the absence of CCR5 did not protect against MS. Moreover, the CCR5 Delta 32 mutation was associated with MS in HLA-DR4-positive Russians (p(corr) < 0.001, odds ratio [OR] = 25.0). The (CCR5 Delta 32,DR4)-positive phenotype was negatively associated with early MS onset (at ages < or = 18 years) (p = 0.0115, OR = 0.1).

In recent years, the number of studies focusing on the genetic basis of common disorders with a complex mode of inheritance, in which multiple genes of small effect are involved, has been steadily increasing. An improved methodology to identify the cumulative contribution of several polymorphous genes would accelerate our understanding of their importance in disease susceptibility and our ability to develop new treatments. A critical bottleneck is the inability of standard statistical approaches, developed for relatively modest predictor sets, to achieve power in the face of the enormous growth in our knowledge of genomics. The inability is due to the combinatorial complexity arising in searches for multiple interacting genes. Similar ''curse of dimensionality'' problems have arisen in other fields, and Bayesian statistical approaches coupled to Markov chain Monte Carlo (MCMC) techniques have led to significant improvements in understanding. We present here an algorithm, APSampler, for the exploration of potential combinations of allelic variations positively or negatively associated with a disease or with a phenotype. The algorithm relies on the rank comparison of phenotype for individuals with and without specific patterns (i.e., combinations of allelic variants) isolated in genetic backgrounds matched for the remaining significant patterns. It constructs a Markov chain to sample only potentially significant variants, minimizing the potential of large data sets to overwhelm the search. We tested APSampler on a simulated data set and on a case-control MS (multiple sclerosis) study for ethnic Russians. For the simulated data, the algorithm identified all the phenotypeassociated allele combinations coded into the data and, for the MS data, it replicated the previously known findings.

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Recombinant interferon-β1b (IFN-β1b) is an effective remedy against multiple sclerosis and other diseases. However, use of small polypeptide (molecular weight is around 18.5 kDa) is limited due to poor solubility, stability, and short half-life in systemic circulation. To solve this problem, we constructed two variants of PASylated IFN-β1b, with PAS sequence at C- or N-terminus of IFN-β1b. The PAS-modified proteins demonstrated 4-fold increase in hydrodynamic volume of the molecule combined with 2-fold increase of in vitro biological activity, as well as advanced stability and solubility of the protein in solution as opposed to unmodified IFN-β1b. Our results demonstrate that PASylation has a positive impact on stability, solubility, and functional activity of IFN-β1b and potentially might improve pharmacokinetic properties of the molecule as a therapeutic agent.

Background: Multiple sclerosis (MS) is an immune-mediated disease of polygenic etiology. Dissection of its genetic background is a complex problem, because of the combinatorial possibilities of gene-gene interactions. As genotyping methods improve throughput, approaches that can explore multigene interactions appropriately should lead to improved understanding of MS.

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