Various catalytic antibodies or abzymes have been detected recently in the sera of patients with several autoimmune pathologies, where their presence is most probably associated with autoimmunization. Recently we have shown that DNase, RNase, and polysaccharide‐hydrolyzing activities are associated with IgGs from the sera of patients with multiple sclerosis (MS). Here we present evidence demonstrating that highly purified MS IgGs (but not Igs from the sera of healthy individuals) catalyze specifically hydrolysis of human myelin basic protein (hMBP). In contrast to many known proteases, IgGs do not hydrolyze many other different proteins. Specific inhibitors of acidic and thiol proteases have no remarkable effect on proteolytic activity of IgGs. However, specific inhibitor of serine (PMSF, AEBSF, and benzamidin) and metal‐dependent (EDTA) proteases significantly inhibit activity of proteolytic abzymes. Interestingly, the ratio of serine‐like and metal‐dependent activities of MS IgGs varied very much from patient to patient. The findings speak in favor of the generation by the immune systems of individual MS patients of a variety of polyclonal anti‐MBP IgGs with different catalytic properties.

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression at the post-transcriptional level through base-pairing predominantly with a 3-untranslated region of target mRNA, followed by mRNA degradation or translational repression. Totally, miRNAs change, through a complex regulatory network, the expression of more than 60% of human genes. MiRNAs are key regulators of the immune response that affect maturation, proliferation, differentiation, and activation of immune cells, as well as antibody secretion and release of inflammatory mediators. Disruption of this regulation may lead to the development of various pathological conditions, including autoimmune inflammation. This review summarizes the data on biogenesis and the mechanisms of miRNA action. We discuss the role of miRNAs in the development and the action of the immune system, as well as in the development of an autoimmune inflammatory response. Special attention is given to the role of miRNAs in the autoimmune inflammation in multiple sclerosis, which is a serious socially significant disease of the central nervous system. Currently, a lot of research is focused on this problem.

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 phenotype-associated allele combinations coded into the data and, for the MS data, it replicated the previously known findings.

Caffeine is an efficient inhibitor of cellular DNA repair, likely through its effects on ATM (ataxia telangiectasia mutated) and ATR (ATM and Rad3-related) kinases. Here, we show that caffeine treatment causes a dose-dependent reduction in the total amount of HIV-1 and avian sarcoma virus retroviral vector DNA that is joined to host DNA in the population of infected cells and also in the number of transduced cells. These changes were observed at caffeine concentrations that had little or no effect on overall cell growth, synthesis, and nuclear import of the viral DNA, or the activities of the viral integrasein vitro. Substantial reductions in the amount of host-viral-joined DNA in the infected population, and in the number of transductants, were also observed in the presence of a dominant-negative form of the ATR protein, ATRkd. After infection, a significant fraction of these cells undergoes cell death. In contrast, retroviral transduction is not impeded in ATM-deficient cells, and addition of caffeine leads to the same reduction that was observed in ATM-proficient cells. These results suggest that activity of the ATR kinase, but not the ATM kinase, is required for successful completion of the viral DNA integration process and/or survival of transduced cells. Components of the cellular DNA damage repair response may represent potential targets for antiretroviral drug development.