Evidence for heterogeneous forms of the snake venom metalloproteinase jararhagin: a factor contributing to snake venom variability

The reprolysin subfamily of metalloproteinases includes snake venom metalloproteinases (SVMP) and mammalian disintegrin/metalloproteinase. These proteins are synthesized as zymogens and undergo proteolytic processing resulting in a variety of multifunctional proteins. Jararhagin is a P-III SVMP isolated from the venom of Bothrops jararaca. In crude venom, two forms of jararhagin are typically found, full-length jararhagin and jararhagin-C, a proteolytically processed form of jararhagin that is composed of the disintegrin-like and cysteine-rich domains of jararhagin. To better understand the structural and mechanistic bases for these forms of jararhagin in the venom of B. jararaca and the source of venom complexity in general, we have examined the jararhagin forms isolated from venom and the autolysis of isolated jararhagin under the conditions of varying pH, calcium ion concentration, and reducing agents. From our results, jararhagin isolated from venom appears as two forms: a predominant form that is stable to in vitro autolysis and a minor form that is susceptible to autolysis under a variety of conditions including alkaline pH, low calcium ion concentrations, or reducing agent. The autolysis site for production of jararhagin-C from isolated jararhagin was different from that observed for jararhagin-C as isolated from crude venom. Taken together, these data lead us to the conclusion that during the biosynthesis of jararhagin in the venom gland at least three forms are present: one form which is rapidly processed to give rise to jararhagin-C, one form which is resistant to processing in the venom and autolysis in vitro, and one minor form which is susceptible to autolysis under conditions that promote destabilization of its structure. The presence of these different forms of jararhagin contributes to greater structural and functional complexity of the venom and may be a common feature among all snake venoms. The biological and biochemical features in the venom gland responsible for these jararhagin isoforms are currently under investigation.     

Cigarette smoke-induced accumulation of lung dendritic cells is interleukin-1α-dependent in mice

Background

Evidence suggests that dendritic cells accumulate in the lungs of COPD patients and correlate with disease severity. We investigated the importance of IL-1R1 and its ligands IL-1α and β to dendritic cell accumulation and maturation in response to cigarette smoke exposure.

Methods

Mice were exposed to cigarette smoke using a whole body smoke exposure system. IL-1R1-, TLR4-, and IL-1α-deficient mice, as well as anti-IL-1α and anti-IL-1β blocking antibodies were used to study the importance of IL-1R1 and TLR4 to dendritic cell accumulation and activation.

Results

Acute and chronic cigarette smoke exposure led to increased frequency of lung dendritic cells. Accumulation and activation of dendritic cells was IL-1R1/IL-1α dependent, but TLR4- and IL-1β-independent. Corroborating the cellular data, expression of CCL20, a potent dendritic cells chemoattractant, was IL-1R1/IL-1α-dependent. Studies using IL-1R1 bone marrow-chimeric mice revealed the importance of IL-1R1 signaling on lung structural cells for CCL20 expression. Consistent with the importance of dendritic cells in T cell activation, we observed decreased CD4⁺ and CD8⁺ T cell activation in cigarette smoke-exposed IL-1R1-deficient mice.

Conclusion

Our findings convey the importance of IL-1R1/IL-1α to the recruitment and activation of dendritic cells in response to cigarette smoke exposure.     

Functional normalization of 450k methylation array data improves replication in large cancer studies

We propose an extension to quantile normalization that removes unwanted technical variation using control probes. We adapt our algorithm, functional normalization, to the Illumina 450k methylation array and address the open problem of normalizing methylation data with global epigenetic changes, such as human cancers. Using data sets from The Cancer Genome Atlas and a large case–control study, we show that our algorithm outperforms all existing normalization methods with respect to replication of results between experiments, and yields robust results even in the presence of batch effects. Functional normalization can be applied to any microarray platform, provided suitable control probes are available.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0503-2) contains supplementary material, which is available to authorized users.     

How old is this mutation? - a study of three Ashkenazi Jewish founder mutations

Background

Several founder mutations leading to increased risk of cancer among Ashkenazi Jewish individuals have been identified, and some estimates of the age of the mutations have been published. A variety of different methods have been used previously to estimate the age of the mutations. Here three datasets containing genotype information near known founder mutations are reanalyzed in order to compare three approaches for estimating the age of a mutation. The methods are: (a) the single marker method used by Risch et al., (1995); (b) the intra-allelic coalescent model known as DMLE, and (c) the Goldgar method proposed in Neuhausen et al. (1996), and modified slightly by our group. The three mutations analyzed were MSH2*1906 G->C, APC*I1307K, and BRCA2*6174delT.

Results

All methods depend on accurate estimates of inter-marker recombination rates. The modified Goldgar method allows for marker mutation as well as recombination, but requires prior estimates of the possible haplotypes carrying the mutation for each individual. It does not incorporate population growth rates. The DMLE method simultaneously estimates the haplotypes with the mutation age, and builds in the population growth rate. The single marker estimates, however, are more sensitive to the recombination rates and are unstable. Mutation age estimates based on DMLE are 16.8 generations for MSH2 (95% credible interval (13, 23)), 106 generations for I1037K (86-129), and 90 generations for 6174delT (71-114).

Conclusions

For recent founder mutations where marker mutations are unlikely to have occurred, both DMLE and the Goldgar method can give good results. Caution is necessary for older mutations, especially if the effective population size may have remained small for a long period of time.