The substrate degradome of meprin metalloproteases reveals an unexpected proteolytic link between meprin β and ADAM10

The in vivo roles of meprin metalloproteases in pathophysiological conditions remain elusive. Substrates define protease roles. Therefore, to identify natural substrates for human meprin α and β we employed TAILS (terminal amine isotopic labeling of substrates), a proteomics approach that enriches for N-terminal peptides of proteins and cleavage fragments. Of the 151 new extracellular substrates we identified, it was notable that ADAM10 (a disintegrin and metalloprotease domain-containing protein 10)—the constitutive α-secretase—is activated by meprin β through cleavage of the propeptide. To validate this cleavage event, we expressed recombinant proADAM10 and after preincubation with meprin β, this resulted in significantly elevated ADAM10 activity. Cellular expression in murine primary fibroblasts confirmed activation. Other novel substrates including extracellular matrix proteins, growth factors and inhibitors were validated by western analyses and enzyme activity assays with Edman sequencing confirming the exact cleavage sites identified by TAILS. Cleavages in vivo were confirmed by comparing wild-type and meprin^?/? mice. Our finding of cystatin C, elafin and fetuin-A as substrates and natural inhibitors for meprins reveal new mechanisms in the regulation of protease activity important for understanding pathophysiological processes.     

A common model approach to macroeconomics: using panel data to reduce sampling error

Is there a common model inherent in macroeconomic data? Macroeconomic theory suggests that market economies of various nations should share many similar dynamic patterns; as a result, individual country empirical models, for a wide variety of countries, often include the same variables. Yet, empirical studies often find important roles for idiosyncratic shocks in the differing macroeconomic performance of countries. We use forecasting criteria to examine the macrodynamic behaviour of 15 OECD countries in terms of a small set of familiar, widely used core economic variables, omitting country‐specific shocks. We find this small set of variables and a simple VAR ‘common model’ strongly support the hypothesis that many industrialized nations have similar macroeconomic dynamics. Copyright © 2005 John Wiley & Sons, Ltd.     

Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1

To identify risk variants for lung cancer, we conducted a multistage genome-wide association study. In the discovery phase, we analyzed 315,450 tagging SNPs in 1,154 current and former (ever) smoking cases of European ancestry and 1,137 frequency-matched, ever-smoking controls from Houston, Texas. For replication, we evaluated the ten SNPs most significantly associated with lung cancer in an additional 711 cases and 632 controls from Texas and 2,013 cases and 3,062 controls from the UK. Two SNPs, rs1051730 and rs8034191, mapping to a region of strong linkage disequilibrium within 15q25.1 containing PSMA4 and the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5, were significantly associated with risk in both replication sets. Combined analysis yielded odds ratios of 1.32 (P < 1 x 10(-17)) for both SNPs. Haplotype analysis was consistent with there being a single risk variant in this region. We conclude that variation in a region of 15q25.1 containing nicotinic acetylcholine receptors genes contributes to lung cancer risk.     

Common 5p15.33 and 6p21.33 variants influence lung cancer risk

We conducted a genome-wide association (GWA) study of lung cancer comparing 511,919 SNP genotypes in 1,952 cases and 1,438 controls. The most significant association was attained at 15q25.1 (rs8042374; P = 7.75 x 10(-12)), confirming recent observations. Pooling data with two other GWA studies (5,095 cases, 5,200 controls) and with replication in an additional 2,484 cases and 3,036 controls, we identified two newly associated risk loci mapping to 6p21.33 (rs3117582, BAT3-MSH5; P(combined) = 4.97 x 10(-10)) and 5p15.33 (rs401681, CLPTM1L; P(combined) = 7.90 x 10(-9)).