Correlation of clinical and deletion data in Duchenne and Becker muscular dystrophy.

Cloned cDNA sequences representing exons from the Duchenne/Becker muscular dystrophy (DMD/BMD) gene were used for deletion screening in a population of 287 males males affected with DMD or BMD. The clinical phenotypes of affected boys were classified into three clinical severity groups based on the age at which ambulation was lost. Boys in group 1 had DMD, losing ambulation before their 13th birthday; those in group 2 had disease of intermediate severity, losing ambulation between the ages of 13 and 16 years; and boys in group 3 had BMD, being ambulant beyond 16 years. A fourth group consisted of patients too young to be classified. Clinical group allocation was made without previous knowledge of the DNA results. A gene deletion was found in 124 cases where the clinical severity group of the affected boy was known. The extent of the deletions was delineated using cDNA probes. There were 74 different deletions. Fifty-five of these were unique to individual patients, but the other 19 were found in at least two unrelated patients. The different clinical groups showed generally similar distributions of deletions, and the number of exon bands deleted (that is, deletion size) was independent of phenotype. Some specific deletion types, however, correlated with the clinical severity of the disease. Deletion of exons containing HindIII fragments 33 and 34 and 33 to 35 were associated with BMD and were not found in patients with DMD. Deletions 3 to 7 occurred in four patients with the intermediate phenotype and one patient with BMD. Other shared deletions were associated with DMD, although in four cases patients with disease of intermediate severity apparently shared the same deletion with boys with DMD. The range of phenotypes observed, and the overlap at the genetic level between severe and intermediate and mild and intermediate forms of dystrophy, emphasizes the essential continuity of the clinical spectrum of DMD/BMD. There were no characteristic deletions found in boys with mental retardation or short stature which differed from deletions in affected boys without these features

Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?: Linking microbes to ecosystem processes

International audienceMicroorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology