The C. elegans glycosyltransferase BUS-8 has two distinct and essential roles in epidermal morphogenesis.

Ventral enclosure in Caenorhabditis elegans involves migration of epidermal cells over a neuroblast substrate and subsequent adhesion at the ventral midline. Organisation of the neuroblast layer by ephrins and their receptors is essential for this migration. We show that bus-8, which encodes a predicted glycosyltransferase, is essential for embryonic enclosure and acts in or with ephrin signalling to mediate neuroblast organisation and to permit epidermal migration. BUS-8 acts non-cell-autonomously in this process, and likely modifies an extracellular regulator of ephrin signalling and cell organisation. Weak and cold-sensitive alleles of bus-8 show that the gene has a separate and distinct post-embryonic role, being essential for epidermal integrity and production of the cuticle surface. This disorganisation of the epidermis and cuticle layers causes increased drug sensitivity, which could aid the growing use of C. elegans in drug screening and chemical genomics. The viable mutants are also resistant to infection by the pathogen Microbacterium nematophilum, due to failure of the bacterium to bind to the host surface. The two separate essential roles of BUS-8 in epidermal morphogenesis add to our growing understanding of the widespread importance of glycobiology in development

Stochastic blockmodeling of the modules and core of the Caenorhabditis elegans connectome

10.1371/journal.pone.0097584PLoS ONE97e9758

Improved regression calibration

The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We propose an improved regression calibration approach, a general pseudo maximum likelihood estimation method based on a conveniently decomposed form of the likelihood. It is both consistent and computationally efficient, and produces point estimates and estimated standard errors which are practically identical to those obtained by maximum likelihood. Simulations suggest that improved regression calibration which is easy to implement in standard software, works well in a range of situations