Binding of the hepatitis C virus E2 glycoprotein to CD81 is strain specific and is modulated by a complex interplay between hypervariable regions 1 and 2.

The envelope glycoprotein E2 of hepatitis C virus (HCV) is the target of neutralizing antibodies and is presently being evaluated as an HCV vaccine candidate. HCV binds to human cells through the interaction of E2 with the tetraspanin CD81, a putative viral receptor component. We have analyzed four different E2 proteins from 1a and 1b viral isolates for their ability to bind to recombinant CD81 in vitro and to the native receptor displayed on the surface of Molt-4 cells. A substantial difference in binding efficiency between these E2 variants was observed, with proteins derived from 1b subtypes showing significantly lower binding than the 1a protein. To elucidate the mechanism of E2-CD81 interaction and to identify critical regions responsible for the different binding efficiencies of the E2 variants, several mutants were generated in E2 protein regions predicted by computer modeling to be exposed on the protein surface. Functional analysis of these E2 derivatives revealed that at least two distinct domains are responsible for interaction with CD81. A first segment centered around amino acid residues 613 to 618 is essential for recognition, while a second element including the two hypervariable regions (HVRs) modulates E2 receptor binding. Binding inhibition experiments with anti-HVR monoclonal antibodies confirmed this mapping and supported the hypothesis that a complex interplay between the two HVRs of E2 is responsible for modulating receptor binding, possibly through intramolecular interactions. Finally, E2 proteins from different isolates displayed a profile of binding to human hepatic cells different from that observed on Molt-4 cells or isolated recombinant CD81, indicating that additional factors are involved in viral recognition by target liver cells.

Evaluation of annotation strategies using an entire genome sequence.

MOTIVATION: Genome-wide functional annotation either by manual or automatic means has raised considerable concerns regarding the accuracy of assignments and the reproducibility of methodologies. In addition, a performance evaluation of automated systems that attempt to tackle sequence analyses rapidly and reproducibly is generally missing. In order to quantify the accuracy and reproducibility of function assignments on a genome-wide scale, we have re-annotated the entire genome sequence of Chlamydia trachomatis (serovar D), in a collaborative manner. RESULTS: We have encoded all annotations in a structured format to allow further comparison and data exchange and have used a scale that records the different levels of potential annotation errors according to their propensity to propagate in the database due to transitive function assignments. We conclude that genome annotation may entail a considerable amount of errors, ranging from simple typographical errors to complex sequence analysis problems. The most surprising result of this comparative study is that automatic systems might perform as well as the teams of experts annotating genome sequences.