Large Diversity of Functional Nanobodies from a Camelid Immune Library Revealed by an Alternative Analysis of Next-Generation Sequencing Data.

Next-generation sequencing (NGS) has been applied successfully to the field of therapeutic antibody discovery, often outperforming conventional screening campaigns which tend to identify only the more abundant selective antibody sequences. We used NGS to mine the functional nanobody repertoire from a phage-displayed camelid immune library directed to the recepteur d'origine nantais (RON) receptor kinase. Challenges to this application of NGS include accurate removal of read errors, correct identification of related sequences, and establishing meaningful inclusion criteria for sequences-of-interest. To this end, a sequence identity threshold was defined to separate unrelated full-length sequence clusters by exploring a large diverse set of publicly available nanobody sequences. When combined with majority-rule consensus building, applying this elegant clustering approach to the NGS data set revealed a wealth of >5,000-enriched candidate RON binders. The huge binding potential predicted by the NGS approach was explored through a set of randomly selected candidates: 90% were confirmed as RON binders, 50% of which functionally blocked RON in an ERK phosphorylation assay. Additional validation came from the correct prediction of all 35 RON binding nanobodies which were identified by a conventional screening campaign of the same immune library. More detailed characterization of a subset of RON binders revealed excellent functional potencies and a promising epitope diversity. In summary, our approach exposes the functional diversity and quality of the outbred camelid heavy chain-only immune response and confirms the power of NGS to identify large numbers of promising nanobodies.

Detection of putative secreted proteins in the plant-parasitic nematode Heterodera schachtii.

The beet cyst nematode Heterodera schachtii is an important pathogen worldwide, but its molecular characterization has been limited to studying individual genes of interest. We undertook a high-throughput genomic approach and drastically increased the number of available sequences for this parasite. A total of 2,662 expressed sequence tags were grouped into 1,212 clusters representing a nonredundant catalog of H. schachtii genes. Implementing a bioinformatic workflow, we identified 50 sequences coding for candidate secreted proteins. All of these contain a putative signal peptide required for entry into the secretory pathway and lack any transmembrane domain. Included are previously postulated cell-wall-degrading enzymes and other parasitism-related genes. Moreover, we provide the first report of an arabinogalactan endo-1,4-beta-galactosidase enzyme (EC 3.2.1.89) in animals. As sequence data increase at a rapid rate, developing high-throughput genomic screening is a necessity. The in silico approach described here is an effective way to identify putative secreted proteins and prioritize candidates for further studies.