Primary vitreoretinal lymphoma (PVRL) is a high-grade lymphoma affecting the vitreous and/or the retina. The vast majority of cases are histopathologically classified as diffuse large B-cell lymphoma (DLBCL) and considered a subtype of primary central nervous system lymphoma (PCNSL). To obtain more insight into the ontogenetic relationship between PVRL and PCNSL, we adopted an immunogenetic perspective and explored the respective immunoglobulin gene repertoire profiles from 55 PVRL cases and 48 PCNSL cases. In addition, considering that both entities are predominantly related to activated B-cell (ABC) DLBCL, we compared their repertoire with that of publicly available 262 immunoglobulin heavy variable domain gene rearrangement sequences from systemic ABC-type DLBCLs. PVRL displayed a strikingly biased repertoire, with the IGHV4-34 gene being used in 63.6% of cases, which was significantly higher than in PCNSL (34.7%) or in DLBCL (30.2%). Further repertoire bias was evident by (1) restricted associations of IGHV4-34 expressing heavy chains, with κ light chains utilizing the IGKV3-20/IGKJ1 gene pair, including 5 cases with quasi-identical sequences, and (2) the presence of a subset of stereotyped IGHV3-7 rearrangements. All PVRL IGHV sequences were highly mutated, with evidence of antigen selection and ongoing mutations. Finally, half of PVRL and PCNSL cases carried the MYD88 L265P mutation, which was present in all 4 PVRL cases with stereotyped IGHV3-7 rearrangements. In conclusion, the massive bias in the immunoglobulin gene repertoire of PVRL delineates it from PCNSL and points to antigen selection as a major driving force in their development.

Huge numbers of protein sequences are now available in public databases. In order to exploit more fully this valuable biological data, these sequences need to be annotated with functional properties such as Enzyme Commission (EC) numbers and Gene Ontology terms. The UniProt Knowledgebase (UniProtKB) is currently the largest and most comprehensive resource for protein sequence and annotation data. In the March 2018 release of UniProtKB, some 556,000 sequences have been manually curated but over 111 million sequences still lack functional annotations. The ability to annotate automatically these unannotated sequences would represent a major advance for the field of bioinformatics. Here, we present a novel network-based approach called GrAPFI for the automatic functional annotation of protein sequences. The underlying assumption of GrAPFI is that proteins may be related to each other by the protein domains, families, and super-families that they share. Several protein domain databases exist such as In-terPro, Pfam, SMART, CDD, Gene3D, and Prosite, for example. Our approach uses Interpro domains, because the InterPro database contains information from several other major protein family and domain databases. Our results show that GrAPFI achieves better EC number annotation performance than several other previously described approaches.