Neutralizing antibodies (inhibitors) to replacement factor VIII (FVIII, either plasma derived or recombinant) impair the effective management of hemophilia A. Individuals with hemophilia A due to major deletions of the FVIII gene (F8) lack antigenically cross-reactive material in their plasma ("CRM-negative"), and the prevalence of inhibitors in these individuals may be as high as 90%. Conversely, individuals with hemophilia A caused by F8 missense mutations are CRM-positive, and their overall prevalence of inhibitors is <10% (ref. 2). Individuals with the F8 intron 22 inversion (found in ∼50% of individuals with severe hemophilia A) have been grouped with the former on the basis of their genetic defect and CRM-negative status. However, only ∼20% of these individuals develop inhibitors. Here we demonstrate that the levels of F8 mRNA and intracellular FVIII protein in B lymphoblastoid cells and liver biopsies from individuals with the intron 22 inversion are comparable to those in healthy controls. These results support the hypothesis that most individuals with the intron 22 inversion are tolerized to FVIII and thus do not develop inhibitors. Furthermore, we developed a new pharmacogenetic algorithm that permits the stratification of inhibitor risk for individuals and subpopulations by predicting the immunogenicity of replacement FVIII using, as input, the number of putative T cell epitopes in the infused protein and the competence of major histocompatibility complex class II molecules to present such epitopes. This algorithm showed statistically significant accuracy in predicting the presence of inhibitors in 25 unrelated individuals with the intron 22 inversion.

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.