Immunoglobulin (Ig) class switching is initiated by deamination of C→U within the immunoglobulin heavy chain locus, catalyzed by activation-induced deaminase (AID). In the absence of uracil-DNA glycosylase (UNG) and the homologue of bacterial MutS (MSH)–2 mismatch recognition protein, the resultant U:G lesions are not processed into switching events but are fixed by replication allowing sites of AID-catalyzed deamination to be identified by the resulting C→T mutations. We find that AID targets cytosines in both donor and acceptor switch regions (S regions) with the deamination domains initiating ∼150 nucleotides 3′ of the I exon start sites and extending over several kilobases (the IgH intronic enhancer is spared). Culturing B cells with interleukin 4 or interferon γ specifically enhanced deamination around Sγ1 and Sγ2a, respectively. Mutation spectra suggest that, in the absence of UNG and MSH2, AID may occasionally act at the μ switch region in an apparently processive manner, but there is no marked preference for targeting of the transcribed versus nontranscribed strand (even in areas capable of R loop formation). The data are consistent with switch recombination being triggered by transcription-associated, strand-symmetric AID-mediated deamination at both donor and acceptor S regions with cytokines directing isotype specificity by potentiating AID recruitment to the relevant acceptor S region.

Retinal gene therapy has shown great promise in treating retinitis pigmentosa (RP), a primary photoreceptor degeneration that leads to severe sight loss in young people 1 , 2 , 3 , 4 , 5 , 6 . Here we report the first in human Phase I/II dose escalation clinical trial for X-linked RP caused by mutations in the RP GTPase regulator (RPGR) gene 7 in 18 patients up to 6 months follow-up ( Clinicaltrials.gov : NCT03116113). The primary outcome of the study was safety and secondary outcomes included visual acuity, microperimetry and central retinal thickness. Apart from steroid-responsive subretinal inflammation in patients at the higher doses, there were no significant safety concerns following subretinal delivery of an adeno-associated viral vector encoding codon-optimized human RPGR (AAV8. coRPGR ) 8 meeting the pre-specified primary endpoint. Visual field improvements beginning at one month and maintained to the last point of follow-up were observed in six patients.

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Regular health screening plays a crucial role in the early detection of common chronic diseases and prevention of their progression. An AI system capable of recapitulating early disease detection, staging and incidence prediction would help to improve healthcare access and delivery, particularly in resource-poor or remote settings. Using a total of 115,344 retinal fundus photographs from 57,672 patients (with data split into mutually exclusive training, internal testing, and external validation sets), we first developed AI models capable of identifying chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) based on fundus images. The AI system was shown to be capable of predicting the clinical indicators of CKD and T2DM (including eGFR and blood glucose levels), which indicates its potential for extracting quantitative clinical metrics embedded subtly within retinal fundus images. We further developed an AI system to predict the risk of disease progression using baseline images of 10,269 patients for whom longitudinal clinical data were available for up to 6 years, which demonstrated potential utility in optimizing health screening intervals and clinical management. The generalizability of the AI system in identifying and predicting the progression of CKD and T2DM was evaluated using population-based external validation cohorts. Moreover, a prospective pilot study with 3,081 patients was also conducted to demonstrate the broader applicability of the AI system at the 'point-of-care' using fundus images captured with smartphones. The results provide proof-of-concept for a reliable and non-invasive AI-based clinical screening tool based on fundus photographs for the early detection and incidence prediction of two common systemic diseases.