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Retrotransposons (RTEs) have been postulated to reactivate with age and contribute to aging through activated innate immune response and inflammation. Here, we analyzed the relationship between RTE expression and aging using published transcriptomic and methylomic datasets of human blood. Despite no observed correlation between RTE activity and chronological age, the expression of most RTE classes and families except short interspersed nuclear elements (SINEs) correlated with biological age-associated gene signature scores. Strikingly, we found that the expression of SINEs was linked to upregulated DNA repair pathways in multiple cohorts. We also observed DNA hypomethylation with aging and the significant increase in RTE expression level in hypomethylated RTEs except for SINEs. Additionally, our single-cell transcriptomic analysis suggested a role for plasma cells in aging mediated by RTEs. Altogether, our multi-omics analysis of large human cohorts highlights the role of RTEs in biological aging and suggests possible mechanisms and cell populations for future investigations.

Abstract Background Retinitis pigmentosa (RP) is a genetically heterogeneous group of degenerative disorders causing progressive vision loss due to photoreceptor death. RP affects other retinal cells, including the retinal pigment epithelium (RPE). MicroRNAs (miRs) are implicated in RP pathogenesis, and downregulating miR-181a/b has shown therapeutic benefit in RP mouse models by improving mitochondrial function. This study investigates the expression profile of miR-181a/b in RPE cells and the neural retina during RP disease progression. We also evaluate how miR-181a/b downregulation, by knocking out miR-181a/b-1 cluster in RPE cells, confers therapeutic efficacy in an RP mouse model and explore the mechanisms underlying this process. Results Our findings reveal distinct expression profiles, with downregulated miR-181a/b in RPE cells suggesting a protective response and upregulated miR-181a/b in the neural retina indicating a role in disease progression. We found that miR-181a/b-2, encoded in a separate genomic cluster, compensates for miR-181a/b-1 ablation in RPE cells at late time points. The transient downregulation of miR-181a/b in RPE cells at post-natal week 6 (PW6) led to improved RPE morphology, retarded photoreceptor degeneration and decreased RPE aerobic glycolysis. Conclusions Our study elucidates the underlying mechanisms associated with the therapeutic modulation of miR-181a/b, providing insights into the metabolic processes linked to its RPE-specific downregulation. Our data further highlights the impact of compensatory regulation between miR clusters with implications for the development of miR-based therapeutics. Graphical Abstract

Summary: Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

Targeted therapy has emerged as a more precise approach to treat glomerular diseases, focusing on specific molecular or cellular processes that contribute to disease development or progression. This approach complements or replaces traditional immunosuppressive therapy, optimizes supportive care, and provides a more personalized treatment strategy. In this review, we summarize the evolving understanding of pathogenic mechanisms in immune-mediated glomerular diseases and the developing targeted therapies based on these mechanisms. We begin by discussing pan-B-cell depletion, anti-CD20 rituximab, and targeting B-cell survival signaling through the BAFF/APRIL pathway. We also exam specific plasma cell depletion with anti-CD38 antibody. We then shift our focus to complement activation in glomerular diseases, which is involved in antibody-mediated glomerular diseases, such as IgA nephropathy, membranous nephropathy, ANCA-associated vasculitis, and lupus nephritis. Non-antibody-mediated complement activation occurs in glomerular diseases, including C3 glomerulopathy, complement-mediated atypical hemolytic uremic syndrome, and focal segmental glomerulosclerosis. We discuss specific inhibition of terminal, lectin, and alternative pathways in different glomerular diseases. Finally, we summarize current clinical trials targeting the final pathways of various glomerular diseases, including kidney fibrosis. We conclude that targeted therapy based on individualized pathogenesis should be the future of treating glomerular diseases.