Radial glia promote microglial development through integrin αVß8 -TGFß1 signaling.

Microglia diversity emerges from interactions between intrinsic genetic programs and environment-derived signals, but how these processes unfold and interact in the developing brain remains unclear. Here, we show that radial glia-expressed integrin beta 8 (ITGB8) expressed in radial glia progenitors activates microglia-expressed TGFß1, permitting microglial development. Domain-restricted deletion of Itgb8 in these progenitors establishes complementary regions with developmentally arrested "dysmature" microglia that persist into adulthood. In the absence of autocrine TGFß1 signaling, we find that microglia adopt a similar dysmature phenotype, leading to neuromotor symptoms almost identical to Itgb8 mutant mice. In contrast, microglia lacking the TGFß signal transducers Smad2 and Smad3 have a less polarized dysmature phenotype and correspondingly less severe neuromotor dysfunction. Finally, we show that non-canonical (Smad-independent) signaling partially suppresses disease and development associated gene expression, providing compelling evidence for the adoption of microglial developmental signaling pathways in the context of injury or disease.

Primary Graft Dysfunction Is Associated With Development of Early Cardiac Allograft Vasculopathy, but Not Other Immune-mediated Complications, After Heart Transplantation.

BACKGROUND: We investigated associations between primary graft dysfunction (PGD) and development of acute cellular rejection (ACR), de novo donor-specific antibodies (DSAs), and cardiac allograft vasculopathy (CAV) after heart transplantation (HT). METHODS: A total of 381 consecutive adult HT patients from January 2015 to July 2020 at a single center were retrospectively analyzed. The primary outcome was incidence of treated ACR (International Society for Heart and Lung Transplantation grade 2R or 3R) and de novo DSA (mean fluorescence intensity >500) within 1 y post-HT. Secondary outcomes included median gene expression profiling score and donor-derived cell-free DNA level within 1 y and incidence of cardiac allograft vasculopathy (CAV) within 3 y post-HT. RESULTS: When adjusted for death as a competing risk, the estimated cumulative incidence of ACR (PGD 0.13 versus no PGD 0.21; P = 0.28), median gene expression profiling score (30 [interquartile range, 25-32] versus 30 [interquartile range, 25-33]; P = 0.34), and median donor-derived cell-free DNA levels was similar in patients with and without PGD. After adjusting for death as a competing risk, estimated cumulative incidence of de novo DSA within 1 y post-HT in patients with PGD was similar to those without PGD (0.29 versus 0.26; P = 0.10) with a similar DSA profile based on HLA loci. There was increased incidence of CAV in patients with PGD compared with patients without PGD (52.6% versus 24.8%; P = 0.01) within the first 3 y post-HT. CONCLUSIONS: During the first year after HT, patients with PGD had a similar incidence of ACR and development of de novo DSA, but a higher incidence of CAV when compared with patients without PGD.

Combining donor derived cell free DNA and gene expression profiling for non-invasive surveillance after heart transplantation.

BACKGROUND: Donor-derived cell free DNA (dd-cfDNA) and gene expression profiling (GEP) offer noninvasive alternatives to rejection surveillance after heart transplantation; however, there is little evidence on the paired use of GEP and dd-cfDNA for rejection surveillance. METHODS: A single center, retrospective analysis of adult heart transplant recipients. A GEP cohort, transplanted from January 1, 2015 through December 31, 2017 and eligible for rejection surveillance with GEP was compared to a paired testing cohort, transplanted July 1, 2018 through June 30, 2020, with surveillance from both dd-cfDNA and GEP. The primary outcomes were survival and rejection-free survival at 1 year post-transplant. RESULTS: In total 159 patients were included, 95 in the GEP and 64 in the paired testing group. There were no differences in baseline characteristics, except for less use of induction in the paired testing group (65.6%) compared to the GEP group (98.9%), P < .01. At 1-year, there were no differences between the paired testing and GEP groups in survival (98.4% vs. 94.7%, P = .23) or rejection-free survival (81.3% vs. 73.7% P = .28). CONCLUSIONS: Compared to post-transplant rejection surveillance with GEP alone, pairing dd-cfDNA and GEP testing was associated with similar survival and rejection-free survival at 1 year while requiring significantly fewer biopsies.

Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping.

Introduction: Regional differences in activation rates may contribute to the electrical substrates that maintain atrial fibrillation (AF), and estimating them non-invasively may help guide ablation or select anti-arrhythmic medications. We tested whether non-invasive assessment of regional AF rate accurately represents intracardiac recordings. Methods: In 47 patients with AF (27 persistent, age 63 ± 13 years) we performed 57-lead non-invasive Electrocardiographic Imaging (ECGI) in AF, simultaneously with 64-pole intracardiac signals of both atria. ECGI was reconstructed by Tikhonov regularization. We constructed personalized 3D AF rate distribution maps by Dominant Frequency (DF) analysis from intracardiac and non-invasive recordings. Results: Raw intracardiac and non-invasive DF differed substantially, by 0.54 Hz [0.13 - 1.37] across bi-atrial regions (R 2 = 0.11). Filtering by high spectral organization reduced this difference to 0.10 Hz (cycle length difference of 1 - 11 ms) [0.03 - 0.42] for patient-level comparisons (R 2 = 0.62), and 0.19 Hz [0.03 - 0.59] and 0.20 Hz [0.04 - 0.61] for median and highest DF, respectively. Non-invasive and highest DF predicted acute ablation success (p = 0.04). Conclusion: Non-invasive estimation of atrial activation rates is feasible and, when filtered by high spectral organization, provide a moderate estimate of intracardiac recording rates in AF. Non-invasive technology could be an effective tool to identify patients who may respond to AF ablation for personalized therapy.