Chronic exposure to ethnic–political and war violence has deleterious effects throughout childhood. Some youths exposed to war violence are more likely to act aggressively afterwards, and some are more likely to experience post-traumatic stress symptoms (PTS symptoms). However, the concordance of these two outcomes is not strong, and it is unclear what discriminates between those who are at more risk for one or the other. Drawing on prior research on desensitization and arousal and on recent social–cognitive theorizing about how high anxious arousal to violence can inhibit aggression, we hypothesized that those who characteristically experience higher anxious arousal when exposed to violence should display a lower increase in aggression after exposure to war violence but the same or a higher increase in PTS symptoms compared to those low in anxious arousal. To test this hypothesis, we analyzed data from our 4-wave longitudinal interview study of 1051 Israeli and Palestinian youths (ages at Wave 1 ranged from 8 to 14, and at Wave 4 from 15–22). We used the 4 waves of data on aggression, PTS symptoms, and exposure to war violence, along with additional data collected during Wave 4 on the anxious arousal participants experienced while watching a very violent film unrelated to war violence (N = 337). Longitudinal analyses revealed that exposure to war violence significantly increased both the risk of subsequent aggression and PTS symptoms. However, anxious arousal in response to seeing the unrelated violent film (measured from skin conductance and self-reports of anxiety) moderated the relation between exposure to war violence and subsequent psychological and behavioral outcomes. Those who experienced greater anxious arousal while watching the violent film showed a weaker positive relation between amount of exposure to war violence and aggression toward their peers but a stronger positive relation between amount of exposure to war violence and PTS symptoms.

Cross-species spillover events are responsible for many of the pandemics in human history including COVID-19; however, the evolutionary mechanisms that enable these events are poorly understood. We have previously modeled this process using a chimeric vaccinia virus expressing the rhesus cytomegalovirus–derived protein kinase R (PKR) antagonist RhTRS1 in place of its native PKR antagonists: E3L and K3L (VACVΔEΔK + RhTRS1). Using this virus, we demonstrated that gene amplification of rhtrs1 occurred early during experimental evolution and was sufficient to fully rescue virus replication in partially resistant African green monkey (AGM) fibroblasts. Notably, this rapid gene amplification also allowed limited virus replication in otherwise completely non-permissive human fibroblasts, suggesting that gene amplification may act as a ‘molecular foothold’ to facilitate viral adaptation to multiple species. In this study, we demonstrate that there are multiple barriers to VACVΔEΔK + RhTRS1 replication in human cells, mediated by both PKR and ribonuclease L (RNase L). We experimentally evolved three AGM-adapted virus populations in human fibroblasts. Each population adapted to human cells bimodally, via an initial 10-fold increase in replication after only two passages followed by a second 10-fold increase in replication by passage 9. Using our Illumina-based pipeline, we found that some single nucleotide polymorphisms (SNPs) which had evolved during the prior AGM adaptation were rapidly lost, while thirteen single-base substitutions and short indels increased over time, including two SNPs unique to human foreskin fibroblast (HFF)–adapted populations. Many of these changes were associated with components of the viral RNA polymerase, although no variant was shared between all three populations. Taken together, our results demonstrate that rhtrs1 amplification was sufficient to increase viral tropism after passage in an ‘intermediate species’ and subsequently enabled the virus to adopt different, species-specific adaptive mechanisms to overcome distinct barriers to viral replication in AGM and human cells.

Genetic diagnosis is valuable for predicting disease risk and prognosis. Exome sequencing can resolve approximately one-third of cases, depending on disease type and patient selection criteria. The difficulty in identifying pathogenic variants among many rare variants is a major challenge. Computational predictors exist, but additional evidence is required for accurate variant interpretation. Functional studies are one option but are rarely completed on a time scale useful for individuals’ diagnosis. As a result, most protein-coding variants are of uncertain significance. Congenital hypopituitarism is a genetically heterogeneous disorder involving > 30 genes, and many are dominant with incomplete penetrance [reviewed in ( 1)]. These issues make it challenging to identify the genetic basis of pituitary dysfunction, and >75% of cases are not solved by exome sequencing. Mutations in POU1F1 cause 2-8% of hypopituitarism cases. Affected individuals have deficiencies in GH, TSH, and prolactin, because POU1F1 transactivates those hormone genes. Akiba and colleagues analyzed the mechanism whereby an intronic c.142-83 A > G variant causes dominant pituitary hormone deficiency characterized by lack of GH, normal TSH with thyroid hormone insufficiency, and variable pituitary size ( 2). They demonstrated that this variant affects splicing and is pathogenic in mice. <!-- /foreach in Model.Sections -->

Background Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes. Results We benchmarked eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compared experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms’ concordance with MPSA measurements, and with each other, was lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieved the best overall performance at distinguishing disruptive and neutral variants. Controlling for overall call rate genome-wide, SpliceAI and Pangolin also showed superior overall sensitivity for identifying SDVs. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues. Conclusion SpliceAI and Pangolin showed the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

Sodium-glucose cotransporter-2 (SGLT2) inhibitors slow diabetic kidney disease progression, but their intrarenal mechanisms remain incompletely understood, particularly in type 1 diabetes (T1D), where kidney protection has not been definitively established. ATTEMPT (NCT04333823) was a placebo-controlled trial in which 98 youth (ages 12 to 21) with T1D and hyperfiltration were randomized 1:1 to dapagliflozin (5 milligrams) or placebo for 16 weeks. Participants underwent sequential kidney biopsies, multiparametric kidney MRI, and plasma and urine proteomics. The sequential research kidney biopsies were performed on adults 18 years or older at one of three sites (baseline n = 16, follow-up n = 11). Single-cell RNA sequencing of 214,415 cells across 27 biopsies revealed coordinated transcriptional shifts across nephron, vascular, and immune compartments. In the proximal tubule, the primary site of SGLT2 expression, dapagliflozin down-regulated glycolysis, gluconeogenesis, and oxidative stress markers. Endothelial cells showed reduced profibrotic and inflammatory gene expression with increased protective factors. Podocytes demonstrated enhanced cytoskeletal reinforcement and suppressed interferon signaling. These molecular changes paralleled clinical improvements, including attenuation of hyperfiltration, improved glycemic control, and normalization of medullary oxygenation. Trajectory analyses revealed dapagliflozin shifted tubular cells from injury-prone toward healthier phenotypes. Cross-cohort comparison against healthy controls showed that more than 55% of dapagliflozin-responsive genes shifted toward healthy control expression patterns. Urine proteomics mirrored tissue changes with decreased injury markers and increased protective proteins. These convergent molecular mechanisms, metabolic reprogramming, dampened inflammation, and normalized oxygen handling provide hypothesis-generating mechanistic insights into potential kidney-protective mechanisms of SGLT2 inhibitor therapy in youth with T1D.