Post-traumatic stress disorder (PTSD) is a debilitating disorder that develops in some people following trauma exposure. Trauma and PTSD have been associated with accelerated cellular aging. This study evaluated the effect of trauma and PTSD on accelerated GrimAge, an epigenetic predictor of lifespan, in traumatized civilians. This study included 218 individuals with current PTSD, 427 trauma-exposed controls without any history of PTSD and 209 subjects with lifetime PTSD history who are not categorized as current PTSD cases. The Traumatic Events Inventory (TEI) and Clinician-Administered PTSD Scale (CAPS) were used to measure lifetime trauma burden and PTSD, respectively. DNA from whole blood was interrogated using the MethylationEPIC or HumanMethylation450 BeadChips. GrimAge estimates were calculated using the methylation age calculator. Cortical thickness of 69 female subjects was assessed by using T1-weighted structural MRI images. Associations between trauma exposure, PTSD, cortical thickness, and GrimAge acceleration were tested with multiple regression models. Lifetime trauma burden (p = 0.03), current PTSD (p = 0.02) and lifetime PTSD (p = 0.005) were associated with GrimAge acceleration, indicative of a shorter predicted lifespan. The association with lifetime PTSD was replicated in an independent cohort (p = 0.04). In the MRI sub sample, GrimAge acceleration also associated with cortical atrophy in the right lateral orbitofrontal cortex (p adj = 0.03) and right posterior cingulate (p adj = 0.04), brain areas associated with emotion-regulation and threat-regulation. Our findings suggest that lifetime trauma and PTSD may contribute to a higher epigenetic-based mortality risk. We also demonstrate a relationship between cortical atrophy in PTSD-relevant brain regions and shorter predicted lifespan.

Single-molecule (particle) tracking is a powerful method to study dynamic processes in cells at highest possible spatial and temporal resolution. We have developed SMTracker, a graphical user interface for automatic quantifying, visualizing and managing of data. Version 2.0 determines distributions of positional displacements in x- and y- direction using multi-state diffusion models, discriminates between Brownian, sub- or superdiffusive behaviour, and locates slow or fast diffusing populations in a standardized cell. Using SMTracker, we show that the Bacillus subtilis RNA degradosome consists of a highly dynamic complex of RNase Y and binding partners. We found similar changes in molecule dynamics for RNase Y, CshA, PNPase and enolase, but not for phosphofructokinase, RNase J1 and J2, to inhibition of transcription. However, the absence of PfkA or of RNase J2 affected molecule dynamics of RNase Y-mVenus, indicating that these two proteins are indeed part of the degradosome. Molecule counting suggests that RNase Y is present as a dimer in cells, at an average copy number of about 500, of which 46% are present in a slow-diffusive state and thus likely engaged within degradosomes. Thus, RNase Y, CshA, PNPase and enolase likely play central roles, and RNase J1, J2 and PfkA more peripheral roles, in degradosome architecture.

Decreased inhibition-related hippocampal activation soon after trauma predicted future PTSD symptom severity. This finding may contribute to early identification of at-risk individuals and reveals potential targets for intervention or symptom prevention in the aftermath of trauma.

Background: Exposure to a traumatic event leads to posttraumatic stress disorder (PTSD) in 10–20% of exposed individuals. Predictors of risk are needed to target early interventions to those who are most vulnerable. The objective of the study was to test whether a noninvasive mobile device that measures a physiological biomarker of autonomic nervous system activation could predict future PTSD symptoms. Methods: Skin conductance response (SCR) was collected during a trauma interview in the emergency department within hours of exposure to trauma in 95 individuals. Trajectories of PTSD symptoms over 12 months post-trauma were identified using Latent Growth Mixture Modeling. Results: SCR was significantly correlated with the probability of being in the chronic PTSD trajectory following trauma exposure in the ED (r=0.489, p<0.000001). Lasso regression with elastic net was performed with demographic and clinical measures obtained in the ED, demonstrating that SCR was the most significant predictor of the chronic PTSD trajectory (p<0.00001). Conclusions: The current study is the first prospective study of PTSD showing SCR in the immediate aftermath of trauma predicts subsequent development of chronic PTSD. This finding points to an easily obtained, and neurobiologically informative, biomarker in emergency departments that can be disseminated to predict the development of PTSD.