Integrated analysis of proteomics, epigenomics and metabolomics data revealed divergent pathway activation patterns in the recent versus chronic post-traumatic stress disorder.

Metabolomics, proteomics and DNA methylome assays, when done in tandem from the same blood sample and analyzed together, offer an opportunity to evaluate the molecular basis of post-traumatic stress disorder (PTSD) course and pathogenesis. We performed separate metabolomics, proteomics, and DNA methylome assays on blood samples from two well-characterized cohorts of 159 active duty male participants with relatively recent onset PTSD (<1.5 years) and 300 male veterans with chronic PTSD (>7 years). Analyses of the multi-omics datasets from these two independent cohorts were used to identify convergent and distinct molecular profiles that might constitute potential signatures of severity and progression of PTSD and its comorbid conditions. Molecular signatures indicative of homeostatic processes such as signaling and metabolic pathways involved in cellular remodeling, neurogenesis, molecular safeguards against oxidative stress, metabolism of polyunsaturated fatty acids, regulation of normal immune response, post-transcriptional regulation, cellular maintenance and markers of longevity were significantly activated in the active duty participants with recent PTSD. In contrast, we observed significantly altered multimodal molecular signatures associated with chronic inflammation, neurodegeneration, cardiovascular and metabolic disorders, and cellular attritions in the veterans with chronic PTSD. Activation status of signaling and metabolic pathways at the early and late timepoints of PTSD demonstrated the differential molecular changes related to homeostatic processes at its recent and multi-system syndromes at its chronic phase. Molecular alterations in the recent PTSD seem to indicate some sort of recalibration or compensatory response, possibly directed in mitigating the pathological trajectory of the disorder.

Rare copy number variation in posttraumatic stress disorder.

Posttraumatic stress disorder (PTSD) is a heritable (h2 = 24-71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029, SE = 0.005, P = 6.3 × 10-8). The genome-wide neurodevelopmental CNV burden identified explains 0.034% of the variation in PTSD symptoms. The 15q11.2 BP1-BP2 microdeletion region was significantly associated with PTSD (beta = 0.0206, SE = 0.0056, P = 0.0002). No individual significant genes interrupted by CNV were identified. 22 gene pathways related to the function of the nervous system and brain were significant in pathway analysis (FDR q < 0.05), but these associations were not significant once NDD regions were removed. A larger sample size, better detection methods, and annotated resources of CNV are needed to explore this relationship further.

Transcriptomes of Wet Skin Biopsies Predict Outcomes after Ionizing Radiation Exposure with Potential Dosimetric Applications in a Mouse Model.

Countermeasures for radiation diagnosis, prognosis, and treatment are trailing behind the proliferation of nuclear energy and weaponry. Radiation injury mechanisms at the systems biology level are not fully understood. Here, mice skin biopsies at h2, d4, d7, d21, and d28 after exposure to 1, 3, 6, or 20 Gy whole-body ionizing radiation were evaluated for the potential application of transcriptional alterations in radiation diagnosis and prognosis. Exposure to 20 Gy was lethal by d7, while mice who received 1, 3, or 6 Gy survived the 28-day time course. A Sammon plot separated samples based on survival and time points (TPs) within lethal (20 Gy) and sublethal doses. The differences in the numbers, regulation mode, and fold change of significantly differentially transcribed genes (SDTGs, p < 0.05 and FC > 2) were identified between lethal and sublethal doses, and down and upregulation dominated transcriptomes during the first post-exposure week, respectively. The numbers of SDTGs and the percentages of upregulated ones revealed stationary downregulation post-lethal dose in contrast to responses to sublethal doses which were dynamic and largely upregulated. Longitudinal up/downregulated SDTGs ratios suggested delayed and extended responses with increasing IR doses in the sublethal range and lethal-like responses in late TPs. This was supported by the distributions of common and unique genes across TPs within each dose. Several genes with potential dosimetric marker applications were identified. Immune, fibrosis, detoxification, hematological, neurological, gastric, cell survival, migration, and proliferation radiation response pathways were identified, with the majority predicted to be activated after sublethal and inactivated after lethal exposures, particularly during the first post-exposure week.

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors.

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

A DNA methylation clock associated with age-related illnesses and mortality is accelerated in men with combat PTSD.

DNA methylation patterns at specific cytosine-phosphate-guanine (CpG) sites predictably change with age and can be used to derive "epigenetic age", an indicator of biological age, as opposed to merely chronological age. A relatively new estimator, called "DNAm GrimAge", is notable for its superior predictive ability in older populations regarding numerous age-related metrics like time-to-death, time-to-coronary heart disease, and time-to-cancer. PTSD is associated with premature mortality and frequently has comorbid physical illnesses suggestive of accelerated biological aging. This is the first study to assess DNAm GrimAge in PTSD patients. We investigated the acceleration of GrimAge relative to chronological age, denoted "AgeAccelGrim" in combat trauma-exposed male veterans with and without PTSD using cross-sectional and longitudinal data from two independent well-characterized veteran cohorts. In both cohorts, AgeAccelGrim was significantly higher in the PTSD group compared to the control group (N = 162, 1.26 vs -0.57, p = 0.001 and N = 53, 0.93 vs -1.60 Years, p = 0.008), suggesting accelerated biological aging in both cohorts with PTSD. In 3-year follow-up study of individuals initially diagnosed with PTSD (N = 26), changes in PTSD symptom severity were correlated with AgeAccelGrim changes (r = 0.39, p = 0.049). In addition, the loss of CD28 cell surface markers on CD8 + T cells, an indicator of T-cell senescence/exhaustion that is associated with biological aging, was positively correlated with AgeAccelGrim, suggesting an immunological contribution to the accelerated biological aging. Overall, our findings delineate cellular correlates of biological aging in combat-related PTSD, which may help explain the increased medical morbidity and mortality seen in this disease.

Epigenetic biotypes of post-traumatic stress disorder in war-zone exposed veteran and active duty males.

Post-traumatic stress disorder (PTSD) is a heterogeneous condition evidenced by the absence of objective physiological measurements applicable to all who meet the criteria for the disorder as well as divergent responses to treatments. This study capitalized on biological diversity observed within the PTSD group observed following epigenome-wide analysis of a well-characterized Discovery cohort (N = 166) consisting of 83 male combat exposed veterans with PTSD, and 83 combat veterans without PTSD in order to identify patterns that might distinguish subtypes. Computational analysis of DNA methylation (DNAm) profiles identified two PTSD biotypes within the PTSD+ group, G1 and G2, associated with 34 clinical features that are associated with PTSD and PTSD comorbidities. The G2 biotype was associated with an increased PTSD risk and had higher polygenic risk scores and a greater methylation compared to the G1 biotype and healthy controls. The findings were validated at a 3-year follow-up (N = 59) of the same individuals as well as in two independent, veteran cohorts (N = 54 and N = 38), and an active duty cohort (N = 133). In some cases, for example Dopamine-PKA-CREB and GABA-PKC-CREB signaling pathways, the biotypes were oppositely dysregulated, suggesting that the biotypes were not simply a function of a dimensional relationship with symptom severity, but may represent distinct biological risk profiles underpinning PTSD. The identification of two novel distinct epigenetic biotypes for PTSD may have future utility in understanding biological and clinical heterogeneity in PTSD and potential applications in risk assessment for active duty military personnel under non-clinician-administered settings, and improvement of PTSD diagnostic markers.

The Genetic Basis for the Increased Prevalence of Metabolic Syndrome among Post-Traumatic Stress Disorder Patients.

Post-traumatic stress disorder (PTSD) is a highly debilitating psychiatric disorder that can be triggered by exposure to extreme trauma. Even if PTSD is primarily a psychiatric condition, it is also characterized by adverse somatic comorbidities. One illness commonly co-occurring with PTSD is Metabolic syndrome (MetS), which is defined by a set of health risk/resilience factors including obesity, elevated blood pressure, lower high-density lipoprotein cholesterol, higher low-density lipoprotein cholesterol, higher triglycerides, higher fasting blood glucose and insulin resistance. Here, phenotypic association between PTSD and components of MetS are tested on a military veteran cohort comprising chronic PTSD presentation (n = 310, 47% cases, 83% male). Consistent with previous observations, we found significant phenotypic correlation between the various components of MetS and PTSD severity scores. To examine if this observed symptom correlations stem from a shared genetic background, we conducted genetic correlation analysis using summary statistics data from large-scale genetic studies. Our results show robust positive genetic correlation between PTSD and MetS (rg[SE] = 0.33 [0.056], p = 4.74E-09), and obesity-related components of MetS (rg = 0.25, SE = 0.05, p = 6.4E-08). Prioritizing genomic regions with larger local genetic correlation implicate three significant loci. Overall, these findings show significant genetic overlap between PTSD and MetS, which may in part account for the markedly increased occurrence of MetS among PTSD patients.

PTSD is associated with increased DNA methylation across regions of HLA-DPB1 and SPATC1L.

Posttraumatic stress disorder (PTSD) is characterized by intrusive thoughts, avoidance, negative alterations in cognitions and mood, and arousal symptoms that adversely affect mental and physical health. Recent evidence links changes in DNA methylation of CpG cites to PTSD. Since clusters of proximal CpGs share similar methylation signatures, identification of PTSD-associated differentially methylated regions (DMRs) may elucidate the pathways defining differential risk and resilience of PTSD. Here we aimed to identify epigenetic differences associated with PTSD. DNA methylation data profiled from blood samples using the MethylationEPIC BeadChip were used to perform a DMR analysis in 187 PTSD cases and 367 trauma-exposed controls from the Grady Trauma Project (GTP). DMRs were assessed with R package bumphunter. We identified two regions that associate with PTSD after multiple test correction. These regions were in the gene body of HLA-DPB1 and in the promoter of SPATC1L. The DMR in HLA-DPB1 was associated with PTSD in an independent cohort. Both DMRs included CpGs whose methylation associated with nearby sequence variation (meQTL) and that associated with expression of their respective genes (eQTM). This study supports an emerging literature linking PTSD risk to genetic and epigenetic variation in the HLA region.

Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

Role of enhanced glucocorticoid receptor sensitivity in inflammation in PTSD: insights from computational model for circadian-neuroendocrine-immune interactions.

Although glucocorticoid resistance contributes to increased inflammation, individuals with posttraumatic stress disorder (PTSD) exhibit increased glucocorticoid receptor (GR) sensitivity along with increased inflammation. It is not clear how inflammation coexists with a hyperresponsive hypothalamic-pituitary-adrenal (HPA) axis. To understand this better, we developed and analyzed an integrated mathematical model for the HPA axis and the immune system. We performed mathematical simulations for a dexamethasone (DEX) suppression test and IC50-dexamethasone for cytokine suppression by varying model parameters. The model analysis suggests that increasing the steepness of the dose-response curve for GR activity may reduce anti-inflammatory effects of GRs at the ambient glucocorticoid levels, thereby increasing proinflammatory response. The adaptive response of proinflammatory cytokine-mediated stimulatory effects on the HPA axis is reduced due to dominance of the GR-mediated negative feedback on the HPA axis. To verify these hypotheses, we analyzed the clinical data on neuroendocrine variables and cytokines obtained from war-zone veterans with and without PTSD. We observed significant group differences for cortisol and ACTH suppression tests, proinflammatory cytokines TNFα and IL6, high-sensitivity C-reactive protein, promoter methylation of GR gene, and IC50-DEX for lysozyme suppression. Causal inference modeling revealed significant associations between cortisol suppression and post-DEX cortisol decline, promoter methylation of human GR gene exon 1F (NR3C1-1F), IC50-DEX, and proinflammatory cytokines. We noted significant mediation effects of NR3C1-1F promoter methylation on inflammatory cytokines through changes in GR sensitivity. Our findings suggest that increased GR sensitivity may contribute to increased inflammation; therefore, interventions to restore GR sensitivity may normalize inflammation in PTSD.

Temporal analysis of mRNA expression profiles in Orientia infected C3HeB/FeJ mouse.

BACKGROUND: Scrub typhus causes up to 35% mortality if left untreated. One billion people living in the endemic regions are at risk. In spite of its heavy disease burden in some of the most populated areas in the world, there is no vaccine available. Although the disease can be effectively treated by proper antibiotics, timely and accurate diagnosis remains a challenge. Orientia tsutsugamushi infects a variety of mammalian cells in vitro and replicates in the cytoplasm of the infected cells. Microarray analysis has been used extensively to study host-pathogen interactions in in vitro models to understand pathogenesis. However there is a lack of in vivo studies. RESULTS: In this study, C3HeB/FeJ (C3H) mice were infected by O. tsutsugamushi via the intraperitoneal route and monitored gene expression at 10 different time points post infection. We observed two distinct types of expression profiles in the genes that we analyzed. There are two valleys (4-18 h and 2-4 days) with low number of differentially expressed genes (DEG) with three peaks with high number of DEG at 2 h, 1-day and 7-day post infection. Further analysis revealed that pathways like complement and coagulation cascade, and blood clotting cascade pathways showed significant global changes throughout entire time course. Real time quantitative Polymerase Chain Reaction (RT-qPCR) confirmed the change of expression for genes involved in complement and coagulation cascade. These results suggested dynamic regulation of the complement and coagulation cascades throughout most of the time post infection while some other specific pathways, such as fatty acid metabolism and tryptophan metabolism, are turned on or off at certain times post infection. CONCLUSIONS: The findings highlight the complex interconnection among all different biological pathways. It is conceivable that specific pathways such as cell growth control and cell development in the host are affected by Orientia in the initial phase of infection for Orientia to grow intracellularly. Once Orientia is replicating successfully inside the host as infection progresses, the infection could activate pathways involved in cellular immune responses to defend for host cell survival and try to eliminate the pathogen.

Functional Heatmap: an automated and interactive pattern recognition tool to integrate time with multi-omics assays.

BACKGROUND: Life science research is moving quickly towards large-scale experimental designs that are comprised of multiple tissues, time points, and samples. Omic time-series experiments offer answers to three big questions: what collective patterns do most analytes follow, which analytes follow an identical pattern or synchronize across multiple cohorts, and how do biological functions evolve over time. Existing tools fall short of robustly answering and visualizing all three questions in a unified interface. RESULTS: Functional Heatmap offers time-series data visualization through a Master Panel page, and Combined page to answer each of the three time-series questions. It dissects the complex multi-omics time-series readouts into patterned clusters with associated biological functions. It allows users to identify a cascade of functional changes over a time variable. Inversely, Functional Heatmap can compare a pattern with specific biology respond to multiple experimental conditions. All analyses are interactive, searchable, and exportable in a form of heatmap, line-chart, or text, and the results are easy to share, maintain, and reproduce on the web platform. CONCLUSIONS: Functional Heatmap is an automated and interactive tool that enables pattern recognition in time-series multi-omics assays. It significantly reduces the manual labour of pattern discovery and comparison by transferring statistical models into visual clues. The new pattern recognition feature will help researchers identify hidden trends driven by functional changes using multi-tissues/conditions on a time-series fashion from omic assays.

Mechanistic inferences on metabolic dysfunction in posttraumatic stress disorder from an integrated model and multiomic analysis: role of glucocorticoid receptor sensitivity.

Posttraumatic stress disorder (PTSD) is associated with neuroendocrine alterations and metabolic abnormalities; however, how metabolism is affected by neuroendocrine disturbances is unclear. The data from combat-exposed veterans with PTSD show increased glycolysis to lactate flux, reduced TCA cycle flux, impaired amino acid and lipid metabolism, insulin resistance, inflammation, and hypersensitive hypothalamic-pituitary-adrenal (HPA) axis. To analyze whether the co-occurrence of multiple metabolic abnormalities is independent or arises from an underlying regulatory defect, we employed a systems biological approach using an integrated mathematical model and multiomic analysis. The models for hepatic metabolism, HPA axis, inflammation, and regulatory signaling were integrated to perform metabolic control analysis (MCA) with respect to the observations from our clinical data. We combined the metabolomics, neuroendocrine, clinical laboratory, and cytokine data from combat-exposed veterans with and without PTSD to characterize the differences in regulatory effects. MCA revealed mechanistic association of the HPA axis and inflammation with metabolic dysfunction consistent with PTSD. This was supported by the data using correlational and causal analysis that revealed significant associations between cortisol suppression, high-sensitivity C-reactive protein, homeostatic model assessment of insulin resistance, γ-glutamyltransferase, hypoxanthine, and several metabolites. Causal mediation analysis indicates that the effects of enhanced glucocorticoid receptor sensitivity (GRS) on glycolytic pathway, gluconeogenic and branched-chain amino acids, triglycerides, and hepatic function are jointly mediated by inflammation, insulin resistance, oxidative stress, and energy deficit. Our analysis suggests that the interventions to normalize GRS and inflammation may help to manage features of metabolic dysfunction in PTSD.

Molecular alterations induced by Yersinia pestis, dengue virus and Staphylococcal enterotoxin B under severe stress.

BACKGROUND: Severe stress can have drastic and systemic effects with dire implications on the health and wellbeing of exposed individuals. Particularly, the effect of stress on the immune response to infection is of interest to public health because of its implications for vaccine efficacy and treatment strategies during stressful scenarios. Severe stress has previously been shown to cause an anergic state in the immune system that persists following exposure to a potent mitogen. METHODS: Transcriptome and microRNA changes were characterized using blood samples collected from U.S. Army Ranger candidates immediately before and after training, followed by exposure to representative pathogenic agents: Yersinia pestis, dengue virus 2, and Staphylococcal enterotoxin B (SEB). We employed experimental and computational approaches to characterize altered gene expression, processes, pathways, and regulatory networks mediating the host's response towards severe stress; to assess the protective immunity status of the stressed host towards infection; and to identify pathogen-induced biomarkers under severe stress conditions. RESULTS: We observed predicted inhibition of pathways significantly associated with lymphopoiesis, wound healing, inflammatory response, lymphocyte activation, apoptosis, and predicted activation of oxidative stress. Using weighted correlation network analyses, we demonstrated preservation of these pathways across infection and stress combinations. Regulatory networks comprising a common set of upstream regulators: transcription factors, microRNAs and post-translational regulators (kinases and phosphatases) may be drivers of molecular alterations leading to compromised protective immunity. Other sets of transcripts were persistently altered in both the pre- and post-stress conditions due to the host's response to each pathogenic agent, forming specific molecular signatures with the potential to distinguish infection from that of severe stress. CONCLUSIONS: Our results suggest that severe stress alters molecules implicated in the development of leukopoietic stem cells, thereby leading to depletion of cellular and molecular repertoires of protective immunity. Suppressed molecules mediating membrane trafficking of recycling endosomes, membrane translocation and localization of the antigen processing mechanisms and cell adhesions indicate suboptimal antigen presentation, impaired formation of productive immunological synapses, and inhibited T-cell activations. These factors may collectively be responsible for compromised protective immunity (infection susceptibility, delayed wound healing, and poor vaccine response) observed in people under severe stress.

PanoromiX: a time-course network medicine platform integrating molecular assays and pathophenotypic data.

BACKGROUND: Network medicine aims to map molecular perturbations of any given diseases onto complex networks with functional interdependencies that underlie a pathological phenotype. Furthermore, investigating the time dimension of disease progression from a network perspective is key to gaining key insights to the disease process and to identify diagnostic or therapeutic targets. Existing platforms are ineffective to modularize the large complex systems into subgroups and consolidate heterogeneous data to web-based interactive animation. RESULTS: We have developed PanoromiX platform, a data-agnostic dynamic interactive visualization web application, enables the visualization of outputs from genome based molecular assays onto modular and interactive networks that are correlated with any pathophenotypic data (MRI, Xray, behavioral, etc.) over a time course all in one pane. As a result, PanoromiX reveals the complex organizing principles that orchestrate a disease-pathology from a gene regulatory network (nodes, edges, hubs, etc.) perspective instead of snap shots of assays. Without extensive programming experience, users can design, share, and interpret their dynamic networks through the PanoromiX platform with rich built-in functionalities. CONCLUSIONS: This emergent tool of network medicine is the first to visualize the interconnectedness of tailored genome assays to pathological networks and phenotypes for cells or organisms in a data-agnostic manner. As an advanced network medicine tool, PanoromiX allows monitoring of panel of biomarker perturbations over the progression of diseases, disease classification based on changing network modules that corresponds to specific patho-phenotype as opposed to clinical symptoms, systematic exploration of complex molecular interactions and distinct disease states via regulatory network changes, and the discovery of novel diagnostic and therapeutic targets.

Altered fecal microbiota composition in all male aggressor-exposed rodent model simulating features of post-traumatic stress disorder.

The bidirectional role of gut-brain axis that integrates the gut and central nervous system activities has recently been investigated. We studied "cage-within-cage resident-intruder" all-male model, where subject male mice (C57BL/6J) are exposed to aggressor mice (SJL albino), and gut microbiota-derived metabolites were identified in plasma after 10 days of exposure. We assessed 16S ribosomal RNA gene from fecal samples collected daily from these mice during the 10-day study. Alpha diversity using Chao indices indicated no change in diversity in aggressor-exposed samples. The abundance profile showed the top phyla were Firmicutes and Bacteroidetes, Tenericutes, Verrucomicrobia, Actinobacteria and Proteobacteria, respectively. The phyla Firmicutes and Bacteroidetes are vulnerable to PTSD-eliciting stress and the Firmicutes/Bacteroidetes ratio increases with stress. Principal coordinate analysis showed the control and aggressor-exposed samples cluster separately where samples from early time points (day 1-3) clustered together and were distinct from late time points (day 4-9). The genus-based analysis revealed all control time points clustered together and aggressor-exposed samples had multiple clusters. The decrease in proportion of Firmicutes after aggressor exposure persisted throughout the study. The proportion of Verrucomicrobia immediately decreased and was significantly shifted at most of the later time points. The genus Oscillospira, Lactobacillus, Akkermansia and Anaeroplasma are the top four genera that differed between control and stressor-exposed mice. The data showed immediate effect on microbiome composition during a 10 day time period of stress exposure. Studying the longitudinal effects of a stressor is an important step toward an improved mechanistic understanding of the microbiome dynamics.

Poisoning with Soman, an Organophosphorus Nerve Agent, Alters Fecal Bacterial Biota and Urine Metabolites: a Case for Novel Signatures for Asymptomatic Nerve Agent Exposure.

The experimental pathophysiology of organophosphorus (OP) chemical exposure has been extensively reported. Here, we describe an altered fecal bacterial biota and urine metabolome following intoxication with soman, a lipophilic G class chemical warfare nerve agent. Nonanesthetized Sprague-Dawley male rats were subcutaneously administered soman at 0.8 (subseizurogenic) or 1.0 (seizurogenic) of the 50% lethal dose (LD50) and evaluated for signs of toxicity. Animals were stratified based on seizing activity to evaluate effects of soman exposure on fecal bacterial biota and urine metabolites. Soman exposure reshaped fecal bacterial biota by altering Facklamia, Rhizobium, Bilophila, Enterobacter, and Morganella genera of the Firmicutes and Proteobacteria phyla, some of which are known to hydrolyze OP chemicals. However, analogous changes were not observed in the bacterial biota of the ileum, which remained the same irrespective of dose or seizing status of animals after soman intoxication. However, at 75 days after soman exposure, the bacterial biota stabilized and no differences were observed between groups. Interestingly, in considering just the seizing status of animals, we found that the urine metabolomes were markedly different. Leukotriene C4, kynurenic acid, 5-hydroxyindoleacetic acid, norepinephrine, and aldosterone were excreted at much higher rates at 72 h in seizing animals, consistent with early multiorgan involvement during soman poisoning. These findings demonstrate the feasibility of using the dysbiosis of fecal bacterial biota in combination with urine metabolome alterations as forensic evidence for presymptomatic OP exposure temporally to enable administration of neuroprotective therapies of the future.IMPORTANCE The paucity of assays to determine physiologically relevant OP exposure presents an opportunity to explore the use of fecal bacteria as sentinels in combination with urine to assess changes in the exposed host. Recent advances in sequencing technologies and computational approaches have enabled researchers to survey large community-level changes of gut bacterial biota and metabolomic changes in various biospecimens. Here, we profiled changes in fecal bacterial biota and urine metabolome following a chemical warfare nerve agent exposure. The significance of this work is a proof of concept that the fecal bacterial biota and urine metabolites are two separate biospecimens rich in surrogate indicators suitable for monitoring OP exposure. The larger value of such an approach is that assays developed on the basis of these observations can be deployed in any setting with moderate clinical chemistry and microbiology capability. This can enable estimation of the affected radius as well as screening, triage, or ruling out of suspected cases of exposures in mass casualty scenarios, transportation accidents involving hazardous materials, refugee movements, humanitarian missions, and training settings when coupled to an established and validated decision tree with clinical features.

Metabolomic analyses reveal lipid abnormalities and hepatic dysfunction in non-human primate model for Yersinia pestis.

INTRODUCTION: Pneumonic plague is caused by the aerosolized form of Yersinia pestis and is a highly virulent infection with complex clinical consequences, and without treatment, the fatality rate approaches 100%. The exact mechanisms of disease progression are unclear, with limited work done using metabolite profiling to study disease progression. OBJECTIVE: The aim of this pilot study was to profile the plasma metabolomics in an animal model of Y. pestis infection. METHODS: In this study, African Green monkeys were challenged with the highly virulent, aerosolized Y. pestis strain CO92, and untargeted metabolomics profiling of plasma was performed using liquid and gas chromatography with mass spectrometry. RESULTS: At early time points post-exposure, we found significant increases in polyunsaturated, long chain fatty acid metabolites with p values ranging from as low as 0.000001 (ratio = 1.94) for the metabolite eicosapentaenoate to 0.04 (ratio = 1.36) for the metabolite adrenate when compared to time-matched controls. Multiple acyl carnitines metabolites were increased at earlier time points and could be a result of fatty acid oxidation defects with p values ranging from as low as 0.00001 (ratio = 2.95) for the metabolite octanoylcarnitine to 0.04 (ratio = 1.33) for metabolite deoxycarnitine when compared to time-matched controls. Dicarboxylic acids are important metabolic products of fatty acids oxidation, and when compared to time matched controls, were higher at earlier time points where metabolite tetradecanedioate has a ratio of 4.09 with significant p value of 0.000002 and adipate with a ratio of 1.12 and p value of 0.004. The metabolites from lysolipids (with significant p values ranging from 0.00006 for 1-oleoylglycerophosphoethanolamine to 0.04 for 1-stearoylglycerophosphoethanolamine and a ratio of 0.47 and 0.78, respectively) and bile acid metabolism (with significant p values ranging from 0.02 for cholate to 0.04 for deoxycholate and a ratio of 0.39 and 0.66, respectively) pathways were significantly lower compared to their time-matched controls during the entire course of infection. Metabolite levels from amino acid pathways were disrupted, and a few from the leucine, isoleucine and valine pathway were significantly higher (p values ranging from 0.002 to 0.04 and ratios ranging from 1.3 to 1.5, respectively), whereas metabolites from the urea cycle, arginine and proline pathways were significantly lower (p values ranging from 0.00008 to 0.02 and ratios ranging from 0.5 to 0.7, respectively) during the course of infection. CONCLUSIONS: The involvement of several lipid pathways post-infection suggested activation of pathways linked to inflammation and oxidative stress. Metabolite data further showed increased energy demand, and multiple metabolites indicated potential hepatic dysfunction. Integration of blood metabolomics and transcriptomics data identified linoleate as a core metabolite with cross-talk with multiple genes from various time points. Collectively, the data from this study provided new insights into the mechanisms of Y. pestis pathogenesis that may aid in development of therapeutics.

Molecular evidence of stress-induced acute heart injury in a mouse model simulating posttraumatic stress disorder.

Posttraumatic stress disorder (PTSD) is a common condition induced by life-threatening stress, such as that experienced by soldiers under battlefield conditions. Other than the commonly recognized behavioral and psychological dysfunction, epidemiological studies have also revealed that PTSD patients have a higher risk of other diseases, such as cardiovascular disorders. Using a PTSD mouse model, we investigated the longitudinal transcriptomic changes in heart tissues after the exposure to stress through intimidation. Our results revealed acute heart injury associated with the traumatic experience, reflecting the underlying biological injury processes of the immune response, extracellular matrix remodeling, epithelial-to-mesenchymal cell transitions, and cell proliferation. Whether this type of injury has any long-term effects on heart function is yet to be determined. The differing responses to stress leading to acute heart injury in different inbred strains of mice also suggest that this response has a genetic as well as an environmental component. Accordingly, the results from this study suggest a molecular basis for the observed higher risk of cardiovascular disorders in PTSD patients, which raises the likelihood of cardiac dysfunction induced by long-term stress exposures.