Ruoting Yang

Core modular blood and brain biomarkers in social defeat mouse model for post traumatic stress disorder

BackgroundPost-traumatic stress disorder (PTSD) is a severe anxiety disorder that affects a substantial portion of combat veterans and poses serious consequences to long-term health. Consequently, the identification of diagnostic and prognostic blood biomarkers for PTSD is of great interest. Previously, we assessed genome-wide gene expression of seven brain regions and whole blood in a social defeat mouse model subjected to various stress conditions.ResultsTo extract biological insights from these data, we have applied a new computational framework for identifying gene modules that are activated in common across blood and various brain regions. Our results, in the form of modular gene networks that highlight spatial and temporal biological functions, provide a systems-level molecular description of response to social stress. Specifically, the common modules discovered between the brain and blood emphasizes molecular transporters in the blood-brain barrier, and the associated genes have significant overlaps with known blood signatures for PTSD, major depression, and bipolar disease. Similarly, the common modules specific to the brain highlight the components of the social defeat stress response (e.g., fear conditioning pathways) in each brain sub-region.ConclusionsMany of the brain-specific genes discovered are consistent with previous independent studies of PTSD or other mental illnesses. The results from this study further our understanding of the mechanism of stress response and contribute to a growing list of diagnostic biomarkers for PTSD.

Whole-genome DNA methylation status associated with clinical PTSD measures of OIF/OEF veterans

Emerging knowledge suggests that post-traumatic stress disorder (PTSD) pathophysiology is linked to the patients’ epigenetic changes, but comprehensive studies examining genome-wide methylation have not been performed. In this study, we examined genome-wide DNA methylation in peripheral whole blood in combat veterans with and without PTSD to ascertain differentially methylated probes. Discovery was initially made in a training sample comprising 48 male Operation Enduring Freedom (OEF)/Operation Iraqi Freedom (OIF) veterans with PTSD and 51 age/ethnicity/gender-matched combat-exposed PTSD-negative controls. Agilent whole-genome array detected ~5600 differentially methylated CpG islands (CpGI) annotated to ~2800 differently methylated genes (DMGs). The majority (84.5%) of these CpGIs were hypermethylated in the PTSD cases. Functional analysis was performed using the DMGs encoding the promoter-bound CpGIs to identify networks related to PTSD. The identified networks were further validated by an independent test set comprising 31 PTSD+/29 PTSD− veterans. Targeted bisulfite sequencing was also used to confirm the methylation status of 20 DMGs shown to be highly perturbed in the training set. To improve the statistical power and mitigate the assay bias and batch effects, a union set combining both training and test set was assayed using a different platform from Illumina. The pathways curated from this analysis confirmed 65% of the pool of pathways mined from training and test sets. The results highlight the importance of assay methodology and use of independent samples for discovery and validation of differentially methylated genes mined from whole blood. Nonetheless, the current study demonstrates that several important epigenetically altered networks may distinguish combat-exposed veterans with and without PTSD.

Gene expression associated with suicide attempts in US veterans

According to a recent report from the Office of Suicide Prevention in the US Department of Veterans Affairs, veterans represent 8.5% of the US population, but account for 18% of all deaths from suicide. The aim of this study of psychiatric patients (n=39; 87% male) was to compare blood gene expression data from veterans with a history of one or more suicide attempts to veterans who had never attempted suicide. The attempter and non-attempter groups were matched for age and race/ethnicity, and both groups included veterans with a diverse psychiatric history that included posttraumatic stress disorder (PTSD) and substance-use disorders. Veterans were interviewed for lifetime psychiatric history, including a detailed assessment of prior suicide attempts and provided a blood sample. Results of Ingenuity Pathway Analysis (IPA) identified several pathways associated with suicide attempts, including the mammalian target of rapamycin (mTOR) and WNT signaling pathways. These pathways are of particular interest, given their role in explaining pharmacological treatments for suicidal behavior, including the use of ketamine and lithium. These results suggest that findings observed in civilians are also relevant for veterans and provide a context for interpreting results observed in post-mortem samples. In conclusion, an emerging body of work that shows consistency in findings across blood and brain samples suggests that it might be possible to identify molecular predictors of suicide attempts.

Control circuitry for fear conditioning associated with Post-Traumatic Stress Disorder (PTSD)

Post-Traumatic Stress Disorder (PTSD) is an anxiety disorder triggered by exposure to traumatic stressors. At a molecular level, traumatic stress triggers the release of neurotransmitter dopamine (DA), and the corresponding receptors take the role of sensors that signals the fear conditioning (FD) circuit. The proteins in the FD neuronal circuit in turn activate the transcription factor CREB in amygdala and Nucleus Accumbens (NAc) to counteract the stress in order to maintain the homeostasis of the system. However, a sustained excessive CREB level due to high stress results in long-lasting fear memory and social avoidance, typical symptoms of PTSD. Therefore, we hypothesize that an excessive production of CREB through the DA-CREB pathway may be one of the causes that lead to PTSD. In order to validate this hypothesis, we construct a chemical kinetic model of DA-CREB pathway in the FD circuit and subject it to both sensitivity and bifurcation analysis. Sensitivity analysis revealed a core positive feedback loop in the FD circuit that is responsible for sustained production of CREB under stressful conditions, and consistent with this analysis, bifurcation analysis also revealed the importance of this feedback loop by exhibiting bistability that causes several proteins in the FD circuit to sustain a high concentration level and attains a difficult to recovery state. This preliminary study underlines the importance of DA-CREB regulatory pathway, which when disrupted due to traumatic stress leads to PTSD symptoms.

8 – Systems Biology

This chapter provides an overview of modelling techniques for biological systems, including mechanistic and phenomenological approaches. The basic steps for building an ordinary differential equation (ODE)-based mechanistic model (i.e., structure characterization, model simulation, parameter estimation, and sensitivity analysis) are reviewed paying special attention to the case of oscillating systems. Moreover, novel statistical models for inferring differentially expressed genes, signaling pathways, functional clusters, and genetic networks are presented. Statistical tools, such as COMBINER (Core Module Biomarker Identification with Network Exploration), provide a comprehensive interaction map within and between pathways that can be translated to ODE models. Combining statistical and ODE modelling techniques can greatly enhance predictive capacity. The power of these approaches to identify biomarkers and drug target mechanisms is illustrated with application to the core mammalian feedback circuit and posttraumatic stress disorder.

Epigenetic Age in Male Combat-Exposed War Veterans: Associations with Posttraumatic Stress Disorder Status

DNA methylation patterns change with age and can be used to derive an estimate of “epigenetic age,” an indicator of biological age. Several studies have shown associations of posttraumatic stress disorder (PTSD) with worse somatic health and early mortality, raising the possibility of accelerated biological aging. This study examined associations between estimated epigenetic age and various variables in 160 male combat-exposed war veterans with (n = 79) and without PTSD (n = 81). DNA methylation was assessed in leukocyte genomic DNA using the Illumina 450K DNA methylation arrays. Epigenetic age was estimated using Horvath’s epigenetic clock algorithm and Δage (epigenetic age-chronological age) was calculated. In veterans with PTSD (Δage = 3.2), Δage was on average lower compared to those without PTSD (Δage = 5.0; p = 0.02; Cohen’s d = 0.42). This between-group difference was not explained by race/ethnicity, lifestyle factors or childhood trauma. Antidepressant use, however, explained part of the association. In the PTSD positive group, telomerase activity was negatively related to Δage (β = –0.35; p = 0.007). In conclusion, veterans with PTSD had significantly lower epigenetic age profiles than those without PTSD. Further, current antidepressant use and higher telomerase activity were related to relatively less epigenetic aging in veterans with PTSD, speculative of a mechanistic pathway that might attenuate biological aging-related processes in the context of PTSD.

A Multimetric Evaluation of Stratified Random Sampling for Classification: A Case Study

Accurate classification of biological phenotypes is an essential task for medical decision making. The selection of subjects for classifier training and validation sets is a crucial step within this task. To evaluate the impact of two approaches for subject selection—randomization and clinical balancing, we applied six classification algorithms to a highly replicated publicly available breast cancer data set. Using six performance metrics, we demonstrate that clinical balancing improves both training and validation performance for all methods on average. We also observed a smaller discrepancy between training and validation performance. Furthermore, a simple analytical argument is presented which suggests that we need only two metrics from the class of metrics based on the entries of the confusion matrix. In light of our results, we recommend: 1) clinical balancing of training and validation data to improve signal-to-noise ratio and 2) the use of multiple classification algorithms and evaluation metrics for a comprehensive evaluation of the decision making process.

Core module network construction for breast cancer metastasis

For prognostic and diagnostic purposes, it is crucial to be able to separate the group of “driver” genes and their first-degree neighbours, (i.e. “core module”) from the general “disease module”. To facilitate this task, we developed a novel computational framework COMBINER: COre Module Biomarker Identification with Network ExploRation. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. We generated a list of “driver genes” by finding the common core modules between two sets of COMBINER markers identified with different module inference protocols. Overlaying the markers on the map of “the hallmarks of cancer” and constructing a weighted regulatory network with sensitivity analysis, we validated 29 driver genes. Our results show the COMBINER framework to be a promising approach for identifying and characterizing core modules and driver genes of many complex diseases.