Towards precision medicine for stress disorders: diagnostic biomarkers and targeted drugs

Elevated SCN11A concentrations associated with lower serum lipid levels in patients with major depressive disorder

The pathogenesis of major depressive disorder (MDD) involves lipid metabolism. Our earlier research also revealed that MDD patients had much lower total cholesterol (TC) concentrations than healthy controls (HCs). However, it is still unclear why TC decreased in MDD. Here, based on the Ingenuity Knowledge Base's ingenuity pathway analysis, we found that sodium voltage-gated channel alpha subunit 11A (SCN11A) might serve as a link between low lipid levels and MDD. We analyzed the TC levels and used ELISA kits to measure the levels of SCN11A in the serum from 139 MDD patients, and 65 HCs to confirm this theory and explore the potential involvement of SCN11A in MDD. The findings revealed that TC levels were considerably lower and SCN11A levels were remarkably increased in MDD patients than those in HCs, while they were significantly reversed in drug-treatment MDD patients than in drug-naïve MDD patients. There was no significant difference in SCN11A levels among MDD patients who used single or multiple antidepressants, and selective serotonin reuptake inhibitors or other antidepressants. Pearson correlation analysis showed that the levels of TC and SCN11A were linked with the Hamilton Depression Rating Scales score. A substantial association was also found between TC and SCN11A. Moreover, a discriminative model made up of SCN11A was discovered, which produced an area under a curve of 0.9571 in the training set and 0.9357 in the testing set. Taken together, our findings indicated that SCN11A may serve as a link between low lipid levels and MDD, and showed promise as a candidate biomarker for MDD.

Precision medicine for psychotic disorders: objective assessment, risk prediction, and pharmacogenomics

Psychosis occurs inside the brain, but may have external manifestations (peripheral molecular biomarkers, behaviors) that can be objectively and quantitatively measured. Blood biomarkers that track core psychotic manifestations such as hallucinations and delusions could provide a window into the biology of psychosis, as well as help with diagnosis and treatment. We endeavored to identify objective blood gene expression biomarkers for hallucinations and delusions, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We were successful in identifying biomarkers that were predictive of high hallucinations and of high delusions states, and of future psychiatric hospitalizations related to them, more so when personalized by gender and diagnosis. Top biomarkers for hallucinations that survived discovery, prioritization, validation and testing include PPP3CB, DLG1, ENPP2, ZEB2, and RTN4. Top biomarkers for delusions include AUTS2, MACROD2, NR4A2, PDE4D, PDP1, and RORA. The top biological pathways uncovered by our work are glutamatergic synapse for hallucinations, as well as Rap1 signaling for delusions. Some of the biomarkers are targets of existing drugs, of potential utility in pharmacogenomics approaches (matching patients to medications, monitoring response to treatment). The top biomarkers gene expression signatures through bioinformatic analyses suggested a prioritization of existing medications such as clozapine and risperidone, as well as of lithium, fluoxetine, valproate, and the nutraceuticals omega-3 fatty acids and magnesium. Finally, we provide an example of how a personalized laboratory report for doctors would look. Overall, our work provides advances for the improved diagnosis and treatment for schizophrenia and other psychotic disorders.

Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.

Evaluating the prospective utility of pharmacogenetics reporting among Canadian Armed Forces personnel receiving pharmacotherapy: a preliminary assessment towards precision psychiatric care

Pharmacological interventions for treating posttraumatic stress disorder in Canadian Armed Forces (CAF) members and Veterans often achieve modest results. The field of pharmacogenetics, or the study of how genes influence an individual's response to different medications, offers insight into how prior knowledge of gene-drug interactions may potentially improve the trial-and-error process of drug selection in pharmacotherapy, thereby improving treatment effects and remission rates. Given the relative recency of pharmacogenetics testing and sparse research in military samples, we used pharmacogenetics testing in a small pilot group (n=23) of CAF members and Veterans who were already engaged in pharmacotherapy for a service-related mental health condition to better understand the associated opportunities and challenges of pharmacogenetics testing in this population. Our preliminary evaluation involved: (1) reporting the prevalence of pharmacogenetics testing 'bin' status according to participants' reports ('green', 'yellow' or 'red'; intending to signal 'go', 'caution' or 'stop', regarding the potential for gene-drug interactions); (2) calculating the percentage of currently prescribed psychotropic medications that were assessed and included in the reports; (3) evaluating whether prescribers used pharmacogenetics testing information according to clinical notes and (4) collecting informal feedback from participating psychiatrists. While pharmacogenetics testing appeared to provide valuable information for a number of clients, a major limitation was the number of commonly prescribed medications not included in the reports.

Towards precision medicine for anxiety disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

Anxiety disorders are increasingly prevalent, affect people's ability to do things, and decrease quality of life. Due to lack of objective tests, they are underdiagnosed and sub-optimally treated, resulting in adverse life events and/or addictions. We endeavored to discover blood biomarkers for anxiety, using a four-step approach. First, we used a longitudinal within-subject design in individuals with psychiatric disorders to discover blood gene expression changes between self-reported low anxiety and high anxiety states. Second, we prioritized the list of candidate biomarkers with a Convergent Functional Genomics approach using other evidence in the field. Third, we validated our top biomarkers from discovery and prioritization in an independent cohort of psychiatric subjects with clinically severe anxiety. Fourth, we tested these candidate biomarkers for clinical utility, i.e. ability to predict anxiety severity state, and future clinical worsening (hospitalizations with anxiety as a contributory cause), in another independent cohort of psychiatric subjects. We showed increased accuracy of individual biomarkers with a personalized approach, by gender and diagnosis, particularly in women. The biomarkers with the best overall evidence were GAD1, NTRK3, ADRA2A, FZD10, GRK4, and SLC6A4. Finally, we identified which of our biomarkers are targets of existing drugs (such as a valproate, omega-3 fatty acids, fluoxetine, lithium, sertraline, benzodiazepines, and ketamine), and thus can be used to match patients to medications and measure response to treatment. We also used our biomarker gene expression signature to identify drugs that could be repurposed for treating anxiety, such as estradiol, pirenperone, loperamide, and disopyramide. Given the detrimental impact of untreated anxiety, the current lack of objective measures to guide treatment, and the addiction potential of existing benzodiazepines-based anxiety medications, there is a urgent need for more precise and personalized approaches like the one we developed.

LRFN5 and OLFM4 as novel potential biomarkers for major depressive disorder: a pilot study

Evidences have shown that both LRFN5 and OLFM4 can regulate neural development and synaptic function. Recent genome-wide association studies on major depressive disorder (MDD) have implicated LRFN5 and OLFM4, but their expressions and roles in MDD are still completely unclear. Here, we examined serum concentrations of LRFN5 and OLFM4 in 99 drug-naive MDD patients, 90 drug-treatment MDD patients, and 81 healthy controls (HCs) using ELISA methods. The results showed that both LRFN5 and OLFM4 levels were considerably higher in MDD patients compared to HCs, and were significantly lower in drug-treatment MDD patients than in drug-naive MDD patients. However, there were no significant differences between MDD patients who received a single antidepressant and a combination of antidepressants. Pearson correlation analysis showed that they were associated with the clinical data, including Hamilton Depression Scale score, age, duration of illness, fasting blood glucose, serum lipids, and hepatic, renal, or thyroid function. Moreover, these two molecules both yielded fairly excellent diagnostic performance in diagnosing MDD. In addition, a combination of LRFN5 and OLFM4 demonstrated a better diagnostic effectiveness, with an area under curve of 0.974 in the training set and 0.975 in the testing set. Taken together, our data suggest that LRFN5 and OLFM4 may be implicated in the pathophysiology of MDD and the combination of LRFN5 and OLFM4 may offer a diagnostic biomarker panel for MDD.

Applications of artificial intelligence-machine learning for detection of stress: a critical overview

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.

Transcriptome-wide analysis reveals the molecular mechanisms of cannabinoid type II receptor agonists in cardiac injury induced by chronic psychological stress

Background: Growing evidence has supported that chronic psychological stress would cause heart damage, However the mechanisms involved are not clear and effective interventions are insufficient. Cannabinoid type 2 receptor (CB2R) can be a potential treatment for cardiac injury. This study is aimed to investigate the protective mechanism of CB2R agonist against chronic psychological stress-induced cardiac injury. Methods: A mouse chronic psychological stress model was constructed based on a chronic unpredictable stress pattern. Mice were performed a three-week psychological stress procedure, and cardiac tissues of them were collected for whole-transcriptome sequencing. Overlap analysis was performed on differentially expressed mRNAs (DE-mRNAs) and ER stress-related genes (ERSRGs), and bioinformatic methods were used to predict the ceRNA networks and conduct pathway analysis. The expressions of the DE-ERSRGs were validated by RT-qPCR. Results: In the comparison of DE mRNA in Case group, Control group and Treatment group, three groups of ceRNA networks and ceRNA (circ) networks were constructed. The DE-mRNAs were mainly enriched in chromatid-relevant terms and Hematopoietic cell lineage pathway. Additionally, 13 DE-ERSRGs were obtained by the overlap analysis, which were utilized to establish a ceRNA network with 15 nodes and 14 edges and a ceRNA (circ) network with 23 nodes and 28 edges. Furthermore, four DE-ERSRGs (Cdkn1a, Atf3, Fkbp5, Gabarapl1) in the networks were key, which were mainly enriched in response to extracellular stimulus, response to nutrient levels, cellular response to external stimulus, and FoxO signaling pathway. Finally, the RT-qPCR results showed almost consistent expression patterns of 13 DE-ERSRGs between the transcriptome and tissue samples. Conclusion: The findings of this study provide novel insights into the molecular mechanisms of chronic psychological stress-induced cardiac diseases and reveal novel targets for the cardioprotective effects of CB2R agonists.

Methylation in MAD1L1 is associated with the severity of suicide attempt and phenotypes of depression

Depression is a multifactorial disorder representing a significant public health burden. Previous studies have linked multiple single nucleotide polymorphisms with depressive phenotypes and suicidal behavior. MAD1L1 is a mitosis metaphase checkpoint protein that has been linked to depression in GWAS. Using a longitudinal EWAS approach in an adolescent cohort at two time points (n = 216 and n = 154), we identified differentially methylated sites that were associated with depression-related genetic variants in MAD1L1. Three methylation loci (cg02825527, cg18302629, and cg19624444) were consistently hypomethylated in the minor allele carriers, being cross-dependent on several SNPs. We further investigated whether DNA methylation at these CpGs is associated with depressive psychiatric phenotypes in independent cohorts. The first site (cg02825527) was hypomethylated in blood (exp(β) = 84.521, p value ~ 0.003) in participants with severe suicide attempts (n = 88). The same locus showed increased methylation in glial cells (exp(β) = 0.041, p value ~ 0.004) in the validation cohort, involving 29 depressed patients and 29 controls, and showed a trend for association with suicide (n = 40, p value ~ 0.089) and trend for association with depression treatment (n = 377, p value ~ 0.075). The second CpG (cg18302629) was significantly hypomethylated in depressed participants (exp(β) = 56.374, p value ~ 0.023) in glial cells, but did not show associations in the discovery cohorts. The last methylation site (cg19624444) was hypomethylated in the whole blood of severe suicide attempters; however, this association was at the borderline for statistical significance (p value ~ 0.061). This locus, however, showed a strong association with depression treatment in the validation cohort (exp(β) = 2.237, p value ~ 0.003) with 377 participants. The direction of associations between psychiatric phenotypes appeared to be different in the whole blood in comparison with brain samples for cg02825527 and cg19624444. The association analysis between methylation at cg18302629 and cg19624444 and MAD1L1 transcript levels in CD¹⁴⁺ cells shows a potential link between methylation at these CpGs and MAD1L1 expression. This study suggests evidence that methylation at MAD1L1 is important for psychiatric health as supported by several independent cohorts.

MANF/EWSR1/ANXA6 pathway might as the bridge between hypolipidemia and major depressive disorder

Major depressive disorder (MDD) involves changes in lipid metabolism, but previous findings are contradictory. Mesencephalic astrocyte-derived neurotrophic factor (MANF) is considered to be a regulator of lipid metabolism. To date, the function of MANF has been studied in many brain disorders, but not in MDD. Therefore, to better understand the role of lipids in MDD, this study was conducted to examine lipid levels in the serum of MDD patients and to investigate the potential function of MANF in MDD. First, the data on total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) in serum from 354 MDD patients and 360 healthy controls (HCs) were collected and analyzed. The results showed that there were significantly lower concentrations of TC and LDL-C in MDD patients compared with HCs, and TC levels were positively correlated with LDL-C levels. Bioinformatics analysis indicated that MANF/EWSR1/ANXA6 pathway might serve as the connecting bridge through which hypolipidemia played a functional role in MDD. Second, to verify this hypothesis, serum samples were collected from 143 MDD patients, and 67 HCs to measure the levels of MANF, EWSR1, and ANXA6 using ELISA kits. The results showed that compared to HCs, MDD patients had a significantly lower level of MANF and higher levels of ANXA6 and EWSR1, and these molecules were significantly correlated with both TC level and Hamilton Depression Rating Scales (HDRS) score. In addition, a discriminative model consisting of MANF, EWSR1, and ANXA6 was identified. This model was capable of distinguishing MDD subjects from HCs, yielded an area under curve of 0.9994 in the training set and 0.9569 in the testing set. Taken together, our results suggested that MANF/EWSR1/ANXA6 pathway might act as the bridge between hypolipidemia and MDD, and these molecules held promise as potential biomarkers for MDD.

Altered gene expression and PTSD symptom dimensions in World Trade Center responders

Despite experiencing a significant trauma, only a subset of World Trade Center (WTC) rescue and recovery workers developed posttraumatic stress disorder (PTSD). Identification of biomarkers is critical to the development of targeted interventions for treating disaster responders and potentially preventing the development of PTSD in this population. Analysis of gene expression from these individuals can help in identifying biomarkers of PTSD. We established a well-phenotyped sample of 371 WTC responders, recruited from a longitudinal WTC responder cohort using stratified random sampling, by obtaining blood, self-reported and clinical interview data. Using bulk RNA-sequencing from whole blood, we examined the association between gene expression and WTC-related PTSD symptom severity on (i) highest lifetime Clinician-Administered PTSD Scale (CAPS) score, (ii) past-month CAPS score, and (iii) PTSD symptom dimensions using a 5-factor model of re-experiencing, avoidance, emotional numbing, dysphoric arousal and anxious arousal symptoms. We corrected for sex, age, genotype-derived principal components and surrogate variables. Finally, we performed a meta-analysis with existing PTSD studies (total N = 1016), using case/control status as the predictor and correcting for these variables. We identified 66 genes significantly associated with total highest lifetime CAPS score (FDR-corrected p < 0.05), and 31 genes associated with total past-month CAPS score. Our more granular analyses of PTSD symptom dimensions identified additional genes that did not reach statistical significance in our analyses with total CAPS scores. In particular, we identified 82 genes significantly associated with lifetime anxious arousal symptoms. Several genes significantly associated with multiple PTSD symptom dimensions and total lifetime CAPS score (SERPINA1, RPS6KA1, and STAT3) have been previously associated with PTSD. Geneset enrichment of these findings has identified pathways significant in metabolism, immune signaling, other psychiatric disorders, neurological signaling, and cellular structure. Our meta-analysis revealed 10 genes that reached genome-wide significance, all of which were downregulated in cases compared to controls (CIRBP, TMSB10, FCGRT, CLIC1, RPS6KB2, HNRNPUL1, ALDOA, NACA, ZNF429 and COPE). Additionally, cellular deconvolution highlighted an enrichment in CD4 T cells and eosinophils in responders with PTSD compared to controls. The distinction in significant genes between total lifetime CAPS score and the anxious arousal symptom dimension of PTSD highlights a potential biological difference in the mechanism underlying the heterogeneity of the PTSD phenotype. Future studies should be clear about methods used to analyze PTSD status, as phenotypes based on PTSD symptom dimensions may yield different gene sets than combined CAPS score analysis. Potential biomarkers implicated from our meta-analysis may help improve therapeutic target development for PTSD.

How to Mitigate Risk of Premature Cardiovascular Disease Among Children and Adolescents with Mental Health Conditions

Purpose of Review

The goal of this article is to characterize the myriad of ways that children with mental health conditions can be at risk for premature cardiovascular disease (CVD) and various modalities to ameliorate this risk in childhood in order to improve the life course of these children.

Review Findings

Child and adolescent mental health conditions are a common yet underrecognized risk factor for premature CVD. The American Heart Association has recently included psychiatric conditions as a CVD risk factor (CVDRF) and the evidence linking childhood adversity to cardiometabolic disease. There are bidirectional and additive effects from the intrinsic emotional dysregulation and inflammatory changes from the mental health condition, the associations with risky health behaviors, and in some cases, metabolic side effects from pharmacotherapy. These pathways can be potentiated by toxic stress, a physiologic response to stressors from childhood adversity. Toxic stress is also associated with development of mental health conditions with epigenetic effects that can result in transgenerational inheritance of cardiometabolic risk. Exposure to toxic stress and mental health conditions in isolation sometimes compounded by pharmacotherapies used in treatment increase the risk of cardiometabolic diseases in childhood. The multiple pathways, which adversely influence cardiometabolic outcomes, encourage clinicians to consider strategies to mitigate these factors and justify the importance of early screening and treatment for CVDRFs.

Summary

Mental health, health behaviors, and environmental factors co-occur and intersect in complex pathways that can increase CVD risk over the lifespan. Early detection and response can mitigate the risks associated with premature development of CVD.

Adaptation of Lipid Profiling in Depression Disease and Treatment: A Critical Review

Major depressive disorder (MDD), also called depression, is a serious disease that impairs the quality of life of patients and has a high incidence, affecting approximately 3.8% of the world population. Its diagnosis is very subjective and is not supported by measurable biomarkers mainly due to the lack of biochemical markers. Recently, disturbance of lipid profiling has been recognized in MDD, in animal models of MDD or in depressed patients, which may contribute to unravel the etiology of the disease and find putative new biomarkers, for a diagnosis or for monitoring the disease and therapeutics outcomes. In this review, we provide an overview of current knowledge of lipidomics analysis, both in animal models of MDD (at the brain and plasma level) and in humans (in plasma and serum). Furthermore, studies of lipidomics analyses after antidepressant treatment in rodents (in brain, plasma, and serum), in primates (in the brain) and in humans (in plasma) were reviewed and give evidence that antidepressants seem to counteract the modification seen in lipids in MDD, giving some evidence that certain altered lipid profiles could be useful MDD biomarkers for future precision medicine.

The Molecular Basis of Resilience: A Narrative Review

Resilience refers to the adaptability of a person - an ability to "bounce-back" from stressors. We question if resilience can be strengthened, potentially to decrease the risk of stress-related disorders. Unfortunately, the molecular origins of resilience are complicated and not yet well understood. In this review, we examine the various physiological biomarkers of resilience, including the associated genes, epigenetic changes, and protein biomarkers associated with resilient phenotypes. In addition to assessing biomarkers that may indicate higher levels of resilience, we also review at length the many biomarkers that confer lower levels of resilience and may lead to disorders of low resilience, such as anxiety and depression. This large and encompassing review may help to identify the possible therapeutic targets of resilience. Hopefully these studies will lead to a future where stress-related disorders can be prevented, rather than treated.

Longitudinal telomere length profile does not reflect HIV and childhood trauma impacts on cognitive function in South African women

HIV-associated neurocognitive disorders (HAND) present a challenge in South Africa where the burden of HIV infection is the highest. Identification of biological correlates of HAND is required to improve diagnosis and inform interventions. Telomeres maintain genomic integrity and their shortening is a marker of biological aging sensitive to environmental influences. This study examined relative telomere length (rTL) as a predictor of cognitive function in the context of HIV and childhood trauma (CT), a risk factor for HAND. Two hundred and eighty-six women completed a neurocognitive assessment battery and the Childhood Trauma Questionnaire-Short Form (CTQ). Quantitative polymerase chain reaction for amplification of telomeric repeats and the reference gene human beta-globin was used to calculate rTL. Neurocognitive and rTL assessments were repeated at 1 year in 110 participants. Cross-sectional and longitudinal data were assessed using linear and mixed models, respectively. Participants with HIV (n = 135 in cross-sectional and n = 62 in longitudinal study groups) reported more severe CT and had shorter baseline rTL compared to seronegative controls. Participants without HIV had a greater 1-year decline in rTL. Global cognitive and attention/working memory scores declined in participants with HIV. Our data indicate that baseline rTL in the context of CT and HIV did not predict decline in cognitive scores. HIV-associated pathophysiological processes driving cognitive decline may also engage mechanisms that protect against telomere shortening. The results highlight the importance of examining biological correlates in longitudinal studies.

Trauma-Informed Care

Most children will experience some type of trauma during childhood, and many children suffer from significant adversities. Research in genetics, neuroscience, and epidemiology all provide evidence that these experiences have effects at the molecular, cellular, and organ level, with consequences on physical, emotional, developmental, and behavioral health across the life span. Trauma-informed care translates that science to inform and improve pediatric care and outcomes. To practically address trauma and promote resilience, pediatric clinicians need tools to assess childhood trauma and adversity experiences as well as practical guidance, resources, and interventions. In this clinical report, we summarize current, practical advice for rendering trauma-informed care across varied medical settings.

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

Transcriptomic analysis plays a key role in biomedical research. Linear dimensionality reduction methods, especially principal-component analysis (PCA), are widely used in detecting sample-to-sample heterogeneity, while recently developed non-linear methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), can efficiently cluster heterogeneous samples in single-cell RNA sequencing analysis. Yet, the application of t-SNE and UMAP in bulk transcriptomic analysis and comparison with conventional methods have not been achieved. We compare four major dimensionality reduction methods (PCA, multidimensional scaling [MDS], t-SNE, and UMAP) in analyzing 71 large bulk transcriptomic datasets. UMAP is superior to PCA and MDS but shows some advantages over t-SNE in differentiating batch effects, identifying pre-defined biological groups, and revealing in-depth clusters in two-dimensional space. Importantly, UMAP generates sample clusters uncovering biological features and clinical meaning. We recommend deploying UMAP in visualizing and analyzing sizable bulk transcriptomic datasets to reinforce sample heterogeneity analysis.

Profiles of Emotion Regulation and Post-Traumatic Stress Severity among Female Victims of Intimate Partner Violence

Emotional dysregulation is a construct that has drawn substantial attention as a transdiagnostic contributing factor to the loss of health. Intimate partner violence (IPV) is a term used to describe physical, psychological, or sexual assault of a spouse or sexual partner. The aim of the study was to determine the variability of emotional dysregulation among women with different types of IPV revictimization and post-traumatic stress. The cross-sectional survey included 120 women attended by the Integrated Monitoring System of Gender Violence of Madrid, Spain, due to a gender violence complaint. The presence of post-traumatic stress disorder (DSM 5 criteria), emotional dysregulation (Emotional Processing Scale (EPS)), childhood trauma, and type of revictimization were evaluated. Cluster analysis found three profiles of emotional regulation: Emotionally Regulated, Avoidance/Non-Impoverished, and Emotional Overwhelm. The results showed that the Emotional Overwhelm group was characterized by a general dysregulation of emotional experiences and a greater intensity of post-traumatic stress symptoms. In addition, women who have suffered several episodes of IPV by different partners showed a differential pattern of emotional regulation than the rest of the victims that entailed greater psychopathology. Findings confirm that emotional dysregulation is a critical pathway to the decrease of health among IPV victims.

Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program

We conducted genome-wide association analyses of over 250,000 participants of European (EUR) and African (AFR) ancestry from the Million Veteran Program using electronic health record-validated post-traumatic stress disorder (PTSD) diagnosis and quantitative symptom phenotypes. Applying genome-wide multiple testing correction, we identified three significant loci in European case-control analyses and 15 loci in quantitative symptom analyses. Genomic structural equation modeling indicated tight coherence of a PTSD symptom factor that shares genetic variance with a distinct internalizing (mood-anxiety-neuroticism) factor. Partitioned heritability indicated enrichment in several cortical and subcortical regions, and imputed genetically regulated gene expression in these regions was used to identify potential drug repositioning candidates. These results validate the biological coherence of the PTSD syndrome, inform its relationship to comorbid anxiety and depressive disorders and provide new considerations for treatment.

The function of apolipoproteins L (APOLs): relevance for kidney disease, neurotransmission disorders, cancer and viral infection

The discovery that apolipoprotein L1 (APOL1) is the trypanolytic factor of human serum raised interest about the function of APOLs, especially following the unexpected finding that in addition to their protective action against sleeping sickness, APOL1 C-terminal variants also cause kidney disease. Based on the analysis of the structure and trypanolytic activity of APOL1, it was proposed that APOLs could function as ion channels of intracellular membranes and be involved in mechanisms triggering programmed cell death. In this review, the recent finding that APOL1 and APOL3 inversely control the synthesis of phosphatidylinositol-4-phosphate (PI(4)P) by the Golgi PI(4)-kinase IIIB (PI4KB) is commented. APOL3 promotes Ca2+ -dependent activation of PI4KB, but due to their increased interaction with APOL3, APOL1 C-terminal variants can inactivate APOL3, leading to reduction of Golgi PI(4)P synthesis. The impact of APOLs on several pathological processes that depend on Golgi PI(4)P levels is discussed. I propose that through their effect on PI4KB activity, APOLs control not only actomyosin activities related to vesicular trafficking, but also the generation and elongation of autophagosomes induced by inflammation.

Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

Mood disorders (depression, bipolar disorders) are prevalent and disabling. They are also highly co-morbid with other psychiatric disorders. Currently there are no objective measures, such as blood tests, used in clinical practice, and available treatments do not work in everybody. The development of blood tests, as well as matching of patients with existing and new treatments, in a precise, personalized and preventive fashion, would make a significant difference at an individual and societal level. Early pilot studies by us to discover blood biomarkers for mood state were promising [1], and validated by others [2]. Recent work by us has identified blood gene expression biomarkers that track suicidality, a tragic behavioral outcome of mood disorders, using powerful longitudinal within-subject designs, validated them in suicide completers, and tested them in independent cohorts for ability to assess state (suicidal ideation), and ability to predict trait (future hospitalizations for suicidality) [3-6]. These studies showed good reproducibility with subsequent independent genetic studies [7]. More recently, we have conducted such studies also for pain [8], for stress disorders [9], and for memory/Alzheimer's Disease [10]. We endeavored to use a similar comprehensive approach to identify more definitive biomarkers for mood disorders, that are transdiagnostic, by studying mood in psychiatric disorders patients. First, we used a longitudinal within-subject design and whole-genome gene expression approach to discover biomarkers which track mood state in subjects who had diametric changes in mood state from low to high, from visit to visit, as measured by a simple visual analog scale that we had previously developed (SMS-7). Second, we prioritized these biomarkers using a convergent functional genomics (CFG) approach encompassing in a comprehensive fashion prior published evidence in the field. Third, we validated the biomarkers in an independent cohort of subjects with clinically severe depression (as measured by Hamilton Depression Scale, (HAMD)) and with clinically severe mania (as measured by the Young Mania Rating Scale (YMRS)). Adding the scores from the first three steps into an overall convergent functional evidence (CFE) score, we ended up with 26 top candidate blood gene expression biomarkers that had a CFE score as good as or better than SLC6A4, an empirical finding which we used as a de facto positive control and cutoff. Notably, there was among them an enrichment in genes involved in circadian mechanisms. We further analyzed the biological pathways and networks for the top candidate biomarkers, showing that circadian, neurotrophic, and cell differentiation functions are involved, along with serotonergic and glutamatergic signaling, supporting a view of mood as reflecting energy, activity and growth. Fourth, we tested in independent cohorts of psychiatric patients the ability of each of these 26 top candidate biomarkers to assess state (mood (SMS-7), depression (HAMD), mania (YMRS)), and to predict clinical course (future hospitalizations for depression, future hospitalizations for mania). We conducted our analyses across all patients, as well as personalized by gender and diagnosis, showing increased accuracy with the personalized approach, particularly in women. Again, using SLC6A4 as the cutoff, twelve top biomarkers had the strongest overall evidence for tracking and predicting depression after all four steps: NRG1, DOCK10, GLS, PRPS1, TMEM161B, GLO1, FANCF, HNRNPDL, CD47, OLFM1, SMAD7, and SLC6A4. Of them, six had the strongest overall evidence for tracking and predicting both depression and mania, hence bipolar mood disorders. There were also two biomarkers (RLP3 and SLC6A4) with the strongest overall evidence for mania. These panels of biomarkers have practical implications for distinguishing between depression and bipolar disorder. Next, we evaluated the evidence for our top biomarkers being targets of existing psychiatric drugs, which permits matching patients to medications in a targeted fashion, and the measuring of response to treatment. We also used the biomarker signatures to bioinformatically identify new/repurposed candidate drugs. Top drugs of interest as potential new antidepressants were pindolol, ciprofibrate, pioglitazone and adiphenine, as well as the natural compounds asiaticoside and chlorogenic acid. The last 3 had also been identified by our previous suicidality studies. Finally, we provide an example of how a report to doctors would look for a patient with depression, based on the panel of top biomarkers (12 for depression and bipolar, one for mania), with an objective depression score, risk for future depression, and risk for bipolar switching, as well as personalized lists of targeted prioritized existing psychiatric medications and new potential medications. Overall, our studies provide objective assessments, targeted therapeutics, and monitoring of response to treatment, that enable precision medicine for mood disorders.

Deficits across multiple behavioral domains align with susceptibility to stress in 129S1/SvImJ mice

Acute physical or psychological stress can elicit adaptive behaviors that allow an organism maintain homeostasis. However, intense and/or prolonged stressors often have the opposite effect, resulting in maladaptive behaviors and curbing goal-directed action; in the extreme, this may contribute to the development of psychiatric conditions like generalized anxiety disorder, major depressive disorder, or post-traumatic stress disorder. While treatment of these disorders generally focuses on reducing reactivity to potentially threatening stimuli, there are in fact impairments across multiple domains including valence, arousal, and cognition. Here, we use the genetically stress-susceptible 129S1 mouse strain to explore the effects of stress across multiple domains. We find that 129S1 mice exhibit a potentiated neuroendocrine response across many environments and paradigms, and that this is associated with reduced exploration, neophobia, decreased novelty- and reward-seeking, and spatial learning and memory impairments. Taken together, our results suggest that the 129S1 strain may provide a useful model for elucidating mechanisms underlying myriad aspects of stress-linked psychiatric disorders as well as potential treatments that may ameliorate symptoms.

Current methods for stress marker detection in saliva

•

Introduction of relevant biomarkers in stress conditions.

•

Reference ranges of biomarkers in normal conditions.

•

Saliva as easy-accessible specimen.

•

Review of analytical methods for biomarker determination in saliva.

•

Possibilities for design of point-of-care devices.

Stress and stress-related diseases are leading to drastic consequences in private and professional life. Therefore, the need for stress prevention strategies is of personal and economic interest. Especially during the recent period related to covid-19 outbreak and lock-down, an ongoing discussion of increasing stress etiology is reported. Biomarker analysis may help to assist diagnosis and classification of stress-related diseases and therefore support therapeutical decisions. Due to its non-invasive sampling, the analysis of saliva has become highly attractive compared to the detection methods in other specimen. This review article summarizes the status of research, innovative approaches, and trends. Scientific literature published since 2011 was excerpted with concentration on the detection of up to seven promising marker substances. Most often reported cortisol represents the currently best evaluated stress marker, while norepinephrine (noradrenaline) or its metabolite 3-methoxy-4-hydroxyphenylglycol is also a quite commonly considered stress marker. Other complementary stress marker candidates are testosterone, dehydroepiandrosterone (DHEA) and its sulfonated analogue DHEA-S, alpha-amylase, secretory immunoglobulin A, and chromogranin A. Several working groups are researching in the field of stress marker detection to develop reliable, fast, and affordable methods. Analytical methods reported mainly focused on immunological and electrochemical as well as chromatographic methods hyphenated to mass spectrometric detection to yield the required detection limits.

Convergent Functional Genomics approach to prioritize molecular targets of risk in early life stress-related psychiatric disorders

There is an overwhelming evidence proving that mental disorders are not the product of a single risk factor - i.e. genetic variants or environmental factors, including exposure to maternal perinatal mental health problems or childhood adverse events - rather the product of a trajectory of cumulative and multifactorial insults occurring during development, such as exposures during the foetal life to adverse mental condition in the mother, or exposures to adverse traumatic events during childhood or adolescence. In this review, we aim to highlight the potential utility of a Convergent Functional Genomics (CFG) approach to clarify the complex brain-relevant molecular mechanisms and alterations induced by early life stress (ELS). We describe different studies based on CFG in psychiatry and neuroscience, and we show how this 'hypothesis-free' tool can prioritize a stringent number of genes modulated by ELS, that can be tested as potential candidates for Gene x Environment (GxE) interaction studies. We discuss the results obtained by using a CFG approach identifying FoxO1 as a gene where genetic variability can mediate the effect of an adverse environment on the development of depression. Moreover, we also demonstrate that FoxO1 has a functional relevance in stress-induced reduction of neurogenesis, and can be a potential target for the prevention or treatment of stress-related psychiatric disorders. Overall, we suggest that CFG approach could include trans-species and tissues data integration and we also propose the application of CFG to examine in depth and to prioritize top candidate genes that are affected by ELS across lifespan and generations.

Blood biomarkers for memory: toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs

Short-term memory dysfunction is a key early feature of Alzheimer's disease (AD). Psychiatric patients may be at higher risk for memory dysfunction and subsequent AD due to the negative effects of stress and depression on the brain. We carried out longitudinal within-subject studies in male and female psychiatric patients to discover blood gene expression biomarkers that track short term memory as measured by the retention measure in the Hopkins Verbal Learning Test. These biomarkers were subsequently prioritized with a convergent functional genomics approach using previous evidence in the field implicating them in AD. The top candidate biomarkers were then tested in an independent cohort for ability to predict state short-term memory, and trait future positive neuropsychological testing for cognitive impairment. The best overall evidence was for a series of new, as well as some previously known genes, which are now newly shown to have functional evidence in humans as blood biomarkers: RAB7A, NPC2, TGFB1, GAP43, ARSB, PER1, GUSB, and MAPT. Additional top blood biomarkers include GSK3B, PTGS2, APOE, BACE1, PSEN1, and TREM2, well known genes implicated in AD by previous brain and genetic studies, in humans and animal models, which serve as reassuring de facto positive controls for our whole-genome gene expression discovery approach. Biological pathway analyses implicate LXR/RXR activation, neuroinflammation, atherosclerosis signaling, and amyloid processing. Co-directionality of expression data provide new mechanistic insights that are consistent with a compensatory/scarring scenario for brain pathological changes. A majority of top biomarkers also have evidence for involvement in other psychiatric disorders, particularly stress, providing a molecular basis for clinical co-morbidity and for stress as an early precipitant/risk factor. Some of them are modulated by existing drugs, such as antidepressants, lithium and omega-3 fatty acids. Other drug and nutraceutical leads were identified through bioinformatic drug repurposing analyses (such as pioglitazone, levonorgestrel, salsolidine, ginkgolide A, and icariin). Our work contributes to the overall pathophysiological understanding of memory disorders and AD. It also opens new avenues for precision medicine- diagnostics (assement of risk) as well as early treatment (pharmacogenomically informed, personalized, and preventive).

Longitudinal epigenome-wide association studies of three male military cohorts reveal multiple CpG sites associated with post-traumatic stress disorder

BACKGROUND

Epigenetic mechanisms have been suggested to play a role in the development of post-traumatic stress disorder (PTSD). Here, blood-derived DNA methylation data (HumanMethylation450 BeadChip) collected prior to and following combat exposure in three cohorts of male military members were analyzed to assess whether DNA methylation profiles are associated with the development of PTSD. A total of 123 PTSD cases and 143 trauma-exposed controls were included in the analyses. The Psychiatric Genomics Consortium (PGC) PTSD EWAS QC pipeline was used on all cohorts, and results were combined using a sample size weighted meta-analysis in a two-stage design. In stage one, we jointly analyzed data of two new cohorts (N = 126 and 78) for gene discovery, and sought to replicate significant findings in a third, previously published cohort (N = 62) to assess the robustness of our results. In stage 2, we aimed at maximizing power for gene discovery by combining all three cohorts in a meta-analysis.

RESULTS

Stage 1 analyses identified four CpG sites in which, conditional on pre-deployment DNA methylation, post-deployment DNA methylation was significantly associated with PTSD status after epigenome-wide adjustment for multiple comparisons. The most significant (intergenic) CpG cg05656210 (p = 1.0 × 10-08) was located on 5q31 and significantly replicated in the third cohort. In addition, 19 differentially methylated regions (DMRs) were identified, but failed replication. Stage 2 analyses identified three epigenome-wide significant CpGs, the intergenic CpG cg05656210 and two additional CpGs located in MAD1L1 (cg12169700) and HEXDC (cg20756026). Interestingly, cg12169700 had an underlying single nucleotide polymorphism (SNP) which was located within the same LD block as a recently identified PTSD-associated SNP in MAD1L1. Stage 2 analyses further identified 12 significant differential methylated regions (DMRs), 1 of which was located in MAD1L1 and 4 were situated in the human leukocyte antigen (HLA) region.

CONCLUSIONS

This study suggests that the development of combat-related PTSD is associated with distinct methylation patterns in several genomic positions and regions. Our most prominent findings suggest the involvement of the immune system through the HLA region and HEXDC, and MAD1L1 which was previously associated with PTSD.

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.

Single-nucleotide polymorphism in the human TIA1 gene interacts with stressful life events to predict the development of pathological anxiety symptoms in a Swedish population

BACKGROUND

The TIA1 gene encodes a prion-related RNA-binding protein that regulates stress-dependent synaptic plasticity and fear memory in mice. It is unknown whether genetic variation in human TIA1 is associated with differences in stress- and fear-related behavior in people.

METHODS

A longitudinal, population-based survey was conducted in Sweden to collect information on demographics, socioeconomic status, exposure to stressful life events and psychiatric symptoms. DNA samples were obtained from study participants to allow genotyping of single-nucleotide polymorphisms in the human TIA1 locus.

RESULTS

We identified a single-nucleotide polymorphism in the human TIA1 gene that interacts with exposure to previous-year stressful life events to predict the development of pathological anxiety symptoms in a non-clinical cohort.

LIMITATIONS

Sample population is limited in both size and scope, and we did not perform functional analysis of allelic variants of TIA1.

CONCLUSIONS

TIA1 may represent a susceptibility locus for stress-dependent psychopathology. These studies support an evolutionarily conserved role of TIA1 in the mammalian brain, and may provide molecular and genetic insight into the development of stress-related psychiatric conditions such as PTSD and anxiety.

Multidimensional informatic deconvolution defines gender-specific roles of hypothalamic GIT2 in aging trajectories

In most species, females live longer than males. An understanding of this female longevity advantage will likely uncover novel anti-aging therapeutic targets. Here we investigated the transcriptomic responses in the hypothalamus - a key organ for somatic aging control - to the introduction of a simple aging-related molecular perturbation, i.e. GIT2 heterozygosity. Our previous work has demonstrated that GIT2 acts as a network controller of aging. A similar number of both total (1079-female, 1006-male) and gender-unique (577-female, 527-male) transcripts were significantly altered in response to GIT2 heterozygosity in early life-stage (2 month-old) mice. Despite a similar volume of transcriptomic disruption in females and males, a considerably stronger dataset coherency and functional annotation representation was observed for females. It was also evident that female mice possessed a greater resilience to pro-aging signaling pathways compared to males. Using a highly data-dependent natural language processing informatics pipeline, we identified novel functional data clusters that were connected by a coherent group of multifunctional transcripts. From these it was clear that females prioritized metabolic activity preservation compared to males to mitigate this pro-aging perturbation. These findings were corroborated by somatic metabolism analyses of living animals, demonstrating the efficacy of our new informatics pipeline.