Identifying blood biomarkers for mood disorders using convergent functional genomics

Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study

BACKGROUND

Depressive disorders have substantial global implications, leading to various social consequences, including decreased occupational productivity and a high disability burden. Early detection and intervention for clinically significant depression have gained attention; however, the existing depression screening tools, such as the Center for Epidemiologic Studies Depression Scale, have limitations in objectivity and accuracy. Therefore, researchers are identifying objective indicators of depression, including image analysis, blood biomarkers, and ecological momentary assessments (EMAs). Among EMAs, user-generated text data, particularly from diary writing, have emerged as a clinically significant and analyzable source for detecting or diagnosing depression, leveraging advancements in large language models such as ChatGPT.

OBJECTIVE

We aimed to detect depression based on user-generated diary text through an emotional diary writing app using a large language model (LLM). We aimed to validate the value of the semistructured diary text data as an EMA data source.

METHODS

Participants were assessed for depression using the Patient Health Questionnaire and suicide risk was evaluated using the Beck Scale for Suicide Ideation before starting and after completing the 2-week diary writing period. The text data from the daily diaries were also used in the analysis. The performance of leading LLMs, such as ChatGPT with GPT-3.5 and GPT-4, was assessed with and without GPT-3.5 fine-tuning on the training data set. The model performance comparison involved the use of chain-of-thought and zero-shot prompting to analyze the text structure and content.

RESULTS

We used 428 diaries from 91 participants; GPT-3.5 fine-tuning demonstrated superior performance in depression detection, achieving an accuracy of 0.902 and a specificity of 0.955. However, the balanced accuracy was the highest (0.844) for GPT-3.5 without fine-tuning and prompt techniques; it displayed a recall of 0.929.

CONCLUSIONS

Both GPT-3.5 and GPT-4.0 demonstrated relatively reasonable performance in recognizing the risk of depression based on diaries. Our findings highlight the potential clinical usefulness of user-generated text data for detecting depression. In addition to measurable indicators, such as step count and physical activity, future research should increasingly emphasize qualitative digital expression.

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.

A mitochondrial nexus in major depressive disorder: Integration with the psycho-immune-neuroendocrine network

Nervous system processes, including cognition and affective state, fundamentally rely on mitochondria. Impaired mitochondrial function is evident in major depressive disorder (MDD), reflecting cumulative detrimental influences of both extrinsic and intrinsic stressors, genetic predisposition, and mutation. Glucocorticoid 'stress' pathways converge on mitochondria; oxidative and nitrosative stresses in MDD are largely mitochondrial in origin; both initiate cascades promoting mitochondrial DNA (mtDNA) damage with disruptions to mitochondrial biogenesis and tryptophan catabolism. Mitochondrial dysfunction facilitates proinflammatory dysbiosis while directly triggering immuno-inflammatory activation via released mtDNA, mitochondrial lipids and mitochondria associated membranes (MAMs), further disrupting mitochondrial function and mitochondrial quality control, promoting the accumulation of abnormal mitochondria (confirmed in autopsy studies). Established and putative mechanisms highlight a mitochondrial nexus within the psycho-immune neuroendocrine (PINE) network implicated in MDD. Whether lowering neuronal resilience and thresholds for disease, or linking mechanistic nodes within the MDD pathogenic network, impaired mitochondrial function emerges as an important risk, a functional biomarker, providing a therapeutic target in MDD. Several treatment modalities have been demonstrated to reset mitochondrial function, which could benefit those with MDD.

Immunoglobulin genes expressed in lymphoblastoid cell lines discern and predict lithium response in bipolar disorder patients

Bipolar disorder (BD) is a neuropsychiatric mood disorder manifested by recurrent episodes of mania and depression. More than half of BD patients are non-responsive to lithium, the first-line treatment drug, complicating BD clinical management. Given its unknown etiology, it is pertinent to understand the genetic signatures that lead to variability in lithium response. We discovered a set of differentially expressed genes (DEGs) from the lymphoblastoid cell lines (LCLs) of 10 controls and 19 BD patients belonging mainly to the immunoglobulin gene family that can be used as potential biomarkers to diagnose and treat BD. Importantly, we trained machine learning algorithms on our datasets that predicted the lithium response of BD subtypes with minimal errors, even when used on a different cohort of 24 BD patients acquired by a different laboratory. This proves the scalability of our methodology for predicting lithium response in BD and for a prompt and suitable decision on therapeutic interventions.

Gene expression profiling in whole blood stimulated ex vivo with lipopolysaccharide as a tool to predict post-stroke depressive symptoms: Proof-of-concept study

Prediction of post-stroke depressive symptoms (DSs) is challenging in patients without a history of depression. Gene expression profiling in blood cells may facilitate the search for biomarkers. The use of an ex vivo stimulus to the blood helps to reveal differences in gene profiles by reducing variation in gene expression. We conducted a proof-of-concept study to determine the usefulness of gene expression profiling in lipopolysaccharide (LPS)-stimulated blood for predicting post-stroke DS. Out of 262 enrolled patients with ischemic stroke, we included 96 patients without a pre-stroke history of depression and not taking any anti-depressive medication before or during the first 3 months after stroke. We assessed DS at 3 months after stroke using the Patient Health Questionnaire-9. We used RNA sequencing to determine the gene expression profile in LPS-stimulated blood samples taken on day 3 after stroke. We constructed a risk prediction model using a principal component analysis combined with logistic regression. We diagnosed post-stroke DS in 17.7% of patients. Expression of 510 genes differed between patients with and without DS. A model containing 6 genes (PKM, PRRC2C, NUP188, CHMP3, H2AC8, NOP10) displayed very good discriminatory properties (area under the curve: 0.95) with the sensitivity of 0.94 and specificity of 0.85. Our results suggest the potential utility of gene expression profiling in whole blood stimulated with LPS for predicting post-stroke DS. This method could be useful for searching biomarkers of post-stroke depression.

Hormonal and inflammatory signatures of different mood episodes in bipolar disorder: a large-scale clinical study

BACKGROUND

Bipolar disorder (BD) is characterized by intensive mood fluctuations. While hormones imbalance plays important role in the mood swings, it is unknown whether peripheral hormones profiles could differentiate the manic and depressive mood episodes in BD. In this study, we investigated the changes of various hormones and inflammatory markers across distinct mood episodes of BD in a large clinical study to provide mood episode-specific peripheral biomarkers for BD.

METHODS

A total of 8332 BD patients (n = 2679 depressive episode; n = 5653 manic episode) were included. All patients were in acute state of mood episodes and need hospitalization. A panel of blood tests were performed for levels of sex hormones (serum levels of testosterone, estradiol, and progesterone), stress hormones (adrenocorticotropic hormone and cortisol), and an inflammation marker (C-reactive protein, CRP). A receiver operating characteristic (ROC) curve was used to analyze the discriminatory potential of the biomarkers for mood episodes.

RESULTS

In overall comparison between mood episodes, the BD patients expressed higher levels of testosterone, estradiol, progesterone, and CRP (P < 0.001) and lower adrenocorticotropic hormone (ACTH) level (P < 0.001) during manic episode. The episode-specific changes of testosterone, ACTH, and CRP levels remained between the two groups (P < 0.001) after correction for the confounding factors including age, sex, BMI, occupation, marital status, tobacco use, alcohol consumption, psychotic symptoms, and age at onset. Furthermore, we found a sex- and age-specific impact of combined biomarkers in mood episodes in male BD patients aged ≥ 45 years (AUC = 0.70, 95% CI, 0.634-0.747), not in females.

CONCLUSIONS

While both hormone and inflammatory change is independently associated with mood episodes, we found that the combination of sex hormones, stress hormones and CRP could be more effective to differentiate the manic and depressive episode. The biological signatures of mood episodes in BD patients may be sex- and age-specific. Our findings not only provide mood episode-related biological markers, but also better support for targeted intervention in BD treatments.

Differences in intracellular protein levels in monocytes and CD4+ lymphocytes between bipolar depressed patients and healthy controls: A pilot study with tyramine-based signal-amplified flow cytometry

BACKGROUND

Molecular biomarkers for bipolar disorder (BD) that distinguish it from other manifestations of depressive symptoms remain unknown. The aim of this study was to determine if a very sensitive tyramine-based signal-amplification technology for flow cytometry (CellPrint™) could facilitate the identification of cell-specific analyte expression profiles of peripheral blood cells for bipolar depression (BPD) versus healthy controls (HCs).

METHODS

The diagnosis of psychiatric disorders was ascertained with Mini International Neuropsychiatric Interview for DSM-5. Expression levels for eighteen protein analytes previously shown to be related to bipolar disorder were assessed with CellPrint™ in CD4⁺ T cells and monocytes of bipolar patients and HCs. Implementation of protein-protein interaction (PPI) network and pathway analysis was subsequently used to identify new analytes and pathways for subsequent interrogations.

RESULTS

Fourteen drug-naïve or -free patients with bipolar I or II depression and 17 healthy controls (HCs) were enrolled. The most distinguishable changes in analyte expression based on t-tests included GSK3β, HMGB1, IRS2, phospho-GSK3αβ, phospho-RELA, and TSPO in CD4⁺ T cells and calmodulin, GSK3β, IRS2, and phospho-HS1 in monocytes. Subsequent PPI and pathway analysis indicated that prolactin, leptin, BDNF, and interleukin-3 signal pathways were significantly different between bipolar patients and HCs.

LIMITATION

The sample size of the study was small and 2 patients were on medications.

CONCLUSION

In this pilot study, CellPrint™ was able to detect differences in cell-specific protein levels between BPD patients and HCs. A subsequent study including samples from patients with BPD, major depressive disorder, and HCs is warranted.

How can childhood maltreatment affect post-traumatic stress disorder in adult: Results from a composite null hypothesis perspective of mediation analysis

Background

A greatly growing body of literature has revealed the mediating role of DNA methylation in the influence path from childhood maltreatment to psychiatric disorders such as post-traumatic stress disorder (PTSD) in adult. However, the statistical method is challenging and powerful mediation analyses regarding this issue are lacking.

Methods

To study how the maltreatment in childhood alters long-lasting DNA methylation changes which further affect PTSD in adult, we here carried out a gene-based mediation analysis from a perspective of composite null hypothesis in the Grady Trauma Project (352 participants and 16,565 genes) with childhood maltreatment as exposure, multiple DNA methylation sites as mediators, and PTSD or its relevant scores as outcome. We effectively addressed the challenging issue of gene-based mediation analysis by taking its composite null hypothesis testing nature into consideration and fitting a weighted test statistic.

Results

We discovered that childhood maltreatment could substantially affected PTSD or PTSD-related scores, and that childhood maltreatment was associated with DNA methylation which further had significant roles in PTSD and these scores. Furthermore, using the proposed mediation method, we identified multiple genes within which DNA methylation sites exhibited mediating roles in the influence path from childhood maltreatment to PTSD-relevant scores in adult, with 13 for Beck Depression Inventory and 6 for modified PTSD Symptom Scale, respectively.

Conclusion

Our results have the potential to confer meaningful insights into the biological mechanism for the impact of early adverse experience on adult diseases; and our proposed mediation methods can be applied to other similar analysis settings.

Risk Prediction for Internet Financial Enterprises by Deep Learning Algorithm and Sustainable Development of Business Transformation

It is necessary to find new ideas of business transformation of traditional financial enterprises under the background of Internet finance. Based on DL (deep learning) algorithm, the BPNN (Back Propagation neural network) model and Vector Autoregression model are used to analyze the business conflict of commercial banks among traditional financial enterprises under Internet finance. The business integration point of the two is found through the impulse response analysis of the impact of the Internet financial business on the traditional financial industry. Then, the DL algorithm based on BPNN is used to obtain the optimal solution of business integration, to promote the transformation of traditional financial services under the background of Internet finance. The results show that there is a close correlation between Internet finance and traditional financial business. The initial conflicts between the two are serious, but as time passes, they have a trend of mutual integration.

Blood and brain gene expression signatures of chronic intermittent ethanol consumption in mice

Alcohol Use Disorder (AUD) is a chronic, relapsing syndrome diagnosed by a heterogeneous set of behavioral signs and symptoms. There are no laboratory tests that provide direct objective evidence for diagnosis. Microarray and RNA-Seq technologies enable genome-wide transcriptome profiling at low costs and provide an opportunity to identify biomarkers to facilitate diagnosis, prognosis, and treatment of patients. However, access to brain tissue in living patients is not possible. Blood contains cellular and extracellular RNAs that provide disease-relevant information for some brain diseases. We hypothesized that blood gene expression profiles can be used to diagnose AUD. We profiled brain (prefrontal cortex, amygdala, and hypothalamus) and blood gene expression levels in C57BL/6J mice using RNA-seq one week after chronic intermittent ethanol (CIE) exposure, a mouse model of alcohol dependence. We found a high degree of preservation (rho range: [0.50, 0.67]) between blood and brain transcript levels. There was small overlap between blood and brain DEGs, and considerable overlap of gene networks perturbed after CIE related to cell-cell signaling (e.g., GABA and glutamate receptor signaling), immune responses (e.g., antigen presentation), and protein processing / mitochondrial functioning (e.g., ubiquitination, oxidative phosphorylation). Blood gene expression data were used to train classifiers (logistic regression, random forest, and partial least squares discriminant analysis), which were highly accurate at predicting alcohol dependence status (maximum AUC: 90.1%). These results suggest that gene expression profiles from peripheral blood samples contain a biological signature of alcohol dependence that can discriminate between CIE and Air subjects.

Ten Years of EWAS

Epigenome-wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next-generation, and third-generation sequencing technologies have prepared a high-quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.

Potential biomarkers of major depression diagnosis and chronicity

BACKGROUND

Molecular biomarkers are promising tools to be routinely used in clinical psychiatry. Among psychiatric diseases, major depression disorder (MDD) has gotten attention due to its growing prevalence and morbidity.

METHODS

We tested some peripheral molecular parameters such as serum mature Brain-Derived Neurotrophic Factor (mBDNF), plasma C-Reactive Protein (CRP), serum cortisol (SC), and the salivary Cortisol Awakening Response (CAR), as well as the Pittsburgh sleep quality inventory (PSQI), as part of a multibiomarker panel for potential use in MDD diagnosis and evaluation of disease's chronicity using regression models, and ROC curve.

RESULTS

For diagnosis model, two groups were analyzed: patients in the first episode of major depression (MD: n = 30) and a healthy control (CG: n = 32). None of those diagnosis models tested had greater power than Hamilton Depression Rating Scale-6. For MDD chronicity, a group of patients with treatment-resistant major depression (TRD: n = 28) was tested across the MD group. The best chronicity model (p < 0.05) that discriminated between MD and TRD included four parameters, namely PSQI, CAR, SC, and mBDNF (AUC ROC = 0.99), with 96% of sensitivity and 93% of specificity.

CONCLUSION

These results indicate that changes in specific biomarkers (CAR, SC, mBDNF and PSQI) have potential on the evaluation of MDD chronicity, but not for its diagnosis. Therefore, these findings can contribute for further studies aiming the development of a stronger model to be commercially available and used in psychiatry clinical practice.

The Roles of Lpar1 in Central Nervous System Disorders and Diseases

Lysophosphatidic acid receptor 1 (Lpar1), which is found in almost all human tissues but is most abundant in the brain, can couple to G protein-coupled receptors (GPCRs) and participate in regulating cell proliferation, migration, survival, and apoptosis. Endothelial differentiation gene-2 receptor (Edg2), the protein encoded by the Lpar1 gene, is present on various cell types in the central nervous system (CNS), such as neural stem cells (NSCs), oligodendrocytes, neurons, astrocytes, and microglia. Lpar1 deletion causes neurodevelopmental disorders and CNS diseases, such as brain cancer, neuropsychiatric disorders, demyelination diseases, and neuropathic pain. Here, we summarize the possible roles and mechanisms of Lpar1/Edg2 in CNS disorders and diseases and propose that Lpar1/Edg2 might be a potential therapeutic target for CNS disorders and diseases.

Epigenetic Targeting of Histone Deacetylases in Diagnostics and Treatment of Depression

Depression is a highly prevalent, disabling, and often chronic illness that places substantial burdens on patients, families, healthcare systems, and the economy. A substantial minority of patients are unresponsive to current therapies, so there is an urgent need to develop more broadly effective, accessible, and tolerable therapies. Pharmacological regulation of histone acetylation level has been investigated as one potential clinical strategy. Histone acetylation status is considered a potential diagnostic biomarker for depression, while inhibitors of histone deacetylases (HDACs) have garnered interest as novel therapeutics. This review describes recent advances in our knowledge of histone acetylation status in depression and the therapeutic potential of HDAC inhibitors.

Biomarkers of Major Depressive Disorder: Knowing is Half the Battle

Major depressive disorder (MDD) is a heterogeneous disease which is why there are currently no specific methods to accurately test the severity, endophenotype or therapy response. This lack of progress is partly attributed to the com-plexity and variability of depression, in association with analytical variability of clinical literature and the wide number of theoretically complex biomarkers. The literature accessible, indicates that markers involved in inflammatory, neuro-trophic and metabolic processes and components of neurotransmitters and neuroendocrine systems are rather strong indicators to be considered clinically and can be measured through genetic and epigenetic, transcriptomic and proteomic, metabolomics and neuroimaging assessments. Promising biologic systems/markers found were i.e., growth biomarkers, endocrine markers, oxidant stress markers, proteomic and chronic inflammatory markers, are discussed in this review. Several lines of evidence suggest that a portion of MDD is a dopamine agonist-responsive subtype. This review analyzes concise reports on the pathophysiological biomarkers of MDD and therapeutic reactions via peripheral developmental factors, inflammative cytokines, endocrine factors and metabolic markers. Various literatures also support that endocrine and metabolism changes are associated with MDD. Accumulating evidence suggests that at least a portion of MDD patients show characteristics pathological changes regarding different clinical pathological biomarkers. By this review we sum up all the different biomarkers playing an important role in the detection or treatment of the different patients suffering from MDD. The review also gives an overview of different biomarker's playing a potential role in modulating effect of MDD.

Transcriptomic analyses of humans and mice provide insights into depression

Accumulating studies have been conducted to identify risk genes and relevant biological mechanisms underlying major depressive disorder (MDD). In particular, transcriptomic analyses in brain regions engaged in cognitive and emotional processes, e.g., the dorsolateral prefrontal cortex (DLPFC), have provided essential insights. Based on three independent DLPFC RNA-seq datasets of 79 MDD patients and 75 healthy controls, we performed differential expression analyses using two alternative approaches for cross-validation. We also conducted transcriptomic analyses in mice undergoing chronic variable stress (CVS) and chronic social defeat stress (CSDS). We identified 12 differentially expressed genes (DEGs) through both analytical methods in MDD patients, the majority of which were also dysregulated in stressed mice. Notably, the mRNA level of the immediate early gene FOS ( Fos proto-oncogene) was significantly decreased in both MDD patients and CVS-exposed mice, and CSDS-susceptible mice exhibited a greater reduction in Fos expression compared to resilient mice. These findings suggest the potential key roles of this gene in the pathogenesis of MDD related to stress exposure. Altered transcriptomes in the DLPFC of MDD patients might be, at least partially, the result of stress exposure, supporting that stress is a primary risk factor for MDD.

A systematic review of childhood maltreatment and DNA methylation: candidate gene and epigenome-wide approaches

Childhood maltreatment is a major risk factor for chronic and severe mental and physical health problems across the lifespan. Increasing evidence supports the hypothesis that maltreatment is associated with epigenetic changes that may subsequently serve as mechanisms of disease. The current review uses a systematic approach to identify and summarize the literature related to childhood maltreatment and alterations in DNA methylation in humans. A total of 100 empirical articles were identified in our systematic review of research published prior to or during March 2020, including studies that focused on candidate genes and studies that leveraged epigenome-wide data in both children and adults. Themes arising from the literature, including consistent and inconsistent patterns of results, are presented. Several directions for future research, including important methodological considerations for future study design, are discussed. Taken together, the literature on childhood maltreatment and DNA methylation underscores the complexity of transactions between the environment and biology across development.

Interleukin-1 Beta in Peripheral Blood Mononuclear Cell Lysates as a Longitudinal Biomarker of Response to Antidepressants: A Pilot Study

Interleukin-1 beta (IL1β) is primarily produced by monocytes in the periphery and the brain. Yet, IL1β protein levels have to date been investigated in major depressive disorder (MDD) and antidepressant response using either plasma or serum assays although with contradictory results, while mononuclear cell assays are lacking despite their extensive use in other contexts. In this pilot study, we comparatively assessed IL1β in mononuclear lysates and plasma in depressed MDD patients over treatment and healthy controls (HC). We recruited 31 consecutive adult MDD inpatients and 25 HC matched on age, sex, and BMI. Twenty-six patients completed an 8-week follow-up under treatment. IL1β was measured in both lysates and plasma in patients at baseline (T0) and at study end (T1) as well as in HC. We calculated ΔIL1β(%) for both lysates and plasma as IL1β percent changes from T0 to T1. Seventeen patients (65.4% of completers) were responders at T1 and had lower baseline BMI than non-responders (p = 0.029). Baseline IL1β from either plasma or lysates could not efficiently discriminate between depressed patients and HC, or between responders and non-responders. However, the two response groups displayed contrasting IL1β trajectories in lysates but not in plasma assays (response group by time interactions, p = 0.005 and 0.96, respectively). ΔIL1β(%) in lysates predicted response (p = 0.025, AUC = 0.81; accuracy = 84.6%) outperforming ΔIL1β(%) in plasma (p = 0.77, AUC=0.52) and was robust to adjusting for BMI. In conclusion, ΔIL1β(%) in mononuclear lysates may be a longitudinal biomarker of antidepressant response, potentially helpful in avoiding untimely switching of antidepressants, thereby warranting further investigation.

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.

EMG-Free Monitorization of the Acoustic Startle Reflex with a Mobile Phone: Implications of Sound Parameters with Posture Related Responses

(1) Background: Acute acoustic (sound) stimulus prompts a state of defensive motivation in which unconscious muscle responses are markedly enhanced in humans. The orbicularis oculi (OO) of the eye is an easily accessed muscle common for acoustic startle reaction/response/reflex (ASR) investigations and is the muscle of interest in this study. Although the ASR can provide insights about numerous clinical conditions, existing methodologies (Electromyogram, EMG) limit the usability of the method in real clinical conditions. (2) Objective: With EMG-free muscle recording in mind, our primary aim was to identify and investigate potential correlations in the responses of individual and cooperative OO muscles to various acoustic stimuli using a mobile and wire-free system. Our secondary aim was to investigate potential altered responses to high and also relatively low intensity acoustics at different frequencies in both sitting and standing positions through the use of biaural sound induction and video diagnostic techniques and software. (3) Methods: This study used a mobile-phone acoustic startle response monitoring system application to collect blink amplitude and velocity data on healthy males, aged 18–28 community cohorts during (n = 30) in both sitting and standing postures. The iPhone X application delivers specific sound parameters and detects blinking responses to acoustic stimulus (in millisecond resolution) to study the responses of the blinking reflex to acoustic sounds in standing and sitting positions by using multiple acoustic test sets of different frequencies and amplitudes introduced as acute sound stimuli (<0.5 s). The single acoustic battery of 15 pure-square wave sounds consisted of frequencies and amplitudes between 500, 1000, 2000, 3000, and 4000 Hz scales using 65, 90, and 105 dB (e.g., 3000 Hz_90 dB). (4) Results: Results show that there was a synchronization of amplitude and velocity between both eyes to all acoustic startles. Significant differences (p = 0.01) in blinking reaction time between sitting vs. standing at the high intensity (105 dB) 500 Hz acoustic test set was discovered. Interestingly, a highly significant difference (p < 0.001) in response times between test sets 500 Hz_105 dB and 4000 Hz_105 dB was identified. (5) Conclusions: To our knowledge, this is the first mobile phone-based acoustic battery used to detect and report significant ASR responses to specific frequencies and amplitudes of sound stimulus with corresponding sitting and standing conditions. The results from this experiment indicate the potential significance of using the specific frequency, amplitude, and postural conditions (as never before identified) which can open new horizons for ASR to be used for diagnosis and monitoring in numerous clinical and remote or isolated conditions.

Positional cloning and comprehensive mutation analysis of a Japanese family with lithium-responsive bipolar disorder identifies a novel DOCK5 mutation

Bipolar disorder (BD) is a severe psychiatric disorder characterized by the recurrence of depressive and manic episodes. Its heritability is high, and many linkage and association studies have been performed. Although various linkage regions and candidate genes have been reported, few have shown sufficient reproducibility, and none have identified the pathogenic genes based on the results of the linkage analysis. To find functional variants that are expected to be rare and have strong genetic effects, we recruited ten healthy individuals, two individuals with unknown status, and six patients with BD or recurrent major depressive disorder (MDD) from a Japanese family consisting of 21 members. We performed a genome-wide linkage analysis using a 100K single-nucleotide polymorphism (SNP) array and microsatellite markers to narrow linkage regions within this family. Subsequently, we performed whole-exome sequencing for two patients with BD to identify genetic mutations in the narrowed linkage regions. Then, we performed co-segregation analysis for DNA variants obtained from the results of the exome sequencing. Finally, we identified a rare heterozygous mutation in exon 31 of DOCK5 (c.3170A>G, p.E1057G). Convergent functional genomics analysis revealed that DOCK5 was listed as one of the biomarkers for mood state and suicidality. Although DOCK5 is still a functionally unknown gene, our findings highlight the possibility of a pathological relationship between BD and DOCK5.

Convergent lines of evidence support NOTCH4 as a schizophrenia risk gene

The association between NOTCH4 and schizophrenia has been repeatedly reported. However, the results from different genetic studies are inconsistent, and the role of NOTCH4 in schizophrenia pathogenesis remains unknown. Here, we provide convergent lines of evidence that support NOTCH4 as a schizophrenia risk gene. We first performed a meta-analysis and found that a genetic variant (rs2071287) in NOTCH4 was significantly associated with schizophrenia (a total of 125 848 subjects, p=8.31×10^-17), with the same risk allele across all tested samples. Expression quantitative trait loci (eQTL) analysis showed that rs2071287 was significantly associated with NOTCH4 expression (p=1.08×10^-14) in human brain tissues, suggesting that rs2071287 may confer schizophrenia risk through regulating NOTCH4 expression. Sherlock integrative analysis using a large-scale schizophrenia GWAS and eQTL data from human brain tissues further revealed that NOTCH4 was significantly associated with schizophrenia (p=4.03×10^-7 in CMC dataset and p=3.06×10^-6 in xQTL dataset), implying that genetic variants confer schizophrenia risk through modulating NOTCH4 expression. Consistently, we found that NOTCH4 was significantly downregulated in brains of schizophrenia patients compared with controls (p=2.53×10^-3), further suggesting that dysregulation of NOTCH4 may have a role in schizophrenia. Finally, we showed that NOTCH4 regulates proliferation, self-renewal, differentiation and migration of neural stem cells, suggesting that NOTCH4 may confer schizophrenia risk through affecting neurodevelopment. Our study provides convergent lines of evidence that support the involvement of NOTCH4 in schizophrenia. In addition, our study also elucidates a possible mechanism for the role of NOTCH4 in schizophrenia pathogenesis.

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).

Association between DNA methylation levels in brain tissue and late-life depression in community-based participants

OBJECTIVE

Major depressive disorder (MDD) arises from a combination of genetic and environmental risk factors and DNA methylation is one of the molecular mechanisms through which these factors can manifest. However, little is known about the epigenetic signature of MDD in brain tissue. This study aimed to investigate associations between brain tissue-based DNA methylation and late-life MDD.

METHODS

We performed a brain epigenome-wide association study (EWAS) of late-life MDD in 608 participants from the Religious Order Study and the Rush Memory and Aging Project (ROS/MAP) using DNA methylation profiles of the dorsal lateral prefrontal cortex generated using the Illumina HumanMethylation450 Beadchip array. We also conducted an EWAS of MDD in each sex separately.

RESULTS

We found epigenome-wide significant associations between brain tissue-based DNA methylation and late-life MDD. The most significant and robust association was found with altered methylation levels in the YOD1 locus (cg25594636, p value = 2.55 × 10-11; cg03899372, p value = 3.12 × 10-09; cg12796440, p value = 1.51 × 10-08, cg23982678, p value = 7.94 × 10-08). Analysis of differentially methylated regions (p value = 5.06 × 10-10) further confirmed this locus. Other significant loci include UGT8 (cg18921206, p value = 1.75 × 10-08), FNDC3B (cg20367479, p value = 4.97 × 10-08) and SLIT2 (cg10946669, p value = 8.01 × 10-08). Notably, brain tissue-based methylation levels were strongly associated with late-life MDD in men more than in women.

CONCLUSIONS

We identified altered methylation in the YOD1, UGT8, FNDC3B, and SLIT2 loci as new epigenetic factors associated with late-life MDD. Furthermore, our study highlights the sex-specific molecular heterogeneity of MDD.

Characterization of the signature of peripheral innate immunity in women with later-life major depressive disorder

The prevalence of depression in later life is higher in women than in men. However, the sex difference in the pathophysiology of depression in elderly patients is not fully understood. Here, we performed gene expression profiling in leukocytes of middle-aged and elderly patients with major depressive disorder, termed later-life depression (LLD) in this context, and we characterized the sex-dependent pathophysiology of LLD. A microarray dataset obtained from leukocytes of patients (aged ≥50 years) with LLD (32 males and 39 females) and age-matched healthy individuals (20 males and 24 females) was used. Differentially expressed probes were determined by comparing the expression levels between patients and healthy individuals, and then functional annotation analyses (Ingenuity Pathway Analysis, Reactome pathway analysis, and cell-type enrichment analysis) were performed. A total of 1656 probes were differentially expressed in LLD females, but only 3 genes were differentially expressed in LLD males. The differentially expressed genes in LLD females were relevant to leukocyte extravasation signaling, Tec kinase signaling and the innate immune response. The upregulated genes were relevant to myeloid lineage cells such as CD14⁺ monocytes. In contrast, the downregulated genes were relevant to CD4⁺ and CD8⁺ T cells. Remarkable innate immune signatures are present in the leukocytes of LLD females but not males. Because inflammation is involved in the pathophysiology of depression, the altered inflammatory activity may be involved in the pathophysiology of LLD in women. In contrast, abnormal inflammation may be an uncommon feature in LLD males.

Study Protocol for a Prospective Longitudinal Cohort Study to Identify Proteomic Predictors of Pluripotent Risk for Mental Illness: The Seoul Pluripotent Risk for Mental Illness Study

BACKGROUND

The Seoul Pluripotent Risk for Mental Illness (SPRIM) study was designed to identify predictors leading to mental illness in help-seeking individuals by securing sufficient statistical power through transdiagnostic approaches. The SPRIM study aims to examine the clinical characteristics of high-risk individuals for mental illness and to identify proteomic biomarkers that can predict the onset of mental illness.

METHODS

This paper describes the study protocol of the SPRIM study. We aim to recruit 150 participants who meet the criteria for high risk for major mental illness, 150 patients with major psychiatric disorders (schizophrenia, bipolar disorder, and major depressive disorder), and 50 matched healthy control subjects for 2 years. Clinical evaluations, self-report measures, and proteomic analyses will be implemented. The assessment points are at baseline, 6, 12, 18, and 24 months.

CONCLUSIONS

In the present study, we introduced the study protocol of the SPRIM study, which is the first prospective cohort study of transdiagnostic high-risk concepts using proteomic biomarkers. This study has a paradigm that encompasses various diseases without aiming at predicting and preventing the development of a specific mental illness in help-seeking individuals. The transdiagnostic high-risk concept could be extended to provide a perspective for people with various psychopathological tendencies below a threshold, such that they do not meet the existing diagnostic criteria of mental illnesses, to determine what may lead them to a specific disease and help identify appropriate preventative interventions.

Integrative analysis of lithium treatment associated effects on brain structure and peripheral gene expression reveals novel molecular insights into mechanism of action

Lithium is a highly effective medication for bipolar disorder, but its mechanism of action remains unknown. In this study, brain MRI scans and blood samples for gene expression (total of 110 scans and 109 blood samples) were collected from 21 bipolar subjects before and after 2 and 8 weeks of lithium monotherapy and at the same time-points from untreated 16 healthy controls. We used linear mixed-effects models to identify brain structural features and genes with expression changed after lithium treatment, with correction for multiple testing, and correlated their concurrent changes to identify molecular pathways associated with lithium effects. There are significant increases in gray matter fraction, global cortical thickness, and the frontal and parietal cortices after 8 weeks of lithium treatment (corrected p < 0.05). Volume increases were also seen for putamen, hippocampus, thalamic nuclei, and thalamic substructures. Several genes showed significant expression changes, and 14 gene pathways were identified for the present integration analysis. Of these, nine pathways had significant correlations with structural changes (FDR < 0.05). Three neurotrophy-related pathways (GDNF family of ligands, NFAT immune-response, and p53-signaling pathway) correlated with structural changes in multiple regions. Mediation analysis showed that the sphingomyelin metabolism pathway is associated with HAM-D change (p < 0.01), and this effect is mediated via the volume of mediodorsal thalamus (p < 0.03). In summary, the integration of lithium effects on brain structural and peripheral gene expression changes revealed effects on several neurotrophic molecular pathways, which provides further insights into the mechanism of lithium action.

Peripheral blood metabolome predicts mood change-related activity in mouse model of bipolar disorder

Bipolar disorder is a major mental illness characterized by severe swings in mood and activity levels which occur with variable amplitude and frequency. Attempts have been made to identify mood states and biological features associated with mood changes to compensate for current clinical diagnosis, which is mainly based on patients' subjective reports. Here, we used infradian (a cycle > 24 h) cyclic locomotor activity in a mouse model useful for the study of bipolar disorder as a proxy for mood changes. We show that metabolome patterns in peripheral blood could retrospectively predict the locomotor activity levels. We longitudinally monitored locomotor activity in the home cage, and subsequently collected peripheral blood and performed metabolomic analyses. We then constructed cross-validated linear regression models based on blood metabolome patterns to predict locomotor activity levels of individual mice. Our analysis revealed a significant correlation between actual and predicted activity levels, indicative of successful predictions. Pathway analysis of metabolites used for successful predictions showed enrichment in mitochondria metabolism-related terms, such as "Warburg effect" and "citric acid cycle." In addition, we found that peripheral blood metabolome patterns predicted expression levels of genes implicated in bipolar disorder in the hippocampus, a brain region responsible for mood regulation, suggesting that the brain-periphery axis is related to mood-change-associated behaviors. Our results may serve as a basis for predicting individual mood states through blood metabolomics in bipolar disorder and other mood disorders and may provide potential insight into systemic metabolic activity in relation to mood changes.

Epigenome-wide association study of depression symptomatology in elderly monozygotic twins

Depression is a severe and debilitating mental disorder diagnosed by evaluation of affective, cognitive and physical depression symptoms. Severity of these symptoms strongly impacts individual's quality of life and is influenced by a combination of genetic and environmental factors. One of the molecular mechanisms allowing for an interplay between these factors is DNA methylation, an epigenetic modification playing a pivotal role in regulation of brain functioning across lifespan. The aim of this study was to investigate if there are DNA methylation signatures associated with depression symptomatology in order to identify molecular mechanisms contributing to pathophysiology of depression. We performed an epigenome-wide association study (EWAS) of continuous depression symptomatology score measured in a cohort of 724 monozygotic Danish twins (346 males, 378 females). Through EWAS analyses adjusted for sex, age, flow-cytometry based blood cell composition, and twin relatedness structure in the data we identified depression symptomatology score to be associated with blood DNA methylation levels in promoter regions of neuropsin (KLK8, p-value = 4.7 × 10^-7) and DAZ associated protein 2 (DAZAP2, p-value = 3.13 × 10^-8) genes. Other top associated probes were located in gene bodies of MAD1L1 (p-value = 5.16 × 10^-6), SLC29A2 (p-value = 6.15 × 10^-6) and AKT1 (p-value = 4.47 × 10^-6), all genes associated before with development of depression. Additionally, the following three measures (a) DNAmAge (calculated with Horvath and Hannum epigenetic clock estimators) adjusted for chronological age, (b) difference between DNAmAge and chronological age, and (c) DNAmAge acceleration were not associated with depression symptomatology score in our cohort. In conclusion, our data suggests that depression symptomatology score is associated with DNA methylation levels of genes implicated in response to stress, depressive-like behaviors, and recurrent depression in patients, but not with global DNA methylation changes across the genome.

Integrative analyses of major histocompatibility complex loci in the genome-wide association studies of major depressive disorder

Recent European genome-wide association studies (GWAS) have revealed strong statistical correlations between MDD and numerous zero-to-high linked variants in the genomic region containing major histocompatibility complex (MHC) genes (MHC region), but the underlying biological mechanisms are still unclear. To better understand the roles of this genomic region in the neurobiology of MDD, we applied a convergent functional genomics approach to integrate GWAS data of MDD relevant biological phenotypes, gene-expression analyses results obtained from brain samples, and genetic analyses of independent Chinese MDD samples. We observed that independent MDD risk variants in the MHC region were also significantly associated with the relevant biological phenotypes in the predicted directions, including the emotional and cognitive-related phenotypes. Gene-expression analyses further revealed that mRNA expression levels of several MHC region genes in the human brain were associated with MDD risk SNPs and diagnostic status. For instance, a brain-enriched gene ZNF603P consistently showed lower mRNA levels in the individuals carrying MDD risk alleles and in MDD patients. Remarkably, we further found that independent MDD risk SNPs in the MHC region likely converged to affect the mRNA level(s) of the same gene(s), and Europeans and Han Chinese populations have a substantial shared genetic and molecular basis underlying MDD risk associations in the MHC region. These results highlighted several potential pivotal genes at the MHC region in the pathogenesis of MDD. Their common impacts on multiple psychiatric relevant phenotypes also implicated the neurological processes shared by different psychological processes, such as mood and/or cognition, shedding lights on their potential biological mechanisms.

Could a blood test for PTSD and depression be on the horizon?

INTRODUCTION

Depression and posttraumatic stress disorder (PTSD) are two complex and debilitating psychiatric disorders that result in poor life and destructive behaviors against self and others. Currently, diagnosis is based on subjective rather than objective determinations leading to misdiagnose and ineffective treatments. Advances in novel neurobiological methods have allowed assessment of promising biomarkers to diagnose depression and PTSD, which offers a new means of appropriately treating patients. Areas covered: Biomarkers discovery in blood represents a fundamental tool to predict, diagnose, and monitor treatment efficacy in depression and PTSD. The potential role of altered HPA axis, epigenetics, NPY, BDNF, neurosteroid biosynthesis, the endocannabinoid system, and their function as biomarkers for mood disorders is discussed. Insofar, we propose the identification of a biomarker axis to univocally identify and discriminate disorders with large comorbidity and symptoms overlap, so as to provide a base of support for development of targeted treatments. We also weigh in on the feasibility of a future blood test for early diagnosis. Expert commentary: Potential biomarkers have already been assessed in patients' blood and need to be further validated through multisite large clinical trial stratification. Another challenge is to assess the relation among several interdependent biomarkers to form an axis that identifies a specific disorder and secures the best-individualized treatment. The future of blood-based tests for PTSD and depression is not only on the horizon but, possibly, already around the corner.

The effects of individualised intermittent theta burst stimulation in the prefrontal cortex: A TMS-EEG study

Recent studies have highlighted variability in response to theta burst stimulation (TBS) in humans. TBS paradigm was originally developed in rodents to mimic gamma bursts coupled with theta rhythms, and was shown to elicit long-term potentiation. The protocol was subsequently adapted for humans using standardised frequencies of stimulation. However, each individual has different rhythmic firing pattern. The present study sought to explore whether individualised intermittent TBS (Ind iTBS) could outperform the effects of two other iTBS variants. Twenty healthy volunteers received iTBS over left prefrontal cortex using 30 Hz at 6 Hz, 50 Hz at 5 Hz, or individualised frequency in separate sessions. Ind iTBS was determined using theta-gamma coupling during the 3-back task. Concurrent use of transcranial magnetic stimulation and electroencephalography (TMS-EEG) was used to track changes in cortical plasticity. We also utilised mood ratings using a visual analogue scale and assessed working memory via the 3-back task before and after stimulation. No group-level effect was observed following either 30 or 50 Hz iTBS in TMS-EEG. Ind iTBS significantly increased the amplitude of the TMS-evoked P60, and decreased N100 and P200 amplitudes. A significant positive correlation between neurophysiological change and change in mood rating was also observed. Improved accuracy in the 3-back task was observed following both 50 Hz and Ind iTBS conditions. These findings highlight the critical importance of frequency in the parameter space of iTBS. Tailored stimulation parameters appear more efficacious than standard paradigms in neurophysiological and mood changes. This novel approach presents a promising option and benefits may extend to clinical applications.

Neurotrophins, cytokines, oxidative stress mediators and mood state in bipolar disorder: systematic review and meta-analyses

BACKGROUND

A reliable biomarker signature for bipolar disorder sensitive to illness phase would be of considerable clinical benefit. Among circulating blood-derived markers there has been a significant amount of research into inflammatory markers, neurotrophins and oxidative stress markers.AimsTo synthesise and interpret existing evidence of inflammatory markers, neurotrophins and oxidative stress markers in bipolar disorder focusing on the mood phase of illness.

METHOD

Following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) guidelines, a systematic review was conducted for studies investigating peripheral biomarkers in bipolar disorder compared with healthy controls. We searched Medline, Embase, PsycINFO, SciELO and Web of Science, and separated studies by bipolar mood phase (mania, depression and euthymia). Extracted data on each biomarker in separate mood phases were synthesised using random-effects model meta-analyses.

RESULTS

In total, 53 studies were included, comprising 2467 cases and 2360 controls. Fourteen biomarkers were identified from meta-analyses of three or more studies. No biomarker differentiated mood phase in bipolar disorder individually. Biomarker meta-analyses suggest a combination of high-sensitivity C-reactive protein/interleukin-6, brain derived neurotrophic factor/tumour necrosis factor (TNF)-α and soluble TNF-α receptor 1 can differentiate specific mood phase in bipolar disorder. Several other biomarkers of interest were identified.

CONCLUSIONS

Combining biomarker results could differentiate individuals with bipolar disorder from healthy controls and indicate a specific mood-phase signature. Future research should seek to test these combinations of biomarkers in longitudinal studies.Declaration of interestNone.

Can an Integrated Science Approach to Precision Medicine Research Improve Lithium Treatment in Bipolar Disorders?

Clinical practice guidelines identify lithium as a first line treatment for mood stabilization and reduction of suicidality in bipolar disorders (BD); however, most individuals show sub-optimal response. Identifying biomarkers for lithium response could enable personalization of treatment and refine criteria for stratification of BD cases into treatment-relevant subgroups. Existing systematic reviews identify potential biomarkers of lithium response, but none directly address the conceptual issues that need to be addressed to enhance translation of research into precision prescribing of lithium. For example, although clinical syndrome subtyping of BD has not led to customized individual treatments, we emphasize the importance of assessing clinical response phenotypes in biomarker research. Also, we highlight the need to give greater consideration to the quality of prospective longitudinal monitoring of illness activity and the differentiation of non-response from partial or non-adherence with medication. It is unlikely that there is a single biomarker for lithium response or tolerability, so this review argues that more research should be directed toward the exploration of biosignatures. Importantly, we emphasize that an integrative science approach may improve the likelihood of discovering the optimal combination of clinical factors and multimodal biomarkers (e.g., blood omics, neuroimaging, and actigraphy derived-markers). This strategy could uncover a valid lithium response phenotype and facilitate development of a composite prediction algorithm. Lastly, this narrative review discusses how these strategies could improve eligibility criteria for lithium treatment in BD, and highlights barriers to translation to clinical practice including the often-overlooked issue of the cost-effectiveness of introducing biomarker tests in psychiatry.

Did you know that your animals have consciousness?

Consciousness is the greatest enigma in human history. For centuries scientists and researchers have tried to describe it without coming to conclusions. In the last years with the neurosciences development, consciousness has become the common goal of numerous studies. But consciousness has always been studied only in humans, but after "Cambridge Declaration on Consciousness" in 2012, even non-human animalsthey feel possessed of the consciousness. According to "theory Orch-OR" of Hameroff and Penrose we have conducted a study on Alaskan malamute and German shepherd to analysed a triplet of platelet fatty acids (linoleic acid; palmitic acid; arachidonic acid). Through these analysis and the relative mapping of subjects within a SOM it was possible to make an assessment of the possible onset of mood disorders in the dogs. A critical analysis of the results obtained shows that animals have molecular analogies with humans compared to mood disorders. The German shepherd and Alaskan malamute, indeed, have, in the case of major depression, a bio-chemical profile, the most similar to man.

Stress, Depression, Resilience and Ageing: A Role for the LPA-LPA1 Pathway

BACKGROUND

Chronic stress affects health and the quality of life, with its effects being particularly relevant in ageing due to the psychobiological characteristics of this population. However, while some people develop psychiatric disorders, especially depression, others seem very capable of dealing with adversity. There is no doubt that along with the identification of neurobiological mechanisms involved in developing depression, discovering which factors are involved in positive adaptation under circumstances of extreme difficulty will be crucial for promoting resilience.

METHODS

Here, we review recent work in our laboratory, using an animal model lacking the LPA1 receptor, together with pharmacological studies and clinical evidence for the possible participation of the LPA1 receptor in mood and resilience to stress.

RESULTS

Substantial evidence has shown that the LPA1 receptor is involved in emotional regulation and in coping responses to chronic stress, which, if dysfunctional, may induce vulnerability to stress and predisposition to the development of depression. Given that there is commonality of mechanisms between those involved in negative consequences of stress and in ageing, this is not surprising, considering that the LPA1 receptor may be involved in coping with adversity during ageing.

CONCLUSION

Alterations in this receptor may be a susceptibility factor for the presence of depression and cognitive deficits in the elderly population. However, because this is only a promising hypothesis based on previous data, future studies should focus on the involvement of the LPA-LPA1 pathway in coping with stress and resilience in ageing.

Plasma disturbance of phospholipid metabolism in major depressive disorder by integration of proteomics and metabolomics

INTRODUCTION

Major depressive disorder (MDD) is a highly prevalent mental disorder affecting millions of people worldwide. However, a clear causative etiology of MDD remains unknown. In this study, we aimed to identify critical protein alterations in plasma from patients with MDD and integrate our proteomics and previous metabolomics data to reveal significantly perturbed pathways in MDD. An isobaric tag for relative and absolute quantification (iTRAQ)-based quantitative proteomics approach was conducted to compare plasma protein expression between patients with depression and healthy controls (CON).

METHODS

For integrative analysis, Ingenuity Pathway Analysis software was used to analyze proteomics and metabolomics data and identify potential relationships among the differential proteins and metabolites.

RESULTS

A total of 74 proteins were significantly changed in patients with depression compared with those in healthy CON. Bioinformatics analysis of differential proteins revealed significant alterations in lipid transport and metabolic function, including apolipoproteins (APOE, APOC4 and APOA5), and the serine protease inhibitor. According to canonical pathway analysis, the top five statistically significant pathways were related to lipid transport, inflammation and immunity.

CONCLUSION

Causal network analysis by integrating differential proteins and metabolites suggested that the disturbance of phospholipid metabolism might promote the inflammation in the central nervous system.

Proteomics analysis reveals differential pattern of widespread protein expression and novel role of histidine-rich glycoprotein and lipopolysaccharide-binding protein in rheumatoid arthritis

Rheumatoid factor (RF) is an auto-antibody against antigen-antibody immune complexes. RF is valuable as a biomarker for the screening of autoimmune and infectious diseases. However, it is suggested that RF would be a more powerful biomarker when used complementarily with RF-correlated proteins. In this study, we utilized a proteomic approach to analyze global protein expression in RF-low and RF-high subjects using high-performance liquid chromatography tandem mass spectrometry. Histidine-rich glycoprotein (HRG) and lipopolysaccharide-binding protein (LBP) were found to be differentially expressed between RF-low and RF-high subjects (cut-off > 2-fold, p < 0.05), which was validated by enzyme-linked immunosorbent assay. To evaluate whether both proteins allow discriminating rheumatoid arthritis patients from healthy controls, receiver-operating characteristic (ROC) curves were analyzed. Areas under the ROC curves of HRG and LBP were 0.861 and 0.888, respectively. The correlation between RF and HRG was statistically significant (p = 0.003), and LBP was also correlated with RF (p = 0.044), as indicated by correlation analysis. HRG and LBP are reportedly involved in RF-producing and RF-correlated diseases. Thus, we propose that HRG and LBP could be useful screening markers for RF-correlated diseases.

Transcriptional profiling of mitochondria associated genes in prefrontal cortex of subjects with major depressive disorder

OBJECTIVES

Recent evidences suggest that mitochondrial dysfunction maybe involved in the pathophysiology of major depressive disorder (MDD); however, the role of mitochondrial genes in this disorder has not been studied systematically. In the present study, we profiled expression of mitochondrial genes in dorsolateral prefrontal cortex (dlPFC) of MDD and non-psychiatric control subjects.

METHODS

Human mitochondrial RT² profile PCR array plates were used to examine differentially expressed genes in dlPFC of 11 MDD and 11 control subjects. Differentially expressed genes were validated independently by qRT-PCR. Biological relevance of differentially expressed genes was analysed by gene ontology (GO) and ingenuity pathways analysis (IPA).

RESULTS

We found that 16 genes were differentially expressed in the MDD group compared with control group. Among them, three genes were downregulated and 13 genes upregulated. None of these genes were affected by confounding variables, such as age, post-mortem interval, brain pH, and antidepressant toxicology. Seven differentially expressed genes were successfully validated in MDD subjects. GO and IPA analyses identified several new regulatory networks associated with mitochondrial dysfunctions in MDD.

CONCLUSIONS

Our findings suggest abnormal mitochondrial systems in the brain of MDD subjects which could be involved in the etiopathogenesis of this disorder.

Role of Protein Kinase C in Bipolar Disorder: A Review of the Current Literature

Bipolar disorder (BD) is a major health problem. It causes significant morbidity and imposes a burden on the society. Available treatments help a substantial proportion of patients but are not beneficial for an estimated 40-50%. Thus, there is a great need to further our understanding the pathophysiology of BD to identify new therapeutic avenues. The preponderance of evidence pointed towards a role of protein kinase C (PKC) in BD. We reviewed the literature pertinent to the role of PKC in BD. We present recent advances from preclinical and clinical studies that further support the role of PKC. Moreover, we discuss the role of PKC on synaptogenesis and neuroplasticity in the context of BD. The recent development of animal models of BD, such as stimulant-treated and paradoxical sleep deprivation, and the ability to intervene pharmacologically provide further insights into the involvement of PKC in BD. In addition, the effect of PKC inhibitors, such as tamoxifen, in the resolution of manic symptoms in patients with BD further points in that direction. Furthermore, a wide variety of growth factors influence neurotransmission through several molecular pathways that involve downstream effects of PKC. Our current understanding identifies the PKC pathway as a potential therapeutic avenue for BD.

Analytical approaches for lipidomics and its potential applications in neuropsychiatric disorders

OBJECTIVES

In this review, the authors discuss an overview of lipidomics followed by in-depth discussion of its application to the study of human diseases, including extraction methods of lipids, analytical techniques and clinical research in neuropsychiatric disorders.

METHODS

Lipidomics is a lipid-targeted metabolomics approach aiming at the comprehensive analysis of lipids in biological systems. Recent technological advancements in mass spectrometry and chromatography have greatly enhanced the development and applications of metabolic profiling of diverse lipids in complex biological samples.

RESULTS

An effective evaluation of the clinical course of diseases requires the application of very precise diagnostic and assessment approaches as early as possible. In order to achieve this, "omics" strategies offer new opportunities for biomarker identification and/or discovery in complex diseases and may provide pathological pathways understanding for diseases beyond traditional methodologies.

CONCLUSIONS

This review highlights the importance of lipidomics for the future perspectives as a tool for biomarker identification and discovery and its clinical application.

Disrupted hippocampal neuregulin-1/ErbB3 signaling and dentate gyrus granule cell alterations in suicide

Neuregulin-1 (NRG1) and ErbB receptors have been associated with psychopathology, and NRG1-ErbB3 signaling has been shown to increase hippocampal neurogenesis and induce antidepressant-like effects. In this study, we aimed to determine whether deficits in NRG1 or ErbBs might be present in the hippocampus of suicide completers. In well-characterized postmortem hippocampal samples from suicides and matched sudden-death controls, we assessed gene expression and methylation using qRT-PCR and EpiTYPER, respectively. Moreover, in hippocampal tissues stained with cresyl violet, stereology was used to quantify numbers of granule cells and of glia. Granule cell body size was examined with a nucleator probe, and granule cell layer volume with a Cavalieri probe. Unmedicated suicides showed sharply decreased hippocampal ErbB3 expression and decreased numbers of ErbB3-expressing granule cell neurons in the anterior dentate gyrus; a phenomenon seemingly reversed by antidepressant treatment. Furthermore, we found ErbB3 expression to be significantly decreased in the dentate gyrus of adult mice exposed to chronic social defeat stress. Taken together, these results reveal novel suicidal endophenotypes in the hippocampus, as well as a putative etiological mechanism underlying suicidality, and suggest that antidepressant or NRG1 treatment may reverse a potential deficit in anterior dentate gyrus granule cell neurons in individuals at risk of dying by suicide.

Distinctive gene expression profile in women with history of postpartum depression

Postpartum depression (PPD) is a disease which incorporates a variety of depressive states differing in nature and severity. To assist in the understanding of the pathogenesis of the disease, we aimed to ascertain a molecular mechanism underlying PPD development. We applied microarray technology to characterize gene expression of euthymic women with a history of PPD and compared the results with healthy controls. Our study demonstrated that women who considered euthymic on a clinical level, in fact, had an altered molecular profile when compared to participants with no PPD history. We identified nine genes significantly distinguished expression in post- depressive women; they may serve as a diagnostic tool for the detection of a predisposition to PPD. Our findings contribute significantly to the understanding of PPD etiology and its pathogenesis, offer a plausible explanation for the risk of the PPD recurrence, and may also contribute to clinical treatment.