Targeted proteomic analysis of cognitive dysfunction in remitted major depressive disorder: Opportunities of multi-omics approaches towards predictive, preventive, and personalized psychiatry

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Epigenetic modification related to cognitive changes during a cognitive training intervention in depression

BACKGROUND

DNA methylation as a biomarker is well suited to investigate dynamic processes, such as symptom improvement. For this study we focus on epigenomic state or trait markers as early signatures of cognitive improvement in individuals receiving a cognitive intervention. We performed a first epigenome-wide association study (EWAS) on patients with cognitive dysfunction in depression comparing those with vs without cognitive dysfunction and those cognitively improving vs non-improving following a cognitive intervention.

METHOD

Data from a randomized controlled trial (RCT) were used for this analysis, where cognitive function of 112 patients randomly assigned to a personalized cognitive intervention was compared to standard cognitive treatment. Cognition was measured for this study using the four cognitive tasks from the THINC-it battery. We compared individuals with cognitive impairment with individuals without cognitive impairment at baseline and after a cognitive intervention of 8 weeks. Blood for DNA methylation analysis (Illumina Infinium MethylationEPIC 850 k BeadChip) was collected at baseline and 8 weeks into the treatment. For the baseline analysis, after quality control, the final sample comprised 90 individuals, and analyses at week 8 were performed on 84 individuals. Data cleaning, quality control, and differential methylation analysis of DNA methylation data was performed using the RnBeads package (R). Analyses were corrected for gender, age, depression score (MADRS), reported years of education, height and weight, as well as surrogate variables estimated by the pipeline used. The within-individual paired longitudinal analysis was performed using Welch's t-test.

RESULTS

Analyses at baseline and at week 8 did not show any genome-wide significant CpGs (p < 5 × 10-8) comparing patients with and without cognitive impairment. The most significant result in the baseline analysis comparing the groups with and without cognitive impairment at baseline is located in an open Sea region with predominantly regulatory qualities (cg10962945; 6.61 × 10-7). The most significant CpG at 8 weeks was also located in open sea, though in exon 13 of the NTRK2-gene, linked to the BDNF pathway (cg13620631, 5.56 × 10-7). Finally, a within-individual paired longitudinal analysis with only patients that show improved cognitive function over time was performed, showing 65 CpGs that overlapped between the 1% most significant of this analysis and the 1% most significant CpGs from the cross-sectional analysis at 8 weeks.

CONCLUSION

Our result suggest that DNA methylation can be suitable to capture early signs of treatment response of a cognitive intervention in depression. In our layered approach we could capture dynamics that can help differentiate between biological trait and state markers of cognitive function in depression. Despite not being genome-wide significant, the CpG locations returned by our analysis comparing patients with and without cognitive impairment, are in line with prior knowledge on pathways and genes relevant for depression treatment and cognition.

Integrative Analyses of scRNA-seq, Bulk mRNA-seq, and DNA Methylation Profiling in Depressed Suicide Brain Tissues

BACKGROUND

Suicidal behaviors have become a serious public health concern globally due to the economic and human cost of suicidal behavior to individuals, families, communities, and society. However, the underlying etiology and biological mechanism of suicidal behavior remains poorly understood.

METHODS

We collected different single omic data, including single-cell RNA sequencing (scRNA-seq), bulk mRNA-seq, DNA methylation microarrays from the cortex of Major Depressive Disorder (MDD) in suicide subjects' studies, as well as fluoxetine-treated rats brains. We matched subject IDs that overlapped between the transcriptome dataset and the methylation dataset. The differential expression genes and differentially methylated regions were calculated with a 2-group comparison analysis. Cross-omics analysis was performed to calculate the correlation between the methylated and transcript levels of differentially methylated CpG sites and mapped transcripts. Additionally, we performed a deconvolution analysis for bulk mRNA-seq and DNA methylation profiling with scRNA-seq as the reference profiles.

RESULTS

Difference in cell type proportions among 7 cell types. Meanwhile, our analysis of single-cell sequence from the antidepressant-treated rats found that drug-specific differential expression genes were enriched into biological pathways, including ion channels and glutamatergic receptors.

CONCLUSIONS

This study identified some important dysregulated genes influenced by DNA methylation in 2 brain regions of depression and suicide patients. Interestingly, we found that oligodendrocyte precursor cells (OPCs) have the most contributors for cell-type proportions related to differential expression genes and methylated sites in suicidal behavior.

Multi-omics approaches in psychoneuroimmunology and health research: Conceptual considerations and methodological recommendations

The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of PNI studies are still based on a relatively small number of analytes. To advance this work, we suggest that PNI, and health research in general, can benefit greatly from adopting a multi-omics approach, which involves integrating data across multiple biological levels (e.g., the genome, proteome, transcriptome, metabolome, lipidome, and microbiome/metagenome) to more comprehensively profile biological functions and relate these profiles to clinical and behavioral outcomes. To assist investigators in this endeavor, we provide an overview of multi-omics research, highlight recent landmark multi-omics studies investigating human health and disease risk, and discuss how multi-omics can be applied to better elucidate links between psychological, nervous system, and immune system activity. In doing so, we describe how to design high-quality multi-omics studies, decide which biological samples (e.g., blood, stool, urine, saliva, solid tissue) are most relevant, incorporate behavioral and wearable sensing data into multi-omics research, and understand key data quality, integration, analysis, and interpretation issues. PNI researchers are addressing some of the most interesting and important questions at the intersection of psychology, neuroscience, and immunology. Applying a multi-omics approach to this work will greatly expand the horizon of what is possible in PNI and has the potential to revolutionize our understanding of mind-body medicine.

Usefulness of candidate mRNAs and miRNAs as biomarkers for mild cognitive impairment and Alzheimer's disease

OBJECTIVE

To explore potential molecular mechanisms and novel biomarkers of mild cognitive impairment (MCI) and Alzheimer's disease (AD).

METHODS

The mRNA expression datasets GSE63060 and GSE63061 and the miRNA expression dataset GSE120584 were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) and miRNA (DEmiRs) were identified in the normal, MCI, and AD groups. Mfuzz clustering and weighted correlation network analyses (WGCNA) were conducted, followed by pathway and functional enrichment analyses and miRNA-mRNA network construction. Furthermore, phenotypic correlation analysis and experimental verification were performed on key DEGs and DEmiRs.

RESULTS

In total, 3,000 intersected DEGs from GSE63060/GSE63061 and 817 DEmiRs from GSE120584 were obtained. Mfuzz and WGCNA analyses revealed 106 DEGs including ribosomal protein L11 (RPL11) and 28 DEmiRs including miR-6764-5p. These DEGs and DEmiRs were mainly enriched in pathways like Ribosome. Moreover, 5 key DEGs including cytohesin 4 (CYTH4) and 6 crucial DEmiRs including miR-6734-3p were identified by miRNA-mRNA interaction network analysis. Phenotypic correlation analysis showed that CYTH4 and miR-6734-3p were correlated with patients' age. The results of quantitative polymerase chain reaction analysis confirmed that RPL11 expression was significantly downregulated in the MCI and AD groups compared to that in the normal group, while the expression of CYTH4, miR-6764-5p, and miR-6734-3p was remarkably upregulated in the MCI and AD groups.

CONCLUSIONS

miR-6764-5p might contribute to MCI and AD by targeting RPL11 in the ribosome pathway. Therefore, miR-6734-3p and its target mRNA CYTH4 might be used as novel biomarkers for MCI and AD.

Metabolomic Identification of Serum Exosome-Derived Biomarkers for Bipolar Disorder

Background

Exosomes are extracellular vesicles that play important roles in various physiological and pathological functions. Previous studies have demonstrated that exosome-derived contents are promising biomarkers to inform the pathogenesis and diagnosis of major depressive disorder and schizophrenia.

Methods

We used ultraperformance liquid chromatography-tandem mass spectrometry to analyze the differentially expressed metabolites in serum exosomes of patients with bipolar disorder (BD) and evaluated the potential of exosomal metabolites as biomarkers for BD.

Results

Our results showed 26 differentially expressed serum exosomal metabolites in patients with BD (n = 32) when compared with healthy control (HC) subjects (n = 40), and these differentially expressed metabolites were enriched in pathways related to sugar metabolism. We then utilized random forest classifier and identified 15 exosomal metabolites that can be used to classify samples from patients with BD and HC subjects with 0.838 accuracy (95% CI, 0.604–1.00) in the training set of participants. These 15 metabolites showed excellent performance in differentiating between patients with BD and HC subjects in the testing set of participants, with 0.971 accuracy (95% CI, 0.865–1.00). Importantly, the 15 exosomal metabolites also showed good to excellent performance in differentiating between BD patients and other major psychiatric diseases (major depressive disorder and schizophrenia).

Conclusion

Collectively, our findings for the first time revealed a potential role of exosomal metabolite dysregulations in the onset and/or development of BD and suggested that blood exosomal metabolites are strong candidates to inform the diagnosis of BD.

iHealth: The ethics of artificial intelligence and big data in mental healthcare

The concept of intelligent health (iHealth) in mental healthcare integrates artificial intelligence (AI) and Big Data analytics. This article is an attempt to outline ethical aspects linked to iHealth by focussing on three crucial elements that have been defined in the literature: self-monitoring, ecological momentary assessment (EMA), and data mining. The material for the analysis was obtained by a database search. Studies and reviews providing outcome data for each of the three elements were analyzed. An ethical framing of the results was conducted that shows the chances and challenges of iHealth. The synergy between self-monitoring, EMA, and data mining might enable the prevention of mental illness, the prediction of its onset, the personalization of treatment, and the participation of patients in the treatment process. Challenges arise when it comes to the autonomy of users, privacy and data security of users, and potential bias.

From genetics to systems biology of stress-related mental disorders

Many individuals will be exposed to some form of traumatic stress in their lifetime which, in turn, increases the likelihood of developing stress-related disorders such as post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and anxiety disorders (ANX). The development of these disorders is also influenced by genetics and have heritability estimates ranging between ∼30 and 70%. In this review, we provide an overview of the findings of genome-wide association studies for PTSD, depression and ANX, and we observe a clear genetic overlap between these three diagnostic categories. We go on to highlight the results from transcriptomic and epigenomic studies, and, given the multifactorial nature of stress-related disorders, we provide an overview of the gene-environment studies that have been conducted to date. Finally, we discuss systems biology approaches that are now seeing wider utility in determining a more holistic view of these complex disorders.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models

Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

Considerations for mass spectrometry-based multi-omic analysis of clinical samples

Introduction: The role of mass spectrometry in biomolecule analysis has become paramount over the last several decades ranging in the analysis across model systems and human specimens. Accordingly, the presence of mass spectrometers in clinical laboratories has also expanded alongside the number of researchers investigating the protein, lipid, and metabolite composition of an array of biospecimens. With this increase in the number of omic investigations, it is important to consider the entire experimental strategy from sample collection and storage, data collection and analysis.Areas covered: In this short review, we outline considerations for working with clinical (e.g. human) specimens including blood, urine, and cerebrospinal fluid, with emphasis on sample handling, profiling composition, targeted measurements and relevance to disease. Discussions of integrated genomic or transcriptomic datasets are not included. A brief commentary is also provided regarding new technologies with clinical relevance.Expert opinion: The role of mass spectrometry to investigate clinically related specimens is on the rise and the ability to integrate multiple omics datasets from mass spectrometry measurements will be crucial to further understanding human health and disease.

Perturbation of Ephrin Receptor Signaling and Glutamatergic Transmission in the Hypothalamus in Depression Using Proteomics Integrated With Metabolomics

Hypothalamic dysfunction is a key pathological factor in inflammation-associated depression. In the present study, isobaric tags for relative-absolute quantitation (iTRAQ) combined with mass spectrometry and gas chromatography-mass spectrometry (GC-MS) were employed to detect the proteomes and metabolomes in the hypothalamus of the lipopolysaccharide (LPS)-induced depression mouse, respectively. A total of 187 proteins and 27 metabolites were differentially expressed compared with the control group. Following the integration of bi-omics data, pertinent pathways and molecular interaction networks were further identified. The results indicated altered molecules were clustered into Ephrin receptor signaling, glutamatergic transmission, and inflammation-related signaling included the LXR/RXR activation, FXR/RXR activation, and acute phase response signaling. First discovered in the hypothalamus, Ephrin receptor signaling regulates N-methyl-D-aspartate receptor (NMDAR)-predominant glutamatergic transmission, and further acted on AKT signaling that contributed to changes in hypothalamic neuroplasticity. Ephrin type-B receptor 2 (EPHB2), a transmembrane receptor protein in Ephrin receptor signaling, was significantly elevated and interacted with the accumulated NMDAR subunit GluN2A in the hypothalamus. Additionally, molecules involved in synaptic plasticity regulation, such as hypothalamic postsynaptic density protein-95 (PSD-95), p-AKT and brain-derived neurotrophic factor (BDNF), were significantly altered in the LPS-induced depressed group. It might be an underlying pathogenesis that the EPHB2-GluN2A-AKT cascade regulates synaptic plasticity in depression. EPHB2 can be a potential therapeutic target in the correction of glutamatergic transmission dysfunction. In summary, our findings point to the previously undiscovered molecular underpinnings of the pathophysiology in the hypothalamus of inflammation-associated depression and offer potential targets to develop antidepressants.

Horizontal and vertical integrative analysis methods for mental disorders omics data

In recent biomedical studies, omics profiling has been extensively conducted on various types of mental disorders. In most of the existing analyses, a single type of mental disorder and a single type of omics measurement are analyzed. In the study of other complex diseases, integrative analysis, both vertical and horizontal integration, has been conducted and shown to bring significantly new insights into disease etiology, progression, biomarkers, and treatment. In this article, we showcase the applicability of integrative analysis to mental disorders. In particular, the horizontal integration of bipolar disorder and schizophrenia and the vertical integration of gene expression and copy number variation data are conducted. The analysis is based on the sparse principal component analysis, penalization, and other advanced statistical techniques. In data analysis, integration leads to biologically sensible findings, including the disease-related gene expressions, copy number variations, and their associations, which differ from the “benchmark” analysis. Overall, this study suggests the potential of integrative analysis in mental disorder research.

A gene co-expression module implicating the mitochondrial electron transport chain is associated with long-term response to lithium treatment in bipolar affective disorder

Lithium is the first-line treatment for bipolar affective disorder (BPAD) but two-thirds of patients respond only partially or not at all. The reasons for this high variability in lithium response are not well understood. Transcriptome-wide profiling, which tests the interface between genes and the environment, represents a viable means of exploring the molecular mechanisms underlying lithium response variability. Thus, in the present study we performed co-expression network analyses of whole-blood-derived RNA-seq data from n = 50 lithium-treated BPAD patients. Lithium response was assessed using the well-validated ALDA scale, which we used to define both a continuous and a dichotomous measure. We identified a nominally significant correlation between a co-expression module comprising 46 genes and lithium response represented as a continuous (i.e., scale ranging 0-10) phenotype (cor = -0.299, p = 0.035). Forty-three of these 46 genes had reduced mRNA expression levels in better lithium responders relative to poorer responders, and the central regulators of this module were all mitochondrially-encoded (MT-ND1, MT-ATP6, MT-CYB). Accordingly, enrichment analyses indicated that genes involved in mitochondrial functioning were heavily over-represented in this module, specifically highlighting the electron transport chain (ETC) and oxidative phosphorylation (OXPHOS) as affected processes. Disrupted ETC and OXPHOS activity have previously been implicated in the pathophysiology of BPAD. Our data adds to previous evidence suggesting that a normalisation of these processes could be central to lithium's mode of action, and could underlie a favourable therapeutic response.