Similarities and dissimilarities between psychiatric cluster disorders

The Ribosome Hypothesis: Decoding Mood Disorder Complexity

Several types of mood disorders lie along a continuum, with nebulous boundaries between them. Understanding the mechanisms that contribute to mood disorder complexity is critical for effective treatment. However, present treatments are largely centered around neurotransmission and receptor-based hypotheses, which, given the high instance of treatment resistance, fail to adequately explain the complexities of mood disorders. In this opinion piece, based on our recent results, we propose a ribosome hypothesis of mood disorders. We suggest that any hypothesis seeking to explain the diverse nature of mood disorders must incorporate infrastructure diversity that results in a wide range of effects. Ribosomes, with their mobility across neurites and complex composition, have the potential to become specialized during stress; thus, ribosome diversity and dysregulation are well suited to explaining mood disorder complexity. Here, we first establish a framework connecting ribosomes to the current state of knowledge associated with mood disorders. Then, we describe the potential mechanisms through which ribosomes could homeostatically regulate systems to manifest diverse mood disorder phenotypes and discuss approaches for substantiating the ribosome hypothesis. Investigating these mechanisms as therapeutic targets holds promise for transdiagnostic avenues targeting mood disorders.

Ribosomal dysregulation: A conserved pathophysiological mechanism in human depression and mouse chronic stress

The underlying biological mechanisms that contribute to the heterogeneity of major depressive disorder (MDD) presentation remain poorly understood, highlighting the need for a conceptual framework that can explain this variability and bridge the gap between animal models and clinical endpoints. Here, we hypothesize that comparative analysis of molecular data from different experimental systems of chronic stress, and MDD has the potential to provide insight into these mechanisms and address this gap. Thus, we compared transcriptomic profiles of brain tissue from postmortem MDD subjects and from mice exposed to chronic variable stress (CVS) to identify orthologous genes. Ribosomal protein genes (RPGs) were down-regulated, and associated ribosomal protein (RP) pseudogenes were up-regulated in both conditions. A seeded gene co-expression analysis using altered RPGs common between the MDD and CVS groups revealed that down-regulated RPGs homeostatically regulated the synaptic changes in both groups through a RP-pseudogene-driven mechanism. In vitro analysis demonstrated that the RPG dysregulation was a glucocorticoid-driven endocrine response to stress. In silico analysis further demonstrated that the dysregulation was reversed during remission from MDD and selectively responded to ketamine but not to imipramine. This study provides the first evidence that ribosomal dysregulation during stress is a conserved phenotype in human MDD and chronic stress-exposed mouse. Our results establish a foundation for the hypothesis that stress-induced alterations in RPGs and, consequently, ribosomes contribute to the synaptic dysregulation underlying MDD and chronic stress-related mood disorders. We discuss the role of ribosomal heterogeneity in the variable presentations of depression and other mood disorders.

A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup

Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.

Gene Enrichment Analysis of Astrocyte Subtypes in Psychiatric Disorders and Psychotropic Medication Datasets

Astrocytes have many important functions in the brain, but their roles in psychiatric disorders and their responses to psychotropic medications are still being elucidated. Here, we used gene enrichment analysis to assess the relationships between different astrocyte subtypes, psychiatric diseases, and psychotropic medications (antipsychotics, antidepressants and mood stabilizers). We also carried out qPCR analyses and "look-up" studies to assess the chronic effects of these drugs on astrocyte marker gene expression. Our bioinformatic analysis identified gene enrichment of different astrocyte subtypes in psychiatric disorders. The highest level of enrichment was found in schizophrenia, supporting a role for astrocytes in this disorder. We also found differential enrichment of astrocyte subtypes associated with specific biological processes, highlighting the complex responses of astrocytes under pathological conditions. Enrichment of protein phosphorylation in astrocytes and disease was confirmed by biochemical analysis. Analysis of LINCS chemical perturbagen gene signatures also found that kinase inhibitors were highly discordant with astrocyte-SCZ associated gene signatures. However, we found that common gene enrichment of different psychotropic medications and astrocyte subtypes was limited. These results were confirmed by "look-up" studies and qPCR analysis, which also reported little effect of psychotropic medications on common astrocyte marker gene expression, suggesting that astrocytes are not a primary target of these medications. Conversely, antipsychotic medication does affect astrocyte gene marker expression in postmortem schizophrenia brain tissue, supporting specific astrocyte responses in different pathological conditions. Overall, this study provides a unique view of astrocyte subtypes and the effect of medications on astrocytes in disease, which will contribute to our understanding of their role in psychiatric disorders and offers insights into targeting astrocytes therapeutically.

Differential vulnerability of anterior cingulate cortex cell types to diseases and drugs

In psychiatric disorders, mismatches between disease states and therapeutic strategies are highly pronounced, largely because of unanswered questions regarding specific vulnerabilities of different cell types and therapeutic responses. Which cellular events (housekeeping or salient) are most affected? Which cell types succumb first to challenges, and which exhibit the strongest response to drugs? Are these events coordinated between cell types? How does disease and drug effect this coordination? To address these questions, we analyzed single-nucleus-RNAseq (sn-RNAseq) data from the human anterior cingulate cortex-a region involved in many psychiatric disorders. Density index, a metric for quantifying similarities and dissimilarities across functional profiles, was employed to identify common or salient functional themes across cell types. Cell-specific signatures were integrated with existing disease and drug-specific signatures to determine cell-type-specific vulnerabilities, druggabilities, and responsiveness. Clustering of functional profiles revealed cell types jointly participating in these events. SST and VIP interneurons were found to be most vulnerable, whereas pyramidal neurons were least. Overall, the disease state is superficial layer-centric, influences cell-specific salient themes, strongly impacts disinhibitory neurons, and influences astrocyte interaction with a subset of deep-layer pyramidal neurons. In absence of disease, drugs profiles largely recapitulate disease profiles, offering a possible explanation for drug side effects. However, in presence of disease, drug activities, are deep layer-centric and involve activating a distinct subset of deep-layer pyramidal neurons to circumvent the disease state's disinhibitory circuit malfunction. These findings demonstrate a novel application of sn-RNAseq data to explain drug and disease action at a systems level.

Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity

Medication and other therapies for psychiatric disorders show unsatisfying efficacy, in part due to the significant clinical/ biological heterogeneity within each disorder and our over-reliance on categorical clinical diagnoses. Alternatively, dimensional transdiagnostic studies have provided a promising pathway toward realizing personalized medicine and improved treatment outcomes. One factor that may influence response to psychiatric treatments is cognitive function, which is reflected in one's intellectual capacity. Intellectual capacity is also reflected in the organization and structure of intrinsic brain networks. Using a large transdiagnostic cohort (n = 1721), we sought to discover neuroimaging biomarkers by developing a resting-state functional connectome-based prediction model for a key intellectual capacity measure, Full-Scale Intelligence Quotient (FSIQ), across the diagnostic spectrum. Our cross-validated model yielded an excellent prediction accuracy (r = 0.5573, p < 0.001). The robustness and generalizability of our model was further validated on three independent cohorts (n = 2641). We identified key transdiagnostic connectome signatures underlying FSIQ capacity involving the dorsal-attention, frontoparietal and default-mode networks. Meanwhile, diagnosis groups showed disorder-specific biomarker patterns. Our findings advance the neurobiological understanding of cognitive functioning across traditional diagnostic categories and provide a new avenue for neuropathological classification of psychiatric disorders.

A Retrospective Study on the Factors Associated with Long-Stay Hospitalization in a Child Neuropsychiatry Unit

The past twenty years have seen a rapid increase in acute psychiatric symptoms in children and adolescents, with a subsequent rise in the number of psychiatric hospitalizations. This paper aims to: (a) describe the epidemiology of hospitalizations and some of the clinical and sociodemographic characteristics of pediatric patients admitted to a regional referral Complex Operative Child Neuropsychiatry Hospital Unit in Northeast Italy and (b) identify potential factors correlated with the length of hospital stay.

METHODS

318 (M = 12.8 years; SD = 3.11; 72% Female) patients hospitalized for mental health disorders from 2013 to 2019.

RESULTS

Around 60% of hospital admissions occurred via the emergency room, mostly due to suicidal ideation and/or suicide attempts (24%). Affective disorders were the most frequent discharge diagnosis (40%). As for factors correlated with length of hospital stay, we found significant links with chronological age, way of hospital admission, cause of admission, discharge diagnosis, presence of psychiatric comorbidity, family conflict, and psychiatric family history.

CONCLUSIONS

These results provide information about global characteristics associated with the length of psychiatric hospital stays in pediatric patients and provide a basis on which specific precautions can be hypothesized with the aim of developing more focused treatments.

Molecular signatures from multi-omics of autism spectrum disorders and schizophrenia

The genetic and phenotypic heterogeneity of autism spectrum disorder (ASD) impedes the unification of multiple biological hypotheses in an attempt to explain the complex features of ASD, such as impaired social communication, social interaction deficits, and restricted and repetitive patterns of behavior. However, recent psychiatric genetic studies have identified numerous risk genes and chromosome loci (copy number variation: CNV) which enable us to analyze at the single gene level and utilize system-level approaches. In this review, we focus on ASD as a major neurodevelopmental disorder and review recent findings mainly from the bioinformatics of omics studies. Additionally, by comparing these data with other major psychiatric disorders, including schizophrenia (SCZ), we identify unique characteristics of both diseases from multiple enrichment, pathway, and protein-protein interaction networks (PPIs) analyses using susceptible genes found in recent large-scale genetic studies. These unified, systematic approaches highlight unique characteristics of both disorders from multiple aspects and demonstrate how convergent pathways can contribute to an understanding of the complex etiology of such neurodevelopmental and neuropsychiatric disorders.

Comorbid Chronic Pain and Depression: Shared Risk Factors and Differential Antidepressant Effectiveness

The bidirectional relationship between depression and chronic pain is well-recognized, but their clinical management remains challenging. Here we characterize the shared risk factors and outcomes for their comorbidity in the Australian Genetics of Depression cohort study (N = 13,839). Participants completed online questionnaires about chronic pain, psychiatric symptoms, comorbidities, treatment response and general health. Logistic regression models were used to examine the relationship between chronic pain and clinical and demographic factors. Cumulative linked logistic regressions assessed the effect of chronic pain on treatment response for 10 different antidepressants. Chronic pain was associated with an increased risk of depression (OR = 1.86 [1.37-2.54]), recent suicide attempt (OR = 1.88 [1.14-3.09]), higher use of tobacco (OR = 1.05 [1.02-1.09]) and misuse of painkillers (e.g., opioids; OR = 1.31 [1.06-1.62]). Participants with comorbid chronic pain and depression reported fewer functional benefits from antidepressant use and lower benefits from sertraline (OR = 0.75 [0.68-0.83]), escitalopram (OR = 0.75 [0.67-0.85]) and venlafaxine (OR = 0.78 [0.68-0.88]) when compared to participants without chronic pain. Furthermore, participants taking sertraline (OR = 0.45 [0.30-0.67]), escitalopram (OR = 0.45 [0.27-0.74]) and citalopram (OR = 0.32 [0.15-0.67]) specifically for chronic pain (among other indications) reported lower benefits compared to other participants taking these same medications but not for chronic pain. These findings reveal novel insights into the complex relationship between chronic pain and depression. Treatment response analyses indicate differential effectiveness between particular antidepressants and poorer functional outcomes for these comorbid conditions. Further examination is warranted in targeted interventional clinical trials, which also include neuroimaging genetics and pharmacogenomics protocols. This work will advance the delineation of disease risk indicators and novel aetiological pathways for therapeutic intervention in comorbid pain and depression as well as other psychiatric comorbidities.