Changes in cortical protein markers of iron transport with gender, major depressive disorder and suicide

Higher levels of AKT-interacting protein in the frontal pole from people with schizophrenia are limited to a sub-group who have a marked deficit in cortical muscarinic M1 receptors

We are studying the molecular pathology of a sub-group within schizophrenia (∼ 25 %: termed Muscarinic Receptor Deficit subgroup of Schizophrenia (MRDS)) who can be separated because they have very low levels of cortical muscarinic M1 receptors (CHRM1). Based on our transcriptomic data from Brodmann's area ((BA) 9, 10 and 33 (controls, schizophrenia and mood disorders) and the cortex of the CHRM1-/- mouse (a molecular model of aberrant CHRM1 signaling), we predicted levels of AKT interacting protein (AKTIP), but not tubulin alpha 1b (TUBA1B) or AKT serine/threonine kinase 1 (AKT1) and pyruvate dehydrogenase kinase 1 (PDK1) (two AKTIP-functionally associated proteins), would be changed in MRDS. Hence, we used Western blotting to measure AKTIP (BA 10: controls, schizophrenia and mood disorders; BA 9: controls and schizophrenia) plus TUBA1B, AKT1 and PDK1 (BA 10: controls and schizophrenia) proteins. The only significant change with diagnosis was higher levels of AKTIP protein in BA 10 (Cohen's d = 0.73; p = 0.02) in schizophrenia compared to controls due to higher levels of AKTIP only in people with MRDS (Cohen's d = 0.80; p = 0.03). As AKTIP is involved in AKT1 signaling, our data suggests that signaling pathway is particularly disturbed in BA 10 in MRDS.

Lower levels of soluble β-amyloid precursor protein, but not β-amyloid, in the frontal cortex in schizophrenia

We identified a sub-group (25%) of people with schizophrenia (muscarinic receptor deficit schizophrenia (MRDS)) that are characterised because of markedly lower levels of cortical muscarinic M1 receptors (CHRM1) compared to most people with the disorder (non-MRDS). Notably, bioinformatic analyses of our cortical gene expression data shows a disturbance in the homeostasis of a biochemical pathway that regulates levels of CHRM1. A step in this pathway is the processing of β-amyloid precursor protein (APP) and therefore we postulated there would be altered levels of APP in the frontal cortex from people with MRDS. Here we measure levels of CHRM1 using [3H]pirenzepine binding, soluble APP (sAPP) using Western blotting and amyloid beta peptides (Aβ1-40 and Aβ1-42) using ELISA in the frontal cortex (Brodmann's area 6: BA 6; MRDS = 14, non-MRDS = 14, controls = 14). We confirmed the MRDS cohort in this study had the expected low levels of [3H]pirenzepine binding. In addition, we showed that people with schizophrenia, independent of their sub-group status, had lower levels of sAPP compared to controls but did not have altered levels of Aβ1-40 or Aβ1-42. In conclusion, whilst changes in sAPP are not restricted to MRDS our data could indicate a role of APP, which is important in axonal and synaptic pruning, in the molecular pathology of the syndrome of schizophrenia.

Integrated entitymetrics analysis for health information on bipolar disorder using social media data and scientific literature

Purpose

This study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature.

Design/methodology/approach

Reddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations.

Findings

Mental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were “depressed” and “severe” in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, “afraid” and “Lithium” and “schizophrenia” and “suicidal,” were identified often in the social media and PMC data, respectively.

Originality/value

Mental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains.

Suicide Biomarkers to Predict Risk, Classify Diagnostic Subtypes, and Identify Novel Therapeutic Targets: 5 Years of Promising Research

BACKGROUND

Suicide is a global health crisis. However, no objective biomarkers of suicide risk currently exist, and self-reported data can be unreliable, which limits prediction, diagnostic, and treatment efforts. Reliable biomarkers that can differentiate between diagnostic subgroups, predict worsening symptoms, or suggest novel therapeutic targets would be extremely valuable for patients, researchers, and clinicians.

METHODS

MEDLINE was searched for reports published between 2016 and 2021 using search terms (suicid*) AND (biomarker*) OR (indicat*). Reports that compared biomarkers between suicidal ideation, suicide attempt, death from suicide, or any suicide subgroup against other neuropsychiatric disorders were included. Studies exclusively comparing suicidal behavior or death from suicide with healthy controls were not included to ensure that biomarkers were specific to suicide and not other psychopathology.

RESULTS

This review summarizes the last 5 years of research into suicide-associated biomarkers and provides a comprehensive guide for promising and novel biomarkers that encompass varying presentations of suicidal ideation, suicide attempt, and death by suicide. The serotonergic system, inflammation, hypothalamic-pituitary-adrenal axis, lipids, and endocannabinoids emerged as the most promising diagnostic, predictive, and therapeutic indicators.

CONCLUSIONS

The utility of diagnostic and predictive biomarkers is evident, particularly for suicide prevention. While larger-scale studies and further in-depth research are required, the last 5 years of research has uncovered essential biomarkers that could ultimately improve predictive strategies, aid diagnostics, and help develop future therapeutic targets.

Comorbidity between Alzheimer's disease and major depression: a behavioural and transcriptomic characterization study in mice

BACKGROUND

Major depression (MD) is the most prevalent psychiatric disease in the population and is considered a prodromal stage of the Alzheimer's disease (AD). Despite both diseases having a robust genetic component, the common transcriptomic signature remains unknown.

METHODS

We investigated the cognitive and emotional behavioural responses in 3- and 6-month-old APP/PSEN1-Tg mice, before β-amyloid plaques were detected. We studied the genetic and pathway deregulation in the prefrontal cortex, striatum, hippocampus and amygdala of mice at both ages, using transcriptomic and functional data analysis.

RESULTS

We found that depressive-like and anxiety-like behaviours, as well as memory impairments, are already present at 3-month-old APP/PSEN1-Tg mutant mice together with the deregulation of several genes, such as Ciart, Grin3b, Nr1d1 and Mc4r, and other genes including components of the circadian rhythms, electron transport chain and neurotransmission in all brain areas. Extending these results to human data performing GSEA analysis using DisGeNET database, it provides translational support for common deregulated gene sets related to MD and AD.

CONCLUSIONS

The present study sheds light on the shared genetic bases between MD and AD, based on a comprehensive characterization from the behavioural to transcriptomic level. These findings suggest that late MD could be an early manifestation of AD.

Potential Biomarkers for Diagnosing Major Depressive Disorder Patients with Suicidal Ideation

Background

Major depressive disorder (MDD) and suicide are two major health problems, but there are still no objective methods to diagnose MDD or suicidal ideation (SI). This study was conducted to identify potential biomarkers for diagnosing MDD patients with SI.

Methods

First-episode drug-naïve MDD patients with SI and demographics-matched healthy controls (HCs) were recruited. First-episode drug-naïve MDD patients without SI were also included. The serum lipids, C-reactive protein (CRP), transferring (TRSF), homocysteine (HCY) and alpha 1-antitrypsin (AAT) in serum were detected. The univariate and multivariate statistical analyses were used to identify and validate the potential biomarkers.

Results

The 86 HCs, 53 MDD patients with SI and 20 MDD patients without SI were included in this study. Four potential biomarkers were identified: AAT, TRSF, high-density lipoprotein cholesterol (HDLC), and apolipoprotein A1 (APOA1). After one month treatment, the levels of AAT and APOA1 were significantly improved. The panel consisting of these potential biomarkers had an excellent diagnostic performance, yielding an area under the ROC curve (AUC) of 0.994 and 0.990 in the training and testing set, respectively. Moreover, this panel could effectively distinguish MDD patients with SI from MDD patients without SI (AUC=0.928).

Conclusion

These results showed that these potential biomarkers could facilitate the development of an objective method for diagnosing MDD patients with SI, and the decreased AAT levels in MDD patients might lead to the appearance of SI by resulting in the elevated inflammation.

Label-Free Liquid Chromatography-Mass Spectrometry Proteomic Analysis of the Urinary Proteome for Measuring the Escitalopram Treatment Response From Major Depressive Disorder

Major depressive disorder (MDD) is a common mental disorder that can cause substantial impairments in quality of life. Clinical treatment is usually built on a trial-and-error method, which lasts ~12 weeks to evaluate whether the treatment is efficient, thereby leading to some inefficient treatment measures. Therefore, we intended to identify early candidate urine biomarkers to predict efficient treatment response in MDD patients. In this study, urine samples were collected twice from 19 respondent and 10 non-respondent MDD patients receiving 0-, 2-, and 12-week treatments with escitalopram. Differential urinary proteins were subsequently analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Our two pilot tests suggested that the urine proteome reflects changes associated with major depressive disorder at the early stage of treatment measures. On week 2, 20 differential proteins were identified in the response group compared with week 0, with 14 of these proteins being associated with the mechanisms of MDD. In the non-response group, 60 differential proteins were identified at week 2, with 28 of these proteins being associated with the mechanisms of MDD. In addition, differential urinary proteins at week 2 between the response and non-response groups can be clearly distinguished by using orthogonal projection on latent structure-discriminant analysis (OPLS-DA). Our small pilot tests indicated that the urine proteome can reflect early effects of escitalopram therapy between the response and non-response groups since at week 2, which may provide potential early candidate urine biomarkers to predict efficient treatment measures in MDD patients.

Metallomics-based platforms for comparing the human blood serum profiles between bipolar disorder and schizophrenia patients

RATIONALE

An evaluation of bipolar disorder (BD) and schizophrenia (SCZ) was carried out, from a metallomics point of view, using native conditions, attempting to preserve the interaction between metals and biomolecules.

METHOD

For this task, blood serum samples from healthy individuals and patients were compared. In addition, the profiles of metal ions and metalloids involved in the pathologies were quantified, and a comparison was carried out of the protein profile in serum samples of healthy individuals and diseased patients.

RESULTS

After optimization and accuracy evaluation of the method, different concentrations of Li, Mg, Mn and Zn were observed in the samples of BD patients and high levels of copper for SCZ patients, indicating an imbalance in the homeostasis of important micronutrients. The treatment, especially with lithium, may be related to competition between metallic ions. BD-related metallobiomolecules were detected, preserving the binding between metal ions and biomolecules, with four fractions detected in the ultraviolet range (280 nm). Four fractions were collected by high-performance liquid chromatography/inductively coupled plasma mass spectrometry (HPLC/ICP-MS) and the proteins were identified by liquid chromatography/tandem mass spectrometry (LC/MS/MS). The Ig lambda chain V-IV region Hil, immunoglobulin heavy constant gama 1 (IGHG1) and beta-2-glycoprotein 1 (or ApoH) was identified in SCZ samples, suggesting its relationship with mood disorders. Surprisingly, Protein IGKV2D-28 was identified only in BD samples, opening up new possibilities for studies regarding the role of this protein in BD.

CONCLUSIONS

This approach brings new perspectives to the comprehension of mood disorders, highlighting the importance of metallomics science in disease development. This strategy showed an innovative potential for evaluating mood disorders at the proteomic level, making it possible to identify proteins related to mood disorders and BD.

Machine Learning Analysis of Blood microRNA Data in Major Depression: A Case-Control Study for Biomarker Discovery

BACKGROUND

There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of machine learning (ML).

METHOD

Supervised and unsupervised ML were applied to blood microRNA expression profiles from a MDD case-control dataset (n = 168) to distinguish between (1) case vs control status, (2) MDD severity levels defined based on the Montgomery-Asberg Depression Rating Scale, and (3) antidepressant responders vs nonresponders.

RESULTS

MDD cases were distinguishable from healthy controls with an area-under-the receiver-operating characteristic curve (AUC) of 0.97 on testing data. High- vs low-severity cases were distinguishable with an AUC of 0.63. Unsupervised clustering of patients, before supervised ML analysis of each cluster for MDD severity, improved the performance of the classifiers (AUC of 0.70 for cluster 1 and 0.76 for cluster 2). Antidepressant responders could not be successfully separated from nonresponders, even after patient stratification by unsupervised clustering. However, permutation testing of the top microRNA, identified by the ML model trained to distinguish responders vs nonresponders in each of the 2 clusters, showed an association with antidepressant response. Each of these microRNA markers was only significant when comparing responders vs nonresponders of the corresponding cluster, but not using the heterogeneous unclustered patient set.

CONCLUSIONS

Supervised and unsupervised ML analysis of microRNA may lead to robust biomarkers for monitoring clinical evolution and for more timely assessment of treatment in MDD patients.