The Clinical Significance of Serum IL-33 and sST2 Alterations in the Post-Stroke Depression

IL-33 in Ischemic Stroke: Brain vs. Periphery

Cerebrovascular disease is the second-leading cause of death and disability worldwide, with stroke being the most common cause. In ischemic stroke, several processes combine to produce immunosuppression, leaving the post-stroke body susceptible to infection, which in turn affects neuroinflammation. Interleukin-33 (IL-33), a member of the interleukin-1 family (IL-1), functions as a modulator of immune responses and inflammation, playing a crucial role in the establishment of immunologic responses. IL-33 has been shown to have a protective effect on brain injury and represents a potential target by modulating inflammatory cytokines and stimulating immune regulatory cells. With an emphasis on preclinical and clinical studies, this review covers the impact of IL-33 on immune system mechanisms following ischemic stroke.

Current landscape of fecal microbiota transplantation in treating depression

Depression, projected to be the predominant contributor to the global disease burden, is a complex condition with diverse symptoms including mood disturbances and cognitive impairments. Traditional treatments such as medication and psychotherapy often fall short, prompting the pursuit of alternative interventions. Recent research has highlighted the significant role of gut microbiota in mental health, influencing emotional and neural regulation. Fecal microbiota transplantation (FMT), the infusion of fecal matter from a healthy donor into the gut of a patient, emerges as a promising strategy to ameliorate depressive symptoms by restoring gut microbial balance. The microbial-gut-brain (MGB) axis represents a critical pathway through which to potentially rectify dysbiosis and modulate neuropsychiatric outcomes. Preclinical studies reveal that FMT can enhance neurochemicals and reduce inflammatory markers, thereby alleviating depressive behaviors. Moreover, FMT has shown promise in clinical settings, improving gastrointestinal symptoms and overall quality of life in patients with depression. The review highlights the role of the gut-brain axis in depression and the need for further research to validate the long-term safety and efficacy of FMT, identify specific therapeutic microbial strains, and develop targeted microbial modulation strategies. Advancing our understanding of FMT could revolutionize depression treatment, shifting the paradigm toward microbiome-targeting therapies.

Cerebrospinal fluid soluble growth stimulation expressed gene 2: A potential predictor of outcome for prognosis after aneurysmal subarachnoid hemorrhage

Background

Serum concentration of soluble growth stimulation expressed gene 2 (sST2) appears to have prognostic value in patients with aneurysmal subarachnoid hemorrhage (aSAH) by now. This study aimed to investigate the relationship between cerebrospinal fluid (CSF) sST2 concentration and outcome in patients with aSAH.

Methods

A total of 65 aSAH patients who met the inclusion criteria in the Neurosurgery Department of Jining No.1 People's Hospital from March 2021 to August 2022 were selected as the research objects. 35 patients with the third month Modified-Rankin-Scale (mRS) score of 0-2 were divided into good prognosis group, and 30 patients with the third month mRS score of 3-5 were divided into poor prognosis group. CSF was collected by lumbar puncture for the first 5 days after aneurysm surgery. CSF sST2 concentration was determined using an enzyme-linked immunosorbent assay.

Results

In all patients, CSF sST2 concentrations initially increased, peaked on day 2, and then decreased. Compared with the good prognosis group, the sST2 concentration was significantly increased in the poor prognosis group at 1, 2, 3, 4 and 5 days after aSAH surgery. CSF sST2 concentration exhibited good diagnostic performance for predicting outcome (area under the receiver operating characteristic curve = 0.988). Additionally, CSF sST2 concentration has good performance for predicting cerebral edema, but only in the poor prognosis group (area under the curve = 0.93).

Conclusions

Elevated CSF sST2 concentration is associated with poor outcome in aSAH patients. CSF sST2 may have a role as a predictive biomarker in these patients.

Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of EPHX2: A Pilot Integrative Machine Learning Study

Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide prediction and prevention within this population. Integrated information from different dimensions, including personality, cognitive function, and social and genetic factors, is necessary to improve the performance of predictive models. Besides, recent studies have indicated the critical roles for EPHX2/P2X2 in the pathophysiology of MDD. Our previous studies found an association of EPHX2 and P2X2 with suicide in MDD. This study is aimed at (1) establishing predictive models with integrated information to distinguish MDD from healthy volunteers, (2) estimating the suicide risk of MDD, and (3) determining the contribution of EPHX2/P2X2. This cross-sectional study was conducted on 472 prospectively collected participants. The machine learning (ML) technique using Extreme Gradient Boosting (XGBoost) classifier was employed to evaluate the performance and relative importance of the extracted characteristics in recognising patients with MDD and depressed suicide attempters (DSA). In independent validation set, the model with clinical and cognitive information could recognise MDD with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval (CI), 0.898-0.977), and genetic information did not improve classification performance. The model with clinical, cognitive, and genetic information resulted in a significantly higher AUC of 0.801 (95% CI, 0.719-0.884) for identifying DSA than the model with only clinical information, in which the three single nucleotide polymorphisms of EPHX2 showed important roles. This study successfully established step-by-step predictive ML models to estimate the risk of suicide attempts in MDD. We found that EPHX2 can help improve the performance of suicidal predictive models. This trial is registered with NCT05575713.

Alzheimer's Disease: Models and Molecular Mechanisms Informing Disease and Treatments

Alzheimer's Disease (AD) is a complex neurodegenerative disease resulting in progressive loss of memory, language and motor abilities caused by cortical and hippocampal degeneration. This review captures the landscape of understanding of AD pathology, diagnostics, and current therapies. Two major mechanisms direct AD pathology: (1) accumulation of amyloid β (Aβ) plaque and (2) tau-derived neurofibrillary tangles (NFT). The most common variants in the Aβ pathway in APP, PSEN1, and PSEN2 are largely responsible for early-onset AD (EOAD), while MAPT, APOE, TREM2 and ABCA7 have a modifying effect on late-onset AD (LOAD). More recent studies implicate chaperone proteins and Aβ degrading proteins in AD. Several tests, such as cognitive function, brain imaging, and cerebral spinal fluid (CSF) and blood tests, are used for AD diagnosis. Additionally, several biomarkers seem to have a unique AD specific combination of expression and could potentially be used in improved, less invasive diagnostics. In addition to genetic perturbations, environmental influences, such as altered gut microbiome signatures, affect AD. Effective AD treatments have been challenging to develop. Currently, there are several FDA approved drugs (cholinesterase inhibitors, Aß-targeting antibodies and an NMDA antagonist) that could mitigate AD rate of decline and symptoms of distress.

Conversion and Obsessive-Phobic Symptoms Predict IL-33 and IL-28A Levels in Individuals Diagnosed with COVID-19

The first epidemiological wave of the incidence of COVID-19 in Bulgaria was registered in June 2020. After the wave peak, we conducted a study in persons diagnosed with COVID-19 (N = 52). They were assessed with the anxiety-depressive scale (ADS), including basic (BS), vegetative (VS), conversion (CS), obsessive-phobic (OPS), and depressive (DS) symptoms. ADS assessment of individuals diagnosed with SARS-CoV-2 indicated a correlation between OPS and IL-33 values. IL-10 levels were higher than reference ranges in all patients. Multiple linear regression analyses demonstrated that combination of CS and OPS explained 28% of IL-33 levels, while combination of symptoms from all ADS dimensions explained 24% of IL-33 levels. It was also found that 21% of IL-28A levels was explained from the combination by all ADS dimensions, whereas OPS was the predictor for lower concentrations. The obtained results revealed meaningful correlations between psycho neuro-immunological factors in pathogenesis of illness from the coronavirus infection.

Association of adenosine triphosphate-related genes to major depression and suicidal behavior: Cognition as a potential mediator

BACKGROUND

Soluble epoxide hydrolase (sEH, encoded by EPHX2) and P2X2 (a subtype of ATP receptors) may mediate the antidepressant-like effects of ATP. We sought to determine whether polymorphisms and mRNA expression of EPHX2 and P2X2 are associated with depression and suicidal behavior and how cognition may mediate such associations.

METHOD

We examined 83 single nucleotide polymorphisms (SNPs) of EPHX2 and P2X2. Subjects were MDD suicide attempters (N = 143), MDD non-suicide attempters (N = 248), and healthy volunteers (HV, N = 110). Data on demographics, depression severity, and suicide attempts were collected. Participants completed a set of cognitive tasks. Polymorphisms were genotyped using MALDI-TOF MS within the MassARRAY system. The expression of mRNA was measured using real-time polymerase chain reaction (RT-PCR).

RESULTS

Cognitive function was a significant mediator (p = 0.006) of the genetic effect on depression. Allele C of rs202059124 was associated with depression risk (OR = 11.57, 95%CI: 2.33-209.87, p = 0.0181). A significant relationship was found between P2X2 mRNA expression and depression (OR = 0.68, 95%CI: 0.49-0.94, p = 0.0199). One haploblock (rs9331942 and rs2279590) was associated with suicide attempts: subjects with haplotype GC (frequency = 19.8 %, p = 0.017) and AT (frequency = 35.2 %, p < 0.001) had a lower rate of suicide attempts.

CONCLUSIONS

Our results confirmed that cognitive impairment plays a role in the effect of rs9331949 on depression. Moreover, we confirmed a relationship between P2X2, EPHX2, and MDD in humans and presented preliminary haplotype-based evidence that implicates EPHX2 in suicide.

LIMITATIONS

The main limitation of this study is the limited sample size. More comprehensive and multi-domain cognition tasks and different assessment measures are required in further study.

Multi-omic modeling of antidepressant response implicates dynamic immune and inflammatory changes in individuals who respond to treatment

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide, and is commonly treated with antidepressant drugs (AD). Although effective, many patients fail to respond to AD treatment, and accordingly identifying factors that can predict AD response would greatly improve treatment outcomes. In this study, we developed a machine learning tool to integrate multi-omic datasets (gene expression, DNA methylation, and genotyping) to identify biomarker profiles associated with AD response in a cohort of individuals with MDD.

MATERIALS AND METHODS

Individuals with MDD (N = 111) were treated for 8 weeks with antidepressants and were separated into responders and non-responders based on the Montgomery-Åsberg Depression Rating Scale (MADRS). Using peripheral blood samples, we performed RNA-sequencing, assessed DNA methylation using the Illumina EPIC array, and performed genotyping using the Illumina PsychArray. To address this rich multi-omic dataset with high dimensional features, we developed integrative Geneset-Embedded non-negative Matrix factorization (iGEM), a non-negative matrix factorization (NMF) based model, supplemented with auxiliary information regarding gene sets and gene-methylation relationships. In particular, we factorize the subjects by features (i.e., gene expression or DNA methylation) into subjects-by-factors and factors-by-features. We define the factors as the meta-phenotypes as they represent integrated composite scores of the molecular measurements for each subject.

RESULTS

Using our model, we identified a number of meta-phenotypes which were related to AD response. By integrating geneset information into the model, we were able to relate these meta-phenotypes to biological processes, including a meta-phenotype related to immune and inflammatory functions as well as other genes related to depression or AD response. The meta-phenotype identified several genes including immune interleukin 1 receptor like 1 (IL1RL1) and interleukin 5 receptor (IL5) subunit alpha (IL5RA), AKT/PIK3 pathway related phosphoinositide-3-kinase regulatory subunit 6 (PIK3R6), and sphingomyelin phosphodiesterase 3 (SMPD3), which has been identified as a target of AD treatment.

CONCLUSIONS

The derived meta-phenotypes and associated biological functions represent both biomarkers to predict response, as well as potential new treatment targets. Our method is applicable to other diseases with multi-omic data, and the software is open source and available on Github (https://github.com/li-lab-mcgill/iGEM).

The role of damage associated molecular pattern molecules (DAMPs) and permeability of the blood-brain barrier in depression and neuroinflammation

Depression is a heterogeneous mental disorder characterized by feelings of sadness and loss of interest that render the subject unable to handle basic daily activities such as sleeping, eating, or working. Neurobiological traits leading to depression include genetic background, early life abuse, life stressors, and systemic and central inflammatory profiles. Several clinical and preclinical reports documented that depression shows an increase in pro-inflammatory markers such as interleukin (IL-)1β, IL-6, IL-12, tumor necrosis factor (TNF), and interferon (IFN)-γ; and a decrease in anti-inflammatory IL-4, IL-10, and transforming growth factor (TGF)-β species. Inflammatory activation may trigger and maintain depression. Dynamic crosstalk between the peripheral immune system and the central nervous system (CNS) such as activated endothelial cells, monocytes, monocyte-derived dendritic cells, macrophages, T cells, and microglia has been proposed as a leading cause of neuroinflammation. Notably, pro-inflammatory cytokines disrupt the hypothalamic-pituitary-adrenal (HPA) axis and serotonergic, noradrenergic, dopaminergic, and glutamatergic neurotransmission. While still under investigation, peripheral cytokines can engage brain pathways and affect the central synthesis of HPA hormones and neurotransmitters through several mechanisms such as activation of the vagus nerve, increasing the permeability of the blood-brain barrier (BBB), altered cytokines transport systems, and engaging toll-like receptors (TLRs) by pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs). However, physiological mechanisms that favor time-dependent central inflammation before or during illness are not totally understood. This review will provide preclinical and clinical evidence of DAMPs and the BBB permeability as contributors to depression and neuroinflammation. We will also discuss pharmacologic approaches that could potentially modulate DAMPs and BBB permeability for future interventions against major depression.

Study on Biomarkers Related to the Treatment of Post-Stroke Depression and Alternative Medical Treatment Methods

PURPOSE

PSD is a syndrome that occurs after a stroke, which manifests as a series of depressive symptoms and corresponding physiological symptoms. Relevant studies have shown that the drug therapy is often accompanied by drug side effects and patient resistance. Acupuncture has attracted attention as a treatment method without adverse reactions of patients. The purpose of this study was to investigate the possible mechanism of action of acupuncture in PSD.

PATIENTS AND METHODS

Download depression and stroke datasets from public databases. Bioinformatics methods were used to analyze the key gene targets related to stroke and depression. Functional enrichment analysis assesses important pathways. Further screen PSD-related biological pathways and genes. After the experimental model was established, the expression differences of key genes and related pathways were compared between the model group and the control group through acupuncture treatment and qPCR verification.

RESULTS

Depression and stroke-related genes were obtained by bioinformatics methods, and then important biological processes and biological pathways related to depression and stroke were analyzed by GO and KEGG. And further screen out the disease targets closely related to PSD. In the follow-up animal experiments, we confirmed that acupuncture can intervene on these key pathways and targets, and then play a role in the targeted therapy of diseases.

CONCLUSION

The results of this study show that five genes ("NRBP1", "SIRT1", "BDNF", "MAPK3", "CREB1".) and key biological pathways such as NFkB, PI3K/AKT activation, and MAPK are the keys to the occurrence and development of PSD biomarkers, which can also be therapeutically intervened by acupuncture.