Genetic variants in miRNAs differentially expressed during brain development and their relevance to psychiatric disorders susceptibility

Exploring the bidirectional causal relationship between Autism Spectrum Disorder and Schizophrenia using Mendelian randomization

Autism Spectrum Disorder (ASD), characterized mainly by stereotyped behaviors and social impairments, affects about one in 100 children worldwide. Schizophrenia (SCZ), a chronic mental illness, affects 1% of the global population. The pathogenesis and specific treatment strategies for ASD and SCZ remain unclear. Previous research has suggested similarities in SCZ and ASD etiology and symptoms. However, no definitive correlation has been confirmed. Therefore, we conducted a Mendelian randomization study to assess the relationship between SCZ and ASD, providing new insights into their etiology and treatment. We used the two-sample Mendelian randomization (TSMR) approach to investigate the bidirectional causal association between SCZ and ASD, employing summary-level genome-wide association studies (GWAS) data. ASD summary data from the IEU GWAS database and SCZ summary data from the Psychiatric Genomics Consortium (PGC) were used as exposure and outcome variables, respectively. Statistical analysis was performed using the TwoSampleMR package in R version 4.3.2, with sensitivity analysis conducted to verify the result's reliability. Based on the results of the MR analysis, we retrieved and analyzed the relevant genetic information from the GWAS Catalog. TSMR analysis revealed higher ASD risk in SCZ (IVW: OR: 1.19, 95% CI: 1.12-1.26, P < .001). Bidirectional MR analysis confirmed a causal relationship between ASD and SCZ (IVW: scz2018clozuk (Clozapine UK), OR: 1.12, 95% CI: 1.04-1.21, P = .003; scz2019asi, OR: 1.14, 95% CI: 1.05-1.23, P = .002). Our study demonstrated a bidirectional relationship between SCZ and ASD in the European population, suggesting that each may induce the onset of the other.

Effects of Cognitive Intervention and Rehabilitation Training on the Expression of miR-134-5p in Elderly Patients with Diabetes Mellitus and Cognitive Impairment

OBJECTIVE

This study aimed to analyze the effect and potential mechanism of cognitive intervention and rehabilitation training in elderly patients with diabetes mellitus complicated with mild cognitive impairment.

METHODS

In this study, 128 elderly patients with diabetes mellitus complicated with mild cognitive impairment were randomly divided into the control group and the training group. The effects of the two groups were compared before and after the cognitive intervention. The expression of miR-134-5p was assessed by qRT-PCR. The relationships between miR-134-5p and Mini-Mental State Examination Scale and Montreal Cognitive Assessment Scale were evaluated.

RESULTS

After 3-month management, the Mini-Mental State Examination Scale, Montreal Cognitive Assessment Scale, the Chinese version of the diabetes self-efficacy rating scale, and WHO quality of life brief were improved in both control group and training group, and the training group showed better improvement. Cognitive intervention and rehabilitation training restricted the expression of miR-134-5p. The levels of miR-134-5p were pertinent to cognitive function.

CONCLUSION

Cognitive intervention and rehabilitation training might prevent the development of diabetes mellitus complicated with mild cognitive impairment by inhibiting miR-134-5p.

DiCleave: a deep learning model for predicting human Dicer cleavage sites

BACKGROUND

MicroRNAs (miRNAs) are a class of non-coding RNAs that play a pivotal role as gene expression regulators. These miRNAs are typically approximately 20 to 25 nucleotides long. The maturation of miRNAs requires Dicer cleavage at specific sites within the precursor miRNAs (pre-miRNAs). Recent advances in machine learning-based approaches for cleavage site prediction, such as PHDcleav and LBSizeCleav, have been reported. ReCGBM, a gradient boosting-based model, demonstrates superior performance compared with existing methods. Nonetheless, ReCGBM operates solely as a binary classifier despite the presence of two cleavage sites in a typical pre-miRNA. Previous approaches have focused on utilizing only a fraction of the structural information in pre-miRNAs, often overlooking comprehensive secondary structure information. There is a compelling need for the development of a novel model to address these limitations.

RESULTS

In this study, we developed a deep learning model for predicting the presence of a Dicer cleavage site within a pre-miRNA segment. This model was enhanced by an autoencoder that learned the secondary structure embeddings of pre-miRNA. Benchmarking experiments demonstrated that the performance of our model was comparable to that of ReCGBM in the binary classification tasks. In addition, our model excelled in multi-class classification tasks, making it a more versatile and practical solution than ReCGBM.

CONCLUSIONS

Our proposed model exhibited superior performance compared with the current state-of-the-art model, underscoring the effectiveness of a deep learning approach in predicting Dicer cleavage sites. Furthermore, our model could be trained using only sequence and secondary structure information. Its capacity to accommodate multi-class classification tasks has enhanced the practical utility of our model.

Blood miRNA levels associated with ADHD traits in children across six European birth cohorts

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent and highly heritable neurodevelopmental disorder of major societal concern. Diagnosis can be challenging and there are large knowledge gaps regarding its etiology, though studies suggest an interplay of genetic and environmental factors involving epigenetic mechanisms. MicroRNAs (miRNAs) show promise as biomarkers of human pathology and novel therapies, and here we aimed to identify blood miRNAs associated with traits of ADHD as possible biomarker candidates and further explore their biological relevance.

METHODS

Our study population consisted of 1126 children (aged 5-12 years, 46% female) from the Human Early Life Exposome study, a study spanning six ongoing population-based European birth cohorts. Expression profiles of miRNAs in whole blood samples were quantified by microarray and tested for association with ADHD-related measures of behavior and neuropsychological functions from questionnaires (Conner's Rating Scale and Child Behavior Checklist) and computer-based tests (the N-back task and Attention Network Test).

RESULTS

We identified 29 miRNAs significantly associated (false discovery rate < .05) with the Conner's questionnaire-rated trait hyperactivity, 15 of which have been linked to ADHD in previous studies. Investigation into their biological relevance revealed involvement in several pathways related to neurodevelopment and function, as well as being linked with other neurodevelopmental or psychiatric disorders known to overlap with ADHD both in symptomology, genetic risk, and co-occurrence, such as autism spectrum disorder or schizophrenia. An additional three miRNAs were significantly associated with Conner's-rated inattention. No associations were found with questionnaire-rated total ADHD index or with computer-based tests.

CONCLUSIONS

The large overlap of our hyperactivity-associated miRNAs with previous studies on ADHD is intriguing and warrant further investigation. Though this study should be considered explorative and preliminary, these findings contribute towards identifying a set of miRNAs for use as blood-based biomarkers to aid in earlier and easier ADHD diagnosis.

MicroRNA-Related Polymorphism and Their Association with Fibromyalgia

MicroRNAs are tissue-specific expressed short RNAs that serve post-transcriptional gene regulation. A specific microRNA can bind to mRNAs of different genes and thereby suppress their protein production. In the context of the complex phenotype of fibromyalgia, we used the Axiom miRNA Target Site Genotyping Array to search genome-wide for DNA variations in microRNA genes, their regulatory regions, and in the 3'UTR of protein-coding genes. To identify disease-relevant DNA polymorphisms, a cohort of 176 female fibromyalgia patients was studied in comparison to a cohort of 162 healthy women. The association between 48,329 markers and fibromyalgia was investigated using logistic regression adjusted for population stratification. Results show that 29 markers had p-values < 1 × 10^-3, and the strongest association was observed for rs758459 (p-value of 0.0001), located in the Neurogenin 1 gene which is targeted by hsa-miR-130a-3p. Furthermore, variant rs2295963 is predicted to affect binding of hsa-miR-1-3p. Both microRNAs were previously reported to be differentially expressed in fibromyalgia patients. Despite its limited statistical power, this study reports two microRNA-related polymorphisms which may play a functional role in the pathogenesis of fibromyalgia. For a better understanding of the disease pattern, further functional analyses on the biological significance of microRNAs and microRNA-related polymorphisms are required.

The non-coding genome in Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders (NDDs) characterized by difficulties in social interaction and communication, repetitive behavior, and restricted interests. While ASD have been proven to have a strong genetic component, current research largely focuses on coding regions of the genome. However, non-coding DNA, which makes up for ∼99% of the human genome, has recently been recognized as an important contributor to the high heritability of ASD, and novel sequencing technologies have been a milestone in opening up new directions for the study of the gene regulatory networks embedded within the non-coding regions. Here, we summarize current progress on the contribution of non-coding alterations to the pathogenesis of ASD and provide an overview of existing methods allowing for the study of their functional relevance, discussing potential ways of unraveling ASD's "missing heritability".

MicroRNAs serve as prediction and treatment-response biomarkers of attention-deficit/hyperactivity disorder and promote the differentiation of neuronal cells by repressing the apoptosis pathway

Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. This study aimed to examine whether miRNA expression abundance in total white blood cells (WBCs) facilitated the identification of ADHD and reflected its response to treatment. Furthermore, whether miRNA markers facilitated the growth of the human cortical neuronal (HCN-2) cells was also investigated. Total WBC samples were collected from 145 patients and 83 controls, followed by RNA extraction and qPCR assays. Subsequently, WBC samples were also collected at the endpoint from ADHD patients who had undergone 12 months of methylphenidate treatment. The determined ΔCt values of 12 miRNAs were applied to develop an ADHD prediction model and to estimate the correlation with treatment response. The prediction model applying the ΔCt values of 12 examined miRNAs (using machine learning algorithm) demonstrated good validity in discriminating ADHD patients from controls (sensitivity: 96%; specificity: 94.2%). Among the 92 ADHD patients completing the 12-month follow-up, miR-140-3p, miR-27a-3p, miR-486-5p, and miR-151-5p showed differential trends of ΔCt values between treatment responders and non-responders. In addition, the in vitro cell model revealed that miR-140-3p and miR-126-5p promoted the differentiation of HCN-2 cells by enhancing the length of neurons and the number of junctions. Microarray and flow cytometry assays confirmed that this promotion was achieved by repressing apoptosis and/or necrosis. The findings of this study suggest that the expression levels of miRNAs have the potential to serve as both diagnostic and therapeutic biomarkers for ADHD. The possible biological mechanisms of these biomarker miRNAs in ADHD pathophysiology were also clarified.

Novel approaches in schizophrenia-from risk factors and hypotheses to novel drug targets

Schizophrenia is a severe psychiatric disorder characterized by emotional, behavioral and cognitive disturbances, and the treatment of schizophrenia is often complicated by noncompliance and pharmacoresistance. The search for the pathophysiological mechanisms underlying schizophrenia has resulted in the proposal of several hypotheses to explain the impacts of environmental, genetic, neurodevelopmental, immune and inflammatory factors on disease onset and progression. This review discusses the newest insights into the pathophysiology of and risk factors for schizophrenia and notes novel approaches in antipsychotic treatment and potential diagnostic and theranostic biomarkers. The current hypotheses focusing on neuromediators (dopamine, glutamate, and serotonin), neuroinflammation, the cannabinoid hypothesis, the gut-brain axis model, and oxidative stress are summarized. Key genetic features, including small nucleotide polymorphisms, copy number variations, microdeletions, mutations and epigenetic changes, are highlighted. Current pharmacotherapy of schizophrenia relies mostly on dopaminergic and serotonergic antagonists/partial agonists, but new findings in the pathophysiology of schizophrenia have allowed the expansion of novel approaches in pharmacotherapy and the establishment of more reliable biomarkers. Substances with promising results in preclinical and clinical studies include lumateperone, pimavanserin, xanomeline, roluperidone, agonists of trace amine-associated receptor 1, inhibitors of glycine transporters, AMPA allosteric modulators, mGLUR_2-3 agonists, D-amino acid oxidase inhibitors and cannabidiol. The use of anti-inflammatory agents as an add-on therapy is mentioned.