Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity

Identification and characterization of noncoding RNAs-associated competing endogenous RNA networks in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a common and serious mental illness. Many novel genes in MDD have been characterized by high-throughput methods such as microarrays or sequencing. Recently, noncoding RNAs (ncRNAs) were suggested to be involved in the complicated environmental-genetic regulatory network of MDD occurrence; however, the interplay among RNA species, including protein-coding RNAs and ncRNAs, in MDD remains unclear.

AIM

To investigate the RNA expression datasets downloaded from a public database and construct a network based on differentially expressed long noncoding RNA (lncRNAs), microRNAs (miRNAs), and mRNAs between MDD and controls.

METHODS

Gene expression data were searched in NCBI Gene Expression Omnibus using the search term “major depressive disorder.” Six array datasets from humans were related to the search term: GSE19738, GSE32280, GSE38206, GSE52790, GSE76826, and GSE81152. These datasets were processed for initial assessment and subjected to quality control and differential expression analysis. Differentially expressed lncRNAs, miRNAs, and mRNAs were determined, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed, and protein-protein interaction network was generated. The results were analyzed for their association with MDD.

RESULTS

After analysis, 3 miRNAs, 12 lncRNAs, and 33 mRNAs were identified in the competing endogenous RNA network. Two of these miRNAs were earlier shown to be involved in psychiatric disorders, and differentially expressed mRNAs were found to be highly enriched in pathways related to neurogenesis and neuroplasticity as per Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The expression of hub gene fatty acid 2-hydroxylase was enriched, and the encoded protein was found to be involved in myelin formation, indicating that neurological development and signal transduction are involved in MDD pathogenesis.

CONCLUSION

The present study presents candidate ncRNAs involved in the neurogenesis and neuroplasticity pathways related to MDD.

M6A RNA Methylation-Based Epitranscriptomic Modifications in Plasticity-Related Genes via miR-124-C/EBPα-FTO-Transcriptional Axis in the Hippocampus of Learned Helplessness Rats

BACKGROUND

Impaired synaptic plasticity has been linked to dynamic gene regulatory network changes. Recently, gene regulation has been introduced with the emerging concept of unique N6-methyladenosine (m6A)-based reversible transcript methylation. In this study, we tested whether m6A RNA methylation may potentially serve as a link between the stressful insults and altered expression of plasticity-related genes.

METHODS

Expression of plasticity genes Nr3c1, Creb1, Ntrk2; m6A-modifying enzymes Fto, methyltransferase like (Mettl)-3 and 14; DNA methylation enzymes Dnmt1, Dnmt3a; transcription factor C/ebp-α; and miRNA-124-3p were determined by quantitative polymerase chain reaction (qPCR) in the hippocampus of rats that showed susceptibility to develop stress-induced depression (learned helplessness). M6A methylation of plasticity-related genes was determined following m6A mRNA immunoprecipitation. Chromatin immunoprecipitation was used to examine the endogenous binding of C/EBP-α to the Fto promoter. MiR-124-mediated post-transcriptional inhibition of Fto via C/EBPα was determined using an in vitro model.

RESULTS

Hippocampus of learned helplessness rats showed downregulation of Nr3c1, Creb1, and Ntrk2 along with enrichment in their m6A methylation. A downregulation in demethylating enzyme Fto and upregulation in methylating enzyme Mettl3 were also noted. The Fto promoter was hypomethylated due to the lower expression of Dnmt1 and Dnmt3a. At the same time, there was a lower occupancy of transcription factor C/EBPα on the Fto promoter. Conversely, C/ebp-α transcript was downregulated via induced miR-124-3p expression.

CONCLUSIONS

Our study mechanistically linked defective C/EBP-α-FTO-axis, epigenetically influenced by induced expression of miR-124-3p, in modifying m6A enrichment in plasticity-related genes. This could potentially be linked with abnormal neuronal plasticity in depression.

Functional brain abnormalities in major depressive disorder using a multiscale community detection approach

Major depressive disorder (MDD) is a serious disease associated with abnormal brain regions, however, the interconnection between specific brain regions related to depression has not been fully explored. To solve this problem, the paper proposes a novel multiscale community detection method to compare the differences in brain regions between normal controls (NC) and MDD patients. This study adopted the Brainnetome Atlas to divide the brain into 246 regions and extract the time series of each region. The Pearson correlation was used to measure the similarity among different brain regions to conduct the brain functional network and to perform multiscale community detection. The optimal brain community structure of each group was further explored based on the modularized Qcut algorithm, normalized mutual information (NMI), and variation of information (VI). The Jaccard index was then applied to compare the abnormalities of each brain region from different community environments between the brain function networks of NC and MDD patients. The experiments revealed several abnormal brain regions between NC and MDD, including the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, orbital gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, posterior superior temporal sulcus, inferior parietal gyrus, precuneus, postcentral gyrus, insular gyrus, cingulate gyrus, hippocampus and basal ganglia. Finally, a new subnetwork related to cognitive function was discovered, which was composed of the island gyrus and inferior frontal gyrus. All experiments indicated that the proposed method is useful in detecting functional brain abnormalities in MDD, and it can provide valuable insights into the diagnosis and treatment of MDD.

miR-218: A Stress-Responsive Epigenetic Modifier

Understanding the epigenetic role of microRNAs (miRNAs) has been a critical development in the field of neuropsychiatry and in understanding their underlying pathophysiology. Abnormalities in miRNA expression are often seen as key to the pathogenesis of many stress-associated mental disorders, including major depressive disorder (MDD). Recent advances in omics biology have further contributed to this understanding and expanded the role of miRNAs in networking a diverse array of molecular pathways, which are essentially related to the stress adaptivity of a healthy brain. Studies have highlighted the role of many such miRNAs in causing maladaptive changes in the brain's stress axis. One such miRNA is miR-218, which is debated as a critical candidate for increased stress susceptibility. miR-218 is expressed throughout the brain, notably in the hippocampus and prefrontal cortex (PFC). It is expressed at various levels through life stages, as seen by adolescent and adult animal models. Until now, a minimal number of studies have been conducted on human subjects to understand its role in stress-related abnormalities in brain circuits. However, several studies, including animal and cell-culture models, have been used to understand the impact of miR-218 on stress response and hypothalamic-pituitary-adrenal (HPA) axis function. So far, expression changes in this miRNA have been found to regulate signaling pathways such as glucocorticoid signaling, serotonergic signaling, and glutamatergic signaling. Recently, the developmental role of miR-218 has generated interest, given its increasing expression from adolescence to adulthood and targeting the Netrin-1/DCC signaling pathway. Since miR-218 expression affects neuronal development and plasticity, it is expected that a change in miR-218 expression levels over the course of development may negatively impact the process and make individuals stress-susceptible in adulthood. In this review, we describe the role of miR-218 in stress-induced neuropsychiatric conditions with an emphasis on stress-related disorders.

Blood-Based miRNA Biomarkers as Correlates of Brain-Based miRNA Expression

The use of easily accessible peripheral samples, such as blood or saliva, to investigate neurological and neuropsychiatric disorders is well-established in genetic and epigenetic research, but the pathological implications of such biomarkers are not easily discerned. To better understand the relationship between peripheral blood- and brain-based epigenetic activity, we conducted a pilot study on captive baboons (Papio hamadryas) to investigate correlations between miRNA expression in peripheral blood mononuclear cells (PBMCs) and 14 different cortical and subcortical brain regions, represented by two study groups comprised of 4 and 6 animals. Using next-generation sequencing, we identified 362 miRNAs expressed at ≥ 10 read counts in 80% or more of the brain samples analyzed. Nominally significant pairwise correlations (one-sided P < 0.05) between peripheral blood and mean brain expression levels of individual miRNAs were observed for 39 and 44 miRNAs in each group. When miRNA expression levels were averaged for tissue type across animals within the groups, Spearman's rank correlations between PBMCs and the brain regions are all highly significant (r _s = 0.47-0.57; P < 2.2 × 10^-16), although pairwise correlations among the brain regions are markedly stronger (r _s = 0.86-0.99). Principal component analysis revealed differentiation in miRNA expression between peripheral blood and the brain regions for the first component (accounting for ∼75% of variance). Linear mixed effects modeling attributed most of the variance in expression to differences between miRNAs (>70%), with non-significant 7.5% and 13.1% assigned to differences between blood and brain-based samples in the two study groups. Hierarchical UPGMA clustering revealed a major co-expression branch in both study groups, comprised of miRNAs globally upregulated in blood relative to the brain samples, exhibiting an enrichment of miRNAs expressed in immune cells (CD14+, CD15+, CD19+, CD3+, and CD56 + leukocytes) among the top blood-brain correlates, with the gene MYC, encoding a master transcription factor that regulates angiogenesis and neural stem cell activation, representing the most prevalent miRNA target. Although some differentiation was observed between tissue types, these preliminary findings reveal wider correlated patterns between blood- and brain-expressed miRNAs, suggesting the potential utility of blood-based miRNA profiling for investigating by proxy certain miRNA activity in the brain, with implications for neuroinflammatory and c-Myc-mediated processes.

Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times

Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals' lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it is a mood disturbance that can persist for an individual for more than a few weeks to months. In MDD, in most cases, the individuals do not consult a professional, and even if being consulted, the results are not significant as the individuals find it challenging to identify whether they are depressed or not. Depression, most of the time, cooccurs with anxiety and leads to suicide in few cases, among the employees, who are about to handle the pressure at work and home and mostly unnoticing such problems. This is why this work aims to analyze the IT employees who are mostly working with targets. The artificial neural network, which is modeled loosely like the brain, has proved in recent days that it can perform better than most of the classification algorithms. This study has implemented the multilayered neural perceptron and experimented with the backpropagation technique over the data samples collected from IT professionals. This study aims to develop a model that can classify depressed individuals from those who are not depressed effectively with the data collected from them manually and through sensors. The results show that deep-MLP with backpropagation outperforms other machine learning-based models for effective classification.