Identifying crucial biomarkers in peripheral blood of schizophrenia and screening therapeutic agents by comprehensive bioinformatics analysis

Analysis of differentially expressed genes in schizophrenia based on bioinformatics and corresponding mRNA expression levels

OBJECTIVE

This study aimed to use bioinformatics analysis to identify differentially expressed genes (DEGs) involved in the pathogenesis of schizophrenia and validate their mRNA expression levels through real-time quantitative PCR (qPCR).

MATERIAL/METHODS

Datasets from the publicly available Gene Expression Omnibus (GEO) database were analyzed using R software to identify DEGs. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were conducted. A protein-protein interaction (PPI) network was constructed using Cytoscape software to identify key genes with notable expression changes. The expression levels of these key genes were subsequently validated in schizophrenia patients using qPCR to assess potential susceptibility genes.

RESULTS

In total, 813 DEGs were identified, with six key genes highlighted through GO analysis and PPI network screening. Among these, HDAC1, UBA52, and FYN demonstrated statistically significant differences in mRNA expression between schizophrenia patients and healthy controls (P < 0.05).

CONCLUSIONS

This study identified several DEGs potentially linked to the pathogenesis of schizophrenia, suggesting that HDAC1, UBA52, and FYN could serve as candidate susceptibility genes and diagnostic biomarkers. These findings provide new insights and directions for future schizophrenia research.

Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies

Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral blood, is of great significance. This study aimed to identify immune-related molecular biomarkers of schizophrenia in peripheral blood. Eighty-four Peripheral blood leukocytes of first-episode drug-naïve (FEDN) patients with schizophrenia and 97 healthy controls were collected and examined using high-throughput RNA-sequencing. Differentially-expressed genes (DEGs) were analysed. Weighted correlation network analysis (WGCNA) was employed to identify schizophrenia-associated module genes. The CIBERSORT algorithm was adopted to analyse immune cell proportions. Then, machine-learning algorithms including random forest, LASSO, and SVM-RFE were employed to screen immune-related predictive genes of schizophrenia. The RNA-seq analyses revealed 734 DEGs. Further machine-learning-based bioinformatic analyses screened out three immune-related predictive genes of schizophrenia (FOSB, NUP43, and H3C1), all of which were correlated with neutrophils and natural killer cells resting. Lastly, external GEO datasets were used to verify the performance of the machine-learning models with these predictive genes. In conclusion, by analysing the peripheral mRNA expression profiles of FEDN patients with schizophrenia, this study identified three predictive genes that could be potential molecular biomarkers for schizophrenia.

miRNA-Based Diagnosis of Schizophrenia Using Machine Learning

Diagnostic practices for schizophrenia are unreliable due to the lack of a stable biomarker. However, machine learning holds promise in aiding in the diagnosis of schizophrenia and other neurological disorders. Dysregulated miRNAs were extracted from public sources. Datasets of miRNAs selected from the literature and random miRNAs with designated gene targets along with related pathways were assigned as descriptors of machine-learning models. These data were preprocessed and classified using WEKA and TensorFlow, and several classifiers were tested to train the model. The Sequential neural network developed by authors performed the best of the classifiers tested, achieving an accuracy of 94.32%. Naïve Bayes was the next best model, with an accuracy of 72.23%. MLP achieved an accuracy of 65.91%, followed by Hoeffding tree with an accuracy of 64.77%, Random tree with an accuracy of 63.64%, Random forest, which achieved an accuracy of 61.36%, and lastly ADABoostM1, which achieved an accuracy of 53.41%. The Sequential neural network and Naïve Bayes classifier were tested to validate the model as they achieved the highest accuracy. Naïve Bayes achieved a validation accuracy of 72.22%, whereas the sequential neural network achieved an accuracy of 88.88%. Our results demonstrate the practicality of machine learning in psychiatric diagnosis. Dysregulated miRNA combined with machine learning can serve as a diagnostic aid to physicians for schizophrenia and potentially other neurological disorders as well.

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms

Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the GEO database and then merged and de-batched for the analyses of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). The WGCNA analysis showed the six schizophrenia-related modules containing 12,888 genes. The functional enrichment analyses indicated that the DEGs were highly involved in immune-related processes and functions. The immune cell infiltration analysis with the CIBERSORT algorithm revealed 12 types of immune cells that were significantly different between schizophrenia subjects and controls. Additionally, by intersecting DEGs, WGCNA module genes, and an immune gene set obtained from online databases, 151 schizophrenia-associated immune-related genes were obtained. Moreover, machine learning algorithms including LASSO and Random Forest were employed to further screen out 17 signature genes, including GRIN1, P2RX7, CYBB, PTPN4, UBR4, LTF, THBS1, PLXNB3, PLXNB1, PI15, RNF213, CXCL11, IL7, ARHGAP10, TTR, TYROBP, and EIF4A2. Then, SVM-RFE was added, and together with LASSO and Random Forest, a hub gene (EIF4A2) out of the 17 signature genes was revealed. Lastly, in a schizophrenia rat model, the EIF4A2 expression levels were reduced in the model rat brains in a brain-regional dependent manner, but can be reversed by risperidone. In conclusion, by using various bioinformatic and biological methods, this study found 17 immune-related signature genes and a hub gene of schizophrenia that might be potential diagnostic biomarkers and therapeutic targets of schizophrenia.

The STAT1-SLC31A1 axis: Potential regulation of cuproptosis in diabetic retinopathy

BACKGROUND

By identifying molecular biological markers linked to cuproptosis in diabetic retinopathy (DR), new pathobiological pathways and more accessible diagnostic markers can be developed.

METHODS

The datasets related to DR were acquired from the Gene Expression Omnibus database, while genes associated with cuproptosis were sourced from previously published compilations. Consensus clustering was conducted to delineate distinct DR subclasses. Feature genes were identified utilizing weighted correlation network analysis (WGCNA). Additionally, two machine-learning algorithms were employed to refine the selection of feature genes. Finally, we conducted preliminary validation experiments to ascertain the involvement of cuproptosis in DR development and the transcriptional regulation of critical genes using both the streptozotocin-induced diabetic mouse model and the high glucose-induced BV2 model.

RESULTS

In the STZ-induced diabetic mouse retinas, a decrease in the expression of cuproptosis signature proteins (FDX1, DLAT, and NDUFS8) suggested the occurrence of cuproptosis in DR. Subsequently, the expression of eight cuproptosis differential genes was validated through the STZ-induced diabetes and oxygen-induced retinopathy (OIR) models, with the key gene SLC31A1 showing upregulation in both models and dataset species. Further analyses, including weighted gene co-expression network analysis, GSVA, and immune infiltration analysis, indicated a close correlation between cuproptosis and microglia function. Additionally, validation in an in vitro model of microglia indicated the occurrence of cuproptosis in microglia under high glucose conditions, alongside abnormal expression of STAT1 with SLC31A1.

CONCLUSION

Our findings suggest that STAT1/SLC31A1 may pave the way for a deeper comprehension of the mechanistic basis of DR and offer potential therapeutic avenues.

Association between the levels of toxic heavy metals and schizophrenia in the population of Guangxi, China: A case-control study

The relationship between body levels of heavy metals and the risk of schizophrenia remains unclear. This study investigates the relationship between plasma levels of toxic heavy metals and the risk of schizophrenia among adults in Guangxi, China. Plasma concentrations of lead (Pb), cadmium (Cd), arsenic (As), and chromium (Cr) were measured using inductively coupled plasma mass spectrometry (ICP-MS). To evaluate both the single and combined effects of metal exposure on the risk of schizophrenia, we employed multivariate logistic regression, Bayesian Kernel Machine Regression (BKMR), and generalized Weighted Quantile Sum (gWQS) models. Additionally, we employed the Comparative Toxicogenomics Database (CTD) to analyze the mechanistic pathways through which metal mixtures may induce schizophrenia. Relative mRNA expression levels were measured using Real-Time Quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to predict potential biological functions. In logistic regression models, compared to the lowest exposure group (Q1), the odds ratios (ORs) for Pb in groups Q2, Q3, and Q4 were 2.18 (95% CI: 1.20-3.94), 4.74 (95% CI: 2.52-8.95), and 3.62 (95% CI: 1.80-7.28), respectively. Both BKMR and gWQS models indicated a positive correlation between the combined effects of toxic heavy metal mixtures and the risk of schizophrenia, with Pb demonstrating the most substantial impact, particularly in older adults and females. Elevated levels of tumor necrosis factor (TNF) and interleukin-1 beta (IL-1β) were observed in patients with schizophrenia, while the expression of tumor protein p53 (TP53) was significantly reduced. These findings underscore the critical need to avoid exposure to toxic heavy metals to prevent schizophrenia, highlighting significant public health implications.

Identification of Potential Biomarkers for Major Depressive Disorder: Based on Integrated Bioinformatics and Clinical Validation

Major depressive disorder (MDD) is a severe mental illness characterized by a lack of objective biomarkers. Mounting evidence suggests there are extensive transcriptional molecular changes in the prefrontal cortex (PFC) of individuals with MDD. However, it remains unclear whether there are specific genes that are consistently altered and possess diagnostic power. In this study, we conducted a systematic search of PFC datasets of MDD patients from the Gene Expression Omnibus database. We calculated the differential expression of genes (DEGs) and identified robust DEGs using the RRA and MetaDE methods. Furthermore, we validated the consistently altered genes and assessed their diagnostic power through enzyme-linked immunosorbent assay experiments in our clinical blood cohort. Additionally, we evaluated the diagnostic power of hub DEGs in independent public blood datasets. We obtained eight PFC datasets, comprising 158 MDD patients and 263 healthy controls, and identified a total of 1468 unique DEGs. Through integrated analysis, we identified 290 robustly altered DEGs. Among these, seven hub DEGs (SLC1A3, PON2, AQP1, EFEMP1, GJA1, CENPD, HSD11B1) were significantly down-regulated at the protein level in our clinical blood cohort. Moreover, these hub DEGs exhibited a negative correlation with the Hamilton Depression Scale score (P < 0.05). Furthermore, these hub DEGs formed a panel with promising diagnostic power in three independent public blood datasets (average AUCs of 0.85) and our clinical blood cohort (AUC of 0.92). The biomarker panel composed of these genes demonstrated promising diagnostic efficacy for MDD and serves as a useful tool for its diagnosis.

Extracellular Vesicle MicroRNAs as Predictive Biomarkers in Postoperative Delirium After Spine Surgery: Preliminary Study

Postoperative delirium (POD) can cause poor patient outcomes in older adults who undergo surgery. In this study, we tested plasma extracellular vesicle (EV) miRNAs obtained before the delirium event to find predictive POD biomarkers after spine surgery. We recruited patients who are more than 70 years old and have undergone spine surgery. Finally, POD patients (n = 31) were included, with no-POD patients matched in age, sex, medical history, and type of surgery (n = 31). Peripheral blood was collected from patients in the operating room after the operation was completed. EVs were isolated from plasma, and the 798 miRNA expression level from EVs was measured using a NanoString platform. Sixty-two patients were included in the study; all were Korean, 67.7% were females, and the median age was 75 years. Preoperative medical history was not statistically different between no-POD and POD patients except for hypertension and the American Society of Anesthesiologists physical status. From the miRNA profiling, we identified 142 significantly differentially expressed miRNAs in POD patients compared with no-POD patients, which are associated with psychological/neurological disorders. The top 10 differentially expressed miRNAs including miR-548ar-5p and miR-627-5p were all upregulated in POD patients and the results were validated using qRT-PCR from the independent sets of samples (n = 96). We demonstrated the potential of plasma EV-miRNAs as predictive biomarkers to identify the risk group of POD after spine surgery. It also provides opportunities for future studies investigating the role of EV-miRNAs in delirium pathology.

Exploration of the shared gene signatures and molecular mechanisms between Alzheimer's disease and intracranial aneurysm

Although Alzheimer's disease (AD) and intracranial aneurysm (IA) were two different types of diseases that occurred in the brain, ruptured IA (RIA) survivors may experience varying degrees of cognitive dysfunction. Neither AD nor IA is easily recognizable by an early onset so that the incidence of adverse clinical outcomes would be on the rise. Therefore, we focused on the exploration of the shared genes and molecular mechanisms between AD and IA, which would be significant for the efficiency of co-screening and co-diagnosis. Two GEO datasets were selected for the weighted gene co-expression network analysis (WGCNA) and differentially expressed gene screening, obtaining 78 overlapped genes. Next, 9 hub genes were identified by the protein-protein interaction network, including PIK3CA, GAB1, IGF1R, PLCB1, PGR, PDGFRB, PLCE1, FGFR3, and SYNJ1. The interactions among the hub genes, miRNA, and TFs were also explored. Meanwhile, we performed GO and KEGG pathway enrichment analyses for the results of WGCNA and hub genes, which showed that the Ras signaling and Rap1 signaling were the main shared pathogenesis. In conclusion, the present bioinformatics analysis revealed that AD and IA had the shared genes and molecular mechanisms, and these outcomes were associated with inflammation and calcium homeostasis, which could provide research clues for further studies.

Genetic determinants of coping, resilience and self-esteem in schizophrenia suggest a primary role for social factors and hippocampal neurogenesis

Schizophrenia is a severe psychiatric disorder, associated with a reduction in life expectancy of 15-20 years. Available treatments are at least partially effective in most affected individuals, and personal resources such as resilience (successful adaptation despite adversity) and coping abilities (strategies used to deal with stressful or threatening situations), are important determinants of disease outcomes and long-term sustained recovery. Published findings support the existence of a genetic background underlying resilience and coping, with variable heritability estimates. However, genome-wide analyses concerning the genetic determinants of these personal resources, especially in the context of schizophrenia, are lacking. Here, we performed a genome-wide association study coupled with accessory analyses to investigate potential genetic determinants of resilience, coping and self-esteem in 490 schizophrenia patients. Results revealed a complex genetic background partly overlapping with that of neuroticism, worry and schizophrenia itself and support the importance of social aspects in shapingthese psychological constructs. Hippocampal neurogenesis and lipid metabolism appear to be potentially relevant biological underpinnings, and specific miRNAs such as miR-124 and miR-137 may warrant further studies as potential biomarkers. In conclusion, this study represents an important first step in the identification of genetic and biological correlates shaping resilience, coping resources and self-esteem in schizophrenia.

Influence of antipsychotic drugs on microRNA expression in schizophrenia patients - A systematic review

Schizophrenia (SCZ) is a severe psychiatric disorder with unclear pathophysiology. Moreover, there is no specific biological marker to help clinicians to define a diagnosis, and medication is decided according to the psychiatrist's experience. In this scenario, microRNAs (miRNAs), which are small noncoding RNA molecules that regulate several genes, emerge as potential peripheral biomarkers to help not only the evaluation of the disease state but also the treatment response. Here, we systematically reviewed indexed literature and evaluated follow-up studies investigating the changes in miRNA expression due to antipsychotic treatment. We also assessed target genes and performed pathway enrichment analysis of miRNAs listed in this systematic review. A total of 11 studies were selected according to research criteria, and we observed that 28 miRNAs play a relevant role in schizophrenia pathogenesis or response to antipsychotic treatment, seven of those of extreme interest as possible biomarkers either for condition or treatment. Predicted targets of the miRNAs reviewed here were previously associated with schizophrenia in genome-wide studies, and pathway analysis showed enrichment for genes related to neural processes. With this review, we expect to highlight the importance of miRNAs in schizophrenia pathogenesis and its treatment and point out interesting miRNAs to future studies.

Exploration on the potential efficacy and mechanism of methyl salicylate glycosides in the treatment of schizophrenia based on bioinformatics, molecular docking and dynamics simulation

The etiological and therapeutic complexities of schizophrenia (SCZ) persist, prompting exploration of anti-inflammatory therapy as a potential treatment approach. Methyl salicylate glycosides (MSGs), possessing a structural parent nucleus akin to aspirin, are being investigated for their therapeutic potential in schizophrenia. Utilizing bioinformation mining, network pharmacology, molecular docking and dynamics simulation, the potential value and mechanism of MSGs (including MSTG-A, MSTG-B, and Gaultherin) in the treatment of SCZ, as well as the underlying pathogenesis of the disorder, were examined. 581 differentially expressed genes related to SCZ were identified in patients and healthy individuals, with 349 up-regulated genes and 232 down-regulated genes. 29 core targets were characterized by protein-protein interaction (PPI) network, with the top 10 core targets being BDNF, VEGFA, PVALB, KCNA1, GRIN2A, ATP2B2, KCNA2, APOE, PPARGC1A and SCN1A. The pathogenesis of SCZ primarily involves cAMP signaling, neurodegenerative diseases and other pathways, as well as regulation of ion transmembrane transport. Molecular docking analysis revealed that the three candidates exhibited binding activity with certain targets with binding affinities ranging from -4.7 to -109.2 kcal/mol. MSTG-A, MSTG-B and Gaultherin show promise for use in the treatment of SCZ, potentially through their ability to modulate the expression of multiple genes involved in synaptic structure and function, ion transport, energy metabolism. Molecular dynamics simulation revealed good binding abilities between MSTG-A, MSTG-B, Gaultherin and ATP2B2. It suggests new avenues for further investigation in this area.

Plasma miRNAs as potential biomarkers for schizophrenia in a Jordanian cohort

Background

Schizophrenia (SZ), a complex and chronic neuropsychiatric disorder affecting approximately 1 % of the general population, presents diagnostic challenges due to the absence of reliable biomarkers, and relying mainly on clinical observations. MicroRNAs (miRNAs) signatures in a wide range of diseases, including psychiatric disorders, hold immense potential for serving as biomarkers. This study aimed to analyze the expression levels of specific microRNAs (miRNAs) namely miR-29b-3p, miR-106b-5p, and miR-199a-3p and explore their diagnostic potential for SZ in Jordanian patients.

Methods

Small RNAs (miRNAs) were extracted from plasma samples of 30 SZ patients and 35 healthy controls. RNA was reverse transcribed and quantified by real-time polymerase chain reaction (qRT-PCR). The expression levels of three miRNAs (miR-29b-3p, miR-106b-5p and miR-199a-3p) were analyzed. Receiver operating characteristic (ROC) curves analysis was performed to evaluate diagnostic value of these miRNAs. Target genes prediction, functional enrichment and pathway analyses were done using miRWalk and Metascape. STRING database was used to construct protein-protein network and identify hub genes.

Results

Notably, miR-106b-5p and miR-199a-3p were significantly upregulated (p < 0.0001), while miRNA-29b-3p was downregulated (p < 0.0001) in SZ patients compared to controls. The diagnostic potential was assessed through ROC curves, revealing substantial diagnostic value for miR-199a-3p (AUC: 0.979) followed by miR-106b-5p (AUC: 0.774), with limited diagnostic efficacy for miR-29b-3p. Additionally, bioinformatic analyses for the predicted target genes of the diagnostically significant miRNAs uncovered Gene Ontology (GO) terms related to neurological development, including morphogenesis, which is involved in neuron differentiation, brain development, head development, and neuron projection morphogenesis. These findings highlight a potential connection between the identified miRNAs and SZ pathophysiology in the studied Jordanian population. Furthermore, a protein-protein interaction network from the target genes identified in association with neurological development in the Gene Ontology (GO) terms deepens our comprehension of the molecular landscape of the regulated target genes.

Conclusions

This comprehensive exploration highlights the promising role of miRNAs in unraveling intricate molecular pathways associated with SZ in the Jordanian cohort and suggests that plasma miRNAs could serve as reliable biomarkers for SZ diagnosis and disease progression. Remarkably, this study represents the first investigation into the role of circulating miRNA expression among Jordanian patients with SZ, providing valuable insights into the diagnostic landscape of this disorder.

Integrated analysis of transcriptional changes in major depressive disorder: Insights from blood and anterior cingulate cortex

Background

Major depressive disorder (MDD) was involved in widely transcriptional changes in central and peripheral tissues. While, previous studies focused on single tissues, making it difficult to represent systemic molecular changes throughout the body. Thus, there is an urgent need to explore the central and peripheral biomarkers with intrinsic correlation.

Methods

We systematically retrieved gene expression profiles of blood and anterior cingulate cortex (ACC). 3 blood datatsets (84 MDD and 88 controls) and 6 ACC datasets (100 MDD and 100 controls) were obtained. Differential expression analysis, RobustRankAggreg (RRA) analysis, functional enrichment analysis, immune associated analysis and protein-protein interaction networks (PPI) were integrated. Furthermore, the key genes were validated in an independent ACC dataset (12 MDD and 15 controls) and a cohort with 120 MDD and 117 controls.

Results

Differential expression analysis identified 2211 and 2021 differential expressed genes (DEGs) in blood and ACC, respectively. RRA identified 45 and 25 robust DEGs in blood and ACC based on DEGs, and all of them were closely associated with immune cells. Functional enrichment results showed both the robust DEGs in blood and ACC were enriched in humoral immune response. Furthermore, PPI identified 8 hub DEGs (CD79A, CD79B, CD19, MS4A1, PLP1, CLDN11, MOG, MAG) in blood and ACC. Independent ACC dataset showed the area under the curve (AUC) based on these hub DEGs was 0.77. Meanwhile, these hub DEGs were validated in the serum of MDD patients, and also showed a promising diagnostic power.

Conclusions

The biomarker panel based on hub DEGs yield a promising diagnostic efficacy, and all of these hub DEGs were strongly correlated with immunity. Humoral immune response may be the key link between the brain and blood in MDD, and our results may provide further understanding for MDD.

Diagnostic value and role of serum miR-15a-5p in patients with schizophrenia

BACKGROUND

More and more studies have confirmed that the heredity plays an important role in mental disorders, especially microRNA. The objective of this research was to explore the level of miR-15a-5p in patients with schizophrenia (SZ), and to evaluate the feasibility of this miRNA as a diagnostic marker of SZ.

METHODS

The serum level of miR-15a-5p in patients with SZ and healthy people was detected by RT-qPCR. ROC curve was established to evaluate the clinical diagnostic significance of miR-15a-5p in SZ. Pearson correlation coefficient was used to evaluate the correlation between miR-15a-5p level and PANSS score. Logistic regression was used to assess the risk factors of SZ. A rat model of SZ was established, and the effects of miR-15a-5p on the behavior of SZ rats were observed through water maze test and open field test.

RESULTS

The serum level of miR-15a-5p in patients with SZ was significantly increased, and ROC analysis revealed that miR-15a-5p had clinical diagnostic value in SZ. High level of miR-15a-5p was positively correlated with the positive symptom, negative symptom and general psychopathology subscore of patients. Logistic regression results showed that miR-15a-5p was a risk factor affecting the occurrence of SZ. Animal studies showed that the serum level of miR-15a-5p was elevated in the SZ rats, and inhibiting the expression of miR-15a-5p has a positive effect on improving the cognitive function and anxiety behavior of SZ rats.

CONCLUSIONS

Serum miR-15a-5p is a risk factor for SZ, which is of great significance for the diagnosis of SZ.

Early parental deprivation during primate infancy has a lifelong impact on gene expression in the male marmoset brain

Adverse early life experiences are well-established risk factors for neurological disorders later in life. However, the molecular mechanisms underlying the impact of adverse experiences on neurophysiological systems throughout life remain incompletely understood. Previous studies suggest that social attachment to parents in early development are indispensable for infants to grow into healthy adults. In situations where multiple offspring are born in a single birth in common marmosets, human hand-rearing is employed to ensure the survival of the offspring in captivity. However, hand-reared marmosets often exhibit behavioral abnormalities, including abnormal vocalizations, excessive attachment to the caretaker, and aggressive behavior. In this study, comprehensive transcriptome analyses were conducted on hippocampus tissues, a neuroanatomical region sensitive to social attachment, obtained from human hand-reared (N = 6) and parent-reared male marmosets (N = 5) at distinct developmental stages. Our analyses revealed consistent alterations in a subset of genes, including those related to neurodevelopmental diseases, across different developmental stages, indicating their continuous susceptibility to the effects of early parental deprivation. These findings highlight the dynamic nature of gene expression in response to early life experiences and suggest that the impact of early parental deprivation on gene expression may vary across different stages of development.

Comparative analysis of methods to reduce activation signature gene expression in PBMCs

Preserving the in vivo cell transcriptome is essential for accurate profiling, yet factors during cell isolation including time ex vivo and temperature induce artifactual gene expression, particularly in stress-responsive immune cells. In this study, we investigated two methods to mitigate ex vivo activation signature gene (ASG) expression in peripheral blood mononuclear cells (PBMCs): transcription and translation inhibitors (TTis) and cold temperatures during isolation. Comparative analysis of PBMCs isolated with TTis revealed reduced ASG expression. However, TTi treatment impaired responsiveness to LPS stimulation in subsequent in vitro experiments. In contrast, cold isolation methods also prevented ASG expression; up to a point where the addition of TTis during cold isolation offered minimal additional advantage. These findings highlight the importance of considering the advantages and drawbacks of different isolation methods to ensure accurate interpretation of PBMC transcriptomic profiles.

Downregulation of microRNA-330-5p induces manic-like behaviors in REM sleep-deprived rats by enhancing tyrosine hydroxylase expression

AIM

In our pilot study, we found an increase in tyrosine hydroxylase (Th) mRNA expression in the prefrontal cortex of 72-h REM sleep-deprived (SD) rats, a mania model. Additionally, the expression levels of miR-325-3p, miR-326-3p, and miR-330-5p, the predicted target miRNAs on TH, were significantly decreased. Based on these results, in this study, we investigated whether miRNA-325-3p, miR-326-3p, and miR-330-5p modulate TH and manic-like behaviors in SD rats.

METHODS

Manic-like behaviors were assessed using the open field test (OFT) and elevated plus-maze (EPM) test. The direct binding activity of miRNAs to the 3'-untranslated region (3'-UTR) of the Th gene was measured in HEK-293 cells using a luciferase reporter system. We also examined mRNA and protein expression of TH after intracerebroventricular (ICV) injection of miR-330-5p agomir to SD rats, along with manic-like behaviors.

RESULTS

We observed an upregulation in mRNA and protein expression of TH and downregulation in miRNA-325-3p, miR-326-3p, and miR-330-5p expressions in the prefrontal cortex of SD rats, together with increased manic-like behaviors. The luciferase reporter assay showed that miR-330-5p could repress TH expression through direct binding to its target site in the 3'-UTR of Th, whereas miR-326-3p and miR-330-5p could not. In addition, ICV injection of miR-330-5p agomir alleviated the increase in TH expression in the prefrontal cortex of SD rats and manic-like behaviors.

CONCLUSIONS

TH expression regulation through miR-330-5p may be implicated in the pathophysiology of mania in SD rats.

Identification of a Hypoxia-Related Signature as Candidate Detector for Schizophrenia Based on Genome-Wide Gene Expression

INTRODUCTION

Schizophrenia (SCZ), a severe neuropsychiatric disorder with high genetic susceptibility, has high rates of misdiagnosis due to the unavoidably subjective factors and heterogeneous clinical presentations. Hypoxia has been identified as an importantly risk factor that participates in the development of SCZ. Therefore, development of a hypoxia-related biomarker for SCZ diagnosis is promising. Therefore, we dedicated to develop a biomarker that could contribute to distinguishing healthy controls and SCZ patients.

METHODS

GSE17612, GSE21935, and GSE53987 datasets, consisting of 97 control samples and 99 SCZ samples, were involved in our study. The hypoxia score was calculated based on the single-sample gene-set enrichment analysis using the hypoxia-related differentially expressed genes to quantify the expression levels of these genes for each SCZ patient. Patients in high-score groups were defined if their hypoxia score was in the upper half of all hypoxia scores and patients in low-score groups if their hypoxia score was in the lower half. GSEA was applied to detect the functional pathway of these differently expressed genes. CIBERSORT algorithm was utilized to evaluate the tumor-infiltrating immune cells of SCZ patients.

RESULTS

In this study, we developed and validated a biomarker consisting of 12 hypoxia-related genes that could distinguish healthy controls and SCZ patients robustly. We found that the metabolism reprogramming might be activated in the patient with high hypoxia score. Finally, CIBERSORT analysis illustrated that lower composition of naive B cells and higher composition of memory B cells might be observed in low-score groups of SCZ patients.

CONCLUSION

These findings revealed that the hypoxia-related signature was acceptable as a detector for SCZ, providing further insight into effective diagnosis and treatment strategies for SCZ.

Identification of TAC1 Associated with Alzheimer's Disease Using a Robust Rank Aggregation Approach

BACKGROUND

Alzheimer's disease (AD) brings heavy burden to society and family. There is an urgent need to find effective methods for disease diagnosis and treatment. The robust rank aggregation (RRA) approach that could aggregate the resulting gene lists has been widely utilized in genomic data analysis.

OBJECTIVE

To identify hub genes using RRA approach in AD.

METHODS

Seven microarray datasets in frontal cortex from GEO database were used to identify differential expressed genes (DEGs) in AD patients using RRA approach. STRING was performed to explore the protein-to-protein interaction (PPI). Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were utilized for enrichment analysis. Human Gene Connectome and Gene Set Enrichment Analysis were used for functional annotation. Finally, the expression levels of hub genes were validated in the cortex of 5xFAD mice by quantitative real-time polymerase chain reaction.

RESULTS

After RRA analysis, 473 DEGs (216 upregulated and 257 downregulated) were identified in AD samples. PPI showed that DEGs had a total of 416 nodes and 2750 edges. These genes were divided into 17 clusters, each of which contains at least three genes. After functional annotation and enrichment analysis, TAC1 is identified as the hub gene and may be related to synaptic function and inflammation. In addition, Tac1 was found downregulated in cortices of 5xFAD mice.

CONCLUSION

In the current study, TAC1 is identified as a key gene in the frontal cortex of AD, providing insight into the possible pathogenesis and potential therapeutic targets for this disease.

Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning

Background

Diabetic nephropathy (DN) is the primary cause of end-stage renal disease, but existing therapeutics are limited. Therefore, novel molecular pathways that contribute to DN therapy and diagnostics are urgently needed.

Methods

Based on the Gene Expression Omnibus (GEO) database and Limma R package, we identified differentially expressed genes of DN and downloaded oxidative stress-related genes based on the Genecard database. Then, immune and oxidative stress-related hub genes were screened by combined WGCNA, machine learning, and protein-protein interaction (PPI) networks and validated by external validation sets. We conducted ROC analysis to assess the diagnostic efficacy of hub genes. The correlation of hub genes with clinical characteristics was analyzed by the Nephroseq v5 database. To understand the cellular clustering of hub genes in DN, we performed single nucleus RNA sequencing through the KIT database.

Results

Ultimately, we screened three hub genes, namely CD36, ITGB2, and SLC1A3, which were all up-regulated. According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Correlation analysis revealed that the expression of hub genes was significantly correlated with the deterioration of renal function, and the results of single nucleus RNA sequencing showed that hub genes were mainly clustered in endothelial cells and leukocyte clusters.

Conclusion

By combining three machine learning algorithms with WGCNA analysis, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of DN.

Mapping miRNA Research in Schizophrenia: A Scientometric Review

Micro RNA (miRNA) research has great implications in uncovering the aetiology of neuropsychiatric conditions due to the role of miRNA in brain development and function. Schizophrenia, a complex yet devastating neuropsychiatric disorder, is one such condition that had been extensively studied in the realm of miRNA. Although a relatively new field of research, this area of study has progressed sufficiently to warrant dozens of reviews summarising findings from past to present. However, as a majority of reviews cannot encapsulate the full body of research, there is still a need to synthesise the diversity of publications made in this area in a systematic but easy-to-understand manner. Therefore, this study adopted bibliometrics and scientometrics, specifically document co-citation analysis (DCA), to review the literature on miRNAs in the context of schizophrenia over the course of history. From a literature search on Scopus, 992 papers were found and analysed with CiteSpace. DCA analysis generated a network of 13 major clusters with different thematic focuses within the subject area. Finally, these clusters are qualitatively discussed. miRNA research has branched into schizophrenia, among other medical and psychiatric conditions, due to previous findings in other forms of non-coding RNA. With the rise of big data, bioinformatics analyses are increasingly common in this field of research. The future of research is projected to rely more heavily on interdisciplinary collaboration. Additionally, it can be expected that there will be more translational studies focusing on the application of these findings to the development of effective treatments.