Identification of hub genes and construction of diagnostic nomogram model in schizophrenia

NECAB family of neuronal calcium-binding proteins in health and disease

The N-terminal EF-hand calcium-binding proteins 1-3 (NECAB1-3) constitute a family of predominantly neuronal proteins characterized by the presence of at least one EF-hand calcium-binding domain and a functionally less well characterized C-terminal antibiotic biosynthesis monooxygenase domain. All three family members were initially discovered due to their interactions with other proteins. NECAB1 associates with synaptotagmin-1, a critical neuronal protein involved in membrane trafficking and synaptic vesicle exocytosis. NECAB2 interacts with predominantly striatal G-protein-coupled receptors, while NECAB3 partners with amyloid-β A4 precursor protein-binding family A members 2 and 3, key regulators of amyloid-β production. This demonstrates the capacity of the family for interactions with various classes of proteins. NECAB proteins exhibit distinct subcellular localizations: NECAB1 is found in the nucleus and cytosol, NECAB2 resides in endosomes and the plasma membrane, and NECAB3 is present in the endoplasmic reticulum and Golgi apparatus. The antibiotic biosynthesis monooxygenase domain, an evolutionarily ancient component, is akin to atypical heme oxygenases in prokaryotes but is not well-characterized in vertebrates. Prokaryotic antibiotic biosynthesis monooxygenase domains typically form dimers, suggesting that calcium-mediated conformational changes in NECAB proteins may induce antibiotic biosynthesis monooxygenase domain dimerization, potentially activating some enzymatic properties. However, the substrate for this enzymatic activity remains uncertain. Alternatively, calcium-mediated conformational changes might influence protein interactions or the subcellular localization of NECAB proteins by controlling the availability of protein-protein interaction domains situated between the EF hands and the antibiotic biosynthesis monooxygenase domain. This review summarizes what is known about genomic organization, tissue expression, intracellular localization, interaction partners, and the physiological and pathophysiological role of the NECAB family.

Probing molecular pathways: Illuminating the connection between COVID-19 and Alzheimer's disease through the endocannabinoid system dynamics

Our study investigates the molecular link between COVID-19 and Alzheimer's disease (AD). We aim to elucidate the mechanisms by which COVID-19 may influence the onset or progression of AD. Using bioinformatic tools, we analyzed gene expression datasets from the Gene Expression Omnibus (GEO) database, including GSE147507, GSE12685, and GSE26927. Intersection analysis was utilized to identify common differentially expressed genes (CDEGs) and their shared biological pathways. Consensus clustering was conducted to group AD patients based on gene expression, followed by an analysis of the immune microenvironment and variations in shared pathway activities between clusters. Additionally, we identified transcription factor-binding sites shared by CDEGs and genes in the common pathway. The activity of the pathway and the expression levels of the CDEGs were validated using GSE164805 and GSE48350 datasets. Six CDEGs (MAL2, NECAB1, SH3GL2, EPB41L3, MEF2C, and NRGN) were identified, along with a downregulated pathway, the endocannabinoid (ECS) signaling pathway, common to both AD and COVID-19. These CDEGs showed a significant correlation with ECS activity (p < 0.05) and immune functions. The ECS pathway was enriched in healthy individuals' brains and downregulated in AD patients. Validation using GSE164805 and GSE48350 datasets confirmed the differential expression of these genes in COVID-19 and AD tissues. Our findings reveal a potential pathogenetic link between COVID-19 and AD, mediated by CDEGs and the ECS pathway. However, further research and multicenter evidence are needed to translate these findings into clinical applications.

Effects of chronic low-level lead (Pb) exposure on cognitive function and hippocampal neuronal ferroptosis: An integrative approach using bioinformatics analysis, machine learning, and experimental validation

Lead (Pb), a pervasive and ancient toxic heavy metal, continues to pose significant neurological health risks, particularly in regions such as Southeast Asia. While previous research has primarily focused on the adverse effects of acute, high-level lead exposure on neurological systems, studies on the impacts of chronic, low-level exposure are less extensive, especially regarding the precise mechanisms linking ferroptosis - a novel type of neuron cell death - with cognitive impairment. This study aims to explore the potential effects of chronic low-level lead exposure on cognitive function and hippocampal neuronal ferroptosis. This research represents the first comprehensive investigation into the impact of chronic low-level lead exposure on hippocampal neuronal ferroptosis, spanning clinical settings, bioinformatic analyses, and experimental validation. Our findings reveal significant alterations in the expression of genes associated with iron metabolism and Nrf2-dependent ferroptosis following lead exposure, as evidenced by comparing gene expression in the peripheral blood of lead-acid battery workers and workers without lead exposure. Furthermore, our in vitro and in vivo experimental results strongly suggest that lead exposure may precipitate cognitive dysfunction and induce hippocampal neuronal ferroptosis. In conclusion, our study indicates that chronic low-level lead exposure may activate microglia, leading to the promotion of ferroptosis in hippocampal neurons.

New insights into the role of mitochondrial metabolic dysregulation and immune infiltration in septic cardiomyopathy by integrated bioinformatics analysis and experimental validation

BACKGROUND

Septic cardiomyopathy (SCM), a common cardiovascular comorbidity of sepsis, has emerged among the leading causes of death in patients with sepsis. SCM's pathogenesis is strongly affected by mitochondrial metabolic dysregulation and immune infiltration disorder. However, the specific mechanisms and their intricate interactions in SCM remain unclear. This study employed bioinformatics analysis and drug discovery approaches to identify the regulatory molecules, distinct functions, and underlying interactions of mitochondrial metabolism and immune microenvironment, along with potential interventional strategies in SCM.

METHODS

GSE79962, GSE171546, and GSE167363 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and module genes were identified using Limma and Weighted Correlation Network Analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms, including support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) regression, and random forest, were used to screen mitochondria-related hub genes for early diagnosis of SCM. Subsequently, a nomogram was developed based on six hub genes. The immunological landscape was evaluated by single-sample gene set enrichment analysis (ssGSEA). We also explored the expression pattern of hub genes and distribution of mitochondria/inflammation-related pathways in UMAP plots of single-cell dataset. Potential drugs were explored using the Drug Signatures Database (DSigDB). In vivo and in vitro experiments were performed to validate the pathogenetic mechanism of SCM and the therapeutic efficacy of candidate drugs.

RESULTS

Six hub mitochondria-related DEGs [MitoDEGs; translocase of inner mitochondrial membrane domain-containing 1 (TIMMDC1), mitochondrial ribosomal protein S31 (MRPS31), F-box only protein 7 (FBXO7), phosphatidylglycerophosphate synthase 1 (PGS1), LYR motif containing 7 (LYRM7), and mitochondrial chaperone BCS1 (BCS1L)] were identified. The diagnostic nomogram model based on the six hub genes demonstrated high reliability and validity in both the training and validation sets. The immunological microenvironment differed between SCM and control groups. The Spearman correlation analysis revealed that hub MitoDEGs were significantly associated with the infiltration of immune cells. Upregulated hub genes showed remarkably high expression in the naive/memory B cell, CD14+ monocyte, and plasma cell subgroup, evidenced by the feature plot. The distribution of mitochondria/inflammation-related pathways varied across subgroups among control and SCM individuals. Metformin was predicted to be the most promising drug with the highest combined score. Its efficacy in restoring mitochondrial function and suppressing inflammatory responses has also been validated.

CONCLUSIONS

This study presents a comprehensive mitochondrial metabolism and immune infiltration landscape in SCM, providing a potential novel direction for the pathogenesis and medical intervention of SCM.

Exploration of programmed cell death-associated characteristics and immune infiltration in neonatal sepsis: new insights from bioinformatics analysis and machine learning

BACKGROUND

Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition.

METHODS

From the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. To further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes.

RESULTS

Within the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay.

CONCLUSION

In our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000).

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Neurological prognostication after cardiac arrest (CA) is important to avoid pursuing futile treatments for poor outcome and inappropriate withdrawal of life-sustaining treatment for good outcome. To predict neurological outcome after CA through biomarkers in peripheral blood mononuclear cells, four datasets were downloaded from the Gene Expression Omnibus database. GSE29546 and GSE74198 were used as training datasets, while GSE92696 and GSE34643 were used as verification datasets. The intersection of differentially expressed genes and hub genes from multiscale embedded gene co-expression network analysis (MEGENA) was utilized in the machine learning screening. Key genes were identified using support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF). The results were validated using receiver operating characteristic curve analysis. An mRNA-miRNA network was constructed. The distribution of immune cells was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Five biomarkers were identified as predictors for neurological outcome after CA, with an area under the curve (AUC) greater than 0.7: CASP8 and FADD-like apoptosis regulator (CFLAR), human protein kinase X (PRKX), miR-483-5p, let-7a-5p, and let-7c-5p. Interestingly, the combination of CFLAR minus PRKX showed an even higher AUC of 0.814. The mRNA-miRNA network consisted of 30 nodes and 76 edges. Statistical differences were found in immune cell distribution, including neutrophils, NK cells active, NK cells resting, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, B cells memory, and mast cells resting between individuals with good and poor neurological outcome after CA. In conclusion, our study identified novel predictors for neurological outcome after CA. Further clinical and laboratory studies are needed to validate our findings.