Distinct microglia alternative splicing in Alzheimer's disease

Transcriptome combined with Mendelian randomization to screen key genes associated with mitochondrial and programmed cell death causally associated with diabetic retinopathy

Background

Mitochondrial dysfunction in the retina can induce apoptosis of retinal capillary cells, leading to diabetic retinopathy (DR). This study aimed to explore key genes related to programmed cell death (PCD) and mitochondria in DR via bioinformatic analysis.

Methods

A differential analysis was performed to identify differentially expressed genes (DEGs) between DR and control samples using the GSE94019 dataset from the Gene Expression Omnibus (GEO) database. Pearson correlation analysis was then utilized to select genes linked to mitochondrial function and PCD (M-PCD). Candidate genes were identified by overlapping DR-DEGs and M-PCD genes, followed by functional annotation. Mendelian randomization (MR) analysis was employed to identify genes with causal relationships to DR. Key genes were identified through protein-protein interaction (PPI) analysis using six algorithms (DEgree, DMNC, EPC, MCC, Genes are BottleNeck, and MNC) within Cytoscape software. The expression patterns of these genes were validated using GSE94019 and GSE60436 datasets, as well as RT-qPCR. Enrichment analysis provided insights into the function and pathways of these key genes in DR. Differential immune cell profiles were determined via immune infiltration analysis, followed by exploring the relationships between immune cells, cytokines, and the identified genes. Correlations between key genes and apoptosis genes were also examined. In vivo experiments using RT-PCR, immunohistochemistry (IHC), and western blot analysis confirmed that MYC and SLC7A11 expression was significantly elevated in DR rat retinal tissues.

Results

From 658 candidate genes, 12 showed significant causal associations with DR. MYC and SLC7A11 were particularly notable, showing upregulated expression in DR samples and involvement in apoptosis and diabetes-related pathways. These genes were significantly associated with apoptotic genes and correlated positively with altered immune cell types and cytokines, suggesting a link between immune response and DR pathogenesis. In vivo findings confirmed that MYC and SLC7A11 expression was elevated in DR rat retinal tissues.

Conclusion

Key genes (MYC and SLC7A11) associated with mitochondrial function and PCD in DR were identified, offering insights into DR's pathological mechanisms and potential targets for diagnostic and therapeutic strategies.

Microglia specific alternative splicing alterations in multiple sclerosis

Several aberrant alternative splicing (AS) events and their regulatory mechanisms are widely recognized in multiple sclerosis (MS). Yet the cell-type specific AS events have not been extensively examined. Here we assessed the diversity of AS events using web-based RNA-seq data of sorted CD15-CD11b+ microglia in white matter (WM) region from 10 patients with MS and 11 control subjects. The GSE111972 dataset was downloaded from GEO and ENA databases, aligned to the GRCh38 reference genome from ENSEMBL via STAR. rMATS was used to assess five types of AS events, alternative 3'SS (A3SS), alternative 5'SS (A5SS), skipped exon (SE), retained intron (RI) and mutually exclusive exons (MXE), followed by visualizing with rmats2sashimiplot and maser. Differential genes or transcripts were analyzed using the limma R package. Gene ontology (GO) analysis was performed with the clusterProfiler R package. 42,663 raw counts of AS events were identified and 132 significant AS events were retained based on the filtered criteria: 1) average coverage >10 and 2) delta percent spliced in (ΔPSI) >0.1. SE was the most common AS event (36.36%), followed by MXE events (32.58%), and RI (18.94%). Genes related to telomere maintenance and organization primarily underwent SE splicing, while genes associated with protein folding and mitochondrion organization were predominantly spliced in the MXE pattern. Conversely, genes experiencing RI were enriched in immune response and immunoglobulin production. In conclusion, we identified microglia-specific AS changes in the white matter of MS patients, which may shed light on novel pathological mechanisms underlying MS.

Identification of prostate cancer bone metastasis related genes and potential therapy targets by bioinformatics and in vitro experiments

The aetiology of bone metastasis in prostate cancer (PCa) remains unclear. This study aims to identify hub genes involved in this process. We utilized machine learning, GO, KEGG, GSEA, Single-cell analysis, ROC methods to identify hub genes for bone metastasis in PCa using the TCGA and GEO databases. Potential drugs targeting these genes were identified. We validated these results using 16 specimens from patients with PCa and analysed the relationship between the hub genes and clinical features. The impact of APOC1 on PCa was assessed through in vitro experiments. Seven hub genes with AUC values of 0.727-0.926 were identified. APOC1, CFH, NUSAP1 and LGALS1 were highly expressed in bone metastasis tissues, while NR4A2, ADRB2 and ZNF331 exhibited an opposite trend. Immunohistochemistry further confirmed these results. The oxidative phosphorylation pathway was significantly enriched by the identified genes. Aflatoxin B1, benzo(a)pyrene, cyclosporine were identified as potential drugs. APOC1 expression was correlated with clinical features of PCa metastasis. Silencing APOC1 significantly inhibited PCa cell proliferation, clonality, and migration in vitro. This study identified 7 hub genes that potentially facilitate bone metastasis in PCa through mitochondrial metabolic reprogramming. APOC1 emerged as a promising therapeutic target and prognostic marker for PCa with bone metastasis.

Identifying MSMO1, ELOVL6, AACS, and CERS2 related to lipid metabolism as biomarkers of Parkinson's disease

The mechanisms underlying lipid metabolic disorders in Parkinson's diseases (PD) remain unclear. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify PD-related modular genes and differentially expressed genes (DEGs). Lipid metabolism-related genes (LMRGs) were extracted from Molecular Signatures Database. Candidate genes were assessed with overlapping modular genes, DEGs, and LMRGs for the purpose of building protein-protein interaction (PPI) networks. Then, biomarkers were generated by machine learning and Backpropagation Neural Network development according to candidate genes. Biomarker-based enrichment and network modulation analyses were executed to investigate related signaling pathways. Following dimensionality reduction clustering and annotation, scRNA-seq was submitted to cellular interactions and trajectory analysis to analyze regulatory mechanisms of critical cells. Finally, qRT-PCR was conducted to confirm the expression of biomarkers in PD patients. Four biomarkers (MSMO1, ELOVL6, AACS, and CERS2) were obtained and highly predictive after analysis mentioned above. Then, OPC, Oli, and Neu cells were the primary expression sites for biomarkers according to scRNA-seq studies. Finally, we confirmed mRNA of MSMO1, ELOVL6 and AACS were downregulated in PD patients comparing with control, while CERS2 was upregulated. In conclusion, MSMO1, ELOVL6, AACS, and CERS2 related to LMRGs could be new biomarkers for diagnosing and treating PD.

Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer

BACKGROUND

Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.

METHODS

We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).

RESULTS

The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.

CONCLUSIONS

In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

The human microglia responsome: a resource to better understand microglia states in health and disease

Microglia, the immune cells of the brain, are increasingly implicated in neurodegenerative disorders through genetic studies. However, how genetic risk factors for these diseases are related to microglial gene expression, microglial function, and ultimately disease, is still largely unknown. Microglia change rapidly in response to alterations in their cellular environment, which is regulated through changes in transcriptional programs, which are as yet poorly understood. Here, we compared the effects of a set of inflammatory and restorative stimuli (lipopolysaccharide, interferon-gamma, resiquimod, tumor necrosis factor-alpha, adenosine triphosphate, dexamethasone, and interleukin-4) on human microglial cells from 67 different donors (N = 398 samples) at the gene and transcript level. We show that microglia from different anatomical brain regions show distinct responses to inflammatory stimuli. We observed a greater overlap between human stimulated microglia and human monocytes than with mouse microglia. We define specific microglial signatures across conditions which are highly relevant for a wide range of biological functions and complex human diseases. Finally, we used our stimulation signatures to interpret associations from Alzheimer's disease (AD) genetic studies and microglia by integrating our inflammatory gene expression profiles with common genetic variants to map cis -expression QTLs (eQTLs). Together, we provide the most comprehensive transcriptomic database of the human microglia responsome.

Highlights

RNA-sequencing of 398 human microglial samples exposed to six different triggers.Microglia from different anatomical regions show distinct stimulation responses.Responses in human microglia show a greater overlap with human monocytes than murine microglia.Mapping of response Quantitative Trait Loci identifies interactions between genotype and effect of stimulation on gene expression.Our atlas provides a reference map for interpreting microglia signatures in health and disease.