Bioinformatics Analysis Discovers Microtubular Tubulin Beta 6 Class V (TUBB6) as a Potential Therapeutic Target in Glioblastoma

Annexin A's Life in Pan-Cancer: Especially in Glioma Immune Cells

The Annexin A (ANXA) family plays a critical role in cancer, with particular emphasis on their prognostic significance in pan-cancer analyses and gliomas. By integrating multi-omics data from The Cancer Genome Atlas (TCGA) and single-cell sequencing analysis, we conducted a comprehensive evaluation of ANXA2 and ANXA4 to investigate their expression patterns and functional impacts across various cancers, with a focus on glioblastoma (GBM). Our analysis encompassed several key components, including literature review, identification of differentially expressed genes (DEGs) in cancer, survival analysis, co-expression studies, competing endogenous RNA networks, cellular functional analysis, tumor microenvironment response to chemotherapy, and tumor stemness. Special attention was given to glioblastoma and low-grade glioma. Notably, our findings highlighted discrepancies among the analytical tools used, underscoring the necessity of employing multiple methods for accurate identification of DEGs. Additionally, we determined that ANXA2 and ANXA4 are predominantly expressed by M2 macrophages in GBM, based on our characterization of human glioma macrophages. These results suggest a strong correlation between ANXA2 and ANXA4 expression levels and the presence of macrophages and CD4 + resting memory T cells in gliomas, offering valuable insights into the complex interplay between the ANXA family and cancer progression.

Therapeutic Potential of TUBB6 Inhibition for Hematoma Reduction, Microtubule Stabilization, and Neurological Recovery in an In Vivo Model of Intracerebral Hemorrhage

This in vivo study explored the impact of TUBB6 inhibition in intracerebral hemorrhage (ICH), focusing on its effects on hematoma volume, microtubule stability, inflammation, neuronal preservation, and sensorimotor recovery. Sprague-Dawley rats was used to induce ICH by collagenase injection into the right striatum, followed by administration of TUBB6 antisense oligonucleotide (ASO) or Control ASO directly into the hematoma site 3 h post-ICH. Outcomes measured included hematoma volume, microtubule stability (acetylated α-tubulin), levels of inflammatory cytokines, mitogen-activated protein kinase (MAPK) pathway activity, neuronal degeneration (Fluoro-Jade C staining), and cell integrity (Cresyl Violet staining). Functional recovery was assessed using neurological severity scores (mNSS), corner turn, forelimb-placing, and rotarod tests, with body weight tracked for up to 28 days. TUBB6 expression increased with the severity of hemorrhage in the ICH models. TUBB6 ASO significantly reduced hematoma volume at 24- and 72-h post-ICH, restored acetylated α-tubulin levels, suppressed MAPK signaling pathway, and decreased pro-inflammatory markers with increased IL-10. TUBB6 ASO also reduced neuronal degeneration and improved cell viability. In terms of functional outcomes, the TUBB6 ASO + ICH group exhibited reduced mNSS scores, improved body weight maintenance, and better performance on corner turn, forelimb-placing and rotarod tests compared to the Control ASO + ICH group. TUBB6 ASO treatment demonstrated therapeutic potential in a rat model of ICH by reducing hematoma volume, stabilizing microtubules, modulating the MAPK signaling pathway, and mitigating inflammation. It also preserved neuronal integrity and enhanced sensorimotor recovery, suggesting its effectiveness as a therapeutic approach to improve ICH outcomes.

Identification of novel Kv1.3 channel-interacting proteins using proximity labelling in T-cells

Potassium channels regulate membrane potential, calcium flux, cellular activation and effector functions of adaptive and innate immune cells. The voltage-activated Kv1.3 channel is an important regulator of T cell-mediated autoimmunity and microglia-mediated neuroinflammation. Kv1.3 channels, via protein-protein interactions, are localized with key immune proteins and pathways, enabling functional coupling between K+ efflux and immune mechanisms. To gain insights into proteins and pathways that interact with Kv1.3 channels, we applied a proximity-labeling proteomics approach to characterize protein interactors of the Kv1.3 channel in activated T-cells. Biotin ligase TurboID was fused to either N or C termini of Kv1.3, stably expressed in Jurkat T cells and biotinylated proteins in proximity to Kv1.3 were enriched and quantified by mass spectrometry. We identified over 1,800 Kv1.3 interactors including known interactors (beta-integrins, Stat1) although majority were novel. We found that the N-terminus of Kv1.3 preferentially interacts with protein synthesis and protein trafficking machinery, while the C-terminus interacts with immune signaling and cell junction proteins. T-cell Kv1.3 interactors included 335 cell surface, T-cell receptor complex, mitochondrial, calcium and cytokine-mediated signaling pathway and lymphocyte migration proteins. 178 Kv1.3 interactors in T-cells also represent genetic risk factors of T cell-mediated autoimmunity, including STIM1, which was further validated using co-immunoprecipitation. Our studies reveal novel proteins and molecular pathways that interact with Kv1.3 channels in adaptive (T-cell) and innate immune (microglia), providing a foundation for how Kv1.3 channels may regulate immune mechanisms in autoimmune and neurological diseases.

Developing and experimental validating a B cell exhaustion-related gene signature to assess prognosis and immunotherapeutic response in bladder cancer

BACKGROUND

B cell exhaustion (BEX) refers to the impairment of normal B cell functions and decreased proliferation capability. However, the prognostic value of BEX-related genes in bladder cancer (BLCA) remains unclear.

METHODS

BLCA cases from TCGA were used for training, while GSE5287, GSE13507, GSE31684, and GSE32894 cohorts from GEO were used for external validation. BEX-related genes were identified through literature retrieval, unsupervised clustering, and genomic difference detection. Gene pairing, LASSO, random forest, and Cox regression were employed to construct a predictive model. B cell samples from scRNAseqDB, GSE111636, and IMvigor210 were utilized to explore immunoprofiles and the predictive ability of the model in immunotherapeutic response. Additionally, 21 pairs of BLCA and paracarcinoma samples from Nanfang Hospital were used to re-confirm our findings through RT-qPCR, immunofluorescence, and flow cytometry.

RESULTS

39 BEX-related genes were identified. A 4-gene-pair signature was constructed and served as a reliable prognostic predictor across multiple datasets (pooled HR = 2.32; 95 % CI = 1.81-2.98). The signature reflected the BEX statuses of B cells (FDR < 0.05) and showed promise in evaluating immunotherapeutic sensitivity (P < 0.001). In the local cohort, CD52, TUBB6, and CAV1 were down-regulated in BLCA tissues, while TGFBI, UBE2L6, TINAGL1, and IL32 were up-regulated (all P < 0.05). Furthermore, the infiltration levels of CD19 + CD52 + and CD19 + TUBB6 + B cells in paracarcinoma samples were higher than those in BLCA samples (all P < 0.05).

CONCLUSION

A BEX-related gene signature was developed to predict prognosis and immunotherapeutic sensitivity in BLCA, providing valuable guidance for personalized treatment.

Systematic transcriptome-wide meta-analysis across endocrine disrupting chemicals reveals shared and unique liver pathways, gene networks, and disease associations

Cardiometabolic disorders (CMD) are a growing public health problem across the world. Among the known cardiometabolic risk factors are compounds that induce endocrine and metabolic dysfunctions, such as endocrine disrupting chemicals (EDCs). To date, how EDCs influence molecular programs and cardiometabolic risks has yet to be fully elucidated, especially considering the complexity contributed by species-, chemical-, and dose-specific effects. Moreover, different experimental and analytical methodologies employed by different studies pose challenges when comparing findings across studies. To explore the molecular mechanisms of EDCs in a systematic manner, we established a data-driven computational approach to meta-analyze 30 human, mouse, and rat liver transcriptomic datasets for 4 EDCs, namely bisphenol A (BPA), bis(2-ethylhexyl) phthalate (DEHP), tributyltin (TBT), and perfluorooctanoic acid (PFOA). Our computational pipeline uniformly re-analyzed pre-processed quality-controlled microarray data and raw RNAseq data, derived differentially expressed genes (DEGs) and biological pathways, modeled gene regulatory networks and regulators, and determined CMD associations based on gene overlap analysis. Our approach revealed that DEHP and PFOA shared stable transcriptomic signatures that are enriched for genes associated with CMDs, suggesting similar mechanisms of action such as perturbations of peroxisome proliferator-activated receptor gamma (PPARγ) signaling and liver gene network regulators VNN1 and ACOT2. In contrast, TBT exhibited highly divergent gene signatures, pathways, network regulators, and disease associations from the other EDCs. In addition, we found that the rat, mouse, and human BPA studies showed highly variable transcriptomic patterns, providing molecular support for the variability in BPA responses. Our work offers insights into the commonality and differences in the molecular mechanisms of various EDCs and establishes a streamlined data-driven workflow to compare molecular mechanisms of environmental substances to elucidate the underlying connections between chemical exposure and disease risks.

The Tubulin Code, from Molecules to Health and Disease

Microtubules are essential dynamic polymers composed of α/β-tubulin heterodimers. They support intracellular trafficking, cell division, cellular motility, and other essential cellular processes. In many species, both α-tubulin and β-tubulin are encoded by multiple genes with distinct expression profiles and functionality. Microtubules are further diversified through abundant posttranslational modifications, which are added and removed by a suite of enzymes to form complex, stereotyped cellular arrays. The genetic and chemical diversity of tubulin constitute a tubulin code that regulates intrinsic microtubule properties and is read by cellular effectors, such as molecular motors and microtubule-associated proteins, to provide spatial and temporal specificity to microtubules in cells. In this review, we synthesize the rapidly expanding tubulin code literature and highlight limitations and opportunities for the field. As complex microtubule arrays underlie essential physiological processes, a better understanding of how cells employ the tubulin code has important implications for human disease ranging from cancer to neurological disorders.

Prediction of anti-microtubular target proteins of tubulins and their interacting proteins using Gene Ontology tools

BACKGROUND

Tubulins are highly conserved globular proteins involved in stabilization of cellular cytoskeletal microtubules during cell cycle. Different isoforms of tubulins are differentially expressed in various cell types, and their protein-protein interactions (PPIs) analysis will help in identifying the anti-microtubular drug targets for cancer and neurological disorders. Numerous web-based PPIs analysis methods are recently being used, and in this paper, I used Gene Ontology (GO) tools, e.g., Stringbase, ProteomeHD, GeneMANIA, and ShinyGO, to identify anti-microtubular target proteins by selecting strongly interacting proteins of tubulins.

RESULTS

I used 6 different human tubulin isoforms (two from each of α-, β-, and γ-tubulin) and found several thousands of node-to-node protein interactions (highest 4956 in GeneMANIA) and selected top 10 strongly interacting node-to-node interactions with highest score, which included 7 tubulin family protein and 6 non-tubulin family proteins (total 13). Functional enrichment analysis indicated a significant role of these 13 proteins in nucleation, polymerization or depolymerization of microtubules, membrane tethering and docking, dorsal root ganglion development, mitotic cycle, and cytoskeletal organization. I found γ-tubulins (TUBG1, TUBGCP4, and TUBBGCP6) were known to contribute majorly for tubulin-associated functions followed by α-tubulin (TUBA1A) and β-tubulins (TUBB AND TUBB3). In PPI results, I found several non-tubular proteins interacting with tubulins, and six of them (HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41) were found closely associated with their functions.

CONCLUSIONS

Increasing number of regulatory proteins and subpopulation of tubulin proteins are being reported with poor understanding in their association with microtubule assembly and disassembly. The functional enrichment analysis of tubulin isoforms using recent GO tools resulted in identification of γ-tubulins playing a key role in microtubule functions and observed non-tubulin family of proteins HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41 strongly interacting functional proteins of tubulins. The present study yields a promising model system using GO tools to narrow down tubulin-associated proteins as a drug target in cancer, Alzheimer's, neurological disorders, etc.

Construction and validation of a prognosis signature based on the immune microenvironment in gastric cancer

Background

Gastric cancer (GC) is an aggressive malignant tumor with a high degree of heterogeneity, and its immune microenvironment is closely associated with tumor growth, development and drug resistance. Therefore, a classification system of gastric cancer based explicitly on the immune microenvironment context might enrich the strategy for gastric cancer prognosis and therapy.

Methods

A total of 668 GC patients were collected from TCGA-STAD (n = 350), GSE15459 (n = 192), GSE57303 (n = 70) and GSE34942 (n = 56) datasets. Three immune-related subtypes (immunity-H, -M, and -L) were identified by hierarchical cluster analysis based on the ssGSEA score of 29 immune microenvironment-related gene sets. The immune microenvironment-related prognosis signature (IMPS) was constructed via univariate Cox regression, Lasso-Cox regression and multivariate Cox regression, and nomogram model combining IMPS and clinical variables was further constructed by the “rms” package. RT-PCR was applied to validate the expression of 7 IMPS genes between two human GC cell lines (AGS and MKN45) and one normal gastric epithelial cell line (GES-1).

Results

The patients classified as immunity-H subtype exhibited highly expressed immune checkpoint and HLA-related genes, with enriched naïve B cells, M1 macrophages and CD8 T cells. We further constructed and validated a 7-gene (CTLA4, CLDN6, EMB, GPR15, ENTPD2, VWF and AKR1B1) prognosis signature, termed as IMPS. The patients with higher IMPS expression were more likely to be associated with higher pathology grade, more advanced TNM stages, higher T and N stage, and higher ratio of death. In addition, the prediction values of the combined nomogram in predicting 1-year (AUC = 0.750), 3-year (AUC = 0.764) and 5-year (AUC = 0.802) OS was higher than IMPS and individual clinical characteristics.

Conclusions

The IMPS is a novel prognosis signature associated with the immune microenvironment and clinical characteristics. The IMPS and the combined nomogram model provide a relatively reliable predictive index for predicting the survival outcomes of gastric cancer.

Bioinformatics Identification of TUBB as Potential Prognostic Biomarker for Worse Prognosis in ERα-Positive and Better Prognosis in ERα-Negative Breast Cancer

Tubulin β class I gene (TUBB) is highly expressed in various cancers and plays several roles in carcinogenesis. However, the prognostic value of TUBB in breast cancer remains to be investigated. GEPIA and Breast Cancer Gene-Expression Miner were used to explore TUBB expression in breast cancer patients. Kaplan–Meier Plotter was used to assess the relationship between TUBB expression and several prognostic indicators including overall, distant metastasis-free, and relapse-free survival in ERα-positive and ERα-negative breast cancer. The genes that correlate with TUBB in ERα-positive and ERα-negative breast cancer were explored and the pathways were investigated using GSCA. The correlation between TUBB and several gene markers of immune cells was explored using GEPIA. ERα-positive breast cancer patients with increased TUBB showed worse prognosis, possibly through the activation of the TSC/mTOR pathway, whereas ERα-negative breast cancer patients with increased TUBB mRNA showed better prognosis. Significant positive correlations were observed between TUBB and gene markers of immune cells in ERα-positive breast cancer patients, whereas significant negative correlations were observed in ERα-negative breast cancer patients. The analysis revealed that TUBB might be considered as a predictive biomarker for worse prognosis in ERα-positive and better prognosis in ERα-negative breast cancer.

Machine learning for the micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in glioblastoma

Glioblastoma (GBM) is the most common primary intracranial tumor in the central nervous system, and resistance to temozolomide is an important reason for the failure of GBM treatment. We screened out that Solute Carrier Family 2 Member 10 (SLC2A10) is significantly highly expressed in GBM with a poor prognosis, which is also enriched in the NF-E2 p45-related factor 2 (NRF2) signalling pathway. The NRF2 signalling pathway is an important defence mechanism against ferroptosis. SLC2A10 related LINC02381 is highly expressed in GBM, which is localized in the cytoplasm/exosomes, and LINC02381 encoded micropeptides are localized in the exosomes. The micropeptide encoded by LINC02381 may be a potential treatment strategy for GBM, but the underlying mechanism of its function is not precise yet. We put forward the hypothesis: “The micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in GBM.” This study innovatively used machine learning for micropeptide to provide personalized diagnosis and treatment plans for precise treatment of GBM, thereby promoting the development of translational medicine. The study aimed to help find new disease diagnoses and prognostic biomarkers and provide a new strategy for experimental scientists to design the downstream validation experiments.

Allopregnanolone suppresses glioblastoma survival through decreasing DPYSL3 and S100A11 expression

Allopregnanolone (allo) is a physiological regulator of neuronal activity that treats multiple neurological disorders. Allo penetrates the blood-brain barrier with very high efficiency, implying that allo can treat CNS-related diseases, including glioblastoma (GBM), which always recurs after standard therapy. Hence, this study aimed to determine whether allo has a therapeutic effect on GBM. We found that allo enhanced temozolomide (TMZ)-suppressed cell survival and proliferation of TMZ-resistant cells. In particular, allo enhanced TMZ-inhibited cell migration and TMZ-induced apoptosis. Additionally, allo strongly induced DNA damage characterized by γH2Ax. Furthermore, quantitative proteomic analysis, iTRAQ, showed that allo significantly decreased the levels of DPYSL3, S100A11, and S100A4, reflecting the poor prognosis of patients with GBM confirmed by differential gene expression and survival analysis. Moreover, single-cell RNA-Seq revealed that S100A11, expressed in malignant cells, oligodendrocytes, and macrophages, was significantly associated with immune cell infiltration. Furthermore, overexpression of DPYSL3 or S100A11 prevented allo-induced cell death. In conclusion, allo suppresses GBM cell survival by decreasing DPYSL3/S100A11 expression and inducing DNA damage.

Integrative analysis of DNA methylation and gene expression reveals key molecular signatures in acute myocardial infarction

Backgrounds

Acute myocardial infarction (AMI) has been one of the most fatal diseases among all types of heart diseases due to its rapid onset and high rates of fatality. Understanding accurately how multi-omics molecular features change at the early stage of AMI is crucial for its treatment. Currently, the changes involved in DNA methylation modification and gene expression of multiple genes have remained unexplored.

Results

We used the RNA-seq and MeDIP-seq on heart tissues from AMI mouse models at series of time points (Sham, AMI 10-min, 1-h, 6-h, 24-h and 72-h), to comprehensively describe the transcriptome and genome-wide DNA methylation changes at above time points. We identified 18814, 18614, 23587, 26018 and 33788 differential methylation positions (DMPs) and 123, 135, 731, 1419 and 2779 differentially expressed genes (DEGs) at 10-min, 1-h, 6-h, 24-h and 72-h AMI, respectively, compared with the sham group. Remarkably, the 6-h AMI with the drastic changes of DEGs and a large number of enriched functional pathways in KEGG may be the most critical stage of AMI process. The 4, 9, 40, 26, and 183 genes were further identified at each time point, based on the negative correlation (P < 0.05) between the differential mRNA expression and the differential DNA methylation. The mRNA and the promoter methylation expressions of five genes (Ptpn6, Csf1r, Col6a1, Cyba, and Map3k14) were validated by qRT-PCR and BSP methods, and the mRNA expressions were further confirmed to be regulated by DNA methylation in cardiomyocytes in vitro.

Conclusions

Our findings profiled the molecular variations from the perspective of DNA methylation in the early stage of AMI and provided promising epigenetic-based biomarkers for the early clinical diagnosis and therapeutic targets of AMI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01267-x.

Network Pharmacology and Inflammatory Microenvironment Strategy Approach to Finding the Potential Target of Siraitia grosvenorii (Luo Han Guo) for Glioblastoma

Background: Glioblastoma (GBM) is the most common and aggressive primary intracranial tumor of the central nervous system, and the prognosis of GBM remains a challenge using the standard methods of treatment—TMZ, radiation, and surgical resection. Traditional Chinese medicine (TCM) is a helpful complementary and alternative medicine. However, there are relatively few studies on TCM for GBM.

Purpose: We aimed to find the connection between TCM and anti-GBM.

Study design: Network pharmacology and inflammatory microenvironment strategy were used to predict Siraitia grosvenorii (Luo Han Guo) target for treating glioblastoma.

Methods: We mainly used network pharmacology and bioinformatics.

Results: CCL5 was significantly highly expressed in GBM with poor prognostics. Uni-cox and randomForest were used to determine that CCL5 was especially a biomarker in GBM. CCL5 was also the target for SG and TMZ. The active ingredient of Luo Han Guo — squalene and CCL5 —showed high binding efficiency. CCL5, a chemotactic ligand, was enriched and positively correlated in eosinophils. CCL5 was also the target of Luo Han Guo, and its effective active integrate compound –— squalene — might act on CCL5.

Conclusion: SG might be a new complementary therapy of the same medicine and food, working on the target CCL5 and playing an anti-GBM effect. CCL5 might affect the immune microenvironment of GBM.

Expression characteristics of long noncoding RNA and messenger RNA in human traumatic brain injury

OBJECTIVES

The role of long noncoding RNAs has attracted significant attention in diseases. However, their expression characteristics in human traumatic brain injury are unclear.

METHODS

The brain contusion tissues and tissues adjacent to the brain contusion from 6 server traumatic brain injury patients were used to analyze differential expression signatures of long noncoding RNAs and mRNAs via full-length transcriptome sequencing, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and establishment of a long noncoding RNA/mRNA coexpression network.

RESULTS

We identified 1720 long noncoding RNAs and 1632 mRNAs differential expression. Microarray analysis showed that 874 long noncoding RNAs and 1405 mRNAs were upregulated, 846 long noncoding RNAs and 227 mRNAs were downregulated. Subsequently, we used Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to reveal signaling pathways that were associated with target genes. Then, a long noncoding RNA/mRNA coexpression network was generated, which showed an absolute correlation coefficient value >0.99 for 559 long noncoding RNA-mRNA pairs. Finally, we comprehensive analyzed long noncoding RNA/mRNA coexpression network and Kyoto Encyclopedia of Genes and Genomes pathway and found the top five pairs of long noncoding RNA/ mRNA. Accordingly, we identified that long noncoding RNA tubulin beta 6 class V/nuclear factor E2-related factor 2 was most closely related to the pathological process after traumatic brain injury.

CONCLUSIONS

Our results indicated that the expression profiles of long noncoding RNAs and mRNAs were different after traumatic brain injury, providing new insight regarding long noncoding RNAs in human traumatic brain injury.

A Fast and Efficient Approach to Obtaining High-Purity Glioma Stem Cell Culture

Glioma is the most common and malignant primary brain tumor. Patients with malignant glioma usually have a poor prognosis due to drug resistance and disease relapse. Cancer stem cells contribute to glioma initiation, progression, resistance, and relapse. Hence, quick identification and efficient understanding of glioma stem cells (GSCs) are of profound importance for therapeutic strategies and outcomes. Ideally, therapeutic approaches will only kill cancer stem cells without harming normal neural stem cells (NSCs) that can inhibit GSCs and are often beneficial. It is key to identify the differences between cancer stem cells and normal NSCs. However, reports detailing an efficient and uniform protocol are scarce, as are comparisons between normal neural and cancer stem cells. Here, we compared different protocols and developed a fast and efficient approach to obtaining high-purity glioma stem cell by tracking observation and optimizing culture conditions. We examined the proliferative and differentiative properties confirming the identities of the GSCs with relevant markers such as Ki67, SRY-box containing gene 2, an intermediate filament protein member nestin, glial fibrillary acidic protein, and s100 calcium-binding protein (s100-beta). Finally, we identified distinct expression differences between GSCs and normal NSCs including cyclin-dependent kinase 4 and tumor protein p53. This study comprehensively describes the features of GSCs, their properties, and regulatory genes with expression differences between them and normal stem cells. Effective approaches to quickly obtaining high-quality GSCs from patients should have the potential to not only help understand the diseases and the resistances but also enable target drug screening and personalized medicine for brain tumor treatment.