Identification of immune-infiltrated hub genes as potential biomarkers of Moyamoya disease by bioinformatics analysis

Characterization of PANoptosis-related genes and the immune landscape in moyamoya disease

Moyamoya disease (MMD) is a cerebrovascular narrowing and occlusive condition characterized by progressive stenosis of the terminal portion of the internal carotid artery and the formation of an abnormal network of dilated, fragile perforators at the base of the brain. However, the role of PANoptosis, an apoptotic mechanism associated with vascular disease, has not been elucidated in MMD. In our study, a total of 40 patients' genetic data were included, and a total of 815 MMD-related differential genes were screened, including 215 upregulated genes and 600 downregulated genes. Among them, DNAJA3, ESR1, H19, KRT18 and STK3 were five key genes. These five key genes were associated with a variety of immune cells and immune factors. Moreover, GSEA (gene set enrichment analysis) and GSVA (gene set variation analysis) showed that the different expression levels of the five key genes affected multiple signaling pathways associated with MMD. In addition, they were associated with the expression of MMD-related genes. Then, based on the five key genes, a transcription factor regulatory network was constructed. In addition, targeted therapeutic drugs against MMD-related genes were obtained by the Cmap drug prediction method: MST-312, bisacodyl, indirubin, and tropanyl-3,5-dimethylbenzoate. These results suggest that the PANoptosis-related genes may contribute to the pathogenesis of MMD through multiple mechanisms.

Small-molecule α-lipoic acid targets ELK1 to balance human neutrophil and erythrocyte differentiation

BACKGROUND

Erythroid and myeloid differentiation disorders are commonly occurred in leukemia. Given that the relationship between erythroid and myeloid lineages is still unclear. To find the co-regulators in erythroid and myeloid differentiation might help to find new target for therapy of myeloid leukemia. In hematopoiesis, ALA (alpha lipoic acid) is reported to inhibit neutrophil lineage determination by targeting transcription factor ELK1 in granulocyte-monocyte progenitors via splicing factor SF3B1. However, further exploration is needed to determine whether ELK1 is a common regulatory factor for erythroid and myeloid differentiation.

METHODS

In vitro culture of isolated CD34+, CMPs (common myeloid progenitors) and CD34+ CD371- HSPCs (hematopoietic stem progenitor cells) were performed to assay the differentiation potential of monocytes, neutrophils, and erythrocytes. Overexpression lentivirus of long isoform (L-ELK1) or the short isoform (S-ELK1) of ELK1 transduced CD34+ HSPCs were transplanted into NSG mice to assay the human lymphocyte and myeloid differentiation differences 3 months after transplantation. Knocking down of SRSF11, which was high expressed in CD371+GMPs (granulocyte-monocyte progenitors), upregulated by ALA and binding to ELK1-RNA splicing site, was performed to analyze the function in erythroid differentiation derived from CD34+ CD123mid CD38+ CD371- HPCs (hematopoietic progenitor cells). RNA sequencing of L-ELK1 and S-ELK1 overexpressed CD34+ CD123mid CD38+ CD371- HPCs were performed to assay the signals changed by ELK1.

RESULTS

Here, we presented new evidence that ALA promoted erythroid differentiation by targeting the transcription factor ELK1 in CD34+ CD371- hematopoietic stem progenitor cells (HSPCs). Overexpression of either the long isoform (L-ELK1) or the short isoform (S-ELK1) of ELK1 inhibited erythroid-cell differentiation, but knockdown of ELK1 did not affect erythroid-cell differentiation. RNAseq analysis of CD34+ CD123mid CD38+ CD371- HPCs showed that L-ELK1 upregulated the expression of genes related to neutrophil activity, phosphorylation, and hypoxia signals, while S-ELK1 mainly regulated hypoxia-related signals. However, most of the genes that were upregulated by L-ELK1 were only moderately upregulated by S-ELK1, which might be due to a lack of serum response factor interaction and regulation domains in S-ELK1 compared to L-ELK1. In summary, the differentiation of neutrophils and erythrocytes might need to rely on the dose of L-ELK1 and S-ELK1 to achieve precise regulation via RNA splicing signals at early lineage commitment.

CONCLUSIONS

ALA and ELK1 are found to regulate both human granulopoiesis and erythropoiesis via RNA spliceosome, and ALA-ELK1 signal might be the target of human leukemia therapy.

GM-CSF Promotes the Development of Dysfunctional Vascular Networks in Moyamoya Disease

Moyamoya disease (MMD) is a chronic occlusive cerebrovascular disease with the development of a network of abnormal vessels. Immune inflammation is associated with the occurrence and development of MMD. However, the mechanisms underlying the formation of the abnormal vascular network remain unclear. Twenty-eight patients with MMD, 26 ischemic stroke patients, and 26 unrelated healthy volunteers were enrolled in this study The data showed that the levels of granulocyte-macrophage colony-stimulating factor (GM-CSF) were higher in MMD patients than in healthy controls (P <0.01), and GM-CSF was mainly from Th1 and Th17 cells in MMD. We found that increased GM-CSF drove monocytes to secrete a series of cytokines associated with angiogenesis, inflammation, and chemotaxis. In summary, our findings demonstrate for the first time the important involvement of GM-CSF in MMD and that GM-CSF is an important factor in the formation of abnormal vascular networks in MMD.

Circulating immune cell landscape and T-cell abnormalities in patients with moyamoya disease

BACKGROUND

Moyamoya disease (MMD) stands as a prominent cause of stroke among children and adolescents in East Asian populations. Although a growing body of evidence suggests that dysregulated inflammation and autoimmune responses might contribute to the development of MMD, a comprehensive and detailed understanding of the alterations in circulating immune cells associated with MMD remains elusive.

METHODS

In this study, we employed a combination of single-cell RNA sequencing (scRNA-seq), mass cytometry and RNA-sequencing techniques to compare immune cell profiles in peripheral blood samples obtained from patients with MMD and age-matched healthy controls.

RESULTS

Our investigation unveiled immune dysfunction in MMD patients, primarily characterized by perturbations in T-cell (TC) subpopulations, including a reduction in effector TCs and an increase in regulatory TCs (Tregs). Additionally, we observed diminished natural killer cells and dendritic cells alongside heightened B cells and monocytes in MMD patients. Notably, within the MMD group, there was an augmented proportion of fragile Tregs, whereas the stable Treg fraction decreased. MMD was also linked to heightened immune activation, as evidenced by elevated expression levels of HLA-DR and p-STAT3.

CONCLUSIONS

Our findings offer a comprehensive view of the circulating immune cell landscape in MMD patients. Immune dysregulation in patients with MMD was characterized by alterations in T-cell populations, including a decrease in effector T-cells and an increase in regulatory T-cells (Tregs), suggest a potential role for disrupted circulating immunity in the aetiology of MMD.

Identification of diagnostic markers for moyamoya disease by combining bulk RNA-sequencing analysis and machine learning

Moyamoya disease (MMD) remains a chronic progressive cerebrovascular disease with unknown etiology. A growing number of reports describe the development of MMD relevant to infection or autoimmune diseases. Identifying biomarkers of MMD is to understand the pathogenesis and development of novel targeted therapy and may be the key to improving the patient's outcome. Here, we analyzed gene expression from two GEO databases. To identify the MMD biomarkers, the weighted gene co-expression network analysis (WGCNA) and the differential expression analyses were conducted to identify 266 key genes. The KEGG and GO analyses were then performed to construct the protein interaction (PPI) network. The three machine-learning algorithms of support vector machine-recursive feature elimination (SVM-RFE), random forest and least absolute shrinkage and selection operator (LASSO) were used to analyze the key genes and take intersection to construct MMD diagnosis based on the four core genes found (ACAN, FREM1, TOP2A and UCHL1), with highly accurate AUCs of 0.805, 0.903, 0.815, 0.826. Gene enrichment analysis illustrated that the MMD samples revealed quite a few differences in pathways like one carbon pool by folate, aminoacyl-tRNA biosynthesis, fat digestion and absorption and fructose and mannose metabolism. In addition, the immune infiltration profile demonstrated that ACAN expression was associated with mast cells resting, FREM1 expression was associated with T cells CD4 naive, TOP2A expression was associated with B cells memory, UCHL1 expression was associated with mast cells activated. Ultimately, the four key genes were verified by qPCR. Taken together, our study analyzed the diagnostic biomarkers and immune infiltration characteristics of MMD, which may shed light on the potential intervention targets of moyamoya disease patients.

Mass cytometry revealed the circulating immune cell landscape across different Suzuki stages of Moyamoya disease

Moyamoya disease (MMD) is a cerebrovascular disorder marked by progressive arterial narrowing, categorized into six stages known as Suzuki stages based on angiographic features. Growing evidence indicates a pivotal role of systemic immune and inflammatory responses in the initiation and advancement of MMD. This study employs high-dimensional mass cytometry to reveal the immunophenotypic characteristics of peripheral blood immune cells (PBMCs) at various Suzuki stages, offering insights into the progression of MMD. PBMC samples from eight patients with early-stage MMD (Suzuki stages II and III) and eight patients with later-stage MMD (Suzuki stages IV, V, and VI) were analyzed using high-dimensional mass cytometry to evaluate the frequency and phenotype of immune cell subtypes. We identified 15 cell clusters and found that the immunological features of early-stage MMD and later-stage MMD are composed of cluster variations. In this study, we confirmed that, compared to later-stage MMD, the early-stage MMD group exhibits an increase in non-classical monocytes. As the Suzuki stage level increases, the proportions of plasmacytoid DCs and monocyte-derived DCs decrease. Furthermore, T cells, monocytes, DCs, and PMN-MDSCs in the early-stage MMD group show activation of the canonical NF-κB signaling pathway. We summarized and compared the similarities and differences between early-stage MMD patients and later-stage MMD patients. There is a potential role of circulating immune dysfunction and inflammatory responses in the onset and development of MMD.

The Progression of Pathophysiology of Moyamoya Disease

Moyamoya disease (MMD) is a chronic steno-occlusive cerebrovascular disease that often leads to hemorrhagic and ischemic strokes; however, its etiology remains elusive. Surgical revascularization by either direct or indirect bypass techniques to restore cerebral hypoperfusion is the treatment of choice to date. This review aims to provide an overview of the current advances in the pathophysiology of MMD, including the genetic, angiogenic, and inflammatory factors related to disease progression. These factors may cause MMD-related vascular stenosis and aberrant angiogenesis in complex manners. With a better understanding of the pathophysiology of MMD, nonsurgical approaches that target the pathogenesis of MMD may be able to halt or slow the progression of this disease.

Bioinformatics analysis reveals the landscape of immune cell infiltration and novel immune-related biomarkers in moyamoya disease

Objective: This study aimed to identify immune infiltration characteristics and new immunological diagnostic biomarkers in the cerebrovascular tissue of moyamoya disease (MMD) using bioinformatics analysis. Methods: GSE189993 and GSE141022 were downloaded from the GEO database. Differentially expressed gene and PPI analysis were performed. After performing WGCNA, the most significant module associated with MMD was obtained. Next, functional pathways according to GSEA, GO, and KEGG were enriched for the aforementioned core genes obtained from PPI and WGCNA. Additionally, immune infiltration, using the CIBERSORT deconvolution algorithm, immune-related biomarkers, and the relationship between these genes, was further explored. Finally, diagnostic accuracy was verified with ROC curves in the validation dataset GSE157628. Results: A total of 348 DEGs were screened, including 89 downregulated and 259 upregulated genes. The thistle^l module was detected as the most significant module associated with MMD. Functional analysis of the core genes was chiefly involved in the immune response, immune system process, protein tyrosine kinase activity, secretory granule, and so on. Among 13 immune-related overlapping genes, 4 genes (BTK, FGR, PTPN11, and SYK) were identified as potential diagnostic biomarkers, where PTPN11 showed the highest specificity and sensitivity. Meanwhile, a higher proportion of eosinophils, not T cells or B cells, was demonstrated in the specific immune infiltration landscape of MMD. Conclusion: Immune activities and immune cells were actively involved in the progression of MMD. BTK, FGR, PTPN11, and SYK were identified as potential immune diagnostic biomarkers. These immune-related genes and cells may provide novel insights for immunotherapy in the future.

Physiological and pathophysiological mechanisms of the molecular and cellular biology of angiogenesis and inflammation in moyamoya angiopathy and related vascular diseases

Rationale

The etiology and pathophysiological mechanisms of moyamoya angiopathy (MMA) remain largely unknown. MMA is a progressive, occlusive cerebrovascular disorder characterized by recurrent ischemic and hemorrhagic strokes; with compensatory formation of an abnormal network of perforating blood vessels that creates a collateral circulation; and by aberrant angiogenesis at the base of the brain. Imbalance of angiogenic and vasculogenic mechanisms has been proposed as a potential cause of MMA. Moyamoya vessels suggest that aberrant angiogenic, arteriogenic, and vasculogenic processes may be involved in the pathophysiology of MMA. Circulating endothelial progenitor cells have been hypothesized to contribute to vascular remodeling in MMA. MMA is associated with increased expression of angiogenic factors and proinflammatory molecules. Systemic inflammation may be related to MMA pathogenesis.

Objective

This literature review describes the molecular mechanisms associated with cerebrovascular dysfunction, aberrant angiogenesis, and inflammation in MMA and related cerebrovascular diseases along with treatment strategies and future research perspectives.

Methods and results

References were identified through a systematic computerized search of the medical literature from January 1, 1983, through July 29, 2022, using the PubMed, EMBASE, BIOSIS Previews, CNKI, ISI web of science, and Medline databases and various combinations of the keywords "moyamoya," "angiogenesis," "anastomotic network," "molecular mechanism," "physiology," "pathophysiology," "pathogenesis," "biomarker," "genetics," "signaling pathway," "blood-brain barrier," "endothelial progenitor cells," "endothelial function," "inflammation," "intracranial hemorrhage," and "stroke." Relevant articles and supplemental basic science articles almost exclusively published in English were included. Review of the reference lists of relevant publications for additional sources resulted in 350 publications which met the study inclusion criteria. Detection of growth factors, chemokines, and cytokines in MMA patients suggests the hypothesis of aberrant angiogenesis being involved in MMA pathogenesis. It remains to be ascertained whether these findings are consequences of MMA or are etiological factors of MMA.

Conclusions

MMA is a heterogeneous disorder, comprising various genotypes and phenotypes, with a complex pathophysiology. Additional research may advance our understanding of the pathophysiology involved in aberrant angiogenesis, arterial stenosis, and the formation of moyamoya collaterals and anastomotic networks. Future research will benefit from researching molecular pathophysiologic mechanisms and the correlation of clinical and basic research results.

APOE as potential biomarkers of moyamoya disease

Objective

The mechanisms underpinning Moyamoya disease (MMD) remain unclear, and effective biomarkers remain unknown. The purpose of this study was to identify novel serum biomarkers of MMD.

Methods

Serum samples were collected from 23 patients with MMD and 30 healthy controls (HCs). Serum proteins were identified using tandem tandem-mass-tag (TMT) labeling combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Differentially expressed proteins (DEPs) in the serum samples were identified using the SwissProt database. The DEPs were assessed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, Gene Ontology (GO), and protein-protein interaction (PPI) networks, and hub genes were identified and visualized using Cytoscape software. Microarray datasets GSE157628, GSE189993, and GSE100488 from the Gene Expression Omnibus (GEO) database were collected. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) were identified, and miRNA targets of DEGs were predicted using the miRWalk3.0 database. Serum apolipoprotein E (APOE) levels were compared in 33 MMD patients and 28 Moyamoya syndrome (MMS) patients to investigate the potential of APOE to be as an MMD biomarker.

Results

We identified 85 DEPs, of which 34 were up- and 51 down-regulated. Bioinformatics analysis showed that some DEPs were significantly enriched in cholesterol metabolism. A total of 1105 DEGs were identified in the GSE157628 dataset (842 up- and 263 down-regulated), whereas 1290 were identified in the GSE189993 dataset (200 up- and 1,090 down-regulated). The APOE only overlaps with the upregulated gene expression in Proteomic Profiling and in GEO databases. Functional enrichment analysis demonstrated that APOE was associated with cholesterol metabolism. Moreover, 149 miRNAs of APOE were predicted in the miRWalk3.0 database, and hsa-miR-718 was the only DE-miRNA overlap identified in MMD samples. Serum APOE levels were significantly higher in patients with MMD than in those without. The performance of APOE as an individual biomarker to diagnose MMD was remarkable.

Conclusions

We present the first description of the protein profile of patients with MMD. APOE was identified as a potential biomarker for MMD. Cholesterol metabolism was found to potentially be related to MMD, which may provide helpful diagnostic and therapeutic insights for MMD.

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease

Background: Growing evidence suggests the links between moyamoya disease (MMD) and autoimmune diseases. However, the molecular mechanism from genetic perspective remains unclear. This study aims to clarify the potential roles of autoimmune-related genes (ARGs) in the pathogenesis of MMD. Methods: Two transcription profiles (GSE157628 and GSE141025) of MMD were downloaded from GEO databases. ARGs were obtained from the Gene and Autoimmune Disease Association Database (GAAD) and DisGeNET databases. Differentially expressed ARGs (DEARGs) were identified using "limma" R packages. GO, KEGG, GSVA, and GSEA analyses were conducted to elucidate the underlying molecular function. There machine learning methods (LASSO logistic regression, random forest (RF), support vector machine-recursive feature elimination (SVM-RFE)) were used to screen out important genes. An artificial neural network was applied to construct an autoimmune-related signature predictive model of MMD. The immune characteristics, including immune cell infiltration, immune responses, and HLA gene expression in MMD, were explored using ssGSEA. The miRNA-gene regulatory network and the potential therapeutic drugs for hub genes were predicted. Results: A total of 260 DEARGs were identified in GSE157628 dataset. These genes were involved in immune-related pathways, infectious diseases, and autoimmune diseases. We identified six diagnostic genes by overlapping the three machine learning algorithms: CD38, PTPN11, NOTCH1, TLR7, KAT2B, and ISG15. A predictive neural network model was constructed based on the six genes and presented with great diagnostic ability with area under the curve (AUC) = 1 in the GSE157628 dataset and further validated by GSE141025 dataset. Immune infiltration analysis showed that the abundance of eosinophils, natural killer T (NKT) cells, Th2 cells were significant different between MMD and controls. The expression levels of HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DRB6, HLA-F, and HLA-G were significantly upregulated in MMD. Four miRNAs (mir-26a-5p, mir-1343-3p, mir-129-2-3p, and mir-124-3p) were identified because of their interaction at least with four hub DEARGs. Conclusion: Machine learning was used to develop a reliable predictive model for the diagnosis of MMD based on ARGs. The uncovered immune infiltration and gene-miRNA and gene-drugs regulatory network may provide new insight into the pathogenesis and treatment of MMD.

The Genetic Landscape of Ischemic Stroke in Children - Current Knowledge and Future Perspectives

Stroke in childhood has multiple etiologies, which are mostly distinct from those in adults. Genetic discoveries over the last decade pointed to monogenic disorders as a rare but significant cause of ischemic stroke in children and young adults, including small vessel and arterial ischemic stroke. These discoveries contributed to the understanding that stroke in children may be a sign of an underlying genetic disease. The identification of these diseases requires a detailed medical and family history collection, a careful clinical evaluation for the detection of systemic symptoms and signs, and neuroimaging assessment. Establishing an accurate etiological diagnosis and understanding the genetic risk factors for stroke are essential steps to decipher the underlying mechanisms, optimize the design of tailored prevention strategies, and facilitate the identification of novel therapeutic targets in some cases. Despite the increasing recognition of monogenic causes of stroke, genetic disorders remain understudied and therefore under-recognized in children with stroke. Increased awareness among healthcare providers is essential to facilitate accurate diagnosis in a timely manner. In this review, we provide a summary of the main single-gene disorders which may present as ischemic stroke in childhood and describe their clinical manifestations. We provide a set of practical suggestions for the diagnostic work up of these uncommon causes of stroke, based upon the stroke subtype and imaging characteristics that may suggest a monogenic diagnosis of ischemic stroke in children. Current hurdles in the genetic analyses of children with ischemic stroke as well as future prospectives are discussed.