Performed Gene Set Enrichment Analysis Research Articles

Identification of CXC Chemokine Receptor 2 (CXCR2) as a Novel Eosinophils-Independent Diagnostic Biomarker of Pediatric Eosinophilic Esophagitis by Integrated Bioinformatic and Machine-Learning Analysis.

Eosinophilic esophagitis (EoE) is a complex allergic condition frequently accompanied by various atopic comorbidities in children, which significantly affects their life qualities. Therefore, this study aimed to evaluate pivotal molecular markers that may facilitate the diagnosis of EoE in pediatric patients. Three available EoE-associated gene expression datasets in children: GSE184182, GSE 197702, GSE55794, along with GSE173895 were downloaded from the GEO database. Differentially expressed genes (DEGs) identified by "limma" were intersected with key module genes identified by weighted gene co-expression network analysis (WGCNA), and the shared genes went through functional enrichment analysis. The protein-protein interaction (PPI) network and the machine learning algorithms: least absolute shrinkage and selection operator (LASSO), random forest (RF), and XGBoost were used to reveal candidate diagnostic markers for EoE. The receiver operating characteristic (ROC) curve showed the efficacy of differential diagnosis of this marker, along with online databases predicting its molecular regulatory network. Finally, we performed gene set enrichment analysis (GSEA) and assessed immune cell infiltration of EoE/control samples by using the CIBERSORT algorithm. The correlations between the key diagnostic biomarker and immune cells were also investigated. The intersection of 936 DEGs and 1446 key module genes in EoE generated 567 genes, which were primarily enriched in immune regulation. Following the construction of the PPI network and filtration by machine learning, CXCR2 served as a potential diagnostic biomarker of pediatric EoE with a perfect diagnostic efficacy (AUC = ~1.00) in regional tissue/peripheral whole blood samples. Multiple infiltrated immune cells were observed to participate in disrupting the homeostasis of esophageal epithelium to varying degrees. The immune-correlated CXCR2 gene was proved to be a promising diagnostic indicator for EoE, and dysregulated regulatory T cells (Tregs)/neutrophils might play a crucial role in the pathogenesis of EoE in children.

Retinol dehydrogenase 10 promotes epithelial-mesenchymal transition in spinal cord gliomas via PI3K/AKT pathway.

Background: Spinal cord glioma (SCG), a rare subset of central nervous system (CNS) glioma, represents a complex challenge in neuro-oncology. There has been research showing that Retinol Dehydrogenase 10 (RDH10) may be a tumor promoting factor in brain glioma, but the biological effects of RDH10 remain undefined in SCG. Methods: We performed gene set enrichment analysis (GSEA) and unsupervised clustering analysis to investigate the roles of EMT (epithelial-mesenchymal transition) in glioma. DEG (differently expressed gene) screening and correlation analysis were conducted to filter the candidate genes which were closely associated with EMT process in SCG. Enrichment analysis and GSVA (Gene Set Variation Analysis) were conducted to investigate the potential mechanism of RDH10 for SCG. Trans-well and healing assay were performed to explore the role of RDH10 in the invasion of SCG. Western blotting was performed to evaluate the levels of markers in PI3K-AKT and EMT pathway. In vivo tests were conducted to verify the role of RDH10 in EMT process. Results: Bioinformatic analysis demonstrated the EMT pathway was associated with dismal prognosis of glioma. Further analysis demonstrated that RDH10 showed the strongest correlation with the EMT process. Retinol Dehydrogenase 10 expression was significantly increased in SCG tissues, correlating with advanced tumor grade and unfavorable prognosis. Functional analysis indicated that decreasing RDH10 levels impeded the invasive and migratory abilities of SCG cells, whereas increasing RDH10 levels augmented them. Enrichment analysis and western blot revealed that RDH10 regulated EMT process of SCG by PI3K-AKT pathway. We observed that the enhanced invasion ability and increased EMT-related protein induced by RDH10 overexpression can be suppressed by PI3K-AKT pathway inhibitor (LY294002). Conclusion: Our research found that RDH10 was an effective biomarker associated with tumor grade and prognosis of SCG. RDH10 could regulate EMT process of SCG through PI3K-AKT pathway.

Comprehensive analysis of autophagy associated genes and immune infiltrates in cervical cancer.

Cervical cancer (CC) is the most common gynecological malignant tumor and the fourth leading cause of cancer-related death in women. The progression of CC is significantly affected by autophagy. Our objective was to use bioinformatics analysis to explore the expression, prognostic significance, and immune infiltration of autophagy-related genes in CC. We identified a set of autophagy-related differentially expressed genes (ARDEGs) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ARDEGs were further validated by The Human Protein Atlas (HPA), GSE52903, and GSE39001 dataset. Hub genes were found by the STRING network and Cytoscape. We performed Gene Set Enrichment Analysis (GSEA), Gene ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and immune infiltration analysis to further understand the functions of the hub genes. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) were used to check the hub genes. A total of 10 up-regulated (CXCR4, BAX, SPHK1, EIF2AK2, TBK1, TNFSF10, ITGB4, CDKN2A, IL24, and BIRC5) and 19 down-regulated (PINK1, ATG16L2, ATG4D, IKBKE, MLST8, MAPK3, ERBB2, ULK3, TP53INP2, MTMR14, BNIP3, FOS, CCL2, FAS, CAPNS1, HSPB8, PTK6, FKBP1B , and DNAJB1) ARDEGs were identified. The ARDEGs were enriched in cell growth, apoptosis, human papillomavirus infection, and cytokine-mediated. Then, we found that low expression of MAPK3 was associated with poor prognosis in CC patients and was significantly enriched in immune pathways. In addition, the expression of MAPK3 was significantly positively correlated with the infiltration levels of macrophages, B cells, mast cell activation, and cancer-associated fibroblasts. Furthermore, MAPK3 was positively correlated with LGALS9, and negatively correlated with CTLA4 and CD40. Our results show that MAPK3 can be used as a new prognostic biomarker to predict the prognosis of patients with CC.

Development and Validation of Potential Molecular Subtypes and Signatures of Thyroid Carcinoma Based on Aging-related Gene Analysis.

Thyroid carcinoma (THCA) is a cancer of the endocrine system that most commonly affects women. Aging-associated genes play a critical role in various cancers. Therefore, we aimed to gain insight into the molecular subtypes of thyroid cancer and whether senescence-related genes can predict the overall prognosis of THCA patients. Thyroid carcinoma (THCA) transcriptome-related expression profiles were obtained from The Cancer Genome Atlas (TCGA) database. These profiles were randomly divided into training and validation subsets at a ratio of 1:1. Unsupervised clustering algorithms were used to compare differences between the two subtypes; prognosis-related senescence genes were used to further construct our prognostic models by univariate and multivariate Cox analyses and construct a nomogram to predict the 1-, 3-, and 5-year overall survival probability of THCA patients. In addition, we performed gene set enrichment analysis (GSEA) to predict the immune microenvironment and somatic mutations between the different risk groups. Finally, real-time PCR was used to verify the expression levels of key model genes. The 'ConsensusClusterPlus' R package was used to cluster thyroid cancer into two categories (Cluster1 and Cluster2) on the basis of 46 differentially expressed aging-related genes (DE-ARGs); patients in Cluster1 demonstrated a better prognosis than those in Cluster2. Cox analysis was used to screen six prognosis-related DE-ARGs. Finally, our real-time PCR results confirmed our hypothesis. Differences exist between the two subtypes of thyroid cancer that help guide treatment decisions. The six DE-ARG genes have a high predictive value for risk stratifying THCA patients.

Low CDKN1B Expression Associated with Reduced CD8+ T Lymphocytes Predicts Poor Outcome in Breast Cancer in a Machine Learning Analysis.

The cyclin-dependent kinase inhibitor 1B (CDKN1B) gene, which encodes the p27Kip1 protein, is important in regulating the cell cycle process and cell proliferation. Its role in breast cancer prognosis is controversial. We evaluated the significance and predictive role of CDKN1B expression in breast cancer prognosis. We investigated the clinicopathologic factors, survival rates, immune cells, gene sets, and prognostic models according to CDKN1B expression in 3794 breast cancer patients. We performed gene set enrichment analysis (GSEA), in silico cytometry, pathway network analyses, gradient boosting machine (GBM) learning, and in vitro drug screening. High CDKN1B expression levels in breast cancer correlated with high lymphocyte infiltration signature scores and increased CD8+ T cells, both of which were associated with improved prognosis in breast cancer. which were associated with a better prognosis. CDKN1B expression was associated with gene sets for the upregulation of T-cell receptor signaling pathways and downregulation of CD8+ T cells. Pathway network analysis revealed a direct link between CDKN1B and the pathway involved in the positive regulation of the protein catabolic process pathway. In addition, an indirect link was identified between CDKN1B and the T-cell receptor signaling pathway. In in vitro drug screening, BMS-345541 demonstrated efficacy as a therapeutic targeting of CDKN1B, effectively impeding the growth of breast cancer cells characterized by low CDKN1B expression. The inclusion of CDKN1B expression in GBM models increased the accuracy of survival predictions. CDKN1B expression plays a significant role in breast cancer progression, implying that targeting CDKN1B might be a promising strategy for treating breast cancer.

Transcriptomic Analysis of Tight Junction Proteins Demonstrates the Aberrant Expression and Function of Zona Occludens 2 (ZO-2) Protein in Stanford Type A Aortic Dissection.

Thoracic aortic aneurysm dissection (TAAD) represents a cardiac surgery emergency characterized by the disrupted integrity of the aortic wall and is associated with poor prognosis. In this context, the identification of biomarkers implicated in the pathobiology of TAAD is crucial. Our aim in the present original in silico study is to assess the differential gene expression profile of the tight junction proteins (TJPs) in patients with TAAD and to propose novel biomarkers for the diagnosis and prognosis of this disease. We implemented bioinformatics methodology in order to construct the gene network of the TJPs family, identify the differentially expressed genes (DEGs) in pathologic aortic tissue excised from patients with TAAD as compared to healthy aortic tissue, and assess the related biological functions and the associated miRNA families. Data regarding the transcriptomic profile of selected genes were retrieved and incorporated from three microarray datasets, including 23 TAAD and 20 healthy control samples. A total of 32 TJPs were assessed. The zona occludens 2 (ZO-2) protein encoded by the gene TJP2 was significantly under-expressed in patients with TAAD compared to the control group (p = 0.009). ZO-2 was associated with fair discrimination and calibration traits in predicting the TAAD presentation. CpG islands of ZO-2 were demonstrated. No important difference was found regarding ZO-2 expression between aneurysmal non-dissected and healthy control aortic tissue. Finally, we performed gene set enrichment analysis (GSEA) and uncovered the major biological functions and miRNA families (hsa-miR-155-5p, hsa-miR-1-3p, hsa-miR-2118-5p, hsa-miR-4691-3p, and hsa-miR-1229-3p) relevant to ZO-2. These outcomes demonstrated the important role of ZO-2 in the pathobiology of TAAD.

Single‐nuclei RNA sequencing provides evidence for glial senescence in Alzheimer’s disease

AbstractBackgroundAgeing is the greatest risk factor for Alzheimer’s disease (AD). While accumulation of senescent cells is one of the major hallmarks of ageing, the effect of the senescence burden is yet to be explored in AD. The aims of this study were to i) quantify senescent microglia, oligodendrocyte and astrocyte in human brain in AD, ii) determine the driver of cell senescence in AD, and iii) AD pathology associated senescence.MethodWe multiplexed Image Mass Cytometry (IMC) to co‐localise cell type markers (IBA1, OLIG2, GFAP), senescence markers (GLB1 and p16) and β‐amyloid (4G8) to detect senescence in tissues from Middle Temporal Gyrus (MTG) of 10 AD and 10 non‐disease control (NDC). We also generated snRNA‐seq from three different brain regions of the same subject namely Entorhinal (EC), MTG (Mid‐temporal Cortex) and Somatosensory Cortex (SSC) of 9 AD and 9 NDC. We performed geneset enrichment analysis using a set of curated senescence associated genesets in snRNA‐seq using AUcell and dream (variancePartition). We further performed trajectory analysis using Monocle3 to characterise β‐amyloid associated senescence signatures in the snRNAseq data.ResultMore than 25% microglia, oligodendrocyte and astrocyte were positive for GLB1 in AD brains while only around 5% glia showed GLB1 expression in NDC. Around 25% peri‐plaque microglia within 10 µm around β‐amyloid plaques in AD showed expression of GLB1 while only 3% of microglia >10 µm had associated senescence markers. Senescence genes were significantly upregulated in AD compared to NDC and were positively correlated with increase amyloid densities in all three glial cells (not in neurons). Trajectories of the glial nuclei described increased expression of pathways related to replicative senescence, cellular activation and stress response in microglia and oxidative stress in oligodendroglia, and astrocyte. Senescence gene signatures were positively associated with both β‐amyloid and pTau.ConclusionOur results highlight the high burden of senescent glia and provide evidence for distinct mechanisms of senescence in microglia (both replicative and stress‐induced) relative to oligodendroglia and astrocyte (stress‐induced) in AD. Both β‐amyloid and pTau associated increase in senescence gene signatures suggest that senescence is an important feature in the late stage of AD.

XPO2 Is Essential for a Subset of AML Cells and Is a Biomarker for Poor Response and Survival in Pediatric and AYA AML

Importins & exportins regulate protein transport across the nuclear membrane. Twenty nuclear transport receptors (NTRs) have been identified; six are involved in nuclear export. NTRs contribute to cancer development, through abnormal localization of protein cargos. The exportin, XPO1, is associated with high risk AML & shorter survival times. However, little is known regarding the functional importance of the related exportin, XPO2 (alias CSE1L). We analyzed a public database of CRISPR/Cas9 depletion screens in 26 AML cell lines. XPO2 ranked in the top 1.4% of essential genes, along with XPO1 (top 1.3%) (DepMap 22Q2 Public+Score, Chronos). An RNAi depletion screen confirmed this, with XPO2 ranked in the top 0.2% of AML genes. Leukemia cells were more dependent on XPO2 vs solid tumors (P = 0.0394) & gene dependency scores indicated a greater reliance on XPO2 vs XPO1 (P <0.0001). Increased expression of XPO2 mRNA was associated with decreased event free survival (EFS) (P = 0.0014) & overall survival (OS) (P = 0.00091) in pediatric & adolescent/young adult (AYA) AML patients (TARGET dataset). In the BeatAML dataset, increased XPO2 mRNA expression was associated with decreased OS in patients <40 years, but not in patients >40 years (P <0.0001). We analyzed protein expression databases & identified XPO2 as the highest expressed exportin in AML cells lines & patient samples. Increased XPO2 expression was detected in 7/7 primary AML samples & 9 AML cell lines by immunoblot (HL60, MV4-11, MOML13, NB4, OCI-AML2, OCI-M2, TEX, U937 & 1254 cells), compared to normal hematopoietic cells. Expression of XPO2 was increased in younger (<40 years) vs older patients (>70 years) (P = 0.0454). Genetic silencing of XPO2 decreased growth & viability of OCI-AML2, TEX, NB4 & 1254 cells. Gambogic acid is an inhibitor of XPO2-mediated nuclear transport. In OCI-AML2, TEX & NB4 cell lines, gambogic acid decreased viability of AML cells (TEX IC50 = 369nM, OCI-AML2 IC50 = 433nM, NB4 IC50 = 161nM). Next, we interrogated a BioID database (PXD007976) & performed gene set enrichment analysis on XPO2 targets. Processes related to ribosome biogenesis were enriched in XPO2 cargo, whereas XPO1 cargo were enriched for snRNA transcription processes (P <0.05). A gene expression signature encompassing XPO2 & its associated cargo genes was enriched in functionally defined LSCs (P = 0.001, in patients with more primitive AML subtypes (P < 2.22 x 10 -16), in more primitive AML blasts as defined by FAB subtype and in high risk subsets, including WT1 mutated pediatric AML. Additionally, the XPO2 gene signature was associated with minimal-residual-disease (MRD) positive disease after induction cycle I (P < 5.1x 10 -6) and II (P = 0.0013), decreased complete remission (CR) rates after cycle I (P = 0.00025) and II (P = 0.02), worse EFS (P = 0.019) and worse OS (P = 0.02) in pediatric and AYA AML patients. In summary, we identified XPO2 as an essential gene for AML . XPO2 is increased in AML cells & primary samples from younger patients. Increased XPO2 expression & increased expression of XPO2 mediated processesare associated with worse outcomes in pediatric AML & correlate with LSC enrichment & primitive AML subtypes. XPO2 may function to enhance ribosomal biogenesis, demonstrating a critical role in leukemogenesis. Thus, this work demonstrates a new dependency of pediatric AML on XPO2 highlighting a new target and biomarker for this disease.

Genome-Wide Methylome and Transcriptome Profiling of Acute Myeloid Leukemia Derived Bone Marrow Mesenchymal Cells Identify Age Group Specific Biological Pathway Dysregulation in the Bone Marrow Microenvironment of AML

Adult patients with acute myeloid leukemia (AML) have a proportionate increase in mortality from their disease and therapy as age increases. In great part, this mortality is due to disease relapse that occurs at a higher rate irrespective of genetic assigned risk. While intensification of therapy in patients with favorable genomics such as core-binding-factor AML (CBF-AML) has yielded a high proportion of long-term remission and potential cure, this benefit has been less frequently observed in patients with AML age 60 and older despite receipt of similar therapy. Moreover, even less benefit has been observed in patients with unfavorable genomics such when bearing a complex karyotype (karyotype with ≥ 3 chromosome abnormalities). Reasons for this unfavorable outcome among older patients is unknown. We hypothesized that tumor-induced aging in cells of bone marrow microenvironment, specifically, in bone marrow mesenchymal stromal cells (BMSCs) plays a significant role in AML survival and progression in older compared to younger patients and that the difference in prognosis may depend on specific aging induced changes in the BMSCs. We expanded BMSCs under physiologic hypoxic conditions (91% nitrogen, 4% oxygen and 5% CO 2) to mimic the bone marrow microenvironment, isolated DNA and RNA from early passaged cells and performed methylation analysis using 850k Illumina DNAmehtylation arrays and gene expression analysis using mRNAseq. We used the Horvath Skin and Blood clock to calculate epigenetic age (DNA methylation age). We determined differentially expressed genes with increasing epigenetic or chronological age in the different AML genetic risk groups (n=9 CBF-AML, n=11 CK-AML and n=7 Normal donor BMSC) as well as in older (>60) and younger (<60) AML-BMSCs, and performed Gene Set Enrichment Analysis (GSEA) for the Hallmarks as well as aging and senescence related genesets (n=33 genesets). We found that BMSCs derived from AML bone marrow cells are more age accelerated than normal donor BMSCs. Our GSEA analysis revealed enrichment for age-associated gene networks that reflected hallmarks of aging, including mitochondrial dysfunction, inflammation, and cellular senescence. Specifically, we observed a positive enrichment for the SenMayo gene signature ( Saul et al. 2022, Nature Communications) and a negative enrichment of proliferation and mitotic gene sets with increasing epigenetic and chronological age (padj<0.05). Gene sets that are related to inhibition of adipogenesis were positively enriched in the CBF-AML BMSCs compared to the CK-AML BMSCs. Our integrated analysis revealed novel observations regarding the cellular changes that occur in the AML stromal microenvironment with increasing age. Most interestingly, our findings suggest that AML derived BMSCs have increased epigenetic age compared to normal donor BMSCs. Additional experiments are ongoing to functionally validate the findings predicted by the enrichment analysis. Our study is the first to suggest an increased epigenetic age in the stromal bone marrow microenvironment of older AML patients.

A Functional Genomic Screen to Identify Novel Genes Involved in Multiple Myeloma Tumour Development

Multiple myeloma (MM) is an incurable malignancy characterised by uncontrolled proliferation of plasma cells (PCs) in the bone marrow (BM). MM is a genetically heterogeneous disease with each patient's PCs harbouring unique genetic mutations, however the development of MM tumours is not only dependent on the underlying genetics but also on the selective pressures applied by the BM microenvironment. Hence, we hypothesise that identifying the dependencies which promote MM cell outgrowth in the BM microenvironment will allow for the identification of new drug targets. This project aims to use an in vivo functional screen, combining CRISPR-Cas9 gene editing, using a single guide RNA (sgRNA) library targeting thousands of genes, with a murine model of MM, to identify novel genes involved in MM tumour development. The Bassik human apoptosis and cancer CRISPR knockout library (Addgene #101926) was used to transduce MM cells with 31,324 unique sgRNAs targeting 3,015 genes and 1,500 control regions. The human MM cell line OPM2 was transduced with the Cas9 transgene (FuCas9GFP), followed by the sgRNA expression vector (pMCB320) generating OPM2-Cas9-sgRNA cells. These cells were then injected (5x10 5 cells/mouse, n=8 mice + 1 tumour-naive control) into the tibiae of immunodeficient NOD.Cg-Prkdc scid Il2rg tm1Wjl/SzJ (NSG) mice. Four weeks post-injection, the primary tumour within the injected tibia was isolated and sgRNA frequencies were assessed via next generation sequencing and compared with that of the initial library and the injected cells using the MAGeCK algorithm. To identify genes that play roles in MM tumour formation in patients, we assessed the expression of our identified genes in CD138-selected BM PCs from newly diagnosed MM patients (n=155) when compared with normal PCs from healthy donors (n=5) in microarray dataset E-MTAB-363 (ArrayExpress). Furthermore, to identify potential pathway involvement of our identified genes we performed gene set enrichment analysis (GSEA) using the University of California San Diego and Broad Institute developed GSEA software, comparing to Hallmark Gene Sets. Comparison of the in vitro cultured cells with the initial library identified 34 genes with undetectable (p<0.05) sgRNAs post in vitro culture, suggesting these genes play key roles in the survival of OPM2 cells. This list contained a number of genes known to control MM cell survival, such as IRF4 and CCND2. Additionally, sgRNAs targeting a further 115 genes were significantly depleted (p<0.05) in vitro and were completely undetectable in vivo, suggesting that these are implicated in cell proliferation and tumour formation in vivo. This gene list contained some already known MM drivers such as MYC, KRAS and DIS3. Furthermore, we identified 28 genes where CRISPR knockout had no significant effect in vitro but were critical dependencies for tumour development, with sgRNAs being undetectable (p<0.05) in vivo. This list contained genes known to be important for MM and BM stromal cell interaction such as ALCAM and SPP1. Notably, 25% of the genes identified as critical dependencies for OPM2 cells in vitro and/or in vivo are significantly upregulated >2-fold (p<0.05; limma) in MM patient PCs when compared with normal PCs (E-MTAB-363), suggesting these may play a role in tumour development in patients. Gene set enrichment analysis revealed that the genes that were essential in vitro were predominantly involved in cell survival related pathways such as cell cycle regulation (G2M checkpoint) and DNA replication/repair; however, the uniquely in vivo genes are predominantly involved in cell metabolism pathways such as MTORC1 signalling, oxidative phosphorylation and glycolysis. Overall, this study has successfully used a functional genetic screen to identify novel gene dependencies required for in vitro MM cell growth and in vivo MM tumour development. A secondary genetic screen will be performed in vivo to validate and refine this list of candidate genes. These validated genes will be further investigated using patient derived data sets to determine if they represent common vulnerabilities in patients and in multiple MM cell lines to investigate their specific mechanistic and pathway involvement. Identification and understanding the driving dependencies which promote MM cell outgrowth in the BM microenvironment will allow for the identification and development of new drug targets to improve patient outcomes.

Stable MGUS and SMM Are Characterized By Distinct Senescent Features in the Pre-Malignant Plasma Cells and the Proximate Bone Microenvironment

Multiple Myeloma (MM) progresses from monoclonal gammopathy of undetermined significance (MGUS) and/or smoldering MM (SMM). Despite similar primary genetic events as MM, MGUS and SMM PCs are non- or low-proliferative. Since oncogene activation and DNA damage drive senescence growth arrest to promote immune clearance of potentially malignant cells, we hypothesized that MGUS and SMM pre-malignant PCs exhibit senescence features. To test this, we performed gene set enrichment analysis (GSEA) of a published human PC gene array dataset (GSE5900) comparing normal, MGUS, and SMM PCs for custom senescence phenotyping gene sets (Senescence Up, SenUp; Senescent Cell Anti-Apoptosis Pathways, SCAPs; Senescence Growth Arrest, SenGA; Inflammatory Senescence Associated Secretory Phenotype, iSASP; Interferon SASP, IFN-SASP) versus biological aging gene sets (SenMayo; CellAge). MGUS and SMM PCs exhibited significant enrichment (NES>1.5, q<0.05) for senescence gene sets SenUp, SenGA, and SCAPs compared to normal PCs, with no enrichment for aging gene sets. MGUS PCs were also enriched for iSASP (NES=1.4, q=0.11), whereas SMM PCs were enriched for IFN-SASP (NES=2.3, q<0.05), characteristic of late-senescence. Thus, both MGUS and SMM PCs show enrichment of senescence gene sets compared to normal PCs with differential SASP profiles. Next, we evaluated MGUS, SMM, and MM patient PCs isolated by magnetic sorting for histological senescence features. PCs were immunostained for LMNB1 and HMGB1, and fluorescent in situ hybridized for α-satellite. Cellular senescence was defined as loss of ≥50% nuclear membrane LMNB1, loss of HMGB1, and ≥2 senescence-associated distensions of satellite (SADS) per nuclei. PCs from MGUS patients (N=12) exhibited increased percentage of senescent PCs (26.9±4.1%, Mean±SEM, p<0.0001) compared to SMM (11.0±1.9%, N=19, p<0.0001) and MM (11.6±1.5%, N=11, p<0.001). A subset analysis of SMM patients for which disease stability was known (≥2 years, N=6-8) showed no differences. These results demonstrate that the percentage of senescent PCs is increased in MGUS but not SMM PCs compared to proliferative disease. To assess PC senescence in the native bone marrow microenvironment (BMME), we evaluated trephine bone biopsies from MGUS and SMM patients that progressed or not to MM ≤10 years, as well as newly diagnosed MM. Biopsies were immunostained for CD138, LMNB1, and HMGB1. Whole biopsy images were analyzed using the artificial intelligence-based software HALO. Cells were classified as senescent if they were double negative and non-senescent if they were double positive for LMNB1 and HMGB1. The percentage of non-senescent PCs increased with progression to MM and positively correlated with total PC burden (R=0.52, p<0.0001), reflective of proliferative disease. Senescent PCs correlated with total senescent BMME (R=0.87, p<0.0001), consistent with a role for senescent cells to drive paracrine senescence. We performed proximity analysis to evaluate the cellular composition within 25um of senescent and non-senescent PCs. As expected, non-senescent PCs exhibited a significantly greater percentage of proximate non-senescent BMME in all groups (p<0.0001). In contrast, senescent PCs in stable MGUS (N=16) and SMM (N=14) were equally surrounded by senescent and non-senescent BMME. In progressing MGUS (N=21) and SMM (N=20), as well as MM (N=18), senescent PCs exhibited significantly reduced senescent BMME in their proximate environment (p<0.05). This suggests that senescent PCs in progressive disease, in contrast to stable disease, fail to drive local paracrine senescence responses. These data demonstrate that PCs from stable MGUS and SMM patients exhibit senescence features, with isolated PCs from stable MGUS showing increased senescence gene expression and histologic features. While stable SMM also exhibits senescence gene expression, they lose morphological features of senescence and show a preferential IFN SASP. Within the bone compartment, senescent PCs in stable MGUS and SMM exhibit the expected proximity to senescent BMME. In contrast, progressing MGUS and SMM, as well as NDMM, show a loss of proximate senescent BMME. Given the role of senescence to drive inflammation to clear potentially tumorigenic cells, these data support that failure to drive paracrine senescence may be key to the immune escape of pre-malignant cells in MGUS and SMM.

Polycom Group Protein Mel18 Inhibits Hematopoietic Stem Cell Self-Renewal through Repressing the Expression of Genes Important for Cellular Senescence and Proliferation

Polycomb group (PcG) proteins are epigenetic gene silencers that have been implicated in stem cell maintenance and cancer development. Genetic and biochemical studies indicate that Polycomb group proteins exist in at least two protein complexes, Polycomb repressive complex 2 (PRC2) and Polycomb repressive complex 1 (PRC1), that act in concert to initiate and maintain stable gene repression. Bmi1 and Mel18 are two major homologs of the PCGF subunit within the canonical PRC1 complex. While Bmi1 is a positive regulator of hematopoietic stem cell (HSC) and leukemia stem cell (LSC) self-renewal, the role of Mel18 in normal and malignant hematopoiesis is not fully understood. To further determine the role of Mel18 in hematopoiesis, we have generated Mel18 conditional knockout mice ( Mel18 f/f-Mx1Cre +). Acute deletion of Mel18 in the hematopoietic compartment did not affect the frequency and survival of hematopoietic stem and progenitor cells (HSPCs). To determine the impact of Mel18 deficiency on HSPC proliferation, we performed serial replating assays and found that loss of Mel18 increases the replating potential of HSPCs in vitro. To determine the role of Mel18 in HSC self-renewal, we first performed serial bone marrow (BM) transplantation assays and found that Mel18 null BM cells show increased engraftment in both primary and secondary transplantation assays. Limiting dilution transplantation showed that there were more functional HSCs in the BM of Mel18 knockout mice. We then performed HSC transplantation assays and found that loss Mel18 enhances HSC self-renewal. Thus, we demonstrated that Mel18 inhibits HSC self-renewal and decreases the number of functional HSCs in vivo. To understand the mechanism by which Mel18 inhibits HSC self-renewal and proliferation, we performed RNA-seq using Mel18 +/+ and Mel18 -/- HSCs. We then performed gene set enrichment analysis (GSEA) to identify potential Mel18 target genes important for HSC behavior. We found that HSC gene signatures are enriched in Mel18 -/- HSCs. We also found that leukemia stem cell gene signatures and CBF-mutant-AML genes are significantly enriched in Mel18 -/- HSCs, suggesting that loss of Mel18 may prime normal HSCs for leukemic transformation. Notably, our RNA-seq assay showed that senescence-related genes, including S100a9 and S100a8, were down-regulated in Mel18 -/- HSPCs. Importantly, loss of Mel18 significantly decreased the number of senescent HSPCs. To understand how Mel18 regulates gene transcription, we performed ATAC-seq assays in Mel18 +/+ and Mel18 -/- HSPCs. Mel18 null HSPCs showed decreased chromatin accessibility near both S100a8 and S100a9 genes. We confirmed that both S100a8 and S100a9 are downregulated in Mel18 -/- HSPCs compared to Mel18 +/+ HSPCs. Given that cell cycle arrest is a common feature of cellular senescence, we also examined the expression of cell cycle related genes in Mel18 -/- HSPCs. The expression of cell cycle regulators, including CDK4 and CCND2, is increased in Mel18 -/- HSPCs. Mel18 can bind to a specific DNA sequence, 5‘-GACTNGACT-3‘, thereby repressing the expression of target genes. Mel18 directly binds to the CCND2 gene locus as revealed by ChIP-seq assays in embryonic stem cells, suggesting that Mel18 may directly repress CCND2 expression in HSPCs. In summary, we demonstrate that Mel18 inhibits HSC self-renewal via repressing the expression of genes that are important for cellular senescence and proliferation.

A Mendelian Randomization-Based Causal Investigation on Bacterial Infection-Related Genes and Neurodegenerative Diseases.

Neurodegenerative diseases remain the most prevalent and unsolved health problems in human society, especially Alzheimer's disease (AD) and Parkinson's disease (PD). The pathogenesis, pathology, and potential clinical treatments of neurodegenerative diseases still require in-depth research. In the wake of the association between pandemics and a growing number of neurodegeneration patients, there has been growing speculation that infections are linked to AD and PD. The Aβ peptide is an important causal-related biomarker of AD and is reported to share structural and functional similarities with certain antimicrobial peptides, suggesting that it has a role in eliciting an immune response against microbes. But how neurodegeneration is related to bacterial chronic infection has not been thoroughly investigated. Using the data from genome-wide association studies (GWAS), we performed Mendelian Randomization (MR) and map 7 genes in multiple bacterial infection pathways as exposure, which show a significant association with the outcome of AD or PD. As co-verification, we perform Gene Set Enrichment Analysis (GSEA) on selected genetic variants incorporating their perturb-seq gene list (combining single-cell RNA-seq and CRISPR-based perturbations). We observed clustering of the differentially expressed genes (DEGs) in the upstream and downstream of AD and PD-related KEGG pathways, hence confirming their causal association with AD and PD and providing new perspectives on the true cause of neurodegeneration.

Protein Disulfide Isomerase A2 Is Correlated with Immune Infiltrates and Is a Novel Prognostic Biomarker in Glioma Patients.

Protein disulfide isomerase A2 (PDIA2), a member of the protein disulfide isomerase family, plays a key role in the folding of nascent proteins in the endoplasmic reticulum by forming disulfide bonds, together with enzymes such as thiol isomerase, oxidase, and reductase. This study investigated the clinical significance and potential functions of PDIA2 in glioma. The expression of PDIA2 in gliomas was explored using The Cancer Genome Atlas and Gene Expression Omnibus databases. We analyzed the clinical characteristics of glioma patients and the prognostic and diagnostic value of PDIA2 expression. Kaplan-Meier and Cox regression analyses were used to examine the effect of PDIA2 expression on overall survival, progression-free interval, and disease-specific survival. Furthermore, we performed Gene Set Enrichment Analysis and immune infiltration analysis to investigate the functions of PDIA2. PDIA2 mRNA and protein expression was evaluated in cell lines and glioma tissues. PDIA2 was expressed at low levels in glioma patients. Kaplan-Meier survival analysis showed that glioma patients with low PDIA2 levels had a worse prognosis than those with high PDIA2 levels. Receiver operating characteristic curve analysis indicated the diagnostic and prognostic ability of PDIA2 (area under the curve = 0.918). Pathways associated with PD1, PI3K/AKT, cancer immunotherapy via PD1 blockade, Fceri-mediated NF-kB activation, FOXM1, and DNA repair were enriched in glioma patients with low levels of PDIA2. PDIA2 expression levels were negatively correlated with immune cell infiltrate levels. PDIA2 levels are significantly downregulated in glioma. PDIA2 expression may be a potential biomarker for the diagnosis and prognosis of glioma patients.

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis.

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

Abstract 14576: Circulating Bone Morphogenetic Protein 10 as a Prognostic Biomarker in Heart Failure: A BIOSTAT-CHF Study

Introduction: Bone morphogenetic protein 10 (BMP10) plays a central role in cardiac embryogenesis and is known to facilitate cardiac repair after myocardial injury in adults. The prognostic value of BMP10 in heart failure (HF) remains largely unknown. Objectives: To identify biological processes related to elevated BMP10 concentrations and to investigate the prognostic utility of circulating BMP10 in HF patients. Methods: Serum BMP10 concentrations were measured in 2085 chronic HF patients from the European BIOSTAT cohort using a prototype electrochemiluminescence immunoassay developed by Roche Diagnostics. First, we performed gene set enrichment analysis to identify biological pathways related to elevated BMP10. Next, we examined the predictive value of BMP10 for all-cause mortality, HF hospitalization and a combined endpoint of mortality and/or HF hospitalization on top of an established risk score, atrial fibrillation (AF) and stroke using Cox-regression analysis after log-transformation and standardization. Results were validated in an independent cohort of HF patients (n=1487). Results: High BMP10 was related to pathways of growth and growth factor activity, regulation of bone, extracellular region, and response to hormones/glucocorticoids. After adjusting for the BIOSTAT risk scores, AF and stroke, log-BMP10 remained a strong predictor of 2-year all-cause mortality (Hazard Ratio [HR] =1.17, Confidence Interval [CI]= 1.06-1.28), HF hospitalization (HR [CI]=1.13 [1.03-1.25]) and the combined endpoint (HR [CI]=1.10 [1.02-1.19]). Similar trends were seen in the validation cohort (Figure 1). Conclusions: BMP10 is an independent predictor of HF-rehospitalization and all-cause mortality in patients with HF.

Single Cell Correlation Analysis: A Novel Method to Analyze Single Cell RNA Sequencing Data Identifies a Self-Renewing Subpopulation of Human Acute Myeloid Leukemia Stem Cells

Introduction Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy. Despite early response to standard chemotherapy, 60-70% of patients relapse with treatment resistant disease and die of relapsed disease. Leukemia stem cells (LSCs), the subpopulation of leukemia cells with self-renewal capacity, can recapitulate leukemia to cause relapse. Ongoing work to define therapeutic targets that can block self-renewal would prevent relapse and disease progression in AML. Prior work has established that LSCs express a distinct gene expression profile. We sought to define the gene expression profile of self-renewal at the single-cell level because a precise understanding of the transcriptional basis for self-renewal could provide more effective therapeutic targets. Our group previously demonstrated that activated NRAS (NRAS G12V) mediates self-renewal in the LSC-enriched subpopulation of AML in the Mll-AF9/NRAS G12V mouse model of AML (Sachs 2014). Single cell RNA-sequencing (scRNA-seq) of the LSC-enriched subpopulation identified three distinct transcriptional profiles. Two genes that encode cell surface markers, Cd36 and Cd69, delineate these distinct profiles. Using in vivo leukemia engraftment assays, we found that only the CD36 -CD69 + LSC subgroup repopulates leukemia while the CD36 +CD69 - LSC subgroup does not, demonstrating that self-renewal capacity is limited to the CD36 -CD69 + subset of the LSC-enriched compartment (Sachs 2020). In contrast, the CD36 +CD69 - LSCs were highly proliferative while the CD36 -CD69 + LSCs were poorly proliferative. These experiments show that self-renewal and rapid proliferation are mutually exclusive features of LSCs, as has previously been shown in normal hematopoietic stem cells (Li 2013). Methods and Results We investigated whether primary human AML stem cells express the murine-derived single-cell self-renewal and proliferation gene expression profiles at the single-cell level. Current computational methods to compare single-cell gene expression profiles use arbitrary similarity scores and do not assess the statistical significance of their findings. Therefore, we developed a new computational method called Single cell Correlation Analysis (SCA). SCA is designed to quantitate the expression of a predefined profile in single-cell RNA sequencing data. The quantification of the expression profiles is assessed on a per-cell basis in an unsupervised manner, meaning that SCA does not rely on any pre-sorting or classification of the query cells. The significance of the quantitative metric (i.e. level of expression) is determined by permutation-based false discovery rate testing. SCA can therefore identify cells that express a profile of interest within a single-cell RNA sequencing dataset and incorporates a measure of statistical significance to that assignment. We used SCA to analyze scRNA-seq data from primary human AML samples. We used data from two samples sequenced by our group and previously published data from 16 samples (van Galen 2019). SCA identified cells that express the murine single-cell self-renewal profile in every sample (0.32-11.5%; FDR < 0.25). We sorted two primary human AML samples to isolate LSCs (CD34 +CD38 -) prior to single-cell capture and sequencing. SCA identified cells that express murine LSC self-renewal profile in both of these samples as well (8.9-11.3%; FDR < 0.25). Furthermore, with these analyses, we found that the single-cell profiles of self-renewal and proliferation were mutually exclusive in human LSCs, as they are in murine LSCs and normal HSCs. We performed gene set enrichment analysis (GSEA) and confirmed that the human cells that express the murine single cell self-renewal profile are enriched in previously reported LSC self-renewal gene expression profiles. Conclusion We present SCA, a novel computational method designed to quantitatively assess single-cell transcriptional datasets and evaluate the statistical significance of its results. Using this method, we demonstrate that primary human AML samples express the single-cell profiles of self-renewal that we previously validated experimentally. SCA-based identification of human self-renewing cells will allow us to interrogate novel transcriptional features of these cells in future studies.

Comparative analysis of genome-wide transcriptional responses to continuous heat stress in Pleurotus tuoliensis

Temperature plays an impactful role in mushroom cultivation. To obtain insights of transcriptomic response in macrofungi against heat stress, we performed RNA-seq analysis using Pleurotus tuoliensis mycelium cells that were treated under 32 °C and 36 °C for consecutive 96 h. By comparing the growth rate data, we found mycelium cells could maintain normal growth rate almost the same as control under 32 °C, yet halted the growths under 36 °C. In total, 2724 differential expressed genes were identified from the three pair-wise comparisons, which were classified to four clusters based on their expression patterns. We also performed gene set enrichment analysis using both GO and KEGG databases, and revealed 48, 113 and 105 enriched GO terms, and 1, 5, and 6 enriched KEGG pathways for three pair-wise comparisons accordingly. In addition, we identified 9 overlapping GO terms and 1 overlapping KEGG pathway shared by the three comparisons. Differentially expressed genes (DEGs) involved in cell communication, amino acid metabolic process, intracellular signal transduction and small molecule biosynthesis were identified in two heat stress treatments despite of the stress intensity. However, the expression of two heat shock protein genes (HSP10 and HSP60) were induced by increasing temperature. Our findings also suggested the DEGs associated with cell cycle regulation had various expression patterns under two heat stress conditions possibly due to different functions. Furthermore, 11 DEGs related to ergosterol biosynthesis were identified with similar expression trends, indicating the ergosterol levels and cell membrane composition may have a tight connection to the acquisition of thermotolerance, which warrant further investigations for deeper understanding of molecular mechanisms in fungal stress responses.

Novel pyroptosis-related lncRNAs and ceRNAs predict osteosarcoma prognosis and indicate immune microenvironment signatures

ObjectiveTo study pyroptosis-related biomarkers that are associated with the prognosis and immune microenvironment characteristics of osteosarcoma (OS). The goal is to establish a foundation for the prognosis and treatment of OS. MethodsWe retrieved transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database for 88 OS patients. Using this data, we constructed a prognostic model to identify pyroptosis-related genes (PRGs) associated with OS prognosis. To further explore the biological function of these PRGs, we performed enrichment analysis. To identify pyroptosis-related long non-coding RNAs (PRLncs) associated with the prognosis of OS, we performed co-expression analysis. Subsequently, a risk prognostic model was constructed using these PRLncs to generate a risk score, termed as PRLncs-score, thereby obtaining PRLncs associated with the prognosis of OS. The accuracy of the prognostic model was verified through survival analysis, risk curve, independent prognostic analysis, receiver operating characteristic (ROC) curve, difference analysis between high- and low-risk groups, and clinical correlation analysis. And to determine whether PRLncs-score is independent prognostic factor for OS. In addition, we further conducted external and internal validation for the risk prognosis model. Further analyses of immune cell infiltration and tumor microenvironment were performed. A pyroptosis-related competitive endogenous RNA (PRceRNA) network was constructed to obtain PRceRNAs associated with the prognosis of OS and performed gene set enrichment analysis (GSEA) on PRceRNA genes. ResultsWe obtained five PRGs (CHMP4C, BAK1, GSDMA, CASP1, and CASP6) that predicted OS prognosis and seven PRLncs (AC090559.1, AP003119.2, CARD8-AS1, AL390728.4, SATB2-AS1, AL133215.2, and AC009495.3) and one PRceRNA (CARD8-AS1-hsa-miR-21-5p-IL1B) that predicted OS prognosis and indicated characteristics of the OS immune microenvironment. The PRLncs-score, in combination with other clinical features, was established as an independent prognostic factor for OS patients. Subsequent scrutiny of the tumor microenvironment and immune infiltration indicated that patients with low-PRLncs-scores were associated with reduced metastatic risk, improved survival rates, heightened levels of immune cells and stroma, and increased immune activity compared to those with high-PRLncs-scores. ConclusionThe study's findings offer insight into the prognosis of OS and its immune microenvironment, and hold promise for improving early diagnosis and immunotherapy.

Comparative transcriptome analysis of eyes reveals the adaptive mechanism of mantis shrimp (oratosquilla oratoria) induced by a dark environment.

The light-dark cycle significantly impacts the growth and development of animals. Mantis shrimps (Oratosquilla oratoria) receive light through their complex photoreceptors. To reveal the adaptive expression mechanism of the mantis shrimp induced in a dark environment, we performed comparative transcriptome analysis with O. oratoria cultured in a light environment (Oo-L) as the control group and O. oratoria cultured in a dark environment (Oo-D) as the experimental group. In the screening of differentially expressed genes (DEGs) between the Oo-L and Oo-D groups, a total of 88 DEGs with |log2FC| > 1 and FDR < 0.05 were identified, of which 78 were upregulated and 10 were downregulated. Then, FBP1 and Pepck were downregulated in the gluconeogenesis pathway, and MKNK2 was upregulated in the MAPK classical pathway, which promoted cell proliferation and differentiation, indicating that the activity of mantis shrimp was slowed and the metabolic rate decreases in the dark environment. As a result, the energy was saved for its growth and development. At the same time, we performed gene set enrichment analysis (GSEA) on all DEGs. In the KEGG pathway analysis, each metabolic pathway in the dark environment showed a slowing trend. GO was enriched in biological processes such as eye development, sensory perception and sensory organ development. The study showed that mantis shrimp slowed down metabolism in the dark, while the role of sensory organs prominent. It provides important information for further understanding the energy metabolism and has great significance to study the physiology of mantis shrimp in dark environment.