Comprehensive Gene Expression Research Articles

KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species.

Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thalianaand Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sitesand chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysisand Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.

Comprehensive Gene Expression Profiling Analysis of Adipose Tissue in Male Individuals from Fat- and Thin-Tailed Sheep Breeds.

It has been shown that tail fat content varies significantly among sheep breeds and plays a significant role in meat quality. Recently, significant efforts have been made to understand the physiological, biochemical, and genomic regulation of fat deposition in sheep tails in order to unravel the mechanisms underlying energy storage and adipose tissue lipid metabolism. RNA-seq has enabled us to provide a high-resolution snapshot of differential gene expression between fat- and thin-tailed sheep breeds. Therefore, three RNA-seq datasets were meta-analyzed for the current work to elucidate the transcriptome profile differences between them. Specifically, we identified hub genes, performed gene ontology (GO) analysis, carried out enrichment analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and validated hub genes using machine learning algorithms. This approach revealed a total of 136 meta-genes, 39 of which were not significant in any of the individual studies, indicating the higher statistical power of the meta-analysis. Furthermore, the results derived from the use of machine learning revealed POSTN, K35, SETD4, USP29, ANKRD37, RTN2, PRG4, and LRRC4C as substantial genes that were assigned a higher weight (0.7) than other meta-genes. Among the decision tree models, the Random Forest ones surpassed the others in adipose tissue predictive power fat deposition in fat- and thin-tailed breeds (accuracy > 0.85%). In this regard, combining meta-analyses and machine learning approaches allowed for the identification of three important genes (POSTN, K35, SETD4) related to lipid metabolism, and our findings could help animal breeding strategies optimize fat-tailed breeds' tail sizes.

Transcriptome Profiling Data of Tea Leaves During Infection by the Dark Brown Tea Leaf Spot Pathogen Epicoccum mackenziei

Dark brown leaf spot on tea ( Camellia sinensis (L.) O. Kuntze) caused by Epicoccum mackenziei Jayasiri, Camporesi & K. D. Hyde has caused a loss in the yield and quality of tea leaves in Guizhou Province, Southwest China. No research has been published on the gene expression profiles of tea leaves during infection by E. mackenziei. In this study, we report the high-quality transcriptome sequencing of tea genes during infection, using high-throughput next-generation sequencing on the Illumina Novaseq 6000 platform. The infection resulted in significantly up- or downregulated expression of the 960 or 397 differentially expressed mRNAs (DEmRNAs) from tea leaves, respectively. The DEmRNAs were annotated using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. The expression of DEmRNA peroxidase 21 (TEA000222.1) was upregulated in response to infection by the pathogen and was enriched in terms of defense response to fungi at the biological process aspect. Expression of 26 or 13 of the tea DEmRNAs enriched in the pathways of plant-pathogen interaction were up- or downregulated, respectively, such as the genes WRKY transcription factor 1 (TEA028099.1), probable disease resistance protein At4g27220 (TEA018574.1), and serine/threonine-protein kinase CDL1 (TEA006388.1), which were downregulated. This comprehensive gene expression profiling study of tea leaves infected by this fungal pathogen will provide a valuable resource for future research on the identification of the host genes associated with the response to pathogen challenge and ultimately with disease resistance in the host. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Abstract 18810: Mitochondria-Rich Extracellular Vesicles From Stem Cell-Derived Cardiomyocytes Restore Bioenergetic Metabolism of Ischemic Myocardium in a Porcine Chronic Heart Failure Model

Introduction: The disruption of mitochondrial energy metabolism underlies the pathophysiology of ischemic heart failure. We previously demonstrated that intramyocardial injection of mitochondria-rich extracellular vesicles (M-EVs) derived from iPSC-derived cardiomyocytes (iCMs) transfers mitochondria and non-mitochondrial biologics into recipient cardiomyocytes. This process stimulates mitochondrial biogenesis and enhances functional capacities in murine and porcine acute myocardial infarction (MI) models. However, the therapeutic potential and underlying mechanism in chronic heart failure models remain to be assessed. Method: M-EVs were isolated from the conditioned medium of human iCMs using differential centrifugation. A total of 11 Yorkshire pigs underwent myocardial ischemia for one hour via balloon occlusion of the left anterior descending artery (Week-0). At week-4, 1.0 x 10 11 M-EVs or PBS control were administered transendocardially to the peri-infarct region (PIR) using a percutaneous catheter delivery system (Biocardia, Inc, CA). Cardiac functions and scar size were analyzed by cardiac MRI with gadolinium contrast at week-4 (pre-injection) and -8 (post-injection). Myocardial tissue samples were collected from the PIR and remote non-ischemic areas at week-8. Proteomic analysis and single-nucleus RNA sequencing were performed to investigate comprehensive protein and gene expression profiles of the myocardium. Results: Left ventricular (LV) ejection fractions were significantly decreased at Week-4 (32.7±5.4%). M-EV treatment significantly improved absolute LVEF from week-4 to -8 in the treatment vs. non-treatment groups, +3.15% vs. -4.99%, respectively, (p<0.0001). No significant difference was observed in the scar size. Multiomic analysis of proteomes and single-nucleus transcriptomes suggested restoration of dysregulated cardiometabolic protein profile and mitochondrial energy metabolism in the myocardium. Conclusion: M-EV-mediated transfer of mitochondria and enriched proteins augmented LV dysfunction in the pig chronic MI model. M-EV therapy may represent a novel biologic approach that directly targets cardiac metabolism by restoring mitochondrial function and cellular bioenergetics.

Polycystin-2-dependent transcriptome reveals early response of autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in polycystin genes, Pkd1 and Pkd2, but the underlying pathogenic mechanisms are poorly understood. To identify genes and pathways that operate downstream of polycystin-2 (PC2), a comprehensive gene expression database was created, cataloging changes in the transcriptome immediately following PC2 protein depletion. To explore cyst initiation processes, an immortalized mouse inner medullary collecting duct line was developed with the ability to knock out the Pkd2 gene conditionally. Genome-wide transcriptome profiling was performed using RNA sequencing in the cells immediately after PC2 was depleted and compared with isogenic control cells. Differentially expressed genes were identified, and a bioinformatic analysis pipeline was implemented. Altered expression of candidate cystogenic genes was validated in Pkd2 knockout mice. The expression of nearly 900 genes changed upon PC2 depletion. Differentially expressed genes were enriched for genes encoding components of the primary cilia, the canonical Wnt pathway, and MAPK signaling. Among the PC2-dependent ciliary genes, the transcription factor Glis3 was significantly downregulated. MAPK signaling formed a key node at the epicenter of PC2-dependent signaling networks. Activation of Wnt and MAPK signaling, concomitant with the downregulation of Glis3, was corroborated in Pkd2 knockout mice. The data identify a PC2 cilia-to-nucleus signaling axis and dysregulation of the Gli-similar subfamily of transcription factors as a potential initiator of cyst formation in ADPKD. The catalog of PC2-regulated genes should provide a valuable resource for future ADPKD research and new opportunities for drug development.NEW & NOTEWORTHY Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease. Mutations in polycystin genes cause the disease, but the underlying mechanisms of cystogenesis are unknown. To help fill this knowledge gap, we created an inducible cell model of ADPKD and assembled a catalog of genes that respond in immediate proximity to polycystin-2 depletion using transcriptomic profiling. The catalog unveils a ciliary signaling-to-nucleus axis proximal to polycystin-2 dysfunction, highlighting Glis, Wnt, and MAPK signaling.

Using ExpressAnalyst for Comprehensive Gene Expression Analysis in Model and Non-Model Organisms.

ExpressAnalyst is a web-based platform that enables intuitive, end-to-end transcriptomics and proteomics data analysis. Users can start from FASTQ files, gene/protein abundance tables, or gene/protein lists. ExpressAnalyst will perform read quantification, gene expression table processing and normalization, differential expression analysis, or meta-analysis with complex study designs. The results are presented via various interactive visualizations such as volcano plots, heatmaps, networks, and ridgeline charts, with built-in functional enrichment analysis to allow flexible data exploration and understanding. ExpressAnalyst currently contains built-in support for 29 common organisms. For non-model organisms without good reference genomes, it can perform comprehensive transcriptome profiling directly from RNA-seq reads. These common tasks are covered in 11 Basic Protocols. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: RNA-seq count table uploading, processing, and normalization Basic Protocol 2: Differential expression analysis with linear models Basic Protocol 3: Functional analysis with volcano plot, enrichment network, and ridgeline visualization Basic Protocol 4: Hierarchical clustering analysis of transcriptomics data using interactive heatmaps Basic Protocol 5: Cross-species gene expression analysis based on ortholog mapping results Basic Protocol 6: Proteomics and microarray data processing and normalization Basic Protocol 7: Preparing multiple gene expression tables for meta-analysis Basic Protocol 8: Statistical and functional meta-analysis of gene expression data Basic Protocol 9: Functional analysis of transcriptomics signatures Basic Protocol 10: Dose-response and time-series data analysis Basic Protocol 11: RNA-seq reads processing and quantification with and without reference transcriptomes.

Sex and age-dependent characterization of the circadian clock as a potential biomarker for physical performance: A prospective study protocol.

Circadian rhythms (CR) regulate daily cycles in behavior, physiology and molecular processes. CRs are endogenous and vary across individuals. Seasonal changes can influence CR. Accordingly, rhythms with different characteristics (amplitude, phase) are depicted during the summer months, as compared to winter. Increasing evidence points to an influence of circadian regulation on physical performance. Here, we aim to obtain a comprehensive circadian gene expression profile for physically active individuals, which can potentially be used for the identification of optimal time intervals for physical exercise. To explore these different aspects, we propose a study where we will carry out a molecular analysis of CR by measuring the expression of specific clock and clock-controlled genes, based on a non-invasive approach using RNA extracted from saliva in physically active, healthy participants. We will collect data across two seasons and use computational algorithms to integrate the molecular data with hormonal data (cortisol and melatonin), and generate a profile of CR in healthy individuals of different sex and age groups. Finally, we will use computational tools to predict optimal time intervals for physical performance based on the above-described data, thereby retrieving valuable data on the circadian clock as a key factor for health maintenance and optimization.

Gene expression profiling of pancreatic neuroendocrine carcinoma and mixed neuroendocrine-non-neuroendocrine neoplasm

Pancreatic neuroendocrine carcinoma (NEC) and mixed neuroendocrine-non-neuroendocrine neoplasm (MiNEN) are rare pancreatic malignant tumors, and comprehensive gene analyses are scarce. In this study, six NECs and six MiNENs were collected, immunohistochemistry for synaptophysin, chromogranin A, INSM1, Ki-67, and Rb was conducted, and KRAS mutational status was examined. Among these cases, comprehensive gene expression analysis of oncogene pathways using nCounter® were performed with six NECs and four MiNENs, and those data were compared with that of three pancreatic ductal adenocarcinomas (PDACs), with that of three normal pancreatic ducts, and with each other. By dividing NEC and MiNEN cases into KRAS-mutated group and KRAS-wild group, the difference of clinicopathological data and gene expression profiling data were examined between the two groups. Compared to the data of normal pancreatic epithelium, all 13 cancer-related pathways were upregulated in PDAC, MiNEN, and NEC group with more upregulation in this order. Compared to the data of PDAC, genes of DNA Damage repair pathway was most upregulated both in NECs and MiNENs. Regarding the difference between KRAS-mutated and KRAS-wild groups, several genes were differentially expressed between the two, where MMP7 was the upregulated gene with highest p-value and NKD1 was the downregulated gene with highest p-value in KRAS-mutated group. From the extent of upregulation of 13 pathways, MiNEN was considered more progressed stage than PDAC, and NEC was considered more progressed than MiNEN. From the comparison of KRAS-mutated and KRAS-wild NECs and MiNENs, several differentially expressed genes were identified in this study.

Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study

Nowadays, anti-TNF therapy remarkably improves the medical management of ulcerative colitis (UC), but approximately 40 % of patients do not respond to this treatment. In this study, we used 79 anti-TNF-naive patients with moderate-to-severe UC from four cohorts to discover alternative therapeutic targets and develop a personalized medicine approach that can diagnose UC non-responders (UCN) prior to receiving anti-TNF therapy. To this end, two microarray data series were integrated to create a discovery cohort with 35 UC samples. A comprehensive gene expression and functional analysis was performed and identified 313 significantly altered genes, among which IL6 and INHBA were highlighted as overexpressed genes in the baseline mucosal biopsies of UCN, whose cooperation may lead to a decrease in the Tregs population. Besides, screening the abundances of immune cell subpopulations showed neutrophils’ accumulation increasing the inflammation. Furthermore, the correlation of KRAS signaling activation with unresponsiveness to anti-TNF mAb was observed using network analysis. Using 50x repeated 10-fold cross-validation LASSO feature selection and a stack ensemble machine learning algorithm, a five-mRNA prognostic panel including IL13RA2, HCAR3, CSF3, INHBA, and MMP1 was introduced that could predict the response of UC patients to anti-TNF antibodies with an average accuracy of 95.3 %. The predictive capacity of the introduced biomarker panel was also validated in two independent cohorts (44 UC patients). Moreover, we presented a distinct immune cell landscape and gene signature for UCN to anti-TNF drugs and further studies should be considered to make this predictive biomarker panel and therapeutic targets applicable in the clinical setting.

Long-term culture induces Bax-dependent apoptosis in rat preimplantation embryos.

Although rat preimplantation embryos are necessary for producing genetically modified rats, their in vitro culture remains a challenge. Rat zygotes can develop from the one-cell stage to the blastocyst stage in vitro; however, long-term culture reduces their developmental competence via an unknown mechanism. In this study, we examined how in vitro conditions affect rat preimplantation embryos, which may explain this reduced competence. Comprehensive gene expression analysis showed that genes related to apoptosis and energy metabolism were differentially expressed in rat embryos cultured long-term in vitro compared with those developed in vivo. Furthermore, we found that the expression of Bak1 and Bax, which are responsible for mitochondrial outer membrane permeabilization, were more upregulated in embryos cultured in vitro than those developed in vivo. Similarly, apoptosis-dependent DNA fragmentation was also exacerbated in in vitro culture conditions. Finally, gene disruption using CRISPR/Cas9 showed that Bax, but not Bak1, was responsible for these effects. These findings suggest that long-term in vitro culture induces Bax-dependent apoptosis through the mitochondrial pathway and may provide clues to improve the long-term culture of rat preimplantation embryos for genetic engineering research.

CIRCUST: A novel methodology for temporal order reconstruction of molecular rhythms; validation and application towards a daily rhythm gene expression atlas in humans.

The circadian system drives near-24-h oscillations in behaviors and biological processes. The underlying core molecular clock regulates the expression of other genes, and it has been shown that the expression of more than 50 percent of genes in mammals displays 24-h rhythmic patterns, with the specific genes that cycle varying from one tissue to another. Determining rhythmic gene expression patterns in human tissues sampled as single timepoints has several challenges, including the reconstruction of temporal order of highly noisy data. Previous methodologies have attempted to address these challenges in one or a small number of tissues for which rhythmic gene evolutionary conservation is assumed to be preserved. Here we introduce CIRCUST, a novel CIRCular-robUST methodology for analyzing molecular rhythms, that relies on circular statistics, is robust against noise, and requires fewer assumptions than existing methodologies. Next, we validated the method against four controlled experiments in which sampling times were known, and finally, CIRCUST was applied to 34 tissues from the Genotype-Tissue Expression (GTEx) dataset with the aim towards building a comprehensive daily rhythm gene expression atlas in humans. The validation and application shown here indicate that CIRCUST provides a flexible framework to formulate and solve the issues related to the analysis of molecular rhythms in human tissues. CIRCUST methodology is publicly available at https://github.com/yolandalago/CIRCUST/.

Comprehensive gene expression analysis of submergence stress response in nucleus-cytoplasm hybrid wheat

Comprehensive gene expression analysis of submergence stress response in nucleus-cytoplasm hybrid wheat

Identification of cuproptosis-related subtypes, characterization of immune microenvironment infiltration, and development of a prognosis model for osteoarthritis.

Osteoarthritis (OA) is a prevalent chronic joint disease with an obscure underlying molecular signature. Cuproptosis plays a crucial role in various biological processes. However, the association between cuproptosis-mediated immune infifiltration and OA progression remains unexplored. Therefore, this study elucidates the pathological process and potential mechanisms underlying cuproptosis in OA by constructing a columnar line graph model and performing consensus clustering analysis. Gene expression profifile datasets GSE12021, GSE32317, GSE55235, and GSE55457 of OA were obtained from the comprehensive gene expression database. Cuproptosis signature genes were screened by random forest (RF) and support vector machine (SVM). A nomogram was developed based on cuproptosis signature genes. A consensus clustering was used to distinguish OA patients into different cuproptosis patterns. To quantify the cuproptosis pattern, a principal component analysis was developed to generate the cuproptosis score for each sample. Single-sample gene set enrichment analysis (ssGSEA) was used to provide the abundance of immune cells in each sample and the relationship between these significant cuproptosis signature genes and immune cells.To quantify the cuproptosis pattern, a principal component analysis technique was developed to generate the cuproptosis score for each sample. Cuproptosis-related genes were extracted and subjected to differential expression analysis to construct a disease prediction model and confifirmed by RT-qPCR. Seven cuproptosis signature genes were screened (DBT, LIPT1, GLS, PDHB, FDX1, DLAT, and PDHA1) to predict the risk of OA disease. A column line graph model was developed based on these seven cuproptosis signature genes, which may assist patients based on decision curve analysis. A consensus clustering method was used to distinguish patients with disorder into two cuproptosis patterns (clusters A and B). To quantify the cuproptosis pattern, a principal component analysis technique was developed to generate the cuproptosis score for each sample. Furthermore, the OA characteristics of patients in cluster A were associated with the inflflammatory factors IL-1b, IL-17, IL-21, and IL-22, suggesting that the cuproptosis signature genes play a vital role in the development of OA. In this study, a risk prediction model based on cuproptosis signature genes was established for the fifirst time, and accurately predicted OA risk. In addition, patients with OA were classifified into two cuproptosis molecule subtypes (clusters A and B); cluster A was highly associated with Th17 immune responses, with higher IL-1b, IL-17, and IL-21 IL-22 expression levels, while cluster B had a higher correlation with cuproptosis. Our analysis will help facilitate future research related cuproptosis-associated OA immunotherapy. However, the specifific mechanisms remain to be elucidated.

TPIA2: an updated tea plant information archive for Camellia genomics.

The genus Camellia consists of about 200 species, which include many economically important species widely used for making tea, ornamental flowersand edible oil. Here, we present an updated tea plant information archive for Camellia genomics (TPIA2; http://tpia.teaplants.cn) by integrating more novel large-scale genomic, transcriptomic, metabolicand genetic variation datasets as well as a variety of useful tools. Specifically, TPIA2 hosts all currently available and well assembled 10 Camellia genomes and their comprehensive annotations from three major sections of Camellia. A collection of 15 million SNPs and 950950 small indels from large-scale genome resequencing of 350 diverse tea accessions were newly incorporated, followed by the implementation of a novel 'Variation' module to facilitate data retrieval and analysis of the functionally annotated variome. Moreover, 116 Camellia transcriptomes were newly assembled and added, leading to a significant extension of expression profiles of Camellia genes to 13 developmental stages and eight abiotic/biotic treatments. An updated 'Expression' function has also been implemented to provide a comprehensive gene expression atlas for Camellia. Two novel analytic tools (e.g. Gene ID Convert and Population Genetic Analysis) were specifically designed to facilitate the data exchange and population genomics in Camellia. Collectively, TPIA2 provides diverse updated valuable genomic resources and powerful functions, and will continue to be an important gateway for functional genomics and population genetic studies in Camellia.

BCL7B, a SWI/SNF complex subunit, orchestrates cancer immunity and stemness

Cancer is one of the main causes of human death. Here, we focus on the B-cell lymphoma 7 protein family member B (BCL7B) gene, an accessory subunit of the SWI/SNF chromatin-remodelling complex. To characterize the function of BCL7B, heterozygous BCL7B-deficient stomach cancer cell lines were generated with the CRISPR/Cas9 genome editing system. The comprehensive gene expression patterns were compared between parental cells and each ΔBCL7B cell line by RNA-seq. The results showed marked downregulation of immune-related genes and upregulation of stemness-related genes in the ΔBCL7B cell lines. Moreover, by ChIP-seq analysis with H3K27me3 antibody, the changes of epigenetic modification sequences were compared between parental cells and each ΔBCL7B cell line. After machine learning, we detected the centroid sequence changes, which exerted an impact on antigen presentation. The regulation of BCL7B expression in cancer cells gives rise to cancer stem cell-like characteristics and the acquisition of an immune evasion phenotype.

Diagnosis- and Prognosis-Related Gene Alterations in BCR::ABL1-Negative Myeloproliferative Neoplasms.

BCR::ABL1-negative myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies in which somatic mutations are acquired in hematopoietic stem/progenitor cells, resulting in an abnormal increase in blood cells in peripheral blood and fibrosis in bone marrow. Mutations in JAK2, MPL, and CALR are frequently found in BCR::ABL1-negative MPNs, and detecting typical mutations in these three genes has become essential for the diagnosis of BCR::ABL1-negative MPNs. Furthermore, comprehensive gene mutation and expression analyses performed using massively parallel sequencing have identified gene mutations associated with the prognosis of BCR::ABL1-negative MPNs such as ASXL1, EZH2, IDH1/2, SRSF2, and U2AF1. Furthermore, single-cell analyses have partially elucidated the effect of the order of mutation acquisition on the phenotype of BCR::ABL1-negative MPNs and the mechanism of the pathogenesis of BCR::ABL1-negative MPNs. Recently, specific CREB3L1 overexpression has been identified in megakaryocytes and platelets in BCR::ABL1-negative MPNs, which may be promising for the development of diagnostic applications. In this review, we describe the genetic mutations found in BCR::ABL1-negative MPNs, including the results of analyses conducted by our group.

Reduced DEFA5 Expression and STAT3 Activation Underlie the Submucosal Invasion of Early Gastric Cancers

Introduction: Submucosal invasion is a core hallmark of early gastric cancer (EGC) with poor prognosis. However, the molecular mechanism of the progression from intramucosal gastric cancer (IMGC) to early submucosal-invasive gastric cancer (SMGC) is not fully understood. The objective of this study was to identify genes and pathways involved in the submucosal invasion in EGC using comprehensive gene expression analysis. Methods: Gene expression profiling was performed for eight cases of IMGC and eight cases of early SMGC with submucosal invasion ≥500 μm. To validate the findings of gene expression analysis and to examine the gene expression pattern in tissues, immunohistochemical (IHC) staining was performed for 50 cases of IMGC and SMGC each. Results: Gene expression analysis demonstrated that the expression levels of small intestine-specific genes were significantly decreased in SMGC. Among them, defensin alpha 5 (DEFA5) was the most downregulated gene in SMGC, which was further validated in SMGC tissues by IHC staining. Gene set enrichment analysis showed a strong association between SMGC, the JAK-STAT signaling pathway, and the upregulation of STAT3-activating cytokines. The expression of phosphorylated STAT3 was significant in the nucleus of tumor cells in SMGC tissues but not in areas expressing DEFA5. Conclusion: The results of this study strongly suggest that the downregulation of DEFA5 and the activation of STAT3 play a significant role in the submucosal invasion of EGC.

CIS deletion by CRISPR/Cas9 enhances human primary natural killer cell functions against allogeneic glioblastoma

BackgroundGlioblastoma (GBM) is the most common malignant brain tumor and has “immunologically cold” features. Changing GBM to an “immunologically hot” tumor requires a strong trigger that induces initial immune responses in GBM. Allogeneic natural killer cells (NKCs) have gained considerable attention as promising immunotherapeutic tools against cancer, where gene-edited NKCs would result in effective anti-cancer treatment. The present study focused on the immune checkpoint molecule cytokine-inducible SH2-containing protein (CISH, or CIS) as a critical negative regulator in NKCs.MethodsThe GBM tumor environment featured with immunological aspect was analyzed with Cancer immunogram and GlioVis. We generated human primary CIS-deleted NKCs (NK dCIS) using clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) with single guide RNA targeting genome sites on CIS coding exons. The genome-edited NKCs underwent microarray with differential expression analysis and gene set enrichment analysis (GSEA). The anti-GBM activity of the genome-edited NKCs was evaluated by apoptosis induction effects against allogeneic GBM cells and spheroids. We further detected in vivo antitumor effects using xenograft brain tumor mice.ResultsWe successfully induced human CIS-deleted NKCs (NK dCIS) by combining our specific human NKC expansion method available for clinical application and genome editing technology. CIS gene-specific guide RNA/Cas9 protein complex suppressed CIS expression in the expanded NKCs with high expansion efficacy. Comprehensive gene expression analysis demonstrated increased expression of 265 genes and decreased expression of 86 genes in the NK dCIS. Gene set enrichment analysis revealed that the enriched genes were involved in NKC effector functions. Functional analysis revealed that the NK dCIS had increased interferon (IFN)ɤ and tumor necrosis factor (TNF) production. CIS deletion enhanced NKC-mediated apoptosis induction against allogeneic GBM cells and spheroids. Intracranial administration of the allogeneic NKCs prolonged the overall survival of xenograft brain tumor mice. Furthermore, the NK dCIS extended the overall survival of the mice.ConclusionThe findings demonstrated the successful induction of human primary NK dCIS with CRISPR/Cas9 with efficient expansion. CIS deletion enhanced the NKC-mediated anti-tumor effects in allogeneic GBM and could be a promising immunotherapeutic alternative for patients with GBM.

Abstract P1144: Single-cell RNA-Sequencing Of The Murine Cardiac Conduction System Reveals Transcriptional Regulators Associated With Conduction Development And Disease

Rhythmic beating of the heart is driven by a small subset of heart cells, altogether known as the cardiac conduction system (CCS). The CCS consists of unique components including the sinoatrial node (SAN), the atrioventricular node (AVN), the bundle of His, bundle branches and Purkinje fiber (PF) system. Due to obstacles inherent in studying the CCS, including small cell numbers, large cell-type heterogeneity and complex anatomy, our understanding of the transcriptional networks driving CCS development remains limited. Here, we harnessed single-cell RNA-sequencing (scRNAseq) and bioinformatic analyses to uncover putative DNA-binding transcription factors (TFs) as well as transcriptional co-regulators enriched within the entire developing murine CCS. Comparison of multiple independent prior datasets, spanning all major components of the CCS, revealed previously known TFs necessary for conduction development including, Islet 1 ( Isl1 ; avg log fold change 0.34, p= 1E-192) in the SAN; T-box transcription factor 5 (Tbx5 ; avg log fold change 0.54, p= 2.1E-110) in the AVN; and ETS variant transcription factor 1 ( Etv1; avg log fold change 0.89, p= 6.7E-105) in the PFs, among others. Notably, an additional 16 novel DNA-binding transcription factors and 40 transcription co-regulators were enriched within specific CCS components at statistical significance. To further evaluate the transcriptional landscape of individual subcomponents within the SAN (compact versus transitional cells), we also performed scRNAseq on FACS-isolated SAN cells from embryonic day 16.5 Hcn4 -GFP transgenic mice revealing differential expression of a host of TFs, both established and novel. Finally, our murine findings were then cross-referenced to over 15 human GWAS studies related to cardiac rhythm abnormalities, revealing an association of a subset of these murine CCS-enriched transcription factors and cardiac arrhythmias in humans. In conclusion, this study provides a comprehensive gene expression atlas of transcription factors and transcriptional co-regulators within individual CCS components at single-cell resolution, laying the groundwork for future studies into the transcriptional regulation of cardiac conduction development and disease.

Based on the Cancer Genome Atlas Database Development of a prognostic model of RNA binding protein in stomach adenocarcinoma

The purpose of this study was to identify potential RNA binding proteins associated with the survival of gastric adenocarcinoma, as well as the corresponding biological characteristics and signaling pathways of these RNA binding proteins. RNA sequencing and clinical data were obtained from the cancer genome map (N = 32, T = 375) and the comprehensive gene expression database (GSE84437, N = 433). The samples in The Cancer Genome Atlas were randomly divided into a development group and a test group. A total of 1495 RNA binding protein related genes were extracted. Using nonparametric tests to analyze the difference of RNA binding protein related genes, 296 differential RNA binding proteins were obtained, 166 were up-regulated and 130 were down regulated. Twenty prognosis-related RNA binding proteins were screened using Cox regression, including 14 high-risk genes (hazard ratio > 1.0) and 6 low-risk genes (hazard ratio < 1.0). Seven RNA binding protein related genes were screened from the final prognostic model and used to construct a new prognostic model. Using the development group and test group, the model was verified with survival analysis, receiver operating characteristics curves and prognosis analysis curves. A prediction nomogram was finally developed and showed good prediction performance.