High-throughput Gene Expression Analysis Research Articles

Teratoma Development in 129.MOLF-Chr19 Mice Elicits Two Waves of Immune Cell Infiltration

Teratomas are a highly differentiated type of testicular germ cell tumors (TGCTs), the most common type of solid cancer in young men. Prominent inflammatory infiltrates are a hallmark of TGCTs, although their compositions and dynamics in teratomas remain elusive. Here, we reached out to characterize the infiltrating immune cells and their activation and polarization state by using high-throughput gene expression analysis of 129.MOLF-Chr19 mice that spontaneously develop testicular teratomas. We showed that inconspicuous testes without any apparent alterations in size or morphology can be clustered into three groups based on their expression of stemness and immune genes, supporting a model in which initial oncogenic transformation elicits a first wave of T-cell infiltration. Moderately and severely enlarged tumorous testes then displayed a progressive infiltration with T cells, monocytes/macrophages, and B cells. Importantly, T cells seem to adopt an inactive state caused by an overexpression of immune checkpoint molecules and the polarization of monocytes/macrophages to an anti-inflammatory phenotype. Our findings are supported by the analysis of metabolic gene expression, which unveiled alterations indicative of tumor growth and immune cell infiltration. Collectively, testicular teratomas, at least in mice, are characterized by a diverse inflammatory infiltrate containing T cells that putatively become inactivated, allowing the tumors to further grow. We believe that these findings may provide a rationale for the development of new immunomodulatory therapies for TGCTs.

Shear stress effects on epididymal epithelial cell via primary cilia mechanosensory signaling.

Shear stress, resulting from fluid flow, is a fundamental mechanical stimulus affecting various cellular functions. The epididymis, essential for sperm maturation, offers a compelling model to study the effects of shear stress on cellular behavior. This organ undergoes extensive proliferation and differentiation until puberty, achieving full functionality as spermatozoa commence their post-testicular maturation. Although the mechanical tension exerted by testicular fluid is hypothesized to drive epithelial proliferation and differentiation, the precise mechanisms remain unclear. Here we assessed whether the responsiveness of the epididymal cells to shear stress depends on functional primary cilia by combining microfluidic strategies on immortalized epididymal cells, calcium signaling assays, and high-throughput gene expression analysis. We identified 97 genes overexpressed in response to shear stress, including early growth response (Egr) 2/3, cellular communication network factor (Ccn) 1/2, and Fos proto-oncogene (Fos). While shear stress triggered a rapid increase of intracellular Ca2+, this response was abrogated following the impairment of primary ciliogenesis through pharmacological and siRNA approaches. Overall, our findings provide valuable insights into how mechanical forces influence the development of the male reproductive system, a requisite to sperm maturation.

FEAtl: a comprehensive web-based expression atlas for functional genomics in tropical and subtropical fruit crops

BackgroundFruit crops, including tropical and subtropical fruits like Avocado (Persea americana), Fig (Ficus carica), Date Palm (Phoenix dactylifera), Mango (Mangifera indica), Guava (Psidium guajava), Papaya (Carica papaya), Pineapple (Ananas comosus), and Banana (Musa acuminata) are economically vital, contributing significantly to global agricultural output, as classified by the FAO’s World Programme for the Census of Agriculture. Advancements in next-generation sequencing, have transformed fruit crop breeding by providing in-depth genomic and transcriptomic data. RNA sequencing enables high-throughput analysis of gene expression, and functional genomics, crucial for addressing horticultural challenges and enhancing fruit production. The genomic and expression data for key tropical and sub-tropical fruit crops is currently lacking a comprehensive expression atlas, revealing a significant gap in resources for horticulturists who require a unified platform with diverse datasets across various conditions and cultivars.ResultsThe Fruit Expression Atlas (FEAtl), available at http://backlin.cabgrid.res.in/FEAtl/, is a first-ever extensive and unified expression atlas for tropical and subtropical fruit crops developed using 3-tier architecture. The expressivity of coding and non-coding genes, encompassing 2,060 RNA-Seq samples across 91 tissue types and 177 BioProjects, it provides a comprehensive view of gene expression patterns for different tissues under various conditions. FEAtl features multiple tabs that cater to different aspects of the dataset, namely, Home, About, Analyze, Statistics, and Team and contains seven central functional modules: Transcript Information,Sample Information, Expression Profiles in FPKM and TPM, Functional Analysis, Genes Based on Tau Score, and Search for Specific Gene. The expression of a transcript of interest can be easily queried by searching by tissue ID and transcript type. Expression data can be displayed as a heat map, along with functional descriptions as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes.ConclusionsThis atlas represents a groundbreaking compilation of a wide array of information pertaining to eight distinct fruit crops and serves as a fundamental resource for comparative analysis among different fruit species and is a catalyst for functional genomic studies. Database availability: http://backlin.cabgrid.res.in/FEAtl/.

A high-throughput gene expression analysis software tool for developmental time series and gene signature analysis of human cardiomyocyte differentiation

Publicly available high-throughput gene expression data enable the investigation of biological processes by the scientific community. Although several bioinformatics tools offer methodologies for basic differential gene expression analysis, difficulties arise in the analysis of multiple sample groups comprising a developmental time series, especially when the identification and classification of unique gene expression patterns is the primary goal of the study. Data analysis using these tools requires programming experience, which limits the accessibility of these tools to the broader community. To streamline developmental time-series investigations, we created the Developmental Gene Expression Analysis (devGEA) tool. This environment can be implemented locally or via web browsers to expedite differential gene expression analysis. This tool provides gene signature determination methods that can classify differentially expressed genes based on their correlation with gene expression patterns. devGEA was used to characterize cardiac development gene expression signatures from high-throughput RNA-seq datasets profiling small-molecule directed cardiomyocyte differentiation of human pluripotent stem cell lines (hiPSCs). After pre-processing, discrete gene expression criteria-based expected changes were used to classify the genes into developmental signatures. Several cardiomyocyte differentiation markers and candidate cardiac genes representing different developmental signatures were experimentally validated using the GIBCOTM hiPSC line. This method was then compared to a gene signature correlation approach that classified expressed genes based on their degree of similarity with key cardiac developmental signatures representing the stages of cardiomyocyte differentiation. Therefore, devGEA provides a robust workflow for investigator-driven analysis of developmental time series, allowing for the identification of differentially expressed genes and gene signatures for extensive experimental investigation. We also introduced a method for classifying genes by their correlation with genes or developmental patterns of interest. Our correlation-based method takes advantage of a priori knowledge of an experiment and is conceptually simpler than unsupervised clustering approaches.

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution.

We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial-indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000+ cells per run. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC has a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has an observed multiplet rate of less than 1.1% and a cell capture rate of ~50%. Although the assay can accurately profile the whole transcriptome (19,683 human or 21,400 mouse genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest - thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of 89,722 cells across multiple sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies across thousands of samples with high data quality.

MicroRNA Biogenesis during Cellular Senesence Induced by Chronic Stress of the Endoplasmic Reticulum

MicroRNAs are small non-coding regulatory RNAs about 22 nt long, post-transcriptional and transcriptional regulators of gene expression that stabilize the cellular phenotype and play an important role in differentiation, development, and apoptosis. MicroRNA biogenesis includes several precisely controlled post-transcriptional stages of processing and transport, including cytoplasmic cleavage of pre-miRNA by type III ribonuclease DICER with the formation of a mature duplex included in the RISC complex. The role of miRNA and its biogenesis are not well understood in such an important process as cellular stress. Cellular stress is a non-specific cellular response to non-physiological stimuli that can switch a cell to death or cellular senescence. The global decrease in microRNA levels is a key feature of cancer cells and an important reason for the formation of a malignant phenotype. In this work, using flow cytometry and high-throughput analysis of gene expression, we showed that chronic endoplasmic reticulum (ER) stress, one of the types of cellular stress associated with impaired protein folding in the ER, leads to the formation of a cellular aging phenotype in fibroblast-like FRSN cells. Despite the fact that acute ER stress can reduce miRNA biogenesis, chronic stress does not lead to a significant drop in global miRNA expression and is accompanied by only a slight decrease in DICER1 mRNA expression. Under chronic ER stress, we found an increase in cell population heterogeneity in terms of lysosomal beta-galactosidase activity, which does not exclude induced or initial cell heterogeneity and in terms of expression of microRNA biogenesis pathway components.

Spatially resolved transcriptomic analysis of the germinating barley grain.

Seeds are a vital source of calories for humans and a unique stage in the life cycle of flowering plants. During seed germination, the embryo undergoes major developmental transitions to become a seedling. Studying gene expression in individual seed cell types has been challenging due to the lack of spatial information or low throughput of existing methods. To overcome these limitations, a spatial transcriptomics workflow was developed for germinating barley grain. This approach enabled high-throughput analysis of spatial gene expression, revealing specific spatial expression patterns of various functional gene categories at a sub-tissue level. This study revealed over 14000 genes differentially regulated during the first 24 hafter imbibition. Individual genes, such as the aquaporin gene family, starch degradation, cell wall modification, transport processes, ribosomal proteins and transcription factors, were found to have specific spatial expression patterns over time. Using spatial autocorrelation algorithms, we identified auxin transport genes that had increasingly focused expression within subdomains of the embryo over time, suggesting their role in establishing the embryo axis. Overall, our study provides an unprecedented spatially resolved cellular map for barley germination and identifies specific functional genomics targets to better understand cellular restricted processes during germination. The data can be viewed at https://spatial.latrobe.edu.au/.

Investigation into the immune microenvironment of gastroenteropancreatic high-grade neuroendocrine carcinoma.

e16244 Background: Gastroenteropancreatic high-grade neuroendocrine carcinomas (GEP HG-NEC) are aggressive cancers with poor survival and limited therapies. Using high-throughput gene expression analysis, we report the tumor-immune system interactions within the tumor microenvironment (TME), differentially expressed genes based on age, tumor location, survival, and objective response rate (ORR) to systemic therapy with the aim to drive future effective therapy research. Methods: We evaluated patients diagnosed with GEP HG-NEC from 2010-2021 for a pilot study and included those with adequate tumor samples. RNA was analyzed using the NanoString nCounter PanCancer IO 360 panel. Logistic regression modeling was used for hypothesis testing associated with best overall response. Cox model was used for testing overall survival (OS). One-sided P values were calculated per the hypothesized positive association between immune signatures and improved clinical outcomes. Results: 21 patients were identified. Mean age 64 years (yrs) with a 2:1 gender ratio (M=14; F=7). Most common primary locations - pancreas (PNEC = 8) and esophagus (3). ORR = 78.6%. TME trends were dampened anti-cancer immune response in patients >60 yrs old and <6-month OS on treatment. TME gene signatures of age >60 yrs (vs. <60 yrs), were indicative of fewer neutrophils, myeloid cells, CD8+ T-cells and CD45+ cells. Downregulated genes in the >60yr old population associated with dampened immune response - ICAM1, TNFAIP6, TWIST1, and PTGER4. A smaller natural killer (NK) cell gene signature was the only notable association with OS <6 months (vs. >6 month). Downregulated genes in >6-month population were HK2 and REN while upregulated genes included CX3CR1, ZAP70, KLRK1, XCL1/2, and MMP7 (immunostimulatory). Increased immune infiltration was associated with prolonged OS, with elevated gene signature of T-cells, lymphoid cells, mast cells, and cytotoxic cells. Downregulated genes related to poor OS were HNF1A and ESR1 (immunosuppressive). A greater potential for a tumor adaptive immune resistance/response were noted; age <60 yrs and prolonged OS having elevated TME PD-L1 and PD-1/TIGIT gene signatures respectively. PNEC was associated with elevated gene signatures of TME MHCII vs. non-PNEC. Upregulated genes in non-PNEC include CXCL2/3 while downregulated genes include TMEM173, IFI16 and RELN (diminished adaptive immune response). Conclusions: This pilot study identified trends in gene expression associated with clinical outcomes. While statistical significance was precluded by limited sample size, our results suggest trends toward increased TME immune cell infiltration in age <60yrs and prolonged OS, increased potential for adaptive immune responses in PNEC, and elevated NK cell infiltration in patients with >6 month OS. Rational drug development for affected patients should target these specific abnormalities.

MicroRNA Biogenesis in Cell Senescence Induced by Chronic Endoplasmic Reticulum Stress

MicroRNAs are small noncoding RNAs that regulate gene expression; stabilize the cell phenotype; and play an important role in cell differentiation, development, and apoptosis. A canonical microRNA biogenesis pathway includes several posttranscriptional steps of processing and transport and ends with cytoplasmic cleavage of pre-miRNA by type III ribonuclease DICER to form a mature duplex, which is included in RISC. MicroRNA biogenesis and role in cell stress are still poorly understood. Using flow cytometry and high-throughput analysis of gene expression, we have shown that chronic endoplasmic reticulum (ER) stress, which is associated with improper protein folding in the ER, induce a cellular senescence phenotype in fibroblast-like FRSN cells. While acute ER stress can reduce miRNA biogenesis, chronic stress does not cause a significant drop in global microRNA expression and is accompanied by only a slight decrease in DICER1 mRNA expression. Heterogeneity with respect to lysosomal β-galactosidase activity was found to increase in the cell population exposed to ER stress. We do not exclude induced cell heterogeneity regarding expression of components of the microRNA biogenesis pathway.

A comprehensive survey on computational learning methods for analysis of gene expression data.

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

Mildly dysplastic oral lesions with optically-detectable abnormalities share genetic similarities with severely dysplastic lesions

ObjectiveOptical imaging studies of oral premalignant lesions have shown that optical markers, including loss of autofluorescence and altered morphology of epithelial cell nuclei, are predictive of high-grade pathology. While these optical markers are consistently positive in lesions with moderate/severe dysplasia or cancer, they are positive only in a subset of lesions with mild dysplasia. This study compared the gene expression profiles of lesions with mild dysplasia (stratified by optical marker status) to lesions with severe dysplasia and without dysplasia. Materials and methodsForty oral lesions imaged in patients undergoing oral surgery were analyzed: nine without dysplasia, nine with severe dysplasia, and 22 with mild dysplasia. Samples were submitted for high throughput gene expression analysis. ResultsThe analysis revealed 116 genes differentially expressed among sites without dysplasia and sites with severe dysplasia; 50 were correlated with an optical marker quantifying altered nuclear morphology. Ten of 11 sites with mild dysplasia and positive optical markers (91%) had gene expression similar to sites with severe dysplasia. Nine of 11 sites with mild dysplasia and negative optical markers (82%) had similar gene expression as sites without dysplasia. ConclusionThis study suggests that optical imaging may help identify patients with mild dysplasia who require more intensive clinical follow-up. If validated, this would represent a significant advance in patient care for patients with oral premalignant lesions.

Extra virgin olive oil extract rich in secoiridoids induces an anti-inflammatory profile in peripheral blood mononuclear cells from obese children

Obesity represents an important public health challenge of the twenty first century reaching epidemic proportions worldwide; this is especially true for the pediatric population. In this context, bioactive compounds from foods are crucial to counteract chronic inflammation as a typical feature of obesity. In particular, extra virgin olive oil (EVOO) is one of the most important functional foods exerting, among others, an anti-inflammatory activity not only due to its major (monounsaturated fatty acids) but also to its minor (phenolics) components, as reported in the last years. However, only a limited number of studies were performed on pediatric population, and even fewer are those focusing on EVOO phenolics that investigate the correlation of the chemical characterization with the biological function. Thus, starting from our in vitro data identifying an EVOO chemical profile characterized by a high content of secoiridoids correlating with an anti-inflammatory effect, we studied the ability of an EVOO extract with the same chemical profile to retain this function ex vivo. Specifically, peripheral blood mononuclear cells (PBMCs) collected from obese children were treated with EVOO and olive oil extracts, characterized by a low polyphenol content, to study the ability of secoiridoids to dampen the inflammatory response. A reduction of pro-inflammatory CD14+CD16+ monocytes was detected by cytofluorimetric analysis when PBMCs were treated with EVOO as compared to olive oil extracts. According to this, a down modulation of CCL2 and CCL4 chemokines involved in the recruitment of inflammatory cells, was reported in the supernatants of EVOO relative to olive oil extracts treated PBMCs. Moreover, a high-throughput gene expression analysis revealed that PBMCs molecular profile from obese children is greatly modulated after the treatment with EVOO extract in terms of metabolic and inflammatory pathways. Importantly, some of the significantly modulated genes were involved in the pathways promoting the development of severe obesity. Overall, our ex vivo data demonstrated the ability of EVOO to reduce the inflammatory milieu of PBMCs from obese children both at protein and molecular levels. Of note, a good correlation between the EVOO chemical profile and the biological modulations in terms of anti-inflammatory activity was reported.

Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology.

Research in synthetic biology and metabolic engineering require a deep understanding on the function and regulation of complex pathway genes. This can be achieved through gene expression profiling which quantifies the transcriptome-wide expression under any condition, such as a cell development stage, mutant, disease, or treatment with a drug. The expression profiling is usually done using high-throughput techniques such as RNA sequencing (RNA-Seq) or microarray. Although both methods are based on different technical approaches, they provide quantitative measures of the expression levels of thousands of genes. The expression levels of the genes are compared under different conditions to identify the differentially expressed genes (DEGs), the genes with different expression levels under different conditions. DEGs, usually involving thousands in number, are then investigated using bioinformatics and data analytic tools to infer and compare their functional roles between conditions. Dealing with such large datasets, therefore, requires intensive data processing and analyses to ensure its quality and produce results that are statistically sound. Thus, there is a need for deep statistical and bioinformatics knowledge to deal with high-throughput gene expression data. This represents a barrier for wet biologists with limited computational, programming, and data analytic skills that prevent them from getting the full potential of the data. In this chapter, we present a step-by-step protocol to perform transcriptome analysis using GeneCloudOmics, a cloud-based web server that provides an end-to-end platform for high-throughput gene expression analysis.

Angiotensin-converting Enzyme-2 (ACE2) Expression in Pediatric Liver Disease.

The membrane protein angiotensin-converting enzyme-2 (ACE2) has gained notoriety as the receptor for severe acute respiratory syndrome coronavirus 2. Prior evidence has shown ACE2 is expressed within the liver but its function has not been fully discerned. Here, we utilized novel methodology to assess ACE2 expression in pediatric immune-mediated liver disease to better understand its presence in liver diseases and its role during infections such as COVID-19. We stained liver tissue with ACE2-specific immunofluorescent antibodies, analyzed via confocal microscopy. Computational deep learning-based segmentation models identified nuclei and cells, allowing the quantification of mean cellular and cytosolic immunofluorescent. Spatial transcriptomics provided high-throughput gene expression analysis in tissue to determine cellular composition for ACE2 expression. ACE2 plasma expression was quantified via enzyme-linked immunosorbent assay. High ACE2 expression was seen at the apical surface of cholangiocytes, with lower expression within hepatocyte cytosol and nonparenchymal cells ( P <0.001). Children with liver disease had higher ACE2 hepatic expression than pediatric control tissue ( P <0.001). Adult control tissue had higher expression than pediatric control ( P <0.001). Plasma ACE2 was not found to be statistically different between samples. Spatial transcriptomics identified cell composition of ACE2-expressing spots containing antibody-secreting cells. Our results show ACE2 expression throughout the liver, with strongest localization to cholangiocyte membranes. Machine learning can be used to rapidly identify hepatic cellular components for histologic analysis. ACE2 expression in the liver may be increased in pediatric liver disease. Future work is needed to better understand the role of ACE2 in chronic disease and acute infections.

Preparation of Intact Nuclei for Single-Nucleus Omics Using Frozen Cell Suspensions from Mutant Embryos of Xenopus tropicalis.

Single-cell omics such as single-cell RNA-sequencing (RNA-seq) have been used extensively to obtain single-cell genome-wide expression data. This technique can be used to compare mutant and wild-type embryos at predifferentiation stages when individual tissues are not yet formed (therefore requiring genotyping to distinguish among embryos), for example, to determine effects of mutations on developmental trajectories or congenital disease phenotypes. It is, however, hard to use single cells for this technique, because such embryos or cells would need to be frozen until genotyping is complete to capture a given developmental stage precisely, but intact cells cannot be isolated from frozen samples. We developed a protocol in which high-quality nuclei are isolated from frozen cell suspensions, allowing for genotyping individual embryos based on a small fraction of a single embryo suspension. The remaining suspension is frozen. After genotyping is complete, nuclei are isolated from embryo suspensions with the desired genotype and encapsulated in 10× Genomics barcoded gel beads for single-nucleus RNA-seq. We provide a step-by-step protocol that can be used for single transcriptomic analysis as well as single-nucleus chromatin accessibility assays such as ATAC-seq. This technique allows for high-quality high-throughput single-nucleus analysis of gene expression in genotyped embryos. This approach may also be valuable for collection of wild-type embryonic material, for example, when collecting tissue from a particular developmental stage. In addition, freezing of tissue suspensions allows precise staging of collected embryos or tissue that may be difficult to manage when collecting and processing cells from living embryos for single-cell RNA-seq.

Probing Carboxylate Anolytes for Photo-Biofuel Cells through Combination of Bioinformatics and Electrochemistry

Photoheterotrophic purple bacterium Rhodobacter capsulatus has recently gained attention for its high halotolerance and its photo-enhanced extracellular electron transfer via exogenous quinone redox mediators, opening opportunity for self-powered decontamination of saline wastewater. Biodegradation of malate and succinate in R. capsulatus electrochemical systems has undergone prior examination, although a consistent bioelectrochemical system to comparatively study multiple carbon sources has not previously been developed. In this study, electrochemical techniques, biological assays, and computational tools were combined to evaluate malate, succinate, propionate, and lactate as oxidizable fuels in photo-enhanced microbial electrochemical systems. Specifically, cyclic voltammetry and amperometry data demonstrated that R. capsulatus generates distinctive photo-enhanced current densities dependent on both fuel identity and concentration. Bacterial growth curve studies were in agreement with the electrochemical data, indicating that lactate, which yielded the greatest bio-anodic current density (9.5 ± 1.9 µA cm-2), allowed the bacteria to grow more rapidly than the other substrates, suggesting its effectiveness as a fuel. Moreover, propionate appeared to be the fuel least efficiently utilized by R. capsulatus, having the slowest growth curve and the lowest current density generation (1.3 ± 0.2 µA cm-2). Finally, high-throughput differential gene expression analysis allowed for illuminating the physiological underpinnings of the observed differences between substrates. Most remarkably, cells grown in lactate were found to overexpress light harvesting complex II proteins and to underexpress flagellar motility proteins, which both correspond to the high photo-enhanced current density in lactate bioanodes compared to malate and propionate.

UBE2S promotes the development of ovarian cancer by promoting PI3K/AKT/mTOR signaling pathway to regulate cell cycle and apoptosis

BackgroundOvarian cancer is one of the important factors that seriously threaten women's health and its morbidity and mortality ranks eighth among female cancers in the world. It is critical to identify potential and promising biomarkers for prognostic evaluation and molecular therapy of OV. Ubiquitin-conjugating enzyme E2S (UBE2S), a potential oncogene, regulates the malignant progression of various tumors; however, its role in OV is still unclear.MethodsThe expression and prognostic significance of UBE2S at the pan-cancer level were investigated through high-throughput gene expression analysis and clinical prognostic data from TCGA, GEPIA, and GEO databases. 181 patients with OV were included in this study. Cell culture and cell transfection were performed on OV cell lines (SKOV3 and A2780) and a normal ovarian cell line (IOSE80). The expression level and prognostic significance of UBE2S in OV were verified by western blot, immunohistochemistry, and Kaplan–Meier survival analysis. Through cell transfection, CCK-8, Ki-67 immunofluorescence, wound healing, Transwell, clonogenic, and flow cytometry assays, the effect and detailed mechanism of UBE2S knockdown on the malignant biological behavior of OV cells were explored.ResultsUBE2S exhibited abnormally high expression at the pan-cancer level. The results of RT-qPCR and Western blotting indicated that UBE2S was significantly overexpressed in ovarian cancer cell lines compared with normal cell lines (P < 0.05). Kaplan–Meier survival analysis and Immunohistochemistry indicated that overexpression of UBE2S was related to poor prognosis of OV (HR > 1, P < 0.05). Results of in vitro experiments indicated that UBE2S gene knockdown might inhibit the proliferation, invasion, and prognosis of OV cells by inhibiting the PI3K/AKT/mTOR signaling pathway, thereby blocking the cell cycle and promoting apoptosis (P < 0.05).ConclusionUBE2S is a potential oncogene strongly associated with a poor prognosis of OV patients. Knockdown of UBE2S could block the cell cycle and promote apoptosis by inhibiting the PI3K/AKT/mTOR pathway and ultimately inhibit the proliferation, migration and prognosis of ovarian cancer, which suggested that UBE2S might be used for molecular therapy and prognostic evaluation of ovarian cancer.

Spatial and Transcriptomic Analysis of Perineural Invasion in Oral Cancer.

Perineural invasion (PNI), a common occurrence in oral squamous cell carcinomas, is associated with poor survival. Consequently, these tumors are treated aggressively. However, diagnostic criteria of PNI vary and its role as an independent predictor of prognosis has not been established. To address these knowledge gaps, we investigated spatial and transcriptomic profiles of PNI-positive and PNI-negative nerves. Tissue sections from 142 patients were stained with S100 and cytokeratin antibodies. Nerves were identified in two distinct areas: tumor bulk and margin. Nerve diameter and nerve-to-tumor distance were assessed; survival analyses were performed. Spatial transcriptomic analysis of nerves at varying distances from tumor was performed with NanoString GeoMx Digital Spatial Profiler Transcriptomic Atlas. PNI is an independent predictor of poor prognosis among patients with metastasis-free lymph nodes. Patients with close nerve-tumor distance have poor outcomes even if diagnosed as PNI negative using current criteria. Patients with large nerve(s) in the tumor bulk survive poorly, suggesting that even PNI-negative nerves facilitate tumor progression. Diagnostic criteria were supported by spatial transcriptomic analyses of >18,000 genes; nerves in proximity to cancer exhibit stress and growth response changes that diminish with increasing nerve-tumor distance. These findings were validated in vitro and in human tissue. This is the first study in human cancer with high-throughput gene expression analysis in nerves with striking correlations between transcriptomic profile and clinical outcomes. Our work illuminates nerve-cancer interactions suggesting that cancer-induced injury modulates neuritogenesis, and supports reclassification of PNI based on nerve-tumor distance rather than current subjective criteria.

Macrophage functional diversity in NAFLD - more than inflammation.

Macrophages have diverse phenotypes and functions due to differences in their origin, location and pathophysiological context. Although their main role in the liver has been described as immunoregulatory and detoxifying, changes in macrophage phenotypes, diversity, dynamics and function have been reported during obesity-related complications such as non-alcoholic fatty liver disease (NAFLD). NAFLD encompasses multiple disease states from hepatic steatosis to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis and hepatocarcinoma. Obesity and insulin resistance are prominent risk factors for NASH, a disease with a high worldwide prevalence and no approved treatment. In this Review, we discuss the turnover and function of liver-resident macrophages (Kupffer cells) and monocyte-derived hepatic macrophages. We examine these populations in both steady state and during NAFLD, with an emphasis on NASH. The explosion in high-throughput gene expression analysis using single-cell RNA sequencing (scRNA-seq) within the last 5 years has revolutionized the study of macrophage heterogeneity, substantially increasing our understanding of the composition and diversity of tissue macrophages, including in the liver. Here, we highlight scRNA-seq findings from the last 5 years on the diversity of liver macrophages in homeostasis and metabolic disease, and reveal hepatic macrophage function beyond their classically described inflammatory role in the progression of NAFLD and NASH pathogenesis.

Preprint Highlight: Adult fibroblasts retain organ-specific transcriptomic identity.

Even with recent advances in lineage tracing and multiomics analyses, it is difficult to specifically identify and target heterogeneous fibroblast populations in an organ-specific manner. This study combined robust multi-organ in vitro, in vivo, and high-throughput gene expression analyses of mouse fibroblast populations derived from various organs to define organ-specific transcriptional signatures. The analyses revealed that fibroblasts derived from different organs retain transcriptional embryonic gene signatures as organ-of-origin identifiers in adulthood. Moreover, organ-specific transcriptome identities of fibroblast populations were maintained in fibroblast co-cultures in vitro and ectopic transplants in vivo. These studies advance our current understanding of the heterogeneity of fibroblast populations and suggest opportunities for their exploitation and targetability in an organ-specific manner.