Transcriptome Sequencing Data Research Articles

Transcriptome sequencing of garlic reveals key genes related to the heat stress response

With global warming, heat stress has become an important factor that seriously affects crop yield and quality. Therefore, understanding plant responses to heat stress is important for agricultural practice, but the molecular mechanism of high-temperature tolerance in garlic remains unclear. In this study, ‘Xusuan No. 6’ was used as the experimental material. After heat stress for 0 (CK), 2 and 24 h, transcriptome sequencing was used to screen metabolic pathways and differentially expressed genes (DEGs) closely related to heat stress and was further verified by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 86,110 unigenes obtained from the raw transcriptome sequencing data were spliced. After 2 h of heat treatment, the expression levels of 8898 genes increased, and 3829 genes were decreased in leaves. After 24 h, the expression levels of 7167 genes were upregulated, and 3176 genes were downregulated. Gene Ontology enrichment analysis showed that DEGs were mainly enriched in seven categories: cellular processes, metabolic processes, binging, catalytic activity, cellular anatomical entity and protein-containing complex response to stimulus. Kyoto Encyclopedia of Genes and Genomes pathway enrichment showed that DEGs are involved in protein processing in the endoplasmic reticulum, plant hormone signal transduction, phenylpropanoid biosynthesis, and photosynthetic antenna proteins. Six genes were selected and further verified by qRT-PCR. In this study, the full-length transcriptome of garlic was constructed, and the regulatory genes related to the heat resistance of garlic were studied. Taken together, these findings can provide a theoretical basis for the cloning of heat resistance genes in garlic and for the analysis of heat resistance mechanisms.

Developing an advanced diagnostic model for hepatocellular carcinoma through multi-omics integration leveraging diverse cell-death patterns.

Hepatocellular carcinoma (HCC), representing more than 80% of primary liver cancer cases, lacks satisfactory etiology and diagnostic methods. This study aimed to elucidate the role of programmed cell death-associated genes (CDRGs) in HCC by constructing a diagnostic model using single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data. Six categories of CDRGs, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA-seq data from blood-derived exosomes were sourced from the exoRBase database, RNA-seq data from cancer tissues from the TCGA database, and scRNA-seq data from the GEO database. Subsequently, we intersected the differentially expressed genes (DEGs) of the HCC cohort from exoRBase and TCGA databases with CDRGs, as well as DEGs obtained from single-cell datasets. Candidate biomarker genes were then screened using clinical indicators and a machine learning approach, resulting in the construction of a seven-gene diagnostic model for HCC. Additionally, scRNA-seq and spatial transcriptome sequencing (stRNA-seq) data of HCC from the Mendeley data portal were used to investigate the underlying mechanisms of these seven key genes and their association with immune checkpoint blockade (ICB) therapy. Finally, we validated the expression of key molecules in tissues and blood-derived exosomes through quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry experiments. Collectively, we obtained a total of 50 samples and 104,288 single cells. Following the meticulous screening, we established a seven-gene diagnostic model for HCC, demonstrating high diagnostic efficacy in both the exoRBase HCC cohort (training set: AUC = 1; testing set: AUC = 0.847) and TCGA HCC cohort (training set: AUC = 1; testing set: AUC = 0.976). Subsequent analysis revealed that HCC cluster 3 exhibited a higher stemness index and could serve as the starting point for the differentiation trajectory of HCC cells, also displaying more abundant interactions with other cell types in the microenvironment. Notably, key genes TRIB3 and NQO1 displayed elevated expression levels in HCC cells. Experimental validation further confirmed their elevated expression in both tumor tissues and blood-derived exosomes of cancer patients. Additionally, stRNA analysis not only substantiated these findings but also suggested that patients with high TRIB3 and NQO1 expression might respond more favorably to ICB therapy. The seven-gene diagnostic model demonstrated remarkable accuracy in HCC screening, with TRIB3 emerging as a promising diagnostic tool and therapeutic target for HCC.

Multi-omics analysis reveals that Cas13d contributes to PI3K-AKT signaling and facilitates cell proliferation via PFKFB4 upregulation

The CRISPR-Cas system is a powerful gene editing technology, the clinical application of which is currently constrained due to safety concerns. A substantial body of safety research concerning Cas9 exists; however, scant attention has been directed toward investigating the safety profile of the emergent Cas13 system, which confers RNA editing capabilities. In particular, uncertainties persist regarding the potential cellular impacts of Cas13d in the absence of reliance on a cleavage effect. In this study, we conducted an initial exploration of the effects of Cas13d on HeLa cells. Total RNA and protein samples were extracted after transfection with a Cas13d-expressing plasmid construct, followed by transcriptomic and proteomic sequencing. Differential gene expression analysis identified 94 upregulated and 847 downregulated genes, while differential protein expression analysis identified 185 upregulated and 231 downregulated proteins. Subsequently, enrichment analysis was conducted on the transcriptome and proteome sequencing data, revealing that the PI3K-Akt signaling pathway is a common term. After intersecting the differentially expressed genes enriched in the PI3K-Akt signaling pathway with all the differentially expressed proteins, it was found that the expression of the related regulatory gene PFKFB4 was upregulated. Moreover, western blot analysis demonstrated that Cas13d can mediate PI3K-Akt signaling upregulation through overexpression of PFKFB4. CCK-8 assay, colony formation, and EdU experiments showed that Cas13d can promote cell proliferation. Our data demonstrate, for the first time, that Cas13d significantly impacts the transcriptomic and proteomic profiles, and proliferation phenotype, of HeLa cells, thus offering novel insights into safety considerations regarding gene editing systems.

P-682 Malic enzyme 1 in granulosa cells promotes ovarian aging through inhibiting the AMPK-FoXO1-IDH2 signaling pathway

Abstract Study question Reduced levels of oxidative phosphorylation in granulosa cells are a key factor in regulating ovarian aging, but the upstream regulatory mechanisms are unknown. Summary answer Our study revealed that elevated malic enzyme 1 in granulosa cells promotes ovarian aging by inhibiting oxidative phosphorylation levels through the AMPK-FoXO1-IDH2 signaling pathway. What is known already With ovarian aging, the ovarian granulosa cells appear hallmarks of high apoptosis activity and low-proliferative activity, which lead to a reduction in ovarian reserve and a decrease in hormone synthesis, resulting in female infertility and reduced quality of life. And the decline of isocitrate dehydrogenase, IDH, in non-human primate ovarian granulosa cells is a key causative factor for the decline in the level of oxidative phosphorylation in granulosa cells. Recently, researchers reported that malic enzyme 1, which encodes a cytosolic, NADP-dependent Enzyme that generates NADPH for fatty acid biosynthesis inhibits IDH2 expression in tumor cells. Study design, size, duration Utilizing clinical samples, cells and animal models, with the help of research tools such as biochemical experiments, gene editing and single-cell transcriptome sequencing, at different levels, including molecular, cellular, animal and human samples, we systematically analyzed the characteristic changes and key molecules in granulosa cells along with ovarian aging and reveal the intracellular molecular pathways through which ME1 and IDH2 regulate the process of ovarian aging for 1 year. Participants/materials, setting, methods We first apply single-cell transcriptome sequencing analysis of young and old mouse ovaries to reveal cellular subpopulation changes and transcriptomic hallmarks of ovarian granulosa cell with aging. And then we validate the biological function and revealing the molecular mechanism to regulate ovarian aging by using physiological ovarian aging mouse model, clinical ovarian granulosa cells from young and old female and human ovarian granulosa cell line KGN utilizing technologies of protein immunology, qPCR and CRISPR/Cas9. Main results and the role of chance Ovarian single-cell transcriptome sequencing data from young and old mice showed that ovarian aging was accompanied by a dramatic decrease in the number of granulosa cells and drastic changes in the proportions of different subpopulations. The proportion of mitotically active granulosa cells was significantly lower in aging ovaries than in young mouse ovaries, and GO enrichment analysis showed that granulosa cell cycle, antioxidant and hormone synthesis-related signaling pathways were significantly down-regulated in aging ovaries. Using immunoblotting, immunofluorescence and qPCR, we demonstrated that the expression of ME1 in granulosa cells of aging mice and human ovaries was significantly elevated, while the expression of IDH2 was significantly decreased. Performing the overexpression and knockdown of ME1 in human ovarian granulosa cell line KGN, which further demonstrated that ME1 could inhibit the transcriptionally regulated IDH2 expression of downstream genes, such as FoXO1, by suppressing the phosphorylation of AMPK. The decrease of IDH2 in mitochondria reduces the level of oxidative phosphorylation in granulosa cells, resulting in the increase of intracellular reactive oxygen species, activation of the intracellular p53-p21 signaling pathway, inhibition of the cell cycle, and promotion of apoptosis, which in turn impairs the function of granulosa cells to synthesize hormones and promotes ovarian aging. Limitations, reasons for caution Malic enzyme 1, which links the glycolytic cycle to the citric acid cycle, expressing in other ovarian cells including oocytes, and the regulation of its expression is complex. Future studies will focus on its biological functions in other ovarian cells and its molecular mechanism in regulating ovarian aging. Wider implications of the findings Our study suggests that granulosa cell ME1 and IDH2 co-regulate oxidative phosphorylation levels to regulate ovarian aging, and that ME1 and IDH2 may serve as potential targets for the finding and treatment of premature ovarian aging and delaying ovarian aging in women. Trial registration number not applicable

Unveiling diabetic nephropathy: a novel diagnostic model through single-cell sequencing and co-expression analysis.

Diabetic nephropathy (DN) is a severe complication of diabetes that affects the kidneys. Disulfidptosis, a newly defined type of programmed cell death, has emerged as a potential area of interest, yet its significance in DN remains unexplored. This study utilized single-cell sequencing data GSE131882 from GEO database combined with bulk transcriptome sequencing data GSE30122, GSE30528 and GSE30529 to investigate disulfidptosis in DN. Single-cell sequencing analysis was performed on samples from DN patients and healthy controls, focusing on cell heterogeneity and communication. Weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA) were employed to identify disulfidptosis-related gene sets and pathways. A diagnostic model was constructed using machine learning techniques based on identified genes, and immunocorrelation analysis was conducted to explore the relationship between key genes and immune cells. PCR validation was performed on blood samples from DN patients and healthy controls. The study revealed significant disulfidptosis heterogeneity and cell communication differences in DN. Specific targets related to disulfidptosis were identified, providing insights into the pathogenesis of DN. The diagnostic model demonstrated high accuracy in distinguishing DN from healthy samples across multiple datasets. Immunocorrelation analysis highlighted the complex interactions between immune cells and key disulfidptosis-related genes. PCR validation supported the differential expression of model genes VEGFA, MAGI2, THSD7A and ANKRD28 in DN. This research advances our understanding of DN by highlighting the role of disulfidptosis and identifying potential biomarkers for early detection and personalized treatment.

Combined analysis of single-cell sequencing and bulk transcriptome sequencing reveals new mechanisms for non-healing diabetic foot ulcers.

Diabetic foot ulcers (DFUs) pose a significant challenge in diabetes care. Yet, a comprehensive understanding of the underlying biological disparities between healing and non-healing DFUs remains elusive. We conducted bioinformatics analysis of publicly available transcriptome sequencing data in an attempt to elucidate these differences. Our analysis encompassed differential analysis to unveil shifts in cell composition and gene expression profiles between non-healing and healing DFUs. Cell communication alterations were explored employing the Cellchat R package. Pseudotime analysis and cytoTRACE allowed us to dissect the heterogeneity within fibroblast subpopulations. Our findings unveiled disruptions in various cell types, localized low-grade inflammation, compromised systemic antigen processing and presentation, and extensive extracellular matrix signaling disarray in non-healing DFU patients. Some of these anomalies partially reverted in healing DFUs, particularly within the abnormal ECM-receptor signaling pathway. Furthermore, we distinguished distinct fibroblast subpopulations in non-healing and healing DFUs, each with unique biological functions. Healing-associated fibroblasts exhibited heightened extracellular matrix (ECM) remodeling and a robust wound healing response, while non-healing-associated fibroblasts showed signs of cellular senescence and complement activation, among other characteristics. This analysis offers profound insights into the wound healing microenvironment, identifies pivotal cell types for DFU healing promotion, and reveals potential therapeutic targets for DFU management.

SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.

The rise of single-cell RNA sequencing (scRNA-seq) technology presents new opportunities for constructing detailed cell type-specific gene regulatory networks (GRNs) to study cell heterogeneity. However, challenges caused by noises, technical errors, and dropout phenomena in scRNA-seq data pose significant obstacles to GRN inference, making the design of accurate GRN inference algorithms still essential. The recent growth of both single-cell and spatial transcriptomic sequencing data enables the development of supervised deep learning methods to infer GRNs on these diverse single-cell datasets. In this study, we introduce a novel deep learning framework based on shared factor neighborhood and integrated neural network (SFINN) for inferring potential interactions and causalities between transcription factors and target genes from single-cell and spatial transcriptomic data. SFINN utilizes shared factor neighborhood to construct cellular neighborhood network based on gene expression data and additionally integrates cellular network generated from spatial location information. Subsequently, the cell adjacency matrix and gene pair expression are fed into an integrated neural network framework consisting of a graph convolutional neural network and a fully-connected neural network to determine whether the genes interact. Performance evaluation in the tasks of gene interaction and causality prediction against the existing GRN reconstruction algorithms demonstrates the usability and competitiveness of SFINN across different kinds of data. SFINN can be applied to infer GRNs from conventional single-cell sequencing data and spatial transcriptomic data. SFINN can be accessed at GitHub: https://github.com/JGuan-lab/SFINN.

WGCNA reveals a biomarker for cancer-associated fibroblasts to predict prognosis in cervical cancer.

Cancer-associated fibroblasts (CAFs) are crucial components of the cervical cancer tumor microenvironment, playing a significant role in cervical cancer progression, treatment resistance, and immune evasion, but whether the expression of CAF-related genes can predict clinical outcomes in cervical cancer is still unknown. In this study, we sought to analyze genes associated with CAFs through weighted gene co-expression network analysis (WGCNA) and to create a predictive model for CAFs in cervical cancer. We acquired transcriptome sequencing data and clinical information on cervical cancer patients from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. WGCNA was conducted to identify genes related to CAFs. We developed a prognostic model based on CAF genes in cervical cancer using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Single-cell sequencing data analysis and in vivo experiments for validation of hub genes in CAFs. A prognostic model for cervical cancer was developed based on CAF genes including COL4A1 , LAMC1 , RAMP3 , POSTN , and SERPINF1 . Cervical cancer patients were divided into low- and high-risk groups based on the optimal cutoff value. Patients in the high-risk group had a significantly worse prognosis. Single-cell RNA sequencing data revealed that hub genes in the CAFs risk model were expressed mainly in fibroblasts. The real-time fluorescence quantitative polymerase chain reaction (PCR) results revealed a significant difference in the expression levels of COL4A1 , LAMC1 , POSTN , and SERPINF1 between the cancer group and the normal group ( p < 0.05). Consistently, the results of the immunohistochemical tests exhibited notable variations in COL4A1, LAMC1, RAMP3, POSTN, and SERPINF1 expression between the cancer and normal groups ( p < 0.001). The CAF risk model for cervical cancer constructed in this study can be used to predict prognosis, while the CAF hub genes can be utilized as crucial markers for cervical cancer prognosis.

Construction of a new prognostic model for colorectal cancer based on bulk RNA-seq combined with The Cancer Genome Atlas data.

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths, and improving the prognosis of CRC patients is an urgent concern. The aim of this study was to explore new immunotherapy targets to improve survival in CRC patients. We analyzed CRC-related single-cell data GSE201348 from the Gene Expression Omnibus (GEO) database, and identified differentially expressed genes (DEGs). Subsequently, we performed differential analysis on the rectum adenocarcinoma (READ) and colon adenocarcinoma (COAD) transcriptome sequencing data [The Cancer Genome Atlas (TCGA)-CRC queue] and clinical data downloaded from TCGA database. Subgroup analysis was performed using CIBERSORTx and cluster analysis. Finally, biomarkers were identified by one-way cox regression as well as least absolute shrinkage and selection operator (LASSO) analysis. In this study, we analyzed CRC-related single-cell data GSE201348, and identified 5,210 DEGs. Subsequently, we performed differential analysis on the TCGA-CRC queue database, and obtained 4,408 DEGs. Then, we categorized the cancer samples in the sequencing data into three groups (k1, k2, and k3), with significant differences observed between the k1 and k2 groups via survival analysis. Further differential analysis on the samples in the k1 and k2 groups identified 1,899 DEGs. A total of 77 DEGs were selected among those DEGs obtained from three differential analyses. Through subsequent Cox univariate analysis and LASSO analysis, seven biomarkers (RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1) were identified and selected to establish a risk score (RS). To sum up, this study demonstrates the potential of the seven-gene prognostic risk model as instrumental variables for predicting the prognosis of CRC.

Dystrophin 71 deficiency causes impaired aquaporin-4 polarization contributing to glymphatic dysfunction and brain edema in cerebral ischemia

ObjectiveThe glymphatic system serves as a perivascular pathway that aids in clearing liquid and solute waste from the brain, thereby enhancing neurological function. Disorders in glymphatic drainage contribute to the development of vasogenic edema following cerebral ischemia, although the molecular mechanisms involved remain poorly understood. This study aims to determine whether a deficiency in dystrophin 71 (DP71) leads to aquaporin-4 (AQP4) depolarization, contributing to glymphatic dysfunction in cerebral ischemia and resulting in brain edema. MethodsA mice model of middle cerebral artery occlusion and reperfusion was used. A fluorescence tracer was injected into the cortex and evaluated glymphatic clearance. To investigate the role of DP71 in maintaining AQP4 polarization, an adeno-associated virus with the astrocyte promoter was used to overexpress Dp71. The expression and distribution of DP71 and AQP4 were analyzed using immunoblotting, immunofluorescence, and co-immunoprecipitation techniques. The behavior ability of mice was evaluated by open field test. Open-access transcriptome sequencing data were used to analyze the functional changes of astrocytes after cerebral ischemia. MG132 was used to inhibit the ubiquitin-proteasome system. The ubiquitination of DP71 was detected by immunoblotting and co-immunoprecipitation. ResultsDuring the vasogenic edema stage following cerebral ischemia, a decline in the efflux of interstitial fluid tracer was observed. DP71 and AQP4 were co-localized and interacted with each other in the perivascular astrocyte endfeet. After cerebral ischemia, there was a notable reduction in DP71 protein expression, accompanied by AQP4 depolarization and proliferation of reactive astrocytes. Increased DP71 expression restored glymphatic drainage and reduced brain edema. AQP4 depolarization, reactive astrocyte proliferation, and the behavior of mice were improved. After cerebral ischemia, DP71 was degraded by ubiquitination, and MG132 inhibited the decrease of DP71 protein level. ConclusionAQP4 depolarization after cerebral ischemia leads to glymphatic clearance disorder and aggravates cerebral edema. DP71 plays a pivotal role in regulating AQP4 polarization and consequently influences glymphatic function. Changes in DP71 expression are associated with the ubiquitin-proteasome system. This study offers a novel perspective on the pathogenesis of brain edema following cerebral ischemia.

Identification of angiogenesis-related genes and molecular subtypes for psoriasis based on random forest algorithm.

Psoriasis is a chronic immune-mediated recurrent skin disease causing systemic damage. Increased angiogenesis has been reported to participate in the progression of psoriasis. However, angiogenesis-related genes (ARGs) in psoriasis have not been systematically elucidated. Therefore, we aim to identify potential biomarkers and subtypes using two algorithmsr. Transcriptome sequencing data of patients with psoriasis were obtained, in which differentially expressed genes were assessed by principal component analysis. A diagnostic model was developed using random forest algorithm and validated by receiver operating characteristic (ROC) curves. Subsequently, we performed consensus clustering to calculate angiogenesis-associated molecular subtypes of psoriasis. Additionally, a correlation analysis was conducted between ARGs and immune cell infiltration. Finally, validation of potential ARG genes was performed by quantitative real-time PCR (qRT-PCR). We identified 29 differentially expressed ARGs, including 13 increased and 16 decreased. Ten ARGs, CXCL8, ANG, EGF, HTATIP2, ANGPTL4, TNFSF12, RHOB, PML, FOXO4, and EMCN were subsequently sifted by the diagnostic model based on a random forest algorithm. Analysis of the ROC curve (area under the curve [AUC] = 1.0) indicated high diagnostic performance in internal validation. The correlation analysis suggested that CXCL8 has a high positive correlation with neutrophil (R =0.8, P < 0.0001) and interleukins pathway (R = 0.79, P < 0.0001). Furthermore, two ARG-mediated subtypes were obtained, indicating potential heterogeneity. Finally, the qRT-PCR demonstrated that the mRNA expression levels of CXCL8 and ANGPTL4 were elevated in psoriasis patients, with a reduced expression of EMCN observed. The current paper indicated potential ARG-related biomarkers of psoriasis, including CXCL8, ANGPTL4, and EMCN, with two molecular subtypes.

Starvation-induced changes in the proteome and transcriptome of the salivary glands of leech (Hirudo nipponia).

Hirudo nipponia is an important medicinal animal in China. Its salivary gland secretions contain a variety of protein bioactive substances. Investigations of its salivary glands are of great significance in the study of the medicinal value and mechanism of leech secretions. Illumina RNA-Seq technology was used to perform transcriptome sequencing of salivary gland tissue of H. nipponia under starvation (D30) and fed (D0) states. A total of 2,650 differentially expressed genes (DEGs) were screened. Using the label-free protein quantification technique and bioinformatics analysis, the expression of differentially expressed proteins (DEPs) in the salivary gland tissue of H. nipponia was compared. A total of 2,021 proteins were identified, among which 181 proteins were differentially expressed between the starvation and fed states, with 72 significantly upregulated and 109 significantly downregulated. The salivary glands of H. nipponia synthesized protein-based active substances after 30 days of starvation and adapted to the starvation environment by weakening respiratory activity and reducing metabolic activity to reduce energy expenditure. Energy was produced by glycolysis and the tricarboxylic acid cycle for the synthesis of substances such as antibiotics. This study combined transcriptome and proteome sequencing data to provide a data reference for an in-depth study of the regulatory mechanism of salivary gland secretions of H. nipponia under starvation stress by analyzing DEGs and DEPs.

Integrative multi-omics analysis reveals cellular and molecular insights into primary Sjögren's syndrome

ObjectiveThis study aims to comprehensively analyze genomic, transcriptomic, proteomic, and single-cell sequencing data to unravel the molecular basis of primary Sjögren's syndrome (pSS) and explore potential therapeutic targets. MethodsMendelian randomization and single-cell RNA sequencing were employed to analyze pSS data. Differentially expressed genes specific to different blood cell types were identified. Integration of multiomics data facilitated the exploration of genetic regulatory relationships. ResultsThe analysis revealed distinct cell clusters representing various immune cell subsets. Several genes, including cathepsin S (CTSS) and glutathione S-transferase omega 1 (GSTO1), were identified as potential biomarkers and therapeutic targets for pSS. Diagnostic utility analysis demonstrated the discriminatory power of CTSS and GSTO1 in distinguishing pSS patients from healthy controls. ConclusionThe findings highlight the importance of integrating multiomics data for understanding pSS pathogenesis. CTSS and GSTO1 show promise as diagnostic biomarkers and potential therapeutic targets for pSS. Further investigations are warranted to elucidate the underlying mechanisms and develop targeted therapies for this complex autoimmune disease.

Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics

BackgroundThis study aimed to develop and evaluate radiomics models to predict CD27 expression and clinical prognosis before surgery in patients with serous ovarian cancer (SOC).MethodsWe used transcriptome sequencing data and contrast-enhanced computed tomography images of patients with SOC from The Cancer Genome Atlas (n = 339) and The Cancer Imaging Archive (n = 57) and evaluated the clinical significance and prognostic value of CD27 expression. Radiomics features were selected to create a recursive feature elimination-logistic regression (RFE-LR) model and a least absolute shrinkage and selection operator logistic regression (LASSO-LR) model for CD27 expression prediction.ResultsCD27 expression was upregulated in tumor samples, and a high expression level was determined to be an independent protective factor for survival. A set of three and six radiomics features were extracted to develop RFE-LR and LASSO-LR radiomics models, respectively. Both models demonstrated good calibration and clinical benefits, as determined by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The LASSO-LR model performed better than the RFE-LR model, owing to the area under the curve (AUC) values of the ROC curves (0.829 vs. 0.736). Furthermore, the AUC value of the radiomics score that predicted the overall survival of patients with SOC diagnosed after 60 months was 0.788 using the LASSO-LR model.ConclusionThe radiomics models we developed are promising noninvasive tools for predicting CD27 expression status and SOC prognosis. The LASSO-LR model is highly recommended for evaluating the preoperative risk stratification for SOCs in clinical applications.

Sigma Factor Engineering in Actinoplanes sp. SE50/110: Expression of the Alternative Sigma Factor Gene ACSP50_0507 (σHAs) Enhances Acarbose Yield and Alters Cell Morphology.

Sigma factors are transcriptional regulators that are part of complex regulatory networks for major cellular processes, as well as for growth phase-dependent regulation and stress response. Actinoplanes sp. SE50/110 is the natural producer of acarbose, an α-glucosidase inhibitor that is used in diabetes type 2 treatment. Acarbose biosynthesis is dependent on growth, making sigma factor engineering a promising tool for metabolic engineering. ACSP50_0507 is a homolog of the developmental and osmotic-stress-regulating Streptomyces coelicolor σHSc. Therefore, the protein encoded by ACSP50_0507 was named σHAs. Here, an Actinoplanes sp. SE50/110 expression strain for the alternative sigma factor gene ACSP50_0507 (sigHAs) achieved a two-fold increased acarbose yield with acarbose production extending into the stationary growth phase. Transcriptome sequencing revealed upregulation of acarbose biosynthesis genes during growth and at the late stationary growth phase. Genes that are transcriptionally activated by σHAs frequently code for secreted or membrane-associated proteins. This is also mirrored by the severely affected cell morphology, with hyperbranching, deformed and compartmentalized hyphae. The dehydrated cell morphology and upregulation of further genes point to a putative involvement in osmotic stress response, similar to its S. coelicolor homolog. The DNA-binding motif of σHAs was determined based on transcriptome sequencing data and shows high motif similarity to that of its homolog. The motif was confirmed by in vitro binding of recombinantly expressed σHAs to the upstream sequence of a strongly upregulated gene. Autoregulation of σHAs was observed, and binding to its own gene promoter region was also confirmed.

Transcriptome sequencing data provide a solid base to understand the phylogenetic relationships, biogeography and reticulated evolution of the genus Zamia L. (Cycadales: Zamiaceae).

Cycads are a key lineage to understand the early evolution of seed plants and their response to past environmental changes. However, tracing the evolutionary trajectory of cycad species is challenging when the robust relationships at inter- or infrageneric level are not well resolved. Here, using 2901 single-copy nuclear genes, we explored the species relationships and gene flow within the second largest genus of cycads, i.e. Zamia, based on phylotranscriptomic analyses of 90 % extant Zamia species. Based on a well-resolved phylogenetic framework, we performed gene flow analyses, molecular dating and biogeographical reconstruction to examine the spatiotemporal evolution of Zamia. We also performed ancestral state reconstruction of a total of 62 traits of the genus to comprehensively investigate its morphological evolution. Zamia comprises seven major clades corresponding to seven distinct distribution areas in the Americas, with at least three reticulation nodes revealed in this genus. Extant lineages of Zamia initially diversified around 18.4-32.6 (29.14) million years ago in Mega-Mexico, and then expanded eastward into the Caribbean and southward into Central and South America. Ancestral state reconstruction revealed homoplasy in most of the morphological characters. This study revealed congruent phylogenetic relationships from comparative methods/datasets, with some conflicts being the result of incomplete lineage sorting and ancient/recent hybridization events. The strong association between the clades and the biogeographic areas suggested that ancient dispersal events shaped the modern distribution pattern, and regional climatic factors may have resulted in the following in situ diversification. Climate cooling starting during the mid-Miocene is associated with the global expansion of Zamia to tropical South America that has dramatically driven lineage diversification in the New World flora, as well as the extinction of cycad species in the nowadays cooler regions of both hemispheres, as indicated by the fossil records.

A mitochondria-related genes associated neuroblastoma signature - based on bulk and single-cell transcriptome sequencing data analysis, and experimental validation.

Neuroblastoma (NB), characterized by its marked heterogeneity, is the most common extracranial solid tumor in children. The status and functionality of mitochondria are crucial in regulating NB cell behavior. While the significance of mitochondria-related genes (MRGs) in NB is still missing in key knowledge. This study leverages consensus clustering and machine learning algorithms to construct and validate an MRGs-related signature in NB. Single-cell data analysis and experimental validation were employed to characterize the pivotal role of FEN1 within NB cells. MRGs facilitated the classification of NB patients into 2 distinct clusters with considerable differences. The constructed MRGs-related signature and its quantitative indicators, mtScore and mtRisk, effectively characterize the MRGs-related patient clusters. Notably, the MRGs-related signature outperformed MYCN in predicting NB patient prognosis and was adept at representing the tumor microenvironment (TME), tumor cell stemness, and sensitivity to the chemotherapeutic agents Cisplatin, Topotecan, and Irinotecan. FEN1, identified as the most contributory gene within the MRGs-related signature, was found to play a crucial role in the communication between NB cells and the TME, and in the developmental trajectory of NB cells. Experimental validations confirmed FEN1's significant influence on NB cell proliferation, apoptosis, cell cycle, and invasiveness. The MRGs-related signature developed in this study offers a novel predictive tool for assessing NB patient prognosis, immune infiltration, stemness, and chemotherapeutic sensitivity. Our findings unveil the critical function of FEN1 in NB, suggesting its potential as a therapeutic target.

LHFPL2 Serves as a Potential Biomarker for M2 Polarization of Macrophages in Renal Cell Carcinoma.

Renal cell carcinoma (RCC) is one of the most common malignant tumors of the kidney, presenting significant challenges for clinical diagnosis and treatment. Macrophages play crucial roles in RCC, promoting tumor progression and warranting further investigation. Previous studies have identified LHFPL2 as a transmembrane protein associated with reproduction, but its relationship with tumors or macrophages has not been discussed. This study utilized transcriptomic sequencing data from 609 KIRC patients in the TCGA database and single-cell sequencing data from 34,326 renal carcinoma cells for subsequent analysis. We comprehensively evaluated the expression of LHFPL2 and its relationship with clinical features, tumor prognosis, immune infiltration, and mutations. Additionally, we further assessed the correlation between LHFPL2 and macrophage M2 polarization using single-cell data and explored its potential as a cancer therapeutic target through molecular docking. The results demonstrated that LHFPL2 is upregulated in RCC and associated with poor survival rates. In clinical staging, the proportion of malignant and high-metastasis patients was higher in the high-LHFPL2 group than in the low-LHFPL2 group. Furthermore, we found that LHFPL2 influences RCC immune infiltration, with its expression positively correlated with various immune checkpoint and M2-related gene expressions, positively associated with M2 macrophage infiltration, and negatively correlated with activated NK cells. Moreover, LHFPL2 showed specific expression in macrophages, with the high-expression subgroup exhibiting higher M2 polarization, hypoxia, immune evasion, and angiogenesis scores, promoting tumor progression. Finally, we predicted several potential drugs targeting LHFPL2, such as conivaptan and nilotinib. Our analysis elaborately delineates the immune characteristics of LHFPL2 in the tumor microenvironment and its positive correlation with macrophage M2 polarization, providing new insights into tumor immunotherapy. We also propose potential FDA-approved drugs targeting this gene, which should be tested for their binding effects with LHFPL2 in future studies.

M133S mutation possibly involve in the ER stress and mitophagy pathway in maintenance hemodialysis patients with occult hepatitis B infection

Occult hepatitis B virus infection (OBI) is characterized by the presence of HBV DNA in the absence of detectable HBsAg. OBI is an important risk factor for cirrhosis and hepatocellular carcinoma, but its pathogenesis has not been fully elucidated. Mutations in the HBV preS/S genes can lead to impaired secretion of either HBsAg or S-protein resulting in the accumulation of defective viruses or S protein in cells. In our previous work, the M133S mutation was present in the HBV S gene of maintenance hemodialysis (MHD) patients with OBI. In this study, we investigated the potential role of amino acid substitutions in S proteins in S protein production and secretion through the construction of mutant S gene plasmids, structural prediction, transcriptome sequencing analysis, and in vitro functional studies. Protein structure prediction showed that the S protein M133S mutant exhibited hydrophilic modifications, with greater aggregation and accumulation of the entire structure within the membrane phospholipid bilayer. Differential gene enrichment analysis of transcriptome sequencing data showed that differentially expressed genes were mainly concentrated in protein processing in the endoplasmic reticulum (ER). The expression of heat shock family proteins and ER chaperone molecules was significantly increased in the wild-type and mutant groups, whereas the expression of mitochondria-associated proteins was decreased. Immunofluorescence staining and protein blotting showed that the endoplasmic reticulum-associated protein PDI, the autophagy marker LC3, and the lysosome-associated protein LAMP2 co-localized with the S proteins in the wild-type and mutant strains, and their expression was increased. The mitochondria-associated TOMM20 protein was also co-expressed with the S protein, but expression was significantly reduced in the mutant. The M133S mutation in the S gene is expressed as a defective and misfolded protein that accumulates in the endoplasmic reticulum causing secretion-impaired endoplasmic reticulum stress, which in turn triggers mitochondrial autophagy and recruits lysosomes to fuse with the autophagosome, leading to mitochondrial clearance. This study preliminarily demonstrated that the mutation of M133S in the S gene can cause OBI and is associated with disease progression, providing a theoretical basis for the diagnosis and treatment of OBI.

Screening of potential drugs for the treatment of diabetic kidney disease using single-cell transcriptome sequencing and connectivity map data

ObjectiveTo explore the feasibility of screening potential drugs for the treatment of diabetic kidney disease (DKD) using a single-cell transcriptome sequencing dataset and Connectivity Map (CMap) database screening. MethodsA DKD single-nucleus transcriptome sequencing dataset was analyzed using Seurat 4.0 to obtain specific podocyte subclusters and differentially expressed genes (DEGs) related to DKD. These DEGs were subsequently subjected to a search against the CMap database to screen for drug candidates. Cell and animal experiments were conducted to evaluate the efficacy of the top 3 drug candidates. ResultsInitially, we analyzed the DKD single-nucleus transcriptome sequencing dataset to obtain intrinsic renal cells such as podocytes, endothelial cells, mesangial cells, proximal tubular cells, collecting duct cells and immune cells. Podocytes were further divided into four subclusters, among which the proportion of POD_1 podcytes was significantly greater in DKD kidneys than in control kidneys (34.0 % vs. 3.4 %). The CMap database was searched using the identified DEGs in the POD_1 subcluster, and the drugs, including tozasertib, paroxetine, and xylazine, were obtained. Cell-based experiments showed that tozasertib, paroxetine and xylazine had no significant podocyte toxicity in the concentration range of 0.01–50 μM. Tozasertib, paroxetine, and xylazine all reversed the advanced glycation end products (AGEs)-induced decrease in podocyte marker levels, but the effect of paroxetine was more prominent. Animal experiments showed that paroxetine decreased urine ALB/Cr levels in DKD model mice by approximately 51.5 % (115.7 mg/g vs. 238.8 mg/g, P < 0.05). Histopathological assessment revealed that paroxetine attenuated basement membrane thickening, restored the number of foot processes of podocytes, and reduced foot process fusion. In addition, paroxetine also attenuated renal tubular-interstitial fibrosis. Mechanistically, paroxetine inhibited the expression of GRK2 and NLRP3, decreased the phosphorylation level of p65, restored NRF2 expression, and relieved inflammation and oxidative stress. ConclusionThis strategy based on single-cell transcriptome sequencing and CMap data can facilitate the identification and aid the rapid development of clinical DKD drugs. Paroxetine, screened by this strategy, has excellent renoprotective effects.