Exploring the midgut physiology of the non-haematophagous mosquito Toxorhynchites theobaldi.

Toxorhynchites mosquitoes have an exclusively phytophagous feeding habit as adults, which leads to significant differences in their morphophysiology compared with haematophagous mosquitoes. However, the molecular mechanisms of digestion in this mosquito are not well understood. In this study, RNA sequencing of the posterior midgut (PMG) of the mosquito Toxorhynchites theobaldi was undertaken, highlighting its significance in mosquito digestion. Subsequently, a comparison was made between the differential gene expression of the PMG and that of the anterior midgut. It was found that the most abundant proteases in the PMG were trypsin and chymotrypsin, and the level of gene expression for enzymes essential for digestion (such as serine protease, α-amylase and pancreatic triacylglycerol lipase) and innate immune response (including catalase, cecropin-A2 and superoxide dismutase) was like that of haematophagous mosquitoes. Peritrophin-1 was detected in the entire midgut, with an elevated expression level in the PMG. Based on our findings, it is hypothesized that a non-haematophagic habit might have been exhibited by the ancestor of Tx. theobaldi, and this trait may have been retained. This study represents a pioneering investigation at the molecular level of midgut contents in a non-haematophagous mosquito. The findings offer valuable insights into the evolutionary aspects of feeding habits in culicids.

Distinct molecular profile of the chick organizer as a stem zone during axial elongation.

The vertebrate organizer plays a crucial role in building the main (antero-posterior) axis of the embryo: it neuralizes the surrounding ectoderm, and is the site of emigration for cells making axial and paraxial mesendoderm during elongation. The chick organizer becomes a stem zone at the onset of elongation; it stops recruiting cells from the neighbouring ectoderm and generates all its derivatives from the small number of resident cells it contains at the end of gastrulation stages. Nothing is known about the molecular identity of this stem zone. Here, we specifically labelled long-term resident cells of the organizer and compared their RNA-seq profile to that of the neighbouring cell populations. Screening by reverse transcription-polymerase chain reaction and in situ hybridization identified four genes (WIF1, PTGDS, ThPO and UCKL1) that are upregulated only in the organizer region when it becomes a stem zone and remain expressed there during axial elongation. In experiments specifically labelling the resident cells of the mature organizer, we show that only these cells express these genes. These findings molecularly define the organizer as a stem zone and offer a key to understanding how this zone is set up, the molecular control of its cells' behaviour and the evolution of axial growth zones.

[The diagnostic value of inflammation-related genes in bronchopulmonary dysplasia].

This study aims to explore the diagnostic value of inflammation-related genes in peripheral blood mononuclear cells in bronchopulmonary dysplasia (BPD). By using bioinformatics analysis, three datasets including GSE32472, GSE125873, and GSE220135, which contain whole-genome expression profile data of 251 neonates, were included. The GSE32472 dataset was used as a training dataset to detect differentially expressed genes between non-BPD and BPD neonates in peripheral blood mononuclear cells. The gene enrichment analysis (GSEA) was used to detect the pathway enrichment of up-regulated genes in BPD newborns. The main regulatory factors analysis (MRA) algorithm was used to filter the main regulatory genes in the inflammation-related pathway (GO:0006954). After obtaining the main regulatory genes, the expression of the main regulatory genes in the GSE32472, GSE125873, and GSE220135 datasets was detected. Through the logistic regression model, risk scoring was conducted for neonates, and the risk scores of non-BPD and BPD neonates were compared. Lastly, the classification performance of the model was evaluated using the area under the curve (AUC). The results showed that compared with non-BPD neonates, there were 486 up-regulated genes and 433 down-regulated genes in the peripheral blood mononuclear cells of BPD neonates. The inflammation-related pathway was highly enriched in the up-regulated genes. Ultimately, phospholipase C beta 1 (PLCB1), nidogen 1 (NID1), serum response factor binding protein 1 (SRFBP1), centrosomal protein 72 (CEP72), excision repair cross complementation group 6 like (ERCC6L), and peptidylprolyl isomerase like 1 (PPIL1) were identified as the main regulatory genes. The prediction model's calculation formula for risk score was PLCB1×0.26+NID1×0.97+SRFBP1×1.58+CEP72×(-0.36)+ERCC6L×2.14+PPIL1×0.67. The AUCs in the GSE32472 test dataset, GSE125873 dataset, and GSE220135 dataset were 0.88, 0.86, and 0.89, respectively. This prediction model could distinguish between non-BPD and BPD neonates. In conclusion, the prediction model based on inflammation-related pathway genes has a certain diagnostic value for BPD.

Construction of m6A-Related Gene Prediction Model and Subtype Analysis in Non-Obstructive Azoospermia Based on Bioinformatics.

PURPOSE: Non-obstructive azoospermia (NOA) is a severe and common cause of male infertility. Currently, the most reliable predictor of sperm retrieval success in NOA is histopathology, but preoperative testicular biopsy often increases the difficulty of sperm retrieval surgery. This study aims to explore the characteristics of N6-methyladenosine (m6A) modification in NOA patients and investigate the potential biomarkers and molecular mechanisms for pathological diagnosis and treatment of NOA using m6A-related genes. METHODS: NOA-related datasets were downloaded from the GEO database. Based on the results of LASSO regression analysis, a prediction model was established from differentially expressed m6A-related genes, and the predictive performance of the model was evaluated using ROC curves. Cluster analysis was performed based on differentially expressed m6A-related genes to evaluate the differences in different m6A modification patterns in terms of differentially expressed genes (DEGs), biological features, and immune features. RESULTS: There were significant differences in eight m6A-related genes between NOA samples and healthy controls. The ROC curves showed excellent predictive performance for the diagnostic models constructed with ALKBH5 and FTO. DEGs of two m6A modification subtypes indicated the influence of m6A-related genes in the biological processes of mitosis and meiosis in NOA patients, and there were significant immune differences between the two subtypes. CONCLUSION: The NOA pathological diagnostic models constructed with FTO and ALKBH5 have good predictive ability. We have identified two different m6A modification subtypes, which may help predict sperm retrieval success rate and treatment selection in NOA patients.

Exploring the role of cellular senescence in cancer prognosis across multiple tumor types.

Background: Cellular senescence is a common biological process with a well-established link to cancer. However, the impact of cellular senescence on tumor progression remains unclear. To investigate this relationship, we utilized transcriptomic data from a senescence gene set to explore the connection between senescence and cancer prognosis. Methods: We developed the senescence score by the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. We obtained transcriptomic information of the senescence gene set from The Cancer Genome Atlas (TCGA) program. Additionally, we created a nomogram that integrates these senescence scores with clinical characteristics, providing a more comprehensive tool for prognosis evaluation. Results: We calculated the senescence score based on the expression level of 42 senescence-related genes. We established the nomogram based on the senescence score and clinical characteristics. The senescence score showed a positive correlation with epithelial-to-mesenchymal transition, cell cycle, and glycolysis, and a negative correlation with autophagy. Furthermore, we carried out Gene Ontology (GO) analysis to explore the signaling pathways and biological process in different senescence score groups. Conclusions: The senescence score, a novel tool constructed in this study, shows promise in predicting survival outcomes across various cancer types. These findings not only highlight the complex interplay between senescence and cancer but also indicate that cellular senescence might serve as a biomarker for tumor prognosis.

[Bioinformatic analysis of immune-related transcription factors in diabetic kidney disease].

Objective To identify immune-related transcription factors (TFs) in renal glomeruli and tubules from diabetic kidney disease (DKD) patients by bioinformatics analysis. Methods Gene expression datasets from GEO (GSE30528, GSE30529) and RNA sequencing (RNA-seq) data from the Karolinska Kidney Research Center were used. Gene set enrichment analysis (GSEA) was conducted to examine differences in immune-related gene expression in the glomeruli and tubules (DKD) patients. To identify immune-related genes (IRGs) and TFs, differential expression analysis was carried out using the Limma and DESeq2 software packages. Key immune-related TFs were pinpointed through co-expression analysis. The interaction network between TFs and IRGs was constructed using the STRING database and Cytoscape software. Furthermore, the Nephroseq database was employed to investigate the correlation between the identified TFs and clinical-pathological features. Results When compared to normal control tissues, significant differences in the expression of immune genes were observed in both the glomeruli and tubules of individuals with Diabetic Kidney Disease (DKD). Through differential and co-expression analysis, 50 immune genes and 9 immune-related transcription factors (TFs) were identified in the glomeruli. In contrast, 131 immune response genes (IRGs) and 41 immune-related TFs were discovered in the renal tubules. The protein-protein interaction (PPI) network highlighted four key immune-related TFs for the glomeruli: Interferon regulatory factor 8 (IRF8), lactotransferrin (LTF), CCAAT/enhancer binding protein alpha (CEBPA), and Runt-related transcription factor 3 (RUNX3). For the renal tubules, the key immune-related TFs were FBJ murine osteosarcoma viral oncogene homolog B (FOSB), nuclear receptor subfamily 4 group A member 1 (NR4A1), IRF8, and signal transducer and activator of transcription 1 (STAT1). These identified TFs demonstrated a significant correlation with the glomerular filtration rate (GFR), highlighting their potential importance in the pathology of DKD. Conclusion Bioinformatics analysis identifies potential genes associated with DKD pathogenesis and immune dysregulation. Further validation of the expression and function of these genes may contribute to immune-based therapeutic research for DKD.

Unique regulatory network of dragon fruit simultaneously mitigates the effect of vanadium pollutant and environmental factors.

Under changing climatic conditions, plants are simultaneously facing conflicting stresses in nature. Plants can sense different stresses, induce systematic ROS signals, and regulate transcriptomic, hormonal, and stomatal responses. We performed transcriptome analysis to reveal the integrative stress response regulatory mechanism underlying heavy metal stress alone or in combination with heat and drought conditions in pitaya (dragon fruit). A total of 70 genes were identified from 31,130 transcripts with conserved differential expression. Furthermore, weighted gene co-expression network analysis (WGCNA) identified trait-associated modules. By integrating information from three modules and protein-protein interaction (PPI) networks, we identified 10 interconnected genes associated with the multifaceted defense mechanism employed by pitaya against co-occurring stresses. To further confirm the reliability of the results, we performed a comparative analysis of 350 genes identified by three trait modules and 70 conserved genes exhibiting their dynamic expression under all treatments. Differential expression pattern of genes and comparative analysis, have proven instrumental in identifying ten putative structural genes. These ten genes were annotated as PLAT/LH2, CAT, MLP, HSP, PB1, PLA, NAC, HMA, and CER1 transcription factors involved in antioxidant activity, defense response, MAPK signaling, detoxification of metals and regulating the crosstalk between the complex pathways. Predictive analysis of putative candidate genes, potentially governing single, double, and multifactorial stress response, by several signaling systems and molecular patterns. These findings represent a valuable resource for pitaya breeding programs, offering the potential to develop resilient "super pitaya" plants.

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.

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment.

Background: Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC). Methods: Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC. Results: ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy. Conclusion: This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

Temperatures above 37°C increase virulence of a convergent Klebsiella pneumoniae sequence type 307 strain.

Background: Convergence of Klebsiella pneumoniae (KP) pathotypes has been increasingly reported in recent years. These pathogens combine features of both multidrug-resistant and hypervirulent KP. However, clinically used indicators for hypervirulent KP identification, such as hypermucoviscosity, appear to be differentially expressed in convergent KP, potential outbreak clones are difficult to identify. We aimed to fill such knowledge gaps by investigating the temperature dependence of hypermucoviscosity and virulence in a convergent KP strain isolated during a clonal outbreak and belonging to the high-risk sequence type (ST)307. Methods: Hypermucoviscosity, biofilm formation, and mortality rates in Galleria mellonella larvae were examined at different temperatures (room temperature, 28°C, 37°C, 40°C and 42°C) and with various phenotypic experiments including electron microscopy. The underlying mechanisms of the phenotypic changes were explored via qPCR analysis to evaluate plasmid copy numbers, and transcriptomics. Results: Our results show a temperature-dependent switch above 37°C towards a hypermucoviscous phenotype, consistent with increased biofilm formation and in vivo mortality, possibly reflecting a bacterial response to fever-like conditions. Furthermore, we observed an increase in plasmid copy number for a hybrid plasmid harboring carbapenemase and rmpA genes. However, transcriptomic analysis revealed no changes in rmpA expression at higher temperatures, suggesting alternative regulatory pathways. Conclusion: This study not only elucidates the impact of elevated temperatures on hypermucoviscosity and virulence in convergent KP but also sheds light on previously unrecognized aspects of its adaptive behavior, underscoring its resilience to changing environments.

Comprehensive analysis of key host gene-microbe networks in the cecum tissues of the obese rabbits induced by a high-fat diet.

The Cecum is a key site for cellulose digestion in nutrient metabolism of intestine, but its mechanisms of microbial and gene interactions has not been fully elucidated during pathogenesis of obesity. Therefore, the cecum tissues of the New Zealand rabbits and their contents between the high-fat diet-induced group (Ob) and control group (Co) were collected and analyzed using multi-omics. The metagenomic analysis indicated that the relative abundances of Corallococcus_sp._CAG:1435 and Flavobacteriales bacterium species were significantly lower, while those of Akkermansia glycaniphila, Clostridium_sp._CAG:793, Mycoplasma_sp._CAG:776, Mycoplasma_sp._CAG:472, Clostridium_sp._CAG:609, Akkermansia_sp._KLE1605, Clostridium_sp._CAG:508, and Firmicutes_bacterium_CAG:460 species were significantly higher in the Ob as compared to those in Co. Transcriptomic sequencing results showed that the differentially upregulated genes were mainly enriched in pathways, including calcium signaling pathway, PI3K-Akt signaling pathway, and Wnt signaling pathway, while the differentially downregulated genes were mainly enriched in pathways of NF-kappaB signaling pathway and T cell receptor signaling pathway. The comparative analysis of metabolites showed that the glycine, serine, and threonine metabolism and cysteine and methionine metabolism were the important metabolic pathways between the two groups. The combined analysis showed that CAMK1, IGFBP6, and IGFBP4 genes were highly correlated with Clostridium_sp._CAG:793, and Akkermansia_glycaniphila species. Thus, the preliminary study elucidated the microbial and gene interactions in cecum of obese rabbit and provided a basis for further studies in intestinal intervention for human obesity.

Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy.

Background: Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD. Method: Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD. Result: Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis. Conclusion: Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.

Combining single-cell and bulk RNA sequencing, NK cell marker genes reveal a prognostic and immune status in pancreatic ductal adenocarcinoma.

The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target.

Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.

CCR7 and CD48 as Predicted Targets in Acute Rejection Related to M1 Macrophage after Pediatric Kidney Transplantation.

Background: Kidney transplantation (KT) is the best treatment for end-stage renal disease. Although long and short-term survival rates for the graft have improved significantly with the development of immunosuppressants, acute rejection (AR) remains a major risk factor attacking the graft and patients. The innate immune response plays an important role in rejection. Therefore, our objective is to determine the biomarkers of congenital immunity associated with AR after KT and provide support for future research. Materials and Methods: A differential expression genes (DEGs) analysis was performed based on the dataset GSE174020 from the NCBI gene Expression Synthesis Database (GEO) and then combined with the GSE5099 M1 macrophage-related gene identified in the Molecular Signatures Database. We then identified genes in DEGs associated with M1 macrophages defined as DEM1Gs and performed gene ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. Cibersort was used to analyze the immune cell infiltration during AR. At the same time, we used the protein-protein interaction (PPI) network and Cytoscape software to determine the key genes. Dataset, GSE14328 derived from pediatric patients, GSE138043 and GSE9493 derived from adult patients, were used to verify Hub genes. Additional verification was the rat KT model, which was used to perform HE staining, immunohistochemical staining, and Western Blot. Hub genes were searched in the HPA database to confirm their expression. Finally, we construct the interaction network of transcription factor (TF)-Hub genes and miRNA-Hub genes. Results: Compared to the normal group, 366 genes were upregulated, and 423 genes were downregulated in the AR group. Then, 106 genes related to M1 macrophages were found among these genes. GO and KEGG enrichment analysis showed that these genes are mainly involved in cytokine binding, antigen binding, NK cell-mediated cytotoxicity, activation of immune receptors and immune response, and activation of the inflammatory NF-κB signaling pathway. Two Hub genes, namely CCR7 and CD48, were identified by PPI and Cytoscape analysis. They have been verified in external validation sets, originated from both pediatric patients and adult patients, and animal experiments. In the HPA database, CCR7 and CD48 are mainly expressed in T cells, B cells, macrophages, and tissues where these immune cells are distributed. In addition to immunoinfiltration, CD4+T, CD8+T, NK cells, NKT cells, and monocytes increased significantly in the AR group, which was highly consistent with the results of Hub gene screening. Finally, we predicted that 19 TFs and 32 miRNAs might interact with the Hub gene. Conclusions: Through a comprehensive bioinformatic analysis, our findings may provide predictive and therapeutic targets for AR after KT.

Integrative analysis of blood transcriptome profiles in small-cell lung cancer patients for identification of novel chemotherapy resistance-related biomarkers.

Introduction: Chemoresistance constitutes a prevalent factor that significantly impacts thesurvival of patients undergoing treatment for smal-cell lung cancer (SCLC). Chemotherapy resistance in SCLC patients is generally classified as primary or acquired resistance, each governedby distinct mechanisms that remain inadequately researched. Methods: In this study, we performed transcriptome screening of peripheral blood plasma obtainedfrom 17 patients before and after receiving combined etoposide and platinum treatment. We firs testimated pseudo-single-cell analysis using xCell and ESTIMATE and identified differentially expressed genes (DEGs), then performed network analysis to discover key hub genes involved in chemotherapy resistance. Results: Our analysis showed a significant increase in class-switched memory B cell scores acrossboth chemotherapy resistance patterns, indicating their potential crucial role in mediatingresistance. Moreover, network analysis identifed PRICKLE3, TNFSFI0, ACSLl and EP300 as potential contributors to primary resistance, with SNWl, SENP2 and SMNDCl emerging assignificant factors in acquired resistance, providing valuable insights into chemotherapy resistancein SCLC. Discussion: These findings offer valuable insights for understanding chemotherapy resistance and related gene signatures in SCLC, which could help further biological validation studies.

Pyroptosis-Related Genes as Diagnostic Markers in Chronic Obstructive Pulmonary Disease and Its Correlation with Immune Infiltration.

Background: Chronic obstructive pulmonary disease (COPD) stands as a predominant cause of global morbidity and mortality. This study aims to elucidate the relationship between pyroptosis-related genes (PRGs) and COPD diagnosis in the context of immune infiltration, ultimately proposing a PRG-based diagnostic model for predicting COPD outcomes. Methods: Clinical data and PRGs of COPD patients were sourced from the GEO database. The "ConsensusClusterPlus" package was employed to generate molecular subtypes derived from PRGs that were identified through differential expression analysis and LASSO Cox analysis. A diagnostic signature including eight genes (CASP4, CASP5, ELANE, GPX4, NLRP1, GSDME, NOD1and IL18) was also constructed. Immune cell infiltration calculated by the ESTIMATE score, Stroma scores and Immune scores were also compared on the basis of pyroptosis-related molecular subtypes and the risk signature. We finally used qRT - PCR to detect the expression levels of eight genes in COPD patient and normal. Results: The diagnostic model, anchored on eight PRGs, underwent validation with an independent experimental cohort. The area under the receiver operating characteristic (ROC) curves (AUC) for the diagnostic model showcased values of 0.809, 0.765, and 0.956 for the GSE76925, GSE8545, and GSE5058 datasets, respectively. Distinct expression patterns and clinical attributes of PRGs were observed between the comparative groups, with functional analysis underscoring a disparity in immune-related functions between them. Conclusion: In this study, we developed a potential as diagnostic biomarkers for COPD and have a significant role in modulating the immune response. Such insights pave the way for novel diagnostic and therapeutic strategies for COPD.

Overexpression of LAG-3: a potential indicator of low immune function in tuberculosis.

Background: Tuberculosis (TB) persists as a global health challenge, with its treatment hampered by the side effects of long-term combination drug therapies and the growing issue of drug resistance. Therefore, the development of novel therapeutic strategies is critical. This study focuses on the role of immune checkpoint molecules (ICs) and functions of CD8+ T cells in the search for new potential targets against TB. Methods: We conducted differential expression genes analysis and CD8+ T cell functional gene analysis on 92 TB samples and 61 healthy individual (HI) samples from TB database GSE83456, which contains data on 34,603 genes. The GSE54992 dataset was used to validated the findings. Additionally, a cluster analysis on single-cell data from primates infected with mycobacterium tuberculosis and those vaccinated with BCG was performed. Results: The overexpression of LAG-3 gene was found as a potentially important characteristic of both pulmonary TB (PTB) and extrapulmonary TB (EPTB). Further correlation analysis showed that LAG-3 gene was correlated with GZMB, perforin, IL-2 and IL-12. A significant temporal and spatial variation in LAG-3 expression was observed in T cells and macrophages during TB infection and after BCG vaccination. Conclusion: LAG-3 was overexpressed in TB samples. Targeting LAG-3 may represent a potential therapeutic target for tuberculosis.

Single-cell transcriptomic analysis to identify endomembrane regulation of metalloproteins and motor proteins in autoimmunity.

TMEM230 promotes antigen processing, trafficking, and presentation by regulating the endomembrane system of membrane bound organelles (lysosomes, proteosomes and mitochondria) and phagosomes. Activation of the immune system requires trafficking of various cargos between the endomembrane system and cell plasma membrane. The Golgi apparatus is the hub of the endomembrane system and essential for the generation, maintenance, recycling, and trafficking of the components of the endomembrane system itself and immune system. Intracellular trafficking and secretion of immune system components depend on mitochondrial metalloproteins for ATP synthesis that powers motor protein transport of endomembrane cargo. Glycan modifying enzyme genes and motor proteins are essential for the activation of the immune system and trafficking of antigens between the endomembrane system and the plasma membrane. Recently, TMEM230 was identified as co-regulated with RNASET2 in lysosomes and with metalloproteins in various cell types and organelles, including mitochondria in autoimmune diseases. Aberrant metalloproteinase secretion by motor proteins is a major contributor to tissue remodeling of synovial membrane and joint tissue destruction in rheumatoid arthritis (RA) by promoting infiltration of blood vessels, bone erosion, and loss of cartilage by phagocytes. In this study, we identified that specific glycan processing enzymes are upregulated in certain cell types (fibroblast or endothelial cells) that function in destructive tissue remodeling in rheumatoid arthritis compared to osteoarthritis (OA). TMEM230 was identified as a regulator in the secretion of metaloproteinases and heparanase necessary tissue remodeling in OA and RA. In dendritic (DC), natural killer and T cells, TMEM230 was expressed at low or no levels in RA compared to OA. TMEM230 expression in DC likely is necessary for regulatory or helper T cells to maintain tolerance to self-antigens and prevent susceptibility to autoimmune disease. To identify how TMEM230 and the endomembrane system contribute to autoimmunity we investigated, glycan modifying enzymes, metalloproteinases and motor protein genes co-regulated with or regulated by TMEM230 in synovial tissue by analyzing published single cell transcriptomic datasets from RA patient derived synovial tissue.

Single-cell analysis of Crohn's disease: Unveiling heterogeneity and evaluating ustekinumab outcomes.

BACKGROUND: The present study aimed to dissect the cellular complexity of Crohn's disease (CD) using single-cell RNA sequencing, focusing on identifying key cell populations and their transcriptional profiles in inflamed tissue. METHODS: We applied scRNA-sequencing to compare the cellular composition of CD patients with healthy controls, utilizing Seurat for clustering and annotation. Differential gene expression analysis and protein-protein interaction networks were constructed to identify crucial genes and pathways. RESULTS: Our study identified eight distinct cell types in CD, highlighting crucial fibroblast and T cell interactions. The analysis revealed key cellular communications and identified significant genes and pathways involved in the disease's pathology. The role of fibroblasts was underscored by elevated expression in diseased samples, offering insights into disease mechanisms and potential therapeutic targets, including responses to ustekinumab treatment, thus enriching our understanding of CD at a molecular level. CONCLUSIONS: Our findings highlight the complex cellular and molecular interplay in CD, suggesting new biomarkers and therapeutic targets, offering insights into disease mechanisms and treatment implications.