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Background: This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data to identify potential biomarkers and therapeutic targets for rheumatoid arthritis (RA)., Method: Firstly, we obtained the synovial scRNA-seq data from the Immport database and bulk RNA-seq data from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene correlation network analysis (WGCNA) to screen for module genes most relevant to RA and intersected them with the differentially expressed genes (DEGs) obtained from scRNA-seq and bulk RNA-seq to obtain intersecting genes. Next, we constructed a protein-protein interaction (PPI) network of hub genes using the STRING database and Cytoscape software and validated its expression using external validation cohorts. Finally, we performed immune cell infiltration analysis using CIBERSORT and explored the expression and drug binding activity of key gene using clinical samples and molecular docking, respectively., Result: We identified six cellular subgroups through dimensionality reduction and clustering, and fibroblasts may be the most important cell cluster in RA based on pseudotime and cell-cell communication analyses. Subsequently, we intersected module genes with DEGs obtained from scRNA-seq and bulk RNA-seq and constructed a PPI network of hub genes (BGN, COL11A1, COL1A1, GUCY1A1, POSTN). In external validation cohorts, POSTN was highly expressed and demonstrated the highest diagnostic performance (AUC = 0.716). In subsequent analyses, we defined POSTN as a key gene and found that its expression level was positively correlated with M2 macrophages in immune cell infiltration analysis. Additionally, POSTN was upregulated in clinical samples and exhibited favorable binding activity with nine anti-rheumatoid arthritis drugs (affinity ≤ -6.0 kcal/mol)., Conclusion: Through bioinformatics analysis, clinical sample validation, and molecular docking, we found that POSTN was highly expressed in RA and stably bound to common anti-rheumatoid arthritis drugs, which will provide new insights into potential biomarkers and therapeutic targets for RA., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: The expression profiles of placental genes are crucial for understanding the pathogenesis of fetal development and placental-origin pregnancy syndromes. However, owing to ethical limitations and the risks of puncture sampling, it is difficult to obtain placental tissue samples repeatedly, continuously, multiple times, or in real time. Establishing a non-invasive method for predicting placental gene expression profiles through maternal plasma cell-free DNA (cfDNA) sequencing, which carries information about the source tissue and gene expression, can potentially solve this problem., Methods: Peripheral blood and placental samples were collected simultaneously from pregnant women who underwent cesarean section. Deep sequencing was performed on the separated plasma cfDNA and single-cell sequencing was performed on peripheral blood mononuclear cells (PBMC), chorioamniotic membranes (CAM), placental villi (PV), and decidua basalis (DB). The aggregation of corresponding information for each gene was combined with the transcriptome of PBMCs and a differential resolution transcriptome of the placenta. This combined information was then utilized for the construction of gene expression prediction models. After training, all models evaluated the correlation between the predicted and actual gene expression levels using external test set data., Results: From five women, more than 20 million reads were obtained using deep sequencing for plasma cfDNA; PBMCs obtained 32,401 single-cell expression profiles; and placental tissue obtained 156,546 single-cell expression profiles (59,069, 44,921, and 52,556 for CAM, PV, and DB, respectively). The cells in the PBMC and placenta were clustered and annotated into five and eight cell types, respectively. A "DEPICT" gene expression prediction model was successfully constructed using deep neural networks. The predicted correlation coefficients were 0.75 in PBMCs, 0.84 in the placenta, and 0.78, 0.80, and 0.77 in CAM, BP, and PV respectively, and greater than 0.68 in different cell lines in the placenta., Conclusion: The DEPICT model, which can noninvasively predict placental gene expression profiles based on maternal plasma cfDNA fragmentation characteristics, was constructed to overcome the limitation of the inability to obtain real-time placental gene expression profiles and to improve research on noninvasive prediction of placental origin pregnancy syndrome., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The Echiura worm Urechis unicinctus refers to a common benthic invertebrate found in the intertidal zone of Huanghai as well as Bohai Bay. U. unicinctus is known to contain various physiologically active substances, making it highly valuable in terms of its edibility, medicinal properties, and economic potential. Nonetheless, the limited study on the immune system of U. unicinctus poses difficulties for its aquaculture and artificial reproduction. Marine invertebrates, including shellfish and U. unicinctus, are thought to primarily depend on their innate immune system for disease protection, owing to the severalinnate immune molecules they possess. Herein, we employed PacBio single-molecule real-time (SMRT) sequencing technology to perform the full-length transcriptome analysis of U. unicinctus individuals under five different conditions (room temperature (RT), low temperature (LT), high temperature (HT), without water (DRY), ultraviolet irradiation (UV)). Concequently, we identified 59,371 unigenes that had a 2,779 bp average length, 2,613 long non-coding RNAs (lncRNAs), 59,190 coding sequences (CDSs), 35,166 simple sequence repeats (SSRs), and 1,733 transcription factors (TFs), successfully annotating 90.58 % (53,778) of the unigenes. Subsequently, key factors associated with immune-related processes, such as non-self-recognition, cellular immune defenses, and humoral immune defenses, were searched. Our study also identified pattern recognition receptors (PRRs) that included 17 peptidoglycan recognition proteins (PGRPs), 13 Gram-negative binding proteins (GNBPs), 18 scavenger receptors (SRs), 74 toll-like receptors (TLRs), and 89 C-type lectins (CLTs). Altogether, the high-quality transcriptome obtained data will offer valuable insights for further investigations into U. unicinctus innate immune response, laying the foundation for subsequent molecular biology studies and aquaculture., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Osteosarcoma is a common malignant tumor with a low survival rate after metastasis. Current treatments have not proven to effectively increase patient survival rates. Immunotherapy is a promising new treatment approach, however, immune target therapy has not shown satisfactory results. This study aims to provide new insights and evidence for the use of immunotherapy in osteosarcoma, based on a comprehensive analysis of gene expression data from databases., Methods: Gene expression and GSAV analysis were conducted on samples from patients with metastatic and non-metastatic osteosarcoma in the TARGET and GEO databases to identify relevant genes. These genes were further analyzed using GO, KEGG, GSVA, correlation analysis, and immune microenvironment scoring techniques. The tissue location of gene expression was confirmed through single-cell analysis. Validation of gene expression patterns was performed using polymerase chain reaction, western blot, and immunohistochemistry., Results: The study identified FUCA1 and NCKAP1L as significantly enriched in non-metastatic osteosarcoma, with higher expression associated with better patient survival rates. Gene function enrichment was primarily related to immune functions, with positive correlations to macrophage phagocytosis, antigen presentation, and macrophage polarization pathways. Analysis of the immune microenvironment revealed a positive correlation between gene expression and immune scores, with increased presence of macrophages, T cells, and B cells in the high expression group. Single-cell analysis and experimental results confirmed the enrichment of FUCA1 and NCKAP1L in macrophages., Conclusion: The identification of FUCA1 and NCKAP1L as potential prognostic biomarkers suggests their potential for improving patient outcomes. Modulation of macrophages may offer a promising strategy for enhancing the immune microenvironment in osteosarcoma., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: B cell exhaustion (BEX) refers to the impairment of normal B cell functions and decreased proliferation capability. However, the prognostic value of BEX-related genes in bladder cancer (BLCA) remains unclear., Methods: BLCA cases from TCGA were used for training, while GSE5287, GSE13507, GSE31684, and GSE32894 cohorts from GEO were used for external validation. BEX-related genes were identified through literature retrieval, unsupervised clustering, and genomic difference detection. Gene pairing, LASSO, random forest, and Cox regression were employed to construct a predictive model. B cell samples from scRNAseqDB, GSE111636, and IMvigor210 were utilized to explore immunoprofiles and the predictive ability of the model in immunotherapeutic response. Additionally, 21 pairs of BLCA and paracarcinoma samples from Nanfang Hospital were used to re-confirm our findings through RT-qPCR, immunofluorescence, and flow cytometry., Results: 39 BEX-related genes were identified. A 4-gene-pair signature was constructed and served as a reliable prognostic predictor across multiple datasets (pooled HR = 2.32; 95 % CI = 1.81-2.98). The signature reflected the BEX statuses of B cells (FDR < 0.05) and showed promise in evaluating immunotherapeutic sensitivity (P < 0.001). In the local cohort, CD52, TUBB6, and CAV1 were down-regulated in BLCA tissues, while TGFBI, UBE2L6, TINAGL1, and IL32 were up-regulated (all P < 0.05). Furthermore, the infiltration levels of CD19 + CD52 + and CD19 + TUBB6 + B cells in paracarcinoma samples were higher than those in BLCA samples (all P < 0.05)., Conclusion: A BEX-related gene signature was developed to predict prognosis and immunotherapeutic sensitivity in BLCA, providing valuable guidance for personalized treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Chronic Obstructive Pulmonary Disease (COPD) is characterized by high morbidity, disability, and mortality rates worldwide. RNA-binding proteins (RBPs) might regulate genes involved in oxidative stress and inflammation in COPD patients. Single-cell transcriptome sequencing (scRNA-seq) offers an accurate tool for identifying intercellular heterogeneity and the diversity of immune cells. However, the role of RBPs in the regulation of various cells, especially AT2 cells, remains elusive., Materials and Methods: A scRNA-seq dataset (GSE173896) and a bulk RNA-seq dataset acquired from airway tissues (GSE124180) were employed for data mining. Next, RNA-seq analysis was performed in both COPD and control patients. Differentially expressed genes (DEGs) were identified using criteria of fold change (FC ≥ 1.5 or ≤ 1.5) and P value ≤ 0.05. Lastly, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and alternative splicing identification analyses were carried out., Results: RBP genes exhibited specific expression patterns across different cell groups and participated in cell proliferation and mitochondrial dysfunction in AT2 cells. As an RBP, AZGP1 expression was upregulated in both the scRNA-seq and RNA-seq datasets. It might potentially be a candidate immune biomarker that regulates COPD progression by modulating AT2 cell proliferation and adhesion by regulating the expression of SAMD5, DNER, DPYSL3, GBP5, GBP3, and KCNJ2. Moreover, AZGP1 regulated alternative splicing events in COPD, particularly DDAH1 and SFRP1, holding significant implications in COPD., Conclusion: RBP gene AZGP1 inhibits epithelial cell proliferation by regulating genes participating in alternative splicing in COPD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Long non-coding RNAs (lncRNAs) dysregulation is key in the pathogenesis of systemic lupus erythematosus (SLE), but the role of exosomal lncRNAs in SLE has not been well studied. We elucidated the profiles of plasma exosomal lncRNAs expression in patients with SLE and predictd their potential clinical significance in SLE., Methods: In the screening stage, six newly diagnosed and untreated patients with SLE and six healthy controls were examined by high-throughput sequencing technology, and differential exosomal lncRNA profiles were constructed. In the validation phase, two differentially selected exosomal lncRNAs from 20 patients each with active and stable SLE and 20 healthy controls were verified with RT-qPCR. The correlation between the selected exosomal lncRNAs and SLE clinical indicators was examined. The diagnostic value of the selected exosomal lncRNAs in SLE was analyzed by the receiver operator characteristic (ROC) curve., Results: Exosomes were successfully extracted from the patients and controls. Sequencing-phase sequencing demonstrated 528 upregulated lncRNAs and 7491 downregulated lncRNAs. In the validation stage, exosomal LINC00667 and DANCR were significantly upregulated in the patients, and positively correlated with Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2 K). Exosomal DANCR expression between the active and stable SLE patients was different. The area under the curve(AUC) of exosomal LINC00667 and DANCR for SLE diagnosis was 0.815 and 0.759, respectively., Conclusions: Exosomal LINC00667 and DANCR were upregulated in SLE, and might be new biomarkers thereof. Exosomal DANCR was associated with SLE activity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Aim: The aim of the present work was to find an efficient method for safe and reliable expansion of human dental pulp cells (hDPCs) in vitro. Here, we examined the effect of a novel recombinant E8 fragment of Laminin-511 (iMatrix-511) in hDPCs regarding viability and cell spreading. Further, we investigated the underlying mechanisms governing its effects in hDPCs using RNA sequencing (RNA-seq)., Methodology: hDPCs were obtained from caries-free maxilla third molars (n = 3). CCK-8 assay was conducted to measure the viability of cells cultured on iMatrix-511 and two other ECM proteins. Cell morphology was observed by phase contrast microscope. RNA-seq of hDPCs cultured on iMatrix-511 or noncoated control was performed on Illumina Novaseq TM 6000 platform., Results: iMatrix-511 (0.5 μg/cm 2 ) enhanced the viability of hDPCs to an extent better than COL-1 and gelatin. Short term culture of hDPCs on iMatrix-511 resulted in 233 differentially expressed genes (DEGs). The top 12 most upregulated genes were XIAP, AL354740, MRFAP1, AC012321, KCND3, TMEM120B, AC009812, GET1-SH3BGR, CNTN3, AC090409, GEN1 and PIK3IP1, whereas the top 12 most downregulated genes were SFN, KRT17, RAB4B-EGLN2, CSTA, KCTD11, ATP6V1G2-DDX39B, AC010323, SBSN, LYPD3, FOSB, AC022400 and CHI3L1. qPCR validation confirmed the significant upregulation of GEN1, KCND3, PIK3IP1 and MRFAP1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, with genes enriched in various extracellular matrix interaction, estrogen and fat metabolism-related functions and pathways., Conclusions: iMatrix-511 facilitated spreading and proliferation of hDPCs. It enhances expression of anti-apoptotic genes, while inhibits expression of epidermis development-related genes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Depression, which is a disease of heterogeneous etiology, is characterized by high disability and mortality rates. Gut microbiota are associated with the development of depression. To further explore any differences in the mechanisms of depression induced by gut microbiota and traditional stresses, as well as facilitate the development of microbiota-based interventions, a fecal microbiota transplantation (FMT) depression model was made. This was achieved by transplanting feces from major depressive disorder (MDD) patients into germ-free mice. Second, the mechanisms of the depression induced by gut microbiota were analyzed in comparison with those of the depression caused by different forms of stress. It turned out that mice exhibited depressive-like behavior after FMT. Then, PCR array analysis was performed on the hippocampus of the depressed mice to identify differentially expressed genes (DEGs). The KEGG analysis revealed that the pathways of depression induced by gut microbes are closely associated with immuno-inflammation. To determine the pathogenic pathways of physiological stress and psychological stress-induced depression, raw data was extracted from several databases and KEGG analysis was performed. The results from the analysis revealed that the mechanisms of depression induced by physiological and psychological stress are closely related to the regulation of neurotransmitters and energy metabolism. Interestingly, the immunoinflammatory response was distinct across different etiologies that induced depression. The findings showed that gut microbiota dysbiosis-induced depression was mainly associated with adaptive immunity, while physiological stress-induced depression was more linked to innate immunity. This study compared the pathogenesis of depression caused by gut microbiota dysbiosis, and physiological and psychological stress. We explored new intervention methods for depression and laid the foundation for precise treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: There is growing evidence indicating immune inflammation is a key factor in the progression of chronic obstructive pulmonary disease (COPD). Immune checkpoints (ICs) are crucial targets for modulating the functional activation and differentiation of immune cells, particularly in relation to immune inflammation and the regulation of T cell activation and exhaustion. However, the precise mechanisms of ICs in COPD remain understood., Methods: COPD datasets were obtained from the Gene Expression Omnibus (GEO) and analyzed using GEO2R and Limma to identify differentially expressed genes. LASSO regression was then applied to screen ICs closely associated with COPD. Finally, target genes were selected based on gene expression profiles. Gene ontology (GO), immune infiltration analysis, and gene set enrichment analysis (GSEA) were utilized to assess the relationship between IC genes (ICGs) and immune cells. Subsequently, tobacco-exposed mice, anti-Tim3-treated mice, and HAVCR2-knockout mice were generated, with flow cytometry being used to confirm the results., Results: Through the analysis of GSE38974 and LASSO regression, five ICGs were identified. Subsequent validation using GSE20257 and GSE76925 confirmed these findings. Gene expression profiling highlighted HAVCR2 as having the strongest correlation with COPD. Further investigation through immune infiltration analysis, GO, and GSEA indicated a link between HAVCR2 and CD8+ T cells in COPD. Flow cytometry experiments demonstrated high Tim3 expression in CD8+ T cells of mice exposed to tobacco, promoting Tc1 and inhibiting Tc17, thus affecting CD8+ Tem activation and CD8+ Tcm formation, leading to an immune imbalance within CD8+ T cells., Conclusion: Prolonged exposure to tobacco upregulates Tim3 in CD8+ T cells, triggering its regulatory effects on Tc1/Tc17. Knocking out HAVCR2 further upregulated the expression of CD8+ Tem while suppressing the expression of CD8+ Tcm, indicating that Tim3 plays a role in the activation and differentiation of CD8+ T cells in the context of tobacco exposure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The gold inner shell of Turbo argyrostomus is an important morphological classification characteristic in Gastropoda. However, the gene sets responsible for shell formation in gastropods remain poorly explored. In this study, we investigated the microstructure using scanning electron microscopy (SEM), hematoxylin-eosin (HE) and Alcian blue staining-periodic acid-Schiff (AB-PAS) staining. The SEM results illustrated that the T. argyrostomus shell exhibited a special "sandwich" microstructure. The results of histological observation demonstrated two major cell types: adipocytes and mucin cells. A total of 318 differentially expressed genes were identified between edge mantle and central mantle, among which whey acidic protein, N66, and nacre-like proteins, and Lam G and EGF domains may be related to shell microstructure. 22.39% - 25.20% of the mucin genes had biomineralization related domains, which supported for the relationship between mucins and shell formation. Moreover, this study revealed energy distribution differences between the edge mantle and central mantle. These results provide insights for further understanding of the biomineralization mechanism in Gastropoda., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

RNA sequencing (RNA-seq) has undergone substantial advancements in recent decades and has emerged as a vital technique for profiling the transcriptome. The transition from bulk sequencing to single-cell and spatial approaches has facilitated the achievement of higher precision at cell resolution. It provides valuable biological knowledge about individual immune cells and aids in the discovery of the molecular mechanisms that contribute to the development of autoimmune diseases. Celiac disease (CeD) is an autoimmune disorder characterized by a strong immune response to gluten consumption. RNA-seq has led to significantly advanced research in multiple fields, particularly in CeD research. It has been instrumental in studies involving comparative transcriptomics, nutritional genomics and wheat research, cancer research in the context of CeD, genetic and noncoding RNA-mediated epigenetic insights, disease monitoring and biomarker discovery, regulation of mitochondrial functions, therapeutic target identification and drug mechanism of action, dietary factors, immune cell profiling and the immune landscape. This review offers a comprehensive examination of recent RNA-seq technology research in the field of CeD, highlighting future challenges and opportunities for its application., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Alcoholic liver cirrhosis (ALC) is a result of excessive and chronic alcohol consumption. Because alchol can cause DNA damage, extrachromosomal circular DNA (eccDNA) was investigated in ALC liver due to it can be a result of DNA damage. Considering eccDNA has ability to lead to genomic instability as an enhancer of gene transcription, we utilized Circle-Seq to identify differences in eccDNA profiles and gene expression patterns in liver samples obtained from ALC patients (n = 3) and healthy controls (n = 3) to investigate the role of eccDNA in the development of ALC. The abundance of eccDNA in ALC (mean = 13,349) were higher than the healthy control (mean = 11,557) without significant difference (pvalue = 0.6530). We observed 1,032 eccDNA containing genes showed higher expression in ALC patients compared to healthy controls (p < 0.05, log2FC > 1). Notably, we discovered seven genes that exhibited a significant positive correlation between eccDNA abundance and gene expression levels. These genes include A disintegrin and metalloproteinase with thrombospondin motifs 2 (ADAMTS2), Voltage-dependent L-type calcium channel subunit alpha-1C (CACNA1C), Protein TANC1 (TANC1), Integrin alpha-2 (ITGA2), EH domain-containing protein 4 (EHD4), Phosphofurin acidic cluster sorting protein 1 (PACS1), and Neuron navigator 2 (NAV2). Through mass spectrometry proteomics, ITGA2 were found to have significantly higher abbudance in ALC. Integrins are a family of proteins plays key roles in the fibrosis development of liver. Thus, our study opens a new perspective for liver fibrosis development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Selecting appropriate reference genes is crucial for ensuring the accuracy and reliability of gene expression study using reverse transcription-quantitative PCR (RT-qPCR). To screen the optimal reference genes for analyzing gene expression in different tissues of the vector leafhopper Psammotettix striatus which causes extensive damage to a wide range of crops by vectoring multiple plant pathogenic microorganisms, the transcriptome data from Malpighian tubules (MTs) of P. striatus were mined. Twenty alternative candidate reference genes were initially selected for screening, among which seven genes with diverse Gene Ontology (GO) annotations were choosed as candidate reference genes, i.e., ribosomal protein L7A (RPL7A), ribosomal protein S28 (RPS28), ribosomal protein L22 (RPL22), ribosomal protein LP2 (RPLP2), H3 histone family 3A (H3F3A), elongation factor 1γ (EF-1γ), and elongation factor 1α (EF-1α). Gene expression levels in different tissues of P. striatus adults were examined using RT-qPCR, and their expression stability was analyzed using multiple reference gene screening software. This study revealed EF-1α as the most abundantly expressed gene, while RPL22 exhibited the lowest expression levels. EF-1α showed the most stable expression, whereas RPS28 showed the least stability. Various software tools confirmed EF-1α as the most stable single reference gene, and EF-1α and RPLP2 an optimal combination. This study provides a foundation for future investigation of the transmission of pathogenic microorganisms mediated by the vector leafhoppers, the function of the MTs, the biosynthesis of brochosomes, the coevolutionary processes and nutritional interactions of symbionts and host insects, and the gene expression study of other sap-sucking insects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Keratoconjunctivitis sicca (KCS) is an ocular condition characterized by insufficient tear production and inflammatory irritation, with Sjögren's syndrome (SS) being a major causative factor. This study aimed to extract patient transcriptomic data from the GEO database to identify signature genes associated with the diagnosis and treatment of KCS and the expression of three key genes were experimentally verified. We performed a difference analysis on the SS patient dataset and performed a Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the resulting genes. Additionally, a Weighted Gene Co-expression Network Analysis (WGCNA) was constructed. Machine learning techniques were employed to analyze the most strongly correlated gene modules with SS traits. These findings were further validated using KCS immune-correlation microarrays as a validation set. The correlation of the three identified genes with 22 immune cells was assessed through immune infiltration analysis. Subsequently, a rat model of desiccated keratoconjunctivitis was established, and the modeling situation and expression of characteristic genes were analyzed at the morphological, tissue, and molecular levels. Bioinformatic prediction revealed that the expression of JAK1, SKI, ZBTB16 not only differed in the machine learning validation set, but also correlated with some immune cells in the immune infiltration analysis. The results of animal experiments showed that the transcription and expression levels of these three genes were significantly different in rat KCS tissues and normal tissues, and there were also differences in the expression of JAK1 and SKI in rat peripheral blood, as well as significant up-regulation of the expression of related inflammatory factors in KCS tissues. Through bioinformatics prediction and animal experimental validation, this study identified three differentially expressed genes in SS mediated KCS patients, which provide new potential biological targets for the diagnosis and treatment of KCS., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The swimming crab, Portunus trituberculatus is one of crucial aquaculture crabs with significant differences in growth and economic performance between male and female swimming crabs. Consequently, the culture of female populations presents higher economic value. The doublesex and mab-3 related transcription factor (Dmrt) gene family are known to play crucial role in gonad differentiation and development. However, there is limited information about this gene family in Portunus trituberculatus. In this study, we identified seven members of the Dmrt gene family in P. trituberculatus based on the published transcriptome and genome data and designated as Ptdmrt-1, Ptdoublesex (Ptdsx), Ptidmrt-1, Ptdmrt-11E, Ptidmrt-2, Ptdmrt-99B, and Ptdmrt-3 based on the homology analysis results, respectively. These Ptdmrt genes distributed across 6 chromosomes and were predicted to encode 283 aa, 288 aa, 529 aa, 436 aa, 523 aa, 224 aa, and 435 aa protein precursors, respectively. The expression patterns of these dmrt genes were characterized by qRT-PCR and gonad transcriptome data. The results showed that five members (Ptdmrt-99B, Ptdsx, Ptdmrt-1, Ptdmrt-3, and Ptdmrt-11E) were differentially expressed between the testis and ovary. In addition, their expression patterns from zoea 2 to juvenile 1 were characterized by published transcriptome data and the results showed that they were lowly expressed and did not exhibit notable difference except for Ptdsx during early development. Overall, majority of Ptdmrt genes were involved in gonad differentiation in the swimming crab. Current findings provide a solid foundation for further exploration of the roles of these genes in gonad development and differentiation in P. trituberculatus., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Objective: To extract exosomes from obese and non-obese mice, screen specifically expressed microRNAs by high-throughput sequencing and explore their roles., Methods: An animal obesity model was constructed, and the successful construction of the obesity model was verified by HE staining, Western Blot and RT-qPCR. In addition, exosomes were extracted and verified by Western Blot. High-throughput sequencing was performed on the extracted serum exosomes to screen for differentially expressed microRNAs. fluorescence quantitative RT-PCR (RT-qPCR) was used to validate the differentially expressed miRNAs and explore their functions., Results: 8 microRNAs were up-regulated and 11 microRNAs were down-regulated. mmu-miR-674-5p and X&#95;28316 were significantly down-regulated and had the greatest impact on protein pathways. 8&#95;13258 was significantly up-regulated and affected multiple protein pathways. GO enrichment analysis suggested that the differentially expressed microRNAs were mainly involved in the cleavage of microtubule activity, transferase activity/transferase pentameric acid. GO enrichment analysis suggested that differentially expressed microRNAs were mainly involved in the processes of cleavage microtubule activity, transferase activity/transfer pentamer, and threonine phosphatase/threonine kinase activity.KEGG pathway enrichment analysis showed that differentially expressed microRNAs were mainly involved in the processes of regulating the phosphorylation of TP53 activity, the G2/M DNA damage checkpoint, and the processing of the ends of DNA double-strand breaks. Protein interaction networks were enriched for Stat3, Fgr, Camk2b, Rac1, Asb6, and Ankfy1. Suggesting that they may be mediated by differential genes to participate in the process of insulin resistance. qRT-PCR results showed that the expression trend of mmu-miR-674-5p was consistent with the sequencing results. It suggests that it may be able to participate in the regulation of insulin resistance as a target gene., Conclusion: microRNAs were differentially expressed in serum exosomes of obese and non-obese mice and might be involved in the specific regulation of insulin resistance. mmu-miR-674-5p was differentially expressed significantly and the validation trend was consistent with it, suggesting that it might be able to participate in the regulation of insulin resistance as a target gene., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

BACKGROUND Peri-implantitis is the main cause of failure of implant treatment, and there is little research on its molecular mechanism. This study aimed to identify key biomarkers and immune infiltration of peri-implantitis using a bioinformatics method. MATERIAL AND METHODS Three Gene Ontology (GO) gene expression profiles were selected from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by the LIMMA package, and functional correlations of DEGs were analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Information on immune-related genes was obtained from ImmPort (https://www.immport.org) and InnateDB (http://www.innatedb.com). Immune-related DEGs were screened by least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE). The single-sample Gene Set Enrichment Analysis algorithm was used to analyze immune cell infiltration in gingival tissue between peri-implantitis and normal controls. Finally, results of bioinformatics analysis were verified by qPCR. RESULTS A total of 398 DEGs were identified, of which 96 were immune-related. Enrichment analysis showed these genes were enriched in inflammatory response, leucocyte chemotaxis, immune response-regulating signaling pathway, and cell activation. Seven key genes were selected by LASSO and SVM-RFE. Receiver operating characteristic curve results showed these genes had excellent diagnostic efficacy. Results of qPCR showed significant differences in the expression of these genes. CONCLUSIONS Differences in key genes and immune infiltration between peri-implantitis and gingival tissues of normal controls may provide new insights into the development of peri-implantitis. Elucidating the difference in immune infiltration between peri-implantitis tissues and normal tissues will help to understand the development of peri-implantitis.

Background: In patients with colorectal cancer (CRC), the therapeutic effects of conventional immune checkpoint inhibitors targeting the adaptive immune system are largely limited to those with microsatellite instability-high tumors. Meanwhile, new immunotherapies targeting the innate immune system are attracting increasing attention. CD47 is a representative innate immune checkpoint involved in the evasion of tumor cell phagocytosis by macrophages. This large-scale study comprehensively examined the molecular significance of CD47 gene expression in CRC., Methods: We analyzed the next-generation sequencing data of DNA and RNA from 14,287 CRC cases included in the data set of a commercial Clinical Laboratory Improvement Amendments-certified laboratory (Caris Life Sciences). The cases were divided into two groups based on the median value of CD47 gene expression levels. The molecular and immune profiles between the groups were compared, and the relationship between CD47 expression and survival outcomes was further examined., Results: In CD47 -high tumors, the proportion of consensus molecular subtypes 1 and 4 was significantly higher than in CD47 -low tumors. The expression levels of damage-associated molecular pattern-related genes showed a positive correlation with CD47 expression levels. Major oncogenic pathways, such as mitogen-activated protein kinase, phosphoinositide 3-kinase, angiogenesis, and transforming growth factor beta, were significantly activated in CD47 -high tumors. Additionally, the expression levels of a panel of adaptive immune checkpoint genes and estimates of immune cells constituting the tumor microenvironment (TME) were significantly higher in CD47 -high tumors., Conclusions: CD47 expression in CRC was associated with the activation of several oncogenic pathways and an immune-engaged TME. Our findings may provide valuable information for considering new therapeutic strategies targeting innate immune checkpoints in CRC., Competing Interests: Competing interests: NG, JX, and WMK are employees of Caris Life Sciences. All remaining authors have declared no conflicts of interest., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

When using the reverse-transcription quantitative polymerase chain reaction (RT-qPCR) technique for quantitative assessment of microRNA (miRNA) expression, normalizing data using a stable endogenous gene is essential; however, no universally adequate reference gene exists. Therefore, in this study, we aimed to determine, via the RNA-Seq technique, the most adequate endogenous normalizer for the expression assessment of plasma miRNAs in patients with coronavirus disease 2019 (COVID-19). Two massive sequencing procedures were performed (a) to identify differentially expressed miRNAs between patients with COVID-19 and healthy volunteers (n = 12), and (b) to identify differentially expressed miRNAs between patients with severe COVID-19 and those with mild COVID-19 (n = 8). The endogenous normalizer candidates were selected according to the following criteria: (1) the miRNA must have a fold regulation = 1; (2) the miRNA must have a p-value > 0.990; and (3) the miRNAs that were discovered the longest ago should be selected. Four miRNAs (hsa-miR-34a-3p, hsa-miR-194-3p, hsa-miR-17-3p, and hsa-miR-205-3p) met all criteria and were selected for validation by RT-qPCR in a cohort of 125 patients. Of these, only hsa-miR-205-3p was eligible endogenous normalizers in the context of COVID-19 because their expression was stable between the compared groups., (© 2024. The Author(s).)

A fundamental question in biology, central to our understanding of cancer and other pathologies, is determining how different cell types coordinate to form and maintain tissues. Recognizing the distinct features and capabilities of the cells that compose these tissues is critical. Unfortunately, the complexity of tissues often hinders our ability to distinguish between neighboring cell types and, in turn, scrutinize their transcriptomes and generate reliable and tractable cell models for studying their inherently different biologies. We have recently introduced a novel method that permits the identification and purification of the 12 cell types that compose the human breast-nearly all of which could be reliably propagated in the laboratory. Here, we explore the nature of these cell types. We sequence mRNAs from each purified population and investigate transcriptional patterns that reveal their distinguishing features. We describe the differentially expressed genes and enriched biological pathways that capture the essence of each cell type, and we highlight transcripts that display intriguing expression patterns. These data, analytic tools, and transcriptional analyses form a rich resource whose exploration provides remarkable insights into the inner workings of the cell types composing the breast, thus furthering our understanding of the rules governing normal cell and tissue function., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Del Toro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Macrophages are pivotal innate immune cells which exhibit high phenotypic plasticity and can exist in different polarization states dependent on exposure to external stimuli. Numerous methods have been employed to simulate macrophage polarization states to test their function in vitro. However, limited research has explored whether these polarization methods yield comparable populations beyond key gene, cytokine, and cell surface marker expression. Here, we employ an unbiased comprehensive analysis using data organized through the all RNA-seq and ChIP-seq sample and signature search (ARCHS 4 ) database, which compiles all RNAseq data deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). In silico analyses were carried out demonstrating that commonly employed macrophage polarization methods generate distinct gene expression profiles in macrophage subsets that remained poorly described until now. Our analyses confirm existing knowledge on broad macrophage polarization, while expanding nuanced differences between M2a and M2c subsets, suggesting non-interchangeable stimuli for M2a polarization. Furthermore, we characterize divergent gene expression patterns in M1 macrophages following standard polarization protocols, indicating significant subset distinctions. Consequently, equivalence cannot be assumed among polarization regimens for in vitro macrophage studies, particularly in simulating diverse pathogen responses., (© 2024. The Author(s).)

Biomarkers associated with the progression from gastric intestinal metaplasia (GIM) to gastric adenocarcinoma (GA), i.e., GA-related GIM, could provide valuable insights into identifying patients with increased risk for GA. The aim of this study was to utilize multi-bioinformatics to reveal potential biomarkers for the GA-related GIM and predict potential drug repurposing for GA prevention in patients. The multi-bioinformatics included gene expression matrix (GEM) by microarray gene expression (MGE), ScType (a fully automated and ultra-fast cell-type identification based solely on a given scRNA-seq data), Ingenuity Pathway Analysis, PageRank centrality, GO and MSigDB enrichments, Cytoscape, Human Protein Atlas and molecular docking analysis in combination with immunohistochemistry. To identify GA-related GIM, paired surgical biopsies were collected from 16 GIM-GA patients who underwent gastrectomy, yielding 64 samples (4 biopsies per stomach x 16 patients) for MGE. Co-analysis was performed by including scRNAseq and immunohistochemistry datasets of endoscopic biopsies of 37 patients. The results of the present study showed potential biomarkers for GA-related GIM, including GEM of individual patients, individual genes (such as RBP2 and CD44), signaling pathways, network of molecules, and network of signaling pathways with key topological nodes. Accordingly, potential treatment targets with repurposed drugs were identified including epidermal growth factor receptor, proto-oncogene tyrosine-protein kinase Src, paxillin, transcription factor Jun, breast cancer type 1 susceptibility protein, cellular tumor antigen p53, mouse double minute 2, and CD44., (© 2024. The Author(s).)

This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential drugs using bioinformatics and machine learning. Datasets for HIV (GSE 37250) and monkeypox (GSE 24125) were obtained from the GEO database. Common differentially expressed genes (DEGs) in co-infection were identified by intersecting DEGs from monkeypox datasets with genes from key HIV modules screened using Weighted Gene Co-Expression Network Analysis (WGCNA). After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and construction of protein-protein interaction (PPI) network, candidate hub genes were further screened based on machine learning algorithms. Transcriptional factors (TFs) and miRNA-candidate hub gene networks were constructed to understand regulatory mechanisms and protein-drug interactions to identify potential therapeutic drugs. Seven candidate hub genes-MX2, ADAR, POLR2H, RPL5, IFI16, IFIT2, and RPS5-were identified. TFs and miRNAs associated with these hub genes, playing a key role in regulating viral infection and inflammation due to the activation of antiviral innate immunity, were also identified through network analysis. Potential therapeutic drugs were screened based on these hub genes: AZT, a nucleotide reverse transcriptase inhibitor, suppressed viral replication in HIV and monkeypox co-infection, while mefloquine inhibited inflammation due to the activation of antiviral innate immunity. In conclusion, the study identified candidate hub genes, their transcriptional regulation, signaling pathways, and small-molecule drugs in HIV and monkeypox co-infection, contributing to understanding the pathogenesis of HIV and monkeypox co-infection and informing precise therapeutic strategies., (© 2024. The Author(s).)

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders., (© 2024. The Author(s).)

Acute liver injury (ALI) presents a challenging problem worldwide, prompting extensive research efforts. Cellular senescence has been found to be induced following ALI, and targeting cellular senescence has shown therapeutic potential. Therefore, understanding the expression of senescence-related genes in ALI can help to explore pathogenesis and treatment of this common disease. Carbon tetrachloride (CCl 4 ) is commonly used to study ALI. Although polymerase chain reaction (PCR) is a convenient and economical molecular biology technique widely used in basic medicine, research on selecting suitable reference genes to obtain objective and reproducible PCR data is scarce. Moreover, evidence supporting the choice of reference genes for experimental studies of CCl4-induced ALI and hepatic senescence in mice is limited. In this study, we obtained murine livers at four time points (0, 12, 24, and 48 h) following CCl4 treatment. We used five algorithms (geNorm, BestKeeper, NormFinder, delta Ct, and RefFinder) to rank 12 candidate genes in real-time reverse-transcription quantitative PCR (RT-qPCR) experiments. Focusing on cellular senescence in this model, we adopted four senescence-associated secretory phenotype (SASP) genes (Il6, Il1b, Ccl2, and Ccl5) as target genes. Our results confirmed Gapdh and Tbp as suitable reference genes in murine CCl 4 -induced ALI models. Furthermore, we provide a table of published studies recommending reference genes for various liver disease models. This study provides a valuable reference for enhancing the reliability and reproducibility of ALI molecular findings., (© 2024. The Author(s).)

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in reality only a small fraction of them may actually be perturbed. Here, using simulations, we demonstrate that the standard approach to single cell analysis fails to isolate the subset of affected case cells and their markers when either the affected subset is small, or when the strength of the perturbation is mild. To address this fundamental limitation, we introduce HiDDEN, a computational method that refines the case-control labels to accurately reflect the perturbation status of each cell. We show HiDDEN's superior ability to recover biological signals missed by the standard analysis workflow in simulated ground truth datasets of cell type mixtures. When applied to a dataset of human multiple myeloma precursor conditions, HiDDEN recapitulates the expert manual annotation and discovers malignancy in early stage samples missed in the original analysis. When applied to a mouse model of demyelination, HiDDEN identifies an endothelial subpopulation playing a role in early stage blood-brain barrier dysfunction. We anticipate that HiDDEN should find wide usage in contexts that require the detection of subtle transcriptional changes in cell types across conditions., (© 2024. The Author(s).)

MicroRNAs (miRNAs) play dual roles in acute lymphoblastic leukemia (ALL) as both tumor suppressors and oncogenes, and miRNA expression profiles can be used for patient risk stratification. Precise assessment of miRNA levels is crucial for understanding their role and function in gene regulation. Quantitative real-time polymerase chain reaction (qPCR) is a reliable, rapid, and cost-effective method for analyzing miRNA expression, assuming that appropriate normalization to stable references is performed to ensure valid data. In this study, we evaluated the stability of six commonly used miRNA references (5sRNA, SNORD44, RNU6, RNU1A1, miR-103a-3p, and miR-532-5p) across nine B-cell precursor (BCP) ALL cell lines, 22 patient-derived xenograft (PDX) BCP ALL samples from different organ compartments of leukemia bearing mice, and peripheral blood mononuclear cells (PBMCs) from six healthy donors. We used four different algorithms (Normfinder, ∆CT, geNorm, and BestKeeper) to assess the most stably expressed reference across all samples. Moreover, we validated our data in an additional set of 13 PDX ALL samples and six healthy controls, identifying miR-103a-3p and miR-532-5p as the most stable references for miRNA normalization in BCP ALL studies. Additionally, we demonstrated the critical importance of using a stable reference to accurately interpret miRNA data., (© 2024. The Author(s).)

The 4T1 model is extensively employed in murine studies to elucidate the mechanisms underlying the carcinogenesis of triple-negative breast cancer. Molecular biology serves as a cornerstone in these investigations. However, accurate gene expression analyses necessitate data normalization employing housekeeping genes (HKGs) to avert spurious results. Here, we initially delve into the characteristics of the tumor evolution induced by 4T1 in mice, underscoring the imperative for additional tools for tumor monitoring and assessment methods for tracking the animals, thereby facilitating prospective studies employing this methodology. Subsequently, leveraging various software platforms, we assessed ten distinct HKGs (GAPDH, 18 S, ACTB, HPRT1, B2M, GUSB, PGK1, CCSER2, SYMPK, ANKRD17) not hitherto evaluated in the 4T1 breast cancer model, across tumors and diverse tissues afflicted by metastasis. Our principal findings underscore GAPDH as the optimal HKG for gene expression analyses in tumors, while HPRT1 emerged as the most stable in the liver and CCSER2 in the lung. These genes demonstrated consistent expression and minimal variation among experimental groups. Furthermore, employing these HKGs for normalization, we assessed TNF-α and VEGF expression in tissues and discerned significant disparities among groups. We posit that this constitutes the inaugural delineation of an ideal HKG for experiments utilizing the 4T1 model, particularly in vivo settings., (© 2024. The Author(s).)

Fibrosing interstitial lung diseases (ILDs) encompass a diverse range of scarring disorders that lead to progressive lung failure. Previous gene expression profiling studies focused on idiopathic pulmonary fibrosis (IPF) and bulk tissue samples. We employed digital spatial profiling to gain new insights into the spatial resolution of gene expression across distinct lung microenvironments (LMEs) in IPF, chronic hypersensitivity pneumonitis (CHP) and non-specific interstitial pneumonia (NSIP). We identified differentially expressed genes between LMEs within each condition, and across histologically similar regions between conditions. Uninvolved regions in IPF and CHP were distinct from normal controls, and displayed potential therapeutic targets. Hallmark LMEs of each condition retained distinct gene signatures, but these could not be reproduced in matched lung tissue samples. Based on these profiles and unsupervised clustering, we grouped previously unclassified ILD cases into NSIP or CHP. Overall, our work uniquely dissects gene expression profiles between LMEs within and across different types of fibrosing ILDs., (© 2024. The Author(s).)

Background: Numerous diseases are associated with the interplay of mitochondrial and macrophage polarization. However, the correlation of mitochondria-related genes (MRGs) and macrophage polarization-related genes (MPRGs) with the prognosis of glioma remains unclear. This study aimed to examine this relationship based on bioinformatic analysis., Methods: Glioma-related datasets (TCGA-GBMLGG, mRNA-seq-325, mRNA-seq-693, GSE16011, GSE4290, and GSE138794) were included in this study. The intersection genes were obtained by overlapping differentially expressed genes (DEGs) from differential expression analysis in GSE16011, key module genes from WGCNA, and MRGs. Subsequently, the intersection genes were further screened to obtain prognostic genes. Following this, a risk model was developed and verified. After that, independent prognostic factors were identified, followed by the construction of a nomogram and subsequent evaluation of its predictive ability. Furthermore, immune microenvironment analysis and expression validation were implemented. The GSE138794 dataset was utilized to evaluate the expression of prognostic genes at a cellular level, followed by conducting an analysis on cell-to-cell communication. Finally, the results were validated in different datasets and tissue samples from patients., Results: ECI2, MCCC2, OXCT1, SUCLG2, and CPT2 were identified as prognostic genes for glioma. The risk model constructed based on these genes in TCGA-GBMLGG demonstrated certain accuracy in predicting the occurrence of glioma. Additionally, the nomogram constructed based on risk score and grade exhibited strong performance in predicting patient survival. Significant differences were observed in the proportion of 27 immune cell types (e.g., activated B cells and macrophages) and the expression of 32 immune checkpoints (e.g., CD70, CD200, and CD48) between the two risk groups. Single-cell RNA sequencing showed that CPT2, ECI2, and SUCLG2 were highly expressed in oligodendrocytes, neural progenitor cells, and BMDMs, respectively. The results of cell-cell communication analysis revealed that both oligodendrocytes and BMDMs exhibited a substantial number of interactions with high strength., Conclusion: This study revealed five genes associated with the prognosis of glioma (ECI2, MCCC2, OXCT1, SUCLG2, and CPT2), providing novel insights into individualized treatment and prognosis., (© 2024 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

Motivation: A crucial component of intuitive data visualization is presenting a hierarchical tree structure with interactive functions. For example, single-cell transcriptomics studies may generate gene expression values with developmental trajectories or cell lineage structures. Two common visualization methods, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), require two separate figures to depict the distribution of cell types and gene expression data, with low-dimension projections that may not capture the hierarchical structures among cells., Results: Here, we present a JavaScript framework and an interactive web app named Collapsible Tree, which presents values jointly with interactive, expandable, and collapsible lineage structures. For example, the Collapsible Tree presents cellular states and gene expression from single-cell transcriptomics within a single hierarchical plot, enabling comparisons of gene expression across lineages and subtle patterns between sub-lineages. Our framework can facilitate the exploration of complicated value patterns that are not evident in UMAP or t-SNE plots., Availability and Implementation: The Collapsible Tree web interface is available at https://collapsibletree.data2in.net. The JavaScript library source code is available at https://github.com/data2intelligence/collapsible&#95;tree., (Published by Oxford University Press 2024.)

Protogyny, being capable of changing from female to male during their lifetime, is prevalent in 20 families of teleosts but is believed to have evolved within specific evolutionary lineages. Therefore, shared regulatory factors governing the sex change process are expected to be conserved across protogynous fishes. However, a comprehensive understanding of this mechanism remains elusive. To identify these factors, we conducted a meta-analysis using gonadal transcriptome data from seven species. We curated data pairs of ovarian tissue and transitional gonad, and employed ratios of expression level as a unified criterion for differential expression, enabling a meta-analysis across species. Our approach revealed that classical sex change-related genes exhibited differential expression levels between the ovary and transitional gonads, consistent with previous reports. These results validate our methodology's robustness. Additionally, we identified novel genes not previously linked to gonadal sex change in fish. Notably, changes in the expression levels of acetoacetyl-CoA synthetase and apolipoprotein Eb, which are involved in cholesterol synthesis and transport, respectively, suggest that the levels of cholesterol, a precursor of steroid hormones crucial for sex change, are decreased upon sex change onset in the gonads. This implies a potential universal influence of cholesterol dynamics on gonadal transformation in protogyny., (© 2024 The Author(s). Genes to Cells published by Molecular Biology Society of Japan and John Wiley & Sons Australia, Ltd.)

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics., (© 2024. The Author(s).)

This study investigated the effectiveness of acoustic separation for platelet analysis in patients with non-small-cell lung cancer (NSCLC), comparing it with traditional centrifugation methods. In total, 10 patients with NSCLC and 10 healthy volunteers provided peripheral blood samples, which were processed using either acoustic separation or centrifugation to isolate platelets. The study included whole transcriptome analysis of platelets, peripheral blood mononuclear cells, and tumor tissue samples, employing hierarchical clustering and Gene Ontology analysis to explore gene expression differences. Acoustic separation proved more efficient than centrifugation in terms of platelet yield, recovery rate, and RNA yield. Gene expression profiles of platelets from patients with NSCLC showed distinct patterns compared with healthy volunteers, indicating tumor-influenced alterations. Gene Ontology analysis revealed enrichment in pathways associated with platelet activation and the tumor microenvironment. This finding indicates the potential of acoustic isolation in platelet separation and its relevance in understanding the unique gene expression profile of platelets in patients with NSCLC. The findings of this study suggested that platelets from cancer patients separated by acoustic techniques exhibited tumor-specific alterations and provided new insights into the diagnosis of cancer in platelet analysis systems in clinical practice., (© 2024 The Author(s). Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.)

Streptomyces produces diverse secondary metabolites of biopharmaceutical importance, yet the rate of biosynthesis of these metabolites is often hampered by complex transcriptional regulation. Therefore, a fundamental understanding of transcriptional regulation in Streptomyces is key to fully harness its genetic potential. Here, independent component analysis (ICA) of 454 high-quality gene expression profiles of the model species Streptomyces coelicolor is performed, of which 249 profiles are newly generated for S. coelicolor cultivated on 20 different carbon sources and 64 engineered strains with overexpressed sigma factors. ICA of the transcriptome dataset reveals 117 independently modulated groups of genes (iModulons), which account for 81.6% of the variance in the dataset. The genes in each iModulon are involved in specific cellular responses, which are often transcriptionally controlled by specific regulators. Also, iModulons accurately predict 25 secondary metabolite biosynthetic gene clusters encoded in the genome. This systemic analysis leads to reveal the functions of previously uncharacterized genes, putative regulons for 40 transcriptional regulators, including 30 sigma factors, and regulation of secondary metabolism via phosphate- and iron-dependent mechanisms in S. coelicolor. ICA of large transcriptomic datasets thus enlightens a new and fundamental understanding of transcriptional regulation of secondary metabolite synthesis along with interconnected metabolic processes in Streptomyces., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Synovial Sarcomas (SS) are characterized by the presence of the SS18::SSX fusion gene, which protein product induce chromatin changes through remodeling of the BAF complex. To elucidate the genomic events that drive phenotypic diversity in SS, we performed RNA and targeted DNA sequencing on 91 tumors from 55 patients. Our results were verified by proteomic analysis, public gene expression cohorts and single-cell RNA sequencing. Transcriptome profiling identified three distinct SS subtypes resembling the known histological subtypes: SS subtype I and was characterized by hyperproliferation, evasion of immune detection and a poor prognosis. SS subtype II and was dominated by a vascular-stromal component and had a significantly better outcome. SS Subtype III was characterized by biphasic differentiation, increased genomic complexity and immune suppression mediated by checkpoint inhibition, and poor prognosis despite good responses to neoadjuvant therapy. Chromosomal abnormalities were an independent significant risk factor for metastasis. KRT8 was identified as a key component for epithelial differentiation in biphasic tumors, potentially controlled by OVOL1 regulation. Our findings explain the histological grounds for SS classification and indicate that a significantly larger proportion of patients have high risk tumors (corresponding to SS subtype I) than previously believed., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

We focus on characterizing cell lines from young and aged-healthy and -AML (acute myeloid leukemia) cell lines, and our goal is to identify the key markers associated with the progression of AML. To characterize the age-related phenotypes in AML cell lines, we consider eigenCell analysis that effectively encapsulates the primary expression level patterns across the cell lines. However, earlier investigations utilizing eigenGenes and eigenCells analysis were based on linear combination of all features, leading to the disturbance from noise features. Moreover, the analysis based on a fully dense loading matrix makes it challenging to interpret the results of eigenCells analysis. In order to address these challenges, we develop a novel computational approach termed network-constrained eigenCells profile estimation, which employs a sparse learning strategy. The proposed method estimates eigenCell based on not only the lasso but also network constrained penalization. The use of the network-constrained penalization enables us to simultaneously select neighborhood genes. Furthermore, the hub genes and their regulator/target genes are easily selected as crucial markers for eigenCells estimation. That is, our method can incorporate insights from network biology into the process of sparse loading estimation. Through our methodology, we estimate sparse eigenCells profiles, where only critical markers exhibit expression levels. This allows us to identify the key markers associated with a specific phenotype. Monte Carlo simulations demonstrate the efficacy of our method in reconstructing the sparse structure of eigenCells profiles. We employed our approach to unveil the regulatory system of immunogenes in both young/aged-healthy and -AML cell lines. The markers we have identified for the age-related phenotype in both healthy and AML cell lines have garnered strong support from previous studies. Specifically, our findings, in conjunction with the existing literature, indicate that the activities within this subnetwork of CD79A could be pivotal in elucidating the mechanism driving AML progression, particularly noting the significant role played by the diminished activities in the CD79A subnetwork. We expect that the proposed method will be a useful tool for characterizing disease-related subsets of cell lines, encompassing phenotypes and clones.

Objective: Osteoporosis is a severe bone disease with a complex pathogenesis involving various immune processes. With the in-depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention and treatment of osteoporosis. This study aims to explore novel bone immune markers related to osteoporosis based on single-cell and transcriptome data, utilizing bioinformatics and machine learning methods, in order to provide novel strategies for the diagnosis and treatment of the disease., Methods: Single cell and transcriptome data sets were acquired from Gene Expression Omnibus (GEO). The data was then subjected to cell communication analysis, pseudotime analysis, and high dimensional WGCNA (hdWGCNA) analysis to identify key immune cell subpopulations and module genes. Subsequently, ConsensusClusterPlus analysis was performed on the key module genes to identify different diseased subgroups in the osteoporosis (OP) training set samples. The immune characteristics between subgroups were evaluated using Cibersort, EPIC, and MCP counter algorithms. OP's hub genes were screened using 10 machine learning algorithms and 113 algorithm combinations. The relationship between hub genes and immunity and pathways was established by evaluating the immune and pathway scores of the training set samples through the ESTIMATE, MCP-counter, and ssGSEA algorithms. Real-time fluorescence quantitative PCR (RT-qPCR) testing was conducted on serum samples collected from osteoporosis patients and healthy adults., Results: In OP samples, the proportions of bone marrow-derived mesenchymal stem cells (BM-MSCs) and neutrophils increased significantly by 6.73% (from 24.01% to 30.74%) and 6.36% (from 26.82% to 33.18%), respectively. We found 16 intersection genes and four hub genes (DND1, HIRA, SH3GLB2, and F7). RT-qPCR results showed reduced expression levels of DND1, HIRA, and SH3GLB2 in clinical blood samples of OP patients. Moreover, the four hub genes showed positive correlations with neutrophils (0.65-0.90), immature B cells (0.76-0.92), and endothelial cells (0.79-0.87), while showing negative correlations with myeloid-derived suppressor cells (negative 0.54-0.73), T follicular helper cells (negative 0.71-0.86), and natural killer T cells (negative 0.75-0.85)., Conclusion: Neutrophils play a crucial role in the occurrence and development of osteoporosis. The four hub genes potentially inhibit metabolic activities and trigger inflammation by interacting with other immune cells, thereby significantly contributing to the onset and diagnosis of OP., (© 2024 The Author(s). Orthopaedic Surgery published by Tianjin Hospital and John Wiley & Sons Australia, Ltd.)

Spatial heterogeneity and plasticity of the mammalian liver are critical for systemic metabolic homeostasis in response to fluctuating nutritional conditions. Here, a spatially resolved transcriptomic landscape of mouse livers across fed, fasted and refed states using spatial transcriptomics is generated. This approach elucidated dynamic temporal-spatial gene cascades and how liver zonation-both expression levels and patterns-adapts to shifts in nutritional status. Importantly, the pericentral nuclear receptor Nr1i3 (CAR) as a pivotal regulator of triglyceride metabolism is pinpointed. It is showed that the activation of CAR in the pericentral region is transcriptionally governed by Pparα. During fasting, CAR activation enhances lipolysis by upregulating carboxylesterase 2a, playing a crucial role in maintaining triglyceride homeostasis. These findings lay the foundation for future mechanistic studies of liver metabolic heterogeneity and plasticity in response to nutritional status changes, offering insights into the zonated pathology that emerge during liver disease progression linked to nutritional imbalances., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Background: Coronary atherosclerotic plaques susceptible to acute coronary syndrome have traditionally been characterized by their surrounding cellular architecture. However, with the advent of intravascular imaging, novel mechanisms of coronary thrombosis have emerged, challenging our contemporary understanding of acute coronary syndrome. These intriguing findings underscore the necessity for a precise molecular definition of plaque stability. Considering this, our study aimed to investigate the vascular microenvironment in patients with stable and unstable plaques using spatial transcriptomics., Methods: Autopsy-derived coronary arteries were preserved and categorized by plaque stability (n=5 patients per group). We utilized the GeoMx spatial profiling platform and Whole Transcriptome Atlas to link crucial histological morphology markers in coronary lesions with differential gene expression in specific regions of interest, thereby mapping the vascular transcriptome. This innovative approach allowed us to conduct cell morphological and spatially resolved transcriptional profiling of atherosclerotic plaques while preserving crucial intercellular signaling., Results: We observed intriguing spatial and cell-specific transcriptional patterns in stable and unstable atherosclerotic plaques, showcasing regional variations within the intima and media. These regions exhibited differential expression of proinflammatory molecules (eg, IFN-γ [interferon-γ], MHC [major histocompatibility complex] class II, proinflammatory cytokines) and prothrombotic signaling pathways. By using lineage tracing through spatial deconvolution of intimal CD68 + (cluster of differentiation 68) cells, we characterized unique, intraplaque subpopulations originating from endothelial, smooth muscle, and myeloid lineages with distinct regional locations associated with plaque instability. In addition, unique transcriptional signatures were observed in vascular smooth muscle and CD68 + cells among plaques exhibiting coronary calcification., Conclusions: Our study illuminates distinct cell-specific and regional transcriptional alterations present in unstable plaques. Furthermore, we characterize spatially resolved, in situ evidence supporting cellular transdifferentiation and intraplaque plasticity as significant contributors to plaque instability in human coronary atherosclerosis. Our results provide a powerful resource for the identification of novel mediators of acute coronary syndrome, opening new avenues for preventative and therapeutic treatments., Competing Interests: None.

Pancreatic cancer remains one of the deadliest malignancies with an overall 5-year survival rate of 13 %. This dismal fact can be partly attributed to currently limited understanding of tumor heterogeneity and immune microenvironment. Traditional bulk-sequencing techniques overlook the diversity of tumor cells, while single-cell sequencing disorganizes the position localizing of cells in tumor microenvironment. The advent of spatial transcriptomics (ST) presents a novel solution by integrating location and whole transcript expression information. This technology allows for detailed observation of spatio-temporal changes across various cell subtypes within the pancreatic tumor microenvironment, providing insights into their potential functions. This review offers an overview of recent studies implementing ST in pancreatic cancer research, highlighting its instrumental role in investigating the heterogeneity and functions of tumor cells, stromal cells, and immune cells. On the basis, we also prospected and summarized the clinical application scenarios, technical limitations and challenges of ST technology in pancreatic cancer., Competing Interests: Declaration of Competing Interest All the authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background Leprosy is no longer considered an imprecation, as an effective multidrug therapy regimen is available worldwide for its cure. However, its diverse clinical manifestations sometimes involve acute inflammatory reactions. These complications result in irreversible nerve damage, neuritis and anatomical deformities that emerge before, during the treatment or after the completion of treatment. Reversal reaction (Type-I) and erythema nodosum leprosum (Type-II) are the leprosy reactions generally seen in patients with lepromatous and borderline forms of leprosy. At present, there is no accurate diagnostic test available to detect these leprosy reactions. Objectives To identify potential biomarkers indicative of Type-I and Type-II leprosy reactions that could help in their early diagnosis. Methods and Results Host-transcriptomics investigations have been utilised in this study to decipher a correlation between host-gene expression-based biomarkers and exacerbation of leprosy reactions. We present a comparative analysis of publicly available host transcriptomics datasets (from Gene Expression Omnibus) related to leprosy reactions. Individual datasets were analysed and integration of results was carried out using meta-analysis. Common differentially expressed genes (DEGs) were identified using the frequentist and Bayesian ratio association test methods. We have identified several genes - ADAMTS5, ADAMTS9, IFITM2, IFITM3, KIRREL, ANK3, CD1E, CTSF, DOCK9 and KRT73 to name a few - which can serve as potential biomarkers for Type-II reaction. Similarly, ACP5, APOC1, CCL17, S100B, SLC11A1 among others may likely serve as biomarkers for Type-I reaction. Limitations The number of datasets related to leprosy reactions found after the systematic search is less (n = 4) and may limit the accuracy of identified biomarker genes. This could be resolved by including more studies in the data analysis. Conclusion We provide a comprehensive list of gene candidates which could be prioritised further in research focusing on immune reactions in leprosy, as they are likely important in understanding its complexities and could be useful in its early diagnosis.

Circular RNA (CircRNA), as a classical noncoding RNA, has been proven to regulate skeletal muscle development (SMD). However, the molecular genetic basis of circRNA regulation in muscle cells remains unclear. In this study, the expression patterns of circRNAs in the longissimus dorsi muscle at embryonic day 75 and postnatal day 1 in DBGs were investigated to identify the key circRNAs that play an important role in SMD in goats. A total of 140 significantly and differentially expressed circRNAs (DEcircRNAs) were identified among the groups at different developmental stages. Among the 116 host genes (HGs) of DEcircRNAs, 76 were significantly and differentially expressed, which was confirmed by previous RNA&#95;seq data. Furthermore, the expression pattern of 10 DEcircRNAs with RT-qPCR was verified, which showed 80% concordance rate with that of RNA&#95;seq datasets. Moreover, the authenticity of seven randomly selected DEcircRNAs was verified by PCR Sanger sequencing. Based on the functional annotation results, among the 76 significantly and differentially expressed HGs, 74 were enriched in 845 GO terms, whereas 35 were annotated to 85 KEGG pathways. The results of this study could provide a comprehensive understanding of the genetic basis of circRNAs involved in SMD and muscle growth.

Background: Hirschsprung's disease (HSCR) is a congenital disorder characterized by aganglionosis in the intermuscular and submucosal nerve plexuses of the gut, leading to impaired gastrointestinal function. Although the precise cause and pathophysiology of HSCR remain elusive, increasing evidence points to a significant role of autophagy in its development, warranting further investigation into its underlying mechanisms., Methods: This study utilized publicly available microarray expression profiling datasets, GSE96854 and GSE98502, from the Gene Expression Omnibus (GEO). The R software (version 4.2.0) was employed to identify autophagy-related genes potentially showing differential expression in HSCR. Subsequent analyses included correlation analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction (PPI) network analysis using the STRING database (version 11.0) and Cytoscape software (version 3.8.2). Ultimately, HSCR samples were used to verify the mRNA levels of important genes by quantitative real-time polymerase chain reaction (qRT-PCR) in a laboratory setting., Results: We have discovered 20 genes that are involved in autophagy and show variable expression. Among these genes, 15 are up-regulated and five are down-regulated. The enrichment analysis using the GO and KEGG pathways revealed a notable enrichment in pathways related to the control of autophagy. Nine hub genes were found via the investigation of the PPI network constructed from STRING database and module analysis using Cytoscape. Moreover, the concordance between SIRT1 expression in the HSCR model and the bioinformatics analysis of mRNA chip findings was validated using qRT-PCR., Conclusion: Utilizing bioinformatics analysis, we identified 20 potential genes associated with Hirschsprung's disease that play a role in autophagy. Notably, the upregulation of SIRT1 may profoundly influence the progression of HSCR by regulating autophagy-related pathways, offering a novel perspective on the disease's pathogenesis., Competing Interests: The authors declare there are no competing interests., (©2024 Yao et al.)

Two correspondences raised concerns or comments about our analyses regarding exaggerated false positives found by differential expression (DE) methods. Here, we discuss the points they raise and explain why we agree or disagree with these points. We add new analysis to confirm that the Wilcoxon rank-sum test remains the most robust method compared to the other five DE methods (DESeq2, edgeR, limma-voom, dearseq, and NOISeq) in two-condition DE analyses after considering normalization and winsorization, the data preprocessing steps discussed in the two correspondences., (© 2024. The Author(s).)

A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested in the previous study., (© 2024. The Author(s).)

A recent study found severely inflated type I error rates for DESeq2 and edgeR, two dominant tools used for differential expression analysis of RNA-seq data. Here, we show that by properly addressing the outliers in the RNA-Seq data using winsorization, the type I error rate of DESeq2 and edgeR can be substantially reduced, and the power is comparable to Wilcoxon rank-sum test for large datasets. Therefore, as an alternative to Wilcoxon rank-sum test, they may still be applied for differential expression analysis of large RNA-Seq datasets., (© 2024. The Author(s).)

Gene expression data holds the potential to shed light on multiple biological processes at once. However, data analysis methods for single cell sequencing mostly focus on finding cell clusters or the principal progression line of the data. Data analysis for spatial transcriptomics mostly addresses clustering and finding spatially variable genes. Existing data analysis methods are effective in finding the main data features, but they might miss less pronounced, albeit significant, processes, possibly involving a subset of the samples. In this work we present SPIRAL: Significant Process InfeRence ALgorithm. SPIRAL is based on Gaussian statistics to detect all statistically significant biological processes in single cell, bulk and spatial transcriptomics data. The algorithm outputs a list of structures, each defined by a set of genes working simultaneously in a specific population of cells. SPIRAL is unique in its flexibility: the structures are constructed by selecting subsets of genes and cells based on statistically significant and consistent differential expression. Every gene and every cell may be part of one structure, more or none. SPIRAL also provides several visual representations of structures and pathway enrichment information. We validated the statistical soundness of SPIRAL on synthetic datasets and applied it to single cell, spatial and bulk RNA-sequencing datasets. SPIRAL is available at https://spiral.technion.ac.il/ ., (© 2024. The Author(s).)

There have been several reports of skin manifestations in patients with coronavirus disease 2019 (COVID-19). However, it is unclear whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be detected on the skin surface, including the sebum, of these patients. In this study, SARS-CoV-2 RNA was detected using real-time reverse-transcription polymerase chain reaction (RT-PCR) assay of skin surface lipids (SSLs) collected using an oil-blotting film from the faces of hospitalized patients with COVID-19. Human transcriptome analysis was also performed using the same samples. In facial SSLs of patients with COVID-19, the RT-PCR positivity rate was 84.6% (11/13 samples) within 5 days and 30.4% (7/23 samples) by 6-10 days of symptom onset. In the transcriptome analysis, the most characteristic SSL-RNA profile was the upregulation of interferon-stimulated gene (ISG)-related genes, such as ISG15, IFITM1, and MX1. This study presents an alternative technique using SSLs for non-invasive SARS-CoV-2 RNA detection and simultaneous analysis of human molecular pathogenesis in patients with COVID-19., (© 2024. The Author(s).)