Inflammatory Response-related Genes Research Articles

Identification of an inflammatory response-related gene prognostic signature and immune microenvironment for cervical cancer.

Background: Cervical cancer (CC) is the fourth most common cancer among women worldwide. As part of the brisk cross-talk between the host and the tumor, prognosis can be affected through inflammatory responses or the tumor microenvironment. However, further exploration of the inflammatory response-related genes that have prognostic value, microenvironment infiltration, and chemotherapeutic therapies in CC is needed. Methods: The clinical data and mRNA expression profiles of CC patients were downloaded from a public database for this study. In the TCGA cohort, a multigene prognostic signature was constructed by least absolute shrinkage and selection operator (LASSO) and Cox analyses. CC patients from the GEO cohort were used for validation. K‒M analysis was used to compare overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors of OS. The immune cell infiltration and immune-related functional score were calculated by single-sample gene set enrichment analysis (GSEA). Immunohistochemistry was utilized to validate the protein expression of prognostic genes in CC tissues. Results: A genetic signature model associated with the inflammatory response was built by LASSO Cox regression analysis. Patients in the high-risk group had a significantly lower OS rate. The predictive ability of the prognostic genes was evaluated by means of receiver operating characteristic (ROC) curve analysis. The risk score was confirmed to be an independent predictor of OS by univariate and multivariate Cox analyses. The immune status differed between the high-risk and low-risk groups, and the cancer-related pathways were enriched in the high-risk group according to functional analysis. The risk score was significantly related to tumor stage and immune infiltration type. The expression levels of five prognostic genes (LCK, GCH1, TNFRSF9, ITGA5, and SLC7A1) were positively related to sensitivity to antitumor drugs. Additionally, the expression of prognostic genes was significantly different between CC tissues and myoma patient cervix (non-tumorous) tissues in the separate sample cohort. Conclusion: A model consisting of 5 inflammation-related genes can be used to predict prognosis and influence immune status in CC patients. Furthermore, the inhibition or enhancement of these genes may become a novel alternative therapy.

Coarse particulate matter (PM10) induce an inflammatory response through the NLRP3 activation

IntroductionPM exposure can induce inflammatory and oxidative responses; however, differences in these adverse effects have been reported depending on the chemical composition and size. Moreover, inflammatory mechanisms such as NLRP3 activation by PM10 have yet to be explored.ObjectiveTo assess the impact of PM10 on cell cytotoxicity and the inflammatory response through in vitro and in vivo models.MethodologyPeripheral blood mononuclear cells (PBMCs) from healthy donors were exposed to PM10. Cytotoxicity was determined using the LDH assay; the expression of inflammasome components and the production of pro-inflammatory cytokines were quantified through qPCR and ELISA, respectively; and the formation of ASC complexes was examined using confocal microscopy. For in vivo analysis, male C57BL6 mice were intranasally challenged with PM10 and bronchoalveolar lavage fluid was collected to determine cell counts and quantification of pro-inflammatory cytokines by ELISA. RNA was extracted from lung tissue, and the gene expression of inflammatory mediators was quantified.ResultsPM10 exposure induced significant cytotoxicity at concentrations over 100 µg/mL. Moreover, PM10 enhances the gene expression and release of pro-inflammatory cytokines in PBMCs, particularly IL-1β; and induces the formation of ASC complexes in a dose-dependent manner. In vivo, PM10 exposure led to cell recruitment to the lungs, which was characterized by a significant increase in polymorphonuclear cells compared to control animals. Furthermore, PM10 induces the expression of several inflammatory response-related genes, such as NLRP3, IL-1β and IL-18, within lung tissue.ConclusionBriefly, PM10 exposure reduced the viability of primary cells and triggered an inflammatory response, involving NLRP3 inflammasome activation and the subsequent production of IL-1β. Moreover, PM10 induces the recruitment of cells to the lung and the expression of multiple cytokines; this phenomenon could contribute to epithelial damage and, thus to the development and exacerbation of respiratory diseases such as viral infections.

Integrated bioinformatics analysis of ferroptosis-related gene signature in inflammation and immunity in intervertebral disc degeneration

Ferroptosis has recently been shown to play a significant role in the progression of intervertebral disk degeneration (IDD), although the underlying mechanism is still unknown. The objective of this work was to use stringent bioinformatic techniques to clarify the crucial roles played by genes associated with ferroptosis in the emergence of IDD. For additional study, the microarray data pertinent to the IDD were acquired from the Gene Expression Omnibus database. The ferroptosis-related and IDD-related genes (FIDDRGs) were identified using a variety of bioinformatic techniques, which were also used to carry out function enrichment analysis, protein–protein correlation analysis, build the correlation regulatory network, and examine the potential connections between ferroptosis and immune abnormalities and inflammatory responses in IDD. A total of 16 FIDDRGs were eliminated for the further function enrichment analysis, and 10 hub FIDDRGs were chosen to build the correlation regulatory network. Hub FIDDRGs were shown to be highly associated with M2 macrophages and hub inflammatory response-related genes in IDD. When seen as a whole, our findings can give fresh perspectives on the mechanistic studies of ferroptosis in the emergence of IDD and new prospective targets for the therapeutic approaches.

Identification of diagnostic signature, molecular subtypes, and potential drugs in allergic rhinitis based on an inflammatory response gene set.

Rhinitis is a complex condition characterized by various subtypes, including allergic rhinitis (AR), which involves inflammatory reactions. The objective of this research was to identify crucial genes associated with inflammatory response that are relevant for the treatment and diagnosis of AR. We acquired the AR-related expression datasets (GSE75011 and GSE50223) from the Gene Expression Omnibus (GEO) database. In GSE75011, we compared the gene expression profiles between the HC and AR groups and identified differentially expressed genes (DEGs). By intersecting these DEGs with inflammatory response-related genes (IRGGs), resulting in the identification of differentially expressed inflammatory response-related genes (DIRRGs). Afterwards, we utilized the protein-protein interaction (PPI) network, machine learning algorithms, namely least absolute shrinkage and selection operator (LASSO) regression and random forest, to identify the signature markers. We employed a nomogram to evaluate the diagnostic effectiveness of the method, which has been confirmed through validation using GSE50223. qRT-PCR was used to confirm the expression of diagnostic genes in clinical samples. In addition, a consensus clustering method was employed to categorize patients with AR. Subsequently, extensive investigation was conducted to explore the discrepancies in gene expression, enriched functions and pathways, as well as potential therapeutic drugs among these distinct subtypes. A total of 22 DIRRGs were acquired, which participated in pathways including chemokine and TNF signaling pathway. Additionally, machine learning algorithms identified NFKBIA, HIF1A, MYC, and CCRL2 as signature genes associated with AR's inflammatory response, indicating their potential as AR biomarkers. The nomogram based on feature genes could offer clinical benefits to AR patients. We discovered two molecular subtypes, C1 and C2, and observed that the C2 subtype exhibited activation of immune- and inflammation-related pathways. NFKBIA, HIF1A, MYC, and CCRL2 are the key genes involved in the inflammatory response and have the strongest association with the advancement of disease in AR. The proposed molecular subgroups could provide fresh insights for personalized treatment of AR.

Investigating the impact of inflammatory response-related genes on renal fibrosis diagnosis: a machine learning-based study with experimental validation

Renal fibrosis plays a crucial role in the progression of renal diseases, yet the lack of effective diagnostic markers poses challenges in scientific and clinical practices. In this study, we employed machine learning techniques to identify potential biomarkers for renal fibrosis. Utilizing two datasets from the GEO database, we applied LASSO, SVM-RFE and RF algorithms to screen for differentially expressed genes related to inflammatory responses between the renal fibrosis group and the control group. As a result, we identified four genes (CCL5, IFITM1, RIPK2, and TNFAIP6) as promising diagnostic indicators for renal fibrosis. These genes were further validated through in vivo experiments and immunohistochemistry, demonstrating their utility as reliable markers for assessing renal fibrosis. Additionally, we conducted a comprehensive analysis to explore the relationship between these candidate biomarkers, immunity, and drug sensitivity. Integrating these findings, we developed a nomogram with a high discriminative ability, achieving a concordance index of 0.933, enabling the prediction of disease risk in patients with renal fibrosis. Overall, our study presents a predictive model for renal fibrosis and highlights the significance of four potential biomarkers, facilitating clinical diagnosis and personalized treatment. This finding presents valuable insights for advancing precision medicine approaches in the management of renal fibrosis. Communicated by Ramaswamy H. Sarma

Validation of new immune and inflammation-related diagnostic biomarkers for RA.

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease, whose development is associated with immune cells and persistent inflammation. Exploring the biomarkers of RA holds immense significance in terms of the prevention, diagnosis, and treatment of RA. The differentially expressed genes (DEGs) in RA patients and the control group were screened by limma package. Through DEGs intersection overlapping 200 inflammatory response-related genes and 2498 immune-related genes, differentially expressed immune and inflammation-related genes (DE-IIRGs) were identified. Lasso regression analysis screened RA diagnostic biomarkers and constructed PPI networks. Finally, immune infiltration analysis and drug prediction were performed. A total of 20 DE-IIRGs were identified by overlapping DEGs with 2498 immune-related genes and 200 inflammatory response-related genes. These DE-IIRGs were primarily enriched in the cytokine-cytokine receptor interaction and other biological processes, and then five biomarker genes (TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15) were identified. It was found that the expression levels of CXCL9, IL15, and TNFSF10 in the disease samples were significantly higher than those in the control group. These biomarker genes have more effective diagnostic potential. The RA samples exhibited significantly higher levels of cell infiltration compared to the control samples. hsa-miR-199a-5p's connections to the ACVR1B and CCR7 genes were identified by creating ceRNA networks from 20 screened DE-IIRGs. There was a connection between CCL5 and AEMA4D and hsa-miR-214-3p. We identified immune- and inflammation-related biomarkers in RA based on bioinformatics analysis and screened TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15 as diagnostic markers for RA. Key Points • TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15 may be new diagnostic biomarkers for RA. • These findings may provide a theoretical basis for early RA diagnosis.

An inflammatory response-related gene signature can predict the prognosis and impact the immune infiltration of multiple myeloma

Multiple myeloma (MM) is a highly heterogeneous and incurable disease. Inflammation plays a vital role in cancer genesis and progression. However, the relationship between inflammatory response-related genes (IRRGs) and the prognosis of MM patients remains unknown. We constructed a IRRGs prognosis model by least absolute shrinkage and selection operator regression analysis. Moreover, clinical multivariate regression was performed to identify clinical implications. Gene set enrichment analysis was implemented to conduct its biological properties. CIBERSORT deconvolution algorithm was utilized to calculate the immune cell infiltration in different risk groups. The flow cytometry was utilized to perform protein expression of prognostic gene. A Six-IRRGs (VCAM1, RGS1, KIT, CD81, BLNK, and BIRC3) prognostic risk model was successfully constructed and validated. The risk model was an independent predictor for overall survival. Enrichment analysis revealed autophagy and PI3K–Akt signaling pathways were enriched in the high-risk group. Furthermore, we found CD81 widely impacted on the infiltration of immune cells, especially on monocytes and macrophages2. At last, the role of CD81 in MM was confirmed to be an adverse prognostic factor in clinical. Our study explores the potential application value of IRRGs in MM. These findings may provide new insights into the treatment for MM patients.

Dietary supplementation with dihydroartemisinin improves intestinal barrier function in weaned piglets with intrauterine growth retardation by modulating the gut microbiota.

The aim of this study was to investigate whether dietary dihydroartemisinin (DHA) supplementation could improve intestinal barrier function and microbiota composition in intrauterine growth restriction (IUGR) weaned piglets. Twelve normal birth weight (NBW) piglets and 24 IUGR piglets at 21 d of age were divided into three groups, which were fed a basal diet (NBW-CON and IUCR-CON groups) and an 80mg/kg DHA diet (IUGR-DHA group). At 49 d of age, eight piglets of each group with similar body weights within groups were slaughtered, and serum and small intestine samples were collected. The results showed that IUGR piglets reduced growth performance, impaired the markers of intestinal permeability, induced intestinal inflammation, decreased intestinal immunity, and disturbed the intestinal microflora. Dietary DHA supplementation increased average daily gain, average daily feed intake, and body weight at 49 d of age in IUGR-weaned piglets (P < 0.05). DHA treatment decreased serum diamine oxidase activity and increased the numbers of intestinal goblet cells and intraepithelial lymphocytes, concentrations of jejunal mucin-2 and ileal trefoil factor 3, and intestinal secretory immunoglobin A and immunoglobin G (IgG) concentrations of IUGR piglets (P < 0.05). Diet supplemented with DHA also upregulated mRNA abundances of jejunal IgG, the cluster of differentiation 8 (CD8), major histocompatibility complex-I (MHC-I), and interleukin 6 (IL-6) and ileal IgG, Fc receptor for IgG (FcRn), cluster of differentiation 8 (CD4), CD8, MHC-I, IL-6 and tumor necrosis factor α (TNF-α), and enhanced mRNA abundance and protein expression of intestinal occludin and ileal claudin-1 in IUGR piglets (P < 0.05). In addition, DHA supplementation in the diet improved the microbial diversity of the small intestine of IUGR piglets and significantly increased the relative abundance of Actinobacteriota, Streptococcus, Blautia and Streptococcus in the jejunum, and Clostridium sensu_ stricto_in the ileum (P < 0.05). The intestinal microbiota was correlated with the mRNA abundance of tight junction proteins and inflammatory response-related genes. These data suggested that DHA could improve the markers of intestinal barrier function in IUGR-weaned piglets by modulating gut microbiota. DHA may be a novel nutritional candidate for preventing intestinal dysfunction in IUGR pigs.

Molecular Classification of HNSCC Based on Inflammatory Response-Related Genes - Integrated Single-Cell and Bulk RNA-Seq Analysis.

Tumor cells, inflammatory cells, and chemical factors collaboratively orchestrate a sophisticated signaling network, culminating in the formation of the inflammatory tumor microenvironment (TME). The present study sought to explore the nature of the inflammatory response in HNSCC and to decipher its influence on immunotherapeutic. A thorough analysis was performed utilizing the TCGA cohort along with two GEO cohorts. Unsupervised clustering of 200 inflammatory response-related genes (IRGs) was applied using the k-means algorithm to explore the heterogeneity of HNSCC. Additionally, a prognostic signature based on IRGs genes was constructed using Lasso regression. Meanwhiles, the expression of IRGs were identified in tumors and paracancerous tissues at the single-cell level. The crosstalk between IRGs was explored using CellChat and the patterns of incoming and outgoing signals were identified. Finally, qPCR was used to verify the expression of hub genes. There were significant differences in immune-cell function and immune-cell infiltration among three inflammatory response clusters. Additionally, we also constructed a prognostic model which could predicted the responses of common chemotherapeutic drugs and immunotherapy. Furthermore, qPCR and sc-RNA seq corroborated that the expression profiles of the prognostic genes were largely in alignment with the findings from the bioinformatics analysis. Ultimately, the molecular docking demonstrated favorable binding affinities between the pivotal gene-SCC7 and four chemotherapeutic drugs. This research has uniquely shed light on the intricate connection between the inflammatory response profiles and the immune infiltration patterns in HNSCC.

A Novel Porphyromonas gingivalis Infection-Related Inflammatory Response-Related Genes Signature Predicts the Prognosis of Esophageal Squamous Cell Carcinoma.

Our previous research showed that Porphyromonas gingivalis (P. gingivalis) infection can activate the inflammatory signaling pathway and promotes the malignancy development of esophageal squamous cell carcinoma (ESCC). However, the prognostic significance of inflammatory response-related genes (IRRGs) in P. gingivalis-infected ESCC requires further elucidation. Hence, our study constructed a prognostic signature based on P. gingivalis and IRRGs to forecast the survival of patients with ESCC, which may provide insight into new treatment options for ESCC patients. Differentially expressed genes (DEGs) were identified in P.gingivalis-infected and P.gingivalis-uninfected ESCC cell by RNA sequencing. A risk model was constructed and validated using the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database by using univariate Cox regression analysis, LASSO, and the multivariate Cox regression analysis. Kaplan-Meier analysis was carried out to compare the overall survival (OS) between high-risk and low-risk groups. Single-sample gene set enrichment analysis was used to analyze the immune cell infiltration. The Genomics of Drug Sensitivity in Cancer database was used to predict drug sensitivity. There were 365 DEGs between the P.gingivalis-infected and P.gingivalis-uninfected groups. Four genes including DKK1, ESRRB, EREG, and RELN were identified to construct the prognostic risk model (P = .012, C-index = 0.73). In both the training and validation sets, patients had a considerably shorter OS in the high-risk group than those in the low-risk group (P < .05). A nomogram was established using the risk score, gender, and N stage which could effectively forecast the prognosis of patients (P = .016, C-index = 0.66). The high-risk group displayed lower immune infiltrating cells, such as activated dendritic cells, type 2 T helper cells, and neutrophils (P < .05). A total of 41 drugs, including dactinomycin, luminespib, and sepantronium bromide, had a significant difference in IC50 between the 2 subgroups. We demonstrated the potential of a novel signature constructed from 4 P. gingivalis-related IRRGs for prognostic prediction in ESCC patients.

Repeated intratracheal instillation of whole-cigarette smoke condensate to assess lung damage in a rat model.

Cigarette smoke induces an inflammatory response in the lungs by recruiting inflammatory cells, leading to lung diseases such as lung cancer, chronic obstructive pulmonary disease, and pulmonary fibrosis. Existing inhalation exposure methods for assessing the adverse effects of cigarette smoke require expensive equipment and are labor-intensive. Therefore, we attempted to develop a novel method to assess these adverse effects using intratracheal instillation (ITI) of whole cigarette smoke condensate (WCSC). The WCSC (0, 5, 10, or 20 mg/mL) was administered by ITI once daily for 6 or 12 days using an automatic video instillator. Repeated WCSC ITI increased the lung weight, and monocyte chemoattractant protein-1 (MCP-1), neutrophil, and lymphocyte levels within bronchoalveolar lavage fluid compared to the control. In the histopathological analysis of the lung tissue, a mild inflammatory response was observed in the 6 and 12 days 20 mg/mL WCSC exposure groups. The genome-wide RNA-seq expression patterns revealed that inflammatory and immune response-related genes, such as the chemokine signaling pathway, Th1/Th2 cell differentiation, and cytokine-cytokine receptor interaction, were employed following WCSC exposure. In addition, MCP-1 was time-dependent and increased in the 10 mg/mL exposure group compared to the control group. These results suggested that the WCSC might induce the potential pulmonary inflammatory response. Furthermore, we proposed that ITI may be a rapid and effective method of evaluating the adverse effects of WCSC within a short exposure period (less than 2 weeks), and it can be used to evaluate cigarette inhalation toxicity studies as an alternative method.

Effects of Dietary Fish Meal Replaced by Fish Steak Meal on Growth Performance, Antioxidant Capacity, Intestinal Health and Microflora, Inflammatory Response, and Protein Metabolism of Large Yellow Croaker Larimichthys crocea.

Although fish steak meal (FSM) is a potentially available protein source, its efficiency as a fish meal (FM) substitute remains unclear to date. To this end, this study was carried out to determine the effects of dietary FM replaced by FSM on growth performance, antioxidant capacity, intestinal health and microflora, inflammatory response, and protein metabolism of large yellow croaker. Five isolipidic and isonitrogenous diets were formulated by substituting FM with FSM at levels of 0% (FSM0, control diet), 25% (FSM25), 50% (FSM50), 75% (FSM75), and 100% (FSM100), and were fed to juvenile large yellow croaker for 8 weeks. Compared with the control diet, the replacement of 25% dietary FM with FSM did not markedly alter the weight gain (WG) and specific growth rate (SGR). When the FM substitution level was over 25%, WG and SGR markedly reduced. The intestinal structure observation found that the FSM75 and FSM100 diets markedly decreased villus height, villus width, and muscle thickness of the anterior intestine. The FSM75 and FSM100 diets significantly decreased enzyme activities of amylase (AMS), lipase (LPS), trypsin, catalase (CAT), and total superoxide dismutase (T-SOD) and the total antioxidant capacity (T-AOC), and increased the malondialdehyde (MDA) content in the liver of large yellow croaker. The mRNA expression levels of intestinal barrier and inflammatory response-related genes suggested that the FSM50, FSM75, and FSM100 diets significantly decreased the mRNA abundances of intestinal barrier-related genes and anti-inflammatory response-related genes, and increased the mRNA abundances of proinflammatory gene il-6 in the anterior intestine. The compositions of intestinal microflora displayed that the FSM50, FSM75, and FSM100 diets decreased relative abundances of Firmicutes phylum and increased relative abundances of Proteobacteria phylum. In addition, the results of protein expression levels showed that the phosphorylation level of mammalian target of rapamycin (mTOR) and 4E-binding protein 1 (4E-BP1) in FSM75 and FSM100 groups were markedly reduced. In conclusion, FSM can replace up to 25% dietary FM without compromising the growth performance, intestinal health, and protein metabolism of the large yellow croaker.

The inflammatory response-related robust machine learning signature in endometrial cancer: Based on multi-cohort studies.

Uterine corpus endometrial carcinoma (UCEC) is a prevalent form of cancer in women, affecting the inner lining of the uterus. Inflammation plays a crucial role in the progression and prognosis of cancer, making it important to identify inflammatory response-related subtypes in UCEC for targeted therapy and personalized medicine. This study discovered significant variation in immune response within UCEC tumors based on molecular subtypes of inflammatory response-related genes. Subtype A showed a more favorable prognosis and better response to immunotherapies like anti-CTLA4 and anti-PDCD1 therapy. Functional analysis revealed subtype-specific differences in immune response, with subtype A exhibiting higher expression of genes related to cytokine signaling pathways, NK cell-mediated cytotoxicity pathways and inflammatory processes. Subtype A also showed increased sensitivity to three chemotherapeutic agents. A 12-gene inflammatory response-related signature was found to have prognostic value for 1, 2 and 3year survival in UCEC patients. Additionally, a validated machine learning-based signature demonstrated significant differences in clinical traits between low-risk and high-risk cohorts. Elevated risk scores were associated with higher pathological grading, older age, advanced stage and immune subtype C2. Low-risk groups had higher infiltration of immune cell types such as CD8 + T cells and activated CD4 + cells. However, the abundance of cytotoxic immune cells decreased with increasing risk scores. Finally, PCR was applied to test the different expression in P2PX4. P2RX4 knockdown inhibited the proliferation and proliferation of the endometrial carcinoma Ishikawa cell line. In conclusion, this developed signature can serve as a clinical prediction index and reveal distinct immune expression patterns. Ultimately, this study has the potential to enhance targeted therapy and personalized medicine for UCEC patients.

Mechanism of Innate Immune Response Induced by Albizia julibrissin Saponin Active Fraction Using C2C12 Myoblasts.

Albizia julibrissin saponin active fraction (AJSAF), is a prospective adjuvant with dual Th1/Th2 and Tc1/Tc2 potentiating activity. Its adjuvant activity has previously been proven to be strictly dependent on its spatial co-localization with antigens, highlighting the role of local innate immunity in its mechanisms. However, its potential targets and pathways remain unclear. Here, its intracellular molecular mechanisms of innate immune response were explored using mouse C2C12 myoblast by integrative analysis of the in vivo and in vitro transcriptome in combination with experimental validations. AJSAF elicited a temporary cytotoxicity and inflammation towards C2C12 cells. Gene set enrichment analysis demonstrated that AJSAF regulated similar cell death- and inflammatory response-related genes in vitro and in vivo through activating second messenger-MAPK-CREB pathways. AJSAF markedly enhanced the Ca2+, cAMP, and reactive oxygen species levels and accelerated MAPK and CREB phosphorylation in C2C12 cells. Furthermore, Ca2+ chelator, CREB inhibitor, and MAPK inhibitors dramatically blocked the up-regulation of IL-6, CXCL1, and COX2 in AJSAF-treated C2C12 cells. Collectively, these results demonstrated that AJSAF induced innate immunity via Ca2+-MAPK-CREB pathways. This study is beneficial for insights into the molecular mechanisms of saponin adjuvants.

Identification of side effects of COVID-19 drug candidates on embryogenesis using an integrated zebrafish screening platform

Drug repurposing is an important strategy in COVID-19 treatment, but many clinically approved compounds have not been extensively studied in the context of embryogenesis, thus limiting their administration during pregnancy. Here we used the zebrafish embryo model organism to test the effects of 162 marketed drugs on cardiovascular development. Among the compounds used in the clinic for COVD-19 treatment, we found that Remdesivir led to reduced body size and heart functionality at clinically relevant doses. Ritonavir and Baricitinib showed reduced heart functionality and Molnupiravir and Baricitinib showed effects on embryo activity. Sabizabulin was highly toxic at concentrations only 5 times higher than Cmax and led to a mean mortality of 20% at Cmax. Furthermore, we tested if zebrafish could be used as a model to study inflammatory response in response to spike protein treatment and found that Remdesivir, Ritonavir, Molnupiravir, Baricitinib as well as Sabizabulin counteracted the inflammatory response related gene expression upon SARS-CoV-2 spike protein treatment. Our results show that the zebrafish allows to study immune-modulating properties of COVID-19 compounds and highlights the need to rule out secondary defects of compound treatment on embryogenesis. All results are available on a user friendly web-interface https://share.streamlit.io/alernst/covasc_dataapp/main/CoVasc_DataApp.py that provides a comprehensive overview of all observed phenotypic effects and allows personalized search on specific compounds or group of compounds. Furthermore, the presented platform can be expanded for rapid detection of developmental side effects of new compounds for treatment of COVID-19 and further viral infectious diseases.

Mesenchymal Stromal Cell-Derived Small Extracellular Vesicles Modulate Apoptosis, TNF Alpha and Interferon Gamma Response Gene mRNA Expression in T Lymphocytes.

Recent studies have highlighted the therapeutic potential of small extracellular bodies derived from mesenchymal stem cells (MSC-sEVs) for various diseases, notably through their ability to alter T-cell differentiation and function. The current study aimed to explore immunomodulatory pathway alterations within T cells through mRNA sequencing of activated T cells cocultured with bone marrow-derived MSC-sEVs. mRNA profiling of activated human T cells cocultured with MSC-sEVs or vehicle control was performed using the QIAGEN Illumina sequencing platform. Pathway networks and biological functions of the differentially expressed genes were analyzed using Ingenuity pathway analysis (IPA)® software, KEGG pathway, GSEA and STRING database. A total of 364 differentially expressed genes were identified in sEV-treated T cells. Canonical pathway analysis highlighted the RhoA signaling pathway. Cellular development, movement, growth and proliferation, cell-to-cell interaction and inflammatory response-related gene expression were altered. KEGG enrichment pathway analysis underscored the apoptosis pathway. GSEA identified enrichment in downregulated genes associated with TNF alpha and interferon gamma response, and upregulated genes related to apoptosis and migration of lymphocytes and T-cell differentiation gene sets. Our findings provide valuable insights into the mechanisms by which MSC-sEVs implement immunomodulatory effects on activated T cells. These findings may contribute to the development of MSC-sEV-based therapies.

The needle in the haystack: Identifying and validating common genes of depression, insomnia, and inflammation

BackgroundInsomnia, inflammation, and depression are often co-occurring conditions. The mechanisms underlying these conditions remain unclear. Materials and methodsWe collected microarray datasets of depression and insomnia from GEO and analyzed them for differentially expressed genes (DEGs). We then overlapped the DEGs with a list of inflammatory response-related genes to identify genes associated with all three conditions. We next performed analyses of enrichment analyses, KEGG mapping, and protein-protein interaction to identify hub genes. Furthermore, we established a depression rat model with inflammation and insomnia to validate the potential genes. At last, a two-sample Mendelian randomization (MR) study was conducted to confirm the association of identified target genes with depression outcomes. ResultsWe obtained 32 common DEGs associated with the depression, insomnia and inflammatory, and found that the PI3K-AKT signaling pathway might be involved in the inflammatory response in insomnia and depression. CREB1, CYBB, FYN, and CCR5 were identified as targets for the next validation. In model rats, the CCR5 and PI3K-AKT pathways were significantly up-regulated, while the model group exhibited significantly lower hippocampal p-CREB protein expression. The MR study suggested a potential causal relationship between CREB1 and the risk of depression (OR = 1.11, p = 0.013). LimitationsThe identified potential genes and pathways require further laboratory and clinical evidence verification. ConclusionWe identified four potential inflammatory related-genes (CREB1, CYBB, FYN, and CCR5). CREB1 may be a potential inflammatory response-related biomarker and drug target for depression and insomnia, as validated by the followed rat model and MR study.

Glycerol monolaurate improved intestinal barrier, antioxidant capacity, inflammatory response and microbiota dysbiosis in large yellow croaker (Larimichthys crocea) fed with high soybean oil diets

Glycerol monolaurate (GML) is a potential candidate for regulating metabolic syndrome and inflammatory response. However, the role of GML in modulating intestinal health in fish has not been well determined. In this study, a 70-d feeding trial was conducted to evaluate the effect of GML on intestinal barrier, antioxidant capacity, inflammatory response and microbiota community of large yellow croaker (13.05 ± 0.09 g) fed with high level soybean oil (SO) diets. Two basic diets with fish oil (FO) or SO were formulated. Based on the SO group diet, three different levels of GML 0.02% (SO0.02), 0.04% (SO0.04) and 0.08% (SO0.08) were supplemented respectively. Results showed that intestinal villus height and perimeter ratio were increased in SO0.04 treatment compared with the SO group. The mRNA expressions of intestinal physical barrier-related gene odc and claudin-11 were significantly up-regulated in different addition of GML treatments compared with the SO group. Fish fed SO diet with 0.04% GML addition showed higher activities of acid phosphatase and lysozyme compared with the SO group. The content of malonaldehyde was significantly decreased and activities of catalase and superoxide dismutase were significantly increased in 0.02% and 0.04% GML groups compared with those in the SO group. The mRNA transcriptional levels of inflammatory response-related genes (il-1β, il-6, tnf-α and cox-2) in 0.04% GML treatment were notably lower than those in the SO group. Meanwhile, sequencing analysis of bacterial 16S rRNA V4–V5 region showed that GML addition changed gut microbiota structure and increased alpha diversity of large yellow croaker fed diets with a high level of SO. The correlation analysis results indicated that the change of intestinal microbiota relative abundance strongly correlated with intestinal health indexes. In conclusion, these results demonstrated that 0.02%–0.04% GML addition could improve intestinal morphology, physical barrier, antioxidant capacity, inflammatory response and microbiota dysbiosis of large yellow croaker fed diets with a high percentage of SO.

Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma

Inflammatory response plays a crucial role in the development and progression of gliomas. Whereas the prognostic esteem of inflammatory response-related genes has never been comprehensively explored in glioma, the RNA-seq information and clinical data of patients with glioma were extracted from TCGA, CGGA, and Rembrandt databases. The differentially expressed genes (DEGs) were picked out between glioma tissue and non-tumor brain tissue (NBT). Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct the prognostic signature in the TCGA cohort and verified in other cohorts. Kaplan–Meier survival analyses were conducted to compare the overall survival (OS) between the high and low-risk groups. Univariate and multivariate Cox analyses were subsequently used to confirm the independent prognostic factors of OS, and then, the nomogram was established based them. Furthermore, immune infiltration, immune checkpoints, and immunotherapy were also probed and compared between high and low-risk groups. The four genes were also analyzed by qRT-PCR, immunohistochemistry, and western blot trials between glioma tissue and NBT. The 39 DEGs were identified between glioma tissue and NBT, of which 31 genes are associated to the prognosis of glioma. The 8 optimal inflammatory response-related genes were selected to construct the prognostic inflammatory response-related signature (IRRS) through the LASSO regression. The effectiveness of the IRRS was verified in the TCGA, CGGA, and Rembrandt cohorts. Meanwhile, a nomogram with better accuracy was established to predict OS based on the independent prognostic factors. The IRRS was highly correlated with clinicopathological features, immune infiltration, and genomic alterations in glioma patients. In addition, four selective genes also verified the difference between glioma tissue and NBT. A novel prognostic signature was associated with the prognosis, immune infiltration, and immunotherapy effect in patients with gliomas. Thus, this study could provide a perspective for glioma prognosis and treatment.

Integrated bioinformatic analysis and experimental validation for exploring the key molecular of brain inflammaging.

Integrating bioinformatics and experimental validation to explore the mechanisms of inflammaging in the Brain. After dividing the GSE11882 dataset into aged and young groups, we identified co-expressed differentially expressed genes (DEGs) in different brain regions. Enrichment analysis revealed that the co-expressed DEGs were mainly associated with inflammatory responses. Subsequently, we identified 12 DEGs that were related to the inflammatory response and used the DGIdb website for drug prediction. By using both the least absolute shrinkage and selection operator (LASSO) and random forest (RF), four biomarkers were screened and an artificial neural network (ANN) was developed for diagnosis. Subsequently, the biomarkers were validated through animal studies. Then we utilized AgeAnno to investigate the roles of biomarkers at the single cell level. Next, a consensus clustering approach was used to classify the aging samples and perform differential analysis to identify inflammatory response-related genes. After conducting a weighted gene co-expression network analysis (WGCNA), we identified the genes that are correlated with both four brain regions and aging. Wayne diagrams were used to identify seven inflammaging-related genes in different brain regions. Finally, we performed immuno-infiltration analysis and identified macrophage module genes. Inflammaging may be a major mechanism of brain aging, and the regulation of macrophages by CX3CL1 may play a role in the development of inflammaging. In summary, targeting CX3CL1 can potentially delay inflammaging and immunosenescence in the brain.