MMP9 and STAT1 are biomarkers of the change in immune infiltration after anti-tuberculosis therapy, and the immune status can identify patients with spinal tuberculosis

Common diagnostic biomarkers and molecular mechanisms of Helicobacter pylori infection and inflammatory bowel disease

Background

Helicobacter pylori (H. pylori) may be present in the intestinal mucosa of patients with inflammatory bowel disease (IBD), which is a chronic inflammation of the gastrointestinal tract. The role of H. pylori in the pathogenesis of IBD remains unclear. In this study, bioinformatics techniques were used to investigate the correlation and co-pathogenic pathways between H. pylori and IBD.

Methods

The following matrix data were downloaded from the GEO database: H. pylori-associated gastritis, GSE233973 and GSE27411; and IBD, GSE3365 and GSE179285. Differential gene analysis was performed via the limma software package in the R environment. A protein-protein interaction (PPI) network of DEGs was constructed via the STRING database. Cytoscape software, through the CytoHubba plugin, filters the PPI subnetwork and identifies Hub genes. Validation of the Hub genes was performed in the validation set. Immune analysis was conducted via the CIBERSORT algorithm. Transcription factor interaction and small molecule drug analyses of the Hub genes were also performed.

Results

Using the GSE233973 and GSE3365 datasets, 151 differentially expressed genes (DEGs) were identified. GO enrichment analysis revealed involvement in leukocyte migration and chemotaxis, response to lipopolysaccharides, response to biostimulatory stimuli, and regulation of interleukin-8 (IL-8) production. Ten Hub genes (TLR4, IL10, CXCL8, IL1B, TLR2, CXCR2, CCL2, IL6, CCR1 and MMP-9) were identified via the PPI network and Cytoscape software. Enrichment analysis of the Hub genes focused on the lipopolysaccharide response, bacterial molecular response, biostimulatory response and leukocyte movement. Validation using the GSE27411 and GSE179285 datasets revealed that MMP-9 was significantly upregulated in both the H. pylori and IBD groups. The CIBERSORT algorithm revealed immune infiltration differences between the control and disease groups of IBD patients. Additionally, the CMap database identified the top 11 small molecule compounds across 10 cell types, including TPCA-1, AS-703026 and memantine, etc.

Conclusion

Our study revealed the co-pathogenic mechanism between H. pylori and IBD and identified 10 Hub genes related to cellular immune regulation and signal transduction. The expression of MMP-9 is significantly upregulated in both H. pylori infection and IBD. This study provides a new perspective for exploring the prevention and treatment of H. pylori infection and IBD.

Molecular Indicator for Distinguishing Multi-drug-Resistant Tuberculosis from Drug Sensitivity Tuberculosis and Potential Medications for Treatment

The issue of multi-drug-resistant tuberculosis (MDR-TB) presents a substantial challenge to global public health. Regrettably, the diagnosis of drug-resistant tuberculosis (DR-TB) frequently necessitates an extended period or more extensive laboratory resources. The swift identification of MDR-TB poses a particularly challenging endeavor. To identify the biomarkers indicative of multi-drug resistance, we conducted a screening of the GSE147689 dataset for differentially expressed genes (DEGs) and subsequently conducted a gene enrichment analysis. Our analysis identified a total of 117 DEGs, concentrated in pathways related to the immune response. Three machine learning methods, namely random forest, decision tree, and support vector machine recursive feature elimination (SVM-RFE), were implemented to identify the top 10 genes according to their feature importance scores. A4GALT and S1PR1, which were identified as common genes among the three methods, were selected as potential molecular markers for distinguishing between MDR-TB and drug-susceptible tuberculosis (DS-TB). These markers were subsequently validated using the GSE147690 dataset. The findings suggested that A4GALT exhibited area under the curve (AUC) values of 0.8571 and 0.7121 in the training and test datasets, respectively, for distinguishing between MDR-TB and DS-TB. S1PR1 demonstrated AUC values of 0.8163 and 0.5404 in the training and test datasets, respectively. When A4GALT and S1PR1 were combined, the AUC values in the training and test datasets were 0.881 and 0.7551, respectively. The relationship between hub genes and 28 immune cells infiltrating MDR-TB was investigated using single sample gene enrichment analysis (ssGSEA). The findings indicated that MDR-TB samples exhibited a higher proportion of type 1 T helper cells and a lower proportion of activated dendritic cells in contrast to DS-TB samples. A negative correlation was observed between A4GALT and type 1 T helper cells, whereas a positive correlation was found with activated dendritic cells. S1PR1 exhibited a positive correlation with type 1 T helper cells and a negative correlation with activated dendritic cells. Furthermore, our study utilized connectivity map analysis to identify nine potential medications, including verapamil, for treating MDR-TB. In conclusion, our research identified two molecular indicators for the differentiation between MDR-TB and DS-TB and identified a total of nine potential medications for MDR-TB.

Follicular CD8+ T cells promote immunoglobulin production and demyelination in multiple sclerosis and a murine model

Emerging evidence underscores the importance of CD8+ T cells in the pathogenesis of multiple sclerosis (MS), but the precise mechanisms remain ambiguous. This study intends to elucidate the involvement of a novel subset of follicular CD8+ T cells (CD8+CXCR5+ T) in MS and an experimental autoimmune encephalomyelitis (EAE) murine model. The expansion of CD8+CXCR5+ T cells was observed in both MS patients and EAE mice during the acute phase. In relapsing MS patients, higher frequencies of circulating CD8+CXCR5+ T cells were positively correlated with new gadolinium-enhancement lesions in the central nervous system (CNS). In EAE mice, frequencies of CD8+CXCR5+ T cells were also positively correlated with clinical scores. These cells were found to infiltrate into ectopic lymphoid-like structures in the spinal cords during the peak of the disease. Furthermore, CD8+CXCR5+ T cells, exhibiting high expression levels of ICOS, CD40L, IL-21, and IL-6, were shown to facilitate B cell activation and differentiation through a synergistic interaction between CD40L and IL-21. Transferring CD8+CXCR5+ T cells into naïve mice confirmed their ability to enhance the production of anti-MOG35-55 antibodies and contribute to the disease progression. Consequently, CD8+CXCR5+ T cells may play a role in CNS demyelination through heightening humoral immune responses.

Unveiling the molecular mechanisms of Dendrobium officinale polysaccharides on intestinal immunity: An integrated study of network pharmacology, molecular dynamics and in vivo experiments

Intestinal immunity plays a pivotal role in overall immunological defenses, constructing mechanisms against pathogens while maintaining balance with commensal microbial communities. Existing therapeutic interventions may lead to drug resistance and potential toxicity when immune capacity is compromised. Dendrobium officinale, a traditional Chinese medicine, contains components identified to bolster immunity. Employing network pharmacology strategies, this study identified constituents of Dendrobium officinale and their action targets in the TCMSP and Swiss Target Prediction databases, and compared them with intestinal immunity-related targets. Protein-protein interaction networks revealed the core targets of Dendrobium officinale polysaccharides, encompassing key pathways such as cell proliferation, inflammatory response, and immune reactions, particularly in association with the Toll-like receptor 4. Molecular docking and molecular dynamics simulation further confirmed the high affinity and stability between Dendrobium officinale polysaccharides and Toll-like receptor 4. In vivo experiments demonstrated that Dendrobium officinale polysaccharides modulates the expression of Toll-like receptor 4 and its downstream key proteins in the colonic mucosa of mice. Consequently, these findings suggest that Dendrobium officinale polysaccharides may serve as a potential modulator for intestinal immune functions, with its mechanism potentially related to the Toll-like receptor 4.

Identification of host immune-related biomarkers in active tuberculosis: A comprehensive analysis of differentially expressed genes

Tuberculosis (TB) is a serious public health issue in India. Numerous molecular mechanisms and immunological responses play significant roles in the pathogenesis of tuberculosis. This study aimed to identify host immune-related biomarkers that are significantly differentially expressed in active TB and that play a vital role in disease progression. The methodology employed in this study included data collection, pre-processing, analysis, and interpretation of the results. Six microarray datasets were used to identify differentially expressed genes (DEGs), and only the common DEGs were used for further downstream analysis, such as hub gene identification, gene ontology, pathway enrichment analysis, and drug-gene interaction analysis. The study identified 1728 DEGs, including 906 upregulated and 822 downregulated genes. Five hub genes were identified that were: STAT1, GBP5, GBP1, FCGR1A, and BATF2. Gene ontology and pathway enrichment revealed that most of the genes were involved in interferon-gamma signaling. In addition, through drug-gene interactions, known drugs have been identified for STAT1, FCGR1A and GBP1. The findings of this study may contribute to early detection and treatment of active TB.

USP9X inhibits metastasis in pulmonary sarcomatoid carcinoma by regulating epithelial-mesenchymal transition, angiogenesis and immune infiltration

BACKGROUND

Pulmonary sarcomatoid carcinoma (PSC) is a highly invasive pulmonary malignancy with an extremely poor prognosis. The results of previous studies suggest that ubiquitin-specific peptidase 9X (USP9X) contributes to the progression of numerous types of cancer. Nevertheless, there is little knowledge about the molecular mechanisms and functions of USP9X in the metastasis of PSC.

METHODS

Immunohistochemistry and western blotting were used to detect USP9X expression levels in PSC tissues and cells. Wound healing, transwell, enzyme-linked immunosorbent assay (ELISA), tube formation, and aortic ring assays were used to examine the function and mechanism of USP9X in the metastasis of PSC.

RESULTS

Expression of USP9X was markedly decreased and significantly correlated with metastasis and prognosis of patients with PSC. Then we revealed that USP9X protein levels were negatively associated with the levels of epithelial-mesenchymal transition (EMT) markers and the migration of PSC cells. It was confirmed that USP9X in PSC cells reduced VEGF secretion and inhibited tubule formation of human umbilical vein endothelial cells (HUVEC) in vitro. USP9X was detected to downregulate MMP9. Meanwhile, MMP9 was positively related to EMT, angiogenesis and was negatively related to immune infiltration in the public databases. USP9X was significantly negatively associated with the expression of MMP9, EMT markers, CD31, and positively associated with CD4, and CD8 in PSC tissues.

CONCLUSION

The present study reveals the vital role of USP9X in regulating EMT, angiogenesis and immune infiltration and inhibiting metastasis of PSC via downregulating MMP9, which provides a new effective therapeutic target for PSC.

Unlocking the Diagnostic Potential: A Systematic Review of Biomarkers in Spinal Tuberculosis

Background/Objectives: Spinal tuberculosis (STB) is frequently misdiagnosed due to the multitude of symptoms it presents with. This review aimed to investigate the biomarkers that have the potential to accurately diagnose spinal TB in its early stages. Methods: A systematic search was conducted across multiple databases, yielding a diverse range of biomarkers categorized into complete blood count parameters, host inflammatory responses, bacterial antigens, and RNA-based markers. This review included studies on spinal tuberculosis patients, including blood serum biomarkers, while exclusion criteria included pediatric cases, cerebrospinal fluid or imaging biomarkers, co-infection with other bacteria, viruses, comorbidities, tumors, immune diseases, HIV infection, metabolic disorders, animal studies, opinion papers, and biomarkers relevant to health problems outside the disease. QUADAS-2 was used as a quality assessment tool for this review. This review identifies several promising biomarkers with significant diagnostic potential. Results: The neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), IFN-γ, CXCR3, CXCL9, CXCL10, PSMB9, STAT1, TAP1, and specific miRNA combinations demonstrated noteworthy diagnostic accuracy in distinguishing STB from other spinal pathologies. Additionally, these biomarkers offer insights into disease severity and progression. The review also highlighted the importance of combining multiple biomarkers to enhance diagnostic precision. This comprehensive systematic review underscores the potential of biomarkers to revolutionize the diagnosis of spinal tuberculosis. By integrating these markers into clinical practice, healthcare providers can achieve earlier and more accurate diagnosis, leading to improved patient care and outcomes. Conclusions: The combination of multiple biomarkers, including NLR, PSMB9, STAT1, and specific miRNAs, demonstrates promising diagnostic accuracy.

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

ERK1/2-CEBPB Axis-Regulated hBD1 Enhances Anti-Tuberculosis Capacity in Alveolar Type II Epithelial Cells

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains a global health crisis with substantial morbidity and mortality rates. Type II alveolar epithelial cells (AEC-II) play a critical role in the pulmonary immune response against Mtb infection by secreting effector molecules such as antimicrobial peptides (AMPs). Here, human β-defensin 1 (hBD1), an important AMP produced by AEC-II, has been demonstrated to exert potent anti-tuberculosis activity. HBD1 overexpression effectively inhibited Mtb proliferation in AEC-II, while mice lacking hBD1 exhibited susceptibility to Mtb and increased lung tissue inflammation. Mechanistically, in A549 cells infected with Mtb, STAT1 negatively regulated hBD1 transcription, while CEBPB was the primary transcription factor upregulating hBD1 expression. Furthermore, we revealed that the ERK1/2 signaling pathway activated by Mtb infection led to CEBPB phosphorylation and nuclear translocation, which subsequently promoted hBD1 expression. Our findings suggest that the ERK1/2-CEBPB-hBD1 regulatory axis can be a potential therapeutic target for anti-tuberculosis therapy aimed at enhancing the immune response of AEC-II cells.

From simple to complex: Protein-based biomarker discovery in tuberculosis

Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.

Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis

Melanoma is one of the deadliest skin cancers. Recently, developed single-cell sequencing has revealed fresh insights into melanoma. Cytokine signaling in the immune system is crucial for tumor development in melanoma. To evaluate melanoma patient diagnosis and treatment, the prediction value of cytokine signaling in immune-related genes (CSIRGs) is needed. In this study, the machine learning method of least absolute selection and shrinkage operator (LASSO) regression was used to establish a CSIRG prognostic signature of melanoma at the single-cell level. We discovered a 5-CSIRG signature that was substantially related to the overall survival of melanoma patients. We also constructed a nomogram that combined CSIRGs and clinical features. Overall survival of melanoma patients can be consistently predicted with good performance as well as accuracy by both the 5-CSIRG signature and nomograms. We compared the melanoma patients in the CSIRG high- and low-risk groups in terms of tumor mutation burden, infiltration of the immune system, and gene enrichment. High CSIRG-risk patients had a lower tumor mutational burden than low CSIRG-risk patients. The CSIRG high-risk patients had a higher infiltration of monocytes. Signaling pathways including oxidative phosphorylation, DNA replication, and aminoacyl tRNA biosynthesis were enriched in the high-risk group. For the first time, we constructed and validated a machine-learning model by single-cell RNA-sequencing datasets that have the potential to be a novel treatment target and might serve as a prognostic biomarker panel for melanoma. The 5-CSIRG signature may assist in predicting melanoma patient prognosis, biological characteristics, and appropriate therapy.