Multi-omic analysis revealed the immunological patterns and diagnostic value of exhausted T cell-derived PTTG1 in patients with psoriasis.

BACKGROUND: Psoriasis, characterized by chronic inflammation, is a persistent skin condition that is notoriously challenging to manage and prone to relapse. Despite significant advancements in its treatment, many adverse reactions still occur. Therefore, exploring the mechanisms behind the occurrence and development of psoriasis is extremely important. METHODS: The weighted correlation network analysis (WGCNA) algorithm was used to identify phenotype-related genes in patients with psoriasis. We recruited clinical samples of patients with psoriasis, and used single-cell RNA sequencing (scRNA-seq) to visualize divergent genes and metabolisms of varied cells for the psoriasis. Various machine-learning methods were used to identify core genes, and molecular docking was used to analyze the stability of leptomycin B targeting pituitary tumor transforming 1 (PTTG1). Immunofluorescence (IHC) analysis, multiplex immunofluorescence (mIF) analysis, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to validate the results. RESULTS: Our results identified 1391 genes associated with the phenotype in patients with psoriasis and highlighted the significant alterations in T-cell functionality observed in the disease by WGCNA. There were nine distinct cellular clusters in psoriasis analyzed with the aid of scRNA-seq data. Each subtype of cell exhibited distinct genetic profiles, functional roles, signaling mechanisms, and metabolic characteristics. Machine-learning methods further demonstrated the potential diagnostic value of T cell-derived PTTG1 and its relationship with T-cell exhaustion in psoriasis. Lastly, the leptomycin B was scrutinized and verified had high stability targeting PTTG1. CONCLUSIONS: This study elucidates the biological basis of psoriasis. At the same time, it was discovered that PTTG1 derived from exhausted T cells serves as a diagnostic biomarker for psoriasis. Leptomycin B could be a potential drug for targeted treatment of psoriasis on PTTG1.

Semaglutide mitigates testicular damage in diabetes by inhibiting ferroptosis.

Diabetes is linked to male infertility, but the mechanisms and therapeutic options remain unclear. This study investigates the effects of semaglutide on testicular function in a diabetes mouse model. Clinical data shows that diabetes affects blood glucose, lipid levels, and sperm quality. Single-cell and transcriptome analyses reveal changes in testicular tissue cell proportions and activation of ferroptosis pathways in diabetic patients/rats. In the diabetes mouse model, sperm quality decreases significantly. Treatment with semaglutide (Sem) and the ferroptosis inhibitor ferrostatin-1 (Fer-1) alleviates testicular damage, as evidenced by improved lipid peroxidation and ferroptosis markers. Moreover, the diabetes-induced decrease in the TM-3 cell line's vitality, increased lipid peroxidation, ROS, ferrous ions, and mitochondrial membrane potential damage are all improved by semaglutide and ferrostatin-1 intervention. Overall, these findings highlight semaglutide's potential as a therapeutic approach for mitigating diabetes-induced testicular damage through modulation of the ferroptosis pathway.

Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis.

BACKGROUND: Although significant advances have been made in intensive care medicine and antibacterial treatment, sepsis is still a common disease with high mortality. The condition of sepsis patients changes rapidly, and each hour of delay in the administration of appropriate antibiotic treatment can lead to a 4-7% increase in fatality. Therefore, early diagnosis and intervention may help improve the prognosis of patients with sepsis. METHODS: We obtained single-cell sequencing data from 12 patients. This included 14,622 cells from four patients with bacterial infectious sepsis and eight patients with sepsis admitted to the ICU for other various reasons. Monocyte differentiation trajectories were analyzed using the "monocle" software, and differentiation-related genes were identified. Based on the expression of differentiation-related genes, 99 machine-learning combinations of prognostic signatures were obtained, and risk scores were calculated for all patients. The "scissor" software was used to associate high-risk and low-risk patients with individual cells. The "cellchat" software was used to demonstrate the regulatory relationships between high-risk and low-risk cells in a cellular communication network. The diagnostic value and prognostic predictive value of Enah/Vasp-like (EVL) were determined. Clinical validation of the results was performed with 40 samples. The "CBNplot" software based on Bayesian network inference was used to construct EVL regulatory networks. RESULTS: We systematically analyzed three cell states during monocyte differentiation. The differential analysis identified 166 monocyte differentiation-related genes. Among the 99 machine-learning combinations of prognostic signatures constructed, the Lasso + CoxBoost signature with 17 genes showed the best prognostic prediction performance. The highest percentage of high-risk cells was found in state one. Cell communication analysis demonstrated regulatory networks between high-risk and low-risk cell subpopulations and other immune cells. We then determined the diagnostic and prognostic value of EVL stabilization in multiple external datasets. Experiments with clinical samples demonstrated the accuracy of this analysis. Finally, Bayesian network inference revealed potential network mechanisms of EVL regulation. CONCLUSIONS: Monocyte differentiation-related prognostic signatures based on the Lasso + CoxBoost combination were able to accurately predict the prognostic status of patients with sepsis. In addition, low EVL expression was associated with poor prognosis in sepsis.

Activation of the Chemokine Receptor CCR1 and Preferential Recruitment of Gαi Suppress RSV Replication: Implications for Developing Novel Respiratory Syncytial Virus Treatment Strategies.

Respiratory syncytial virus (RSV) is a major pathogen that can cause acute respiratory infectious diseases of the upper and lower respiratory tract, especially in children, elderly individuals, and immunocompromised people. Generally, following viral infection, respiratory epithelial cells secrete cytokines and chemokines to recruit immune cells and initiate innate and/or adaptive immune responses. However, whether chemokines affect viral replication in nonimmune cells is rarely clear. In this study, we detected that chemokine CCL5 was highly expressed, while expression of its receptor, CCR1, was downregulated in respiratory epithelial cells after RSV infection. When we overexpressed CCR1 on respiratory epithelial cells in vivo or in vitro, viral load was significantly suppressed, which can be restored by the neutralizing antibody for CCR1. Interestingly, the antiviral effect of CCR1 was not related to type I interferon (IFN-I), apoptosis induction, or viral adhesion or entry inhibition. In contrast, it was related to the preferential recruitment and activation of the adaptor Gαi, which promoted inositol 1,4,5-triphosphate receptor type 3 (ITPR3) expression, leading to inhibited STAT3 phosphorylation; explicitly, phosphorylated STAT3 (p-STAT3) was verified to be among the important factors regulating the activity of HSP90, which has been previously reported to be a chaperone of RSV RNA polymerase. In summary, we are the first to reveal that CCR1 on the surface of nonimmune cells regulates RSV replication through a previously unknown mechanism that does not involve IFN-I induction. IMPORTANCE Our results revealed a novel mechanism by which RSV escapes innate immunity. That is, although it induces high CCL5 expression, RSV might attenuate the binding of CCL5 by downregulating the expression of CCR1 in respiratory epithelial cells to weaken the inhibitory effect of CCR1 on HSP90 activity and thereby facilitate RSV replication in nonimmune cells. This study provides a new target for the development of co-antiviral inhibitors against other components of the host and co-molecular chaperone/HSP90 and provides a scientific basis for the search for effective broad-spectrum antiviral drugs.

Identification of immune patterns in idiopathic pulmonary fibrosis patients driven by PLA2G7-positive macrophages using an integrated machine learning survival framework.

Patients with advanced idiopathic pulmonary fibrosis (IPF), a complex and incurable lung disease with an elusive pathology, are nearly exclusive candidates for lung transplantation. Improved identification of patient subtypes can enhance early diagnosis and intervention, ultimately leading to better prognostic outcomes for patients. The goal of this study is to identify new immune patterns and biomarkers in patients. Immune subtypes in IPF patients were identified using single-sample gene set enrichment analysis, and immune subtype-related genes were explored using the weighted correlation network analysis algorithm. A machine learning integration framework was used to establish the optimal prognostic model, known as the immune-related risk score (IRS). Single-cell sequencing was conducted to investigate the major role of macrophage-derived PLA2G7 in the immune microenvironment. We assessed the stability of celecoxib in targeting PLA2G7 through molecular docking and surface plasmon resonance. IPF patients present two distinct immune subtypes, one characterized by immune activation and inflammation, and the other by immune suppression. IRS can predict the immune status and prognosis of IPF patients. Furthermore, multi-cohort analysis and single-cell sequencing analysis demonstrated the diagnostic and prognostic value of PLA2G7 derived from macrophages and its role in shaping the inflammatory immune microenvironment in IPF patients. Celecoxib could effectively and stably bind with PLA2G7. PLA2G7, as identified through IRS, demonstrates marked stability in diagnosing and predicting the prognosis of IPF patients as well as predicting their immune status. It can serve as a novel biomarker for IPF patients.

Single-cell sequencing and multiple machine learning algorithms to identify key T-cell differentiation gene for progression of NAFLD cirrhosis to hepatocellular carcinoma.

Introduction: Hepatocellular carcinoma (HCC), which is closely associated with chronicinflammation, is the most common liver cancer and primarily involves dysregulated immune responses in the precancerous microenvironment. Currently, most studies have been limited to HCC incidence. However, the immunopathogenic mechanisms underlying precancerous lesions remain unknown. Methods: We obtained single-cell sequencing data (GSE136103) from two nonalcoholic fatty liver disease (NAFLD) cirrhosis samples and five healthy samples. Using pseudo-time analysis, we systematically identified five different T-cell differentiation states. Ten machine-learning algorithms were used in 81 combinations to integrate the frameworks and establish the best T-cell differentiation-related prognostic signature in a multi-cohort bulk transcriptome analysis. Results: LDHA was considered a core gene, and the results were validated using multiple external datasets. In addition, we validated LDHA expression using immunohistochemistry and flow cytometry. Conclusion: LDHA is a crucial marker gene in T cells for the progression of NAFLD cirrhosis to HCC.

Long noncoding RNA Malat1 protects against osteoporosis and bone metastasis.

MALAT1, one of the few highly conserved nuclear long noncoding RNAs (lncRNAs), is abundantly expressed in normal tissues. Previously, targeted inactivation and genetic rescue experiments identified MALAT1 as a suppressor of breast cancer lung metastasis. On the other hand, Malat1-knockout mice are viable and develop normally. On a quest to discover the fundamental roles of MALAT1 in physiological and pathological processes, we find that this lncRNA is downregulated during osteoclastogenesis in humans and mice. Remarkably, Malat1 deficiency in mice promotes osteoporosis and bone metastasis of melanoma and mammary tumor cells, which can be rescued by genetic add-back of Malat1. Mechanistically, Malat1 binds to Tead3 protein, a macrophage-osteoclast-specific Tead family member, blocking Tead3 from binding and activating Nfatc1, a master regulator of osteoclastogenesis, which results in the inhibition of Nfatc1-mediated gene transcription and osteoclast differentiation. Notably, single-cell transcriptome analysis of clinical bone samples reveals that reduced MALAT1 expression in pre-osteoclasts and osteoclasts is associated with osteoporosis and metastatic bone lesions. Altogether, these findings identify Malat1 as a lncRNA that protects against osteoporosis and bone metastasis.

Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5.

Sepsis is a disease with a very high mortality rate, mainly involving an immune-dysregulated response due to bacterial infection. Most studies are currently limited to the whole blood transcriptome level; however, at the single cell level, there is still a great deal unknown about specific cell subsets and disease markers. We obtained 29 peripheral blood single-cell sequencing data, including 66,283 cells from 10 confirmed samples of sepsis infection and 19 healthy samples. Cells related to the sepsis phenotype were identified and characterized by the "scissor" method. The regulatory relationships of sepsis-related phenotype cells in the cellular communication network were clarified using the "cell chat" method. The least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) were used to identify sepsis signature genes of diagnostic value. External validation was performed using multiple datasets from the GEO database (GSE28750, GSE185263, GSE57065) and 40 clinical samples. Bayesian algorithm was used to calculate the regulatory network of LILRA5 co-expressed genes. The stability of atenolol-targeting LILRA5 was determined by molecular docking techniques. Ultimately, action trajectory and survival analyses demonstrate the effectiveness of atenolol-targeted LILRA5 in treating patients with sepsis. We successfully identified 1215 healthy phenotypic cells and 462 sepsis phenotypic cells. We focused on 447 monocytes of the sepsis phenotype. Among the cellular communications, there were a large number of differences between these cells and other immune cells showing a significant inflammatory phenotype compared to the healthy phenotypic cells. Together, the three machine learning algorithms identified the LILRA5 marker gene in sepsis patients, and validation results from multiple external datasets as well as real-world clinical samples demonstrated the robust diagnostic performance of LILRA5. The AUC values of LILRA5 in the external datasets GSE28750, GSE185263, and GSE57065 could reach 0.875, 0.940, and 0.980, in that order. Bayesian networks identified a large number of unknown regulatory relationships for LILRA5 co-expression. Molecular docking results demonstrated the possibility of atenolol targeting LILRA5 for the treatment of sepsis. Behavioral trajectory analysis and survival analysis demonstrate that atenolol has a desirable therapeutic effect. LILRA5 is a marker gene in sepsis patients, and atenolol can stably target LILRA5.

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer.

Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

Quantitative determination of d-psicose based on flexible copper film materials and electrochemical scanning methods.

In this paper, a three-electrode electrochemical detection system was designed. Platinum electrode was used as the counter electrode, saturated KCl electrode as reference electrode, and copper film material as working electrode, respectively. Cyclic voltammetry (CV) and chronoamperometry (i-t) methods were used for d-psicose scanning. CV results indicated that d-psicose presented the oxidation-reduction reacting procedure on the surface of copper film electrode. Testing parameters optimization was conducted using CV scanning in different scanning rates. d-psicose quantitative determination model was developed by i-t scanning results. The sensitivity was9419.1A×cm-2·mol/L, the detection limit was1.04311×10-8mol/L. The proposed method has some advantages including high sensitivity, low detection line, and fast response speed. Negative control testing results by using glucose, sucrose, and NaCl solutions demonstrated that the proposed method had good selectivity.

Coupling thermophilic composting and vermicomposting processes to remove Cr from biogas residues and produce high value-added biofertilizers.

Removing pollutants and producing high value-added products are essential steps for sustainable disposal and utilization of biogas residues. Here, a coupled thermophilic composting and vermicomposting process was used to remove Cr from biogas residues, and the composting products were co-fermented with the plant growth-promoting fungus Trichoderma to produce high value-added biofertilizers. The results showed that thermophilic composting for 37 d markedly increased the total content of Cr but decreased the percentage of available Cr fractions. Synchrotron-radiation-based observations further provided direct evidence of the binding sites to support the results from traditional sequential extraction. At a density of 60 g earthworm/kg biogas residues, vermicomposting removed 23-31% of Cr from biogas residues. After vermicomposting, co-fermentation of biogas residues and Trichoderma was optimized, in which Trichoderma spores were 2-5 × 108 cfu/g substrates. Together, coupling thermophilic composting and vermicomposting processes is a promising technique to remove a portion of heavy metals from biogas residues.

Biological removal of cadmium from biogas residues during vermicomposting, and the effect of earthworm hydrolysates on Trichoderma guizhouense sporulation.

The removal efficiency of Cd from biogas residues (BR) by earthworms (Eisenia fetida) during vermicomposting and the optimum addition of earthworm hydrolysates for production of Trichoderma guizhouense NJAU 4742 spores were determined. The results showed that vermicomposting could effectively remove Cd (up to 18.9%) from the BR. Synchrotron radiation based FTIR spectromicroscopy demonstrated a weakened correlation between functional groups after vermicomposting, suggesting that the activity of earthworms affects the binding sites and bioavailability of heavy metals. Under optimum conditions, the hydrolysis rate of earthworms was ~97% and the removal efficiency of Cd was up to 93%. Furthermore, addition of 20% of earthworm hydrolysate promoted the largest production of Trichoderma sporulation (~2.95 × 108 cfu/g straw), indicating the possibility of earthworm hydrolysates promoting the growth of Trichoderma guizhouense is a suitable way to recycle earthworms after vermicomposting.