Mitofusin 1-Mediated Redistribution of Mitochondrial Antiviral Signaling Protein Promotes Type 1 Interferon Response in Human Cytomegalovirus Infection.

One of the most potent anti-human cytomegalovirus (HCMV) immune mechanisms possessed by host cells is type I interferon (IFN1), which induces the expression of IFN-stimulated genes (ISGs). During this process, mitochondria play an important role in the IFN1 response, and mitofusin 1 (MFN1) is a key regulator of mitochondrial fusion located on the outer mitochondrial membrane. However, the underlying mechanism of MFN1's promotion of IFN1 during HCMV infection still remains unknown. In this study, HCMV infection promoted IFN1 production and enhanced ISG expression. Meanwhile, it promoted the increase of mitochondrial fusion in THP-1 cells and peripheral blood mononuclear cells (PBMCs), especially the expression of MFN1. Phosphorylation of tank binding kinase 1 (p-TBK1), interferon regulatory factor 3 (p-IRF3), and ISGs was significantly decreased in MFN1 or mitochondrial antiviral signaling protein (MAVS)-knockdown THP-1 cells, and MFN1 was constitutively associated with MAVS, positively regulated mitochondrial fusion, and IFN1 production. Knockdown of MFN1 inhibited the MAVS redistribution without affecting the MAVS expression, whereas the HCMV-induced IFN1 production decreased. Conversely, leflunomide could induce the expression of MFN1, thereby producing IFN1 and stimulating the expression of ISG in leflunomide-treated THP-1 cells. These observations reveal that HCMV infection leads to MFN1-mediated redistribution of MAVS and then induces an antiviral response of IFN1 and that the MFN-agonist leflunomide promotes IFN1 responses and may serve as a potential anti-HCMV therapy. IMPORTANCE Human cytomegalovirus (HCMV) infection is ubiquitous and is often asymptomatic in healthy individuals, but it can cause great damage to newborns, AIDS patients, and other immune deficiency patients. In this study, we found that HCMV infection caused mitochondrial fusion, and expression of mitofusin 1 (MFN1), which is a protein associated with mitochondrial antiviral signaling protein (MAVS), positively regulates mitochondrial fusion and HCMV-induced IFN1 response. Knockdown of MFN1 or MAVS can inhibit the HCMV-induced IFN1 production. What is more, confocal laser-scanning microscope showed that knockdown of MFN1 inhibits the HCMV-induced redistribution of MAVS. Conversely, MFN1 agonist leflunomide could induce IFN1 production. In conclusion, we provide new insight into the relationship between MFN1 and IFN1 during HCMV infection and show that MFN1 may serve as a potential strategy against HCMV infection.

Development and evaluation of machine learning models and nomogram for the prediction of severe acute pancreatitis.

BACKGROUND AND AIM: Severe acute pancreatitis (SAP) in patients progresses rapidly and can cause multiple organ failures associated with high mortality. We aimed to train a machine learning (ML) model and establish a nomogram that could identify SAP, early in the course of acute pancreatitis (AP). METHODS: In this retrospective study, 631 patients with AP were enrolled in the training cohort. For predicting SAP early, five supervised ML models were employed, such as random forest (RF), K-nearest neighbors (KNN), and naive Bayes (NB), which were evaluated by accuracy (ACC) and the areas under the receiver operating characteristic curve (AUC). The nomogram was established, and the predictive ability was assessed by the calibration curve and AUC. They were externally validated by an independent cohort of 109 patients with AP. RESULTS: In the training cohort, the AUC of RF, KNN, and NB models were 0.969, 0.954, and 0.951, respectively, while the AUC of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Ranson and Glasgow scores were only 0.796, 0.847, and 0.837, respectively. In the validation cohort, the RF model also showed the highest AUC, which was 0.961. The AUC for the nomogram was 0.888 and 0.955 in the training and validation cohort, respectively. CONCLUSIONS: Our findings suggested that the RF model exhibited the best predictive performance, and the nomogram provided a visual scoring model for clinical practice. Our models may serve as practical tools for facilitating personalized treatment options and improving clinical outcomes through pre-treatment stratification of patients with AP.

Human cytomegalovirus-induced immune regulation is correlated with poor prognosis in patients with colorectal cancer.

Evidence suggests that human cytomegalovirus (HCMV) infection may be implicated in the progression of colorectal cancer (CRC). However, the correlation between HCMV infection and survival outcomes in patients with CRC remains unclear. Here, we constructed a flow algorithm to identify HCMV sequences based on the RNA-seq data of patients with CRC derived from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The patients' clinical information matrix was used to calculate the Euclidean distance to filter out suitable patients not infected with HCMV, combined with patients' survival outcomes, to reveal how HCMV infection is involved in CRC progression. HCMV infection is widespread in patients with CRC, and the prevalence of HCMV infection ranges from 10 to 36% in four independent CRC datasets, with infection being concentrated in carcinoma tissue rather than in normal tissue. In addition, HCMV-positive patients had a poor survival prognosis, with three HCMV genes, UL82, UL42, and UL117, associated with poor patient survival outcomes. Most importantly, we suppose that the regulation of immune function by HCMV may be key to the poor prognosis of patients with CRC. We found that HCMV infection was associated with poor prognosis in CRC patients and identified three prognosis-associated HCMV genes. The regulation of immune function caused by HCMV infection was the key factor, while HCMV-positive patients with CRC mostly presented with a state of immunosuppression. This may provide new ideas for the personalized treatment of patients with CRC, especially with respect to immunotherapy.

Plasma Phage Load is Positively Related to the Immune Checkpoints in Patients Living with Human Immunodeficiency Virus.

BACKGROUND: Microbial Translocation (MT) and altered gut microbiota are involved in immune activation and inflammation, whereas immune checkpoint proteins play an important role in maintaining immune self-tolerance and preventing excessive immune activation. OBJECTIVE: This study aims to investigate the relationship between plasma phage load and immune homeostasis in people living with HIV(PLWH). METHODS: We recruited 15 antiretroviral therapy (ART)-naive patients, 23 ART-treated (AT) patients, and 34 Healthy Participants (HP) to explore the relationship between the plasma phage load and immune checkpoint proteins. The Deoxyribonucleic Acid (DNA) load of the lambda (λ) phage was detected using fluorescence quantitative Polymerase Chain Reaction (PCR). The Immune Checkpoints (ICPs) were detected using multiplex immunoassay. RESULTS: Our study demonstrated that the plasma phage load was increased in people living with HIV (PLWH) (P<0.05), but not in the ART-naive and AT groups (P>0.05). Plasma ICPs, including cluster of differentiation 27 (CD27), soluble glucocorticoid-induced Tumor Necrosis Factor (TNF) receptor (sGITR), soluble cluster of differentiation 80 (sCD80), sCD86, soluble glucocorticoidinduced TNF receptor-related ligand (sGITRL), soluble induced T-cell Costimulatory (sICOS), sCD40, soluble toll-like receptor 2 (sTLR2), and sCD28, were markedly decreased among the ARTnaive group (P<0.05) but not in the AT and HP groups (P>0.05). The plasma phage load was positively correlated with ICP and C-reactive protein (CRP) levels in PLWH (P<0.05). CONCLUSION: Our study indicated that the plasma phage load in PLWH was positively related to the expression of ICPs and inflammation, which may be used as a promising marker for the immune level of PLWH.

Single-Cell Transcriptome Reveals the Metabolic and Clinical Features of a Highly Malignant Cell Subpopulation in Pancreatic Ductal Adenocarcinoma.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a high mortality rate. PDAC exhibits significant heterogeneity as well as alterations in metabolic pathways that are associated with its malignant progression. In this study, we explored the metabolic and clinical features of a highly malignant subgroup of PDAC based on single-cell transcriptome technology. Methods: A highly malignant cell subpopulation was identified at single-cell resolution based on the expression of malignant genes. The metabolic landscape of different cell types was analyzed based on metabolic pathway gene sets. In vitro experiments to verify the biological functions of the marker genes were performed. PDAC patient subgroups with highly malignant cell subpopulations were distinguished according to five glycolytic marker genes. Five glycolytic highly malignant-related gene signatures were used to construct the glycolytic highly malignant-related genes signature (GHS) scores. Results: This study identified a highly malignant tumor cell subpopulation from the single-cell RNA sequencing (scRNA-seq) data. The analysis of the metabolic pathway revealed that highly malignant cells had an abnormally active metabolism, and enhanced glycolysis was a major metabolic feature. Five glycolytic marker genes that accounted for the highly malignant cell subpopulations were identified, namely, EN O 1, LDHA, PKM, PGK1, and PGM1. An in vitro cell experiment showed that proliferation rates of PANC-1 and CFPAC-1 cell lines decreased after knockdown of these five genes. Patients with metabolic profiles of highly malignant cell subpopulations exhibit clinical features of higher mortality, higher mutational burden, and immune deserts. The GHS score evaluated using the five marker genes was an independent prognostic factor for patients with PDAC. Conclusion: We revealed a subpopulation of highly malignant cells in PDAC with enhanced glycolysis as the main metabolic feature. We obtained five glycolytic marker gene signatures, which could be used to identify PDAC patient subgroups with highly malignant cell subpopulations, and proposed a GHS prognostic score.