Cancer SLC6A6-mediated taurine uptake transactivates immune checkpoint genes and induces exhaustion in CD8+ T cells.

Taurine is used to bolster immunity, but its effects on antitumor immunity are unclear. Here, we report that cancer-related taurine consumption causes T cell exhaustion and tumor progression. The taurine transporter SLC6A6 is correlated with aggressiveness and poor outcomes in multiple cancers. SLC6A6-mediated taurine uptake promotes the malignant behaviors of tumor cells but also increases the survival and effector function of CD8+ T cells. Tumor cells outcompete CD8+ T cells for taurine by overexpressing SLC6A6, which induces T cell death and malfunction, thereby fueling tumor progression. Mechanistically, taurine deficiency in CD8+ T cells increases ER stress, promoting ATF4 transcription in a PERK-JAK1-STAT3 signaling-dependent manner. Increased ATF4 transactivates multiple immune checkpoint genes and induces T cell exhaustion. In gastric cancer, we identify a chemotherapy-induced SP1-SLC6A6 regulatory axis. Our findings suggest that tumoral-SLC6A6-mediated taurine deficiency promotes immune evasion and that taurine supplementation reinvigorates exhausted CD8+ T cells and increases the efficacy of cancer therapies.

A dual-process of targeted and unbiased Nanopore sequencing enables accurate and rapid diagnosis of lower respiratory infections.

BACKGROUND: Nanopore metagenomics has been used for infectious disease diagnosis for bacterial pathogens. However, this technology currently lacks comprehensive performance studies in clinical settings for simultaneous detection of bacteria, fungi, and viruses. METHODS: We developed a dual-process of Nanopore sequencing for one sample, with unbiased metagenomics in Meta process and target enrichment in Panel process (Nanopore Meta-Panel process, NanoMP) and prospectively enrolled 450 respiratory specimens from multiple centers. The filter system of pathogen detection was established with machine learning and receiver operator characteristic (ROC) curve to optimize the detection accuracy based on orthogonal test of 21 species. Antimicrobial resistance (AMR) genes were identified based on the Comprehensive Antibiotic Resistance Database (CARD) and single-nucleotide polymorphism matrix. FINDINGS: Our approach showed high sensitivity in Meta process, with 82.9%, 88.7%, and 75.0% for bacteria, fungi (except Aspergillus), and Mycobacterium tuberculosis groups, respectively. Moreover, target amplification improved the sensitivity of virus (>80.0% vs. 39.4%) and Aspergillus (81.8% vs. 42.3%) groups in Panel process compared with Meta process. Overall, NanoMP achieved 80.2% sensitivity and 98.8% specificity compared with the composite reference standard, and we were able to accurately detect AMR genes including blaKPC-2, blaOXA-23 and mecA and distinguish their parent organisms in patients with mixed infections. INTERPRETATION: We combined metagenomic and enriched Nanopore sequencing for one sample in parallel. Our NanoMP approach simultaneously covered bacteria, viruses and fungi in respiratory specimens and demonstrated good diagnostic performance in real clinical settings. FUNDING: National Key Research and Development Program of China and National Natural Science Foundation of China.

Altered Respiratory Microbiomes, Plasma Metabolites, and Immune Responses in Influenza A Virus and Methicillin-Resistant Staphylococcus aureus Coinfection.

Influenza A virus (IAV)-methicillin-resistant Staphylococcus aureus (MRSA) coinfection causes severe respiratory infections. The host microbiome plays an important role in respiratory tract infections. However, the relationships among the immune responses, metabolic characteristics, and respiratory microbial characteristics of IAV-MRSA coinfection have not been fully studied. We used specific-pathogen-free (SPF) C57BL/6N mice infected with IAV and MRSA to build a nonlethal model of IAV-MRSA coinfection and characterized the upper respiratory tract (URT) and lower respiratory tract (LRT) microbiomes at 4 and 13 days postinfection by full-length 16S rRNA gene sequencing. Immune response and plasma metabolism profile analyses were performed at 4 days postinfection by flow cytometry and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The relationships among the LRT microbiota, the immune response, and the plasma metabolism profile were analyzed by Spearman's correlation analysis. IAV-MRSA coinfection showed significant weight loss and lung injury and significantly increased loads of IAV and MRSA in bronchoalveolar lavage fluid (BALF). Microbiome data showed that coinfection significantly increased the relative abundances of Enterococcus faecalis, Enterobacter hormaechei, Citrobacter freundii, and Klebsiella pneumoniae and decreased the relative abundances of Lactobacillus reuteri and Lactobacillus murinus. The percentages of CD4+/CD8+ T cells and B cells in the spleen; the levels of interleukin-9 (IL-9), interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), IL-6, and IL-8 in the lung; and the level of mevalonolactone in plasma were increased in IAV-MRSA-coinfected mice. L. murinus was positively correlated with lung macrophages and natural killer (NK) cells, negatively correlated with spleen B cells and CD4+/CD8+ T cells, and correlated with multiple plasma metabolites. Future research is needed to clarify whether L. murinus mediates or alters the severity of IAV-MRSA coinfection. IMPORTANCE The respiratory microbiome plays an important role in respiratory tract infections. In this study, we characterized the URT and LRT microbiota, the host immune response, and plasma metabolic profiles during IAV-MRSA coinfection and evaluated their correlations. We observed that IAV-MRSA coinfection induced severe lung injury and dysregulated host immunity and plasma metabolic profiles, as evidenced by the aggravation of lung pathological damage, the reduction of innate immune cells, the strong adaptation of the immune response, and the upregulation of mevalonolactone in plasma. L. murinus was strongly correlated with immune cells and plasma metabolites. Our findings contribute to a better understanding of the role of the host microbiome in respiratory tract infections and identified a key bacterial species, L. murinus, that may provide important references for the development of probiotic therapies.

Circulating cell-free DNA sequencing for early detection of lung cancer.

INTRODUCTION: Lung cancer is a leading cause of death in patients with cancer. Early diagnosis is crucial to improve the prognosis of patients with lung cancer. Plasma circulating cell-free DNA (cfDNA) contains comprehensive genetic and epigenetic information from tissues throughout the body, suggesting that early detection of lung cancer can be done non-invasively, conveniently, and cost-effectively using high-sensitivity techniques such as sequencing. AREAS COVERED: In this review, we summarize the latest technological innovations, coupled with next-generation sequencing (NGS), regarding genomic alterations, methylation, and fragmentomic features of cfDNA for the early detection of lung cancer, as well as their clinical advances. Additionally, we discuss the suitability of study designs for diagnostic accuracy evaluation for different target populations and clinical questions. EXPERT OPINION: Currently, cfDNA-based early screening and diagnosis of lung cancer faces many challenges, such as unsatisfactory performance, lack of quality control standards, and poor repeatability. However, the progress of several large prospective studies employing epigenetic features has shown promising predictive performance, which has inspired cfDNA sequencing for future clinical applications. Furthermore, the development of multi-omics markers for lung cancer, including genome-wide methylation and fragmentomics, is expected to play an increasingly important role in the future.

Topography of respiratory tract and gut microbiota in mice with influenza A virus infection.

Introduction: Influenza A virus (IAV)-induced dysbiosis may predispose to severe bacterial superinfections. Most studies have focused on the microbiota of single mucosal surfaces; consequently, the relationships between microbiota at different anatomic sites in IAV-infected mice have not been fully studied. Methods: We characterized respiratory and gut microbiota using full-length 16S rRNA gene sequencing by Nanopore sequencers and compared the nasopharyngeal, oropharyngeal, lung and gut microbiomes in healthy and IAV-infected mice. Results: The oropharyngeal, lung and gut microbiota of healthy mice were dominated by Lactobacillus spp., while nasopharyngeal microbiota were comprised primarily of Streptococcus spp. However, the oropharyngeal, nasopharyngeal, lung, and gut microbiota of IAV-infected mice were dominated by Pseudomonas, Escherichia, Streptococcus, and Muribaculum spp., respectively. Lactobacillus murinus was identified as a biomarker and was reduced at all sites in IAV-infected mice. The microbiota composition of lung was more similar to that of the nasopharynx than the oropharynx in healthy mice. Discussion: These findings suggest that the main source of lung microbiota in mice differs from that of adults. Moreover, the similarity between the nasopharyngeal and lung microbiota was increased in IAV-infected mice. We found that IAV infection reduced the similarity between the gut and oropharyngeal microbiota. L. murinus was identified as a biomarker of IAV infection and may be an important target for intervention in post-influenza bacterial superinfections.

Endotypes of chronic rhinosinusitis based on inflammatory and remodeling factors.

BACKGROUND: Previous studies on the endotyping of chronic rhinosinusitis (CRS) that were based on inflammatory factors have broadened our understanding of the disease. However, the endotype of CRS combined with inflammatory and remodeling features has not yet been clearly elucidated. OBJECTIVE: We sought to identify the endotypes of patients with CRS according to inflammatory and remodeling factors. METHODS: Forty-eight inflammatory and remodeling factors in the nasal mucosal tissues of 128 CRS patients and 24 control subjects from northern China were analyzed by Luminex, ELISA, and ImmunoCAP. Sixteen factors were used to perform the cluster analysis. The characteristics of each cluster were analyzed using correlation analysis and validated by immunofluorescence staining. RESULTS: Patients were classified into 5 clusters. Clusters 1 and 2 showed non-type 2 signatures with low biomarker concentrations, except for IL-19 and IL-27. Cluster 3 involved a low type 2 endotype with the highest expression of neutrophil factors, such as granulocyte colony-stimulating factor, IL-8, and myeloperoxidase, and remodeling factors, such as matrix metalloproteinases and fibronectin. Cluster 4 exhibited moderate type 2 inflammation. Cluster 5 exhibited high type 2 inflammation, which was associated with relatively higher levels of neutrophil and remodeling factors. The proportion of CRS with nasal polyps, asthma, allergies, anosmia, aspirin sensitivity, and the recurrence of CRS increased from clusters 1 to 5. CONCLUSION: Diverse inflammatory mechanisms result in distinct CRS endotypes and remodeling profiles. The explicit differentiation and accurate description of these endotypes will guide targeted treatment decisions.

Molecular Epidemiology and Antifungal Resistance of Cryptococcus neoformans From Human Immunodeficiency Virus-Negative and Human Immunodeficiency Virus-Positive Patients in Eastern China.

Cryptococcosis is an opportunistic and potentially lethal infection caused by Cryptococcus neoformans and Cryptococcus gattii complex, which affects both immunocompromised and immunocompetent people, and it has become a major public health concern worldwide. In this study, we characterized the molecular epidemiology and antifungal susceptibility of 133 C. neoformans isolates from East China Invasive Fungal Infection Group (ECIFIG), 2017-2020. Isolates were identified to species level by matrix-assisted laser desorption ionization-time of flight mass spectrometry and confirmed by IGS1 sequencing. Whole-genome sequencing (WGS) was conducted on three multidrug-resistant isolates. Among the 133 strains, 61 (45.86%) were isolated from HIV-positive patients and 72 (54.16%) were isolated from HIV-negative patients. In total, C. neoformans var. grubii accounted for 97.74% (130/133), while C. neoformans var. neoformans was rare (2.06%, 3/133). The strains were further classified into nine sequence types (STs) dominated by ST5 (90.23%, 120/133) with low genetic diversity. No association was observed between STs and HIV status. All strains were wild type to voriconazole, while high antifungal minimal inhibitory concentrations (MICs) above the epidemiological cutoff values (ECVs) were observed in C. neoformans strains, and more than half of isolates were non-wild-type to amphotericin B (89.15%, 109/133). Eight isolates were resistant to fluconazole, and eight isolates were non-wild type to 5-fluorocytosine. Furthermore, WGS has verified the novel mutations of FUR1 in 5-fluorocytosine-resistant strains. In one isolate, aneuploidy of chromosome 1 with G484S mutation of ERG11 was observed, inducing high-level resistance (MIC: 32 µg/ml) to fluconazole. In general, our data showed that there was no significant difference between HIV-positive and HIV-negative patients on STs, and we elucidate the resistant mechanisms of C. neoformans from different perspectives. It is important for clinical therapy and drug usage in the future.

Development and validation of a blood-based genomic mutation signature to predict the clinical outcomes of atezolizumab therapy in NSCLC.

OBJECTIVES: We designed this study to develop a blood-based genomic mutation signature (bGMS) model for predicting the efficacy of atezolizumab therapy in non-small cell lung cancer (NSCLC) in a non-invasive manner. MATERIALS AND METHODS: Patients with NSCLC treated with atezolizumab from POPLAR and OAK clinical trials were included in our study. OAK cohort was defined as the training group, and POPLAR cohort was defined as the validation group. LASSO Cox regressions were applied to the training group to develop the gene mutation signature model to predict the overall survival (OS). Then the model was validated in the validation group. The combined impact of bGMS and other factors was explored with multivariable Cox regression. RESULTS: A bGMS risk model including 15 genes was established to classify patients into high-bGMS and low-bGMS groups. High-bGMS patients had shorter overall survival (OS) and progression-free survival (PFS) compared with low-bGMS in both training cohort (OS 7.9 vs. 19.9 months, p < 0.0001; PFS 1.7 vs. 4 months, p = 0.011) and validation cohort (OS 8.4 vs. 18.6 months, p = 0.0019; PFS 1.5 vs. 4.4 months, p = 0.013). The bGMS was superior to the blood tumor mutation burden (bTMB), LAF-bTMB, MSAF, PD-L1 expression, and a 5-genomic mutation signature in predicting OS for patients receiving atezolizumab. In addition, low-bGMS patients receiving atezolizumab therapy had a better OS rate compared with those receiving docetaxel therapy in both training (P < 0.0001) and validation groups (P = 0.018). Multivariate Cox regression analysis showed that bGMS was an independent prognostic factor on OS and PFS for patients receiving atezolizumab. Furthermore, a nomogram was developed to combine bGMS with the clinical characteristics to improve the predictive power further. CONCLUSION: bGMS could predict OS benefit for patients with NSCLC receiving atezolizumab therapy. BGMS and other non-invasive clinical characteristics can be combined to develop a more accurate model.

Risk assessment of type 2 diabetes in northern China based on the logistic regression model.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a complex disease with high incidence and serious harm associated with polygenic determination. This study aimed to develop a predictive model so as to assess the risk of T2DM and apply it to health care and disease prevention in northern China. OBJECTIVE: Based on genotyping results, a risk warning model for type 2 diabetes was established. METHODS: Blood samples of 1042 patients with T2DM in northern China were collected. Multiplex polymerase chain reaction and high-throughput sequencing (NGS) techniques were used to design the amplification-based targeted sequencing panel to sequence the 21 T2DM susceptibility genes. RESULT: The related key gene KQT-like subfamily member 1 played an important role in the T2DM risk model, and single-nucleotide polymorphism rs2237892 was highly significant, with a P value of 1.2 × 10-5. CONCLUSIONS: Susceptibility genes in different populations were examined, and a model was developed to assess the risk-based genetic analysis. The performance of the model reached 92.8%.

Online reaction based single-step capillary electrophoresis-systematic evolution of ligands by exponential enrichment for ssDNA aptamers selection.

Capillary electrophoresis-systematic evolution of ligands by exponential enrichment (CE-SELEX) has proven to be an effective technique for aptamers selection. In this study, we present an online reaction based convenient single-step CE-SELEX (ssCE-SELEX) mode with human thrombin (H-Thr) as a model target. The selection progress was monitored through bulk Kd analysis, which showed more than a 1000-fold improvement over the initial library after two rounds of selection. Three selected candidate sequences presented high binding affinities against H-Thr with nanomolar (nM) Kd determined by nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM, 56.4-177.1 nM) and CE based non-linear fitting (CE-NLF, 98.2-199.7 nM). They also exhibited high specificities towards H-Thr compared with bovine thrombin, IgG, lysozyme, and lactoferrin. Meanwhile, the Kd results by isothermal titration calorimetry (ITC) confirmed the effective CE in measuring the aptamer affinity. In addition, three candidates were applied as aptasensors in the AuNPs based colorimetric assay, which showed visible color change and good linear relationships (R2 > 0.93) with H-Thr concentration. Furthermore, molecular dynamics (MD) simulation was performed to validate the binding of the three candidates with H-Thr by binding sites and binding free energy. The ssCE-SELEX method avoids off-line incubation, saves time and sample, and may provide a universal and convenient method for aptamers selection.

Retinoic acid receptor ß, a potential therapeutic target in the inhibition of adenovirus replication.

Human adenoviruses (HAdVs) usually cause mild respiratory infections, but they can also lead to fatal outcomes for immunosuppressive patients. Unfortunately, there has been no specific anti-HAdV drug approved for medical use. A better understanding of the nature of virus-host interactions during infection is beneficial to the discovery of potential antiviral targets and new antiviral drugs. In this study, a time-course transcriptome analysis of HAdV-infected human lung epithelial cells (A549 cells) was performed to investigate virus-host interactions, and several key host molecules involved in the HAdV infection process were identified. The RARß (retinoic acid receptor ß) molecule, one of the upstream regulatory factors of differentially expressed genes (DEGs), played important roles in HAdV replication. The results of reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting showed that RARß mRNA and protein were downregulated by HAdV infection in the A549 cells. The knockdown of RARß by RARß siRNA increased the HAdV production and the overexpression of RARß decreased the HAdV production. Furthermore, FDA-approved Tazarotene, which is an RAR selective agonist with relatively more selectivity for RARß, was found to inhibit HAdV replication in vitro. Taken together, our study presents a key host molecule in adenovirus infection, which could be developed as a potential host target to an anti-adenovirus drug. In addition, this study provides evidence for the re-exploitation of an FDA-approved small molecule for therapeutic applications in adenovirus replication.

Risk-Predicting Model for Incident of Essential Hypertension Based on Environmental and Genetic Factors with Support Vector Machine.

Essential hypertension (EH) has become a major chronic disease around the world. To build a risk-predicting model for EH can help to interpose people's lifestyle and dietary habit to decrease the risk of getting EH. In this study, we constructed a EH risk-predicting model considering both environmental and genetic factors with support vector machine (SVM). The data were collected through Epidemiological investigation questionnaire from Beijing Chinese Han population. After data cleaning, we finally selected 9 environmental factors and 12 genetic factors to construct the predicting model based on 1200 samples, including 559 essential hypertension patients and 641 controls. Using radial basis kernel function, predictive accuracy via SVM with function with only environmental factor and only genetic factor were 72.8 and 54.4%, respectively; after considering both environmental and genetic factor the accuracy improved to 76.3%. Using the model via SVM with Laplacian function, the accuracy with only environmental factor and only genetic factor were 76.9 and 57.7%, respectively; after combining environmental and genetic factor, the accuracy improved to 80.1%. The predictive accuracy of SVM model constructed based on Laplacian function was higher than radial basis kernel function, as well as sensitivity and specificity, which were 63.3 and 86.7%, respectively. In conclusion, the model based on SVM with Laplacian kernel function had better performance in predicting risk of hypertension. And SVM model considering both environmental and genetic factors had better performance than the model with environmental or genetic factors only.

An Integrative Analysis of the Putative Gefitinib-resistance Related Genes in a Lung Cancer Cell Line Model System.

The epidermal growth factor receptor-tyrosine kinase inhibitors (EGFRTKI), such as gefitinib and erlotinib have improved the survival of patients with nonsmall cell lung cancer (NSCLC). Unfortunately, acquired resistance will eventually develop in most patients who initially respond to the therapy. Currently known molecular mechanisms for such an acquired resistance may interpret only about 70% of clinical cases. In this study, using NSCLC cell model H1650, we constructed a gefitinib resistant cell line H1650GR through long term drug exposure with increased doses. RNA sequencing and whole genome SNP array were applied to investigate the transcriptome and genome alterations possibly involved in gefitinib resistance. By comparing the expression profiles between H1650GR and H1650 cells, we identified a large set of differentially expressed genes (DEGs), including FOXM1. In the PI3K/AKT pathway, AKT activity was predicted to be inhibited. However, genes that play important roles in gefitinib-induced apoptosis, including TP53, FOXO3 and BAD, were not up-regulated. Ingenuity Pathway Analysis (IPA) canonical pathway analysis showed that p53 signaling was inhibited in H1650GR cells, with down-regulation of pro-apoptosis genes FAS, PUMA, NOXA, and upregulation of anti-apoptosis genes BIRC5/Survivin. Besides, a large number of immune response-related genes were differently expressed, the role of which in gefitinib resistance requires further investigation. Whole genome copy number alterations (CNAs) were also analyzed and NOXA was located in the H1650GR unique copy number loss region, 18q21. Our results suggested that the much higher EGFR-TKI resistance in H1650GR may be produced by the integration of multi-aspect factors.