Machine learning based on metabolomics unveils neutrophil extracellular trap-related metabolic signatures in non-small cell lung cancer patients undergoing chemoimmunotherapy.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the primary form of lung cancer, and the combination of chemotherapy with immunotherapy offers promising treatment options for patients suffering from this disease. However, the emergence of drug resistance significantly limits the effectiveness of these therapeutic strategies. Consequently, it is imperative to devise methods for accurately detecting and evaluating the efficacy of these treatments. AIM: To identify the metabolic signatures associated with neutrophil extracellular traps (NETs) and chemoimmunotherapy efficacy in NSCLC patients. METHODS: In total, 159 NSCLC patients undergoing first-line chemoimmunotherapy were enrolled. We first investigated the characteristics influencing clinical efficacy. Circulating levels of NETs and cytokines were measured by commercial kits. Liquid chromatography tandem mass spectrometry quantified plasma metabolites, and differential metabolites were identified. Least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest algorithms were employed. By using plasma metabolic profiles and machine learning algorithms, predictive metabolic signatures were established. RESULTS: First, the levels of circulating interleukin-8, neutrophil-to-lymphocyte ratio, and NETs were closely related to poor efficacy of first-line chemoimmunotherapy. Patients were classed into a low NET group or a high NET group. A total of 54 differential plasma metabolites were identified. These metabolites were primarily involved in arachidonic acid and purine metabolism. Three key metabolites were identified as crucial variables, including 8,9-epoxyeicosatrienoic acid, L-malate, and bis(monoacylglycerol)phosphate (18:1/16:0). Using metabolomic sequencing data and machine learning methods, key metabolic signatures were screened to predict NET level as well as chemoimmunotherapy efficacy. CONCLUSION: The identified metabolic signatures may effectively distinguish NET levels and predict clinical benefit from chemoimmunotherapy in NSCLC patients.

Dynamic A-to-I RNA editing during acute neuroinflammation in sepsis-associated encephalopathy.

Introduction: The activation of cerebral endothelial cells (CECs) has recently been reported to be the earliest acute neuroinflammation event in the CNS during sepsis-associated encephalopathy (SAE). Importantly, adenosine-to-inosine (A-to-I) RNA editing mediated by ADARs has been associated with SAE, yet its role in acute neuroinflammation in SAE remains unclear. Methods: Our current study systematically analyzed A-to-I RNA editing in cerebral vessels, cerebral endothelial cells (CECs), and microglia sampled during acute neuroinflammation after treatment in a lipopolysaccharide (LPS)-induced SAE mouse model. Results: Our results showed dynamic A-to-I RNA editing activity changes in cerebral vessels during acute neuroinflammation. Differential A-to-I RNA editing (DRE) associated with acute neuroinflammation were identified in these tissue or cells, especially missense editing events such as S367G in antizyme inhibitor 1 (Azin1) and editing events in lincRNAs such as maternally expressed gene 3 (Meg3), AW112010, and macrophage M2 polarization regulator (Mm2pr). Importantly, geranylgeranyl diphosphate synthase 1 (Ggps1) and another three genes were differentially edited across cerebral vessels, CECs, and microglia. Notably, Spearman correlation analysis also revealed dramatic time-dependent DRE during acute neuroinflammation, especially in GTP cyclohydrolase1 (Gch1) and non-coding RNA activated by DNA damage (Norad), both with the editing level positively correlated with both post-LPS treatment time and edited gene expression in cerebral vessels and CECs. Discussion: The findings in our current study demonstrate substantial A-to-I RNA editing changes during acute neuroinflammation in SAE, underlining its potential role in the disease.

Foliar uptake screening: A promising strategy for identifying wheat varieties with low lead accumulation.

Lead (Pb) contamination in wheat grain is of great concern, especially in North China. Atmospheric deposition is a major contributor to Pb accumulation in wheat grain. Screening low Pb accumulating wheat varieties has been an effective method for addressing Pb contamination in wheat grain. However, identifying wheat varieties with low Pb accumulation based on foliar uptake of atmospheric Pb has been neglected. Therefore, two field trials with distinct atmospheric Pb deposition were conducted to screen for stable varieties with low Pb accumulation. It was verified that YB700 and CH58, which have high thousand-grain weights and stable low Pb accumulation in field 1 (0.19 and 0.13 mg kg-1) and field 2 (0.17 and 0.20 mg kg-1), respectively, were recommended for cultivation in atmospheric Pb contaminated farmlands in North China. Furthermore, indoor experiments were conducted to investigate Pb uptake by the roots and leaves of different wheat varieties. Our findings indicate that Pb accumulation in different wheat varieties is primarily influenced by foliar Pb uptake rather than root Pb uptake. Interestingly, there was a positive correlation (p < 0.05) between the Pb concentrations in leaves and the stomatal width and trichome length of the adaxial epidermal surface. Additionally, there is a positive correlation (p < 0.01) between the Pb concentration in the wheat grain and trichome length. In conclusion, the screening of wheat varieties with narrower stomatal widths or shorter trichomes based on foliar uptake pathways is an effective strategy for ensuring food safety in areas contaminated by atmospheric Pb.