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Background An increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers. Methods We retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building. Results Among LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99). Conclusions These results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC.

Identification of novel non‐invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle‐based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi‐modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP‐NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met‐NN (p < 0.001). Based on MP‐NN, the tri modal model MPI‐RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools. A multiplexed assay is developed on an optimized nanoparticle‐based laser desorption/ionization mass spectrometry platform for the sensitive detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi‐modal platforms are constructed for lung adenocarcinoma early detection by deep learning. The strategy is demonstrated to be practically feasible to aid the existing diagnostic approaches of lung cancer.

The Bin/Amphiphysin/Rvs (BAR) domain protein FAM92A1 is a multifunctional protein engaged in regulating mitochondrial ultrastructure and ciliogenesis, but its physiological role in the brain remains unclear. Here, we show that FAM92A1 is expressed in neurons starting from embryonic development. FAM92A1 knockout in mice results in altered brain morphology and age-associated cognitive deficits, potentially due to neuronal degeneration and disrupted synaptic plasticity. Specifically, FAM92A1 deficiency impairs diverse neuronal membrane morphology, including the mitochondrial inner membrane, myelin sheath, and synapses, indicating its roles in membrane remodeling and maintenance. By determining the crystal structure of the FAM92A1 BAR domain, combined with atomistic molecular dynamics simulations, we uncover that FAM92A1 interacts with phosphoinositide- and cardiolipin-containing membranes to induce lipid-clustering and membrane curvature. Altogether, these findings reveal the physiological role of FAM92A1 in the brain, highlighting its impact on synaptic plasticity and neural function through the regulation of membrane remodeling and endocytic processes. The conserved BAR domain protein FAM92A1 is known to induce membrane curvature. Here, the authors report the crystal structure of its BAR domain and perform an extensive characterization of the brain and behavior of FAM92A1 knockout mice.

Introduction: Methamphetamine (METH) abuse by pregnant drug addicts causes toxic effects on fetal neurodevelopment; however, the mechanism underlying such effect of METH is poorly understood. Methods: In the present study, we applied three-dimensional (3D) neurospheres derived from the embryonic rat hippocampal tissue to investigate the effect of METH on neurodevelopment. Through the combination of whole genome transcriptional analyses, the involved cell signalings were identified and investigated. Results: We found that METH treatment for 24 h significantly and concentration-dependently reduced the size of neurospheres. Analyses of genome-wide transcriptomic profiles found that those down-regulated differentially expressed genes (DEGs) upon METH exposure were remarkably enriched in the cell cycle progression. By measuring the cell cycle and the expression of cell cycle-related checkpoint proteins, we found that METH exposure significantly elevated the percentage of G0/G1 phase and decreased the levels of the proteins involved in the G1/S transition, indicating G0/G1 cell cycle arrest. Furthermore, during the early neurodevelopment stage of neurospheres, METH caused aberrant cell differentiation both in the neurons and astrocytes, and attenuated migration ability of neurospheres accompanied by increased oxidative stress and apoptosis. Conclusion: Our findings reveal that METH induces an aberrant cell cycle arrest and neuronal differentiation, impairing the coordination of migration and differentiation of neurospheres.

Cocaine-induced endoplasmic reticulum (ER) stress has been increasingly recognized, but its neuronal specificity and functional significance remain unclear. Because the ER is also a major site for lipid and sphingolipid biosynthesis, cocaine-triggered ER stress may influence metabolic pathways linked to cellular stress signaling. Here, we sought to define the cell-type specificity and downstream consequences of cocaine-induced ER stress in the nucleus accumbens (NAc). We combined cocaine administration with ultrastructural analysis of ER morphology, immunohistochemical and molecular profiling of ER stress pathways, and assays of sphingolipid biosynthesis in the NAc. We also evaluated the effects of pharmacological inhibition of ER stress and sphingolipid synthesis, and performed D1-MSN-specific knockdown of and to assess their contributions to cocaine-induced behavioral and neuroplastic adaptations. Cocaine selectively activated ER stress in dopamine receptor 1 (D1)-expressing medium spiny neurons (MSNs), marked by induction of activating transcription factor 4 (ATF4). Cocaine also upregulated serine palmitoyltransferase long-chain base subunit 1 (SPTLC1), and promoter analysis with functional validation identified as a direct target of ATF4. ATF4 activation was thus coupled to remodeling of sphingolipid metabolism. Blocking ER stress or sphingolipid synthesis-and D1-MSN-specific knockdown of or -markedly reduced cocaine-induced behavioral and neuroplastic changes. These findings identify a D1-MSN ER stress response that promotes cocaine-induced neuroadaptations via the ATF4-SPTLC1 signaling axis and suggest a potential therapeutic target for cocaine addiction.

Depression is a heterogeneous disorder influenced by cell type-specific gene transcription in the brain. Peroxisome proliferator-activated receptor gamma (PPARγ) plays an important role in modulating the pathophysiology of depression. However, the role of PPARγ signaling in modulating depression-responsive neuronal ensembles remains largely unknown. We established a chronic restraint stress mouse model and integrated multi-omics and functional approaches to investigate the role of glucosylceramide (GlcCer)-PPARγ signaling in stress-induced depression. Conditional knockout mice targeting glucosylceramide synthase (GCS) or in dopamine D2 receptor-expressing medium spiny neurons (D2-MSNs) were generated using a Cre-loxP system, and molecular assays were used to characterize GlcCer as an endogenous activator of PPARγ-driven transcriptional programs. GlcCer as a crucial native activator of PPARγ that specifically modulates depression by binding to the activation function 1 domain of PPARγ in D2-MSNs in the dorsal striatum. Genetic ablation of GCS in D2-MSNs disrupts PPARγ signaling and neuronal function, leading to depression-like behaviors in mice. Selective deletion of in D2-MSNs produces a similar effect through dopamine D2 receptor. Administration of GlcCer or the PPARγ agonist pioglitazone reverses stress-induced depression-like behaviors, combined GlcCer and pioglitazone exerts additive antidepressant effects. These findings demonstrate a pivotal role for GlcCer-PPARγ signaling in D2-MSNs in depression, highlighting the therapeutic potential of targeting PPARγ activity in depression.

The expression of linear DNA sequences are precisely regulated by the three-dimensional (3D) architecture of chromatin. Morphine-induced aberrant gene networks of neurons have been extensively investigated; however, how morphine impacts the 3D genomic architecture of neurons is still unknown. Here, we applied digestion-ligation-only high-throughput chromosome conformation capture (DLO Hi-C) technology to investigate the affection of morphine on the 3D chromatin architecture of primate cortical neurons. After receiving continuous morphine administration for 90 days on rhesus monkeys, we discovered that morphine re-arranged chromosome territories, with a total of 391 segmented compartments being switched. Morphine altered over half of the detected topologically associated domains (TADs), most of which exhibited a variety of shifts, followed by separating and fusing types. Analysis of the looping events at kilobase-scale resolution revealed that morphine increased not only the number but also the length of differential loops. Moreover, all identified differentially expressed genes (DEGs) from the RNA sequencing (RNA-seq) were mapped to the specific TAD boundaries or differential loops, and were further validated to be significantly changed. Collectively, an altered 3D genomic architecture of cortical neurons may regulate the gene networks associated-morphine effects. Our finding provides critical hubs connecting chromosome spatial organization and gene networks associated with the morphine effects in humans.Competing Interest StatementThe authors have declared no competing interest.