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High-dose radiation activates caspases in tumor cells to produce abundant DNA fragments for DNA sensing in antigen-presenting cells, but the intrinsic DNA sensing in tumor cells after radiation is rather limited. Here we demonstrate that irradiated tumor cells hijack caspase 9 signaling to suppress intrinsic DNA sensing. Instead of apoptotic genomic DNA, tumor-derived mitochondrial DNA triggers intrinsic DNA sensing. Specifically, loss of mitochondrial DNA sensing in Casp9 −/− tumors abolishes the enhanced therapeutic effect of radiation. We demonstrated that combining emricasan, a pan-caspase inhibitor, with radiation generates synergistic therapeutic effects. Moreover, loss of CASP9 signaling in tumor cells led to adaptive resistance by upregulating programmed death-ligand 1 (PD-L1) and resulted in tumor relapse. Additional anti-PD-L1 blockade can further overcome this acquired immune resistance. Therefore, combining radiation with a caspase inhibitor and anti-PD-L1 can effectively control tumors by sequentially blocking both intrinsic and extrinsic inhibitory signaling. Therapeutic irradiation can trigger DNA-sensing pathways and trigger antitumor immunity. Fu and colleagues demonstrate that tumors can co-opt intrinsic apoptotic pathways to avoid immunogenic cell death following irradiation.

Lignin-derived aromatic compounds have significant potential for multiple industrial applications, and elucidating the processes for bacterial lignin degradation processes can facilitate the utilization of plant biomass. A lignin-degrading bacterial strain, designated HZC-1, was newly isolated from saline-alkali soil and exhibited robust growth in 1–18% (w/v) NaCl and across a pH range of 5.0–11.0. The isolate showed the highest 16S rRNA gene sequence similarity (≤ 97.7%) to known Salinicoccus species. Furthermore, average nucleotide identity (≤ 82.34) and digital DNA-DNA hybridization (≤ 52.9%) analyses supported its classification as a potentially novel species within the genus Salinicoccus . Genomic annotation indicated that strain HZC-1 adapted to saline-alkali environments via multiple mechanisms such as Na + /H + antiporter and glycine betaine transport systems. By combining genomic and untargeted metabolomic data, it can be inferred that this strain was capable of metabolizing lignin derivatives through non-classical pathways involving enzymes such as β-glucosidase, aromatic cyclohydroxyl dioxygenase and those associated with naphthalene degradation. These findings suggest the potential lignin-degrading capacity of Salinicoccus sp. HZC-1 under saline-alkali conditions, presenting a potentially novel bacterial taxon for waste lignin valorization and bioremediation of aromatic pollutants.

Being isolated from the peripheral system by the blood–brain barrier, the brain has long been considered a completely impervious tissue. However, recent findings show that the gut microbiome (GM) influences gastrointestinal and brain disorders such as Alzheimer’s disease (AD). Despite several hypotheses, such as neuroinflammation, tau hyperphosphorylation, amyloid plaques, neurofibrillary tangles, and oxidative stress, being proposed to explain the origin and progression of AD, the pathogenesis remains incompletely understood. Epigenetic, molecular, and pathological studies suggest that GM influences AD development and have endeavored to find predictive, sensitive, non-invasive, and accurate biomarkers for early disease diagnosis and monitoring of progression. Given the growing interest in the involvement of GM in AD, current research endeavors to identify prospective gut biomarkers for both preclinical and clinical diagnoses, as well as targeted therapy techniques. Here, we discuss the most recent findings on gut changes in AD, microbiome-based biomarkers, prospective clinical diagnostic uses, and targeted therapy approaches. Furthermore, we addressed herbal components, which could provide a new venue for AD diagnostic and therapy research.

Chlorogenic acid (CGA), a phenolic compound with diverse bioactivities, plays a crucial role in plant defense mechanisms and has significant therapeutic potential in human inflammatory and cardiovascular diseases. The biosynthesis and accumulation of CGA in plants result from a complex interplay between internal factors (e.g., hormones, enzymes, and genes) and external factors (e.g., microbial interactions, drought, and temperature fluctuations). This review systematically investigates the influence of microbes on internal regulatory factors governing CGA biosynthesis in plants. CGA is synthesized through four distinct metabolic pathways, with hormones, enzymes, and genes as key regulators. Notably, microbes enhance CGA biosynthesis by improving plant nutrient uptake, supplying essential hormones, regulating the expression of related enzymes and genes, and the interaction between bacteria and fungi. In addition, our review summarizes the challenges currently present in the research and proposes a series of innovative strategies. These include in-depth investigations into the molecular mechanisms of microbial regulation of plant gene expression, gene editing, development of microbial inoculants, construction of synthetic microbial communities, and exogenous application of plant hormones.

Entorhinal cortex (EC), thought to be the location of the earliest lesions in Alzheimer’s disease (AD), has been widely studied in recent years. With the irreversible pathological changes of AD, there is an urgent need to find biomarkers that can be used to predict the presence of the disease before it is clinically expressed. The aim of this review is to summarize and analyze recent findings that are relevant to the important role of EC in the diagnosis of mild cognitive impairment (MCI) and mild AD and to describe a range of neuroimaging techniques used to define the EC boundary. A comprehensive literature search for articles published up to May 2015 was performed. Our research highlights the finding that atrophy in EC reflects the early pathological changes of AD and can be a strong predictor of prodromal AD. The early changes in EC are a good imaging biomarker that can be used to discriminate individuals with MCI from normal control subjects. A larger degree of atrophy in EC predicts increased disease severity, and the right EC in patients with mild AD exhibited greater changes than the left side. In addition, the EC seems to have an obvious advantage over the hippocampus as a biomarker when predicting future conversion to AD in individuals with MCI, and it may be of help in following the course of disease progression. In this review, we also summarize the main differences observed between the hippocampus and the EC when differentiating diseases. These findings will hopefully provide an opportunity for the effective prevention and early treatment of AD.

Synthetic microbial community has widely concerned in the fields of agriculture, food and environment over the past few years. However, there is little consensus on the method to synthetic microbial community from construction to functional verification. Here, we review the concept, characteristics, history and applications of synthetic microbial community, summarizing several methods for synthetic microbial community construction, such as isolation culture, core microbiome mining, automated design, and gene editing. In addition, we also systematically summarized the design concepts, technological thresholds, and applicable scenarios of various construction methods, and highlighted their advantages and limitations. Ultimately, this review provides four efficient, detailed, easy-to-understand and -follow steps for synthetic microbial community construction, with major implications for agricultural practices, food production, and environmental governance.

Assessing microbial community stability and soil quality requires understanding the role of keystone microbial taxa in maintaining diversity and functionality. This study collected soil samples from four major habitats in the urban and peri-urban areas of 20 highly urbanized provinces in China using both the five-point method and the S-shape method and explored their microbiota through high-throughput sequencing techniques. The data was used to investigate changes in the structural diversity and co-occurrence patterns of keystone microbial communities from peri-urban (agricultural land) to urban environments (hospitals, wastewater treatment plants, and zoos) across different regions. Using network analysis, we examined the structure and symbiosis of soil keystone taxa and their association with environmental factors during urbanization. Results revealed that some urban soils exhibited higher microbial diversity, network complexity, and community stability compared to peri-urban soil. Significant differences were observed in the composition, structure, and potential function of keystone microbial taxa between these environments. Correlation analysis showed a significant negative relationship between keystone taxa and mean annual precipitation (p < 0.05), and a strong positive correlation with soil nutrients, microbial diversity, and community stability (p < 0.05). These findings suggest that diverse keystone taxa are vital for sustaining microbial community stability and that urbanization-induced environmental changes modulate their composition. Shifts in keystone taxa composition reflect alterations in soil health and ecosystem functioning, emphasizing their role as indicators of soil quality during urban development. This study highlights the ecological importance of keystone taxa in shaping microbial resilience under urbanization pressure.

Early diagnosis of Alzheimer’s disease (AD) remains a significant challenge due to the lack of objective biomarkers. Accurate identification of morphological and metabolic alterations during the different stages of AD is crucial for timely intervention and effective management of the disease. Surface-based morphometry (SBM) and amide proton transfer (APT) imaging are innovative techniques that offer the potential to visualize these changes in the brain. By leveraging these advanced imaging modalities, this study aims to investigate the specific alterations that occur in AD, thereby exploring potential imaging biomarkers that could facilitate early and precise diagnosis of the condition. The identification of such biomarkers is essential for improving the diagnostic accuracy and efficacy of therapeutic strategies for Alzheimer’s disease. In this prospective study, we enrolled 26 patients with AD, thirty-six patients with amnestic mild cognitive impairment (aMCI), and 32 healthy controls (HCs). All participants underwent 3D T1-weighted imaging (T1WI) and 3D-APT imaging. Morphological parameters, including cortical thickness (CTh), sulcal depth (SD), fractal dimension (FD), and gyrification index (GI), were calculated using the SBM algorithm. Magnetization transfer ratio asymmetry (MTR asym ) values were computed for 106 brain regions utilizing the vendor’s post-processing workstation. AD Patients exhibited cortical thinning in the frontal, temporal, parietal lobes, and cingulate gyrus, along with a decrease in GI in the left parietal lobe. MCI patients showed a reduction in GI in the left parietal and temporal lobes. Multivariate logistic regression analysis identified widespread increased MTR asym values in AD patients, affecting the frontal, temporal, parietal, occipital, cingulate, basal ganglia regions, and white matter. The right amygdala shows the highest diagnostic performance. Additionally, a negative correlation was observed between MTR asym values and clinical assessments. SBM analysis revealed morphological changes in the brain across different stages of AD, while APT imaging identified metabolic alterations in AD patients. These findings suggest that the combination of SBM and APT imaging may hold potential as a non-invasive diagnostic and monitoring approach for AD.

This study aimed to develop a machine learning model based on multimodal radiomics features from cerebellar subregions, utilizing the complementarity of cerebellar structural and metabolic imaging data for accurate diagnosis of Alzheimer's disease (AD). A total of 164 cognitively normal (CN) subjects and 146 AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were included. All participants had 3DT1-weighted magnetic resonance imaging (3DT1W MRI) and [ F]fluorodeoxyglucose positron emission tomography ([ F]FDG PET) imaging data. The cerebellum was divided into 26 subregions, and radiomics features were extracted from different cerebellar regions of these two modality images, respectively. After feature selection, single-modality ([ F]FDG PET, 3DT1W MRI) and multimodal ([ F]FDG PET + 3DT1W MRI) random forest classification models were constructed. Model performance and clinical value were assessed using area under the curve (AUC), calibration curves, and decision curve analysis (DCA). In addition, we also used Shapley Additive exPlanations (SHAP) to clarify the contributions of features, thereby enhancing the interpretability of the model. All three models could effectively diagnose AD, with the multimodal model showing the best performance. In the independent test set, the multimodal model achieved an AUC of 0.903, which was higher than the single-modality models based on [ F]FDG PET (AUC = 0.842) and 3DT1W MRI (AUC = 0.804). The calibration curves and DCA demonstrated that all three models had good calibration and clinical applicability, especially the multimodal model. SHAP analysis of the multimodal model revealed that among the 15 selected features, the top seven features with the highest SHAP values were derived from [ F]FDG PET images, with R_FDG_CER_III_original_firstorder_90Percentile and R_FDG_CER_VI_original_firstorder_Median being the two most important features for distinguishing AD from CN. The multimodal radiomics model based on cerebellar subregions, which integrates [ F]FDG PET and 3DT1W MRI data, can effectively diagnose AD and provide potential biomarkers for clinical applications.