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Summary: Background: Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited. Methods: We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties. Findings: Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells. Interpretation: This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism. Funding: National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Summary: Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in the breast cancer and activation of regulatory T cells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across the training, test, and independent validation cohorts. Inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in patients with TNBC. This study deepens our understanding of nucleotide metabolism in breast cancer development and introduces a promising non-invasive method for early breast cancer detection and predicting NAC response in patients with TNBC.

Background: Following traumatic bone loss or removal of bone tumors, the failure of bone allograft transplantation for large bone defect repair remains a significant problem in orthopedics. Therefore, new strategies that can efficiently enhance allograft healing and long-term incorporation are critically needed. Method: In this study, we first injected Notch-activating Jagged1 peptide to mice and then isolated bone marrow tissues and cells for proliferation and differentiation assays. Femur bone allograft surgery was also performed in Jagged1 pre-treated mice, and bone defect healing process were monitored by histology, Micro-CT and biomechanical testing. Result: Our results showed that Jagged1 therapeutic injection is sufficient to maximally activate Notch and promote bone marrow stromal cell proliferation in vivo, while no effects on bone structure were observed. More importantly, Jagged1 pre-treatment significantly promoted bone callus formation and increased bone mechanical strength during allograft healing in a femur bone defect mouse model. Conclusion: This study reveals that Notch in vivo activation can be induced by injection of Jagged1 peptide for expansion of local native stromal cells that will significantly enhance bone callus formation. The Translational potential of this Article: The clinical uses of this therapeutic strategy would be immediately applicable for chronic long bone defect repair. More importantly, this devised strategy for expansion of endogenous BMSCs can also be applied to enhance other tissue and organ repair.

Summary: Background: Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs. Methods: Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers. Findings: A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866. Interpretation: The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Objectives: The aim of this study was to characterise a high biofilm-forming capacity, hypermucoviscous, blaKPC and blaNDM co-producing Klebsiella pneumoniae strain (KSH203). Methods: Antimicrobial susceptibility, biofilm formation and hypermucoviscous phenotype were determined by the disk diffusion method, crystal violet staining and positive string test, respectively. Whole-genome sequencing was performed using a PacBio RS II Sequencer. High-quality reads were de novo assembled using Celera Assembler v.8.0. Genome annotation was performed using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP), and the genome characteristics were analysed by bioinformatics methods. Results: Klebsiella pneumoniae strain KSH203 was resistant to all antibiotics tested but was only intermediate-resistant to polymyxin B. This strain showed high biofilm-forming ability and a hypermucoviscous phenotype with serotype K25 belonging to the ST11 clone. KSH203 consists of a 5 464 059-bp single chromosome and four plasmids including pKSH203-NDM (53 144 bp), pKSH203-KPC (159 467 bp), pKSH203-CTX-M-3 (156 910 bp) and pKSH203-qnrS (253 705 bp). A total of 44 antimicrobial resistance genes and a large number virulence-associated genes were identified in the genome of strain KSH203. Conclusion: In this study, we illustrate the whole genome sequence of high biofilm-forming capacity, hypermucoviscous K. pneumoniae isolate KSH203 with capsular serotype K25 belonging to ST11 isolated from a patient in China, which carried a large number of antimicrobial resistance genes and virulence-associated genes. Future studies are needed to be aware of dissemination of this type of strain among environmental, animal and human isolates.

Traditional agro-ecosystems have sustained diverse aquatic plants, but have been greatly damaged by modern agricultural practices. Ottelia alismoides is a submerged macrophyte occurring in and around paddy fields, and a vulnerable species in Japan. Using four primer pairs of amplified fragment length polymorphism (AFLP) markers, genetic variation was estimated in 12 O. alismoides populations from Honshu, Shikoku and Kyushu. O. alismoides demonstrated low intra-population genetic variation (Pp = 15.7%, HE = 0.052 and I = 0.080), which was explained by its annual life history, high level of autogamy and repeated genetic bottlenecks. These factors, combined with limited gene flows (Nm = 0.113–0.127, M = 0.091), also resulted in the high genetic differentiation among populations, contributing about 70% of the total genetic variation. The gene flow might be hindered by geographic distance, mountains within islands and the strait between islands. It is, therefore, recommended that conservation of all extant populations be prioritized to maximize gene representation.