Yiming Li

After optic nerve injury in mature mammals, retinal ganglion cells (RGCs) are normally unable to regenerate their axons and undergo delayed apoptosis. However, if the lens is damaged at the time of nerve injury, many RGCs survive axotomy and regenerate their axons into the distal optic nerve. Lens injury induces macrophage activation, and we show here that factors secreted by macrophages stimulate RGCs to regenerate their axons. When macrophages were activated by intravitreal injections of Zymosan, a yeast cell wall preparation, the number of RGC axons regenerating into the distal optic nerve was even greater than after lens injury. These effects were further enhanced if Zymosan was injected 3 d after nerve crush. In a grafting paradigm, intravitreal Zymosan increased the number of RGCs that regenerated their axons through a 1.5 cm peripheral nerve graft twofold relative to uninjected controls and threefold if injections were delayed 3 d. In cell culture, media conditioned by activated macrophages stimulated adult rat RGCs to regenerate their axons; this effect was potentiated by a low molecular weight factor that is constitutively present in the vitreous humor. After gel-filtration chromatography, macrophage-derived proteins > or =30 kDa were found to be toxic to RGCs, whereas proteins <30 kDa reversed this toxicity and promoted axon regeneration. The protein(s) that stimulated axon growth is distinct from identified polypeptide trophic factors that were tested. Thus, macrophages produce proteins with both positive and negative effects on RGCs, and the effects of macrophages can be optimized by the timing of their activation.

OBJECTIVE: The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature. METHODS: In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS. RESULTS: There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts. CONCLUSIONS: PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors.

Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T2-weighted MRI-based radiomics model for predicting the survival of gliomas. A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T2-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T2-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining. The 14 radiomic features-based prediction model was constructed from preoperative T2-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration. The preoperative T2-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours.

Patients with high ALDH1A3-expressing glioblastoma (ALDH1A3hi GBM) show limited benefit from postoperative chemoradiotherapy. Understanding the mechanisms underlying such resistance in these patients is crucial for the development of new treatments. Here, we show that the interaction between ALDH1A3 and PKM2 enhances the latter’s tetramerization and promotes lactate accumulation in glioblastoma stem cells (GSCs). By scanning the lactylated proteome in lactate-accumulating GSCs, we show that XRCC1 undergoes lactylation at lysine 247 (K247). Lactylated XRCC1 shows a stronger affinity for importin α, allowing for greater nuclear transposition of XRCC1 and enhanced DNA repair. Through high-throughput screening of a small-molecule library, we show that D34-919 potently disrupts the ALDH1A3-PKM2 interaction, preventing the ALDH1A3-mediated enhancement of PKM2 tetramerization. In vitro and in vivo treatment with D34-919 enhanced chemoradiotherapy-induced apoptosis of GBM cells. Together, our findings show that ALDH1A3-mediated PKM2 tetramerization is a potential therapeutic target to improve the response to chemoradiotherapy in ALDH1A3hi GBM.