The SUVR4 histone lysine methyltransferase binds ubiquitin and converts H3K9me1 to H3K9me3 on transposon chromatin in Arabidopsis.

Chromatin structure and gene expression are regulated by posttranslational modifications (PTMs) on the N-terminal tails of histones. Mono-, di-, or trimethylation of lysine residues by histone lysine methyltransferases (HKMTases) can have activating or repressive functions depending on the position and context of the modified lysine. In Arabidopsis, trimethylation of lysine 9 on histone H3 (H3K9me3) is mainly associated with euchromatin and transcribed genes, although low levels of this mark are also detected at transposons and repeat sequences. Besides the evolutionarily conserved SET domain which is responsible for enzyme activity, most HKMTases also contain additional domains which enable them to respond to other PTMs or cellular signals. Here we show that the N-terminal WIYLD domain of the Arabidopsis SUVR4 HKMTase binds ubiquitin and that the SUVR4 product specificity shifts from di- to trimethylation in the presence of free ubiquitin, enabling conversion of H3K9me1 to H3K9me3 in vitro. Chromatin immunoprecipitation and immunocytological analysis showed that SUVR4 in vivo specifically converts H3K9me1 to H3K9me3 at transposons and pseudogenes and has a locus-specific repressive effect on the expression of such elements. Bisulfite sequencing indicates that this repression involves both DNA methylation-dependent and -independent mechanisms. Transcribed genes with high endogenous levels of H3K4me3, H3K9me3, and H2Bub1, but low H3K9me1, are generally unaffected by SUVR4 activity. Our results imply that SUVR4 is involved in the epigenetic defense mechanism by trimethylating H3K9 to suppress potentially harmful transposon activity.

Feasibility of deep learning-based tumor segmentation for target delineation and response assessment in grade-4 glioma using multi-parametric MRI.

Background: Tumor burden assessment is essential for radiation therapy (RT), treatment response evaluation, and clinical decision-making. However, manual tumor delineation remains laborious and challenging due to radiological complexity. The objective of this study was to investigate the feasibility of the HD-GLIO tool, an ensemble of pre-trained deep learning models based on the nnUNet-algorithm, for tumor segmentation, response prediction, and its potential for clinical deployment. Methods: We analyzed the predicted contrast-enhanced (CE) and non-enhancing (NE) HD-GLIO output in 49 multi-parametric MRI examinations from 23 grade-4 glioma patients. The volumes were retrospectively compared to corresponding manual delineations by 2 independent operators, before prospectively testing the feasibility of clinical deployment of HD-GLIO-output to a RT setting. Results: For CE, median Dice scores were 0.81 (95% CI 0.71-0.83) and 0.82 (95% CI 0.74-0.84) for operator-1 and operator-2, respectively. For NE, median Dice scores were 0.65 (95% CI 0.56-0,69) and 0.63 (95% CI 0.57-0.67), respectively. Comparing volume sizes, we found excellent intra-class correlation coefficients of 0.90 (P < .001) and 0.95 (P < .001), for CE, respectively, and 0.97 (P < .001) and 0.90 (P < .001), for NE, respectively. Moreover, there was a strong correlation between response assessment in Neuro-Oncology volumes and HD-GLIO-volumes (P < .001, Spearman's R2 = 0.83). Longitudinal growth relations between CE- and NE-volumes distinguished patients by clinical response: Pearson correlations of CE- and NE-volumes were 0.55 (P = .04) for responders, 0.91 (P > .01) for non-responders, and 0.80 (P = .05) for intermediate/mixed responders. Conclusions: HD-GLIO was feasible for RT target delineation and MRI tumor volume assessment. CE/NE tumor-compartment growth correlation showed potential to predict clinical response to treatment.

Meclofenamate causes loss of cellular tethering and decoupling of functional networks in glioblastoma.

BACKGROUND: Glioblastoma cells assemble to a syncytial communicating network based on tumor microtubes (TMs) as ultra-long membrane protrusions. The relationship between network architecture and transcriptional profile remains poorly investigated. Drugs that interfere with this syncytial connectivity such as meclofenamate (MFA) may be highly attractive for glioblastoma therapy. METHODS: In a human neocortical slice model using glioblastoma cell populations of different transcriptional signatures, three-dimensional tumor networks were reconstructed, and TM-based intercellular connectivity was mapped on the basis of two-photon imaging data. MFA was used to modulate morphological and functional connectivity; downstream effects of MFA treatment were investigated by RNA sequencing and fluorescence-activated cell sorting (FACS) analysis. RESULTS: TM-based network morphology strongly differed between the transcriptional cellular subtypes of glioblastoma and was dependent on axon guidance molecule expression. MFA revealed both a functional and morphological demolishment of glioblastoma network architectures which was reflected by a reduction of TM-mediated intercellular cytosolic traffic as well as a breakdown of TM length. RNA sequencing confirmed a downregulation of NCAM and axon guidance molecule signaling upon MFA treatment. Loss of glioblastoma communicating networks was accompanied by a failure in the upregulation of genes that are required for DNA repair in response to temozolomide (TMZ) treatment and culminated in profound treatment response to TMZ-mediated toxicity. CONCLUSION: The capacity of TM formation reflects transcriptional cellular heterogeneity. MFA effectively demolishes functional and morphological TM-based syncytial network architectures. These findings might pave the way to a clinical implementation of MFA as a TM-targeted therapeutic approach.

Sequential bortezomib and temozolomide treatment promotes immunological responses in glioblastoma patients with positive clinical outcomes: A phase 1B study.

BACKGROUND: Glioblastoma (GBM) is an aggressive malignant brain tumor where median survival is approximately 15 months after best available multimodal treatment. Recurrence is inevitable, largely due to O6 methylguanine DNA methyltransferase (MGMT) that renders the tumors resistant to temozolomide (TMZ). We hypothesized that pretreatment with bortezomib (BTZ) 48 hours prior to TMZ to deplete MGMT levels would be safe and tolerated by patients with recurrent GBM harboring unmethylated MGMT promoter. The secondary objective was to investigate whether 26S proteasome blockade may enhance differentiation of cytotoxic immune subsets to impact treatment responses measured by radiological criteria and clinical outcomes. METHODS: Ten patients received intravenous BTZ 1.3 mg/m2 on days 1, 4, and 7 during each 4th weekly TMZ-chemotherapy starting on day 3 and escalated from 150 mg/m2 per oral 5 days/wk via 175 to 200 mg/m2 in cycles 1, 2, and 3, respectively. Adverse events and quality of life were evaluated by CTCAE and EQ-5D-5L questionnaire, and immunological biomarkers evaluated by flow cytometry and Luminex enzyme-linked immunosorbent assay. RESULTS: Sequential BTZ + TMZ therapy was safe and well tolerated. Pain and performance of daily activities had greatest impact on patients' self-reported quality of life and were inversely correlated with Karnofsky performance status. Patients segregated a priori into three groups, where group 1 displayed stable clinical symptoms and/or slower magnetic resonance imaging radiological progression, expanded CD4+ effector T-cells that attenuated cytotoxic T-lymphocyte associated protein-4 and PD-1 expression and secreted interferon γ and tumor necrosis factor α in situ and ex vivo upon stimulation with PMA/ionomycin. In contrast, rapidly progressing group 2 patients exhibited tolerised T-cell phenotypes characterized by fourfold to sixfold higher interleukin 4 (IL-4) and IL-10 Th-2 cytokines after BTZ + TMZ treatment, where group 3 patients exhibited intermediate clinical/radiological responses. CONCLUSION: Sequential BTZ + TMZ treatment is safe and promotes Th1-driven immunological responses in selected patients with improved clinical outcomes (Clinicaltrial.gov (NCT03643549)).