Lymphatic endothelial-like cells promote glioblastoma stem cell growth through cytokine-driven cholesterol metabolism.

Glioblastoma is the most lethal primary brain tumor with glioblastoma stem cells (GSCs) atop a cellular hierarchy. GSCs often reside in a perivascular niche, where they receive maintenance cues from endothelial cells, but the role of heterogeneous endothelial cell populations remains unresolved. Here, we show that lymphatic endothelial-like cells (LECs), while previously unrecognized in brain parenchyma, are present in glioblastomas and promote growth of CCR7-positive GSCs through CCL21 secretion. Disruption of CCL21-CCR7 paracrine communication between LECs and GSCs inhibited GSC proliferation and growth. LEC-derived CCL21 induced KAT5-mediated acetylation of HMGCS1 on K273 in GSCs to enhance HMGCS1 protein stability. HMGCS1 promoted cholesterol synthesis in GSCs, favorable for tumor growth. Expression of the CCL21-CCR7 axis correlated with KAT5 expression and HMGCS1K273 acetylation in glioblastoma specimens, informing patient outcome. Collectively, glioblastomas contain previously unrecognized LECs that promote the molecular crosstalk between endothelial and tumor cells, offering potentially alternative therapeutic strategies.

Knockdown of PTEN promotes colon cancer progression and induces M2 macrophage polarization in the colon cancer cell environment.

Objective: This article aims to study the effect of phosphate and tension homolog deleted on chromosome ten (PTEN) knockdown on colon cancer progression and macrophage polarization in the cancer environment. Materials and Methods and Results: The expression of PTEN in colon cancer tissues and colon cancer cells was significantly lower than in precancerous tissues or CCD-18Co cells, and the decrease was most evident in SW620 cells. The expressions of phosphate (p)-p38, c-Jun N-terminal kinase (JNK), activator protein 1 (AP-1), B-cell lymphoma-2 (Bcl-2) protein in colon cancer tissues and cells were significantly higher than in precancerous tissues or CCD-18Co cells (P-values < 0.05). Bcl-2-associated X (Bax) and Caspase-3 expressions in colon cancer tissues and cells were significantly lower than in precancerous tissues or CCD-18Co cells (P-values < 0.05). 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) was applied to measure cell viability. Transwell evaluated the cell migration and invasion ability. Si-PTEN improved the proliferation, migration, and invasion of SW620 cells (P-values < 0.05). The expression levels of arginase-1 (Arg-1), CD163, CD206 in colon cancer tissues were significantly higher than in precancerous tissues (P-values < 0.05). The cell cycle, the number of M1 and M2 double-positive cells were assessed by flow cytometry. Si-PTEN reduced the expression of tumor necrosis factor-alpha (TNF-α), interleukin-1beta (IL-1ß), and inducible nitric oxide synthase (iNOS), which upregulated the expression of Arg-1, CD206, CD163, p-p38, JNK, and AP-1 (P-values < 0.05). Conclusion: Si-PTEN promoted colon cancer progression and induced the polarization of M2 tumor-associated macrophages in the colon cancer cell environment.

Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics.

Computational tools are commonly used in untargeted metabolomics to automatically extract metabolic features from liquid chromatography-mass spectrometry (LC-MS) raw data. However, due to the incapability of software to accurately determine chromatographic peak heights/areas for features with poor chromatographic peak shape, automated data processing in untargeted metabolomics faces additional quantitative variation (i.e., computational variation) besides the well-recognized analytical and biological variations. In this work, using multiple biological samples, we investigated how experimental factors, including sample concentrations, LC separation columns, and data processing programs, contribute to computational variation. For example, we found that the peak height (PH)-based quantification is more precise when MS-DIAL was used for data processing. We further systematically compared the different patterns of computational variation between PH- and peak area (PA)-based quantitative measurements. Our results suggest that the magnitude of computational variation is highly consistent at a given concentration. Hence, we proposed a quality control (QC) sample-based correction workflow to minimize computational variation by automatically selecting PH or PA-based measurement for each intensity value. This bioinformatic solution was demonstrated in a metabolomic comparison of leukemia patients before and after chemotherapy. Our novel workflow can be effectively applied on 652 out of 915 metabolic features, and over 31% (206 out of 652) of corrected features showed distinctly changed statistical significance. Overall, this work highlights computational variation, a considerable but underinvestigated quantitative variability in omics-scale quantitative analyses. In addition, the proposed bioinformatic solution can minimize computational variation, thus providing a more confident statistical comparison among biological groups in quantitative metabolomics.

Fold-Change Compression: An Unexplored But Correctable Quantitative Bias Caused by Nonlinear Electrospray Ionization Responses in Untargeted Metabolomics.

The nonlinear signal response of electrospray ionization (ESI) presents a critical limitation for mass spectrometry (MS)-based quantitative analysis. In the field of metabolomics research, this issue has largely remained unaddressed; MS signal intensities are usually directly used to calculate fold changes for quantitative comparison. In this work, we demonstrate that, due to the nonlinear ESI response, signal intensity ratios of a metabolic feature calculated between two samples may not reflect their real metabolic concentration ratios (i.e., fold-change compression), implying that conventional fold-change calculations directly using MS signal intensities can be misleading. In this regard, we developed a quality control (QC) sample-based signal calibration workflow to overcome the quantitative bias caused by the nonlinear ESI response. In this workflow, calibration curves for every metabolic feature are first established using a QC sample injected in serial injection volumes. The MS signals of each metabolic feature are then calibrated to their equivalent QC injection volumes for comparative analysis. We demonstrated this novel workflow in a targeted metabolite analysis, showing that the accuracy of fold-change calculations can be significantly improved. Furthermore, in a metabolomic comparison of the bone marrow interstitial fluid samples from leukemia patients before and after chemotherapy, an additional 59 significant metabolic features were found with fold changes larger than 1.5, and an additional 97 significant metabolic features had fold changes corrected by more than 0.1. This work enables high-quality quantitative analysis in untargeted metabolomics, thus providing more confident biological hypotheses generation.

Cortical deficiency of laminin gamma1 impairs the AKT/GSK-3beta signaling pathway and leads to defects in neurite outgrowth and neuronal migration.

Laminins have dramatic and varied actions on neurons in vitro. However, their in vivo function in brain development is not clear. Here we show that knockout of laminin gamma1 in the cerebral cortex leads to defects in neuritogenesis and neuronal migration. In the mutant mice, cortical layer structures were disrupted, and axonal pathfinding was impaired. During development, loss of laminin expression impaired phosphorylation of FAK and paxillin, indicating defects in integrin signaling pathways. Moreover, both phosphorylation and protein levels of GSK-3beta were significantly decreased, but only phosphorylation of AKT was affected in the mutant cortex. Knockout of laminin gamma1 expression in vitro, dramatically inhibited neurite growth. These results indicate that laminin regulates neurite growth and neuronal migration via integrin signaling through the AKT/GSK-3beta pathway, and thus reveal a novel mechanism of laminin function in brain development.

Proteolytic fragments of laminin promote excitotoxic neurodegeneration by up-regulation of the KA1 subunit of the kainate receptor.

Degradation of the extracellular matrix (ECM) protein laminin contributes to excitotoxic cell death in the hippocampus, but the mechanism of this effect is unknown. To study this process, we disrupted laminin gamma1 (lamgamma1) expression in the hippocampus. Lamgamma1 knockout (KO) and control mice had similar basal expression of kainate (KA) receptors, but the lamgamma1 KO mice were resistant to KA-induced neuronal death. After KA injection, KA1 subunit levels increased in control mice but were unchanged in lamgamma1 KO mice. KA1 levels in tissue plasminogen activator (tPA)-KO mice were also unchanged after KA, indicating that both tPA and laminin were necessary for KA1 up-regulation after KA injection. Infusion of plasmin-digested laminin-1 into the hippocampus of lamgamma1 or tPA KO mice restored KA1 up-regulation and KA-induced neuronal degeneration. Interfering with KA1 function with a specific anti-KA1 antibody protected against KA-induced neuronal death both in vitro and in vivo. These results demonstrate a novel pathway for neurodegeneration involving proteolysis of the ECM and KA1 KA receptor subunit up-regulation.