Omics profile of iPSC-derived astrocytes from Progressive Supranuclear Palsy (PSP) patients.

INTRODUCTION: Progressive Supranuclear Palsy (PSP) is a neurodegenerative tauopathy and, to date, the pathophysiological mechanisms in PSP that lead to Tau hyperphosphorylation and neurodegeneration are not clear. In some brain areas, Tau pathology in glial cells appears to precede Tau aggregation in neurons. The development of a model using astrocyte cell lines derived from patients has the potential to identify molecules and pathways that contribute to early events of neurodegeneration. We developed a model of induced pluripotent stem cells (iPSC)-derived astrocytes to investigate the pathophysiology of PSP, particularly early events that might contribute to Tau hyperphosphorylation, applying omics approach to detect differentially expressed genes, metabolites, and proteins, including those from the secretome. METHODS: Skin fibroblasts from PSP patients (without MAPT mutations) and controls were reprogrammed to iPSCs, further differentiated into neuroprogenitor cells (NPCs) and astrocytes. In the 5th passage, astrocytes were harvested for total RNA sequencing. Intracellular and secreted proteins were processed for proteomics experiments. Metabolomics profiling was obtained from supernatants only. RESULTS: We identified hundreds of differentially expressed genes. The main networks were related to cell cycle re-activation in PSP. Several proteins were found exclusively secreted by the PSP group. The cellular processes related to the cell cycle and mitotic proteins, TriC/CCT pathway, and redox signaling were enriched in the secretome of PSP. Moreover, we found distinct sets of metabolites between PSP and controls. CONCLUSION: Our iPSC-derived astrocyte model can provide distinct molecular signatures for PSP patients and it is useful to elucidate the initial stages of PSP pathogenesis.

Metabolomics Approach Reveals Important Glioblastoma Plasma Biomarkers for Tumor Biology.

Glioblastoma (GB) is the most aggressive and frequent primary malignant tumor of the central nervous system and is associated with poor overall survival even after treatment. To better understand tumor biochemical alterations and broaden the potential targets of GB, this study aimed to evaluate differential plasma biomarkers between GB patients and healthy individuals using metabolomics analysis. Plasma samples from both groups were analyzed via untargeted metabolomics using direct injection with an electrospray ionization source and an LTQ mass spectrometer. GB biomarkers were selected via Partial Least Squares Discriminant and Fold-Change analyses and were identified using tandem mass spectrometry with in silico fragmentation, consultation of metabolomics databases, and a literature search. Seven GB biomarkers were identified, some of which were unprecedented biomarkers for GB, including arginylproline (m/z 294), 5-hydroxymethyluracil (m/z 143), and N-acylphosphatidylethanolamine (m/z 982). Notably, four other metabolites were identified. The roles of all seven metabolites in epigenetic modulation, energy metabolism, protein catabolism or folding processes, and signaling pathways that activate cell proliferation and invasion were elucidated. Overall, the findings of this study highlight new molecular targets to guide future investigations on GB. These molecular targets can also be further evaluated to derive their potential as biomedical analytical tools for peripheral blood samples.

Unraveling the Metabolic Alterations Induced by Zika Infection in Prostate Epithelial (PNT1a) and Adenocarcinoma (PC-3) Cell Lines.

The outbreak of Zika virus infection in 2016 led to the identification of its presence in several types of biofluids, including semen. Later discoveries associated Zika infection with sexual transmission and persistent replication in cells of the male reproductive tract. Prostate epithelial and carcinoma cells are favorable to virus replication, with studies pointing to transcriptomics alterations of immune and inflammation genes upon persistence. However, metabolome alterations promoted by the Zika virus in prostate cells are unknown. Given its chronic effects and oncolytic potential, we aim to investigate the metabolic alterations induced by the Zika virus in prostate epithelial (PNT1a) and adenocarcinoma (PC-3) cells using an untargeted metabolomics approach and high-resolution mass spectrometry. PNT1a cells were viable up to 15 days post ZIKV infection, in contrast to its antiproliferative effect in the PC-3 cell lineage. Remarkable alterations in the PNT1a cell metabolism were observed upon infection, especially regarding glycerolipids, fatty acids, and acylcarnitines, which could be related to viral cellular resource exploitation, in addition to the over-time increase in oxidative stress metabolites associated with carcinogenesis. The upregulation of FA20:5 at 5 dpi in PC-3 cells corroborates the antiproliferative effect observed since this metabolite was previously reported to induce PC-3 cell death. Overall, Zika virus promotes extensive lipid alterations on both PNT1a and PC-3 cells, promoting different outcomes based on the cellular metabolic state.

Differential Plasma Metabolites between High- and Low-Grade Meningioma Cases.

Meningiomas (MGMs) are currently classified into grades I, II, and III. High-grade tumors are correlated with decreased survival rates and increased recurrence rates. The current grading classification is based on histological criteria and determined only after surgical tumor sampling. This study aimed to identify plasma metabolic alterations in meningiomas of different grades, which would aid surgeons in predefining the ideal surgical strategy. Plasma samples were collected from 51 patients with meningioma and classified into low-grade (LG) (grade I; n = 43), and high-grade (HG) samples (grade II, n = 5; grade III, n = 3). An untargeted metabolomic approach was used to analyze plasma metabolites. Statistical analyses were performed to select differential biomarkers among HG and LG groups. Metabolites were identified using tandem mass spectrometry along with database verification. Five and four differential biomarkers were identified for HG and LG meningiomas, respectively. To evaluate the potential of HG MGM metabolites to differentiate between HG and LG tumors, a receiving operating characteristic curve was constructed, which revealed an area under the curve of 95.7%. This indicates that the five HG MGM metabolites represent metabolic alterations that can differentiate between LG and HG meningiomas. These metabolites may indicate tumor grade even before the appearance of histological features.