From Analysis:
Selective vulnerability of entorhinal cortex layer II neurons in AD
Why do entorhinal cortex layer II stellate neurons die first in AD? Their unique electrophysiological properties, grid cell function, and high metabolic demand may contribute, but the molecular basis of selective vulnerability is unknown.
These hypotheses emerged from the same multi-agent debate that produced this hypothesis.
Molecular Mechanism and Rationale
Grid cells in layer II of the entorhinal cortex represent one of the brain's most metabolically demanding neuronal populations due to their continuous spatial computation and persistent theta-frequency firing patterns. These specialized neurons maintain hexagonal firing fields that require sustained high-frequency oscillations at 4-12 Hz, creating extraordinary metabolic stress that may contribute to their selective vulnerability in neurodegenerative diseases. The molecular basis of this vulnerability centers on the imbalance between energy demands and antioxidant capacity, particularly involving the mitochondrial enzyme isocitrate dehydrogenase 2 (IDH2).
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BACKGROUND: A recent genomewide mutational analysis of glioblastomas (World Health Organization [WHO] grade IV glioma) revealed somatic mutations of the isocitrate dehydrogenase 1 gene (IDH1) in a fraction of such tumors, most frequently in tumors that were known to have evolved from lower-grade gliomas (secondary glioblastomas). METHODS: We determined the sequence of the IDH1 gene and the related IDH2 gene in 445 central nervous system (CNS) tumors and 494 non-CNS tumors. The enzymatic activity of the proteins that were produced from normal and mutant IDH1 and IDH2 genes was determined in cultured glioma cells that were transfected with these genes. RESULTS: We identified mutations that affected amino acid 132 of IDH1 in more than 70% of WHO grade II and III astrocytomas and oligodendrogliomas and in glioblastomas that developed from these lower-grade lesions. Tumors without mutations in IDH1 often had mutations affecting the analogous amino acid (R172) of the IDH2 gene. Tumors with I
Liver cancer has the second highest worldwide cancer mortality rate and has limited therapeutic options. We analyzed 363 hepatocellular carcinoma (HCC) cases by whole-exome sequencing and DNA copy number analyses, and we analyzed 196 HCC cases by DNA methylation, RNA, miRNA, and proteomic expression also. DNA sequencing and mutation analysis identified significantly mutated genes, including LZTR1, EEF1A1, SF3B1, and SMARCA4. Significant alterations by mutation or downregulation by hypermethylation in genes likely to result in HCC metabolic reprogramming (ALB, APOB, and CPS1) were observed. Integrative molecular HCC subtyping incorporating unsupervised clustering of five data platforms identified three subtypes, one of which was associated with poorer prognosis in three HCC cohorts. Integrated analyses enabled development of a p53 target gene expression signature correlating with poor survival. Potential therapeutic targets for which inhibitors exist include WNT signaling, MDM4, MET, VE
Drug resistance contributes to poor therapeutic response in urothelial carcinoma (UC). Metabolomic analysis suggested metabolic reprogramming in gemcitabine-resistant urothelial carcinoma cells, whereby increased aerobic glycolysis and metabolic stimulation of the pentose phosphate pathway (PPP) promoted pyrimidine biosynthesis to increase the production of the gemcitabine competitor deoxycytidine triphosphate (dCTP) that diminishes its therapeutic effect. Furthermore, we observed that gain-of-function of isocitrate dehydrogenase 2 (IDH2) induced reductive glutamine metabolism to stabilize Hif-1α expression and consequently stimulate aerobic glycolysis and PPP bypass in gemcitabine-resistant UC cells. Interestingly, IDH2-mediated metabolic reprogramming also caused cross resistance to CDDP, by elevating the antioxidant defense via increased NADPH and glutathione production. Downregulation or pharmacological suppression of IDH2 restored chemosensitivity. Since the expression of key meta
Hepatocellular carcinoma (HCC) is an aggressive human cancer with increasing incidence worldwide. Multiple efforts have been made to explore pharmaceutical therapies to treat HCC, such as targeted tyrosine kinase inhibitors, immune based therapies and combination of chemotherapy. However, limitations exist in current strategies including chemoresistance for instance. Tumor initiation and progression is driven by reprogramming of metabolism, in particular during HCC development. Recently, metabolic associated fatty liver disease (MAFLD), a reappraisal of new nomenclature for non-alcoholic fatty liver disease (NAFLD), indicates growing appreciation of metabolism in the pathogenesis of liver disease, including HCC, thereby suggesting new strategies by targeting abnormal metabolism for HCC treatment. In this review, we introduce directions by highlighting the metabolic targets in glucose, fatty acid, amino acid and glutamine metabolism, which are suitable for HCC pharmaceutical interventio
The efficacy of chimeric antigen receptor (CAR) T cell therapy is hampered by relapse in hematologic malignancies and by hyporesponsiveness in solid tumors. Long-lived memory CAR T cells are critical for improving tumor clearance and long-term protection. However, during rapid ex vivo expansion or in vivo tumor eradication, metabolic shifts and inhibitory signals lead to terminal differentiation and exhaustion of CAR T cells. Through a mitochondria-related compound screening, we find that the FDA-approved isocitrate dehydrogenase 2 (IDH2) inhibitor enasidenib enhances memory CAR T cell formation and sustains anti-leukemic cytotoxicity in vivo. Mechanistically, IDH2 impedes metabolic fitness of CAR T cells by restraining glucose utilization via the pentose phosphate pathway, which alleviates oxidative stress, particularly in nutrient-restricted conditions. In addition, IDH2 limits cytosolic acetyl-CoA levels to prevent histone acetylation that promotes memory cell formation. In combinat
The tumor microenvironment predominantly polarizes tumor-associated macrophages (TAMs) toward an M2-like phenotype, thereby inhibiting antitumor immune responses. This process is substantially affected by metabolic reprogramming; however, reeducating TAMs to enhance their antitumor capabilities through metabolic remodeling remains a challenge. Here, we show that tumor-derived microparticles loaded with succinate (SMPs) can remodel the metabolic state of TAMs. SMPs promote classical M1-like polarization of macrophages by enhancing glycolysis and attenuating the tricarboxylic acid (TCA) cycle in a protein succinylation-dependent manner. Mechanistically, succinate is delivered into the mitochondria and nucleus by SMPs, leading to succinylation of isocitrate dehydrogenase 2 (IDH2) and histone H3K122 within the lactate dehydrogenase A (Ldha) promoter region. Our findings provide a distinct approach for TAM polarization using cell membrane-derived microparticles loaded with endogenous metabo
Macroautophagy (hereafter referred to as autophagy) is a catabolic membrane trafficking process that degrades a variety of cellular constituents and is associated with human diseases. Although extensive studies have focused on autophagic turnover of cytoplasmic materials, little is known about the role of autophagy in degrading nuclear components. Here we report that the autophagy machinery mediates degradation of nuclear lamina components in mammals. The autophagy protein LC3/Atg8, which is involved in autophagy membrane trafficking and substrate delivery, is present in the nucleus and directly interacts with the nuclear lamina protein lamin B1, and binds to lamin-associated domains on chromatin. This LC3-lamin B1 interaction does not downregulate lamin B1 during starvation, but mediates its degradation upon oncogenic insults, such as by activated RAS. Lamin B1 degradation is achieved by nucleus-to-cytoplasm transport that delivers lamin B1 to the lysosome. Inhibiting autophagy or the
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previo
Isocitrate dehydrogenases (IDHs) are critical metabolic enzymes that catalyze the oxidative decarboxylation of isocitrate to α-ketoglutarate (αKG), NAD(P)H, and CO2. IDHs epigenetically control gene expression through effects on αKG-dependent dioxygenases, maintain redox balance and promote anaplerosis by providing cells with NADPH and precursor substrates for macromolecular synthesis, and regulate respiration and energy production through generation of NADH. Cancer-associated mutations in IDH1 and IDH2 represent one of the most comprehensively studied mechanisms of IDH pathogenic effect. Mutant enzymes produce (R)-2-hydroxyglutarate, which in turn inhibits αKG-dependent dioxygenase function, resulting in a global hypermethylation phenotype, increased tumor cell multipotency, and malignancy. Recent studies identified wild-type IDHs as critical regulators of normal organ physiology and, when transcriptionally induced or down-regulated, as contributing to cancer and neurodegeneration, re
The advances of genome-wide 'discovery platforms' and the increasing affordability of the analysis of significant sample sizes have led to the identification of novel mutations in brain tumours that became diagnostically and prognostically relevant. The development of mutation-specific antibodies has facilitated the introduction of these convenient biomarkers into most neuropathology laboratories and has changed our approach to brain tumour diagnostics. However, tissue diagnosis will remain an essential first step for the correct stratification for subsequent molecular tests, and the combined interpretation of the molecular and tissue diagnosis ideally remains with the neuropathologist. This overview will help our understanding of the pathobiology of common intrinsic brain tumours in adults and help guiding which molecular tests can supplement and refine the tissue diagnosis of the most common adult intrinsic brain tumours. This article will discuss the relevance of 1p/19q codeletions,
Fluoroquinolones (FQs) are an important class of potent broad-spectrum antibiotics. However, their general use is more and more limited by adverse side effects. While general mechanisms for the fluoroquinolone-associated disability (FQAD) have been identified, the underlying molecular targets of toxicity remain elusive. In this study, focusing on the most commonly prescribed FQs Ciprofloxacin and Levofloxacin, whole proteome analyses revealed prominent mitochondrial dysfunction in human cells, specifically of the complexes I and IV of the electron transport chain (ETC). Furthermore, global untargeted chemo-proteomic methodologies such as photo-affinity profiling with FQ-derived probes, as well as derivatization-free thermal proteome profiling, were applied to elucidate human protein off-targets of FQs in living cells. Accordingly, the interactions of FQs with mitochondrial AIFM1 and IDH2 have been identified and biochemically validated for their contribution to mitochondrial dysfunctio
BACKGROUND: Methanesulfonic acid sodium salt (Dipyrone), an antipyretic and analgesic drug, has been demonstrated to improve cerebral ischemia through the inhibition of mitochondrial cell death cascades. The aim of this study was to evaluate the potential photoprotective activity of methanesulfonic acid sodium salt in a model of light-induced retinopathy. METHODS: One hundred mice were assigned randomly into vehicle (V), methanesulfonic acid sodium salt (D), light damage model plus vehicle (MV) and light damage model plus methanesulfonic acid sodium salt (MD) groups (n = 25 each). In the MD group, methanesulfonic acid sodium salt (100 mg/kg) was administered by intraperitoneal injection 30 minutes before light exposure. Twenty-four hours after light exposure, hematoxylin and eosin staining and transmission electron microscopy (TEM) were used for histological evaluation. The thickness of the outer plus inner-segment and outer nuclear layer was measured on sections parallel to the vertic
Loop-based multiple heart-cutting (MHC) two-dimensional liquid chromatography (2D-LC) is presented as a solution to quantify target components in complex matrices, such as additives in polymers, at very high chromatographic resolution. The determination of hexabromocyclododecane (HBCD) in polystyrene (PS) is described. One dimensional ((1)D) LC analysis with UV detection did not allow quantitation of the main isomers of HBCD due to peak overlap with polymer components. MHC 2D-LC analysis provided the separation power, accuracy, and repeatability needed for quantitative analysis of the additives of interest. Heart-cuts from peaks of the (1)D-chromatogram or entire regions of interest are sampled into loops, where they remain parked until their sequential reinjection onto the second dimension ((2)D) column. A column set consisting of phenyl ((1)D) and C18 ((2)D) stationary phases gave baseline separation in (2)D between HBCD and PS background. Linearity for spiked polymer samples was ach
Target: HCN1 (hyperpolarization-activated cyclic nucleotide-gated channel 1)
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Specific Weaknesses:
Chemical Matter Challenges:
| Event | Price | Change | Source | Time | |
|---|---|---|---|---|---|
| 📄 | New Evidence | $0.413 | ▲ 2.7% | evidence_batch_update | 2026-04-13 02:18 |
| 📄 | New Evidence | $0.402 | ▲ 6.8% | evidence_batch_update | 2026-04-13 02:18 |
| ⚖ | Recalibrated | $0.376 | ▼ 0.5% | 2026-04-12 10:15 | |
| ⚖ | Recalibrated | $0.378 | ▼ 1.6% | 2026-04-10 15:58 | |
| ⚖ | Recalibrated | $0.384 | ▲ 1.9% | 2026-04-10 14:28 | |
| ⚖ | Recalibrated | $0.377 | ▲ 2.9% | 2026-04-08 18:39 | |
| ⚖ | Recalibrated | $0.366 | ▲ 1.7% | 2026-04-06 04:04 | |
| ⚖ | Recalibrated | $0.360 | ▼ 0.9% | 2026-04-04 16:38 | |
| ⚖ | Recalibrated | $0.364 | ▼ 3.5% | 2026-04-04 16:02 | |
| 📄 | New Evidence | $0.377 | ▲ 4.0% | evidence_batch_update | 2026-04-04 09:08 |
| ⚖ | Recalibrated | $0.362 | ▼ 2.3% | 2026-04-03 23:46 | |
| ⚖ | Recalibrated | $0.370 | ▼ 0.4% | 2026-04-02 21:55 | |
| ⚖ | Recalibrated | $0.372 | ▼ 11.8% | market_recalibrate | 2026-04-02 19:14 |
| 💬 | Debate Round | $0.422 | ▲ 2.6% | debate_engine | 2026-04-02 17:18 |
| 📄 | New Evidence | $0.411 | ▼ 5.7% | market_dynamics | 2026-04-02 17:18 |
Molecular pathway showing key causal relationships underlying this hypothesis
graph TD
IDH2["IDH2"] -->|regulates| Grid_Cell_Specific_Metabo["Grid Cell-Specific Metabolic Reprogramming via IDH"]
IDH2_1["IDH2"] -->|regulates| Tau_Propagation["Tau Propagation"]
IDH2_2["IDH2"] -->|associated with| neurodegeneration["neurodegeneration"]
RELN["RELN"] -->|co discussed| IDH2_3["IDH2"]
MAP6["MAP6"] -->|co discussed| IDH2_4["IDH2"]
HCN1["HCN1"] -->|co discussed| IDH2_5["IDH2"]
MCU["MCU"] -->|co discussed| IDH2_6["IDH2"]
PPARGC1A["PPARGC1A"] -->|co discussed| IDH2_7["IDH2"]
SLC16A2["SLC16A2"] -->|co discussed| IDH2_8["IDH2"]
IDH2_9["IDH2"] -->|co discussed| MAP6_10["MAP6"]
IDH2_11["IDH2"] -->|co discussed| PPARGC1A_12["PPARGC1A"]
IDH2_13["IDH2"] -->|co discussed| SLC16A2_14["SLC16A2"]
IDH2_15["IDH2"] -->|co discussed| HCN1_16["HCN1"]
IDH2_17["IDH2"] -->|co discussed| RELN_18["RELN"]
IDH2_19["IDH2"] -->|co discussed| MCU_20["MCU"]
style IDH2 fill:#ce93d8,stroke:#333,color:#000
style Grid_Cell_Specific_Metabo fill:#4fc3f7,stroke:#333,color:#000
style IDH2_1 fill:#ce93d8,stroke:#333,color:#000
style Tau_Propagation fill:#ffd54f,stroke:#333,color:#000
style IDH2_2 fill:#ce93d8,stroke:#333,color:#000
style neurodegeneration fill:#ef5350,stroke:#333,color:#000
style RELN fill:#ce93d8,stroke:#333,color:#000
style IDH2_3 fill:#ce93d8,stroke:#333,color:#000
style MAP6 fill:#ce93d8,stroke:#333,color:#000
style IDH2_4 fill:#ce93d8,stroke:#333,color:#000
style HCN1 fill:#ce93d8,stroke:#333,color:#000
style IDH2_5 fill:#ce93d8,stroke:#333,color:#000
style MCU fill:#ce93d8,stroke:#333,color:#000
style IDH2_6 fill:#ce93d8,stroke:#333,color:#000
style PPARGC1A fill:#ce93d8,stroke:#333,color:#000
style IDH2_7 fill:#ce93d8,stroke:#333,color:#000
style SLC16A2 fill:#ce93d8,stroke:#333,color:#000
style IDH2_8 fill:#ce93d8,stroke:#333,color:#000
style IDH2_9 fill:#ce93d8,stroke:#333,color:#000
style MAP6_10 fill:#ce93d8,stroke:#333,color:#000
style IDH2_11 fill:#ce93d8,stroke:#333,color:#000
style PPARGC1A_12 fill:#ce93d8,stroke:#333,color:#000
style IDH2_13 fill:#ce93d8,stroke:#333,color:#000
style SLC16A2_14 fill:#ce93d8,stroke:#333,color:#000
style IDH2_15 fill:#ce93d8,stroke:#333,color:#000
style HCN1_16 fill:#ce93d8,stroke:#333,color:#000
style IDH2_17 fill:#ce93d8,stroke:#333,color:#000
style RELN_18 fill:#ce93d8,stroke:#333,color:#000
style IDH2_19 fill:#ce93d8,stroke:#333,color:#000
style MCU_20 fill:#ce93d8,stroke:#333,color:#000
neurodegeneration | 2026-04-01 | completed