The hypoxia-inducible transcription factor HIF1α drives expression of many glycolytic enzymes. Here, we show that hypoxic glycolysis, in turn, increases HIF1α transcriptional activity and stimulates tumor growth, revealing a novel feed-forward mechanism of glycolysis-HIF1α signaling. Negative regulation of HIF1α by AMPK1 is bypassed in hypoxic cells, due to ATP elevation by increased glycolysis, thereby preventing phosphorylation and inactivation of the HIF1α transcriptional coactivator p300. Notably, of the HIF1α-activated glycolytic enzymes we evaluated by gene silencing, aldolase A (ALDOA) blockade produced the most robust decrease in glycolysis, HIF-1 activity, and cancer cell proliferation. Furthermore, either RNAi-mediated silencing of ALDOA or systemic treatment with a specific small-molecule inhibitor of aldolase A was sufficient to increase overall survival in a xenograft model of metastatic breast cancer. In establishing a novel glycolysis-HIF-1α feed-forward mechanism in hypoxic tumor cells, our results also provide a preclinical rationale to develop aldolase A inhibitors as a generalized strategy to treat intractable hypoxic cancer cells found widely in most solid tumors. Cancer Res; 76(14); 4259-69. ©2016 AACR.
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http://dx.doi.org/10.1158/0008-5472.CAN-16-0401 | DOI Listing |
Sensors (Basel)
January 2025
Engineering Training Center, Nantong University, Nantong 226019, China.
The issue of obstacle avoidance and safety for visually impaired individuals has been a major topic of research. However, complex street environments still pose significant challenges for blind obstacle detection systems. Existing solutions often fail to provide real-time, accurate obstacle avoidance decisions.
View Article and Find Full Text PDFFree Radic Biol Med
January 2025
Graduate Program in Biological Sciences: Toxicological Biochemistry, Centre of Natural and Exact Sciences, Federal University of Santa Maria, Santa Maria, RS, Brazil.
Fibromyalgia (FM) is a complex and multifaceted condition characterized by a range of clinical symptoms, including widespread pain and a strong association with migraine headaches. Recent findings have underscored the role of oxidative stress and transient receptor potential ankyrin 1 (TRPA1) channel in migraine and FM. However, the precise mechanisms underlying the comorbidity between migraine and FM are unclear.
View Article and Find Full Text PDFThe process by which neocortical neurons and circuits amplify their response to an unexpected change in stimulus, often referred to as deviance detection (DD), has long been thought to be the product of specialized cell types and/or routing between mesoscopic brain areas. Here, we explore a different theory, whereby DD emerges from local network-level interactions within a neocortical column. We propose that deviance-driven neural dynamics can emerge through interactions between ensembles of neurons that have a fundamental inhibitory motif: competitive inhibition between reciprocally connected ensembles under modulation from feed-forward selective (dis)inhibition.
View Article and Find Full Text PDFCogn Sci
January 2025
Department of Psychology, Binghamton University.
It has become widely accepted that standard connectionist models are unable to show identity-based relational reasoning that requires universal generalization. The purpose of this brief report is to show how one of the simplest forms of such models, feed-forward auto-associative networks, satisfies two of the most well-known challenges: universal generalization of the identity function and the reduplication rule. Given the simplicity of the modeling account provided, along with the clarity of the evidence, these demonstrations invite a shift in this high-profile debate over the nature of cognitive architecture and point to a way to bridge some of the presumed gulf between characteristically symbolic forms of reasoning and connectionist mechanisms.
View Article and Find Full Text PDFHeliyon
July 2024
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge.
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