PK-M2-mediated metabolic changes in breast cancer cells induced by ionizing radiation.

PURPOSE: Radiotherapy (RT) constitutes an important part of breast cancer treatment. However, triple negative breast cancers (TNBC) exhibit remarkable resistance to most therapies, including RT. Developing new ways to radiosensitize TNBC cells could result in improved patient outcomes. The M2 isoform of pyruvate kinase (PK-M2) is believed to be responsible for the re-wiring of cancer cell metabolism after oxidative stress. The aim of the study was to determine the effect of ionizing radiation (IR) on PK-M2-mediated metabolic changes in TNBC cells, and their survival. In addition, we determine the effect of PK-M2 activators on breast cancer stem cells, a radioresistant subpopulation of breast cancer stem cells. METHODS: Glucose uptake, lactate production, and glutamine consumption were assessed. The cellular localization of PK-M2 was evaluated by western blot and confocal microscopy. The small molecule activator of PK-M2, TEPP46, was used to promote its pyruvate kinase function. Finally, effects on cancer stem cell were evaluated via sphere forming capacity. RESULTS: Exposure of TNBC cells to IR increased their glucose uptake and lactate production. As expected, PK-M2 expression levels also increased, especially in the nucleus, although overall pyruvate kinase activity was decreased. PK-M2 nuclear localization was shown to be associated with breast cancer stem cells, and activation of PK-M2 by TEPP46 depleted this population. CONCLUSIONS: Radiotherapy can induce metabolic changes in TNBC cells, and these changes seem to be mediated, at least in part by PK-M2. Importantly, our results show that activators of PK-M2 can deplete breast cancer stem cells in vitro. This study supports the idea of combining PK-M2 activators with radiation to enhance the effect of radiotherapy in resistant cancers, such as TNBC.

Norepinephrine Modulates Coding of Complex Vocalizations in the Songbird Auditory Cortex Independent of Local Neuroestrogen Synthesis.

The catecholamine norepinephrine plays a significant role in auditory processing. Most studies to date have examined the effects of norepinephrine on the neuronal response to relatively simple stimuli, such as tones and calls. It is less clear how norepinephrine shapes the detection of complex syntactical sounds, as well as the coding properties of sensory neurons. Songbirds provide an opportunity to understand how auditory neurons encode complex, learned vocalizations, and the potential role of norepinephrine in modulating the neuronal computations for acoustic communication. Here, we infused norepinephrine into the zebra finch auditory cortex and performed extracellular recordings to study the modulation of song representations in single neurons. Consistent with its proposed role in enhancing signal detection, norepinephrine decreased spontaneous activity and firing during stimuli, yet it significantly enhanced the auditory signal-to-noise ratio. These effects were all mimicked by clonidine, an α-2 receptor agonist. Moreover, a pattern classifier analysis indicated that norepinephrine enhanced the ability of single neurons to accurately encode complex auditory stimuli. Because neuroestrogens are also known to enhance auditory processing in the songbird brain, we tested the hypothesis that norepinephrine actions depend on local estrogen synthesis. Neither norepinephrine nor adrenergic receptor antagonist infusion into the auditory cortex had detectable effects on local estradiol levels. Moreover, pretreatment with fadrozole, a specific aromatase inhibitor, did not block norepinephrine's neuromodulatory effects. Together, these findings indicate that norepinephrine enhances signal detection and information encoding for complex auditory stimuli by suppressing spontaneous "noise" activity and that these actions are independent of local neuroestrogen synthesis.

A Suite of Mouse Reagents for Studying Amelogenesis.

Amelogenesis, the formation of dental enamel, is driven by specialized epithelial cells called ameloblasts, which undergo successive stages of differentiation. Ameloblasts secrete enamel matrix proteins (EMPs), proteases, calcium, and phosphate ions in a stage-specific manner to form mature tooth enamel. Developmental defects in tooth enamel are common in humans, and they can greatly impact the well-being of affected individuals. Our understanding of amelogenesis and developmental pathologies is rooted in past studies using epithelial Cre driver and knockout alleles. However, the available mouse models are limited, as most do not allow targeting different ameloblast sub-populations, and constitutive loss of EMPs often results in severe phenotype in the mineral, making it difficult to interpret defect mechanisms. Herein, we report on the design and verification of a toolkit of twelve mouse alleles that include ameloblast-stage specific Cre recombinases, fluorescent reporter alleles, and conditional flox alleles for the major EMPs. We show how these models may be used for applications such as sorting of live stage specific ameloblasts, whole mount imaging, and experiments with incisor explants. The full list of new alleles is available at https://dev.facebase.org/enamelatlas/mouse-models/ .

M2 isoform of pyruvate kinase rewires glucose metabolism during radiation therapy to promote an antioxidant response and glioblastoma radioresistance.

BACKGROUND: Resistance to existing therapies is a significant challenge in improving outcomes for glioblastoma (GBM) patients. Metabolic plasticity has emerged as an important contributor to therapy resistance, including radiation therapy (RT). Here, we investigated how GBM cells reprogram their glucose metabolism in response to RT to promote radiation resistance. METHODS: Effects of radiation on glucose metabolism of human GBM specimens were examined in vitro and in vivo with the use of metabolic and enzymatic assays, targeted metabolomics, and FDG-PET. Radiosensitization potential of interfering with M2 isoform of pyruvate kinase (PKM2) activity was tested via gliomasphere formation assays and in vivo human GBM models. RESULTS: Here, we show that RT induces increased glucose utilization by GBM cells, and this is accompanied with translocation of GLUT3 transporters to the cell membrane. Irradiated GBM cells route glucose carbons through the pentose phosphate pathway (PPP) to harness the antioxidant power of the PPP and support survival after radiation. This response is regulated in part by the PKM2. Activators of PKM2 can antagonize the radiation-induced rewiring of glucose metabolism and radiosensitize GBM cells in vitro and in vivo. CONCLUSIONS: These findings open the possibility that interventions designed to target cancer-specific regulators of metabolic plasticity, such as PKM2, rather than specific metabolic pathways, have the potential to improve the radiotherapeutic outcomes in GBM patients.

Radiosensitizing Pancreatic Cancer via Effective Autophagy Inhibition.

Despite aggressive treatments, pancreatic ductal adenocarcinoma (PDAC) remains an intractable disease, largely because it is refractory to therapeutic interventions. To overcome its nutrient-poor microenvironment, PDAC heavily relies on autophagy for metabolic needs to promote tumor growth and survival. Here, we explore autophagy inhibition as a method to enhance the effects of radiotherapy on PDAC tumors. Hydroxychloroquine is an autophagy inhibitor at the focus of many PDAC clinical trials, including in combination with radiotherapy. However, its acid-labile properties likely reduce its intratumoral efficacy. Here, we demonstrate that EAD1, a synthesized analogue of HCQ, is a more effective therapeutic for sensitizing PDAC tumors of various KRAS mutations to radiotherapy. Specifically, in vitro models show that EAD1 is an effective inhibitor of autophagic flux in PDAC cells, accompanied by a potent inhibition of proliferation. When combined with radiotherapy, EAD1 is consistently superior to HCQ not only as a single agent, but also in radiosensitizing PDAC cells, and perhaps most importantly, in decreasing the self-renewal capacity of PDAC cancer stem cells (PCSC). The more pronounced sensitizing effects of autophagy inhibitors on pancreatic stem over differentiated cells points to a new understanding that PCSCs may be more dependent on autophagy to counter the effects of radiation toxicity, a potential mechanism explaining the resistance of PCSCs to radiotherapy. Finally, in vivo subcutaneous tumor models demonstrate that combination of radiotherapy and EAD1 is the most successful at controlling tumor growth. The models also confirmed a similar toxicity profile between EAD1 and Hydroxychloroquine.

1-[(4-Nitrophenyl)sulfonyl]-4-phenylpiperazine treatment after brain irradiation preserves cognitive function in mice.

BACKGROUND: Normal tissue toxicity is an inevitable consequence of primary or secondary brain tumor radiotherapy. Cranial irradiation commonly leads to neurocognitive deficits that manifest months or years after treatment. Mechanistically, radiation-induced loss of neural stem/progenitor cells, neuroinflammation, and demyelination are contributing factors that lead to progressive cognitive decline. METHODS: The effects of 1-[(4-nitrophenyl)sulfonyl]-4-phenylpiperazine (NSPP) on irradiated murine neurospheres, microglia cells, and patient-derived gliomaspheres were assessed by sphere-formation assays, flow cytometry, and interleukin (IL)-6 enzyme-linked immunosorbent assay. Activation of the hedgehog pathway was studied by quantitative reverse transcription PCR. The in vivo effects of NSPP were analyzed using flow cytometry, sphere-formation assays, immunohistochemistry, behavioral testing, and an intracranial mouse model of glioblastoma. RESULTS: We report that NSPP mitigates radiation-induced normal tissue toxicity in the brains of mice. NSPP treatment significantly increased the number of neural stem/progenitor cells after brain irradiation in female animals, and inhibited radiation-induced microglia activation and expression of the pro-inflammatory cytokine IL-6. Behavioral testing revealed that treatment with NSPP after radiotherapy was able to successfully mitigate radiation-induced decline in memory function of the brain. In mouse models of glioblastoma, NSPP showed no toxicity and did not interfere with the growth-delaying effects of radiation. CONCLUSIONS: We conclude that NSPP has the potential to mitigate cognitive decline in patients undergoing partial or whole brain irradiation without promoting tumor growth and that the use of this compound as a radiation mitigator of radiation late effects on the central nervous system warrants further investigation.

Development and verification of prediction models for preventing cardiovascular diseases.

OBJECTIVES: Cardiovascular disease (CVD) is one of the major causes of death worldwide. For improved accuracy of CVD prediction, risk classification was performed using national time-series health examination data. The data offers an opportunity to access deep learning (RNN-LSTM), which is widely known as an outstanding algorithm for analyzing time-series datasets. The objective of this study was to show the improved accuracy of deep learning by comparing the performance of a Cox hazard regression and RNN-LSTM based on survival analysis. METHODS AND FINDINGS: We selected 361,239 subjects (age 40 to 79 years) with more than two health examination records from 2002-2006 using the National Health Insurance System-National Health Screening Cohort (NHIS-HEALS). The average number of health screenings (from 2002-2013) used in the analysis was 2.9 ± 1.0. Two CVD prediction models were developed from the NHIS-HEALS data: a Cox hazard regression model and a deep learning model. In an internal validation of the NHIS-HEALS dataset, the Cox regression model showed a highest time-dependent area under the curve (AUC) of 0.79 (95% CI 0.70 to 0.87) for in females and 0.75 (95% CI 0.70 to 0.80) in males at 2 years. The deep learning model showed a highest time-dependent AUC of 0.94 (95% CI 0.91 to 0.97) for in females and 0.96 (95% CI 0.95 to 0.97) in males at 2 years. Layer-wise Relevance Propagation (LRP) revealed that age was the variable that had the greatest effect on CVD, followed by systolic blood pressure (SBP) and diastolic blood pressure (DBP), in that order. CONCLUSION: The performance of the deep learning model for predicting CVD occurrences was better than that of the Cox regression model. In addition, it was confirmed that the known risk factors shown to be important by previous clinical studies were extracted from the study results using LRP.

Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination.

BACKGROUND: With the increase in the world's aging population, there is a growing need to prevent and predict dementia among the general population. The availability of national time-series health examination data in South Korea provides an opportunity to use deep learning algorithm, an artificial intelligence technology, to expedite the analysis of mass and sequential data. OBJECTIVE: This study aimed to compare the discriminative accuracy between a time-series deep learning algorithm and conventional statistical methods to predict all-cause dementia and Alzheimer dementia using periodic health examination data. METHODS: Diagnostic codes in medical claims data from a South Korean national health examination cohort were used to identify individuals who developed dementia or Alzheimer dementia over a 10-year period. As a result, 479,845 and 465,081 individuals, who were aged 40 to 79 years and without all-cause dementia and Alzheimer dementia, respectively, were identified at baseline. The performance of the following 3 models was compared with predictions of which individuals would develop either type of dementia: Cox proportional hazards model using only baseline data (HR-B), Cox proportional hazards model using repeated measurements (HR-R), and deep learning model using repeated measurements (DL-R). RESULTS: The discrimination indices (95% CI) for the HR-B, HR-R, and DL-R models to predict all-cause dementia were 0.84 (0.83-0.85), 0.87 (0.86-0.88), and 0.90 (0.90-0.90), respectively, and those to predict Alzheimer dementia were 0.87 (0.86-0.88), 0.90 (0.88-0.91), and 0.91 (0.91-0.91), respectively. The DL-R model showed the best performance, followed by the HR-R model, in predicting both types of dementia. The DL-R model was superior to the HR-R model in all validation groups tested. CONCLUSIONS: A deep learning algorithm using time-series data can be an accurate and cost-effective method to predict dementia. A combination of deep learning and proportional hazards models might help to enhance prevention strategies for dementia.

Osteoprogenitor cells from bone marrow and cortical bone: understanding how the environment affects their fate.

Bone is a dynamic organ where skeletal progenitors and hematopoietic cells share and compete for space. Presumptive mesenchymal stem cells (MSC) have been identified and harvested from the bone marrow (BM-MSC) and cortical bone fragments (CBF-MSC). In this study, we demonstrate that despite the cells sharing a common ancestor, the differences in the structural properties of the resident tissues affect cell behavior and prime them to react differently to stimuli. Similarly to the bone marrow, the cortical portion of the bone contains a unique subset of cells that stains positively for the common MSC-associated markers. These cells display different multipotent differentiation capability, clonogenic expansion, and immunosuppressive potential. In particular, when compared with BM-MSC, CBF-MSC are bigger in size, show a lower proliferation rate at early passages, have a greater commitment toward the osteogenic lineage, constitutively produce nitric oxide as a mediator for bone remodeling, and more readily respond to proinflammatory cytokines. Our data suggest that the effect of the tissue's microenvironment makes the CBF-MSC a superior candidate in the development of new strategies for bone repair.

Serotonin (5-HT) excites rat masticatory muscle afferent fibers through activation of peripheral 5-HT3 receptors.

In the present study, we combined immunohistochemical experiments with in vivo single unit recordings to examine whether 5-HT(3) receptors are expressed by masticatory (masseter and temporalis) sensory ganglion neurons and to investigate the effects of intramuscular injection of 5-HT on the excitability and mechanical threshold of rat masticatory muscle afferent fibers. The expression of 5-HT(3) receptors by masticatory ganglion neurons was examined using immunohistochemical techniques. In vivo extracellular single unit recording techniques were used to assess changes in the excitability of individual masticatory muscle afferent fibers. Immunohistochemical experiments detected a relatively high frequency (52%) of 5-HT(3) receptor expression by masticatory ganglion neurons. Injection of 5-HT (10(-4), 10(-3), 10(-2)M) evoked concentration-related increases in the magnitude of afferent discharge, but did not significantly sensitize muscle afferent fibers to mechanical stimuli. No significant sex-related differences in 5-HT-evoked afferent discharge were identified. Afferent discharge evoked by 5-HT was significantly attenuated by co-injection with the selective 5-HT(3) receptor antagonist tropisetron (10(-3)M). Afferent discharge was also evoked by the selective 5-HT(3) receptor agonist 2-methyl-5-HT. Unexpectedly, a significant concentration-related decrease in median blood pressure in response to 5-HT injection was found. This 5-HT-induced decrease in blood pressure was not antagonized by tropisetron or mimicked by 2-methyl-5-HT, indicating that the drop in blood pressure was not 5-HT(3) receptor-mediated. The present results indicate that 5-HT excites slowly conducting masticatory muscle afferent fibers through activation of peripheral 5-HT(3) receptors, and suggest that similar mechanisms may contribute to 5-HT-evoked muscle pain in human subjects.