SUMMARY It remains unclear how cancer cells coordinate glycolysis and biosynthesis to support rapidly growing tumors. We found that glycolytic enzyme phosphoglycerate mutase 1 (PGAM1), commonly upregulated in human cancers due to loss of TP53, contributes to biosynthesis regulation in part by controlling intracellular levels of its substrate 3-phosphoglycerate (3-PG) and product 2-phosphoglycerate (2-PG). 3-PG binds to and inhibits 6-phosphogluconate dehydrogenase in the oxidative pentose phosphate pathway (PPP), while 2-PG activates 3-phosphoglycerate dehydrogenase to provide feedback control of 3-PG levels. Inhibition of PGAM1 by shRNA or a small molecule inhibitor PGMI-004A results in increased 3-PG and decreased 2-PG levels in cancer cells, leading to significantly decreased glycolysis, PPP flux and biosynthesis, as well as attenuated cell proliferation and tumor growth.

SUMMARY Mitochondrial pyruvate dehydrogenase complex (PDC) is crucial for glucose homoeostasis in mammalian cells. The current understanding of PDC regulation involves inhibitory serine phosphorylation of pyruvate dehydrogenase (PDH) by PDH kinase (PDK), whereas dephosphorylation of PDH by PDH phosphatase (PDP) activates PDC. Here we report that lysine acetylation of PDHA1 and PDP1 is common in EGF-stimulated cells and diverse human cancer cells. K321 acetylation inhibits PDHA1 by recruiting PDK1 and K202 acetylation inhibits PDP1 by dissociating its substrate PDHA1, both of which are important to promote glycolysis in cancer cells and consequent tumor growth. Moreover, we identified mitochondrial ACAT1 and SIRT3 as the upstream acetyltransferase and deacetylase, respectively, of PDHA1 and PDP1, while knockdown of ACAT1 attenuates tumor growth. Furthermore, Y381 phosphorylation of PDP1 dissociates SIRT3 and recruits ACAT1 to PDC. Together, hierarchical, distinct post-translational modifications act in concert to control molecular composition of PDC and contribute to the Warburg effect.

The oxidative pentose phosphate pathway (PPP) contributes to tumor growth, but the precise contribution of 6-phosphogluconate dehydrogenase (6PGD), the third enzyme in this pathway, to tumorigenesis remains unclear. We found that suppression of 6PGD decreased lipogenesis and RNA biosynthesis and elevated ROS levels in cancer cells, attenuating cell proliferation and tumor growth. 6PGD-mediated production of ribulose-5-phosphate (Ru-5-P) inhibits AMPK activation by disrupting the active LKB1 complex, thereby activating acetyl-CoA carboxylase 1 and lipogenesis. Ru-5-P and NADPH are thought to be precursors in RNA biosynthesis and lipogenesis, respectively; thus, our findings provide an additional link between oxidative PPP and lipogenesis through Ru-5-P-dependent inhibition of LKB1-AMPK signaling. Moreover, we identified and developed 6PGD inhibitors, Physcion and its derivative S3, that effectively inhibited 6PGD, cancer cell proliferation and tumor growth in nude mice xenografts without obvious toxicity, suggesting that 6PGD could be an anticancer target.

The role of mitochondria in cancer is controversial. Using a genome-wide shRNA screen, we now show that tumours reprogram a network of mitochondrial dynamics operative in neurons, including syntaphilin (SNPH), kinesin KIF5B and GTPase Miro1/2 to localize mitochondria to the cortical cytoskeleton and power the membrane machinery of cell movements. When expressed in tumours, SNPH inhibits the speed and distance travelled by individual mitochondria, suppresses organelle dynamics, and blocks chemotaxis and metastasis, in vivo. Tumour progression in humans is associated with downregulation or loss of SNPH, which correlates with shortened patient survival, increased mitochondrial trafficking to the cortical cytoskeleton, greater membrane dynamics and heightened cell invasion. Therefore, a SNPH network regulates metastatic competence and may provide a therapeutic target in cancer.

Mitochondria must buffer the risk of proteotoxic stress to preserve bioenergetics, but the role of these mechanisms in disease is poorly understood. Using a proteomics screen, we now show that the mitochondrial unfoldase-peptidase complex ClpXP associates with the oncoprotein survivin and the respiratory chain Complex II subunit succinate dehydrogenase B (SDHB) in mitochondria of tumor cells. Knockdown of ClpXP subunits ClpP or ClpX induces the accumulation of misfolded SDHB, impairing oxidative phosphorylation and ATP production while activating “stress” signals of 5′ adenosine monophosphate-activated protein kinase (AMPK) phosphorylation and autophagy. Deregulated mitochondrial respiration induced by ClpXP targeting causes oxidative stress, which in turn reduces tumor cell proliferation, suppresses cell motility, and abolishes metastatic dissemination in vivo. ClpP is universally overexpressed in primary and metastatic human cancer, correlating with shortened patient survival. Therefore, tumors exploit ClpXP-directed proteostasis to maintain mitochondrial bioenergetics, buffer oxidative stress, and enable metastatic competence. This pathway may provide a “drugable” therapeutic target in cancer.

SUMMARY Hypoxia is a universal driver of aggressive tumor behavior, but the underlying mechanisms are not completely understood. Using a phosphoproteomics screen, we now show that active Akt accumulates in the mitochondria during hypoxia and phosphorylates pyruvate dehydrogenase kinase 1 (PDK1) on Thr346 to inactivate the pyruvate dehydrogenase complex. In turn, this pathway switches tumor metabolism towards glycolysis, antagonizes apoptosis and autophagy, dampens oxidative stress, and maintains tumor cell proliferation in the face of severe hypoxia. Mitochondrial Akt-PDK1 signaling correlates with unfavorable prognostic markers and shorter survival in glioma patients and may provide an “actionable” therapeutic target in cancer.

SUMMARY Many human cancers share similar metabolic alterations, including the Warburg effect. However, it remains unclear whether oncogene-specific metabolic alterations are required for tumor development. Here we demonstrate a “synthetic lethal” interaction between oncogenic BRAF V600E and a ketogenic enzyme 3-hydroxy-3-methylglutaryl-CoA lyase (HMGCL). HMGCL expression is upregulated in BRAF V600E-expressing human primary melanoma and hairy cell leukemia cells. Suppression of HMGCL specifically attenuates proliferation and tumor growth potential of human melanoma cells expressing BRAF V600E. Mechanistically, active BRAF upregulates HMGCL through an octamer transcription factor Oct-1, leading to increased intracellular levels of HMGCL product, acetoacetate, which selectively enhances binding of BRAF V600E but not BRAF wild type to MEK1 in V600E-positive cancer cells to promote activation of MEK-ERK signaling. These findings reveal a mutation-specific mechanism by which oncogenic BRAF V600E “rewires” metabolic and cell signaling networks and signals through the Oct-1-HMGCL-acetoacetate axis to selectively promote BRAF V600E-dependent tumor development.

Background COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.