• 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br Fig Phenotype screening method Left panel After five


    Fig. 1. Phenotype screening method. (Left panel) After five baseline measurements were taken, a test sample was injected. Next, changes in OCR and ECAR were simultaneously monitored for 1 h, followed by measurements using the Seahorse XF Cell Mito Stress Test kit. Oligomycin A (OMA), FCCP, and rotenone/antimycin A (R/A) were added in a stepwise fashion. (Right panel) Percentage changes in OCR or ECAR were calculated as follows: (measurement #6)/(measurement #5) × 100 (6 min after drug treatment) or (measurement #15)/(measurement #5) × 100 (60 min after drug treatment). Details of the assay conditions are summarized in the panel.
    2.6. In vitro reduced nicotinamide 186689-07-6 dinucleotide-coenzyme Q (NADH-CoQ) reductase assay
    The enzymatic activity of complex I was determined using MitoCheck Complex I Activity Assay Kit (Cat. No: 700930; Cayman Chemical, Ann Arbor, MI, USA) according to the manufacturer's in-structions.
    3. Results
    3.1. Construction of phenotypic screening system based on bioenergetic and proteomic profiling
    The following method using the Seahorse XFe96 analyzer was used to screen for the inhibitors of cancer metabolism (Fig. 1). First, HeLa cells were treated with test samples, after which OCR and ECAR were simultaneously monitored for 1 h. Percentage changes in OCR and ECAR were calculated, after which test samples that caused noticeable changes in the ratio between the two values were identified for further study. Subsequently, Mito Stress Test was performed, in which oligo-mycin A, FCCP, and rotenone/antimycin A were added to the cells in a stepwise manner. The pattern of changes in OCR values provided in-formation about key parameters of mitochondrial function (basal re-spiration, adenosine triphosphate (ATP) turnover, proton leak, and maximal respiration) and a possible mode of action of the test samples. Test-sample-treated cells that were still sensitive to the protonophore FCCP were identified as complex V inhibitors. An example of such was oligomycin A (Fig. S2).
    Before the screening was started, we created a standard set of ty-pical antimetabolic inhibitors; this included glycolysis inhibitors (2- 
    deoxyglucose (2-DG), iodoacetic acid), mitochondrial ETC complex inhibitors (rotenone, antimycin A, oligomycin A), a glutaminolysis in-hibitor (bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide, BPTES), a mitochondrial uncoupler (FCCP), and inhibitors of mi-tochondrial substrate uptake (etomoxir, UK5099). The effects of these reference compounds on cellular metabolism were then examined (Fig. 2 and Fig. S2).
    Our results indicated that inhibition of the glycolytic pathway led to
    a decrease in ECAR (Fig. 2 “Glycolysis” panel). Furthermore, direct inhibitors of mitochondrial respiration dramatically decreased OCR and correspondingly increased ECAR to compensate for ATP loss caused by OCR inhibition (Fig. 2 “Respiration” panel). FCCP induced an influx of H+ into mitochondria and stimulated uncoupled respiration, which resulted in significant increases in OCR and ECAR (Fig. 2 “Uncoupler” panel). BPTES induced a little change in OCR/ECAR ratio (Fig. 2 “Glutaminolysis” panel). UK5099, an inhibitor of mitochondrial pyr-uvate carrier, and etomoxir, an irreversible inhibitor of carnitine pal-mitoyltransferase 1, caused partial OXPHOS inhibition due to the pathway-specific blockade of mitochondrial uptake of substrates for the tricarboxylic acid cycle (Fig. 2 “Substrate uptake” panel). These data indicated that the profiling system used could help with the selection of hit samples in an unbiased fashion.
    Meanwhile, we have constructed a proteomic profiling system, ChemProteoBase, which identifies the targets of compounds via pro-teome analyses using 2-D DIGE. The expression of the 296 spots (Fig. S3) obtained from the analysis of HeLa cells treated with the sample compounds was compared to the expression data in the ChemProteoBase database. We identified 274 spots among the 296 spots in the master gel image using HeLa cell lysate and 183 different proteins in the list due to the duplication of different spots identified as
    Fig. 2. Changes in cellular metabolism induced by well characterized antimetabolic inhibitors. Percentage changes in OCR and ECAR (relative to their respective baseline values) induced by the indicated compounds were calculated from Fig. S1. Each data point represents the mean ± standard deviation value at 6 min (all inhibitors except glycolysis inhibitors) or 60 min (glycolysis inhibitors) after treatment. Data are mean ± s.d. (n = 3 technical replicates) from one representative experiment out of two independent experiments.
    the same protein (Table S1). In order to identify spots of glycolytic enzymes that are involved in cellular metabolism, we searched for the word “glycolysis” in the Comment line, the Database cross-Reference line, and the KeyWord line of the UniProt record for each identified protein. We found that 24 spots (8 proteins) designated in Fig. 3A were glycolytic enzymes. After measuring these spots, the expression of the eight glycolytic enzymes was determined simultaneously in the cells. Thereafter, we examined the relationships between changes in the ex-pression of the glycolytic enzymes and the mode of action of each drug (Fig. 3B). Our data showed that inhibitors of mitochondrial respiration, such as antimycin A, tend to increase the expression levels of enzymes that are involved in glycolysis. The glycolysis inhibitor 2-DG induced accumulation of proteins associated with glycolysis. On the other hand, dasatinib and rapamycin tend to decrease the expression levels of gly-colytic enzymes. This suggests that unique proteomic patterns of these proteins would be helpful in validating antimetabolic candidates.