Person:

Seth, Pankaj

Malic enzyme 2 (ME2) is a mitochondrial enzyme that catalyzes the conversion of malate to pyruvate and CO2 and uses NAD as a cofactor. Higher expression of this enzyme correlates with the degree of cell de-differentiation. We found that ME2 is expressed in K562 erythroleukemia cells, in which a number of agents have been found to induce differentiation either along the erythroid or the myeloid lineage. We found that knockdown of ME2 led to diminished proliferation of tumor cells and increased apoptosis in vitro. These findings were accompanied by differentiation of K562 cells along the erythroid lineage, as confirmed by staining for glycophorin A and hemoglobin production. ME2 knockdown also totally abolished growth of K562 cells in nude mice. Increased ROS levels, likely reflecting increased mitochondrial production, and a decreased (NADPH/NADP^+) ratio were noted but use of a free radical scavenger to decrease inhibition of ROS levels did not reverse the differentiation or apoptotic phenotype, suggesting that ROS production is not causally involved in the resultant phenotype. As might be expected, depletion of ME2 induced an increase in the (NAD^+/NADH) ratio and ATP levels fell significantly. Inhibition of the malate-aspartate shuttle was insufficient to induce K562 differentiation. We also examined several intracellular signaling pathways and expression of transcription factors and intermediate filament proteins whose expression is known to be modulated during erythroid differentiation in K562 cells. We found that silencing of ME2 leads to phospho-ERK1/2 inhibition, phospho-AKT activation, increased GATA-1 expression and diminished vimentin expression. Metabolomic analysis, conducted to gain insight into intermediary metabolic pathways that ME2 knockdown might affect, showed that ME2 depletion resulted in high orotate levels, suggesting potential impairment of pyrimidine metabolism. Collectively our data point to ME2 as a potentially novel metabolic target for leukemia therapy.

Background: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

Many malignant cells produce increased amounts of lactate, which promotes the development of myeloid-derived suppressor cells (MDSCs). MDSCs, lactate, and a low pH in the tumor microenvironment inhibit the function of natural killer (NK) cells and T lymphocytes, hence allowing for disease progression. Ketogenic diets can deplete tumor-bearing animals from MDSCs and regulatory T cells, thereby improving their immunological profile.

Mitochondrial malic enzyme 2 (ME2) catalyzes the oxidative decarboxylation of malate to yield CO2 and pyruvate, with concomitant reduction of dinucleotide cofactor NAD+ or NADP+. We find that ME2 is highly expressed in many solid tumors. In the A549 non-small cell lung cancer (NSCLC) cell line, ME2 depletion inhibits cell proliferation and induces cell death and differentiation, accompanied by increased reactive oxygen species (ROS) and NADP+/NADPH ratio, a drop in ATP, and increased sensitivity to cisplatin. ME2 knockdown impacts phosphoinositide-dependent protein kinase 1 (PDK1) and phosphatase and tensin homolog (PTEN) expression, leading to AKT inhibition. Depletion of ME2 leads to malate accumulation and pyruvate decrease, and exogenous cell permeable dimethyl-malate (DMM) mimics the ME2 knockdown phenotype. Both ME2 knockdown and DMM treatment reduce A549 cell growth in vivo. Collectively, our data suggest that ME2 is a potential target for cancer therapy.

During the type-setting of the final version of the article [1] some of the additional files were swapped. The correct files are republished in this Erratum. The online version of the original article can be found under doi:10.1186/s13059-015-0658-5.