Adaptive stimulation of macropinocytosis overcomes aspartate limitation in cancer cells under hypoxia.

Stress-adaptive mechanisms enable tumour cells to overcome metabolic constraints under nutrient and oxygen shortage. Aspartate is an endogenous metabolic limitation under hypoxic conditions, but the nature of the adaptive mechanisms that contribute to aspartate availability and hypoxic tumour growth are poorly understood. Here we identify GOT2-catalysed mitochondrial aspartate synthesis as an essential metabolic dependency for the proliferation of pancreatic tumour cells under hypoxic culture conditions. In contrast, GOT2-catalysed aspartate synthesis is dispensable for pancreatic tumour formation in vivo. The dependence of pancreatic tumour cells on aspartate synthesis is bypassed in part by a hypoxia-induced potentiation of extracellular protein scavenging via macropinocytosis. This effect is mutant KRAS dependent, and is mediated by hypoxia-inducible factor 1 (HIF1A) and its canonical target carbonic anhydrase-9 (CA9). Our findings reveal high plasticity of aspartate metabolism and define an adaptive regulatory role for macropinocytosis by which mutant KRAS tumours can overcome nutrient deprivation under hypoxic conditions.

Dietary thiamine influences l-asparaginase sensitivity in a subset of leukemia cells.

Tumor environment influences anticancer therapy response but which extracellular nutrients affect drug sensitivity is largely unknown. Using functional genomics, we determine modifiers of l-asparaginase (ASNase) response and identify thiamine pyrophosphate kinase 1 as a metabolic dependency under ASNase treatment. While thiamine is generally not limiting for cell proliferation, a DNA-barcode competition assay identifies leukemia cell lines that grow suboptimally under low thiamine and are characterized by low expression of solute carrier family 19 member 2 (SLC19A2), a thiamine transporter. SLC19A2 is necessary for optimal growth and ASNase resistance, when standard medium thiamine is lowered ~100-fold to human plasma concentrations. In addition, humanizing blood thiamine content of mice through diet sensitizes SLC19A2-low leukemia cells to ASNase in vivo. Together, our work reveals that thiamine utilization is a determinant of ASNase response for some cancer cells and that oversupplying vitamins may affect therapeutic response in leukemia.

Genetic analysis of stilbenoid profiles in grapevine stems reveals a major mQTL hotspot on chromosome 18 associated with disease-resistance motifs.

Grapevine (Vitis spp.) contains a wealth of phytochemicals that have received considerable attention due to health-promoting properties and biological activities as phytoalexins. To date, the genetic basis of the quantitative variations for these potentially beneficial compounds has been limited. Here, metabolic quantitative trait locus (mQTL) mapping was conducted using grapevine stems of a segregating F2 population. Metabolic profiling of grapevine stems was performed using liquid chromatography-high-resolution mass spectrometry (LC-HRMS), resulting in the detection of 1317 ions/features. In total, 19 of these features matched with literature-reported stilbenoid masses and were genetically mapped using a 1449-SNP linkage map and R/qtl software, resulting in the identification of four mQTLs. Two large-effect mQTLs that corresponded to a stilbenoid dimer and a trimer were mapped on chromosome 18, accounting for phenotypic variances of 29.0% and 38.4%. Functional annotations of these large-effect mQTLs on the VitisNet network database revealed a major hotspot of disease-resistance motifs on chromosome 18. This 2.8-Mbp region contains 48 genes with R-gene motifs, including variants of TIR, NBS, and LRR, that might potentially confer resistance to powdery mildew, downy mildew, or other pathogens. The locus also encompasses genes associated with flavonoid and biosynthetic pathways that are likely involved in the production of secondary metabolites, including phytoalexins. In addition, haplotype dosage effects of the five mQTLs further characterized the genomic regions for differential production of stilbenoids that can be applied in resistance breeding through manipulation of stilbenoid production in planta.

Botanical metabolite ions extraction from full electrospray ionization mass spectrometry using high-dimensional penalized regression.

INTRODUCTION: Mass spectrometric data analysis of complex biological mixtures can be a challenge due to its vast datasets. There is lack of data treatment pipelines to analyze chemical signals versus noise. These tasks, so far, have been up to the discretion of the analysts. OBJECTIVES: The aim of this work is to demonstrate an analytical workflow that would enhance the confidence in metabolomics before answering biological questions by serial dilution of botanical complex mixture and high-dimensional data analysis. Furthermore, we would like to provide an alternative approach to a univariate p-value cutoff from t-test for blank subtraction procedure between negative control and biological samples. METHODS: A serial dilution of complex mixture analysis under electrospray ionization was proposed to study firsthand chemical complexity of metabolomics. Advanced statistical models using high-dimensional penalized regression were employed to study both the concentration and ion intensity relationship and the ion-ion relationship per second of retention time sub dataset. The multivariate analysis was carried out with a tool built in-house, so called metabolite ions extraction and visualization, which was implemented in R environment. RESULTS: A test case of the medicinal plant goldenseal (Hydrastis canandensis L.), showed an increase in metabolome coverage of features deemed as "important" by a multivariate analysis compared to features deemed as "significant" by a univariate t-test. For an illustration, the data analysis workflow suggested an unexpected putative compound, 20-hydroxyecdysone. This suggestion was confirmed with MS/MS acquisition and literature search. CONCLUSION: The multivariate analytical workflow selects "true" metabolite ions signals and provides an alternative approach to a univariate p-value cutoff from t-test, thus enhancing the data analysis process of metabolomics.