Statistical hypothesis test of factor loading in principal component analysis and its application to metabolite set enrichment analysis

Principal component analysis (PCA) has been widely used to visualize high-dimensional metabolomic data in a two- or three-dimensional subspace. In metabolomics, some metabolites (e.g. top 10 metabolites) have been subjectively selected when using factor loading in PCA, and biological inferences for these metabolites are made. However, this approach is possible to lead biased biological inferences because these metabolites are not objectively selected by statistical criterion. We proposed a statistical procedure to pick up metabolites by statistical hypothesis test of factor loading in PCA and make biological inferences by metabolite set enrichment analysis (MSEA) for these significant metabolites. This procedure depends on the fact that the eigenvector in PCA for autoscaled data is proportional to the correlation coefficient between PC score and each metabolite levels. We applied this approach for two metabolomic data of mice liver samples. 136 of 282 metabolites in first case study and 66 of 275 metabolites in second case study were statistically significant. This result suggests that to set the previously-determined number of metabolites is not appropriate because the number of significant metabolites is different in each study when using factor loading in PCA. Moreover, MSEA was performed for these significant metabolites and significant metabolic pathways can be detected. These results are acceptable when compared with previous biological knowledge. It is essential to select metabolites statistically for making unbiased biological inferences from metabolome data, when using factor loading in PCA. We proposed a statistical procedure to pick up metabolites by statistical hypothesis test of factor loading in PCA and make biological inferences by MSEA for these significant metabolites. We developed an R package mseapca to perform this approach. The “mseapca” package is publicity available on CRAN website

Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis

BACKGROUND: Principal component analysis (PCA) has been widely used to visualize high-dimensional metabolomic data in a two- or three-dimensional subspace. In metabolomics, some metabolites (e.g., the top 10 metabolites) have been subjectively selected when using factor loading in PCA, and biological inferences are made for these metabolites. However, this approach may lead to biased biological inferences because these metabolites are not objectively selected with statistical criteria. RESULTS: We propose a statistical procedure that selects metabolites with statistical hypothesis testing of the factor loading in PCA and makes biological inferences about these significant metabolites with a metabolite set enrichment analysis (MSEA). This procedure depends on the fact that the eigenvector in PCA for autoscaled data is proportional to the correlation coefficient between the PC score and each metabolite level. We applied this approach to two sets of metabolomic data from mouse liver samples: 136 of 282 metabolites in the first case study and 66 of 275 metabolites in the second case study were statistically significant. This result suggests that to set the number of metabolites before the analysis is inappropriate because the number of significant metabolites differs in each study when factor loading is used in PCA. Moreover, when an MSEA of these significant metabolites was performed, significant metabolic pathways were detected, which were acceptable in terms of previous biological knowledge. CONCLUSIONS: It is essential to select metabolites statistically to make unbiased biological inferences from metabolomic data when using factor loading in PCA. We propose a statistical procedure to select metabolites with statistical hypothesis testing of the factor loading in PCA, and to draw biological inferences about these significant metabolites with MSEA. We have developed an R package “mseapca” to facilitate this approach. The “mseapca” package is publicly available at the CRAN website

SHMT2 drives glioma cell survival in the tumor microenvironment but imposes a dependence on glycine clearance

SUMMARY Cancer cells adapt their metabolic processes to support rapid proliferation, but less is known about how cancer cells alter metabolism to promote cell survival in a poorly vascularized tumor microenvironment1–3. Here, we identify a key role for serine and glycine metabolism in the survival of brain cancer cells within the ischemic zones of gliomas. In human glioblastoma multiforme (GBM), mitochondrial serine hydroxymethyltransferase (SHMT2) and glycine decarboxylase (GLDC) are highly expressed in the pseudopalisading cells that surround necrotic foci. We find that SHMT2 activity limits that of pyruvate kinase (PKM2) and reduces oxygen consumption, eliciting a metabolic state that confers a profound survival advantage to cells in poorly vascularized tumor regions. GLDC inhibition impairs cells with high SHMT2 levels as the excess glycine not metabolized by GLDC can be converted to the toxic molecules aminoacetone and methylglyoxal. Thus, SHMT2 is required for cancer cells to adapt to the tumor environment, but also renders these cells sensitive to glycine cleavage system inhibition

SHMT2 drives glioma cell survival in ischaemia but imposes a dependence on glycine clearance

Cancer cells adapt their metabolic processes to support rapid proliferation, but less is known about how cancer cells alter metabolism to promote cell survival in a poorly vascularized tumour microenvironment1, 2, 3. Here we identify a key role for serine and glycine metabolism in the survival of brain cancer cells within the ischaemic zones of gliomas. In human glioblastoma multiforme, mitochondrial serine hydroxymethyltransferase (SHMT2) and glycine decarboxylase (GLDC) are highly expressed in the pseudopalisading cells that surround necrotic foci. We find that SHMT2 activity limits that of pyruvate kinase (PKM2) and reduces oxygen consumption, eliciting a metabolic state that confers a profound survival advantage to cells in poorly vascularized tumour regions. GLDC inhibition impairs cells with high SHMT2 levels as the excess glycine not metabolized by GLDC can be converted to the toxic molecules aminoacetone and methylglyoxal. Thus, SHMT2 is required for cancer cells to adapt to the tumour environment, but also renders these cells sensitive to glycine cleavage system inhibition.American Brain Tumor Association (Basic Research Fellowship)Massachusetts Institute of Technology. School of Science (Fellowship in Cancer Research)Jane Coffin Childs Memorial Fund for Medical Research (Fellowship)Leukemia & Lymphoma Society of America (Fellowship)National Institutes of Health (U.S.) (Grants T32GM007287, K99 CA168940, R01CA168653, 5P30CA14051, CA103866, CA129105, and AI07389)American Cancer Society (Fellowship)American Brain Tumor Association (Discovery Grant)National Institute on Aging (Fellowship)Smith Family FoundationBurroughs Wellcome FundDamon Runyon Cancer Research FoundationStern FamilyUnited States. Dept. of Defense. Congressionally Directed Medical Research Programs (Discovery Award)David H. Koch Institute for Integrative Cancer Research at MITAlexander and Margaret Stewart Trus

Arginine Deprivation Inhibits the Warburg Effect and Upregulates Glutamine Anaplerosis and Serine Biosynthesis in ASS1-Deficient Cancers

Targeting defects in metabolism is an underutilized strategy for the treatment of cancer. Arginine auxotrophy resulting from the silencing of argininosuccinate synthetase 1 (ASS1) is a common metabolic alteration reported in a broad range of aggressive cancers. To assess the metabolic effects that arise from acute and chronic arginine starvation in ASS1-deficient cell lines, we performed metabolite profiling. We found that pharmacologically induced arginine depletion causes increased serine biosynthesis, glutamine anaplerosis, oxidative phosphorylation, and decreased aerobic glycolysis, effectively inhibiting the Warburg effect. The reduction of glycolysis in cells otherwise dependent on aerobic glycolysis is correlated with reduced PKM2 expression and phosphorylation and upregulation of PHGDH. Concurrent arginine deprivation and glutaminase inhibition was found to be synthetic lethal across a spectrum of ASS1-deficient tumor cell lines and is sufficient to cause in vivo tumor regression in mice. These results identify two synthetic lethal therapeutic strategies exploiting metabolic vulnerabilities of ASS1-negative cancers