Difference in acute and chronic stage ischemic stroke metabolic markers with controls.

BACKGROUND: Acute Ischemic Stroke (AIS), a major cause of disability, was previously associated with multiple metabolomic changes, but many findings were contradictory. Case-control and longitudinal study designs could have played a role in that. To clarify metabolomic changes, we performed a simultaneous comparison of ischemic stroke metabolome in acute, chronic stages of stroke and controls. METHODS: Through the nuclear magnetic resonance (NMR) platform, we evaluated 271 serum metabolites from a cohort of 297 AIS patients in acute and chronic stages and 159 controls. We used Sparse Partial Least Squares-Discriminant analysis (sPLS-DA) to evaluate group disparity; multivariate regression to compare metabolome in acute, chronic stages of stroke and controls; and mixed regression to compare metabolome acute and chronic stages of stroke. We applied false discovery rate (FDR) to our calculations. RESULTS: The sPLS-DA revealed separation of the metabolome in acute, chronic stages of stroke and controls. Regression analysis identified 38 altered metabolites. Ketones, branched-chain amino acids (BCAAs), energy, and inflammatory compounds were mostly elevated, while alanine and glutamine were decreased in the acute stage. These metabolites declined/increased in the chronic stage, often to the same levels as in controls. Levels of fatty acids, phosphatidylcholines, phosphoglycerides, and sphingomyelins did not change between acute and chronic stages, but were different comparing to controls. CONCLUSION: Our pilot study identified metabolites associated with acute stage of ischemic stroke and those that are altered in stroke patients comparing to controls regardless of stroke acuity. Future investigation in a larger independent cohort is needed to validate these findings.

Comparison of Acute and Chronic Stage Ischemic Stroke Metabolome with Controls.

Background: Acute Ischemic Stroke (AIS), a major cause of disability, was previously associated with multiple metabolomic changes, but many findings were contradictory. Case-control and longitudinal study designs could have played a role in that. To clarify metabolomic changes, we performed a simultaneous comparison of ischemic stroke metabolome in acute, chronic stages of stroke and controls. Methods: Through the nuclear magnetic resonance (NMR) platform, we evaluated 271 serum metabolites from a cohort of 297 AIS patients in acute and chronic stages and 159 controls. We used Sparse Partial Least Squares-Discriminant analysis (sPLS-DA) to evaluate group disparity; multivariate regression to compare metabolome in acute, chronic stages of stroke and controls; and mixed regression to compare metabolome acute and chronic stages of stroke. We applied false discovery rate (FDR) to our calculations. Results: The sPLS-DA revealed separation of the metabolome in acute, chronic stages of stroke and controls. Regression analysis identified 38 altered metabolites. Ketone bodies, branched-chain amino acids (BCAAs), energy, and inflammatory compounds were elevated in the acute stage, but declined in the chronic stage, often to the same levels as in controls. Levels of other amino acids, phosphatidylcholines, phosphoglycerides, and sphingomyelins mainly did not change between acute and chronic stages, but was different comparing to controls. Conclusion: Our pilot study identified metabolites associated with acute stage of ischemic stroke and those that are altered in stroke patients comparing to controls regardless of stroke acuity. Future investigation in a larger independent cohort is needed to validate these findings.

Predictive bioinformatics identifies novel regulators of proliferation in a cancer stem cell model.

The cancer stem cell model postulates that tumors are hierarchically organized with a minor population, the cancer stem cells, exhibiting unlimited proliferative potential. These cells give rise to the bulk of tumor cells, which retain a limited ability to divide. Without successful targeting of cancer stem cells, tumor reemergence after therapy is likely. However, identifying target pathways essential for cancer stem cell proliferation has been challenging. Here, using a transcriptional network analysis termed GAMMA, we identified 50 genes whose correlation patterns suggested involvement in cancer stem cell division. Using RNAi depletion, we found that 21 of these target genes showed preferential growth inhibition in a breast cancer stem cell model. More detailed initial analysis of 6 of these genes revealed 4 with clear roles in the fidelity of chromosome segregation. This study reveals the strong predictive potential of transcriptional network analysis in increasing the efficiency of successful identification of novel proliferation dependencies for cancer stem cells.