Electronic Structure and Redox Properties of the Open-Shell Metal−Carbide Endofullerene Sc₃C₂@C₈₀: A Density Functional Theory Investigation

Density functional theory calculations have shown that the open-shell metal−carbide endofullerene Sc3C2@C80 has the valence state (Sc3+)3(C2)3-@C806-. A lot of low-lying isomers differing in geometries and locations of the endohedral [(Sc3+)3(C2)3-] cluster have been located, indicating unusual dual intramolecular dynamic behaviors of this endofullerene at room temperature. The electrochemical redox properties of this endofullerene have been elucidated in terms of electronic structure theory. Its redox states are found to follow the general charge−state formula (Sc3+)3C2(3-q)-@C806- (q is the charge of the whole molecule ranging from +1 to −3), demonstrating the high charge flexibility of the endohedral metal−carbide cluster. The structure of the endohedral [(Sc3+)3C2(3-q)-] cluster varies with the redox processes, shifting from a planar structure (for q = 0 and −1) to a trifoliate structure (for q = +1, −2, −3)

Performance comparison of DNA methylation-based classifiers for GBM patient prognosis.

<p>Promoter DNA methylation data of 42 GBM patients was used to derive the set of mModules. Top-gene set is top 38 (size-matched to the mModule set) most significantly differentially methylated genes between LTS and STS GBM patients. G-CIMP+ set, a set of 1228 discriminative genes reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Noushmehr1" target="_blank">[6]</a>. Two hundred thirty seven additional GBM patients from TCGA were used for testing classification accuracy. Error bar is the standard deviation based on 100 leave-one-out cross validations. P-values are based on t-tests comparing the average classification accuraciy of the mModule-based classifier and those of other classifiers.</p

Performance of multi-analyte modules for GBM patient prognosis.

<p><b>A</b>) Prognostic accuracies of GBM patients by four marker sets. Expression data of 42 GBM patient was used to derive the module set. Two hundred thirty seven additional GBM patients from TCGA were used for classification using the module set. Support Vector Machine algorithm was used to build a classifier based on each marker set. Top-gene sets were size-matched to the network module sets (i.e., the same number of genes as in the network module sets). Error bar is the standard deviation based on 100 leave-one-out cross validations. P-values are based on t-tests comparing the average classification accuracy of the multi-analyte-module-based classifier to those of other classifiers. <b>B–D</b>) Kaplan-Meier survival curves for LTS and STS GBM patients classified using the combined module set (B), 38-gene set (C), and G-CIMP+ set (D). P-value indicates the significance of separation between the two curves and is computed using logrank test.</p

Overview of the MAPIT algorithm.

<p>Using clinical data, GBM patients are classified as either Long Term Survivors (LTS, >2 yrs.) or Short Term Survivors (STS, <2 yrs.). Two types of global measures of tumor samples are combined with protein-protein interactome (PPI) for network module identification: mRNA expression and promoter DNA methylation. Significance of change in either gene expression or promoter DNA methylation profiles between LTS and STS patients are overlaid on top of the PPI network to generate single-analyte networks, eNetwork and mNetwork. Network modules from each single-analyte network are identified using the extended miPALM algorithm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Kim1" target="_blank">[24]</a> independently. Significant modules from each network are then combined to train a classifier for GBM prognosis using Support Vector Machine (SVM). A Recursive Feature Elimination (RFE) algorithm is implemented with the SVM classifier to select a final set of most discriminative network modules for patient prognosis.</p

Multi-Analyte Network Markers for Tumor Prognosis

<div><p>Deregulation of gene expression, a hallmark of cancer, is caused by both genetic and epigenetic mechanisms. The rapid accumulation of epigenome maps of various cancers suggests a new avenue of research, namely integrating epigenomic data with other types of omic data for cancer diagnosis, prognosis, and biomarker discovery. We introduce the MAPIT algorithm (<u>M</u>ulti <u>A</u>nalyte <u>P</u>athway <u>I</u>nference <u>T</u>ool), to enable principled integration of epigenomic, transcriptomic, and protein interactome data. As a proof-of-principle, we apply MAPIT to glioblastoma multiforme (GBM), the most common and aggressive form of brain tumor. Few predictive markers were reported for the prognosis of GBM patients. By integrating mRNA transcriptome, promoter DNA methylome and protein-protein physical interactome, we find ten expression- and three methylation-based network markers, involving 118 genes. When tested on additional GBM patient samples, the prognostic accuracy of the multi-analyte network markers (73.5%) is 9.7% and 8.6% higher than previous prognostic signatures built on gene expression or DNA methylation alone. Our results highlight the critical role of two novel pathways in the prognosis of GBM patients, small GTPase-mediated protein trafficking and ubiquitination-dependent protein degradation. A better understanding of these two pathways could lead to personalized therapies for subgroups of GBM patients. Our study demonstrates that integrating epigenomic, transcriptomic, and interactomic data can improve the accuracy network-based prognosis markers and lead to novel mechanistic understanding of cancer.</p> </div

The set of multi-analyte prognostic modules identified by the MAPIT algorithm.

<p>Ten eModules (<i>A–J</i>) and three mModules (<i>K–M</i>) are shown. Node colour represents gene expression change of LTS patients compared to STS patients. Red, down-regulation; Green, up-regulation. Shade is proportional to the −log (p-value) of the change. Node border colour represents DNA methylation change of LTS patients compared to STS patients. Red, hypomethylation; Green, hypermethylation. Shade is proportional to the −log (p-value) of the change. Diamond nodes, genes reported to bear somatic mutations in GBM patients. Rectangular nodes, genes identified as GBM prognostic markers in either <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Colman1" target="_blank">[4]</a> or <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Noushmehr1" target="_blank">[6]</a>. Hexagonal nodes, genes both reported to bear somatic mutations and identified as prognostic markers in either <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Colman1" target="_blank">[4]</a> or <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Noushmehr1" target="_blank">[6]</a>. Purple star: genes located in CNV regions identified in GBM patients <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-The1" target="_blank">[37]</a>. Edge, protein-protein interaction. Edge width is proportional to the combined significance of expression changes of the two involved nodes (see Methods for details).</p

Performance comparison of gene-expression-based classifiers for GBM patient prognosis.

<p><b>A</b>) Prognostic accuracy of various marker sets. Classification accuracy is defined as the ratio of the number of correctly classified patients to the total number of patients tested. Expression data of 42 GBM patients was used to derive the eModule set. Top-gene set is top 156 (size-matched to the number of genes in the eModule set) most significantly differentially expressed genes between LTS and STS patients. 38-gene set, a set of 38 discriminative genes reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Colman1" target="_blank">[4]</a>. Two hundred thirty seven additional GBM patients from TCGA were used for testing classification accuracy. Error bar is the standard deviation based on 100 leave-one-out cross validations. <b>B</b>) Performance of eModule set and 38-gene set using three external microarray data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Freije1" target="_blank">[28]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Phillips1" target="_blank">[30]</a> from which the 38-gene signature was derived. Numbers in parenthesis indicate number of LTS and STS in each data set, respectively. P-values are based on t-tests comparing the average classification accuracy of the eModule-based classifier and those of other classifiers.</p

Unprecedented μ₄-C₂^6- Anion in Sc₄C₂@C₈₀

Metal carbide compound containing highly charged C2q- (q = 5, 6) moiety is rather scarce. We show by means of density functional calculations that an unprecedented μ4-C26- anion can viably exist as an endohedral [Sc4C2]6+ cluster in the endofullerene Sc4C2@C80. The electronic structure, ionization energy, electron affinity, 13C NMR chemical shifts, vibrational frequencies, and electrochemical redox potentials of this unique endofullerene have been predicted to assist future experimental characterization

Additional file 3: of SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data

Tables S1 and S2. Supplementary tables. (PDF 41 kb