Heterogeneity plays an important role in cancer genesis and progression. In this paper, we theoretically and computationally show that the increase of heterogeneity signals predeterioration during tumor progression by unified random ordinary differential equations (RODEs). Tumor formation results from a systematic change in organisms, and its causality is complex. Heterogeneity may be one of the key factors under some conditions. Specifically, the interactions between cell populations are modeled by random community matrices in normal tissues, benign tumors, and malignant tumors, and then we prove that the RODEs system describing the cell density of normal tissues or benign tumors becomes unstable as heterogeneity of cell populations or species increases. The increase of heterogeneity is an important signal. Heterogeneity can be viewed as a feature to distinguish benign tumors from malignant tumors under certain circumstances. Furthermore, we show that with the increase of heterogeneity, the divergence speed of the RODEs system becomes faster, which implies that malignant tumors with higher heterogeneity may develop faster and have poorer prognoses. Our theoretical findings can explain some noteworthy phenomena in various datasets in tumor patients and our biological experiments in mice from a mathematical viewpoint. Particularly, clinical data and mutation information from the cancer genome atlas and our experiments in mice revealed that tumors with higher heterogeneity usually show shorter survival time, whereas tumors with lower heterogeneity tend to have better prognosis, which also indicates that heterogeneity can be used as a potential biomarker in future clinical diagnosis. Targeting the heterogeneity may be a potential strategy for the cancer treatment.
Abstract Cadmium (Cd) has long been recognized as toxic pollutant to crops worldwide. The biosynthesis of glutathione-dependent phytochelatin plays crucial roles in the detoxification of Cd in plants. However, its regulatory mechanism remains elusive. Here, we revealed that Arabidopsis transcription factor WRKY45 confers Cd tolerance via promoting the expression of PC synthesis-related genes PCS1 and PCS2, respectively. Firstly, we found that Cd stress induces the transcript levels of WRKY45 and its protein abundance. Accordingly, in contrast to wild type Col-0, the increased sensitivity to Cd is observed in wrky45 mutant, while overexpressing WRKY45 plants are more tolerant to Cd. Secondly, quantitative real-time PCR revealed that the expression of AtPCS1 and AtPCS2 is stimulated in overexpressing WRKY45 plants, but decreased in wrky45 mutant. Thirdly, WRKY45 promotes the expression of PCS1 and PCS2, electrophoresis mobility shift assay analysis uncovered that WRKY45 directly bind to the W-box cis-element of PCS2 promoter. Lastly, the overexpression of WRKY45 in Col-0 leads to more accumulation of PCs in Arabidopsis, and the overexpression of PCS1 or PCS2 in wrky45 mutant plants rescues the phenotypes induced by Cd stress. In conclusion, our results show that AtWRKY45 positively regulate Cd tolerance in Arabidopsis via activating PCS1 and PCS2 expression. Environmental implication Accumulation of cadmium (Cd) in soils poses a threat to crop productivity and food safety. It has been revealed that phytochelatin (PC) plays an essential role in plants to alleviate Cd toxicity, yet the regulatory mechanisms governing its expression remain unclear. We have demonstrated that the Arabidopsis transcription factor WRKY45 directly activates the expression of PCS1 and PCS2 , which encode PC synthase, thereby increasing the content of PC and enhancing Arabidopsis tolerance to Cd stress. These findings offer insights into precise regulation strategies for crop Cd tolerance via modulation of WRKY45 homologue in crops.
Each phase of eukaryotic cell cycle is tightly controlled by multicomponent regulatory networks based on complex relationships of protein phosphorylation. In order to better understand the relationships between kinases and their substrate proteins during the progression of cell cycle, we analyzed phosphoproteome of HeLa cells during G1, S, and G2/M phases of cell cycle using our developed quantitative phosphoproteomic approaches. A total of 4776 high-confidence phosphorylation sites (phosphosites) in 1177 proteins were identified. Bioinformatics analysis for predicting kinase groups revealed that 46 kinase groups could be assigned to 4321 phosphosites. The majority of phosphoproteins harboring two or more phosphosites could be phosphorylated by different kinase groups, in which nine major kinase groups accounted for more than 90% phosphosites. Further analyses showed that approximately half of the examined two phosphosite combinations were correlatively regulated, regardless of whether the kinase groups were same or not. In general, the majority of proteins containing correlated phosphosites had solely co-regulated or counter-regulated phosphosites, and co-regulation was significantly more frequent than counter-regulation, suggesting that the former may be more important for regulating the cell cycle. In conclusion, our findings provide new insights into the complex regulatory mechanisms of protein phosphorylation networks during eukaryotic cell cycle.
In this study, a software tool (IFGFA) for identification of featured genes from gene expression data based on latent factor analysis was developed.Despite the availability of computational methods and statistical models appropriate for analyzing special genomic data, IFGFA provides a platform for predicting colon cancer-related genes and can be applied to other cancer types.The computational framework behind IFGFA is based on the well-established Bayesian factor and regression model and prior knowledge about the gene from OMIM.We validated the predicted genes by analyzing somatic mutations in patients.An interface was developed to enable users to run the computational framework efficiently through visual programming.IFGFA is executable in a Windows system and does not require other C.H.
Data-independent acquisition (DIA) has significant advantages for mass spectrometry (MS)-based peptide quantification, while mixed spectra remain challenging for precise stoichiometry. We here choose to analyze the library spectra in specific sets preferentially and locally. Accordingly, the featured ions are defined as the fragment ions uniquely assigned to corresponding precursors in a given spectrum set, which are generated by dynamic deconvolution of the mixed mass spectra. Then, we present featured ion-guided stoichiometry (FIGS), a universal method for accurate and robust peptide quantification for the DIA-MS data. We validate the high performance on the quantification sensitivity, accuracy, and efficiency of FIGS. Notably, our FIGS dramatically improves the quantification accuracy for the full dynamic range, especially for low-abundance peptides.
Abstract Background We have recently identified a number of active regulatory networks involved in diabetes progression in Goto-Kakizaki (GK) rats by network screening. The networks were quite consistent with the previous knowledge of the regulatory relationships between transcription factors (TFs) and their regulated genes. To study the underlying molecular mechanisms directly related to phenotype changes, such as diseases, we also previously developed a computational procedure for identifying transcriptional master regulators (MRs) in conjunction with network screening and network inference, by effectively perturbing the phenotype states. Results In this work, we further improved our previous method for identifying MR candidates, by listing them in a more reliable manner, and applied the method to reveal the MR candidates for diabetes progression in GK rats from the active networks. Specifically, the active TF-gene pairs for different time periods in GK rats were first extracted from the networks by network screening. Another set of active TF-gene pairs was selected by network inference, by considering the gene expression signatures for those periods between GK and Wistar-Kyoto (WKY) rats. The TF-gene pairs extracted by the two methods were then further selected, from the viewpoints of the emergence specificity of TF in GK rats and the regulated-gene coverage of TF in the expression signature. Finally, we narrowed all of the genes down to only 5 TFs (Etv4, Fus, Nr2f1, Sp2, and Tcfap2b) as the candidates of MRs, with 54 regulated genes, by merging the selected TF-gene pairs. Conclusions The present method has successfully identified biologically plausible MR candidates, including the TFs related to diabetes in previous reports. Although the experimental verifications of the candidates and the present procedure are beyond the scope of this study, we narrowed down the candidates to 5 TFs, which can be used to perform the verification experiments relatively easily. The numerical results showed that our computational method is an efficient way to detect the key molecules responsible for biological phenomena.
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