Adipogenesis licensing and execution are disparately linked to cell proliferation

Coordination of cell differentiation and proliferation is a key issue in the development process of multi-cellular organisms and stem cells. Here we provide evidence that the establishment of adipocyte differentiation of 3T3-L1 cells requires two processes： the licensing of an adipogenesis gene-expression program within a particular growth-arrest stage, i.e., the contact-inhibition stage, and then the execution of this program in a cell-cycle-independent manner, by which the licensed progenitors are differentiated into adipocytes in the presence of inducing factors. Our results showed that differentiation licensing of 3T3-L1 cells during the contact-inhibition stage involved epigenetic modifications such as DNA methylation and histone modifications, whereas disturbing these epigenetic modifications by DNA methylation inhibitors or RNAi during the contact-inhibition stage significantly reduced adipogenesis efficiency. More importantly, when these licensed 3T3-L1 cells were re-cultured under non-differentiating conditions or treated only with insulin, this adipogenesis commitment could be maintained from one cell generation to the next, whereby the licensed program could be activated in a cell-cycle-independent manner once these cells were subjected to adipo- genesis-inducing conditions. This result suggests that differentiation licensing and differentiation execution can be uncoupled and disparately linked to cell proliferation. Our findings deliver a new concept that cell-fate decision can be subdivided into at least two stages, licensing and execution, which might have different regulatory relationships with cell proliferation. In addition, this new concept may provide a clue for developing new strategies against obesity.

Construction of a chloroplast protein interaction network and functional mining of photosynthetic proteins in Arabidopsis thaliana

Chloroplast is a typical plant cell organelle where photosynthesis takes place. In this study, a total of 1 808 chloroplast core proteins in Arabidopsis thaliana were reliably identified by combining the results of previously published studies and our own predictions. We then constructed a chloroplast protein interaction network primarily based on these core protein interactions. The network had 22 925 protein interaction pairs which involved 2 214 proteins. A total of 160 previously uncharacterized proteins were annotated in this network. The subunits of the photosynthetic complexes were modularized, and the functional relationships among photosystem Ⅰ （PSI）, photosystem Ⅱ （PSII）, light harvesting complex of photosystem Ⅰ （LHC Ⅰ） and light harvesting complex of photosystem Ⅰ （LHC Ⅱ） could be deduced from the predicted protein interactions in this network. We further confirmed an interaction between an unknown protein AT1G52220 and a photosynthetic subunit PSI-D2 by yeast two-hybrid analysis. Our chloroplast protein interaction network should be useful for functional mining of photosynthetic proteins and investigation of chloroplast-related functions at the systems biology level in Arabidopsis.

High-coverage proteome analysis reveals the first insight of protein modification systems in the pathogenic spirochete Leptospira interrogans

Leptospirosis is a widespread zoonotic disease caused by pathogenic spirochetes of the genus Leptospira that infects humans and a wide range of animals. By combining computational prediction and high-accuracy tandem mass spectra, we revised the genome annotation of Leptospira interrogans serovar Lai, a free-living pathogenic spirochete responsible for leptospirosis, providing substantial peptide evidence for novel genes and new gene boundaries. Subsequently, we presented a high-coverage proteome analysis of protein expression and multiple posttranslational modifications （PTMs）. Approximately 64.3% of the predicted L. interrogans proteins were cataloged by detecting 2 540 proteins. Meanwhile, a profile of multiple PTMs was concurrently established, containing in total 32 phosphorylated, 46 acetylated and 155 methylated proteins. The PTM systems in the serovar Lai show unique features. Unique eukaryotic-like features of L. interrogans protein modifications were demonstrated in both phosphorylation and arginine methylation. This systematic analysis provides not only comprehensive information of high-coverage protein expression and multiple modifications in prokaryotes but also a view suggesting that the evolutionarily primitive L. interrogans shares significant similarities in protein modification systems with eukaryotes.

MIRE: A GRAPHICAL R PACKAGE FOR MICRORNARELATED ANALYSIS

Objective To provide a set of useful analysis tools for the researchers to explore the microRNA data. Methods The R language was used for generating the Graphical Users Interface and implementing most functions. Some Practical Extraction and Report Language (Perl) scripts were used for parsing source files. Results We developed a graphical R package named miRE, which was designated for the analysis of microRNA functions, genomic organization, etc. This package provided effective and convenient tools for molecular biologists to deal with routine analyses in microRNA-related research. With its help, the users would be able to build a desktop- centered microRNA research environment quite easily and effectively. miRE is freely available at http://www. biosino.org/~kanghu/WorkPresentation/miRE/miRE.html. A detailed user manual and tutorials with example code and image are also available. Conclusion miRE is a tool providing an open-source, user-friendly, integrated interface for microRNA-related analysis. With its help, researchers can perform microRNA-related analysis more efficiently.

Pan-cancer network disorders revealed by overall and local signaling entropy

Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics,which is driven by intracellular rewiring of signaling network.The measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of tumors.Here,we proposed signaling entropy(SR)to assess the degree of tumor network disorder.We calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcrip-tomic and proteomic data.The SR of tumors was significantly higher than that of normal samples and was highly correlated with cell sternness,cancer type,tumor grade,and metastasis.We further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response evaluation.Overall,SR could reveal cancer network disorders related to tumor malignant potency,clinical prognosis,and drug response.

Shanghai Score： A Prognostic and Adjuvant Treatment-evaluating System Constructed for Chinese Patients with Hepatocellular Carcinoma after Curative Resection

Background： For Chinese patients with hepatocellular carcinoma （HCC）, surgical resection is the most important treatment to achieve long-term survival for patients with an early-stage tumor, and yet the prognosis after surgery is diverse. We aimed to construct a scoring system （Shanghai Score） for individualized prognosis estimation and adjuvant treatment evaluation. Methods： A multivariate Cox proportional hazards model was constructed based on 4166 HCC patients undergoing resection during 2001-2008 at Zhongshan Hospital. Age, hepatitis B surface antigen, hepatitis B e antigen, partial thromboplastin time, total bilirubin, alkaline phosphatase, y-glutamyltransferase, a-fetoprotein, tumor size, cirrhosis, vascular invasion, differentiation, encapsulation, and tumor number were finally retained by a backward step-down selection process with the Akaike information criterion. The Harrell＇s concordance index （C-index） was used to measure model performance. Shanghai Score is calculated by summing the products of the 14 variable values times each variable＇s corresponding regression coefficient. Totally 1978 patients from Zhongshan Hospital undergoing resection during 2009-2012, 808 patients from Eastern Hepatobiliary Surgery Hospital during 2008-2010, and 244 patients from Tianjin Medical University Cancer Hospital during 2010-2011 were enrolled as external validation cohorts. Shanghai Score was also implied in evaluating adjuvant treatment choices based on propensity score matching analysis.Results： Shanghai Score showed good calibration and discrimination in postsurgical HCC patients. The bootstrap-corrected C-index （confidence interval [CI]） was 0.74 for overall survival （OS） and 0.68 for recurrence-free survival （RFS） in derivation cohort （4166 patients）, and in the three independent validation cohorts, the CIs for OS ranged 0.70 0.72 and that for RFS ranged 0.63 0.68. Furthermore, Shanghai Score provided evaluation for adjuvant treatment choices （transcatheter arterial chemoembolization or interferon-a）. The identified subset of patients at low risk could be ideal candidates for curative surgery, and subsets of patients at moderate or high risk could be recommended with possible adjuvant therapies after surgery. Finally, a web server with individualized outcome prediction and treatment recommendation was constructed. Conclusions： Based on the largest cohort up to date, we established Shanghai Score - an individualized outcome prediction system specifically designed for Chinese HCC patients after surgery. The Shanghai Score web server provides an easily accessible tool to stratify the prognosis of patients undergoing liver resection for HCC.

A comparison of next-generation sequencing analysis methods for cancer xenograft samples

The application of next-generation sequencing （NGS） technology in cancer is influenced by the quality and purity of tissue samples. This issue is especially critical for patient-derived xenograft （PDX） models, which have proven to be by far the best preclinical tool for investigating human tumor biology, because the sensitivity and specificity of NGS analysis in xenograft samples would be compromised by the contamination of mouse DNA and RNA. This definitely affects downstream analyses by causing inaccurate mutation calling and gene expression estimates. The reliability of NGS data analysis for cancer xenograft samples is therefore highly dependent on whether the sequencing reads derived from the xenograft could be distinguished from those originated from the host. That is, each sequence read needs to be accurately assigned to its original species. Here, we review currently available methodologies in this field, including Xenome, Disambiguate, bamcmp and pdxBlacklist, and provide guidelines for users.

Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer

The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits.Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability.Mechanism-driven strategy has recently emerged,aiming to build signatures with both predictive power and explanatory power.Driven by this strategy,we developed a robust gene dysregulation analysis framework with machine learning algorithms,which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration.We then applied the framework to a colorectal cancer(CRC)cohort from The Cancer Genome Atlas.The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect.By choosing dysregulations with greedy strategy,we built a four-dysregulation(4-DysReg)signature,which has the capability of predicting prognosis and adjuvant chemotherapy benefit.4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation.These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine,and furthermore,elucidate the mechanisms of carcinogenesis.