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1.  Macrophage Migration Inhibitory Factor Polymorphism Is Associated with Susceptibility to Inflammatory Coronary Heart Disease 
BioMed Research International  2015;2015:315174.
Background. Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine. This study explored the association of 173G/C polymorphism of the MIF gene with coronary heart disease (CHD). Methods. Sequencing was carried out after polymerase chain reaction with DNA specimens from 186 volunteers without CHD and 70 patients with CHD. Plasma MIF levels on admission were measured by ELISA. Patients were classified into either stable angina pectoris (SAP) or unstable angina pectoris (UAP). Genotype distribution between cases and controls and the association of patients' genotypes with MIF level and plaque stability were statistically evaluated (ethical approval number: 2012-01). Results. The frequency of the C genotype was higher in CHD patients than in the control (P = 0.014). The frequency of the 173*CC genotype was higher in CHD patients than in the control (P = 0.005). The plasma MIF level was higher in MIF173*C carriers than in MIF173*G carriers (P = 0.033). CHD patients had higher plasma MIF levels than the control (P = 0.000). Patients with UAP had higher plasma MIF levels than patients with SAP (P = 0.014). Conclusions. These data suggest that MIF −173G/C polymorphism may be related to the development of CHD in a Chinese population. Plasma MIF level is a predictor of plaque stability. This trial is registered with NCT01750502 .
PMCID: PMC4364024  PMID: 25821795
2.  SRSF10 Regulates Alternative Splicing and Is Required for Adipocyte Differentiation 
Molecular and Cellular Biology  2014;34(12):2198-2207.
During adipocyte differentiation, significant alternative splicing changes occur in association with the adipogenic process. However, little is known about roles played by splicing factors in this process. We observed that mice deficient for the splicing factor SRSF10 exhibit severely impaired development of subcutaneous white adipose tissue (WAT) as a result of defects in adipogenic differentiation. To identify splicing events responsible for this, transcriptome sequencing (RNA-seq) analysis was performed using embryonic fibroblast cells. Several SRSF10-affected splicing events that are implicated in adipogenesis have been identified. Notably, lipin1, known as an important regulator during adipogenesis, was further investigated. While lipin1β is mainly involved in lipogenesis, its alternatively spliced isoform lipin1α, generated through the skipping of exon 7, is primarily required for initial adipocyte differentiation. Skipping of exon 7 is controlled by an SRSF10-regulated cis element located in the constitutive exon 8. The activity of this element depends on the binding of SRSF10 and correlates with the relative abundance of lipin1α mRNA. A series of experiments demonstrated that SRSF10 controls the production of lipin1α and thus promotes adipocyte differentiation. Indeed, lipin1α expression could rescue SRSF10-mediated adipogenic defects. Taken together, our results identify SRSF10 as an essential regulator for adipocyte differentiation and also provide new insights into splicing control by SRSF10 in lipin1 pre-mRNA splicing.
PMCID: PMC4054296  PMID: 24710272
3.  Genome-wide analysis of SRSF10-regulated alternative splicing by deep sequencing of chicken transcriptome 
Genomics Data  2014;2:20-23.
Splicing factor SRSF10 is known to function as a sequence-specific splicing activator that is capable of regulating alternative splicing both in vitro and in vivo. We recently used an RNA-seq approach coupled with bioinformatics analysis to identify the extensive splicing network regulated by SRSF10 in chicken cells. We found that SRSF10 promoted both exon inclusion and exclusion. Functionally, many of the SRSF10-verified alternative exons are linked to pathways of response to external stimulus. Here we describe in detail the experimental design, bioinformatics analysis and GO/pathway enrichment analysis of SRSF10-regulated genes to correspond with our data in the Gene Expression Omnibus with accession number GSE53354. Our data thus provide a resource for studying regulation of alternative splicing in vivo that underlines biological functions of splicing regulatory proteins in cells.
•Alternative splicing events regulated by SRSF10 were examined in cells.•SRSF10 promoted both exon inclusion and exclusion.•SRSF10-regulated alternative exons are associated with multiple signaling pathways.
PMCID: PMC4535984  PMID: 26484059
Alternative splicing; SRSF10; RNA-seq; Bioinformatics
4.  Training Set Selection for the Prediction of Essential Genes 
PLoS ONE  2014;9(1):e86805.
Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.
PMCID: PMC3899339  PMID: 24466248
5.  Transcriptome analysis of alternative splicing events regulated by SRSF10 reveals position-dependent splicing modulation 
Nucleic Acids Research  2014;42(6):4019-4030.
Splicing factor SRSF10 is known to function as a sequence-specific splicing activator. Here, we used RNA-seq coupled with bioinformatics analysis to identify the extensive splicing network regulated by SRSF10 in chicken cells. We found that SRSF10 promoted both exon inclusion and exclusion. Motif analysis revealed that SRSF10 binding to cassette exons was associated with exon inclusion, whereas the binding of SRSF10 within downstream constitutive exons was associated with exon exclusion. This positional effect was further demonstrated by the mutagenesis of potential SRSF10 binding motifs in two minigene constructs. Functionally, many of SRSF10-verified alternative exons are linked to pathways of stress and apoptosis. Consistent with this observation, cells depleted of SRSF10 expression were far more susceptible to endoplasmic reticulum stress-induced apoptosis than control cells. Importantly, reconstituted SRSF10 in knockout cells recovered wild-type splicing patterns and considerably rescued the stress-related defects. Together, our results provide mechanistic insight into SRSF10-regulated alternative splicing events in vivo and demonstrate that SRSF10 plays a crucial role in cell survival under stress conditions.
PMCID: PMC3973337  PMID: 24442672
6.  A new computational strategy for predicting essential genes 
BMC Genomics  2013;14:910.
Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms.
We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naïve Bayes model (FWM), which is based on Naïve Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naïve Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction.
FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.
PMCID: PMC3880044  PMID: 24359534
Essential genes; Naïve Bayes; Support vector machine; Gene essentiality
7.  A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data 
BioData Mining  2012;5:8.
Several biclustering algorithms have been proposed to identify biclusters, in which genes share similar expression patterns across a number of conditions. However, different algorithms would yield different biclusters and further lead to distinct conclusions. Therefore, some testing and comparisons between these algorithms are strongly required.
In this study, five biclustering algorithms (i.e. BIMAX, FABIA, ISA, QUBIC and SAMBA) were compared with each other in the cases where they were used to handle two expression datasets (GDS1620 and pathway) with different dimensions in Arabidopsis thaliana (A. thaliana)
GO (gene ontology) annotation and PPI (protein-protein interaction) network were used to verify the corresponding biological significance of biclusters from the five algorithms. To compare the algorithms’ performance and evaluate quality of identified biclusters, two scoring methods, namely weighted enrichment (WE) scoring and PPI scoring, were proposed in our study. For each dataset, after combining the scores of all biclusters into one unified ranking, we could evaluate the performance and behavior of the five biclustering algorithms in a better way.
Both WE and PPI scoring methods has been proved effective to validate biological significance of the biclusters, and a significantly positive correlation between the two sets of scores has been tested to demonstrate the consistence of these two methods.
A comparative study of the above five algorithms has revealed that: (1) ISA is the most effective one among the five algorithms on the dataset of GDS1620 and BIMAX outperforms the other algorithms on the dataset of pathway. (2) Both ISA and BIMAX are data-dependent. The former one does not work well on the datasets with few genes, while the latter one holds well for the datasets with more conditions. (3) FABIA and QUBIC perform poorly in this study and they may be suitable to large datasets with more genes and more conditions. (4) SAMBA is also data-independent as it performs well on two given datasets. The comparison results provide useful information for researchers to choose a suitable algorithm for each given dataset.
PMCID: PMC3447720  PMID: 22824157
8.  The Influence of Deleterious Mutations on Adaptation in Asexual Populations 
PLoS ONE  2011;6(11):e27757.
We study the dynamics of adaptation in asexual populations that undergo both beneficial and deleterious mutations. In particular, how the deleterious mutations affect the fixation of beneficial mutations was investigated. Using extensive Monte Carlo simulations, we find that in the “strong-selection weak mutation (SSWM)” regime or in the “clonal interference (CI)” regime, deleterious mutations rarely influence the distribution of “selection coefficients of the fixed mutations (SCFM)”; while in the “multiple mutations” regime, the accumulation of deleterious mutations would lead to a decrease in fitness significantly. We conclude that the effects of deleterious mutations on adaptation depend largely on the supply of beneficial mutations. And interestingly, the lowest adaptation rate occurs for a moderate value of selection coefficient of deleterious mutations.
PMCID: PMC3215719  PMID: 22110756
9.  Vertebrate Paralogous MEF2 Genes: Origin, Conservation, and Evolution 
PLoS ONE  2011;6(3):e17334.
The myocyte enhancer factor 2 (MEF2) gene family is broadly expressed during the development and maintenance of muscle cells. Although a great deal has been elucidated concerning MEF2 transcription factors' regulation of specific gene expression in diverse programs and adaptive responses, little is known about the origin and evolution of the four members of the MEF2 gene family in vertebrates.
Methodology/Principal Findings
By phylogenetic analyses, we investigated the origin, conservation, and evolution of the four MEF2 genes. First, among the four MEF2 paralogous branches, MEF2B is clearly distant from the other three branches in vertebrates, mainly because it lacks the HJURP_C (Holliday junction recognition protein C-terminal) region. Second, three duplication events might have occurred to produce the four MEF2 paralogous genes and the latest duplication event occurred near the origin of vertebrates producing MEF2A and MEF2C. Third, the ratio (Ka/Ks) of non-synonymous to synonymous nucleotide substitution rates showed that MEF2B evolves faster than the other three MEF2 proteins despite purifying selection on all of the four MEF2 branches. Moreover, a pair model of M0 versus M3 showed that variable selection exists among MEF2 proteins, and branch-site analysis presented that sites 53 and 64 along the MEF2B branch are under positive selection. Finally, and interestingly, substitution rates showed that type II MADS genes (i.e., MEF2-like genes) evolve as slowly as type I MADS genes (i.e., SRF-like genes) in animals, which is inconsistent with the fact that type II MADS genes evolve much slower than type I MADS genes in plants.
Our findings shed light on the relationship of MEF2A, B, C, and D with functional conservation and evolution in vertebrates. This study provides a rationale for future experimental design to investigate distinct but overlapping regulatory roles of the four MEF2 genes in various tissues.
PMCID: PMC3048864  PMID: 21394201
10.  The MADS Symphonies of Transcriptional Regulation 
PMCID: PMC3355769  PMID: 22645529
11.  Deciphering Heterogeneity in Pig Genome Assembly Sscrofa9 by Isochore and Isochore-Like Region Analyses 
PLoS ONE  2010;5(10):e13303.
The isochore, a large DNA sequence with relatively small GC variance, is one of the most important structures in eukaryotic genomes. Although the isochore has been widely studied in humans and other species, little is known about its distribution in pigs.
Principal Findings
In this paper, we construct a map of long homogeneous genome regions (LHGRs), i.e., isochores and isochore-like regions, in pigs to provide an intuitive version of GC heterogeneity in each chromosome. The LHGR pattern study not only quantifies heterogeneities, but also reveals some primary characteristics of the chromatin organization, including the followings: (1) the majority of LHGRs belong to GC-poor families and are in long length; (2) a high gene density tends to occur with the appearance of GC-rich LHGRs; and (3) the density of LINE repeats decreases with an increase in the GC content of LHGRs. Furthermore, a portion of LHGRs with particular GC ranges (50%–51% and 54%–55%) tend to have abnormally high gene densities, suggesting that biased gene conversion (BGC), as well as time- and energy-saving principles, could be of importance to the formation of genome organization.
This study significantly improves our knowledge of chromatin organization in the pig genome. Correlations between the different biological features (e.g., gene density and repeat density) and GC content of LHGRs provide a unique glimpse of in silico gene and repeats prediction.
PMCID: PMC2952626  PMID: 20948965

Results 1-11 (11)