Minping Qian

Characterizing the strand-specific distribution of non-CpG methylation in human pluripotent cells

DNA methylation is an important defense and regulatory mechanism. In mammals, most DNA methylation occurs at CpG sites, and asymmetric non-CpG methylation has only been detected at appreciable levels in a few cell types. We are the first to systematically study the strand-specific distribution of non-CpG methylation. With the divide-and-compare strategy, we show that CHG and CHH methylation are not intrinsically different in human embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). We also find that non-CpG methylation is skewed between the two strands in introns, especially at intron boundaries and in highly expressed genes. Controlling for the proximal sequences of non-CpG sites, we show that the skew of non-CpG methylation in introns is mainly guided by sequence skew. By studying subgroups of transposable elements, we also found that non-CpG methylation is distributed in a strand-specific manner in both short interspersed nuclear elements (SINE) and long interspersed nuclear elements (LINE), but not in long terminal repeats (LTR). Finally, we show that on the antisense strand of Alus, a non-CpG site just downstream of the A-box is highly methylated. Together, the divide-and-compare strategy leads us to identify regions with strand-specific distributions of non-CpG methylation in humans.

Gene-Centric Genomewide Association Study via Entropy

Genes are the functional units in most organisms. Compared to genetic variants located outside genes, genic variants are more likely to affect disease risk. The development of the human HapMap project provides an unprecedented opportunity for genetic association studies at the genomewide level for elucidating disease etiology. Currently, most association studies at the single-nucleotide polymorphism (SNP) or the haplotype level rely on the linkage information between SNP markers and disease variants, with which association findings are difficult to replicate. Moreover, variants in genes might not be sufficiently covered by currently available methods. In this article, we present a gene-centric approach via entropy statistics for a genomewide association study to identify disease genes. The new entropy-based approach considers genic variants within one gene simultaneously and is developed on the basis of a joint genotype distribution among genetic variants for an association test. A grouping algorithm based on a penalized entropy measure is proposed to reduce the dimension of the test statistic. Type I error rates and power of the entropy test are evaluated through extensive simulation studies. The results indicate that the entropy test has stable power under different disease models with a reasonable sample size. Compared to single SNP-based analysis, the gene-centric approach has greater power, especially when there is more than one disease variant in a gene. As the genomewide genic SNPs become available, our entropy-based gene-centric approach would provide a robust and computationally efficient way for gene-based genomewide association study.

Combinatorial regulation of transcription factors and microRNAs

BackgroundGene regulation is a key factor in gaining a full understanding of molecular biology. Cis-regulatory modules (CRMs), consisting of multiple transcription factor binding sites, have been confirmed as the main regulators in gene expression. In recent years, a novel regulator known as microRNA (miRNA) has been found to play an important role in gene regulation. Meanwhile, transcription factor and microRNA co-regulation has been widely identified. Thus, the relationships between CRMs and microRNAs have generated interest among biologists.ResultsWe constructed new combinatorial regulatory modules based on CRMs and miRNAs. By analyzing their effect on gene expression profiles, we found that genes targeted by both CRMs and miRNAs express in a significantly similar way. Furthermore, we constructed a regulatory network composed of CRMs, miRNAs, and their target genes. Investigating its structure, we found that the feed forward loop is a significant network motif, which plays an important role in gene regulation. In addition, we further analyzed the effect of miRNAs in embryonic cells, and we found that mir-154, as well as some other miRNAs, have significant co-regulation effect with CRMs in embryonic development.ConclusionsBased on the co-regulation of CRMs and miRNAs, we constructed a novel combinatorial regulatory network which was found to play an important role in gene regulation, particularly during embryonic development.

Modeling stochastic phenotype switching and bet-hedging in bacteria: stochastic nonlinear dynamics and critical state identification

Fluctuating environments pose tremendous challenges to bacterial populations. It is observed in numerous bacterial species that individual cells can stochastically switch among multiple phenotypes for the population to survive in rapidly changing environments. This kind of phenotypic heterogeneity with stochastic phenotype switching is generally understood to be an adaptive bet-hedging strategy. Mathematical models are essential to gain a deeper insight into the principle behind bet-hedging and the pattern behind experimental data. Traditional deterministic models cannot provide a correct description of stochastic phenotype switching and bet-hedging, and traditional Markov chain models at the cellular level fail to explain their underlying molecular mechanisms. In this paper, we propose a nonlinear stochastic model of multistable bacterial systems at the molecular level. It turns out that our model not only provides a clear description of stochastic phenotype switching and bet-hedging within isogenic bacterial populations, but also provides a deeper insight into the analysis of multidimensional experimental data. Moreover, we use some deep mathematical theories to show that our stochastic model and traditional Markov chain models are essentially consistent and reflect the dynamic behavior of the bacterial system at two different time scales. In addition, we provide a quantitative characterization of the critical state of multistable bacterial systems and develop an effective data-driven method to identify the critical state without resorting to specific mathematical models.

Population dynamics of cancer cells with cell state conversions

Cancer stem cell (CSC) theory suggests a cell-lineage structure in tumor cells in which CSCs are capable of giving rise to the other non-stem cancer cells (NSCCs) but not vice versa. However, an alternative scenario of bidirectional interconversions between CSCs and NSCCs was proposed very recently. Here we present a general population model of cancer cells by integrating conventional cell divisions with direct conversions between different cell states, namely, not only can CSCs differentiate into NSCCs by asymmetric cell division, NSCCs can also dedifferentiate into CSCs by cell state conversion. Our theoretical model is validated when applying the model to recent experimental data. It is also found that the transient increase in CSCs proportion initiated from the purified NSCCs subpopulation cannot be well predicted by the conventional CSC model where the conversion from NSCCs to CSCs is forbidden, implying that the cell state conversion is required especially for the transient dynamics. The theoretical analysis also gives the condition such that our general model can be equivalently reduced into a simple Markov chain with only cell state transitions keeping the same cell proportion dynamics.

Etiologic Diagnosis of Lower Respiratory Tract Bacterial Infections Using Sputum Samples and Quantitative Loop-Mediated Isothermal Amplification

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship. Trial Registration ClinicalTrials.gov NCT00567827

Hybridization modeling of oligonucleotide SNP arrays for accurate DNA copy number estimation

Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms.

Integrative Approaches for microRNA Target Prediction: Combining Sequence Information and the Paired mRNA and miRNA Expression Profiles

Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transactional regulators, and play important roles in cancer. One essential step to understand the regulatory effect of miRNAs is the reliable prediction of their target mRNAs. Typically, the predictions are solely based on the sequence information, which unavoidably have high false detection rates. Recently, some novel approaches are developed to predict miRNA targets by integrating the typical algorithm with the paired expression profiles of miRNA and mRNA. Here we review and discuss these integrative approaches and propose a new algorithm called HCTarget. Applying HCtarget to the expression data in multiple myeloma, we predict target genes for ten specific miRNAs. The experimental verification and a loss of function study validate our predictions. Therefore, the integrative approach is a reliable and effective way to predict miRNA targets, and could improve our comprehensive understanding of gene regulation.

Modular analysis of the probabilistic genetic interaction network

Motivation: Epistatic Miniarray Profiles (EMAP) has enabled the mapping of large-scale genetic interaction networks; however, the quantitative information gained from EMAP cannot be fully exploited since the data are usually interpreted as a discrete network based on an arbitrary hard threshold. To address such limitations, we adopted a mixture modeling procedure to construct a probabilistic genetic interaction network and then implemented a Bayesian approach to identify densely interacting modules in the probabilistic network. Results: Mixture modeling has been demonstrated as an effective soft-threshold technique of EMAP measures. The Bayesian approach was applied to an EMAP dataset studying the early secretory pathway in Saccharomyces cerevisiae. Twenty-seven modules were identified, and 14 of those were enriched by gold standard functional gene sets. We also conducted a detailed comparison with state-of-the-art algorithms, hierarchical cluster and Markov clustering. The experimental results show that the Bayesian approach outperforms others in efficiently recovering biologically significant modules. Contact: dengmh@pku.edu.cn; fangtingli@pku.edu.cn; zhuyp@hupo.org.cn Supplementary Information: Supplementary data are available at Bioinformatics online.

Predicting MicroRNA targets by integrating sequence and expression data in cancer

Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transactional regulators, and play important parts in cancer. One essential step to understand the regulatory effect of miRNAs is the reliable prediction of their target mRNAs. Typically, the predictions are solely based on sequence information, which unavoidably have high false detection rates. Here we develop a new algorithm called HCTarget, which predict miRNA targets by integrating the typical algorithm and the paired expression profiles of miRNA and mRNA. HCTarget formulates a linear model to characterize the relationship between mRNA and miRNA, and use a Markov Chain Monto Carlo algorithm to learn the target probabilities. When applying HCtarget to the expression data in multiple myeloma, we predict target genes for ten cancer related miRNAs. The experimental verification and a loss of function study of hsa-miR-16 validate our predictions. Compared with the previous approaches, our target sets have increased functional enrichment. Meanwhile, our predicted target pair hsa-miR-19b and SULF1 plays an important role in multiple myeloma. Therefore, HCtarget is a reliable and effective approach to predict miRNA target genes, and could improve our comprehensive understanding of gene regulation.