Xun Lan

Using Amino Acid Physicochemical Distance Transformation for Fast Protein Remote Homology Detection

Protein remote homology detection is one of the most important problems in bioinformatics. Discriminative methods such as support vector machines (SVM) have shown superior performance. However, the performance of SVM-based methods depends on the vector representations of the protein sequences. Prior works have demonstrated that sequence-order effects are relevant for discrimination, but little work has explored how to incorporate the sequence-order information along with the amino acid physicochemical properties into the prediction. In order to incorporate the sequence-order effects into the protein remote homology detection, the physicochemical distance transformation (PDT) method is proposed. Each protein sequence is converted into a series of numbers by using the physicochemical property scores in the amino acid index (AAIndex), and then the sequence is converted into a fixed length vector by PDT. The sequence-order information can be efficiently included into the feature vector with little computational cost by this approach. Finally, the feature vectors are input into a support vector machine classifier to detect the protein remote homologies. Our experiments on a well-known benchmark show the proposed method SVM-PDT achieves superior or comparable performance with current state-of-the-art methods and its computational cost is considerably superior to those of other methods. When the evolutionary information extracted from the frequency profiles is combined with the PDT method, the profile-based PDT approach can improve the performance by 3.4% and 11.4% in terms of ROC score and ROC50 score respectively. The local sequence-order information of the protein can be efficiently captured by the proposed PDT and the physicochemical properties extracted from the amino acid index are incorporated into the prediction. The physicochemical distance transformation provides a general framework, which would be a valuable tool for protein-level study.

Genomic Targets of the KRAB and SCAN Domain-containing Zinc Finger Protein 263

Half of all human transcription factors use C2H2 zinc finger domains to specify site-specific DNA binding and yet very little is known about their role in gene regulation. Based on in vitro studies, a zinc finger code has been developed that predicts a binding motif for a particular zinc finger factor (ZNF). However, very few studies have performed genome-wide analyses of ZNF binding patterns, and thus, it is not clear if the binding code developed in vitro will be useful for identifying target genes of a particular ZNF. We performed genome-wide ChIP-seq for ZNF263, a C2H2 ZNF that contains 9 finger domains, a KRAB repression domain, and a SCAN domain and identified more than 5000 binding sites in K562 cells. Our results suggest that ZNF263 binds to a 24-nt site that differs from the motif predicted by the zinc finger code in several positions. Interestingly, many of the ZNF263 binding sites are located within the transcribed region of the target gene. Although ZNFs containing a KRAB domain are thought to function mainly as transcriptional repressors, many of the ZNF263 target genes are expressed at high levels. To address the biological role of ZNF263, we identified genes whose expression was altered by treatment of cells with ZNF263-specific small interfering RNAs. Our results suggest that ZNF263 can have both positive and negative effects on transcriptional regulation of its target genes.

High Resolution Detection and Analysis of CpG Dinucleotides Methylation Using MBD-Seq Technology

Methyl-CpG binding domain protein sequencing (MBD-seq) is widely used to survey DNA methylation patterns. However, the optimal experimental parameters for MBD-seq remain unclear and the data analysis remains challenging. In this study, we generated high depth MBD-seq data in MCF-7 cell and developed a bi-asymmetric-Laplace model (BALM) to perform data analysis. We found that optimal efficiency of MBD-seq experiments was achieved by sequencing ∼100 million unique mapped tags from a combination of 500 mM and 1000 mM salt concentration elution in MCF-7 cells. Clonal bisulfite sequencing results showed that the methylation status of each CpG dinucleotides in the tested regions was accurately detected with high resolution using the proposed model. These results demonstrated the combination of MBD-seq and BALM could serve as a useful tool to investigate DNA methylome due to its low cost, high specificity, efficiency and resolution.

Epigenetic silencing mediated through activated PI3K/AKT signaling in breast cancer.

Trimethylation of histone 3 lysine 27 (H3K27me3) is a critical epigenetic mark for the maintenance of gene silencing. Additional accumulation of DNA methylation in target loci is thought to cooperatively support this epigenetic silencing during tumorigenesis. However, molecular mechanisms underlying the complex interplay between the two marks remain to be explored. Here we show that activation of PI3K/AKT signaling can be a trigger of this epigenetic processing at many downstream target genes. We also find that DNA methylation can be acquired at the same loci in cancer cells, thereby reinforcing permanent repression in those losing the H3K27me3 mark. Because of a link between PI3K/AKT signaling and epigenetic alterations, we conducted epigenetic therapies in conjunction with the signaling-targeted treatment. These combined treatments synergistically relieve gene silencing and suppress cancer cell growth in vitro and in xenografts. The new finding has important implications for improving targeted cancer therapies in the future.

Multivalent epigenetic marks confer microenvironment-responsive epigenetic plasticity to ovarian cancer cells

“Epigenetic plasticity” refers to the capability of mammalian cells to alter their differentiation status via chromatin remodeling-associated alterations in gene expression. While epigenetic plasticity has been best associated with lineage commitment of embryonic stem cells, recent studies have demonstrated chromatin remodeling even in terminally differentiated normal cells, and advanced-stage melanoma and breast cancer cells, in context-dependent responses to alterations in their microenvironment. In the current study, we extend this attribute of epigenetic plasticity to aggressive ovarian cancer cells, by using an integrative approach to associate cellular phenotypes with chromatin modifications (“ChIP-chip”) and mRNA and microRNA expression. While we identified numerous gene promoters possessing the well-known “bivalent mark” of H3K27me3/H3K4me2, we also report 14 distinct, lesser-known bi-, tri-, and tetravalent combinations of activating and repressive chromatin modifications, in platinum-resistant CP70 ovarian cancer cells. The vast majority (>90%) of all the histone marks studied localized to regions within 2000 bp of transcription start sites, supporting a role in gene regulation. Upon a simple alteration in the microenvironment, transition from two- to three-dimensional culture, an increase (17% to 38%) in repressive-only marked promoters was observed, concomitant with a decrease (31% to 21%) in multivalent (i.e., juxtaposed permissive and repressive histone marked) promoters. Like embryonic/tissue stem and other (non-ovarian) carcinoma cells, ovarian cancer cell epigenetic plasticity reflects an inherent transcriptional flexibility for context-responsive alterations in phenotype. It is possible that this plasticity could be therapeutically exploited for the management of this lethal gynecologic malignancy.

Amplification of Distant Estrogen Response Elements Deregulates Target Genes Associated with Tamoxifen Resistance in Breast Cancer

A causal role of gene amplification in tumorigenesis is well known, whereas amplification of DNA regulatory elements as an oncogenic driver remains unclear. In this study, we integrated next-generation sequencing approaches to map distant estrogen response elements (DEREs) that remotely control the transcription of target genes through chromatin proximity. Two densely mapped DERE regions located on chromosomes 17q23 and 20q13 were frequently amplified in estrogen receptor-α-positive luminal breast cancer. These aberrantly amplified DEREs deregulated target gene expression potentially linked to cancer development and tamoxifen resistance. Progressive accumulation of DERE copies was observed in normal breast progenitor cells chronically exposed to estrogenic chemicals. These findings may extend to other DNA regulatory elements, the amplification of which can profoundly alter target transcriptome during tumorigenesis.

Agonist and antagonist switch DNA motifs recognized by human androgen receptor in prostate cancer

Human transcription factors recognize specific DNA sequence motifs to regulate transcription. It is unknown whether a single transcription factor is able to bind to distinctly different motifs on chromatin, and if so, what determines the usage of specific motifs. By using a motif‐resolution chromatin immunoprecipitation‐exonuclease (ChIP‐exo) approach, we find that agonist‐liganded human androgen receptor (AR) and antagonist‐liganded AR bind to two distinctly different motifs, leading to distinct transcriptional outcomes in prostate cancer cells. Further analysis on clinical prostate tissues reveals that the binding of AR to these two distinct motifs is involved in prostate carcinogenesis. Together, these results suggest that unique ligands may switch DNA motifs recognized by ligand‐dependent transcription factors in vivo. Our findings also provide a broad mechanistic foundation for understanding ligand‐specific induction of gene expression profiles.

QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions.

BackgroundMany computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples.ResultsIn this study, we developed a novel quantitative method for comparing two biological ChIP-seq samples, called QChIPat. Our method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. Our program was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells.ConclusionsSeveral advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions. QChIPat is implemented in R, Perl and C++, and has been tested under Linux. The R package is available at http://motif.bmi.ohio-state.edu/QChIPat.

Ligand-dependent genomic function of glucocorticoid receptor in triple-negative breast cancer.

Glucocorticoids (GCs) have been widely used as coadjuvants in the treatment of solid tumours, but GC treatment may be associated with poor pharmacotherapeutic response or prognosis. The genomic action of GC in these tumours is largely unknown. Here we find that dexamethasone (Dex, a synthetic GC)-regulated genes in triple-negative breast cancer (TNBC) cells are associated with drug resistance. Importantly, these GC-regulated genes are aberrantly expressed in TNBC patients and are associated with unfavourable clinical outcomes. Interestingly, in TNBC cells, Compound A (CpdA, a selective GR modulator) only regulates a small number of genes not involved in carcinogenesis and therapy resistance. Mechanistic studies using a ChIP-exo approach reveal that Dex- but not CpdA-liganded glucocorticoid receptor (GR) binds to a single glucocorticoid response element (GRE), which drives the expression of pro-tumorigenic genes. Our data suggest that development of safe coadjuvant therapy should consider the distinct genomic function between Dex- and CpdA-liganded GR.

Computational analysis reveals a correlation of exon-skipping events with splicing, transcription and epigenetic factors

Alternative splicing (AS), in higher eukaryotes, is one of the mechanisms of post-transcriptional regulation that generate multiple transcripts from the same gene. One particular mode of AS is the skipping event where an exon may be alternatively excluded or constitutively included in the resulting mature mRNA. Both transcript isoforms from this skipping event site, i.e. in which the exon is either included (inclusion isoform) or excluded (skipping isoform), are typically present in one cell, and maintain a subtle balance that is vital to cellular function and dynamics. However, how the prevailing conditions dictate which isoform is expressed and what biological factors might influence the regulation of this process remain areas requiring further exploration. In this study, we have developed a novel computational method, graph-based exon-skipping scanner (GESS), for de novo detection of skipping event sites from raw RNA-seq reads without prior knowledge of gene annotations, as well as for determining the dominant isoform generated from such sites. We have applied our method to publicly available RNA-seq data in GM12878 and K562 cells from the ENCODE consortium and experimentally validated several skipping site predictions by RT-PCR. Furthermore, we integrated other sequencing-based genomic data to investigate the impact of splicing activities, transcription factors (TFs) and epigenetic histone modifications on splicing outcomes. Our computational analysis found that splice sites within the skipping-isoform-dominated group (SIDG) tended to exhibit weaker MaxEntScan-calculated splice site strength around middle, ‘skipping’, exons compared to those in the inclusion-isoform-dominated group (IIDG). We further showed the positional preference pattern of splicing factors, characterized by enrichment in the intronic splice sites immediately bordering middle exons. Finally, our analysis suggested that different epigenetic factors may introduce a variable obstacle in the process of exon–intron boundary establishment leading to skipping events.