ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet. 2012;13:840-852. [PMID: 23090257 DOI: 10.1038/nrg3306]

Network dynamics in the tumor microenvironment

The evolutionary path from tumor initiation to metastasis can only be fully understood by considering cancer cells as part of a multi-species ecosystem within the tumor microenvironment. This paper reviews and suggests two important recent trends. Firstly, I review arguments that interactions among diverse cells in the tumor microenvironment create a distinct cellular environment that can confer growth advantages, resist interventions, and allow tumors to remain dormant for long periods. Second, I review and highlight a trend toward data-rich, molecularly detailed, computational models of the tumor microenvironment. I argue that data-driven molecularly detailed tumor microenvironment models can now be built using data from multiple emerging high-throughput technologies, and that such models can pinpoint mechanisms of dysregulation and suggest specific drug targets and follow up experiments.

The future of whole-cell modeling

Integrated whole-cell modeling is poised to make a dramatic impact on molecular and systems biology, bioengineering, and medicine--once certain obstacles are overcome. From our group's experience building a whole-cell model of Mycoplasma genitalium, we identified several significant challenges to building models of more complex cells. Here we review and discuss these challenges in seven areas: first, experimental interrogation; second, data curation; third, model building and integration; fourth, accelerated computation; fifth, analysis and visualization; sixth, model validation; and seventh, collaboration and community development. Surmounting these challenges will require the cooperation of an interdisciplinary group of researchers to create increasingly sophisticated whole-cell models and make data, models, and simulations more accessible to the wider community.

Library construction for next-generation sequencing: overviews and challenges

High-throughput sequencing, also known as next-generation sequencing (NGS), has revolutionized genomic research. In recent years, NGS technology has steadily improved, with costs dropping and the number and range of sequencing applications increasing exponentially. Here, we examine the critical role of sequencing library quality and consider important challenges when preparing NGS libraries from DNA and RNA sources. Factors such as the quantity and physical characteristics of the RNA or DNA source material as well as the desired application (i.e., genome sequencing, targeted sequencing, RNA-seq, ChIP-seq, RIP-seq, and methylation) are addressed in the context of preparing high quality sequencing libraries. In addition, the current methods for preparing NGS libraries from single cells are also discussed.

Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models

MOTIVATION

Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification.

RESULTS

We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated.

AVAILABILITY AND IMPLEMENTATION

R/Bioconductor package epigenomix.

CONTACT

h.klein@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-wide profiling of transcription factor binding and epigenetic marks in adipocytes by ChIP-seq

The recent advances in high-throughput sequencing combined with various other technologies have allowed detailed and genome-wide insight into the transcriptional networks that control adipogenesis. Chromatin immunoprecipitation (ChIP) combined with high-throughput sequencing (ChIP-seq) is one of the most widely used of these technologies. Using these methods, association of transcription factors, cofactors, and epigenetic marks can be mapped to DNA in a genome-wide manner. Here, we provide a detailed protocol for performing ChIP-seq analyses in preadipocytes and adipocytes. We have focused mainly on critical points, limitations of the assay, and quality controls required in order to obtain reproducible ChIP-seq data.

Detecting protein-DNA interactions using a modified yeast one-hybrid system

The yeast one-hybrid (Y1H) system has been among the methods of choice to detect protein-DNA interactions. However, conventional Y1H systems with a single auxotrophic reporter gene often suffer from high incidence of false positives to demonstrate a limited power in large-scale screenings. Here we describe a refined Y1H system that uses two independent bait sequences, each controlling a distinct reporter gene integrated in the host genome. With these modifications and a method of targeted DNA methylation, we succeeded in efficient isolation of clones for methylated DNA-binding proteins from mammalian cDNA libraries.

Gene networks controlling Arabidopsis thaliana flower development

The formation of flowers is one of the main models for studying the regulatory mechanisms that underlie plant development and evolution. Over the past three decades, extensive genetic and molecular analyses have led to the identification of a large number of key floral regulators and to detailed insights into how they control flower morphogenesis. In recent years, genome-wide approaches have been applied to obtaining a global view of the gene regulatory networks underlying flower formation. Furthermore, mathematical models have been developed that can simulate certain aspects of this process and drive further experimentation. Here, we review some of the main findings made in the field of Arabidopsis thaliana flower development, with an emphasis on recent advances. In particular, we discuss the activities of the floral organ identity factors, which are pivotal for the specification of the different types of floral organs, and explore the experimental avenues that may elucidate the molecular mechanisms and gene expression programs through which these master regulators of flower development act.

Development of an Illumina-based ChIP-exonuclease method provides insight into FoxA1-DNA binding properties

ChIP-exonuclease (ChIP-exo) is a modified ChIP-seq approach for high resolution mapping of transcription factor DNA sites. We describe an Illumina-based ChIP-exo method which provides a global improvement of the data quality of estrogen receptor (ER) ChIP and insights into the motif structure for key ER-associated factors. ChIP-exo of the ER pioneer factor FoxA1 identifies protected DNA with a predictable 8 bp overhang from the Forkhead motif, which we term mesas. We show that mesas occur in multiple cellular contexts and exist as single or overlapping motifs. Our Illumina-based ChIP-exo provides high resolution mapping of transcription factor binding sites.

Discriminative motif optimization based on perceptron training

MOTIVATION

Generating accurate transcription factor (TF) binding site motifs from data generated using the next-generation sequencing, especially ChIP-seq, is challenging. The challenge arises because a typical experiment reports a large number of sequences bound by a TF, and the length of each sequence is relatively long. Most traditional motif finders are slow in handling such enormous amount of data. To overcome this limitation, tools have been developed that compromise accuracy with speed by using heuristic discrete search strategies or limited optimization of identified seed motifs. However, such strategies may not fully use the information in input sequences to generate motifs. Such motifs often form good seeds and can be further improved with appropriate scoring functions and rapid optimization.

RESULTS

We report a tool named discriminative motif optimizer (DiMO). DiMO takes a seed motif along with a positive and a negative database and improves the motif based on a discriminative strategy. We use area under receiver-operating characteristic curve (AUC) as a measure of discriminating power of motifs and a strategy based on perceptron training that maximizes AUC rapidly in a discriminative manner. Using DiMO, on a large test set of 87 TFs from human, drosophila and yeast, we show that it is possible to significantly improve motifs identified by nine motif finders. The motifs are generated/optimized using training sets and evaluated on test sets. The AUC is improved for almost 90% of the TFs on test sets and the magnitude of increase is up to 39%.

AVAILABILITY AND IMPLEMENTATION

DiMO is available at http://stormo.wustl.edu/DiMO

The role of replicates for error mitigation in next-generation sequencing

Advances in next-generation sequencing (NGS) technologies have rapidly improved sequencing fidelity and substantially decreased sequencing error rates. However, given that there are billions of nucleotides in a human genome, even low experimental error rates yield many errors in variant calls. Erroneous variants can mimic true somatic and rare variants, thus requiring costly confirmatory experiments to minimize the number of false positives. Here, we discuss sources of experimental errors in NGS and how replicates can be used to abate such errors.

Widespread misinterpretable ChIP-seq bias in yeast

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to detect genome-wide interactions between a protein of interest and DNA in vivo. Loci showing strong enrichment over adjacent background regions are typically considered to be sites of binding. Insufficient attention has been given to systematic artifacts inherent to the ChIP-seq procedure that might generate a misleading picture of protein binding to certain loci. We show here that unrelated transcription factors appear to consistently bind to the gene bodies of highly transcribed genes in yeast. Strikingly, several types of negative control experiments, including a protein that is not expected to bind chromatin, also showed similar patterns of strong binding within gene bodies. These false positive signals were evident across sequencing platforms and immunoprecipitation protocols, as well as in previously published datasets from other labs. We show that these false positive signals derive from high rates of transcription, and are inherent to the ChIP procedure, although they are exacerbated by sequencing library construction procedures. This expression bias is strong enough that a known transcriptional repressor like Tup1 can erroneously appear to be an activator. Another type of background bias stems from the inherent nucleosomal structure of chromatin, and can potentially make it seem like certain factors bind nucleosomes even when they don't. Our analysis suggests that a mock ChIP sample offers a better normalization control for the expression bias, whereas the ChIP input is more appropriate for the nucleosomal periodicity bias. While these controls alleviate the effect of the biases to some extent, they are unable to eliminate it completely. Caution is therefore warranted regarding the interpretation of data that seemingly show the association of various transcription and chromatin factors with highly transcribed genes in yeast.

Searching for repetitions in biological networks: methods, resources and tools

We present here a compact overview of the data, models and methods proposed for the analysis of biological networks based on the search for significant repetitions. In particular, we concentrate on three problems widely studied in the literature: 'network alignment', 'network querying' and 'network motif extraction'. We provide (i) details of the experimental techniques used to obtain the main types of interaction data, (ii) descriptions of the models and approaches introduced to solve such problems and (iii) pointers to both the available databases and software tools. The intent is to lay out a useful roadmap for identifying suitable strategies to analyse cellular data, possibly based on the joint use of different interaction data types or analysis techniques.

Lane-by-lane sequencing using Illumina's Genome Analyzer II

Next-generation sequencing has become an essential tool in molecular biology that has been successfully applied to a broad variety of experimental approaches. While several platforms for next-generation sequencing exist, the most commonly used approach is sequencing-by-synthesis, implemented on Illumina's Genome Analyzer II (GAII) and HiSeq2000 systems. A key constraint of these sequencers is the need to run multiple lanes of samples with identical parameters as part of a single flowcell. Here, we present a series of modifications to the Illumina Genome Analyzer II, along with a script generating tool, that allow users to run the GAII in a lane-by-lane manner. Any number of lanes can be run at one time. Repeated use of the same flowcell on multiple sequencing runs does not appreciably reduce the intensity, cluster density, or accuracy of the run. These modifications will enable smaller-scale experiments with unusual design parameters to be run routinely on the GAII.

Practical guidelines for the comprehensive analysis of ChIP-seq data

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections.

Chromatin position effects assayed by thousands of reporters integrated in parallel

Reporter genes integrated into the genome are a powerful tool to reveal effects of regulatory elements and local chromatin context on gene expression. However, so far such reporter assays have been of low throughput. Here, we describe a multiplexing approach for the parallel monitoring of transcriptional activity of thousands of randomly integrated reporters. More than 27,000 distinct reporter integrations in mouse embryonic stem cells, obtained with two different promoters, show ∼1,000-fold variation in expression levels. Data analysis indicates that lamina-associated domains act as attenuators of transcription, likely by reducing access of transcription factors to binding sites. Furthermore, chromatin compaction is predictive of reporter activity. We also found evidence for crosstalk between neighboring genes and estimate that enhancers can influence gene expression on average over ∼20 kb. The multiplexed reporter assay is highly flexible in design and can be modified to query a wide range of aspects of gene regulation.

High-resolution network biology: connecting sequence with function

Proteins are not monolithic entities; rather, they can contain multiple domains that mediate distinct interactions, and their functionality can be regulated through post-translational modifications at multiple distinct sites. Traditionally, network biology has ignored such properties of proteins and has instead examined either the physical interactions of whole proteins or the consequences of removing entire genes. In this Review, we discuss experimental and computational methods to increase the resolution of protein-protein, genetic and drug-gene interaction studies to the domain and residue levels. Such work will be crucial for using interaction networks to connect sequence and structural information, and to understand the biological consequences of disease-associated mutations, which will hopefully lead to more effective therapeutic strategies.

SUMOylation regulates the chromatin occupancy and anti-proliferative gene programs of glucocorticoid receptor

In addition to the glucocorticoids, the glucocorticoid receptor (GR) is regulated by post-translational modifications, including SUMOylation. We have analyzed how SUMOylation influences the activity of endogenous GR target genes and the receptor chromatin binding by using isogenic HEK293 cells expressing wild-type GR (wtGR) or SUMOylation-defective GR (GR3KR). Gene expression profiling revealed that both dexamethasone up- and downregulated genes are affected by the GR SUMOylation and that the affected genes are significantly associated with pathways of cellular proliferation and survival. The GR3KR-expressing cells proliferated more rapidly, and their anti-proliferative response to dexamethasone was less pronounced than in the wtGR-expressing cells. ChIP-seq analyses indicated that the SUMOylation modulates the chromatin occupancy of GR on several loci associated with cellular growth in a fashion that parallels with their differential dexamethasone-regulated expression between the two cell lines. Moreover, chromatin SUMO-2/3 marks, which were associated with active GR-binding sites, showed markedly higher overlap with the wtGR cistrome than with the GR3KR cistrome. In sum, our results indicate that the SUMOylation does not simply repress the GR activity, but regulates the activity of the receptor in a target locus selective fashion, playing an important role in controlling the GR activity on genes influencing cell growth.

Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins

Chromatin immunoprecipitation (ChIP) is the gold-standard technique for localizing nuclear proteins in the genome. We used ChIP, in combination with deep sequencing (Seq), to study the genome-wide distribution of the Silent information regulator (Sir) complex in Saccharomyces cerevisiae. We analyzed ChIP-Seq peaks of the Sir2, Sir3, and Sir4 silencing proteins and discovered 238 unexpected euchromatic loci that exhibited enrichment of all three. Surprisingly, published ChIP-Seq datasets for the Ste12 transcription factor and the centromeric Cse4 protein indicated that these proteins were also enriched in the same euchromatic regions with the high Sir protein levels. The 238 loci, termed "hyper-ChIPable", were in highly expressed regions with strong polymerase II and polymerase III enrichment signals, and the correlation between transcription level and ChIP enrichment was not limited to these 238 loci but extended genome-wide. The apparent enrichment of various proteins at hyper-ChIPable loci was not a consequence of artifacts associated with deep sequencing methods, as confirmed by ChIP-quantitative PCR. The localization of unrelated proteins, including the entire silencing complex, to the most highly transcribed genes was highly suggestive of a technical issue with the immunoprecipitations. ChIP-Seq on chromatin immunoprecipitated with a nuclear-localized GFP reproduced the above enrichment in an expression-dependent manner: induction of the GAL genes resulted in an increased ChIP signal of the GFP protein at these loci, with presumably no biological relevance. Whereas ChIP is a broadly valuable technique, some published conclusions based upon ChIP procedures may merit reevaluation in light of these findings.

Gene regulatory scenarios of primary 1,25-dihydroxyvitamin d3 target genes in a human myeloid leukemia cell line

Genome- and transcriptome-wide data has significantly increased the amount of available information about primary 1,25-dihydroxyvitamin D3 (1,25(OH)2D3) target genes in cancer cell models, such as human THP-1 myelomonocytic leukemia cells. In this study, we investigated the genes G0S2, CDKN1A and MYC as master examples of primary vitamin D receptor (VDR) targets being involved in the control of cellular proliferation. The chromosomal domains of G0S2 and CDKN1A are 140-170 kb in size and contain one and three VDR binding sites, respectively. This is rather compact compared to the MYC locus that is 15 times larger and accommodates four VDR binding sites. All eight VDR binding sites were studied by chromatin immunoprecipitation in THP-1 cells. Interestingly, the site closest to the transcription start site of the down-regulated MYC gene showed 1,25(OH)2D3-dependent reduction of VDR binding and is not associated with open chromatin. Four of the other seven VDR binding regions contain a typical DR3-type VDR binding sequence, three of which are also occupied with VDR in macrophage-like cells. In conclusion, the three examples suggest that each VDR target gene has an individual regulatory scenario. However, some general components of these scenarios may be useful for the development of new therapy regimens.

Complexity of the alternative splicing landscape in plants

Alternative splicing (AS) of precursor mRNAs (pre-mRNAs) from multiexon genes allows organisms to increase their coding potential and regulate gene expression through multiple mechanisms. Recent transcriptome-wide analysis of AS using RNA sequencing has revealed that AS is highly pervasive in plants. Pre-mRNAs from over 60% of intron-containing genes undergo AS to produce a vast repertoire of mRNA isoforms. The functions of most splice variants are unknown. However, emerging evidence indicates that splice variants increase the functional diversity of proteins. Furthermore, AS is coupled to transcript stability and translation through nonsense-mediated decay and microRNA-mediated gene regulation. Widespread changes in AS in response to developmental cues and stresses suggest a role for regulated splicing in plant development and stress responses. Here, we review recent progress in uncovering the extent and complexity of the AS landscape in plants, its regulation, and the roles of AS in gene regulation. The prevalence of AS in plants has raised many new questions that require additional studies. New tools based on recent technological advances are allowing genome-wide analysis of RNA elements in transcripts and of chromatin modifications that regulate AS. Application of these tools in plants will provide significant new insights into AS regulation and crosstalk between AS and other layers of gene regulation.

A more complete picture of metal hyperaccumulation through next-generation sequencing technologies

The mechanistic understanding of metal hyperaccumulation has benefitted immensely from the use of molecular genetics tools developed for Arabidopsis thaliana. The revolution in DNA sequencing will enable even greater strides in the near future, this time not restricted to the family Brassicaceae. Reference genomes are within reach for many ecologically interesting species including heterozygous outbreeders. They will allow deep RNA-seq transcriptome studies and the re-sequencing of contrasting individuals to unravel the genetic basis of phenotypic variation. Cell-type specific transcriptome analyses, which will be essential for the dissection of metal translocation pathways in hyperaccumulators, can be achieved through the combination of RNA-seq and translatome approaches. Affordable high-resolution genotyping of many individuals enables the elucidation of quantitative trait loci in intra- and interspecific crosses as well as through genome-wide association mapping across large panels of accessions. Furthermore, genome-wide scans have the power to detect loci under recent selection. Together these approaches will lead to a detailed understanding of the evolutionary path towards the emergence of hyperaccumulation traits.

Unraveling regulatory programs for NF-kappaB, p53 and microRNAs in head and neck squamous cell carcinoma

In head and neck squamous cell carcinoma (HNSCC), mutations of p53 usually coexist with aberrant activation of NF-kappaB (NF-κB), other transcription factors and microRNAs, which promote tumor pathogenesis. However, how these factors and microRNAs interact to globally modulate gene expression and mediate oncogenesis is not fully understood. We devised a novel bioinformatics method to uncover interactive relationships between transcription factors or microRNAs and genes. This approach is based on matrix decomposition modeling under the joint constraints of sparseness and regulator-target connectivity, and able to integrate gene expression profiling and binding data of regulators. We employed this method to infer the gene regulatory networks in HNSCC. We found that the majority of the predicted p53 targets overlapped with those for NF-κB, suggesting that the two transcription factors exert a concerted modulation on regulatory programs in tumor cells. We further investigated the interrelationships of p53 and NF-κB with five additional transcription factors, AP1, CEBPB, EGR1, SP1 and STAT3, and microRNAs mir21 and mir34ac. The resulting gene networks indicate that interactions among NF-κB, p53, and the two miRNAs likely regulate progression of HNSCC. We experimentally validated our findings by determining expression of the predicted NF-κB and p53 target genes by siRNA knock down, and by examining p53 binding activity on promoters of predicted target genes in the tumor cell lines. Our results elucidating the cross-regulations among NF-κB, p53, and microRNAs provide insights into the complex regulatory mechanisms underlying HNSCC, and shows an efficient approach to inferring gene regulatory programs in biological complex systems.

Quantifying transcription factor kinetics: at work or at play?

Transcription factors (TFs) interact dynamically in vivo with chromatin binding sites. Here we summarize and compare the four different techniques that are currently used to measure these kinetics in live cells, namely fluorescence recovery after photobleaching (FRAP), fluorescence correlation spectroscopy (FCS), single molecule tracking (SMT) and competition ChIP (CC). We highlight the principles underlying each of these approaches as well as their advantages and disadvantages. A comparison of data from each of these techniques raises an important question: do measured transcription kinetics reflect biologically functional interactions at specific sites (i.e. working TFs) or do they reflect non-specific interactions (i.e. playing TFs)? To help resolve this dilemma we discuss five key unresolved biological questions related to the functionality of transient and prolonged binding events at both specific promoter response elements as well as non-specific sites. In support of functionality, we review data suggesting that TF residence times are tightly regulated, and that this regulation modulates transcriptional output at single genes. We argue that in addition to this site-specific regulatory role, TF residence times also determine the fraction of promoter targets occupied within a cell thereby impacting the functional status of cellular gene networks. Thus, TF residence times are key parameters that could influence transcription in multiple ways.

HMCan: a method for detecting chromatin modifications in cancer samples using ChIP-seq data

MOTIVATION

Cancer cells are often characterized by epigenetic changes, which include aberrant histone modifications. In particular, local or regional epigenetic silencing is a common mechanism in cancer for silencing expression of tumor suppressor genes. Though several tools have been created to enable detection of histone marks in ChIP-seq data from normal samples, it is unclear whether these tools can be efficiently applied to ChIP-seq data generated from cancer samples. Indeed, cancer genomes are often characterized by frequent copy number alterations: gains and losses of large regions of chromosomal material. Copy number alterations may create a substantial statistical bias in the evaluation of histone mark signal enrichment and result in underdetection of the signal in the regions of loss and overdetection of the signal in the regions of gain.

RESULTS

We present HMCan (Histone modifications in cancer), a tool specially designed to analyze histone modification ChIP-seq data produced from cancer genomes. HMCan corrects for the GC-content and copy number bias and then applies Hidden Markov Models to detect the signal from the corrected data. On simulated data, HMCan outperformed several commonly used tools developed to analyze histone modification data produced from genomes without copy number alterations. HMCan also showed superior results on a ChIP-seq dataset generated for the repressive histone mark H3K27me3 in a bladder cancer cell line. HMCan predictions matched well with experimental data (qPCR validated regions) and included, for example, the previously detected H3K27me3 mark in the promoter of the DLEC1 gene, missed by other tools we tested.

Identification of a novel and unique transcription factor in the intraerythrocytic stage of Plasmodium falciparum

The mechanisms of stage-specific gene regulation in the malaria parasite Plasmodium falciparum are largely unclear, with only a small number of specific regulatory transcription factors (AP2 family) having been identified. In particular, the transcription factors that function in the intraerythrocytic stage remain to be elucidated. Previously, as a model case for stage-specific transcription in the P. falciparum intraerythrocytic stage, we analyzed the transcriptional regulation of pf1-cys-prx, a trophozoite/schizont-specific gene, and suggested that some nuclear factors bind specifically to the cis-element of pf1-cys-prx and enhance transcription. In the present study, we purified nuclear factors from parasite nuclear extract by 5 steps of chromatography, and identified a factor termed PREBP. PREBP is not included in the AP2 family, and is a novel protein with four K-homology (KH) domains. The KH domain is known to be found in RNA-binding or single-stranded DNA-binding proteins. PREBP is well conserved in Plasmodium species and partially conserved in phylum Apicomplexa. To evaluate the effects of PREBP overexpression, we used a transient overexpression and luciferase assay combined approach. Overexpression of PREBP markedly enhanced luciferase expression under the control of the pf1-cys-prx cis-element. These results provide the first evidence of a novel transcription factor that activates the gene expression in the malaria parasite intraerythrocytic stage. These findings enhance our understanding of the evolution of specific transcription machinery in Plasmodium and other eukaryotes.

Navigating the transcriptional roadmap regulating plant secondary cell wall deposition

The current status of lignocellulosic biomass as an invaluable resource in industry, agriculture, and health has spurred increased interest in understanding the transcriptional regulation of secondary cell wall (SCW) biosynthesis. The last decade of research has revealed an extensive network of NAC, MYB and other families of transcription factors regulating Arabidopsis SCW biosynthesis, and numerous studies have explored SCW-related transcription factors in other dicots and monocots. Whilst the general structure of the Arabidopsis network has been a topic of several reviews, they have not comprehensively represented the detailed protein-DNA and protein-protein interactions described in the literature, and an understanding of network dynamics and functionality has not yet been achieved for SCW formation. Furthermore the methodologies employed in studies of SCW transcriptional regulation have not received much attention, especially in the case of non-model organisms. In this review, we have reconstructed the most exhaustive literature-based network representations to date of SCW transcriptional regulation in Arabidopsis. We include a manipulable Cytoscape representation of the Arabidopsis SCW transcriptional network to aid in future studies, along with a list of supporting literature for each documented interaction. Amongst other topics, we discuss the various components of the network, its evolutionary conservation in plants, putative modules and dynamic mechanisms that may influence network function, and the approaches that have been employed in network inference. Future research should aim to better understand network function and its response to dynamic perturbations, whilst the development and application of genome-wide approaches such as ChIP-seq and systems genetics are in progress for the study of SCW transcriptional regulation in non-model organisms.

Integration of cancer genomics with treatment selection: from the genome to predictive biomarkers

The field of cancer genomics is rapidly advancing as new technology provides detailed genetic and epigenetic profiling of human cancers. The amount of new data available describing the genetic make-up of tumors is paralleled by rapid advances in drug discovery and molecular therapy currently under investigation to treat these diseases. This review summarizes the challenges and approaches associated with the integration of genomic data into the development of new biomarkers in the management of cancer.

Unraveling genomic variation from next generation sequencing data

Elucidating the content of a DNA sequence is critical to deeper understand and decode the genetic information for any biological system. As next generation sequencing (NGS) techniques have become cheaper and more advanced in throughput over time, great innovations and breakthrough conclusions have been generated in various biological areas. Few of these areas, which get shaped by the new technological advances, involve evolution of species, microbial mapping, population genetics, genome-wide association studies (GWAs), comparative genomics, variant analysis, gene expression, gene regulation, epigenetics and personalized medicine. While NGS techniques stand as key players in modern biological research, the analysis and the interpretation of the vast amount of data that gets produced is a not an easy or a trivial task and still remains a great challenge in the field of bioinformatics. Therefore, efficient tools to cope with information overload, tackle the high complexity and provide meaningful visualizations to make the knowledge extraction easier are essential. In this article, we briefly refer to the sequencing methodologies and the available equipment to serve these analyses and we describe the data formats of the files which get produced by them. We conclude with a thorough review of tools developed to efficiently store, analyze and visualize such data with emphasis in structural variation analysis and comparative genomics. We finally comment on their functionality, strengths and weaknesses and we discuss how future applications could further develop in this field.

The impact of trans-regulation on the evolutionary rates of metazoan proteins

Transcription factor (TF) and microRNA (miRNA) are two crucial trans-regulatory factors that coordinately control gene expression. Understanding the impacts of these two factors on the rate of protein sequence evolution is of great importance in evolutionary biology. While many biological factors associated with evolutionary rate variations have been studied, evolutionary analysis of simultaneously accounting for TF and miRNA regulations across metazoans is still uninvestigated. Here, we provide a series of statistical analyses to assess the influences of TF and miRNA regulations on evolutionary rates across metazoans (human, mouse and fruit fly). Our results reveal that the negative correlations between trans-regulation and evolutionary rates hold well across metazoans, but the strength of TF regulation as a rate indicator becomes weak when the other confounding factors that may affect evolutionary rates are controlled. We show that miRNA regulation tends to be a more essential indicator of evolutionary rates than TF regulation, and the combination of TF and miRNA regulations has a significant dependent effect on protein evolutionary rates. We also show that trans-regulation (especially miRNA regulation) is much more important in human/mouse than in fruit fly in determining protein evolutionary rates, suggesting a considerable variation in rate determinants between vertebrates and invertebrates.

Contribution of nucleosome binding preferences and co-occurring DNA sequences to transcription factor binding

BACKGROUND

Chromatin plays a critical role in regulating transcription factors (TFs) binding to their canonical transcription factor binding sites (TFBS). Recent studies in vertebrates show that many TFs preferentially bind to genomic regions that are well bound by nucleosomes in vitro. Co-occurring secondary motifs sometimes correlated with functional TFBS.

RESULTS

We used a logistic regression to evaluate how well the propensity for nucleosome binding and co-occurrence of a secondary motif identify which canonical motifs are bound in vivo. We used ChIP-seq data for three transcription factors binding to their canonical motifs: c-Jun binding the AP-1 motif (TGA(C)/(G)TCA), GR (glucocorticoid receptor) binding the GR motif (G-ACA---(T)/(C)GT-C), and Hoxa2 (homeobox a2) binding the Pbx (Pre-B-cell leukemia homeobox) motif (TGATTGAT). For all canonical TFBS in the mouse genome, we calculated intrinsic nucleosome occupancy scores (INOS) for its surrounding 150-bps DNA and examined the relationship with in vivo TF binding. In mouse mammary 3134 cells, c-Jun and GR proteins preferentially bound regions calculated to be well-bound by nucleosomes in vitro with the canonical AP-1 and GR motifs themselves contributing to the high INOS. Functional GR motifs are enriched for AP-1 motifs if they are within a nucleosome-sized 150-bps region. GR and Hoxa2 also bind motifs with low INOS, perhaps indicating a different mechanism of action.

CONCLUSION

Our analysis quantified the contribution of INOS and co-occurring sequence to the identification of functional canonical motifs in the genome. This analysis revealed an inherent competition between some TFs and nucleosomes for binding canonical TFBS. GR and c-Jun cooperate if they are within 150-bps. Binding of Hoxa2 and a fraction of GR to motifs with low INOS values suggesting they are not in competition with nucleosomes and may function using different mechanisms.

Synthetic genetic targeting of genome instability in cancer

Cancer is a leading cause of death throughout the World. A limitation of many current chemotherapeutic approaches is that their cytotoxic effects are not restricted to cancer cells, and adverse side effects can occur within normal tissues. Consequently, novel strategies are urgently needed to better target cancer cells. As we approach the era of personalized medicine, targeting the specific molecular defect(s) within a given patient's tumor will become a more effective treatment strategy than traditional approaches that often target a given cancer type or sub-type. Synthetic genetic interactions are now being examined for their therapeutic potential and are designed to target the specific genetic and epigenetic phenomena associated with tumor formation, and thus are predicted to be highly selective. In general, two complementary approaches have been employed, including synthetic lethality and synthetic dosage lethality, to target aberrant expression and/or function associated with tumor suppressor genes and oncogenes, respectively. Here we discuss the concepts of synthetic lethality and synthetic dosage lethality, and explain three general experimental approaches designed to identify novel genetic interactors. We present examples and discuss the merits and caveats of each approach. Finally, we provide insight into the subsequent pre-clinical work required to validate novel candidate drug targets.

Overview of high throughput sequencing technologies to elucidate molecular pathways in cardiovascular diseases

High throughput sequencing technologies have become essential in studies on genomics, epigenomics, and transcriptomics. Although sequencing information has traditionally been elucidated using a low throughput technique called Sanger sequencing, high throughput sequencing technologies are capable of sequencing multiple DNA molecules in parallel, enabling hundreds of millions of DNA molecules to be sequenced at a time. This advantage allows high throughput sequencing to be used to create large data sets, generating more comprehensive insights into the cellular genomic and transcriptomic signatures of various diseases and developmental stages. Within high throughput sequencing technologies, whole exome sequencing can be used to identify novel variants and other mutations that may underlie many genetic cardiac disorders, whereas RNA sequencing can be used to analyze how the transcriptome changes. Chromatin immunoprecipitation sequencing and methylation sequencing can be used to identify epigenetic changes, whereas ribosome sequencing can be used to determine which mRNA transcripts are actively being translated. In this review, we will outline the differences in various sequencing modalities and examine the main sequencing platforms on the market in terms of their relative read depths, speeds, and costs. Finally, we will discuss the development of future sequencing platforms and how these new technologies may improve on current sequencing platforms. Ultimately, these sequencing technologies will be instrumental in further delineating how the cardiovascular system develops and how perturbations in DNA and RNA can lead to cardiovascular disease.

PscanChIP: Finding over-represented transcription factor-binding site motifs and their correlations in sequences from ChIP-Seq experiments

Chromatin immunoprecipitation followed by sequencing with next-generation technologies (ChIP-Seq) has become the de facto standard for building genome-wide maps of regions bound by a given transcription factor (TF). The regions identified, however, have to be further analyzed to determine the actual DNA-binding sites for the TF, as well as sites for other TFs belonging to the same TF complex or in general co-operating or interacting with it in transcription regulation. PscanChIP is a web server that, starting from a collection of genomic regions derived from a ChIP-Seq experiment, scans them using motif descriptors like JASPAR or TRANSFAC position-specific frequency matrices, or descriptors uploaded by users, and it evaluates both motif enrichment and positional bias within the regions according to different measures and criteria. PscanChIP can successfully identify not only the actual binding sites for the TF investigated by a ChIP-Seq experiment but also secondary motifs corresponding to other TFs that tend to bind the same regions, and, if present, precise positional correlations among their respective sites. The web interface is free for use, and there is no login requirement. It is available at http://www.beaconlab.it/pscan_chip_dev.

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.

Emerging tools for synthetic genome design

Synthetic biology is an emerging discipline for designing and synthesizing predictable, measurable, controllable, and transformable biological systems. These newly designed biological systems have great potential for the development of cheaper drugs, green fuels, biodegradable plastics, and targeted cancer therapies over the coming years. Fortunately, our ability to quickly and accurately engineer biological systems that behave predictably has been dramatically expanded by significant advances in DNA-sequencing, DNA-synthesis, and DNA-editing technologies. Here, we review emerging technologies and methodologies in the field of building designed biological systems, and we discuss their future perspectives.

An epigenetic roadmap for cardiomyocyte differentiation

Wamstad et al have provided a robust global analysis of histone markers and gene expression at 4 stages of murine embryonic stem (ES) cell differentiation into cardiac myocytes. This detailed data set will provide a rich opportunity for generating and testing hypotheses related to combinatorial transcriptional regulation of gene expression and epigenetic regulation of cell fate decisions in cardiac lineages.

Somewhat in control--the role of transcription in regulating microbial metabolic fluxes

The most common way for microbes to control their metabolism is by controlling enzyme levels through transcriptional regulation. Yet recent studies have shown that in many cases, perturbations to the transcriptional regulatory network do not result in altered metabolic phenotypes on the level of the flux distribution. We suggest that this may be a consequence of cells protecting their metabolism against stochastic fluctuations in expression as well as enabling a fast response for those fluxes that may need to be changed quickly. Furthermore, it is impossible for a regulatory program to guarantee optimal expression levels in all conditions. Several studies have found examples of demonstrably suboptimal regulation of gene expression, and improvements to the regulatory network have been investigated in laboratory evolution experiments.

GATA-1 genome-wide occupancy associates with distinct epigenetic profiles in mouse fetal liver erythropoiesis

We report the genomic occupancy profiles of the key hematopoietic transcription factor GATA-1 in pro-erythroblasts and mature erythroid cells fractionated from day E12.5 mouse fetal liver cells. Integration of GATA-1 occupancy profiles with available genome-wide transcription factor and epigenetic profiles assayed in fetal liver cells enabled as to evaluate GATA-1 involvement in modulating local chromatin structure of target genes during erythroid differentiation. Our results suggest that GATA-1 associates preferentially with changes of specific epigenetic modifications, such as H4K16, H3K27 acetylation and H3K4 di-methylation. Furthermore, we used random forest (RF) non-linear regression to predict changes in the expression levels of GATA-1 target genes based on the genomic features available for pro-erythroblasts and mature fetal liver-derived erythroid cells. Remarkably, our prediction model explained a high proportion of 62% of variation in gene expression. Hierarchical clustering of the proximity values calculated by the RF model produced a clear separation of upregulated versus downregulated genes and a further separation of downregulated genes in two distinct groups. Thus, our study of GATA-1 genome-wide occupancy profiles in mouse primary erythroid cells and their integration with global epigenetic marks reveals three clusters of GATA-1 gene targets that are associated with specific epigenetic signatures and functional characteristics.

Epigenetic therapy: use of agents targeting deacetylation and methylation in cancer management

The emergence of epigenetic mechanisms as key regulators of gene expression has led to dramatic advances in understanding cancer biology. Driven by complex layers that include aberrant DNA methylation and histone modification, epigenetic aberrations have emerged as critical processes that disrupt cellular machinery and homeostasis. Recent discoveries have already translated into successful clinical trials and improved patient care, with several agents approved for hematologic disease and others undergoing study. As the field matures, substantial challenges persist that will require resolution. These include the need to decipher more fully the interplay between the epigenetic and genetic machinery, patient selection and improving treatment efficacy in solid tumors, and optimizing combination therapies to counteract chemoresistance and minimize adverse effects. Here, we review recent progress in epigenetic treatments and consider their implications for future cancer therapy.