Mapping the genome landscape using tiling array technology

Pan-cancer analysis of PLAU indicates its potential prognostic value and correlation with neutrophil infiltration in BLCA

BACKGROUND

PLAU is known as a selected serine protease converting plasminogen to plasmin. The role of PLAU in the development of pan-cancer, especially bladder urothelial carcinoma (BLCA) remains unclear.

METHOD

A variety of online tools and cancer databases, including TCGA, GETx, HPA database, GSCALite, UALCAN, ESTIMATE, CIBERSORT, ssGSEA algorithms and SangerBox website, were applied to investigate the associations between PLAU expression and prognosis, genetic alterations, pathway activation, and tumor immunity in pan-cancer. Through cBioPortal and STITCH platforms, the oncogenic role of PLAU and related targeting medicines in BLCA were also explored. We verified the expression of PLAU in pan-cancer cells and its function in bladder cancer cell lines using wet-lab experiments.

RESULTS

PLAU expression levels were significantly higher in most cancer tissues. PLAU had a certain accuracy in the diagnosis of various types of cancers (90 % AUC > 0.700). In BLCA, PLAU has abundant methylated sites and showed statistical differences in clinical features. PLAU was involved in tumor immune infiltration, and especially positively correlated with neutrophil infiltration. High-expressed PLAU indicated poorer prognosis in the BLCA patients receiving Atezolizumab. A high mRNA and protein expression levels of PLAU were observed in pan-cancer cell lines, especially BLCA cells. Knockdown of PLAU inhibited the invasive, proliferative, and aggressive phenotypes of bladder cancer cells. Immunohistochemical staining validated PLAU's higher expression in BLCA tissues than in adjacent non-cancerous tissues. And overexpression of PLAU was associated with more advanced TNM stage, and high infiltrating depth.

CONCLUSION

Our study revealed that PLAU can serve as a potential therapeutic target and prognostic marker for various malignancies, especially BLCA.

What's Wrong in a Jump? Prediction and Validation of Splice Site Variants

Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research.

Anno genominis XX: 20 years of Arabidopsis genomics

Twenty years ago, the Arabidopsis thaliana genome sequence was published. This was an important moment as it was the first sequenced plant genome and explicitly brought plant science into the genomics era. At the time, this was not only an outstanding technological achievement, but it was characterized by a superb global collaboration. The Arabidopsis genome was the seed for plant genomic research. Here, we review the development of numerous resources based on the genome that have enabled discoveries across plant species, which has enhanced our understanding of how plants function and interact with their environments.

Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on high-throughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.

A Brief Overview of lncRNAs in Endothelial Dysfunction-Associated Diseases: From Discovery to Characterization

Long non-coding RNAs (lncRNAs) are a novel class of regulatory RNA molecules and they are involved in many biological processes and disease developments. Several unique features of lncRNAs have been identified, such as tissue-and/or cell-specific expression pattern, which suggest that they could be potential candidates for therapeutic and diagnostic applications. More recently, the scope of lncRNA studies has been extended to endothelial biology research. Many of lncRNAs were found to be critically involved in the regulation of endothelial function and its associated disease progression. An improved understanding of endothelial biology can thus facilitate the discovery of novel biomarkers and therapeutic targets for endothelial dysfunction-associated diseases, such as abnormal angiogenesis, hypertension, diabetes, and atherosclerosis. Nevertheless, the underlying mechanism of lncRNA remains undefined in previous published studies. Therefore, in this review, we aimed to discuss the current methodologies for discovering and investigating the functions of lncRNAs and, in particular, to address the functions of selected lncRNAs in endothelial dysfunction-associated diseases.

An Overview of Methodologies in Studying lncRNAs in the High-Throughput Era: When Acronyms ATTACK!

The discovery of pervasive transcription in eukaryotic genomes provided one of many surprising (and perhaps most surprising) findings of the genomic era and led to the uncovering of a large number of previously unstudied transcriptional events. This pervasive transcription leads to the production of large numbers of noncoding RNAs (ncRNAs) and thus opened the window to study these diverse, abundant transcripts of unclear relevance and unknown function. Since that discovery, recent advances in high-throughput sequencing technologies have identified a large collection of ncRNAs, from microRNAs to long noncoding RNAs (lncRNAs). Subsequent discoveries have shown that many lncRNAs play important roles in various eukaryotic processes; these discoveries have profoundly altered our understanding of the regulation of eukaryotic gene expression. Although the identification of ncRNAs has become a standard experimental approach, the functional characterization of these diverse ncRNAs remains a major challenge. In this chapter, we highlight recent progress in the methods to identify lncRNAs and the techniques to study the molecular function of these lncRNAs and the application of these techniques to the study of plant lncRNAs.

Long non-coding RNAs in hematological malignancies: translating basic techniques into diagnostic and therapeutic strategies

Recent studies have revealed that non-coding regions comprise the vast majority of the human genome and long non-coding RNAs (lncRNAs) are a diverse class of non-coding RNAs that has been implicated in a variety of biological processes. Abnormal expression of lncRNAs has also been linked to different human diseases including cancers, yet the regulatory mechanisms and functional effects of lncRNAs are still ambiguous, and the molecular details also need to be confirmed. Unlike protein-coding gene, it is much more challenging to unravel the roles of lncRNAs owing to their unique and complex features such as functional diversity and low conservation among species, which greatly hamper their experimental characterization. In this review, we summarize and discuss both conventional and advanced approaches for the identification and functional characterization of lncRNAs related to hematological malignancies. In particular, the utility and advancement of clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system as gene-editing tools are envisioned to facilitate the molecular dissection of lncRNAs via different knock-in/out strategies. Besides experimental considerations specific to lncRNAs, the roles of lncRNAs in the pathogenesis and progression of leukemia are also highlighted in the review. We expect that these insights may ultimately lead to clinical applications including development of biomarkers and novel therapeutic approaches targeting lncRNAs.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

Discovery and functional analysis of lncRNAs: Methodologies to investigate an uncharacterized transcriptome

It is known that more than 70% of mammalian genomes are transcribed, yet the vast majority of transcripts do not code for proteins. Are these noncoding transcripts merely transcriptional noise, or do they serve a biological purpose? Recent developments in genomic analysis technologies, especially sequencing methods, have allowed researchers to create a large atlas of transcriptomes, study subcellular localization, and investigate potential interactions with proteins for a growing number of transcripts. Here, we review the current methodologies available for discovering and investigating functions of long noncoding RNAs (lncRNAs), which require a wide variety of applications to study their potential biological roles. This article is part of a Special Issue entitled: Clues to long noncoding RNA taxonomy1, edited by Dr. Tetsuro Hirose and Dr. Shinichi Nakagawa.

The quest for tolerant varieties: the importance of integrating "omics" techniques to phenotyping

The primary objective of crop breeding is to improve yield and/or harvest quality while minimizing inputs. Global climate change and the increase in world population are significant challenges for agriculture and call for further improvements to crops and the development of new tools for research. Significant progress has been made in the molecular and genetic analysis of model plants. However, is science generating false expectations? Are 'omic techniques generating valuable information that can be translated into the field? The exploration of crop biodiversity and the correlation of cellular responses to stress tolerance at the plant level is currently a challenge. This viewpoint reviews concisely the problems one encounters when working on a crop and provides an outline of possible workflows when initiating cellular phenotyping via "-omic" techniques (transcriptomics, proteomics, metabolomics).

Role of CpG deserts in the epigenetic transgenerational inheritance of differential DNA methylation regions

Background

Previously a variety of environmental toxicants were found to promote the epigenetic transgenerational inheritance of disease through differential DNA methylation regions (DMRs), termed epimutations, present in sperm. The transgenerational epimutations in sperm and somatic cells identified in a number of previous studies were further investigated.

Results

The epimutations from six different environmental exposures were found to be predominantly exposure specific with negligible overlap. The current report describes a major genomic feature of all the unique epimutations identified (535) as a very low (<10 CpG/100 bp) CpG density in sperm and somatic cells associated with transgenerational disease. The genomic locations of these epimutations were found to contain DMRs with small clusters of CpG within a general region of very low density CpG. The potential role of these epimutations on gene expression is suggested to be important.

Conclusions

Observations suggest a potential regulatory role for lower density CpG regions termed “CpG deserts”. The potential evolutionary origins of these regions is also discussed.

Next-generation technologies and data analytical approaches for epigenomics

Epigenetics refers to the collection of heritable features that modulate the genome-environment interaction without being encoded in the actual DNA sequence. While being mitotically and sometimes even meiotically transmitted, epigenetic traits often demonstrate extensive flexibility. This allows cells to acquire diverse gene expression patterns during differentiation, but also to adapt to a changing environment. However, epigenetic alterations are not always beneficial to the organism, as they are, for example, frequently identified in human diseases such as cancer. Accurate and cost-efficient genome-scale profiling of epigenetic features is thus of major importance to pinpoint these "epimutations," for example, to monitor the epigenetic impact of environmental exposure. Over the last decade, the field of epigenetics has been revolutionized by several innovative "epigenomics" technologies exactly addressing this need. In this review, we discuss and compare widely used next-generation methods to assess DNA methylation and hydroxymethylation, noncoding RNA expression, histone modifications, and nucleosome positioning. Although recent methods are typically based on "second-generation" sequencing, we also pay attention to still commonly used array- and PCR-based methods, and look forward to the additional advantages of single-molecule sequencing. As the current bottleneck in epigenomics research is the analysis rather than generation of data, the basic difficulties and problem-solving strategies regarding data preprocessing and statistical analysis are introduced for the different technologies. Finally, we also consider the complications associated with epigenomic studies of species with yet unsequenced genomes and possible solutions.

Accessing epigenetic variation in the plant methylome

Cytosine DNA methylation is the addition of a methyl group to the 5' position of a cytosine, which plays a crucial role in plant development and gene silencing. Genome-wide profiling of DNA methylation is now possible using various techniques and strategies. Using these technologies, we are beginning to elucidate the extent and impact of variation in DNA methylation between individuals and/or tissues. Here, we review the different techniques used to analyze the methylomes at the whole-genome level and their applications to better understand epigenetic variations in plants.

Genome-wide survey of cold stress regulated alternative splicing in Arabidopsis thaliana with tiling microarray

Alternative splicing plays a major role in expanding the potential informational content of eukaryotic genomes. It is an important post-transcriptional regulatory mechanism that can increase protein diversity and affect mRNA stability. Alternative splicing is often regulated in a tissue-specific and stress-responsive manner. Cold stress, which adversely affects plant growth and development, regulates the transcription and splicing of plant splicing factors. This can affect the pre-mRNA processing of many genes. To identify cold regulated alternative splicing we applied Affymetrix Arabidopsis tiling arrays to survey the transcriptome under cold treatment conditions. A novel algorithm was used for detection of statistically relevant changes in intron expression within a transcript between control and cold growth conditions. A reverse transcription polymerase chain reaction (RT-PCR) analysis of a number of randomly selected genes confirmed the changes in splicing patterns under cold stress predicted by tiling array. Our analysis revealed new types of cold responsive genes. While their expression level remains relatively unchanged under cold stress their splicing pattern shows detectable changes in the relative abundance of isoforms. The majority of cold regulated alternative splicing introduced a premature termination codon (PTC) into the transcripts creating potential targets for degradation by the nonsense mediated mRNA decay (NMD) process. A number of these genes were analyzed in NMD-defective mutants by RT-PCR and shown to evade NMD. This may result in new and truncated proteins with altered functions or dominant negative effects. The results indicate that cold affects both quantitative and qualitative aspects of gene expression.

Genomics approaches for crop improvement against abiotic stress

As sessile organisms, plants are inevitably exposed to one or a combination of stress factors every now and then throughout their growth and development. Stress responses vary considerably even in the same plant species; stress-susceptible genotypes are at one extreme, and stress-tolerant ones are at the other. Elucidation of the stress responses of crop plants is of extreme relevance, considering the central role of crops in food and biofuel production. Crop improvement has been a traditional issue to increase yields and enhance stress tolerance; however, crop improvement against abiotic stresses has been particularly compelling, given the complex nature of these stresses. As traditional strategies for crop improvement approach their limits, the era of genomics research has arisen with new and promising perspectives in breeding improved varieties against abiotic stresses.

A brief introduction to tiling microarrays: principles, concepts, and applications

Technological achievements have always contributed to the advancement of biomedical research. It has never been more so than in recent times, when the development and application of innovative cutting-edge technologies have transformed biology into a data-rich quantitative science. This stunning revolution in biology primarily ensued from the emergence of microarrays over two decades ago. The completion of whole-genome sequencing projects and the advance in microarray manufacturing technologies enabled the development of tiling microarrays, which gave unprecedented genomic coverage. Since their first description, several types of application of tiling arrays have emerged, each aiming to tackle a different biological problem. Although numerous algorithms have already been developed to analyze microarray data, new method development is still needed not only for better performance but also for integration of available microarray data sets, which without doubt constitute one of the largest collections of biological data ever generated. In this chapter we first introduce the principles behind the emergence and the development of tiling microarrays, and then discuss with some examples how they are used to investigate different biological problems.

Detection of Alternatively Spliced or Processed RNAs in Cancer Using Oligonucleotide Microarray

Deregulation of gene expression plays a pivotal role in tumorigenesis, so the ability to detect RNA alterations is of great value in cancer diagnosis and management. DNA microarrays have been used to measure changes in mRNA or microRNA level, but less often the change of RNA isoforms. Here we appraise the utilization of microarray in detecting alternatively processed RNAs, which have alternative splice forms, retained introns, or altered 3' untranslated regions. We cover the methodology and focus on cancer studies. Recent development in parallel or deep sequencing used in transcriptome analysis is also discussed.

Empirical bayes model comparisons for differential methylation analysis

A number of empirical Bayes models (each with different statistical distribution assumptions) have now been developed to analyze differential DNA methylation using high-density oligonucleotide tiling arrays. However, it remains unclear which model performs best. For example, for analysis of differentially methylated regions for conservative and functional sequence characteristics (e.g., enrichment of transcription factor-binding sites (TFBSs)), the sensitivity of such analyses, using various empirical Bayes models, remains unclear. In this paper, five empirical Bayes models were constructed, based on either a gamma distribution or a log-normal distribution, for the identification of differential methylated loci and their cell division—(1, 3, and 5) and drug-treatment-(cisplatin) dependent methylation patterns. While differential methylation patterns generated by log-normal models were enriched with numerous TFBSs, we observed almost no TFBS-enriched sequences using gamma assumption models. Statistical and biological results suggest log-normal, rather than gamma, empirical Bayes model distribution to be a highly accurate and precise method for differential methylation microarray analysis. In addition, we presented one of the log-normal models for differential methylation analysis and tested its reproducibility by simulation study. We believe this research to be the first extensive comparison of statistical modeling for the analysis of differential DNA methylation, an important biological phenomenon that precisely regulates gene transcription.

Clarifying omics concepts, challenges, and opportunities for Prunus breeding in the postgenomic era

The recent sequencing of the complete genome of the peach, together with the availability of new high-throughput genome, transcriptome, proteome, and metabolome analysis technologies, offers new possibilities for Prunus breeders in what has been described as the postgenomic era. In this context, new biological challenges and opportunities for the application of these technologies in the development of efficient marker-assisted selection strategies in Prunus breeding include genome resequencing using DNA-Seq, the study of RNA regulation at transcriptional and posttranscriptional levels using tilling microarray and RNA-Seq, protein and metabolite identification and annotation, and standardization of phenotype evaluation. Additional biological opportunities include the high level of synteny among Prunus genomes. Finally, the existence of biases presents another important biological challenge in attaining knowledge from these new high-throughput omics disciplines. On the other hand, from the philosophical point of view, we are facing a revolution in the use of new high-throughput analysis techniques that may mean a scientific paradigm shift in Prunus genetics and genomics theories. The evaluation of scientific progress is another important question in this postgenomic context. Finally, the incommensurability of omics theories in the new high-throughput analysis context presents an additional philosophical challenge.

Revealing the transcriptome landscape of mouse spermatogonial cells by tiling microarray

In the past decade, the advent of microarray technologies has allowed functional genomic studies of male germ cell development, resulting in the identification of genes governing various processes. A major limitation with conventional gene expression microarray is that results obtained are biased due to gene probe design. The gene probes for expression microarrays are usually represented by a small number of probes located at the 3' end of a transcript. Tiling microarrays eliminate such issue by interrogating the genome in an unbiased fashion through probes tiled across the entire genome. These arrays provide higher genomic resolution and allow the identification of novel transcripts. To reveal the complexity of the genomic landscape of developing male germ cells, we applied tiling microarray to evaluate the transcriptome in spermatogonial cells. Over 50% of all known mouse genes are expressed during testicular development. More than 47% of the transcripts are uncharacterized. The results suggested that the transcription machinery in spermaotogonial cells is more complex than previously envisioned.

Tissue-specific differences in cytosine methylation and their association with differential gene expression in sorghum

It has been well established that DNA cytosine methylation plays essential regulatory roles in imprinting gene expression in endosperm, and hence normal embryonic development, in the model plant Arabidopsis (Arabidopsis thaliana). Nonetheless, the developmental role of this epigenetic marker in cereal crops remains largely unexplored. Here, we report for sorghum (Sorghum bicolor) differences in relative cytosine methylation levels and patterns at 5'-CCGG sites in seven tissues (endosperm, embryo, leaf, root, young inflorescence, anther, and ovary), and characterize a set of tissue-specific differentially methylated regions (TDMRs). We found that the most enriched TDMRs in sorghum are specific for the endosperm and are generated concomitantly but imbalanced by decrease versus increase in cytosine methylation at multiple 5'-CCGG sites across the genome. This leads to more extensive demethylation in the endosperm than in other tissues, where TDMRs are mainly tissue nonspecific rather than specific to a particular tissue. Accordingly, relative to endosperm, the other six tissues showed grossly similar levels though distinct patterns of cytosine methylation, presumably as a result of a similar extent of concomitant decrease versus increase in cytosine methylation that occurred at variable genomic loci. All four tested TDMRs were validated by bisulfite genomic sequencing. Diverse sequences were found to underlie the TDMRs, including those encoding various known-function or predicted proteins, transposable elements, and those bearing homology to putative imprinted genes in maize (Zea mays). We further found that the expression pattern of at least some genic TDMRs was correlated with its tissue-specific methylation state, implicating a developmental role of DNA methylation in regulating tissue-specific or -preferential gene expression in sorghum.

Deciphering transcription factor binding patterns from genome-wide high density ChIP-chip tiling array data

Background

The binding events of DNA-interacting proteins and their patterns can be extensively characterized by high density ChIP-chip tiling array data. The characteristics of the binding events could be different for different transcription factors. They may even vary for a given transcription factor among different interaction loci. The knowledge of binding sites and binding occupancy patterns are all very useful to understand the DNA-protein interaction and its role in the transcriptional regulation of genes.

Results

In the view of the complexity of the DNA-protein interaction and the opportunity offered by high density tiled ChIP-chip data, we present a statistical procedure which focuses on identifying the interaction signal regions instead of signal peaks using moving window binomial testing method and deconvolving the patterns of interaction using peakedness and skewness scores. We analyzed ChIP-chip data of 4 different DNA interacting proteins including transcription factors and RNA polymerase in fission yeast using our procedure. Our analysis revealed the variation of binding patterns within and across different DNA interacting proteins. We present their utility in understanding transcriptional regulation from ChIP-chip data.

Conclusions

Our method can successfully detect the signal regions and characterize the binding patterns in ChIP-chip data which help appropriate analysis of the ChIP-chip data.

New approaches to Prunus transcriptome analysis

The recent sequencing of the complete genome of the peach offers new opportunities for further transcriptomic studies in Prunus species in the called post-genomics era. First works on transcriptome analysis in Prunus species started in the early 2000s with the development of ESTs (expressed sequence tags) and the analysis of several candidate genes. Later, new strategies of massive analysis (high throughput) of transcriptomes have been applied, producing larger amounts of data in terms of expression of a large number of genes in a single experiment. One of these systems is massive transcriptome analysis using cDNA biochips (microarrays) to analyze thousands of genes by hybridization of mRNA labelled with fluorescence. However, the recent emergence of a massive sequencing methodology ("deep-sequencing") of the transcriptome (RNA-Seq), based on lowering the costs of DNA (in this cases complementary, cDNA) sequencing, could be more suitable than the application of microarrays. Recent papers have described the tremendous power of this technology, both in terms of profiling coverage and quantitative accuracy in transcriptomic studies. Now this technology is being applied to plant species, including Prunus. In this work, we analyze the potential in using this RNA-Seq technology in the study of Prunus transcriptomes and the development of genomic tools. In addition, the strengths and limitations of RNA-Seq relative to microarray profiling have been discussed.

Tiling array data analysis: a multiscale approach using wavelets

BACKGROUND

Tiling array data is hard to interpret due to noise. The wavelet transformation is a widely used technique in signal processing for elucidating the true signal from noisy data. Consequently, we attempted to denoise representative tiling array datasets for ChIP-chip experiments using wavelets. In doing this, we used specific wavelet basis functions, Coiflets, since their triangular shape closely resembles the expected profiles of true ChIP-chip peaks.

RESULTS

In our wavelet-transformed data, we observed that noise tends to be confined to small scales while the useful signal-of-interest spans multiple large scales. We were also able to show that wavelet coefficients due to non-specific cross-hybridization follow a log-normal distribution, and we used this fact in developing a thresholding procedure. In particular, wavelets allow one to set an unambiguous, absolute threshold, which has been hard to define in ChIP-chip experiments. One can set this threshold by requiring a similar confidence level at different length-scales of the transformed signal. We applied our algorithm to a number of representative ChIP-chip data sets, including those of Pol II and histone modifications, which have a diverse distribution of length-scales of biochemical activity, including some broad peaks.

CONCLUSIONS

Finally, we benchmarked our method in comparison to other approaches for scoring ChIP-chip data using spike-ins on the ENCODE Nimblegen tiling array. This comparison demonstrated excellent performance, with wavelets getting the best overall score.

The epigenome and plant development

The epigenomic regulation of chromatin structure and genome stability is essential for the interpretation of genetic information and ultimately the determination of phenotype. High-resolution maps of plant epigenomes have been obtained through a combination of chromatin technologies and genomic tiling microarrays and through high-throughput sequencing-based approaches. The transcriptomic activity of a plant at a certain stage of development is controlled by genome-wide combinatorial interactions of epigenetic modifications. Tissue- or environment-specific epigenomes are established during plant development. Epigenomic reprogramming triggered by the activation and movement of small RNAs is important for plant gametogenesis. Genome-wide loss of DNA methylation in the endosperm and the accompanying endosperm-specific gene expression during seed development provide a genomic insight into epigenetic regulation of gene imprinting in plants. Global changes of histone modifications during plant responses to different light environments play an important regulatory role in a sophisticated light-regulated transcriptional network. Epigenomic natural variation that developed during evolution is important for phenotypic diversity and can potentially contribute to the molecular mechanisms of complex biological phenomena such as heterosis in plants.

The current state of chromatin immunoprecipitation

The biological significance of interactions of nuclear proteins with DNA in the context of gene expression, cell differentiation, or disease has immensely been enhanced by the advent of chromatin immunoprecipitation (ChIP). ChIP is a technique whereby a protein of interest is selectively immunoprecipitated from a chromatin preparation to determine the DNA sequences associated with it. ChIP has been widely used to map the localization of post-translationally modified histones, histone variants, transcription factors, or chromatin modifying enzymes on the genome or on a given locus. In spite of its power, ChIP has for a long time remained a cumbersome procedure requiring large numbers of cells. These limitations have sparked the development of modifications to shorten the procedure, simplify sample handling and make ChIP amenable to small numbers of cells. In addition, the combination of ChIP with DNA microarray and high-throughput sequencing technologies has in recent years enabled the profiling of histone modification, histone variants, and transcription factor occupancy on a genome-wide scale. This review highlights the variations on the theme of the ChIP assay, the various detection methods applied downstream of ChIP, and examples of their application.

Generalizing moving averages for tiling arrays using combined p-value statistics

High density tiling arrays are an effective strategy for genome-wide identification of transcription factor binding regions. Sliding window methods that calculate moving averages of log ratios or t-statistics have been useful for the analysis of tiling array data. Here, we present a method that generalizes the moving average approach to evaluate sliding windows of p-values by using combined p-value statistics. In particular, the combined p-value framework can be useful in situations when taking averages of the corresponding test-statistic for the hypothesis may not be appropriate or when it is difficult to assess the significance of these averages. We exhibit the strengths of the combined p-values methods on Drosophila tiling array data and assess their ability to predict genomic regions enriched for transcription factor binding. The predictions are evaluated based on their proximity to target genes and their enrichment of known transcription factor binding sites. We also present an application for the generalization of the moving average based on integrating two different tiling array experiments.

Experimental annotation of the human pathogen Candida albicans coding and noncoding transcribed regions using high-resolution tiling arrays

BACKGROUND

Compared to other model organisms and despite the clinical relevance of the pathogenic yeast Candida albicans, no comprehensive analysis has been done to provide experimental support of its in silico-based genome annotation.

RESULTS

We have undertaken a genome-wide experimental annotation to accurately uncover the transcriptional landscape of the pathogenic yeast C. albicans using strand-specific high-density tiling arrays. RNAs were purified from cells growing under conditions relevant to C. albicans pathogenicity, including biofilm, lab-grown yeast and serum-induced hyphae, as well as cells isolated from the mouse caecum. This work provides a genome-wide experimental validation for a large number of predicted ORFs for which transcription had not been detected by other approaches. Additionally, we identified more than 2,000 novel transcriptional segments, including new ORFs and exons, non-coding RNAs (ncRNAs) as well as convincing cases of antisense gene transcription. We also characterized the 5' and 3' UTRs of expressed ORFs, and established that genes with long 5' UTRs are significantly enriched in regulatory functions controlling filamentous growth. Furthermore, we found that genomic regions adjacent to telomeres harbor a cluster of expressed ncRNAs. To validate and confirm new ncRNA candidates, we adapted an iterative strategy combining both genome-wide occupancy of the different subunits of RNA polymerases I, II and III and expression data. This comprehensive approach allowed the identification of different families of ncRNAs.

CONCLUSIONS

In summary, we provide a comprehensive expression atlas that covers relevant C. albicans pathogenic developmental stages in addition to the discovery of new ORF and non-coding genetic elements.

Light signal transduction: an infinite spectrum of possibilities

The past 30 years has seen a tremendous increase in our understanding of the light-signaling networks of higher plants. This short review emphasizes the role that Arabidopsis genetics has played in deciphering this complex network. Importantly, it outlines how genetic studies led to the identification of photoreceptors and signaling components that are not only relevant in plants, but play key roles in mammals.

A systematic approach to understand the functional consequences of non-protein coding risk regions

A primary goal of genetic association studies is to elucidate genes and novel biological mechanisms involved in disease. Recently, genome-wide association studies have identified many common genetic variants that are significantly associated with complex diseases such as cancer. In contrast to Mendelian disorders, a sizable fraction of the variants lies outside known protein-coding regions; therefore, understanding their biological consequences presents a major challenge in human genetics. Here we describe an integrated framework to allow non-protein coding loci to be annotated with respect to regulatory functions. This will facilitate identification of target genes as well as prioritize variants for functional testing.

Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed using whole-genome tiling arrays

The responses of plants to abiotic stresses are accompanied by massive changes in transcriptome composition. To provide a comprehensive view of stress-induced changes in the Arabidopsis thaliana transcriptome, we have used whole-genome tiling arrays to analyze the effects of salt, osmotic, cold and heat stress as well as application of the hormone abscisic acid (ABA), an important mediator of stress responses. Among annotated genes in the reference strain Columbia we have found many stress-responsive genes, including several transcription factor genes as well as pseudogenes and transposons that have been missed in previous analyses with standard expression arrays. In addition, we report hundreds of newly identified, stress-induced transcribed regions. These often overlap with known, annotated genes. The results are accessible through the Arabidopsis thaliana Tiling Array Express (At-TAX) homepage, which provides convenient tools for displaying expression values of annotated genes, as well as visualization of unannotated transcribed regions along each chromosome.

The Listeria transcriptional landscape from saprophytism to virulence

The bacterium Listeria monocytogenes is ubiquitous in the environment and can lead to severe food-borne infections. It has recently emerged as a multifaceted model in pathogenesis. However, how this bacterium switches from a saprophyte to a pathogen is largely unknown. Here, using tiling arrays and RNAs from wild-type and mutant bacteria grown in vitro, ex vivo and in vivo, we have analysed the transcription of its entire genome. We provide the complete Listeria operon map and have uncovered far more diverse types of RNAs than expected: in addition to 50 small RNAs (<500 nucleotides), at least two of which are involved in virulence in mice, we have identified antisense RNAs covering several open-reading frames and long overlapping 5' and 3' untranslated regions. We discovered that riboswitches can act as terminators for upstream genes. When Listeria reaches the host intestinal lumen, an extensive transcriptional reshaping occurs with a SigB-mediated activation of virulence genes. In contrast, in the blood, PrfA controls transcription of virulence genes. Remarkably, several non-coding RNAs absent in the non-pathogenic species Listeria innocua exhibit the same expression patterns as the virulence genes. Together, our data unravel successive and coordinated global transcriptional changes during infection and point to previously unknown regulatory mechanisms in bacteria.

Genome and proteome annotation: organization, interpretation and integration

Recent years have seen a huge increase in the generation of genomic and proteomic data. This has been due to improvements in current biological methodologies, the development of new experimental techniques and the use of computers as support tools. All these raw data are useless if they cannot be properly analysed, annotated, stored and displayed. Consequently, a vast number of resources have been created to present the data to the wider community. Annotation tools and databases provide the means to disseminate these data and to comprehend their biological importance. This review examines the various aspects of annotation: type, methodology and availability. Moreover, it puts a special interest on novel annotation fields, such as that of phenotypes, and highlights the recent efforts focused on the integrating annotations.

Comprehensive analysis of Arabidopsis expression level polymorphisms with simple inheritance

In Arabidopsis thaliana, gene expression level polymorphisms (ELPs) between natural accessions that exhibit simple, single locus inheritance are promising quantitative trait locus (QTL) candidates to explain phenotypic variability. It is assumed that such ELPs overwhelmingly represent regulatory element polymorphisms. However, comprehensive genome-wide analyses linking expression level, regulatory sequence and gene structure variation are missing, preventing definite verification of this assumption. Here, we analyzed ELPs observed between the Eil-0 and Lc-0 accessions. Compared with non-variable controls, 5' regulatory sequence variation in the corresponding genes is indeed increased. However, approximately 42% of all the ELP genes also carry major transcription unit deletions in one parent as revealed by genome tiling arrays, representing a >4-fold enrichment over controls. Within the subset of ELPs with simple inheritance, this proportion is even higher and deletions are generally more severe. Similar results were obtained from analyses of the Bay-0 and Sha accessions, using alternative technical approaches. Collectively, our results suggest that drastic structural changes are a major cause for ELPs with simple inheritance, corroborating experimentally observed indel preponderance in cloned Arabidopsis QTL.

The state-of-the-art of chromatin immunoprecipitation

The biological significance of interactions of nuclear proteins with DNA in the context of gene expression, cell differentiation, or disease has immensely been enhanced by the advent of chromatin immunoprecipitation (ChIP). ChIP is a technique whereby a protein of interest is selectively immunoprecipitated from a chromatin preparation to determine the DNA sequences associated with it. ChIP has been widely used to map the localization of post-translationally modified histones, histone variants, transcription factors, or chromatin-modifying enzymes on the genome or on a given locus. In spite of its power, ChIP has for a long time remained a cumbersome procedure requiring large number of cells. These limitations have sparked the development of modifications to shorten the procedure, simplify the sample handling, and make the ChIP amenable to small number of cells. In addition, the combination of ChIP with DNA microarray, paired-end ditag, and high-throughput sequencing technologies has in recent years enabled the profiling of histone modifications and transcription factor occupancy on a genome-wide scale. This review highlights the variations on the theme of the ChIP assay, the various detection methods applied downstream of ChIP, and examples of their application.

Genome-wide analyses of alternative splicing in plants: opportunities and challenges

Alternative splicing (AS) creates multiple mRNA transcripts from a single gene. While AS is known to contribute to gene regulation and proteome diversity in animals, the study of its importance in plants is in its early stages. However, recently available plant genome and transcript sequence data sets are enabling a global analysis of AS in many plant species. Results of genome analysis have revealed differences between animals and plants in the frequency of alternative splicing. The proportion of plant genes that have one or more alternative transcript isoforms is approximately 20%, indicating that AS in plants is not rare, although this rate is approximately one-third of that observed in human. The majority of plant AS events have not been functionally characterized, but evidence suggests that AS participates in important plant functions, including stress response, and may impact domestication and trait selection. The increasing availability of plant genome sequence data will enable larger comparative analyses that will identify functionally important plant AS events based on their evolutionary conservation, determine the influence of genome duplication on the evolution of AS, and discover plant-specific cis-elements that regulate AS. This review summarizes recent analyses of AS in plants, discusses the importance of further analysis, and suggests directions for future efforts.

Systems approaches to identifying gene regulatory networks in plants

Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed.