Transcript mapping with high-density oligonucleotide tiling arrays

Defining pervasive transcription units using chromatin RNA-sequencing data

Pervasive transcripts (PTs) are difficult to detect by steady-state RNA-seq, because they are degraded immediately by the nuclear exosome complex. Here, we describe a protocol illustrating a bioinformatic pipeline for genome-wide PTs de novo annotation via chromatin-associated RNA-seq data upon DIS3 depletion. Compared to defining PTs by nascent RNA-seq such as TT-seq and PRO-seq, this protocol is more convenient and cost efficient. In addition, this protocol defines 3′-end of PTs more precisely, while reads from PRO-seq have a skew at the 5′-end.

For complete details on the use and execution of this protocol, please refer to Liu et al. (2022).

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Efficient chromatin RNA extraction with spike-in RNA for RNA-seq normalization

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Detection of accumulated PTs by chromatin RNA-seq upon Dis3 depletion

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Annotate genome-wide PTs de novo

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Bioinformatic pipeline for identification of sample-specific eRNAs and PROMPTs

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Oct4 differentially regulates chromatin opening and enhancer transcription in pluripotent stem cells

The transcription factor Oct4 is essential for the maintenance and induction of stem cell pluripotency, but its functional roles are not fully understood. Here, we investigate the functions of Oct4 by depleting and subsequently recovering it in mouse embryonic stem cells (ESCs) and conducting a time-resolved multiomics analysis. Oct4 depletion leads to an immediate loss of its binding to enhancers, accompanied by a decrease in mRNA synthesis from its target genes that are part of the transcriptional network that maintains pluripotency. Gradual decrease of Oct4 binding to enhancers does not immediately change the chromatin accessibility but reduces transcription of enhancers. Conversely, partial recovery of Oct4 expression results in a rapid increase in chromatin accessibility, whereas enhancer transcription does not fully recover. These results indicate different concentration-dependent activities of Oct4. Whereas normal ESC levels of Oct4 are required for transcription of pluripotency enhancers, low levels of Oct4 are sufficient to retain chromatin accessibility, likely together with other factors such as Sox2.

The PAF1 complex promotes 3' processing of pervasive transcripts

The PAF1 complex (PAF1C) functions in multiple transcriptional processes involving RNA polymerase II (RNA Pol II). Enhancer RNAs (eRNAs) and promoter upstream transcripts (PROMPTs) are pervasive transcripts transcribed by RNA Pol II and degraded rapidly by the nuclear exosome complex after 3' endonucleolytic cleavage by the Integrator complex (Integrator). Here we show that PAF1C has a role in termination of eRNAs and PROMPTs that are cleaved 1-3 kb downstream of the transcription start site. Mechanistically, PAF1C facilitates recruitment of Integrator to sites of pervasive transcript cleavage, promoting timely cleavage and transcription termination. We also show that PAF1C recruits Integrator to coding genes, where PAF1C then dissociates from Integrator upon entry into processive elongation. Our results demonstrate a function of PAF1C in limiting the length and accumulation of pervasive transcripts that result from non-productive transcription.

Efficient change-points detection for genomic sequences via cumulative segmented regression

MOTIVATION

Knowing the number and the exact locations of multiple change points in genomic sequences serves several biological needs. The cumulative-segmented algorithm (cumSeg) has been recently proposed as a computationally efficient approach for multiple change-points detection, which is based on a simple transformation of data and provides results quite robust to model mis-specifications. However, the errors are also accumulated in the transformed model so that heteroscedasticity and serial correlation will show up, and thus the variations of the estimated change points will be quite different, while the locations of the change points should be of the same importance in the original genomic sequences.

RESULTS

In this study, we develop two new change-points detection procedures in the framework of cumulative segmented regression. Simulations reveal that the proposed methods not only improve the efficiency of each change point estimator substantially but also provide the estimators with similar variations for all the change points. By applying these proposed algorithms to Coriel and SNP genotyping data, we illustrate their performance on detecting copy number variations.

AVAILABILITY AND IMPLEMENTATION

The proposed algorithms are implemented in R program and the codes are provided in the online supplementary material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Plant RNA-mediated gene regulatory network

Not all transcribed RNAs are protein-coding RNAs. Many of them are non-protein-coding RNAs in diverse eukaryotes. However, some of them seem to be non-functional and are resulted from spurious transcription. A lot of non-protein-coding transcripts have a significant function in the translation process. Gene expressions depend on complex networks of diverse gene regulatory pathways. Several non-protein-coding RNAs regulate gene expression in a sequence-specific system either at the transcriptional level or post-transcriptional level. They include a significant part of the gene expression regulatory network. RNA-mediated gene regulation machinery is evolutionarily ancient. They well-evolved during the evolutionary time and are becoming much more complex than had been expected. In this review, we are trying to summarizing the current knowledge in the field of RNA-mediated gene silencing.

Aptardi predicts polyadenylation sites in sample-specific transcriptomes using high-throughput RNA sequencing and DNA sequence

Annotation of polyadenylation sites from short-read RNA sequencing alone is a challenging computational task. Other algorithms rooted in DNA sequence predict potential polyadenylation sites; however, in vivo expression of a particular site varies based on a myriad of conditions. Here, we introduce aptardi (alternative polyadenylation transcriptome analysis from RNA-Seq data and DNA sequence information), which leverages both DNA sequence and RNA sequencing in a machine learning paradigm to predict expressed polyadenylation sites. Specifically, as input aptardi takes DNA nucleotide sequence, genome-aligned RNA-Seq data, and an initial transcriptome. The program evaluates these initial transcripts to identify expressed polyadenylation sites in the biological sample and refines transcript 3'-ends accordingly. The average precision of the aptardi model is twice that of a standard transcriptome assembler. In particular, the recall of the aptardi model (the proportion of true polyadenylation sites detected by the algorithm) is improved by over three-fold. Also, the model-trained using the Human Brain Reference RNA commercial standard-performs well when applied to RNA-sequencing samples from different tissues and different mammalian species. Finally, aptardi's input is simple to compile and its output is easily amenable to downstream analyses such as quantitation and differential expression.

Transcription activation depends on the length of the RNA polymerase II C-terminal domain

Eukaryotic RNA polymerase II (Pol II) contains a tail‐like, intrinsically disordered carboxy‐terminal domain (CTD) comprised of heptad‐repeats, that functions in coordination of the transcription cycle and in coupling transcription to co‐transcriptional processes. The CTD repeat number varies between species and generally increases with genome size, but the reasons for this are unclear. Here, we show that shortening the CTD in human cells to half of its length does not generally change pre‐mRNA synthesis or processing in cells. However, CTD shortening decreases the duration of promoter‐proximal Pol II pausing, alters transcription of putative enhancer elements, and delays transcription activation after stimulation of the MAP kinase pathway. We suggest that a long CTD is required for efficient enhancer‐dependent recruitment of Pol II to target genes for their rapid activation.

Transcriptionally active enhancers in human cancer cells

The growth of human cancer cells is driven by aberrant enhancer and gene transcription activity. Here, we use transient transcriptome sequencing (TT-seq) to map thousands of transcriptionally active putative enhancers in fourteen human cancer cell lines covering seven types of cancer. These enhancers were associated with cell type-specific gene expression, enriched for genetic variants that predispose to cancer, and included functionally verified enhancers. Enhancer-promoter (E-P) pairing by correlation of transcription activity revealed ~ 40,000 putative E-P pairs, which were depleted for housekeeping genes and enriched for transcription factors, cancer-associated genes, and 3D conformational proximity. The cell type specificity and transcription activity of target genes increased with the number of paired putative enhancers. Our results represent a rich resource for future studies of gene regulation by enhancers and their role in driving cancerous cell growth.

Single-cell analysis of structural variations and complex rearrangements with tri-channel processing

Structural variation (SV), involving deletions, duplications, inversions and translocations of DNA segments, is a major source of genetic variability in somatic cells and can dysregulate cancer-related pathways. However, discovering somatic SVs in single cells has been challenging, with copy-number-neutral and complex variants typically escaping detection. Here we describe single-cell tri-channel processing (scTRIP), a computational framework that integrates read depth, template strand and haplotype phase to comprehensively discover SVs in individual cells. We surveyed SV landscapes of 565 single cells, including transformed epithelial cells and patient-derived leukemic samples, to discover abundant SV classes, including inversions, translocations and complex DNA rearrangements. Analysis of the leukemic samples revealed four times more somatic SVs than cytogenetic karyotyping, submicroscopic copy-number alterations, oncogenic copy-neutral rearrangements and a subclonal chromothripsis event. Advancing current methods, single-cell tri-channel processing can directly measure SV mutational processes in individual cells, such as breakage-fusion-bridge cycles, facilitating studies of clonal evolution, genetic mosaicism and SV formation mechanisms, which could improve disease classification for precision medicine.

The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact

Metastatic castration-resistant prostate cancer (mCRPC) has a highly complex genomic landscape. With the recent development of novel treatments, accurate stratification strategies are needed. Here we present the whole-genome sequencing (WGS) analysis of fresh-frozen metastatic biopsies from 197 mCRPC patients. Using unsupervised clustering based on genomic features, we define eight distinct genomic clusters. We observe potentially clinically relevant genotypes, including microsatellite instability (MSI), homologous recombination deficiency (HRD) enriched with genomic deletions and BRCA2 aberrations, a tandem duplication genotype associated with CDK12-/- and a chromothripsis-enriched subgroup. Our data suggests that stratification on WGS characteristics may improve identification of MSI, CDK12-/- and HRD patients. From WGS and ChIP-seq data, we show the potential relevance of recurrent alterations in non-coding regions identified with WGS and highlight the central role of AR signaling in tumor progression. These data underline the potential value of using WGS to accurately stratify mCRPC patients into clinically actionable subgroups.

The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies

The whole-genome sequencing of prospectively collected tissue biopsies from 442 patients with metastatic breast cancer reveals that, compared to primary breast cancer, tumor mutational burden doubles, the relative contributions of mutational signatures shift and the mutation frequency of six known driver genes increases in metastatic breast cancer. Significant associations with pretreatment are also observed. The contribution of mutational signature 17 is significantly enriched in patients pretreated with fluorouracil, taxanes, platinum and/or eribulin, whereas the de novo mutational signature I identified in this study is significantly associated with pretreatment containing platinum-based chemotherapy. Clinically relevant subgroups of tumors are identified, exhibiting either homologous recombination deficiency (13%), high tumor mutational burden (11%) or specific alterations (24%) linked to sensitivity to FDA-approved drugs. This study provides insights into the biology of metastatic breast cancer and identifies clinically useful genomic features for the future improvement of patient management.

The pause-initiation limit restricts transcription activation in human cells

Eukaryotic gene transcription is often controlled at the level of RNA polymerase II (Pol II) pausing in the promoter-proximal region. Pausing Pol II limits the frequency of transcription initiation ('pause-initiation limit'), predicting that the pause duration must be decreased for transcriptional activation. To test this prediction, we conduct a genome-wide kinetic analysis of the heat shock response in human cells. We show that the pause-initiation limit restricts transcriptional activation at most genes. Gene activation generally requires the activity of the P-TEFb kinase CDK9, which decreases the duration of Pol II pausing and thereby enables an increase in the productive initiation frequency. The transcription of enhancer elements is generally not pause limited and can be activated without CDK9 activity. Our results define the kinetics of Pol II transcriptional regulation in human cells at all gene classes during a natural transcription response.

The Paf1 Complex Broadly Impacts the Transcriptome of Saccharomyces cerevisiae

The Polymerase Associated Factor 1 complex (Paf1C) is a multifunctional regulator of eukaryotic gene expression important for the coordination of transcription with chromatin modification and post-transcriptional processes. In this study, we investigated the extent to which the functions of Paf1C combine to regulate the Saccharomyces cerevisiae transcriptome. While previous studies focused on the roles of Paf1C in controlling mRNA levels, here, we took advantage of a genetic background that enriches for unstable transcripts, and demonstrate that deletion of PAF1 affects all classes of Pol II transcripts including multiple classes of noncoding RNAs (ncRNAs). By conducting a de novo differential expression analysis independent of gene annotations, we found that Paf1 positively and negatively regulates antisense transcription at multiple loci. Comparisons with nascent transcript data revealed that many, but not all, changes in RNA levels detected by our analysis are due to changes in transcription instead of post-transcriptional events. To investigate the mechanisms by which Paf1 regulates protein-coding genes, we focused on genes involved in iron and phosphate homeostasis, which were differentially affected by PAF1 deletion. Our results indicate that Paf1 stimulates phosphate gene expression through a mechanism that is independent of any individual Paf1C-dependent histone modification. In contrast, the inhibition of iron gene expression by Paf1 correlates with a defect in H3 K36 trimethylation. Finally, we showed that one iron regulon gene, FET4, is coordinately controlled by Paf1 and transcription of upstream noncoding DNA. Together, these data identify roles for Paf1C in controlling both coding and noncoding regions of the yeast genome.

Whole-genome sequence of the bovine blood fluke Schistosoma bovis supports interspecific hybridization with S. haematobium

Mesenteric infection by the parasitic blood fluke Schistosoma bovis is a common veterinary problem in Africa and the Middle East and occasionally in the Mediterranean Region. The species also has the ability to form interspecific hybrids with the human parasite S. haematobium with natural hybridisation observed in West Africa, presenting possible zoonotic transmission. Additionally, this exchange of alleles between species may dramatically influence disease dynamics and parasite evolution. We have generated a 374 Mb assembly of the S. bovis genome using Illumina and PacBio-based technologies. Despite infecting different hosts and organs, the genome sequences of S. bovis and S. haematobium appeared strikingly similar with 97% sequence identity. The two species share 98% of protein-coding genes, with an average sequence identity of 97.3% at the amino acid level. Genome comparison identified large continuous parts of the genome (up to several 100 kb) showing almost 100% sequence identity between S. bovis and S. haematobium. It is unlikely that this is a result of genome conservation and provides further evidence of natural interspecific hybridization between S. bovis and S. haematobium. Our results suggest that foreign DNA obtained by interspecific hybridization was maintained in the population through multiple meiosis cycles and that hybrids were sexually reproductive, producing viable offspring. The S. bovis genome assembly forms a highly valuable resource for studying schistosome evolution and exploring genetic regions that are associated with species-specific phenotypic traits.

In this article we detail the assembly and functional annotation of the Schistosoma bovis genome. S. bovis is a parasitic flatworm that primarily infects bovines, with important economic consequences in affected countries. However, it is also a close relative of the human carcinogenic parasite Schistosoma haematobium which is a serious health issue in many endemic countries in Sub-Saharan Africa. The close relationship and overlapping geographical distribution of S. bovis and S. haematobium allows these to hybridise in the wild increasing their genetic diversity and presenting the risk of zoonotic transmission, i.e. the transmission from animals to humans. The hybridization between human and ruminant schistosomes is of particular interest as interspecific hybridization may have dramatic impacts on transmission rates, disease dynamics, control interventions and parasite evolution. By whole-genome sequencing and comparative genomics we present evidence that fertile hybrids are indeed present in the wild, presenting the potential risk of transmission from animal reservoirs to humans.

NAD(P)HX repair deficiency causes central metabolic perturbations in yeast and human cells

NADHX and NADPHX are hydrated and redox inactive forms of the NADH and NADPH cofactors, known to inhibit several dehydrogenases in vitro. A metabolite repair system that is conserved in all domains of life and that comprises the two enzymes NAD(P)HX dehydratase and NAD(P)HX epimerase, allows reconversion of both the S- and R-epimers of NADHX and NADPHX to the normal cofactors. An inherited deficiency in this system has recently been shown to cause severe neurometabolic disease in children. Although evidence for the presence of NAD(P)HX has been obtained in plant and human cells, little is known about the mechanism of formation of these derivatives in vivo and their potential effects on cell metabolism. Here, we show that NAD(P)HX dehydratase deficiency in yeast leads to an important, temperature-dependent NADHX accumulation in quiescent cells with a concomitant depletion of intracellular NAD⁺ and serine pools. We demonstrate that NADHX potently inhibits the first step of the serine synthesis pathway in yeast. Human cells deficient in the NAD(P)HX dehydratase also accumulated NADHX and showed decreased viability. In addition, those cells consumed more glucose and produced more lactate, potentially indicating impaired mitochondrial function. Our results provide first insights into how NADHX accumulation affects cellular functions and pave the way for a better understanding of the mechanism(s) underlying the rapid and severe neurodegeneration leading to early death in NADHX repair-deficient children.

Considerations on Experimental Design and Data Analysis of Chromatin Immunoprecipitation Experiments

Arguably one of the most valuable techniques to study chromatin organization, ChIP is the method of choice to map the contacts established between proteins and genomic DNA. Ever since its inception, more than 30 years ago, ChIP has been constantly evolving, improving, and expanding its capabilities and reach. Despite its widespread use by many laboratories across a wide variety of disciplines, ChIP assays can be sometimes challenging to design, and are often sensitive to variations in practical implementation.In this chapter, we provide a general overview of the ChIP method and its most common variations, with a special focus on ChIP-seq. We try to address some of the most important aspects that need to be taken into account in order to design and perform experiments that generate the most reproducible, high-quality data. Some of the main topics covered include the use of properly characterized antibodies, alternatives to chromatin preparation, the need for proper controls, and some recommendations about ChIP-seq data analysis.

Untranslated Parts of Genes Interpreted: Making Heads or Tails of High-Throughput Transcriptomic Data via Computational Methods: Computational methods to discover and quantify isoforms with alternative untranslated regions

In this review we highlight the importance of defining the untranslated parts of transcripts, and present a number of computational approaches for the discovery and quantification of alternative transcription start and poly-adenylation events in high-throughput transcriptomic data. The fate of eukaryotic transcripts is closely linked to their untranslated regions, which are determined by the position at which transcription starts and ends at a genomic locus. Although the extent of alternative transcription starts and alternative poly-adenylation sites has been revealed by sequencing methods focused on the ends of transcripts, the application of these methods is not yet widely adopted by the community. We suggest that computational methods applied to standard high-throughput technologies are a useful, albeit less accurate, alternative to the expertise-demanding 5' and 3' sequencing and they are the only option for analysing legacy transcriptomic data. We review these methods here, focusing on technical challenges and arguing for the need to include better normalization of the data and more appropriate statistical models of the expected variation in the signal.

INO80 represses osmostress induced gene expression by resetting promoter proximal nucleosomes

The conserved INO80 chromatin remodeling complex is involved in regulation of DNA damage repair, replication and transcription. It is commonly recruited to the transcription start region and contributes to the establishment of promoter-proximal nucleosomes. We find a substantial influence of INO80 on nucleosome dynamics and gene expression during stress induced transcription. Transcription induced by osmotic stress leads to genome-wide remodeling of promoter proximal nucleosomes. INO80 function is required for timely return of evicted nucleosomes to the 5΄ end of induced genes. Reduced INO80 function in Arp8-deficient cells leads to correlated prolonged transcription and nucleosome eviction. INO80 and the related complex SWR1 regulate incorporation of the H2A.Z isoform at promoter proximal nucleosomes. However, H2A.Z seems not to influence osmotic stress induced gene regulation. Furthermore, we show that high rates of transcription promote INO80 recruitment to promoter regions, suggesting a connection between active transcription and promoter proximal nucleosome remodeling. In addition, we find that absence of INO80 enhances bidirectional promoter activity at highly induced genes and expression of a number of stress induced transcripts. We suggest that INO80 has a direct repressive role via promoter proximal nucleosome remodeling to limit high levels of transcription in yeast.

Similarity-Based Segmentation of Multi-Dimensional Signals

The segmentation of time series and genomic data is a common problem in computational biology. With increasingly complex measurement procedures individual data points are often not just numbers or simple vectors in which all components are of the same kind. Analysis methods that capitalize on slopes in a single real-valued data track or that make explicit use of the vectorial nature of the data are not applicable in such scenaria. We develop here a framework for segmentation in arbitrary data domains that only requires a minimal notion of similarity. Using unsupervised clustering of (a sample of) the input yields an approximate segmentation algorithm that is efficient enough for genome-wide applications. As a showcase application we segment a time-series of transcriptome sequencing data from budding yeast, in high temporal resolution over ca. 2.5 cycles of the short-period respiratory oscillation. The algorithm is used with a similarity measure focussing on periodic expression profiles across the metabolic cycle rather than coverage per time point.

TT-seq captures enhancer landscapes immediately after T-cell stimulation

To monitor transcriptional regulation in human cells, rapid changes in enhancer and promoter activity must be captured with high sensitivity and temporal resolution. Here, we show that the recently established protocol TT-seq ("transient transcriptome sequencing") can monitor rapid changes in transcription from enhancers and promoters during the immediate response of T cells to ionomycin and phorbol 12-myristate 13-acetate (PMA). TT-seq maps eRNAs and mRNAs every 5 min after T-cell stimulation with high sensitivity and identifies many new primary response genes. TT-seq reveals that the synthesis of 1,601 eRNAs and 650 mRNAs changes significantly within only 15 min after stimulation, when standard RNA-seq does not detect differentially expressed genes. Transcription of enhancers that are primed for activation by nucleosome depletion can occur immediately and simultaneously with transcription of target gene promoters. Our results indicate that enhancer transcription is a good proxy for enhancer regulatory activity in target gene activation, and establish TT-seq as a tool for monitoring the dynamics of enhancer landscapes and transcription programs during cellular responses and differentiation.

PIPE-chipSAD: A Pipeline for the Analysis of High Density Arrays of Bacterial Transcriptomes

PIPE-chipSAD is a pipeline for bacterial transcriptome studies based on high-density microarray experiments. The main algorithm chipSAD, integrates the analysis of the hybridization signal with the genomic position of probes and identifies portions of the genome transcribing for mRNAs. The pipeline includes a procedure, align-chipSAD, to build a multiple alignment of transcripts originating in the same locus in multiple experiments and provides a method to compare mRNA expression across different conditions. Finally, the pipeline includes anno-chipSAD a method to annotate the detected transcripts in comparison to the genome annotation. Overall, our pipeline allows transcriptional profile analysis of both coding and non-coding portions of the chromosome in a single framework. Importantly, due to its versatile characteristics, it will be of wide applicability to analyse, not only microarray signals, but also data from other high throughput technologies such as RNA-sequencing. The current PIPE-chipSAD implementation is written in Python programming language and is freely available at https://github.com/silviamicroarray/chipSAD.

Staphylococcus aureus Transcriptome Architecture: From Laboratory to Infection-Mimicking Conditions

Staphylococcus aureus is a major pathogen that colonizes about 20% of the human population. Intriguingly, this Gram-positive bacterium can survive and thrive under a wide range of different conditions, both inside and outside the human body. Here, we investigated the transcriptional adaptation of S. aureus HG001, a derivative of strain NCTC 8325, across experimental conditions ranging from optimal growth in vitro to intracellular growth in host cells. These data establish an extensive repertoire of transcription units and non-coding RNAs, a classification of 1412 promoters according to their dependence on the RNA polymerase sigma factors SigA or SigB, and allow identification of new potential targets for several known transcription factors. In particular, this study revealed a relatively low abundance of antisense RNAs in S. aureus, where they overlap only 6% of the coding genes, and only 19 antisense RNAs not co-transcribed with other genes were found. Promoter analysis and comparison with Bacillus subtilis links the small number of antisense RNAs to a less profound impact of alternative sigma factors in S. aureus. Furthermore, we revealed that Rho-dependent transcription termination suppresses pervasive antisense transcription, presumably originating from abundant spurious transcription initiation in this A+T-rich genome, which would otherwise affect expression of the overlapped genes. In summary, our study provides genome-wide information on transcriptional regulation and non-coding RNAs in S. aureus as well as new insights into the biological function of Rho and the implications of spurious transcription in bacteria.

The major human pathogen Staphylococcus aureus can survive under a wide range of conditions, both inside and outside the human body. The goal of this study was to determine how S. aureus adapts to such different conditions and, additionally, we wanted to identify general factors governing the staphylococcal transcriptome architecture. Therefore, we performed a precise analysis of all RNA transcripts of S. aureus across experimental conditions ranging from in vitro growth in different media to internalization by eukaryotic host cells. We systematically mapped all transcription units, annotated non-coding RNAs, and assigned promoters controlled by particular RNA polymerase sigma factors and transcription factors. By a comparison with data available for the related Gram-positive bacterium Bacillus subtilis, we made key observations concerning the abundance and origin of antisense RNAs. Intriguingly, these findings support the view that many antisense RNAs in a bacterium like B. subtilis could be byproducts of spurious promoter recognition by condition-specific alternative sigma factors. We also report that the transcription termination factor Rho prevents widespread antisense transcription, presumably caused by pervasive transcription initiation in the A+T-rich genome of S. aureus. Altogether our study presents new perspectives on the biological significance of antisense and pervasive transcription in bacteria.

Loss of the Yeast SR Protein Npl3 Alters Gene Expression Due to Transcription Readthrough

Yeast Npl3 is a highly abundant, nuclear-cytoplasmic shuttling, RNA-binding protein, related to metazoan SR proteins. Reported functions of Npl3 include transcription elongation, splicing and RNA 3’ end processing. We used UV crosslinking and analysis of cDNA (CRAC) to map precise RNA binding sites, and strand-specific tiling arrays to look at the effects of loss of Npl3 on all transcripts across the genome. We found that Npl3 binds diverse RNA species, both coding and non-coding, at sites indicative of roles in both early pre-mRNA processing and 3’ end formation. Tiling arrays and RNAPII mapping data revealed 3’ extended RNAPII-transcribed RNAs in the absence of Npl3, suggesting that defects in pre-mRNA packaging events result in termination readthrough. Transcription readthrough was widespread and frequently resulted in down-regulation of neighboring genes. We conclude that the absence of Npl3 results in widespread 3' extension of transcripts with pervasive effects on gene expression.

Npl3 is a yeast mRNA binding protein with many reported functions in RNA processing. We wanted to identify direct targets and therefore combined analyses of the transcriptome-wide effects of the loss of Npl3 on gene expression with UV crosslinking and bioinformatics to identify RNA-binding sites for Npl3. We found that Npl3 binds diverse sites on large numbers of transcripts, and that the loss of Npl3 results in transcriptional readthrough on many genes. One effect of this transcription readthrough is that the expression of numerous flanking genes is strongly down regulated. This underlines the importance of faithful termination for the correct regulation of gene expression. The effects of the loss of Npl3 are seen on both mRNAs and non-protein coding RNAs. These have distinct but overlapping termination mechanisms, with both classes requiring Npl3 for correct RNA packaging.

Chromatin Dynamics and the RNA Exosome Function in Concert to Regulate Transcriptional Homeostasis

The histone variant H2A.Z is a hallmark of nucleosomes flanking promoters of protein-coding genes and is often found in nucleosomes that carry lysine 56-acetylated histone H3 (H3-K56Ac), a mark that promotes replication-independent nucleosome turnover. Here, we find that H3-K56Ac promotes RNA polymerase II occupancy at many protein-coding and noncoding loci, yet neither H3-K56Ac nor H2A.Z has a significant impact on steady-state mRNA levels in yeast. Instead, broad effects of H3-K56Ac or H2A.Z on RNA levels are revealed only in the absence of the nuclear RNA exosome. H2A.Z is also necessary for the expression of divergent, promoter-proximal non-coding RNAs (ncRNAs) in mouse embryonic stem cells. Finally, we show that H2A.Z functions with H3-K56Ac to facilitate formation of chromosome interaction domains (CIDs). Our study suggests that H2A.Z and H3-K56Ac work in concert with the RNA exosome to control mRNA and ncRNA expression, perhaps in part by regulating higher-order chromatin structures.

High-resolution analysis of the m-xylene/toluene biodegradation subtranscriptome of Pseudomonas putida mt-2

Pseudomonas putida mt-2 metabolizes m-xylene and other aromatic compounds through the enzymes encoded by the xyl operons of the TOL plasmid pWW0 along with other chromosomally encoded activities. Tiling arrays of densely overlapping oligonucleotides were designed to cover every gene involved in this process, allowing dissection of operon structures and exposing the interplay of plasmid and chromosomal functions. All xyl sequences were transcribed in response to aromatic substrates and the 3'-termini of both upper and lower mRNA operons extended beyond their coding regions, i.e. the 3'-end of the lower operon mRNA penetrated into the convergent xylS regulatory gene. Furthermore, xylR mRNA for the master m-xylene responsive regulator of the system was decreased by aromatic substrates, while the cognate upper operon mRNA was evenly stable throughout its full length. RNA sequencing confirmed these data at a single nucleotide level and refined the formerly misannotated xylL sequence. The chromosomal ortho route for degradation of benzoate (the ben, cat clusters and some pca genes) was activated by this aromatic, but not by the TOL substrates, toluene or m-xylene. We advocate this scenario as a testbed of natural retroactivity between a pre-existing metabolic network and a new biochemical pathway implanted through gene transfer.

Computational implementation of a tunable multicellular memory circuit for engineered eukaryotic consortia

Cells are complex machines capable of processing information by means of an entangled network of molecular interactions. A crucial component of these decision-making systems is the presence of memory and this is also a specially relevant target of engineered synthetic systems. A classic example of memory devices is a 1-bit memory element known as the flip-flop. Such system can be in principle designed using a single-cell implementation, but a direct mapping between standard circuit design and a living circuit can be cumbersome. Here we present a novel computational implementation of a 1-bit memory device using a reliable multicellular design able to behave as a set-reset flip-flop that could be implemented in yeast cells. The dynamics of the proposed synthetic circuit is investigated with a mathematical model using biologically-meaningful parameters. The circuit is shown to behave as a flip-flop in a wide range of parameter values. The repression strength for the NOT logics is shown to be crucial to obtain a good flip-flop signal. Our model also shows that the circuit can be externally tuned to achieve different memory states and dynamics, such as persistent and transient memory. We have characterized the parameter domains for robust memory storage and retrieval as well as the corresponding time response dynamics.

The Paf1 complex represses small-RNA-mediated epigenetic gene silencing

RNA interference (RNAi) refers to the ability of exogenously introduced double-stranded RNA (dsRNA) to silence expression of homologous sequences. Silencing is initiated when the enzyme Dicer processes the dsRNA into small interfering RNAs (siRNAs). Small RNA molecules are incorporated into Argonaute protein-containing effector complexes, which they guide to complementary targets to mediate different types of gene silencing, specifically post-transcriptional gene silencing (PTGS) and chromatin-dependent gene silencing¹. Although endogenous small RNAs play critical roles in chromatin-mediated processes across kingdoms, efforts to initiate chromatin modifications in trans by using siRNAs have been inherently difficult to achieve in all eukaryotic cells. Using fission yeast, we show that RNAi-directed heterochromatin formation is negatively controlled by the highly conserved RNA polymerase-associated factor 1 complex (Paf1C). Temporary expression of a synthetic hairpin RNA in Paf1C mutants triggers stable heterochromatin formation at homologous loci, effectively silencing genes in trans. This repressed state is propagated across generations by continual production of secondary siRNAs, independently of the synthetic hairpin RNA. Our data support a model where Paf1C prevents targeting of nascent transcripts by the siRNA-containing RNA-induced transcriptional silencing (RITS) complex and thereby epigenetic gene silencing, by promoting efficient transcription termination and rapid release of the RNA from the site of transcription. We show that although compromised transcription termination is sufficient to initiate the formation of bi-stable heterochromatin by trans-acting siRNAs, impairment of both transcription termination and nascent transcript release is imperative to confer stability to the repressed state. Our work uncovers a novel mechanism for small RNA- mediated epigenome regulation and highlights fundamental roles for Paf1C and the RNAi machinery in building epigenetic memory.

Transcriptome wide annotation of eukaryotic RNase III reactivity and degradation signals

Detection and validation of the RNA degradation signals controlling transcriptome stability are essential steps for understanding how cells regulate gene expression. Here we present complete genomic and biochemical annotations of the signals required for RNA degradation by the dsRNA specific ribonuclease III (Rnt1p) and examine its impact on transcriptome expression. Rnt1p cleavage signals are randomly distributed in the yeast genome, and encompass a wide variety of sequences, indicating that transcriptome stability is not determined by the recurrence of a fixed cleavage motif. Instead, RNA reactivity is defined by the sequence and structural context in which the cleavage sites are located. Reactive signals are often associated with transiently expressed genes, and their impact on RNA expression is linked to growth conditions. Together, the data suggest that Rnt1p reactivity is triggered by malleable RNA degradation signals that permit dynamic response to changes in growth conditions.

RNA degradation is essential for gene regulation. The amount and timing of protein synthesis is determined, at least in part, by messenger RNA stability. Although RNA stability is determined by specific structural and sequence motif, the distribution of the degradation signals in eukaryotic genomes remains unclear. In this study, we describe the genomic distribution of the RNA degradation signals required for selective nuclear degradation in yeast. The results indicate that most RNAs in the yeast transcriptome are predisposed for degradation, but only few are catalytically active. The catalytic reactivity of messenger RNAs were mostly determined by the overall structural context of the degradation signals. Strikingly, most active RNA degradation signals are found in genes associated with respiration and fermentation. Overall, the findings reported here demonstrate how certain RNA are selected for cleavage and illustrated the importance of this selective RNA degradation for fine tuning gene expression in response to changes in growth condition.

A discrete transition zone organizes the topological and regulatory autonomy of the adjacent tfap2c and bmp7 genes

Despite the well-documented role of remote enhancers in controlling developmental gene expression, the mechanisms that allocate enhancers to genes are poorly characterized. Here, we investigate the cis-regulatory organization of the locus containing the Tfap2c and Bmp7 genes in vivo, using a series of engineered chromosomal rearrangements. While these genes lie adjacent to one another, we demonstrate that they are independently regulated by distinct sets of enhancers, which in turn define non-overlapping regulatory domains. Chromosome conformation capture experiments reveal a corresponding partition of the locus in two distinct structural entities, demarcated by a discrete transition zone. The impact of engineered chromosomal rearrangements on the topology of the locus and the resultant gene expression changes indicate that this transition zone functionally organizes the structural partition of the locus, thereby defining enhancer-target gene allocation. This partition is, however, not absolute: we show that it allows competing interactions across it that may be non-productive for the competing gene, but modulate expression of the competed one. Altogether, these data highlight the prime role of the topological organization of the genome in long-distance regulation of gene expression.

The specificity of enhancer-gene interactions is fundamental to the execution of gene regulatory programs underpinning embryonic development and cell differentiation. However, our understanding of the mechanisms conferring specificity to enhancers and target gene interactions is limited. In this study, we characterize the cis-regulatory organization of a large genomic locus consisting of two developmental genes, Tfap2c and Bmp7. We show that this locus is structurally partitioned into two distinct domains by the constitutive action of a discrete transition zone located between the two genes. This separation restricts selectively the functional action of enhancers to the genes present within the same domain. Interestingly, the effects of this region as a boundary are relative, as it allows some competing interactions to take place across domains. We show that these interactions modulate the functional output of a brain enhancer on its primary target gene resulting in the spatial restriction of its expression domain. These results support a functional link between topological chromatin domains and allocation of enhancers to genes. They further show that a precise adjustment of chromatin interaction levels fine-tunes gene regulation by long-range enhancers.

IsoSCM: improved and alternative 3' UTR annotation using multiple change-point inference

Major applications of RNA-seq data include studies of how the transcriptome is modulated at the levels of gene expression and RNA processing, and how these events are related to cellular identity, environmental condition, and/or disease status. While many excellent tools have been developed to analyze RNA-seq data, these generally have limited efficacy for annotating 3' UTRs. Existing assembly strategies often fragment long 3' UTRs, and importantly, none of the algorithms in popular use can apportion data into tandem 3' UTR isoforms, which are frequently generated by alternative cleavage and polyadenylation (APA). Consequently, it is often not possible to identify patterns of differential APA using existing assembly tools. To address these limitations, we present a new method for transcript assembly, Isoform Structural Change Model (IsoSCM) that incorporates change-point analysis to improve the 3' UTR annotation process. Through evaluation on simulated and genuine data sets, we demonstrate that IsoSCM annotates 3' termini with higher sensitivity and specificity than can be achieved with existing methods. We highlight the utility of IsoSCM by demonstrating its ability to recover known patterns of tissue-regulated APA. IsoSCM will facilitate future efforts for 3' UTR annotation and genome-wide studies of the breadth, regulation, and roles of APA leveraging RNA-seq data. The IsoSCM software and source code are available from our website https://github.com/shenkers/isoscm.

Annotation of genomics data using bidirectional hidden Markov models unveils variations in Pol II transcription cycle

DNA replication, transcription and repair involve the recruitment of protein complexes that change their composition as they progress along the genome in a directed or strand-specific manner. Chromatin immunoprecipitation in conjunction with hidden Markov models (HMMs) has been instrumental in understanding these processes, as they segment the genome into discrete states that can be related to DNA-associated protein complexes. However, current HMM-based approaches are not able to assign forward or reverse direction to states or properly integrate strand-specific (e.g., RNA expression) with non-strand-specific (e.g., ChIP) data, which is indispensable to accurately characterize directed processes. To overcome these limitations, we introduce bidirectional HMMs which infer directed genomic states from occupancy profiles de novo. Application to RNA polymerase II-associated factors in yeast and chromatin modifications in human T cells recovers the majority of transcribed loci, reveals gene-specific variations in the yeast transcription cycle and indicates the existence of directed chromatin state patterns at transcribed, but not at repressed, regions in the human genome. In yeast, we identify 32 new transcribed loci, a regulated initiation–elongation transition, the absence of elongation factors Ctk1 and Paf1 from a class of genes, a distinct transcription mechanism for highly expressed genes and novel DNA sequence motifs associated with transcription termination. We anticipate bidirectional HMMs to significantly improve the analyses of genome-associated directed processes.

biomvRhsmm: genomic segmentation with hidden semi-Markov model

High-throughput technologies like tiling array and next-generation sequencing (NGS) generate continuous homogeneous segments or signal peaks in the genome that represent transcripts and transcript variants (transcript mapping and quantification), regions of deletion and amplification (copy number variation), or regions characterized by particular common features like chromatin state or DNA methylation ratio (epigenetic modifications). However, the volume and output of data produced by these technologies present challenges in analysis. Here, a hidden semi-Markov model (HSMM) is implemented and tailored to handle multiple genomic profile, to better facilitate genome annotation by assisting in the detection of transcripts, regulatory regions, and copy number variation by holistic microarray or NGS. With support for various data distributions, instead of limiting itself to one specific application, the proposed hidden semi-Markov model is designed to allow modeling options to accommodate different types of genomic data and to serve as a general segmentation engine. By incorporating genomic positions into the sojourn distribution of HSMM, with optional prior learning using annotation or previous studies, the modeling output is more biologically sensible. The proposed model has been compared with several other state-of-the-art segmentation models through simulation benchmarking, which shows that our efficient implementation achieves comparable or better sensitivity and specificity in genomic segmentation.

Transcript mapping based on dRNA-seq data

Background

RNA-seq and its variant differential RNA-seq (dRNA-seq) are today routine methods for transcriptome analysis in bacteria. While expression profiling and transcriptional start site prediction are standard tasks today, the problem of identifying transcriptional units in a genome-wide fashion is still not solved for prokaryotic systems.

Results

We present RNAseg, an algorithm for the prediction of transcriptional units based on dRNA-seq data. A key feature of the algorithm is that, based on the data, it distinguishes between transcribed and un-transcribed genomic segments. Furthermore, the program provides many different predictions in a single run, which can be used to infer the significance of transcriptional units in a consensus procedure. We show the performance of our method based on a well-studied dRNA-seq data set for Helicobacter pylori.

Conclusions

With our algorithm it is possible to identify operons and 5’- and 3’-UTRs in an automated fashion. This alleviates the need for labour intensive manual inspection and enables large-scale studies in the area of comparative transcriptomics.

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.

Parseq: reconstruction of microbial transcription landscape from RNA-Seq read counts using state-space models

MOTIVATION

The most common RNA-Seq strategy consists of random shearing, amplification and high-throughput sequencing of the RNA fraction. Methods to analyze transcription level variations along the genome from the read count profiles generated by the RNA-Seq protocol are needed.

RESULTS

We developed a statistical approach to estimate the local transcription levels and to identify transcript borders. This transcriptional landscape reconstruction relies on a state-space model to describe transcription level variations in terms of abrupt shifts and more progressive drifts. A new emission model is introduced to capture not only the read count variance inside a transcript but also its short-range autocorrelation and the fraction of positions with zero counts. The estimation relies on a particle Gibbs algorithm whose running time makes it more suited to microbial genomes. The approach outperformed read-overlapping strategies on synthetic and real microbial datasets.

AVAILABILITY

A program named Parseq is available at: http://www.lgm.upmc.fr/parseq/.

CONTACT

bodgan.mirauta@upmc.fr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The role of Ctk1 kinase in termination of small non-coding RNAs

Transcription termination in Saccharomyces cerevisiae can be performed by at least two distinct pathways and is influenced by the phosphorylation status of the carboxy-terminal domain (CTD) of RNA polymerase II (Pol II). Late termination of mRNAs is performed by the CPF/CF complex, the recruitment of which is dependent on CTD-Ser2 phosphorylation (Ser2P). Early termination of shorter cryptic unstable transcripts (CUTs) and small nucleolar/nuclear RNAs (sno/snRNAs) is performed by the Nrd1-Nab3-Sen1 (NNS) complex that binds phosphorylated CTD-Ser5 (Ser5P) via the CTD-interacting domain (CID) of Nrd1p. In this study, mutants of the different termination pathways were compared by genome-wide expression analysis. Surprisingly, the expression changes observed upon loss of the CTD-Ser2 kinase Ctk1p are more similar to those derived from alterations in the Ser5P-dependent NNS pathway, than from loss of CTD-Ser2P binding factors. Tiling array analysis of ctk1Δ cells reveals readthrough at snoRNAs, at many cryptic unstable transcripts (CUTs) and stable uncharacterized transcripts (SUTs), but only at some mRNAs. Despite the suggested predominant role in termination of mRNAs, we observed that a CTK1 deletion or a Pol II CTD mutant lacking all Ser2 positions does not result in a global mRNA termination defect. Rather, termination defects in these strains are widely observed at NNS-dependent genes. These results indicate that Ctk1p and Ser2 CTD phosphorylation have a wide impact in termination of small non-coding RNAs but only affect a subset of mRNA coding genes.

Transcriptomic and phylogenetic analysis of a bacterial cell cycle reveals strong associations between gene co-expression and evolution

BACKGROUND

The genetic network involved in the bacterial cell cycle is poorly understood even though it underpins the remarkable ability of bacteria to proliferate. How such network evolves is even less clear. The major aims of this work were to identify and examine the genes and pathways that are differentially expressed during the Caulobacter crescentus cell cycle, and to analyze the evolutionary features of the cell cycle network.

RESULTS

We used deep RNA sequencing to obtain high coverage RNA-Seq data of five C. crescentus cell cycle stages, each with three biological replicates. We found that 1,586 genes (over a third of the genome) display significant differential expression between stages. This gene list, which contains many genes previously unknown for their cell cycle regulation, includes almost half of the genes involved in primary metabolism, suggesting that these "house-keeping" genes are not constitutively transcribed during the cell cycle, as often assumed. Gene and module co-expression clustering reveal co-regulated pathways and suggest functionally coupled genes. In addition, an evolutionary analysis of the cell cycle network shows a high correlation between co-expression and co-evolution. Most co-expression modules have strong phylogenetic signals, with broadly conserved genes and clade-specific genes predominating different substructures of the cell cycle co-expression network. We also found that conserved genes tend to determine the expression profile of their module.

CONCLUSION

We describe the first phylogenetic and single-nucleotide-resolution transcriptomic analysis of a bacterial cell cycle network. In addition, the study suggests how evolution has shaped this network and provides direct biological network support that selective pressure is not on individual genes but rather on the relationship between genes, which highlights the importance of integrating phylogenetic analysis into biological network studies.

Detecting splicing variants in idiopathic pulmonary fibrosis from non-differentially expressed genes

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease of unknown cause that lacks a proven therapy for altering its high mortality rate. Microarrays have been employed to investigate the pathogenesis of IPF, but are presented mostly at the gene-expression level due to technologic limitations. In as much as, alternative RNA splicing isoforms are increasingly identified as potential regulators of human diseases, including IPF, we propose a new approach with the capacity to detect splicing variants using RNA-seq data. We conducted a joint analysis of differential expression and differential splicing on annotated human genes and isoforms, and identified 122 non-differentially expressed genes with a high degree of "switch" between major and minor isoforms. Three cases with variant mechanisms for alternative splicing were validated using qRT-PCR, among the group of genes in which expression was not significantly changed at the gene level. We also identified 35 novel transcripts that were unique to the fibrotic lungs using exon-exon junction evidence, and selected a representative for qRT-PCR validation. The results of our study are likely to provide new insight into the pathogenesis of pulmonary fibrosis and may eventuate in new treatment targets.

Bivariate segmentation of SNP-array data for allele-specific copy number analysis in tumour samples

Background

SNP arrays output two signals that reflect the total genomic copy number (LRR) and the allelic ratio (BAF), which in combination allow the characterisation of allele-specific copy numbers (ASCNs). While methods based on hidden Markov models (HMMs) have been extended from array comparative genomic hybridisation (aCGH) to jointly handle the two signals, only one method based on change-point detection, ASCAT, performs bivariate segmentation.

Results

In the present work, we introduce a generic framework for bivariate segmentation of SNP array data for ASCN analysis. For the matter, we discuss the characteristics of the typically applied BAF transformation and how they affect segmentation, introduce concepts of multivariate time series analysis that are of concern in this field and discuss the appropriate formulation of the problem. The framework is implemented in a method named CnaStruct, the bivariate form of the structural change model (SCM), which has been successfully applied to transcriptome mapping and aCGH.

Conclusions

On a comprehensive synthetic dataset, we show that CnaStruct outperforms the segmentation of existing ASCN analysis methods. Furthermore, CnaStruct can be integrated into the workflows of several ASCN analysis tools in order to improve their performance, specially on tumour samples highly contaminated by normal cells.

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.

Extensive degradation of RNA precursors by the exosome in wild-type cells

The exosome is a complex involved in the maturation of rRNA and sn-snoRNA, in the degradation of short-lived noncoding RNAs, and in the quality control of RNAs produced in mutants. It contains two catalytic subunits, Rrp6p and Dis3p, whose specific functions are not fully understood. We analyzed the transcriptome of combinations of Rrp6p and Dis3p catalytic mutants by high-resolution tiling arrays. We show that Dis3p and Rrp6p have both overlapping and specific roles in degrading distinct classes of substrates. We found that transcripts derived from more than half of intron-containing genes are degraded before splicing. Surprisingly, we also show that the exosome degrades large amounts of tRNA precursors despite the absence of processing defects. These results underscore the notion that large amounts of RNAs produced in wild-type cells are discarded before entering functional pathways and suggest that kinetic competition with degradation proofreads the efficiency and accuracy of processing.

Analysis of the regulated transcriptome of Neisseria meningitidis in human blood using a tiling array

Neisseria meningitidis is the major cause of septicemia and meningococcal meningitis. During the course of infection, the bacterium must adapt to different host environments as a crucial factor for survival and dissemination; in particular, one of the crucial factors in N. meningitidis pathogenesis is the ability to grow and survive in human blood. We recently showed that N. meningitidis alters the expression of 30% of the open reading frames (ORFs) of the genome during incubation in human whole blood and suggested the presence of fine regulation at the gene expression level in order to control this step of pathogenesis. In this work, we used a customized tiling oligonucleotide microarray to define the changes in the whole transcriptional profile of N. meningitidis in a time course experiment of ex vivo bacteremia by incubating bacteria in human whole blood and then recovering RNA at different time points. The application of a newly developed bioinformatic tool to the tiling array data set allowed the identification of new transcripts--small intergenic RNAs, cis-encoded antisense RNAs, mRNAs with extended 5' and 3' untranslated regions (UTRs), and operons--differentially expressed in human blood. Here, we report a panel of expressed small RNAs, some of which can potentially regulate genes involved in bacterial metabolism, and we show, for the first time in N. meningitidis, extensive antisense transcription activity. This analysis suggests the presence of a circuit of regulatory RNA elements used by N. meningitidis to adapt to proliferate in human blood that is worthy of further investigation.

Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling)

BACKGROUND

Existing statistical methods for tiling array transcriptome data either focus on transcript discovery in one biological or experimental condition or on the detection of differential expression between two conditions. Increasingly often, however, biologists are interested in time-course studies, studies with more than two conditions or even multiple-factor studies. As these studies are currently analyzed with the traditional microarray analysis techniques, they do not exploit the genome-wide nature of tiling array data to its full potential.

RESULTS

We present an R Bioconductor package, waveTiling, which implements a wavelet-based model for analyzing transcriptome data and extends it towards more complex experimental designs. With waveTiling the user is able to discover (1) group-wise expressed regions, (2) differentially expressed regions between any two groups in single-factor studies and in (3) multifactorial designs. Moreover, for time-course experiments it is also possible to detect (4) linear time effects and (5) a circadian rhythm of transcripts. By considering the expression values of the individual tiling probes as a function of genomic position, effect regions can be detected regardless of existing annotation. Three case studies with different experimental set-ups illustrate the use and the flexibility of the model-based transcriptome analysis.

CONCLUSIONS

The waveTiling package provides the user with a convenient tool for the analysis of tiling array trancriptome data for a multitude of experimental set-ups. Regardless of the study design, the probe-wise analysis allows for the detection of transcriptional effects in both exonic, intronic and intergenic regions, without prior consultation of existing annotation.

Wavelet-based detection of transcriptional activity on a novel Staphylococcus aureus tiling microarray

BACKGROUND

High-density oligonucleotide microarray is an appropriate technology for genomic analysis, and is particulary useful in the generation of transcriptional maps, ChIP-on-chip studies and re-sequencing of the genome.Transcriptome analysis of tiling microarray data facilitates the discovery of novel transcripts and the assessment of differential expression in diverse experimental conditions. Although new technologies such as next-generation sequencing have appeared, microarrays might still be useful for the study of small genomes or for the analysis of genomic regions with custom microarrays due to their lower price and good accuracy in expression quantification.

RESULTS

Here, we propose a novel wavelet-based method, named ZCL (zero-crossing lines), for the combined denoising and segmentation of tiling signals. The denoising is performed with the classical SUREshrink method and the detection of transcriptionally active regions is based on the computation of the Continuous Wavelet Transform (CWT). In particular, the detection of the transitions is implemented as the thresholding of the zero-crossing lines. The algorithm described has been applied to the public Saccharomyces cerevisiae dataset and it has been compared with two well-known algorithms: pseudo-median sliding window (PMSW) and the structural change model (SCM). As a proof-of-principle, we applied the ZCL algorithm to the analysis of the custom tiling microarray hybridization results of a S. aureus mutant deficient in the sigma B transcription factor. The challenge was to identify those transcripts whose expression decreases in the absence of sigma B.

CONCLUSIONS

The proposed method archives the best performance in terms of positive predictive value (PPV) while its sensitivity is similar to the other algorithms used for the comparison. The computation time needed to process the transcriptional signals is low as compared with model-based methods and in the same range to those based on the use of filters. Automatic parameter selection has been incorporated and moreover, it can be easily adapted to a parallel implementation. We can conclude that the proposed method is well suited for the analysis of tiling signals, in which transcriptional activity is often hidden in the noise. Finally, the quantification and differential expression analysis of S. aureus dataset have demonstrated the valuable utility of this novel device to the biological analysis of the S. aureus transcriptome.

Evaluation of alternative RNA labeling protocols for transcript profiling with Arabidopsis AGRONOMICS1 tiling arrays

Microarrays are routine tools for transcript profiling, and genomic tiling arrays such as the Arabidopsis AGRONOMICS1 arrays have been found to be highly suitable for such experiments because changes in genome annotation can be easily integrated at the data analysis level. In a transcript profiling experiment, RNA labeling is a critical step, most often initiated by oligo-dT-primed reverse transcription. Although this has been found to be a robust and reliable method, very long transcripts or non-polyadenylated transcripts might be labeled inefficiently. In this study, we first provide data handling methods to analyze AGRONOMICS1 tiling microarrays based on the TAIR10 genome annotation. Second, we describe methods to easily quantify antisense transcripts on such tiling arrays. Third, we test a random-primed RNA labeling method, and find that on AGRONOMICS1 arrays this method has similar general performance as the conventional oligo-dT-primed method. In contrast to the latter, however, the former works considerably better for long transcripts and for non-polyadenylated transcripts such as found in mitochondria and plastids. We propose that researchers interested in organelle function use the random-primed method to unleash the full potential of genomic tiling arrays.

Genome-wide analysis of GLD-1-mediated mRNA regulation suggests a role in mRNA storage

Translational repression is often accompanied by mRNA degradation. In contrast, many mRNAs in germ cells and neurons are “stored" in the cytoplasm in a repressed but stable form. Unlike repression, the stabilization of these mRNAs is surprisingly little understood. A key player in Caenorhabditis elegans germ cell development is the STAR domain protein GLD-1. By genome-wide analysis of mRNA regulation in the germ line, we observed that GLD-1 has a widespread role in repressing translation but, importantly, also in stabilizing a sub-population of its mRNA targets. Additionally, these mRNAs appear to be stabilized by the DDX6-like RNA helicase CGH-1, which is a conserved component of germ granules and processing bodies. Because many GLD-1 and CGH-1 stabilized mRNAs encode factors important for the oocyte-to-embryo transition (OET), our findings suggest that the regulation by GLD-1 and CGH-1 serves two purposes. Firstly, GLD-1–dependent repression prevents precocious translation of OET–promoting mRNAs. Secondly, GLD-1– and CGH-1–dependent stabilization ensures that these mRNAs are sufficiently abundant for robust translation when activated during OET. In the absence of this protective mechanism, the accumulation of OET–promoting mRNAs, and consequently the oocyte-to-embryo transition, might be compromised.

One of the most striking developmental events is the oocyte-to-embryo transition that, in the absence of Pol II–dependent transcription, depends on regulated translation of maternal mRNAs. Prior to their activation, these maternal mRNAs need to be “stored" in the egg cytoplasm in a repressed but stable form. Surprisingly little is known about how the stored mRNAs are stabilized. The STAR family of RNA–binding proteins includes the C. elegans GLD-1, which controls many aspects of germ cell development. To obtain a comprehensive picture of GLD-1–dependent mRNA regulation, we performed a genome-wide survey of translational repression and mRNA stability of GLD-1 targets. This uncovered a potential role of GLD-1 in mRNA storage, as we found that GLD-1 both represses and stabilizes a subpopulation of its targets. The stabilization also involves a DDX6-like RNA helicase, CGH-1, which is a component of repressive germ granules and processing bodies. Remarkably, the GLD-1 and CGH-1 stabilized mRNAs encode regulators of the oocyte-to-embryo transition, providing an insight into how these functionally related mRNAs are specifically stabilized during germ cell formation. These findings have potential implications for oocyte quality and reproductive fitness, and for mRNA storage in other cell types such as neurons.

Comparative transcriptomics of pathogenic and non-pathogenic Listeria species

Comparative RNA-seq analysis of two related pathogenic and non-pathogenic bacterial strains reveals a hidden layer of divergence in the non-coding genome as well as conserved, widespread regulatory structures called ‘Excludons', which mediate regulation through long non-coding antisense RNAs.

Comparative transcriptome sequencing of two closely related bacterial strains reveals a hidden layer of divergence in the non-coding genome.

Pathogen-specific non-coding RNAs, which might contribute to virulence, are revealed.

The Listeria genome contains a class of unusually long antisense RNAs (lasRNAs) which spans divergent genes and repress expression of the genes located opposite to them while activating the other. The genetic organization of these lasRNAs and operon was named an excludon.

The exhaustive transcriptome information from this publication is provided as an open resource with a web-accessible transcriptome browser.

Listeria monocytogenes is a human, food-borne pathogen. Genomic comparisons between L. monocytogenes and Listeria innocua, a closely related non-pathogenic species, were pivotal in the identification of protein-coding genes essential for virulence. However, no comprehensive comparison has focused on the non-coding genome. We used strand-specific cDNA sequencing to produce genome-wide transcription start site maps for both organisms, and developed a publicly available integrative browser to visualize and analyze both transcriptomes in different growth conditions and genetic backgrounds. Our data revealed conservation across most transcripts, but significant divergence between the species in a subset of non-coding RNAs. In L. monocytogenes, we identified 113 small RNAs (33 novel) and 70 antisense RNAs (53 novel), significantly increasing the repertoire of ncRNAs in this species. Remarkably, we identified a class of long antisense transcripts (lasRNAs) that overlap one gene while also serving as the 5′ UTR of the adjacent divergent gene. Experimental evidence suggests that lasRNAs transcription inhibits expression of one operon while activating the expression of another. Such a lasRNA/operon structure, that we named ‘excludon', might represent a novel form of regulation in bacteria.