A Riboswitch-Based Inducible Gene Expression System for Mycobacteria

Research on the human pathogen Mycobacterium tuberculosis (Mtb) would benefit from novel tools for regulated gene expression. Here we describe the characterization and application of a synthetic riboswitch-based system, which comprises a mycobacterial promoter for transcriptional control and a riboswitch for translational control. The system was used to induce and repress heterologous protein overexpression reversibly, to create a conditional gene knockdown, and to control gene expression in a macrophage infection model. Unlike existing systems for controlling gene expression in Mtb, the riboswitch does not require the co-expression of any accessory proteins: all of the regulatory machinery is encoded by a short DNA segment directly upstream of the target gene. The inducible riboswitch platform has the potential to be a powerful general strategy for creating customized gene regulation systems in Mtb

Identifying differentially methylated genes using mixed effect and generalized least square models

<p>Abstract</p> <p>Background</p> <p>DNA methylation plays an important role in the process of tumorigenesis. Identifying differentially methylated genes or CpG islands (CGIs) associated with genes between two tumor subtypes is thus an important biological question. The methylation status of all CGIs in the whole genome can be assayed with differential methylation hybridization (DMH) microarrays. However, patient samples or cell lines are heterogeneous, so their methylation pattern may be very different. In addition, neighboring probes at each CGI are correlated. How these factors affect the analysis of DMH data is unknown.</p> <p>Results</p> <p>We propose a new method for identifying differentially methylated (DM) genes by identifying the associated DM CGI(s). At each CGI, we implement four different mixed effect and generalized least square models to identify DM genes between two groups. We compare four models with a simple least square regression model to study the impact of incorporating random effects and correlations.</p> <p>Conclusions</p> <p>We demonstrate that the inclusion (or exclusion) of random effects and the choice of correlation structures can significantly affect the results of the data analysis. We also assess the false discovery rate of different models using CGIs associated with housekeeping genes.</p

Linking the Epigenome to the Genome: Correlation of Different Features to DNA Methylation of CpG Islands

DNA methylation of CpG islands plays a crucial role in the regulation of gene expression. More than half of all human promoters contain CpG islands with a tissue-specific methylation pattern in differentiated cells. Still today, the whole process of how DNA methyltransferases determine which region should be methylated is not completely revealed. There are many hypotheses of which genomic features are correlated to the epigenome that have not yet been evaluated. Furthermore, many explorative approaches of measuring DNA methylation are limited to a subset of the genome and thus, cannot be employed, e.g., for genome-wide biomarker prediction methods. In this study, we evaluated the correlation of genetic, epigenetic and hypothesis-driven features to DNA methylation of CpG islands. To this end, various binary classifiers were trained and evaluated by cross-validation on a dataset comprising DNA methylation data for 190 CpG islands in HEPG2, HEK293, fibroblasts and leukocytes. We achieved an accuracy of up to 91% with an MCC of 0.8 using ten-fold cross-validation and ten repetitions. With these models, we extended the existing dataset to the whole genome and thus, predicted the methylation landscape for the given cell types. The method used for these predictions is also validated on another external whole-genome dataset. Our results reveal features correlated to DNA methylation and confirm or disprove various hypotheses of DNA methylation related features. This study confirms correlations between DNA methylation and histone modifications, DNA structure, DNA sequence, genomic attributes and CpG island properties. Furthermore, the method has been validated on a genome-wide dataset from the ENCODE consortium. The developed software, as well as the predicted datasets and a web-service to compare methylation states of CpG islands are available at http://www.cogsys.cs.uni-tuebingen.de/software/dna-methylation/

A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing.

As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼ 100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.We thank the DKFZ Genomics and Proteomics Core Facility and the OICR Genome Technologies Platform for provision of sequencing services. Financial support was provided by the consortium projects READNA under grant agreement FP7 Health-F4-2008-201418, ESGI under grant agreement 262055, GEUVADIS under grant agreement 261123 of the European Commission Framework Programme 7, ICGC-CLL through the Spanish Ministry of Science and Innovation (MICINN), the Instituto de Salud Carlos III (ISCIII) and the Generalitat de Catalunya. Additional financial support was provided by the PedBrain Tumor Project contributing to the International Cancer Genome Consortium, funded by German Cancer Aid (109252) and by the German Federal Ministry of Education and Research (BMBF, grants #01KU1201A, MedSys #0315416C and NGFNplus #01GS0883; the Ontario Institute for Cancer Research to PCB and JDM through funding provided by the Government of Ontario, Ministry of Research and Innovation; Genome Canada; the Canada Foundation for Innovation and Prostate Cancer Canada with funding from the Movember Foundation (PCB). PCB was also supported by a Terry Fox Research Institute New Investigator Award, a CIHR New Investigator Award and a Genome Canada Large-Scale Applied Project Contract. The Synergie Lyon Cancer platform has received support from the French National Institute of Cancer (INCa) and from the ABS4NGS ANR project (ANR-11-BINF-0001-06). The ICGC RIKEN study was supported partially by RIKEN President’s Fund 2011, and the supercomputing resource for the RIKEN study was provided by the Human Genome Center, University of Tokyo. MDE, LB, AGL and CLA were supported by Cancer Research UK, the University of Cambridge and Hutchison-Whampoa Limited. SD is supported by the Torres Quevedo subprogram (MI CINN) under grant agreement PTQ-12-05391. EH is supported by the Research Council of Norway under grant agreements 221580 and 218241 and by the Norwegian Cancer Society under grant agreement 71220-PR-2006-0433. Very special thanks go to Jennifer Jennings for administrating the activity of the ICGC Verification Working Group and Anna Borrell for administrative support.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1000

Genomic features and computational identification of human microRNAs under long-range developmental regulation

<p>Abstract</p> <p>Background</p> <p>Recent functional studies have demonstrated that many microRNAs (miRNAs) are expressed by RNA polymerase II in a specific spatiotemporal manner during the development of organisms and play a key role in cell-lineage decisions and morphogenesis. They are therefore functionally related to a number of key protein coding developmental genes, that form genomic regulatory blocks (GRBs) with arrays of highly conserved non-coding elements (HCNEs) functioning as long-range enhancers that collaboratively regulate the expression of their target genes. Given this functional similarity as well as recent zebrafish transgenesis assays showing that the miR-9 family is indeed regulated by HCNEs with enhancer activity, we hypothesized that this type of miRNA regulation is prevalent. In this paper, we therefore systematically investigate the regulatory landscape around conserved self-transcribed miRNAs (ST miRNAs), with their own known or computationally inferred promoters, by analyzing the hallmarks of GRB target genes. These include not only the density of HCNEs in their vicinity but also the presence of large CpG islands (CGIs) and distinct patterns of histone modification marks associated with developmental genes.</p> <p>Results</p> <p>Our results show that a subset of the conserved ST miRNAs we studied shares properties similar to those of protein-coding GRB target genes: they are located in regions of significantly higher HCNE/enhancer binding density and are more likely to be associated with CGIs. Furthermore, their putative promoters have both activating as well as silencing histone modification marks during development and differentiation. Based on these results we used both an elevated HCNE density in the genomic vicinity as well as the presence of a bivalent promoter to identify 29 putative GRB target miRNAs/miRNA clusters, over two-thirds of which are known to play a role during development and differentiation. Furthermore these predictions include miRNAs of the miR-9 family, which are the only experimentally verified GRB target miRNAs.</p> <p>Conclusions</p> <p>A subset of the conserved miRNA loci we investigated exhibits typical characteristics of GRB target genes, which may partially explain their complex expression profiles during development.</p

Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements

Background: Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is important, but current approaches tackle average methylation within a genomic locus and are often limited to specific genomic regions. Results: We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict CpG site methylation levels using as features neighboring CpG site methylation levels and genomic distance, and co-localization with coding regions, CGIs, and regulatory elements from the ENCODE project, among others. Our approach achieves 91% -- 94% prediction accuracy of genome-wide methylation levels at single CpG site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs. Our classifier outperforms state-of-the-art methylation classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation status, CpG island status, co-localized DNase I hypersensitive sites, and specific transcription factor binding sites were found to be most predictive of methylation levels. Conclusions: Our observations of DNA methylation patterns led us to develop a classifier to predict site-specific methylation levels that achieves the best DNA methylation predictive accuracy to date. Furthermore, our method identified genomic features that interact with DNA methylation, elucidating mechanisms involved in DNA methylation modification and regulation, and linking different epigenetic processes

Dynamic regulation of the transcription initiation landscape at single nucleotide resolution during vertebrate embryogenesis

Spatiotemporal control of gene expression is central to animal development. Core promoters represent a previously unanticipated regulatory level by interacting with cis-regulatory elements and transcription initiation in different physiological and developmental contexts. Here, we provide a first and comprehensive description of the core promoter repertoire and its dynamic use during the development of a vertebrate embryo. By using cap analysis of gene expression (CAGE), we mapped transcription initiation events at single nucleotide resolution across 12 stages of zebrafish development. These CAGE-based transcriptome maps reveal genome-wide rules of core promoter usage, structure, and dynamics, key to understanding the control of gene regulation during vertebrate ontogeny. They revealed the existence of multiple classes of pervasive intra- and intergenic post-transcriptionally processed RNA products and their developmental dynamics. Among these RNAs, we report splice donor site-associated intronicRNA(sRNA) to be specific to genes of the splicing machinery. For the identification of conserved features, we compared the zebrafish data sets to the first CAGE promoter map of Tetraodon and the existing human CAGE data. We show that a number of features, such as promoter type, newly discovered promoter properties such as a specialized purine-rich initiator motif, as well as sRNAs and the genes in which they are detected, are conserved in mammalian and Tetraodon CAGE-defined promoter maps. The zebrafish developmental promoterome represents a powerful resource for studying developmental gene regulation and revealing promoter features shared across vertebrates

A systematic review of maternal antidepressant use in pregnancy and short- and long-term offspring's outcomes

The relative safety of antidepressants during pregnancy has received substantial attention, but most syntheses fail to account for mental illness effects. We aimed to evaluate the literature comparing low birth weight (LBW) and neurodevelopmental and neurobehavioural outcomes for children whose mothers took antidepressants in pregnancy compared to those whose mothers had common mental disorders, or symptoms, but who did not take antidepressants during pregnancy. A systematic review was conducted searching PubMed, MEDLINE, PsycINFO and Embase in January 2015. A modified version of the Newcastle Ottawa Scale was used to assess study quality. Eleven cohort studies were included: four reporting a LBW outcome (all with higher risk of bias) and seven reporting a neurodevelopmental outcome (five with higher risk of bias). We found only limited evidence of gestational age-adjusted LBW in exposed children in two studies which had a higher risk of bias and did not control for depressive symptom severity. Only five (7.5%) neurodevelopmental outcomes and one (12.5%) neurobehavioural outcome showed evidence of a statistically significant effect, three out of four were from studies with a higher risk of bias. There is little robust evidence indicating a detrimental effect of antidepressant use during pregnancy on LBW and neurodevelopmental and neurobehavioural outcomes. More rigorous study designs are needed