Long interspersed nuclear elements-1 (LINE-1 or L1s) are abundant retrotransposons that comprise approximately 20 % of mammalian genomes1–3. Active L1 retrotransposons can impact the genome in a variety of ways, creating insertions, deletions, new splice sites or gene expression fine-tuning4–6. We have previously shown that L1 retrotransposons are capable of mobilization in neuronal progenitor cells from rodents and humans and evidence of massive L1 insertions was observed in adult brain tissues but not in other somatic tissues7,8. In addition, L1 mobility in the adult hippocampus can also be influenced by the environment9. The neuronal specificity of somatic L1 retrotransposition in neural progenitors is partially due to the transition of a Sox2/HDAC1 repressor complex to a Wnt-mediated TCF/LEF transcriptional activation7,10. The transcriptional switch accompanies chromatin remodeling during neuronal differentiation, allowing a transient stimulation of L1 transcription7. The activity of L1 retrotransposons during brain development can impact gene expression and neuronal function, thereby increasing brain-specific genetic mosaicism11,12. Further understanding of the molecular mechanisms that regulate L1 expression should provide new insights into the role of L1 retrotransposition during brain

Predicting how and where proteins, especially transcription factors (TFs), interact with DNA is an important problem in biology. We present here a systematic study of predictive modeling approaches to the TF-DNA binding problem, which have been frequently shown to be more efficient than those methods only based on position-specific weight matrices (PWMs). In these approaches, a statistical relationship between genomic sequences and gene expression or ChIPbinding intensities is inferred through a regression framework; and influential sequence features are identified by variable selection. We examine a few state-of-the-art learning methods including stepwise linear regression, multivariate adaptive regression splines (MARS), neural networks, support vector machines, boosting, and Bayesian additive regression trees (BART). These methods are applied to both simulated datasets and two whole-genome ChIP-chip datasets on the TFs Oct4 and Sox2, respectively, in human embryonic stem cells. We find that, with proper learning methods, predictive modeling approaches can significantly improve the predictive power and identify more biologically interesting features, such as TF-TF interactions, than the PWM approach. In particular, BART and boosting show the best and the most robust overall performance among all the methods.

Previous investigations of the core gene regulatory circuitry that controls the pluripotency of embryonic stem (ES) cells have largely focused on the roles of transcription, chromatin and non-coding RNA regulators A core set of transcription factors that includes OCT4 (also called POU5F1), NANOG and SOX2, together with specific microRNAs and long non-coding RNAs, control the expression of genes required for the establishment and maintenance of ES-cell pluripotency To identify such factors, we used high-throughput RNA sequencing (RNA-seq) data to define human and mouse cassette alternative exons that are differentially spliced between ES cells and induced pluripotent stem cells (iPSCs), and diverse differentiated cells and tissues, referred to below as 'ES-cell-differential alternative splicing'. A splicing code analysis 17 was then performed to identify cis-elements that may promote or repress these exons. The RNA-seq data used to profile alternative splicing were also used to detect human and mouse splicing factor genes that are differentially expressed between ES cells/iPSCs and non-ES cells/tissues. By integrating these data sources, we sought to identify differentially expressed splicing regulators with defined binding sites that match cis-elements predicted by the code analysis to function in ES-cell-differential alternative splicing. We identified 181 human and 103 mouse ES-cell-differential alternative splicing events, with comparable proportions of exons that are $25% more included or more skipped in ES cells versus the other profiled cells and tissues 216 ; hypergeometric test). The human and mouse ES-cell-differential alternative splicing events are significantly enriched in genes associated with the cytoskeleton (for example, DST, ADD3), plasma membrane (for example, DNM2, ITGA6) and kinase activity (for example, CASK, MARK2 and MAP2K7) (Supplementary The splicing code analysis revealed that motifs corresponding to consensus binding sites of the conserved MBNL proteins are the most strongly associated with ES-cell-differential alternative splicing in human and mouse. The presence of MBNL motifs in downstream flanking intronic sequences is associated with exon skipping in ES cells, whereas their presence in upstream flanking intronic sequences is associated with exon inclusion in ES cells From RNA-seq expression profiling of 221 known or putative splicing factors, 11 genes showed significant differential expression between human ES cells/iPSCs and other cells and tissues (Bonferronicorrected P , 0.05, Wilcoxon rank-sum test)

Embryonic stem (ES) cells are pluripotent cells derived from the inner cell mass of blastocyst-stage embryos. They can maintain an undifferentiated state indefinitely and can differentiate into derivatives of all three germ layers, namely ectoderm, endoderm and mesoderm. Although much progress has been made in the propagation and differentiation of ES cells, induction of photoreceptors has generally required coculture with or transplantation into developing retinal tissue. Here, we describe a protocol for generating retinal cells from ES cells by stepwise treatment with defined factors. This method preferentially induces photoreceptor and retinal pigment epithelium (RPE) cells from mouse and human ES cells. In our protocol, differentiation of RPE and photoreceptors from mouse ES cells requires 28 d and the differentiation of human ES cells into mature RPE and photoreceptors requires 120 and 150 d, respectively. This differentiation system and the resulting pluripotent stem cell-derived retinal cells will facilitate the development of transplantation therapies for retinal diseases, drug testing and in vitro disease modeling. It will also improve our understanding of the development of the central nervous system, especially the eye.

Morinda citrifolia L. (Noni): A review of the scientific validation for its nutritional and therapeutic properties

Article: Plews, J.R., Li, J.L., Jones, M. et al. (4 more authors) (2010) Activation of pluripotency genes in human fibroblast cells by a novel mRNA based approach. Plos One, 5 (12). Art no.e14397. ISSN 1932-6203 https://doi.org/10.1371/journal.pone.0014397 eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Activation of Pluripotency Genes in Human Fibroblast Abstract Background: Several methods have been used to induce somatic cells to re-enter the pluripotent state. Viral transduction of reprogramming genes yields higher efficiency but involves random insertions of viral sequences into the human genome. Although induced pluripotent stem (iPS) cells can be obtained with the removable PiggyBac transposon system or an episomal system, both approaches still use DNA constructs so that resulting cell lines need to be thoroughly analyzed to confirm they are free of harmful genetic modification. Thus a method to change cell fate without using DNA will be very useful in regenerative medicine.

Owing to their strong migratory capacity, tumor tropism, and tumor inhibitory effect, neural stem cells (NSCs) have recently emerged as one of the most attractive gene delivery vectors for cancer therapy. However, further animal studies found that proportional NSC vectors were distributed to nontarget organs after intravenous injection and the nonspecific transgene expression led to significant cytotoxic effects in these organs. Hence, an expression cassette that controls the transgene expression within NSC vectors in a tumor site-specific manner is desired. Considering hypoxia as a hallmark of tumor microenvironment, we have developed a novel NSC vector platform coupling transcriptional targeting with microRNA (miRNA) regulation for tumor hypoxia targeting. This combinatorial vector employed a hypoxia-responsive promoter and repeated targeting sequences of an miRNA that is enriched in NSCs but downregulated upon hypoxia induction to control the transgene expression. This resulted in significantly improved hypoxic selectivity over the use of a control vector without miRNA regulation. Thus, incorporating miRNA regulation into a transcriptional targeting vector adds an extra layer of security to prevent off-target transgene expression and should be useful for the development of NSC vectors with high targeting specifcity for cancer therapy.

Here we introduce a new design framework for synthetic biology that exploits the advantages of Bayesian model selection. We will argue that the difference between inference and design is that in the former we try to reconstruct the system that has given rise to the data that we observe, while in the latter, we seek to construct the system that produces the data that we would like to observe, i.e. the desired behavior. Our approach allows us to exploit methods from Bayesian statistics, including efficient exploration of models spaces and high-dimensional parameter spaces, and the ability to rank models with respect to their ability to generate certain types of data. Bayesian model selection furthermore automatically strikes a balance between complexity and (predictive or explanatory) performance of mathematical models. In order to deal with the complexities of molecular systems we employ an approximate Bayesian computation scheme which only requires us to simulate from different competing models in order to arrive at rational criteria for choosing between them. We illustrate the advantages resulting from combining the design and modeling (or in-silico prototyping) stages currently seen as separate in synthetic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-like behavior, as well as bacterial two-component signaling systems. 1

It has been debated whether human induced pluripotent stem cells (iPSCs) and embryonic stem cells (ESCs) express distinctive transcriptomes. By using the method of weighted gene co-expression network analysis, we showed here that iPSCs exhibit altered functional modules compared with ESCs. Notably, iPSCs and ESCs differentially express 17 modules that primarily function in transcription, metabolism, development, and im-mune response. These module activations (up- and downregulation) are highly conserved in a variety of iPSCs, and genes in each module are coherently co-expressed. Furthermore, the activation levels of these modular genes can be used as quantitative variables to discriminate iPSCs and ESCs with high accuracy (96%). Thus, differential activations of these functional modules are the conserved features distinguishing iPSCs from ESCs. Strikingly, the overall activation level of these modules is inversely correlated with the DNA methylation level, suggesting that DNA methylation may be one mechanism regulating the module differences. Overall, we conclude that human iPSCs and ESCs exhibit distinct gene expression networks, which are likely associated with different epigenetic reprogramming events during the derivation of iPSCs and ESCs.

mediated epigenetic modifications during reprogramming to pluripotency