15,208 research outputs found
TREEOME: A framework for epigenetic and transcriptomic data integration to explore regulatory interactions controlling transcription
Motivation: Predictive modelling of gene expression is a powerful framework
for the in silico exploration of transcriptional regulatory interactions
through the integration of high-throughput -omics data. A major limitation of
previous approaches is their inability to handle conditional and synergistic
interactions that emerge when collectively analysing genes subject to different
regulatory mechanisms. This limitation reduces overall predictive power and
thus the reliability of downstream biological inference.
Results: We introduce an analytical modelling framework (TREEOME: tree of
models of expression) that integrates epigenetic and transcriptomic data by
separating genes into putative regulatory classes. Current predictive modelling
approaches have found both DNA methylation and histone modification epigenetic
data to provide little or no improvement in accuracy of prediction of
transcript abundance despite, for example, distinct anti-correlation between
mRNA levels and promoter-localised DNA methylation. To improve on this, in
TREEOME we evaluate four possible methods of formulating gene-level DNA
methylation metrics, which provide a foundation for identifying gene-level
methylation events and subsequent differential analysis, whereas most previous
techniques operate at the level of individual CpG dinucleotides. We demonstrate
TREEOME by integrating gene-level DNA methylation (bisulfite-seq) and histone
modification (ChIP-seq) data to accurately predict genome-wide mRNA transcript
abundance (RNA-seq) for H1-hESC and GM12878 cell lines.
Availability: TREEOME is implemented using open-source software and made
available as a pre-configured bootable reference environment. All scripts and
data presented in this study are available online at
http://sourceforge.net/projects/budden2015treeome/.Comment: 14 pages, 6 figure
The modern versus extended evolutionary synthesis : sketch of an intra-genomic gene's eye view for the evolutionary-genetic underpinning of epigenetic and developmental evolution
Studying the phenotypic evolution of organisms in terms of populations of genes and genotypes,
the Modern Synthesis (MS) conceptualizes biological evolution in terms of 'inter-organismal'
interactions among genes sitting in the different individual organisms that constitute a population.
It 'black-boxes' the complex 'intra-organismic' molecular and developmental epigenetics mediating
between genotypes and phenotypes. To conceptually integrate epigenetics and evo-devo into
evolutionary theory, advocates of an Extended Evolutionary Synthesis (EES) argue that the MS's
reductive gene-centrism should be abandoned in favor of a more inclusive organism-centered approach.
To push the debate to a new level of understanding, we introduce the evolutionary biology
of 'intra-genomic conflict' (IGC) to the controversy. This strategy is based on a twofold rationale.
First, the field of IGC is both âgene-centeredâ and 'intra-organismic' and, as such, could build a
bridge between the gene-centered MS and the intra-organismic fields of epigenetics and evo-devo.
And second, it is increasingly revealed that IGC plays a significant causal role in epigenetic and
developmental evolution and even in speciation. Hence, to deal with the âdiscrepancyâ between
the âgene-centeredâ MS and the âintra-organismicâ fields of epigenetics and evo-devo, we sketch
a conceptual solution in terms of âintra-genomic conflict and compromiseâ â an âintra-genomic
geneâs eye viewâ that thinks in terms of intra-genomic âevolutionarily stable strategiesâ (ESSs)
among numerous and various DNA regions and elements â to evolutionary-genetically underwrite
both epigenetic and developmental evolution, as such questioning the âgene-de-centeredâ
stance put forward by EES-advocates
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Epigenetic memory in induced pluripotent stem cells.
Somatic cell nuclear transfer and transcription-factor-based reprogramming revert adult cells to an embryonic state, and yield pluripotent stem cells that can generate all tissues. Through different mechanisms and kinetics, these two reprogramming methods reset genomic methylation, an epigenetic modification of DNA that influences gene expression, leading us to hypothesize that the resulting pluripotent stem cells might have different properties. Here we observe that low-passage induced pluripotent stem cells (iPSCs) derived by factor-based reprogramming of adult murine tissues harbour residual DNA methylation signatures characteristic of their somatic tissue of origin, which favours their differentiation along lineages related to the donor cell, while restricting alternative cell fates. Such an 'epigenetic memory' of the donor tissue could be reset by differentiation and serial reprogramming, or by treatment of iPSCs with chromatin-modifying drugs. In contrast, the differentiation and methylation of nuclear-transfer-derived pluripotent stem cells were more similar to classical embryonic stem cells than were iPSCs. Our data indicate that nuclear transfer is more effective at establishing the ground state of pluripotency than factor-based reprogramming, which can leave an epigenetic memory of the tissue of origin that may influence efforts at directed differentiation for applications in disease modelling or treatment
Advances in computational immunology
Published versio
Induced pluripotent stem cells, a giant leap for mankind therapeutic applications
Induced pluripotent stem cells (iPSC) technology has propelled the field of stem
cells biology, providing new cells to explore the molecular mechanisms of
pluripotency, cancer biology and aging. A major advantage of human iPSC,
compared to the pluripotent embryonic stem cells, is that they can be generated
from virtually any embryonic or adult somatic cell type without destruction of
human blastocysts. In addition, iPSC can be generated from somatic cells
harvested from normal individuals or patients, and used as a cellular tool to
unravel mechanisms of human development and to model diseases in a manner
not possible before. Besides these fundamental aspects of human biology and
physiology that are revealed using iPSC or iPSC-derived cells, these cells hold an
immense potential for cell-based therapies, and for the discovery of new or
personalized pharmacological treatments for many disorders. Here, we review
some of the current challenges and concerns about iPSC technology. We
introduce the potential held by iPSC for research and development of novel
health-related applications. We briefly present the efforts made by the scientific
and clinical communities to create the necessary guidelines and regulations to
achieve the highest quality standards in the procedures for iPSC generation,
characterization and long-term preservation. Finally, we present some of the
audacious and pioneer clinical trials in progress with iPSC-derived cells.info:eu-repo/semantics/publishedVersio
Detection of Epigenomic Network Community Oncomarkers
In this paper we propose network methodology to infer prognostic cancer
biomarkers based on the epigenetic pattern DNA methylation. Epigenetic
processes such as DNA methylation reflect environmental risk factors, and are
increasingly recognised for their fundamental role in diseases such as cancer.
DNA methylation is a gene-regulatory pattern, and hence provides a means by
which to assess genomic regulatory interactions. Network models are a natural
way to represent and analyse groups of such interactions. The utility of
network models also increases as the quantity of data and number of variables
increase, making them increasingly relevant to large-scale genomic studies. We
propose methodology to infer prognostic genomic networks from a DNA
methylation-based measure of genomic interaction and association. We then show
how to identify prognostic biomarkers from such networks, which we term
`network community oncomarkers'. We illustrate the power of our proposed
methodology in the context of a large publicly available breast cancer dataset
The ITALK project : A developmental robotics approach to the study of individual, social, and linguistic learning
This is the peer reviewed version of the following article: Frank Broz et al, âThe ITALK Project: A Developmental Robotics Approach to the Study of Individual, Social, and Linguistic Learningâ, Topics in Cognitive Science, Vol 6(3): 534-544, June 2014, which has been published in final form at doi: http://dx.doi.org/10.1111/tops.12099 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." Copyright © 2014 Cognitive Science Society, Inc.This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.Peer reviewe
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