Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99


Any time

2020 ( 2 )

2019 ( 210 )

2018 ( 279 )

2017 ( 283 )

Custom range...

Search Results: 1 - 10 of 206712 matches for " Mark D Biggin "
All listed articles are free for downloading (OA Articles)
Page 1 /206712
Display every page Item
System wide analyses have underestimated protein abundances and the importance of transcription in mammals
Jingyi Jessica Li,Peter J. Bickel,Mark D. Biggin
PeerJ , 2015, DOI: 10.7717/peerj.270
Abstract: Large scale surveys in mammalian tissue culture cells suggest that the protein expressed at the median abundance is present at 8,000–16,000 molecules per cell and that differences in mRNA expression between genes explain only 10–40% of the differences in protein levels. We find, however, that these surveys have significantly underestimated protein abundances and the relative importance of transcription. Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al. (2011), we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 9% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R2 = 0.14). Based on this, our second strategy suggests that mRNA levels explain ~84% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role. Finally, the above estimates apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the absence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the ~40% of genes that are non-expressed.
Quantitative Models of the Mechanisms That Control Genome-Wide Patterns of Transcription Factor Binding during Early Drosophila Development
Tommy Kaplan,Xiao-Yong Li,Peter J. Sabo,Sean Thomas,John A. Stamatoyannopoulos,Mark D. Biggin ,Michael B. Eisen
PLOS Genetics , 2011, DOI: 10.1371/journal.pgen.1001290
Abstract: Transcription factors that drive complex patterns of gene expression during animal development bind to thousands of genomic regions, with quantitative differences in binding across bound regions mediating their activity. While we now have tools to characterize the DNA affinities of these proteins and to precisely measure their genome-wide distribution in vivo, our understanding of the forces that determine where, when, and to what extent they bind remains primitive. Here we use a thermodynamic model of transcription factor binding to evaluate the contribution of different biophysical forces to the binding of five regulators of early embryonic anterior-posterior patterning in Drosophila melanogaster. Predictions based on DNA sequence and in vitro protein-DNA affinities alone achieve a correlation of ~0.4 with experimental measurements of in vivo binding. Incorporating cooperativity and competition among the five factors, and accounting for spatial patterning by modeling binding in every nucleus independently, had little effect on prediction accuracy. A major source of error was the prediction of binding events that do not occur in vivo, which we hypothesized reflected reduced accessibility of chromatin. To test this, we incorporated experimental measurements of genome-wide DNA accessibility into our model, effectively restricting predicted binding to regions of open chromatin. This dramatically improved our predictions to a correlation of 0.6–0.9 for various factors across known target genes. Finally, we used our model to quantify the roles of DNA sequence, accessibility, and binding competition and cooperativity. Our results show that, in regions of open chromatin, binding can be predicted almost exclusively by the sequence specificity of individual factors, with a minimal role for protein interactions. We suggest that a combination of experimentally determined chromatin accessibility data and simple computational models of transcription factor binding may be used to predict the binding landscape of any animal transcription factor with significant precision.
The role of chromatin accessibility in directing the widespread, overlapping patterns of Drosophila transcription factor binding
Xiao-Yong Li, Sean Thomas, Peter J Sabo, Michael B Eisen, John A Stamatoyannopoulos, Mark D Biggin
Genome Biology , 2011, DOI: 10.1186/gb-2011-12-4-r34
Abstract: Here we use data resulting from the DNaseI digestion of isolated embryo nuclei to provide a biophysical measure of the degree to which proteins can access different regions of the genome. We show that the in vivo binding patterns of 21 developmental regulators are quantitatively correlated with DNA accessibility in chromatin. Furthermore, we find that levels of factor occupancy in vivo correlate much more with the degree of chromatin accessibility than with occupancy predicted from in vitro affinity measurements using purified protein and naked DNA. Within accessible regions, however, the intrinsic affinity of the factor for DNA does play a role in determining net occupancy, with even weak affinity recognition sites contributing. Finally, we show that programmed changes in chromatin accessibility between different developmental stages correlate with quantitative alterations in factor binding.Based on these and other results, we propose a general mechanism to explain the widespread, overlapping DNA binding by animal transcription factors. In this view, transcription factors are expressed at sufficiently high concentrations in cells such that they can occupy their recognition sequences in highly accessible chromatin without the aid of physical cooperative interactions with other proteins, leading to highly overlapping, graded binding of unrelated factors.In vivo crosslinking studies show that a wide range of animal transcription factors each bind to many thousands of DNA regions throughout the genome and that not all of this binding is necessarily functional (for example, [1-19]). For example, our studies of over 20 transcriptional regulators in the Drosophila blastoderm embryo show that the few hundred most highly bound DNA regions include all of these proteins' known target cis-regulatory modules (CRMs) and are preferentially associated with developmental control genes and genes whose expression is strongly patterned in the blastoderm [1-3,14,17,19]. In contrast, th
A7DB: a relational database for mutational, physiological and pharmacological data related to the α7 nicotinic acetylcholine receptor
Steven D Buckingham, Luanda Pym, Andrew K Jones, Laurence Brown, Mark SP Sansom, David B Sattelle, Philip C Biggin
BMC Neuroscience , 2005, DOI: 10.1186/1471-2202-6-2
Abstract: A7DB http://www.lgics.org/a7db/ webcite is a new relational database of manually curated experimental physiological data associated with the α7 nAChR. It aims to store as much of the pharmacology, physiology and structural data pertaining to the α7 nAChR. The data is accessed via web interface that allows a user to search the data in multiple ways: 1) a simple text query 2) an incremental query builder 3) an interactive query builder and 4) a file-based uploadable query. It currently holds more than 460 separately reported experiments on over 85 mutations.A7DB will be a useful tool to molecular biologists and bioinformaticians not only working on the α7 receptor family of proteins but also in the more general context of nicotinic receptor modelling. Furthermore it sets a precedent for expansion with the inclusion of all nicotinic receptor families and eventually all cys-loop receptor families.Nicotinic acetylcholine receptors (nAChRs) are the most studied members of the cys-loop family of ligand-gated ion channels (LGICs) which also contains γ-aminobutyric acid (GABA) receptors, glycine receptors and 5-HT3 receptors [1]. Distinct subtypes of nAChRs mediate, for example, fast synaptic transmission in the brain and at neuromuscular junctions [2]. All are believed to be pentameric assemblies of various combinations of different subunits (α, β, γ/ε, δ), some of which exist as multiple isoforms [3]. nAChRs are important targets for novel analgesics as well as new drugs being devised for Alzheimer's disease and schizophrenia [4,5]. Mutations in nAChRs are associated with certain forms of epilepsy [6,7] and several congenital myasthenias [8]. There is a substantial and growing body of physiological, pharmacological, genomic structural and modeling data on receptors formed from these subunits. The volume and diversity of these data present severe challenges for their efficient storage and interpretation. Here we describe a relational database, initially relating to the α7 s
A model for sequential evolution of ligands by exponential enrichment (SELEX) data
Juli Atherton,Nathan Boley,Ben Brown,Nobuo Ogawa,Stuart M. Davidson,Michael B. Eisen,Mark D. Biggin,Peter Bickel
Quantitative Biology , 2012, DOI: 10.1214/12-AOAS537
Abstract: A Systematic Evolution of Ligands by EXponential enrichment (SELEX) experiment begins in round one with a random pool of oligonucleotides in equilibrium solution with a target. Over a few rounds, oligonucleotides having a high affinity for the target are selected. Data from a high throughput SELEX experiment consists of lists of thousands of oligonucleotides sampled after each round. Thus far, SELEX experiments have been very good at suggesting the highest affinity oligonucleotide, but modeling lower affinity recognition site variants has been difficult. Furthermore, an alignment step has always been used prior to analyzing SELEX data. We present a novel model, based on a biochemical parametrization of SELEX, which allows us to use data from all rounds to estimate the affinities of the oligonucleotides. Most notably, our model also aligns the oligonucleotides. We use our model to analyze a SELEX experiment containing double stranded DNA oligonucleotides and the transcription factor Bicoid as the target. Our SELEX model outperformed other published methods for predicting putative binding sites for Bicoid as indicated by the results of an in-vivo ChIP-chip experiment.
Large-Scale Turnover of Functional Transcription Factor Binding Sites in Drosophila
Alan M Moses,Daniel A Pollard,David A Nix,Venky N Iyer,Xiao-Yong Li,Mark D Biggin,Michael B Eisen
PLOS Computational Biology , 2006, DOI: 10.1371/journal.pcbi.0020130
Abstract: The gain and loss of functional transcription factor binding sites has been proposed as a major source of evolutionary change in cis-regulatory DNA and gene expression. We have developed an evolutionary model to study binding-site turnover that uses multiple sequence alignments to assess the evolutionary constraint on individual binding sites, and to map gain and loss events along a phylogenetic tree. We apply this model to study the evolutionary dynamics of binding sites of the Drosophila melanogaster transcription factor Zeste, using genome-wide in vivo (ChIP–chip) binding data to identify functional Zeste binding sites, and the genome sequences of D. melanogaster, D. simulans, D. erecta, and D. yakuba to study their evolution. We estimate that more than 5% of functional Zeste binding sites in D. melanogaster were gained along the D. melanogaster lineage or lost along one of the other lineages. We find that Zeste-bound regions have a reduced rate of binding-site loss and an increased rate of binding-site gain relative to flanking sequences. Finally, we show that binding-site gains and losses are asymmetrically distributed with respect to D. melanogaster, consistent with lineage-specific acquisition and loss of Zeste-responsive regulatory elements.
Binding Site Turnover Produces Pervasive Quantitative Changes in Transcription Factor Binding between Closely Related Drosophila Species
Robert K. Bradley,Xiao-Yong Li,Cole Trapnell,Stuart Davidson,Lior Pachter,Hou Cheng Chu,Leath A. Tonkin,Mark D. Biggin,Michael B. Eisen
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.1000343
Abstract: Changes in gene expression play an important role in evolution, yet the molecular mechanisms underlying regulatory evolution are poorly understood. Here we compare genome-wide binding of the six transcription factors that initiate segmentation along the anterior-posterior axis in embryos of two closely related species: Drosophila melanogaster and Drosophila yakuba. Where we observe binding by a factor in one species, we almost always observe binding by that factor to the orthologous sequence in the other species. Levels of binding, however, vary considerably. The magnitude and direction of the interspecies differences in binding levels of all six factors are strongly correlated, suggesting a role for chromatin or other factor-independent forces in mediating the divergence of transcription factor binding. Nonetheless, factor-specific quantitative variation in binding is common, and we show that it is driven to a large extent by the gain and loss of cognate recognition sequences for the given factor. We find only a weak correlation between binding variation and regulatory function. These data provide the first genome-wide picture of how modest levels of sequence divergence between highly morphologically similar species affect a system of coordinately acting transcription factors during animal development, and highlight the dominant role of quantitative variation in transcription factor binding over short evolutionary distances.
Nonparametric identification of regulatory interactions from spatial and temporal gene expression data
Anil Aswani, Soile VE Ker?nen, James Brown, Charless C Fowlkes, David W Knowles, Mark D Biggin, Peter Bickel, Claire J Tomlin
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-413
Abstract: Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for eve mRNA pattern formation in the Drosophila melanogaster blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same cis-regulatory module depending on the factors' concentration, and implies different modes of activation and repression.Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.Inferring transcriptional regulatory networks in animals is challenging. For example, the large number of genes, the spatial and temporal complexity of expression patterns, and the presence of many redundant and indirect interactions all make it difficult to learn the network. In the long term, it will be necessary to use multiple data sets--including gene expression, genome wide in vivo DNA binding, and network perturbation data--to accurately represent all interactions. Combining multiple data classes in this way, however, is an open and challenging problem.An alternative, intermediate approach is to use only g
Three-dimensional morphology and gene expression in the Drosophila blastoderm at cellular resolution II: dynamics
Soile VE Ker?nen, Charless C Fowlkes, Cris L Luengo Hendriks, Damir Sudar, David W Knowles, Jitendra Malik, Mark D Biggin
Genome Biology , 2006, DOI: 10.1186/gb-2006-7-12-r124
Abstract: Using automated image analysis methods, we provide the first quantitative description of temporal changes in morphology and gene expression at cellular resolution in whole embryos, using the Drosophila blastoderm as a model. Analyses based on both fixed and live embryos reveal complex, previously undetected three-dimensional changes in nuclear density patterns caused by nuclear movements prior to gastrulation. Gene expression patterns move, in part, with these changes in morphology, but additional spatial shifts in expression patterns are also seen, supporting a previously proposed model of pattern dynamics based on the induction and inhibition of gene expression. We show that mutations that disrupt either the anterior/posterior (a/p) or the dorsal/ventral (d/v) transcriptional cascades alter morphology and gene expression along both the a/p and d/v axes in a way suggesting that these two patterning systems interact via both transcriptional and morphological mechanisms.Our work establishes a new strategy for measuring temporal changes in the locations of cells and gene expression patterns that uses fixed cell material and computational modeling. It also provides a coordinate framework for the blastoderm embryo that will allow increasingly accurate spatio-temporal modeling of both the transcriptional control network and morphogenesis.The transcription network controlling pattern formation in the Drosophila blastoderm is one of the best characterized animal regulatory networks [1-4] and, because of its relative simplicity, is one of the most tractable for computational modeling (for example, [5-8]). In this network, a hierarchical cascade of transcription factors drives expression of increasing numbers of genes in more and more spatially refined patterns through developmental stage 5. For example, along the a/p axis, the gap genes are among the first zygotically expressed transcriptional regulators, which cross-regulate each other and pair rule gene expression.As part
Developmental roles of 21 Drosophila transcription factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions
Stewart MacArthur, Xiao-Yong Li, Jingyi Li, James B Brown, Hou Cheng Chu, Lucy Zeng, Brandi P Grondona, Aaron Hechmer, Lisa Simirenko, Soile VE Ker?nen, David W Knowles, Mark Stapleton, Peter Bickel, Mark D Biggin, Michael B Eisen
Genome Biology , 2009, DOI: 10.1186/gb-2009-10-7-r80
Abstract: Here we show that an additional 15 transcription factors that regulate other aspects of embryo patterning show a similar quantitative continuum of function and binding to thousands of genomic regions in vivo. Collectively, the 21 regulators show a surprisingly high overlap in the regions they bind given that they belong to 11 DNA binding domain families, specify distinct developmental fates, and can act via different cis-regulatory modules. We demonstrate, however, that quantitative differences in relative levels of binding to shared targets correlate with the known biological and transcriptional regulatory specificities of these factors.It is likely that the overlap in binding of biochemically and functionally unrelated transcription factors arises from the high concentrations of these proteins in nuclei, which, coupled with their broad DNA binding specificities, directs them to regions of open chromatin. We suggest that most animal transcription factors will be found to show a similar broad overlapping pattern of binding in vivo, with specificity achieved by modulating the amount, rather than the identity, of bound factor.Sequence-specific transcription factors regulate spatial and temporal patterns of mRNA expression in animals by binding in different combinations to cis-regulatory modules (CRMs) located generally in the non-protein coding portions of the genome (reviewed in [1-4]). Most of these factors recognize short, degenerate DNA sequences that occur multiple times in every gene locus. Yet only a subset of these recognition sequences are thought to be functional targets [1,5,6]. Because we do not sufficiently understand the rules determining DNA binding in vivo or the transcriptional output that results from particular combinations of bound factors, we cannot at present predict the locations of CRMs or patterns of gene expression from genome sequence and in vitro DNA binding specificities alone.To address this challenge, the Berkeley Drosophila Transcriptio
Page 1 /206712
Display every page Item

Copyright © 2008-2017 Open Access Library. All rights reserved.