Neil D. Clarke

Integration of External Signaling Pathways with the Core Transcriptional Network in Embryonic Stem Cells

Transcription factors (TFs) and their specific interactions with targets are crucial for specifying gene-expression programs. To gain insights into the transcriptional regulatory networks in embryonic stem (ES) cells, we use chromatin immunoprecipitation coupled with ultra-high-throughput DNA sequencing (ChIP-seq) to map the locations of 13 sequence-specific TFs (Nanog, Oct4, STAT3, Smad1, Sox2, Zfx, c-Myc, n-Myc, Klf4, Esrrb, Tcfcp2l1, E2f1, and CTCF) and 2 transcription regulators (p300 and Suz12). These factors are known to play different roles in ES-cell biology as components of the LIF and BMP signaling pathways, self-renewal regulators, and key reprogramming factors. Our study provides insights into the integration of the signaling pathways into the ES-cell-specific transcription circuitries. Intriguingly, we find specific genomic regions extensively targeted by different TFs. Collectively, the comprehensive mapping of TF-binding sites identifies important features of the transcriptional regulatory networks that define ES-cell identity.

Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

Three distinct cell types are present within the 64-cell stage mouse blastocyst. We have investigated cellular development up to this stage using single-cell expression analysis of more than 500 cells. The 48 genes analyzed were selected in part based on a whole-embryo analysis of more than 800 transcription factors. We show that in the morula, blastomeres coexpress transcription factors specific to different lineages, but by the 64-cell stage three cell types can be clearly distinguished according to their quantitative expression profiles. We identify Id2 and Sox2 as the earliest markers of outer and inner cells, respectively. This is followed by an inverse correlation in expression for the receptor-ligand pair Fgfr2/Fgf4 in the early inner cell mass. Position and signaling events appear to precede the maturation of the transcriptional program. These results illustrate the power of single-cell expression analysis to provide insight into developmental mechanisms. The technique should be widely applicable to other biological systems.

A Library of Yeast Transcription Factor Motifs Reveals a Widespread Function for Rsc3 in Targeting Nucleosome Exclusion at Promoters

The sequence specificity of DNA-binding proteins is the primary mechanism by which the cell recognizes genomic features. Here, we describe systematic determination of yeast transcription factor DNA-binding specificities. We obtained binding specificities for 112 DNA-binding proteins representing 19 distinct structural classes. One-third of the binding specificities have not been previously reported. Several binding sequences have striking genomic distributions relative to transcription start sites, supporting their biological relevance and suggesting a role in promoter architecture. Among these are Rsc3 binding sequences, containing the core CGCG, which are found preferentially approximately 100 bp upstream of transcription start sites. Mutation of RSC3 results in a dramatic increase in nucleosome occupancy in hundreds of proximal promoters containing a Rsc3 binding element, but has little impact on promoters lacking Rsc3 binding sequences, indicating that Rsc3 plays a broad role in targeting nucleosome exclusion at yeast promoters.

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

Background Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.

Assessment of CASP7 structure predictions for template free targets.

In CASP7, protein structure prediction targets that lacked substantial similarity to a protein in the PDB at the time of assessment were considered to be free modeling targets (FM). We assessed predictions for 14 FM targets as well as four other targets that were deemed to be on the borderline between FM targets and template based modeling targets (TBM/FM). GDT_TS was used as one measure of model quality. Model quality was also assessed by visual inspection. Visual inspection was performed by three independent assessors who were blinded to GDT_TS scores and other quantitative measures of model quality. The best models by visual inspection tended to rank among the top few percent by GDT_TS, but were typically not the highest scoring models. Thus, visual inspection remains an essential component of assessment for FM targets. Overall, group TS020 (Baker) performed best, but success on individual targets was widely distributed among many groups. Among these other groups, TS024 and TS025 (Zhang and Zhang server) performed notably well without exceptionally large computing resources. This should be considered encouraging for future CASPs. There was a sense of progress in template FM relative to CASP6, but we were unable to demonstrate this progress objectively. Proteins 2007.

Automated server predictions in CASP7

With each round of CASP (Critical Assessment of Techniques for Protein Structure Prediction), automated prediction servers have played an increasingly important role. Today, most protein structure prediction approaches in some way depend on automated methods for fold recognition or model building. The accuracy of server predictions has significantly increased over the last years, and, in CASP7, we observed a continuation of this trend. In the template‐based modeling category, the best prediction server was ranked third overall, i.e. it outperformed all but two of the human participating groups. This server also ranked among the very best predictors in the free modeling category as well, being clearly beaten by only one human group. In the high accuracy (HA) subset of TBM, two of the top five groups were servers. This article summarizes the contribution of automated structure prediction servers in the CASP7 experiment, with emphasis on 3D structure prediction, as well as information on their prediction scope and public availability. Proteins 2007.

Dynamic Deposition of Histone Variant H3.3 Accompanies Developmental Remodeling of the Arabidopsis Transcriptome

In animals, replication-coupled histone H3.1 can be distinguished from replication-independent histone H3.3. H3.3 variants are enriched at active genes and their promoters. Furthermore, H3.3 is specifically incorporated upon gene activation. Histone H3 variants evolved independently in plants and animals, and it is unclear whether different replication-independent H3.3 variants developed similar properties in both phyla. We studied Arabidopsis H3 variants in order to find core properties of this class of histones. Here we present genome-wide maps of H3.3 and H3.1 enrichment and the dynamic changes of their profiles upon cell division arrest. We find H3.3 enrichment to positively correlate with gene expression and to be biased towards the transcription termination site. In contrast with H3.1, heterochromatic regions are mostly depleted of H3.3. We report that, in planta, dynamic changes in H3.3 profiles are associated with the extensive remodeling of the transcriptome that occurs during cell differentiation. We propose that H3.3 dynamics are linked to transcription and are involved in resetting covalent histone marks at a genomic scale during plant development. Our study suggests that H3 variants properties likely result from functionally convergent evolution.

Assessment of intramolecular contact predictions for CASP7

Predictions of intramolecular residue–residue contacts were assessed as part of the seventh community‐wide Critical Assessment of Structure Prediction experiment (CASP7). As in past assessments, we focused on contacts that lie far apart in sequence as these are likely to be more informative in predicting protein structure. One lab did somewhat better than others according to our assessment, and there is some reason to think that this labs results represent progress over CASP6. In general, contacts inferred from 3D structural predictions are similar in accuracy to those predicted by contact prediction methods. However, contact prediction methods were more accurate for some targets. Proteins 2007.

Domain definition and target classification for CASP7.

Experimentally determined protein structures formed the basis of the CASP7 prediction assessments. These target structures were assigned to one or more tertiary structure prediction categories and where necessary were divided into structural domains. Boundaries for these domains were based on visual inspection of the targets and superpositions of the target with template structures. Target domains were classified into three different categories for assessment: “high accuracy modeling,” “template‐based modeling,” and “free modeling.” Assessment categories were determined by structural similarity between the target domain and the nearest structural templates in the PDB and by the accuracy of the models submitted by the predictors or by whether or not template information was used to generate the predictions. In CASP7 108 of the 123 target domains were evaluated in the template‐based modeling category and the remaining 15 target domains were classified as free modeling. A total of 28 target domains from the template‐based modeling category were also assessed in the high accuracy category and four overlapped with the free modeling category. Proteins 2007.

Blurring of high-resolution data shows that the effect of intrinsic nucleosome occupancy on transcription factor binding is mostly regional, not local.

Genome wide maps of nucleosome occupancy in yeast have recently been produced through deep sequencing of nuclease-protected DNA. These maps have been obtained from both crosslinked and uncrosslinked chromatin in vivo, and from chromatin assembled from genomic DNA and nucleosomes in vitro. Here, we analyze these maps in combination with existing ChIP-chip data, and with new ChIP-qPCR experiments reported here. We show that the apparent nucleosome density in crosslinked chromatin, when compared to uncrosslinked chromatin, is preferentially increased at transcription factor (TF) binding sites, suggesting a strategy for mapping generic transcription factor binding sites that would not require immunoprecipitation of a particular factor. We also confirm previous conclusions that the intrinsic, sequence dependent binding of nucleosomes helps determine the localization of TF binding sites. However, we find that the association between low nucleosome occupancy and TF binding is typically greater if occupancy at a site is averaged over a 600bp window, rather than using the occupancy at the binding site itself. We have also incorporated intrinsic nucleosome binding occupancies as weights in a computational model for TF binding, and by this measure as well we find better prediction if the high resolution nucleosome occupancy data is averaged over 600bp. We suggest that the intrinsic DNA binding specificity of nucleosomes plays a role in TF binding site selection not so much through the specification of precise nucleosome positions that permit or occlude binding, but rather through the creation of low occupancy regions that can accommodate competition from TFs through rearrangement of nucleosomes.