Stability and evolution of transcriptional regulatory networks

How do transcriptional regulatory networks elicit stable gene expression patterns? How do such networks evolve? These topics have kept molecular biologists occupied for years. The recent elucidation of regulatory networks that control endoderm development, including ‘genomic location analysis ’ of transcription factors in endoderm-derived liver cells, provides more comprehensive views than in the past. But how much more have we learned? Genome location analysis, or ChIP-on-chip, first involves crosslinking chromatin in native cells, breaking it into small fragments (e.g., 0.5–1 kb), immunoprecipitating the fragments that contain a specific transcription factor antigen (ChIP), and performing ligation-mediated PCR to amplify the ChIP fragments. The amplified DNA pool is then labeled and hybridized to a microarray (chip) containing single-stranded DNA probe

Genome-Scale Validation of Deep-Sequencing Libraries

Chromatin immunoprecipitation followed by high-throughput (HTP) sequencing (ChIP-seq) is a powerful tool to establish protein-DNA interactions genome-wide. The primary limitation of its broad application at present is the often-limited access to sequencers. Here we report a protocol, Mab-seq, that generates genome-scale quality evaluations for nucleic acid libraries intended for deep-sequencing. We show how commercially available genomic microarrays can be used to maximize the efficiency of library creation and quickly generate reliable preliminary data on a chromosomal scale in advance of deep sequencing. We also exploit this technique to compare enriched regions identified using microarrays with those identified by sequencing, demonstrating that they agree on a core set of clearly identified enriched regions, while characterizing the additional enriched regions identifiable using HTP sequencing

Comparative Hi-C Reveals that CTCF Underlies Evolution of Chromosomal Domain Architecture

Topological domains are key architectural building blocks of chromosomes, but their functional importance and evolutionary dynamics are not well defined. We performed comparative high-throughput chromosome conformation capture (Hi-C) in four mammals and characterized the conservation and divergence of chromosomal contact insulation and the resulting domain architectures within distantly related genomes. We show that the modular organization of chromosomes is robustly conserved in syntenic regions and that this is compatible with conservation of the binding landscape of the insulator protein CTCF. Specifically, conserved CTCF sites are co-localized with cohesin, are enriched at strong topological domain borders, and bind to DNA motifs with orientations that define the directionality of CTCF’s long-range interactions. Conversely, divergent CTCF binding between species is correlated with divergence of internal domain structure, likely driven by local CTCF binding sequence changes, demonstrating how genome evolution can be linked to a continuous flux of local conformation changes. We also show that large-scale domains are reorganized during genome evolution as intact modules

Cell Cycle Genes Are the Evolutionarily Conserved Targets of the E2F4 Transcription Factor

Maintaining quiescent cells in G0 phase is achieved in part through the multiprotein subunit complex known as DREAM, and in human cell lines the transcription factor E2F4 directs this complex to its cell cycle targets. We found that E2F4 binds a highly overlapping set of human genes among three diverse primary tissues and an asynchronous cell line, which suggests that tissue-specific binding partners and chromatin structure have minimal influence on E2F4 targeting. To investigate the conservation of these transcription factor binding events, we identified the mouse genes bound by E2f4 in seven primary mouse tissues and a cell line. E2f4 bound a set of mouse genes that was common among mouse tissues, but largely distinct from the genes bound in human. The evolutionarily conserved set of E2F4 bound genes is highly enriched for functionally relevant regulatory interactions important for maintaining cellular quiescence. In contrast, we found minimal mRNA expression perturbations in this core set of E2f4 bound genes in the liver, kidney, and testes of E2f4 null mice. Thus, the regulatory mechanisms maintaining quiescence are robust even to complete loss of conserved transcription factor binding events

Aging increases cell-to-cell transcriptional variability upon immune stimulation

Aging is characterized by progressive loss of physiological and cellular functions, but the molecular basis of this decline remains unclear. We explored how aging affects transcriptional dynamics using single-cell RNA sequencing of unstimulated and stimulated naïve and effector memory CD4(+) T cells from young and old mice from two divergent species. In young animals, immunological activation drives a conserved transcriptomic switch, resulting in tightly controlled gene expression characterized by a strong up-regulation of a core activation program, coupled with a decrease in cell-to-cell variability. Aging perturbed the activation of this core program and increased expression heterogeneity across populations of cells in both species. These discoveries suggest that increased cell-to-cell transcriptional variability will be a hallmark feature of aging across most, if not all, mammalian tissues.Funded by the European Research Council (F.C., T.F.R., D.T.O., S.A.T., and M.J.T.S.), EMBO Young Investigators Programme (D.T.O.), Cancer Research UK (H.-C.C., M.d.l.R., D.T.O., and J.C.M.), Janet Thornton Fellowship (WT098051 to C.P.M.-J.), Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (107609/Z/15/Z to M.d.l.R.), European Molecular Biology Laboratory (N.E., A.A.K., M.J.T.S., S.A.T., and J.C.M.), Medical Research Council Biostatistics Unit (MRC_MC_UP_0801/1 to C.A.V.), WTSI (C.P.M.-J., S.A.T., J.C.M., and D.T.O.), and Biotechnology and Biological Sciences Research Council–Collaborative Awards in Science and Engineering Studentship with Abcam plc (A.A.K.)

Protein Complex Evolution Does Not Involve Extensive Network Rewiring

The formation of proteins into stable protein complexes plays a fundamental role in the operation of the cell. The study of the degree of evolutionary conservation of protein complexes between species and the evolution of protein-protein interactions has been hampered by lack of comprehensive coverage of the high-throughput (HTP) technologies that measure the interactome. We show that new high-throughput datasets on protein co-purification in yeast have a substantially lower false negative rate than previous datasets when compared to known complexes. These datasets are therefore more suitable to estimate the conservation of protein complex membership than hitherto possible. We perform comparative genomics between curated protein complexes from human and the HTP data in Saccharomyces cerevisiae to study the evolution of co-complex memberships. This analysis revealed that out of the 5,960 protein pairs that are part of the same complex in human, 2,216 are absent because both proteins lack an ortholog in S. cerevisiae, while for 1,828 the co-complex membership is disrupted because one of the two proteins lacks an ortholog. For the remaining 1,916 protein pairs, only 10% were never co-purified in the large-scale experiments. This implies a conservation level of co-complex membership of 90% when the genes coding for the protein pairs that participate in the same protein complex are also conserved. We conclude that the evolutionary dynamics of protein complexes are, by and large, not the result of network rewiring (i.e. acquisition or loss of co-complex memberships), but mainly due to genomic acquisition or loss of genes coding for subunits. We thus reveal evidence for the tight interrelation of genomic and network evolution

A ChIP-Seq Benchmark Shows That Sequence Conservation Mainly Improves Detection of Strong Transcription Factor Binding Sites

Transcription factors are important controllers of gene expression and mapping transcription factor binding sites (TFBS) is key to inferring transcription factor regulatory networks. Several methods for predicting TFBS exist, but there are no standard genome-wide datasets on which to assess the performance of these prediction methods. Also, it is believed that information about sequence conservation across different genomes can generally improve accuracy of motif-based predictors, but it is not clear under what circumstances use of conservation is most beneficial.Here we use published ChIP-seq data and an improved peak detection method to create comprehensive benchmark datasets for prediction methods which use known descriptors or binding motifs to detect TFBS in genomic sequences. We use this benchmark to assess the performance of five different prediction methods and find that the methods that use information about sequence conservation generally perform better than simpler motif-scanning methods. The difference is greater on high-affinity peaks and when using short and information-poor motifs. However, if the motifs are specific and information-rich, we find that simple motif-scanning methods can perform better than conservation-based methods.Our benchmark provides a comprehensive test that can be used to rank the relative performance of transcription factor binding site prediction methods. Moreover, our results show that, contrary to previous reports, sequence conservation is better suited for predicting strong than weak transcription factor binding sites