We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor–binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor–binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.

SUMMARY Autism spectrum disorder (ASD) is a complex developmental syndrome of unknown etiology. Recent studies employing exome- and genome-wide sequencing have identified nine high-confidence ASD (hcASD) genes. Working from the hypothesis that ASD-associated mutations in these biologically pleiotropic genes will disrupt intersecting developmental processes to contribute to a common phenotype, we have attempted to identify time periods, brain regions, and cell types in which these genes converge. We have constructed coexpression networks based on the hcASD “seed” genes, leveraging a rich expression data set encompassing multiple human brain regions across human development and into adulthood. By assessing enrichment of an independent set of probable ASD (pASD) genes, derived from the same sequencing studies, we demonstrate a key point of convergence in midfetal layer 5/6 cortical projection neurons. This approach informs when, where, and in what cell types mutations in these specific genes may be productively studied to clarify ASD pathophysiology.

1,4-Butanediol (BDO) is an important commodity chemical used to manufacture over 2.5 million tons annually of valuable polymers, and it is currently produced exclusively through feedstocks derived from oil and natural gas. Herein we report what are to our knowledge the first direct biocatalytic routes to BDO from renewable carbohydrate feedstocks, leading to a strain of Escherichia coli capable of producing 18 g l(-1) of this highly reduced, non-natural chemical. A pathway-identification algorithm elucidated multiple pathways for the biosynthesis of BDO from common metabolic intermediates. Guided by a genome-scale metabolic model, we engineered the E. coli host to enhance anaerobic operation of the oxidative tricarboxylic acid cycle, thereby generating reducing power to drive the BDO pathway. The organism produced BDO from glucose, xylose, sucrose and biomass-derived mixed sugar streams. This work demonstrates a systems-based metabolic engineering approach to strain design and development that can enable new bioprocesses for commodity chemicals that are not naturally produced by living cells.

Recent studies implicate chromatin modifiers in autism spectrum disorder (ASD) through the identification of recurrent de novo loss of function mutations in affected individuals. ASD risk genes are co-expressed in human midfetal cortex, suggesting that ASD risk genes converge in specific regulatory networks during neurodevelopment. To elucidate such networks we identify genes targeted by CHD8, a chromodomain helicase strongly associated with ASD, in human midfetal brain, human neural stem cells (hNSCs) and embryonic mouse cortex. CHD8 targets are strongly enriched for other ASD risk genes in both human and mouse neurodevelopment, and converge in ASD-associated co-expression networks in human midfetal cortex. CHD8 knockdown in hNSCs results in dysregulation of ASD risk genes directly targeted by CHD8. Integration of CHD8 binding data into ASD risk models improves detection of risk genes. These results suggest loss of CHD8 contributes to ASD by perturbing an ancient gene regulatory network during human brain development.

Regulation of gene expression by sequence-specific transcription factors is central to developmental programs and depends on the binding of transcription factors with target sites in the genome. To date, most such analyses in Caenorhabditis elegans have focused on the interactions between a single transcription factor with one or a few select target genes. As part of the modENCODE Consortium, we have used chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq) to determine the genome-wide binding sites of 22 transcription factors (ALR-1, BLMP-1, CEH-14, CEH-30, EGL-27, EGL-5, ELT-3, EOR-1, GEI-11, HLH-1, LIN-11, LIN-13, LIN-15B, LIN-39, MAB-5, MDL-1, MEP-1, PES-1, PHA-4, PQM-1, SKN-1, and UNC-130) at diverse developmental stages. For each factor we determined candidate gene targets, both coding and non-coding. The typical binding sites of almost all factors are within a few hundred nucleotides of the transcript start site. Most factors target a mixture of coding and non-coding target genes, although one factor preferentially binds to non-coding RNA genes. We built a regulatory network among the 22 factors to determine their functional relationships to each other and found that some factors appear to act preferentially as regulators and others as target genes. Examination of the binding targets of three related HOX factors-LIN-39, MAB-5, and EGL-5-indicates that these factors regulate genes involved in cellular migration, neuronal function, and vulval differentiation, consistent with their known roles in these developmental processes. Ultimately, the comprehensive mapping of transcription factor binding sites will identify features of transcriptional networks that regulate C. elegans developmental processes.

At Class II catabolite activator protein (CAP)-dependent promoters, CAP activates transcription from a DNA site overlapping the DNA site for RNA polymerase. We show that transcription activation at Class II CAP-dependent promoters requires not only the previously characterized interaction between an activating region of CAP and the RNA polymerase alpha subunit C-terminal domain, but also an interaction between a second, promoter-class-specific activating region of CAP and the RNA polymerase alpha subunit N-terminal domain. We further show that the two interactions affect different steps in transcription initiation. Transcription activation at Class II CAP-dependent promoters provides a paradigm for understanding how an activator can make multiple interactions with the transcription machinery, each interaction being responsible for a specific mechanistic consequence.

We demonstrate here that the previously described bacterial promoter upstream element (UP element) consists of two distinct subsites, each of which, by itself, can bind the RNA polymerase holoenzyme ␣ subunit carboxy-terminal domain (RNAP ␣CTD) and stimulate transcription. Using binding-site-selection experiments, we identify the consensus sequence for each subsite. The selected proximal subsites (positions −46 to −38; consensus 5-AAAAAARNR-3) stimulate transcription up to 170-fold, and the selected distal subsites (positions −57 to −47; consensus 5-AWWWWWTTTTT-3) stimulate transcription up to 16-fold. RNAP has subunit composition ␣ 2 ␤␤ and thus contains two copies of ␣CTD. Experiments with RNAP derivatives containing only one copy of ␣CTD indicate, in contrast to a previous report, that the two ␣CTDs function interchangeably with respect to UP element recognition. Furthermore, function of the consensus proximal subsite requires only one copy of ␣CTD, whereas function of the consensus distal subsite requires both copies of ␣CTD. We propose that each subsite constitutes a binding site for a copy of ␣CTD, and that binding of an ␣CTD to the proximal subsite region (through specific interactions with a consensus proximal subsite or through nonspecific interactions with a nonconsensus proximal subsite) is a prerequisite for binding of the other ␣CTD to the distal subsite.[Key Words: Promoter; RNA polymerase; ␣ subunit; UP element; transcription initiation] Received May 18, 1999; revised version accepted July 6, 1999.Bacterial promoters consist of at least three RNA polymerase (RNAP) recognition sequences: The −10 element, the −35 element, and the UP element (Hawley and McClure 1983;Ross et al. 1993). The −10 and −35 elements are recognized by the RNAP subunit (Dombroski et al. 1992), and the UP element, located upstream of the −35 element, is recognized by the RNAP ␣ subunit (Ross et al. 1993;Blatter et al. 1994). The best-characterized UP element is in the rrnB P1 promoter, in which the sequence determinants are located between positions −40 and −60 with respect to the transcription start site (Rao et al. 1994), and UP element-␣ interactions facilitate initial binding of RNAP and subsequent step(s) in transcription initiation (Rao et al. 1994;Strainic et al. 1998). A consensus UP element sequence (referred to here as the consensus full UP element), derived from binding-siteselection experiments, consists almost exclusively of A and T residues and increases promoter activity >300-fold . UP elements have been identified upstream of many bacterial and phage promoters and can function with RNAPs containing different factors (e.g., Newlands et al. 1993;Ross et al. 1993Ross et al. , 1998Fredrick et al. 1995).Each RNAP ␣ subunit consists of two domains connected by a long unstructured and/or flexible linker (Blatter et al. 1994;Jeon et al. 1997). The 28-kD aminoterminal domain (␣NTD) is responsible for dimerization of ␣ and for interaction with the remainder of RNAP (Igarashi and Ishihama 1991;Busby and Ebright 1994). The...

We have used systematic fluorescence resonance energy transfer and distance-constrained docking to define the three-dimensional structures of bacterial RNA polymerase holoenzyme and the bacterial RNA polymerase-promoter open complex in solution. The structures provide a framework for understanding sigma(70)-(RNA polymerase core), sigma(70)-DNA, and sigma(70)-RNA interactions. The positions of sigma(70) regions 1.2, 2, 3, and 4 are similar in holoenzyme and open complex. In contrast, the position of sigma(70) region 1.1 differs dramatically in holoenzyme and open complex. In holoenzyme, region 1.1 is located within the active-center cleft, apparently serving as a "molecular mimic" of DNA, but, in open complex, region 1.1 is located outside the active center cleft. The approach described here should be applicable to the analysis of other nanometer-scale complexes.