Technologies to measure whole-genome mRNA abundances and methods to organize and display such data are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes. We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo. Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions. The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected cis-regulatory elements. Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast-without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known.

SUMMARY N6-methyladenosine (m6A) is the most abundant internal modification of messenger RNA. While the presence of m6A on transcripts can impact alternative splicing, a nuclear reader of this mark that mediates the processing of nuclear transcripts has not been identified. We find that the RNA-binding HNRNPA2B1 protein binds m6A-bearing RNAs in vivo and in vitro and its biochemical footprint matches the m6A consensus motif. HNRNPA2B1 directly binds a set of nuclear transcripts and modulates their alternative splicing in a similar manner as the m6A ‘writer’ METTL3. Moreover, HNRNPA2B1 binds to m6A marks in a subset of primary-miRNA transcripts, interacts with the microRNA Microprocessor complex protein DGCR8, and promotes primary miRNA processing—phenocopying the effect of METTL3 depletion on the processing of these precursor transcripts. We propose HNRNPA2B1 to be a nuclear reader of the m6A mark and to mediate, in part, this mark’s effects on primary microRNA processing and alternative splicing.

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Microglia are resident immune cells of the CNS that are activated by infection, neuronal injury and inflammation. Here we utilize flow cytometry and deep RNA sequencing of acutely isolated spinal cord microglia to define their activation in vivo. Analysis of resting microglia identified 29 genes that distinguish microglia from other CNS cells and peripheral macrophages/monocytes. We then analyzed molecular changes in microglia during neurodegenerative disease activation using the SOD1G93A mouse model of ALS. We find that SOD1G93A microglia are not derived from infiltrating monocytes, and that both potentially neuroprotective and toxic factors are concurrently up-regulated, including Alzheimer’s disease genes. Mutant microglia differed from SOD1WT, LPS activated microglia, and M1/M2 macrophages, that define an ALS-specific phenotype. Concurrent mRNA/FACS analysis revealed post-transcriptional regulation of microglia surface receptors, and T cell-associated changes in the transcriptome. These results provide insights into microglia biology and establish a resource for future studies of neuroinflammation.

We describe a systematic genome-wide approach for learning the complex combinatorial code underlying gene expression. Our probabilistic approach identifies local DNA-sequence elements and the positional and combinatorial constraints that determine their context-dependent role in transcriptional regulation. The inferred regulatory rules correctly predict expression patterns for 73% of genes in Saccharomyces cerevisiae, utilizing microarray expression data and sequences in the 800 bp upstream of genes. Application to Caenorhabditis elegans identifies predictive regulatory elements and combinatorial rules that control the phased temporal expression of transcription factors, histones, and germline specific genes. Successful prediction requires diverse and complex rules utilizing AND, OR, and NOT logic, with significant constraints on motif strength, orientation, and relative position. This system generates a large number of mechanistic hypotheses for focused experimental validation, and establishes a predictive dynamical framework for understanding cellular behavior from genomic sequence.

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Histone acetyltransferases and deacetylases with specificities for different sites of acetylation affect common chromatin regions. This could generate unique patterns of acetylation that may specify downstream biological processes. To search for existence of these patterns and their relationship to gene activity, we analyzed the genome-wide acetylation profiles for eleven lysines in the four core histones of Saccharomyces cerevisiae. We find that both hyper- and hypoacetylation of individual lysines are associated with transcription, generating distinct patterns of acetylation that define groups of biologically related genes. The genes within these groups are significantly coexpressed, mediate similar physiological processes, share unique cis-regulatory DNA motifs, and are enriched for binding of specific transcription factors. Our data also indicate that the in vivo binding of the transcription factor Bdf1 is associated with acetylation on most lysines but relative deacetylation on H4 lysine 16. Thus, certain acetylation patterns may be used as surfaces for specific protein-histone interactions, providing one mechanism for coordinate regulation of chromatin processes that are biologically related.

During the maternal-to-zygotic transition, a developing embryo integrates post-transcriptional regulation of maternal mRNAs with transcriptional activation of its own genome. By combining chromosomal ablation in Drosophila with microarray analysis, we characterized the basis of this integration. We show that the expression profile for at least one third of zygotically active genes is coupled to the concomitant degradation of the corresponding maternal mRNAs. The embryo uses transcription and degradation to generate localized patterns of expression, and zygotic transcription to degrade distinct classes of maternal transcripts. Although degradation does not appear to involve a simple regulatory code, the activation of the zygotic genome starts from intronless genes sharing a common cis-element. This cis-element interacts with a single protein, the Bicoid stability factor, and acts as a potent enhancer capable of timing the activity of an exogenous transactivator. We propose that this regulatory mode links morphogen gradients with temporal regulation during the maternal-to-zygotic transition.