Measuring the prevalence of regional mutation rates: an analysis of silent substitutions in mammals, fungi, and insects

BackgroundThe patterns of mutation vary both within and across genomes. It has been shown for a few mammals that mutation rates vary within the genome, while for unknown reasons, the sensu stricto yeasts have uniform rates instead. The generality of these observations has been unknown. Here we examine silent site substitutions in a more expansive set (20 mammals, 27 fungi, 4 insects) to determine why some genomes demonstrate this mosaic distribution and why others are uniform.ResultsWe applied several intragene and intergene correlation tests to measure regional substitution patterns. Assuming that silent sites are a reasonable approximation to neutrally mutating sequence, our results show that all multicellular eukaryotes exhibit mutational heterogeneity. In striking contrast, all fungi are mutationally uniform - with the exception of three Candida species: C. albicans, C. dubliniensis, and C. tropicalis. We speculate that aspects of replication timing may be responsible for distinguishing these species. Our analysis also reveals classes of genes whose silent sites behave anomalously with respect to the mutational background in many species, indicating prevalent selective pressures. Genes associated with nucleotide binding or gene regulation have consistently low silent substitution rates in every mammalian species, as well as multiple fungi. On the other hand, receptor genes repeatedly exhibit high silent substitution rates, suggesting they have been influenced by diversifying selection.ConclusionOur findings provide a framework for understanding the regional mutational properties of eukaryotes, revealing a sharp difference between fungi and multicellular species. They also elucidate common selective pressures acting on eukaryotic silent sites, with frequent evidence for both purifying and diversifying selection

Interlocking Transcriptional Feedback Loops Control White-Opaque Switching in Candida albicans

The human pathogen Candida albicans can assume either of two distinct cell types, designated “white” and “opaque.” Each cell type is maintained for many generations; switching between them is rare and stochastic, and occurs without any known changes in the nucleotide sequence of the genome. The two cell types differ dramatically in cell shape, colony appearance, mating competence, and virulence properties. In this work, we investigate the transcriptional circuitry that specifies the two cell types and controls the switching between them. First, we identify two new transcriptional regulators of white-opaque switching, Czf1 and white-opaque regulator 2 (Wor2). Analysis of a large set of double mutants and ectopic expression strains revealed genetic relationships between CZF1, WOR2, and two previously identified regulators of white-opaque switching, WOR1 and EFG1. Using chromatin immunoprecipitation, we show that Wor1 binds the intergenic regions upstream of the genes encoding three additional transcriptional regulators of white-opaque switching (CZF1, EFG1, and WOR2), and also occupies the promoters of numerous white- and opaque-enriched genes. Based on these interactions, we have placed these four genes in a circuit controlling white-opaque switching whose topology is a network of positive feedback loops, with the master regulator gene WOR1 occupying a central position. Our observations indicate that a key role of the interlocking feedback loop network is to stably maintain each epigenetic state through many cell divisions

The Transcriptomes of Two Heritable Cell Types Illuminate the Circuit Governing Their Differentiation

The differentiation of cells into distinct cell types, each of which is heritable for many generations, underlies many biological phenomena. White and opaque cells of the fungal pathogen Candida albicans are two such heritable cell types, each thought to be adapted to unique niches within their human host. To systematically investigate their differences, we performed strand-specific, massively-parallel sequencing of RNA from C. albicans white and opaque cells. With these data we first annotated the C. albicans transcriptome, finding hundreds of novel differentially-expressed transcripts. Using the new annotation, we compared differences in transcript abundance between the two cell types with the genomic regions bound by a master regulator of the white-opaque switch (Wor1). We found that the revised transcriptional landscape considerably alters our understanding of the circuit governing differentiation. In particular, we can now resolve the poor concordance between binding of a master regulator and the differential expression of adjacent genes, a discrepancy observed in several other studies of cell differentiation. More than one third of the Wor1-bound differentially-expressed transcripts were previously unannotated, which explains the formerly puzzling presence of Wor1 at these positions along the genome. Many of these newly identified Wor1-regulated genes are non-coding and transcribed antisense to coding transcripts. We also find that 5′ and 3′ UTRs of mRNAs in the circuit are unusually long and that 5′ UTRs often differ in length between cell-types, suggesting UTRs encode important regulatory information and that use of alternative promoters is widespread. Further analysis revealed that the revised Wor1 circuit bears several striking similarities to the Oct4 circuit that specifies the pluripotency of mammalian embryonic stem cells. Additional characteristics shared with the Oct4 circuit suggest a set of general hallmarks characteristic of heritable differentiation states in eukaryotes

The Evolution of Combinatorial Gene Regulation in Fungi

It is widely suspected that gene regulatory networks are highly plastic. The rapid turnover of transcription factor binding sites has been predicted on theoretical grounds and has been experimentally demonstrated in closely related species. We combined experimental approaches with comparative genomics to focus on the role of combinatorial control in the evolution of a large transcriptional circuit in the fungal lineage. Our study centers on Mcm1, a transcriptional regulator that, in combination with five cofactors, binds roughly 4% of the genes in Saccharomyces cerevisiae and regulates processes ranging from the cell-cycle to mating. In Kluyveromyces lactis and Candida albicans, two other hemiascomycetes, we find that the Mcm1 combinatorial circuits are substantially different. This massive rewiring of the Mcm1 circuitry has involved both substantial gain and loss of targets in ancient combinatorial circuits as well as the formation of new combinatorial interactions. We have dissected the gains and losses on the global level into subsets of functionally and temporally related changes. One particularly dramatic change is the acquisition of Mcm1 binding sites in close proximity to Rap1 binding sites at 70 ribosomal protein genes in the K. lactis lineage. Another intriguing and very recent gain occurs in the C. albicans lineage, where Mcm1 is found to bind in combination with the regulator Wor1 at many genes that function in processes associated with adaptation to the human host, including the white-opaque epigenetic switch. The large turnover of Mcm1 binding sites and the evolution of new Mcm1-cofactor interactions illuminate in sharp detail the rapid evolution of combinatorial transcription networks

Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks

Background: Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches. Methodology/Principal Findings: Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1), glycolysis (HLF), mitochondrial transcription (TFB2M), adipogenesis (PIAS1), neuronal development (TLX3), immune function (IRF1) and vasculogenesis (SOX17), within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C) were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal) to 30 months post natal (adulthood) for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a 'metabolic axis' formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes. Conclusions/Significance: The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo transcriptional regulation of skeletal muscle development

Tumor Transcriptome Sequencing Reveals Allelic Expression Imbalances Associated with Copy Number Alterations

Due to growing throughput and shrinking cost, massively parallel sequencing is rapidly becoming an attractive alternative to microarrays for the genome-wide study of gene expression and copy number alterations in primary tumors. The sequencing of transcripts (RNA-Seq) should offer several advantages over microarray-based methods, including the ability to detect somatic mutations and accurately measure allele-specific expression. To investigate these advantages we have applied a novel, strand-specific RNA-Seq method to tumors and matched normal tissue from three patients with oral squamous cell carcinomas. Additionally, to better understand the genomic determinants of the gene expression changes observed, we have sequenced the tumor and normal genomes of one of these patients. We demonstrate here that our RNA-Seq method accurately measures allelic imbalance and that measurement on the genome-wide scale yields novel insights into cancer etiology. As expected, the set of genes differentially expressed in the tumors is enriched for cell adhesion and differentiation functions, but, unexpectedly, the set of allelically imbalanced genes is also enriched for these same cancer-related functions. By comparing the transcriptomic perturbations observed in one patient to his underlying normal and tumor genomes, we find that allelic imbalance in the tumor is associated with copy number mutations and that copy number mutations are, in turn, strongly associated with changes in transcript abundance. These results support a model in which allele-specific deletions and duplications drive allele-specific changes in gene expression in the developing tumor

Intercalation of a new tier of transcription regulation into an ancient circuit

Changes in gene regulatory networks are a major source of evolutionary novelty1–3. Here we describe a specific type of network rewiring event, one that intercalates a new level of transcriptional control into an ancient circuit. We deduce that, over evolutionary time, the direct ancestral connections between a regulator and its target genes were broken and replaced by indirect connections, preserving the overall logic of the ancestral circuit but producing a new behaviour. The example was uncovered through a series of experiments in three ascomycete yeasts: the bakers’ yeast Saccharomyces cerevisiae, the dairy yeast Kluyveromyces lactis and the human pathogen Candida albicans. All three species have three cell types: two mating-competent cell forms (a and α) and the product of their mating (a/α), which is mating-incompetent. In the ancestral mating circuit, two homeodomain proteins, Mata1 and Matα2, form a heterodimer that directly represses four genes that are expressed only in a and α cells and are required for mating4–6. In a relatively recent ancestor of K. lactis, a reorganization occurred. The Mata1–Matα2 heterodimer represses the same four genes (known as the core haploid-specific genes) but now does so indirectly through an intermediate regulatory protein, Rme1. The overall logic of the ancestral circuit is preserved (haploid-specific genes ON in a and α cells and OFF in a/α cells), but a new phenotype was produced by the rewiring: unlike S. cerevisiae and C. albicans, K. lactis integrates nutritional signals, by means of Rme1, into the decision of whether or not to mate