FLOWERING LOCUS C (FLC) has a key role in the timing of the initiation of flowering in Arabidopsis. FLC binds and represses two genes that promote flowering, FT and SOC1. We show that FLC binds to many other genes, indicating that it has regulatory roles other than the repression of flowering. We identified 505 FLC binding sites, mostly located in the promoter regions of genes and containing at least one CArG box, the motif known to be associated with MADS-box proteins such as FLC. We examined 40 of the target genes, and 20 showed increased transcript levels in an flc mutant compared with the wild type. Five genes showed decreased expression in the mutant, indicating that FLC binding can result in either transcriptional repression or activation. The genes we identified as FLC targets are involved in developmental pathways throughout the life history of the plant, many of which are associated with reproductive development. FLC is also involved in vegetative development, as evidenced by its binding to SPL15, delaying the progression from juvenile to adult phase. Some of the FLC target genes are also bound by two other MADSbox proteins, AP1 and SEP3, suggesting that MADS-box genes may operate in a network of control at different stages of the life cycle, many ultimately contributing to the development of the reproductive phase of the plant.reproductive transition | phase change | environmental response | floral morphology | ChIP sequencing E ncoding a MADS-box transcription factor, FLOWERING LOCUS C (FLC) is a major repressor of flowering in Arabidopsis (1, 2). The regulatory role of FLC in the control of flowering initiation is of special significance in vernalization (2), a period of low temperature that stimulates flowering. Before vernalization, FLC represses the initiation of flowering, preventing the changes that convert the apical meristem to one producing the reproductive structures. After the prolonged period of low temperature, FLC expression is repressed and plants are able to initiate flowering. The repression of FLC is associated with modifications to FLC chromatin, which prevent transcriptional activity of the gene (3, 4). The state of reduced transcriptional activity is maintained through the subsequent cell divisions of the developing plant when growing under normal temperature conditions (5). Two loci regulating FLC are FRIGIDA (FRI) (6) and VERNALIZATION INSENSITIVE 3 (VIN3) (4). FRI is responsible for a high level of production of the FLC protein and VIN3, which is induced by low temperature, reduces FLC transcriptional activity during vernalization. The vernalization process overrides the FRI-mediated control of FLC, resulting in the repression of transcriptional activity and the promotion of flowering initiation (7).The transition of the vegetative apical meristem to one producing reproductive structures involves the interaction of FLC with a small number of key genes. Three flowering-time genes,

The heterotic hybrid offspring of Arabidopsis accessions C24 and Landsberg erecta have altered methylomes. Changes occur most frequently at loci where parental methylation levels are different. There are context-specific biases in the nonadditive methylation patterns with m CG generally increased and m CHH decreased relative to the parents. These changes are a result of two main mechanisms, Trans Chromosomal Methylation and Trans Chromosomal deMethylation, where the methylation level of one parental allele alters to resemble that of the other parent. Regions of altered methylation are enriched around genic regions and are often correlated with changes in siRNA levels. We identified examples of genes with altered expression likely to be due to methylation changes and suggest that in crosses between the C24 and Ler accessions, epigenetic controls can be important in the generation of altered transcription levels that may contribute to the increased biomass of the hybrids.I n the formation of a hybrid, the genome and epigenome of each of the parents are brought together within the one nucleus. The interactions of these two sets of genetic instructions result in the unique characteristics of the hybrid, including superior performance. Both the level and pattern of expression of many genes are altered in hybrids (1-3). Altered transcription levels have mostly been explained by the interaction between the alleles of a gene delivered by the parents involving a range of interactions such as dominance, overdominance, and epistatic interactions between loci (1). Despite these genetic analyses, there is a lack of understanding of the molecular mechanisms underpinning heterosis.It has been suggested that the magnitude of hybrid vigor is positively correlated with the genetic distance or amount of sequence variation between the parental genomes (4, 5). However, crosses between genetically similar parents such as Arabidopsis accessions or subspecies of rice can produce hybrids displaying significant heterosis, apparently breaking down the relationship between genetic distance and extent of hybrid vigor (6, 7). It has been reported that the epigenome evolves at a significantly faster rate than the genetic sequence (8-10), consistent with genetically similar parents having markedly different epigenomes (11)(12)(13)(14)(15)(16)(17)(18)(19)(20). These epigenomic systems, such as DNA methylation and small RNAs, play a vital role in genomic stability, development, and the regulation of genes within a plant. The epigenome may contribute the allelic variability needed to generate heterosis in crosses between genetically similar parents. We previously reported that the Arabidopsis C24 and Ler accessions have different siRNA distributions and that the reciprocal heterotic hybrids show a 27% reduction in the levels of 24-nt siRNAs (18). The major reduction in these 24-nt siRNA sequences corresponded to those segments of the genome, primarily the gene bodies and their flanking sequences, where the two parents had unequal levels of 24-nt ...

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We have implemented in Python the COmparative GENomic Toolkit, a fully integrated and thoroughly tested framework for novel probabilistic analyses of biological sequences, devising workflows, and generating publication quality graphics. PyCogent includes connectors to remote databases, built-in generalized probabilistic techniques for working with biological sequences, and controllers for third-party applications. The toolkit takes advantage of parallel architectures and runs on a range of hardware and operating systems, and is available under the general public license from http://sourceforge.net/projects/pycogent. RationaleThe genetic divergence of species is affected by both DNA metabolic processes and natural selection. Processes contributing to genetic variation that are undetectable with intraspecific data may be detectable by inter-specific analyses because of the accumulation of signal over evolutionary time scales. As a consequence of the greater statistical power, there is interest in applying comparative analyses to address an increasing number and diversity of problems, in particular analyses that integrate sequence and phenotype. Significant barriers that hinder the extension of comparative analyses to exploit genome indexed phenotypic data include the narrow focus of most analytical tools, and the diverse array of data sources, formats, and tools available. Theoretically coherent integrative analyses can be conducted by combining probabilistic models of different aspects of genotype. Probabilistic models of sequence change underlie many core bioinformatics tasks, including similarity search, sequence alignment, phylogenetic inference, and ancestral state reconstruction. Probabilistic models allow usage of likelihood inference, a powerful approach from statistics, to establish the significance of differences in support of competing hypotheses. Linking different analyses through a shared and explicit probabilistic model of sequence change is thus extremely valuable, and provides a basis for generalizing analyses to more complex models of evolution (for example, to incorporate dependence between sites). Numerous studies have established how biological factors representing metabolic or selective influences can be represented in substitution models as specific parameters that affect rates of interchange between sequence motifs or the spatial occurrence of such rates [1][2][3][4]. Given this solid grounding, it is desirable to have a toolkit that allows flexible parameterization of probabilistic models and interchange of appropriate modules.There are many existing software packages that can manipulate biological sequences and structures, but few allow specification of both truly novel statistical models and detailed workflow control for genome scale datasets. Traditional phylogenetic analysis applications [5,6] typically provide a number of explicitly defined statistical models that are difficult to modify. One exception in which the parameterization of entirely novel substitution models was poss...

Genetic signatures caused by demographic and adaptive processes during past climatic shifts can inform predictions of species’ responses to anthropogenic climate change. To identify these signatures in Acropora tenuis, a reef-building coral threatened by global warming, we first assembled the genome from long reads and then used shallow whole-genome resequencing of 150 colonies from the central inshore Great Barrier Reef to inform population genomic analyses. We identify population structure in the host that reflects a Pleistocene split, whereas photosymbiont differences between reefs most likely reflect contemporary (Holocene) conditions. Signatures of selection in the host were associated with genes linked to diverse processes including osmotic regulation, skeletal development, and the establishment and maintenance of symbiosis. Our results suggest that adaptation to post-glacial climate change in A. tenuis has involved selection on many genes, while differences in symbiont specificity between reefs appear to be unrelated to host population structure.