Evolution acts on enhancer organization to fine-tune gradient threshold readouts

The elucidation of principles governing evolution of gene regulatory sequence is critical to the study of metazoan diversification. We are therefore exploring the structure and organizational constraints of regulatory sequences by studying functionally equivalent cis-regulatory modules (CRMs) that have been evolving in parallel across several loci. Such an independent dataset allows a multi-locus study that is not hampered by nonfunctional or constrained homology. The neurogenic ectoderm enhancers (NEEs) of Drosophila melanogaster are one such class of coordinately regulated CRMs. The NEEs share a common organization of binding sites and as a set would be useful to study the relationship between CRM organization and CRM activity across evolving lineages. We used the D. melanogaster transgenic system to screen for functional adaptations in the NEEs from divergent drosophilid species. We show that the individual NEE modules across a genome in any one lineage have independently evolved adaptations to compensate for lineage-specific developmental and/or genomic changes. Specifically, we show that both the site composition and the site organization of NEEs have been finely tuned by distinct, lineage-specific selection pressures in each of the three divergent species that we have examined: D. melanogaster, D. pseudoobscura, and D. virilis. Furthermore, by precisely altering the organization of NEEs with different morphogen gradient threshold readouts, we show that CRM organizational evolution is sufficient for explaining changes in enhancer activity. Thus, evolution can act on CRM organization to fine-tune morphogen gradient threshold readouts over a wide dynamic range. Our study demonstrates that equivalence classes of CRMs are powerful tools for detecting lineage-specific adaptations by gene regulatory sequences.     

MOPAT: a graph-based method to predict recurrent cis-regulatory modules from known motifs

The identification of cis-regulatory modules (CRMs) can greatly advance our understanding of eukaryotic regulatory mechanism. Current methods to predict CRMs from known motifs either depend on multiple alignments or can only deal with a small number of known motifs provided by users. These methods are problematic when binding sites are not well aligned in multiple alignments or when the number of input known motifs is large. We thus developed a new CRM identification method MOPAT (motif pair tree), which identifies CRMs through the identification of motif modules, groups of motifs co-occurring in multiple CRMs. It can identify 'orthologous' CRMs without multiple alignments. It can also find CRMs given a large number of known motifs. We have applied this method to mouse developmental genes, and have evaluated the predicted CRMs and motif modules by microarray expression data and known interacting motif pairs. We show that the expression profiles of the genes containing CRMs of the same motif module correlate significantly better than those of a random set of genes do. We also show that the known interacting motif pairs are significantly included in our predictions. Compared with several current methods, our method shows better performance in identifying meaningful CRMs.     

Glocal alignment: finding rearrangements during alignment

MOTIVATION: To compare entire genomes from different species, biologists increasingly need alignment methods that are efficient enough to handle long sequences, and accurate enough to correctly align the conserved biological features between distant species. The two main classes of pairwise alignments are global alignment, where one string is transformed into the other, and local alignment, where all locations of similarity between the two strings are returned. Global alignments are less prone to demonstrating false homology as each letter of one sequence is constrained to being aligned to only one letter of the other. Local alignments, on the other hand, can cope with rearrangements between non-syntenic, orthologous sequences by identifying similar regions in sequences; this, however, comes at the expense of a higher false positive rate due to the inability of local aligners to take into account overall conservation maps. RESULTS: In this paper we introduce the notion of glocal alignment, a combination of global and local methods, where one creates a map that transforms one sequence into the other while allowing for rearrangement events. We present Shuffle-LAGAN, a glocal alignment algorithm that is based on the CHAOS local alignment algorithm and the LAGAN global aligner, and is able to align long genomic sequences. To test Shuffle-LAGAN we split the mouse genome into BAC-sized pieces, and aligned these pieces to the human genome. We demonstrate that Shuffle-LAGAN compares favorably in terms of sensitivity and specificity with standard local and global aligners. From the alignments we conclude that about 9% of human/mouse homology may be attributed to small rearrangements, 63% of which are duplications.     

Validation of skeletal muscle cis-regulatory module predictions reveals nucleotide composition bias in functional enhancers

We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs) using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions.     

Evolution of lineage-specific functions in ancient cis-regulatory modules

Morphological evolution is driven both by coding sequence variation and by changes in regulatory sequences. However, how cis-regulatory modules (CRMs) evolve to generate entirely novel expression domains is largely unknown. Here, we reconstruct the evolutionary history of a lens enhancer located within a CRM that not only predates the lens, a vertebrate innovation, but bilaterian animals in general. Alignments of orthologous sequences from different deuterostomes sub-divide the CRM into a deeply conserved core and a more divergent flanking region. We demonstrate that all deuterostome flanking regions, including invertebrate sequences, activate gene expression in the zebrafish lens through the same ancient cluster of activator sites. However, levels of gene expression vary between species due to the presence of repressor motifs in flanking region and core. These repressor motifs are responsible for the relatively weak enhancer activity of tetrapod flanking regions. Ray-finned fish, however, have gained two additional lineage-specific activator motifs which in combination with the ancient cluster of activators and the core constitute a potent lens enhancer. The exploitation and modification of existing regulatory potential in flanking regions but not in the highly conserved core might represent a more general model for the emergence of novel regulatory functions in complex CRMs.     

Multiple protein sequence alignment

Multiple sequence alignments are essential in computational analysis of protein sequences and structures, with applications in structure modeling, functional site prediction, phylogenetic analysis and sequence database searching. Constructing accurate multiple alignments for divergent protein sequences remains a difficult computational task, and alignment speed becomes an issue for large sequence datasets. Here, I review methodologies and recent advances in the multiple protein sequence alignment field, with emphasis on the use of additional sequence and structural information to improve alignment quality.     

Modeling the evolution of regulatory elements by simultaneous detection and alignment with phylogenetic pair HMMs

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation.