CAGE: Combinatorial Analysis of Gene-cluster Evolution

Fast computation of a string duplication history under no-breakpoint-reuse

In this paper, we provide an O(n log(2) n log log n log* n) algorithm to compute a duplication history of a string under no-breakpoint-reuse condition. The motivation of this problem stems from computational biology, in particular, from analysis of complex gene clusters. The problem is also related to computing edit distance with block operations, but, in our scenario, the start of the history is not fixed, but chosen to minimize the distance measure.

Sp sites contribute to basal and inducible expression of the human TNIP1 (TNFα-inducible protein 3-interacting protein 1) promoter

TNIP1 [TNFα (tumour necrosis factor α)-induced protein 3-interacting protein 1] is a co-repressor of RAR (retinoic acid receptor) and PPAR (peroxisome-proliferator-activated receptor). Additionally, it can reduce signalling stemming from cell membrane receptors such as those for TNFα and EGF (epidermal growth factor). Consequently, it influences a variety of receptor-mediated events as diverse as transcription, programmed cell death and cell cycling. Thus changes in TNIP1 expression levels are likely to affect multiple important biological end points. TNIP1 expression level changes have been linked to psoriasis and systemic sclerosis. As such, it is crucial to determine what controls its expression levels, starting with constitutive control of its promoter. Our analysis of the TNIP1 promoter revealed multiple transcription start sites in its GC-rich proximal regions along with two transcriptionally active Sp (specificity protein) sites, responsive to both Sp1 and Sp3. EMSA (electrophoretic mobility-shift assay) and ChIP (chromatin immunoprecipitation) demonstrated physical binding between Sp1 and Sp3 at these sites. A decrease in Sp1 protein levels via siRNA (short interfering RNA) or diminished Sp1 DNA binding by mithramycin decreased TNIP1 mRNA levels. This Sp-binding GC-rich region of the TNIP1 promoter also participates in transcriptional activation by ligand-bound RAR. Together, these results demonstrate newly identified regulators of TNIP1 expression and suggest possible transcription factor targets which in turn control TNIP1-related biological end points ranging from apoptosis to inflammatory diseases.

Clusters of ancestrally related genes that show paralogy in whole or in part are a major feature of the genomes of humans and other species

Arrangements of genes along chromosomes are a product of evolutionary processes, and we can expect that preferable arrangements will prevail over the span of evolutionary time, often being reflected in the non-random clustering of structurally and/or functionally related genes. Such non-random arrangements can arise by two distinct evolutionary processes: duplications of DNA sequences that give rise to clusters of genes sharing both sequence similarity and common sequence features and the migration together of genes related by function, but not by common descent [1], [2], [3]. To provide a background for distinguishing between the two, which is important for future efforts to unravel the evolutionary processes involved, we here provide a description of the extent to which ancestrally related genes are found in proximity.

Towards this purpose, we combined information from five genomic datasets, InterPro, SCOP, PANTHER, Ensembl protein families, and Ensembl gene paralogs. The results are provided in publicly available datasets (http://cgd.jax.org/datasets/clustering/paraclustering.shtml) describing the extent to which ancestrally related genes are in proximity beyond what is expected by chance (i.e. form paraclusters) in the human and nine other vertebrate genomes, as well as the D. melanogaster, C. elegans, A. thaliana, and S. cerevisiae genomes.

With the exception of Saccharomyces, paraclusters are a common feature of the genomes we examined. In the human genome they are estimated to include at least 22% of all protein coding genes. Paraclusters are far more prevalent among some gene families than others, are highly species or clade specific and can evolve rapidly, sometimes in response to environmental cues. Altogether, they account for a large portion of the functional clustering previously reported in several genomes.

Revealing mammalian evolutionary relationships by comparative analysis of gene clusters

Many software tools for comparative analysis of genomic sequence data have been released in recent decades. Despite this, it remains challenging to determine evolutionary relationships in gene clusters due to their complex histories involving duplications, deletions, inversions, and conversions. One concept describing these relationships is orthology. Orthologs derive from a common ancestor by speciation, in contrast to paralogs, which derive from duplication. Discriminating orthologs from paralogs is a necessary step in most multispecies sequence analyses, but doing so accurately is impeded by the occurrence of gene conversion events. We propose a refined method of orthology assignment based on two paradigms for interpreting its definition: by genomic context or by sequence content. X-orthology (based on context) traces orthology resulting from speciation and duplication only, while N-orthology (based on content) includes the influence of conversion events. We developed a computational method for automatically mapping both types of orthology on a per-nucleotide basis in gene cluster regions studied by comparative sequencing, and we make this mapping accessible by visualizing the output. All of these steps are incorporated into our newly extended CHAP 2 package. We evaluate our method using both simulated data and real gene clusters (including the well-characterized α-globin and β-globin clusters). We also illustrate use of CHAP 2 by analyzing four more loci: CCL (chemokine ligand), IFN (interferon), CYP2abf (part of cytochrome P450 family 2), and KIR (killer cell immunoglobulin-like receptors). These new methods facilitate and extend our understanding of evolution at these and other loci by adding automated accurate evolutionary inference to the biologist's toolkit. The CHAP 2 package is freely available from http://www.bx.psu.edu/miller_lab.

Conversion events in gene clusters

BACKGROUND

Gene clusters containing multiple similar genomic regions in close proximity are of great interest for biomedical studies because of their associations with inherited diseases. However, such regions are difficult to analyze due to their structural complexity and their complicated evolutionary histories, reflecting a variety of large-scale mutational events. In particular, conversion events can mislead inferences about the relationships among these regions, as traced by traditional methods such as construction of phylogenetic trees or multi-species alignments.

RESULTS

To correct the distorted information generated by such methods, we have developed an automated pipeline called CHAP (Cluster History Analysis Package) for detecting conversion events. We used this pipeline to analyze the conversion events that affected two well-studied gene clusters (α-globin and β-globin) and three gene clusters for which comparative sequence data were generated from seven primate species: CCL (chemokine ligand), IFN (interferon), and CYP2abf (part of cytochrome P450 family 2). CHAP is freely available at http://www.bx.psu.edu/miller_lab.

CONCLUSIONS

These studies reveal the value of characterizing conversion events in the context of studying gene clusters in complex genomes.