Studying genomes through the aeons: protein families, pseudogenes and proteome evolution

Millions of years of evolution preserved: a comprehensive catalog of the processed pseudogenes in the human genome

Processed pseudogenes were created by reverse-transcription of mRNAs; they provide snapshots of ancient genes existing millions of years ago in the genome. To find them in the present-day human, we developed a pipeline using features such as intron-absence, frame-disruption, polyadenylation, and truncation. This has enabled us to identify in recent genome drafts approximately 8000 processed pseudogenes (distributed from http://pseudogene.org). Overall, processed pseudogenes are very similar to their closest corresponding human gene, being 94% complete in coding regions, with sequence similarity of 75% for amino acids and 86% for nucleotides. Their chromosomal distribution appears random and dispersed, with the numbers on chromosomes proportional to length, suggesting sustained "bombardment" over evolution. However, it does vary with GC-content: Processed pseudogenes occur mostly in intermediate GC-content regions. This is similar to Alus but contrasts with functional genes and L1-repeats. Pseudogenes, moreover, have age profiles similar to Alus. The number of pseudogenes associated with a given gene follows a power-law relationship, with a few genes giving rise to many pseudogenes and most giving rise to few. The prevalence of processed pseudogenes agrees well with germ-line gene expression. Highly expressed ribosomal proteins account for approximately 20% of the total. Other notables include cyclophilin-A, keratin, GAPDH, and cytochrome c.

TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics

Biological networks are a topic of great current interest, particularly with the publication of a number of large genome-wide interaction datasets. They are globally characterized by a variety of graph-theoretic statistics, such as the degree distribution, clustering coefficient, characteristic path length and diameter. Moreover, real protein networks are quite complex and can often be divided into many sub-networks through systematic selection of different nodes and edges. For instance, proteins can be sub-divided by expression level, length, amino-acid composition, solubility, secondary structure and function. A challenging research question is to compare the topologies of sub- networks, looking for global differences associated with different types of proteins. TopNet is an automated web tool designed to address this question, calculating and comparing topological characteristics for different sub-networks derived from any given protein network. It provides reasonable solutions to the calculation of network statistics for sub-networks embedded within a larger network and gives simplified views of a sub-network of interest, allowing one to navigate through it. After constructing TopNet, we applied it to the interaction networks and protein classes currently available for yeast. We were able to find a number of potential biological correlations. In particular, we found that soluble proteins had more interactions than membrane proteins. Moreover, amongst soluble proteins, those that were highly expressed, had many polar amino acids, and had many alpha helices, tended to have the most interaction partners. Interestingly, TopNet also turned up some systematic biases in the current yeast interaction network: on average, proteins with a known functional classification had many more interaction partners than those without. This phenomenon may reflect the incompleteness of the experimentally determined yeast interaction network.

An activated 5? cryptic splice site in the human ALG3 gene generates a premature termination codon insensitive to nonsense-mediated mRNA decay in a new case of congenital disorder of glycosylation type Id (CDG-Id)

A defect of the dolichyl-P-Man:Man5GlcNAc2-PP-dolichyl mannosyltransferase encoded by the ALG3 gene (alias NOT56L) causes congenital disorder of glycosylation type Id (CDG-Id). In this work, a new mutation in the ALG3 gene causing atypical splicing is described with characterization of expression levels and transcript stabilities of the different splice products. A silent mutation in exon 1 of the ALG3 gene (c.165C<T) resulted in a deletion in the corresponding transcripts (c.160_196del) due to the activation of a cryptic donor splice site. Expression studies revealed that negligible amounts of normal transcripts were present in the patient. The deletion in the ALG3 gene generated a premature termination codon (PTC) coding for an ALG3 protein truncated after the first N-terminal transmembranous domain (p.Val54fsX66). Nonsense mediated decay (NMD) of mRNA is a general mechanism for clearing of RNA molecules containing suitable PTCs. However, suppression of NMD using cycloheximide had no influence on ALG3 transcript levels, although the PTCs of the transcript fulfill the criteria for the initiation of NMD. The results presented in this work demonstrate that factors abrogating NMD of the ALG3 gene exists and that the ALG3 gene can serve as a valuable tool for further investigations of the regulation of NMD.

A "polyORFomic" analysis of prokaryote genomes using disabled-homology filtering reveals conserved but undiscovered short ORFs

Prokaryote gene annotation is complicated by large numbers of short open reading frames (ORFs) that arise naturally from genetic code design. Historically, many hypothetical ORFs have been annotated as genes in microbes, usually with an arbitrary length threshold (e.g. greater than 100 codons). Given the use of such thresholds, what is the extent of genuine undiscovered short genes in the current sampling of prokaryote genomes? To assess rigorously the potential under-annotation of short ORFs with homology, we exhaustively compared the polyORFome--all possible ORFs in 64 prokaryotes (53 bacteria and 11 archaea) plus budding yeast--to itself and to all known proteins. The novelty of our analysis is that, firstly, sequence comparisons to/between both annotated and un-annotated ORFs are considered, and secondly a two-step disabled-homology filter is applied to set aside putative pseudogenes and spurious ORFs. We find that un-annotated homologous short ORFs (uhORFs) correspond to a small but non-negligible fraction of the annotated prokaryote proteomes (0.5-3.8%, depending on selection criteria). Moreover, the disabled-homology filter indicates that about a third of uhORFs correspond to putative pseudogenes or spurious ORFs. Our analysis shows that the use of annotation length thresholds is unnecessary, as there are manageable numbers of short ORF homologies conserved (without disablements) across microbial genomes. Data on uhORFs are available from http://pseudogene.org/polyo

The evolutionary relationships of zebrafish genes tbx6, tbx16/spadetail and mga

The T-box (Tbx) genes encode a family of transcription factors required for development of vertebrate embryos. In an attempt to discover human orthologues of the zebrafish (Danio rerio) tbx6 and tbx16/spadetail genes, we found that the most similar human (Homo sapiens) gene is the orthologue of mouse (Mus musculus) Mga (MAX gene associated). We have identified the zebrafish orthologue of Mga using analyses of sequence similarity and the orthologies of syntenic genes. Zebrafish mga maps close to dtk (developmental receptor tyrosine kinase), the orthologue of human TYRO3 (TYRO3 protein tyrosine kinase). Like its human and mouse orthologues, zebrafish mga lacks the three conserved introns within the T-box coding sequences that are characteristic of the vertebrate T-box gene family. This suggests that these genes are derived from an ancient reverse transcription event. The human genome does not appear to possess orthologues of zebrafish tbx6 or tbx16/spadetail.

Mystery of intron gain

For nearly 15 years, it has been widely believed that many introns were recently acquired by the genes of multicellular organisms. However, the mechanism of acquisition has yet to be described for a single animal intron. Here, we report a large-scale computational analysis of the human, Drosophila melanogaster, Caenorhabditis elegans, and Arabidopsis thaliana genomes. We divided 147,796 human intron sequences into batches of similar lengths and aligned them with each other. Different types of homologies between introns were found, but none showed evidence of simple intron transposition. Also, 106,902 plant, 39,624 Drosophila, and 6021 C. elegans introns were examined. No single case of homologous introns in nonhomologous genes was detected. Thus, we found no example of transposition of introns in the last 50 million years in humans, in 3 million years in Drosophila and C. elegans, or in 5 million years in Arabidopsis. Either new introns do not arise via transposition of other introns or intron transposition must have occurred so early in evolution that all traces of homology have been lost.

Evolutionary connections between bacterial and eukaryotic signaling systems: a genomic perspective

Recent advances in microbial genomics suggest that several protein domains are common to bacterial and eukaryotic regulatory proteins. In particular, developmentally and morphologically complex prokaryotes appear to share several signaling modules with eukaryotes. New experimental studies and information from domain architectures point to several similar mechanistic themes in bacterial and eukaryotic signaling proteins. Laterally transferred protein domains, originally of bacterial provenance, appear to have contributed to the evolution of sensory pathways related to light, redox and nitric oxide signaling, and developmental pathways, such as Notch, cytokine and cytokinin signaling in eukaryotes.

Vertebrate gene predictions and the problem of large genes

To find unknown protein-coding genes, annotation pipelines use a combination of ab initio gene prediction and similarity to experimentally confirmed genes or proteins. Here, we show that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistently low false-negative rate. The incorporation of similarity information is meant to reduce the false-positive rate, but in doing so it increases the false-negative rate. The crucial variable is gene size (including introns)--genes of the most extreme sizes, especially very large genes, are most likely to be incorrectly predicted.

The DNA sequence of chromosome I of an African trypanosome: gene content, chromosome organisation, recombination and polymorphism

The African trypanosome, Trypanosoma brucei, causes sleeping sickness in humans in sub-Saharan Africa. Here we report the sequence and analysis of the 1.1 Mb chromosome I, which encodes approximately 400 predicted genes organised into directional clusters, of which more than 100 are located in the largest cluster of 250 kb. A 160-kb region consists primarily of three gene families of unknown function, one of which contains a hotspot for retroelement insertion. We also identify five novel gene families. Indeed, almost 20% of predicted genes are members of families. In some cases, tandemly arrayed genes are 99-100% identical, suggesting an active process of amplification and gene conversion. One end of the chromosome consists of a putative bloodstream-form variant surface glycoprotein (VSG) gene expression site that appears truncated and degenerate. The other chromosome end carries VSG and expression site-associated genes and pseudogenes over 50 kb of subtelomeric sequence where, unusually, the telomere-proximal VSG gene is oriented away from the telomere. Our analysis includes the cataloguing of minor genetic variations between the chromosome I homologues and an estimate of crossing-over frequency during genetic exchange. Genetic polymorphisms are exceptionally rare in sequences located within and around the strand-switches between several gene clusters.

Protein families and TRIBES in genome sequence space

Accurate detection of protein families allows assignment of protein function and the analysis of functional diversity in complete genomes. Recently, we presented a novel algorithm called TribeMCL for the detection of protein families that is both accurate and efficient. This method allows family analysis to be carried out on a very large scale. Using TribeMCL, we have generated a resource called TRIBES that contains protein family information, comprising annotations, protein sequence alignments and phylogenetic distributions describing 311 257 proteins from 83 completely sequenced genomes. The analysis of at least 60 934 detected protein families reveals that, with the essential families excluded, paralogy levels are similar between prokaryotes, irrespective of genome size. The number of essential families is estimated to be between 366 and 426. We also show that the currently known space of protein families is scale free and discuss the implications of this distribution. In addition, we show that smaller families are often formed by shorter proteins and discuss the reasons for this intriguing pattern. Finally, we analyse the functional diversity of protein families in entire genome sequences. The TRIBES protein family resource is accessible at http://www.ebi.ac.uk/research/cgg/tribes/.

Protein prenyltransferases: anchor size, pseudogenes and parasites

Lipid modification of eukaryotic proteins by protein prenyltransferases is required for critical signaling pathways, cell cycle progression, cytoskeleton remodeling, induction of apoptosis and vesicular trafficking. This review analyzes the influence of distinct states of sequential posttranslational processing that can be obtained after single or double prenylation, reversible palmitoylation, proteolytic cleavage of the C-terminus and possible reversible carboxymethylation. This series of modifications, as well as the exact length of the prenyl anchor, are determinants in protein-membrane and specific protein-protein interactions of protein prenyltransferase substrates. Furthermore, the occurrence and distribution of pseudogenes of protein prenyltransferase subunits are discussed. Besides being developed as anti-cancer agents, prenyltransferase inhibitors are effective against an increasing number of parasitic diseases. Extensive screens for protein prenyltransferases in genomic data of fungal and protozoan pathogens unveil a series of new pharmacologic targets for prenyltransferase inhibition, including the parasites Brugia malayi, Onchocerca volvulus, Aspergillus nidulans, Pneumocystis carinii, Entamoeba histolytica, Strongyloides stercoralis, Trichinella spiralis and Cryptosporidium parvum.

Identification of pseudogenes in the Drosophila melanogaster genome

Pseudogenes are copies of genes that cannot produce a protein. They can be detected from disruptions to their apparent coding sequence, caused by frameshifts and premature stop codons. They are classed as either processed pseudogenes (made by reverse transcription from an mRNA) or duplicated pseudogenes, arising from duplication in the genomic DNA and subsequent disablement. Historically, there is anecdotal evidence that the fruit fly (Drosophila melanogaster) has few pseudogenes. Investigators have linked this to a high deletion rate of genomic DNA, for which there is evidence from genetic experiments on genome size. Here, we apply a homology-based pipeline that was developed previously to identify pseudogenes in other eukaryotic genomes, to the fruit fly, so as to derive the first complete survey of its pseudogene population. We find approximately 100 pseudogenes, with at least a sixth of these as candidate processed pseudogenes. This gives a much lower proportion of pseudogenes (compared with the size of the proteome) than in the genomes of other eukaryotes for which data are available (human, nematode and budding yeast). Closest matching proteins to Drosophila pseudogenes are significantly longer than the average protein in its proteome (up to approximately 60% more than the average protein's length), in contrast to the situation in the three other eukaryotic genomes. This may be due to the persistence of fragments of longer genes. In the fly pseudogene population, we found most pseudogenes for serine proteases (which are more abundant in the Drosophila lineage compared with the other eukaryotes), immunoglobulin-motif-containing proteins and cytochromes P450. Data on the sequences and positions of the putative pseudogenes are available at: http://www.pseudogene.org/fly. The detection of a small number of pseudogenes in the Drosophila genome and the higher mean length for the closest matching proteins to pseudogenes (possibly because remnants of genes encoding longer proteins are more likely to persist) are further evidence for a high deletion rate of genomic DNA in the fruit fly. The data are useful for molecular evolution study in Drosophila.

The structure of the protein universe and genome evolution

Despite the practically unlimited number of possible protein sequences, the number of basic shapes in which proteins fold seems not only to be finite, but also to be relatively small, with probably no more than 10,000 folds in existence. Moreover, the distribution of proteins among these folds is highly non-homogeneous -- some folds and superfamilies are extremely abundant, but most are rare. Protein folds and families encoded in diverse genomes show similar size distributions with notable mathematical properties, which also extend to the number of connections between domains in multidomain proteins. All these distributions follow asymptotic power laws, such as have been identified in a wide variety of biological and physical systems, and which are typically associated with scale-free networks. These findings suggest that genome evolution is driven by extremely general mechanisms based on the preferential attachment principle.

The language of genes

Linguistic metaphors have been woven into the fabric of molecular biology since its inception. The determination of the human genome sequence has brought these metaphors to the forefront of the popular imagination, with the natural extension of the notion of DNA as language to that of the genome as the 'book of life'. But do these analogies go deeper and, if so, can the methods developed for analysing languages be applied to molecular biology? In fact, many techniques used in bioinformatics, even if developed independently, may be seen to be grounded in linguistics. Further interweaving of these fields will be instrumental in extending our understanding of the language of life.

Digging deep for ancient relics: a survey of protein motifs in the intergenic sequences of four eukaryotic genomes

We have examined conserved protein motifs in the non-coding, intergenic regions ("pseudomotif patterns") and surveyed their occurrence in the fly, worm, yeast and human genomes (chromosomes 21 and 22 only). To identify these patterns, we masked out annotated genes, pseudogenes and repeat regions from the raw genomic sequence and then compared the remaining sequence, in six-frame translation, against 1319 patterns from the PROSITE database. For each pseudomotif pattern, the absolute number of occurrences is not very informative unless compared against a statistical expectation; consequently, we calculated the expected occurrence of each pattern using a Poisson model and verified this with simulations. Using a p-value cut-off of 0.01, we found 67 pseudomotif patterns over-represented in fly intergenic regions, 34 in worm, 21 in human and six in yeast. These include the zinc finger, leucine zipper, nucleotide-binding motif and EGF domain. Many of the over-represented patterns were common to two or more organisms, but there were a few that were unique to specific ones. Furthermore, we found more over-represented patterns in the fly than in the worm, although the fly has fewer pseudogenes. This puzzling observation can be explained by a higher deletion rate in the fly genome. We also surveyed under-represented patterns, finding 23 in the fly, 12 in the worm, 18 in human and two in yeast. If intergenic sequences were truly random, we would expect an equal number of over and under-represented patterns. The fact that for each organism the number of over-represented patterns is greater than the number of under-represented ones implies that a fraction of the intergenic regions consist of ancient protein fragments that, due to accumulated disablements, have become unrecognizable by conventional techniques for gene and pseudogene identification. Moreover, we find that in aggregate the over-represented pseudomotif patterns occupy a substantial fraction of the intergenic regions. Further information is available at http://pseudogene.org

Comprehensive analysis of amino acid and nucleotide composition in eukaryotic genomes, comparing genes and pseudogenes

Based on searches for disabled homologs to known proteins, we have identified a large population of pseudogenes in four sequenced eukaryotic genomes-the worm, yeast, fly and human (chromosomes 21 and 22 only). Each of our nearly 2500 pseudogenes is characterized by one or more disablements mid-domain, such as premature stops and frameshifts. Here, we perform a comprehensive survey of the amino acid and nucleotide composition of these pseudogenes in comparison to that of functional genes and intergenic DNA. We show that pseudogenes invariably have an amino acid composition intermediate between genes and translated intergenic DNA. Although the degree of intermediacy varies among the four organisms, in all cases, it is most evident for amino acid types that differ most in occurrence between genes and intergenic regions. The same intermediacy also applies to codon frequencies, especially in the worm and human. Moreover, the intermediate composition of pseudogenes applies even though the composition of the genes in the four organisms is markedly different, showing a strong correlation with the overall A/T content of the genomic sequence. Pseudogenes can be divided into 'ancient' and 'modern' subsets, based on the level of sequence identity with their closest matching homolog (within the same genome). Modern pseudogenes usually have a much closer sequence composition to genes than ancient pseudogenes. Collectively, our results indicate that the composition of pseudogenes that are under no selective constraints progressively drifts from that of coding DNA towards non-coding DNA. Therefore, we propose that the degree to which pseudogenes approach a random sequence composition may be useful in dating different sets of pseudogenes, as well as to assess the rate at which intergenic DNA accumulates mutations. Our compositional analyses with the interactive viewer are available over the web at http://genecensus.org/pseudogene.