Martin J. Lercher

The evolutionary dynamics of eukaryotic gene order

In eukaryotes, unlike in bacteria, gene order has typically been assumed to be random. However, the first statistically rigorous analyses of complete genomes, together with the availability of abundant gene-expression data, have forced a paradigm shift: in every complete eukaryotic genome that has been analysed so far, gene order is not random. It seems that genes that have similar and/or coordinated expression are often clustered. Here, we review this evidence and ask how such clusters evolve and how this relates to mechanisms that control gene expression.

Clustering of housekeeping genes provides a unified model of gene order in the human genome.

It is often supposed that, except for tandem duplicates, genes are randomly distributed throughout the human genome. However, recent analyses suggest that when all the genes expressed in a given tissue (notably placenta and skeletal muscle) are examined, these genes do not map to random locations but instead resolve to clusters. We have asked three questions: (i) is this clustering true for most tissues, or are these the exceptions; (ii) is any clustering simply the result of the expression of tandem duplicates and (iii) how, if at all, does this relate to the observed clustering of genes with high expression rates? We provide a unified model of gene clustering that explains the previous observations. We examined Serial Analysis of Gene Expression (SAGE) data for 14 tissues and found significant clustering, in each tissue, that persists even after the removal of tandem duplicates. We confirmed clustering by analysis of independent expressed-sequence tag (EST) data. We then tested the possibility that the human genome is organized into subregions, each specializing in genes needed in a given tissue. By comparing genes expressed in different tissues, we show that this is not the case: those genes that seem to be tissue-specific in their expression do not, as a rule, cluster. We report that genes that are expressed in most tissues (housekeeping genes) show strong clustering. In addition, we show that the apparent clustering of genes with high expression rates is a consequence of the clustering of housekeeping genes.

An integrated view of protein evolution

Why do proteins evolve at different rates? Advances in systems biology and genomics have facilitated a move from studying individual proteins to characterizing global cellular factors. Systematic surveys indicate that protein evolution is not determined exclusively by selection on protein structure and function, but is also affected by the genomic position of the encoding genes, their expression patterns, their position in biological networks and possibly their robustness to mistranslation. Recent work has allowed insights into the relative importance of these factors. We discuss the status of a much-needed coherent view that integrates studies on protein evolution with biochemistry and functional and structural genomics.

Adaptive evolution of bacterial metabolic networks by horizontal gene transfer

Numerous studies have considered the emergence of metabolic pathways, but the modes of recent evolution of metabolic networks are poorly understood. Here, we integrate comparative genomics with flux balance analysis to examine (i) the contribution of different genetic mechanisms to network growth in bacteria, (ii) the selective forces driving network evolution and (iii) the integration of new nodes into the network. Most changes to the metabolic network of Escherichia coli in the past 100 million years are due to horizontal gene transfer, with little contribution from gene duplicates. Networks grow by acquiring genes involved in the transport and catalysis of external nutrients, driven by adaptations to changing environments. Accordingly, horizontally transferred genes are integrated at the periphery of the network, whereas central parts remain evolutionarily stable. Genes encoding physiologically coupled reactions are often transferred together, frequently in operons. Thus, bacterial metabolic networks evolve by direct uptake of peripheral reactions in response to changed environments.

Human SNP variability and mutation rate are higher in regions of high recombination.

Understanding the co-variation of nucleotide diversity and local recombination rates is important both for the mapping of disease-associated loci and in understanding the causes of sequence evolution. It is known that single nucleotide polymorphisms (SNPs) around protein coding genes show higher diversity in regions of high recombination. Here, we find that this correlation holds for SNPs across the entire human genome, the great majority of which are not near exons or control elements. Contrasting with results from coding regions, we provide evidence that the higher nucleotide diversity in regions of high recombination is most likely due, at least in part, to a higher mutation rate. One possible explanation for this is that recombination is mutagenic.

Prediction of effective genome size in metagenomic samples

We introduce a novel computational approach to predict effective genome size (EGS; a measure that includes multiple plasmid copies, inserted sequences, and associated phages and viruses) from short sequencing reads of environmental genomics (or metagenomics) projects. We observe considerable EGS differences between environments and link this with ecologic complexity as well as species composition (for instance, the presence of eukaryotes). For example, we estimate EGS in a complex, organism-dense farm soil sample at about 6.3 megabases (Mb) whereas that of the bacteria therein is only 4.7 Mb; for bacteria in a nutrient-poor, organism-sparse ocean surface water sample, EGS is as low as 1.6 Mb. The method also permits evaluation of completion status and assembly bias in single-genome sequencing projects.

Chance and necessity in the evolution of minimal metabolic networks

It is possible to infer aspects of an organisms lifestyle from its gene content. Can the reverse also be done? Here we consider this issue by modelling evolution of the reduced genomes of endosymbiotic bacteria. The diversity of gene content in these bacteria may reflect both variation in selective forces and contingency-dependent loss of alternative pathways. Using an in silico representation of the metabolic network of Escherichia coli, we examine the role of contingency by repeatedly simulating the successive loss of genes while controlling for the environment. The minimal networks that result are variable in both gene content and number. Partially different metabolisms can thus evolve owing to contingency alone. The simulation outcomes do preserve a core metabolism, however, which is over-represented in strict intracellular bacteria. Moreover, differences between minimal networks based on lifestyle are predictable: by simulating their respective environmental conditions, we can model evolution of the gene content in Buchnera aphidicola and Wigglesworthia glossinidia with over 80% accuracy. We conclude that, at least for the particular cases considered here, gene content of an organism can be predicted with knowledge of its distant ancestors and its current lifestyle.

Gene Transfer from Bacteria and Archaea Facilitated Evolution of an Extremophilic Eukaryote

Hot, Toxic Eukaryote Unusually, the single-celled eukaryote red alga, Galdieria sulphuraria, can thrive in hot, acidic springs. This organism is endowed with extraordinary metabolic talents and can consume a variety of strange carbohydrates, as well as turn on photosynthesis when the food runs out. Schönknecht et al. (p. 1207; see the Perspective by Rocha) discerned from phylogenetic analysis of its genome that during its evolution, G. sulphuraria appears to have commandeered at least 75 bacterial and archaeal genes by horizontal gene transfer and then applied gene expansion to boost its metabolic repertoire. A mosaic of genes acquired from various phyla enable a red alga to grow abundantly in hot, acidic, and toxic niches. [Also see Perspective by Rocha] Some microbial eukaryotes, such as the extremophilic red alga Galdieria sulphuraria, live in hot, toxic metal-rich, acidic environments. To elucidate the underlying molecular mechanisms of adaptation, we sequenced the 13.7-megabase genome of G. sulphuraria. This alga shows an enormous metabolic flexibility, growing either photoautotrophically or heterotrophically on more than 50 carbon sources. Environmental adaptation seems to have been facilitated by horizontal gene transfer from various bacteria and archaea, often followed by gene family expansion. At least 5% of protein-coding genes of G. sulphuraria were probably acquired horizontally. These proteins are involved in ecologically important processes ranging from heavy-metal detoxification to glycerol uptake and metabolism. Thus, our findings show that a pan-domain gene pool has facilitated environmental adaptation in this unicellular eukaryote.

Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes

The nematode Caenorhabditis elegans is a popular model system in genetics, not least because a majority of human disease genes are conserved in C. elegans. To generate a comprehensive inventory of its expressed proteome, we performed extensive shotgun proteomics and identified more than half of all predicted C. elegans proteins. This allowed us to confirm and extend genome annotations, characterize the role of operons in C. elegans, and semiquantitatively infer abundance levels for thousands of proteins. Furthermore, for the first time to our knowledge, we were able to compare two animal proteomes (C. elegans and Drosophila melanogaster). We found that the abundances of orthologous proteins in metazoans correlate remarkably well, better than protein abundance versus transcript abundance within each organism or transcript abundances across organisms; this suggests that changes in transcript abundance may have been partially offset during evolution by opposing changes in protein abundance.

Evidence for widespread degradation of gene control regions in hominid genomes.

Although sequences containing regulatory elements located close to protein-coding genes are often only weakly conserved during evolution, comparisons of rodent genomes have implied that these sequences are subject to some selective constraints. Evolutionary conservation is particularly apparent upstream of coding sequences and in first introns, regions that are enriched for regulatory elements. By comparing the human and chimpanzee genomes, we show here that there is almost no evidence for conservation in these regions in hominids. Furthermore, we show that gene expression is diverging more rapidly in hominids than in murids per unit of neutral sequence divergence. By combining data on polymorphism levels in human noncoding DNA and the corresponding human–chimpanzee divergence, we show that the proportion of adaptive substitutions in these regions in hominids is very low. It therefore seems likely that the lack of conservation and increased rate of gene expression divergence are caused by a reduction in the effectiveness of natural selection against deleterious mutations because of the low effective population sizes of hominids. This has resulted in the accumulation of a large number of deleterious mutations in sequences containing gene control elements and hence a widespread degradation of the genome during the evolution of humans and chimpanzees.