Changing the light environment: chloroplast signalling and response mechanisms

Light is an essential environmental factor required for photosynthesis, but it also mediates signals to control plant development and growth and induces stress tolerance. The photosynthetic organelle (chloroplast) is a key component in the signalling and response network in plants. This theme issue of Philosophical Transactions of the Royal Society of London B: Biology provides updates, highlights and summaries of the most recent findings on chloroplast-initiated signalling cascades and responses to environmental changes, including light and biotic stress. Besides plant molecular cell biology and physiology, the theme issue includes aspects from the cross-disciplinary fields of environmental adaptation, ecology and agronomy.Oxygenic photosynthetic organisms carry out the most intriguing reaction on Earth, namely the conversion of light energy from the sun into chemical energy, which also results in oxygen as a by-product. The photosynthetic end products (sugars) drive most processes in living cells on Earth. As photosynthetic organisms represent the basis of our daily life (food, energy, materials), effects on their primary productivity have an impact on the society in various aspects, for instance economy, ecological sustainability and even our lifestyle. Photosynthetic organisms, particularly plants which are essentially sessile, have to constantly deal with changes in a wide range of abiotic and biotic factors in their immediate environment on a seasonal as well as daily basis. The chloroplast is a light-driven energy factory, but besides this primary mission it perceives signals from surroundings to adjust plant development and induce adaptation to ever-changing environmental cues.The signalling cascades start from various chloroplast processes but merge later or crosstalk with each other and with other signalling cascades (figure 1). For example, acclimation of plants to excess light conditions may also simultaneously increase the tolerance to other abiotic stress factors [1]. Recently, chloroplasts were also recognized to perceive and mediate signals that promote tolerance against plant pathogens (immune defence) or that are involved in hormone perception [2]. Resolving the crosstalk between the cascades is most important for understanding physiological responses in plants under ever-changing environments, and for predicting how plants survive under natural growth conditions. Open in a separate windowFigure 1.Overview of light-induced chloroplast signalling and response mechanisms, covered by papers in this theme issue. Chl, chlorophyll; NPQ, non-photochemical quenching; ROS, reactive oxygen species; ST, state transition; Trx, thioredoxin. (Online version in colour.)This theme issue of Philosophical Transactions of the Royal Society of London B: Biology covers the most recent findings and updates on the molecular short-term mechanisms used by the chloroplast to adjust its function to changes in light conditions, and on the signalling pathways that induce long-term adaptive responses, such as stress tolerance and immune defence in plants (figure 1). It focuses on the current understanding of the crosstalk between signalling networks activated by chloroplasts and mitochondria, light receptors and those induced by biotic stress. It also focuses on the variation of the adaptive mechanisms in natural population and on their agricultural and ecological impacts. Thus, besides plant molecular cell biology and physiology, the theme issue includes aspects from the cross-disciplinary fields of environmental adaptation, ecology and agronomy. It consists of 10 research articles and nine reviews covering the following four topics: (i) short-term adaptive responses in chloroplasts, (ii) chloroplast-to-nucleus signalling and crosstalk with other signalling pathways, (iii) natural variation of regulatory mechanisms to allow for adaptation and (iv) agricultural and ecological perspective of light responses in chloroplasts.Light signals perceived by chlorophyll (Chl) in the thylakoid membrane and by photoreceptors in the cytosol activate various short-term adaptive responses including enzyme regulation, photoprotection and repair (figure 1). Ebenhöh et al. [3] propose a mathematical model for relative contributions of non-photochemical quenching (NPQ) and state transition (ST) in light acclimation. The paper by Cazzaniga et al. [4] identifies photoreceptor-dependent chloroplast movement as an additional pathway used to dissipate the excess absorbed energy, whereas Ruban & Belgio [5] investigate NPQ in relation to maximum light intensity tolerated by plants. Bertrand et al. [6] investigate the different mechanisms involved in NPQ relaxation in diatoms. Nikkanen & Rintamäki [7] and Kirchhoff [8] review the current knowledge on chloroplast processes regulated by thioredoxins under changing light environment and processes in the thylakoid membrane associated with the photosystem II repair cycle in high light stress, respectively.Together with adjustments of metabolic processes and induction of photoprotective mechanisms, light initiates signalling to the nucleus for gene expression, resulting in various long-term adaptive responses, including development and growth, stress and programmed cell death (figure 1). Larkin [9] provides an updated insight into the impact of the GENOMES UNCOUPLED genes on plastid-to-nucleus signalling and reviews the influence of plastids on light receptor signalling and development, whereas the contribution by Blanco et al. [10] searches for new components integrating mitochondrial and plastid retrograde signals that regulate plant energy metabolism. Alsharafa et al. [11] investigate the kinetics of events involved in initiation of high light acclimation, and Tikkanen et al. [12] show that chloroplast signalling interacts with both reactive oxygen species (ROS) and hormonal signalling. ROS signalling is also highlighted in the papers by Heyno et al. (hydrogen peroxide) [13] and by Zhang et al. (singlet oxygen) [14]. Foyer et al. [15] introduce a chloroplast protein belonging to the WHIRLY family and propose that the redox state of the photosynthetic electron transport chain triggers the movement of this protein from the chloroplast to the nucleus where it regulates the gene expression leading to cross tolerance, including light acclimation and immune defence. Gorecka et al. [16] identify novel components for crosstalk of immune reaction-induced signalling networks with two short-term photoprotective mechanisms, ST and NPQ. Trotta et al. [17] further review the increasing evidence for crosstalk between light-induced chloroplast signalling and immune reactions in plants.To allow for adaptation to a changing environment, natural selection of existing genetic variation takes place. Flood, Yin et al. [18] report natural variation in photosystem II protein phosphorylation in the model plant Arabidopsis thaliana and propose a possible role in the adaptation to diverse environments. In addition, Serõdio et al. [19] review the current knowledge of adaptation of macroalgal chloroplasts to life in sea slug following ingestion. Finally, the review by Darko et al. [20] uses selected examples to show how artificial lighting can be used to improve plant growth in agriculture and for production of functional food and materials, whereas Demmig-Adams et al. [21] provide an ecophysiological perspective of light responses in the chloroplast to optimize its function and of the whole plant in a changing environment.This research on light-induced signalling and response is developing in many directions, as reflected by the broad field coverage of the papers of this theme issue. It highlights and summarizes the present knowledge from the individual chloroplast reactions to the variation of the adaptive mechanisms in natural populations and on their agricultural and ecological impacts.     

Genetic Control of Photoperiod Sensitivity in Maize Revealed by Joint Multiple Population Analysis

Variation in maize for response to photoperiod is related to geographical adaptation in the species. Maize possesses homologs of many genes identified as regulators of flowering time in other species, but their relation to the natural variation for photoperiod response in maize is unknown. Candidate gene sequences were mapped in four populations created by crossing two temperate inbred lines to two photoperiod-sensitive tropical inbreds. Whole-genome scans were conducted by high-density genotyping of the populations, which were phenotyped over 3 years in both short- and long-day environments. Joint multiple population analysis identified genomic regions controlling photoperiod responses in flowering time, plant height, and total leaf number. Four key genome regions controlling photoperiod response across populations were identified, referred to as ZmPR1–4. Functional allelic differences within these regions among phenotypically similar founders suggest distinct evolutionary trajectories for photoperiod adaptation in maize. These regions encompass candidate genes CCA/LHY, CONZ1, CRY2, ELF4, GHD7, VGT1, HY1/SE5, TOC1/PRR7/PPD-1, PIF3, ZCN8, and ZCN19.MAIZE (Zea mays L. subsp. mays) was domesticated in southern Mexico and its center of diversity is in tropical Latin America (Goodman 1999; Matsuoka et al. 2002), where precipitation rates and day lengths cycle annually. The presumed ancestor of maize, teosinte (Zea mays L. subsp. parviglumis), likely evolved photoperiod sensitivity to synchronize its reproductive phases to the wetter, short-day growing season (Ribaut et al. 1996; Campos et al. 2006). A critical event in the postdomestication evolution of maize was its spread from tropical to temperate regions of the Americas (Goodman 1988), requiring adaptation to longer day lengths. The result of this adaptation process is manifested today as a major genetic differentiation between temperate and tropical maize (Liu et al. 2003) and substantially reduced photoperiod sensitivity of temperate maize (Gouesnard et al. 2002). Tropical maize exhibits delayed flowering time, increased plant height, and a greater total leaf number when grown in temperate latitudes with daily dark periods <11 hr (Allison and Daynard 1979; Warrington and Kanemasu 1983a,b). Identifying the genes underlying maize photoperiod sensitivity will provide insight into the postdomestication evolution of maize and may reduce barriers to the use of diverse tropical germplasm resources for improving temperate maize production (Holland and Goodman 1995; Liu et al. 2003; Ducrocq et al. 2009).Natural variation at key genes in flowering time pathways is related to adaptation and evolution of diverse plant species (Caicedo et al. 2004; Shindo et al. 2005; Turner et al. 2005; Cockram et al. 2007; Izawa 2007; Slotte et al. 2007). Identification of some of the genes controlling adaptation in numerous plant species relied on regulatory pathways elucidated in Arabidopsis (Simpson and Dean 2002). Many key genes in the Arabidopsis flowering time regulatory pathways are conserved across diverse plant species (Kojima et al. 2002; Hecht et al. 2007; Kwak et al. 2008), but their functions have diverged, resulting in unique regulatory pathways in some phylogenetic groups (Colasanti and Coneva 2009). For example, FRI and FLC control most natural variation for vernalization response in Arabidopsis (Caicedo et al. 2004; Shindo et al. 2005), but wheat and barley appear to lack homologs of these genes and regulate vernalization response with different genes (Yan et al. 2004).Maize exhibits tremendous natural variation for flowering time (Gouesnard et al. 2002; Camus-Kulandaivelu et al. 2006), for which numerous QTL have been identified (Chardon et al. 2004). In contrast, only a few flowering time mutants are known and only a handful of flowering time genes, including DWARF8 (D8), DELAYED FLOWERING1 (DLF1), VEGETATIVE TO GENERATIVE TRANSITION1 (VGT1), and INDETERMINATE GROWTH1 (ID1), have been cloned in maize (Thornsberry et al. 2001; Colasanti et al. 2006; Muszynski et al. 2006; Salvi et al. 2007; Colasanti and Coneva 2009). Variation at or near D8 and VGT1 is related to latitudinal adaptation, but these genes do not appear to regulate photoperiod responses and account for only a limited proportion of the standing flowering time variation in maize (Camus-Kulandaivelu et al. 2006, 2008; Ducrocq et al. 2008; Buckler et al. 2009).Quantitative trait loci (QTL) mapping was a key first step to identifying the genes underlying natural variation for flowering time in Arabidopsis (Koornneef et al. 2004). Photoperiodic QTL have been mapped previously in individual biparental maize mapping populations (Koester et al. 1993; Moutiq et al. 2002; Wang et al. 2008; Ducrocq et al. 2009). Such studies are informative with respect to the parents from which the populations were derived, but often do not reflect the genetic heterogeneity of broader genetic reference populations (Holland 2007).Association mapping (Thornsberry et al. 2001; Ersoz et al. 2007) and combined analysis of multiple biparental crosses (Rebaï et al. 1997; Rebaï and Goffinet 2000; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008) represent alternative approaches to understanding the variation in genetic control for complex traits among diverse germplasm. Association mapping has limited power to identify genes that affect traits closely associated with population structure, such as flowering time in maize (Camus-Kulandaivelu et al. 2006; Ersoz et al. 2007). In contrast, joint QTL analysis of multiple populations is not hindered by the associations between causal genes and population structure. Combined QTL analysis of multiple mapping populations provides improved power to detect QTL, more precise estimation of their effects and positions, and better understanding of their functional allelic variation and distribution across more diverse germplasm compared to single-population mapping (Rebaï et al. 1997; Wu and Jannink 2004; Jourjon et al. 2005; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008; Buckler et al. 2009). Joint analysis also provides a direct test of the importance of higher-order epistatic interactions between founder alleles at individual loci with genetic backgrounds (Jannink and Jansen 2001; Blanc et al. 2006). In this study, joint analysis of multiple populations was used to test directly the hypothesis that diverse tropical maize lines carry functionally similar alleles at key photoperiod loci, which would imply genetic homogeneity for a common set of mutations and a shared evolutionary pathway for photoperiod insensitivity.The objective of this study was to integrate candidate gene analyses with photoperiod QTL mapping across multiple maize populations. We tested candidate floral regulators known from other species for associations with natural variation for photoperiod response in maize. We analyzed flowering time in four interrelated recombinant inbred line (RIL) populations, each derived from crosses between temperate and tropical maize parents (Figure 1), in both long- and short-day environments to characterize their responses to distinct photoperiods. Joint population analysis provided high resolution of many QTL positions, permitting robust testing of underlying candidate genes. We directly and indirectly mapped homologs of flowering time candidates genes from Arabidopsis, rice, and barley on a dense consensus genetic map of these four populations, permitting identification of homologs that colocalize with genome regions associated with variation for photoperiod response. These mapping families are being integrated into the maize nested association mapping (NAM) population (Buckler et al. 2009; McMullen et al. 2009) because they were genotyped with the maize NAM map SNP markers, they involve the common parent B73, and their seed and genotypic information (File S1 cont.) are publicly available. Their availability further expands the genetic diversity represented by the maize NAM population and enhances this valuable public community resource.Open in a separate windowFigure 1.—Factorial mating of two temperate (B73 and B97) and two tropical (CML254 and Ki14) inbred maize lines to create four related recombinant inbred line mapping populations.     

Massive parallel MHC genotyping: titanium that shines

The power of population genetic analyses is often limited by sample size resulting from constraints in financial resources and time to genotype large numbers of individuals. This particularly applies to nonmodel species where detailed genomic knowledge is lacking. Next‐generation sequencing technology using primers ‘tagged’ with an individual barcode of a few nucleotides offers the opportunity to genotype hundreds of individuals at several loci in parallel ( Binladen et al. 2007 ; Meyer et al. 2008 ). The large number of sequence reads can also be used to identify artefacts by frequency distribution thresholds intrinsically determined for each run and data set. In Babik et al. (2009 ), next‐generation deep sequencing was used to genotype several major histocompatibility complex (MHC) class IIB loci of the European bank vole ( Fig. 1 ). Their approach can be useful for many researchers working with complex multiallelic templates and large sample sizes.

Figure 1 Open in figure viewer PowerPoint Hypothetical example of parallel genotyping of two individuals using individually bar‐coded primers. Polymerase chain reactions (PCRs) are performed separately for each individual using a forward primer with a unique Tag‐sequence of four nucleotides. After sequencing of pooled PCR products, sequences can be sorted by their forward primer Tag (Tag‐sorting error rate was estimated < 0.1%). Rare sequences most likely represent artefacts and due to the large amount of sequences obtained (up to 10⁶) the artefact threshold can be determined intrinsically for each data set and was estimated to be around 3% in the case of bank vole MHC class IIB genes ( Babik et al. 2009 ). Photos by Gabriela Bydlon.     

Introgression of Drosophila simulans Nuclear Pore Protein 160 in Drosophila melanogaster Alone Does Not Cause Inviability but Does Cause Female Sterility

We have been analyzing genes for reproductive isolation by replacing Drosophila melanogaster genes with homologs from Drosophila simulans by interspecific backcrossing. Among the introgressions established, we found that a segment of the left arm of chromosome 2, Int(2L)S, carried recessive genes for hybrid sterility and inviability. That nuclear pore protein 160 (Nup160) in the introgression region is involved in hybrid inviability, as suggested by others, was confirmed by the present analysis. Male hybrids carrying an X chromosome of D. melanogaster were not rescued by the Lethal hybrid rescue (Lhr) mutation when the D. simulans Nup160 allele was made homozygous or hemizygous. Furthermore, we uniquely found that Nup160 is also responsible for hybrid sterility. Females were sterile when D. simulans Nup160 was made homozygous or hemizygous in the D. melanogaster genetic background. Genetic analyses indicated that the D. simulans Nup160 introgression into D. melanogaster was sufficient to cause female sterility but that other autosomal genes of D. simulans were also necessary to cause lethality. The involvement of Nup160 in hybrid inviability and female sterility was confirmed by transgene experiment.INVESTIGATING the genetic bases of reproductive isolation is important for understanding speciation (Sawamura and Tomaru 2002; Coyne and Orr 2004; Wu and Ting 2004; Noor and Feder 2006; Presgraves 2010). In fact, continued interest in this issue has led to the isolation of several genes that are responsible for hybrid sterility and inviability in Drosophila (Ting et al. 1998; Barbash et al. 2003; Presgraves et al. 2003; Brideau et al. 2006; Masly et al. 2006; Phadnis and Orr 2009; Prigent et al. 2009; Tang and Presgraves 2009). Drosophila melanogaster and Drosophila simulans are the best pair for such genetic analyses (Sturtevant 1920). Hybrid male lethality in the cross between D. melanogaster females and D. simulans males is caused by incompatibility involving chromatin-binding proteins (Barbash et al. 2003; Brideau et al. 2006), and hybrid female lethality in the reciprocal cross is caused by incompatibility between a maternally supplied factor and a repetitive satellite DNA (Sawamura et al. 1993a; Sawamura and Yamamoto 1997; Ferree and Barbash 2009). Furthermore, individuals with the genotype equivalent to the backcrossed generation exhibit different incompatibilities (Pontecorvo 1943; Presgraves 2003), two components of which have been identified (Presgraves et al. 2003; Tang and Presgraves 2009). Because of the discovery of rescuing mutations that prevent hybrid inviability and sterility (Watanabe 1979; Hutter and Ashburner 1987; Sawamura et al. 1993a,b; Davis et al. 1996; Barbash and Ashburner 2003), chromosome segments from D. simulans can be introgressed into the D. melanogaster genome (Sawamura et al. 2000; Masly et al. 2006). For example, introgression of the D. simulans chromosome 4 or Y into D. melanogaster results in male sterility (Muller and Pontecorvo 1940; Orr 1992), and the recessive sterility by the chromosome 4 introgression is attributed to an interspecific gene transposition between chromosomes (Masly et al. 2006).The other successful introgressions of this type are the tip and the middle regions of the left arm of chromosome 2, Int(2L)D and Int(2L)S, respectively (Sawamura et al. 2000). Both female and male Int(2L)S homozygotes are sterile (Figure 1A), and the recessive sterility genes have been mapped with recombination and complementation assays against deficiencies. The male sterility genes are polygenic and interact epistatically with each other (Sawamura and Yamamoto 2004; Sawamura et al. 2004b), but the female sterility gene has been mapped to a 170-kb region containing only 20 open reading frames (ORFs) (Sawamura et al. 2004a). Interestingly, Int(2L)S also carries a recessive lethal gene whose effect is detected only in a specific genotype (Figure 1B). Lethality in hybrid males from the cross between D. melanogaster females and D. simulans males is rescued by the Lethal hybrid rescue (Lhr) mutation in D. simulans (Watanabe 1979), but the hybrid males cannot be rescued if they carry the introgression, presumably because of incompatibility between an X-linked gene(s) of D. melanogaster and a homozygous D. simulans gene in the Int(2L)S region (Sawamura 2000). Because the female sterility gene and the lethal gene were not separated by recombination, Sawamura et al. (2004a) suggested that female sterility and lethality may be a consequence of the pleiotropic effects of a single gene.Open in a separate windowFigure 1.—Viability and fertility of flies with various genotypes. (A) Females and males that are heterozygous or homozygous for the D. simulans introgression Int(2L)S (Int) in the D. melanogaster genetic background. (B) Four genotypic classes from the cross between introgression heterozygote [Int(2L)S/CyO] females and D. simulans Lethal hybrid rescue (Lhr) males. (C) Four genotypic classes from the cross between D. melanogaster females with a deficiency (Df) [Df(2L)/CyO] and D. simulans Lhr males. Open chromosome regions are from D. melanogaster, and shaded ones are from D. simulans.Because the hybrid lethal gene on Int(2L)S is recessive, the gene can be mapped by deficiencies instead of using introgression (Figure 1C) (Sawamura 2000; Sawamura et al. 2004a). In fact, hybrid males carrying a deficiency encompassing this region (hemizygous for the D. simulans genes) and the D. melanogaster X chromosome are lethal even if they carry the hybrid rescue mutation (see also Presgraves 2003). Tang and Presgraves (2009) subsequently narrowed down this region with multiple deficiencies and identified the hybrid lethal gene with a complementation test and transformation. We confirmed their conclusion and report our data here. In the transformation experiment, we used the natural promoter of the gene, instead of overexpressing the gene (Tang and Presgraves 2009), and we directly indicated, for the first time, that the hybrid lethal gene is also responsible for the female sterility of introgression homozygotes. The D. simulans allele of the gene seems to be nonfunctional on the genetic background of D. melanogaster. Moreover, our results indicated that this gene and chromosome X of D. melanogaster are not sufficient to explain the inviability and that another autosomal gene(s) in D. simulans is required.     

Evolution of a Distinct Genomic Domain in Drosophila: Comparative Analysis of the Dot Chromosome in Drosophila melanogaster and Drosophila virilis

The distal arm of the fourth (“dot”) chromosome of Drosophila melanogaster is unusual in that it exhibits an amalgamation of heterochromatic properties (e.g., dense packaging, late replication) and euchromatic properties (e.g., gene density similar to euchromatic domains, replication during polytenization). To examine the evolution of this unusual domain, we undertook a comparative study by generating high-quality sequence data and manually curating gene models for the dot chromosome of D. virilis (Tucson strain 15010–1051.88). Our analysis shows that the dot chromosomes of D. melanogaster and D. virilis have higher repeat density, larger gene size, lower codon bias, and a higher rate of gene rearrangement compared to a reference euchromatic domain. Analysis of eight “wanderer” genes (present in a euchromatic chromosome arm in one species and on the dot chromosome in the other) shows that their characteristics are similar to other genes in the same domain, which suggests that these characteristics are features of the domain and are not required for these genes to function. Comparison of this strain of D. virilis with the strain sequenced by the Drosophila 12 Genomes Consortium (Tucson strain 15010–1051.87) indicates that most genes on the dot are under weak purifying selection. Collectively, despite the heterochromatin-like properties of this domain, genes on the dot evolve to maintain function while being responsive to changes in their local environment.EUKARYOTIC genomes are packaged into two major types of chromatin: euchromatin is gene rich and has a diffuse appearance during interphase, while heterochromatin is gene poor and remains densely packaged throughout the cell cycle (Grewal and Elgin 2002). The distal 1.2 Mb of the fourth chromosome of Drosophila melanogaster, known as the dot chromosome or Muller F element, is unusual in exhibiting an amalgamation of heterochromatic and euchromatic properties. This domain has a gene density that is similar to the other autosomes (Bartolomé et al. 2002; Slawson et al. 2006). However, it appears heterochromatic by many criteria, including late replication and very low levels of meiotic recombination (Wang et al. 2002; Arguello et al. 2010). It exhibits high levels of association with heterochromatin protein 1 (HP1) and histone H3 di- and trimethylated at lysine 9 (H3K9me2/3), as shown by immunofluorescent staining of the polytene chromosomes (Riddle and Elgin 2006; Slawson et al. 2006). This association with heterochromatin marks has recently been confirmed by the modENCODE Project [N. C. Riddle, A. Minoda, P. V. Kharchenko, A. A. Alekseyenko, Y. B. Schwartz, M. Y. Tolstorukov, A. A. Gorchakov, C. Kennedy, D. Linder-Basso, J. D. Jaffe, G. Shanower, M. I. Kuroda, V. Pirrotta, P. J. Park, S. C. R. Elgin, G. H. Karpen, and the modENCODE Consortium (http://www.modencode.org), unpublished results]. To understand this unique domain and to examine the evolution of a region with very low levels of recombination, we have undertaken a comparative study using the dot chromosome of D. virilis, a species that diverged from D. melanogaster 40–60 million years ago (Powell and Desalle 1995). We sequenced and improved the assembly of the D. virilis dot chromosome and created a manually curated set of gene models to ensure that both the assembly and the gene annotations are at a quality comparable to those in D. melanogaster. We then compared the sequence organization and gene characteristics of the distal portion of the D. virilis dot chromosome with the corresponding region from the D. melanogaster dot chromosome.In addition to examining the long-term dot chromosome evolution, we also investigated the short-term dot chromosome evolution by comparing the genomic sequences from two different strains of D. virilis. Agencourt Biosciences (AB) has previously produced a whole genome shotgun assembly of Tucson strain 15010–1051.87, while we have sequenced Tucson strain 15010–1051.88 of D. virilis [the Genomics Education Partnership (GEP) assembly]. The AB assembly has been improved by the Drosophila 12 Genomes Consortium and released as part of the comparative analysis freeze 1 (CAF1) assembly (Drosophila 12 Genomes Consortium et al. 2007).Using the GEP and CAF1 assemblies from D. virilis, and the high-quality D. melanogaster assembly and its gene annotations from FlyBase (Crosby et al. 2007), we compared the gene properties and sequence organization of the dot chromosomes and reference euchromatic and heterochromatic domains. The dot chromosomes from D. melanogaster and D. virilis are distinct from the heterochromatic and euchromatic regions of the two genomes, both in organization (e.g., repeat density) and in characteristics of the genes (e.g., size, codon bias). The two dot chromosomes resemble each other by most criteria and differ only in the types of repetitive sequences present and in relative gene order and orientation. Despite the very low rate of meiotic recombination, comparison of the two D. virilis strains shows that dot chromosome genes are under weak purifying selection. Our analysis of genes that are present in a euchromatic chromosome arm in one species and on the dot chromosome in the other (the “wanderer” genes) shows that this set of genes evolves to maintain function while responding to the changes in the local chromosomal environment.     

On Relevance of Codon Usage to Expression of Synthetic and Natural Genes in Escherichia coli

A recent investigation concluded that codon bias did not affect expression of green fluorescent protein (GFP) variants in Escherichia coli, while stability of an mRNA secondary structure near the 5′ end played a dominant role. We demonstrate that combining the two variables using regression trees or support vector regression yields a biologically plausible model with better support in the GFP data set and in other experimental data: codon usage is relevant for protein levels if the 5′ mRNA structures are not strong. Natural E. coli genes had weaker 5′ mRNA structures than the examined set of GFP variants and did not exhibit a correlation between the folding free energy of 5′ mRNA structures and protein expression.IN genomes, natural selection may act on silent sites of codons to make translation of highly expressed genes more efficient, an effect linked primarily to abundances of tRNA isoacceptor molecules (Ikemura 1985; Bulmer 1987; Kanaya et al. 1999). Codon choice may also be linked to formation of secondary structures in mRNA that reduce protein levels, as has been shown with haplotypes of the human COMT gene (Nackley et al. 2006). Kudla et al. (2009) have recently reported an experiment that contributes toward understanding how synonymous codon usage shapes gene expression. They have constructed a library of 154 synthetic variants of a green fluorescent protein (GFP) gene that varied randomly at synonymous sites while retaining the original amino acid sequence. The authors concluded that codon usage (CU) bias did not correlate with protein levels measured as fluorescence of the GFP, but also that the minimum free energy of a mRNA secondary structure in a 42-nucleotide region at [−4,37] that overlaps the start codon (“hairpin stability”) bears a great significance. CU bias was quantified by the widely used codon adaptation index (CAI) method (Sharp and Li 1987), essentially a measure of the distance of a gene''s codon usage to the codon usage of a predefined set of highly expressed genes. The CAI and some of its more recent alternatives, such as measure independent of length and composition (MILC) (Supek and Vlahovicek 2005), have been shown to be a viable surrogate for gene expression in various unicellular organisms. Additionally, in a multiple linear regression of rank fluorescence against a number of sequence-derived attributes, including CAI and the abovementioned hairpin stability, Kudla et al. (2009) did not find CAI to contribute significantly toward the prediction of protein levels, in contrast to the hairpin stability.

Both the codon adaptation index and the 5′ mRNA secondary structures influence protein levels in the Kudla et al. data:

The described statistical analyses, however, failed to address the case in which a nonlinear three-way dependency between hairpin stability, codon usage, and fluorescence might exist; data are visualized in Figure 1, A–C, and in figure 2B in Kudla et al. Such complex patterns in data are readily captured by the support vector machines (SVM) algorithm, reviewed in Noble (2006) and Ben-Hur et al. (2008). We have employed the SVM with a radial basis function kernel to regress fluorescence against both hairpin stability and CAI simultaneously (Figure 1B) and computed the Pearson''s correlation coefficient in cross-validation (here denoted as Q) between true and predicted values of fluorescence (See File S1). A linear model based solely on hairpin stability as employed by Kudla et al. (Figure 1A) can explain Q² = 38.6% of variance in protein levels, while the nonlinear SVM regression that takes CAI into account explains Q² = 52.2% of variance. The difference in Q is statistically significant at P = 10⁻¹⁹⁰ (paired t-test). Note that Kudla et al. utilize the Spearman rank correlation coefficient (ρ) in their article; the hairpin stability would explain ρ² = 44.6% of the variance in expression levels if the requirement for a linear relationship was abandoned in this manner.Open in a separate windowFigure 1.—Regression of protein levels against folding free energy of an mRNA hairpin at nucleotides −4 through 37 (A), against the hairpin free energy and the codon adaptation index (Sharp and Li 1987) (B and C), or against the hairpin free energy and the codon frequencies (D and E). The colors show the measured protein levels, while the background shading reflects the protein levels predicted by the specific model. (A) Predictions by linear regression. (B and E) Predictions by a support vector machine with a radial basis function kernel. (C) Predictions by an M5′ regression tree. (D) A schematic of the M5′ model, where coefficients in the terminal nodes are derived from data where protein levels, all codon frequencies, and hairpin free energies were normalized to [0,1] to facilitate comparison between the influence of codons, the hairpin stability, and the constant in the regression equation. All coefficients ≥0.1 are in boldface type. In the plots (A–C and E), a slight amount of random “jitter” was introduced to the point positions (at most, 3% of the range of each axis) to better visualize overlapping points. In the plot in E, a single outlying point is not shown. See Figure S2 for the same plots without jitter and with the outlier in E included. R² is the squared Pearson''s correlation coefficient between actual and model-predicted protein levels; Q² is similar, but obtained in cross-validation (10-fold, 100 runs), and is a more conservative estimate of regression accuracy.Open in a separate windowFigure 2.—The distributions of RNA folding free energies of a 42-nucleotide window in the mRNA between positions −4 and +37, where the “A” in the “AUG” start codon has index zero. The distributions are shown separately for the 154 gene variants from Kudla et al. (2009) and for the genes from the E. coli K12 genome. The dotted line indicates the 5th percentile of the E. coli values at −10.9 kcal/mol.Compared to the SVM, a more interpretable generalization of the data can be achieved by a different nonlinear regression approach, the M5′ tree (Wang and Witten 1997), which recursively divides the data to reduce the variance of the dependant variable within each partition and then builds separate linear models for the partitions. The resulting regression tree (Figure 1C; supporting information, Figure S1) better explains the correlation between protein levels on one side and hairpin stability and CAI on the other side when compared to a linear model employed by Kudla et al. that regresses protein levels against hairpin stability only [see figure 2B in Kudla et al. (2009) and Figure 1A]; 9.3% more variance is explained by the M5′, P = 10⁻⁹¹ (paired t-test). An interpretation that follows from the general structure of the M5′ tree (Figure S1) is that, at high mRNA hairpin stability, protein levels will generally be quite low and not dependant on CAI; in contrast, with less stable mRNA hairpins, both hairpin stability and CAI play a role in determining protein levels. In the interpretation of the M5′ tree structure, we would place less emphasis on the exact coefficients of the linear models in the leaves because the reliability of these fine-grained features of the M5′ model can strongly depend on the good coverage of all parts of the mRNA–CAI space data points.

The CAI may not be an optimal summary of codon usage for predicting expression of overexpressed genes:

Regarding use of CAI in the present context, it should be noted that CAI''s original purpose was to serve as a proxy for gene expression in conditions of abundance that result in fast growth in the organism''s environmental niche. The CAI or related approaches (Supek and Vlahovicek 2005) may not, however, be an ideal representation of codon usage when examining overexpression of a foreign protein at levels that exceed the natural abundances of the host''s most highly expressed proteins. This was indeed shown to be the case in a recent article by Welch et al. (2009) in which the authors reported an experiment with heterologous expression of variants of two proteins in E. coli: an antibody fragment and a phage DNA polymerase. Welch et al. found that codon frequencies in general, but not CAI specifically, correlated well with protein levels and postulated that for overexpressed proteins optimal codons would correspond to the codons translated efficiently under amino acid starvation (Elf et al. 2003; Dittmar et al. 2005). Analogously to Welch et al., we now apply our regression algorithms not to the CAI, but directly to the codon frequencies that CAI attempts to summarize in the Kudla et al. data (See File S2). An M5′ regression tree trained on the hairpin stability and codon frequencies (Figure 1D) explains 10.6% more variance (P = 10⁻⁸³, paired t-test) in protein levels than an M5′ tree trained on hairpin stability and CAI (Figure 1C, Figure S1). A SVM regression model trained on the hairpin stability and a simple linear combination of selected codon frequencies (Figure 1E) explains 8.8% more variance (P = 10⁻⁸², paired t-test) than the SVM that uses CAI (Figure 1B). An SVM trained on the hairpin stability and the full set of codon frequencies (not shown in Figure 1) explains Q² = 65.0% of variance in the protein abundances, a sizable increase (P ≈ 10⁻²⁶⁰, paired t-test) compared to a linear regression on solely the [−4,37] hairpin stability (Q² = 38.6%) as originally employed by Kudla et al. and also as compared to a set of randomized controls (Q² = 20.1–30.7%; Table S1). Therefore, not relying on a predefined notion of codon optimality—as embodied in the CAI—further strengthens the argument that the correlation of CU and protein levels is far from negligible in this data set.Additionally, we found some correlation between codon frequencies and 5′ mRNA hairpin stability in the Kudla et al. gene variants (Figure S4). The fact that the two factors were not completely independent adds weight to the relevance of CU to protein levels since one could not be certain that even the variance in protein levels explained by 5′ mRNA structures is wholly due to the structures themselves and not to the confounding variables—here, the codon frequencies.The M5′ tree trained on codon frequencies (Figure 1D) follows the same general structure as the M5′ tree trained on the CAI (Figure S1) where the codon frequencies become relevant with mRNA hairpins weaker than −9.75 kcal/mol, while with stronger [−4,37] mRNA hairpins protein levels are generally low. Our interpretation is that the lack of a stable secondary structure that could obstruct translational initiation is a necessary but not a sufficient condition for high protein expression. When the initiation phase is unhindered, the bottleneck would shift to the elongation phase in which codon optimality plays an important role. In the literature, theoretical models of translation may consider either the initiation (Bulmer 1991) or the elongation phase (Xia 1998) as the rate-limiting step of translation under physiological conditions; we are not aware of such analyses describing translation of artificially overexpressed genes.The codons identified as relevant by our M5′ model of the Kudla et al. data are different from, but not inconsistent with, those proposed by Welch et al. (Table S2). We anticipate that the rules for codon optimality for overexpression in an Escherichia coli host will become better defined as more large-scale experiments, such as the two discussed here (Kudla et al. 2009; Welch et al. 2009), are carried out.

The “RNA structure + codon usage” model agrees with independent experimental data and is robust to removal of extreme values:

Our reanalysis of the Kudla et al. data should be viewed in light of the conclusions of Welch et al. (2009) who find that codon usage, but not the 5′ hairpin stability, correlates with protein levels in their data, while noting that their gene variants generally have considerably weaker 5′ mRNA hairpins than the sequences in Kudla et al. Welch et al. reconcile the different outcomes of the two experiments by noting that “inhibition of initiation by especially strong mRNA structure would obscure effects resulting from factors that influence elongation, such as codon usage” (page 9). Here we propose that precisely the same model can be derived solely from the Kudla et al. data. Furthermore, we find that the 154 gene variants from Kudla et al. indeed do have unusually stable 5′ mRNA hairpins (mean free energy = −9.68 kcal/mol) in comparison to natural E. coli genes (mean free energy = −6.15 kcal/mol) (P = 10⁻³⁸ by Mann–Whitney U-test; see Figure 2). The part of the distribution of Kudla et al. gene variants that overlaps with the bulk of the E. coli genes, with 5′ mRNA hairpin free energies lower than ∼ −10 kcal/mol, corresponds to the range where our M5′ model indicates a stronger influence of CU on protein levels (Figure S1, Figure 1D).We investigate to what extent the presence of a group of sequences extreme in their 5′ mRNA hairpin stabilities in the Kudla et al. data set (left peak in Figure 2) influenced the authors'' conclusion that the hairpin stabilities have an overarching influence on protein levels. After removing the sequences below the 5th percentile of the E. coli natural hairpin stabilities (−10.9 kcal/mol), we were left with 109 of the original 154 Kudla et al. sequences. The accuracy of regressing protein levels against mRNA hairpin stability deteriorates greatly (Q² = 18.5%) after removing the 45 sequences, but less so with SVM and M5′ regression that take into account both CU and the hairpin stability (udla et al. basically captured the difference between these extreme cases—in which very strong 5′ mRNA secondary structures blocked expression—and all other sequences. However, to explain the variation in protein levels within the nonextreme set, hairpin stabilities by themselves are not sufficient and need to be complemented with CU.

TABLE 1

Accuracy of the regression of protein levels against 5′ mRNA hairpin stability or against 5′ mRNA hairpin stability and codon frequencies

Data set	Linear regression, hairpin stability only (%)	SVM, hairpin stability + codon frequencies (%)	M5′, hairpin stability + codon frequencies (%)
Full (n = 154)	38.6	65.0	56.7
No strong hairpins (n = 109)	18.5	53.0	40.4

Open in a separate windowThe cross-validation correlation coefficient squared (Q²) is compared with the full Kudla et al. data set (154 proteins) and the reduced data set (109 proteins) where mRNA hairpin folding energies are ≥ −10.9 kcal/mol, the 5th percentile of natural E. coli genes.In addition to measuring protein levels in the 154-sequence data set, Kudla et al. performed an additional experiment where an unstructured 28-codon tag was fused to 5′ ends of 72 (of 154) GFP sequence variants. Adding the tag was found to enhance protein levels, supporting the conclusion of Kudla et al. that 5′ structure of mRNA had a strong influence on protein production. After an analysis of the data, we found (see File S3) that data from this specific experiment are not well suited to serve as a direct verification of our existing M5′ and SVM regression models. Still, we can compare the protein level predictions of our existing SVM model on the same set of sequences before and after adding the unstructured tag. We found that the predicted expression levels have increased for 67 of 72 sequences (Table S3) after adding the tag that fixes 5′ mRNA folding energy at a weak −6.1 kcal/mol, a result consistent with the Kudla et al. experiment. Additionally, we have trained a new SVM regression model only on the tagged 72-sequence set (See File S2) and found that, within this set, SVM regression can again predict GFP levels solely from codon usage (5′ mRNA structure is invariant among these sequences) at Q² = 37.7%. This amount of variance is similar, or even somewhat larger than, the difference in the variance explained by mRNA vs. mRNA+codons (38.6% vs. 65.0%) in the original data. Therefore, codon usage is of similar importance in shaping the protein levels within the tagged 72-sequence set, as it was in the original 154-sequence set.

mRNA 5′ end secondary structure stabilities do not correlate with protein levels for natural E.

coli genes: To further verify our proposed model, we analyzed the relative contributions of mRNA hairpin stabilities and CU on expression levels of natural E. coli genes (See File S2). If the hairpin stabilities were limiting for expression in the range of folding free energies spanned by the E. coli mRNAs, one would expect to see a correlation between the free energy of mRNA 5′ end folding and the abundance of the corresponding protein. We found no such correlation using the folding free energies of the [−4,37] mRNA region (Figure 3) or equal-sized regions centered around the start codon at [−20,21] or on the expected location of a Shine–Dalgarno sequence (Shultzaberger et al. 2001) at [−30,11] (see Figure S3). Unsurprisingly, CAI correlated well with protein levels (Figure 3) in all examined experimental data sets (Lopez-Campistrous et al. 2005; Lu et al. 2007; Ishihama et al. 2008). Therefore, within the boundaries of the mRNA folding free energies spanned by E. coli genes, the CU plays a dominant role in shaping gene expression (or the CU may possibly be shaped by the expression; see Concluding remarks). As for the stronger mRNA hairpins with < −11 kcal/mol, they are present in the Kudla et al. data, but are very rare in the E. coli genome, which could be explained by one of two scenarios: (i) Above a certain threshold, the mRNA hairpin stability may become so detrimental to expression that all the mutants having such hairpins are subject to very strong negative selection and therefore are absent from the genome. And/or (ii) the Kudla et al. data set may not be representative of the genes in the E. coli genome or the mutational processes they undergo; for example, the amino acid sequence of the GFP''s beginning might be unusually conducive to forming RNA hairpins. Unless further analyses prove differently, it seems reasonable to surmise that in natural E. coli genes mRNA secondary structures would shape expression if they were highly stable, consistent with the finding of a universal (albeit not particularly strong) trend toward avoidance of 5′ mRNA structures in genomes (Gu et al. 2010). However, it can also be concluded at this point—and with more confidence—that at lower secondary structure stabilities the CU has an overarching influence on expression. Such a model of expression-related gene sequence determinants in E. coli is fully consistent with our interpretation of the M5′ regression tree that we have derived from the Kudla et al. data.Open in a separate windowFigure 3.—Correlations between the E. coli absolute protein abundances measured in three independent experiments (Lopez-Campistrous et al. 2005; Lu et al. 2007; Ishihama et al. 2008) and the codon adaptation index (CAI) or the free energy of folding of a secondary structure in the mRNA [−4,37] region (in kcal/mol; more negative values denote a more stable RNA secondary structure). “ρ” is the Spearman''s rank correlation coefficient.

Concluding remarks:

We argue that Kudla et al. worked with a set of gene sequences in which strong mRNA secondary structures (that effectively abolished expression) were frequent enough to mask the relevance of codon frequencies on protein levels when examined only with linear regression methods. While mRNA secondary structures can certainly occur when designing synthetic genes, it is highly questionable to what extent Kudla et al.''s conclusion that CU is of little importance for expression would be generally valid for biotechnological applications, especially since we have shown that the influence of CU is nevertheless present even in the Kudla et al. data. What is beyond doubt, however, is that a strong 5′ mRNA secondary structure can be a roadblock in heterologous expression, and therefore the synthetic gene variants harboring such structures should be avoided. The more specific rules regarding the exact location of the hairpin on the gene sequence, the hairpin''s length, or the tolerable levels of folding free energy will have to be established by further experimentation.A recent algorithm for estimating the efficiency of ribosomal binding sites from the mRNA sequence (Salis et al. 2009) explicitly takes into account the folding free energy of RNA secondary structures, along with other factors. When protein overexpression is desired, the conclusions of Welch et al. and (by our reanalysis) the Kudla et al. data indicate that CU should be optimized in addition to the ribosome binding site sequence to ensure that both initiation and elongation phases of translation are free of impediments.On the basis of their results, Kudla et al. also discuss the evolutionary link between the CU of natural genes and the expression levels of proteins for which they code. They propose that selection for translational efficiency acts at a global level in cells; the codons that accelerate elongation would be preferred in a highly expressed gene not because they facilitate production of that particular protein, but to free up ribosomes for the rate-determining initiation phase of translation of the total cellular mRNA pool. Effectively, the flow of causality between CU and expression would be reversed in comparison to the established view. This hypothesis should be critically reevaluated because it depends on the assertion that manipulating a gene''s CU cannot cause protein levels to increase, an assertion poorly supported by the Kudla et al. data.     

Protein synthesis meets ABC ATPases: new roles for Rli1/ABCE1

The essential NTPase Rli1/ABCE1 has been implicated in translation initiation, ribosome biogenesis, and human immunodeficiency virus capsid assembly. Two recent papers by the Krebber and Pestova groups —the former published in this issue of EMBO reports— suggest new important roles of Rli1/ABCE1 in translation termination and ribosome recycling in eukaryotes.EMBO Rep (2010) 11: 3 214–219. doi:10.1038/embor.2009.272The essential, conserved NTPase Rli1/ABCE1—a member of the ABC (ATP-binding cassette) superfamily of ATPases—has been implicated in translation initiation, ribosome biogenesis and human immunodeficiency virus capsid assembly. Two recent papers by the Krebber and Pestova groups—the former published in this issue of EMBO reports—suggest new important roles of Rli1/ABCE1 in translation termination and ribosome recycling in eukaryotes (Khoshnevis et al, 2010; Pisarev et al, 2010).Two recent papers […] suggest new important roles of Rli1/ABCE1 in translation termination and ribosome recycling in eukaryotesProtein synthesis is divided into four phases—initiation, elongation, termination and ribosome recycling—which are catalysed by several translation factors. The fundamental reactions of protein synthesis, such as mRNA decoding, peptide bond formation and tRNA translocation, follow the same basic principles in prokaryotes and eukaryotes. However, some steps are quite different and require a larger set of factors in eukaryotes. The best-studied example of eukaryotic complexity is the initiation of protein synthesis. In prokaryotes, initiation is catalysed by only three factors—IF1, IF2 and IF3—whereas in mammals it requires at least 13. Two recent papers shed new light on termination and ribosome recycling in the yeast and mammalian systems, suggesting that these two steps are also different in eukaryotes and prokaryotes (Khoshnevis et al, 2010; Pisarev et al, 2010).…new [research] on termination and ribosome recycling in the yeast and mammalian systems [suggests] that these two steps are also different in eukaryotes and prokaryotesIn prokaryotes, translation termination is promoted by three release factors: RF1, RF2 and RF3. RF1 and RF2 recognize the three stop codons and catalyse the hydrolysis of the peptidyl-tRNA. RF3, a GTP-binding protein that is not essential in bacteria, does not participate in peptide release but, at the expense of GTP hydrolysis, promotes the dissociation of RF1 and RF2, thereby accelerating their turnover (Kisselev et al, 2003). To free the ribosome for initiation on another mRNA (a process known as recycling), the post-termination ribosome is disassembled in a step that requires ribosome recycling factor (RRF) and one of the elongation factors, the GTPase EF-G. Together, these factors promote the dissociation of the post-termination complex into subunits. The subsequent dissociation of tRNA and mRNA from the small ribosomal subunit is promoted by initiation factors, in particular IF3 (Peske et al, 2005).In eukaryotes, translation termination is mediated by only two factors: eRF1 recognizes all three termination codons and triggers the hydrolysis of peptidyl-tRNA, whereas eRF3 accelerates the process in a GTP-dependent manner (Fig 1; Alkalaeva et al, 2006). Unlike prokaryotic RF1 or RF2—which have no measurable affinity for RF3—eRF1 binds tightly to eRF3, and it is probably the complex of the two proteins that enters the ribosome. The mechanism of guanine nucleotide exchange on eRF3 is also different from that on prokaryotic RF3, suggesting that termination in eukaryotes and prokaryotes differs in almost every detail except, probably, the mechanism of peptidyl-tRNA hydrolysis itself. Nevertheless, the identification of an additional factor that facilitates termination was unexpected. In this issue of EMBO reports, Khoshnevis and colleagues use the power of yeast genetics to show that a protein named Rli1 (RNase L inhibitor 1) interacts physically with the termination factors eRF1 (known as Sup45 in yeast) and, to a lesser extent, eRF3 (Sup35; Khoshnevis et al, 2010). The downregulation of Rli1 expression increases stop codon read-through in a dual reporter system, indicating a lower efficiency of termination. Conversely, upregulation of Rli1 partly suppresses the increased read-through caused by certain mutations of eRF1. Although the mechanism by which Rli1 affects translation termination is not understood, the results of the Krebber lab provide strong evidence that Rli1 mediates the function of eRF1 and eRF3 in vivo (Fig 1).…the identification [in eukaryotes] of an additional factor that facilitates termination was unexpectedOpen in a separate windowFigure 1New roles of Rli1/ABCE1 in translation termination and ribosome recycling in eukaryotes. During termination, translating ribosomes contain peptidyl-tRNA (peptide is shown in dark blue and tRNA in dark red) in the P site and expose a stop codon in the A site. The stop codon is recognized by termination factor eRF1, which enters the ribosome together with eRF3-GTP. After GTP hydrolysis, catalysed by eRF3, the peptide is released from the peptidyl-tRNA with the help of eRF1. The point at which Rli1/ABCE1 binds to the ribosome is unknown, but the order shown is consistent with the effect of the factor on both termination and recycling. After NTP hydrolysis by Rli1/ABCE1, the 60S subunit and factors dissociate from the 40S subunit. Finally, tRNA and mRNA are released from the 40S subunit with the help of initiation factors (not shown). ABCE1, ATP-binding cassette, sub-family E member 1; eRF, eukaryotic release factor; Rli1, RNase L inhibitor 1.Surprisingly, modulating the efficiency of termination seems not to be the only function of Rli1 in translation. In a parallel study, Pestova and co-workers show that in higher eukaryotes, the homologue of Rli1—ABCE1—strongly enhances ribosome recycling (Pisarev et al, 2010). Eukaryotes lack a homologue of bacterial RRF and thus have to use other factors to disassemble the post-termination ribosome. Ribosome recycling can be brought about to some extent by eIF3, eIF1 and eIF1A (Pisarev et al, 2007), which is reminiscent of the IF3/IF1-mediated slow ribosome recycling that seems to occur in some conditions in bacterial systems. In eukaryotes, the initiation-factor-driven recycling operates only in a narrow range of low Mg²⁺ concentrations, probably because the affinity of the subunits to one another increases steeply with Mg²⁺ (Pisarev et al, 2010). By contrast, ABCE1 seems to catalyse efficient subunit dissociation in various conditions. To bind to the ribosome, ABCE1 requires the presence of eRF1, which is thought to induce a conformational change of the ribosome that unmasks the binding site for ABCE1. Subunit dissociation requires NTP (ATP, GTP, CTP or UTP) hydrolysis by ABCE1 (Fig 1). Subsequently, the dissociation of mRNA and tRNA from the small ribosomal subunit is promoted by initiation factors, which also inhibit the spontaneous reassociation of the subunits. Thus, the sequence of events during ribosome recycling in the eukaryotic system is remarkably similar to that in prokaryotes, and ABCE1 and eRF1 (possibly together with eRF3) seem to act as genuine ribosome recycling factors, similar to bacterial RRF/EF-G, despite the lack of any similarity in sequence or structure.Rli1/ABCE1 is a member of the ABC ATPases and comprises four structural domains (Karcher et al, 2008). Two nucleotide-binding domains (1 and 2) are connected by a hinge and arranged in a head-to-tail orientation. In contrast to other ABC enzymes, ABCE1 has an amino-terminal iron–sulphur (Fe–S) cluster domain, which is located in close proximity to, and presumably interacts with the nucleotide-binding loop of domain 1. Thus, there is a potential link between Fe–S domain function and NTP-induced conformational control of the ABC tandem cassette. Interestingly, although Khosnevis and colleagues map the eRF1 binding site on the second, carboxy-terminal ATPase domain, the Fe–S cluster is required for the function of Rli1/ABCE1 in termination and recycling (Khoshnevis et al, 2010). One might speculate that NTP hydrolysis is coupled to splitting the ribosome into subunits, in analogy to the prokaryotic recycling factors RRF/EF-G that couple the free energy of GTP hydrolysis and phosphate release into subunit dissociation (Savelsbergh et al, 2009). Kinetic experiments measuring single-round rates of subunit dissociation and NTP hydrolysis would be required to establish the existence and nature of such coupling.Another intriguing question is the role of the Fe–S cluster in Rli1/ABCE1. Fe–S protein biogenesis is the only known function of mitochondria that is indispensable for the viability of yeast cells (Lill, 2009). As yeast mitochondria do not contain essential Fe–S proteins, the essential character of the mitochondrial Fe–S protein assembly machinery could be attributed to its role in the maturation of extra-mitochondrial Fe–S proteins, such as Rli1/ABCE1, which is essential in all organisms tested.Another interesting finding by the Krebber group is that Rli1 can bind to Hcr1 (known as eIF3j in higher eukaryotes; Khoshnevis et al, 2010). Hcr1/eIF3j is an RNA-binding subunit of initiation factor eIF3, which is involved in initiation and required for Rli1/ABCE1-independent ribosome recycling. The fact that Rli1/ABCE1 binds to both eRF1 and Hcr1/eIF3j might indicate a functional or regulatory link between the termination, recycling and initiation machineries eukaryotes. It is unclear why eukaryotes require termination and recycling machinery that is so different from that of prokaryotes. One possibility is that Rli1/ABCE1, in contrast to its prokaryotic counterparts, not only acts in termination and recycling but also provides a platform for the recruitment of initiation factors to the ribosome, thereby acting as an additional checkpoint for translational control. Thus, the results of the Krebber and Pestova labs open a new, exciting avenue of research on eukaryotic protein synthesis.     

Genotype-by-Diet Interactions Drive Metabolic Phenotype Variation in Drosophila melanogaster

The rising prevalence of complex disease suggests that alterations to the human environment are increasing the proportion of individuals who exceed a threshold of liability. This might be due either to a global shift in the population mean of underlying contributing traits, or to increased variance of such underlying endophenotypes (such as body weight). To contrast these quantitative genetic mechanisms with respect to weight gain, we have quantified the effect of dietary perturbation on metabolic traits in 146 inbred lines of Drosophila melanogaster and show that genotype-by-diet interactions are pervasive. For several metabolic traits, genotype-by-diet interactions account for far more variance (between 12 and 17%) than diet alone (1–2%), and in some cases have as large an effect as genetics alone (11–23%). Substantial dew point effects were also observed. Larval foraging behavior was found to be a quantitative trait exhibiting significant genetic variation for path length (P = 0.0004). Metabolic and fitness traits exhibited a complex correlation structure, and there was evidence of selection minimizing weight under laboratory conditions. In addition, a high fat diet significantly increases population variance in metabolic phenotypes, suggesting decreased robustness in the face of dietary perturbation. Changes in metabolic trait mean and variance in response to diet indicates that shifts in both population mean and variance in underlying traits could contribute to increases in complex disease.METABOLIC syndrome (MetS) is a complex disease that is promoted by interactions between genetic and environmental effects (O''Rahilly and Farooqi 2006), and seems to be increasing in prevalence in response to a transition from traditional toward Westernized lifestyles (Lee et al. 2004; Schulz et al. 2006). MetS is a constellation of metabolic symptoms including insulin resistance, abdominal obesity, and dyslipidemia, and is predictive of cardiovascular disease and type-2 diabetes (Alberti et al. 2006). The condition has reached epidemic proportions in many Westernized countries (Isomaa et al. 2001; Ford and Giles 2003; Lorenzo et al. 2003; Alberti et al. 2006). Not all individuals are susceptible to the deleterious effects of a Westernized lifestyle, but some individuals are very sensitive to the effects of their environment (Schulz et al. 2006).We argued previously that environmental perturbation contributes to the recent increases in chronic disease in Westernized societies by exposing cryptic genetic variation, a phenomenon that may be particularly evident in metabolic syndrome (Gibson 2009). Increases in complex disease after an environmental shift can be caused by both a change in the population mean or increased variance in a predisposing underlying trait, or endophenotype, causing a larger portion of the population to exceed a disease threshold (Gibson and Reed 2008). Endophenotypes can be molecular, such as rate of uptake of glucose into cells, but also include visible disease covariates, such as body mass. The transition from traditional diets and lifestyles may have perturbed our metabolic homeostasis, thereby promoting increased susceptibility to, and in turn prevalence of, obesity, hyperlipidemia, diabetes, and cardiovascular illness.The complexity of genetic and environmental interactions leads to major challenges in successful disease treatment and prevention strategies, in that it is very difficult to accurately model the relative contributions of nature and nurture to disease susceptibility in a human population. Dietary factors have been demonstrated to interact with specific genetic variants to increase the risk of metabolic disease in humans (Corella and Ordovas 2005; Ordovas 2006; Corella et al. 2009; Warodomwichit et al. 2009), but the relative contribution of overall genotype and environmental effects on human variation is difficult to determine. Modeling population level genotype-by-environment interactions using a model organism like Drosophila can compensate for the research challenges of parameter estimation in human populations.Drosophila share great homology to humans in a number of systems including central metabolism, insulin-signaling pathways, and organs responsible for physiological homoeostasis (e.g., heart, liver, and kidney) (Rizki 1978; Bodmer 1995; Nation 2002; Rulifson et al. 2002; Denholm et al. 2003; Wessells et al. 2004). It has been shown that Drosophila with ablated insulin-producing neurons have elevated hemolymph trehalose levels, considered to parallel a diabetic phenotype (Rulifson et al. 2002). Loss of insulin signaling also restores normal rhythmicity of adult heart rate in old flies (Wessells et al. 2004), providing a link between the obesity and cardiac components of MetS. We have used 146 natural genetic isolates of Drosophila melanogaster to model the relative contributions of genetics, diet, and other environmental effects on the MetS-like phenotypes of larval weight gain, blood sugar concentration, lipid storage, and survival. Individuals from each of these genetic lines were raised on four different diets: their normal lab food, a calorie restricted (0.75% glucose) food, a high (4%) glucose food, and a high fat diet containing (3%) coconut oil.Using this approach, we sought evidence pertaining to two major hypotheses. There are six general types of phenotypic reaction scenarios that a genetically variable population can exhibit in response to an environmental transition: (1) no phenotypic variation in response to genetic or environmental factors, (2) genetic variation in mean phenotype but no change across environments, (3) an additive change in phenotypic mean across genotypes between environments, (4) an interaction effect between genetic and environment leading to a crossing of line means, and (5) a decrease or (6) an increase in variance in the new environment (Gibson and Vanhelden 1997). First, we considered whether the predominant source of metabolic variation within a Drosophila population is genetic, environmental, or the interaction between genetic and environmental effects. Our null was that none of these factors significantly influence weight gain or other phenotypes (scenario 1 above), but it was expected that genetic variation would be prevalent. The more fundamental issue is which of two alternate hypotheses apply: that dietary effects are essentially additive across genotypes (3) or that they are largely genotype dependent (4), possibly also with contributions of behavior and the external environment. Second, we considered whether the transition from a standard laboratory diet to a perturbing diet would change the environmental and/or genetic variance observed in the population: a decrease (5) indicating physiological limitation to the variation or an increase (6) indicating decanalization of the metabolic phenotypes due to loss of physiological buffering.     

Butterfly genomics sheds light on the process of hybrid speciation

How common is hybridization between species and what effect does it have on the evolutionary process? Can hybridization generate new species and what indeed is a species? In this issue, Gompert et al. (2014) show how massive, genome‐scale data sets can be used to shed light on these questions. They focus on the Lycaeides butterflies, and in particular, several populations from the western USA, which have characteristics suggesting that they may contain hybrids of two or more different species (Gompert et al. 2006). They demonstrate that these populations do contain mosaic genomes made up of components from different parental species. However, this appears to have been largely driven by historical admixture, with more recent processes appearing to be isolating the populations from each other. Therefore, these populations are on their way to becoming distinct species (if they are not already) but have arisen following extensive hybridization between other distinct populations or species (Fig.  1 ).

Figure 1 Open in figure viewer PowerPoint There has been extensive historical admixture between Lycaeides species with some new species arising from hybrid populations. (Photo credits: Lauren Lucas, Chris Nice, and James Fordyce).

Their data set must be one of the largest outside of humans, with over one and half thousand butterflies genotyped at over 45 thousand variable nucleotide positions. It is this sheer amount of data that has allowed the researchers to distinguish between historical and more recent evolutionary and demographic processes. This is because it has allowed them to partition the data into common and rare genetic variants and perform separate analyses on these. Common genetic variants are likely to be older while rare variants are more likely to be due to recent mutations. Therefore, by splitting the genetic variation into these components, the researchers were able to show more admixture among common variants, while rare variants showed less admixture and clear separation of the populations. The extensive geographic sampling of individuals, including overlapping distributions of several of the putative species, also allowed the authors to rule out the possibility that the separation of the populations was simply due to geographical distance. The authors have developed a new programme for detecting population structure and admixture, which does the same job as STRUCTURE (Pritchard et al. 2000 ), identifying genetically distinct populations and admixture between these populations, but is designed to be used with next generation sequence data. They use the output of this model for another promising new method to distinguish between contemporary and historical admixtures. They fixed the number of source populations in the model at two and estimated the proportion of each individual's genome coming from these two populations. Therefore, an individual can either be purely population 1, or population 2 or some mixture of the two (they call this value q, the same parameter exists in STRUCTURE). They then compared this to the level of heterozygosity coming from the two source populations in the individual's genome. If an individual is an F1 hybrid of two source populations, then it would have a q of 0.5 and also be heterozygous at all loci that distinguish the parental populations. On the other hand, if it is a member of a stable hybrid lineage, it might also have a q of 0.5 but would not be expected to be heterozygous at these loci, because over time the population would become fixed for one or other of the source population states either by drift or selection (Fig.  2 ). This is indeed what they find in the hybrid populations. They tend to have intermediate q values, but the level of heterozygosity coming from the source populations (which they call Q₁₂) was consistently lower than expected.

Figure 2 Open in figure viewer PowerPoint The Q‐matrix analysis used by Gompert et al. ( 2014 ) to distinguish between contemporary (hybrid swarm) and historical (stable hybrid lineage) admixture.

Overall, the results support several of the populations as being stable hybrid lineages. Nevertheless, the strictest definitions of hybrid species specify that the process of hybridization between the parental species must be instrumental in driving the reproductive isolation of the new species from both parental populations (Abbott et al. 2013 ). This is extremely hard to demonstrate conclusively because it requires us to first of all identify the isolating mechanisms that operated in the early evolution of the species and then to show that these were caused by the hybridization event itself. One advantage of the Lycaeides system is that the species appear to be in the early stages of divergence, so barriers to gene flow that are operating currently are likely to be those that are driving the species divergence. While there is some evidence that hybridization gave rise to traits that allowed the new populations to colonize new environments (Gompert et al. 2006 ; Lucas et al. 2008 ), there is clearly further work to be carried out in this direction. One of the rare examples of homoploid hybrid speciation (hybrid speciation without a change in chromosome number) where the reproductive isolation criterion has been demonstrated, comes from the Heliconius butterflies. In this case, hybridization of two species has been shown to give rise to a new colour pattern that instantly becomes reproductively isolated from the parental species due to mate preference for that pattern (Mavárez et al. 2006 ). However, while this has become a widely accepted example (Abbott et al. 2013 ), the naturally occurring ‘hybrid species’ in fact has derived most of its genome from one of the parental species, with largely just the colour pattern controlling locus coming from the other parent, a process that has been termed ‘hybrid trait speciation’ (Salazar et al. 2010 ). This distinction is an important one in terms of our understanding of the organization of biological diversity. While hybrid trait speciation will still largely fit the model of a neatly branching evolutionary tree, with perhaps only the region surrounding the single introgressed gene deviating from this model, hybrid species that end up with mosaic genomes, like Lycaeides, will not fit this model when considering the genome as a whole. This distinction also more broadly applies when comparing the patterns of divergence between Heliconius and Lycaeides. These two butterfly genera have been driving forward our understanding of the prevalence and importance of hybridization at the genomic level, but they reveal different ways in which hybridization can influence the organization of biological diversity. Recent work in Heliconius has shown that admixture is extensive and has been ongoing over a large portion of the evolutionary history of species (Martin et al. 2013 ; Nadeau et al. 2013 ). Nevertheless, this has not obscured the clear and robust pattern of a bifurcating evolutionary tree when considering the genome as a whole (Nadeau et al. 2013 ). In contrast in Lycaeides, the genome‐wide phylogeny clearly does not fit a bifurcating tree, resembling more of a messy shrub, with hybrid taxa falling at intermediate positions on the phylogeny (Gompert et al. 2014 ). The extent to which we need to rethink the way we describe and organize biological diversity will depend on the relative prevalence of these different outcomes of hybridization. We are likely to see many more of these types of large sequence data sets for ecologically interesting organisms. Gompert et al. ( 2014 ) show that these data need not only be a quantitative advance, but can also qualitatively change our understanding of the evolutionary history of these organisms. In particular, analysing common and rare genetic variants separately may provide information that would otherwise be missed. The emerging field of ‘speciation genomics’ (Seehausen et al. 2014 ) should follow this lead in developing new ways of making the most of the flood of genomic data that is being generated, but also improve methods for integrating this with field observations and experiments to identify the sources and targets of selection and divergence.

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This article was written and figures prepared by N.N. except as specified in the text (photo credits).

Citing Literature

Number of times cited according to CrossRef: 4

• V. Alex Sotola, David S. Ruppel, Timothy H. Bonner, Chris C. Nice, Noland H. Martin, Asymmetric introgression between fishes in the Red River basin of Texas is associated with variation in water quality, Ecology and Evolution, 10.1002/ece3.4901, 9 , 4, (2083-2095), (2019). Wiley Online Library
• Matej Bocek, Dominik Kusy, Michal Motyka, Ladislav Bocak, Persistence of multiple patterns and intraspecific polymorphism in multi-species Müllerian communities of net-winged beetles, Frontiers in Zoology, 10.1186/s12983-019-0335-8, 16 , 1, (2019). Crossref
• Nicola J. Nadeau, Takeshi Kawakami, Population Genomics of Speciation and Admixture, , 10.1007/13836_2018_24, (2018). Crossref
• Amanda Roe, Julian Dupuis, Felix Sperling, Molecular Dimensions of Insect Taxonomy in the Genomics Era, Insect Biodiversity, 10.1002/9781118945568, (547-573), (2017). Wiley Online Library

Volume 23 , Issue 18 September 2014

Pages 4441-4443