Flagellated Algae Protein Evolution Suggests the Prevalence of Lineage-Specific Rules Governing Evolutionary Rates of Eukaryotic Proteins

Abstract

Understanding the general rules governing the rate of protein evolution is fundamental to evolutionary biology. However, attempts to address this issue in yeasts and mammals have revealed considerable differences in the relative importance of determinants for protein evolutionary rates. This phenomenon was previously explained by the fact that yeasts and mammals are different in many cellular and genomic properties. Flagellated algae species have several cellular and genomic characteristics that are intermediate between yeasts and mammals. Using partial correlation analyses on the evolution of 6,921 orthologous proteins from Chlamydomonas reinhardtii and Volvox carteri, we examined factors influencing evolutionary rates of proteins in flagellated algae. Previous studies have shown that mRNA abundance and gene compactness are strong determinants for protein evolutionary rates in yeasts and mammals, respectively. We show that both factors also influence algae protein evolution with mRNA abundance having a larger impact than gene compactness on the rates of algae protein evolution. More importantly, among all the factors examined, coding sequence (CDS) length has the strongest (positive) correlation with protein evolutionary rates. This correlation between CDS length and the rates of protein evolution is not due to alignment-related issues or domain density. These results suggest no simple and universal rules governing protein evolutionary rates across different eukaryotic lineages. Instead, gene properties influence the rate of protein evolution in a lineage-specific manner.

Introduction

The general rules governing protein evolutionary rates have been studied not only because of their fundamental importance in molecular evolution but also for their broad implications in genomics, bioinformatics, and systems biology. For example, evolutionary sequence conservation has been widely used in identifying functional coding or noncoding regions in the genome that are important for an organism’s fitness (Boffelli et al. 2003; Elnitski et al. 2003; Thomas et al. 2003; Pennacchio et al. 2006). Recent studies have identified several gene properties, including gene essentiality (Hirsh and Fraser 2001; Jordan et al. 2002; Zhang and He 2005; Liao et al. 2006) and mRNA abundance (Pal et al. 2001; Rocha and Danchin 2004; Drummond and Wilke 2008; Slotte et al. 2011), that correlate with protein evolutionary rates in a wide phylogenetic spectrum. However, the relative importance of determinants for protein evolutionary rates varies widely between yeasts and mammals (Liao et al. 2006, 2010; Hudson and Conant 2011). In yeasts, mRNA abundance is the predominant factor determining the rate of protein evolution (Drummond et al. 2006), whereas in mammals, gene compactness, measured by averaged length of introns or untranslated regions (UTRs), has a stronger influence on protein evolutionary rates compared with the abundance of mRNA (Liao et al. 2006, 2010). This discrepancy between mammalian and yeast protein evolution was explained by the specialization of more than 150 cell types in mammals (Vogel and Chothia 2006) that requires additional layers of gene regulation (e.g., tissue specificity and alternative splicing) associated with multicellularity and organismal complexity (Schad et al. 2011) to influence protein evolution (Gu and Su 2007). Additionally, mammalian cells (10–100 µm diameter) are larger than yeast cells (3–4 µm diameter) and can better tolerate cell toxicity of misfolded proteins caused by mistranslation (Drummond et al. 2005; Liao et al. 2006). Furthermore, although more than 95% yeast genes are intron-less, more than 90% mammalian genes contain at least one intron, which can increase the efficacy of natural selection (Comeron and Kreitman 2002; Liao et al. 2006).

Chlamydomonas reinhardtii and Volvox carteri are flagellated algae that diverged approximately 200 Ma (Herron et al. 2009). These two algae have several cellular and genomic characteristics that are intermediate between yeasts and mammals. First, C. reinhardtii is a unicellular organism with two cell types, sexual or asexual (Rochaix 1995), whereas V. carteri is a colonial/multicellular organism composed of approximately 2,000 Chlamydomonas-like somatic cells and approximately 16 asexual reproductive cells called gonidia (Hallmann 2011). Second, C. reinhardtii and V. carteri cells are 8–10 µm in diameter (Kirk et al. 1993; Rochaix 1995). Third, unlike yeast genes, most C. reinhardtii genes (91%) and V. carteri genes (92%) have at least one intron (Merchant et al. 2007; Prochnik et al. 2010). Although most genes have an exon–intron structure similar to that of mammals (Waterston et al. 2002), the average intron size of C. reinhardtii genes (373 bp) (Merchant et al. 2007) is less than one-tenth of the average intron size (3,888 bp) of mouse genes (Waterston et al. 2002). Because of these intermediate characteristics, it is interesting to examine which gene properties are determinants of protein evolutionary rates in flagellated green algae.

To investigate protein evolutionary rates, we calculate nonsynonymous substitution rates (d_N) and the ratios of d_N to synonymous substitution rates (d_S) of approximately 7,000 C. reinhardtii–V. carteri one-to-one orthologous proteins. By examining how mRNA abundance, gene compactness, and other gene features are associated with d_N or d_N/d_S, we address 1) whether factors correlated with d_N (or d_N/d_S) in other eukaryotes are also correlated with d_N (or d_N/d_S) of flagellated green algae with similar trends, 2) whether gene compactness or mRNA abundance plays a greater role in determining evolutionary rates of flagellated algae proteins in terms of d_N or d_N/d_S, and 3) whether either gene compactness or mRNA abundance is the most important factor in determining flagellated algae d_N (or d_N/d_S). Our results indicate that gene properties often influence the rate of protein evolution in a lineage-specific manner. Hence, there is no general rule for interpreting evolutionary rate variation among proteins in a wide range of eukaryotic lineages.

Materials and Methods

Orthologous Genes and the Calculation of Evolutionary Rates

The genomes and annotations of two flagellated green algae C. reinhardtii and V. carteri were downloaded from Phytozome 7.0 (http://www.phytozome.net/, last accessed April 13, 2011) (Goodstein et al. 2012). Although many C. reinhardtii or V. carteri genes are likely alternatively spliced (Kianianmomeni et al. 2008; Labadorf et al. 2010), based on the current annotations of these algal genes, each C. reinhardtii or V. carteri gene only corresponds to one transcript and one protein. In total, information from 17,114 C. reinhardtii genes (proteins) and 14,542 V. carteri genes (proteins) were obtained. On the basis of the protein sequences of the algae genes, we used reciprocal best hits to define 6,921 C. reinhardtii–V. carteri one-to-one orthologous gene pairs using E value less than 10⁻¹⁰ in BLASTp (v2.2.24, http://blast.ncbi.nlm.nih.gov/, last accessed April 13, 2011) searches (Zhang and He 2005; Wyder et al. 2007). In addition to the BLASTp-based method, we also used InParanoid (version 4.1) (O'Brien et al. 2005) to define and obtained 3,850 C. reinhardtii–V. carteri one-to-one orthologous genes. Both sets of orthologs generated similar results, so we present the results using BLASTp-based one-to-one orthologs, unless otherwise noted. To calculate the evolutionary rates of the 6,921 genes after the C. reinhardtii–V. carteri divergence, the amino acid sequences of orthologous gene pairs were aligned using ClustalW (v1.83, ftp://ftp.ebi.ac.uk/pub/software/unix/clustalw/, last accessed April 15, 2011) (Thompson et al. 1994) or MUSCLE (v3.8.31, http://www.drive5.com/muscle/, last accessed July 7, 2011) (Edgar 2004) with default parameters and back translated to the corresponding nucleotide coding sequences (CDSs). Because results generated based on the two aligners are virtually identical, we only present those based on ClustalW alignments as the main results, unless otherwise noted. The number of synonymous substitutions per synonymous site (d_S) and the number of nonsynonymous substitutions per nonsynonymous site (d_N) of each ortholog were estimated using the codeml module of PAML 3.14 (Yang 1997) with following parameters: runmode = −2, seqtype = 1, CodonFreq = 2, model = 0, and NSsites = 0.

Expression and Structural Properties of Genes

Codon adaptation index (CAI) (Sharp and Li 1987) ranges from 0 to 1, and a higher CAI implies a higher expression level of the gene. To calculate CAI of each C. reinhardtii and V. carteri gene, we used CodonW (v1.4.2, http://codonw.sourceforge.net/, last accessed May 11, 2011). RNA-seq-based transcriptome data (GSM600876 and GSM449827) were obtained from National Center for Biotechnology Information (NCBI) GEO Data Sets (http://www.ncbi.nlm.nih.gov/geo/, last accessed May 19, 2011), which were generated from directly measuring mRNA expression levels of C. reinhardtii genes grown in Tris-acetate-phosphate medium. Raw n-mer RNA-seq reads of GSM600876 (n = 75) and GSM449827 (n = 35) were mapped to annotated exons of all C. reinhardtii genes by SOAP (v2.21, http://soap.genomics.org.cn/, last accessed January 24, 2013). The number of uniquely mapped RNA-seq reads per gene was divided by the number of unique n-mers per gene to generate normalized mRNA expression signals (Sultan et al. 2008; Qian et al. 2010; Xiong et al. 2010; Chang and Liao 2012), and the signals of different replicates of the same experiment were averaged. A total of 3,480 C. reinhardtii genes with one-to-one orthologs in V. carteri had detectable expression signals in both C. reinhardtii data sets of GSM600876 and GSM449827. For each gene, the expression level in terms of mRNA abundance (ExpLev_mRNA) was the average expression signal from the two RNA-seq data sets. To examine whether using a direct measure of mRNA abundance changes the importance of expression as a determinant of protein evolutionary rate in mammals or yeasts (Liao et al. 2006, 2010), ExpLev_mRNA was also calculated from n-mer single read RNA-seq data of mouse (Mus musculus, n = 25) tissues (Mortazavi et al. 2008) and yeast (Saccharomyces cerevisiae, n = 33 or 35) (Nagalakshmi et al. 2008). Estimating expression signals of mouse genes in liver, muscle, and brain has been described previously (Qian et al. 2010; Chang and Liao 2012). Expression signals from these three tissues were averaged to yield ExpLev_mRNA for each mouse gene. The yeast RNA-seq raw reads (GSM282598) were obtained from NCBI GEO Data Set. Expression signals from three random hexamer-primed samples (n = 33, one sample; n = 35, two samples) were calculated as described above and were averaged to yield ExpLev_mRNA for each yeast gene.

To investigate the potential effect of the protein domain density (domain%, see Results and Discussion) on the evolution of algae proteins, we used C. reinhardtii protein domain annotations (JGI v.4) downloaded from SUPERFAMILY (v.1.75) (Wilson et al. 2007). Upstream start codons (uAUG) within the 5′-UTR can inhibit protein translation of an mRNA (Calvo et al. 2009; Yun et al. 2012). We defined #uAUG as the numbers of AUG codons in the 5′-UTR of the representative transcript for each C. reinhardtii gene and V. carteri gene. To investigate the independent influence of each gene property (CDS length in nucleotides, CDS Length; RNA-seq-based mRNA expression level, ExpLev_mRNA; CAI; average intron length in nucleotides, Avg Intron Length; length of UTR in nucleotides, UTR Length; number of uAUG motifs, #uAUG) on C. reinhardtii–V. carteri d_N or d_N/d_S, we performed partial correlation analysis using modules of the “ppcor” package (v.1.0) (Kim and Yi 2007) for R (v2.15.1, http://www.r-project.org/, last accessed June 22, 2012). Information of UTR or intron structures of mammalian genes and yeast genes were obtained from Liao et al. (2010).

Results and Discussion

Orthologous Genes Used for the Correlation Analysis

RNA-seq experiments can directly assess absolute mRNA abundance of genes. This data can be used to address fundamental questions in biology, including questions related to transcriptome evolution (Xiong et al. 2010; Brawand et al. 2011; Chang and Liao 2012). Among the 6,921 C. reinhardtii–V. carteri one-to-one orthologous genes, only 3,480 C. reinhardtii genes had RNA-seq-based mRNA abundance signals in our analysis (see Materials and Methods). The remaining 3,441 genes either had no unique n-mer mappable region in the coding region (three genes) or had no mapped RNA-seq read for the calculation of mRNA abundance due to absent or weak expression (3,438 genes). The variation in expression levels of these 3,438 genes without an estimable ExpLev_mRNA can only be observed by deeper sequencing. Because the estimation of protein evolutionary rates was not affected by sequencing coverage of the transcriptome, the inclusion of these 3,438 genes in the correlation analysis could substantially underestimate the contribution of ExpLev_mRNA to the variation in d_N or d_N/d_S. Thus, we focused on the 3,480 orthologs with detectable RNA-seq data in C. reinhardtii and examined whether this subset of orthologs is representative of all one-to-one orthologs for the correlation analysis.

For each of two data sets, (i) all 6,921 one-to-one orthologs and (ii) 3,480 orthologs with detectable RNA-seq-based mRNA abundance (ExpLev_mRNA > 0) in C. reinhardtii, Spearman’s rank correlation analyses were performed for C. reinhardtii genes versus C. reinhardtii–V. carteri d_N or d_N/d_S on the following gene properties: CDS Length, ExpLev_mRNA, CAI, Avg Intron Length, 3′-UTR Length, 5′-UTR Length, and #uAUG. The rank correlation coefficient (ρ) between the examined factor and d_N (or d_N/d_S) was similar for both data sets for nearly all cases (table 1). The results from data set (i) are generally more statistically significant than those of data set (ii) due to the larger sample size in data set (i). Similar patterns were observed when V. carteri genes were used to define gene properties (supplementary table S1, Supplementary Material online). The consistent ρ observed between data sets (i) and (ii) suggest that the evolution of the 3,480 orthologs with C. reinhardtii RNA-seq data is representative of the genome as a whole. Therefore, we used these 3,480 orthologs for the subsequent analyses that required direct measurement of mRNA abundance (ExpLev_mRNA).

Table 1.

Spearman’s Rank Correlations of Chlamydomonas reinhardtii Gene Properties with d_N and d_N/d_S

Gene Propertiesa	All 6,921 Orthologs		3,480 Orthologsb
	ρ (P Valuec) with dN	ρ (P Valuec) with dN/dS	ρ (P Valuec) with dN	ρ (P Valuec) with dN/dS
CDS Length	0.549 (<10−300)	0.401 (<10−265)	0.552 (<10−275)	0.403 (<10−135)
#uAUG	0.269 (<10−114)	0.191 (<10−57)	0.281 (<10−63)	0.205 (<10−33)
Gene expression level
ExpLevmRNA	NA	NA	−0.546 (<10−268)	−0.356 (<10−104)
CAI	−0.287 (<10−130)	−0.184 (<10−52)	−0.281 (<10−63)	−0.175 (<10−24)
Gene compactness
Avg Intron Length	−0.055 (<10−5)	−0.069 (<10−8)	−0.085 (<10−6)	−0.075 (<10−5)
5′-UTR Length	0.154 (<10−37)	0.073 (<10−8)	0.147 (<10−17)	0.064 (<10−3)
3′-UTR Length	−0.020 (0.09)	−0.032 (<10−2)	−0.020 (0.23)	−0.038 (0.03)

Note.—NA, not applicable.

^aAll gene properties were based on C. reinhardtii genes.

^bThe subset of one-to-one orthologs with RNA-seq expression levels in C. reinhardtii genes.

^cP values show the probabilities of the observations under the hypothesis of no correlation.

Gene properties with greater influence on protein evolutionary rates were expected to have higher rank correlations with d_N. Gene properties can affect the mutation rate or the local selection environment, which both comprise protein evolutionary rates. To distinguish between properties that influenced protein evolutionary rates at the selection level from those that influence properties at the mutation level, we calculated both ρ to d_N and ρ to d_N/d_S. If the influence on protein evolutionary rates was at the selection level, rather than at the mutation level, its ρ to d_N and ρ to d_N/d_S should not differ substantially. Because some gene properties were inter-related (fig. 1), partial rank correlation was used to assess the direct influence of a specific gene property on d_N or d_N/d_S by controlling for potential confounding effects of all the other properties (table 2).

Fig. 1.—

The network showing the inter-relatedness of the gene properties in (A) Chlamydomonas reinhardtii or (B) Volvox carteri. Gene properties are represented by nodes. Edges between nodes represent a highly significant rank correlation coefficient (ρ) between the two corresponding features. Edge thickness corresponds to the magnitude of ρ; edge color corresponds to the sign of ρ (orange = positive, blue = negative). Volvox carteri genes have no ExpLev_mRNA data.

Table 2.

Partial Rank Correlations of the Gene Properties with d_N or d_N/d_S After Controlling for All the Other Gene Properties

Gene Propertiesa	Chlamydomonas reinhardtii Genesb		Volvox carteri Genesb
	ρp (P Valuec) with dN	ρp (P Valuec) with dN/dS	ρp (P Valuec) with dN	ρp (P Valuec) with dN/dS
CDS Length	0.340 (<10−99)	0.247 (<10−50)	0.592 (<10−300)	0.446 (<10−188)
#uAUG	0.041 (0.01)	0.074 (<10−4)	0.035 (0.04)	−0.050 (<10−2)
Gene expression level
ExpLevmRNA	−0.276 (<10−63)	−0.128 (<10−13)	NA	NA
CAI	−0.140 (<10−16)	−0.067 (<10−4)	−0.173 (<10−24)	−0.043 (0.01)
Gene compactness
Avg Intron Length	−0.077 (<10−5)	−0.062 (<10−3)	−0.016 (0.34)	−0.039 (0.02)
5′-UTR Length	−0.002 (0.93)	−0.063 (<10−3)	−0.073 (<10−4)	0.025 (0.13)
3′-UTR Length	−0.019 (0.25)	−0.012 (0.47)	−0.099 (<10−8)	−0.030 (0.08)

Note.—NA, not applicable.

^aAll gene properties were based on C. reinhardtii genes.

^bGene properties can be defined based on C. reinhardtii genes or Volvox carteri genes, as indicated.

^cP values show the probabilities of the observations under the hypothesis of no correlation.

Influence of mRNA Abundance and Gene Compactness on the Rate of Algae Protein Evolution

In yeasts, mRNA abundance is the predominant factor determining the rate of protein evolution, as actively transcribed yeast genes evolve slowly (Drummond et al. 2005, 2006). In mammals, gene compactness, measured by average length of introns or length of UTRs, has a stronger influence on protein evolutionary rates than does gene expression level, as mammalian proteins encoded by a more compact gene evolve more rapidly (Liao et al. 2006).

Codon usage bias has been frequently used in predicting mRNA expression levels (Fraser et al. 2004; Goetz and Fuglsang 2005), including that of C. reinhardtii (Popescu et al. 2006). Consistent with a previous study on 67 nuclear Chlamydomonas genes (Popescu et al. 2006), we found CAI to be negatively correlated with d_N (Spearman’s correlation coefficient ρ = −0.281, P < 10⁻⁶³) and d_N/d_S (ρ = −0.175, P < 10⁻²⁴) (based on 3,480 orthologs; table 1). Using direct mRNA abundance from RNA-seq experiments (ExpLev_mRNA), this negative correlation between mRNA abundance and evolutionary rates became stronger (d_N: ρ = −0.546, P < 10⁻²⁶⁸; d_N/d_S: ρ = −0.356, P < 10⁻¹⁰⁴) (table 1). These results suggest that the influence of mRNA abundance on protein evolutionary rates of flagellate algae was underestimated when indirect expression estimates, such as CAI, are used (Popescu et al. 2006). Direct mRNA abundance data were used to reassess the effect of mRNA abundance on protein evolution of yeast genes and mammalian genes. In yeast genes, mRNA abundance remained the predominant evolutionary rate determinant for yeast proteins (supplementary table S2, Supplementary Material online). In mammalian genes, the correlation between mRNA abundance from oligonucleotide microarrays (Liao et al. 2010) and mammalian d_N or d_N/d_S was previously reported to be statistically insignificant (Liao et al. 2010), but with direct measurements of protein abundance, the correlation between mRNA abundance and protein evolutionary rated was significant and negative (supplementary table S2, Supplementary Material online). Therefore, the direct measurement of RNA-seq experiments clarified how mRNA abundance affects protein evolution. A limitation of microarray data in quantifying mRNA abundance lies in its inability to distinguish between alternative splicing events. This limitation only applied to the mammalian data, as most of the yeasts genes are not alternatively spliced. As a result, the influence of mRNA abundance in yeast protein evolution was the same whether mRNA was measured directly or indirectly.

For flagellated algae proteins, we observed a strong positive correlation between the number of start codons in the UTR (#uAUG) and d_N (ρ = 0.281, P < 10⁻⁶³) and d_N/d_S (ρ = 0.205, P < 10⁻³³) (table 1). UTR start codons can inhibit gene activities at both the transcription and the translation levels (Calvo et al. 2009; Yun et al. 2012). Along these lines, we observed significant negative correlations between both #uAUG and ExpLev_mRNA (ρ = −0.317, P < 10⁻⁸¹) and #uAUG and CAI (ρ = −0.149, P < 10⁻¹⁸) for C. reinhardtii genes (fig. 1A). In the partial correlation analysis, the influence of #uAUG on d_N became only marginally significant (P = 0.01, based on #uAUG of C. reinhardtii genes; P = 0.04, based on #uAUG of V. carteri genes) (table 2), whereas ExpLev_mRNA remained significantly negatively correlated with d_N (ρ_p = −0.276, P < 10⁻⁶³) and d_N/d_S(ρ_p = −0.128, P < 10⁻¹³) (table 2). Thus, mRNA abundance is an important and independent factor determining the rate of algae protein evolution. Although our analyses on proteins of algae (tables 1 and 2), yeasts (supplementary table S2, Supplementary Material online), and mammals (supplementary table S2, Supplementary Material online) suggested that mRNA abundance has a universal effect on d_N or d_N/d_S, the observed correlation can be caused by avoidance of protein misfolding (Drummond et al. 2005; Drummond and Wilke 2008) or misinteraction (Yang et al. 2012), avoidance of mRNA misfolding (Park et al. 2013), selection on protein function (Cherry 2010), or a combination of all. Whether mRNA abundance affects protein evolution in all three lineages due to a common cause deserves further investigations.

Gene compactness was defined by the lengths of introns and UTRs. Gene compactness was positively correlated with d_N and d_N/d_S in mammals (Liao et al. 2006). In flagellated algae, average intron length (Avg Intron Length) was negatively correlated with d_N (ρ = −0.085, P < 10⁻⁶) and d_N/d_S (ρ = −0.075, P < 10⁻⁵) (3,480 orthologs; table 1). Partial correlation analysis confirmed an independent effect of intron length on d_N and d_N/d_S (table 2), although the correlation between V. carteri-based intron length and d_N was not statistically significant (ρ_p = −0.016, P = 0.34). The correlations between lengths of UTRs (5′-UTR Length and 3′-UTR Length) and the rates of protein evolution were more difficult to interpret (table 1). 5′-UTR Length was positively correlated with d_N (ρ = 0.147, P < 10⁻¹⁷) and d_N/d_S (ρ = 0.064, P < 10⁻³), whereas 3′-UTR Length was not significantly correlated with d_N (P = 0.23) and negatively correlated with or d_N/d_S (ρ = −0.038, P = 0.03) (3,480 orthologs; table 1). The inconsistency was likely confounded by the inter-relatedness of gene properties (fig. 1). For example, genes with longer 5′-UTRs tend to have more uAUGs (5′-UTR Length vs. #uAUG: ρ = 0.651, P < 10⁻³⁰⁰) and lower mRNA expression levels (5′-UTR Length vs. ExpLev_mRNA: ρ = −0.165, P < 10⁻²¹) (fig. 1). The partial correlations for 5′-UTR Length or 3′-UTR Length versus d_N or d_N/d_S were negative or insignificant (table 2), similar to the results for gene compactness defined by average intron length. It is possible that the lack of partial correlation between 5′-UTR Length or 3′-UTR Length and d_N or d_N/d_S was due to misannotation of UTRs in algal genes. Nevertheless, together these results suggest that flagellated algal genes with greater gene compactness tend to evolve more rapidly. However, the influence of gene compactness is not as important as expression level in determining the rates of protein evolution after the divergence of C. reinhardtii and V. carteri. Using C. reinhardtii and V. carteri orthologous genes defined using the InParanoid algorithm yielded similar results (supplementary table S3, Supplementary Material online).

Protein Length Is the Most Important Gene Property Influencing Algae Protein Evolutionary Rates

Although both mRNA abundance and gene compactness affect d_N (or d_N/d_S) after the divergence of C. reinhardtii and V. carteri, neither had the most significant correlation with d_N (or d_N/d_S) (tables 1 and 2). Instead, CDS length had the strongest effect on d_N (or d_N/d_S) among all the gene properties examined (CDS Length vs. d_N: ρ = 0.552, P < 10⁻²⁷⁵; CDS Length and d_N/d_S: ρ = 0.403, P < 10⁻¹³⁵) (3,480 orthologs, table 1). This result holds even after controlling for other gene properties (table 2). Using C. reinhardtii–V. carteri orthologs defined by the InParanoid algorithm generated a minor difference for C. reinhardtii-based gene properties to d_N (ExpLev_mRNA-d_N ρ_p was slightly stronger than CDS Length-d_N ρ_p), not d_N/d_S (supplementary table S3, Supplementary Material online). The role of CDS length in determining the rate of protein evolution has been difficult to discern. Using 363 mouse–rat orthologs, there was a significant, but weak, negative correlation between protein lengths and evolutionary rates (Zhang 2000). However, with a larger data set (∼3,500 mouse–rat orthologs), no correlation was found between protein lengths and evolutionary rates (Liao et al. 2006). In the fruit fly, longer proteins were reported to evolve more rapidly (Lemos et al. 2005). In yeasts, the correlation between protein lengths and evolutionary rates depends on the range of protein sizes (Bloom et al. 2006). The widely different effect of protein length effect on d_N or d_N/d_S suggested that any observed correlation was minor or a byproduct caused by confounding factors, such as mRNA expression. However, our findings indicated that CDS Length plays a major, independent role in generating the evolutionary rate variation of algae proteins.

To evaluate the validity of the strong positive correlation between CDS Length and d_N or d_N/d_S in algae, we examined how potential alignment errors in C. reinhardtii–V. carteri orthologs affect this correlation. Errors in sequencing, CDS annotation, and alignment should inflate both d_N and d_N/d_S estimates because these errors do not differentiate between nonsynonymous and synonymous sites (Stoletzki and Eyre-Walker 2011). We repeated our analysis on a subset of C. reinhardtii–V. carteri orthologs with “high quality” alignments defined by Heads-or-tails (HoT) scores (Landan and Graur 2007). The HoT column score (or residue score) is the fraction of identical aligned columns (or paired residues that are identical) between the “Heads” alignment, generated from the original sequences, and the “Tails” alignment, generated from the reversed sequences. These scores were calculated for each orthologous pair. The analysis for table 2 was repeated on a subset of 2,923 (or 2,903) orthologs with high HoT column scores ≥ 0.8 (or residue scores ≥ 0.8) and supplementary table S4, Supplementary Material online (or supplementary table S5, Supplementary Material online), was generated. Compared with table 2, values of ρ_p shown in supplementary table S4 or S5, Supplementary Material online, were in general weaker. This can be due to the fact that the orthologs with truly and very diverged CDSs were unavoidably excluded after the data filtering and a bias was introduced. Nevertheless, within both subsets of orthologs, although mRNA abundance had a slightly stronger correlation with d_N than CDS length did, CDS length still had the strongest correlation with d_N/d_S among the factors examined.

We expected that orthologs that prone to alignment errors should have greater differences in protein length and a higher fraction of unalignable residues. To group genes according to their propensity to have inaccurate alignments, we calculated protein length dissimilarity as ΔL = |L_C − L_V|/(L_C + L_V) and the proportion of unalignable residues as UnalignRes = 1 − [2 × N_aligned/(L_C + L_V)] for each ortholog, where N_aligned is the number of aligned codons, and L_C and L_V are lengths of the protein sequence in C. reinhardtii and V. carteri genes, respectively. CDS length in C. reinhardtii genes was positively correlated with both ΔL (ρ = 0.393, P < 10⁻¹²⁸) and UnalignRes regardless of alignment tool used (ClustalW: ρ = 0.412, P < 10⁻¹⁴²; MUSCLE: ρ = 0.481, P < 10⁻²⁰⁰), indicating in general there was better alignment for orthologs encoding shorter proteins. To evaluate whether an overestimated d_N or d_N/d_S of longer proteins led to the strong correlation between CDS Length and d_N or d_N/d_S (tables 1 and 2), we divided all orthologs into two equal-sized groups according to ΔL or UnalignRes and examined the relative strengths of correlation for CDS length and mRNA expression level versus d_N (or d_N/d_S) (table 3). These correlations were stronger in the groups of orthologs that predicted to have fewer alignment errors (lower ΔL and lower UnalignRes), indicating that high quality annotation and alignment facilitates the identification of protein evolutionary rate determinants. Within the groups with low ΔL or low UnalignRes, the correlation between CDS Length and d_N (or d_N/d_S) was stronger than that of ExpLev_mRNA and d_N (or d_N/d_S). In contrast, the correlation between ExpLev_mRNA and d_N (or d_N/d_S) was stronger than that of CDS Length and d_N (or d_N/d_S) in the groups of orthologs prone to have alignment errors (table 3), suggesting alignment issues inflate rather than underestimate the relative importance of mRNA expression on protein evolutionary rate in our analysis. Additionally, the partial correlations of CDS Length and d_N (or d_N/d_S) after controlling for ΔL (CDS Length vs. d_N: ρ_p = 0.543, P < 10⁻³⁰⁰; CDS Length vs. d_N/d_S: ρ_p = 0.415, P < 10⁻¹⁵⁸) or UnalignRes (CDS Length vs. d_N: ρ_p = 0.528, P < 10⁻²⁹³; CDS Length vs. d_N/d_S: ρ_p = 0.409, P < 10⁻¹⁵³) were slightly stronger than those of ExpLev_mRNA and d_N (or d_N/d_S) after controlling for ΔL (ExpLev_mRNA vs. d_N: ρ_p = −0.535, P < 10⁻³⁰⁰; ExpLev_mRNA vs. d_N/d_S: ρ_p = −0.363, P < 10⁻¹¹⁵) or UnalignRes (ExpLev_mRNA vs. d_N: ρ_p = −0.520, P < 10⁻²⁸¹; ExpLev_mRNA vs. d_N/d_S: ρ_p = −0.355, P < 10⁻¹¹⁰), suggesting that alignment quality does not explain the stronger correlation between CDS length and protein evolutionary rate compared with that of mRNA abundance and protein evolutionary rate. Furthermore, the observed influence of CDS length on protein evolutionary rate is real and unlikely an artifact of poor sequence alignment.

Table 3.

Effect of Alignment on Rank Correlations of CDS Length or ExpLev_mRNA with d_N and d_N/d_S

Orthologs	ClustalW		MUSCLE
	ρ (P Valuea) with dN	ρ (P Valuea) with dN/dS	ρ (P Valuea) with dN	ρ (P Valuea) with dN/dS
Similar CDS Length (bottom 50% ΔL)
CDS Length	0.687 (<10−243)	0.483 (<10−101)	0.666 (<10−222)	0.444 (<10−84)
ExpLevmRNA	−0.647 (<10−206)	−0.361 (<10−54)	−0.636 (<10−197)	−0.319 (<10−41)
Dissimilar CDS Length (top 50% ΔL)
CDS Length	0.390 (<10−63)	0.318 (<10−41)	0.371 (<10−57)	0.295 (<10−35)
ExpLevmRNA	−0.418 (<10−73)	−0.340 (<10−47)	−0.410 (<10−70)	−0.323 (<10−42)
Strongly alignable (bottom 50% UnalignRes)
CDS Length	0.661 (<10−218)	0.467 (<10−94)	0.592 (<10−163)	0.361 (<10−53)
ExpLevmRNA	−0.630 (<10−192)	−0.346 (<10−49)	−0.573 (<10−151)	−0.236 (<10−22)
Poorly aligned (top 50% UnalignRes)
CDS Length	0.382 (<10−60)	0.311 (<10−39)	0.371 (<10−57)	0.319 (<10−41)
ExpLevmRNA	−0.414 (<10−72)	−0.340 (<10−47)	−0.416 (<10−73)	−0.353 (<10−51)

^aP values show the probabilities of the observations under the hypothesis of no correlation.

Domain Density Does Not Account for the Influence of Protein Length on Evolutionary Rates

It is interesting to consider why CDS length is important to algae protein evolution. Decreased protein length has been associated with increased efficiency of protein synthesis (e.g., Akashi [2003]) that is needed for highly expressed proteins (Coghlan and Wolfe 2000; Jansen and Gerstein 2000), which evolve slowly. However, according to the results of our partial correlation analysis (table 2), mRNA expression levels do not contribute to the influence of CDS length on protein evolutionary rates. In prokaryotes, variation in protein length is caused by variation in the length of linkers (nondomain protein regions) connecting protein domains (Wang et al. 2011). These nondomain regions of the protein were less constrained in sequence evolution, likely due to lower functional importance (Wilson et al. 1977; Steiner et al. 1985) or higher intrinsic disorderness (Orengo and Thornton 2005; Brown et al. 2011). Therefore, we tested the hypothesis that in algae, longer proteins tend to evolve more rapidly because they contain a larger proportion of linker sequences or a smaller proportion of domain sequences (domain%).

Among the 3,480 C. reinhardtii genes with a one-to-one ortholog in the V. carteri genome, there were 626 C. reinhardtii genes containing protein domains annotated by SUPERFAMILY. The small number of C. reinhardtii genes with SUPERFAMILY domains reflects the incompleteness of current protein domain annotation. Despite the small sample size, we observed a strong negative correlation between CDS Length and domain%, defined as the percent of amino acids residing in SUPERFAMILY-annotated protein domains of a protein (ρ = −0.490, P < 10⁻³⁸; fig. 2A), suggesting that longer proteins have a higher proportion of residues outside of protein domains, which potentially explains the strong correlation between CDS Length and d_N (or d_N/d_S). Furthermore, when we divided the 626 orthologs into three groups according to domain%: domain% ≤ 50% (n = 212), 50% < domain% ≤ 75% (n = 167), and domain% > 75% (n = 247), we found that within each domain% group, CDS Length had a similar positive correlation with d_N (ρ = 0.441–0.551, P < 10⁻⁸–10⁻²⁰; fig. 2B) and d_N/d_S (ρ = 0.231–0.339, P < 10⁻³–10⁻⁷; fig. 2C). We divided each protein sequence into domain regions and nondomain regions, generated a concatenate domain and nondomain protein for each protein, and calculated the domain-specific or nondomain-specific d_N (or d_N/d_S) for each gene. CDS Length was positively correlated with both domain-specific d_N (ρ = 0.346, P < 10⁻¹⁴) (or d_N/d_S, ρ = 0.134, P < 10⁻²) and nondomain-specific d_N (ρ = 0.411, P < 10⁻²¹) (or d_N/d_S, ρ = 0.118, P < 10⁻²). These results suggest that domain density does not sufficiently explain the effect of CDS length on protein evolution.

Fig. 2.—

(A) Genes with longer CDS (larger CDS Length) encode proteins with a lower percent of the total sequence length annotated as protein domains (domain%). Controlling for domain%, CDS Length remains positively correlated with (B) d_N and (C) d_N/d_S. The linear regression line and the Spearman’s rank correlation coefficient are shown for each domain% bin.

Concluding Remarks

As previously observed in yeasts (Drummond et al. 2006) and mammals (Liao et al. 2006), we found that mRNA abundance and gene compactness influence the evolutionary rates of flagellated algae proteins. However, for algae proteins, gene compactness was only a minor determinant of protein evolutionary rates, and its influence was confounded by the number of start codons in the UTR. Although the hypothesis that compact genes evolve faster because they lack introns that promote the efficacy of natural selection (Liao et al. 2006) cannot be disproven, it is also possible that gene properties associated with regulatory motifs (e.g., uAUG-like motifs and miRNA target sites) better explain the difference in relative importance of gene compactness in determining protein evolutionary rate in yeasts, mammals and algae.

In flagellated algae, CDS length had the greatest independent influence on protein evolutionary rate. This finding was unexpected as genome-wide investigations in yeasts (Drummond et al. 2006) and mammals (Liao et al. 2006) found no correlation between protein length and d_N (or d_N/d_S). Although the underlying cause for this correlation remains to be understood, it is independent from associations with gene expression (Drummond et al. 2005; Yang et al. 2012) and protein domain density, which is similar to functional density (Wilson et al. 1977). Recent studies have shown that the chaperone-mediated protein folding can accelerate protein evolution (Bogumil and Dagan 2010; Warnecke and Hurst 2010). It is unknown whether algae genes encoding longer proteins tend to code for substrates of chaperones. When chaperonin-dependency data or protein–protein interaction data become available for algae, it will be interesting to examine whether chaperone–substrate interaction plays a role in the effect of CDS length on flagellated algae protein evolutionary rates.

Although many studies have searched for universal rules explaining sequence evolution (Li 1997; Wilson et al. 1977; Zeldovich and Shakhnovich 2008; Koonin 2011), our study suggests that although determinants of protein evolutionary rates can be common among multiple eukaryotic lineages (e.g., Drummond and Wilke [2008]), their relative importance can differ between lineages. Consequently, the implications of sequence conservation in a less well-studied lineage are often unpredictable and require further lineage-specific investigation.

Supplementary Material

Supplementary tables S1–S5 are available at Genome Biology and Evolution online (http://www.gbe.oxfordjournals.org/).