Many biological processes can be better understood in the framework of reliable phylogenetic analyses. This is not only true for our understanding of evolutionary systematics and phylogenetics, including TOL, but it will also largely contribute to our understanding of diversification at the subcellular, cellular and organismal levels of integration. One well documented example in this respect is the postulated whole-genome duplication (WGD) that occurred during the evolution of some species belonging to the Saccharomycotina 1. Only using a correctly inferred phylogenetic TOL it was possible to distinguish between "pre-WGD" and "post-WGD" species of Saccharomycotina. Other examples refer to our understanding of evolution of metabolic pathways 2, structure of genomes 34, life styles 5, and pathogenicity 6.

Until recently, our understanding of the (fungal) TOL has been based on two approaches, which basically differ in number of species and genes considered: (1) few genes and large number of species; (2) large number of genes and few species. The clear advantage of the first approach is the availability of many sequences, e.g. of the rDNA locus, in publicly available databases (i.e. National Center for Biotechnology Information – NCBI), and, secondly, it is generally rather easy to generate complete or partial sequences of a few genes for a large number of species. Besides, the rDNA loci have the clear advantage of being universally present in all branches of TOL, universal primers are well known and it has been successfully explored in many branches of TOL. The disadvantage of the rDNA loci, however, is that the deeper branches are usually less supported 7. As an answer to this, various authors started to include multiple protein coding genes in their phylogenetic analyses 8910. Unfortunately, the rationale behind the selection of these protein coding genes is not always clear, and discrepancies and incongruences between individual gene trees may result in unresolved phylogenetic trees 78. This may be due to different evolutionary rates, and/or different origins of the genes, e.g. whether nuclearly encoded (e.g. RPB1 and RPB2) or mitochondrial in origin (e.g. ATP6). In the second approach, large numbers of genes have been used for phylogenetic studies as an attempt to contribute to the first approach described above. This was firstly applied in the prokaryotes 11 and, more recently, in eukaryotes as well 121314. A large selection of genes and/or proteins are concatenated and used for inferring phylogenetic relationships, thereby increasing the phylogenetic signal considerably 1214151617. However, although this approach resolved the fungal phylogenetic tree 12141617 it also suffers from some limitations. For instance, it does not take into consideration the evolutionary history of each individual gene and it depends on the availability of complete genome data.

Here, we explored the usefulness of comparing the cophenetic correlation coefficients (CCCs) among distance matrices of individual gene trees in order to make a phylogenetically meaningful selection of orthologs to be considered for further phylogenomics studies as well as large scale TOL and barcoding applications. We used the fungal kingdom as an example as it represents one of the major clades of life with approximately 1.5 million species 18, of which only approximately 80.000 have been described. Moreover, the fungi are morphologically, metabolically and ecologically highly diverse and, importantly, the number of completely sequenced genomes is high among the eukaryotes.

Candidate proteins to be considered for TOL and/or barcoding studies were assessed from 33 fungal proteomes by comparing (i) distance matrices of each individual orthologous protein (KOGs) matrix, (ii) to compare these with that of a well supported guide tree 14, and (iii) analyze for their phylogenetic signal. The method presented here may be universally applied for the selection of markers in various TOL and barcoding studies.

The 33 genomes investigated shared 4852 KOGs from which 70 were single copy proteins. The function of these 70 KOGs was assessed from the Saccharomyces cerevisiae genome database 19 (Additional file 1). The corresponding systematic name, standard name, description, chromosome number and knock out phenotype are presented in Table 1 (Additional file 1). Knock out phenotypes of 32 genes were lethal (Table 1) when deleted in S. cerevisiae 19, thus suggesting that they code for essential proteins. Genes coding for the 70 KOG proteins are distributed on almost all chromosomes of S. cerevisiae, except chromosome VI (Table 1), thus representing the entire genome.

Comparing the CCC values of a 531 × 531 distance matrices analyzed before 14 using Pearson's correlation, indicated that KOG2671 represents the single copy protein with the highest correlation value of 0.96 (Additional file 2). This KOG2671 protein (putative RNA methylase KOG annotation) corresponds to ORF YOL124c of S. cerevisiae [Catalytic subunit of an adoMet-dependent tRNA methyltransferase complex (Trm11p-Trm112p), required for the methylation of the guanosine nucleotide at position 10 (m2G10) in tRNAs; contains a THUMP domain and a methyltransferase domain]. The CCC values of the remaining 69 single copy KOGs were compared with that of KOG2671. Any of the subsequent five single protein KOGs present in the list of 531 KOG proteins 14, namely KOG2728 (Uncharacterized conserved protein with similarity to phosphopantothenoylcysteine synthetase/decarboxylase), KOG0991 (Replication factor C, subunit RFC2), KOG0340, (ATP-dependent RNA helicase), KOG0809 (SNARE protein TLG2/Syntaxin 16), and KOG3786 (RNA polymerase II assessory factor Cdc73p), could be used as a starting point for this comparison, because the correlation values ranged between 0.95 and 0.96 (Additional file 2). The correlation values between the distance matrix of KOG2671 and that of each of the remaining 69 KOG proteins ranged from 0.08 to 0.93 (Table 1), and were statistically significant (Additional file 3). The majority of the KOGs (i.e. 64 from 70 KOGs) gave correlation values higher than 0.50 (Table 1). As an example, we constructed a phylogenetic tree based on concatenation of these 64 KOGs (Fig. 1), which is in accordance with previously published trees. Four KOGs gave CCC values below 0.36 (Table 1), thus indicating that they have different phylogenetic signals. This is sustained by the resulting phylogenetic tree showing a different topology (Additional file 4) if compared with that based on 64 KOGs (Fig. 1). For instance, the Pezizomycotina formed a sister clade to the Basidiomycetes and, S. pombe occured as a basal lineage to both of them, but without statistical support (Additional file 4).

Among the KOG proteins with CCC values above 0.50, are many proteins involved in cellular processes and signaling. The other tree KOG categories 20, namely information storage and processing, metabolism, and poorly characterized categories seem to be less informative (Fig. 2). When the KOG proteins are concatenated in increasing numbers (e.g. the 10 with the highest CCC values; the 20 with the highest CCC values and so on) it can be seen that the CCC values remains above 0.8 until 44 proteins have been concatenated (Fig. 2). Thereafter, the CCC values showed a sharp decline, indicating that the KOG proteins 44–64 have different phylogenetic signals. Interestingly, the topology of the phylogenetic trees stabilizes after the concatenation of 40 proteins (Additional file 5). After concatenation of only 10 and 20 proteins the lineages with C. glabrata, S. kluyveri, K. lactis and A. gossypii, and that of C. lusitaniae, D. hansenii, C. guilliermondii and C. albicans, and finally the Euascomycete lineage of C. globosum, N. crassa, M. grisea and F. graminearum showed varying topologies (Additional file 5). Bootstrap values of most branches were high irrespective the number of proteins concatenated (Fig. 1, Additional file 5). However, for two branches, labeled 7 and 9 in Additional file 5, that received lower bootstrap values, the maximum value (85%) was obtained after concatenation of 40 KOG proteins. The A. gossypii-K. lactis-Sac. kluyveri lineage (labeled as branches 4 and 5 in Additional file 5) received only low support, and this was even true after concatenation of 531 orthologues 14. This most likely indicates that further improvement can only be obtained by further species sampling in this lineage. Summarizing we estimate that 40–45 concatenated single copy protein KOGs are needed to fully resolve fungal TOL. Below this number the tree topology may be different, and above this number the CCC values as well as the support values tend to drop.

In short, the set of proteins resulting from our studies presents a good selection to be elaborated in further studies on fungal TOL, which may include many non-sequenced species. As the proteins were selected across the fungal kingdom and because they represent single KOG proteins, they may also be suitable for the development of molecular barcodes. This proposed method is universal and can be extended easily to bacterial and archaeal TOLs as well as other eukaryote lineages of TOL.

KOG: Clusters of orthologous groups for eukaryotic complete genomes.

EEK participated in the design of the study, analyses and drafted the manuscript.

VR participated in the design of the study, analyses and drafted the manuscript.

CEE participated in the sequence alignment and drafted the manuscript.

TB participated in the design of the study, analyses and drafted the manuscript.

All authors contributed to the final manuscript preparation.