- Research article
- Open Access
Implications of high level pseudogene transcription in Mycobacterium leprae
- Diana L Williams1Email author,
- Richard A Slayden2,
- Amol Amin^2,
- Alejandra N Martinez1, 3,
- Tana L Pittman1,
- Alex Mira4,
- Anirban Mitra5,
- Valakunja Nagaraja5,
- Norman E Morrison6,
- Milton Moraes3 and
- Thomas P Gillis1
© Williams et al; licensee BioMed Central Ltd. 2009
Received: 23 September 2008
Accepted: 25 August 2009
Published: 25 August 2009
The Mycobacterium leprae genome has less than 50% coding capacity and 1,133 pseudogenes. Preliminary evidence suggests that some pseudogenes are expressed. Therefore, defining pseudogene transcriptional and translational potentials of this genome should increase our understanding of their impact on M. leprae physiology.
Gene expression analysis identified transcripts from 49% of all M. leprae genes including 57% of all ORFs and 43% of all pseudogenes in the genome. Transcribed pseudogenes were randomly distributed throughout the chromosome. Factors resulting in pseudogene transcription included: 1) co-orientation of transcribed pseudogenes with transcribed ORFs within or exclusive of operon-like structures; 2) the paucity of intrinsic stem-loop transcriptional terminators between transcribed ORFs and downstream pseudogenes; and 3) predicted pseudogene promoters. Mechanisms for translational "silencing" of pseudogene transcripts included the lack of both translational start codons and strong Shine-Dalgarno (SD) sequences. Transcribed pseudogenes also contained multiple "in-frame" stop codons and high Ka/Ks ratios, compared to that of homologs in M. tuberculosis and ORFs in M. leprae. A pseudogene transcript containing an active promoter, strong SD site, a start codon, but containing two in frame stop codons yielded a protein product when expressed in E. coli.
Approximately half of M. leprae's transcriptome consists of inactive gene products consuming energy and resources without potential benefit to M. leprae. Presently it is unclear what additional detrimental affect(s) this large number of inactive mRNAs has on the functional capability of this organism. Translation of these pseudogenes may play an important role in overall energy consumption and resultant pathophysiological characteristics of M. leprae. However, this study also demonstrated that multiple translational "silencing" mechanisms are present, reducing additional energy and resource expenditure required for protein production from the vast majority of these transcripts.
Bacterial pseudogenes are inactivated, presumably nonfunctional genes that can accumulate in the genomes of bacterial species, especially those undergoing processes such as niche selection or host specialization [1, 2]. When a bacterial gene is under low selection pressure, it undergoes a period of frequent nucleotide substitutions because deleterious mutations are not efficiently purged. These mutations can cause the accumulation of in-frame stop codons, reading frame-shifts, or removal of traditional translational start codons or vital sections of the gene, giving rise to a pseudogene . Mutations that destroy promoter or regulatory sequences can result in the "silencing" of transcription or translation or premature termination of protein synthesis .
The case of pseudogenes in Mycobacterium leprae, an obligate intracellular bacterium and etiologic agent of leprosy, is very dramatic. Its ~3.3 Mb genome consists of 1,614 open reading frames (ORFs) and 1,133 pseudogenes . The pseudogenes represent 41% of the total genes http://genolist.pasteur.fr/Leproma/help/current.html; the largest percentage found in any bacterial genome sequenced to date http://www.pseudogene.org/cgi-bin/db-gen.cgi?type=Prokaryote.
The overall G+C content of M. leprae's genome is 57.8% . This is 8% lower than that of its close relative M. tuberculosis , a feature usually described in species undergoing low selection pressure [7, 8]. Interestingly, the G+C content of pseudogenes (56.5%) is lower than that of its ORFs (60.1%).
Pseudogenes are distributed throughout the M. leprae chromosome and are assigned to the majority of functional groups [5, 9, 10]. If these genes are indeed nonfunctional in M. leprae, they should no longer be required for survival in the specialized intracellular niche in which M. leprae resides. Therefore, the study of M. leprae pseudogenes is important to expand our understanding of the evolution of this obligate intracellular parasite and to establish the role that pseudogenes play in M. leprae's unique metabolism and parasitism.
Recently, pseudogene transcripts in M. leprae have been identified [11, 12]. However, the extent of pseudogene transcription and the potential impact that these transcribed genes have on M. leprae have not been analyzed critically. The cost of expressing non-functional ORFs could be especially dramatic for M. leprae because the speed of pseudogene deletion appears to be slower than in other bacteria .
To address this, a study of the overall pseudogene transcriptional profile of M. leprae using a global M. leprae DNA microarray and reverse transcriptase-PCR analyses was conducted. The results demonstrated that a large number of M. leprae pseudogenes were transcribed during growth in the nude mouse foot pad, a model for lepromatous leprosy in man. Analyses of these transcribed pseudogenes using bioinformatics tools and in vitro methods demonstrated that potential mechanisms for transcription of these pseudogenes were associated with their residing within gene clusters or downstream of functional ORFs. In addition some of these genes contained functional promoters within their 5' upstream sequence. Since translation of this large number of pseudogenes could have a major impact on M. leprae's resources and energy consumption without apparent benefit to its survival and growth, mechanisms for translational "silencing" of these transcripts were also investigated using bioinformatics tools. Results demonstrated that the vast majority of these pseudogenes are "silenced". The "silencing" of these transcripts was found to be associated with: 1) the lack of a strong Shine-Dalgarno (SD), ribosomal binding site, in the 5'-UTR of the majority of these genes; 2) the lack of traditional translational start codons; 3) the presence of multiple in-frame stop codons; and 4) high Ka/Ks ratios indicating low functionality of putative protein products. These data indicated that the majority of pseudogenes were nonfunctional, inactivated genes. However, when one pseudogene containing a functional promoter, SD site, a traditional start codon, a very low Ka/Ks ratio, and encoding 3/4 of its M. tuberculosis ortholog was tested for protein production in E. coli, the predicted product was observed.
Transcriptome of M. leprae
Transcriptional analysis of M. leprae ORFs and pseudogenes using the M. leprae DNA microarray demonstrated that that 1,353 transcripts had a mean signal to noise ratio (SNR) cutoff value ≥ 2 (raw data are accessible through GEO Series accession # GSE17191 study at: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17191). Several genes, positive on only 1 of 4 arrays were further analyzed and found positive using RT-PCR analysis (Additional File 1). Therefore the current transcriptome of nude mouse footpad-derived M. leprae consists of a total of 1,353 transcripts (Additional File 1). RT-PCR analysis was also used to validate the transcription of 20.5% of gene transcripts positive on the array. The transcriptome represents 49% of the total 2,747 genes surveyed and 867/1,353 (64%) transcripts were from ORFs or protein coding genes from a variety of functional gene categories (Additional File 1). Approximately 11% of these ORFs have previously been shown to produce proteins in armadillo-derived M. leprae, however no protein product was observed for any pseudogene in these studies (Additional File 1) [14, 15]. The present study demonstrated that 486/1,353 (36%) of the gene transcripts detected were from pseudogenes, demonstrating that 43% of all pseudogenes found in the M. leprae genome were transcriptionally active (Additional file 1). This represents the largest number of transcriptionally-active pseudogenes reported to date.
Read-though transcription of pseudogenes
Transcribed M. leprae pseudogenes within gene clusters.
ML# in Cluster
ML0211 – ML0214
ML0475 – ML0483
ML0475 – ML0483
ML0475 – ML0483
ML0491 – ML0501
ML0491 – ML0501
ML0491 – ML0501
ML0510 – ML0523
ML0532 – ML0537
ML0540 – ML0548
ML0540 – ML0548
ML0540 – ML0548
ML0582 – ML0587
ML0582 – ML0587
ML0624 – ML0633
ML0778 – ML0782
ML0831 – ML0833
ML1195 – ML1200
ML1452 – ML1468
ML1452 – ML1468
ML1581 – ML1598
ML1581 – ML1598
ML1581 – ML1598
ML1658 – ML1664
ML1691 – ML1696
ML1768 – ML1800
ML1768 – ML1800
ML1840 – ML1895
ML1840 – ML1895
ML1840 – ML1895
ML1840 – ML1895
ML1840 – ML1895
ML1840 – ML1895
ML1840 – ML1895
ML1840 – ML1895
ML1840 – ML1895
ML1840 – ML1895
ML2211 – ML2230
ML2211 – ML2230
ML2211 – ML2230
ML2211 – ML2230
ML2211 – ML2230
ML2211 – ML2230
ML2326 – ML2330
ML2697 – ML2713
ML2697 – ML2713
ML2697 – ML2713
Identification of intrinsic stem loop structures
Prediction of intrinsic stem loop terminators in the 3'-UTR of transcribed ORFs located upstream of transcribed pseudogenes.
M. leprae Chromosomal Location
Stem Loop ΔG2
Analysis of M. leprae pseudogene promoters in silico and in vitro promoter in E. coli analyses.
Distance Upstream of Start Codon
Promoter Sequence (5'3')4
-35 -10 (+1)
ML gfp Express5
AATCAG ccagagcaggcgagcaaaCTGAAT-- acagtcccgT
TATGCG ccaggacaagcgagcaagCCGAAT-- acggtgccgT
TCACGT cgaattgcaccgtgtcggCCTTAA atct---agctA
TCACGT cgaattggcacgcgtcggCCTTCA gatcagagtgcA
GTCGCT ggattcagagactagaacGTGTTA caaccgggaagA
TAGGCT gcccggcagtcgacgtCCTTTA tc-----gatccgT
TACGCT gccgagcggtcgacatCCTTTA ac-----gatccgT
GCCAAA acgacgcgcggatggaAGACGT cc-----ggccggG
ACGCTG gcgctcatgaccgcgttgcAGCCTA-- ccgtatcgC
ACGCTG gcgctcatgaccgactcgaAGCCTA-- gcgcatggC
CGGTGA cagtcatactgtcaagaTACCTC atcccgaaccggT
GTGGTT gcccacaacagtcgcTATCCT gag-------ctggC
GTGCTT gccggggac-gtcggTATCCT agg-------acggC
TGCGTA gctttcgcgacggattTACAGT cc-----gctcccA
ATGACA aagtggtcgatcacatgcCCGATC-- accagcaatT
ATGCCC gatcggtcgatcagctggCCGATC-- aacaacagcT
GTTAAA aacgtgtttaagagttGAAGAG gg-----ggttaaC
GTTAAG accttgtttaggagttGAAGAT cg-----gtttaaC
ACCTTC aggtcgccaccgagcgTGAACG ct-----ccggatG
ACGGTC aggtcgccgccgagggTCAACG tt-----ccggatG
TACACT tcggtttctaatctgtg-gaATCCAT-- ggcagtcA
TAAACC tcggcgtcgaatcggcgagaACCCAT-- gtcagccA
AGTGCC gcgtctacttgctcatcAGTTAG cac----agccaT
AGTGCG gcgtcgacctgctcatcCGTTAA cac----agccaT
GTGCAG tttagggcgatcgTAAGCG cggcgct--------tG
GTTGGA ttcagggcgatcgCAAGCT cggcgct--------tG
Translational start codons in transcribed pseudogenes
Potential mechanisms for translational "silencing" of pseudogenes transcribed in M. leprae were analyzed in silico using bioinformatics tools. Results demonstrated that 363/486 (75%) of transcribed pseudogenes lacked traditional translational start codons (AUG, GUG, UUG), greatly reducing the translation potential of transcribed pseudogenes into protein products (Additional File 4).
Ribosome binding strength of the SD regions of transcribed pseudogenes
In frame stop codons within transcribed pseudogenes
Functionality predictions for transcribed pseudogenes
Thus, most pseudogenes, including those that are transcribed, appear to be under very low selection strength. It is also worth mentioning that 15/216 (7%) have Ka/Ks ratios > 1.4 (Fig. 8B), whereas these cases are basically absent in functional genes. These higher-than-expected non-synonymous substitutions are characteristic of proteins under positive selection, but only 4 of them are transcribed, and therefore, it is unlikely that these high ratios indicate accelerated protein evolution but simply sequence deterioration. These analyses could be considerably improved when the genome of the second strain of M. leprae BR4923 (just recently available) is analyzed, so that sequence evolutionary patterns could be compared between pseudogenes in both strains.
Pseudogenes are considered disabled copies of functional genes that were once active in the ancient genome and their identification has been relatively rare until the recent availability of a large number of fully sequenced and annotated genomes and the improvement in detection algorithms [17–19]. Analysis of these genomes demonstrated that pseudogenes are much more common than previously thought and that pseudogenes can represent a significant fraction of the genome [[5, 18, 19]; http://www.pseudogene.org/main.php]. As a result, the coding potential of genomes has been shown to be substantially lower than originally predicted. For example, the human genome contains 16,326 pseudogenes and Escherichia coli K-12 genome, once thought to only possess a few pseudogenes, has been shown to harbor 134 inactivated genes. Mycobacterial species are no exception. M. tuberculosis H37Rv contains 278 inactivated genes http://www.pseudogene.org/cgi-bin/db-gen.cgi?type=Prokaryote and the recently sequenced M. ulcerans genome has 727 pseudogenes (BuruList Web Server: http://genolist.pasteur.fr/BuruList/) . The case of pseudogenes in M. leprae is very dramatic with over 1100 being documented [4, 5, 9]. This represents the largest number of any bacterial genome sequenced to date. These data strongly suggest that genome down-sizing through the accumulation of pseudogenes, as well as gene loss, has resulted in the very specialized requirements for M. leprae growth.
Although the precise mechanism resulting in the formation of this large number of pseudogenes in M. leprae is unclear, several possible mechanisms have been defined. It has been suggested that the loss of dnaQ-mediated proofreading activities of the DNA polymerase III and large-scale rearrangements and deletions arising from homologous recombination events may have contributed to this accumulation of pseudogenes [4, 5]. The loss of sigma factors  and two-component systems  have also been proposed as possible mechanisms in M. leprae pseudogenization. The dynamics of this reductive process in M. leprae has recently been studied by reconstructing the gene content of the last common ancestor of M. leprae and its closest relative M. tuberculosis and comparing it with the present M. leprae genome . Data from this study suggest that the loss of ancestral genes resulted in the loss of functional genes of M. leprae's ancestor and its divergence from M. tuberculosis and that pseudogenization events appear to be recent gradual evolutionary events in M. leprae's lineage (within the last 20 million years).
Pseudogene accumulation might promote adaptive microevolution resulting in transitioning from a free-living to a mutualistic lifestyle [1, 2]; from multiple hosts to specific hosts and ultimately specific host cells. Therefore, pseudogenization of M. leprae's sigma factors  and stress response genes, resulting in limited response to environmental stress conditions , may have contributed at least in part to its adaptive evolution and to its extremely specialized niche within peripheral macrophages [24, 25] and Schwann cells of peripheral nerves in humans .
In general, pseudogenes are considered to be 'junk' DNA sequences that are in the process of being removed from the genome. However, recently we and others have demonstrated the presence of a small number of pseudogene transcripts in M. leprae [11, 12] and other bacterial species [27, 28]. In addition, others have found that transcribed pseudogenes can be functional .
In the present study, further characterization of the overall pseudogene transcriptional profile of M. leprae in the nu/nu mouse foot pad granulomatous tissue by global DNA array and RT-PCR analyses demonstrated that not only does M. leprae possess the highest number of pseudogenes/genome it also possesses the highest rate of bacterial pseudogene transcription documented to date. There was no apparent bias for transcription of pseudogenes in M. leprae based on chromosomal location or functional gene category. Although the highest percentage of transcribed pseudogenes was found in functional category V (hypothetical proteins), this finding was not surprising as this category contains the largest percentage of pseudogenes in the genome . Many pseudogenes belong to gene families that are large in close relatives such as M. tuberculosis but are simplified during the loss of redundancy that takes place after niche specialization . Results of the present study demonstrated that a large number of these degenerated ORFs, which may no longer code for their appropriate functions, were expressed in M. leprae using transcriptional machinery, metabolic resources and energy without potential benefit to this organism. These direct and indirect costs have previously been suggested to select against the expression of pseudogenes in M. leprae by the erosion of sequences involved in transcription initiation . Therefore, even though a large number of M. leprae pseudogenes are transcriptionally active, approximately 60% of M. leprae's pseudogenes are transcriptionally silent, presumably by this or similar mechanisms.
In silico analysis of transcribed pseudogenes suggested potential mechanisms for their transcription. Their positioning within gene clusters (operon-like organizations), or downstream of transcribed ORFs, along with the paucity of intrinsic terminators between functional ORFs and transcribed pseudogenes implies that several pseudogenes are transcribed via a read-through manner. These data support a previous study which demonstrated that ~74% of M. leprae ORFs lacked detectable intrinsic transcriptional terminators . An exception to this was found in the present study when the transcriptional pair ML0180c-ML0179c (pseudogene), containing a strong terminator sequence (ΔG -38.4) within the ML0180c coding region, was found to be transcribed as a single gene transcript product. The question is why is the terminator not functional? Previous work by our group has shown that terminators do not function if they are inside coding regions. There could be various reasons for this, prominently the presence of ribosomes or formation of antitermination complexes. In this case, the terminator is inside the pseudogene coding region and factor(s) which prevent termination functions inside coding regions could come into play. Sequences upstream and downstream of terminators have also been shown to be important in some cases. These could be the reason(s) for its lack of functioning. Also it must be noted that ΔG is an important, but not the sole indicator of terminator efficiency. In fact, our work has also shown that most terminators in M. leprae have a ΔG lower than this value.
The present study also demonstrated that rho (ML1132) and ndk (ML1469c), a nucleoside diphosphate kinase associated with its activity , were among the 1353 genes expressed. However, to date nothing is known about rho-dependent transcript termination in M. leprae and therefore, the significance of this for pseudogene gene expression is unknown. In addition co-transcription of genes of unrelated function has been shown in intracellular species that have undergone massive genome reduction and low selection strength such as Buchnera, where after the elimination of DNA segments that included promoter regions, two unrelated genes ended up physically linked  and were shown by microarray analysis to be co-transcribed . Thus, these imperfect regulatory mechanisms in which promoter-less ORFs or pseudogenes are unnecessarily expressed may not be uncommon in species undergoing low selection strength, such as those under episodes of genetic drift and small population sizes.
However, not all M. leprae pseudogenes appear to rely on read-through transcription as a mechanism of transcription. Putative promoters were identified in silico in the upstream region of M. leprae pseudogenes. When 10 of these were tested for promoter activity in a promoterless reporter E. coli system, all were positive. Therefore, while the selection against the expression of pseudogenes in M. leprae by the erosion of sequences involved in transcription initiation appears to be an effective transcriptional mechanism for "silencing" M. leprae pseudogenes, the presence of functional promoters contributes to pseudogene transcription in M. leprae.
Prokaryotic mRNAs generally contain within their 5'-UTRs an SD sequence that serves as a ribosome-binding site . The loss of functional SD sequences results in the lack of efficient translational capability and therefore results in a reduction or loss of protein production. Recently it has been reported that the SD sequences of M. leprae pseudogenes are highly degraded or degenerate suggesting that translation is impaired in nonfunctional open reading frames (pseudogenes) in this pathogen and that this potentially reduces the metabolic investment on faulty proteins because, although pseudogenes can persist for long time periods in the genome, they would be effectively "silenced" . The present study confirmed these results and further demonstrated that although they have lower ribosomal binding strength than ORFs, transcribed pseudogenes have higher ribosomal binding strength than non-transcribed pseudogenes. Therefore these data strongly suggest that some transcribed pseudogenes are actually translated in M. leprae. To test this hypothesis, the promoter, SD (strong ribosomal binding strength), start codon and partial coding region of the pyrR (ML0531) pseudogene was fused into the gfp gene in a promoterless reporter plasmid lacking a SD site and was transformed into E. coli. Results of this preliminary experiment suggested that the pyrR SD site initiated ribosomal binding and resulted in the translation of the pyrR-gfp fusion protein product yielding the green fluorescent phenotype. Thus, although the results of this study indicate that most pseudogenes have either no recognizable SD or weak SD sequences for binding to the anti-SD sequence of the 3' region of the 16S rRNA, some of the transcribed pseudogenes have intact ribosome-binding sequences of similar strength to the orthologs in M. tuberculosis.
In addition, the current study demonstrated that the majority of transcribed pseudogenes lack traditional prokaryotic translational start codons. It has been shown that alteration of start codons results in loss of translational efficiency [32, 33]. Even though the lack of these sequences in the majority of M. leprae pseudogene transcripts appears to be an effective mechanism for translational "silencing", to date this has not yet been experimentally confirmed.
In-frame stop codons (elementary property that distinguishes a pseudogene from a functioning gene) were present in 95% of transcribed pseudogenes, whether or not they contained start codons. Therefore, if translation of transcribed pseudogenes initiates, a truncated protein product should result from the majority of M. leprae pseudogenes. In rare instances, the protein fragment is still functional as bad codons can also be bypassed or edited at the level of mRNA by recoding mechanisms. Recoding is the reprogramming of mRNA translation by localized alterations in the standard translational rules and recoding products can play critical cellular roles . Typically three classes of recoding are known: 1) frameshift recoding; 2) bypass (hopping) recoding; and 3) codon redefinition involving site-specific recognition (usually but not limited to stop codon). Recoding is utilized in the expression of a minority of genes in probably all organisms and has been documented in M. avium, [Selenocysteine incorporation at the stop codon (UGA) to yield formate dehydrogenase http://recode.genetics.utah.edu/display.cfm#fdh_s_pro_mavi]. To date recoding has not been documented in M. leprae or its close relative M. tuberculosis. However, if recoding does occur in M. leprae, it is unlikely that transcripts would be recoded to yield full length sequences when multiple stop codons occur in a single coding sequence. It is estimated that 80% of transcribed pseudogenes contain at least 3 stop codons within their sequence and 90% of these pseudogenes have < 50% of the predicted full-length protein when compared to the M. tuberculosis homolog due to deletion mutations. Therefore, it is predicted that if translated, these sequences will result in truncated proteins.
Using the non-synonymous to synonymous substitutions analysis as a measure of potential functionality of pseudogenes, we showed that only one third of these genes had similar Ka/Ks ratios to functional genes, regardless of whether they are transcribed or not. As explained above, this is an upper limit because part of the analyzed sequence evolution corresponds to the M. tuberculosis functional orthologs and because the pseudogenization process could be recent for some genes and therefore their Ka/Ks ratios would be close to normal. Therefore, although the number of pseudogenes for which unambiguous Ka/Ks ratios could be obtained was small, and at least one of these with a low Ka/Ks ratio was translated, these data suggest that most transcribed pseudogenes are in the process of degradation. However, this is an upper estimate because of the potential short time passed after pseudogenization and because part of the substitutions correspond to the functional homolog in M tuberculosis taken as reference. Additional support for these conclusions is that even though protein expression data has demonstrated the presence of > 300 proteins in protein extracts from armadillo-derived M. leprae, no pseudogene products were identified [14, 15].
The data presented in this study strongly suggest that even though a large number of M. leprae's pseudogenes are transcriptionally active, translational "silencing" mechanisms ensure that valuable metabolites and energy are not wasted to produce proteins from the majority of these transcripts which have no apparent benefit for cellular survival or growth of M. leprae. However, it is unclear whether these pseudogene transcripts have an additional detrimental effect on M. leprae. Nevertheless, some pseudogene transcripts do appear to be capable of producing protein products. These genes and their potential translational products need to be studied more extensively to understand their full biological impact on M. leprae.
RNA purification and cDNA production
M. leprae was purified from the granulomatous hind footpad tissue of four individual nu/nu nude mice, six months post-infection as previously described . M. leprae RNA was purified from individual bacterial preparations as previously described using TRIzol® (Invitrogen, Carlsbad, CA), FastPrep Blue RNA tubes and mechanical extraction using a FastPrep® 120 Instrument . DNA was removed from these preparations using the Turbo DNA-free™ kit (Ambion, Austin, TX). DNA-free RNA aliquots were then stored at -80°C. This RNA was used for DNA microarray analysis or converted to cDNA for RT-PCR analysis using 1 μg RNA and the Advantage RT-for-PCR Kit (BD BioSciences, Clontech, Mountain View, CA) using random hexamers. Controls for DNA contamination consisted of 1 μg RNA incubated with the reverse transcription reagents, excluding the reverse transcriptase (RT-). Template cDNA was also made from BALB/c mouse spleen total RNA (BD Biosciences, Clontech).
M. leprae gene expression by microarray analysis
M. leprae whole genome DNA microarrays representing the 1,614 annotated ORFs and 1,133 identified pseudogenes, were obtained from the Leprosy Research Support and Maintenance of an Armadillo Colony Post-Genome Era, Part I: Leprosy Research Support Contract (NO1 AI-25469) at Colorado State University. Microarray experiments were performed using previously described protocols . Microarrays were scanned using a Bio-Rad VersArray Chip Reader (Bio-Rad, Carlsbad, CA) and using SpotFinder Software (manufacturer) to quantify fluorescence. Genes were positive for transcription if the average mean signal to noise ratios (SNR) were > 2-fold for samples analyzed. Transcribed pseudogenes were then mapped to the M. leprae chromosome and functional gene categories which have transcribed pseudogenes were identified http://www.sanger.ac.uk/Projects/M_leprae/.
To validate ~20% of genes positive by microarray analysis, RT-PCR was performed using Thai-53 M. leprae cDNA, primers for PCR amplification [based on gene sequences from the M. leprae TN genome http://genolist.pasteur.fr/Leproma/ using PrimerQuest http://www.idtdna.com], and PCR analysis using recommended primer annealing temperature and 40 cycles of PCR. M. leprae DNA (1 ng) was used as a positive control. Reactions without reverse transcriptase (RT (-) reactions), buffer and mouse cDNA were used as negative controls for each assay. Amplicons were observed in ethidium bromide-stained 2% agarose gels using Gel Doc 2000 Gel Documentation System (Bio-Rad) and the amplicon sequence was confirmed using automated DNA sequencing.
Identification of transcriptional read-through mechanisms
To identify M. leprae pseudogenes potentially transcribed as a result of read-through transcription, the presence of these pseudogenes within gene clusters or directly downstream of transcribed ORFs was analyzed using Gene Cluster analysis (GeneChords-http://genomics10.bu.edu/cgi-bin/GeneChords/GeneChords.cgi) and the M. leprae TN mapping data http://genolist.pasteur.fr/Leproma/. M. leprae cDNA was used as the template to amplify fragments representing the read-trough products of predicted size using PCR and forward primers within the upstream ORFs and reverse primers within the pseudogenes or in the genes downstream of the pseudogene (Fig. 3A). Products were analyzed for their predicted fragment sizes using agarose gel electrophoresis and the DNA sequence of the resultant PCR amplicons was confirmed by automated DNA sequencing.
Identification of stem loop structures indicative of intrinsic termination sequences
Since read-through transcription relies on the absence of transcript termination structures between the 3'-UTRs of transcribed ORFs and downstream genes or pseudogenes, the presence of stem loop structures, indicative of intrinsic terminators, was investigated in the annotated genome of M. leprae TN strain using the algorithm Genome Scanner for Terminators (GeSTer) [16, 38]. The program accepts whole genome sequence information from GenBank (NCBI), and searches the sequences -20 to +270 with respect to the stop codon in the 3'-UTR of upstream ORFs for palindromic sequences, which could potentially form stem-loop structures when transcribed. It then sorts out the structures based on their ΔG. GeSTer further defines a genomic ΔGcutoff which is a function of the genomic G+C content of the bacterial species. Palindromic structures with ΔG value more negative than this cut-off are only considered as potential intrinsic terminators.
In silico identification of putative pseudogene promoters
Independent pseudogene transcription requires the presence of functional a promoter in the (5'UTR) of these genes. Promoters need DNA bend regions as the RNA polymerase complex initiates strand separation at the promoter -10 regions . Putative pseudogene promoters were located by DNA curvature (bend) analysis [40, 41] with the "bend-it" server http://hydra.icgeb.trieste.it/dna/index.php using DNase I parameters  and the consensus bendability scale  with a 31 size sliding window and simple smoothing of plots. Upstream 200–300 nucleotide plots of intrinsic curvature, bendability, complexity and GC content troughs were used to locate promoter regions through coincidence of peaks and troughs. Intrinsic curvature peak heights of less than 5 degrees per helical turn were discarded . The sigA promoter region 38 was used as a standard . When these peaks and troughs coincided, the region was assigned a putative promoter site. To avoid pseudogenization effects on upstream regions it was necessary to use ClustalW alignments with upstream regions from normal mycobacterial genes of the Mtb complex, the MMAR-MUL and MAV-MAP complexes. If alignments could not be made, the ML data was discarded. ClustalW http://www.ebi.ac.uk/Tools/clustalw2/index.html cross-species mycobacterial DNA alignments were used to assign putative promoter -10, -35 elements, initiation sites (I) and start codons to the predicted promoter-like regions and determine the distance (bp) of the promoter upstream from the start codon.
In vitro promoter analysis
To further characterize pseudogene promoters previously identified by "bend-it" DNA curvature and alignment analyses, approximately 150 bp fragments of upstream sequence of 10 pseudogenes containing putative promoter regions were amplified from M. leprae Thai-53 DNA using standard PCR, primers (Additional File 6) and cloned into the p Glow-TOPO-TA vector which contains a promoterless Cycle 3 GFP mutant reporter from the p Glow-TOPO-TA-Expression-Kit (Invitrogen). E. coli XL1-Blue Supercompetent cells were then transformed with these plasmids. Fluorescence was analyzed from ampicillin-resistant (100 μg/ml ampicillin in Luria Bertani agar) bacterial clones by mixing a portion of each colony with 10 μl sterile PBS and placing the bacterial suspension onto a glass slide. A cover slip was applied and slides were examined using a Nikon Eclipse E400 fluorescent microscope using a FITC/gfp filter (excitation/emissionmaxima of 480 nm/560 nm). Positive clones were considered those that contained fluorescence levels above cultures that contained only the p Glow-TOPO-TA re-circularized vector.
Prediction of pseudogene translational potential
The translational potential for transcribed pseudogenes was determined in silico by analysis of start codons (AUG, GUG, and UUG) at the 5' end of predicted pseudogenes using the M. leprae TN strain genome sequence http://genolist.pasteur.fr/Leproma/. Since the ability of a transcript to be translated appears to be dependent on conservation of SD sequences which bind the transcript to its complementary sequence in the 3'region of the 16S rRNA, upstream regions of these pseudogenes were examined for SD binding strength using previously described protocols . These procedures calculate, based on base pair formation rules , the free energy values for the binding of the 3' end of the 16S rRNA with the region preceding each ORF at different positions (position zero indicates gene start). Lower free-energy values indicate higher binding strength. Estimates of SD sequence conservation for individual ORFs were obtained by quantifying the difference between the maximum and minimum free energy along the 50 nucleotides preceding the start of the gene (Method 1) and the difference between the free-energy value at position zero and the minimum value along the preceding 50 nucleotides (Method 2). Based on SD sequence conservation in M. tuberculosis, which has not undergone a pseudogenization process, values > 7 and 5 were taken as indicative of a conserved, functional SD region by the first and second measure, respectively.
Prediction of pseudogene functionality
The translational potential for transcribed pseudogenes was also investigated by estimating selection strength by calculating the rates of synonymous (Ks) and nonsynonomous (Ka) DNA substitutions (Ka/Ks ratios) in these sequences and compared to their corresponding functional homologs in M. tuberculosis H37Rv. Sequences of M. leprae pseudogenes and their corresponding M. tuberculosis H37Rv functional homologs were aligned using the program Pileup of the GCG Wisconsin package (Genetics Computer Group 1997). Only unambiguous alignments were considered. Rates of synonymous and non-synonymous substitutions were calculated using the Diverge command in GCG that applies Li's algorithm  with modifications . The Ka/Ks ratio is indicative of selection strength: in functional genes, synonymous substitutions are more frequent than replacement substitutions. Nevertheless, the mean ratio was also calculated for M. leprae functional ORFs for comparison to pseudogenes. Typically in other bacteria if the Ka/Ks ratio is around 1, substitutions are equally frequent at all three codon positions and the ORF is likely to be a pseudogene. However, only 216 pseudogenes could be analyzed because of lack of homology with M. tuberculosis, alignment ambiguity, or because the start codon and/or reading frame could not be unequivocally identified.
A subset of transcriptionally active pseudogenes with translational start codons were further analyzed to determine the percentage of the predicted full-length protein of pseudogene when compared to the M. tuberculosis H37Rv homolog (% Rv) using the deduced translated protein sequences for M. leprae ExPasy Translate Tool http://us.expasy.org/tools/dna.html and ClustalW alignment http://www.ebi.ac.uk/Tools/clustalw2/index.html to the M. tuberculosis homolog.
Cloning and expression of the pyrR pseudogene protein
Primers designed to amplify the coding region corresponding to the entire 532 bp coding region of the pyrR pseudogene (including stop codons at codon 158 and 166 and beyond the end of the coding sequence) from Thai-53 DNA and containing CACC on the 5'terminus of the forward primer (Fig. 10A) were used with standard PCR and directionally cloned into the linearized, topoisomerase I-activated Champion™ pET200/D-TOPO vector (Invitrogen), containing an N-terminus 6× His-tag, an N-terminal tag of a β-galactosidase and T7lac promoter for high-level expression, and transformed into One Shot® BL21 Star™ (DE3) and Top10 for storage (Invitrogen) according to manufacturer's recommendations. A kanamycin-resistant (50 μg/ml in LB agar) clone BL21 Star™ was verified to contain the pyrR insert by PCR and subsequent automated DNA sequencing (E. coli:: pET200/D-TOPO/MLpyrR). A culture was grown to an OD600 = 0.6 and induced for several hours with a final concentration of 1 mM isopropyl β-D thiogalactopyranoside (IPTG) (Sigma-Aldrich, St. Louis, MO). Crude cell lysates from both non-induced and induced E. coli:: pET200/D-TOPO/MLpyrR after 18 hr incubation at 37°C were separated using a SDS-PAGE (4% to 20% discontinuous gradient polyacrylamide gel, stained with coomassie brilliant blue (Bio-Rad), and compared against Kaleidoscope Pre-stained Standard markers (Bio-Rad). A Western blot using the Anti-Xpress™ antibody (Invitrogen) was used to verify the presence of the polyhistidine epitope on the recombinant protein product. Crude cell lysates from E. coli:: pET200/D-TOPO/lacZ (Champion™ pET200/D-TOPO vector Kit), treated under the same conditions described above, were used as a control for this experiment however, this control lacked the histidine epitope tag.
Statistical analysis of data
Statistical analysis of data for this study was obtained from the comparison of means and standard deviations of raw data and performed using One-way Analysis of Variance (ANOVA) using Tukey-Kramer Multiple Comparisons Test GraphPad InStat software. All data with p < 0.05 were considered significant.
We wish to thank Mr. J.P. Pasqua, Dr. Maria T. Pena and Ms. Heidi Zhang for their excellent technical service to this project. This project was funded in part by: NIH-NIAID Contract # YI-AI-2646-01; HRSA/BPHC/Division of National Hansen's Disease Programs; CAPES, Coordenaçao de aperfeiçoamento de pessoal de nivel superior Fundação do Ministério da Educação, Brazil; and NIH NO1 AI-25469, N01 AI-40091, and AI-55298.
This manuscript recognizes the scientific contributions of Dr. Amol Amin, who tragically passed away before completion of this work.
- Tong Z, Zhou D, Song Y, Zhang L, Pei D, Han Y, Pang X, Li M, Cui B, Wang J, Guo Z, Qi Z, Jin L, Zhai J, Du Z, Wang J, Wang X, Yu J, Wang J, Huang P, Yang H, Yang R: Pseudogene accumulation might promote the adaptive microevolution of Yersinia pestis. J Med Microbiol. 2005, 54 (Pt 3): 259-268. 10.1099/jmm.0.45752-0.View ArticlePubMedGoogle Scholar
- Toh H, Weiss BL, Perkin SA, Yamashita A, Oshima K, Hattori M, Aksoy S: Massive genome erosion and functional adaptations provide insights into the symbiotic lifestyle of Sodalis glossinidius in the tsetse host. Genome Res. 2006, 16 (2): 149-156. 10.1101/gr.4106106.PubMed CentralView ArticlePubMedGoogle Scholar
- Andersson JO, Andersson SG: Genome degradation is an ongoing process in Rickettsia. Mol Biol Evol. 1999, 16: 1178-91.View ArticlePubMedGoogle Scholar
- Mira A, Pushker R: The silencing of pseudogenes. Mol Biol Evol. 2005, 22 (11): 2135-38. 10.1093/molbev/msi209.View ArticlePubMedGoogle Scholar
- Cole ST, Eiglmeier K, Parkhill J, James KD, Thomson NR, Wheeler PR, Honore N, Garnier T, Churcher C, Harris D, Mungall K, Basham D, Brown D, Chillingworth T, Connor R, Davies RM, Devlin K, Duthoy S, Feltwell T, Fraser A, Hamlin N, Holroyd S, Hornsby T, Jagels K, Lacroix C, Maclean J, Moule S, Murphy L, Oliver K, Quail MA, Rajandream MA, Rutherford KM, Rutter S, Seeger K, Simon S, Simmonds M, Skelton J, Squares R, Squares S, Stevens K, Taylor K, Whitehead S, Woodward JR, Barrell BG: Massive gene decay in the leprosy bacillus. Nature. 2001, 409: 1007-11. 10.1038/35059006.View ArticlePubMedGoogle Scholar
- Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, Gordon SV, Eiglmeier K, Gas S, Barry CE, Tekaia F, Badcock K, Basham D, Brown D, Chillingworth T, Connor R, Davies R, Devlin K, Feltwell T, Gentles S, Hamlin N, Holroyd S, Hornsby T, Jagels K, Krogh A, McLean J, Moule S, Murphy L, Oliver K, Osborne J, Quail MA, Rajandream MA, Rogers J, Rutter S, Seeger K, Skelton J, Squares R, Squares S, Sulston JE, Taylor K, Whitehead S, Barrell BG: Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature. 1998, 393 (6685): 537-44. 10.1038/31159. Erratum in: Nature 1998, 396(6707):190View ArticlePubMedGoogle Scholar
- Moran NA, Wernegreen JJ: Lifestyle evolution in symbiotic bacteria: insights from genomics. Trends Ecol Evol. 2000, 15 (8): 321-26. 10.1016/S0169-5347(00)01902-9.View ArticlePubMedGoogle Scholar
- Rocha EP, Danchin A: Base composition bias might result from competition for metabolic resources. Trends Genet. 2002, 18 (6): 291-94. 10.1016/S0168-9525(02)02690-2.View ArticlePubMedGoogle Scholar
- Eiglmeier K, Parkhill J, Honore N, Garnier T, Tekaia F, Telenti A, Klatser P, James KD, Thomson NR, Wheeler PR, Churcher C, Harris D, Mungall K, Barrell BG, Cole ST: The decaying genome of Mycobacterium leprae. Lepr Rev. 2001, 72: 387-98.PubMedGoogle Scholar
- Pushker R, Mira A, Rodríguez-Valera F: Comparative genomics of gene-family size in closely related bacteria. Genome Biol. 2004, 5 (4): R27-10.1186/gb-2004-5-4-r27.PubMed CentralView ArticlePubMedGoogle Scholar
- Williams DL, Torrero M, Wheeler PR, Truman RW, Yoder M, Morrison N, Bishai WR, Gillis TP: Biological implications of Mycobacterium leprae gene expression during infection. J Mol Microbiol Biotechnol. 2004, 8 (1): 58-72. 10.1159/000082081.View ArticlePubMedGoogle Scholar
- Suzuki K, Nakata N, Bang PD, Ishii N, Makino M: High-level expression of pseudogenes in Mycobacterium leprae. FEMS Microbiol Lett. 2006, 259: 208-14. 10.1111/j.1574-6968.2006.00276.x.View ArticlePubMedGoogle Scholar
- Mira A, Pushker R, Rodriguez-Valera F: The Neolithic revolution of bacterial genomes. Trends Microbiol. 2006, 14 (5): 200-6. 10.1016/j.tim.2006.03.001.View ArticlePubMedGoogle Scholar
- Marques MA, Espinosa BJ, Xavier da Silveira EK, Pessolani MC, Chapeaurouge A, Perales J, Dobos KM, Belisle JT, Spencer JS, Brennan PJ: Continued proteomic analysis of Mycobacterium leprae subcellular fractions. Proteomics. 2004, 4 (10): 2942-53. 10.1002/pmic.200400945.View ArticlePubMedGoogle Scholar
- Marques MA, Neves-Ferreira AG, da Silveira EK, Valente RH, Chapeaurouge A, Perales J, da Silva Bernardes R, Dobos KM, Spencer JS, Brennan PJ, Pessolani MC: Deciphering the proteomic profile of Mycobacterium leprae cell envelope. Proteomics. 2008, 8 (12): 2477-91. 10.1002/pmic.200700971.View ArticlePubMedGoogle Scholar
- Unniraman S, Prakash R, Nagaraja V: Alternate paradigm for intrinsic transcription termination in eubacteria. J Biol Chem. 2001, 276: 41850-55. 10.1074/jbc.M106252200.View ArticlePubMedGoogle Scholar
- Lerat E, Ochman H: Psi-Phi: exploring the outer limits of bacterial pseudogenes. Genome Res. 2004, 14: 2273-78. 10.1101/gr.2925604.PubMed CentralView ArticlePubMedGoogle Scholar
- Lerat E, Ochman H: Recognizing the pseudogenes in bacterial genomes. Nucleic Acids Res. 2005, 33: 3125-32. 10.1093/nar/gki631.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu Y, Harrison PM, Kunin V, Gerstein M: Comprehensive analysis of pseudogenes in prokaryotes: widespread gene decay and failure of putative horizontally transferred genes. Genome Biol. 2004, 5 (9): R64-10.1186/gb-2004-5-9-r64.PubMed CentralView ArticlePubMedGoogle Scholar
- Stinear TP, Seemann T, Pidot S, Frigui W, Reysset G, Garnier T, Meurice G, Simon D, Bouchier C, Ma L, Tichit M, Porter JL, Ryan J, Johnson PD, Davies JK, Jenkin GA, Small PL, Jones LM, Tekaia F, Laval F, Daffé M, Parkhill J, Cole ST: Reductive evolution and niche adaptation inferred from the genome of Mycobacterium ulcerans, the causative agent of Buruli ulcer. Genome Res. 2007, 17 (2): 192-200. 10.1101/gr.5942807.PubMed CentralView ArticlePubMedGoogle Scholar
- Madan Babu M: Did the loss of sigma factors initiate pseudogene accumulation in M. leprae?. Trends Microbiol. 2003, 11: 59-61. 10.1016/S0966-842X(02)00031-8.View ArticlePubMedGoogle Scholar
- Tyagi JS, Saini DK: Did the loss of two-component systems initiate pseudogene accumulation in Mycobacterium leprae?. Microbiology. 2004, 150: 4-7. 10.1099/mic.0.26863-0.View ArticlePubMedGoogle Scholar
- Gomez-Valero L, Rocha E PC, Latorre A, Silva F: Reconstructing the ancestor of Mycobacterium leprae: The dynamics of gene loss and genome reduction. Genome Res. 2007, 17 (8): 1178-1185. 10.1101/gr.6360207.PubMed CentralView ArticlePubMedGoogle Scholar
- Williams DL, Pittman TL, Deshotel M, Oby-Robinson S, Smith I, Husson R: Molecular basis of the defective heat stress response in Mycobacterium leprae. J Bacti. 2007, 189: 8818-27. 10.1128/JB.00601-07.View ArticleGoogle Scholar
- Krahenbuhl J, Adams LB: The role of the macrophage in resistance to the leprosy bacillus. Macrophage-Pathogen Interactions. Edited by: Zwilling BS, Eisenstein TK. 1994, Marcel Dekker, NY, NY, 281-302.Google Scholar
- Hagge DA, Oby Robinson S, Scollard D, McCormick G, Williams DL: A new model for studying the effects of Mycobacterium leprae on Schwann cell and neuron interactions. J Infect Dis. 2002, 186 (9): 1283-96. 10.1086/344313. Epub 2002 Oct 8View ArticlePubMedGoogle Scholar
- Takahashi H, Watanabe H: A gonococcal homologue of meningococcal gamma-glutamyl transpeptidase gene is a new type of bacterial pseudogene that is transcriptionally active but phenotypically silent. BMC Microbiol. 2005, 5: 56-10.1186/1471-2180-5-56.PubMed CentralView ArticlePubMedGoogle Scholar
- Davids W, Amiri H, Andersson SG: Small RNAs in Rickettsia: are they functional?. Trends Genet. 2002, 18 (7): 331-334. 10.1016/S0168-9525(02)02685-9.View ArticlePubMedGoogle Scholar
- Hirotsune S, Yoshida N, Chen A, Garrett L, Suglyama F, Takahashi S, Yagaml K-I, Wynshaw-Boris A, Yoshiki A: An expressed pseudogene regulates the messenger-RNA stability of its homologous coding gene. Nature. 2003, 423 (6935): 26-8. 10.1038/nature01535.View ArticleGoogle Scholar
- Ingham C, Hunter IS, Smith MCM: Isolation and sequencing of the rho gene from Streptomyces lividans ZX7 and characterization of the RNA-dependent NTPase activity of the over expressed protein. JBC ONLINE. 1996, 271: 21803-807. [http://www.jbc.org/cgi/content/full/271/36/21803]View ArticleGoogle Scholar
- Moran NA, Mira A: The process of genome shrinkage in the obligate symbiont Buchnera aphidicola. Genome Biol. 2001, 2 (12): 1-research0054. 10.1186/gb-2001-2-12-research0054.View ArticleGoogle Scholar
- Wilcox JL, Dunbar HE, Wolfinger RD, Moran NA: Consequences of reductive evolution for gene expression in an obligate endosymbiont. Mol Microbiol. 2003, 48 (6): 1491-1500. 10.1046/j.1365-2958.2003.03522.x.View ArticlePubMedGoogle Scholar
- Hirose T, Sugiura M: Multiple elements required for translation of plastid atpB mRNA lacking the Shine-Dalgarno sequence. Nucleic Acids Res. 2004, 32 (11): 3503-10. 10.1093/nar/gkh682.PubMed CentralView ArticlePubMedGoogle Scholar
- Baranov PV, Gurvich OL, Fayet O, Prere MF, Miller WA, Gesteland RF, Atkins JF, Giddings MC: RECODE: a database of frameshifting, bypassing and codon redefinition utilized for gene expression. Nucl Acids Res. 2001, 29: 264-267. 10.1093/nar/29.1.264.PubMed CentralView ArticlePubMedGoogle Scholar
- Truman RW, Krahenbuhl J: Viable M. leprae as a research reagent. Int J Lepr Other Mycobact Dis. 2001, 69 (1): 1-12.PubMedGoogle Scholar
- Williams DL, Oby-Robinson S, Pittman TL, Scollard D: Purification of Mycobacterium leprae RNA for gene expression analysis from leprosy biopsy specimens. BioTechniques. 2003, 35: 534-536, 538, 540PubMedGoogle Scholar
- Groathouse NA, Brown SE, Knudson DL, Brennan PJ, Slayden RA: Isothermal amplification and molecular typing of the obligate intracellular pathogen Mycobacterium leprae isolated from tissues of unknown origins. J Clin Microbiol. 2006, 44 (4): 1502-1508. 10.1128/JCM.44.4.1502-1508.2006.PubMed CentralView ArticlePubMedGoogle Scholar
- Unniraman S, Prakash R, Nagaraja V: Conserved economics of transcription termination in eubacteria. Nucleic Acids Res. 2002, 30 (3): 675-684. 10.1093/nar/30.3.675.PubMed CentralView ArticlePubMedGoogle Scholar
- Rivetti C, Guthold M, Bustamente C: Wrapping of DNA around the E. coli RNA polymerase open-promoter complex. EMBO Journal. 1999, 18: 4464-75. 10.1093/emboj/18.16.4464.PubMed CentralView ArticlePubMedGoogle Scholar
- Munteanu MG, Vlahovicek K, Parthasaraty S, Simon I, Pongor S: Rod models of DNA: sequence-dependent anisotropic elastic modeling of local bending phenomena. Trends Biochem Sci. 1998, 23 (9): 341-346. 10.1016/S0968-0004(98)01265-1.View ArticlePubMedGoogle Scholar
- Goodsell DS, Dickerson RE: Bending and curvature calculations in B-DNA. Nucleic Acids Res. 1994, 22: 5497-503. 10.1093/nar/22.24.5497.PubMed CentralView ArticlePubMedGoogle Scholar
- Brukner I, Sánchez R, Suck D, Pongor S: Sequence-dependent bending propensity of DNA as revealed by DNase I: parameters for trinucleotides. EMBO J. 1995, 14: 1812-8.PubMed CentralPubMedGoogle Scholar
- Hu Y, Coates AR: Transcription of two sigma 70 homologue genes, sigA and sigB, in stationary-phase Mycobacterium tuberculosis. J Bacteriol. 1999, 181 (2): 469-76.PubMed CentralPubMedGoogle Scholar
- Osada Y, Saito R, Tomita M: Analysis of base-pairing potentials between 16S rRNA and 5'-UTR for translation initiation in various prokaryotes. Bioinformatics. 1999, 15: 578-581. 10.1093/bioinformatics/15.7.578.View ArticlePubMedGoogle Scholar
- Turner DH, Sugimoto N, Jaeger JA, Longfellow CE, Freier SM, Kierzek R: Improved parameters for prediction of RNA structure. Cold Spring Harb Symp Quant Biol. 1987, 52: 123-133.View ArticlePubMedGoogle Scholar
- Li WH: Unbiased estimation of the rates of synonymous and nonsynonymous substitution. J Mol Evol. 1993, 36: 96-99. 10.1007/BF02407308.View ArticlePubMedGoogle Scholar
- Pamilo P, Bianchi NO: Evolution of the Zfx and Zfy genes: rates and interdependence between the genes. Mol Biol Evol. 1993, 10 (2): 271-281.PubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.