Functional Divergence and Convergent Evolution in the Plastid-Targeted Glyceraldehyde-3-Phosphate Dehydrogenases of Diverse Eukaryotic Algae

<div><p>Background</p><p>Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a key enzyme of the glycolytic pathway, reversibly catalyzing the sixth step of glycolysis and concurrently reducing the coenzyme NAD⁺ to NADH. In photosynthetic organisms a GAPDH paralog (Gap2 in Cyanobacteria, GapA in most photosynthetic eukaryotes) functions in the Calvin cycle, performing the reverse of the glycolytic reaction and using the coenzyme NADPH preferentially. In a number of photosynthetic eukaryotes that acquired their plastid by the secondary endosymbiosis of a eukaryotic red alga (Alveolates, haptophytes, cryptomonads and stramenopiles) GapA has been apparently replaced with a paralog of the host’s own cytosolic GAPDH (GapC1). Plastid GapC1 and GapA therefore represent two independent cases of functional divergence and adaptations to the Calvin cycle entailing a shift in subcellular targeting and a shift in binding preference from NAD⁺ to NADPH.</p> <p>Methods</p><p>We used the programs FunDi, GroupSim, and Difference Evolutionary-Trace to detect sites involved in the functional divergence of these two groups of GAPDH sequences and to identify potential cases of convergent evolution in the Calvin-cycle adapted GapA and GapC1 families. Sites identified as being functionally divergent by all or some of these programs were then investigated with respect to their possible roles in the structure and function of both glycolytic and plastid-targeted GAPDH isoforms.</p> <p>Conclusions</p><p>In this work we found substantial evidence for convergent evolution in GapA/B and GapC1. In many cases sites in GAPDHs of these groups converged on identical amino acid residues in specific positions of the protein known to play a role in the function and regulation of plastid-functioning enzymes relative to their cytosolic counterparts. In addition, we demonstrate that bioinformatic software like FunDi are important tools for the generation of meaningful biological hypotheses that can then be tested with direct experimental techniques.</p> </div

A simplified representation of the organismal phylogeny for relevant taxa in this study.

<p>Green oval represents the endosymbiotic event in the ancestor of the Archaeplastida (Green Algae, Red Algae, Glaucophytes, Land Plants, etc) that gave rise to the modern chloroplast. Gap2 of cyanobacteria became the GapA of the Archaeplastida, with a further gene duplication event in land plants giving rise to GapB. Many of the relationships between eukaryotic supergroups are currently unresolved, and are represented with multifurcations. Indicated with red ovals are lineages that contain the GapC1 gene. While the gene itself is monophyletic, it is unlikely that the lineages that harbour the red-algal derived plastids they are found in represent a clade, with the Haptophytes and Cryptophytes having uncertain affiliation but probably not with the Stramenopiles and Alveolata as once was thought.</p

Simplified maximum-likelihood phylogenetic tree of GAPH, shown as arbitrarily rooted with cyanobacterial sequences.

<p>Cytosolic GAPDH collapsed clades are coloured in blue. Gap A/B and Gap2 are in green, with GapC1 in red. Several ciliates form the immediate outgroup to GapC1 sequences, although without bootstrap support. Only bootstrap support values above 50 are shown. Several clades, indicated as Mixed Eukaryotic Groups, belong sequences from taxa that do not form monophyletic clades. Given the many paralogs included in this analysis, and the fact that it is a single gene phylogeny, this is not unexpected. In the majority of these groups taxa are predominantly from a single supergroup with the addition of a few “rogue” taxa. The full phylogenetic tree can be found in data S1.</p

CRISPR MultiTargeter: A Web Tool to Find Common and Unique CRISPR Single Guide RNA Targets in a Set of Similar Sequences

<div><p>Genome engineering has been revolutionized by the discovery of clustered regularly interspaced palindromic repeats (CRISPR) and CRISPR-associated system genes (Cas) in bacteria. The type IIB <i>Streptococcus pyogenes</i> CRISPR/Cas9 system functions in many species and additional types of CRISPR/Cas systems are under development. In the type II system, expression of CRISPR single guide RNA (sgRNA) targeting a defined sequence and Cas9 generates a sequence-specific nuclease inducing small deletions or insertions. Moreover, knock-in of large DNA inserts has been shown at the sites targeted by sgRNAs and Cas9. Several tools are available for designing sgRNAs that target unique locations in the genome. However, the ability to find sgRNA targets common to several similar sequences or, by contrast, unique to each of these sequences, would also be advantageous. To provide such a tool for several types of CRISPR/Cas system and many species, we developed the CRISPR MultiTargeter software. Similar DNA sequences in question are duplicated genes and sets of exons of different transcripts of a gene. Thus, we implemented a basic sgRNA target search of input sequences for single-sgRNA and two-sgRNA/Cas9 nickase targeting, as well as common and unique sgRNA target searches in 1) a set of input sequences; 2) a set of similar genes or transcripts; or 3) transcripts a single gene. We demonstrate potential uses of the program by identifying unique isoform-specific sgRNA sites in 71% of zebrafish alternative transcripts and common sgRNA target sites in approximately 40% of zebrafish duplicated gene pairs. The design of unique targets in alternative exons is helpful because it will facilitate functional genomic studies of transcript isoforms. Similarly, its application to duplicated genes may simplify multi-gene mutational targeting experiments. Overall, this program provides a unique interface that will enhance use of CRISPR/Cas technology.</p></div

Unique transcript isoform-specific sgRNA target sites for Type II CRISPR sgRNAs in zebrafish genes.

<p><b>A</b>. Proportions of genes with identified transcript isoform-specific sgRNA sites, transcripts with isoform-specific sgRNA sites and proportions of these sites in the sense and anti-sense orientation. sgRNA sites are 20 bp long with the NN 5’-dinucleotide and NGG PAM sequence. <b>B</b>. Distribution of total target site numbers for transcript isoforms. The mean number of sgRNA target sites (48.7) is indicated by a dashed line over the histogram. The graph axes are scaled using the square root function. The histogram bars are colored according to the frequency scale as shown.</p

Workflows of guide RNA target search in CRISPR MultiTargeter.

<p><b>A</b>. Simple CRISPR guide RNA search. A user enters a number of sequences or sequence identifiers and specifications for a target search. The program then runs these data, performs a regular expression match, stores the resulting coordinates and generates visual and table views of targets in each sequence. <b>B</b>. Common guide RNA target search in multiple sequences. Input sequences are used to generate a multiple sequence alignment. As in <b>(A)</b>, a regular expression with target specifications is run on the alignment consensus in both forward and reverse orientations. A successful match is defined as one having a maximum of one mismatch in the consensus sequence if the user allows mismatches. These matches are then highlighted in the multiple sequence alignment. In addition another algorithm is run on the input sequences to find unique target sites in each sequence (not shown). <b>C</b>. Common and unique guide RNA target search <b>in similar genes or transcripts</b>. In this workflow, gene or transcript sequences are retrieved from the database. Common targets are detected based on the multiple sequence alignment and unique target sites are found using an exhaustive string comparison algorithm (not shown). All targets sites are also checked to lie within a single exon to ensure successful targeting of the genomic sequence. In the illustration, locations of different target sites in genes A and B are shown together with the expected output of the program run. <b>D</b>. Common and unique guide RNA target search in transcripts of <b>a single gene</b>. Search for target sites is performed as described in <b>(C)</b>. In the illustration, input sequences are transcript isoforms A1, A2 and A3 of the gene A. The different types of target sites are shown as well as the expected program output. In <b>(C)</b> an <b>(D)</b>, common targets are indicated in pink and unique targets are in orange.</p

Examples of input and output pages of CRISPR MultiTargeter.

<p><b>A</b>. The input form for the multiple genes or transcripts input consists of the parameters for the sgRNA target site specification and the identifiers input area. <b>B</b>. The output page consists of the overall header indicating the type of design performed followed by the list of input identifiers which the user provided with the links to Ensembl gene pages where available. This example is from the multiple genes/transcripts workflow performed on <i>sox9a</i> and <i>sox9b</i> zebrafish genes. The main part of the output is focused on common sgRNA target sites and is organized in Visual and Table Views. The user can see the details of these views by clicking on the “expand or hide” links. Visual View consists of links to alignment with the target sites highlighted and markers for the start sites of target sites. Table View contains HTML tables with the relevant information on sgRNA target sites such as their ID numbers, sequences, start, end as well as computed sequence features such as GC % and predicted annealing temperature (Tm) of sgRNA:DNA interaction, exon numbers and predicted scores. There is also a “Unique sgRNA targets” part of the page which is organized similarly.</p

Search algorithm for sgRNA target sites in individual and multiple similar sequences.

<p>Input data for this algorithm consist of a sgRNA target site specification and sequence data. The dashed lines to the sequence boxes represent two possible branches of the algorithm: simple CRISPR sgRNA search and a search for common and unique target sites in multiple similar sequences. Target site specification is common to both branches of the algorithm and consists of a target site length, PAM sequence and its location as well as the sequence of the 5’-dinucleotide and the region where a single mismatch is allowed. The simple sgRNA search is achieved by running a regular expression (search pattern) for the target site specification on all input sequences in both orientations. The program can provide output for the sequence and location of identified target sites in visual and table formats. In the second branch of the algorithm, multiple similar sequences are first aligned using the ClustalW2 program. The resulting multiple sequence alignment is read by the program and the consensus sequence is computed. Running the target site specification expression on this consensus sequence results in the identification of candidate common target sites. If exon sequences are available for a particular sequence (indicated by “?” and dashed lines), each candidate target site in both common and unique sets is checked to ensure that this site lies completely within an exon sequence. Final identified common target sites are then displayed in visual and table formats. The search for unique target sites is accomplished by computing all possible target sites in both orientations in all sequences. Each target site is then compared to all identified target sites in these sequences. The speed of comparison depends on a mismatch count variable (MM count), which ensures that the comparison is stopped (“End”) as soon as there are more than 2 mismatches (identities are indicated by “*”). The target sites which pass this comparison test and the subsequent test for location within exon sequences are confirmed unique target sites. These unique target sites can then be output as before.</p

Comparison of different sgRNA design software programs.

<p>*Bioc—Bioconductor package of the R programming and statistical environment</p><p>Comparison of different sgRNA design software programs.</p

Hem25 is required for the effective import of glycine into mitochondria.

<p>A) Glycine can serve as the sole nitrogen source in <i>S</i>. <i>cerevisiae</i>. The GCV converts glycine to NH₃, as the GCV resides in the mitochondria the use of glycine as a nitrogen source requires efficient uptake of glycine into the mitochondria. B) Inactivation of the <i>HEM25</i> gene in yeast substantially decreased their ability to grow on glycine as the sole nitrogen source. Cells were grown in SD medium containing 30 g/l glycine as nitrogen source. Growth was determined by optical density (OD) of the culture at 600 nm. Data shown are the mean ± SEM for four replicates for wild type and <i>lpd1</i>Δ cells and nine replicates for <i>hem25</i>Δ cells. C) Serine is synthesized from the glycolytic intermediate 3-phosphoglycerate through a series of reactions that includes phosphoserine transaminase (PSAT1 in humans, Ser1 in <i>S</i>. <i>cerevisiae</i>). Serine is normally the main source of one carbon units (CH₂-THF, 5,10 methylenetetrahydrofolate and its metabolites) in cells. Inactivation of the <i>SER1</i> gene in yeast results in yeast cells that are auxotrophic for serine. Glycine supplementation can also overcome a mutation in the <i>SER1</i> gene as glycine can serve as a metabolic source for both serine and one carbon units. However, this capacity depends entirely on mitochondrial glycine import. The import of glycine into the mitochondria can generate one carbon units in the form of CH₂-THF through the activity of the glycine cleavage system (GCV). In addition, mitochondrial serine hydroxymethyltransferase (SHMT2 in humans, Shm1 in yeast) catalyzes the synthesis of serine from glycine and CH₂-THF, with serine exported into the cytoplasm to be consumed for several anabolic pathways including the synthesis of CH₂-THF. Simultaneously, CH₂-THF generated from glycine is oxidized to formate and also exported into the cytoplasm as a source of cytoplasmic one carbon units. D) An inability to import glycine into the mitochondria prevents glycine supplementation from providing serine and one carbon units to cells with an inactivated <i>SER1</i> gene. This was found to be the case upon inactivation of the yeast <i>HEM25</i> gene. Cells were grown to mid-log phase in SD medium containing 1 mM serine, and 1:10 serial dilutions plated on SD medium with no supplements or supplemented with serine or glycine.</p