Transitivity and Context-dependent Preferences Irrational Decision-making in an Amoeboid Organism: Supplementary Data Subject Collections Irrational Decision-making in an Amoeboid Organism: Transitivity and Context- Dependent Preferences

(1172 articles) ecology (845 articles) behaviour Articles on similar topics can be found in the following collections Email alerting service here right-hand corner of the article or click Receive free email alerts when new articles cite this article-sign up in the box at the top publication. Citations to Advance online articles must include the digital object identifier (DOIs) and date of initial online articles are citable and establish publication priority; they are indexed by PubMed from initial publication. the paper journal (edited, typeset versions may be posted when available prior to final publication). Advance Advance online articles have been peer reviewed and accepted for publication but have not yet appeared in Most models of animal foraging and consumer choice assume that individuals make choices based on the absolute value of items and are therefore 'economically rational'. However, frequent violations of ration-ality by animals, including humans, suggest that animals use comparative valuation rules. Are comparative valuation strategies a consequence of the way brains process information, or are they an intrinsic feature of biological decision-making? Here, we examine the principles of rationality in an organism with radically different information-processing mechanisms: the brainless, unicellular, slime mould Physarum polycephalum. We offered P. polycephalum amoebas a choice between food options that varied in food quality and light exposure (P. polycephalum is photophobic). The use of an absolute valuation rule will lead to two properties: transitivity and independence of irrelevant alternatives (IIA). Transitivity is satisfied if preferences have a consistent, linear ordering, while IIA states that a decision maker's preference for an item should not change if the choice set is expanded. A violation of either of these principles suggests the use of comparative rather than absolute valuation rules. Physarum polycephalum satisfied tran-sitivity by having linear preference rankings. However, P. polycephalum's preference for a focal alternative increased when a third, inferior quality option was added to the choice set, thus violating IIA and suggesting the use of a comparative valuation process. The discovery of comparative valuation rules in a unicellular organism suggests that comparative valuation rules are ubiquitous, if not universal, among biological decision makers. 1. INTRODUCTION How do individuals make decisions when choosing between items that vary in two or more attributes, particularly when these attributes are in conflict? A common example involves choosing a restaurant: should we select the higher priced, but higher quality restaurant or the lower priced, lower quality restaurant? Many models …


Summary
Saccharomyces cerevisiae strain W303 is a widely used model organism. However, little is known about its genetic origins, as it was created in the 1970s from crossing yeast strains of uncertain genealogy. To obtain insights into its ancestry and physiology, we sequenced the genome of its variant W303-K6001, a yeast model of ageing research. The combination of two next-generation sequencing (NGS) technologies (Illumina and Roche/454 sequencing) yielded an 11.8 Mb genome assembly at an N50 contig length of 262 kb. Although sequencing was substantially more precise and sensitive than whole-genome tiling arrays, both NGS platforms produced a number of false positives. At a 378Â average coverage, only 74 per cent of called differences to the S288c reference genome were confirmed by both techniques. The consensus W303-K6001 genome differs in 8133 positions from S288c, predicting altered amino acid sequence in 799 proteins, including factors of ageing and stress resistance. The W303-K6001 (85.4%) genome is virtually identical (less than equal to 0.5 variations per kb) to S288c, and thus originates in the same ancestor. Non-S288c regions distribute unequally over the genome, with chromosome XVI the most (99.6%) and chromosome XI the least (54.5%) S288c-like. Several of these clusters are shared with S1278B, another widely used S288c-related model, indicating that these strains share a second ancestor. Thus, the W303-K6001 genome pictures details of complex genetic relationships between the model strains that date back to the early days of experimental yeast genetics. Moreover, this study underlines the necessity of combining multiple NGS and genome-assembling techniques for achieving accurate variant calling in genomic studies. medical interest. Because the lifespan of mammalian organisms is considerably long, short-living species such as the yeast Saccharomyces cerevisiae are popular models in experimental ageing research [1,2]. Widely used measures of yeast ageing are (i) chronological lifespan, defined as survival of a stationary culture at 308C [3], or in its special case 'hibernating lifespan' at 48C [4]; and (ii) replicative lifespan (RLS), defined as the number of cell cycles an individual yeast cell can complete [5]. Determination of RLS is time-consuming and technically challenging, as it requires continuous micromanipulation of the target strains [6,7], or single cell trapping and microscopy [8]. To simplify RLS analysis, a genetic assay based on the yeast strain W303-K6001 was introduced around a decade ago, and has become popular [9][10][11][12][13][14]. The W303-K6001 RLS assay bases on differential expression of the essential CDC6 gene. Placed under control of two promoters, CDC6 is always expressed in mother cells (HO promoter), but expressed in daughters only when they grow on galactose (GAL1 promoter) [11]. Thus, on glucose, daughters arrest, whereas mothers continue to divide until senescence. The cell number in a W303-K6001 glucose microcolony is therefore a direct-and the stationary biomass of a W303-K6001 glucose culture an indirect-measure of RLS [9].
W303-K6001 is a direct descendant of the yeast strain W303-1A, which is commonly used in biomedical research laboratories around the world [15]. This strain is a laboratory mutt that was generated through a series of strain crosses, mainly conducted by Rodney Rothstein during his PhD thesis. W303 derivatives therefore have a complex and not thoroughly documented ancestry. The founding W303 strain W303-1A was derived from W301-18A [16], which was transformed by a plasmid containing the HO gene [17]. W301-18A itself originated from crosses of W87 derivatives [18,19], which are themselves partially descended from yeast strain S288c, the source of the S. cerevisiae reference genome [20]. W303-1A further contains genetic material from yeast strains D311-3A [21,22] and a historical yeast strain, D190-9C. Nothing seems to be known about D190-9C, except that it has originated in the laboratory of Jack Szostak ( personal communication of R. Rothstein to the Saccharomyces genome database, SGD [23]). This complex genealogy mixes with the ancestry of other laboratory strains commonly used today, such as S1278B and SK1 [24].
Proteomic profiling and tiling microarrays indicated that W303-1A derivatives maintain high similarity to 288c [24,25]. Population genomics confirmed these large genetic similarities, but also revealed the presence of substantial non-S288c material in the W303 genome. For example, on chromosome 2 on the left arm, there is a region similar to west African yeast strains, while a region on the right arm clustered with European strains. Surprisingly, regions that resembled Japanese sake strains were also found in the W303 genome [26]. This genetic divergence probably contributes to physiological differences that have been reported for W303 and S288c derivatives BY4741 and BY4742, the most widespread S288c descendants [27]. These strains differ not only in important physiological parameters such as cell size and volume, but also in their relative plasma-membrane potential and tolerance to alkali-metal cations [28]. Moreover, although S288c strains and W303 have a relatively similar RLS, RLS of W303-K6001 is shortened on glucose media [9,29], and aged W303 cells have considerably larger volumes. As the average protein concentration was reversely changed from 64 pg mm 23 (BY4741) to 24 pg mm 23 (W303), it was concluded that senescent W303 cells possess larger vacuoles [30].
To provide a comprehensive basis for the interpretation of ageing experiments performed with W303-K6001, and to understand genetic origins and physiological properties of W303-1A derivatives, we constructed a reference genome sequence for W303-K6001. Both high-coverage pysrosequencing (Roche/454) and sequencing by synthesis (Illumina) technologies were then used for highly accurate variant calling against the S288c reference genome. The high sequencing depth (in total 378Â average coverage) achieved with both platforms on this 11.8 Mb haploid genome facilitated a profound comparison on their performances for de novo assembly and variant calling. Roche/454 sequencing performed better in de novo and reference-guided assembly, indicating that the much higher coverage of the short-read technology could not compensate for read length. Both sequencing strategies, however, called a significant number of false positives in variant detection. Merging the outputs of both platforms reduced this number markedly, indicating that, at present, parallel sequencing with more than one NGS technology is essential for generating precise reference genomes and for avoiding false positives in variant detection.
The proportion of the W303-K6001 genome that is highly similar to its main ancestor, S288c, was 85.4 per cent, whereas the remaining genetic material is of different genetic origin. In part, these non-S288c clusters are shared with S1278B, another commonly used mutt yeast strain. These regions encode for 799 proteins that have altered amino acid sequence compared with S288c.

Results and discussion
3.1. Sequencing and assembly of the W303-K6001 genome We have previously presented a draft genome sequence for W303-K6001 and one of its variants, K6001-B7. Isolated after chemical mutagenesis, K6001-B7 is a W303-K6001 progeny that exhibits a dominantly inherited increase in resistance to oxidative stress, while having a premature ageing phenotype [13]. This genome sequence was generated by massive parallel pyrosequencing (Roche) and led to the identification of 13 single-nucleotide exchanges that distinguish the parent and its progeny. One of these, a C-T transition in the peroxiredoxin locus TSA1 (tsa1-B7), was shown to be responsible for the stress-resistance phenotype. Here, this W303-K6001 genome draft was improved by including the newest version of the S288c genome (EF4, Ensembl annotated version of S288c genome R64-1-1, GenBank GCA_000146045.2) for referencebased genome assembly. Moreover, we resequenced W303-K6001 using sequencing by synthesis technology (Illumina). We combined the sequencing information obtained with both technologies to correct for platform-dependent sequencing errors (false-positive variant calls) as reported to occur in NGS experiments [31]. To keep experimental variation at a minimum, we used the very same DNA purifications for Roche and Illumina library preparation. At a median read length of 527 bp, we had collected 662.12 Mb of sequence information with the Roche FLX pyrosequencing system. Analysing the W303-K6001 DNA on a Genome Analyzer IIx (GAIIx) with a 120 bp paired-end protocol yielded 3.9 Gb of sequencing data rsob.royalsocietypublishing.org Open Biol 2: 120093 after quality clipping; on average, 101.22 bp per 120 bp GAIIx read were used. The insert size of the paired ends was 225 + 60 bp (table 1). Obtained sequencing information was then assembled using the Newbler mapper/assembler v. 2.6, CLC bio reference mapper (included in CLC Genomics Workbench v. 5.1) and SOAPdenovo (63mer v. 1.05). Using Newbler, we tested different strategies for de novo assembly of the W303-K6001 genome (table 2). First, we compared the performance of de novo assemblies using 454 data alone and in combination of 454 and Illumina data. Interestingly, de novo assembly of pure 454 data yielded the largest N50 contig size of 262 kb; adding higher coverage of the Illumina platform did not result in larger contigs. Indicated by a high number of shorter contigs, this might signify that the assembler software was limited in estimating the accurate genome size at this unusual high coverage. Moreover, the insert size distribution of the Illumina paired-end library was below the average read length of the 454 data, and thus did not provide significant additional information for resolving repetitive structures in the yeast genome. Nevertheless, scaffolds produced by the paired-end information of Illumina data resulted in better long-range continuity than the assembly of 454 single reads alone. To exclude that these findings were specific for the Newbler de novo assembler, we assembled the Illumina reads also by SOAPdenovo. After optimizing the k-mer value to 57, the results were still lagging behind the Newbler results, however (table 3).
Next, we assembled the different W303-K6001 datasets by mapping to the S. cerevisiae S288c reference sequence (table 3). We obtained higher contig sizes and better long-range continuity than by the different de novo assembly approaches, reflecting the high similarity of the two strains. Also, short Illumina reads resulted in lower length coverage of the reference genome than using 454 reads. Additionally, we did apply the CLC bio reference mapper, which gave similar results for 454 and 454 þ Illumina reference-guided assemblies, but was better than Newbler when assembling Illumina-only data (table 3).
For the generation of a W303-K6001 reference genome sequence, we combined de novo and reference-guided assembly. We did simulate paired-end reads 400 bp in length with insert sizes ranging from 2000 to 4000 bp, based on the reference-guided assembly (Newbler/454 data only) by applying the 'simulate_reads' tool (CLC bio). The simulated reads comprised a 10Â genome coverage and were de novo assembled together with the 454 data. In this way, we did obtain the best 'de novo' results, but they were still behind the referenceguided strategy. For this reason, we finally did scaffold the

Comparison of the W303-K6001 genome assembly with whole-genome tiling arrays
The W303 genome has previously been analysed using wholegenome tiling arrays [32]. To be able to compare our SNP calling with this array, we used Newbler to map the W303-K6001 genome also to the SC genome version EF2 (Ensembl annotated version of R63-1-1, GCA_000146045.1), on which this study was based. Of the 9334 mutant positions, 583 (9.2%) were identified in the tiling array ( figure 1).   (see the electronic supplementary material, table  S2), and the list of mutations continues with 41 small insertions/deletions, and 87 more complex variations. Several of the latter may lead to a loss of the protein function as they affect the reading frame. Identified mutant genes include ade2-1, trp1-1, can1-100, leu2-3,112 and his3-11,15 alleles, which were actively crossed into W303-1A as auxotrophic markers. The genome sequencing identified either nonsense mutations (ade2 and trp1) or frameshift mutations (can1, leu2, his3) as cause of their loss of function (table 5).
In contrast to W303, BY4741/BY4742 are wild-type for TRP1 and ADE2, but deficient in met15 or lys2, respectively. As these mutations all block central and essential metabolic pathways [33], it is likely that they make a major contribution to the physiological differences reported for W303 and BY4741 [28,30]. The list of genes containing frameshift mutations also includes genetic factors that have been implicated in ageing-related physiological processes (table 6 lists genes belonging to gene ontology categories with more than two genes carrying a frameshift mutation). We detected four non-synonymous mutations, one nonsense mutation and two frameshift mutations within the coding sequence of the MET1 gene involved in methionine biosynthesis. This pathway is used for auxotrophic selection, and closely connected to the oxidative stress defence, glutathione as well as homocysteine metabolism [34]. The nonsense mutation terminates Met1p at its penultimate amino acid, and the two frameshifts are in close proximity so that the second one restores the open reading frame. Indeed, met1-W303 appears to be at least partially functional, as the strain is methionine prototroph (data not shown).
In this context, we noticed that the phenomena of cooccurring frameshift mutations that neutralize each other was not unique among gene ontology terms related to ageing (table 6). We found two neutralizing frameshifts within the telomere organizers TEL2 (12 SNPs and two neutralizing frameshifts), EST1 (two neutralizing frameshifts) and the manganese carrier MTM1 (two neutralizing frameshifts). Telomere organization has been closely associated with ageing in vertebrates, and in yeast their length is kept constant during replicative ageing [35]. MTM1 is required for the mitochondrial activation of superoxide dismutase (SOD2) and oxidative stress resistance [36]. Thus, this complex mutation appears to have occurred more than once in the history of W303. It is likely that the altered amino acids sequence within the frameshifts has influence on the functionality of these genes.
Other genes related to ageing that contain frameshift mutations include eight proteins involved in translation and the biogenesis of the small ribosomal subunit, and  Figure 1. Comparing the W303-K6001 genome sequence with whole-genome tiling arrays. The tiling array correctly identified the yeast background, as a significant number of SNV positions were overlapping. However, the tiling array did not reach sensitivity and accuracy of whole-genome resequencing. All unrelated Non-W303 yeast strains share a number of mutant coordinates, indicating errors in the reference genome or tiling array, or private mutations of the S288c line. YHL008C, a protein that is involved in chloride ion uptake [37,38]. Thus, although the artificial expression of CDC6 might explain large parts of the W303-K6001 lifespan, the strain carries several other mutations within genes involved in this biological property.

Phylogeny of W303-K6001
As mentioned earlier, W303 is a mutt of different laboratory strains, including S288c/W87, D311-3A and D190-9C [18,19,21,22]. Proteomic profiling, tiling arrays and what is known about its history indicated that most of the W303 background is S288c-like [24,25]. The W303-K6001 genome sequence allowed us to define the regions that derived from S288c, as they are virtually identical to the reference genome (less then 0.5 SNV per kb; figure 2a). In contrast, the sequence reveals distinct clusters of much higher genetic variability, identifying the genetic material derived from other parents. In total, the clusters with sequence divergence larger than 1 SNV per kb span 1744 kb, corresponding to 14.6 per cent of the W303-K6001 genome. The differences between the chromosomes are, however, relatively large. W303-K6001 chromosome XVI, for instance, is virtually identical to S288c, whereas just half of chromosome XI is S288c-like  X  IV  V  VI  III  median  VIII  I  XIV  VII  XIII  II  XII  XI 2b). Chromosome XVI is also an indicator of the genome stability of W303-K6001; except for a small cluster that spans 0.4 per cent of its sequence, there is virtually no variation compared with the S288c reference genome. Thus, the approximately four decades since the divergence of W303 and S288c did not lead to a significant number of secondary genetic changes, indicating that W303-K6001 still resembles the status of W303-1A after the crosses that led to its generation [15]. However, this also implies that smaller molecular incompatibilities (i.e. those that might be caused by non co-evolved subunits of protein complexes) might still exist in the W303 genome and impact its robustness. The median percentage of non-S288c-like DNA sequences per W303 chromosome was 13.9 per cent (figure 2b). Analysing the SNP frequency in clusters greater than 15 000 bp, we noticed that most have a median difference of two to five variations per kb (no chromosomal region had a difference between 0.5 and 2 SNVs per kb), but three clusters had higher median divergence of 9-12 SNVs per kb (figure 2c). One could speculate that for this reason one might be able to assign the origin of these regions to a different ancestor; however, the low number of these highly divergent clusters might equally point to regions that were exposed to different selection pressure. We then performed a number of BLAST searches, using the BLASTALL v. 2.2.24 tool [39], querying the yeast genome resources available at SGD January 2012 [23]. Confirming the results from Liti et al. [26], several non-S288c clusters had similarities to quite different yeast genomes, including sake strains. Interestingly, we found that a large non-S288c cluster on chromosome XIV (730 000 -760 000) was not only similar, but identical, to the genome sequence of another yeast strain commonly used in research, S1278B [40]. Exemplary CLUSTALW-generated alignments (figure 3a) and a corresponding distance diagram (figure 3b) are illustrated. Extending our search, we could then identify several other regions in the W303 genome that were similar to the S1278B genome (figure 3c). We illustrate one exemplary breakpoint of the Chr XI cluster, where the W303 genome switches from a non-S288c and non-S1278B region to a S1278B-like genome, indicating differing phylogenetic origins of this cluster (figure 3d). As S1278B shares part of its genealogy with S288c (47% of its genome does not differ from S288c [24]), these results indicate that W303 and S1278B share a second ancestor, or (less likely) that a third strain contributed to the genome of S288c after W303/ S1278B split from the lineage.

Concluding remarks
The K6001-W303 genome sequence facilitates comprehensive insights into its physiology and genealogy. In comparing W303-K6001 and S288c genome sequences, cross-platform sequencing and mapping eliminated 3308 false-positive nucleotide calls and 2389 mapping artefacts. The resulting consensus genome sequence differs in 8133 positions (including 8049 SNPs) from the S288c genome. These data demonstrate that it is vital to reproduce genetic variations identified by different sequencing strategies. Generated by crossing mutt yeast strains W87, D311-3A and D190-9C, W303-K6001 remains closely related to S288c, sharing 85.4 per cent of its genome. Remaining genetic differences cause altered amino acid composition or reading frame in 799 proteins, some of high relevance for physiology and ageing. Individual studies now have to clarify to what extent these mutations contribute to the physiological differences between these common yeast backgrounds. Unequal genomic SNV distribution allowed conclusions on the W303 genealogy, and identified a close genetic relationship of W303 with S1278B. This strain also shares 47 per cent of its background with S288c, but overall differs from it in 3.2 SNPs per kb, which cause 44 genes to be uniquely essential to S1278b and 13 to S288c [40]. Because W303-K6001 and S1278B share genomic regions not found in their common ancestor, it is likely that they have a second mutual ancestor. Thus, W303 represents a partial hybrid of S288c and S1278b, shedding new light into the complex relationships of today's widely used laboratory strains.

Illumina sequencing
Genomic DNA from both strains was sheared by sonification to fragment sizes of around 225 bp, cleaned (Zymo Research) and universal sequencing adaptors were ligated. After library quantification at a Qubit (Invitrogen), a 10 nmol stock solution of the amplified library was created. We loaded 8 pM of the stock solution onto the channels of a 1.4 mm flow cell, and cluster amplification was performed. Sequencing-by-synthesis was performed on an Illumina Genome Analyzer (GAIIx). After quality control of the first base incorporation (signal intensities, cluster density), the run was started. All samples were subjected to 120 bp paired-end sequencing.

Raw data processing of 454 reads
After default raw data processing, we used a resequencing trimming filter to increase the data output. ( parameters: doValleyFilterTrimBack ¼ false, vfBadFlowThreshold ¼ 6, vfLastFlowToTest ¼ 168, errorQscoreWindowTrim ¼ 0.01). With these parameters, we got an average quality score of greater than Q30 per base.

Raw data processing of Illumina GAIIx sequence reads
Illumina data were provided as qseq files generated by the Bustard 1.8.0 pipeline. High-quality data were extracted using homemade scripts ( perl/awk). As a first step, reads were trimmed in such a way that only the longest sequence range of the reads, which did not contain bases of quality lower than Phred 12, was used. Additionally, adaptor sequences were clipped, if at least 15 bp of the adaptor's 3 0 end was found in each read. After trimming and adaptor removal, only reads equal to or longer than 64 bp were used in the mapping/de novo assemblies. In a final step, most of the duplicate reads resulting from amplification bias during library construction were removed, if the first 64 bases of the reads were identical. Finally, sequence data were stored in fasta files with Newbler-compatible headers.

Assembly and mapping
Assemblies were computed by the Roche/454 Newbler v. 2.6 assembler or mapper software applying default parameters. Additional de novo assemblies were performed by the 127mer or 63mer version of SOAPdenovo (v. 1.05, downloaded from http://soap.genomics.org.cn). After running several assemblies, we found that a kmer size of 57 was giving the best results in terms of N50 contig sizes after scaffolding and gap filling and in terms of total consensus length. Additional reference-guided assemblies were carried out by the CLC Genomics Workbench v. 5.1 (CLC BIO, Aarhus, Denmark), and its reference mapper and probabilistic variant detection modules (default parameters for the mapping algorithm, for variant detection the ploidy parameter was set to 1).