Variable absorption of mutational trends by prion-forming domains during Saccharomycetes evolution

Data

The reference set of proteomes for Saccharomycetes (73 organisms) was sourced in June 2017 from UniProt (Boeckmann et al., 2003) (http://www.uniprot.org). Sets of proteins with prion-forming domains (Data S1) and their orthologs across Saccharomycetes were collated as previously described (Su & Harrison, 2019).

Prion-like composition

Prion-like composition in orthologs was calculated in two ways, firstly using the PLAAC prion-like domain annotation program (Lancaster et al., 2014), and secondly using the fLPS program for annotation of compositional biases (Harrison, 2017). These were both run using default parameters, except that for fLPS the expected frequency for glutamine and asparagine residues was set equal to 0.05. For PLAAC, both the PRD score and the LLR score were analysed; the former is an indicator of the overall amount of prion-like composition in an annotated bounded prion-like region, while the latter indicates the prion-like sequence composition of the best sequence window (Lancaster et al., 2014). PLAAC scores < 0.0 or labelled ‘N/A’ in the output are set equal to 0.0 here.

Measures of proteome bias

Several measures of compositional bias across proteomes/genomes were examined:

1. %N (asparagine) in the proteome;

2. %Q (glutamine) in the proteome;

3. % poly-N in the proteome (with a minimum tract length of 3);

4. % poly-Q in the proteome (with a minimum tract length of 3);

5. % poly-Q + poly-N in the proteome (with a minimum tract length of 3);

6. %GC in the DNA;

7. The fraction of N/Q-rich proteins in the proteome according to a specific fLPS bias P-value threshold (either 1e−08, 1e−10 or 1e−12);

8. The fraction of proteins in the proteome with prion-like composition according to the program PLAAC (with PRD score >0.0, ≥15.0 or ≥30.0, or similarly for LLR score).

Measures (i) to (v) were chosen since there are indicative of general mutational trends that are relevant to the predominant compositional biases of prion-forming domains in S. cerevisiae, namely bias towards asparagine and glutamine and tracts of these residue types (An, Fitzpatrick & Harrison, 2016). Measure (vi) (%GC) is the most basic compositional trend that can be analyzed for genomic DNA, which might underlie trends at the amino-acid level. Measures (vii) to (viii) indicate the degree to which individual proteins throughout the proteome have prion-like compositional biases to a certain level, and so would indicate how every protein is on average affected by mutational trends.

Correlations

Both weighted and unweighted Pearson correlation coefficients were calculated to assess the correlations of individual prion-like composition with the general trends in the proteome. Weightings for plot points were calculated according to their closest similarity with another protein, calculated as (1-%I/100), where %I is the percentage sequence identity in the most significant BLASTP sequence alignment (Altschul et al., 1997). These weightings were summed appropriately, as described in previous analyses (Harrison, 2019; Su & Harrison, 2019). Results indicate that the overall outcomes for specific proteins are not affected by non-usage of such weightings (see below).

Initial example: the Ure2 prion-forming domain demonstrates strong absorption of mutational trends

As an initial example, the evolutionary behaviour of compositional biases in the prion-forming domain of Ure2p, which underlies the [URE3] prion, was examined (Figs. 1–2). The current data indicate that an ancestor of the Ure2p prion-forming domain with a strong N/Q-rich prion-like composition originated early in Saccharomycetes evolution (at least in the last common ancestor of the diverse families Debaryomycetaceae and Saccharomycetaceae), in agreement with results in previous publications (An, Fitzpatrick & Harrison, 2016; Harrison et al., 2007) (Fig. 3; the organismal branching pattern from recent fungal phylogenies was used (Kurtzman & Robnett, 2013; Shen et al., 2016). In general, there is a strong correlation between the degree of bias in the N/Q-rich region of Ure2p and the degree of compositional bias in the whole proteome/genome by several indicators (%polyasparagine or %[polyasparagine + polyglutamine] or DNA GC% or fraction of N/Q-rich prion-like proteins with fLPS P-value < 10 ⁻¹⁰) (Fig. 1). The correlations with PLAAC prion-like composition score are lower, but both measures have strong correlations with %GC in DNA (Fig. 2). Thus, during the surge in formation of prion-like regions during Saccharomycetes evolution (An, Fitzpatrick & Harrison, 2016), the degree of N-bias in the individual prion-former Ure2p also increased in correlation with the general trend as it panned out across various sub-clades.

Other prion-forming proteins show a variable spectrum of absorption of mutational trends across Saccharomycetes

Of the known amyloid-based prions—as well as Ure2p—Swi1p, Cyc8p and Sup35p have domains of prion-like composition or N/Q bias that are widespread across Saccharomycetes (in 84% of orthologs for Cyc8p, 98% for Swi1p, and 90% for Sup35p; Table S1), with such domains of these latter three also arising in other Ascomycota clades (An, Fitzpatrick & Harrison, 2016; Harrison et al., 2007).

In general, there are strong correlations for Ure2p, Swip and Cyc8p with %N, %poly-N, %GC in DNA and with the numbers of proteins with prion-like composition (Tables 1–2). Within these general trends, these four demonstrate a spectrum of responses to the overall proteome-wide mutational trends, with Ure2p being the strongest correlator. Sup35p stands out as an exception; it shows on the whole weaker correlations generally with %N and %poly-N, and stronger correlations with %poly-Q than the other three. This may be because there is selection pressure to maintain a specific proportion of Qs in specific local patterns or ratios (MacLea et al., 2015).

Furthermore, Pin3 protein also has a widespread prion-like domain across Saccharomycetes, there being 52/55 (95%) Saccharomycetes Pin3 orthologs having PLAAC LLR scores > 15.0. However, the degree of conservation of N/Q-rich bias per se is lower for this protein with 38/55 (75%) having a fLPS compositional bias P-value ≤1e–10. The metastable prion domain of Pin3 is the only known amyloid-based prion in S. cerevisiae to demonstrate very little correlation for its prion-like compositional biases, indicating some selection pressure for composition of a different sort, that nonetheless may preserve prion-forming ability (Tables 1–2).

The other three cases (Mot3p, Rnq1p and Nup100p) have either more recent ancestry as novel prion-like domains within Saccharomycetes (in the case of Mot3p and Rnq1p), or they arise sporadically in fungal species (Nup100p) (An, Fitzpatrick & Harrison, 2016; Su & Harrison, 2019). These three are thus not expected to demonstrate many significant correlations with measures of compositional bias, but nonetheless we see a mild negative correlation for the fLPS compositional bias of Rnq1p and Mot3p versus %Q in the proteome (Table 1), which is not typical of the other prion-forming proteins, suggesting selection pressures against Q bias in these evolutionarily recently emergent proteins.

There is one species that is often a far outlier when these trends are examined, Ascoidea rubescens (see for example, for Ure2p in Figs. 1–2), an uncharacterized species that is the sole member of the family Ascoideaceae, which is geographically widely distributed and typically grows in beetle galleries in dead wood. It has a very high proportion of poly-N-rich proteins (Tables 1–2). Removal of this outlier species from the correlation analysis causes a substantial increase in correlations with %N and %poly-N, but not for %GC in DNA.

Thus, the three S. cerevisiae prion-forming proteomes Ure2p, Cyc8p and Swi1p appear to absorb the general mutational trends linked to the surge in formation of prion-like domains, that was observed previously (An, Fitzpatrick & Harrison, 2016). This trend is linked to a general decrease in %GC in the DNA (Tables 1–2).

Two other separately studied prion-forming domains are from New1p and Pub1p (Li et al., 2014; Osherovich & Weissman, 2001). These are both strongly correlated proteome-bias absorbers, with Pub1p (which is a hub for protein interaction with other prion-like proteins (Harbi & Harrison, 2014b) uniquely amongst all of the prion-forming domains displaying a strong correlation for both poly-N and poly-Q (Tables 1–2). Pub1p is strongly correlated despite having a low number of orthologous prion domains that have high bias for N and Q residues (53% with fLPS P-value ≤1e–10; Table S1) indicating that there is still correlated behavior for the weaker N/Q biases for this protein. Other prion-forming domains observed in the analysis of Alberti et al. (2009), also display a similar spectrum of bias absorption across Saccharomycetes evolution (Table S1). Highly-correlated bias absorbers from this data whose prion-like domains are widespread in Saccharomycetes include Lsm4p and Gln3p, whereas other widespread prion-like domains show little or no correlation, such as Ngr1p (Tables S1, S2).

Compared to the results for N/Q-compositional bias calculated using fLPS (Table 1), the trends for prion-like composition calculated using the PRDscore from PLAAC, are similar except that New1p loses many significant correlations, and an increased correlation is captured for Sup35p versus the general mutational trends linked to the large-scale surge in formation of prion-like domains (An, Fitzpatrick & Harrison, 2016). Similar trends for PLAAC are observed if Spearman correlation coefficients are applied (by reason of some proteins having several 0.0-value PLAAC PRDscores in orthologs) (Table S3).

The above analysis uses the PLAAC PRDscore, to define the amount of prion-like composition in a bounded region, and so reflecting more absorption of biases in a way analogous to the working of the fLPS algorithm (Harrison, 2017; Lancaster et al., 2014). The PLAAC log-likelihood ratio (LLR) score has been used in the literature to pick out the most likely prion-forming sequence window within proteins (Alberti et al., 2009; An, Fitzpatrick & Harrison, 2016; Sideri et al., 2017; Tetz & Tetz, 2018). Despite the restriction of a window of fixed size (41 amino-acid residues), these LLR scores also demonstrate a similar spectrum of bias absorption, with both strong and weak absorbers evident, albeit generally with less significance (Table S4).

Prion-like N/Q-rich regions generally maintain lower lysine content than the rest of the proteome in Saccharomycetes

It was checked whether the N/Q-rich regions are also rich in lysine, which is encoded by AT%-rich codons, like N (asparagine). Lysine has low prion formation propensity and charged residues are disruptive to prion formation and have low prion formation propensity (Lancaster et al., 2014; Osherovich & Weissman, 2001). Lysine is a disorder-promoting residue (Oldfield & Dunker, 2014) and some intrinsically disordered regions have high positive charge (Hatos et al., 2020; Necci et al., 2018). However, the N/Q-rich regions consistently in general have lower lysine content that the remainder of the Saccharomycetes proteomes (Fig. 4). That is, the vast majority of Saccharomycetes species (∼98%) are below the x=y line on the scatter plot (Fig. 4A). This is also obvious in the distributions of K fraction (Fig. 4B, values for prion-formers are lower, t-test P = 1e−140). Thus, these regions are not simply absorbing higher levels of AT% in their DNA through the embedding within them of amino-acid residues encoded by codons with high AT%.