Natural and pathogenic protein sequence variation affecting prion-like domains within and across human proteomes

Abstract

Background: Impaired proteostatic regulation of proteins with prion-like domains (PrLDs) is associated with a variety of human diseases including neurodegenerative disorders, myopathies, and certain forms of cancer. For many of these disorders, current models suggest a prion-like molecular mechanism of disease, whereby proteins aggregate and spread to neighboring cells in an infectious manner. The development of prion prediction algorithms has facilitated the large-scale identification of PrLDs among "reference" proteomes for various organisms. However, the degree to which intraspecies protein sequence diversity influences predicted prion propensity has not been systematically examined.

Results: Here, we explore protein sequence variation introduced at genetic, post-transcriptional, and post-translational levels, and its influence on predicted aggregation propensity for human PrLDs. We find that sequence variation is relatively common among PrLDs and in some cases can result in relatively large differences in predicted prion propensity. Sequence variation introduced at the post-transcriptional level (via alternative splicing) also commonly affects predicted aggregation propensity, often by direct inclusion or exclusion of a PrLD. Finally, analysis of a database of sequence variants associated with human disease reveals a number of mutations within PrLDs that are predicted to increase prion propensity.

Conclusions: Our analyses expand the list of candidate human PrLDs, quantitatively estimate the effects of sequence variation on the aggregation propensity of PrLDs, and suggest the involvement of prion-like mechanisms in additional human diseases.

Keywords: Neurodegenerative disease; Prion; Prion prediction; Prion-like domains; Protein aggregation; Sequence variation.

Fig. 1

Protein sequence variation introduced at the genetic, post-transcriptional, and post-translational stages. Graphical model depicting sources of protein sequence variation potentially affecting PrLD regions

Fig. 2

Sampling of human PrLD sequence variants yields broad ranges of aggregation propensity scores. a Histogram indicating the frequencies corresponding to the number of unique PAPA scores per protein. b The distribution of aggregation propensity ranges, defined as the difference between the maximum and minimum aggregation propensity scores from sampled sequence variants, is indicated for all PrLDs scoring above PAPA = 0.0 and with at least one annotated sequence variant. c Histograms indicating categorical distributions of aggregation propensity scores for the theoretical minimum and maximum aggregation propensity scores attained from PrLD sequence variant sampling, as well as original aggregation propensity scores derived from the corresponding reference sequences. d Modified box plots depict the theoretical minimum and maximum PAPA scores (lower and upper bounds, respectively), along with the reference sequence score (the color transition point) for all isoforms of prototypical prion-like proteins associated with human disease

Fig. 3

Alternative splicing influences predicted aggregation propensity for a number of human PrLDs. a Minimum and maximum aggregation propensity scores (indicated in blue and orange respectively) are indicated for all proteins with at least one isoform below the classical PAPA = 0.05 threshold and at least one isoform above the PAPA = 0.05 threshold. For simplicity, only the highest and lowest PAPA score are indicated for each unique protein (n = 159), though many of the indicated proteins that cross the 0.05 threshold have multiple isoforms within the corresponding aggregation propensity range (n = 414 total isoforms; Additional file 2). b For all protein isoforms with an aggregation propensity score exceeding the PAPA = 0.05 threshold and with at least one higher-scoring isoform (n = 48 total isoforms, corresponding to 15 unique proteins), scores corresponding to the lower-scoring and higher-scoring isoforms are indicated in blue and orange respectively. In both panels, asterisks (*) indicate proteins for which a PrLD is also identified by PLAAC. Only isoforms for which splicing affected the PAPA score are depicted

Fig. 4

Disease-associated mutations influence predicted aggregation propensities of known PrLDs and new candidate prion-like proteins. a For all disease-associated single-amino acid substitutions that map to high-scoring PrLDs (PAPA score > 0.05) and increase predicted aggregation propensity score, scores corresponding to the wild-type and mutant sequences are indicated in blue and orange respectively. b Wild-type and mutant aggregation propensity scores are similarly plotted for all proteins with wild-type PAPA score < 0.05 but mutant PAPA score > 0.05. In both panels, asterisks (*) indicate proteins also containing a PLAAC-positive PrLD, and amino acid substitutions are indicated above each bar

Fig. 5

Certain PTM types are enriched or depleted within human PrLDs. a Distributions depicting the number of modifications within each PrLD for each of the main PTM types. b Estimated degree of enrichment (blue) or depletion (red) for each PTM type within human PrLDs. Error bars represent the standard error

Fig. 6

The hnRNPA1 PrLD is affected by genetic, post-transcriptional, and post-translational sequence variation. a Aggregation propensity scores for all hnRNPA1 splice variants, as well as all disease-associated variants, are plotted separately. Note that the N319S, D314V, and D314N mutations correspond to N267S, D262V, and D262N mutations in the short isoform, which are the more commonly referenced locations of these mutations [33]. b For comparison, similar analyses were performed for FUS. For each line in both plots, regions corresponding to FoldIndex scores > 0.0 (which are not assigned aggregation propensity scores in PAPA) are plotted as thin grey segments, whereas all regions scored by PAPA (FoldIndex< 0.0) are plotted as thick colored segments. All PTMs mapping to regions with relatively high-scoring regions (PAPA> 0.0) are indicated by vertical red lines, with line styles indicating distinct types of PTMs. For simplicity, only PTMs mapping to the longest isoform are indicated. The classical PAPA = 0.05 threshold is indicated with a dashed grey line