Genetic variation associated with condensate dysregulation in disease

Abstract

A multitude of cellular processes involve biomolecular condensates, which has led to the suggestion that diverse pathogenic mutations may dysregulate condensates. Although proof-of-concept studies have identified specific mutations that cause condensate dysregulation, the full scope of the pathological genetic variation that affects condensates is not yet known. Here, we comprehensively map pathogenic mutations to condensate-promoting protein features in putative condensate-forming proteins and find over 36,000 pathogenic mutations that plausibly contribute to condensate dysregulation in over 1,200 Mendelian diseases and 550 cancers. This resource captures mutations presently known to dysregulate condensates, and experimental tests confirm that additional pathological mutations do indeed affect condensate properties in cells. These findings suggest that condensate dysregulation may be a pervasive pathogenic mechanism underlying a broad spectrum of human diseases, provide a strategy to identify proteins and mutations involved in pathologically altered condensates, and serve as a foundation for mechanistic insights into disease and therapeutic hypotheses.

Keywords: Mendelian disease; biomolecular condensates; cancer; clinical genetics; condensate dysregulation; human genetics; pathogenic variants.

Figure 1.. A proteome-wide map of pathogenic mutations in condensate-promoting features. See also Figures S1–S2 and Table S1.

A. Multivalent interacting features in proteins that promote biomolecular condensate formation, including modular interacting domains (MIDs, left, green and purple) and low complexity sequences (LCSs, right, blue and green). B. Approach to generate a map of pathogenic mutations that affect condensate-promoting features across the proteome (see also Figure S1A). MIDs and LCSs were mapped across the proteome (left, top) and used to define multivalent proteins (middle). Mendelian and cancer variants were mapped across the proteome (left, bottom), in particular on across the set of multivalent proteins (middle), to identify pathogenic mutations that affect MIDs and LCSs (Methods). The approach allows analysis of diseases, condensates, and mutational signatures associated with pathogenic mutations that affect condensate-promoting features in multivalent proteins (right).

Figure 2.. Condensate dysregulation across the spectrum of disease. See also Table S4I–K.

A. Proportion of pathogenic mutations (depicted as distance from center of radar plot) affecting condensate-promoting features in multivalent proteins across Mendelian diseases. Mendelian diseases are stratified by organ systems in which the diseases had a phenotypic effect (Methods). B. Proportion of pathogenic mutations (depicted as distance from center of radar plot) affecting condensate-promoting features in multivalent proteins across cancers. Cancers are stratified by tissues of origin (Methods). C. Enrichment of GO terms among the set of condensate-forming proteins that have pathogenic mutations that affect condensate-promoting features. GO terms (black dots) are ranked (x-axis) by statistical significance (−log₁₀(FDR), y-axis). Red line denotes GO term rank corresponding to threshold for statistical significance (FDR < 0.05). The subset of significantly enriched GO terms that correspond to biomolecular condensates (Table S4I) are highlighted (black open circles and labels). Nuclear, cytoplasmic, and plasma membrane-associated condensates are indicated by purple, blue, or gray labels, respectively. D. Significant associations between specific diseases and specific condensates. The set of condensate-forming proteins with pathogenic mutations affecting condensate-promoting features were mapped to specific condensates using Gene Ontology (see Methods) as well as associated with specific diseases. Overlaps between subsets of proteins associated with specific condensates (y-axis) and those associated with specific diseases (x-axis) were tested for statistical significance. Selected examples of Mendelian diseases (left) and cancer types (right) are shown (see also Table S4J–K). Filled data points correspond to a statistically significant association between the indicated disease with the indicated condensate, with the data point color corresponding to the Benjamini-Hochberg adjusted p-value (FDR) for the enrichment of proteins defined as components of the indicated condensate based on GO (Methods) among the set of condensate-forming proteins that have pathogenic mutations involved in the indicated disease that affect condensate-promoting features. Unfilled datapoints correspond to a lack of a statistically significant enrichment. Size of data point is proportional to the fraction of the indicated disease-associated condensate-forming proteins that are components of the indicated condensates.

Figure 3.. Pathogenic mutations in condensate-promoting features alter condensate properties in live cells. See also Figures S3–S4 and Table S3.

A. Experimental approach for testing the effect of a subset of identified mutations predicted to affect condensates. N-terminal mEGFP-tagged wild-type or mutant forms of candidate proteins were stably expressed in mESCs and condensate properties were assessed using live cell imaging and quantitative image analysis. B. Representative images of wild-type MECP2 (positive control for condensate incorporation) mEGFP alone (negative control). Nuclei are outlined with white dashed lines. C. Representative images of wild-type versus mutant mEGFP-tagged candidate proteins BARD1, DAXX, SALL1, BRD3, RBM10, BCL11A, NONO, BCOR, TCOF1, HP1A, SRSF2, ESRP1. Specific mutations that were tested along with their associated disease are indicated adjacent to the images.

Figure 4.. Mutations in condensate-promoting features cause diverse condensate dysregulation phenotypes.

Models for observed types of condensate dysregulation resulting from pathogenic mutations that affect condensate-promoting features of condensate-forming proteins, including altered condensate incorporation (left), enhanced condensate formation (middle), and altered condensate localization (right). Candidate where these phenotypes were observed (Figure 3C) are listed.