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. 2021 Mar 16;22(6):3017.
doi: 10.3390/ijms22063017.

Integration of Data from Liquid-Liquid Phase Separation Databases Highlights Concentration and Dosage Sensitivity of LLPS Drivers

Nazanin Farahi  1   2   3 , Tamas Lazar  1   2 , Shoshana J Wodak  1   2 , Peter Tompa  1   2   4 , Rita Pancsa  4
Affiliations

Integration of Data from Liquid-Liquid Phase Separation Databases Highlights Concentration and Dosage Sensitivity of LLPS Drivers

Nazanin Farahi et al. Int J Mol Sci. .

Abstract

Liquid-liquid phase separation (LLPS) is a molecular process that leads to the formation of membraneless organelles, representing functionally specialized liquid-like cellular condensates formed by proteins and nucleic acids. Integrating the data on LLPS-associated proteins from dedicated databases revealed only modest agreement between them and yielded a high-confidence dataset of 89 human LLPS drivers. Analysis of the supporting evidence for our dataset uncovered a systematic and potentially concerning difference between protein concentrations used in a good fraction of the in vitro LLPS experiments, a key parameter that governs the phase behavior, and the proteomics-derived cellular abundance levels of the corresponding proteins. Closer scrutiny of the underlying experimental data enabled us to offer a sound rationale for this systematic difference, which draws on our current understanding of the cellular organization of the proteome and the LLPS process. In support of this rationale, we find that genes coding for our human LLPS drivers tend to be dosage-sensitive, suggesting that their cellular availability is tightly regulated to preserve their functional role in direct or indirect relation to condensate formation. Our analysis offers guideposts for increasing agreement between in vitro and in vivo studies, probing the roles of proteins in LLPS.

Keywords: data integration; dosage sensitivity; liquid demixing; liquid–liquid phase separation; local concentration; membraneless organelles; protein abundance; quantitative proteomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Content and overlap of the four liquid–liquid phase separation (LLPS) datasets. The contents of the four databases (PhaSePro, DrLLPS, LLPSDB, PhaSepDB) are represented in the upper portion, with subsets of the data used in this analysis highlighted in color, whereas the rest of their data are depicted in different shades of gray. The total number of proteins as well as the number of proteins in the different subsets are indicated. In the Venn diagram at the bottom, the overlap of the colored subsets of the four LLPS databases is shown.
Figure 2
Figure 2
Filtering steps for obtaining a consolidated set of high-confidence human LLPS drivers. (A) Venn diagram representing the overlap of the three LLPS databases (251 proteins from PhaSePro, DrLLPS, and LLPSDB) after the exclusion of data in PhaSepDB. (B) Venn diagram representing the same overlap after filtering for human proteins (117 proteins). (C) Human proteins were further filtered for high-confidence (co)drivers by excluding those that could not be accepted as LLPS (co)drivers according to our definition of this role, resulting in a set of 89 proteins. (D) Among these, we identified 78 in vitro verified proteins, which could be used in the concentration analysis.
Figure 3
Figure 3
Protein concentrations applied in in vitro LLPS experiments frequently exceed those calculated from proteomics-derived cellular protein abundances. Comparison of protein concentrations used in in vitro LLPS experiments (in blue), concentrations calculated from proteomics-derived cellular protein abundances stored in the PaxDb database (tissue-specific integrated values in orange and cell-line integrated values in green), and local concentrations estimated or referred to by authors (in red) for human LLPS driver proteins integrated from the three LLPS resources and filtered for in vitro verified true LLPS (co)drivers. Concentrations are provided in micromolar units, while LLPS proteins are represented by the respective gene names on the horizontal axis. Functional groups of the proteins are indicated on the top of the figure. For the products of genes LAT, SYN2, and YTHDF3, PaxDb concentration values were not available.
Figure 4
Figure 4
Human LLPS driver proteins are of relatively low abundance compared to the proteome average. The highest tissue- or cell type-specific integrated abundance value was derived for each human protein from PaxDb. These maximal abundances of human LLPS driver proteins (red) are compared to those of the proteome (grey) using histograms. The abundances of LLPS drivers are also separately depicted in the inset histogram to ensure better resolution of the data. Abundances are provided in parts per million (ppm) units on the X-axis.
Figure 5
Figure 5
The genes of human LLPS drivers are overrepresented in dosage-sensitive genes. The Venn diagrams (left) show the overlap between human LLPS driver genes and the set of most reliable dosage-sensitive (MRDS) genes (A), the set of most reliable dosage insensitive (MRDIS) genes (B), and the set of haploinsufficient genes (C). The genes in the intersections are listed for (A,B). The detected overlaps represent statistically highly significant enrichments (A,C) or depletion (B) based on chi2 tests using the whole human-reviewed UniProt proteome as background. Histograms (right) showing the difference between the overlaps of human LLPS-associated genes (red) or equivalent sets of randomly selected well-annotated genes (blue) with (A) MRDS, (B) MRDIS, and (C) haploinsufficient genes.
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