Research Article
Distinctive Network Topology of Phase-Separated Proteins in Human Interactome

https://doi.org/10.1016/j.jmb.2021.167292Get rights and content

Highlights

  • Constructing the integrated human interactome from public databases.

  • Network parameters reveal distinct network topology of phase-separated proteins.

  • Clustering analysis reveals phase-separated communities in human interactome.

  • Node similarity can discriminate the proteome of known membraneless organelles.

Abstract

Liquid-liquid phase separation (LLPS) is an important mechanism that mediates the formation of biomolecular condensates. Despite the immense interest in LLPS, phase-separated proteins verified by experiments are still limited, and identification of phase-separated proteins at proteome-scale is a challenging task. Multivalent interaction among macromolecules is the driving force of LLPS, which suggests that phase-separated proteins may harbor distinct biological characteristics in protein–protein interactions (PPIs). In this study, we constructed an integrated human PPI network (HPIN) and mapped phase-separated proteins into it. Analysis of the network parameters revealed differences of network topology between phase-separated proteins and others. The results further suggested the efficiency when applying topological similarities in distinguishing components of MLOs. Furthermore, we found that affinity purification mass spectrometry (AP-MS) detects PPIs more effectively than yeast-two hybrid system (Y2H) in phase separation-driven condensates. Our work provides the first global view of the distinct network topology of phase-separated proteins in human interactome, suggesting incorporation of PPI network for LLPS prediction in further studies.

Introduction

Liquid-liquid phase separation (LLPS) is a widely occurring biomolecular process that underlies the formation of membrane-less organelle (MLO).1, 2 It is increasingly appreciated that phase separation and MLO are involved in diverse biological processes, including transcriptional regulation and signal transduction,3, 4, 5, 6 and various pathological processes, such as neurodegenerative diseases and oncogenesis.7, 8 Several studies reported that phase separation is driven by multivalent interactions, including collective protein–protein interactions (PPIs) and protein-nucleic acid interactions.9, 10 Jain et al. discovered that stress granule proteome forms a dense PPI network in both yeast and mammalian, and P-body proteome also exhibits a high degree of interconnectivity.11 Yang et al. used betweenness centrality to find that the central node of core stress granule network is G3BP1 which regulates RNA-dependent LLPS.12 Savojardo et al. reviewed the computational methods in prediction of PPI sites and applied them in phase-separated proteins.13 Shen et al. reviewed the current phase separation predictors and highlighted the importance of integrating PPI network information into phase separation predictors.14 With these inspiring results, we would believe that PPI network can be useful in the study of LLPS mechanism and MLO formation.

Phase separation has gained considerable attentions over the past few years, while the number of known PPIs has also increased substantially with the development of experimental techniques. The yeast two-hybrid (Y2H) screens system and affinity purification followed by mass spectrometry (AP-MS) are powerful tools to identify PPIs,15, 16 which can be applied in high-throughput approach to detect a proteome-scale map of PPI in an organism. With respect to the growing PPI data, a number of public databases have set out for collection and storage. For example, STRING database aims to collect, score and integrate all publicly available sources of PPI information.17 HuRI presented a human reference interactome map with approximately 53,000 binary PPIs.18 Despite the growing interest in LLPS and PPI fields, there is a lack of systematic analysis for characteristics of phase-separated proteins in PPI network.

Here, we constructed an integrated Human Protein-Protein Interaction Network (HPIN) by combining PPI data from five public databases, trying to give a relatively comprehensive coverage of the human interactome. Furthermore, LLPS is generally driven by multivalent interactions which can be classified into two categories: one type by intrinsically disordered regions (IDRs) and the other type by multiple modular domains or motifs.9, 19, 20 We then categorized phase-separated proteins into three classes according to interaction types, which were self-assembling phase-separated proteins (PS-self), partner-dependent phase-separated proteins (PS-part), and candidate phase-separated proteins (PS-candidate). We carefully analyzed the network properties of different classes of phase separated proteins in human interactome, and provided insight into distinguishing components of MLOs based on topological similarities.

Section snippets

Collection of protein–protein interaction network and phase-separated proteins

To systematically study phase separation from protein interactome perspective, the integrated Human Protein-Protein Interaction Network (HPIN) was constructed by combining five publicly available PPI databases: HuRI,18 BioPlex3.0,21 XLink-DB,22 STRINGv1117 and PDB.23 As a result, HPIN included 16,882 proteins and 290,312 PPIs, covering 81.1% of the human proteome24 (Figure 1(A and B), Supplementary Table 1). The overlaps among these PPI data were displayed as Venn diagram in Figure 1(C). It

Discussion

In this study, we constructed a large-scale, integrated human Protein-Protein Interaction network (HPIN), trying to give a relatively comprehensive coverage of human interactome. For the first time, we found distinct network topology between different classes of proteins and detected communities with biological functions in HPIN and condensate interactome. The similarity-based discrimination performed well in discriminating the known components of membrane-less organelles, indicating components

Data source of protein–protein interactions

PPI data were obtained on 11 Jan 2021 from five public databases. The HuRI18 presents a reference interactome map of human binary protein interaction through systematic yeast two-hybrid (Y2H) screening. The BioPlex3.021 systemically profiles proteome-scale interactions in multiple human cell lines via affinity purification mass spectrometry. The dataset with the version of BioPlex3.0 interactions (293T Cells) was downloaded, which had been filtered to removed background and nonspecific

CRediT authorship contribution statement

Chunyu Yu: Conceptualization, Methodology, Writing – original draft. Yunzhi Lang: Formal analysis, Writing – original draft. Chao Hou: Resources, Data curation. Ence Yang: Supervision. Xianwen Ren: Conceptualization, Methodology. Tingting Li: Conceptualization, Supervision, Writing – review & editing, Funding acquisition.

Acknowledgements

We gratefully acknowledge the invitation from Dr. Monika Fuxreiter (University of Padova, Italy). We thank Zhaoming Chen (Peking University, China) for the help with intrinsically disordered region calculation. This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFA0507504); the National Natural Science Foundation of China (Grant Nos. 61773025 and 32070666); Clinical Medicine Plus X – Young Scholars Project of Peking University (PKU2021LCXQ012)

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References (51)

  • S.F. Banani et al.

    Compositional control of phase-separated cellular bodies

    Cell

    (2016)
  • H.B. Schmidt et al.

    Transport selectivity of nuclear pores, phase separation, and membraneless organelles

    Trends Biochem. Sci.

    (2016)
  • E. Gomes et al.

    The molecular language of membraneless organelles

    J. Biol. Chem.

    (2019)
  • S. Alberti et al.

    Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates

    Cell

    (2019)
  • C.P. Brangwynne et al.

    Germline P granules are liquid droplets that localize by controlled dissolution/condensation

    Science

    (2009)
  • A.A. Hyman et al.

    Liquid-liquid phase separation in biology

    Annu. Rev. Cell Dev. Biol.

    (2014)
  • B.R. Sabari et al.

    Coactivator condensation at super-enhancers links phase separation and gene control

    Science

    (2018)
  • P. Li et al.

    Phase transitions in the assembly of multivalent signalling proteins

    Nature

    (2012)
  • X. Su et al.

    Phase separation of signaling molecules promotes T cell receptor signal transduction

    Science

    (2016)
  • E. Lester et al.

    Tau aggregates are RNA-protein assemblies that mislocalize multiple nuclear speckle components

    Neuron

    (2021)
  • Y. Shin et al.

    Liquid phase condensation in cell physiology and disease

    Science

    (2017)
  • C. Savojardo et al.

    Protein-protein interaction methods and protein phase separation

    Annu. Rev. Biomed. Data Sci.

    (2020)
  • B. Shen et al.

    Computational screening of biological phase-separating proteins

    Genom. Proteom. Bioinf.

    (2021)
  • S. Fields et al.

    A novel genetic system to detect protein–protein interactions

    Nature

    (1989)
  • G. Rigaut et al.

    A generic protein purification method for protein complex characterization and proteome exploration

    Nature Biotechnol.

    (1999)
  • Cited by (0)

    These authors are equal contributors.

    View full text