Research Focus

Our research focuses on understanding the mechanisms gearing protein folding and misfolding and their relation to human disease. In particular, we are investigating how protein aggregation affects the interactome by suppressing native interactions but also by introducing novel aggregation-specific interactions. These latter are especially relevant as they are usually associated with gain of function activities such as neurotoxicity (neurodegeneration). 

The significance of protein misfolding and aggregation in the development of human disease is often underestimated, and its consequences poorly understood. Indeed, so far, only a few dozen aggregation-associated diseases have been identified, mostly neurodegenerative disorders or systemic amyloidoses. However, protein folding is often an inefficient process that has to be tightly controlled by the protein quality control machinery of the cell. Moreover, the risk of protein misfolding and aggregation increases with age due to gradual waning of the cell’s capacity to direct protein folding and degradation. Why then are so few aggregation-associated diseases identified? 

Neurodegenerative diseases are associated with a cytotoxic gain-of-function phenotype facilitating the incrimination of protein aggregates. However, many aggregating proteins may lack such cytotoxic gain-of-function or display gain-of-function phenotypes, which are currently not associated with protein aggregation. Indeed, we showed that although protein aggregation can lead to cytotoxic gain-of-function in neurodegeneration, we also showed that in cancer on the contrary, protein aggregation contributes to cell proliferation and immortalisation. How can protein aggregation lead to such diverse gain-of-function activities? 

Are these differences due to cell-specific or function-specific contexts, or does the ability of different proteins aggregates to interact with cellular chaperones dictate their physiological effect?

Our goal is to create a more accurate picture of the aggregation propensity of the human proteome and the effect of genome variability hereupon. To achieve this, we created a set of bioinformatics tools (TANGO & WALTZ) that captured the sequence-specific determinants of protein aggregation and performed an analysis on the impact of aggregation on disease-associated mutations (SNPeffect). In doing so we found that aggregation is not limited to a set of amyloid diseases but that many other diseases are also affected by aggregation.

We use an interdisciplinary experimental approach to tackle these questions, combining bioinformatics, biophysical analysis of protein aggregates but also molecular and cellular biological studies of aggregation in human cell culture and model organisms including zebrafish and mice. ​

APRS -The short-stretch hypothesis of protein aggregation

Protein aggregates are often classified by their morphology (“amorphous” vs “amyloid-like”) but even amorphous aggregates contain amyloid-like structures made up of β-sheets [1].

The Switch laboratory has championed the concept that protein aggregation is initiated by short aggregation-prone regions (APRs): 5-15 residue-long stretches that have the intrinsic propensity to self-associate by β-strand interactions [2,3].

APRs are necessary and sufficient for driving protein aggregation: grafting APRs of known amyloid-associated proteins onto proteins that do not aggregate by themselves can turn these proteins into aggregation-prone proteins [4,5].

Most proteins possess at least one APR, but APRs usually do not induce aggregation because they are buried inside the hydrophobic core of the folded protein. The self-assembly of APRs can drive protein aggregation only in situations where proteins are partially or completely unfolded, and the APRs are exposed, e.g. during protein translation or translocation, during transient protein breathing motions, under situations of physiological stress or due to mutations that destabilise the native conformation (See Figure).

References

[1] Tyedmers J, Mogk A, Bukau B (2010) Cellular strategies for controlling protein aggregation. Nat Rev Mol Cell Biol 11 (11):777-788. doi:10.1038/nrm2993

[2] Rousseau F, Serrano L, Schymkowitz JW (2006) How evolutionary pressure against protein aggregation shaped chaperone specificity. J Mol Biol 355 (5):1037-1047.

[3] Goldschmidt L, Teng PK, Riek R, Eisenberg D (2010) Identifying the amylome, proteins capable of forming amyloid-like fibrils. Proceedings of the National Academy of Sciences of the United States of America 107 (8):3487-3492. doi:10.1073/pnas.0915166107

[4] Ventura S, Zurdo J, Narayanan S, Parreno M, Mangues R, Reif B, Chiti F, Giannoni E, Dobson CM, Aviles FX, Serrano L (2004) Short amino acid stretches can mediate amyloid formation in globular proteins: the Src homology 3 (SH3) case. Proc Natl Acad Sci U S A 101 (19):7258-7263. doi:10.1073/pnas.0308249101

[5] Teng PK, Eisenberg D (2009) Short protein segments can drive a non-fibrillizing protein into the amyloid state. Protein Engineering Design & Selection 22 (8):531-536. doi:10.1093/protein/gzp037

PEPT-INS - Targeted Protein Aggregation

Exposed aggregation-prone regions (APRs) can drive otherwise non-aggregating proteins towards the formation of ordered amyloid aggregates. APRs can drive intermolecular interactions of proteins by self-assembling into structures where identical or highly similar APRs are stapled together into -sheets through backbone hydrogen bonds and these -sheets pack in layers via tightly interdigitated sidechains forming stable structures, known as “steric zippers” [1,2]. Electron microscopy image reconstruction and X-ray diffraction data [3,4] from ex-vivo and in vitro amyloids confirmed the generic cross-β backbone organization of amyloids [2,1] (Figure 1).

The extremely ordered, tight packing of side chains in amyloid fibrils explains why the assembly of such structures is highly sequence specific and thus selective [5]. The sequence selectivity of amyloid assembly is the basis of the Pept-In (Peptidic Interferors) technology invented in the Switch laboratory by profs. Joost Schymkowitz and Frederic Rousseau: supplying short peptides containing amino acid sequences homologous to the APR of the target protein leads to the aggregation of the target protein (Figure 2). Since most proteins possess at least one APR [6-8] and many APRs have a unique sequence [9], APRs can be used as barcodes for protein detection or protein functional inactivation.

The Switch laboratory has extensively documented that APRs can induce aggregation of a target protein if it harbours a region very similar to the APR in plants, mammalian cells, and in both Gram-positive and Gram-negative bacteria.

We have developed aggregation-prone peptides for the selective suppression of several protein targets in Arabidopsis and maize, resulting in transgenic plants displaying phenotypes such as increased growth or increased starch content [10,11].

Subsequently, we designed an anti-tumoral peptide targeting an APR located in the human vascular endothelial growth factor receptor 2 (VEGFR2). This peptide induced the aggregation of VEGFR2, thereby knocking down its function and reducing VEGFR2-dependent growth of tumour allografts of the mouse B16 melanoma line [12].

We also created anti-bacterial peptides against the S. aureus proteome that were capable of curing bacterial sepsis without any apparent toxic side-effects in mice [13]. Most recently, we demonstrated that targeting redundant APRs in Gram-negative bacteria can cause the co-aggregation of many proteins, causing the collapse of protein homeostasis (proteostasis), the formation of inclusion bodies and subsequently the death of the bacteria [14].

We have also designed Pept-Ins for the specific detection of proteins in a complex biological matrix, including cell lysates or human serum samples (detection of C-reactive protein and prostate-specific antigen) [9].

The targeted aggregation technology has been patented worldwide and is currently licensed to the spin-off Aelin Therapeutics (Leuven, Belgium). The company is developing the platform technology as a novel human therapeutic modality along antimicrobial and oncological therapeutic programs (https://aelintx.com).

References

[1] Nelson R, Sawaya MR, Balbirnie M, Madsen AO, Riekel C, Grothe R, Eisenberg D (2005) Structure of the cross-beta spine of amyloid-like fibrils. Nature 435 (7043):773-778. doi:10.1038/nature03680

[2] Sawaya MR, Sambashivan S, Nelson R, Ivanova MI, Sievers SA, Apostol MI, Thompson MJ, Balbirnie M, Wiltzius JJ, McFarlane HT, Madsen AO, Riekel C, Eisenberg D (2007) Atomic structures of amyloid cross-beta spines reveal varied steric zippers. Nature 447 (7143):453-457. doi:10.1038/nature05695

[3] Sunde M, Serpell LC, Bartlam M, Fraser PE, Pepys MB, Blake CC (1997) Common core structure of amyloid fibrils by synchrotron X-ray diffraction. J Mol Biol 273 (3):729-739. doi:10.1006/jmbi.1997.1348

[4] Serpell LC, Sunde M, Fraser PE, Luther PK, Morris EP, Sangren O, Lundgren E, Blake CC (1995) Examination of the structure of the transthyretin amyloid fibril by image reconstruction from electron micrographs. J Mol Biol 254 (2):113-118. doi:10.1006/jmbi.1995.0604

[5] O'Nuallain B, Williams AD, Westermark P, Wetzel R (2004) Seeding specificity in amyloid growth induced by heterologous fibrils. J Biol Chem 279 (17):17490-17499

[6] Rousseau F, Serrano L, Schymkowitz JW (2006) How evolutionary pressure against protein aggregation shaped chaperone specificity. J Mol Biol 355 (5):1037-1047. doi:10.1016/j.jmb.2005.11.035

[7] Goldschmidt L, Teng PK, Riek R, Eisenberg D (2010) Identifying the amylome, proteins capable of forming amyloid-like fibrils. Proceedings of the National Academy of Sciences of the United States of America 107 (8):3487-3492. doi:10.1073/pnas.0915166107

[8] Monsellier E, Ramazzotti M, Taddei N, Chiti F (2008) Aggregation Propensity of the Human Proteome. Plos Computational Biology 4 (10). doi:10.1371/journal.pcbi.1000199

[9] Ganesan A, Debulpaep M, Wilkinson H, Van Durme J, De Baets G, Jonckheere W, Ramakers M, Ivarsson Y, Zimmermann P, Van Eldere J, Schymkowitz J, Rousseau F (2015) Selectivity of aggregation-determining interactions. J Mol Biol 427 (2):236-247. doi:10.1016/j.jmb.2014.09.027

[10] Betti C, Vanhoutte I, Coutuer S, De Rycke R, Mishev K, Vuylsteke M, Aesaert S, Rombaut D, Gallardo R, De Smet F, Xu J, Van Lijsebettens M, Van Breusegem F, Inze D, Rousseau F, Schymkowitz J, Russinova E (2016) Sequence-Specific Protein Aggregation Generates Defined Protein Knockdowns in Plants. Plant Physiol 171 (2):773-787. doi:10.1104/pp.16.00335

[11] Betti C, Schymkowitz J, Rousseau F, Russinova E (2018) Selective Knockdowns in Maize by Sequence-Specific Protein Aggregation. In: Lagrimini LM (ed) Maize: Methods and Protocols, vol 1676. Methods in Molecular Biology. Humana Press Inc, Totowa, pp 109-127. doi:10.1007/978-1-4939-7315-6_6

[12] Gallardo R, Ramakers M, De Smet F, Claes F, Khodaparast L, Khodaparast L, Couceiro JR, Langenberg T, Siemons M, Nystrom S, Young LJ, Laine RF, Young L, Radaelli E, Benilova I, Kumar M, Staes A, Desager M, Beerens M, Vandervoort P, Luttun A, Gevaert K, Bormans G, Dewerchin M, Van Eldere J, Carmeliet P, Vande Velde G, Verfaillie C, Kaminski CF, De Strooper B, Hammarstrom P, Nilsson KP, Serpell L, Schymkowitz J, Rousseau F (2016) De novo design of a biologically active amyloid. Science 354 (6313). doi:10.1126/science.aah4949

[13] Bednarska NG, van Eldere J, Gallardo R, Ganesan A, Ramakers M, Vogel I, Baatsen P, Staes A, Goethals M, Hammarstrom P, Nilsson KP, Gevaert K, Schymkowitz J, Rousseau F (2016) Protein aggregation as an antibiotic design strategy. Mol Microbiol 99 (5):849-865. doi:10.1111/mmi.13269

[14] Khodaparast L, Khodaparast L, Gallardo R, Louros NN, Michiels E, Ramakrishnan R, Ramakers M, Claes F, Young L, Shahrooei M, Wilkinson H, Desager M, Mengistu Tadesse W, Nilsson KPR, Hammarstrom P, Aertsen A, Carpentier S, Van Eldere J, Rousseau F, Schymkowitz J (2018) Aggregating sequences that occur in many proteins constitute weak spots of bacterial proteostasis. Nat Commun 9 (1):866. doi:10.1038/s41467-018-03131-0

Gatekeeper Biology

We analyzed the aggregation propensity of over 20 full proteomes of all kingdoms of life using our proprietary TANGO-algorithm and found that about 20% of all residues in a typical globular domain are within aggregation-prone regions (APRs).

Importantly, this study also demonstrated that APRs of all proteomes are systematically flanked by the charged amino acids Arg, Lys, Asp and Glu, a pattern that was soon confirmed independently [1]. We proposed that these residues counteract the aggregation propensity of APRs by charge repulsion and coined these charged residues in the flanks of APRs as ‘aggregation gatekeepers’ (GKs) in reference to the proposition of Otzen et al. that protein sequences undergo negative selection against aggregation [2]. We chose the denomination ‘aggregation gatekeepers’ rather than ‘structural gatekeepers’ to emphasize the specific position of these residues at the flanks of APRs. Several other studies confirmed the prevalence and importance of the gatekeeper pattern [2-8].

We later confirmed that GKs constitute a bona fide and ubiquitous functional class specifically devoted to protein homeostasis. Indeed GKs are evolutionarily conserved at a significant cost to the thermodynamic stability of the native structure (0,5 kcal/mol on average) [9]. Accordingly, we found that mutating GKs not only affects aggregation but also protein synthesis and degradation rates, heat stress response and overall cellular fitness [10].

In our most recent work, we focuses on the close relationship between GKs and the proteostatic machinery and we showed that whereas acidic GKs are fully autonomous in preventing aggregation, basic GKs rely heavily on the assistance of molecular chaperones. Structural constraints, namely incompatibility with burial into the hydrophobic core of globular proteins, however prevent acidic GKs from being used ubiquitously. Our work therefore suggests the use of the inferior basic GKs was necessitated by the emergence of bigger and more complex globular protein folds. As a compensatory mechanism, molecular chaperones adapted to specifically hone in on APRs flanked by basic residues, while leaving APRs protected by acidic GKs to fend for themselves (Houben et al. 2020).

References

[1] Monsellier E, Ramazzotti M, Taddei N, Chiti F (2008) Aggregation Propensity of the Human Proteome. PLoS Comput Biol 4 (10):e1000199. doi:10.1371/journal.pcbi.1000199

[2] Otzen DE, Kristensen O, Oliveberg M (2000) Designed protein tetramer zipped together with a hydrophobic Alzheimer homology: a structural clue to amyloid assembly. Proc Natl Acad Sci U S A 97 (18):9907-9912. doi:10.1073/pnas.160086297

[3] Markiewicz BN, Oyola R, Du D, Gai F (2014) Aggregation Gatekeeper and Controlled Assembly of Trpzip β-Hairpins. Biochemistry 53 (7):1146-1154. doi:10.1021/bi401568a

[4] Buell AK, Tartaglia GG, Birkett NR, Waudby CA, Vendruscolo M, Salvatella X, Welland ME, Dobson CM, Knowles TPJ (2009) Position-Dependent Electrostatic Protection against Protein Aggregation. Chembiochem 10 (8):1309-1312. doi:10.1002/cbic.200900144

[5] Sant'Anna R, Braga C, Varejão N, Pimenta KM, Graña-Montes R, Alves A, Cortines J, Cordeiro Y, Ventura S, Foguel D (2014) The Importance of a Gatekeeper Residue on the Aggregation of Transthyretin: IMPLICATIONS FOR TRANSTHYRETIN-RELATED AMYLOIDOSES. J Biol Chem 289 (41):28324-28337. doi:10.1074/jbc.M114.563981

[6] Estácio SG, Leal SS, Cristóvão JS, Faísca PFN, Gomes CM (2015) Calcium binding to gatekeeper residues flanking aggregation-prone segments underlies non-fibrillar amyloid traits in superoxide dismutase 1 (SOD1). Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1854 (2):118-126. doi:https://doi.org/10.1016/j.bbapap.2014.11.005

[7] Wang X, Zhou Y, Ren J-J, Hammer ND, Chapman MR (2010) Gatekeeper residues in the major curlin subunit modulate bacterial amyloid fiber biogenesis. Proceedings of the National Academy of Sciences 107 (1):163-168. doi:10.1073/pnas.0908714107

[8] Tartaglia GG, Vendruscolo M (2008) The Zyggregator method for predicting protein aggregation propensities. Chem Soc Rev 37 (7):1395-1401. doi:10.1039/B706784B

[9] De Baets G, Van Durme J, Rousseau F, Schymkowitz J (2014) A genome-wide sequence-structure analysis suggests aggregation gatekeepers constitute an evolutionary constrained functional class. J Mol Biol 426 (12):2405-2412. doi:10.1016/j.jmb.2014.04.007

[10] Beerten J, Jonckheere W, Rudyak S, Xu J, Wilkinson H, De Smet F, Schymkowitz J, Rousseau F (2012) Aggregation gatekeepers modulate protein homeostasis of aggregating sequences and affect bacterial fitness. Protein Engineering, Design and Selection 25 (7):357-366. doi:10.1093/protein/gzs031