Poly(ADP-ribose) drives condensation of FUS via a transient interaction.

Poly(ADP-ribose) (PAR) is an RNA-like polymer that regulates an increasing number of biological processes. Dysregulation of PAR is implicated in neurodegenerative diseases characterized by abnormal protein aggregation, including amyotrophic lateral sclerosis (ALS). PAR forms condensates with FUS, an RNA-binding protein linked with ALS, through an unknown mechanism. Here, we demonstrate that a strikingly low concentration of PAR (1 nM) is sufficient to trigger condensation of FUS near its physiological concentration (1 µM), which is three orders of magnitude lower than the concentration at which RNA induces condensation (1 µM). Unlike RNA, which associates with FUS stably, PAR interacts with FUS transiently, triggering FUS to oligomerize into condensates. Moreover, inhibition of a major PAR-synthesizing enzyme, PARP5a, diminishes FUS condensation in cells. Despite their structural similarity, PAR and RNA co-condense with FUS, driven by disparate modes of interaction with FUS. Thus, we uncover a mechanism by which PAR potently seeds FUS condensation.

ALS/FTLD-Linked Mutations in FUS Glycine Residues Cause Accelerated Gelation and Reduced Interactions with Wild-Type FUS.

The RNA-binding protein fused in sarcoma (FUS) can form pathogenic inclusions in neurodegenerative diseases like amyotrophic lateral sclerosis (ALS) and frontotemporal lobar dementia (FTLD). Over 70 mutations in Fus are linked to ALS/FTLD. In patients, all Fus mutations are heterozygous, indicating that the mutant drives disease progression despite the presence of wild-type (WT) FUS. Here, we demonstrate that ALS/FTLD-linked FUS mutations in glycine (G) strikingly drive formation of droplets that do not readily interact with WT FUS, whereas arginine (R) mutants form mixed condensates with WT FUS. Remarkably, interactions between WT and G mutants are disfavored at the earliest stages of FUS nucleation. In contrast, R mutants physically interact with the WT FUS such that WT FUS recovers the mutant defects by reducing droplet size and increasing dynamic interactions with RNA. This result suggests disparate molecular mechanisms underlying ALS/FTLD pathogenesis and differing recovery potential depending on the type of mutation.

Single-molecule and ensemble methods to probe RNP nucleation and condensate properties.

Biomolecular condensates often consist of intrinsically disordered protein and RNA molecules, which together promote the formation of membraneless organelles in cells. The nucleation, condensation, and maturation of condensates is a critical yet poorly understood process. Here, we present single-molecule and accompanying ensemble methods to quantify these processes more comprehensively. In particular, we focus on how to properly design and execute a single-molecule nucleation assay, in which we detect signals arising from individual units of fluorescently labeled RNA-binding proteins associating with an RNA substrate. The analysis of this data allows one to determine the kinetics involved with each step of nucleation. Complemented with meso-scale techniques that measure the biophysical properties of ribonucleoprotein condensates, the methods described herein are powerful tools that can be adopted for studying any protein-RNA interactions that promote phase separation.

Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed?

The ICH M7 (R1) guideline recommends the use of complementary (Q)SAR models to assess the mutagenic potential of drug impurities as a state-of-the-art, high-throughput alternative to empirical testing. Additionally, it includes a provision for the application of expert knowledge to increase prediction confidence and resolve conflicting calls. Expert knowledge, which considers structural analogs and mechanisms of activity, has been valuable when models return an indeterminate (equivocal) result or no prediction (out-of-domain). A retrospective analysis of 1002 impurities evaluated in drug regulatory applications between April 2017 and March 2019 assessed the impact of expert review on (Q)SAR predictions. Expert knowledge overturned the default predictions for 26% of the impurities and resolved 91% of equivocal predictions and 75% of out-of-domain calls. Of the 261 overturned default predictions, 15% were upgraded to equivocal or positive and 79% were downgraded to equivocal or negative. Chemical classes with the most overturns were primary aromatic amines (46%), aldehydes (45%), Michael-reactive acceptors (37%), and non-primary alkyl halides (33%). Additionally, low confidence predictions were the most often overturned. Collectively, the results suggest that expert knowledge continues to play an important role in an ICH M7 (Q)SAR prediction workflow and triaging predictions based on chemical class and probability can improve (Q)SAR review efficiency.