Biology subjects

Bolognesi, B.

Publications and source records attributed to Bolognesi, B..

9 recordsLinked to original sources

bioRxiv2026

The folding, dimerization and allosteric landscapes of the ALS protein SOD1: a comprehensive mutational atlas

Mutations in superoxide dismutase 1 (SOD1) can disturb monomer folding and dimerization, ultimately leading to the development of amyotrophic lateral sclerosis (ALS). Genotype-phenotype knowledge of SOD1 monomer folding and dimerization is largely incomplete, impeding our ability to treat and understand SOD1-ALS. To address this issue, we performed multidimensional deep mutational scanning of [~]6,000 SOD1 variants (amino acid substitutions, insertions, deletions), quantifying folded monomer abundance, and measuring wild-type-variant heterodimerization. Importantly, both abundance and heterodimerization capture pathogenicity, with heterodimerization being a slightly better classifier of pathogenic variants. Notably, [~]80% of pathogenic variants and [~]70% of variants of uncertain significance in SOD1 perturb both monomer abundance and heterodimerization, pointing to protein destabilization as a major driver of SOD1-ALS. Orthogonal validation in mammalian cells shows that protein abundance strongly correlates with other disease-relevant phenotypes, including protein aggregation and toxicity. Although monomer abundance and heterodimerization are strongly coupled, residual effects identify variants that perturb the heterodimer beyond their impact on monomer folding. Mapping residual effects uncovers potential allosteric sites that modulate dimer stability independently of monomer folding, revealing mechanistic insights and potential therapeutic entry points to stabilize SOD1 as a dimer. Together, these results demonstrate that multidimensional phenotype variant mapping improves mechanistic understanding of SOD1 variants and clinical variant interpretation, while uncovering structural targets for SOD1-ALS treatment.

biophysics↗

bioRxiv2026

Systematic mutational mapping reveals optimal amyloid formation for RIPK function

Amyloid formation, typically associated with neurodegeneration, can also serve essential biological functions. RIPK1 and RIPK3 kinases assemble functional amyloids via their RHIM domains to drive necrosome formation and necroptosis. Here, we systematically map the sequence-function relationship of these domains using deep mutagenesis combined with massively parallel assays that directly report on amyloid nucleation and necroptotic signaling. Analysis of [~]3,000 mutations across RIPK1 and RIPK3 reveals that both proteins rely on a conserved aliphatic tetrad to nucleate amyloids, but diverge mechanistically: RIPK3 nucleation is largely driven by this core interface, whereas RIPK1 requires a second aliphatic surface to achieve efficient nucleation. Cross-species comparisons with mutational atlases from mouse RIPKs show conservation of these principles, underscoring their evolutionary selection. Strikingly, functional outcomes depend on finely balanced nucleation: variants that either reduce or enhance amyloid formation impair necroptosis. Consistently, human population genetics indicates that such variants are extremely rare, suggesting evolutionary optimization of the RHIM domain at a "sweet-spot" of amyloid propensity required for signaling. By linking quantitative mutational landscapes to functional amyloid assembly, this work uncovers how evolution has tuned the amyloid propensity of RIPKs to enable robust cell death signaling. These insights not only advance our understanding of amyloid biology but also provide a framework for therapeutic modulation of necroptosis and for engineering synthetic amyloids with tailored activities.

biophysics↗

bioRxiv2025

Massively parallel quantification of mutational impact on IAPP amyloidformation

Amyloid fibrils formed by the islet amyloid polypeptide (IAPP) cause pancreatic beta-cell damage, resulting in reduced insulin secretion and Type 2 diabetes (T2D). Variations in the primary amino acid sequence of IAPP can influence its aggregation rate and animals expressing IAPP variants that do not form amyloids, do not develop T2D. Conversely, specific single amino-acid changes in IAPP are enough to accelerate its aggregation rate. Understanding how mutations impact IAPP aggregation can help gain mechanistic understanding into the process of pathogenic amyloid formation of this peptide and preventively identify mutations that may contribute to the risk of developing T2D. Here, we employ deep mutational scanning to measure the ability to nucleate amyloids for 1663 IAPP variants, including substitutions, insertions, truncations and deletions and identify variants that increase amyloid formation in all mutation classes. Our results point at a continuous stretch of residues (15-32) which likely is structured in IAPP amyloids and that matches the core of the early aggregated species formed by IAPP in vitro. Inside this region, mutations have a more drastic effect in the 21-27 NNFGAIL segment, suggesting tighter structural constraints for this stretch in IAPP amyloids. Finally, by comparing this mutational atlas to that of another amyloid, Amyloid beta (A{beta}42), the peptide that aggregates in Alzheimers Disease, we find that the effects of mutations that slow down nucleation correlate between the two amyloids, but that when it comes to mutations that accelerate nucleation one single amyloid dataset cannot be used to predict mutational effects in the other.

biophysics↗

bioRxiv2024

Energetic portrait of the amyloid beta nucleation transition state

Amyloid protein aggregates are pathological hallmarks of more than fifty human diseases but how soluble proteins nucleate to form amyloids is poorly understood. Here we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively perturb the energetics of the nucleation reaction of amyloid beta (A{beta}42), the protein that aggregates in Alzheimers disease. In total, we quantify the nucleation rates of >140,000 variants of A{beta}42. This allows us to accurately quantify the changes in reaction activation energy for all possible amino acid substitutions in a protein for the first time and, in addition, to quantify >600 energetic interactions between mutations. The data reveal the simple and interpretable genetic architecture of an amyloid nucleation reaction. Strikingly, strong energetic couplings are rare and identify a subset of structural contacts in mature fibrils. Together with the activation energy changes, this strongly suggests that the A{beta}42 nucleation reaction transition state is structured in a short C-terminal region, providing a structural model for the reaction that may initiate Alzheimers disease. We believe this approach can be widely applied to probe the energetics and transition state structures of protein reactions.

biophysics↗

bioRxiv2024

Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

Protein aggregation is a pathological hallmark of more than fifty human diseases and a major problem for biotechnology. Methods have been proposed to predict aggregation from sequence, but these have been trained and evaluated on small and biased experimental datasets. Here we directly address this data shortage by experimentally quantifying the amyloid nucleation of >100,000 protein sequences. This unprecedented dataset reveals the limited performance of existing computational methods and allows us to train CANYA, a convolution-attention hybrid neural network that accurately predicts amyloid nucleation from sequence. We adapt genomic neural network interpretability analyses to reveal CANYAs decision-making process and learned grammar. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict amyloid nucleation.

genomics↗

bioRxiv2023

Deep mutagenesis reveals the distinct mutational landscape of ADan and ABri amyloid nucleation

Stop-loss mutations cause over twenty different diseases. The effects of stop-loss mutations can have multiple consequences that are, however, hard to predict. Stop-loss in ITM2B/BRI2 results in C-terminal extension of the encoded protein and, upon furin cleavage, in the production of two 34 amino acid long peptides, ADan and ABri, that accumulate as amyloids in the brains of patients affected by familial Danish and British Dementia. To systematically explore the consequences of Bri2 C-terminal extension, here, we measure amyloid formation for 676 ADan substitutions and identify the region that forms the putative amyloid core of ADan fibrils, located between positions 20 and 26, where stop-loss occurs. Moreover, we measure amyloid formation for [~]18,000 random C-terminal extensions of Bri2 and find that [~]32% of these sequences can nucleate amyloids. We find that the amino acid composition of these nucleating sequences varies with peptide length and that short extensions of 2 specific amino acids (Aliphatics, Aromatics and Cysteines) are sufficient to generate novel amyloid cores. Overall, our results show that the C-terminus of Bri2 contains an incomplete amyloid motif that can turn amyloidogenic upon extension. C-terminal extension with de novo formation of amyloid motifs may thus be a widespread pathogenic mechanism resulting from stop-loss, highlighting the importance of determining the impact of these mutations for other sequences across the genome.

genomics↗

bioRxiv2022

An atlas of amyloid aggregation: the impact of substitutions, insertions, deletions and truncations on amyloid beta fibril nucleation

Multiplexed assays of variant effects (MAVEs) guide clinical variant interpretation and reveal disease mechanisms. To date, MAVEs have focussed on a single mutation type - amino acid (AA) substitutions - despite the diversity of coding variants that cause disease. Here we use Deep Indel Mutagenesis (DIM) to generate the first comprehensive atlas of diverse variant effects for a disease protein, the amyloid beta (A{beta}) peptide that aggregates in Alzheimers disease (AD) and is mutated in familial AD (fAD). The atlas identifies known fAD mutations and reveals many variants beyond substitutions accelerate A{beta} aggregation and are likely to be pathogenic. Truncations, substitutions, insertions, single- and internal multi-AA deletions differ in their propensity to enhance or impair aggregation, but likely pathogenic variants from all classes are highly enriched in the polar N-terminus of A{beta}. This first comparative atlas highlights the importance of including diverse mutation types in MAVEs and provides important mechanistic insights into amyloid nucleation.

genomics↗

bioRxiv2020

The genetic landscape for amyloid beta fibril nucleation accurately discriminates familial Alzheimer's disease mutations

Amyloid fibrils are associated with many human diseases but how mutations alter the propensity of proteins to form fibrils has not been comprehensively investigated and is not well understood. Alzheimers Disease (AD) is the most common form of dementia with amyloid plaques of the amyloid beta (A{beta}) peptide a pathological hallmark of the disease. Mutations in A{beta} also cause familial forms of AD (fAD). Here we use deep mutational scanning to quantify the effects of >14,000 mutations on the aggregation of A{beta}. The resulting genetic landscape reveals fundamental mechanistic insights into fibril nucleation, including the importance of charge and gatekeeper residues in the disordered region outside of the amyloid core in preventing nucleation. Strikingly, unlike computational predictors and previous measurements, the in vivo nucleation scores accurately identify all known dominant fAD mutations, validating this simple cell-based assay as highly relevant to the human genetic disease and suggesting accelerated fibril nucleation is the ultimate cause of fAD. Our results provide the first comprehensive map of how mutations alter the formation of any amyloid fibril and a validated resource for the interpretation of genetic variation in A{beta}. HighlightsO_LIFirst comprehensive map of how mutations alter the propensity of a protein to form amyloid fibrils. C_LIO_LICharge and gatekeeper residues in the disordered N-terminus of amyloid beta prevent fibril nucleation. C_LIO_LIRates of nucleation in a cell-based assay accurately identify the mutations that cause dominant familial Alzheimers disease. C_LIO_LIThe combination of deep mutational scanning and human genetics provides a general strategy to quantify the disease-relevance of in vitro and in vivo assays. C_LI

genomics↗

bioRxiv2018

Phase Separations Driven by RNA Scaffolds and Protein Sequestration in FXTAS

Recent evidence indicates that specific RNAs promote formation of ribonucleoprotein condensates by acting as scaffolds for RNA-binding proteins (RBPs).\n\nWe systematically investigated RNA-RBP interaction networks to understand ribonucleoprotein assembly. We found that highly-contacted RNAs are highly structured, have long untranslated regions (UTRs) and contain nucleotide repeat expansions. Among the RNAs with such properties, we identified the FMR1 3 UTR that harbors CGG expansions implicated in Fragile X-associated Tremor/Ataxia Syndrome (FXTAS).\n\nWe studied FMR1 binding partners in silico and in vitro and prioritized the splicing regulator TRA2A for further characterization. In a FXTAS cellular model we validated TRA2A-FRM1 interaction and investigated implications of its sequestration at both transcriptomic and post-transcriptomic levels. We found that TRA2A co-aggregates with FMR1 in a FXTAS mouse model and in post mortem human samples.\n\nOur integrative study identifies key components of ribonucleoprotein aggregates, providing links to neurodegenerative disease and allowing the discovery of new therapeutic targets.

molecular biology↗