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Amyloids “at the border”: deep mutagenesis and random sequence extension reveal an incomplete amyloid-forming motif in Bri2 that turns amyloidogenic upon C-terminal extension

ABSTRACT: Amyloids “at the border”: deep mutagenesis and random sequence extension reveal an incomplete amyloid-forming motif in Bri2 that turns amyloidogenic upon C-terminal extension

PROVIDER: PRJNA1128177 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR29550570_1.fastq.gz	Fastqsanger.gz
	SRR29550570_2.fastq.gz	Fastqsanger.gz
	SRR29550571_1.fastq.gz	Fastqsanger.gz
	SRR29550571_2.fastq.gz	Fastqsanger.gz
	SRR29550572_1.fastq.gz	Fastqsanger.gz

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Amyloids “at the border”: deep mutagenesis and random sequence extension reveal an incomplete amyloid-forming motif in Bri2 that turns amyloidogenic upon C-terminal extension

Project description: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.

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A enzymatic program that assembles amyloids

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A enzymatic program that assembles amyloids [ePAT]

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Cell membrane proteome analysis in HEK293T cells challenged with α-Synuclein amyloids

Project description:Amyloids interact with plasma membranes. Extracellular amyloids cross the plasma membrane barrier. Internalized extracellular amyloids are reported to trigger amyloidogenesis of endogenous proteins in recipient cells. To what extent these extracellular and intracellular amyloids perturb the plasma membrane proteome is not investigated. Using α-Synuclein as a model amyloid protein, we performed membrane shaving followed by mass spectrometry experiment to identify the conformational changes in the cell surface proteins after extracellular amyloid challenge. We also performed membrane proteomics after the biogenesis of intracellular α-Synuclein amyloids. Our results suggest that promiscuous interaction with extracellular amyloids stochastically alter the conformation of plasma membrane proteins. This affects the biological process through the plasma membrane and result in loss in cell viability. Cells that survive the extracellular amyloid shock can grow normally and gradually develop intracellular amyloids which do not directly impact the plasma membrane proteome and associated biological processes. Thus, α-Synuclein amyloids can damage the plasma membrane and related processes only during cell to cell transfer and not during their intracellular biogenesis.

2024-07-09 | PXD046280 | Pride

Transcription profiling of Drosophila of wild type migratory border cells (WTBC), non-migrating slbo mutant border cells (slboBC) and non-migrating follicle cells (FC)

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A enzymatic program that assembles amyloids [FLAM-seq]

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Amyloids from WT and mstr C. elegans

Project description:200,000 worms were grown to the young adult stage, collected, and washed. Worms were homogenized in lysis buffer (10mM Tris, pH 7.5, 138mM NaCl, 2mM CaCl2, and 0.1% NP-40). After homogenization, 125U/mL Benzonase nuclease was added to homogenates and samples were incubated for 30 mins at room temperature with gentle rocking. Samples were spun at 21,000g for 5 mins, followed by resuspension of the pellet in lysis buffer. Soluble proteins were removed from the pellet with repeated washes in lysis buffer until A280 of the supernatant was equal to A280 of lysis buffer. Resulting pellet was resuspended in ddH2O, spun at 21,000g for 5 mins, and supernatant was transferred to a collection tube. Water resuspensions and recoveries were continued until A280 of H2O supernatants was 0. Recovered water washes were pooled and amyloids salted out by adding NaCl to 200mM final concentration, followed by 1-3 days incubation at 4oC. Precipitated amyloids were recovered by centrifuging at 21,000g for 30 mins, followed by resuspension in 50-100uL ddH2O. Amyloids were digested (trypsin and chymotrypsin) and prepared for mass spectrometry as described in the methods files.

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

Project description:More than 50 human diseases are characterized by the deposition of specific protein aggregates in the form of insoluble amyloid fibrils. However, only a very small number of proteins are known to form amyloids with high propensity, limiting our ability to understand, predict and engineer amyloid aggregation from sequence. Here we use a massively parallel assay to quantify the amyloid nucleation propensity of >100,000 random 20 amino acid sequences. Approximately 5% of assayed random sequences nucleate the formation of aggregates, generating a very large and diverse training dataset from which to train models to predict amyloid nucleation. We use this dataset to train CANYA, a convolution-attention hybrid neural network that predicts the propensity of any primary sequence to form amyloids. CANYA outperforms previous predictors of protein aggregation on additional random sequences and out-of-sample datasets including human disease-causing amyloids, with very stable performance across diverse prediction tasks. We adapt and extend recent advances in interpretability of genomic neural networks to elucidate CANYA’s decision-making process and learned grammar and to provide mechanistic insights into amyloid formation. Our results demonstrate the power of massive experimental random sequence-space exploration and provide an interpretable and robust neural network model for understanding, predicting and designing amyloid-forming proteins.

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Transcriptional characterization of the myocardial infarct border zone

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Reversible amyloids of pyruvate kinase couple cell metabolism and stress granule disassembly

Project description:Cells respond to stress by blocking translation, rewiring metabolism, and forming transient mRNP assemblies called stress granules (SGs). After stress release, re-establishing homeostasis requires energy-consuming processes. However, the molecular mechanisms whereby cells restore energy production to disassemble SGs and reinitiate growth after stress remain poorly understood. Here we show that, upon stress, the ATP-producing enzyme Cdc19 forms inactive amyloids, and that their rapid re-solubilization is essential to restore energy production and SG disassembly. Cdc19 re-solubilization is initiated by the glycolytic metabolite fructose-1,6-bisphosphate (FBP), which directly binds Cdc19 amyloids and facilitates conformational changes that allow Hsp104 and Ssa2 chaperone recruitment. FBP then promotes Cdc19 tetramerization and activity, which together with trehalose breakdown boosts glycolysis to further enhance SG disassembly. Together, these results provide a molecular mechanism protecting cells during stress by directly coupling metabolism and ATP production with SG dynamics via regulation of Cdc19 amyloids.

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