Biology subjects

Meier, F.

Publications and source records attributed to Meier, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Collective cancer cell calcium activity drives brain metastasis

Communication in multicellular networks is a cancer-intrinsic neural feature and crucial for primary brain tumor growth and resistance, but it is unclear whether brain metastases (BrM), the most common and deadliest brain malignancy, are also driven by communicating cancer networks. Using intravital two-photon microscopy in awake mice, clinical specimens, and Ca2+ integrators, we demonstrate that brain-colonizing breast and lung cancer and melanoma cells display gap-junction-dependent, coordinated Ca2+ activity in multicellular, cancer-cell intrinsic networks, which drives their proliferation. Mechanistically, Ca2+ oscillations induce transcription of immediate early genes, adoption of a neuronal expression profile, and cell cycle progression. While many of those features are enriched in BrM, all investigated cancer cell lines showed collective Ca2+ activity. Therapeutically, blocking Ca2+ activity with gap junction inhibitors reduces BrM burden in mouse models. Here we show communicating cancer cell syncytia as drivers of BrM growth, pointing to a targetable pathomechanism, and potentially a new pan-cancer hallmark. Graphical AbstractIn brief Brain metastases form gap-junction-coupled networks exhibiting spontaneous, coordinated Ca2+ activity linked to immediate early gene activation, neuronal gene programs, and cell cycle progression. Disrupting Ca2+ network communication with gap junction inhibitors induces cell cycle arrest and reduces brain metastatic burden in vivo. O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/723715v1_ufig1.gif" ALT="Figure 1"> View larger version (88K): org.highwire.dtl.DTLVardef@1130ea0org.highwire.dtl.DTLVardef@254dcdorg.highwire.dtl.DTLVardef@117021forg.highwire.dtl.DTLVardef@18c4f1e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIBrain metastases display collective Ca2+ activity in gap-junction-coupled networks C_LIO_LICa2+ co-activity is conserved across cancers but enriched in brain metastasis C_LIO_LICa2+ oscillations robustly induce neuronal gene programs and cell cycle progression C_LIO_LIGap junction inhibition reduces brain metastasis burden in mice C_LI

cancer biology↗

bioRxiv2025

Multiomic profiling of a unique in-transit melanoma cohort identifies melanoma differentiation as predictor of tumor progression and therapy response

Melanoma patients with in-transit metastasis (ITM), a stage of disease where melanoma has metastasized to sites in between the primary lesion and draining lymph node, vary significantly in their clinical outcomes, but the biology driving differential outcomes in ITM is poorly understood. To elucidate the mechanisms of differential outcomes, we utilized multimodal molecular profiling (WES, RNA-seq, highly multiplexed immunofluorescence, spatial transcriptomics) in 1) evolutionary analysis of longitudinal tumor samples and 2) identifying prognostic tumor intrinsic and microenvironmental features in a unique cohort of patients with unresectable ITM. Among other findings, we observed a persistent dedifferentiated AXL/NGFR clonal lineage pre-existing and following immune checkpoint blockade in in-transit and distant metastases. Concordantly, we found that low pigmentation and high T cell exhaustion signatures were independently associated with distant progression. Our findings highlight tumor cell state and immune dysfunction as key predictors and potential biomarkers of metastatic risk in ITM. STATEMENT OF SIGNIFICANCEWhat drives distant progression in melanoma is unclear. Analyzing tumor and immune features in a rare in-transit melanoma patient cohort, we identify biological signals highlighting how immune and tumor states observable in pre-distant metastasis melanomas shape long-term outcomes, and nominate potential prognostic biomarkers.

cancer biology↗

bioRxiv2024

EXCRETE enables deep proteomics of the cyanobacterial extracellular environment

Extracellular proteins play a significant role in shaping microbial communities which, in turn, can impact ecosystem function, human health, and biotechnological processes. Yet, for many ubiquitous microbes, there is limited knowledge regarding the identity and function of secreted proteins. Here, we introduce EXCRETE (enhanced exoproteome characterization by mass spectrometry), a workflow that enables comprehensive description of microbial exoproteomes from minimal starting material. Using cyanobacteria as a case study, we benchmark EXCRETE and show a significant increase over current methods in the identification of extracellular proteins. Subsequently, we show that EXCRETE can be miniaturized and adapted to a 96-well high-throughput format. Application of EXCRETE to cyanobacteria from different habitats (Synechocystis sp. PCC 6803, Synechococcus sp. PCC 11901, and Nostoc punctiforme PCC 73102), and in different cultivation conditions, identified up to 85% of all predicted secreted proteins. Finally, functional analysis reveals that cell envelope maintenance and nutrient acquisition are central functions of the cyanobacterial secretome. Collectively, these findings challenge the general belief that cyanobacteria lack secretory proteins and point to a functional conservation of the secretome across freshwater, marine, and terrestrial species.

biochemistry↗

bioRxiv2023

Preventing injuries: How augmented reality reduces fall risks in pseudo-natural environments

Falling is a major contributor to injury-related incidents and the 2nd most likely cause for unintentional injury deaths world-wide. Here, we therefore tested a vision aid for obstacle detection during a navigation task in pseudo-natural environments (virtually created cities). The vision aid had nine different setups (i.e. no highlighting of hazardous objects vs. eight different hit / false alarm ratios [cue validity]). Elderly and healthy, young adults completed the navigation task. As in real life, elderly walked slower and fell more often in pseudo-natural environments, suggesting the ecological validity of such safe experimental setups to test hazardous situations. Importantly, the vision aid reduced the risk of falling (by up to 33 %) for both age groups. Thus, our study highlights that a vision aid which augments reality could be a substantial contributor in preventing falls and related injuries, decreasing the financial burden on the health system and increasing life quality.

neuroscience↗

bioRxiv2023

μPhos: a scalable and sensitive platform for functional phosphoproteomics

Mass spectrometry has revolutionized cell signaling research by vastly simplifying the identification and quantification of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to internal or external perturbations in space and time is crucial for further illuminating functionality of the phosphoproteome. Here we describe {micro}Phos, an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture experiments in < 8 hours total processing time. By minimizing transfer steps and reducing liquid volumes to < 200 {micro}L, we demonstrate increased sensitivity, over 90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify more than 20,000 unique phosphopeptides in a human cancer cell line using 20 {micro}g starting material, and confidently localize > 5,000 phosphorylation sites from 5 {micro}g. This depth covers key intracellular signaling pathways, rendering sample-limited applications and extensive perturbation experiments with hundreds of samples viable. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/535617v1_ufig1.gif" ALT="Figure 1"> View larger version (87K): org.highwire.dtl.DTLVardef@43d3e9org.highwire.dtl.DTLVardef@10d975forg.highwire.dtl.DTLVardef@14ce090org.highwire.dtl.DTLVardef@2b2e67_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Peptide collision cross sections of 22 post-translational modifications

Recent advances have rekindled the interest in ion mobility spectrometry as an additional dimension of separation in mass spectrometry (MS)-based proteomics. It separates ions according to their size and shape in the gas phase. Here, we set out to investigate the effect of 22 different post-translational modifications (PTMs) on the collision cross section (CCS) of peptides. In total, we analyzed [~]4700 pairs of matching modified and unmodified peptide ions by trapped ion mobility spectrometry (TIMS). Linear alignment based on spike-in reference peptides resulted in highly reproducible CCS values with a median coefficient of variation of 0.3%. On a global level, we observed a redistribution in the m/z vs. ion mobility space for modified peptides upon changes in their charge state. Pairwise comparison between modified and unmodified peptides of the same charge state revealed median shifts in CCS between - 1.1% (lysine formylation) and +4.5% (O-GlcNAcylation). In general, increasing modified peptide masses were correlated with higher CCS values, in particular within homologous PTM series. However, investigating the ion populations in more detail, we found that the change in CCS can vary substantially for a given PTM depending on the gas phase structure of its unmodified counterpart. In conclusion, our study shows PTM- and sequence-specific effects on the cross section of peptides, which could be further leveraged for proteome-wide PTM analysis.

systems biology↗

bioRxiv2022

Synchro-PASEF allows precursor-specific fragment ion extraction and interference removal in data-independent acquisition

Data-independent acquisition (DIA) methods have become increasingly popular in mass spectrometry (MS)-based proteomics because they enable continuous acquisition of fragment spectra for all precursors simultaneously. However, these advantages come with the challenge of correctly reconstructing the precursor-fragment relationships in these highly convoluted spectra for reliable identification and quantification. Here we introduce a scan mode for the combination of trapped ion mobility spectrometry (TIMS) with parallel accumulation - serial fragmentation (PASEF) that seamlessly and continuously follows the natural shape of the ion cloud in ion mobility and peptide precursor mass dimensions. Termed synchro-PASEF, it increases the detected fragment ion current several-fold at sub-second cycle times. Consecutive quadrupole selection windows move synchronously through the mass and ion mobility range, defining precursor-quadrupole relationships. In this process, the quadrupole slices through the peptide precursors, which separates fragment ion signals of each precursor into adjacent synchro-PASEF scans. This precisely defines precursor - fragment relationships in ion mobility and mass dimensions and effectively deconvolutes the DIA fragment space. Importantly, the partitioned parts of the fragment ion transitions provide a further dimension of specificity via a lock and key mechanism. This is also advantageous for quantification, where signals from interfering precursors in the DIA selection window do not affect all partitions of the fragment ion, allowing to retain only the specific parts for quantification. Overall, we establish the defining features of synchro-PASEF and explore its potential for proteomic analyses.

biochemistry↗

bioRxiv2022

Ion mobility-resolved phosphoproteomics with dia-PASEF and short gradients

Mass spectrometry-based phosphoproteomics has identified >150,000 post-translational phosphorylation sites in the human proteome. To disentangle their functional relevance, complex experimental designs that require increased throughput are now coming into focus. Here, we apply dia-PASEF on a trapped ion mobility (TIMS) mass spectrometer to analyze the phosphoproteome of a human cancer cell line in short liquid chromatography gradients. At low sample amounts equivalent to [~]20 ug protein digest per analysis, we quantified over 12,000 phosphopeptides including [~]8,000 class I phosphosites in one hour without a spectral library. Decreasing the gradient time to 15 min yielded virtually identical coverage of the phosphoproteome, and with 7 min gradients we still quantified about 80% of the class I sites with a median coefficient of variation <10% in quadruplicates. We attribute this in part to the increased peak capacity, which effectively compensates for the higher peptide density per time unit in shorter gradients. Our data shows a five-fold reduction in the number of co-isolated peptides with TIMS. In the most extreme case, these were positional isomers of nearby phosphosites that remained unresolved with fast chromatography. In summary, we demonstrate how key features of dia-PASEF translate to phosphoproteomics, resulting in high throughput and sensitivity.

systems biology↗

bioRxiv2022

Rapid and in-depth coverage of the (phospho-)proteome with deep libraries and optimal window design for dia-PASEF

Data-independent acquisition (DIA) methods have become increasingly attractive in mass spectrometry (MS)-based proteomics, because they enable high data completeness and a wide dynamic range. Recently, we combined DIA with parallel accumulation - serial fragmentation (dia-PASEF) on a Bruker trapped ion mobility separated (TIMS) quadrupole time-of-flight (TOF) mass spectrometer. This requires alignment of the ion mobility separation with the downstream mass selective quadrupole, leading to a more complex scheme for dia-PASEF window placement compared to DIA. To achieve high data completeness and deep proteome coverage, here we employ variable isolation windows that are placed optimally depending on precursor density in the m/z and ion mobility plane. This Automatic Isolation Design procedure is implemented in the freely available py_diAID package. In combination with in-depth project-specific proteomics libraries and the Evosep LC system, we reproducibly identified over 7,700 proteins in a human cancer cell line in 44 minutes with quadruplicate single-shot injections at high sensitivity. Even at a throughput of 100 samples per day (11 minutes LC gradients), we consistently quantified more than 6,000 proteins in mammalian cell lysates by injecting four replicates. We found that optimal dia-PASEF window placement facilitates in-depth phosphoproteomics with very high sensitivity, quantifying more than 35,000 phosphosites in a human cancer cell line stimulated with an epidermal growth factor (EGF) in triplicate 21 minutes runs. This covers a substantial part of the regulated phosphoproteome with high sensitivity, opening up for extensive systems-biological studies.

biochemistry↗

bioRxiv2022

Dynamic human liver proteome atlas reveals functional insights into disease pathways

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell-type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence-unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time-course of primary cell cultures and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serves as spectral library to characterize a human cohort of non-alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web-based dashboard application for the interactively exploration of our resource. HighlightsO_LICell-type resolved liver proteome with copy numbers for 10,500 proteins C_LIO_LITime-course of human liver primary cells uncovers functional proteome shifts C_LIO_LIA human cohort study reveals liver proteome changes in NASH and cirrhosis C_LIO_LIAn interactive web portal integrates the results for easy exploration C_LI

systems biology↗

bioRxiv2021

The social architecture of an in-depth cellular protein interactome

Nearly all cellular functions are mediated by protein-protein interactions and mapping the interactome provides fundamental insights into the regulation and structure of biological systems. In principle, affinity purification coupled to mass spectrometry (AP-MS) is an ideal and scalable tool, however, it has been difficult to identify low copy number complexes, membrane complexes and those disturbed by protein-tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive, high-throughput, and highly reproducible AP-MS technology combined with a quantitative two-dimensional analysis strategy for comprehensive interactome mapping of Saccharomyces cerevisiae. We reduced required cell culture volumes thousand-fold and employed 96-well formats throughout, allowing replicate analysis of the endogenous green fluorescent protein (GFP) tagged library covering the entire expressed yeast proteome. The 4159 pull-downs generated a highly structured network of 3,909 proteins connected by 29,710 interactions. Compared to previous large-scale studies, we double the number of proteins (nodes in the network) and triple the number of reliable interactions (edges), including very low abundant epigenetic complexes, organellar membrane complexes and non-taggable complexes interfered by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 15 interactors, the majority of them unreported so far. Similar to social networks between humans, the average shortest distance is 4.2 interactions. A web portal (www.yeast-interactome.org) enables exploration of our dataset by the network and biological communities and variations of our AP-MS technology can be employed in any organism or dynamic conditions.

systems biology↗

bioRxiv2021

AlphaPept, a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making their efficient analysis a principal challenge. There is a plethora of different computational tools that process the raw MS data and derive peptide and protein identification and quantification. During the last decade, there has been dramatic progress in computer science and software engineering, including collaboration tools that have transformed research and industry. To leverage these advances, we developed AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Using Numba for just-in-time machine code compilation on CPU and GPU, we achieve hundred-fold speed improvements while maintaining clear syntax and rapid development speed. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while providing access to the latest advances in machine learning. We provide an easy on-ramp for community validation and contributions through the concept of literate programming, implemented in Jupyter Notebooks of the different modules. A framework for continuous integration, testing, and benchmarking enforces solid software engineering principles. Large datasets can rapidly be processed as shown by the analysis of hundreds of cellular proteomes in minutes per file, many-fold faster than the data acquisiton. The AlphaPept framework can be used to build automated processing pipelines using efficient HDF5 based file formats, web-serving functionality and compatibility with downstream analysis tools. Easy access for end-users is provided by one-click installation of the graphical user interface, for advanced users via a modular Python library, and for developers via a fully open GitHub repository.

bioinformatics↗

bioRxiv2020

Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation

Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing1-3. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary4,5. Chemical labeling-based single-cell approaches introduce hundreds of cells into the MS, but direct analysis of single cells has not yet reached the necessary sensitivity, robustness and quantitative accuracy to answer biological questions6,7. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography and a novel trapped ion mobility mass spectrometer, resulting in a more than ten-fold improved sensitivity. We accurately and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators such as CDK2NA, the E2 ubiquitin ligase UBE2S, DNA topoisomerases TOP2A/B and the chromatin regulator HMGA1. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable core proteome despite perturbation, while the transcriptome appears volatile. This emphasizes substantial regulation of translation and sets the stage for its elucidation at the single cell level. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material8, posttranslational modifications and small molecule studies to gain unprecedented insights into cellular heterogeneity in health and disease.

systems biology↗

bioRxiv2020

A paired liver biopsy and plasma proteomics study reveals circulating biomarkers for alcohol-related liver disease

Existing tests for detecting liver fibrosis, inflammation and steatosis, three stages of liver disease that are still reversible are severely hampered by limited accuracy or invasive nature. Here, we present a paired liver-plasma proteomics approach to infer molecular pathophysiology and to identify biomarkers in a cross-sectional alcohol-related liver disease cohort of nearly 600 individuals. Metabolic functions were downregulated whereas fibrosis-associated signaling and novel immune responses were upregulated, but only half of tissue proteome changes were transmitted to the circulation. Machine learning models based on our biomarker panels outperformed existing tests, laying the foundation for a generic proteomic liver health assessment.

biochemistry↗

bioRxiv2020

Deep learning the collisional cross sections of the peptide universe from a million training samples

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To explore the nature and utility of the entire peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation - serial fragmentation (PASEF). The scale and precision (CV <1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools library validate the model within a 1.3% median relative error (R > 0.99). Hydrophobicity, position of prolines and histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

systems biology↗

bioRxiv2020

Ectopic bone formation and systemic bone loss in a transmembrane TNF-driven model of human spondyloarthritis

Background The transmembrane-TNF transgenic mouse, TgA86, has been shown to develop spontaneously peripheral arthritis with signs of axial involvement. To assess similarity to human spondyloarthritis we performed detailed characterization of the axial, peripheral and comorbid pathologies of this model.Methods TgA86 bone pathologies were assessed at different ages using CT imaging of the spine, tail vertebrae and hind limbs and characterized in detail by histopathological and immunohistochemical analysis. Cardiac function was examined by echocardiography and electrocardiography and bone structural parameters by µCT analysis. The response of TgA86 mice to either early or late anti-TNF treatment was evaluated clinically, histopathologically and by µCT analysis.Results TgA86 mice developed with 100% penetrance spontaneous axial and peripheral pathology which progressed with time and manifested as reduced body weight and body length, kyphosis, tail bendings as well as swollen and distorted hind joints. Whole body CT analysis at advanced ages revealed bone erosions of sacral and caudal vertebrae as well as of sacroiliac joints and hind limps, and also, new ectopic bone formation and eventually vertebral fusion. The pathology of these mice highly resembled that of SpA patients, as it evolved through an early inflammatory phase, evident as enthesitis and synovitis in the affected joints, characterized by mesenchymal cell accumulation and neutrophilic infiltration. Subsequently, regression of inflammation was accompanied by ectopic bone formation, leading to ankylosis. In addition, both systemic bone loss and comorbid heart valve pathology were evident. Importantly, early anti-TNF treatment, similar to clinical treatment protocols, significantly reduced the inflammatory phase of both the axial and peripheral pathology of TgA86 mice.Conclusions The TgA86 mice develop a spontaneous peripheral and axial biphasic pathology accompanied by comorbid heart valvular dysfunction and osteoporosis, overall faithfully reproducing the progression of pathognomonic features of human spondyloarthritis. Therefore, the TgA86 mouse represents a valuable model for deciphering the pathogenic mechanisms of spondyloarthritis and for assessing the efficacy of human therapeutics targeting different phases of the disease.Competing Interest StatementProf. George Kollias participates in the BoD of Biomedcode; Dr. Florian Meier has received grants from Pfizer. All other authors declare no competing interests.List of abbreviationsASAnkylosing SpondylitisCTComputed TomographyCVDCardiovascular DiseaseEDTAEthylenediaminetetraacetic acidH&EHematoxylin&EosinIBDInflammatory Bowel DiseaseMRIMagnetic Resonance ImagingOPOsteoporosisOPNOsteopontinPsAPsoriatic arthritisRARheumatoid ArthritisReAReactive arthritiss/oSafranin/OSpASpondyloarthritisTgtransgenictmTNFtransmembrane mouseTNF TNF-αTumor Necrosis Factor αTRAPTartrate-resistant acid phosphataseUTRUntranslated RegionwtWild TypeµCTmicro CTView Full Text

pathology↗

bioRxiv2019

Adaptive Tuning Curve Widths Improve Sample Efficient Learning

Natural brains perform miraculously well in learning new tasks from a small number of samples, whereas sample efficient learning is still a major open problem in the field of machine learning. Here, we raise the question, how the neural coding scheme affects sample efficiency, and make first progress on this question by proposing and analyzing a learning algorithm that uses a simple reinforce-type plasticity mechanism and does not require any gradients to learn low dimensional mappings. It harnesses three bio-plausible mechanisms, namely, population codes with bell shaped tuning curves, continous attractor mechanisms and probabilistic synapses, to achieve sample efficient learning. We show both theoretically and by simulations that population codes with broadly tuned neurons lead to high sample efficiency, whereas codes with sharply tuned neurons account for high final precision. Moreover, a dynamic adaptation of the tuning width during learning gives rise to both, high sample efficiency and high final precision. We prove a sample efficiency guarantee for our algorithm that lies within a logarithmic factor from the information theoretical optimum. Our simulations show that for low dimensional mappings, our learning algorithm achieves comparable sample efficiency to multi-layer perceptrons trained by gradient descent, although it does not use any gradients. Furthermore, it achieves competitive sample efficiency in low dimensional reinforcement learning tasks. From a machine learning perspective, these findings may inspire novel approaches to improve sample efficiency. From a neuroscience perspective, these findings suggest sample efficiency as a yet unstudied functional role of adaptive tuning curve width.

neuroscience↗

bioRxiv2019

Huntingtin fibrils with different toxicity, structure, and seeding potential can be reversibly interconverted

The first exon of the huntingtin protein (HTTex1) important in Huntingtons Disease (HD) can form cross-{beta} fibrils of varying toxicity. We find that the difference between these fibrils is the degree of entanglement and dynamics of the C-terminal proline-rich domain (PRD) in a mechanism analogous to polyproline film formation. In contrast to fibril strains found for other cross-{beta} fibrils, these HTTex1 fibril types can be reversibly interconverted. This is because the structure of their polyQ fibril core remains unchanged. Further, we find that more toxic fibrils of low entanglement have higher affinities for protein interactors and are more effective seeds for recombinant HTTex1 and HTTex1 in cells. Together these data show how the structure of a framing sequence at the surface of a fibril can modulate seeding, protein-protein interactions, and thereby toxicity in neurodegenerative disease.

biophysics↗