Biology subjects

Cortes, J.

Publications and source records attributed to Cortes, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Systemic and kinematic properties of Amoeba proteus migration correlate strongly across environments

Recent work has shown that cell migration is a systemic property of the whole cell, yet how the different properties of a migratory trajectory relate to one another remains largely uncharacterized. Here we quantified the migration of 150 Amoeba proteus cells under three experimental scenarios (absence of stimuli, a direct-current electric field, and a chemotactic peptide gradient) with eleven metrics that describe either the systemic dynamics of the trajectory (long-range correlations, long-term persistence, super-diffusive exploration, regularity of the move steps and memory of the velocity) or its kinematic output (net displacement, straightness and speed). Screening all 55 metric pairs for statistical dependence with distance covariance identified six couplings that held in all three experimental scenarios, besides the pairs linked by definition. Long-term persistence and super-diffusive exploration were coupled with each other, in agreement with the scaling relation expected for self-similar trajectories, and each of them was coupled with the net displacement and the straightness of the trajectory: more persistent and more super-diffusive cells moved farther and along straighter paths. In addition, the long-range correlations of the move steps were coupled with the memory of speed between consecutive steps. All six couplings were positive and monotonic, with no trend reversal over the observed range. The stimuli shifted six of the eleven metrics, the electric field mainly increasing the straightness and the net displacement of the trajectories and the chemical gradient mainly increasing the speed memory, but they left the six couplings intact and reorganized part of the dependence structure around speed. These results show that several systemic and kinematic properties of Amoeba proteus migration are not independent observables but vary together as parts of an integrated system whose couplings are preserved across environments.

cell biology↗

bioRxiv2026

Loss of the lncRNA SOX1-OT promotes p53-dependent cell-cycle arrest in astrocytes

Long non-coding RNAs (lncRNAs) are increasingly recognized as regulators of brain cell function, but their roles in astrocyte biology and neurodegeneration remain poorly understood. Here, we identify Sox1ot/SOX1-OT as a conserved, brain-enriched lncRNA that is downregulated in Alzheimers disease and in reactive astrocyte states. Antisense oligonucleotide-mediated depletion of Sox1ot in astrocytes revealed a transcriptional program marked by activation of p53 target genes selectively associated with cell-cycle inhibitory pathways. Consistent with this, Sox1ot depletion enhanced p53 occupancy at target promoters such as Cdkn1a, increased Cdkn1a expression and levels of its protein product p21, and thereby induced G1 arrest and reduced astrocyte proliferation. In contrast, other canonical p53 outputs, including apoptosis and senescence, were not affected, indicating that Sox1ot selectively modulates distinct branches of p53 signaling. Notably, loss of Sox1ot/SOX1-OT was accompanied by impaired glutamate uptake, reduced lactate secretion, and altered astrocyte support functions, suggesting that these deficits arise as downstream consequences of the p53-dependent transcriptional shift rather than direct primary effects of Sox1ot loss. Together, these findings identify SOX1-OT as an astrocyte-enriched regulatory layer that constrains a p53-dependent cell-cycle program and highlight its role in shaping astrocyte state transitions in Alzheimers disease.

neuroscience↗

bioRxiv2026

Multi-timescale PTM architecture in human RNA polymerase II

Human RNA Polymerase II (Pol II) is characterized by a dense layer of over 775 post-translational modifications (PTMs) that form a dynamic, rewritable regulatory architecture integrating large numbers of cellular signals to coordinate transcription initiation, elongation, termination, and co-transcriptional RNA processing. While genomic information has been extensively catalogued, the potential information capacity associated with Pol II PTM patterns has remained largely unquantified. Here, we analyze the PTM sites across the human Pol II complex (Rpb1-Rpb12) and estimate the state-space information capacity using Shannon entropy theory. We first provide a theoretical upper bound of ~707.98 bits per Pol II molecule (~88.50 bytes) corresponding to ~5.68 x 107 bits per nucleus (~7.10 MB), assuming ~80,200 Pol II molecules per cell. We distinguish this maximal capacity from a conservative, kinetically addressable estimate of ~114.88 bits per molecule (~1.15 MB per nucleus), reflecting physiological kinetic constraints and site coupling that restrict the simultaneously addressable PTM state space in vivo. Finally, we show that major PTM classes (phosphorylation, proline isomerization, O-GlcNAcylation and ubiquitination) operate over distinct lifetimes, from seconds to minutes and hours-scale processes, supporting a multi-timescale biochemical architecture of this enzyme. Together, these results provide a quantitative information framework that distinguishes maximal PTM state-space capacity from kinetically addressable physiological regulatory capacity, supporting a view of Pol II PTM patterning as a high-dimensional, dynamically reconfigurable, multi-timescale regulatory information layer. HighlightsA systems-level framework quantifies regulatory information in Pol II PTMs Known PTM modification sites provide 707.98 bits per Pol II as a regulatory upper bound Physiological kinetic constraints reduce accessible regulatory capacity to 114.88 bits Distinct PTM modification classes define fast, intermediate, and slow regulatory layers Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/734958v1_ufig1.gif" ALT="Figure 1"> View larger version (84K): org.highwire.dtl.DTLVardef@1101c1borg.highwire.dtl.DTLVardef@a551fcorg.highwire.dtl.DTLVardef@1a97bd3org.highwire.dtl.DTLVardef@68db_HPS_FORMAT_FIGEXP M_FIG C_FIG

Systems Biology↗

bioRxiv2026

Oxygen Minimum Zones Drive Changes in Deep-sea Coral Species Distribution, Diversity, and Community Structure On Seamounts in the Eastern Tropical Pacific

Oxygen minimum zones (OMZs) are among the most significant abiotic environmental gradients found in the ocean. Yet, fine-scale species distribution patterns of organisms inhabiting OMZs are still spatially limited, hindering our understanding on how these oceanographic features influence species diversity and community structure. Cold-water corals are ecologically important habitat-forming species that are often considered to be sensitive to low seawater dissolved oxygen concentration and thus likely to be useful indicators for exploring change in megafaunal abundance and biodiversity across the OMZ. In the eastern tropical Pacific Ocean, widespread oxygen minimum zones and oxygen limiting zones encompass several thousand square kilometers of area and span several hundred meters of the water column, but typically are strongest between 300-700 m depth. A January 2019 cruise aboard the R/V Falkor using the ROV SuBastian, conducted video transects along 7 seamounts between the Costa Rica Margin and Isla del Coco, as well as within one submarine incised canyon on the north side of Isla del Coco. In this study, we analyzed survey data for patterns in cold-water coral species distribution, diversity, and coral community structure relative to abiotic oceanographic variables in order to gain biogeographic insights to this area. Across all sites, we identified 3675 coral occurrences and 75 unique morphospecies between 177-1565 m. Rapid species turnover with increasing depth occurred primarily across the upper (300 m) and lower OMZ boundaries (700 m). Coral assemblages within the OMZ depths were observed to contain distinct groups of species compared to those below at deeper bathyal depths. Stylasterid hydrocorals were disproportionately abundant above and within the OMZ, while octocoral and black coral species dominated in the more well-oxygenated waters below. Coral assemblage diversity and abundance was depressed within the OMZ, but bathyal diversity peaked at intermediate water depths between 1200-1500 m. In addition to assessing the impact of OMZs on coral communities, these results provide unique insights to the abundance, diversity, and environmental drivers of deep-water coral community assembly in a data-deficient locality, thus improving biodiversity metrics and informing marine conservation efforts off Costa Rica. These baseline data are particularly salient in the light of projected expansion and shoaling of eastern tropical Pacific oxygen minimum zones as a result of decreasing ocean oxygen concentrations driven by ocean warming and other climate drivers.

ecology↗

bioRxiv2026

Generative design of intrinsically disordered proteins based on conditioned protein language models: Data is the limit

Intrinsically disordered proteins and regions (IDRs) are central to a multitude of biological processes. Despite extensive studies of their structural and physicochemical properties, the rational design of IDRs with defined conformational behavior remains challenging due to their ensemble nature. Here we present a generative framework for designing disordered protein sequences conditioned on target conformational ensemble descriptors using protein language models (pLMs). We formulate IDR design as the task of generating amino acid sequences predicted to realize specified biophysical properties and implement a Transformer encoder-decoder architecture that maps numerical descriptors to protein sequences. By training models on datasets spanning two orders of magnitude in size, we show that accurate control of conformational and physicochemical properties is achieved only at large data scale. These results demonstrate the feasibility of conditioning generative models on ensemble-level descriptors for IDR design. More broadly, these results support a data-centric paradigm for protein engineering, in which data availability emerges as a key limiting factor for the accurate design of IDRs.

bioinformatics↗

bioRxiv2026

Bridging Higher-Order Information Theory and Neuroimaging: A Voxel-Wise O-Information Framework

The brains functional organization has been extensively studied through pairwise connectivity analyses. While these approaches have provided important insights into brain network organization, they fall short in capturing the complexity of high-order functional interactions (HOI). Particularly relevant is the investigation of redundancy and synergy patterns -not addressable with pairwise interactions-, revealing fundamental mechanisms of brain integration and information processing across various cognitive functions and clinical conditions. Conventional neuroimaging software packages are primarily designed for classical (general linear model-like) analyses but lack native support for HOI metrics. To address this gap, this study introduces a novel framework that bridges high-order information theory with conventional neuroimaging analysis pipelines and is subsequently applied to resting-state functional MRI to demonstrate its practical utility. By representing HOI into voxel-level metrics, our approach allows standard neuroimaging analyses to probe complex multivariate dependencies. Moreover, voxel-level group-comparison analyses show age differences linked with reduced redundancy in default mode network interactions. These findings advance our understanding of the complex relationship between multivariate functional interactions, voxel-level neuroimaging, and behavior, highlighting novel analytic strategies to study high-order information processing underlying cognitive function and its alterations in pathological conditions.

neuroscience↗

bioRxiv2026

Conformational ensembles of flexible multidomain proteins: How close are we to accurate and reliable predictions?

Multidomain proteins connected by flexible linkers populate conformational ensembles that are challenging to characterize using conventional structural biology methods. In domain-linker-domain (DLD) proteins, linker-mediated inter-domain relative positions and orientations are functionally relevant, yet their dynamical behavior in solution normally remain poorly described. Small-angle X-ray scattering (SAXS) provides ensemble-averaged structural information for such systems; however, coupling with computational modeling is required to accurately describe the dynamic behavior of this family of proteins in solution. Here, we present a systematic evaluation of five ensemble-generation strategies applied to a set of eighteen proteins sharing the same two globular domains, connected by naturally occurring linkers of varying length and composition. Modeling methods based on different underlying principles are compared by assessing their agreement to experimental SAXS data, showing a large disparity and systematic structural biases among them. Furthermore, for each approach, we examine the effect of refinement against SAXS restraints and assess its capacity to describe the experimental data, as well as the induced biases in global dimensions and inter-domain distance distributions. This analysis underlines the importance of the initial conformational pool for deriving experimentally compatible ensembles. Overall, this work provides a high-quality benchmark for SAXS-driven ensemble modeling of flexible, multidomain proteins and establishes a framework for the critical interpretation of solution scattering data in systems with pronounced conformational heterogeneity.

biophysics↗

bioRxiv2025

Evidence for the Progressive Improvement of All-Atom Force Fields in Reproducing Local Conformational Preferences of Flexible Peptides

AbstractClassical protein force fields are widely used to probe the conformational properties of intrinsically disordered regions, yet their accuracy in reproducing local structural preferences remains uneven. We evaluated seven Amber and CHARMM force fields across three generations using molecular dynamics simulations of glycine-X-glycine tripeptides, with guest residues that span diverse physicochemical properties. Conformational ensembles were compared against distributions of conformations extracted from the crystallographic structures in the Protein Data Bank, and a statistical model derived from NMR observables. Analysis of secondary structure populations and Ramachandran distributions analyzed via Wasserstein distances reveals a clear historical progression. Early models display strong helical bias, intermediate ones approach Protein Data Bank trends, and recent versions shift toward solution-like ensembles dominated by polyproline II structure. None of the force fields fully captures the experimental distributions, although recent models show marked improvement over earlier generations. The remaining discrepancies point to specific aspects of local structure that still require tuning, while the overall progress underscores a steady trajectory toward more reliable descriptions of disordered peptides.

biophysics↗

bioRxiv2025

Interplay Between Intrinsically Disordered Proteins and Atomically Precise Gold Nanoclusters Modulates their Optical Properties

Understanding how structural and optical properties of metallic nanoclusters can be tuned by proteins is crucial for the use of these hybrid molecules in biomedical applications. The interaction of proteins with ultrasmall, atomically-precise gold nanoclusters (Au-NCs) has been mainly investigated in the context of structured proteins, while their behavior with intrinsically disordered proteins (IDPs) remains unexplored. This work examines the structural and optical properties of Au-NCs interacting with bioengineered IDPs containing up to three cysteines. We show that, by exploiting the conformational flexibility of cysteine-containing IDPs, we can anchor proteins to Au-NCs in a position-specific manner, leading to new bioconjugates with properties that differ from those of the individual components. We observed an up to 15-fold photoluminescence enhancement depending on the number of cysteines anchored. By combining mass spectrometry, small-angle X-ray scattering (SAXS), and computational modelling, the ensemble structures of nine bioconjugates with different stoichiometries were elucidated, indicating their overall compactness. Our results suggest that the interface between these atomically-precise species and the conformationally fluctuating protein is responsible for the optical properties of these nanobioconjugates. This research improves our understanding of Au-NC- protein interactions, paving the way to novel nano-molecular hybrid conjugates with tunable properties for bioimaging and therapeutic applications.

biochemistry↗

bioRxiv2025

Brain structural modules associated to functional high-order interactions in the human brain,

The brains modular organization, ranging from microcircuits to large-scale networks, has been extensively studied in terms of its structural and functional properties. Particularly insightful has been the investigation of the coupling between structural connectivity (SC) and functional connectivity (FC), whose analysis has revealed important insights into the brains efficiency and adaptability related to various cognitive functions. Interestingly, links in SC are intrinsically pairwise but this is not the case for FC; and while recent work demonstrates the relevance of the brains high-order interactions (HOI), the coupling of between SC and functional HOI remains unexplored. To address this gap, this study leverages functional MRI and diffusion weighted imaging to delineate the brains modular structure by investigating the coupling between SC and functional HOI. Our results demonstrates that structural networks can be associated with both redundant and synergistic functional interactions. In particular, SC exhibits both positive and negative correlations with redundancy, it shows consistent positive correlations with synergy, indicating that a higher density of structural connections is linked to increased synergistic interactions. These findings advance our understanding of the complex relationship between structural and high-order functional properties, shedding light on the brains architecture underlying its modular organization. Significance StatementUnderstanding the intricate relationship between the brain structural and functional connectivity is instrumental for enabling a richer characterization of cognitive functions. This challenge is complex, since it involves comparing pairwise links representing white-matter fibers with higher-order functional interactions that can involve multiple brain regions sharing information -- indeed, these interactions can be redundant in presence of shared information, or synergistic when novel information arises in the whole subsystem. Here we advance the investigation of the coupling between brain structure and high-order interactions, offering new insights into how structural properties are associated with both redundant and synergistic functional modules. Furthermore, the approach presented here opens new doors to investigate how this coupling between structural connectivity and functional HOI is modulated during development or under diverse brain pathologies.

neuroscience↗

bioRxiv2024

Single-cell RNA sequencing on formalin-fixed and paraffin-embedded (FFPE) tissue identified multi-ciliary cells in breast cancer

The purpose of this study was to evaluate the suitability of paraffin-embedded (FFPE) samples and fixed fresh (FF) samples for single-cell RNA sequencing (scRNAseq). To this end, we compared whether single-cell profiles from FFPE and matched FF tissue samples from one invasive carcinoma of no especial type carcinoma (invasive ductal carcinoma -IDC) and one invasive lobular carcinoma (ILC) were similar when captured using the common immunohistochemical (IHC) and immune features of the tumors. FF and FFPE tissue derived libraries showed high-quality parameters. The cell heterogeneity obtained was similar, although, there were some differences in the number of cells captured, but there were no specific cell populations captured exclusively by one of the two different approaches. The combined analysis of the four samples identified six types of epithelial cells (normal basal cells, four subpopulations of neoplastic cells in the IDC and one in the ILC), and different tumoral microenvironment populations. scRNAseq results from epithelial neoplastic cells were concordant with common IHC markers. In addition, after quantitative assessment of the whole slide images using QuPath, the proportion of immune cells identified by IHQ in FFPE sections were similar to those obtained by scRNAseq. Confirming the suitability of scRNAseq using FFPE tissue, this technique identified one subpopulation of neoplastic cells present only in the IDC, characterized by the expression of genes related with multi-ciliated cell (MCC) differentiation (FOXJ1, ROPN1L, TPPP3, CFAP45). IHC analysis of FOXJ1 expression confirmed the presence of positive cells only in the IDC in a similar proportion to that obtained by scRNAseq. Analysis of FOXJ1 in a cohort of 214 ER-positive invasive carcinomas demonstrated protein expression in at least 1% of cells in 33% of tumors, suggesting frequent focal MCC differentiation. Our results support the suitability of scRNAseq analysis using FFPE tissue and identified a subpopulation of neoplastic MCC in breast cancer.

cancer biology↗

bioRxiv2024

Generative deep learning models for cognitive performance trajectories in real-world scenarios

The increasing prevalence of cognitive disorders, such as Alzheimers disease, imposes significant challenges on healthcare systems and society. The ability to predict the future cognitive performance (CP) is crucial for professionals in neuropsychology, and real-world data emerges as an important source of complete and reliable information. However, its inherent complexities requires the use of advanced models to make predictions. To do so, we have implemented and compared three deep learning predictive strategies from CP trajectories: multilayer perceptron (MLP), convolutional neural networks (CNN) and long short-term memory (LSTM). The three models showed robustness on their predictions in different patient datasets. The CNN was the most suitable architecture due to its local pattern recognition capabilities and its robustness to overfitting. Therefore, professionals can have a complementary support for targeting treatment approaches to patients needs and anticipate undesired outcomes (e.g. cognitive impairment). Nonetheless, further studies are needed to validate whether neuropsychological interventions based on score predictions lead to improved intervention efficacy compared to traditional approaches for controlled patient groups.

neuroscience↗

bioRxiv2024

Systemic migrations in enucleated cells

Directional locomotion is a fundamental characteristic of many cells with great relevance in essential physiological and human pathological processes. For decades, research efforts have focused on studying specific individual processes and their corresponding biomolecular components involved in cellular locomotion movements. However, the notion that migratory displacements are functionally integrated and regulated at the systemic level has never been recognized. Recently, we have shown that locomotion movements correspond to an emergent systemic behavior which depends on a complex integrated self-organized system carefully regulated at a global cellular level. Here, to study the forces driving the locomotion movement of the cell, and to corroborate the thesis on the systemic character of the cellular migratory responses we have carried out an extensive study of locomotion movements with enucleated cells (cytoplasts) belonging to Amoeba proteus. The migratory behavior of both enucleated and non-enucleated cells has been individually studied in four different scenarios: in absence of stimuli, under a galvanotactic field, in chemotactic gradient, and under very complex conditions such as simultaneous galvanotactic and chemotactic stimuli. All these experimental displacement trajectories, obtained on flat two-dimensional surfaces, have been analyzed using advanced non-linear quantitative approaches. Our results show that both non-enucleated amoebas and cytoplasts display the same complex kind of dynamic structure in their migratory trajectories. The locomotion displacements of enucleated cells are regulated by complex self-organized integrative dynamics, modulated at a global-systemic level which seems to depend on the cooperative interaction of most, if not all, molecular components of cells.

systems biology↗

bioRxiv2024

AgeML: Age modelling with Machine Learning

An approach to age modeling involves the supervised prediction of age using machine learning from subject features. The derived age metrics are used to study the relationship between healthy and pathological aging in multiple body systems, as well as the interactions between them. We lack a standard for this type of age modeling. In this work we developed AgeML, an OpenSource software for age-prediction from any type of tabular clinical data following well-established and tested methodologies. The objective is to set standards for reproducibility and standardization of reporting in supervised age modeling tasks. AgeML does age modeling, calculates age deltas, the difference between predicted and chronological age, measures correlations between age deltas and factors, visualizes differences in age deltas of different clinical populations and classifies clinical populations based on age deltas. With this software we are able to reproduce published work and unveil novel relationships between body organs and polygenetic risk scores. AgeML is age modeling made easy for standardization and reproducibility.

bioinformatics↗

bioRxiv2024

Forces directing the systemic correlations of cell migration

Directional motility is an essential property of cells. Despite its enormous relevance in many fundamental physiological and pathological processes, how cells control their locomotion movements remains an unresolved question. Here we have addressed the systemic processes driving the directed locomotion of cells. Specifically, we have performed an exhaustive study analyzing the trajectories of 700 individual cells belonging to three different species (Amoeba proteus, Metamoeba leningradensis and Amoeba borokensis) in four different scenarios: in absence of stimuli, under an electric field (galvanotaxis), in a chemotactic gradient (chemotaxis), and under simultaneous galvanotactic and chemotactic stimuli. All movements were analyzed using advanced quantitative tools. The results show that the trajectories are mainly characterized by coherent integrative responses that operate at the global cellular scale. These systemic migratory movements depend on the cooperative non-linear interaction of most, if not all, molecular components of cells. SignificanceCellular migration is a cornerstone issue in many human physiological and pathological processes. For years, the scientific attention has been focused on the individualized study of the diverse molecular parts involved in directional motility; however, locomotion movements have never been regarded as a systemic process that operates at a global cellular scale. In our quantitative experimental analysis essential systemic properties underlying locomotion movements were detected. Such emergent systemic properties are not found specifically in any of the molecular parts, partial mechanisms, or individual processes of the cell. Cellular displacements seem to be regulated by integrative processes operating at systemic level.

systems biology↗

bioRxiv2022

Statistical tests to detect differences between codon-specific Ramachandran plots

In their recent work, Rosenberg et al. [1] studied the dependence between the identity of synonymous codons and the distribution of the backbone dihedral angles of the translated amino acids. In the past, it has been shown that the use of synonymous codons is highly relevant in multiple biological processes including, among others, mRNA splicing, translational rates and protein folding [2, 3]. While the correlation between synonymous codons and secondary structure in translated proteins has been widely studied [4-6], Rosenberg et al. evaluated the effect of codon identity on a finer scale, analyzing whether the distribution of ({phi}, {psi}) dihedral angles within secondary structure elements is significantly altered when synonymous codons are used. Their conclusion, showing significant differences, particularly for amino acid residues involved in {beta}-strands, would represent a new paradigm for the role played by synonymous codons in defining protein structure. However, the statistical methodology used in that study was formally incorrect, casting doubt on the obtained results. Besides, it is based on density estimates that might be imprecise for small sample sizes, yielding misleading comparisons. These methodological errors are described in the following section. Then, using an appropriate methodology, we reanalyzed the data presented in [1]. Our results show that the influence of the codon on the distribution of the dihedral angles is statistically non-significant for all types of secondary structures, contradicting the conclusion by Rosenberg et al.. These results were corroborated by repeating the analysis on structures extracted from the AlphaFold Database [7, 8] for the same set of proteins, and shown to be robust with respect to the definition secondary structural classes and also when considering the nature of the neighbor residues. Overall, our observations demonstrate that the influence of the synonymous codons on the backbone dihedral angles can not be inferred with current data.

bioinformatics↗

bioRxiv2022

WASCO: A Wasserstein-based statistical tool to compare conformational ensembles of intrinsically disordered proteins

The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, being one ensemble characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the interest of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO.

bioinformatics↗

bioRxiv2022

The structure of pathogenic huntingtin exon-1 defines the bases of its aggregation propensity

Huntingtons Disease is a neurodegenerative disorder caused by a CAG expansion of the first exon of the HTT gene, resulting in an extended poly-glutamine (poly-Q) tract in the N-terminus of the protein huntingtin (httex1). The structural changes occurring to the poly-Q when increasing its length remain poorly understood mainly due to its intrinsic flexibility and the strong compositional bias of the protein. The systematic application of site-specific isotopic labeling has enabled residue-specific NMR investigations of the poly-Q tract of pathogenic httex1 variants with 46 and 66 consecutive glutamines. The integrative analysis of the data reveals that the poly-Q tract adopts long -helical conformations stabilized by glutamine side-chain to backbone hydrogen bonds. 19F-NMR of site-specifically incorporated fluoro-glutamines and molecular dynamics simulations demonstrate that the mechanism propagating -helical conformations towards the poly-Q from the upstream N17 domain is independent of the poly-Q track length. Aggregation and atomic force microscopy experiments show that the presence of long and persistent -helices in the poly-Q tract is a stronger signature in defining the aggregation kinetics and the structure of the resulting fibrils than the number of glutamines. The ensemble of our observations provides a structural perspective of the pathogenicity of expanded httex1 and paves the way to a deeper understanding of poly-Q related diseases.

biophysics↗