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Koren, G.

Publications and source records attributed to Koren, G..

5 recordsLinked to original sources

Not All Charges Are Equal: Side-Chain Chemistry Reshapes the Disordered Ensemble of α-synuclein

The conformational ensembles of intrinsically disordered proteins (IDPs) are governed by the balance of electrostatic and hydrophobic interactions encoded in their primary sequences. Current polymer-physics models of IDPs frequently group amino acids by coarse-grained properties, such as net charge, often overlooking the distinct residues side-chain chemistries. Analysis across the IDP database reveals that these sequences are sensitive to specific residue identities, where the substitution of aromatic, proline, or hydrophobic groups serves as a primary driver of chain dimensions. However, these data also highlight that even subtle chemical variations between similarly charged residues can consistently shift global compaction. Here, we explore the role of residue identity using small angle X-ray scattering (SAXS) of seven -synuclein variants with progressively increasing numbers of lysine-to-arginine substitutions, two positively charged amino acids with different side-chain chemistry. We show that increasing arginine content drives a systematic compaction of the conformational ensemble, although variants with identical number of substitutions but different positional arrangements suggest influence to the sequence context. Moreover, while increasing salt concentrations shift the structural ensemble from a Gaussian toward a self-avoiding-walk statistics, the arginine-dependent contraction trend remains robust across both regimes. Molecular-dynamics simulations combined with SAXS data reveal that arginine substitutions reduce ensemble heterogeneity by stabilizing transient long-range contacts. Finally, aggregation assays demonstrate that this arginine-driven compaction correlates with an accelerated transition to amyloid fibrils. Our findings demonstrate that chemically subtle substitutions between similarly charged residues can fundamentally reshape the conformational ensemble of IDPs, suggesting that side-chain identity is a critical, yet underappreciated, determinant of protein disorder and proteotoxicity. O_TEXTBOXSignificance StatementIntrinsically disordered proteins (IDPs) are critical to cellular signaling and neurodegeneration, yet our ability to predict their behavior remains limited by a "coarse-grained" understanding of their sequences. We expand the view that net charge and patterning is a primary determinant of IDP dimensions by showing that lysine and arginine, residues identical in charge, exert opposite effects on the conformational landscape of -synuclein model-system. We find that arginine substitutions act as a "molecular glue", driving protein compaction and reducing ensemble heterogeneity. Crucially, this compaction accelerates amyloid aggregation, overturning the conventional assumption that collapsed states protect against fibrillization. This work demonstrates that side-chain identity and patterning are vital for protein homeostasis, providing a new framework for the rational design of IDP-based therapeutics. C_TEXTBOX

biophysics↗

Neurofilament Light Disordered Tail Mutations Reshape Its Self-Assembled Network Structure

Proteins with intrinsically disordered regions (IDRs) perform essential cellular functions despite lacking stable structures, challenging the traditional structure-function paradigm. Neurofilament-light (NFL) proteins assemble into bottlebrush filaments, whose disordered tail domains mediate nematic hydrogel formation critical for neuronal integrity. Mutations in NFL are linked to Charcot-Marie-Tooth (CMT) disease, yet their molecular effects remain unclear. Here, aiming to gain insight into these molecular mechanisms, we combine small-angle X-ray scattering, microscopy, and deep-learning conformational analysis to investigate CMT-associated NFL tail mutations. We find that these mutations induce pathological hydrogel compaction, disrupt filament nematic order by generating microdomains, and alter water retention dynamics by reshaping of sequence-dependent conformational ensembles, leading to macroscopic network rearrangements. These findings provide mechanistic insight into how subtle sequence changes in IDRs modulate protein network organization and function, informing an understanding of IDR-related pathologies and mutation-based disease characterization.

biophysics↗

Self-Assembly of Tunable Intrinsically DisorderedPeptide Amphiphiles

Intrinsically disordered peptide amphiphiles (IDPAs) present a novel class of synthetic conjugates that consist of short hydrophilic polypeptides anchored to hydrocarbon chains. These hybrid polymer-lipid block constructs spontaneously self-assemble into dispersed nanoscopic aggregates or ordered mesophases in aqueous solution due to hydrophobic interactions. Yet, the possible sequence variations and their influence on the self-assembly structures is vast and have hardly been explored. Here, we measure the nanoscopic self-assembled structures of four IDPA systems that differ by their amino acid sequence. We show that permutations in the charge pattern along the sequence remarkably alter the headgroup conformation and consequently alters the pH-triggered phase transitions between spherical, cylindrical micelles and hexagonal condensed phases. We demonstrate that even a single amino acid mutation is sufficient to tune structural transitions in the condensed IDPA mesophases, while peptide conformations remain unfolded and disordered. Furthermore, alteration of the peptide sequence can render IDPAs to become susceptible to enzymatic cleavage and induces enzymatically activated phase transitions. These results hold great potential for embedding multiple functionalities into lipid nanoparticle delivery systems by incorporating IDPAs with desired properties.

biophysics↗

Myofibroblast Senescence Promotes Arrhythmogenic Remodeling in the Aged Infarcted Rabbit Heart

Progressive tissue remodeling after myocardial infarction (MI) promotes cardiac arrhythmias. This process is well studied in young animals, but little is known about pro-arrhythmic changes in aged animals. Senescent cells accumulate with age and accelerate age-associated diseases. Senescent cells interfere with cardiac function and outcome post-MI with age, but studies have not been performed in large animals, and the mechanisms are unknown. Here, we investigated the role of senescence in regulating inflammation, fibrosis, and arrhythmogenesis in young and aged infarcted rabbits. Aged rabbits exhibited increased peri-procedural mortality and arrhythmogenic electrophysiological remodeling at the infarct border zone (IBZ) compared to young rabbits. Studies of the aged infarct zone revealed persistent myofibroblast senescence and increased inflammatory signaling over a twelve-week timecourse. Senescent IBZ myofibroblasts in aged rabbits appear to be coupled to myocytes, and our computational modeling showed that senescent myofibroblast- cardiomyocyte coupling prolongs action potential duration (APD) and facilitates conduction block permissive of arrhythmias. Aged infarcted human ventricles show levels of senescence consistent with aged rabbits, and senescent myofibroblasts also couple to IBZ myocytes. Our findings suggest that senolytic drugs may mitigate arrhythmias post-MI.

cell biology↗

Personal clinical history predicts antibiotic resistance in urinary tract infections

The prevalence of antibiotic resistance in urinary tract infections (UTIs) often renders the prescribed antimicrobial treatment ineffective, highlighting the need for personalized prediction of resistance at time of care. Here, crossing a 10-year longitudinal dataset of over 700,000 community-acquired UTIs with over 6,000,000 personally-linked records of antibiotic purchases, we show that the resistance profile of infections can be predicted based on patient-specific demographics and clinical history. Age, gender, and retirement home residence had strong, yet differential and even non-monotonic, associations with resistance to different antibiotics. Resistance profiles were also associated with the patients records of past urine samples and antibiotic usage, with these associations persisting for months and even longer than a year. Drug usage selected specifically for its own cognate resistance, which led indirectly, through genetic linkage, also to resistance to other, even mechanistically unrelated, drugs. Applying machine learning models, these association patterns allowed good personalized predictions of resistance, which could inform and better optimize empirical prescription of antibiotics.

epidemiology↗