Local environment in biomolecular condensates modulates enzymatic activity across length scales.

The mechanisms that underlie the regulation of enzymatic reactions by biomolecular condensates and how they scale with compartment size remain poorly understood. Here we use intrinsically disordered domains as building blocks to generate programmable enzymatic condensates of NADH-oxidase (NOX) with different sizes spanning from nanometers to microns. These disordered domains, derived from three distinct RNA-binding proteins, each possessing different net charge, result in the formation of condensates characterized by a comparable high local concentration of the enzyme yet within distinct environments. We show that only condensates with the highest recruitment of substrate and cofactor exhibit an increase in enzymatic activity. Notably, we observe an enhancement in enzymatic rate across a wide range of condensate sizes, from nanometers to microns, indicating that emergent properties of condensates can arise within assemblies as small as nanometers. Furthermore, we show a larger rate enhancement in smaller condensates. Our findings demonstrate the ability of condensates to modulate enzymatic reactions by creating distinct effective solvent environments compared to the surrounding solution, with implications for the design of protein-based heterogeneous biocatalysts.

The heavy subunit of ferritin stimulates NLRP3 inflammasomes in hepatic stellate cells through ICAM-1 to drive hepatic inflammation.

Serum ferritin concentrations increase during hepatic inflammation and correlate with the severity of chronic liver disease. Here, we report a molecular mechanism whereby the heavy subunit of ferritin (FTH) contributes to hepatic inflammation. We found that FTH induced activation of the NLRP3 inflammasome and secretion of the proinflammatory cytokine interleukin-1ß (IL-1ß) in primary rat hepatic stellate cells (HSCs) through intercellular adhesion molecule-1 (ICAM-1). FTH-ICAM-1 stimulated the expression of Il1b, NLRP3 inflammasome activation, and the processing and secretion of IL-1ß in a manner that depended on plasma membrane remodeling, clathrin-mediated endocytosis, and lysosomal destabilization. FTH-ICAM-1 signaling at early endosomes stimulated Il1b expression, implying that this endosomal signaling primed inflammasome activation in HSCs. In contrast, lysosomal destabilization was required for FTH-induced IL-1ß secretion, suggesting that lysosomal damage activated inflammasomes. FTH induced IL-1ß production in liver slices from wild-type mice but not in those from Icam1-/- or Nlrp3-/- mice. Thus, FTH signals through its receptor ICAM-1 on HSCs to activate the NLRP3 inflammasome. We speculate that this pathway contributes to hepatic inflammation, a key process that stimulates hepatic fibrogenesis associated with chronic liver disease.

Microscopic and Macroscopic Characterization of Hydrogels Based on Poly(vinyl-alcohol)-Glutaraldehyde Mixtures for Fricke Gel Dosimetry.

In recent decades, hydrogels have emerged as innovative soft materials with widespread applications in the medical and biomedical fields, including drug delivery, tissue engineering, and gel dosimetry. In this work, a comprehensive study of the macroscopic and microscopic properties of hydrogel matrices based on Poly(vinyl-alcohol) (PVA) chemically crosslinked with Glutaraldehyde (GTA) was reported. Five different kinds of PVAs differing in molecular weight and degree of hydrolysis were considered. The local microscopic organization of the hydrogels was studied through the use of the 1H nuclear magnetic resonance relaxometry technique. Various macroscopic properties (gel fraction, water loss, contact angle, swelling degree, viscosity, and Young's Modulus) were investigated with the aim of finding a correlation between them and the features of the hydrogel matrix. Additionally, an optical characterization was performed on all the hydrogels loaded with Fricke solution to assess their dosimetric behavior. The results obtained indicate that the degree of PVA hydrolysis is a crucial parameter influencing the structure of the hydrogel matrix. This factor should be considered for ensuring stability over time, a vital property in the context of potential biomedical applications where hydrogels act as radiological tissue-equivalent materials.

RNA modulates hnRNPA1A amyloid formation mediated by biomolecular condensates.

Several RNA binding proteins involved in membraneless organelles can form pathological amyloids associated with neurodegenerative diseases, but the mechanisms of how this aggregation is modulated remain elusive. Here we investigate how heterotypic protein-RNA interactions modulate the condensation and the liquid to amyloid transition of hnRNPA1A, a protein involved in amyothropic lateral sclerosis. In the absence of RNA, formation of condensates promotes hnRNPA1A aggregation and fibrils are localized at the interface of the condensates. Addition of RNA modulates the soluble to amyloid transition of hnRNPA1A according to different pathways depending on RNA/protein stoichiometry. At low RNA concentrations, RNA promotes both condensation and amyloid formation, and the catalytic effect of RNA adds to the role of the interface between the dense and dilute phases. At higher RNA concentrations, condensation is suppressed according to re-entrant phase behaviour but formation of hnRNPA1A amyloids is observed over longer incubation times. Our findings show how heterotypic nucleic acid-protein interactions affect the kinetics and molecular pathways of amyloid formation.

In situ analysis of osmolyte mechanisms of proteome thermal stabilization.

Organisms use organic molecules called osmolytes to adapt to environmental conditions. In vitro studies indicate that osmolytes thermally stabilize proteins, but mechanisms are controversial, and systematic studies within the cellular milieu are lacking. We analyzed Escherichia coli and human protein thermal stabilization by osmolytes in situ and across the proteome. Using structural proteomics, we probed osmolyte effects on protein thermal stability, structure and aggregation, revealing common mechanisms but also osmolyte- and protein-specific effects. All tested osmolytes (trimethylamine N-oxide, betaine, glycerol, proline, trehalose and glucose) stabilized many proteins, predominantly via a preferential exclusion mechanism, and caused an upward shift in temperatures at which most proteins aggregated. Thermal profiling of the human proteome provided evidence for intrinsic disorder in situ but also identified potential structure in predicted disordered regions. Our analysis provides mechanistic insight into osmolyte function within a complex biological matrix and sheds light on the in situ prevalence of intrinsically disordered regions.

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting.

We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7-T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary-based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2 , and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k-space sampling percentage, with respect to the dictionary-based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.

Ferritin microheterogeneity, subunit composition, functional, and physiological implications.

Ferritin is a ubiquitous intracellular iron storage protein that plays a crucial role in iron homeostasis. Animal tissue ferritins consist of multiple isoforms (or isoferritins) with different proportions of H and L subunits that contribute to their structural and compositional heterogeneity, and thus physiological functions. Using size exclusion and anion exchange chromatography, capillary isoelectric focusing (cIEF), and SDS-capillary gel electrophoresis (SDS-CGE), we reveal for the first time a significant variation in ferritin subunit composition and isoelectric points, in both recombinant and native ferritins extracted from animal organs. Our results indicate that subunits composition is the main determinant of the mean pI of recombinant ferritin heteropolymers, and that ferritin microheterogeneity is a common property of both natural and recombinant proteins and appears to be an intrinsic feature of the cellular machinery during ferritin expression, regulation, post-translational modifications, and post-subunits assembly. The functional significance and physiological implications of ferritin heterogeneity in terms of iron metabolism, response to oxidative stress, tissue-specific functions, and pathological processes are discussed.

A bone density-measuring micromotor distinguishes different cortical/cancellous combinations in vitro: a study using polyurethane foam blocks.

PURPOSE: In the clinical setting, assessing bone quality and quantity at the implant site is the basis to select implant characteristics and the insertion protocol to be applied. However, a quantitative method to classify bone quality and quantity is still lacking. A recently introduced implant placement micromotor that provides site-specific, operator-independent cancellous bone density measurements may be useful for this purpose, but it remains unknown whether this device can detect the presence of a cortical bone layer and measure its thickness and density. MATERIALS AND METHODS: Thus, an in vitro experiment was performed on six double-layer polyurethane foam blocks mimicking the jaw bone with different cortical thickness/cancellous density combinations. The densities were measured using the micromotor, either removing the cortical layer or leaving it intact, with and without irrigation. RESULTS: The results collected in each condition were compared by means of non-parametric statistical tests. Independent of irrigation, the micromotor detected the cortical layer when it was left intact and accurately estimated its thickness. The micromotor did not discriminate each block from the other ones when they were considered separately, but it did when they were grouped into four or three classes. CONCLUSION: The present study suggested that the micromotor may represent a valid device to quantitatively assess bone quality and density. If the micromotor can quantitatively distinguish different cortical/cancellous bone combinations in humans, it may be a helpful tool to define finely-tuned, patient-tailored preparations of the implant seat, making teeth rehabilitation in challenging clinical conditions more predictable.

Surface Passivation Method for the Super-repellence of Aqueous Macromolecular Condensates.

Solutions of macromolecules can undergo liquid-liquid phase separation to form droplets with ultralow surface tension. Droplets with such low surface tension wet and spread over common surfaces such as test tubes and microscope slides, complicating in vitro experiments. The development of a universal super-repellent surface for macromolecular droplets has remained elusive because their ultralow surface tension requires low surface energies. Furthermore, the nonwetting of droplets containing proteins poses additional challenges because the surface must remain inert to a wide range of chemistries presented by the various amino acid side chains at the droplet surface. Here, we present a method to coat microscope slides with a thin transparent hydrogel that exhibits complete dewetting (contact angles θ ≈ 180°) and minimal pinning of phase-separated droplets in aqueous solution. The hydrogel is based on a swollen matrix of chemically cross-linked polyethylene glycol diacrylate of molecular weight 12 kDa (PEGDA), and can be prepared with basic chemistry laboratory equipment. The PEGDA hydrogel is a powerful tool for in vitro studies of weak interactions, dynamics, and the internal organization of phase-separated droplets in aqueous solutions.

A human antibody against pathologic IAPP aggregates protects beta cells in type 2 diabetes models.

In patients with type 2 diabetes, pancreatic beta cells progressively degenerate and gradually lose their ability to produce insulin and regulate blood glucose. Beta cell dysfunction and loss is associated with an accumulation of aggregated forms of islet amyloid polypeptide (IAPP) consisting of soluble prefibrillar IAPP oligomers as well as insoluble IAPP fibrils in pancreatic islets. Here, we describe a human monoclonal antibody selectively targeting IAPP oligomers and neutralizing IAPP aggregate toxicity by preventing membrane disruption and apoptosis in vitro. Antibody treatment in male rats and mice transgenic for human IAPP, and human islet-engrafted mouse models of type 2 diabetes triggers clearance of IAPP oligomers resulting in beta cell protection and improved glucose control. These results provide new evidence for the pathological role of IAPP oligomers and suggest that antibody-mediated removal of IAPP oligomers could be a pharmaceutical strategy to support beta cell function in type 2 diabetes.

The interface of condensates of the hnRNPA1 low-complexity domain promotes formation of amyloid fibrils.

The maturation of liquid-like protein condensates into amyloid fibrils has been associated with several neurodegenerative diseases. However, the molecular mechanisms underlying this liquid-to-solid transition have remained largely unclear. Here we analyse the amyloid formation mediated by condensation of the low-complexity domain of hnRNPA1, a protein involved in amyotrophic lateral sclerosis. We show that phase separation and fibrillization are connected but distinct processes that are modulated by different regions of the protein sequence. By monitoring the spatial and temporal evolution of amyloid formation we demonstrate that the formation of fibrils does not occur homogeneously inside the droplets but is promoted at the interface of the condensates. We further show that coating the interface of the droplets with surfactant molecules inhibits fibril formation. Our results reveal that the interface of biomolecular condensates of hnRNPA1 promotes fibril formation, therefore suggesting interfaces as a potential novel therapeutic target against the formation of aberrant amyloids mediated by condensation.

Multiparametric Orthogonal Characterization of Extracellular Vesicles by Liquid Chromatography Combined with In-Line Light Scattering and Fluorescence Detection.

Extracellular vesicles (EVs) are membrane-enclosed biological nanoparticles with potential as diagnostic markers and carriers for therapeutics. Characterization of EVs poses severe challenges due to their complex structure and composition, requiring the combination of orthogonal analytical techniques. Here, we demonstrate how liquid chromatography combined with multi-angle light scattering (MALS) and fluorescence detection in one single apparatus can provide multiparametric characterization of EV samples, including concentration of particles, average diameter of the particles, protein amount to particle number ratio, presence of EV surface markers and lipids, EV shape, and sample purity. The method requires a small amount of sample of approximately 107 EVs, limited handling of the sample and data analysis time in the order of minutes; it is fully automatable and can be applied to both crude and purified samples.

α-Synuclein Aggregation Is Triggered by Oligomeric Amyloid-ß 42 via Heterogeneous Primary Nucleation.

An increasing number of cases where amyloids of different proteins are found in the same patient are being reported. This observation complicates diagnosis and clinical intervention. Amyloids of the amyloid-ß peptide or the protein α-synuclein are traditionally considered hallmarks of Alzheimer's and Parkinson's diseases, respectively. However, the co-occurrence of amyloids of these proteins has also been reported in patients diagnosed with either disease. Here, we show that soluble species containing amyloid-ß can induce the aggregation of α-synuclein. Fibrils formed under these conditions are solely composed of α-synuclein to which amyloid-ß can be found associated but not as part of the core of the fibrils. Importantly, by global kinetic analysis, we found that the aggregation of α-synuclein under these conditions occurs via heterogeneous primary nucleation, triggered by soluble aggregates containing amyloid-ß.

Comparison of the Protective Effect of Polysorbates, Poloxamer and Brij on Antibody Stability Against Different Interfaces.

Therapeutic proteins and antibodies are exposed to a variety of interfaces during their lifecycle, which can compromise their stability. Formulations, including surfactants, must be carefully optimized to improve interfacial stability against all types of surfaces. Here we apply a nanoparticle-based approach to evaluate the instability of four antibody drugs against different solid-liquid interfaces characterized by different degrees of hydrophobicity. We considered a model hydrophobic material as well as cycloolefin-copolymer (COC) and cellulose, which represent some of the common solid-liquid interfaces encountered during drug production, storage, and delivery. We assess the protective effect of polysorbate 20, polysorbate 80, Poloxamer 188 and Brij 35 in our assay and in a traditional agitation study. While all nonionic surfactants stabilize antibodies against the air-water interface, none of them can protect against hydrophilic charged cellulose. Polysorbates and Brij increase antibody stability in the presence of COC and the model hydrophobic interface, although to a lesser extent compared to the air-water interface, while Poloxamer 188 has a negligible stabilizing effect against these interfaces. These results highlight the challenge of fully protecting antibodies against all types of solid-liquid interfaces with traditional surfactants. In this context, our high-throughput nanoparticle-based approach can complement traditional shaking assays and assist in formulation design to ensure protein stability not only at air-water interfaces, but also at relevant solid-liquid interfaces encountered during the product lifecycle.

The effect of size, shape, coating and functionalization on nuclear relaxation properties in iron oxide core-shell nanoparticles: a brief review of the situation.

In this perspective article, we present a short selection of some of the most significant case studies on magnetic nanoparticles for potential applications in nanomedicine, mainly magnetic resonance. For almost 10 years, our research activity focused on the comprehension of the physical mechanisms on the basis of the nuclear relaxation of magnetic nanoparticles in the presence of magnetic fields; taking advantage of the insights gathered over this time span, we report on the dependence of the relaxation behaviour on the chemico-physical properties of magnetic nanoparticles and discuss them in full detail. In particular, a critical review is carried out on the correlations between their efficiency as contrast agents in magnetic resonance imaging and the magnetic core of magnetic nanoparticles (mainly iron oxides), their size and shape, and the coating and solvent used for making them biocompatible and well dispersible in physiological media. Finally, the heuristic model proposed by Roch and coworkers is presented, as it was extensively adopted to describe most of the experimental data sets. The large amount of data analyzed allowed us to highlight both the advantages and limitations of the model.

Proton Therapy, Magnetic Nanoparticles and Hyperthermia as Combined Treatment for Pancreatic BxPC3 Tumor Cells.

We present an investigation of the effects on BxPC3 pancreatic cancer cells of proton therapy combined with hyperthermia, assisted by magnetic fluid hyperthermia performed with the use of magnetic nanoparticles. The cells' response to the combined treatment has been evaluated by means of the clonogenic survival assay and the estimation of DNA Double Strand Breaks (DSBs). The Reactive Oxygen Species (ROS) production, the tumor cell invasion and the cell cycle variations have also been studied. The experimental results have shown that the combination of proton therapy, MNPs administration and hyperthermia gives a clonogenic survival that is much smaller than the single irradiation treatment at all doses, thus suggesting a new effective combined therapy for the pancreatic tumor. Importantly, the effect of the therapies used here is synergistic. Moreover, after proton irradiation, the hyperthermia treatment was able to increase the number of DSBs, even though just at 6 h after the treatment. Noticeably, the magnetic nanoparticles' presence induces radiosensitization effects, and hyperthermia increases the production of ROS, which contributes to cytotoxic cellular effects and to a wide variety of lesions including DNA damage. The present study indicates a new way for clinical translation of combined therapies, also in the vision of an increasing number of hospitals that will use the proton therapy technique in the near future for different kinds of radio-resistant cancers.

1H-NMR Relaxation of Ferrite Core-Shell Nanoparticles: Evaluation of the Coating Effect.

We investigated the effect of different organic coatings on the 1H-NMR relaxation properties of ultra-small iron-oxide-based magnetic nanoparticles. The first set of nanoparticles, with a magnetic core diameter ds1 = 4.4 ± 0.7 nm, was coated with polyacrylic acid (PAA) and dimercaptosuccinic acid (DMSA), while the second set, ds2 = 8.9 ± 0.9 nm, was coated with aminopropylphosphonic acid (APPA) and DMSA. At fixed core diameters but different coatings, magnetization measurements revealed a similar behavior as a function of temperature and field. On the other hand, the 1H-NMR longitudinal r1 nuclear relaxivity in the frequency range ν = 10 kHz ÷ 300 MHz displayed, for the smallest particles (diameter ds1), an intensity and a frequency behavior dependent on the kind of coating, thus indicating different electronic spin dynamics. Conversely, no differences were found in the r1 relaxivity of the biggest particles (ds2) when the coating was changed. It is concluded that, when the surface to volume ratio, i.e., the surface to bulk spins ratio, increases (smallest nanoparticles), the spin dynamics change significantly, possibly due to the contribution of surface spin dynamics/topology.

Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties.

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.

AF4 and PEG Precipitation as Predictive Assays for Antibody Self-Association.

Monoclonal antibodies (mAbs) are often formulated as high-protein-concentration solutions, which in some cases can exhibit physical stability issues such as high viscosity and opalescence. To ensure that mAb-based drugs can meet their manufacturing, stability, and delivery requirements, it is advantageous to screen for and select mAbs during discovery that are not prone to such behaviors. It has been recently shown that both these macroscopic properties can be predicted to a certain extent from the diffusion interaction parameter (kD), which is a measure of self-association under dilute conditions.1 However, kD can be challenging to measure at the early stage of discovery, where a relatively large amount of a high-purity material, which is required by traditional methods, is often not available. In this study, we demonstrate asymmetric field-flow fractionation (AF4) as a tool to measure self-association and therefore identify antibodies with problematic issues at high concentrations. The principle lies on the ability to concentrate the sample close to the membrane during the injection mode, which can reach formulation-relevant concentrations (>100 mg/mL).2 By analyzing a well-characterized library of commercial antibodies, we show that the measured retention time of the antibodies allows us to pinpoint molecules that exhibit issues at high concentrations. Remarkably, our AF4 assay requires very little (30 µg) sample under dilute conditions and does not need extensive sample purification. Furthermore, we show that a polyethylene glycol (PEG) precipitation assay provides results consistent with AF4 and moreover can further differentiate molecules with issues of opalescence or high viscosity. Overall, our results delineate a two-step strategy for the identification of problematic variants at high concentrations, with AF4 for early developability screening, followed by a PEG assay to validate the problematic molecules and further discriminate between opalescence or high-viscosity issues. This two-step antibody selection strategy enables us to select antibodies early in the discovery process, which are compatible with high-concentration formulations.

Improving extracellular vesicles production through a Bayesian optimization-based experimental design.

With the growing demand and diversity of biological drugs, developing optimal processes for their accelerated production with minimal resource utilization is a pressing challenge. Typically, such optimization involves multiple target properties, such as production yield, biological activity, and product purity. Therefore, strategic experimental design techniques that can characterize the parameter space while simultaneously arriving at the optimal process satisfying multiple target properties are required. To achieve this, we propose the use of a multi-objective batch Bayesian optimization (MOBBO) algorithm and illustrate its successful application for the production of extracellular vesicles (EVs) from a 3D culture of mesenchymal stem cells (MSCs) considering three objectives, namely to maximize the vesicle-to-protein ratio, maximize the enzymatic activity of the MSC-EV protein CD73, and minimize the amount of calregulin impurities. We show that the optimal combination of the process parameters to address the intended objectives could be achieved with only 32 experiments. For the four parameters considered (i.e., microcarrier concentration, seeding density, centrifugation time, and impeller speed), this number of experiments is comparable to or lower than the classical design of experiments (DoE) and the traditional one-factor-at-a-time (OFAT) approach. We illustrate how the algorithm adaptively samples in the process parameter space, selectively excluding unfavorable regions, thus minimizing the number of experiments required to reach optimal conditions. Finally, we compare the obtained solutions to the literature data and present possible applications of the collected data for other modeling activities such as Quality by Design, process monitoring, control, and scale-up.