In vivo measurement of RBC survival in patients with sickle cell disease before or after hematopoietic stem cell transplantation

ABSTRACT

Stable, mixed-donor-recipient chimerism after allogeneic hematopoietic stem cell transplantation (HSCT) for patients with sickle cell disease (SCD) is sufficient for phenotypic disease reversal, and results from differences in donor/recipient-red blood cell (RBC) survival. Understanding variability and predictors of RBC survival among patients with SCD before and after HSCT is critical for gene therapy research which seeks to generate sufficient corrected hemoglobin to reduce polymerization thereby overcoming the red cell pathology of SCD. This study used biotin labeling of RBCs to determine the lifespan of RBCs in patients with SCD compared with patients who have successfully undergone curative HSCT, participants with sickle cell trait (HbAS), and healthy (HbAA) donors. Twenty participants were included in the analysis (SCD pre-HSCT: N = 6, SCD post-HSCT: N = 5, HbAS: N = 6, and HbAA: N = 3). The average RBC lifespan was significantly shorter for participants with SCD pre-HSCT (64.1 days; range, 35-91) compared with those with SCD post-HSCT (113.4 days; range, 105-119), HbAS (126.0 days; range, 119-147), and HbAA (123.7 days; range, 91-147) (P<.001). RBC lifespan correlated with various hematologic parameters and strongly correlated with the average final fraction of sickled RBCs after deoxygenation (P<.001). No adverse events were attributable to the use of biotin and related procedures. Biotin labeling of RBCs is a safe and feasible methodology to evaluate RBC survival in patients with SCD before and after HSCT. Understanding differences in RBC survival may ultimately guide gene therapy protocols to determine hemoglobin composition required to reverse the SCD phenotype as it relates directly to RBC survival. This trial was registered at www.clinicaltrials.gov as #NCT04476277.

Functional and multi-omics signatures of mitapivat efficacy upon activation of pyruvate kinase in red blood cells from patients with sickle cell disease

Mitapivat, a pyruvate kinase (PK) activator, shows great potential as a sickle cell disease (SCD)- modifying therapy. Safety and efficacy of mitapivat as a long-term maintenance therapy is currently being evaluated in two open-label studies. Here we apply a comprehensive multi-omics approach to investigate the impact of activating PK on red blood cells (RBCs) from 15 SCD patients. HbSS patients were enrolled in one of the open label, extended studies (NCT04610866). Leuko-depleted RBCs obtained from fresh whole blood at baseline (visit 1, V1), prior to drug initiation and longitudinal time points over the course of the study were processed for multiomics through a stepwise extraction of metabolites, lipids and proteins. Mitapivat therapy had significant effects on the metabolome, lipidome and proteome of SCD RBCs. Mitapivat decreased 2,3-diphosphoglycerate (DPG) levels, increased adenosine triphosphate (ATP) levels, and improved hematologic and sickling parameters in patients with SCD. Agreement between omics measurements and clinical measurements confirmed the specificity of mitapivat on targeting late glycolysis, with glycolytic metabolites ranking as the top correlates to parameters of hemoglobin S (HbS) oxygen affinity (p50) and sickling kinetics (t50) during treatment. Mitapivat markedly reduced levels of proteins of mitochondrial origin within 2 weeks of initiation of drug treatment, with minimal changes in the reticulocyte counts. The first six months of treatment also witnessed transient elevation of lysophosphatidylcholines and oxylipins with depletion in free fatty acids, suggestive of an effect on membrane lipid remodeling. Multi-omics analysis of RBCs identified benefits for glycolysis, as well as activation of the Lands cycle.

Dynamics of Structural Elements of GB1 β-Hairpin Revealed by Tryptophan-Cysteine Contact Formation Experiments

Quenching of the triplet state of tryptophan by close contact with cysteine provides a tool for measuring the rate of intramolecular contact formation, one of the most elementary events in the folding process, in peptides and proteins using only natural probes. Here we present a study performed on a stabilized mutant of the second β-hairpin of the GB1 domain, where we combine steady-state fluorescence, laser-induced temperature-jump, and contact formation measurements to unveil the role of elementary structural components on hairpin dynamics and overall stability. In particular, our methodology provides access to the conformational dynamics of both the folded and unfolded state of the hairpin under native conditions, revealing the presence of extremely slow dynamics on the microsecond time scale in the unfolded state and coexistence of structures with partial pairing of the tails in the folded state. Comparing model peptides that mimic the turn sequence, we found that both ion pairing and hydrogen bonding due to the threonine side chain contribute to the propensity of turn formation but not to the much slower dynamics of the hydrophobic core formation. Interestingly, the dynamics of the turn region in isolation are significantly faster than the dynamics measured for the unfolded state of the complete hairpin, suggesting that non-native hydrophobic contacts slow down the reconfiguration dynamics of the unfolded state. Overall, the information extracted from these experiments provides kinetic limits on interconversions among conformational populations, hence enabling a simplified multistate free-energy landscape for the GB1 hairpin to be drawn.

Theoretical Simulation of Red Cell Sickling Upon Deoxygenation Based on the Physical Chemistry of Sickle Hemoglobin Fiber Formation

The polymerization of the mutant hemoglobin S upon deoxygenation to form fibers in red blood cells of patients suffering from sickle-cell anemia results in changes in cell shape and rigidity, also known as sickling, which underlie the pathology of the disease. While much has been learned about the fundamental physical chemistry of the polymerization process, transferring these insights to sickling of red cells under in vivo conditions requires being able to monitor, and ultimately predict, the time course of cellular sickling under physiological conditions of deoxygenation. To this end, we have developed an experimental technique for tracking the temporal evolution of the sickling of red blood cells under laboratory deoxygenation conditions, based on the automated analysis of sequences of microscope images and machine-learning analysis to characterize cell morphology. As an aid in the quantitative understanding of these experiments, we have developed a computational framework for simulating the time dependence of sickling in populations of red blood cells which incorporates the current theoretical and empirical understanding of the physical chemistry of the sickling process. In order to apply these techniques to our experiments, we have theoretically determined the time course of deoxygenation by solving the diffusion equation for oxygen in our experimental geometry. With this combined description, we are able to reproduce our experimentally observed kinetics of sickling, suggesting that our theoretical approach should be applicable to physiological deoxygenation scenarios.

Coarse-grained simulations of protein aggregation

Protein aggregation is believed to be responsible for a number of human diseases and limits the yields of pharmaceutical proteins during production. Computer simulations can be used to develop novel experimentally testable hypotheses pertaining to aggregation. While all-atom simulations with explicit solvent are too computationally intensive to address the multitude of relevant time scales, coarse-grained models make it possible to observe the transition of monomers to an equilibrium containing aggregates. Here, we provide the reader with background information and a list of steps for setting up, performing, and analyzing computer simulations of aggregating coarse-grained (CG) proteins.

Extracting rate coefficients from single-molecule photon trajectories and FRET efficiency histograms for a fast-folding protein

Recently developed statistical methods by Gopich and Szabo were used to extract folding and unfolding rate coefficients from single-molecule Förster resonance energy transfer (FRET) data for proteins with kinetics too fast to measure waiting time distributions. Two types of experiments and two different analyses were performed. In one experiment bursts of photons were collected from donor and acceptor fluorophores attached to a 73-residue protein, α(3)D, freely diffusing through the illuminated volume of a confocal microscope system. In the second, the protein was immobilized by linkage to a surface, and photons were collected until one of the fluorophores bleached. Folding and unfolding rate coefficients and mean FRET efficiencies for the folded and unfolded subpopulations were obtained from a photon by photon analysis of the trajectories using a maximum likelihood method. The ability of the method to describe the data in terms of a two-state model was checked by recoloring the photon trajectories with the extracted parameters and comparing the calculated FRET efficiency histograms with the measured histograms. The sum of the rate coefficients for the two-state model agreed to within 30% with the relaxation rate obtained from the decay of the donor-acceptor cross-correlation function, confirming the high accuracy of the method. Interestingly, apparently reliable rate coefficients could be extracted using the maximum likelihood method, even at low (<10%) population of the minor component where the cross-correlation function was too noisy to obtain any useful information. The rate coefficients and mean FRET efficiencies were also obtained in an approximate procedure by simply fitting the FRET efficiency histograms, calculated by binning the donor and acceptor photons, with a sum of three-Gaussian functions. The kinetics are exposed in these histograms by the growth of a FRET efficiency peak at values intermediate between the folded and unfolded peaks as the bin size increases, a phenomenon with similarities to NMR exchange broadening. When comparable populations of folded and unfolded molecules are present, this method yields rate coefficients in very good agreement with those obtained with the maximum likelihood method. As a first step toward characterizing transition paths, the Viterbi algorithm was used to locate the most probable transition points in the photon trajectories.

Relaxation rate for an ultrafast folding protein is independent of chemical denaturant concentration

The connection between free-energy surfaces and chevron plots has been investigated in a laser temperature jump kinetic study of a small ultrafast folding protein, the 35-residue subdomain from the villin headpiece. Unlike all other proteins that have been studied so far, no measurable dependence of the unfolding/refolding relaxation rate on denaturant concentration was observed over a wide range of guanidinium chloride concentration. Analysis with a simple Ising-like theoretical model shows that this denaturant-invariant relaxation rate can be explained by a large movement of the major free energy barrier, together with a denaturant- and reaction coordinate-dependent diffusion coefficient.

Protein aggregation in silico

Protein aggregation is a challenge to the successful manufacture of protein therapeutics; it can impose severe limitations on purification yields and compromise formulation stability. Advances in computer power, and the wealth of computational studies pertaining to protein folding, have facilitated the development of molecular simulation as a tool to investigate protein misfolding and aggregation. Here, we highlight the successes of protein aggregation studies carried out in silico, with a particular emphasis on studies related to biotechnology. To conclude, we discuss future prospects for the field, and identify several biotechnology-related problems that would benefit from molecular simulation.

Thermodynamics of folding and association of lattice-model proteins

Closely related to the "protein folding problem" is the issue of protein misfolding and aggregation. Protein aggregation has been associated with the pathologies of nearly 20 human diseases and presents serious difficulties during the manufacture of pharmaceutical proteins. Computational studies of multiprotein systems have recently emerged as a powerful complement to experimental efforts aimed at understanding the mechanisms of protein aggregation. We describe the thermodynamics of systems containing two lattice-model 64-mers. A parallel tempering algorithm abates problems associated with glassy systems and the weighted histogram analysis method improves statistical quality. The presence of a second chain has a substantial effect on single-chain conformational preferences. The melting temperature is substantially reduced, and the increase in the population of unfolded states is correlated with an increase in interactions between chains. The transition from two native chains to a non-native aggregate is entropically favorable. Non-native aggregates receive approximately 25% of their stabilizing energy from intraprotein contacts not found in the lowest-energy structure. Contact maps show that for non-native dimers, nearly 50% of the most probable interprotein contacts involve pairs of residues that form native contacts, suggesting that a domain-swapping mechanism is involved in self-association.

Kinetic studies of protein L aggregation and disaggregation

We have investigated the aggregation of protein L in 25% (vol/vol) TFE and 10 mM HCl. Under both conditions, aggregates adopt a fibrillar structure and bind dyes Congo Red and Thioflavin T consistent with the presence of amyloid fibrils. The kinetics of aggregation in 25% TFE suggest a linear-elongation mechanism with critical nucleus size of either two or three monomers. Aggregation kinetics in 10 mM HCl show a prolonged lag phase prior to a rapid increase in aggregation. The lag phase is time-dependent, but the time dependence can be eliminated by the addition of pre-formed seeds. Disaggregation studies show that for aggregates formed in TFE, aggregate stability is a strong function of aggregate age. For example, after 200 min of aggregation, 40% of the aggregation reaction is irreversible, while after 3 days over 60% is irreversible. When the final concentration of the denaturant, TFE, is reduced from 5% to 0, the amount of reversible aggregation doubles. Disaggregation studies of aggregates formed in TFE and 10 mM HCl reveal a complicated effect of pH on aggregate stability.

Effect of single-point sequence alterations on the aggregation propensity of a model protein

Sequences of contemporary proteins are believed to have evolved through a process that optimized their overall fitness, including their resistance to deleterious aggregation. Biotechnological processing may expose therapeutic proteins to conditions that are much more conducive to aggregation than those encountered in a cellular environment. An important task of protein engineering is to identify alternative sequences that would protect proteins when processed at high concentrations without altering their native structure associated with specific biological function. Our computational studies exploit parallel tempering simulations of coarse-grained model proteins to demonstrate that isolated amino acid residue substitutions can result in significant changes in the aggregation resistance of the protein in a crowded environment while retaining protein structure in isolation. A thermodynamic analysis of protein clusters subject to competing processes of folding and association shows that moderate mutations can produce effects similar to those caused by changes in system conditions, including temperature, concentration, and solvent composition, that affect the aggregation propensity. The range of conditions where a protein can resist aggregation can therefore be tuned by sequence alterations, although the protein generally may retain its generic ability for aggregation.

Molecular simulation of protein aggregation

Computer simulation offers unique possibilities for investigating molecular-level phenomena difficult to probe experimentally. Drawing from a wealth of studies concerning protein folding, computational studies of protein aggregation are emerging. These studies have been successful in capturing aspects of aggregation known from experiment and are being used to refine experimental methods aimed at abating aggregation. Here we review molecular-simulation studies of protein aggregation conducted in our laboratory. Specific attention is devoted to issues with implications for biotechnology.

Protein-folding landscapes in multichain systems

Computational studies of proteins have significantly improved our understanding of protein folding. These studies are normally carried out by using chains in isolation. However, in many systems of practical interest, proteins fold in the presence of other molecules. To obtain insight into folding in such situations, we compare the thermodynamics of folding for a Miyazawa-Jernigan model 64-mer in isolation to results obtained in the presence of additional chains. The melting temperature falls as the chain concentration increases. In multichain systems, free-energy landscapes for folding show an increased preference for misfolded states. Misfolding is accompanied by an increase in interprotein interactions; however, near the folding temperature, the transition from folded chains to misfolded and associated chains is entropically driven. A majority of the most probable interprotein contacts are also native contacts, suggesting that native topology plays a role in early stages of aggregation.

The competition between protein folding and aggregation: off-lattice minimalist model studies

Protein aggregation has been associated with a number of human diseases, and is a serious problem in the manufacture of recombinant proteins. Of particular interest to the biotechnology industry is deleterious aggregation that occurs during the refolding of proteins from inclusion bodies. As a complement to experimental efforts, computer simulations of multi-chain systems have emerged as a powerful tool to investigate the competition between folding and aggregation. Here we report results from Langevin dynamics simulations of minimalist model proteins. Order parameters are developed to follow both folding and aggregation. By mapping natural units to real units, the simulations are shown to be carried out under experimentally relevant conditions. Data pertaining to the contacts formed during the association process show that multiple mechanisms for aggregation exist, but certain pathways are statistically preferred. Kinetic data show that there are multiple time scales for aggregation, although most association events take place at times much shorter than those required for folding. Last, we discuss results presented here as a basis for future work aimed at rational design of mutations to reduce aggregation propensity, as well as for development of small-molecular weight refolding enhancers.

Interactions of lysozyme in guanidinium chloride solutions from static and dynamic light-scattering measurements

The interactions of partially unfolded proteins provide insight into protein folding and protein aggregation. In this work, we studied partially unfolded hen egg lysozyme interactions in solutions containing up to 7 M guanidinium chloride (GdnHCl). The osmotic second virial coefficient (B(22)) of lysozyme was measured using static light scattering in GdnHCl aqueous solutions at 20 degrees C and pH 4.5. B(22) is positive in all solutions, indicating repulsive protein-protein interactions. At low GdnHCl concentrations, B(22) decreases with rising ionic strength: in the absence of GdnHCl, B(22) is 1.1 x 10(-3) mLmol/g(2), decreasing to 3.0 x 10(-5) mLmol/g(2) in the presence of 1 M GdnHCl. Lysozyme unfolds in solutions at GdnHCl concentrations higher than 3 M. Under such conditions, B(22) increases with ionic strength, reaching 8.0 x 10(-4) mLmol/g(2) at 6.5 M GdnHCl. Protein-protein hydrodynamic interactions were evaluated from concentration-dependent diffusivity measurements, obtained from dynamic light scattering. At moderate GdnHCl concentrations, lysozyme interparticle interactions are least repulsive and hydrodynamic interactions are least attractive. The lysozyme hydrodynamic radius was calculated from infinite-dilution diffusivity and did not change significantly during protein unfolding. Our results contribute toward better understanding of protein interactions of partially unfolded states in the presence of a denaturant; they may be helpful for the design of protein refolding processes that avoid protein aggregation.