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

Abstract

Mitapivat, a pyruvate kinase activator, shows great potential as a sickle cell disease (SCD)-modifying therapy. The safety and efficacy of mitapivat as a long-term maintenance therapy are currently being evaluated in two open-label studies. Here we applied a comprehensive multi-omics approach to investigate the impact of activating pyruvate kinase on red blood cells (RBC) from 15 SCD patients. HbSS patients were enrolled in one of the open-label, extended studies (NCT04610866). Leukodepleted RBC obtained from fresh whole blood at baseline, prior to drug initiation, and at longitudinal timepoints over the course of the study were processed for multi-omics through a stepwise extraction of metabolites, lipids and proteins. Mitapivat therapy had significant effects on the metabolome, lipidome and proteome of SCD RBC. Mitapivat decreased 2,3-diphosphoglycerate levels, increased adenosine triphosphate 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 oxygen affinity (p50) and sickling kinetics (t50) during treatment. Mitapivat markedly reduced levels of proteins of mitochondrial origin within 2 weeks of initiation of treatment, with minimal changes in reticulocyte counts. In the first 6 months of treatment there were also transient elevations of lysophosphatidylcholines and oxylipins with depletion of free fatty acids, suggestive of an effect on membrane lipid remodeling. Multi-omics analysis of RBC identified benefits for glycolysis, as well as activation of the Lands cycle.

Figure 1.

Alterations of the metabolome in sickle red blood cells from patients on treatment with mitapivat. (A) Overview of the clinical study. Fifteen sickle cell patients (SS genotype) were enrolled in this clinical trial, with all 15 patients being treated for 6 months, 14 for a whole year and six for up to 2 years (visit 12). Red blood cell (RBC) samples underwent multi-omics characterization. (B, C) Linear model analysis of metabolomics and lipidomics data (B) or proteomics data (C) identified molecules associated with the treatments, either unadjusted (x axis) or adjusted by patient-specific responses (y axis). Highlighted metabolites (B) or proteins (C) represent the variables with the highest weights across linear discriminant analysis 1 (LDA1). (D) Line plots of mitapivat, ATP and carnitine, the very drug being administered, along with the levels of the top metabolites affected by the treatment. In light blue, median metabolite levels across all samples, while range intervals are shown in light gray. Data are shown as peak area abundance (arbitrary unit on the y axis), while the x axis represents visits 1-12. (E) LDA identified two major components (LDA1 and LDA2 – x and y, respectively) discriminating samples across visits (z axis).

Figure 2.

Heatmap and network analysis of top metabolites and lipids or proteins affected by mitapivat treatment in sickle red blood cells. (A, B) The top 50 metabolites/lipids (A) and proteins (B) (based on linear discriminant analyses) affected by mitapivat treatment are shown as a function of time (visits). A full list of these features is provided in Online Supplementary Table S1. (C) Merged data from these analyses were uploaded to Omicsnet to perform combined pathway analyses.

Figure 3.

Impact of mitapivat treatment on red blood cell residual mitochondrial proteins. (A) Heatmap of median values of peak areas for proteins identified despite the gene ontology classification as proteins of mitochondrial origin or localization. (B) Selected line plots for the most significantly affected members of this group through the course of the study.

Figure 4.

Impact of mitapivat on sickle red blood cell glycolysis. (A) Overview of glycolysis, showing the reaction catalyzed by red cell pyruvate kinase (PKR, Uniprot name KPYR) - the target of mitapivat. (B, C) Line plots for mass spectrometry-based measurements of peak areas of glycolytic metabolites and enzymes during the course of the study.

Figure 5.

Sickle red blood cell membrane remodeling after mitapivat treatment. (A, B) Acyl-carnitines and free fatty acids are significantly affected by mitapivat treatment. (A) Heatmap of the median values of each metabolite in these pathways across all subjects for up to 2 years of treatment (visit 12). (B) Highlight of acyl-carnitine depletion during the course of the treatment, especially free and saturated fatty acid-conjugated acyl-carnitines, (C) with an of the pathway overview. (D) Longitudinal phosphoproteomics analyses suggest a transient increase in protein phosphorylation at visits 2-3. (E) Significant changes in band 3 (SLC4A1), hemoglobin beta (HBB) and ankyrin (ANK1) were observed, with diverging trends at different S/T/Y residues).

Figure 6.

Omics correlates to mass spectrometry-detected mitapivat in sickle red blood cells. (A-F) Metabolite (A-C) and protein (D-F) correlates to mitapivat levels in red blood cells from patients with sickle cell disease during a 2-year period of treatment (visits 1-12; N=6) or just within the first 6 months (N=15). Volcano plots indicate Spearman correlations (x axis) and -log₁₀ of related P values. (C and F) Line plots for selected metabolites (C) and proteins (F), with mitapivat levels (independent variable) shown on the y axis upon 90 degree rotation of the original graph for ease of visualization.

Figure 7.

Multi-omics findings correlate to functional readouts on red blood cells from patients with sickle cell disease on mitapivat treatment for 2 years. Correlates (Spearman) are shown for 2,3-DPG and ATP upon normalization to hematocrit (A, B), PKR activity and levels (C, D), p50 (E) and sickling time - t50 (F). Whole blood levels of ATP and 2,3-DPG were measured using a validated liquid chromatography tandem mass spectrometry assay with lower limit of quantitation at 50.0 μg/mL and converted to intracellular concentrations by dividing by the hematocrit (as a fraction).