Biology subjects

Haiman, Z. B.

Publications and source records attributed to Haiman, Z. B..

8 recordsLinked to original sources

bioRxiv2026

A population-scale red blood cell proteome reveals genetically encoded aging clocks predictive of hemolysis and blood donor activity

As the most abundant human cell and the foundation of transfusion medicine, red blood cells (RBCs) offer a unique readout of systemic health, yet they have never been characterized at population scale. We generated a proteome atlas of 13,091 blood donors with multi-omics longitudinal phenotyping, characterizing the influence of demographics and genetic variation on the reproducibility of RBC proteomes across donations. Elastic-net aging clocks captured biological aging with high accuracy and uncovered genetic regulators of {Delta}Age at FN1, C4/IKZF1, CRAT, PFAS, TRIM58. Across independent cohorts, {Delta}Age was accelerated in G6PD deficiency, sickle cell trait/disease, and iron deficiency, reversed by iron repletion, and slowed in high-frequency donors, linking molecular aging to brain iron/myelin and cognitive performance. Molecular aging signatures predicted storage, osmotic, and oxidative hemolysis, hemoglobin increments after transfusion, and long-term donor activity over 12-years. These results establish RBC proteomics as a scalable biomarker of aging, donor healthspan, and transfusion outcomes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/710284v1_ufig1.gif" ALT="Figure 1"> View larger version (96K): org.highwire.dtl.DTLVardef@15bd46eorg.highwire.dtl.DTLVardef@1d7c107org.highwire.dtl.DTLVardef@1c1d870org.highwire.dtl.DTLVardef@168dc6f_HPS_FORMAT_FIGEXP M_FIG Dzieciatkowska et al. generate the first population-scale atlas of the RBC proteome across 13,000 donors and develop proteomic and metabolomic aging clocks that quantify biological age. Molecular {Delta}Age is reproducible across donations, genetically encoded and accelerated in G6PD deficiency, sickle cell trait/disease, and iron deficiency - yet reset by iron repletion, tracking with cognitive function and brain iron/myelin. RBC aging clocks predict hemolytic fragility, transfusion efficacy, and donor activity 12 years later. C_FIG HighlightsO_LIRBC proteome atlas of 13,091 donors reveals demographic and genetic programs C_LIO_LIGenetically encoded RBC aging clocks identify regulators of molecular {Delta}age C_LIO_LIMolecular aging features predict hemolysis and transfusion response across cohorts C_LIO_LIRBC molecular age forecasts long-term donor activity over a 12-year follow-up C_LI

biochemistry↗

bioRxiv2025

Deep Red Blood Cell Proteome Defines the Band 3 N-Terminus Interactome as a Regulator of Hypoxic Adaptation via BLVRB-Dependent S-Nitroso Transfer

Red blood cells (RBCs) have long been regarded as passive oxygen carriers, yet growing evidence reveals a complex, dynamic proteome independent of de novo gene expression. Here, we define the erythrocyte as an oxygen-responsive system organized around a Band 3 (SLC4A1)-centered metabolon. Using deep proteomics of ultra-pure RBCs and cross-linking interactomics, we identify biliverdin reductase B (BLVRB) as a previously unrecognized Band 3 interactor that binds the N-terminal cytosolic domain under normoxia and dissociates under hypoxia, when band 3-deoxyhemoglobin interactions increase threefold. This reversible interaction forms an oxygen-sensitive switch coupling structural, redox, and metabolic remodeling. In humanized mice, truncation of the Band 3 N-terminus disrupted glycolytic activation, reduced 2,3-bisphosphoglycerate synthesis, and impaired exercise tolerance despite preserved cardiopulmonary function, establishing the physiological relevance of this module. Population-scale proteome quantitative trait locus (pQTL) analyses revealed coordinated variation of SLC4A1 and BLVRB abundance but minimal association of biliverdin levels with BLVRB genotype, suggesting alternative functions beyond heme catabolism. Mechanistically, BLVRB Cys109 acts as a nitric oxide (NO) relay, trans-nitrosating glycolytic enzymes such as GAPDH at active site Cys152, transiently inhibiting glycolysis. This S-nitrosation-mediated feedback mirrors conserved mechanisms in plants, where GAPDH-SNO redirects carbon flow toward the Calvin-Benson cycle under nitrosative stress, revealing an evolutionary convergence in gas-responsive metabolic control. Collectively, our findings define a Band 3-BLVRB-hemoglobin axis that links oxygen sensing, NO signaling, and redox homeostasis, providing a unifying model for how an anucleate cell achieves environmental adaptability through reversible protein-protein interactions and post-translational chemistry. Graphic abstractIssaian et al. define the most comprehensive proteome of ultra-pure human red blood cells (3,775 proteins) and map the O2-dependent interactome, revealing a Band 3-BLVRB-hemoglobin module that links oxygen sensing to metabolic remodeling via reversible inhibitory S-nitrosation of GAPDH C152. In plants this redirects carbon toward photosynthesis, illustrating a conserved NO-dependent metabolic reprogramming mechanism across oxygen-regulated systems. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/691178v1_ufig1.gif" ALT="Figure 1"> View larger version (89K): org.highwire.dtl.DTLVardef@122468org.highwire.dtl.DTLVardef@114428dorg.highwire.dtl.DTLVardef@11d40c4org.highwire.dtl.DTLVardef@1870fb7_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDeep proteomics defines a complete, contamination-free RBC proteome (3,775 proteins) C_LIO_LICross-linking proteomics maps an oxygen-sensitive Band 3-centered interactome C_LIO_LIO2-dependent BLVRB-Band 3 binding regulates metabolism via S-nitrosation of GAPDH C_LIO_LIBand 3 N-terminus is required for hypoxic remodeling and exercise tolerance in vivo C_LI

biochemistry↗

bioRxiv2025

Genetic variation of human G6PD impacts Red Blood Cell transfusion efficacy

Glucose-6-phosphate dehydrogenase (G6PD) deficiency, the most common human enzymopathy, affects 6% of the global population, yet its impact on blood storage and transfusion efficacy remains undefined. We integrated genome-metabolome-proteome analyses of 13,091 blood donors (362 G6PD SNPs), validated in a recalled cohort (n=643), linked donor-recipient databases, humanized mouse models (canonical, African A- [V68M+N126D], Mediterranean [S188F]), and a prospective sickle cell disease study. Common G6PD variants reduced protein abundance, reprogrammed redox metabolism, and increased storage hemolysis. In mice, G6PD-deficient RBCs showed lower post-transfusion recovery, higher oxidative stress, and impaired renal oxygenation. Clinically, recipients of G6PD-deficient units exhibited smaller hemoglobin increments and reduced RBC L{superscript 1}Cr-survival (-8% at 24 h; -12% at 4 weeks). Structural studies revealed kinetic fragility for A- and thermodynamic fragility for Med-, linking genotype to protein instability and transfusion outcome. These findings identify donor G6PD genotype as a determinant of transfusion efficacy, supporting genotype-aware inventory-management strategies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/689741v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@1897489org.highwire.dtl.DTLVardef@1420587org.highwire.dtl.DTLVardef@178e8ddorg.highwire.dtl.DTLVardef@1003b44_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

RBC-GEM: a Knowledge Base for Systems Biology of Human Red Blood Cell Metabolism

Advancements with cost-effective, high-throughput omics technologies have had a transformative effect on both fundamental and translational research in the medical sciences. These advancements have facilitated a departure from the traditional view of human red blood cells (RBCs) as mere carriers of hemoglobin, devoid of significant biological complexity. Over the past decade, proteomic analyses have identified a growing number of different proteins present within RBCs, enabling systems biology analysis of their physiological functions. Here, we introduce RBC-GEM, the most extensive and meticulously curated metabolic reconstruction of a specific human cell type to-date. It was developed through meta-analysis of proteomic data from 28 studies published over the past two decades resulting in a RBC proteome composed of more than 4,600 distinct proteins. Through workflow-guided manual curation, we have compiled the metabolic reactions carried out by this proteome. RBC-GEM is hosted on a version-controlled GitHub repository, ensuring adherence to the standardized protocols for metabolic reconstruction quality control and data stewardship principles. This reconstruction of the RBC metabolic network is a knowledge base consisting of 718 genes encoding proteins acting on 1,590 unique metabolites through 2,554 biochemical reactions: a 700% size expansion over its predecessor. This reconstruction as an up-to-date curated knowledge base can be used for contextualization of data and for the construction of a computational whole-cell model of a human RBC. Author SummaryHuman red blood cells (RBCs) have been studied for decades because of their unique physiology, essential oxygen delivery functions, and general accessibility. RBCs are the simplest yet most numerous of human cell types due to the loss of cellular organelles during their development process. This process has evolved to maximize hemoglobin content per cell to facilitate RBCs main function in gas transport. RBCs are integral to a variety of medical applications, such as blood storage for transfusion. Recent advancements in high-throughput data collection have greatly expanded our understanding of RBC metabolism, highlighting important roles and functions for RBCs in maintaining homeostasis in the organism in addition to oxygen transport. Here we provide a knowledge base for the human RBC as a genome-scale metabolic reconstruction. Our results highlight the complexity of RBC metabolism, supported by recent advancements in high-throughput data collection methods for detecting low-abundance proteins in RBCs. We make knowledge about the RBC findable, accessible, interoperable, and reusable (FAIR). As RBC research is likely to see many translational medical advancements, a knowledge base for the contextualization of RBC data will serve as an essential resource for further research and medical application development.

systems biology↗

bioRxiv2023

A data-driven approach for timescale decomposition of biochemical reaction networks

Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications.

systems biology↗

bioRxiv2021

Mining all publicly available expression data to compute dynamic microbial transcriptional regulatory networks

We are firmly in the era of biological big data. Millions of omics datasets are publicly accessible and can be employed to support scientific research or build a holistic view of an organism. Here, we introduce a workflow that converts all public gene expression data for a microbe into a dynamic representation of the organisms transcriptional regulatory network. This five-step process walks researchers through the mining, processing, curation, analysis, and characterization of all available expression data, using Bacillus subtilis as an example. The resulting reconstruction of the B. subtilis regulatory network can be leveraged to predict new regulons and analyze datasets in the context of all published data. The results are hosted at https://imodulondb.org/, and additional analyses can be performed using the PyModulon Python package. As the number of publicly available datasets increases, this pipeline will be applicable to a wide range of microbial pathogens and cell factories.

bioinformatics↗

bioRxiv2020

MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics

Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner; the Systems Biology Markup Language (SBML) simulation engine. Three case studies demonstrate how to use MASSpy: 1) to simulate dynamics of detailed mechanisms of enzyme regulation; 2) to generate an ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify uncertainty, and 3) to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenge that arise in dynamic modeling of metabolic networks, both at a small and large scale. Author SummaryGenome-scale reconstructions of metabolism appeared shortly after the first genome sequences became available. Constraint-based models are widely used to compute steady state properties of such reconstructions, but the attainment of dynamic models has remained elusive. We thus developed the MASSpy software package, a framework that enables the construction, simulation, and visualization of dynamic metabolic models. MASSpy is based on the mass action kinetics for each elementary step in an enzymatic reaction mechanism. MASSpy seamlessly unites existing software packages within its framework to provide the user with various modeling tools in one package. MASSpy integrates community standards to facilitate the exchange of models, giving modelers the freedom to use the software for different aspects of their own modeling workflows. Furthermore, MASSpy contains methods for generating and simulating ensembles of models, and for explicitly accounting for biological uncertainty. MASSpy has already demonstrated success in a classroom setting. We anticipate that the suite of modeling tools incorporated into MASSpy will enhance the ability of the modeling community to construct and interrogate complex dynamic models of metabolism.

systems biology↗

bioRxiv2017

Catalytic potential and disturbance rejection of glycolytic kinases in the human red blood cell

The allosteric regulation of metabolic enzymes plays a key role in controlling the flux through metabolic pathways. The activity of such enzymes is traditionally described by allosteric rate laws in complex kinetic models of metabolic network function. As an alternative, we describe the fraction of the regulated enzyme that is in an active form by developing a detailed reaction network of all known ligand binding events to the enzyme. This fraction is the fundamental result of metabolic regulation as it represents the \"tug of war\" among the various regulators and substrates that determine the utilization of the enzyme. The active fraction corresponds to the utilization of the catalytic potential of the enzyme. Using well developed kinetic models of human red blood cell (RBC) glycolysis, we characterize the catalytic potential of its three key kinases: hexokinase (HEX), phosphofructokinase (PFK), and pyruvate kinase (PYK). We then compute their time-dependent interacting catalytic potentials. We show how detailed kinetic models of the management of the catalytic potential of the three kinases elucidates disturbance rejection capabilities of glycolysis. Further, we examine the sensitivity of the catalytic potential through an examination of existing personalized RBC models, providing a physiologically-meaningful sampling of the feasible parameter space. The graphical representation of the dynamic interactions of the individual kinase catalytic potential adjustment provides an easy way to understand how a robust homeostatic state is maintained through interacting allosteric regulatory mechanisms.

systems biology↗