Reprogramming of red blood cell metabolism in Zika virus–infected donors

Multiomics Approach Reveals Serum Biomarker Candidates for Congenital Zika Syndrome

The Zika virus (ZIKV) can be vertically transmitted, causing congenital Zika syndrome (CZS) in fetuses. ZIKV infection in early gestational trimesters increases the chances of developing CZS. This syndrome involves several pathologies with a complex diagnosis. In this work, we aim to identify biological processes and molecular pathways related to CZS and propose a series of putative protein and metabolite biomarkers for CZS prognosis in early pregnancy trimesters. We analyzed serum samples of healthy pregnant women and ZIKV-infected pregnant women bearing nonmicrocephalic and microcephalic fetuses. A total of 1090 proteins and 512 metabolites were identified by bottom-up proteomics and untargeted metabolomics, respectively. Univariate and multivariate statistical approaches were applied to find CZS differentially abundant proteins (DAP) and metabolites (DAM). Enrichment analysis (i.e., biological processes and molecular pathways) of the DAP and the DAM allowed us to identify the ECM organization and proteoglycans, amino acid metabolism, and arachidonic acid metabolism as CZS signatures. Five proteins and four metabolites were selected as CZS biomarker candidates. Serum multiomics analysis led us to propose nine putative biomarkers for CZS prognosis with high sensitivity and specificity.

Regulation of kynurenine metabolism by blood donor genetics and biology impacts red cell hemolysis in vitro and in vivo

ABSTRACT

In the field of transfusion medicine, the clinical relevance of the metabolic markers of the red blood cell (RBC) storage lesion is incompletely understood. Here, we performed metabolomics of RBC units from 643 donors enrolled in the Recipient Epidemiology and Donor Evaluation Study, REDS RBC Omics. These units were tested on storage days 10, 23, and 42 for a total of 1929 samples and also characterized for end-of-storage hemolytic propensity after oxidative and osmotic insults. Our results indicate that the metabolic markers of the storage lesion poorly correlated with hemolytic propensity. In contrast, kynurenine was not affected by storage duration and was identified as the top predictor of osmotic fragility. RBC kynurenine levels were affected by donor age and body mass index and were reproducible within the same donor across multiple donations from 2 to 12 months apart. To delve into the genetic underpinnings of kynurenine levels in stored RBCs, we thus tested kynurenine levels in stored RBCs on day 42 from 13 091 donors from the REDS RBC Omics study, a population that was also genotyped for 879 000 single nucleotide polymorphisms. Through a metabolite quantitative trait loci analysis, we identified polymorphisms in SLC7A5, ATXN2, and a series of rate-limiting enzymes (eg, kynurenine monooxygenase, indoleamine 2,3-dioxygenase, and tryptophan dioxygenase) in the kynurenine pathway as critical factors affecting RBC kynurenine levels. By interrogating a donor-recipient linkage vein-to-vein database, we then report that SLC7A5 polymorphisms are also associated with changes in hemoglobin and bilirubin levels, suggestive of in vivo hemolysis in 4470 individuals who were critically ill and receiving single-unit transfusions.

Red Blood Cell Storage: From Genome to Exposome Towards Personalized Transfusion Medicine

Over the last decade, the introduction of omics technologies-especially high-throughput genomics and metabolomics-has contributed significantly to our understanding of the role of donor genetics and nongenetic determinants of red blood cell storage biology. Here we briefly review the main advances in these areas, to the extent these contributed to the appreciation of the impact of donor sex, age, ethnicity, but also processing strategies and donor environmental, dietary or other exposures - the so-called exposome-to the onset and severity of the storage lesion. We review recent advances on the role of genetically encoded polymorphisms on red cell storage biology, and relate these findings with parameters of storage quality and post-transfusion efficacy, such as hemolysis, post-transfusion intra- and extravascular hemolysis and hemoglobin increments. Finally, we suggest that the combination of these novel technologies have the potential to drive further developments towards personalized (or precision) transfusion medicine approaches.

Red Blood Cell Metabolism In Vivo and In Vitro

Red blood cells (RBC) are the most abundant cell in the human body, with a central role in oxygen transport and its delivery to tissues. However, omics technologies recently revealed the unanticipated complexity of the RBC proteome and metabolome, paving the way for a reinterpretation of the mechanisms by which RBC metabolism regulates systems biology beyond oxygen transport. The new data and analytical tools also informed the dissection of the changes that RBCs undergo during refrigerated storage under blood bank conditions, a logistic necessity that makes >100 million units available for life-saving transfusions every year worldwide. In this narrative review, we summarize the last decade of advances in the field of RBC metabolism in vivo and in the blood bank in vitro, a narrative largely influenced by the authors' own journeys in this field. We hope that this review will stimulate further research in this interesting and medically important area or, at least, serve as a testament to our fascination with this simple, yet complex, cell.

Red Blood Cell Omics and Machine Learning in Transfusion Medicine: Singularity Is Near

Background

Blood transfusion is a life-saving intervention for millions of recipients worldwide. Over the last 15 years, the advent of high-throughput, affordable omics technologies - including genomics, proteomics, lipidomics, and metabolomics - has allowed transfusion medicine to revisit the biology of blood donors, stored blood products, and transfusion recipients.

Summary

Omics approaches have shed light on the genetic and non-genetic factors (environmental or other exposures) impacting the quality of stored blood products and efficacy of transfusion events, based on the current Food and Drug Administration guidelines (e.g., hemolysis and post-transfusion recovery for stored red blood cells). As a treasure trove of data accumulates, the implementation of machine learning approaches promises to revolutionize the field of transfusion medicine, not only by advancing basic science. Indeed, computational strategies have already been used to perform high-content screenings of red blood cell morphology in microfluidic devices, generate in silico models of erythrocyte membrane to predict deformability and bending rigidity, or design systems biology maps of the red blood cell metabolome to drive the development of novel storage additives.

Key Message

In the near future, high-throughput testing of donor genomes via precision transfusion medicine arrays and metabolomics of all donated products will be able to inform the development and implementation of machine learning strategies that match, from vein to vein, donors, optimal processing strategies (additives, shelf life), and recipients, realizing the promise of personalized transfusion medicine.

Unraveling the Metabolic Alterations Induced by Zika Infection in Prostate Epithelial (PNT1a) and Adenocarcinoma (PC-3) Cell Lines

The outbreak of Zika virus infection in 2016 led to the identification of its presence in several types of biofluids, including semen. Later discoveries associated Zika infection with sexual transmission and persistent replication in cells of the male reproductive tract. Prostate epithelial and carcinoma cells are favorable to virus replication, with studies pointing to transcriptomics alterations of immune and inflammation genes upon persistence. However, metabolome alterations promoted by the Zika virus in prostate cells are unknown. Given its chronic effects and oncolytic potential, we aim to investigate the metabolic alterations induced by the Zika virus in prostate epithelial (PNT1a) and adenocarcinoma (PC-3) cells using an untargeted metabolomics approach and high-resolution mass spectrometry. PNT1a cells were viable up to 15 days post ZIKV infection, in contrast to its antiproliferative effect in the PC-3 cell lineage. Remarkable alterations in the PNT1a cell metabolism were observed upon infection, especially regarding glycerolipids, fatty acids, and acylcarnitines, which could be related to viral cellular resource exploitation, in addition to the over-time increase in oxidative stress metabolites associated with carcinogenesis. The upregulation of FA20:5 at 5 dpi in PC-3 cells corroborates the antiproliferative effect observed since this metabolite was previously reported to induce PC-3 cell death. Overall, Zika virus promotes extensive lipid alterations on both PNT1a and PC-3 cells, promoting different outcomes based on the cellular metabolic state.