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. 2026 Jan 13;116(5):535–544. doi: 10.1111/ejh.70114

Whole Blood Transcriptomic Analysis of Sickle Cell Trait

Mari Johnson 1, Yanwei Cai 1, Ana Gabriela Vasconcelos 2, Peter Orchard 3, Paul L Auer 4, Guillaume Lettre 5, Jia Wen 6, Nora Franceschini 7, Charles Kooperberg 1, Wei Sun 1, Li Hsu 1, Laura M Raffield 7, Alex P Reiner 1,8,✉
PMCID: PMC13050677  PMID: 41528117

ABSTRACT

Sickle cell trait (SCT) is the heterozygous carrier state for the HBB missense variant which causes sickle cell disease (SCD). SCT has been associated with increased risk of venous thromboembolism and chronic kidney disease as well as alterations in clinical laboratory parameters. To investigate differential gene expression in SCT, we used RNA sequencing of whole blood samples collected from 805 African American female participants (143 SCT; 660 controls) from the Women's Health Initiative Long Life Study (mean age = 76). We identified 226 differentially expressed genes (DEGs) in SCT compared to non‐carriers (FDR < 0.05). Enriched pathways included those related to erythropoiesis, hemoglobin synthesis, and proteasomal degradation. Many of the SCT‐associated DEGs were previously reported as differentially expressed in blood from individuals with SCD. Among the DEGs associated with SCT, we observed enrichment of upregulated ubiquitin‐related genes normally downregulated during the later stages of erythroid differentiation, a pattern previously reported in SCD. Several of the SCT‐associated DEGs highlight mechanisms that potentially link hemolysis or erythropoiesis to hypoxic kidney tubular injury. Future investigation of these genes using single cell transcriptomic analysis in relevant tissues may be useful in understanding mechanisms for adverse health outcomes in individuals with SCT.

Keywords: chronic kidney disease, erythropoiesis, sickle cell disease, sickle cell trait, ubiquitination

1. Introduction

Sickle cell disease (SCD) is an autosomal recessive disorder caused by a missense variant (rs334) in the hemoglobin subunit β (HBB) gene which produces sickle hemoglobin (HbS). Upon deoxygenation, HbS polymerizes, forming rigid sickled red blood cells (RBC's) which can obstruct small blood vessels, leading to acute vaso‐occlusive pain crises and progressive organ damage. The shearing of red blood cells triggers chronic hemolytic anemia and the release of free heme, generating reactive oxygen species, chronic inflammation, and heightened coagulation activation, all of which contribute to adverse outcomes in SCD [1]. Protective modifiers include increased levels of fetal hemoglobin (HbF) and the co‐inheritance of alpha thalassemia, which may reduce the relative concentrations of pathogenic HbS.

Individuals who are heterozygous carriers for the rs344 allele have sickle cell trait (SCT). Owing to partial conferred malaria resistance, SCT is common in areas of historic malaria endemicity and occurs in approximately 300 million people worldwide, including 1 in 13 African American adults. Previously thought to be a relatively benign condition, emerging research indicates that SCT is associated with increased risk of developing several complications including venous thromboembolism (VTE), renal medullary carcinoma, and chronic kidney disease (CKD) [2]. SCT is also associated with alterations in several hematological assays, including higher D‐dimer levels, higher neutrophil counts, lower lymphocyte counts, lower hemoglobin concentration, lower mean corpuscular volume (MCV), and higher mean corpuscular hemoglobin concentration (MCHC) [3, 4]. However, the mechanisms underlying these associations remain poorly understood.

High‐throughput omics approaches are increasingly used to study biological systems and cellular processes associated with complex disorders. Previously, whole blood gene expression profiling has been performed in SCD to identify genes or pathways underlying HbF regulation, SCD, and its clinical sequelae [5, 6, 7]. Recent proteomics and genome‐wide DNA methylation analyses have highlighted proteins related to kidney injury and inflammation [8] and epigenetic alterations associated with SCT [9]. Here we use RNA sequencing to provide the first characterization of the adult whole blood SCT transcriptome. Our goals are to investigate pathways and functional consequences related to SCT‐associated gene expression and compare genes differentially expressed in SCT to previously published blood transcriptome analyses of SCD.

2. Methods

2.1. Study Design

Study participants included 805 self‐identified Black/African American post‐menopausal women aged 65–95 years, who were originally recruited as part of the Women's Health Initiative (WHI). Participants were genotyped at rs334 encoding the beta‐globin p.Glu6Val variant and include 144 women with SCT (Hb A/S) and 661 as non‐SCT controls (Hb A/A). This research was approved by the Fred Hutchinson Cancer Center institutional review board, and all participants gave written informed consent. See Supplemental Methods for additional cohort details.

2.2. RNA Sequencing Data Preprocessing

Whole blood RNA samples were collected using PAXgene blood tubes and total RNA was extracted (PreAnalytiX, Qiagen). Following initial RNA quantification and QC, poly‐A selection and cDNA synthesis were performed using the TruSeq Stranded mRNA kit (Illumina), without globin mRNA removal. Final RNASeq libraries were quantified using the Quant‐iT dsDNA High Sensitivity assay, and library insert size distribution checked using a fragment analyzer. RNA sequencing was performed on a NovaSeq6000 instrument (RTA 3.1.5). Demultiplexed, unaligned BAM files were converted to FASTQ format using SamTools bam2fq (v1.4) and sequence read and base quality checked using the FASTX‐toolkit (v0.0.13). Raw RNA sequencing reads were mapped to the human reference genome (Hg38) using Bowtie, followed by alignment employing TopHat. Target coverage was ≥ 75 million mapped reads. Genes with < 10 transcripts in more than 70% of the samples were removed, leaving 19 254 transcripts. Expression data was examined both by PCA and the relative log expression distribution per sample (Figure S1). Two outliers were removed from further analysis, leaving a total of 143 participants with SCT and 660 non‐SCT controls.

2.3. Differential Gene Expression Analysis

Differential gene expression between SCT individuals and controls was evaluated using the limma‐voom pipeline [10]. Limma fits a linear model per gene adjusting for covariates, while incorporating global gene‐wise variance estimation using empirical bayes shrinkage. To minimize read count depth associated variance, a voom transformation was applied to counts per million (CPM) data. Model covariates included age, sequencing plate, and RNASeq principal components (PCs) 1–10 chosen based on scree plot analysis (Figure S2) and their capture of a large proportion of the blood cell count variation (Figure S3). Significant differences in log fold change of gene expression were adjusted using the Benjamini‐Hochberg method, with a false discovery rate (FDR) of 5%. In secondary analyses, we explored the inferred cell‐type expression of any gene observed as differentially expressed in whole blood (Supplemental Methods). Weighted gene co‐expression network analysis (WGCNA) was also performed to identify modules of genes co‐expressed among individuals with SCT (Supplemental Methods).

2.4. Gene Set Enrichment Analysis

ClusterProfiler was used to implement the FGSEA algorithm [11]. Genes were ranked by their t‐statistics in the DGE analysis, and a normalized enrichment score (NES) was calculated to determine the over‐representation of a gene set at the top or bottom of the ranked list. For testing transcription factor (TF) enrichment, DEGs were examined against TF gene sets identified from the ARCHS4 database. Benjamini‐Hochberg (BH) method was used for q value calculation (FDR 5%).

2.5. Variable of Importance Analysis

For each differentially expressed gene, the contribution of measured blood cell counts, along with age, SCT status, and sequencing plate, was assessed using a linear mixed model with the R package variancePartition [12]. The model parameters were estimated using maximum likelihood, and the variance explained by each variable was calculated as a proportion of total variance. A small subset of genes (n = 5) failed to converge due to numerical instability during model optimization and were excluded.

2.6. Association of Differential Gene Expression With eGFR and eGFR Decline

To evaluate the relationship between SCT‐associated gene expression and kidney function, we fitted linear or logistic regression models separately for each of the DEGs identified in the primary analysis with (1) estimated glomerular filtration rate (eGFR) at the time of RNA collection; (2) CKD (defined as a dichotomous outcome of eGFR ≤ 60 mL/min/1.73 m2). All regression models were adjusted for age, sequencing plate, RNAseq PCs 1–10, and SCT status. Bonferroni multiple testing correction was applied (p < 0.00022, 0.05/226). We additionally assessed whether each gene significantly improved CKD discrimination by comparing the AUC from a base ROC model containing only age, SCT, and technical factors to the AUC for a model additionally containing gene expression. Model improvement was assessed using the difference in AUC (DeLong test).

3. Results

3.1. Participant Characteristics

A total of 143 African American SCT cases and 660 non‐SCT African American controls were included, with an overall mean age of 76 years. Compared to African American controls, SCT individuals had lower hematocrit and MCV/MCH, but a higher MCHC (p < 0.001) (Table 1). Measured total lymphocyte count was lower in SCT compared with controls (Table 1), but there were no significant differences observed for lymphocyte or other WBC subtypes as estimated from bulk RNA sequencing deconvolution (Figure S4). eGFR was lower in SCT and the age‐adjusted odds ratio for CKD (stage 3 or higher) was 1.95 (95% CI 1.33–2.86; p = 0.001).

TABLE 1.

Participant characteristics stratified by SCT status.

Characteristic SCT (N = 144) a Control (N = 661) a p b
Age (years) 75.7 (5.5) 75.9 (6.0) 0.9
BMI 29.5 (6.0) 29.6 (6.1) 0.8
Smoking status > 0.9
Current 12 (8.5%) 59 (9.1%)
Never 73 (52%) 338 (52%)
Past 56 (40%) 254 (39%)
Mean corpuscular volume (fL) 84.3 (6.2) 88.3 (6.5) < 0.001
Mean corpuscular hemoglobin (pg) 28.0 (2.1) 28.8 (2.2) < 0.001
Mean corpuscular hemoglobin conc. (g/dL) 33.2 (1.0) 32.6 (1.1) < 0.001
Hemoglobin (g/dL) 12.5 (1.0) 12.7 (1.1) 0.062
Hematocrit (%) 37.7 (3.1) 38.9 (3.2) < 0.001
Reticulocyte count (×109/L) 54.8 (17.6) 56.6 (16.4) 0.15
Red blood cell count (×1012/L) 4.5 (0.5) 4.4 (0.5) 0.2
Lymphocyte count (×109/L) 1.9 (0.7) 2.0 (0.8) 0.036
Monocyte count (×109/L) 0.5 (0.2) 0.6 (0.2) 0.068
Neutrophil count (×109/L) 3.2 (1.3) 3.3 (1.6) 0.7
Serum creatinine (mg/dL) 1.0 (0.2) 0.9 (0.4) < 0.001
eGFR (mL/min/1.73 m2) 63.6 (16.4) 69.6 (17.3) < 0.001
Chronic kidney disease (CKD) stage 0.001
Stage 1 (eGFR ≥ 90 mL/min/1.73 m2) 9 (6.3%) 94 (14.3%)
Stage 2 (eGFR 60–89 mL/min/1.73 m2) 75 (52.1%) 381 (57.9%)
Stage 3 (eGFR 30–59 mL/min/1.73 m2) 58 (40.3%) 170 (25.8%)
Stage 4 (eGFR 15–29 mL/min/1.73 m2) 2 (1.4%) 10 (1.5%)
Stage 5 (eGFR < 15 mL/min/1.73 m2) 0 (0%) 3 (0.5%)
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Note: Participant characteristics were ascertained during the Women's Health Initiative (WHI) Long‐Life Study (LLS) exam in 2012–2013, the same visit at which blood was collected for RNA sequencing. Displayed are the mean and SD of each value. Differences between groups were compared using Wilcoxon rank sum test or Chi‐squared test.

a

Mean (SD); n (%).

b

Wilcoxon rank sum test; Pearson's chi‐squared test.

3.2. Differential Gene Expression in Sickle Cell Trait

We identified 226 differentially expressed genes (DEGs) in SCT cases compared with controls (5% FDR) (Figure 1; Table S1; Figure S5). Most DEGs (188 or 83%) demonstrated increased expression in SCT. Enriched pathways upregulated in SCT included erythrocyte development, hemoglobin synthesis and metabolism, inflammation, RNA splicing, and proteasomal protein degradation (Figure 2A; Table S2). The TAL1 hematopoietic transcription factor was the most significant gene associated with SCT (Log2FC = 0.35, q value = 1.38 × 10−17). We subsequently performed a gene set enrichment analysis of the 226 SCT DEGs using sets of genes co‐expressed with transcription factors (Table S3). Besides TAL1 and GATA1, the strongest enrichment was observed for several other transcription factors involved in erythropoiesis and transcriptional regulation of the beta‐globin locus, including ZFPM1, NFE2, KLF1, E2F2, FOXO3A, and MXI1 (Figure 2B).

FIGURE 1.

FIGURE 1

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Differential gene expression in sickle cell trait. Volcano plot shows the Log‐fold change (LogFC) in gene expression between sickle cell trait participants and race‐matched controls. Each point represents an individual gene. The X‐axis shows the log fold change (logFC) in gene expression between groups, while the y‐axis represents the −log10 p value of each gene. Genes above the top dashed line meet the Benjamini‐Hochberg adjusted p value < 0.05. Labeled are a subset of the top differentially expressed genes.

FIGURE 2.

FIGURE 2

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Gene set enrichment analysis showing selected significant pathways and transcription factors. All genes were ranked by their t‐statistics and evaluated for enrichment against (A) gene sets corresponding to biological processes annotated in MSigDB, KEGG, and Reactome, plus (B) transcription factor co‐expression from ARCHS4. Bubble size corresponds to the normalized enrichment score (NES) observed in the gene set, and the color scale denotes the adjusted q value. Displayed are selected gene sets (Figure 2A) and transcription factors (Figure 2B) with positive enrichment scores from Tables S2 and S3, respectively.

The second strongest DEG associated with SCT was RUNDC3A (Log2FC = 0.61, q value = 1.30 × 10−13), which is highly expressed during terminal erythroid differentiation [13]. Other significant DEGs involved in terminal erythroid differentiation include YPEL4, DYRK3, ITGB1, PPP2R5B, RNF10, TMCC2 [14, 15, 16, 17]. Additional DEG's upregulated in SCT are involved in (1) heme biosynthesis and iron metabolism (TFR2, ALA2, HEMGN, ABCB6, SLC25A37, SLC25A38, FAM210B, RBM38); (2) mitophagy and autophagy (TBC1D25, BMP2K, PINK1, and XPO7), which are required for terminal erythroblast enucleation [18, 19]; and/or (3) proteosome and ubiquitin pathways (PSME4, TRIM10, TRIM58, WDR26, RANBP10, UBE2H), which encode components of E2 and E3 ubiquitin enzyme complexes implicated in regulation of terminal erythropoiesis [20, 21]. We also detected DEGs related to ferroptosis, an iron‐dependent non‐apoptotic form of cell death recently highlighted as a driver of organ damage in mouse models of SCD [22]. Several ferroptosis genes were upregulated (SLC1A5, SELENBP1, ZRAMB1, NEDD4L, SLC25A37), whereas antioxidant and anti‐ferroptosis genes PRDX4 and PRDX6 were downregulated in SCT. Finally, we observed downregulation of translation‐related pathways in SCT, concordant with the expulsion and degradation of organelles during terminal erythropoiesis. Overall, the observed pattern of SCT‐associated differential gene expression is consistent with an increase in erythropoiesis and cell turnover, commonly observed following hemolysis of sickled erythrocytes.

3.3. Impact of Blood Cell Composition on Observed Differential Gene Expression

To exclude residual confounding of our bulk whole blood transcriptomic analysis due to cell type specific gene expression, we performed a sensitivity analysis in which measured blood cell counts were included as additional covariates in our differential gene expression analysis (Table S4). The additional adjustment of blood cell counts did not substantively alter the overall DEG association results (Figure S6), likely due to the correlation of blood cell counts with RNA‐seq principal components (already accounted for in our original DEG analysis). In an exploratory cell type specific analysis, we did not detect significant differences in SCT‐associated gene expression according to blood cell type for any of the 226 genes identified in bulk RNA analysis (Supplemental Results; Table S5). Moreover, 111 of the 226 genes were significantly associated with SCT adjusting for cell proportions estimated from bulk RNAseq data, providing complementary evidence that most SCT‐associated differentially expressed genes detected in whole blood are not due to cellular heterogeneity (Table S5).

As a complementary way to assess the contribution of blood cell type heterogeneity to gene expression variability in bulk whole blood RNA, we used a variance partition approach to quantify the proportion of variability in gene expression attributable to participant characteristics or technical factors (Table S6) [12]. Overall, across the 221 DEGs available for analysis, the mean proportion of variance explained was highest for reticulocytes (8.1%) (Figure 3A). However, there was also considerable variability in the source of phenotypic variation for each gene (Figure 3B). We also note that six of our DEGs (BNIP3L, SLC25A37, EPB41, ALAS2, FBOX7, ADIPOR1) have been reported to be expressed in mature erythrocytes [23].

FIGURE 3.

FIGURE 3

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Proportion of bulk whole blood gene expression variance explained by blood cell counts. Gene expression for each of our differentially expressed genes (DEGs) was modelled individually using a linear mixed model including cell counts, RNA sequencing plate, and SCT, with the remaining model variance captured by residuals. (A) Histogram shows the proportion of variance explained across all DEGs and variables. (B) Bar chart illustrating the proportion of variance explained for our top 24 DEGs.

3.4. Comparison of SCT‐Associated DEGs With Transcriptomic Signatures in SCD

To evaluate gene expression patterns shared between SCT heterozygotes and SCD homozygotes, we next examined the overlap between our 226 SCT‐associated DEGs and DEGs identified in a meta‐analysis of array‐based blood gene expression studies in SCD patients (n = 247) compared to controls (n = 96) by Ben Hamda et al. [7] Comparing our 226 SCT‐associated DEGs with the 335 SCD‐associated DEGs identified by Ben Hamda et al. there is an overlap of 61 genes (27%) (Table 2), which include our top‐ranked genes TAL1 and RUNDC3A (Table S7). We also note that among the genes differentially expressed in both SCT and steady‐state SCD, the magnitude of gene expression difference (fold‐change) compared to Hb A/A controls is uniformly greater in SCD than SCT (Table S7). The younger age and inclusion of males among the published SCD transcriptomic studies suggest the dysregulated gene expression pattern observed for SCT may not be restricted to older females.

TABLE 2.

Overlap of SCT differentially expressed genes (DEGs) with published gene expression signatures in SCD.

Effect direction in SCT # of SCT DEGs SCD whole blood [7]
# (%) of SCT DEGs overlapping with SCD
Upregulated 188 60 (32%)
Downregulated 38 1 (3%)
Total 226 61 (27%)
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Note: Differentially expressed genes (n = 226) were identified in SCT versus matched controls and compared with published significant DEGs (n = 335) identified in a metanalysis of SCD whole blood gene expression versus healthy controls [7] (FDR < 0.05).

3.5. Comparison of SCT‐Associated DEGs With Transcriptome During Normal Erythropoiesis

Erythropoiesis involves differentiation of progenitor cells through several distinct morphologic stages (pro‐, early basophilic, late basophilic, polychromatic, and orthochromatic erythroblasts), each characterized by stage‐specific patterns of gene expression, to produce anucleate reticulocytes. Reticulocytes transcripts are retained from earlier erythroblast precursors, and therefore may leave a fingerprint of gene expression, reflective of transcriptional changes occurring during earlier stages of erythropoiesis. Consequently, we compared our SCT differentially expressed genes with an RNA‐seq transcriptional gene set identified by An et al. from erythroblast precursors obtained from healthy, CD34+ cord blood donors [13].

The majority of our 226 SCT‐related DEGs (n = 169 or 75%) overlapped with genes that exhibited significant change in expression from an earlier stage to a consecutive later stage of erythropoiesis in one of the four analyses reported by An et al. [13], suggesting that erythrocyte transcription dynamics contribute substantially to the transcriptomic signature observed in SCT (Table S8). Moreover, our SCT‐associated DEGs demonstrated a strong concordance of direction of gene expression across earlier erythropoietic stage transitions (100% concordance for both proerythroblast‐to‐early basophilic and early basophilic‐to‐late basophilic transitions), with decreasing concordance observed during later transitions (86% concordance for late basophilic to polychromatic and 57% concordance from polychromatic to orthochromatic stages) (Table S9). Specifically, the discordant DEGs upregulated in SCT but downregulated during the polychromatic to orthochromatic transition include genes involved in ubiquitination and proteasome pathways (NEDD4L, SUGT1, WDTC1, SPSB3, CDK8, ZER1, PCGF5, COPS6, TTC1, TRIP12, USP12, CHORDC1, RANBP10). A similar pattern of discordant upregulation of genes involved in protein ubiquitination pathways during late erythroid differentiation was observed in reticulocytes from individuals with SCD reported by Zhang et al. [24].

3.6. Relationship of Gene Expression Patterns to SCT‐Related Clinical Phenotypes

We tested whether the 226 individual SCT‐associated DEGs were associated with kidney function (continuous eGFR or dichotomous CKD stage 3 or higher). After correction for multiple testing across the 226 DEGs, higher expression of 10 genes (TSPAN5, DCAF6, SPTA1, YOD1, SLC1A5, ARHGEF12, SPTB, YIPF2, SLC25A37, and FBXO7) significantly associated with lower eGFR (Table S10), and higher expression of four of these eGFR associated genes (TSPAN5, DCAF6, YIPF2, and SLC25A37) was associated with stage 3 or higher CKD (Table S11). Compared to a CKD base prediction model AUC of 0.69, addition of SLCA2537 gene expression improved the AUC to 0.71 (FDR p value = 0.08) (Table S11). To further explore the relationship of gene expression to SCT‐related clinical phenotypes, we characterized the correlations between WGCNA co‐expression gene networks in SCT with laboratory values previously measured concurrently at the WHI LLS exam (Supplemental Results, Tables S12 and S13, Figures S7–S10).

4. Discussion

We provide the first investigation of gene expression in SCT using whole blood RNA‐sequencing data measured in a cohort of older African American women. We identified 226 DEGs, the majority of which are upregulated in SCT. Pathway analyses highlighted enrichment for erythropoiesis, hemoglobin synthesis, ubiquitination and proteasomal degradation among DEGs. Notably, many of the 226 SCT‐associated genes were previously reported to be differentially expressed in transcriptomic studies of steady‐state SCD [7, 24]. These results support the hypothesis that SCT may involve subclinical sickling and hemolysis which ultimately may contribute to adverse chronic health outcomes in these older individuals. Given that gene expression is influenced by both age‐ and sex‐related epigenetic and transcriptomic changes, generalization of the dysregulated gene expression patterns from this cohort to the broader adult SCT population will require additional study.

4.1. Evidence for Compensatory Erythropoiesis in SCT

In SCD, hemolysis due to polymerization of deoxygenated Hb S results in a compensatory increase in erythropoiesis and reticulocytosis. Prior transcriptomic studies of SCD have reported upregulation of genes involved in early‐stage erythroid differentiation along with enrichment of upregulated ubiquitin‐related genes that are normally downregulated during the later stages of erythroid differentiation (i.e., transition from polychromatic to orthochromatic stages) [5, 24]. In our DEG analysis of SCT, we observed qualitatively similar gene expression patterns among individuals with SCT compared to controls. The SCT expression signature includes upregulation of genes encoding erythroid‐enriched hematopoietic transcription factors and genes involved in terminal erythroid maturation, particularly upregulation of ubiquitination‐related genes. Together with higher plasma erythropoietin levels observed in SCT individuals [8], this suggests the presence of a compensatory erythropoietic response in SCT like that observed in SCD. The magnitude of gene expression differences (fold changes) observed in SCT were lower than in SCD, which likely reflects the lower levels of HbS and milder degree of hemolysis and milder phenotypic changes (e.g., hematologic parameters) in SCT individuals compared to SCD.

A consequence of HbS and resultant hemolysis is an increase in oxidative damage and accumulation of misfolded proteins. The increase in ubiquitin‐related genes in SCT and SCD during later stages of erythroid maturation may therefore represent a state of ineffective erythropoiesis, whereby despite the initial expansion of erythroid progenitors, abnormalities during terminal erythroid differentiation result in only a limited number of RBCs produced. Ineffective erythropoiesis may also occur because of hemolysis‐induced inflammation that leads to impaired erythropoietic response [25]. Other genes upregulated in SCT such as DYRK3 may contribute to decreased mature red cell production following hemolysis [15]. Despite the lower hemoglobin and hematocrit in our SCT cases, we did not observe an overall increase in reticulocytes compared to non‐SCT controls. This contrasts with higher reticulocytes reported in younger individuals with SCT [26] and may be a consequence of diminished erythropoiesis in older adults.

4.2. HbS Polymerization, Red Cell Sickling, and Hemolysis in SCT

The lower hemoglobin/hematocrit among SCT individuals compared with controls is likely due at least in part to increased propensity to red cell sickling and hemolysis, increased mechanical fragility, and reduced membrane deformability of Hb S‐containing red cells under hypoxic conditions [27, 28]. The hypoxia‐induced rheologic changes in SCT reported ex vivo were generally intermediate between Hb S/S and Hb A/A RBCs [27, 28]. The occurrence of low‐grade hemolysis in SCT in vivo is additionally supported by the association of SCT with higher MCHC and higher circulating levels of free hemoglobin and of hemolysis‐scavenger proteins such as heme oxygenase‐1 and alpha‐1‐microglobulin [8].

Red cell volume is an important determinant of HbS concentration and subsequent RBC fragility/hemolysis and is controlled predominantly by membrane ion transport channels and cytoskeletal proteins [29]. We observed several genes (SLC4A1, SLC2A1, ANK1, SPTA1, SPTB, EPB42, EPB41, DMTN) encoding these physically interacting structural proteins upregulated in SCT. SLC4A1 (anion exchanger 1 or band 3) has also been implicated in the formation of externalized autophagic phosphatidylserine (PS)‐containing vesicles, which are increased in SCD red cells [30]. The increase in PS exposure on erythrocytes and reticulocytes may contribute to increased coagulation activation observed in SCD and potentially the higher D‐dimer levels and increased VTE risk observed in both SCD and SCT [31].

4.3. Alterations of Lymphocytes and Neutrophils in SCT

SCT has been associated with higher neutrophil count, lower lymphocyte count, and lower proportion of CD8‐T cells [3]. SCD similarly has been associated with alterations of adaptive immunity including lower proportions of CD4+ and CD8+ T cells [32] which may contribute to susceptibility of SCD or SCT individuals to infectious diseases. Several hematopoietic transcription factors upregulated in SCT and SCD contribute to earlier stages of hematopoiesis or hematopoietic stem cell lineage specification. For example, increased expression of TAL1 promotes apoptosis and prevents lymphoid differentiation [33].

Other transcriptomic alterations and gene pathways associated with SCT may provide additional clues as to mechanisms underlying the higher neutrophil count and pro‐inflammatory state associated with SCT. For example, we observed differential expression of genes related to TGFβ signaling, which has multiple, context‐dependent roles in neutrophil recruitment and inflammatory responses. TGFβ1 levels are elevated in SCD and correlate with higher white blood cell counts [34]; whereas in animal models of SCD, TGF‐β1 has anti‐inflammatory effects and prevents neutrophil adhesion and vaso‐occlusion [35]. Increased neutrophil survival may also contribute to leukocytosis associated with sickling and exacerbation of the chronic inflammatory state. In this regard, we detected increased expression in SCT of neutrophil anti‐apoptotic genes BCL2L1 and BIRC2 [36]. Given that increased neutrophil counts are positively correlated with SCD‐related complications, future investigation of SCT‐associated immune‐related genes may highlight relevant pathological mechanisms and/or lead to novel drugs targeting neutrophil counts and activity. The ameliorating effects of some current SCD treatments such as hydroxyurea may be partly mediated by lowering neutrophil counts. Since adhesion and aggregation of neutrophils and platelets to activated endothelial cells contribute to recurrent vaso‐occlusive events in SCD, supplementing hydroxyurea with drugs that target these heterotypic adhesive interactions (such as AKT2 inhibitors) may be useful in reducing SCD severity [37].

4.4. SCT and Kidney Disease

In several population‐based studies, SCT has been associated with increased risk of CKD, albuminuria, faster eGFR decline, and progression to end‐stage kidney disease, along with plasma biomarkers of kidney injury such as KIM‐1, uromodulin, and soluble uPAR [8, 38]. RBC sickling following hypoxia may have specific consequences in the low oxygen environment of the renal medulla where sickled erythrocytes can cause micro‐occlusion of renal tubules and local ischemia. Of the 226 SCT‐related DEGs, higher expression levels of 10 genes were associated with lower eGFR and 5 genes with CKD. These include genes expressed in renal tubules (TSPAN5, YIPF2, SLC25A37) [39, 40], glomerulus (ARHGEF12) [41], and podocyte cytoskeleton (SPTA1, SPTB) [42]. In the kidney, these SCT‐associated DEGs play important roles in maintaining podocyte integrity and function, glomerular filtration, concentrating ability, and/or are markers of kidney injury. YOD1 encodes a deubiquitinating enzyme that regulates the Hippo signaling pathway and plays an important role in hematopoiesis [43] as well as renal fibrosis and kidney cancer [44].

Several of our SCT‐associated DEGs were enriched for pathways related to hypoxia responses and ROS generation, including BPGM which catalyzes the conversion of 1,3‐bisphosphoglycerate (BPG) to 2,3‐BPG in red blood cells [45]. Elevated concentrations of 2,3‐BPG have been demonstrated to enhance the oxygen release from hemoglobin, increasing oxygen delivery to hypoxic kidney tissues, and may therefore represent a potential therapeutic target for sickle nephropahty [46]. Genes related to ferroptosis (iron‐mediated cell death) were differentially expressed in SCT and included the upregulation of the mitochondrial iron transporter SLC25A37 (mitoferrin‐1) and downregulation of peroxiredoxins PRDX4 and PRDX6, which have anti‐ferroptosis and cytoprotective roles in organs such as the kidney [47]. Ferroptosis can also trigger oxidative RBC membrane alterations in proteins such as SLC4A1 and microparticle formation, which may exacerbate SCD clinical outcomes [48]. On the other hand, resistance to ferroptosis may play a role in the development of renal medullary carcinoma, a rare malignancy that occurs in individuals with SCT or SCD [49]. Renal tubular cells are rich in mitochondria and therefore particularly vulnerable to hypoxic or oxidative kidney injury. We observed the association of a mitochondrial pathway‐enriched WGCNA co‐expression module with decreased kidney function in SCT (Supplemental Results, Tables S12 and S13), as well as upregulation of several genes involved in mitophagy and autophagy (e.g., TBC1D25, BMP2K, PINK1, SLC25A37, TANGO2). Interestingly, pathogenic variants of TANGO2 are associated with severe, recurrent rhabdomyolysis, which also occurs as a rare complication of SCT [50].

5. Strengths, Limitations, and Future Directions

Our study provides the first examination of transcriptional alterations in SCT individuals. However, we are limited in drawing tissue‐specific conclusions from these data as differential gene expression was determined using bulk gene expression from whole blood. Nonetheless, our DEG results were largely robust to covariate adjustment of measured blood cell counts, mitigating concern of bias due to cell‐type heterogeneity. Future studies incorporating single cell transcriptomic profiles of relevant tissues such as blood or kidney may provide finer resolution of the findings presented here. Our DEG analyses were limited to older postmenopausal African American women and may not be generalizable to other demographic groups such as men or younger individuals with SCT. Validation of levels in other age groups or in male participants would strengthen the conclusions. Moreover, the lack of any additional SCT transcriptomic data sets along with the Eurocentricity of existing large‐scale whole blood eQTL resources limited our ability to perform independent replication of the 226 DEGs and enriched pathways. Nonetheless, many of the 226 SCT‐associated genes were previously reported to be differentially expressed in whole blood transcriptomic studies of steady‐state SCD [7]. These results support the hypothesis that SCT may involve subclinical sickling and hemolysis which ultimately may contribute to adverse chronic health outcomes in these individuals. Besides cell type heterogeneity, another potential confounder relatively common among African ancestry individuals is the co‐inheritance of alpha‐thalassemia trait (3.7 kb HBA2 deletion) [4], which we were unable to account for. While we highlighted several DEGs and pathways which may be relevant to sickle nephropathy, larger studies including demographically diverse populations with well‐defined clinical outcomes could investigate the relationship between relevant DEGs with SCT and SCD complications including kidney disease and VTE. Single‐cell RNA sequencing of bone marrow or kidney tissues may further dissect cell‐type contributions and confirm erythropoiesis‐kidney injury links.

Author Contributions

M.J., Y.C., L.M.R., and A.P.R. contributed to the study conceptualization. M.J., Y.C., L.M.R., and A.P.R. drafted the manuscript. M.J. and Y.C. performed data curation, software development, and formal analysis. L.H., C.K., and A.P.R. provided funding support. L.M.R., W.S., and A.P.R. supervised the project. A.G.V., Y.C., L.H., N.F., P.L.A., W.S., C.K., L.M.R., and G.L. contributed to the review and editing of the manuscript. All authors critically reviewed and approved the final version of the manuscript.

Funding

The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services (HHS) through contracts 75N92021D00002, 75N92021D00003, 75N92021D00004, 75N92021D00005, and supported by the clinical coordinating center at Fred Hutchinson Cancer Center. For a full list of WHI investigators please visit: https://s3‐us‐west‐2.amazonaws.com/www‐whi‐org/wp‐content/uploads/WHI‐Investigator‐Long‐List.pdf. Whole genome sequencing and RNA‐sequencing were funded under the NHLBI Trans‐Omic Precision Medicine (TOPMed) study (phs001237) and performed at the Broad Institute of MIT and Harvard, and the Northwest Genomics Center (NWGC), respectively (3U54HG003067‐13S1, HHSN268201500014C, X01HL153408). M.J., A.P.R., C.K., and L.H. were supported by the NHLBI grant R01HL152439. A.P.R. was additionally supported by grants R01HL146500 and U01HG01172.

Ethics Statement

Ethical approval for the original WHI study was conducted at the Clinical Coordinating Center at Fred Hutchinson Cancer Center (approval number: IR# 3467), and by the original 40 clinical center site IRBs.

Consent

All procedures in this study followed the ethical standards of institutional review boards and national regulations, in accordance with the revised version of the Helsinki Declaration of 1975.

Conflicts of Interest

L.M.R. is a consultant for the TOPMed Administrative Coordinating Center (through Westat).

Supporting information

Data S1: ejh70114‐sup‐0001‐Supinfo.docx.

EJH-116-535-s001.docx (3.3MB, docx)

Acknowledgments

We would like to acknowledge all the participants who contributed samples for genomic and RNA sequencing as part of the Women's Health Initiative (WHI).

Data Availability Statement

All data has been submitted or is pending submission to dbGaP (phs000200 and phs001237). Data are also available through WHI clinical coordinating center (https://www.whi.org) with an approved manuscript proposal.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1: ejh70114‐sup‐0001‐Supinfo.docx.

EJH-116-535-s001.docx (3.3MB, docx)

Data Availability Statement

All data has been submitted or is pending submission to dbGaP (phs000200 and phs001237). Data are also available through WHI clinical coordinating center (https://www.whi.org) with an approved manuscript proposal.

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