Demographic and Clinical Characteristics of the Study Participants
Admission demographic and clinical characteristics of the children selected for RNA-Seq in whole blood are shown in Table 1. Based on the selection criteria, sex (p = 0.800), overall age (p = 0.797), and distribution within age categories (p = 0.461) were comparable. Glucose levels (p = 0.967) and auxiliary temperature (p = 0.051) were comparable between the groups. Consistent with more profound anemia in children with SMA, hematocrit (p = 1.242E-11) and red blood cells (p = 1.790E-11) were lower, while red cell distribution width (p = 4.050E-4) and mean corpuscular volume (p = 0.002) were elevated. White blood cells (p = 1.390E-4) and lymphocyte (p = 1.000E-6) counts were also elevated in children with SMA. Other hematological measures were comparable between the groups, as were parasitological indices. There was a lower distribution of HbAS and higher proportion of HbSS in the SMA group (p = 0.029), yet not significant after multiple test correction.
Table 1
Demographic and clinical characteristics of the study participants.
Characteristics
|
Total
|
non-SMA
(Hb ≥ 6.0 g/dL)
|
SMA
(Hb < 6.0 g/dL)
|
p-value
|
No. of participants, n
|
66
|
41
|
25
|
|
Sex, n (%)
|
|
|
|
|
Male
|
33 (50.0)
|
20 (48.8)
|
13 (52.0)
|
0.800a
|
Female
|
33 (50.0)
|
21 (51.2)
|
12 (48.0)
|
Age, months
|
24.5 (23.3)
|
24.0 (22.0)
|
25.0 (28.5)
|
0.797b
|
0–12.9
|
12 (18.2)
|
7 (17.1)
|
5 (20.0)
|
0.461
|
13–24.9
|
21 (31.8)
|
14 (34.1)
|
7 (28.0)
|
25–35.9
|
14 (21.2)
|
9 (22.0)
|
5 (20.0)
|
36–48.9
|
17 (25.8)
|
11 (26.8)
|
6 (24.0)
|
> 49
|
2 (0.0)
|
0 (0.0)
|
2 (8.0)
|
Glucose, mmol/L
|
5.0 (2.0)
|
5.0 (2.3)
|
5.0 (1.7)
|
0.967b
|
Admission temperature, °C
|
37.9 (1.1)
|
38.0 (1.2)
|
37.7 (0.8)
|
0.051b
|
Hematological Parameters
|
Hemoglobin, g/dL
|
9.2 (5.2)
|
9.9 (1.4)
|
4.6 (1.2)
|
NA
|
Hematocrit, %
|
25.1 (15.8)
|
29.8 (5.9)
|
14.4 (2.9)
|
1.242E-11b
|
Red blood cells, × 106/µL
|
3.5 (2.5)
|
4.3 (1.0)
|
1.9 (0.9)
|
1.790E-11b
|
Red cell distribution width, %
|
19.8 (5.9)
|
18.7 (3.4)
|
22.3 (8.9)
|
4.050E-4b
|
Mean corpuscular volume, fL
|
71.0 (14.2)
|
69.5 (9.2)
|
78.6 (29.9)
|
0.002b
|
Mean corpuscular hemoglobin, pg
|
23.9 (6.3)
|
22.9 (4.8)
|
26.7 (9.4)
|
0.022b
|
Mean corpuscular hemoglobin concentration, g/dL
|
31.9 (5.9)
|
32.4 (6.7)
|
31.4 (5.6)
|
0.372b
|
Platelets, ×103/µL
|
130.3 (99.2)
|
124.4 (85.7)
|
134.0 (139.7)
|
0.615b
|
Platelet distribution width, %
|
16.9 (1.2)
|
16.5 (1.3)
|
17.3 (0.9)
|
0.730c
|
Mean platelet volume, fL
|
8.6 (1.8)
|
8.5 (1.6)
|
8.9 (1.9)
|
0.124b
|
WBCs, ×103/µL
|
12.7 (10.3)
|
11.3 (6.9)
|
19.8 (11.5)
|
1.390E-4b
|
Lymphocytes, ×103/µL
|
4.2 (4.7)
|
3.7 (1.6)
|
10.0 (9.2)
|
1.000E-6b
|
Monocytes, ×103/µL
|
1.4 (1.3)
|
1.2 (1.3)
|
1.7 (1.4)
|
0.022b
|
Neutrophils, ×103/µL
|
5.4 (6.1)
|
5.3 (4.2)
|
6.0 (6.9)
|
0.438c
|
Granulocytes, ×103/µL
|
7.0 (4.3)
|
6.7 (3.0)
|
9.1 (5.8)
|
0.373c
|
Parasitological Indices
|
Parasite density, MPS/µL
|
38250 (82263)
|
57915 (81568)
|
14191 (68728)
|
0.155b
|
Low (1–5,000)
|
14 (21.2)
|
6 (14.6)
|
8 (32.0)
|
0.134a
|
Moderate (5001–50,000)
|
23 (34.8)
|
13 (31.7)
|
10 (40.0)
|
High (50,001–100,000)
|
17 (25.8)
|
14 (34.1)
|
3 (12.0)
|
Hyper (> 100,001)
|
12 (18.2)
|
8 (19.5)
|
4 (16.0)
|
Geomean parasitemia, /µL
|
23,647
|
28,888
|
17,029
|
0.447c
|
Genetic Variants
|
Sickle cell trait, n (%)
|
|
|
|
0.029a
|
Hb AA
|
51 (77.3)
|
35 (85.4)
|
16 (64.0)
|
Hb AS
|
6 (9.1)
|
4 (9.8)
|
2 (8.0)
|
Hb SS
|
9 (13.6)
|
2 (4.9)
|
7 (28.0)
|
Data are the median (interquartile range; IQR) unless otherwise noted. Children (n = 66) presenting with malaria at SCRH were recruited. Based on hemoglobin (Hb) levels, children were categorized into either non-severe malaria anemia (non-SMA; Hb ≥ 6.0 g/dL, n = 41) or severe malarial anemia (SMA; Hb < 6.0 g/dL, n = 25). aFisher’s exact test with exact p-values for homogeneity was performed. bTwo-sided Mann-Whitney-U tests were used to compare the non-SMA and SMA groups, cGroup means were compared by two-sided, two-sample t-test, with equal variance. All p-values shown in bold remained below the significance level after multiple test correction using the Bonferroni-Holm method (familywise error rate, significance level 0.050). Abbreviations: MPS - malaria parasites presented as mean (standard deviation). |
Differential Gene Expression and Central Regulatory Features in SMA
Differential expression analysis identified 3,420 up- and 3,442 down-regulated genes (padj < 0.050) in children with SMA (Fig. 1A). Comparison of the DEGs revealed 992 genes that were uniquely expressed in non-SMA, 328 in SMA, and 15,592 co-expressed genes (Fig. 1B). A non-supervised hierarchical cluster analysis of the top 1000 DEGs identified unique co-regulated gene clusters in children with SMA, and computed the distribution of clinical variables (i.e., parasitemia, sickle cell status, and age) in each of the groups (Fig. 1C).
Canonical pathway maps for direct functional interactions were generated to gain insight into the networks for two of the major clusters, one down-regulated (n = 156, cluster 1) and one up-regulated (n = 114, cluster 2). The network for the down-regulated genes (cluster 1) was IRF1↔IL-1β↔Caspase-1↔Caspase-4↔FasR with the transcription factor, IRF1, as the central divergence hub (36 direct interactions) and IL-1β as the central convergence hub (27 direct interactions, Fig. 1D). The top process associated with the functional interactions for cluster 1 was Response to Stress (p = 2.404E-08). Cluster 2 (up-regulated) generated a TCF7L1↔E2A↔RING2 network with two transcription factors, TCF7L1, as the central divergence hub (26 direct interactions) and E2A, as a secondary divergence hub (10 functional interactions). The central convergence hub in cluster 2 was RING2 with 5 functional interactions (Fig. 1E). Regulation of Metabolic Process was the top represented process for cluster 2 (p = 2.671E-09). Collectively, these results suggest that SMA is characterized by enhanced stress responses and perturbations in metabolic processes.
Altered Leukocytic Immune Cell Profiles in SMA
To determine if leukocytic immune profiles differed in children who developed SMA, a bioinformatic approach was implemented using CIBERSORTx. Although there was interindividual variability, 10 immune cell types were differentially expressed at p < 0.050 (Fig. 2A). Children with SMA had increased expression of naïve B cells (p = 9.741E-05), CD8 T cells (p = 0.009), CD4 memory resting T cells (p = 0.001), resting NK cells (p = 0.002), monocytes (p = 0.039), and M2 macrophages (p = 0.002) (Fig. 2B). In contrast, the SMA group had a lower proportion of expression for activated dendritic cells (p = 0.001), activated mast cells (p = 0.014), and neutrophils (p = 4.826E-04), along with marginally reduced expression of naïve CD4 T cells (p = 0.053) (Fig. 2B). The immune cell type patterns observed indicate that children with SMA have a decreased antigenic response, reduced immune priming, and enhanced polarization towards cellular proliferation and tissue repair.
Functional Enrichment Analysis Reveals Distinct Features of SMA
To identify biological processes characteristic of developing SMA, enrichment analysis was performed. Gene ontology (GO) enrichment analysis was performed for three domains (i.e., biological process, cellular components, and molecular functions) with the top 20 in each presented in Fig. 3A. Of the three domains, biological processes showed the greatest enrichment in children with SMA, for which altered neutrophil responses (padj = 1.041E-20 to 1.528E-20) and autophagy-related processes (padj = 3.215E-20 to 6.970E-15) ranked among the highest. The top cellular components enriched in SMA included endosome responses (padj = 5.427E-10 to 5.427E-10). For molecular functions, ubiquitin-related process showed the greatest enrichment (padj = 2.867E-07 to 1.779E-03). Consistent with GO process results, the top-ranked Reactome enriched pathways in children with SMA were neutrophil degranulation (padj = 8.388E-18), class I MHC-mediated antigen processing and presentation (padj = 4.400E-08), and antigen processing involving the ubiquitination and proteasome degradation (padj = 3.810E-05, Fig. 3B and C). Results from the GO and Reactome enrichment analyses converge on common biological processes and suggest that SMA is characterized by altered neutrophil responses, perturbations in autophagy and endosomal pathways, and activation of ubiquitin-related processes.
KEGG Canonical Pathways Implicated in SMA: Functional classification of the DEGs between SMA and non-SMA for the KEGG pathways is shown in Fig. 4A. The top-ranked pathways were: (i) cellular processes - endocytosis (padj = 3.380E-05); (ii) environmental information processing - TNF signaling (padj = 3.829E-04); (iii) genetic information processing - protein processing in ER (padj = 7.880E-09), which was also the most significant of all pathways; (iv) metabolism - inositol phosphate metabolism (padj = 4.369E-02); and (v) organismal systems - FcγR-mediated phagocytosis (padj = 3.829E-04). Collectively, the KEGG results suggest that children with SMA exhibit significant alterations in immune response triggering, cellular recycling processes, and protein regulation.
Endoplasmic Reticulum Quality Control Dysfunction in SMA: To further characterize the pathogenesis of SMA, we focused on the top emergent pathway generated from the KEGG database: Protein Processing in Endoplasmic Reticulum (ER; Fig. 4B). Children with SMA had elevated expression of ERManI and PERK and decreased expression of HSP40, EDEM, PDIs, and TRAP, indicating that the ER quality control system is impaired, leading to potential protein misfolding and cellular dysfunction. This is consistent with up-regulation of several transcripts in the ubiquitin ligase complex in the ER membrane (i.e., Ubx, gp78, and Derlin1) and dysregulation in the ER cytoplasm (i.e., elevated HSP40, RBX1, and Skp1, and decreased UbcH5 and FBP). Because of misfolded or damaged proteins, the ER-associated degradation pathway was activated, as illustrated by increased Otu1 and RAD23 expression. Further indications of ER-associated cellular stress and attempts to maintain cellular homeostasis in children with SMA are demonstrated by elevated Sec62/Sec63 complex subunits and MKK7.
MetaCore™ Canonical Pathways Implicated in SMA: Additional characterization of SMA pathogenesis was carried out by exploring the top 20 pathway maps generated with MetaCore™ (Fig. 4C). The top-ranked pathways were: (i) apoptosis and survival - p53/p73-dependent apoptosis (padj = 3.706E-08); (ii) autophagy – autophagy (padj = 1.027E-07); (iii) cytoskeleton remodeling - regulation of actin cytoskeleton organization by the kinase effectors of Rho GTPases (padj = 4.491E-08); (iv) development - positive regulation of WNT β-catenin signaling (padj = 5.429E-10); and (v) immune response - IL-5 signaling via JAK/STAT (padj = 5.429E-10). These results show that the pathogenesis of SMA is a complex and multi-faceted process that involves multiple molecular mechanisms involved in cell growth and differentiation, as well as cellular and immune stress responses.
Deciphering Cellular Stress Responses in SMA
Of the top canonical pathways that emerged from MetaCore™, we focused on positive regulation of WNT/β-catenin signaling to further define cellular and immune stress responses in SMA (Fig. 4D). Children with SMA had reduced expression of WNT, but increased expression of the WNT receptor, LRP5/LRP6, and its target, Axin, which was also signaled by increased expression of ZBED3, SIAH2, PP2C, PP1-cat, and tankyrases. This transcriptional pattern indicates enhanced proteasomal degradation in the context of reduced protection from degradation (i.e., down-regulation of NKD1 and NKD2), despite increased expression of Dsh signaled by up-regulation of the WNT receptor (i.e., Frizzled), transmembrane receptor (i.e., ITGB1), IRS-2, and USP9X, accentuated by decreased phosphorylation from RIPK4 and reduced binding by GRB2 and β-aresstin2. Children with SMA also had increased expression for SMAD3, SMAD4, TBLR1, Makorin-1, Trabid, UBE2B, RNF220, USP7, PKB, and USB47 in the context of decreased expression in β-catenin, indicating activation of the non-canonical WNT signaling pathway. This pattern of expression also suggests cellular stress responses and attempts to regulate protein degradation and stabilize cell survival through alterations in ubiquitination and de-ubiquitination processes.
Validation of Whole Blood Transcriptome Data using a Targeted Gene Array: To validate the RNA-Seq results, we utilized a targeted gene approach with a qRT-PCR array that captured 84 genes involved in ubiquitination. A heatmap cluster analysis showed strong concordance in the fold-change and directionality in the two platforms (Fig. 5A). Cluster analysis of the significant DEGs in the qRT-PCR array showed a significant correlation with the same genes captured from RNA-Seq (r = 0.834, p < 0.001; Fig. 5B). To further compare findings from the two datasets, a workflow was generated in MetaCore™ to identify common (shared) genes that mapped to pathways, GO processes, and process networks. The combined data objects (gene set) from the two platforms revealed the following top-ranked features: (i) pathway map – Proteolysis Ubiquitination (padj = 2.193E-11); (ii) GO process – Protein ubiquitination (padj = 1.083E-12); and (iii) process network - Proteolysis Ubiquitin - proteasomal proteolysis padj = 2.250E-05) (Supplemental Fig. 1A-C).