# Genomics Analysis of Metabolic Pathways of Human Stem Cell-Derived Microglia-Like Cells and the Integrated Cortical Spheroids

Authors: Julie Bejoy; Xuegang Yuan; Liqing Song; Thien Hua; Richard Jeske; Sébastien Sart; Qing-Xiang Amy Sang; Yan Li
Journal: Stem Cells International
 (2019)

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## Abstract

Brain spheroids or organoids derived from human pluripotent stem cells (hiPSCs) are still not capable of completely recapitulatingin vivo human brain tissue, and one of the limitations is lack of microglia. To add built-in immune function, coculture of the dorsal forebrain spheroids with isogenic microglia-like cells (D-MG) was performed in our study. The three-dimensional D-MG spheroids were analyzed for their transcriptome and compared with isogenic microglia-like cells (MG). Cortical spheroids containing microglia-like cells displayed different metabolic programming, which may affect the associated phenotype. The expression of genes related to glycolysis and hypoxia signaling was increased in cocultured D-MG spheroids, indicating the metabolic shift to aerobic glycolysis, which is in favor of M1 polarization of microglia-like cells. In addition, the metabolic pathways and the signaling pathways involved in cell proliferation, cell death, PIK3/AKT/mTOR signaling, eukaryotic initiation factor 2 pathway, and Wnt and Notch pathways were analyzed. The results demonstrate the activation of mTOR and p53 signaling, increased expression of Notch ligands, and the repression of NF-κB and canonical Wnt pathways, as well as the lower expression of cell cycle genes in the cocultured D-MG spheroids. This analysis indicates that physiological 3-D microenvironment may reshape the immunity of in vitro cortical spheroids and better recapitulate in vivo brain tissue function for disease modeling and drug screening.


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## Body

## 1. Introduction

Understanding the models established by human induced pluripotent stem cells (hiPSCs) requires genome-wide mapping to elucidate gene regulatory networks [1, 2]. Therefore, transcriptome analysis has been used to compare hiPSC-derived lineage-specific cells with somatic counterparts [3]. Recently, forebrain spheroids or organoids were derived from hiPSCs for disease modeling and as potential platforms for drug screening [4–7]. These spheroids need to contain critical components of the human brain, such as vascular cells and microglia, for proper function. Our previous study characterized microglia-like cells differentiated from hiPSCs and introduced isogenic microglia-like cells into forebrain spheroids [8]. The microglia-like cells were cocultured with isogenic dorsal cortical spheroids in order to build immune function within the spheroids. While extensive phenotypic characterizations were performed in our previous study, the fundamental metabolic pathways and signaling pathways in different culture systems were not analyzed yet. It is postulated that the microglia-like cells inside the spheroids retain more in vivo-like metabolic program and the associated phenotype due to the environmental change in intimate cell-cell and cell-matrix interactions compared to 2-D culture.Metabolic programming was found to play important roles in homeostasis, tissue repair, immune function, epigenetic change, and cellular phenotype. For hiPSC reprogramming, the somatic cells with oxidative phosphorylation (OXPHOS) gain the glycolysis metabolism when they gain pluripotency [9, 10]. For human mesenchymal stem cell (MSC) aggregates, metabolic reconfiguration towards glycolysis supports reacquisition of the primitive stem cell phenotype [11]. Similarly, for microglia, limited data suggest that polarization to an M1 phenotype (proinflammatory) may be accompanied by a metabolic shift from oxidative phosphorylation to aerobic glycolysis for energy production [12, 13]. It has been suggested that default polarization of resident microglia uses OXPHOS (mitochondrial respiration, i.e., anti-inflammatory M2 phenotype) for the functions involved in tissue repair, while the metabolic program shifts towards glycolysis as well as nitric oxide and citrulline production for M1 polarization [12]. In addition, the metabolic environment of microglia can affect brain region-dependent gene regulation [13]. Therefore, modifications of immune response by the physiological 3-D microenvironment may reshape the innate immunity that gains the “memory” and “trained” inflammation response [12].Based on the literature for metabolic programming of 3-D HepG2/C3A spheroids [14], this present study analyzed the genomics data for hiPSC-derived microglia-like cells (i.e., single cells in semiadherent culture) and the isogenic 3-D dorsal spheroids containing microglia-like cells. The focus is on changes in metabolic pathways and the signaling pathways that are involved in cell proliferation, cell death, inflammation, PIK3/AKT/mTOR signaling, and Wnt and Notch pathways in 3-D cortical spheroids derived from hiPSCs. This study provides global genomic details on the impact of 3-D culturing on the metabolic phenotype of cells inside the cortical spheroids. The analysis in this study can also help to establish better coculturing strategies for mimicking the in vivo structure and functions of the central nervous system in vitro.


## 2. Materials and Methods

### 2.1. Microglia-Like Cells Derived from iPSCs

Our lab has derived microglia-like cells from human iPSK3 cells free of feeders using a modified protocol [15]. Mesoderm induction was performed using activin A, BMP4, SCF, and VEGF, followed by SCF, Flt3L, IL-3, and GM-CSF treatment. Nonadherent cells were replated to tissue culture-treated plates in the presence of IL-3 and M-CSF. The day 23 cells expressed 48-59% CD11b and 52-64% CD45. Day 28 cells expressed 70±6% CD11b and 80±5% IBA-1. Day 30-33 cells expressed 68±12% P2RY12 and 51±10% CX3CR1 (Figure 1(a)) [8].Figure 1
Illustration of the MG group and D-MG group. (a) Derivation of microglia-like cells (MG) from hiPSCs. (b) Incorporation of MG into the isogenic dorsal spheroids (DMG). (c) Morphology of the cells in microglial differentiation and the D-MG spheroids. Day 30 MGs were labeled with CellTracker Red. White scale bar: 100μm. Black scale bar: 200 μm.
(a)
(b)
(c)


### 2.2. Generation of Isogenic Dorsal Cortical Spheroids and Coculturing with Microglia-Like Cells

Neural differentiation was induced using dual SMAD inhibitors LDN193189 (LDN) and SB431542 (SB) on human iPSK3 cells in low attachment 96-well plates. The neural progenitor spheres were treated with patterning factors cyclopamine (a sonic hedgehog inhibitor) and fibroblast growth factor- (FGF-) 2 [7, 16]. The dorsal identity is defined by TBR1, PAX6, BRN2, and SATB2.Dorsal spheroids (day 30) were cocultured with isogenic microglia-like cells at a 4 : 1 ratio (8×105 neurons to 2×105 microglia-like cells, i.e., 20%) in 50% DMEM/10% FBS and 50% neural differentiation medium composed of DMEM/F12 plus 2% B27 (Figures 1(b) and 1(c)). The CellTracker Red-labeled MGs were observed to move into the D-MG spheroids (Figure 1(c)). After three days of coculture (day 33), the dorsal spheroids containing microglia-like cells (D-MG group) and the microglia cells only (MG group) were harvested for RNA isolation and RNA-sequencing.


### 2.3. RNA-Sequencing

mRNA was isolated from the total RNA using the NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs). cDNA libraries were generated from the isolated mRNA using an NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs), with a unique 6-nucleotide index primer (NEBNext Multiplex Oligos for Illumina), using Beckman Biomek 4000. The multiplexed sample was quantified with qPCR (Kapa Biosystems) specific for Illumina sequencing primers, and the average fragment size was determined with a Bioanalyzer High-Sensitivity DNA Chip (Agilent Technologies). The sequencing was performed on Illumina HiSeq 2500 at the Translational Science Laboratory of Florida State University. The pooled data were demultiplexed into individual sample data, and adapter primer sequences were removed [17].


### 2.4. RNA-Sequencing Data Analysis

The sequencing reads were analyzed using RNA-Seq Alignment version 1.1.1 (Illumina BaseSpace application). The reads were aligned with TopHat 2 [18] to the human genome (genome release GRCh38) using default parameters, and counts for each gene were generated. This workflow uses Cufflinks to generate FPKM (fragments per kilobase per million reads) normalized values [19]. DESeq2 was used to determine statistically significant differentially expressed genes (a false discovery rate (FDR) of <0.05 was used). 15,585 genes were considered to be expressed in this study by the DESeq2 software [20]. The top 500 genes that were upregulated and downregulated (1000 total genes) in the microglial culture versus the D-MG group were assessed for GO, KEGG pathway, and phenotype pathway analysis using WebGestalt [21, 22]. Significant enrichment was determined in WebGestalt using the hypergeometric test and the Benjamini-Hochberg FDR method [23] for multiple testing adjustment.


### 2.5. Reverse Transcription Polymerase Chain Reaction (RT-PCR) Analysis

Total RNA was isolated from different cell samples using the RNeasy Mini Kit (Qiagen, Valencia, CA) according to the manufacturer’s protocol followed by the treatment with a DNA-Free RNA Kit (Zymo, Irvine, CA) [24]. Reverse transcription was carried out using 2 g of total RNA, anchored oligo-dT primers (Operon, Huntsville, AL), and Superscript III (Invitrogen, Carlsbad, CA) (according to the protocol of the manufacturer). Primers specific for target genes (Supplementary Table S1) were designed using the software Oligo Explorer 1.2 (GeneLink, Hawthorne, NY). The gene β-actin was used as an endogenous control for normalization of expression levels. Real-time RT-PCR reactions were performed on an ABI7500 instrument (Applied Biosystems, Foster City, CA), using SYBR1 Green PCR Master Mix (Applied Biosystems). The amplification reactions were performed as follows: 2 min at 50°C, 10 min at 95°C, and 40 cycles of 95°C for 15 sec and 55°C for 30 sec and 68°C for 30 sec. Fold variation in gene expression was quantified by means of the comparative Ct method: 2−ΔCttreatment−ΔCtcontrol, which is based on the comparison of expression of the target gene (normalized to the endogenous control β-actin) between the compared samples.


## 2.1. Microglia-Like Cells Derived from iPSCs

Our lab has derived microglia-like cells from human iPSK3 cells free of feeders using a modified protocol [15]. Mesoderm induction was performed using activin A, BMP4, SCF, and VEGF, followed by SCF, Flt3L, IL-3, and GM-CSF treatment. Nonadherent cells were replated to tissue culture-treated plates in the presence of IL-3 and M-CSF. The day 23 cells expressed 48-59% CD11b and 52-64% CD45. Day 28 cells expressed 70±6% CD11b and 80±5% IBA-1. Day 30-33 cells expressed 68±12% P2RY12 and 51±10% CX3CR1 (Figure 1(a)) [8].Figure 1
Illustration of the MG group and D-MG group. (a) Derivation of microglia-like cells (MG) from hiPSCs. (b) Incorporation of MG into the isogenic dorsal spheroids (DMG). (c) Morphology of the cells in microglial differentiation and the D-MG spheroids. Day 30 MGs were labeled with CellTracker Red. White scale bar: 100μm. Black scale bar: 200 μm.
(a)
(b)
(c)


## 2.2. Generation of Isogenic Dorsal Cortical Spheroids and Coculturing with Microglia-Like Cells

Neural differentiation was induced using dual SMAD inhibitors LDN193189 (LDN) and SB431542 (SB) on human iPSK3 cells in low attachment 96-well plates. The neural progenitor spheres were treated with patterning factors cyclopamine (a sonic hedgehog inhibitor) and fibroblast growth factor- (FGF-) 2 [7, 16]. The dorsal identity is defined by TBR1, PAX6, BRN2, and SATB2.Dorsal spheroids (day 30) were cocultured with isogenic microglia-like cells at a 4 : 1 ratio (8×105 neurons to 2×105 microglia-like cells, i.e., 20%) in 50% DMEM/10% FBS and 50% neural differentiation medium composed of DMEM/F12 plus 2% B27 (Figures 1(b) and 1(c)). The CellTracker Red-labeled MGs were observed to move into the D-MG spheroids (Figure 1(c)). After three days of coculture (day 33), the dorsal spheroids containing microglia-like cells (D-MG group) and the microglia cells only (MG group) were harvested for RNA isolation and RNA-sequencing.


## 2.3. RNA-Sequencing

mRNA was isolated from the total RNA using the NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs). cDNA libraries were generated from the isolated mRNA using an NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs), with a unique 6-nucleotide index primer (NEBNext Multiplex Oligos for Illumina), using Beckman Biomek 4000. The multiplexed sample was quantified with qPCR (Kapa Biosystems) specific for Illumina sequencing primers, and the average fragment size was determined with a Bioanalyzer High-Sensitivity DNA Chip (Agilent Technologies). The sequencing was performed on Illumina HiSeq 2500 at the Translational Science Laboratory of Florida State University. The pooled data were demultiplexed into individual sample data, and adapter primer sequences were removed [17].


## 2.4. RNA-Sequencing Data Analysis

The sequencing reads were analyzed using RNA-Seq Alignment version 1.1.1 (Illumina BaseSpace application). The reads were aligned with TopHat 2 [18] to the human genome (genome release GRCh38) using default parameters, and counts for each gene were generated. This workflow uses Cufflinks to generate FPKM (fragments per kilobase per million reads) normalized values [19]. DESeq2 was used to determine statistically significant differentially expressed genes (a false discovery rate (FDR) of <0.05 was used). 15,585 genes were considered to be expressed in this study by the DESeq2 software [20]. The top 500 genes that were upregulated and downregulated (1000 total genes) in the microglial culture versus the D-MG group were assessed for GO, KEGG pathway, and phenotype pathway analysis using WebGestalt [21, 22]. Significant enrichment was determined in WebGestalt using the hypergeometric test and the Benjamini-Hochberg FDR method [23] for multiple testing adjustment.


## 2.5. Reverse Transcription Polymerase Chain Reaction (RT-PCR) Analysis

Total RNA was isolated from different cell samples using the RNeasy Mini Kit (Qiagen, Valencia, CA) according to the manufacturer’s protocol followed by the treatment with a DNA-Free RNA Kit (Zymo, Irvine, CA) [24]. Reverse transcription was carried out using 2 g of total RNA, anchored oligo-dT primers (Operon, Huntsville, AL), and Superscript III (Invitrogen, Carlsbad, CA) (according to the protocol of the manufacturer). Primers specific for target genes (Supplementary Table S1) were designed using the software Oligo Explorer 1.2 (GeneLink, Hawthorne, NY). The gene β-actin was used as an endogenous control for normalization of expression levels. Real-time RT-PCR reactions were performed on an ABI7500 instrument (Applied Biosystems, Foster City, CA), using SYBR1 Green PCR Master Mix (Applied Biosystems). The amplification reactions were performed as follows: 2 min at 50°C, 10 min at 95°C, and 40 cycles of 95°C for 15 sec and 55°C for 30 sec and 68°C for 30 sec. Fold variation in gene expression was quantified by means of the comparative Ct method: 2−ΔCttreatment−ΔCtcontrol, which is based on the comparison of expression of the target gene (normalized to the endogenous control β-actin) between the compared samples.


## 3. Results and Discussions

Previously, our study derived and characterized microglia-like cells from the healthy iPSK3 cells and Ep-iPSC cells [8]. The cells were cocultured and integrated with isogenic dorsal cortical spheroids (Supplementary Figure S1). The whole-cell transcriptome analysis was performed for the microglial phenotype, neural inflammation, and the genes involved in Alzheimer’s disease. Our current study focuses on different attributes of the 3-D dorsal cortical spheroids containing isogenic microglia-like cells (D-MG group) vs. microglia-like cells (MG group), including metabolic pathways and the signaling pathways that are involved in cell proliferation, cell death, inflammation, PIK3/AKT/mTOR signaling, EIF2 pathway, and Wnt and Notch pathways, which were not reported previously.


### 3.1. Central Metabolic Pathway

The 3-D spheroid culture usually is associated with slow growth rates, cytoskeleton reorganization, enhanced tight junctions and polarity, and the relocated membrane transporters [25–27]. In aerobic glycolysis, glucose is consumed through the glycolytic pathway to produce lactic acid, nucleotides, amino acids, and other metabolites, while glutamine is converted through glutaminolysis to citrate for cholesterol and lipid production. In oxidative phosphorylation, the generated pyruvate is oxidized to CO2 and water through the tricarboxylic (TCA) cycle, which produces ATP and coverts NADH to NADPH [28–30].The central metabolic pathways are shown in Figure2. The values were calculated using log2 (DMG/MG). Relative enzyme expression levels indicate an increase in glycolytic and pentose phosphate pathways in the D-MG group as well as the increased amino acid synthesis (Figure 2 and Supplementary Figure S2) [31]. The increased glycolysis is permissive to trigger microbiocidal activity of microglia and allows the cells to survive in the 3-D spheroids. However, enzymes in the glutaminolytic pathway, hexosamine, nucleotide, lipid synthesis, and TCA cycle have small differences among the two groups. Genes related to ATP synthesis and mitochondrial complexes I, III, and IV are shown in Supplementary Table S2 and Supplementary Figure S2. The differences between the two groups are small (within ±0.5 except NDUFA13 at -0.68). But the values are all negative for genes related to ATP synthesis and mitochondrial complexes I and III, indicating lower expression in the D-MG group. Together, these results indicate the different energetic/metabolic phenotypes between the D-MG and the MG groups.Figure 2
Ratios of gene abundance of central metabolic pathways in the D-MG group in comparison to the MG group. The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. (a) Central metabolic pathways. (b) Log2 values of glycolysis genes of D-MG/MG ratios.
(a)
(b)Metabolic shift towards glycolysis usually results in the elevated dependence on glutaminolysis or fatty acid synthesis in cell metabolism [32]. However, in this study, the cells did not show a significant increase in glutaminolysis in the spheroids (Figure 2 and Supplementary Figure S2). Cytoplasmic isocitrate dehydrogenase 1 (IDH-1) is involved in the reaction of isocitrate production from α-ketoglutarate in the cytosol. IDH-1 expression is decreased in the D-MG spheroids, which may be associated with NF-κB activation in a hypoxia-inducible factor- (HIF-1α-) dependent manner [33].Similarly, 3-D culture of iPSC-derived endothelial cells also showed the glycolysis-dominated metabolism compared to ones in 2-D culture [34]. Moreover, human MSCs treated with interferon gamma exert an immunosuppressive phenotype by secreting PGE2/IDO and reconfigure the metabolic phenotype to aerobic glycolysis [35]. It was also suggested that activation of the AKT/mTOR signaling pathway is required for metabolic shift under this immune polarization.The 3-D cultures may not always enhance the glycolysis, which is dependent on a specific cell type. For example, it was reported that 3-D cultures of hiPSC-derived cardiomyocytes displayed downregulation of genes involved in glycolysis and lipid biosynthesis and upregulation of genes involved in oxidative phosphorylation. Accordingly, the 3-D cultures showed lower fluxes for fatty acid synthesis and the increased TCA cycle activity, which improved both cell purity and metabolic maturation [36].


### 3.2. Hypoxia (Glycolysis and Oxidative Phosphorylation)

#### 3.2.1. HIF-1α Pathway

In 3-D spheroid culture, the inside of the spheroids is thought to be more hypoxic than the surface due to mass transfer limitation of oxygen [37], while this has been challenged by other studies as nonhypoxia-stabilized HIF expression [25]. Hypoxia is an important factor in regulating stem cell metabolism and phenotype [38]. When oxygen concentrations decrease, the oxygen-dependent prolyl hydroxylase domain proteins are inactivated and the HIF-1α protein is accumulated, which promotes HIF-1α translocation to the nucleus and its binding to hypoxia response elements, such as glucose transporters and glycolytic enzymes [39, 40]. Our results do not show the higher HIF-1α gene expression in the D-MG group but demonstrate the increased expression of HIF-1α pathway downstream genes, including SIAH2 (1.29), PDK1 (3.84), LDHA (1.99), LONP1 (1.94), and P4HA1 (1.79) (Figures 3(a)–3(c)). These results may indicate the nonhypoxia-stabilized HIF expression in the D-MG group. The downregulated HIF-1α gene expression in the D-MG group was also validated using RT-PCR, along with the upregulated glycolytic gene expression in the D-MG group (Figures 3(d) and 3(e)).Figure 3
HIF-1α-related pathway. (a) RNA-sequencing results for glycolytic genes and HIF-1α; relative transcript expression between D-MG and MG for HIF-1α and its downstream targets (b) on tricarboxylic cycle (TCA) and (c) on glycolysis and extracellular matrix (ECM) production. ∗ indicates p<0.05 (n=3). (d) Validation of glycolytic genes using RT-PCR. ∗ indicates p<0.05 (n=3). (e) Schematic diagram showing the major changes in D-MG versus MG for HIF-1α signaling: D-MG shows enhanced HIF-1α activities that reduce TCA and ATP production, increasing glycolysis mediated by HIF-1α.
(a)
(b)
(c)
(d)
(e)HIF-1α induces pyruvate dehydrogenase kinase 1 (PDK1) expression, which inhibits mitochondrial pyruvate dehydrogenase (PDH) [38, 41]. This reduces pyruvate flux into the TCA cycle and lowers the mitochondrial oxygen requirements. The lactate production and secretion would be increased, as observed by Sart et al. [9]. HIF-1α also induces E3-ubiquitin ligase SIAH2 synthesis, which mediates the proteasomal degradation of the OGDH subunit of α-ketoglutarate dehydrogenase (α-KD) and forms a part of the feedback control of HIF-1α signaling. A modest reduction of the α-KD enzyme complex was observed in the D-MG group (DLD, -0.75, OGDH, -1.71) (Table 1), which may slow down TCA cycle activity. Interestingly, the citrate transportation into the cytoplasm by citrate transporter protein (SLC25A1, -0.25) was relatively comparable between these two groups. The extracellular matrix remodeling via collagen hydroxylases (P4HA1, 1.79) was upregulated in the D-MG group. The amounts of anabolic rate-limiting enzymes were increased or remained similar, while catabolic enzymes were unchanged between the two groups (Table 2). The rate-limiting glycolytic pathway steps (HK2, PKM, and PFKL) are the mostly increased enzymes of the pathway. These findings are consistent with the metabolic shift to aerobic glycolysis. In addition, high PFK level may also indicate high levels of AMP, since the spheroids usually have high ATP amounts [14].Table 1
Glycolysis vs. oxidative phosphorylation.
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
DMG/MG
LOG2 (DMG/MG)
P4HA1
140.340
135.633
141.039
389.230
546.723
505.399
139.004
480.451
3.456
1.79
SLC2A1 (GLUT1)
579.1676
469.9516
562.8377
912.218
1098.52
1056.15
537.319
1022.293
1.903
0.93
DLST
17.881
18.114
17.362
26.470
26.133
24.754
17.786
25.786
1.450
0.54
SLC16A3
304.016
219.663
293.687
255.760
244.563
255.972
272.455
252.098
0.925
-0.11
COX4I1
238.629
185.922
239.199
186.813
225.728
199.922
221.250
204.154
0.923
-0.12
SLC25A1
72.139
59.151
72.118
54.289
60.972
56.058
67.803
57.107
0.842
-0.25
DLD
22.476
21.521
21.854
12.507
12.003
14.700
21.950
13.070
0.595
-0.75
OGDH
28.692
23.353
27.126
8.233
7.421
8.492
26.390
8.049
0.305
-1.71
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.Table 2
Rate-limiting enzymes for central metabolic pathways and the ratios of their expression in the D-MG group compared to the MG group.
Pathway
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
LOG2 (DMG/MG)
Glucose phosphorylation
HK2
8.057
7.382
8.514
373.333
367.184
391.036
7.984
377.184
5.56
Glycolysis
PKM
144.193
130.476
142.160
1071.419
1269.244
1061.022
138.943
1133.895
3.03
Glycine synthesis
SHMT2
34.121
29.636
33.060
146.550
175.435
141.673
32.272
154.553
2.26
Asparagine synthesis
ASNS
28.779
28.424
28.878
93.603
122.579
101.086
28.694
105.756
1.88
Glycogenolysis
PYGB
25.047
22.544
24.888
34.525
34.766
30.397
24.159
33.229
0.46
Pyrimidine synthesis
CAD
8.199
9.554
8.489
13.220
12.638
10.185
8.747
12.015
0.46
Glycolysis
PFKL
62.436
52.127
63.963
83.298
79.339
73.566
59.509
78.734
0.40
Aspartate synthesis
GOT1
16.089
14.826
14.507
18.869
20.440
19.010
15.141
19.439
0.36
Fatty acid synthesis
FASN
28.814
22.644
29.725
36.264
34.796
31.673
27.061
34.244
0.34
Purine synthesis
PRPS1
23.085
20.259
22.740
24.847
28.698
27.976
22.028
27.173
0.30
Pentose phosphate
G6PD
15.355
12.245
15.422
12.237
14.129
12.097
14.341
12.821
-0.16
Fatty acid synthesis
ACAA1
21.219
19.891
21.847
17.973
18.352
15.681
20.986
17.336
-0.28
Glutamine-glutamate conversion
GLUD1
49.375
44.127
51.590
35.530
41.837
38.198
48.364
38.522
-0.33
Methionine synthesis
MTR
7.799
9.359
8.511
7.162
5.777
6.920
8.556
6.620
-0.37
TCA cycle
IDH2
103.753
84.981
101.212
66.326
79.889
75.170
96.649
73.795
-0.39
Serine synthesis
PHGDH
141.285
116.771
138.838
92.636
114.237
92.781
132.298
99.884
-0.41
Hexose
GFPT1
46.208
45.599
46.304
26.099
32.086
29.340
46.037
29.175
-0.66
Fatty acid oxidation
CRAT
23.955
20.886
25.945
14.810
14.697
14.392
23.596
14.633
-0.69
Proline synthesis
PYCR1
102.329
84.127
98.191
46.173
65.047
53.249
94.883
54.823
-0.79
Urea synthesis
CPS1
7.949
7.541
8.583
4.041
3.943
4.177
8.024
4.054
-0.99
Tyrosine synthesis
PAH
0.441
0.551
0.468
0.171
0.184
0.224
0.487
0.193
-1.33
Cysteine synthesis
MAT1A
5.443
4.897
5.280
0.793
0.724
0.529
5.207
0.682
-2.93
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.HIF-1α signaling can induce the expression of the mitochondrial protease LONP1 (1.94) (Figure 3). LONP1 degrades cytochrome C oxidase 4 subunit 1 (COX4-1) through electron transport chain complex IV, allowing the replacement of COX4-1 by COX4-2 [42], which is more efficient in enzymatic reaction. LONP1 is an essential central regulator of mitochondrial activity and is overexpressed during oncogenesis [43]. Although LONP1 was increased (1.94) based on our results, there was little change in the level of COX4-1 (-0.12). Reduced mitochondrial respiration normally results in fewer reactive oxygen species (ROS), correlated with the reduced level of catalase (CAT, -1.55). The reduced oxidative stress results in the diminished hydrogen peroxide damage and less oxidized proteins [14].


#### 3.2.2. Glutamine Metabolism and Hexosamine Pathway

While the D-MG group mainly uses glycolysis as its major energetic metabolism, our results did not show an increased reliance on glutamine metabolism (Figure2 and Supplementary Figure S2). Intracellular glutamine levels are regulated by plasma membrane transporters SLC38A2 and SLC1A5 [14]. Endoplasmic reticulum stress would induce the degradation of transporters and ultimately autophagy and cell death [14]. In D-MG spheroids of this study, both SLC38A2 (-0.53) and SLC1A5 (-1.86) were decreased, which may suggest enhanced autophagy in D-MG spheroids.(1) Conversion of Glutamine into Glutamate. The glutamate demand of the cells is indicated by the expression of GFPT1 (-0.66). GFPT1 is the first and rate-limiting step of the hexosamine pathway and catalyzes the conversion of fructose 6-phosphate and glutamine to glucosamine 6-phosphate and glutamate. Consistently in this study, several enzymes in polysaccharide, proteoglycan, and glycosylation synthetic pathways were downregulated in the D-MG spheroids, including UDP-glucose pyrophosphorylase UGP2 (-0.51), UDP-glucose 6-dehydrogenase UGDH (-1.18), and UDP-glucose 4-epimerase GALE (-1.31).(2) α-Ketoglutarate. Glutamate can be converted to α-ketoglutarate by the mitochondrial GLUD1 (-0.33) or by cytoplasmic alanine or aspartate aminotransferases [44]. The cytoplasmic enzymes were slightly upregulated (GOT1, 0.36, GOT2, 0.69) in the D-MG group. There are three possible routes by which α-ketoglutarate can be converted to citrate (Figure 2) [1]. It can be converted through the TCA cycle [2]. It could be converted from isocitrate to citrate (by IDH2, -0.39, and ACO2, 0.01), which affects the normal TCA cycle flux by reductive glutamate metabolism [32]. In this study, IDH3B was slightly increased (0.36) and it can only catalyze the “forward” reaction [3]. Cytoplasmic α-ketoglutarate, produced through GFPT1 (-0.66) and GOT1 (0.36), can be converted by IDH1 (-1.01) and ACO1 (-0.73), which are both downregulated in the D-MG group compared to the MG group.(3) NADH. NADH/NAD+ and NADPH/NADP+ conversions are critical in the central metabolic pathways (Figure 2 and Supplementary Figure S2). The reduction in the mitochondrial MDH1 (-0.49) and the downregulation of NAD(P) transhydrogenase (NNT, -1.24) suggest that the conversions in mitochondria may have some significance. MDH1 was decreased (-0.49) but lacked a malate source (e.g., the SLC25A11 transporter was essentially unchanged (0.25)). The conversion of cytoplasmic pyruvate to lactate would consume the produced NADH. The richest source of NADPH is the pentose phosphate pathway, where G6PD (-0.16) was essentially unchanged between the two groups. These results indicate the reliance on mitochondrial NAD+/NADH in the D-MG group.(3) Citrate. Citrate can be used for fatty acid synthesis. ATP-citrate synthase uses citrate to generate cytosolic acetyl-CoA. Acetyl-CoA is used for (1) histone acetylation by acetyl-CoA acyltransferase (ACAA1, -0.28), (2) fatty acid synthesis (FASN, 0.34), and (3) synthesis of cholesterol, steroid hormones, and other biomolecules. Glutamine is as important as glucose in metabolism, and it can block glutamate-dependent cellular pathways at either IDH1 or ACL step [14].


### 3.3. Signaling Pathways Involved in Metabolic Reprogramming

The analysis so far shows the metabolic shift to aerobic glycolysis in the 3-D spheroids, which may be driven by diffusion gradients [30, 37]. In order to investigate how metabolic reprogramming is orchestrated, the status of critical pathways for cellular function, including PI3K/AKT/mTOR, Myc, p53, NF-κB, EIF2, Wnt, and Notch, was analyzed.


#### 3.3.1. PIK3/AKT/mTOR Signaling

PI3K (a class I phosphoinositide 3-kinase) is one of the key signaling enzymes that are activated in 3-D spheroid cultures [45]. And the PIK3 pathway is directly related to cellular metabolism and growth. When cells consume the nutrients and growth factors, they generate amino acids, glucose, oxygen, and energy (i.e., ATP) and use these products to activate GTPase, such as Rheb, through PI3K. One key regulator of metabolism and growth-activated downstream of PI3K signaling is the mechanistic target of rapamycin (mTOR) [46]. Our results showed that the expression of mTOR (0.66) was slightly elevated in the D-MG spheroids and the downstream effectors were inhibited (e.g., ribosomal protein s6 kinase RPS6KA3, -1.87) (Figure 4(a)). mTOR stimulates pyrimidine synthesis via the RPS6KA-mediated phosphorylation of CAD (0.46), whereas AKT can phosphorylate ACL, enhancing its lipogenic activities and mTOR signaling. The expression of ACLY (-0.13) indicates that the activity of the AKT pathway was similar in the two groups. mTOR regulates several anabolic and catabolic pathways through fatty acid and cholesterol biosynthetic genes via the SREBP family (SREBP1 and SREBP2) [47].Figure 4
mTOR signaling in the D-MG group. (a) RNA-sequencing results. (b) Validation of ERK genes using RT-PCR.∗ indicates p<0.05 (n=3). (c) Schematic diagram showing the major changes in D-MG versus MG condition for mTOR signaling. D-MG shows enhanced mTOR activities that reduce tricarboxylic cycle (TCA), and D-MG shows increased glycolysis mediated by mTOR.
(a)
(b)
(c)mTOR signaling was reported to increase the translation of HIF-1α, glucose transporters, and glycolytic enzymes and promote metabolic reprogramming. The activation of the mTOR protein complex mTORC1 leads to the induction of genes encoding the enzymes of glycolysis, the pentose phosphate pathway, and lipid and sterol biosynthesis [35, 48]. mTOR was also reported to coordinate the activation of cell growth machinery together with amino acids in the presence of growth signals [49]. However, in the RT-PCR validation, mTOR gene expression was statistically insignificant, possibly due to assay variations (Figures 4(b) and 4(c)). In addition, ERK1 was downregulated while ERK2 was upregulated in the D-MG group.


#### 3.3.2. Myc Signaling

Myc is a transcription factor that dimerizes with MAX to bind to DNA and regulate gene expression involved in metabolism (glycolysis and glutaminolysis) and biosynthesis (nucleotide and lipid synthesis) [50]. Myc supports specific mRNA splice variants, such as glycolysis-affecting PKM2 over PKM1. Myc targets glucose membrane transporters such as GLUT1 (or SLC2A1) and glutamine transporter SLC1A5, which are important in cell proliferation [50]. Myc transactivates the gene expression of PFK, ENO, and LDHA (1.99) and indirectly increases GAPDH and PGK1, based on our genomics analysis (PGK1, 3.54; GPI, 5.07; ENO1, 3.30; and GAPDH, 2.02), indicating the relation between Myc and glycolysis (Figure 5). The gene expression of Myc-related proteins was downregulated in the D-MG spheroids including PTBP1 (-0.60), SLC1A5 (-1.86), and PRDX3 (-1.77) (Figure 5). The low Myc activity inside the D-MG spheroids might indicate the slow proliferation of cells.Figure 5
c-Myc signaling in the D-MG group. (a) RNA-sequencing results related to Myc. (b) Relative transcript expression between D-MG and MG conditions for c-Myc and its downstream targets;∗ indicates p<0.05 (N=3). (c) Schematic diagram showing the major changes in D-MG versus MG for c-Myc signaling: D-MG shows enhanced c-Myc that mediates increased glycolysis.
(a)
(b)
(c)


#### 3.3.3. p53 Signaling

In addition, the expression of the transcriptional suppressor CDK5RAP3 is comparable between the D-MG group and the MG group (0.34). CDK5RAP3 is a novel activator of PAK4 and processes important prometastatic function [51]. It has been reported that CDK5RAP3 knockdown upregulated the tumor suppressor p14ARF at protein and mRNA levels, and ectopic expression of CDK5RAP3 repressed the transcription of p14ARF [51]. Therefore, the CDK5RAP3 expression may allow for the synthesis of p14ARF and its binding to MDM2. The MDM2 (-0.75) expression was decreased in the D-MG spheroids (Figure 6). The MDM2 expression can release p53 from inhibition and thereby lead to the stabilization, accumulation, and activation of p53. Also, the negative regulators of p53 are subjected to tight feedback regulation. For example, BCCIPβ overexpression delays the G1 to S progression and results in an elevated p21 expression, which would inhibit CDK1 induction of cell cycle progression. Though the D-MG spheroids did not show a significant increase in BCCIPβ expression (0.13) compared to the MG group (Figure 6), the expression of CDK1 was decreased (-1.72) in the D-MG group, which indicated a potential cell cycle arrest.Figure 6
p53 signaling in the D-MG group. (a) RNA-sequencing results related to p53. (b) Relative transcript expression between D-MG and MG for p53 and its downstream targets;∗ indicates p<0.05 (N=3). (c) Schematic diagram showing the major changes in D-MG versus MG for p53 signaling: D-MG shows the increased p53 activities, which results in the decreased expression of proteins related to cell proliferation.
(a)
(b)
(c)


#### 3.3.4. Wnt Pathway

In the canonical Wnt pathway, Wnt interacts with the Frizzled receptor that results in the inhibition of glycogen synthase kinase-3β (GSK-3β) [52]. Due to the inhibition, GSK-3β would not be able to phosphorylate β-catenin, leading to the nuclear entry of β-catenin. In the nucleus, β-catenin interacts with members of TCF transcriptional factors (i.e., T cell factor/lymphoid enhancer factor) and modulates target gene expression [53]. In our D-MG spheroids, the expression of β-catenin was slightly reduced compared to that in the MG group (CTNNB1, -0.66) (Figure 7). Wnt 3 (-1.06) and Wnt 5B (-1.55) were also decreased. The histidine triad nucleotide-binding protein 1 (HINT1, 0.23), which keeps the Wnt/β-catenin pathway inactive [54], and protein phosphorylase 2A (PPP2RA1, 0.28) were comparable in the two groups. Our previous studies evaluated the influence of the canonical Wnt pathway on neural patterning of hiPSCs [55, 56]. The reduced canonical Wnt/β-catenin signaling at a late stage of neural differentiation of hiPSCs can enrich cells with rostral forebrain identity [57]. In the noncanonical pathway, Wnt binds to the Frizzled receptor which activates Dishevelled and forms a complex with RAC1, mediating profilin binding to actin [58]. In the current study, the noncanonical Wnt pathway activity was comparable for the D-MG spheroids and MG group (RAC1, 0.27).Figure 7
Wnt signaling in the D-MG group. (a) RNA-sequencing results related to Wnt. (b) Relative transcript expression between D-MG and MG for Wnt and its downstream targets.∗ indicates p<0.05 (N=3). (c) Schematic diagram showing the major changes in D-MG versus MG conditions for Wnt signaling; i.e., D-MG shows the decreased canonical Wnt signaling while noncanonical Wnt is increased, which results in the increased cytoskeletal rearrangement.
(a)
(b)
(c)


#### 3.3.5. NF-κB Pathway

NF-κB was found to be involved in cellular responses to various stimuli such as stress, cytokines, free radicals, and viral antigens [59, 60]. The two classes of NF-κB proteins (p50/p52 and ReIA/ReIB) form heterodimers to function as transcriptional activators. In an inactive state, NF-κB dimers are sequestered in the cytoplasm by IkB inhibitors. They mask the nuclear localization signals of NF-κB proteins and keep NF-κB signaling in the inactive state. When active, NF-κB proteins enter the nucleus and turn on the IkBα repressor. It has been reported that hypoxia upregulated PRMT1 which asymmetrically methylated ReIA, inhibiting the binding of ReIA to DNA and further repressing NF-κB [61]. NF-κB signaling was found to be dysregulated in TGFβR3 epicardial cells, which also showed the impaired cell invasion and revealed the role of NF-κB signaling in TGFβR3 activation [62]. In our D-MG spheroids, the lower expression of TGFβR3 (-2.61) and TGFB1 (-0.60) indicates the less activated NF-κB pathway (Supplementary Table S3 and Figure 4(b)). TGFBI expression is essentially similar (0.03), and PRMT1 expression (0.63) is slightly upregulated.Microglial M1 immune response usually involves NF-κB signaling [59]. Knocking down of LRP1 in primary microglia led to the activation of both c-Jun N-terminal kinase and NF-κB pathways. The sensitivity to lipopolysaccharide (LPS) stimulation in the production of proinflammatory cytokines was also enhanced. The NF-κB inhibitor was shown not only to suppress the production of cytokines induced by the knockdown of LRP1 but also to restore the downregulated expression of LRP1 due to LPS stimulation.


#### 3.3.6. Notch Pathway

Most Notch receptors were upregulated in the D-MG group, including DLL1 (5.85), DLL3 (2.84), DLL4 (1.49), JAG1 (2.55), JAG2 (1.70), HES1 (0.62), HES4 (2.44), and HES5 (7.93) (Figure8(a)), indicating the enhanced cell-cell communications in 3-D culture. Our previous study also showed that Notch-1 expression was upregulated in the hybrid stem cell spheroids [63]. In addition, Notch signaling regulates the balance between the progenitor pool and the neuron pool, preventing premature neurogenesis [64]. Global deletion of Notch signaling leads to the accelerated differentiation into neurons. The activation of Notch by DLL1 causes neural stem cells to irreversibly commit to a glial fate and prevents the stem cells from adopting a neuronal fate [65]. When a Notch receptor interacts with its ligands, Delta and Serrate, it activates the membrane-tethered transcription factor and leads to the release of intracellular domain (NICD) and its translocation to the nucleus. Then, NICD interacts with CSL family regulators and transcript Hes1 and Hes5 expression. Notch signaling also promotes Wnt expression in Drosophila development [66]. Conversely, Wnt protein can promote the expression of Notch ligands DLLs, forming a positive feedback loop to maintain Notch signaling and Wnt protein expression.Figure 8
Log2 expression levels of the D-MG/MG ratios for genes about cell-cell interactions, cell death, and cell cycle. (a) Notch signaling. (b) Cell death. (c) Cell cycle and cell proliferation. (d) EIF2 signaling as a part of integrated stress response (ISR).
(a)
(b)
(c)
(d)


#### 3.3.7. Cell Death and Cell Cycle

It is important to evaluate cell death pathways (necrosis or apoptosis) in the 3-D spheroids. The major changes associated with cell death are free radical damage, swelling, rupture, and cytolysis. Under stress, the cells sense DNA damage by the serine/threonine kinase ATM and p53 [67]. p53 accumulates in the mitochondrial matrix and triggers mitochondrial permeability transition (MPT) pore (PTP) opening. Then, p53 physically interacts with anti- and proapoptotic Bcl-2 and BAX family members to inhibit or activate their respective functions, leading to mitochondrial outer membrane permeabilization (MOMP) and apoptosis [68]. From our results, PPID (0.74), the essential component of the MPT pore located in the mitochondrial matrix, is slightly increased in the D-MG group (Figure 8(b)).The exact molecular composition of the MOMP complex is assumed to contain hexokinase, voltage-dependent anion channel (VDAC, on the outer membrane), the adenine nucleotide translocase (ANT, in the inner membrane), and cyclophilin D (a peptidyl-prolyl isomerase in the matrix). When PTP opens, it leads to matrix swelling and depolarization of the membrane potential, causing subsequent rupture of the outer membrane and consequently apoptosis [68]. Based on our results, VDACs were increased (VDAC1, 1.84; VDAC3, 0.90) in the D-MG group (Figure 8(b)). MPT-driven necrosis works not only by the dissipation of mitochondrial transmembrane potential but also by the latent chromatinolytic activity of AIFM1 (apoptosis-inducing factor mitochondrion-associated 1) [69]. The slightly decreased AIFM1 (-0.38) in the D-MG spheroids indicates that necrosis is favored over apoptosis.The mitochondrial HINT2 promotes angiogenesis via p53 and BAX. The activated NF-κB pathway also leads to the increased expression of antiapoptotic proteins, including Bcl-XL-binding protein v68 (PGAM5, 1.09) and Bcl-2-associated transcription factor 1 (BCLAF1, 0.96) in the D-MG spheroids (Figure 8(b)). The defender against apoptotic cell death (DAD1, 0.11) and Bcl-2 inhibitor of transcription 1 (PTRH2, 0.07) were unchanged. These antiapoptotic proteins can bind to and inactivate proapoptotic proteins. The proapoptotic protein BAX, Bcl-2-like protein 4, was unchanged (-0.02), but Bcl-2-associated athanogene 2 (BAG2, -2.90) was decreased in the D-MG spheroids. Consistent with higher expression of death genes in the D-MG group, the cell cycle-related genes were downregulated in the D-MG spheroids but highly expressed in the MG group (Figure 8(c)).The sirtuin signaling proteins SIRT1 (0.48) and SIRT6 (1.01) were upregulated in the D-MG spheroids (Table3), which regulate DNA damage and oxidative stress. The autophagy-related genes were essentially similar between the two groups. Poly (ADP-ribose) polymerase (PARP) is a family of proteins involved in a number of cellular processes such as DNA repair, genomic stability, and programmed cell death [70]. PARP6 (1.32), PARP3 (1.12), and PARP2 (0.97) were upregulated while PARP4 (-1.34), PARP14 (-2.44), PARP12 (-2.54), and PARP9 (-2.76) were downregulated in the D-MG group (Table 4). PARP1 (-0.01) was essentially unchanged between the two groups.Table 3
Sirtuin signaling and autophagy.
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
DMG/MG
LOG2 (D-MG/MG)
Sirtuin signaling
SIRT6
11.268
9.088
9.289
20.250
21.520
17.741
9.882
19.837
2.007
1.01
SIRT1
8.625
8.714
9.529
12.048
12.060
13.258
8.956
12.455
1.391
0.48
SIRT4
3.945
5.113
3.877
5.457
4.254
5.735
4.312
5.148
1.194
0.26
SIRT7
7.837
8.696
7.100
8.127
7.686
7.362
7.877
7.725
0.981
-0.03
SIRT5
1.763
2.161
1.591
1.743
1.551
1.505
1.838
1.599
0.870
-0.20
SIRT3
13.160
13.397
12.523
8.864
7.755
8.300
13.027
8.306
0.638
-0.65
SIRT2
50.715
42.689
48.365
27.236
31.360
27.618
47.256
28.738
0.608
-0.72
Autophagy
TFEB
5.357
4.069
5.025
4.723
5.425
5.416
4.817
5.188
1.077
0.11
BECN1
13.675
11.764
13.135
12.298
12.239
12.236
12.858
12.257
0.953
-0.07
LAMP1
57.074
48.954
58.426
46.114
51.499
47.309
54.818
48.307
0.881
-0.72
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.Table 4
Gene expression of poly (ADP-ribose) polymerase (PARP) family proteins.
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
DMG/MG
LOG2 (D-MG/MG)
PARP6
19.908
24.159
20.683
55.689
59.154
54.018
21.583
56.287
2.608
1.38
PARP3
3.171
3.020
3.221
6.723
7.323
6.366
3.137
6.804
2.169
1.12
PARP2
17.659
22.634
16.142
35.570
39.811
34.828
18.812
36.736
1.953
0.97
PARP11
3.485
3.789
3.403
6.320
6.097
6.702
3.559
6.373
1.791
0.84
PARP15
0.163
0.124
0.127
0.286
0.263
0.179
0.138
0.243
1.759
0.81
PARP8
2.142
2.526
2.428
3.305
3.204
3.578
2.365
3.362
1.422
0.51
PARP1
42.914
40.611
42.447
41.423
43.048
40.686
41.990
41.719
0.994
-0.01
PARP10
1.451
1.234
1.709
1.518
1.198
1.385
1.465
1.367
0.933
-0.10
PARP16
12.871
12.281
11.812
7.840
8.730
9.558
12.321
8.709
0.707
-0.50
PARP4
8.746
9.413
8.348
3.874
3.104
3.526
8.836
3.501
0.396
-1.34
PARP14
7.616
10.770
8.298
1.482
1.613
1.818
8.895
1.638
0.184
-2.44
PARP12
1.519
1.168
1.288
0.182
0.274
0.228
1.325
0.228
0.172
-2.54
PARP9
3.633
3.688
3.397
0.533
0.460
0.586
3.573
0.526
0.147
-2.76
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.


#### 3.3.8. Eukaryotic Initiation Factor 2 (eIF2) Pathway

eIF2 is one of the critical translation G proteins that are tightly regulated in the integrated stress response. In response to stress stimuli, eIF2α undergoes phosphorylation via different kinases, such as PKR-like endoplasmic reticulum kinase (PERK), which results in the formation of eIF2B. This blocks the p-eIF2 from its active GTP-bound state, causing a reduction of the translation of most mRNAs [71]. In the operative nervous systems, the eIF pathway was found to support the connectivity between neurons. The physiological extracellular cue Sema3A can trigger rapid and transient phosphorylation of eIF2a in axons for the axonal translational changes [72]. The D-MG group displayed the slightly elevated expression of eIF proteins, such as eIF-2α/EIF2S1 (0.220), eIF2β/EIF2S2 (0.274), and eIF2B proteins, such as EIF2B1 (0.43), EIF2B2 (0.47), EIF2B3 (0.45), and EIF2B5 (0.66) (Figure 8(d)), indicating potential stress such as hypoxia or insufficient nutrient uptake in the D-MG group. Also, the expression of EIF2AK3 (0.64), a stress sensing protein kinase that phosphorylates the alpha subunit of eIF2, was slightly elevated in the D-MG group. DDIT3 (0.58), which plays an essential role in inducing cell cycle arrest and apoptosis in response to ER stress, was also slightly elevated in the D-MG group. PPP1R15A (1.17) dephosphorylates the eIF-2α/EIF2S1, thereby reversing the shutoff of protein synthesis initiated by stress-inducible kinases and then facilitating cell recovery from stress.


#### 3.3.9. Extracellular Matrix

The extracellular matrix- (ECM-) related genes were found to be differentially expressed in the two groups (Supplementary FigureS3) [1]. The D-MG group had higher expression of some collagen genes including COL7A1, COL20A1, COL9A1, and COL11A2. The MG group expressed high levels of different types of collagens, including COL23A1, COL6A1, COL4A1, COL13A1, COL5A2, COL5A1, COL14A1, and COL4A1 [2]. The D-MG group mainly enriched laminin LAMB4, while the MG group enriched LAMA3, LAMB1, LAMC1, LAMA2, LAMA1, etc. [3]. The D-MG group expressed higher levels of specific integrins, including ITGA7, ITGB8, ITGA2, and ITGAB3, while the MG group enriched ITGA6, ITGA11, ITGA8, ITGB6, ITGA1, ITGB5, etc. [4]. For ECM remodeling genes, the D-MG group enriched proteases MMP17, MMP10, MMP24, MMP7, and MMP1; the MG group had higher expression of proteases MMP23, MMP2, MMP14, TIMP1, MMP28, MMP26, and MMP15, etc.Other pathways of our interests are shown in Supplementary TableS4, which include the AMPK pathway and PDL1/PD1 pathway (Figure 9 shows the relationship between miRNAs and PDL1/PD1 interactions) (Figure 9) [73]. Microglia, the main antigen-presenting cells in the human brain, maintain the equilibrium with T cells through the PD1 pathway as reported [74].Figure 9
Illustration of microglia with PDL1/PD1 interactions. The numbers in the illustration are from the normal DMG/MG ratios. The arrows show the ways of tumor cell and T cell interactions. MGs act as antigen-presenting cells (APC).


## 3.1. Central Metabolic Pathway

The 3-D spheroid culture usually is associated with slow growth rates, cytoskeleton reorganization, enhanced tight junctions and polarity, and the relocated membrane transporters [25–27]. In aerobic glycolysis, glucose is consumed through the glycolytic pathway to produce lactic acid, nucleotides, amino acids, and other metabolites, while glutamine is converted through glutaminolysis to citrate for cholesterol and lipid production. In oxidative phosphorylation, the generated pyruvate is oxidized to CO2 and water through the tricarboxylic (TCA) cycle, which produces ATP and coverts NADH to NADPH [28–30].The central metabolic pathways are shown in Figure2. The values were calculated using log2 (DMG/MG). Relative enzyme expression levels indicate an increase in glycolytic and pentose phosphate pathways in the D-MG group as well as the increased amino acid synthesis (Figure 2 and Supplementary Figure S2) [31]. The increased glycolysis is permissive to trigger microbiocidal activity of microglia and allows the cells to survive in the 3-D spheroids. However, enzymes in the glutaminolytic pathway, hexosamine, nucleotide, lipid synthesis, and TCA cycle have small differences among the two groups. Genes related to ATP synthesis and mitochondrial complexes I, III, and IV are shown in Supplementary Table S2 and Supplementary Figure S2. The differences between the two groups are small (within ±0.5 except NDUFA13 at -0.68). But the values are all negative for genes related to ATP synthesis and mitochondrial complexes I and III, indicating lower expression in the D-MG group. Together, these results indicate the different energetic/metabolic phenotypes between the D-MG and the MG groups.Figure 2
Ratios of gene abundance of central metabolic pathways in the D-MG group in comparison to the MG group. The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. (a) Central metabolic pathways. (b) Log2 values of glycolysis genes of D-MG/MG ratios.
(a)
(b)Metabolic shift towards glycolysis usually results in the elevated dependence on glutaminolysis or fatty acid synthesis in cell metabolism [32]. However, in this study, the cells did not show a significant increase in glutaminolysis in the spheroids (Figure 2 and Supplementary Figure S2). Cytoplasmic isocitrate dehydrogenase 1 (IDH-1) is involved in the reaction of isocitrate production from α-ketoglutarate in the cytosol. IDH-1 expression is decreased in the D-MG spheroids, which may be associated with NF-κB activation in a hypoxia-inducible factor- (HIF-1α-) dependent manner [33].Similarly, 3-D culture of iPSC-derived endothelial cells also showed the glycolysis-dominated metabolism compared to ones in 2-D culture [34]. Moreover, human MSCs treated with interferon gamma exert an immunosuppressive phenotype by secreting PGE2/IDO and reconfigure the metabolic phenotype to aerobic glycolysis [35]. It was also suggested that activation of the AKT/mTOR signaling pathway is required for metabolic shift under this immune polarization.The 3-D cultures may not always enhance the glycolysis, which is dependent on a specific cell type. For example, it was reported that 3-D cultures of hiPSC-derived cardiomyocytes displayed downregulation of genes involved in glycolysis and lipid biosynthesis and upregulation of genes involved in oxidative phosphorylation. Accordingly, the 3-D cultures showed lower fluxes for fatty acid synthesis and the increased TCA cycle activity, which improved both cell purity and metabolic maturation [36].


## 3.2. Hypoxia (Glycolysis and Oxidative Phosphorylation)

### 3.2.1. HIF-1α Pathway

In 3-D spheroid culture, the inside of the spheroids is thought to be more hypoxic than the surface due to mass transfer limitation of oxygen [37], while this has been challenged by other studies as nonhypoxia-stabilized HIF expression [25]. Hypoxia is an important factor in regulating stem cell metabolism and phenotype [38]. When oxygen concentrations decrease, the oxygen-dependent prolyl hydroxylase domain proteins are inactivated and the HIF-1α protein is accumulated, which promotes HIF-1α translocation to the nucleus and its binding to hypoxia response elements, such as glucose transporters and glycolytic enzymes [39, 40]. Our results do not show the higher HIF-1α gene expression in the D-MG group but demonstrate the increased expression of HIF-1α pathway downstream genes, including SIAH2 (1.29), PDK1 (3.84), LDHA (1.99), LONP1 (1.94), and P4HA1 (1.79) (Figures 3(a)–3(c)). These results may indicate the nonhypoxia-stabilized HIF expression in the D-MG group. The downregulated HIF-1α gene expression in the D-MG group was also validated using RT-PCR, along with the upregulated glycolytic gene expression in the D-MG group (Figures 3(d) and 3(e)).Figure 3
HIF-1α-related pathway. (a) RNA-sequencing results for glycolytic genes and HIF-1α; relative transcript expression between D-MG and MG for HIF-1α and its downstream targets (b) on tricarboxylic cycle (TCA) and (c) on glycolysis and extracellular matrix (ECM) production. ∗ indicates p<0.05 (n=3). (d) Validation of glycolytic genes using RT-PCR. ∗ indicates p<0.05 (n=3). (e) Schematic diagram showing the major changes in D-MG versus MG for HIF-1α signaling: D-MG shows enhanced HIF-1α activities that reduce TCA and ATP production, increasing glycolysis mediated by HIF-1α.
(a)
(b)
(c)
(d)
(e)HIF-1α induces pyruvate dehydrogenase kinase 1 (PDK1) expression, which inhibits mitochondrial pyruvate dehydrogenase (PDH) [38, 41]. This reduces pyruvate flux into the TCA cycle and lowers the mitochondrial oxygen requirements. The lactate production and secretion would be increased, as observed by Sart et al. [9]. HIF-1α also induces E3-ubiquitin ligase SIAH2 synthesis, which mediates the proteasomal degradation of the OGDH subunit of α-ketoglutarate dehydrogenase (α-KD) and forms a part of the feedback control of HIF-1α signaling. A modest reduction of the α-KD enzyme complex was observed in the D-MG group (DLD, -0.75, OGDH, -1.71) (Table 1), which may slow down TCA cycle activity. Interestingly, the citrate transportation into the cytoplasm by citrate transporter protein (SLC25A1, -0.25) was relatively comparable between these two groups. The extracellular matrix remodeling via collagen hydroxylases (P4HA1, 1.79) was upregulated in the D-MG group. The amounts of anabolic rate-limiting enzymes were increased or remained similar, while catabolic enzymes were unchanged between the two groups (Table 2). The rate-limiting glycolytic pathway steps (HK2, PKM, and PFKL) are the mostly increased enzymes of the pathway. These findings are consistent with the metabolic shift to aerobic glycolysis. In addition, high PFK level may also indicate high levels of AMP, since the spheroids usually have high ATP amounts [14].Table 1
Glycolysis vs. oxidative phosphorylation.
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
DMG/MG
LOG2 (DMG/MG)
P4HA1
140.340
135.633
141.039
389.230
546.723
505.399
139.004
480.451
3.456
1.79
SLC2A1 (GLUT1)
579.1676
469.9516
562.8377
912.218
1098.52
1056.15
537.319
1022.293
1.903
0.93
DLST
17.881
18.114
17.362
26.470
26.133
24.754
17.786
25.786
1.450
0.54
SLC16A3
304.016
219.663
293.687
255.760
244.563
255.972
272.455
252.098
0.925
-0.11
COX4I1
238.629
185.922
239.199
186.813
225.728
199.922
221.250
204.154
0.923
-0.12
SLC25A1
72.139
59.151
72.118
54.289
60.972
56.058
67.803
57.107
0.842
-0.25
DLD
22.476
21.521
21.854
12.507
12.003
14.700
21.950
13.070
0.595
-0.75
OGDH
28.692
23.353
27.126
8.233
7.421
8.492
26.390
8.049
0.305
-1.71
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.Table 2
Rate-limiting enzymes for central metabolic pathways and the ratios of their expression in the D-MG group compared to the MG group.
Pathway
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
LOG2 (DMG/MG)
Glucose phosphorylation
HK2
8.057
7.382
8.514
373.333
367.184
391.036
7.984
377.184
5.56
Glycolysis
PKM
144.193
130.476
142.160
1071.419
1269.244
1061.022
138.943
1133.895
3.03
Glycine synthesis
SHMT2
34.121
29.636
33.060
146.550
175.435
141.673
32.272
154.553
2.26
Asparagine synthesis
ASNS
28.779
28.424
28.878
93.603
122.579
101.086
28.694
105.756
1.88
Glycogenolysis
PYGB
25.047
22.544
24.888
34.525
34.766
30.397
24.159
33.229
0.46
Pyrimidine synthesis
CAD
8.199
9.554
8.489
13.220
12.638
10.185
8.747
12.015
0.46
Glycolysis
PFKL
62.436
52.127
63.963
83.298
79.339
73.566
59.509
78.734
0.40
Aspartate synthesis
GOT1
16.089
14.826
14.507
18.869
20.440
19.010
15.141
19.439
0.36
Fatty acid synthesis
FASN
28.814
22.644
29.725
36.264
34.796
31.673
27.061
34.244
0.34
Purine synthesis
PRPS1
23.085
20.259
22.740
24.847
28.698
27.976
22.028
27.173
0.30
Pentose phosphate
G6PD
15.355
12.245
15.422
12.237
14.129
12.097
14.341
12.821
-0.16
Fatty acid synthesis
ACAA1
21.219
19.891
21.847
17.973
18.352
15.681
20.986
17.336
-0.28
Glutamine-glutamate conversion
GLUD1
49.375
44.127
51.590
35.530
41.837
38.198
48.364
38.522
-0.33
Methionine synthesis
MTR
7.799
9.359
8.511
7.162
5.777
6.920
8.556
6.620
-0.37
TCA cycle
IDH2
103.753
84.981
101.212
66.326
79.889
75.170
96.649
73.795
-0.39
Serine synthesis
PHGDH
141.285
116.771
138.838
92.636
114.237
92.781
132.298
99.884
-0.41
Hexose
GFPT1
46.208
45.599
46.304
26.099
32.086
29.340
46.037
29.175
-0.66
Fatty acid oxidation
CRAT
23.955
20.886
25.945
14.810
14.697
14.392
23.596
14.633
-0.69
Proline synthesis
PYCR1
102.329
84.127
98.191
46.173
65.047
53.249
94.883
54.823
-0.79
Urea synthesis
CPS1
7.949
7.541
8.583
4.041
3.943
4.177
8.024
4.054
-0.99
Tyrosine synthesis
PAH
0.441
0.551
0.468
0.171
0.184
0.224
0.487
0.193
-1.33
Cysteine synthesis
MAT1A
5.443
4.897
5.280
0.793
0.724
0.529
5.207
0.682
-2.93
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.HIF-1α signaling can induce the expression of the mitochondrial protease LONP1 (1.94) (Figure 3). LONP1 degrades cytochrome C oxidase 4 subunit 1 (COX4-1) through electron transport chain complex IV, allowing the replacement of COX4-1 by COX4-2 [42], which is more efficient in enzymatic reaction. LONP1 is an essential central regulator of mitochondrial activity and is overexpressed during oncogenesis [43]. Although LONP1 was increased (1.94) based on our results, there was little change in the level of COX4-1 (-0.12). Reduced mitochondrial respiration normally results in fewer reactive oxygen species (ROS), correlated with the reduced level of catalase (CAT, -1.55). The reduced oxidative stress results in the diminished hydrogen peroxide damage and less oxidized proteins [14].


### 3.2.2. Glutamine Metabolism and Hexosamine Pathway

While the D-MG group mainly uses glycolysis as its major energetic metabolism, our results did not show an increased reliance on glutamine metabolism (Figure2 and Supplementary Figure S2). Intracellular glutamine levels are regulated by plasma membrane transporters SLC38A2 and SLC1A5 [14]. Endoplasmic reticulum stress would induce the degradation of transporters and ultimately autophagy and cell death [14]. In D-MG spheroids of this study, both SLC38A2 (-0.53) and SLC1A5 (-1.86) were decreased, which may suggest enhanced autophagy in D-MG spheroids.(1) Conversion of Glutamine into Glutamate. The glutamate demand of the cells is indicated by the expression of GFPT1 (-0.66). GFPT1 is the first and rate-limiting step of the hexosamine pathway and catalyzes the conversion of fructose 6-phosphate and glutamine to glucosamine 6-phosphate and glutamate. Consistently in this study, several enzymes in polysaccharide, proteoglycan, and glycosylation synthetic pathways were downregulated in the D-MG spheroids, including UDP-glucose pyrophosphorylase UGP2 (-0.51), UDP-glucose 6-dehydrogenase UGDH (-1.18), and UDP-glucose 4-epimerase GALE (-1.31).(2) α-Ketoglutarate. Glutamate can be converted to α-ketoglutarate by the mitochondrial GLUD1 (-0.33) or by cytoplasmic alanine or aspartate aminotransferases [44]. The cytoplasmic enzymes were slightly upregulated (GOT1, 0.36, GOT2, 0.69) in the D-MG group. There are three possible routes by which α-ketoglutarate can be converted to citrate (Figure 2) [1]. It can be converted through the TCA cycle [2]. It could be converted from isocitrate to citrate (by IDH2, -0.39, and ACO2, 0.01), which affects the normal TCA cycle flux by reductive glutamate metabolism [32]. In this study, IDH3B was slightly increased (0.36) and it can only catalyze the “forward” reaction [3]. Cytoplasmic α-ketoglutarate, produced through GFPT1 (-0.66) and GOT1 (0.36), can be converted by IDH1 (-1.01) and ACO1 (-0.73), which are both downregulated in the D-MG group compared to the MG group.(3) NADH. NADH/NAD+ and NADPH/NADP+ conversions are critical in the central metabolic pathways (Figure 2 and Supplementary Figure S2). The reduction in the mitochondrial MDH1 (-0.49) and the downregulation of NAD(P) transhydrogenase (NNT, -1.24) suggest that the conversions in mitochondria may have some significance. MDH1 was decreased (-0.49) but lacked a malate source (e.g., the SLC25A11 transporter was essentially unchanged (0.25)). The conversion of cytoplasmic pyruvate to lactate would consume the produced NADH. The richest source of NADPH is the pentose phosphate pathway, where G6PD (-0.16) was essentially unchanged between the two groups. These results indicate the reliance on mitochondrial NAD+/NADH in the D-MG group.(3) Citrate. Citrate can be used for fatty acid synthesis. ATP-citrate synthase uses citrate to generate cytosolic acetyl-CoA. Acetyl-CoA is used for (1) histone acetylation by acetyl-CoA acyltransferase (ACAA1, -0.28), (2) fatty acid synthesis (FASN, 0.34), and (3) synthesis of cholesterol, steroid hormones, and other biomolecules. Glutamine is as important as glucose in metabolism, and it can block glutamate-dependent cellular pathways at either IDH1 or ACL step [14].


## 3.2.1. HIF-1α Pathway

In 3-D spheroid culture, the inside of the spheroids is thought to be more hypoxic than the surface due to mass transfer limitation of oxygen [37], while this has been challenged by other studies as nonhypoxia-stabilized HIF expression [25]. Hypoxia is an important factor in regulating stem cell metabolism and phenotype [38]. When oxygen concentrations decrease, the oxygen-dependent prolyl hydroxylase domain proteins are inactivated and the HIF-1α protein is accumulated, which promotes HIF-1α translocation to the nucleus and its binding to hypoxia response elements, such as glucose transporters and glycolytic enzymes [39, 40]. Our results do not show the higher HIF-1α gene expression in the D-MG group but demonstrate the increased expression of HIF-1α pathway downstream genes, including SIAH2 (1.29), PDK1 (3.84), LDHA (1.99), LONP1 (1.94), and P4HA1 (1.79) (Figures 3(a)–3(c)). These results may indicate the nonhypoxia-stabilized HIF expression in the D-MG group. The downregulated HIF-1α gene expression in the D-MG group was also validated using RT-PCR, along with the upregulated glycolytic gene expression in the D-MG group (Figures 3(d) and 3(e)).Figure 3
HIF-1α-related pathway. (a) RNA-sequencing results for glycolytic genes and HIF-1α; relative transcript expression between D-MG and MG for HIF-1α and its downstream targets (b) on tricarboxylic cycle (TCA) and (c) on glycolysis and extracellular matrix (ECM) production. ∗ indicates p<0.05 (n=3). (d) Validation of glycolytic genes using RT-PCR. ∗ indicates p<0.05 (n=3). (e) Schematic diagram showing the major changes in D-MG versus MG for HIF-1α signaling: D-MG shows enhanced HIF-1α activities that reduce TCA and ATP production, increasing glycolysis mediated by HIF-1α.
(a)
(b)
(c)
(d)
(e)HIF-1α induces pyruvate dehydrogenase kinase 1 (PDK1) expression, which inhibits mitochondrial pyruvate dehydrogenase (PDH) [38, 41]. This reduces pyruvate flux into the TCA cycle and lowers the mitochondrial oxygen requirements. The lactate production and secretion would be increased, as observed by Sart et al. [9]. HIF-1α also induces E3-ubiquitin ligase SIAH2 synthesis, which mediates the proteasomal degradation of the OGDH subunit of α-ketoglutarate dehydrogenase (α-KD) and forms a part of the feedback control of HIF-1α signaling. A modest reduction of the α-KD enzyme complex was observed in the D-MG group (DLD, -0.75, OGDH, -1.71) (Table 1), which may slow down TCA cycle activity. Interestingly, the citrate transportation into the cytoplasm by citrate transporter protein (SLC25A1, -0.25) was relatively comparable between these two groups. The extracellular matrix remodeling via collagen hydroxylases (P4HA1, 1.79) was upregulated in the D-MG group. The amounts of anabolic rate-limiting enzymes were increased or remained similar, while catabolic enzymes were unchanged between the two groups (Table 2). The rate-limiting glycolytic pathway steps (HK2, PKM, and PFKL) are the mostly increased enzymes of the pathway. These findings are consistent with the metabolic shift to aerobic glycolysis. In addition, high PFK level may also indicate high levels of AMP, since the spheroids usually have high ATP amounts [14].Table 1
Glycolysis vs. oxidative phosphorylation.
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
DMG/MG
LOG2 (DMG/MG)
P4HA1
140.340
135.633
141.039
389.230
546.723
505.399
139.004
480.451
3.456
1.79
SLC2A1 (GLUT1)
579.1676
469.9516
562.8377
912.218
1098.52
1056.15
537.319
1022.293
1.903
0.93
DLST
17.881
18.114
17.362
26.470
26.133
24.754
17.786
25.786
1.450
0.54
SLC16A3
304.016
219.663
293.687
255.760
244.563
255.972
272.455
252.098
0.925
-0.11
COX4I1
238.629
185.922
239.199
186.813
225.728
199.922
221.250
204.154
0.923
-0.12
SLC25A1
72.139
59.151
72.118
54.289
60.972
56.058
67.803
57.107
0.842
-0.25
DLD
22.476
21.521
21.854
12.507
12.003
14.700
21.950
13.070
0.595
-0.75
OGDH
28.692
23.353
27.126
8.233
7.421
8.492
26.390
8.049
0.305
-1.71
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.Table 2
Rate-limiting enzymes for central metabolic pathways and the ratios of their expression in the D-MG group compared to the MG group.
Pathway
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
LOG2 (DMG/MG)
Glucose phosphorylation
HK2
8.057
7.382
8.514
373.333
367.184
391.036
7.984
377.184
5.56
Glycolysis
PKM
144.193
130.476
142.160
1071.419
1269.244
1061.022
138.943
1133.895
3.03
Glycine synthesis
SHMT2
34.121
29.636
33.060
146.550
175.435
141.673
32.272
154.553
2.26
Asparagine synthesis
ASNS
28.779
28.424
28.878
93.603
122.579
101.086
28.694
105.756
1.88
Glycogenolysis
PYGB
25.047
22.544
24.888
34.525
34.766
30.397
24.159
33.229
0.46
Pyrimidine synthesis
CAD
8.199
9.554
8.489
13.220
12.638
10.185
8.747
12.015
0.46
Glycolysis
PFKL
62.436
52.127
63.963
83.298
79.339
73.566
59.509
78.734
0.40
Aspartate synthesis
GOT1
16.089
14.826
14.507
18.869
20.440
19.010
15.141
19.439
0.36
Fatty acid synthesis
FASN
28.814
22.644
29.725
36.264
34.796
31.673
27.061
34.244
0.34
Purine synthesis
PRPS1
23.085
20.259
22.740
24.847
28.698
27.976
22.028
27.173
0.30
Pentose phosphate
G6PD
15.355
12.245
15.422
12.237
14.129
12.097
14.341
12.821
-0.16
Fatty acid synthesis
ACAA1
21.219
19.891
21.847
17.973
18.352
15.681
20.986
17.336
-0.28
Glutamine-glutamate conversion
GLUD1
49.375
44.127
51.590
35.530
41.837
38.198
48.364
38.522
-0.33
Methionine synthesis
MTR
7.799
9.359
8.511
7.162
5.777
6.920
8.556
6.620
-0.37
TCA cycle
IDH2
103.753
84.981
101.212
66.326
79.889
75.170
96.649
73.795
-0.39
Serine synthesis
PHGDH
141.285
116.771
138.838
92.636
114.237
92.781
132.298
99.884
-0.41
Hexose
GFPT1
46.208
45.599
46.304
26.099
32.086
29.340
46.037
29.175
-0.66
Fatty acid oxidation
CRAT
23.955
20.886
25.945
14.810
14.697
14.392
23.596
14.633
-0.69
Proline synthesis
PYCR1
102.329
84.127
98.191
46.173
65.047
53.249
94.883
54.823
-0.79
Urea synthesis
CPS1
7.949
7.541
8.583
4.041
3.943
4.177
8.024
4.054
-0.99
Tyrosine synthesis
PAH
0.441
0.551
0.468
0.171
0.184
0.224
0.487
0.193
-1.33
Cysteine synthesis
MAT1A
5.443
4.897
5.280
0.793
0.724
0.529
5.207
0.682
-2.93
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.HIF-1α signaling can induce the expression of the mitochondrial protease LONP1 (1.94) (Figure 3). LONP1 degrades cytochrome C oxidase 4 subunit 1 (COX4-1) through electron transport chain complex IV, allowing the replacement of COX4-1 by COX4-2 [42], which is more efficient in enzymatic reaction. LONP1 is an essential central regulator of mitochondrial activity and is overexpressed during oncogenesis [43]. Although LONP1 was increased (1.94) based on our results, there was little change in the level of COX4-1 (-0.12). Reduced mitochondrial respiration normally results in fewer reactive oxygen species (ROS), correlated with the reduced level of catalase (CAT, -1.55). The reduced oxidative stress results in the diminished hydrogen peroxide damage and less oxidized proteins [14].


## 3.2.2. Glutamine Metabolism and Hexosamine Pathway

While the D-MG group mainly uses glycolysis as its major energetic metabolism, our results did not show an increased reliance on glutamine metabolism (Figure2 and Supplementary Figure S2). Intracellular glutamine levels are regulated by plasma membrane transporters SLC38A2 and SLC1A5 [14]. Endoplasmic reticulum stress would induce the degradation of transporters and ultimately autophagy and cell death [14]. In D-MG spheroids of this study, both SLC38A2 (-0.53) and SLC1A5 (-1.86) were decreased, which may suggest enhanced autophagy in D-MG spheroids.(1) Conversion of Glutamine into Glutamate. The glutamate demand of the cells is indicated by the expression of GFPT1 (-0.66). GFPT1 is the first and rate-limiting step of the hexosamine pathway and catalyzes the conversion of fructose 6-phosphate and glutamine to glucosamine 6-phosphate and glutamate. Consistently in this study, several enzymes in polysaccharide, proteoglycan, and glycosylation synthetic pathways were downregulated in the D-MG spheroids, including UDP-glucose pyrophosphorylase UGP2 (-0.51), UDP-glucose 6-dehydrogenase UGDH (-1.18), and UDP-glucose 4-epimerase GALE (-1.31).(2) α-Ketoglutarate. Glutamate can be converted to α-ketoglutarate by the mitochondrial GLUD1 (-0.33) or by cytoplasmic alanine or aspartate aminotransferases [44]. The cytoplasmic enzymes were slightly upregulated (GOT1, 0.36, GOT2, 0.69) in the D-MG group. There are three possible routes by which α-ketoglutarate can be converted to citrate (Figure 2) [1]. It can be converted through the TCA cycle [2]. It could be converted from isocitrate to citrate (by IDH2, -0.39, and ACO2, 0.01), which affects the normal TCA cycle flux by reductive glutamate metabolism [32]. In this study, IDH3B was slightly increased (0.36) and it can only catalyze the “forward” reaction [3]. Cytoplasmic α-ketoglutarate, produced through GFPT1 (-0.66) and GOT1 (0.36), can be converted by IDH1 (-1.01) and ACO1 (-0.73), which are both downregulated in the D-MG group compared to the MG group.(3) NADH. NADH/NAD+ and NADPH/NADP+ conversions are critical in the central metabolic pathways (Figure 2 and Supplementary Figure S2). The reduction in the mitochondrial MDH1 (-0.49) and the downregulation of NAD(P) transhydrogenase (NNT, -1.24) suggest that the conversions in mitochondria may have some significance. MDH1 was decreased (-0.49) but lacked a malate source (e.g., the SLC25A11 transporter was essentially unchanged (0.25)). The conversion of cytoplasmic pyruvate to lactate would consume the produced NADH. The richest source of NADPH is the pentose phosphate pathway, where G6PD (-0.16) was essentially unchanged between the two groups. These results indicate the reliance on mitochondrial NAD+/NADH in the D-MG group.(3) Citrate. Citrate can be used for fatty acid synthesis. ATP-citrate synthase uses citrate to generate cytosolic acetyl-CoA. Acetyl-CoA is used for (1) histone acetylation by acetyl-CoA acyltransferase (ACAA1, -0.28), (2) fatty acid synthesis (FASN, 0.34), and (3) synthesis of cholesterol, steroid hormones, and other biomolecules. Glutamine is as important as glucose in metabolism, and it can block glutamate-dependent cellular pathways at either IDH1 or ACL step [14].


## 3.3. Signaling Pathways Involved in Metabolic Reprogramming

The analysis so far shows the metabolic shift to aerobic glycolysis in the 3-D spheroids, which may be driven by diffusion gradients [30, 37]. In order to investigate how metabolic reprogramming is orchestrated, the status of critical pathways for cellular function, including PI3K/AKT/mTOR, Myc, p53, NF-κB, EIF2, Wnt, and Notch, was analyzed.


### 3.3.1. PIK3/AKT/mTOR Signaling

PI3K (a class I phosphoinositide 3-kinase) is one of the key signaling enzymes that are activated in 3-D spheroid cultures [45]. And the PIK3 pathway is directly related to cellular metabolism and growth. When cells consume the nutrients and growth factors, they generate amino acids, glucose, oxygen, and energy (i.e., ATP) and use these products to activate GTPase, such as Rheb, through PI3K. One key regulator of metabolism and growth-activated downstream of PI3K signaling is the mechanistic target of rapamycin (mTOR) [46]. Our results showed that the expression of mTOR (0.66) was slightly elevated in the D-MG spheroids and the downstream effectors were inhibited (e.g., ribosomal protein s6 kinase RPS6KA3, -1.87) (Figure 4(a)). mTOR stimulates pyrimidine synthesis via the RPS6KA-mediated phosphorylation of CAD (0.46), whereas AKT can phosphorylate ACL, enhancing its lipogenic activities and mTOR signaling. The expression of ACLY (-0.13) indicates that the activity of the AKT pathway was similar in the two groups. mTOR regulates several anabolic and catabolic pathways through fatty acid and cholesterol biosynthetic genes via the SREBP family (SREBP1 and SREBP2) [47].Figure 4
mTOR signaling in the D-MG group. (a) RNA-sequencing results. (b) Validation of ERK genes using RT-PCR.∗ indicates p<0.05 (n=3). (c) Schematic diagram showing the major changes in D-MG versus MG condition for mTOR signaling. D-MG shows enhanced mTOR activities that reduce tricarboxylic cycle (TCA), and D-MG shows increased glycolysis mediated by mTOR.
(a)
(b)
(c)mTOR signaling was reported to increase the translation of HIF-1α, glucose transporters, and glycolytic enzymes and promote metabolic reprogramming. The activation of the mTOR protein complex mTORC1 leads to the induction of genes encoding the enzymes of glycolysis, the pentose phosphate pathway, and lipid and sterol biosynthesis [35, 48]. mTOR was also reported to coordinate the activation of cell growth machinery together with amino acids in the presence of growth signals [49]. However, in the RT-PCR validation, mTOR gene expression was statistically insignificant, possibly due to assay variations (Figures 4(b) and 4(c)). In addition, ERK1 was downregulated while ERK2 was upregulated in the D-MG group.


### 3.3.2. Myc Signaling

Myc is a transcription factor that dimerizes with MAX to bind to DNA and regulate gene expression involved in metabolism (glycolysis and glutaminolysis) and biosynthesis (nucleotide and lipid synthesis) [50]. Myc supports specific mRNA splice variants, such as glycolysis-affecting PKM2 over PKM1. Myc targets glucose membrane transporters such as GLUT1 (or SLC2A1) and glutamine transporter SLC1A5, which are important in cell proliferation [50]. Myc transactivates the gene expression of PFK, ENO, and LDHA (1.99) and indirectly increases GAPDH and PGK1, based on our genomics analysis (PGK1, 3.54; GPI, 5.07; ENO1, 3.30; and GAPDH, 2.02), indicating the relation between Myc and glycolysis (Figure 5). The gene expression of Myc-related proteins was downregulated in the D-MG spheroids including PTBP1 (-0.60), SLC1A5 (-1.86), and PRDX3 (-1.77) (Figure 5). The low Myc activity inside the D-MG spheroids might indicate the slow proliferation of cells.Figure 5
c-Myc signaling in the D-MG group. (a) RNA-sequencing results related to Myc. (b) Relative transcript expression between D-MG and MG conditions for c-Myc and its downstream targets;∗ indicates p<0.05 (N=3). (c) Schematic diagram showing the major changes in D-MG versus MG for c-Myc signaling: D-MG shows enhanced c-Myc that mediates increased glycolysis.
(a)
(b)
(c)


### 3.3.3. p53 Signaling

In addition, the expression of the transcriptional suppressor CDK5RAP3 is comparable between the D-MG group and the MG group (0.34). CDK5RAP3 is a novel activator of PAK4 and processes important prometastatic function [51]. It has been reported that CDK5RAP3 knockdown upregulated the tumor suppressor p14ARF at protein and mRNA levels, and ectopic expression of CDK5RAP3 repressed the transcription of p14ARF [51]. Therefore, the CDK5RAP3 expression may allow for the synthesis of p14ARF and its binding to MDM2. The MDM2 (-0.75) expression was decreased in the D-MG spheroids (Figure 6). The MDM2 expression can release p53 from inhibition and thereby lead to the stabilization, accumulation, and activation of p53. Also, the negative regulators of p53 are subjected to tight feedback regulation. For example, BCCIPβ overexpression delays the G1 to S progression and results in an elevated p21 expression, which would inhibit CDK1 induction of cell cycle progression. Though the D-MG spheroids did not show a significant increase in BCCIPβ expression (0.13) compared to the MG group (Figure 6), the expression of CDK1 was decreased (-1.72) in the D-MG group, which indicated a potential cell cycle arrest.Figure 6
p53 signaling in the D-MG group. (a) RNA-sequencing results related to p53. (b) Relative transcript expression between D-MG and MG for p53 and its downstream targets;∗ indicates p<0.05 (N=3). (c) Schematic diagram showing the major changes in D-MG versus MG for p53 signaling: D-MG shows the increased p53 activities, which results in the decreased expression of proteins related to cell proliferation.
(a)
(b)
(c)


### 3.3.4. Wnt Pathway

In the canonical Wnt pathway, Wnt interacts with the Frizzled receptor that results in the inhibition of glycogen synthase kinase-3β (GSK-3β) [52]. Due to the inhibition, GSK-3β would not be able to phosphorylate β-catenin, leading to the nuclear entry of β-catenin. In the nucleus, β-catenin interacts with members of TCF transcriptional factors (i.e., T cell factor/lymphoid enhancer factor) and modulates target gene expression [53]. In our D-MG spheroids, the expression of β-catenin was slightly reduced compared to that in the MG group (CTNNB1, -0.66) (Figure 7). Wnt 3 (-1.06) and Wnt 5B (-1.55) were also decreased. The histidine triad nucleotide-binding protein 1 (HINT1, 0.23), which keeps the Wnt/β-catenin pathway inactive [54], and protein phosphorylase 2A (PPP2RA1, 0.28) were comparable in the two groups. Our previous studies evaluated the influence of the canonical Wnt pathway on neural patterning of hiPSCs [55, 56]. The reduced canonical Wnt/β-catenin signaling at a late stage of neural differentiation of hiPSCs can enrich cells with rostral forebrain identity [57]. In the noncanonical pathway, Wnt binds to the Frizzled receptor which activates Dishevelled and forms a complex with RAC1, mediating profilin binding to actin [58]. In the current study, the noncanonical Wnt pathway activity was comparable for the D-MG spheroids and MG group (RAC1, 0.27).Figure 7
Wnt signaling in the D-MG group. (a) RNA-sequencing results related to Wnt. (b) Relative transcript expression between D-MG and MG for Wnt and its downstream targets.∗ indicates p<0.05 (N=3). (c) Schematic diagram showing the major changes in D-MG versus MG conditions for Wnt signaling; i.e., D-MG shows the decreased canonical Wnt signaling while noncanonical Wnt is increased, which results in the increased cytoskeletal rearrangement.
(a)
(b)
(c)


### 3.3.5. NF-κB Pathway

NF-κB was found to be involved in cellular responses to various stimuli such as stress, cytokines, free radicals, and viral antigens [59, 60]. The two classes of NF-κB proteins (p50/p52 and ReIA/ReIB) form heterodimers to function as transcriptional activators. In an inactive state, NF-κB dimers are sequestered in the cytoplasm by IkB inhibitors. They mask the nuclear localization signals of NF-κB proteins and keep NF-κB signaling in the inactive state. When active, NF-κB proteins enter the nucleus and turn on the IkBα repressor. It has been reported that hypoxia upregulated PRMT1 which asymmetrically methylated ReIA, inhibiting the binding of ReIA to DNA and further repressing NF-κB [61]. NF-κB signaling was found to be dysregulated in TGFβR3 epicardial cells, which also showed the impaired cell invasion and revealed the role of NF-κB signaling in TGFβR3 activation [62]. In our D-MG spheroids, the lower expression of TGFβR3 (-2.61) and TGFB1 (-0.60) indicates the less activated NF-κB pathway (Supplementary Table S3 and Figure 4(b)). TGFBI expression is essentially similar (0.03), and PRMT1 expression (0.63) is slightly upregulated.Microglial M1 immune response usually involves NF-κB signaling [59]. Knocking down of LRP1 in primary microglia led to the activation of both c-Jun N-terminal kinase and NF-κB pathways. The sensitivity to lipopolysaccharide (LPS) stimulation in the production of proinflammatory cytokines was also enhanced. The NF-κB inhibitor was shown not only to suppress the production of cytokines induced by the knockdown of LRP1 but also to restore the downregulated expression of LRP1 due to LPS stimulation.


### 3.3.6. Notch Pathway

Most Notch receptors were upregulated in the D-MG group, including DLL1 (5.85), DLL3 (2.84), DLL4 (1.49), JAG1 (2.55), JAG2 (1.70), HES1 (0.62), HES4 (2.44), and HES5 (7.93) (Figure8(a)), indicating the enhanced cell-cell communications in 3-D culture. Our previous study also showed that Notch-1 expression was upregulated in the hybrid stem cell spheroids [63]. In addition, Notch signaling regulates the balance between the progenitor pool and the neuron pool, preventing premature neurogenesis [64]. Global deletion of Notch signaling leads to the accelerated differentiation into neurons. The activation of Notch by DLL1 causes neural stem cells to irreversibly commit to a glial fate and prevents the stem cells from adopting a neuronal fate [65]. When a Notch receptor interacts with its ligands, Delta and Serrate, it activates the membrane-tethered transcription factor and leads to the release of intracellular domain (NICD) and its translocation to the nucleus. Then, NICD interacts with CSL family regulators and transcript Hes1 and Hes5 expression. Notch signaling also promotes Wnt expression in Drosophila development [66]. Conversely, Wnt protein can promote the expression of Notch ligands DLLs, forming a positive feedback loop to maintain Notch signaling and Wnt protein expression.Figure 8
Log2 expression levels of the D-MG/MG ratios for genes about cell-cell interactions, cell death, and cell cycle. (a) Notch signaling. (b) Cell death. (c) Cell cycle and cell proliferation. (d) EIF2 signaling as a part of integrated stress response (ISR).
(a)
(b)
(c)
(d)


### 3.3.7. Cell Death and Cell Cycle

It is important to evaluate cell death pathways (necrosis or apoptosis) in the 3-D spheroids. The major changes associated with cell death are free radical damage, swelling, rupture, and cytolysis. Under stress, the cells sense DNA damage by the serine/threonine kinase ATM and p53 [67]. p53 accumulates in the mitochondrial matrix and triggers mitochondrial permeability transition (MPT) pore (PTP) opening. Then, p53 physically interacts with anti- and proapoptotic Bcl-2 and BAX family members to inhibit or activate their respective functions, leading to mitochondrial outer membrane permeabilization (MOMP) and apoptosis [68]. From our results, PPID (0.74), the essential component of the MPT pore located in the mitochondrial matrix, is slightly increased in the D-MG group (Figure 8(b)).The exact molecular composition of the MOMP complex is assumed to contain hexokinase, voltage-dependent anion channel (VDAC, on the outer membrane), the adenine nucleotide translocase (ANT, in the inner membrane), and cyclophilin D (a peptidyl-prolyl isomerase in the matrix). When PTP opens, it leads to matrix swelling and depolarization of the membrane potential, causing subsequent rupture of the outer membrane and consequently apoptosis [68]. Based on our results, VDACs were increased (VDAC1, 1.84; VDAC3, 0.90) in the D-MG group (Figure 8(b)). MPT-driven necrosis works not only by the dissipation of mitochondrial transmembrane potential but also by the latent chromatinolytic activity of AIFM1 (apoptosis-inducing factor mitochondrion-associated 1) [69]. The slightly decreased AIFM1 (-0.38) in the D-MG spheroids indicates that necrosis is favored over apoptosis.The mitochondrial HINT2 promotes angiogenesis via p53 and BAX. The activated NF-κB pathway also leads to the increased expression of antiapoptotic proteins, including Bcl-XL-binding protein v68 (PGAM5, 1.09) and Bcl-2-associated transcription factor 1 (BCLAF1, 0.96) in the D-MG spheroids (Figure 8(b)). The defender against apoptotic cell death (DAD1, 0.11) and Bcl-2 inhibitor of transcription 1 (PTRH2, 0.07) were unchanged. These antiapoptotic proteins can bind to and inactivate proapoptotic proteins. The proapoptotic protein BAX, Bcl-2-like protein 4, was unchanged (-0.02), but Bcl-2-associated athanogene 2 (BAG2, -2.90) was decreased in the D-MG spheroids. Consistent with higher expression of death genes in the D-MG group, the cell cycle-related genes were downregulated in the D-MG spheroids but highly expressed in the MG group (Figure 8(c)).The sirtuin signaling proteins SIRT1 (0.48) and SIRT6 (1.01) were upregulated in the D-MG spheroids (Table3), which regulate DNA damage and oxidative stress. The autophagy-related genes were essentially similar between the two groups. Poly (ADP-ribose) polymerase (PARP) is a family of proteins involved in a number of cellular processes such as DNA repair, genomic stability, and programmed cell death [70]. PARP6 (1.32), PARP3 (1.12), and PARP2 (0.97) were upregulated while PARP4 (-1.34), PARP14 (-2.44), PARP12 (-2.54), and PARP9 (-2.76) were downregulated in the D-MG group (Table 4). PARP1 (-0.01) was essentially unchanged between the two groups.Table 3
Sirtuin signaling and autophagy.
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
DMG/MG
LOG2 (D-MG/MG)
Sirtuin signaling
SIRT6
11.268
9.088
9.289
20.250
21.520
17.741
9.882
19.837
2.007
1.01
SIRT1
8.625
8.714
9.529
12.048
12.060
13.258
8.956
12.455
1.391
0.48
SIRT4
3.945
5.113
3.877
5.457
4.254
5.735
4.312
5.148
1.194
0.26
SIRT7
7.837
8.696
7.100
8.127
7.686
7.362
7.877
7.725
0.981
-0.03
SIRT5
1.763
2.161
1.591
1.743
1.551
1.505
1.838
1.599
0.870
-0.20
SIRT3
13.160
13.397
12.523
8.864
7.755
8.300
13.027
8.306
0.638
-0.65
SIRT2
50.715
42.689
48.365
27.236
31.360
27.618
47.256
28.738
0.608
-0.72
Autophagy
TFEB
5.357
4.069
5.025
4.723
5.425
5.416
4.817
5.188
1.077
0.11
BECN1
13.675
11.764
13.135
12.298
12.239
12.236
12.858
12.257
0.953
-0.07
LAMP1
57.074
48.954
58.426
46.114
51.499
47.309
54.818
48.307
0.881
-0.72
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.Table 4
Gene expression of poly (ADP-ribose) polymerase (PARP) family proteins.
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
DMG/MG
LOG2 (D-MG/MG)
PARP6
19.908
24.159
20.683
55.689
59.154
54.018
21.583
56.287
2.608
1.38
PARP3
3.171
3.020
3.221
6.723
7.323
6.366
3.137
6.804
2.169
1.12
PARP2
17.659
22.634
16.142
35.570
39.811
34.828
18.812
36.736
1.953
0.97
PARP11
3.485
3.789
3.403
6.320
6.097
6.702
3.559
6.373
1.791
0.84
PARP15
0.163
0.124
0.127
0.286
0.263
0.179
0.138
0.243
1.759
0.81
PARP8
2.142
2.526
2.428
3.305
3.204
3.578
2.365
3.362
1.422
0.51
PARP1
42.914
40.611
42.447
41.423
43.048
40.686
41.990
41.719
0.994
-0.01
PARP10
1.451
1.234
1.709
1.518
1.198
1.385
1.465
1.367
0.933
-0.10
PARP16
12.871
12.281
11.812
7.840
8.730
9.558
12.321
8.709
0.707
-0.50
PARP4
8.746
9.413
8.348
3.874
3.104
3.526
8.836
3.501
0.396
-1.34
PARP14
7.616
10.770
8.298
1.482
1.613
1.818
8.895
1.638
0.184
-2.44
PARP12
1.519
1.168
1.288
0.182
0.274
0.228
1.325
0.228
0.172
-2.54
PARP9
3.633
3.688
3.397
0.533
0.460
0.586
3.573
0.526
0.147
-2.76
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.


### 3.3.8. Eukaryotic Initiation Factor 2 (eIF2) Pathway

eIF2 is one of the critical translation G proteins that are tightly regulated in the integrated stress response. In response to stress stimuli, eIF2α undergoes phosphorylation via different kinases, such as PKR-like endoplasmic reticulum kinase (PERK), which results in the formation of eIF2B. This blocks the p-eIF2 from its active GTP-bound state, causing a reduction of the translation of most mRNAs [71]. In the operative nervous systems, the eIF pathway was found to support the connectivity between neurons. The physiological extracellular cue Sema3A can trigger rapid and transient phosphorylation of eIF2a in axons for the axonal translational changes [72]. The D-MG group displayed the slightly elevated expression of eIF proteins, such as eIF-2α/EIF2S1 (0.220), eIF2β/EIF2S2 (0.274), and eIF2B proteins, such as EIF2B1 (0.43), EIF2B2 (0.47), EIF2B3 (0.45), and EIF2B5 (0.66) (Figure 8(d)), indicating potential stress such as hypoxia or insufficient nutrient uptake in the D-MG group. Also, the expression of EIF2AK3 (0.64), a stress sensing protein kinase that phosphorylates the alpha subunit of eIF2, was slightly elevated in the D-MG group. DDIT3 (0.58), which plays an essential role in inducing cell cycle arrest and apoptosis in response to ER stress, was also slightly elevated in the D-MG group. PPP1R15A (1.17) dephosphorylates the eIF-2α/EIF2S1, thereby reversing the shutoff of protein synthesis initiated by stress-inducible kinases and then facilitating cell recovery from stress.


### 3.3.9. Extracellular Matrix

The extracellular matrix- (ECM-) related genes were found to be differentially expressed in the two groups (Supplementary FigureS3) [1]. The D-MG group had higher expression of some collagen genes including COL7A1, COL20A1, COL9A1, and COL11A2. The MG group expressed high levels of different types of collagens, including COL23A1, COL6A1, COL4A1, COL13A1, COL5A2, COL5A1, COL14A1, and COL4A1 [2]. The D-MG group mainly enriched laminin LAMB4, while the MG group enriched LAMA3, LAMB1, LAMC1, LAMA2, LAMA1, etc. [3]. The D-MG group expressed higher levels of specific integrins, including ITGA7, ITGB8, ITGA2, and ITGAB3, while the MG group enriched ITGA6, ITGA11, ITGA8, ITGB6, ITGA1, ITGB5, etc. [4]. For ECM remodeling genes, the D-MG group enriched proteases MMP17, MMP10, MMP24, MMP7, and MMP1; the MG group had higher expression of proteases MMP23, MMP2, MMP14, TIMP1, MMP28, MMP26, and MMP15, etc.Other pathways of our interests are shown in Supplementary TableS4, which include the AMPK pathway and PDL1/PD1 pathway (Figure 9 shows the relationship between miRNAs and PDL1/PD1 interactions) (Figure 9) [73]. Microglia, the main antigen-presenting cells in the human brain, maintain the equilibrium with T cells through the PD1 pathway as reported [74].Figure 9
Illustration of microglia with PDL1/PD1 interactions. The numbers in the illustration are from the normal DMG/MG ratios. The arrows show the ways of tumor cell and T cell interactions. MGs act as antigen-presenting cells (APC).


## 3.3.1. PIK3/AKT/mTOR Signaling

PI3K (a class I phosphoinositide 3-kinase) is one of the key signaling enzymes that are activated in 3-D spheroid cultures [45]. And the PIK3 pathway is directly related to cellular metabolism and growth. When cells consume the nutrients and growth factors, they generate amino acids, glucose, oxygen, and energy (i.e., ATP) and use these products to activate GTPase, such as Rheb, through PI3K. One key regulator of metabolism and growth-activated downstream of PI3K signaling is the mechanistic target of rapamycin (mTOR) [46]. Our results showed that the expression of mTOR (0.66) was slightly elevated in the D-MG spheroids and the downstream effectors were inhibited (e.g., ribosomal protein s6 kinase RPS6KA3, -1.87) (Figure 4(a)). mTOR stimulates pyrimidine synthesis via the RPS6KA-mediated phosphorylation of CAD (0.46), whereas AKT can phosphorylate ACL, enhancing its lipogenic activities and mTOR signaling. The expression of ACLY (-0.13) indicates that the activity of the AKT pathway was similar in the two groups. mTOR regulates several anabolic and catabolic pathways through fatty acid and cholesterol biosynthetic genes via the SREBP family (SREBP1 and SREBP2) [47].Figure 4
mTOR signaling in the D-MG group. (a) RNA-sequencing results. (b) Validation of ERK genes using RT-PCR.∗ indicates p<0.05 (n=3). (c) Schematic diagram showing the major changes in D-MG versus MG condition for mTOR signaling. D-MG shows enhanced mTOR activities that reduce tricarboxylic cycle (TCA), and D-MG shows increased glycolysis mediated by mTOR.
(a)
(b)
(c)mTOR signaling was reported to increase the translation of HIF-1α, glucose transporters, and glycolytic enzymes and promote metabolic reprogramming. The activation of the mTOR protein complex mTORC1 leads to the induction of genes encoding the enzymes of glycolysis, the pentose phosphate pathway, and lipid and sterol biosynthesis [35, 48]. mTOR was also reported to coordinate the activation of cell growth machinery together with amino acids in the presence of growth signals [49]. However, in the RT-PCR validation, mTOR gene expression was statistically insignificant, possibly due to assay variations (Figures 4(b) and 4(c)). In addition, ERK1 was downregulated while ERK2 was upregulated in the D-MG group.


## 3.3.2. Myc Signaling

Myc is a transcription factor that dimerizes with MAX to bind to DNA and regulate gene expression involved in metabolism (glycolysis and glutaminolysis) and biosynthesis (nucleotide and lipid synthesis) [50]. Myc supports specific mRNA splice variants, such as glycolysis-affecting PKM2 over PKM1. Myc targets glucose membrane transporters such as GLUT1 (or SLC2A1) and glutamine transporter SLC1A5, which are important in cell proliferation [50]. Myc transactivates the gene expression of PFK, ENO, and LDHA (1.99) and indirectly increases GAPDH and PGK1, based on our genomics analysis (PGK1, 3.54; GPI, 5.07; ENO1, 3.30; and GAPDH, 2.02), indicating the relation between Myc and glycolysis (Figure 5). The gene expression of Myc-related proteins was downregulated in the D-MG spheroids including PTBP1 (-0.60), SLC1A5 (-1.86), and PRDX3 (-1.77) (Figure 5). The low Myc activity inside the D-MG spheroids might indicate the slow proliferation of cells.Figure 5
c-Myc signaling in the D-MG group. (a) RNA-sequencing results related to Myc. (b) Relative transcript expression between D-MG and MG conditions for c-Myc and its downstream targets;∗ indicates p<0.05 (N=3). (c) Schematic diagram showing the major changes in D-MG versus MG for c-Myc signaling: D-MG shows enhanced c-Myc that mediates increased glycolysis.
(a)
(b)
(c)


## 3.3.3. p53 Signaling

In addition, the expression of the transcriptional suppressor CDK5RAP3 is comparable between the D-MG group and the MG group (0.34). CDK5RAP3 is a novel activator of PAK4 and processes important prometastatic function [51]. It has been reported that CDK5RAP3 knockdown upregulated the tumor suppressor p14ARF at protein and mRNA levels, and ectopic expression of CDK5RAP3 repressed the transcription of p14ARF [51]. Therefore, the CDK5RAP3 expression may allow for the synthesis of p14ARF and its binding to MDM2. The MDM2 (-0.75) expression was decreased in the D-MG spheroids (Figure 6). The MDM2 expression can release p53 from inhibition and thereby lead to the stabilization, accumulation, and activation of p53. Also, the negative regulators of p53 are subjected to tight feedback regulation. For example, BCCIPβ overexpression delays the G1 to S progression and results in an elevated p21 expression, which would inhibit CDK1 induction of cell cycle progression. Though the D-MG spheroids did not show a significant increase in BCCIPβ expression (0.13) compared to the MG group (Figure 6), the expression of CDK1 was decreased (-1.72) in the D-MG group, which indicated a potential cell cycle arrest.Figure 6
p53 signaling in the D-MG group. (a) RNA-sequencing results related to p53. (b) Relative transcript expression between D-MG and MG for p53 and its downstream targets;∗ indicates p<0.05 (N=3). (c) Schematic diagram showing the major changes in D-MG versus MG for p53 signaling: D-MG shows the increased p53 activities, which results in the decreased expression of proteins related to cell proliferation.
(a)
(b)
(c)


## 3.3.4. Wnt Pathway

In the canonical Wnt pathway, Wnt interacts with the Frizzled receptor that results in the inhibition of glycogen synthase kinase-3β (GSK-3β) [52]. Due to the inhibition, GSK-3β would not be able to phosphorylate β-catenin, leading to the nuclear entry of β-catenin. In the nucleus, β-catenin interacts with members of TCF transcriptional factors (i.e., T cell factor/lymphoid enhancer factor) and modulates target gene expression [53]. In our D-MG spheroids, the expression of β-catenin was slightly reduced compared to that in the MG group (CTNNB1, -0.66) (Figure 7). Wnt 3 (-1.06) and Wnt 5B (-1.55) were also decreased. The histidine triad nucleotide-binding protein 1 (HINT1, 0.23), which keeps the Wnt/β-catenin pathway inactive [54], and protein phosphorylase 2A (PPP2RA1, 0.28) were comparable in the two groups. Our previous studies evaluated the influence of the canonical Wnt pathway on neural patterning of hiPSCs [55, 56]. The reduced canonical Wnt/β-catenin signaling at a late stage of neural differentiation of hiPSCs can enrich cells with rostral forebrain identity [57]. In the noncanonical pathway, Wnt binds to the Frizzled receptor which activates Dishevelled and forms a complex with RAC1, mediating profilin binding to actin [58]. In the current study, the noncanonical Wnt pathway activity was comparable for the D-MG spheroids and MG group (RAC1, 0.27).Figure 7
Wnt signaling in the D-MG group. (a) RNA-sequencing results related to Wnt. (b) Relative transcript expression between D-MG and MG for Wnt and its downstream targets.∗ indicates p<0.05 (N=3). (c) Schematic diagram showing the major changes in D-MG versus MG conditions for Wnt signaling; i.e., D-MG shows the decreased canonical Wnt signaling while noncanonical Wnt is increased, which results in the increased cytoskeletal rearrangement.
(a)
(b)
(c)


## 3.3.5. NF-κB Pathway

NF-κB was found to be involved in cellular responses to various stimuli such as stress, cytokines, free radicals, and viral antigens [59, 60]. The two classes of NF-κB proteins (p50/p52 and ReIA/ReIB) form heterodimers to function as transcriptional activators. In an inactive state, NF-κB dimers are sequestered in the cytoplasm by IkB inhibitors. They mask the nuclear localization signals of NF-κB proteins and keep NF-κB signaling in the inactive state. When active, NF-κB proteins enter the nucleus and turn on the IkBα repressor. It has been reported that hypoxia upregulated PRMT1 which asymmetrically methylated ReIA, inhibiting the binding of ReIA to DNA and further repressing NF-κB [61]. NF-κB signaling was found to be dysregulated in TGFβR3 epicardial cells, which also showed the impaired cell invasion and revealed the role of NF-κB signaling in TGFβR3 activation [62]. In our D-MG spheroids, the lower expression of TGFβR3 (-2.61) and TGFB1 (-0.60) indicates the less activated NF-κB pathway (Supplementary Table S3 and Figure 4(b)). TGFBI expression is essentially similar (0.03), and PRMT1 expression (0.63) is slightly upregulated.Microglial M1 immune response usually involves NF-κB signaling [59]. Knocking down of LRP1 in primary microglia led to the activation of both c-Jun N-terminal kinase and NF-κB pathways. The sensitivity to lipopolysaccharide (LPS) stimulation in the production of proinflammatory cytokines was also enhanced. The NF-κB inhibitor was shown not only to suppress the production of cytokines induced by the knockdown of LRP1 but also to restore the downregulated expression of LRP1 due to LPS stimulation.


## 3.3.6. Notch Pathway

Most Notch receptors were upregulated in the D-MG group, including DLL1 (5.85), DLL3 (2.84), DLL4 (1.49), JAG1 (2.55), JAG2 (1.70), HES1 (0.62), HES4 (2.44), and HES5 (7.93) (Figure8(a)), indicating the enhanced cell-cell communications in 3-D culture. Our previous study also showed that Notch-1 expression was upregulated in the hybrid stem cell spheroids [63]. In addition, Notch signaling regulates the balance between the progenitor pool and the neuron pool, preventing premature neurogenesis [64]. Global deletion of Notch signaling leads to the accelerated differentiation into neurons. The activation of Notch by DLL1 causes neural stem cells to irreversibly commit to a glial fate and prevents the stem cells from adopting a neuronal fate [65]. When a Notch receptor interacts with its ligands, Delta and Serrate, it activates the membrane-tethered transcription factor and leads to the release of intracellular domain (NICD) and its translocation to the nucleus. Then, NICD interacts with CSL family regulators and transcript Hes1 and Hes5 expression. Notch signaling also promotes Wnt expression in Drosophila development [66]. Conversely, Wnt protein can promote the expression of Notch ligands DLLs, forming a positive feedback loop to maintain Notch signaling and Wnt protein expression.Figure 8
Log2 expression levels of the D-MG/MG ratios for genes about cell-cell interactions, cell death, and cell cycle. (a) Notch signaling. (b) Cell death. (c) Cell cycle and cell proliferation. (d) EIF2 signaling as a part of integrated stress response (ISR).
(a)
(b)
(c)
(d)


## 3.3.7. Cell Death and Cell Cycle

It is important to evaluate cell death pathways (necrosis or apoptosis) in the 3-D spheroids. The major changes associated with cell death are free radical damage, swelling, rupture, and cytolysis. Under stress, the cells sense DNA damage by the serine/threonine kinase ATM and p53 [67]. p53 accumulates in the mitochondrial matrix and triggers mitochondrial permeability transition (MPT) pore (PTP) opening. Then, p53 physically interacts with anti- and proapoptotic Bcl-2 and BAX family members to inhibit or activate their respective functions, leading to mitochondrial outer membrane permeabilization (MOMP) and apoptosis [68]. From our results, PPID (0.74), the essential component of the MPT pore located in the mitochondrial matrix, is slightly increased in the D-MG group (Figure 8(b)).The exact molecular composition of the MOMP complex is assumed to contain hexokinase, voltage-dependent anion channel (VDAC, on the outer membrane), the adenine nucleotide translocase (ANT, in the inner membrane), and cyclophilin D (a peptidyl-prolyl isomerase in the matrix). When PTP opens, it leads to matrix swelling and depolarization of the membrane potential, causing subsequent rupture of the outer membrane and consequently apoptosis [68]. Based on our results, VDACs were increased (VDAC1, 1.84; VDAC3, 0.90) in the D-MG group (Figure 8(b)). MPT-driven necrosis works not only by the dissipation of mitochondrial transmembrane potential but also by the latent chromatinolytic activity of AIFM1 (apoptosis-inducing factor mitochondrion-associated 1) [69]. The slightly decreased AIFM1 (-0.38) in the D-MG spheroids indicates that necrosis is favored over apoptosis.The mitochondrial HINT2 promotes angiogenesis via p53 and BAX. The activated NF-κB pathway also leads to the increased expression of antiapoptotic proteins, including Bcl-XL-binding protein v68 (PGAM5, 1.09) and Bcl-2-associated transcription factor 1 (BCLAF1, 0.96) in the D-MG spheroids (Figure 8(b)). The defender against apoptotic cell death (DAD1, 0.11) and Bcl-2 inhibitor of transcription 1 (PTRH2, 0.07) were unchanged. These antiapoptotic proteins can bind to and inactivate proapoptotic proteins. The proapoptotic protein BAX, Bcl-2-like protein 4, was unchanged (-0.02), but Bcl-2-associated athanogene 2 (BAG2, -2.90) was decreased in the D-MG spheroids. Consistent with higher expression of death genes in the D-MG group, the cell cycle-related genes were downregulated in the D-MG spheroids but highly expressed in the MG group (Figure 8(c)).The sirtuin signaling proteins SIRT1 (0.48) and SIRT6 (1.01) were upregulated in the D-MG spheroids (Table3), which regulate DNA damage and oxidative stress. The autophagy-related genes were essentially similar between the two groups. Poly (ADP-ribose) polymerase (PARP) is a family of proteins involved in a number of cellular processes such as DNA repair, genomic stability, and programmed cell death [70]. PARP6 (1.32), PARP3 (1.12), and PARP2 (0.97) were upregulated while PARP4 (-1.34), PARP14 (-2.44), PARP12 (-2.54), and PARP9 (-2.76) were downregulated in the D-MG group (Table 4). PARP1 (-0.01) was essentially unchanged between the two groups.Table 3
Sirtuin signaling and autophagy.
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
DMG/MG
LOG2 (D-MG/MG)
Sirtuin signaling
SIRT6
11.268
9.088
9.289
20.250
21.520
17.741
9.882
19.837
2.007
1.01
SIRT1
8.625
8.714
9.529
12.048
12.060
13.258
8.956
12.455
1.391
0.48
SIRT4
3.945
5.113
3.877
5.457
4.254
5.735
4.312
5.148
1.194
0.26
SIRT7
7.837
8.696
7.100
8.127
7.686
7.362
7.877
7.725
0.981
-0.03
SIRT5
1.763
2.161
1.591
1.743
1.551
1.505
1.838
1.599
0.870
-0.20
SIRT3
13.160
13.397
12.523
8.864
7.755
8.300
13.027
8.306
0.638
-0.65
SIRT2
50.715
42.689
48.365
27.236
31.360
27.618
47.256
28.738
0.608
-0.72
Autophagy
TFEB
5.357
4.069
5.025
4.723
5.425
5.416
4.817
5.188
1.077
0.11
BECN1
13.675
11.764
13.135
12.298
12.239
12.236
12.858
12.257
0.953
-0.07
LAMP1
57.074
48.954
58.426
46.114
51.499
47.309
54.818
48.307
0.881
-0.72
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.Table 4
Gene expression of poly (ADP-ribose) polymerase (PARP) family proteins.
Gene name
MG-1
MG-2
MG-3
D-MG-1
D-MG-2
D-MG-3
MG ave
DMG ave
DMG/MG
LOG2 (D-MG/MG)
PARP6
19.908
24.159
20.683
55.689
59.154
54.018
21.583
56.287
2.608
1.38
PARP3
3.171
3.020
3.221
6.723
7.323
6.366
3.137
6.804
2.169
1.12
PARP2
17.659
22.634
16.142
35.570
39.811
34.828
18.812
36.736
1.953
0.97
PARP11
3.485
3.789
3.403
6.320
6.097
6.702
3.559
6.373
1.791
0.84
PARP15
0.163
0.124
0.127
0.286
0.263
0.179
0.138
0.243
1.759
0.81
PARP8
2.142
2.526
2.428
3.305
3.204
3.578
2.365
3.362
1.422
0.51
PARP1
42.914
40.611
42.447
41.423
43.048
40.686
41.990
41.719
0.994
-0.01
PARP10
1.451
1.234
1.709
1.518
1.198
1.385
1.465
1.367
0.933
-0.10
PARP16
12.871
12.281
11.812
7.840
8.730
9.558
12.321
8.709
0.707
-0.50
PARP4
8.746
9.413
8.348
3.874
3.104
3.526
8.836
3.501
0.396
-1.34
PARP14
7.616
10.770
8.298
1.482
1.613
1.818
8.895
1.638
0.184
-2.44
PARP12
1.519
1.168
1.288
0.182
0.274
0.228
1.325
0.228
0.172
-2.54
PARP9
3.633
3.688
3.397
0.533
0.460
0.586
3.573
0.526
0.147
-2.76
The FPKM (fragments per kilobase per million reads) normalized values for these genes are listed for both samples. The numbers are the log2 values of ratios of D-MG to MG. Negative values indicate that the genes are present in higher amounts in the MG group, while positive values indicate that the genes are present in higher amounts in the D-MG group. The value 1 indicates two-fold change.


## 3.3.8. Eukaryotic Initiation Factor 2 (eIF2) Pathway

eIF2 is one of the critical translation G proteins that are tightly regulated in the integrated stress response. In response to stress stimuli, eIF2α undergoes phosphorylation via different kinases, such as PKR-like endoplasmic reticulum kinase (PERK), which results in the formation of eIF2B. This blocks the p-eIF2 from its active GTP-bound state, causing a reduction of the translation of most mRNAs [71]. In the operative nervous systems, the eIF pathway was found to support the connectivity between neurons. The physiological extracellular cue Sema3A can trigger rapid and transient phosphorylation of eIF2a in axons for the axonal translational changes [72]. The D-MG group displayed the slightly elevated expression of eIF proteins, such as eIF-2α/EIF2S1 (0.220), eIF2β/EIF2S2 (0.274), and eIF2B proteins, such as EIF2B1 (0.43), EIF2B2 (0.47), EIF2B3 (0.45), and EIF2B5 (0.66) (Figure 8(d)), indicating potential stress such as hypoxia or insufficient nutrient uptake in the D-MG group. Also, the expression of EIF2AK3 (0.64), a stress sensing protein kinase that phosphorylates the alpha subunit of eIF2, was slightly elevated in the D-MG group. DDIT3 (0.58), which plays an essential role in inducing cell cycle arrest and apoptosis in response to ER stress, was also slightly elevated in the D-MG group. PPP1R15A (1.17) dephosphorylates the eIF-2α/EIF2S1, thereby reversing the shutoff of protein synthesis initiated by stress-inducible kinases and then facilitating cell recovery from stress.


## 3.3.9. Extracellular Matrix

The extracellular matrix- (ECM-) related genes were found to be differentially expressed in the two groups (Supplementary FigureS3) [1]. The D-MG group had higher expression of some collagen genes including COL7A1, COL20A1, COL9A1, and COL11A2. The MG group expressed high levels of different types of collagens, including COL23A1, COL6A1, COL4A1, COL13A1, COL5A2, COL5A1, COL14A1, and COL4A1 [2]. The D-MG group mainly enriched laminin LAMB4, while the MG group enriched LAMA3, LAMB1, LAMC1, LAMA2, LAMA1, etc. [3]. The D-MG group expressed higher levels of specific integrins, including ITGA7, ITGB8, ITGA2, and ITGAB3, while the MG group enriched ITGA6, ITGA11, ITGA8, ITGB6, ITGA1, ITGB5, etc. [4]. For ECM remodeling genes, the D-MG group enriched proteases MMP17, MMP10, MMP24, MMP7, and MMP1; the MG group had higher expression of proteases MMP23, MMP2, MMP14, TIMP1, MMP28, MMP26, and MMP15, etc.Other pathways of our interests are shown in Supplementary TableS4, which include the AMPK pathway and PDL1/PD1 pathway (Figure 9 shows the relationship between miRNAs and PDL1/PD1 interactions) (Figure 9) [73]. Microglia, the main antigen-presenting cells in the human brain, maintain the equilibrium with T cells through the PD1 pathway as reported [74].Figure 9
Illustration of microglia with PDL1/PD1 interactions. The numbers in the illustration are from the normal DMG/MG ratios. The arrows show the ways of tumor cell and T cell interactions. MGs act as antigen-presenting cells (APC).


## 4. Conclusions

The genomics data reveal that the D-MG spheroids have higher expression of genes for glycolysis and hypoxia signaling, showing the metabolic shift to aerobic glycolysis, consistent with M1 polarization of microglia. However, glutamine conversion is not activated. The signaling pathway activities (activation of mTOR and p53, repression of NF-κB and canonical Wnt) are consistent with the slower proliferation rate and the accumulation of differentiated cells. The additional NADPH needed for citrate and lipid synthesis is mainly generated by pentose phosphate pathway activation. The reduction in the proliferation rate allows the cells to achieve higher ATP levels in the spheroids. The D-MG group enriches genes for NOTCH signaling, but not canonical Wnt signaling. The MG group has higher expression of genes related to cell cycle and proliferation. These results can help to establish better coculturing methods for efficiently mimicking the in vivo structure of the central nervous system.



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*Source: 2382534-2019-11-18.xml*