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In Silico Comparative analysis of Cancer and Stem Cell Microarray data to demonstrate Molecular Transitions and the relative involvement of Glycolysis and Oxidative Phosphorylation cost-effective correlation of Bioenergetics and differentiation Processes -Applications In Drug Development

Ayyappa Kumar S. K, Suresh P. K*
Bio Molecules & Genetics Division, School of Bio Sciences and Technology, VIT University Vellore, Vellore Dt. PIN:632014 (Tamil Nadu, India).
Corresponding Author: Suresh P, Mobile: 9444047684 E-mail: p.k.suresh@vit.ac.in; indian.ethos@gmail.com
Date of Submission: 22-01-2015 Date of Acceptance: 09-02-2015 Conflict of Interest: NIL Source of Support: NONE
Copyright: © 2015 Suresh P. K. et al, publisher and licensee IYPF. This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited.
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Abstract

In the present study, we have validated the molecular switching between Glycolysis and Oxidative phosphorylation (OXPHOS) in certain cell types using open access in silico tools. Most of the cancers cells predominantly show higher rate of glycolysis (pyruvate formation) followed by lactic acid fermentation, in contrast with the predominant role of mitochondria in non- cancerous cells. This type of molecular switching is also noticed during the early stages of embryonic development, wherein the metabolic shift from OXPHOS to glycolysis occurs. Hence, it is necessary to study the switching between these pathways to most accurately replicate physiological conditions ex vivo/in vitro in differentiation/targeted differentiation-based studies. Further, an approach of this nature can help in further documenting the role of aberrations in cellular bioenergetics, contributing to neoplasia. A set of microarray datasets were selected from the NCBI GEO database, modelling three physiological conditions i.e., early development (stem cell), cancer, and Induced pluripotent cells (iPSCs). Data was analyzed using the 'R' software with packages imported from Bioconductor. Our results confirm the aforesaid switches/metabolic transitions in the selected data sets representing key cell types at various stages in the differentiation pathway. To the best of our knowledge, this is the first study of its kind wherein an in silico approach has been adopted to document/demonstrate the cellular, molecular transitions in two major metabolic pathways with ramifications in basic biology and drug development.

Keywords

In silico; microarray; glycolysis; oxidative phosphorylation; Gene Expression Omnibus database (GEO), Oxidative Phosphorylation (OXPHOS), Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA).

Introduction

There are primarily two sources for ATP production– critical energy that is utilized for cell proliferation and survival (Glycolysis and Oxidative phosphorylation). Glycolysis (series of 10 enzymecatalyzed reactions), involves the conversion of glucose to pyruvate to produce 2 ATPs per molecule of glucose. Tri-carboxylic acid (TCA) or Krebs cycle uses the resultant glycolysis-derived pyruvate, in a sequential set of electron-donating reactions. These electrons are channelled to the respiratory chain in mitochondria, using NADH and FADH2 as the electron carriers. The end result is the ATP-synthase-mediated production of 36 ATPs per glucose molecule, produced via proton flow (down the electrochemical gradient) favouring the energetically unfavourable reaction between ADP and Pi. Apart from ATP production, oxygen, the final electron acceptor is reduced to water. Under relatively hypoxic conditions, as in the case of the muscle tissue of an individual after prolonged exercise, anaerobic glycolysis may be the preferred route with the metabolism of pyruvate to lactic acid by lactate dehydrogenase [1][2]. Similarly, hypoxic conditions in the early embryonic stages cause a metabolic shift from oxidative phosphorylation to glycolysis. However, oxidative phosphorylation is fully reinstituted upon preimplantation [3]. Such a relatively higher glycolysis-dependency, has also been observed in the case of the Inner Cell Mass (ICM), correlatable with spherical, depolarized, immature perinuclearly localized mitochondria with low oxygen consumption, in contrast with those seen in the case of the Trophoectoderm [4][5][6]. Human embryonic pluripotent stem cells differentiation is accompanied by the acquisition of a more mature mitochondrial phenotype and a metabolic transition from glycolysis to OXPHOS [6][7][8][9]. This finding, corroborates the majority view of several authors, in terms of hypoxia being beneficial for the maintenance of hESCs in a pluripotent state [10,11] with reprogramming efficiency being favoured, under relatively high hypoxic conditions (low O2 tensions), in both mouse and human somatic cells [12].
It is known that glycolysis and OXPHOS usually work in a coordinated manner, and the latter pathway produces around 15 times more ATPs per molecule of oxidized glucose than the former [13]. The conversion of glucose to glucose- 6-phosphate by hexokinases is the one of the major rate-limiting steps in glycolysis, apart from membrane transport. Among other factors, Increased hexokinase activity, HK compartmentalization and amount of inhibitors are important regulators [14]. Further, the end result of the aforesaid 10 enzyme-catalysed reactions culminates in the production of two molecules each of pyruvate, NADH and ATP. Subsequently, pyruvate enters the TCA and is converted to acetyl coenzyme-A (source of reducing agents for the ETC-mediated ATP synthesis). This Krebs cycle intermediate links glycolysis with the OXPHOS pathway [15]. On the other hand, a relatively hypoxic microenvironment (wherein low O2 tensions prevail, and/or the presence of dysfunctional mitochondria) will promote lactate production from pyruvate [16]. One of the important mitochondrial matrix enzyme complexes is the pyruvate dehydrogenase (PDH) complex, and this enzyme provides an important link between glycolysis and the TCA cycle. The enzyme has 3 subunits (E1, E2 and E3) and several copies of this enzyme are involved in the irreversible decarboxylation of pyruvate to acetyl-coenzyme A and NADH. Phosphorylation of the E1-a subunit, by one of the four pyruvate dehydrogenase kinase isoforms (PDHK1-4), leads to the complexbeing inactivated. Conversely, removal of one phosphate group by one of the two pyruvate dehydrogenase phosphatases (PDP1 and PDP2) activates the complex [17]
Earlier studies have confirmed that glycolysis uptake increases during the morula stage. It was studied in morula, that glucose is required to express the glucose transporter GLUT3 which is essential for blastocyst formation and is also responsible for the expression of other monocarboxylate transporters [18,19]. In pluripotent stem cells, the hexokinases and lactate dehydrogenases are highly expressed and their activity is the highly rate-limiting reactions in glycolysis. These genes are further controlled by the pro-survival mTOR and PI3K pathways [20]. In cancer cells, the high consumption of ATP, pyruvate conversion to acetyl CoA by PDH and entry in to mitochondria, is blocked in hypoxic cancer cells by hypoxia inducible factor 1A (HIF1A) [21]. The metabolic shift between glycolysis and oxidative phosphorylation in stem cells, cancer cells and Induced pluripotent stem cells [22] microarray data were studied to verify the experimentally-determined differentially expressed gene [23]. Our in silico analysis has demonstrated that glucose transporters, hexokinase, pyruvate dehydrogenese as well as the pyruvate kinases are expressed highly in stem cells, cancer cells and pluripotent stem cells. Based on the oxygen consumption rate, the expression of the oxidative phosphorylation genes are differentially expressed in stem cells, iPSCs as well as in the early embryonic stages. Due to the underdevelopment of mitochondria before the pre-implantation stage, the oxidative phosphorylation genes are less expressed. Our study, for the first time, provides an in silico strategy for better understanding molecular transitions in glycolysis and OXPHOS in certain cell types with ramifications in drug/small molecule development.

Materials and Methods

A set of microarray data sets were selected from the NCBI GEO repository http://www.ncbi.nlm.nih.gov/geo/. The accession IDs of these datasets are GSE18290 [24], GSE35373 [25], GSE13834 [26], GSE15744 [27] and GSE26910 [28]. These data sets represent gene expression patterns in three physiological conditions at the cellular level (early embryonic stem cells, Cancer cells and iPSCs).

Statistical Analysis:

Microarray data analysis was performed using the R statistical software. The microarray analysis packages were imported from the Bioconductor website. The affymetrix datasets were analyzed with the packages like 'affy'[29], 'genefilter' [30] 'simpleaffy'[31]. The Affymetrix datasets were normalized using the RMA algorithm. This algorithm performs probe summarization, background correction and normalization (lowess normalisation). Later, the normalized data was subjected to quality control analysis by filtering the data (based on the expression values) on expression using the 'simpleaffy' and ‘genefilter’ packages. The experiment grouping information was done based on the respective experimental design. Based on the sample grouping, statistical analysis was performed using the 'limma' [32] package by fitting the models. The false positives and negatives were removed using the Benjamini Hochberg FDR multiple testing correction method (p <0.05) to identify statistically significant data.

Biological Contextualization:

Biological relevance of the statistically significant data was evaluated by retrieving the annotations from the Database for Annotation, Visualization and Integrated Discovery (DAVID) [33, 34] and R. Later, the data was scanned for the biological relevance using the Gene Ontology (GO) [35] and Gene Set Enrichment analysis (GSEA) [36]. Our study mainly focused on metabolic shifts between Glycolysis and Oxidative phosphorylation in early embryonic development, cancer progression and stem cells. The entities involved in these two pathways were taken from the Wiki pathways. The overlapping/distinct, significantly altered genes (involved in the above two pathways) in the different microarray expression datasets, were converted into a Venn diagram, using the Venny online software application. Gene Set Enrichment Analysis (GSEA) was performed on the statistically significant gene list on the c1, c2, c3, c4, c5, c6, c7gene sets using GSEA software, the following gene setsKEGG_Oxidative_Phosphorylation,KEGG_Citrat e_Cycle_TCA_Cycle,BIOCARTA_Glycolysis_pathw ay, KEGG_Pyruvate_metabolism were obtained at a enrichment factor of 0.3 to 0.4 and at p-value of 0.05. The above results show that genes involved in glycolysis and oxidative phosphorylation are highly enriched at different stages of embryonic development, cancer progression and also in induced pluripotent stem cells.

Results and Discussions

The bioenergetic studies can provide a better understanding of their regulatory role in stem cells. Hence, two major metabolic pathways were selected to better understand their relative importance in stem cells as well as possibly provide overlapping/distinct bioenergetic snapshots that can be correlated with their differentiation status. Hence, we have selected the microarray datasets belonging to three physiological states (early embryonic stem cells, cancer cells and iPSCs). The GSE 18290, GSE35373, GSE 13834 represents data from stem cells and iPSCs. Differentially expressed significant genes were taken from the above datasets. Statistical analysis was done using limma at a p-value of 0.05.
In GSE 18290 data, the samples were divided in to six conditions (1st, 2nd, 4th, 8th, 16th and 32nd cell stages) based on their source. The expression pattern among the progressing cell stages were examined by performing fold change analysis on the statistically significant data, using the widely accepted 2-fold cut off. Analysis of microarray data showed high expression of glycolysis-related genes (8-cell, 16-cell and 32-cell stages), subsequent to their down-regulation at the earlier stages (2 and 4 cell stages). Aldolase A, a glycolysis enzyme, is upregulated in the 16 and 32 cell stages. All the three isoforms of Enolases, involved in glycolysis, at different of development are upregulated. The observed upregulation of Lactate Dehydrogenase (LDHA and LDHB) and the downregulation of testis-specific LDHC Genes again underscores their importance in anerobic glycolysis. Despite the up-regulation of malate dehydrogenase, involved in the of malate oxaloacetate shuttle, the down-regulation of the upstream enzyme (Glutamic-oxaloacetic transaminase 2) indirectly provides evidence of the glycolysis predominating. The up-regulation of the enzymes that are part of the pyruvate dehydrogenase complex (PDHA1 & PDHB) as well as Dihydrolipoamide S-acetyltransferase (DLAT-E2 component of the complex) indicates the progression towards the citric acid cycle. Also, upregulation of PDHX also indicates a similar role for this enzyme, since mutations in this enzyme have been linked to lactic acid build up. The down-regulation of Mitochondrial pyruvate carrier 1 (MPC1) or (BRP44L) indicates that the transport of pyruvate to this organelle is blocked, thereby providing inferential evidence of shuttling of the pyruvate to lactate. Glucose transporters (SLC2A1, SLC2A3) as well as hexokinase (HK1 and HK2) were highly up regulated, showing its significance in the early events in glycolysis-mediated glucose metabolism. All these biochemical events points towards the increasing dependence on glycolysis and Krebs cycle, especially in the 16 and 32 cell stages (Table 1A, Table2A; Figure 1A). This in silico finding is consistent with the published report that 85% of glucose is utilized in the blastocyst stage by aerobic respiration, unlike the markedly lower level of this biomolecule being utilized in the early stages. However, this can possibly be explained by the reported 14-fold higher efficiency of aerobic respiration, compensating for the lower amounts of glucose being utilized. Earlier studies indicate that during the pre-blastocyst stage, metabolism is tightly controlled by rate limiting glycolysis enzymes hexokinase and phosphofructokinases1. Thus pyruvate and its analogs are the only carbon source for the developing cells. Similarly, other cell types (primordial germ cells, oocytes and spermatocytes), representing early stages in the development process also use the aforesaid, same metabolic intermediates (secreted by Ovarian Follicle and Sertoli cells). Later, the pyruvate oxidation occurs in mitochondria through a series of biochemical reactions (Kreb’s cycle and followed by Oxidative phosphorylation) [24]. The hypoxic cellular conditions in the preblastocyst stages as well as the relatively immature mitochondria favouring glycolysis/lactate production is substantiated/mirrored by our in silico data. The majority of the genes (Table 1B, Table 2B, Figure 1B) involved in oxidative phosphorylation are also reported to be upregulated indicating some flexibility in energy metabolism. The upregulation of NDUFA5 & NDUFC1, at the 32 in comparison with the 16 cell stage, indicates the possible preferential transcript of these isoforms in the inner mitochondrial membrane. The NDUFB5 downnregulation possibly indicates redundancy in mitochondrial oxidative phosphorylation. Further, importance of this isoform can be gauged by the fact that obesity and high-fat diet (risk factors for diabetes) is associated with decreases, (among other enzymes that are part of complex 1) specifically in this isoform [37].
The upregulation of GOT2 (Table 3A, Table 3B, Figure 2A, Figure 2B) indicates that the mitochondrial upregulation of aspartate to oxaloacetate, with the possible activation of the oxaloacetate-malate shuttle. The increased expression of PGK1, DLAT (Conversion of pyruvate to acetyl co-A) as well as the increases in the conversion of phosphoenol pyruvate to pyruvate (PKM) and the pyruvate transport protein – BRP44L and BRP44) into the mitochondria, provides evidence for the predominant involvement of glycolysis and TCA cycle in the cardiomyocyte differentiated from human embryonic stem cells. The down-regulation of an important protein in Complex 1 (ND6) perhaps indicates that could be mutations in the nuclear background that can possibly compensate for deficiency of this protein [38]. Our analysis of microarray data of selected data sets has also reported the relative, up regulation of glucose transporters, hexokinases in the 16 and 32 early embryonic stem cell stages. These proteins/enzymes have been known to be required for fixing the glucose molecules and are also important in the rate-limiting initial steps of glycolysis. In order to maintain the pH, anaerobic respiration-derived lactic acid is expelled from the cell. The reconversion of lactate back to pyruvate can occur through lactate dehydrogenases and this can avoid the build-up of lactic acid. NADH dehydrogenase 6 is the gene which was common among all the physiological conditions which is the core enzyme of the Oxidative phosphorylation pathway. However, the preference for pyruvate, by mammalian oocytes and preimplantation embryos [39] [40] eliminates neither the need for aerobic or for anaerobic respiration. Both glycolysis and OXPHOS are active and is correlated anatomically with more compact mitochondrial cristae. Replication of mitochondrial DNA commences and an increased amount of glucose is consumed at this stage [41] [42]. These data suggest that there is flexibility in energy metabolism during both oocyte maturation and embryo preimplantation development, with a shift in the balance towards aerobic respiration during blastocyst development and implantation. More experimental data from mice show that mitochondrial respiration is dispensable for embryonic development and adequate implantation, even though there may be defects subsequently [43] [44]. Removal of pyruvate was associated with a drastic reduction in the development of human embryos, to be contrasted with glucose and lactate elimination having little effect [45]. These data suggest that, anaerobic respiration is necessary and yet not sufficient, again suggesting that both pathways are required for optimal development [46] as well as in mammalian cells. The differential upregulation of the nonneural ENO1 (Table 3A; Table 3B, Figure 4A & 4B) and the downregulation of the neuronal ENO2 is slightly at variance with the data presented in Figure 1A. However, more information is needed about the differentiation status (the equivalent stage of development) of the human embryonic cells (hESCs) to better explain this observed variance. The upregulation of PDK1 and PDHA1 (glycolytic and TCA-related enzymes) is consistent with glycolysis being important in hESCs as well as the most presumably dedifferentiated embryonal cancers. The redundancy of the enzymes can be inferred by the fact that the pathway is active despite the downregulation of PDHX. The downregulation of DLAT & PKM in hESCs, in comparison with the results obtained with hESCs, is ineresting and may be compensated for by the upregulation of LDHC (lactate as a source of energy) and hence needs further evaluation experimentally. The downregulation of GOT2 (a mitochondrial enzyme) and the relative upregulation of SLC2A1 provides evidence for glycolysis enzymes upregulated in hESCs. Other solute transporters like SLC2A3 could possibly be compensating for the downregulated SLC2A1 in embryonal cancers and CpS. The striking downregulation of most of the glycolysis enzymes, CpS is worthy of mention and is consistent with the reported upregulation of oxidative phosphorylation enzymes in differentiated cells. molecular switching between Oxidative phosphorylation to glycolysis is also seen during cellular conversion from differentiated cells to pluripotent stem cells. It is also noticed that promotion of glycolysis enhances iPSC reprogramming, where as pharmacological inhibition of glycolysis or stimulation of Oxidative phosphorylation impairs iPSC reprogramming – important statement [47][48][49]. Since Complex 1 contains 45 distinct, nuclear and mitochondrial subunits, the downregulation of some of the subunits (NDUAFB1) can be plausibly compensated for by the presence of other subunits with similar functions (NDUAFV1). Simlarly, in hESCs, NDUFB9 is compensated for by the presence of NDUFS1..

Conclusion:

According to our analysis, the cancerous cells show high expression of glycolysis genes showing a cellular transition from glycolysis to oxidative phosphorylation as illustrated by Warburg effect. Early embryonic stem cells (pre-blastocyst) has shown an initial decline in both glycolysis and oxidative phosphorylation during 2,4 and 8 cell stages and a progressive increase in expression of those genes from 16 and 32-cell stages. The early embryonic stem cell upon differentiation has shown a transition in their metabolism from oxidative phosphorylation to Glycolysis. In the dedifferentiated cells (IPSCs), the cells showed a high expression of genes involved in glycolysis followed by oxidative phosphorylation. However, in keeping with our in silico findings and the observed experimental data corroborating the flexibility/transitions in energy metabolism, expression profiling and next generation sequencing data and metabolomics (for e.g., GC-MS, LC-MS, NMR) that mimic the in vivo situation would generate more molecular information that would help in further refining the predictability of the in silico bioenergetic signature with respect to glycolysis and oxidative phosphorylation. This approach can help in the identification/refinement of small molecules that can possibly replace all of the four retroviralmediated transduction of genes (Yamanaka factors) important for the generation of patientspecific embryonic stem cell-like cells [50].
Figure 1A and Figure 1B: Regulation of Glycolysis and Oxidative phosphorylation genes (Early Embryonic Stem cells): This graph represents the regulation (Up and down regulation) of glycolysis and oxidative phosphorylation genes differentially expressed during the early embryonic stages (Early ESC’s), on y-axis the regulation of genes in the form of fold change was plotted and on x-axis the gene symbols were plotted. The microarray data was obtained from different cell stages of human early embryo (1, 2- cell, 4-cell, 8-cell, 16-cell and 32-cell). The gene regulations in different cell stages were compared against 1-cell stage, to study the gene expression patterns in developing cell stages.
Figure 2A & Figure 2B: Regulation of Glycolysis and Oxidative phosphorylation genes (hESC's against hESC CM, beating EB, Fetal heart): This graph represents the regulation (Up and down regulation) of glycolysis and oxidative phosphorylation genes differentially expressed among three different physiological conditions they are: early embryonic stem cells, human embryonic stem cells-cardiomyocyte, fetal heart and beating heart. On y-axis the regulation of genes in the form of fold change was noted and on x-axis the gene symbols were plotted. The gene expression patterns of developing heart through hESC’s to hESC’s-CM, fetal heart and beating heart were studied.
Figure 3A and Figure 3B: Regulation of Glycolysis and Oxidative phosphorylation genes (HESC's, Embryonal Cancers, CpS (vs) IPS): This graph represents the regulation (Up and down regulation) of glycolysis and oxidative phosphorylation genes differentially expressed among three different physiological conditions they are: early embryonic stem cells, embryonal cancers, and induced pluripotent stem cells. On y-axis the regulation of genes in the form of fold change was noted and on x-axis the gene symbols were plotted. The gene expression patterns of prostate cancer stromal cells, embryonal cancers and human embryonic stemcells were compared against gene expression patterns of Induced pluripotent stem cells.

Tables at a glance

Table icon Table icon Table icon Table icon
Table 1a Table 1b Table 2a Table 2b
Table icon Table icon Table icon Table icon
Table 3a Table 3b Table 4a Table 4b

Figures at a glance

Figure 1a Figure 1b Figure 2a
Figure 1a Figure 1b Figure 2a
Figure 2b Figure 3a Figure 3b
Figure 2b Figure 3a Figure 3b

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