Abstract

   The DNA Damage Response (DDR) pathways sense DNA damage and coordinate
   robust DNA repair and bypass mechanisms. A series of repair proteins
   are recruited depending on the type of breaks and lesions to ensure
   overall survival. An increase in glucose levels was shown to induce
   genome instability, yet the links between DDR and glucose are still not
   well investigated. In this study, we aimed to identify dysregulation in
   the transcriptome of normal and cancerous breast cell lines upon
   changing glucose levels. We first performed bioinformatics analysis
   using a microarray dataset containing the triple-negative breast cancer
   (TNBC) MDA-MB-231 and the normal human mammary epithelium MCF10A cell
   lines grown in high glucose (HG) or in the presence of the glycolysis
   inhibitor 2-deoxyglucose (2DG). Interestingly, multiple DDR genes were
   significantly upregulated in both cell lines grown in HG. In the wet
   lab, we remarkably found that HG results in severe DNA damage to TNBC
   cells as observed using the comet assay. In addition, several DDR genes
   were confirmed to be upregulated using qPCR analysis in the same cell
   line. Our results propose a strong need for DDR pathways in the
   presence of HG to oppose the severe DNA damage induced in cells.

   Keywords: DNA damage response (DDR), hyperglycemia (HG), DNA damage,
   metabolic diseases, cancer, diabetes mellitus

1. Introduction

   The DNA damage response (DDR) is a series of controlled, complex
   protein pathways that cells have evolved to ensure genomic integrity.
   This pivotal signaling explains how cells preserve their function
   following damage caused by base alteration, aberrant DNA protein
   function, oxidation or exogenous genotoxic factors [[36]1,[37]2]. The
   DDR is robustly activated in response to DNA damage, which allows
   sufficient time for specified DNA repair pathways to physically remove
   damage [[38]3]. The major DNA repair pathways—base excision repair
   (BER), nucleotide excision repair (NER), mismatch repair (MMR),
   homologous recombination (HR) and non-homologous end joining (NHEJ)—are
   activated throughout different stages of the cell cycle, allowing cells
   to repair the DNA damage [[39]4,[40]5]. This is in addition to pathways
   that deal with specific lesions or tolerate them [[41]5,[42]6].
   Programmed cell death or apoptosis is activated when the damage
   persists, which removes cells with extensive genome instability
   [[43]5].

   Many physiological events depend on the DDR network’s coordination of
   DNA breaks/repair [[44]1]. Deviations in this fine-tuning are known to
   destabilize cellular metabolic homeostasis, as exemplified in diverse
   cancers and many metabolic diseases such as diabetes mellitus, where
   disruption or deregulation of DNA repair pathways results in genome
   instability [[45]7,[46]8,[47]9]. High glucose (HG) was reported to
   enhance the cell’s mutation rate and slow the repair ability. This
   leads to increasing the cell’s susceptibility to oxidative DNA damage
   [[48]10,[49]11,[50]12,[51]13] and disruption of DNA integrity
   [[52]14,[53]15,[54]16,[55]17]. Furthermore, the CHK1-mediated DNA
   damage response is not activated properly in HG conditions [[56]17]. In
   addition, endogenously induced chemical modifications and adducts in
   the DNA were reported to be increased due to high glucose metabolism in
   diabetic models. Metabolic diseases also elevate the circulating
   glucose levels, resulting in the accumulation of DNA-advanced glycation
   end products that increase the rate of G transversions. Eventually,
   these products lead to instability of the genome and a high risk of
   cancer [[57]18]. For instance, HG levels interfere with the removal of
   the guanosine derivative produced by the breakdown of glucose:
   N2-(1-carboxyethyl)-2’-deoxyguanosine (CEdG). HG also destabilizes
   Hypoxia-inducible factor 1-alpha (HIF1α), which activates several genes
   involved in the DNA repair process such as the NER genes [[58]19]. This
   promotes genomic instability and increases cancer susceptibility in
   people with Type 2 diabetes (T2D) [[59]20,[60]21,[61]22]. An increase
   in γH2AX protein expression corresponding to DNA breaks was also
   noticed in high glucose conditions [[62]12,[63]23]. These defects lead
   to an accumulation of mutations due to the interference of the
   error-prone NHEJ repair mechanism [[64]24], which is consistent with
   the negative effects of high glucose and the correlation between
   diabetes and cancer.

   The relationship between HG and DNA damage response/repair mechanisms
   is still understudied. Therefore, in this research article, we used the
   triple negative breast cancer (TNBC) cell line MDA-MB-231 and the
   non-malignant breast cancer cell line MCF10A as a model to analyze the
   effects of HG on DNA damage and highlight the specific DDR genes that
   are affected. This work was executed as presented in [65]Figure 1.
   Weighted Gene Co-expression Network Construction Analysis (WGCNA) was
   carried out to find the clusters (modules) of highly correlated genes
   and relate the modules to glucose status of the samples. Then, the
   Reactome tool was utilized to find the enriched pathways in the
   selected modules [[66]25]. Differential expression analysis (DEA)
   highlighted changes in transcription levels in the presence and absence
   of 2-deoxyglucose (2DG), a widely used competitive inhibitor of glucose
   uptake and metabolism acting at the level of hexokinase [[67]26]. We
   then shifted to the wet lab to study a representative group of genes
   and confirm the findings.

Figure 1.

   [68]Figure 1
   [69]Open in a new tab

   Schematic representation of the workflow for this project “Created with
   BiRender.com”.

2. Materials and Methods

2.1. Data Retrieval

   The gene expression profiles for mammary cell lines ([70]GSE59228) were
   downloaded from the Gene Expression Omnibus database
   ([71]https://www.ncbi.nlm.nih.gov/geo/, accessed on 6 August 2021). The
   dataset was composed of 8 MCF10A and 8 MDA-MB-231 samples; 4 of each
   sample type were grown at the low confluence in standard growth
   conditions and the other 4 were incubated for 24 h with 2-deoxy-glucose
   (2-DG, 50 mM) to inhibit glucose metabolism.

2.2. Data Pre-Processing

   The Affy package (version 1.72.0) was used to perform background
   correction and quantile normalization of the raw .CEL files using the
   robust multiarray average algorithm (RMA) [[72]27]. After that, the
   Avereps function implemented in the limma package (version 3.50.3) was
   used to summarize the expression of the multiple probes for the same
   gene [[73]28]. To decrease the number of genes for WGCNA, the varFilter
   function in the gene filter package (version 1.76.0) was used to obtain
   genes exhibiting the most variation (top 50%) in the expression levels
   across samples [[74]29].

2.3. Weighted Gene Co-Expression Analysis (WGCNA)

   The R package WGCNA (version 1.71) was used to perform the weighted
   correlation network analysis [[75]30]. Firstly, the gene co-expression
   similarity between genes m and n was defined as Smn = |cor(m, n)|.
   Given that the scale-free topology was less than 0.8 due to the nature
   of heterogeneity of the data, the signed network was built using power
   equals (18) to tackle the high variation between the different cell
   lines of MCF10A and MDA-MB-231 [[76]30]. Finally, the adjacency matrix
   was transformed into a topological overlap matrix, and the dynamic tree
   cut method was also used to identify the modules with hierarchical
   clustering of the genes using TOM as the distance measure with a deep
   split value of 2 and minimum module size of 30. Additionally, the
   corresponding gene information for each module was extracted for
   further analysis.

2.4. Establishment of Module Related Trait Relationships

   After the identification of the modules, the module eigengene (ME) was
   summarized using the first principal component of the module expression
   levels. Pearson’s correlation analysis was used to identify the
   association between each individual module and the different phenotypes
   of high- and low-glucose MDA-MB-231 and MCF10 cells. Each phenotype has
   a strongly related module which can be considered as its signature
   [[77]30].

2.5. Pathway Enrichment Analysis

   The ReactomePA R package was used to identify the enriched pathway
   based on the Reactome database. The EnrichPathway function utilized a
   hypergeometric model to evaluate if the number of selected genes
   involved in a Reactome pathway is larger than expected to evaluate the
   significance of the enrichment. The cutoff for the adjusted p-value was
   <0.05 [[78]31]. The Enrichplot (version1.14.2) R package was used to
   visualize the enriched pathways in both the green and brown modules.
   The tree plot function was utilized to perform hierarchal clustering in
   a more holistic approach [[79]32].

2.6. Differential Gene Expression Analysis

   Differential expression analysis was performed using the Limma package
   (version 3.50.3) [[80]28]. The adjusted p-values (adj p-value) were
   implemented to avoid the occurrence of false-positive results. Genes
   with |log2 fold change (FC)| larger than 1 and adj p-value < 0.05 were
   considered as DEGs.

2.7. Cell Culture

   The MDA-231 cell line was cultured in DMEM supplemented with 4.5 g/L
   Glucose (High Glucose) or 1 g/L Glucose (Low Glucose), L-Glutamine, 1%
   Penicillin/Streptomycin and 10% Fetal Bovine Serum (FBS). The
   high-glucose medium (25 mM glucose, HG) mimics hyperglycemia in cancer
   cells, and the low-glucose medium (5.55 mM glucose, LG) is close to the
   normal physiological conditions of 4–8 mM [[81]33,[82]34].

2.8. Alkaline Comet Assay

   The alkaline comet assay procedure was executed as described in
   [[83]35,[84]36]. The Comet Assay IV software (Perceptive Instruments,
   Suffolk, UK) was used to calculate the tail moment.

2.9. Quantitative Real-Time Polymerase Chain Reaction (qPCR)

   For the gene expression analyses using qPCR, RNA was extracted from
   MDA-MB-231 cells using TRIzol. mRNAs from the total extracted RNA were
   reverse transcribed into the single stranded cDNA using the iScript™
   cDNA synthesis kit (Bio-Rad, Hercules, CA, USA) according to
   manufacturer’s instructions and stored at −80 °C. Quantitative
   real-time polymerase chain reaction (qPCR) was performed using SYBR™
   Select Master Mix (Thermo Fisher Scientific, Waltham, MA, USA) on the
   QuantStudio™ 12K Flex Real-Time PCR System (Applied Biosystems™,
   Waltham, MA, USA). Glyceraldehydes-3-phosphate dehydrogenase (GAPDH)
   was used as a housekeeping gene.

3. Results

   To explore new possible links between glucose metabolism and changes in
   gene transcription, we performed bioinformatics analysis using the
   publicly available [85]GSE59228 microarray dataset. This dataset
   includes gene expression data for the MCF10A immortalized mammary
   epithelial cells and the MDA-MB-231 metastatic breast cancer cells
   cultured in high-glucose media (HG) (25 mM) +/− 2-deoxyglucose (2-DG,
   50 mM); an inhibitor of glucose uptake that mimics low glucose
   conditions [[86]26].

3.1. Identification of Key Modules Using WGCNA in Cells Cultured in HG +/−
2-DG

   The [87]GSE59228 dataset used for the analysis included 16 samples, 8
   MDA-MB-231 and 8 MCF10A. Four samples from each type were cultured in
   high glucose and the remaining four were treated with 2-DG. Firstly,
   the average link method was used to cluster the samples. The 16 samples
   were clustered into 2 large clusters. Then, each MDA-MB-23 and MCF10A
   large cluster was divided into two small clusters according to their
   glucose level, high glucose (HG) or HG + 2-DG (LG), as shown in
   [88]Figure 2. According to the average link method, samples from the
   same cluster exhibit related expressions across all genes.
   Consequently, the two different cell lines clustered away from each
   other, and then each cell line clustered into two sub-clusters
   according to their glucose conditions, which reveals the effect of the
   difference in glucose levels on the gene expression levels in both cell
   types.

Figure 2.

   [89]Figure 2
   [90]Open in a new tab

   Hierarchical clustering of the samples analyzed. HG, high glucose; LG,
   50 mM 2-DG added to inhibit glucose uptake.

   Using the same dataset, the WGCNA R package was used to cluster genes
   that are similar in their expression pattern into distinctive modules
   with the average linkage method. A total of 11 modules were identified
   ([91]Figure 3a). Then, a module–trait relationship was executed to find
   the relationship between gene expression profiles and their phenotypes.
   Pearson’s correlation coefficient was used to identify the association
   between the module eigengenes and the status of the cells such as the
   glucose level and the cell type. Furthermore, the p-value was
   calculated for the given correlation in [92]Figure 3a.

Figure 3.

   [93]Figure 3
   [94]Open in a new tab

   WGCNA analysis to identify the key modules associated with different
   glucose levels. (a) The module–trait relationships were demonstrated
   using correlation values and p-values with a range of colors; the
   degree of correlation between modules and glucose levels is shown. HG,
   high glucose; LG, 50 mM 2-DG added to inhibit glucose uptake. (b)
   Median rank and Z-summary statistics in the module preservation tests.
   Left plot shows the module position in the test dataset based on the
   median rank. Right plot illustrates the analysis of the Z-summary
   between different modules.

   The brown and green modules had a significant correlation with the
   MDA-MD-231 and MCF10A cells in high glucose and their p-values
   were <0.05. The module stability test was used on the same dataset to
   validate the stability of the identified modules [[95]30,[96]37]
   ([97]Figure 3b). Since the Z-summary of the brown and green modules was
   higher than 10 and the median rank was close to the minimum in the test
   dataset, the modules showed considerable stability. Therefore, we
   selected these modules for further analysis. The full lists of genes
   for these two modules are summarized in [98]Tables S1 and S2.

3.2. DDR Pathways Are Enriched in the Significant Modules

   To identify pathways that correlated to fluctuation in glucose levels,
   the ReactomePA R package (version 3.1.1 March 2017) was used to
   identify the enriched pathways in the brown ([99]Figure 4) and green
   modules ([100]Figure 5) [[101]25]. Several DDR pathways were found to
   be enriched in at least one of the two modules. For example, Homology
   Directed Repair (HDR), DNA double-stranded break repair, DNA Repair,
   Cell cycle check points, S-phase, chromosomal maintenance, DNA protein
   crosslink repair/protein-linked repair, etc. ([102]Figure 4 and
   [103]Figure 5). The full list of the enriched pathways for the brown
   and green modules and their significance are summarized in [104]Table
   S3 and Table S4, respectively.

Figure 4.

   [105]Figure 4
   [106]Open in a new tab

   Pathway enrichment analysis for the brown module. The ReactomePA
   package [[107]38] was used to identify the pathways enriched in the
   brown modules. The pathways represented are statistically significant
   (adjusted p-value < 0.05) as indicated by the color of the nodes (right
   panel). The number of genes retrieved in our analysis and identified in
   the pathways are represented using the size of the circles as indicated
   in the right panel.

Figure 5.

   [108]Figure 5
   [109]Open in a new tab

   Pathway enrichment analysis for the green module. The Reactome tool
   [[110]38] was used to identify the pathways enriched in the green
   modules. The pathways represented are statistically significant
   (adjusted p-value < 0.05) as indicated by the color of the nodes (right
   panel). The number of genes retrieved in our analysis and identified in
   the pathways is represented using the size of the circles as indicated
   in the right panel.

3.3. Multiple DDR Genes Are Differentially Expressed in Cells Cultured in
High Glucose vs. Low Glucose

   Differential expression analysis (DEA) was completed for the MDA-MB-231
   cells and MCF10A cells to identify the differentially expressed genes
   (DEGs) between the high- and low-glucose status in both cell lines. A
   p-value < 0.05 and the log2 fold change (lfc) >|1| were set as a
   threshold to identify the DEGs. Interestingly, we observed that
   multiple DDR genes are down-regulated in LG-cultured cells in
   comparison to HG cells. Excitingly, the same pattern of expression was
   observed for numerous genes in both the MDA-MB-231 and MFC10A,
   indicating that the increase in glucose affects both normal and
   malignant cells. This is also in line with the WGCNA and the pathway
   enrichment analysis data. The full list of DEGs for both cell lines is
   provided in [111]Table S5.

3.4. High Glucose Increases the Expression of Multiple DDR Genes Which Is
Reversed in Low-Glucose Conditions

   To validate the bioinformatics results, we shifted to the wet lab and
   experimentally exposed MDA-MB-231 cells to LG after culturing in HG. To
   investigate the effects of high- and low-glucose conditions on DNA
   damage, a comet assay was performed under alkaline conditions (pH > 13)
   to detect DNA double-strand breaks, single-strand breaks, alkali-labile
   sites, DNA-DNA/DNA-protein cross-linking, incomplete excision repair
   sites and oxidative base alterations [[112]39,[113]40,[114]41,[115]42].
   Cells were cultured in high glucose and the comet tail moment was
   recorded. A higher level of DNA damage was observed in HG cells
   compared to LG as presented in the increased tail moment ([116]Figure
   6).

Figure 6.

   [117]Figure 6
   [118]Open in a new tab

   Alkaline comet assay representing significant DNA damage in HG-
   compared to LG-cultured cells. MDA-MB-231 cells were cultured in high
   glucose (25 mM glucose, HG) or low glucose (5 mM glucose, LG). Each bar
   represents the mean ± SEM of three repeats. Results were analyzed using
   an unpaired Student’s t-test where each error bar represents SEM. ** =
   p < 0.01.

   To study the effects of changing glucose levels on DDR, we chose
   representative genes for several DDR pathways for confirmation using
   qPCR analysis. The genes selected (BARD1, BRCA2, COPS8, DNA2, FANCD2,
   LIG1, MSH6, NSD2, PARP1, RAD1, RAD51, TDP1) were identified as DEGs and
   were also part of either the brown or the green modules to cover a
   broad range of mechanisms ([119]Tables S1, S2 and S5). Moreover, the
   biological repeats of these genes in the qPCR analysis were consistent
   and reproducible; therefore, we present the expression levels of these
   particular genes in the microarray dataset in [120]Figure 7 for
   MDA-MB-231 and in [121]Figure S1 for MCF-10A. Moreover, to identify
   major similarities and differences in the transcriptional changes of
   these genes in the different levels of glucose, we performed
   hierarchical clustering on normalized expression of DDR genes for each
   sample. The heatmap shows a clear pattern where the LG and HG cells
   were clustered together based on the DDR gene expression levels
   ([122]Figure 8). Finally, the qPCR data is represented in [123]Figure 9
   and [124]Figure S2. The Relative quantification (RQ) was calculated for
   the genes. The data showed that RAD51 (p = 0.0003), BRCA2 (p = 0.0235),
   DNA2 (p = 0.0431), FANCD2 (p = 0.0223), MSH6 (p = 0.0017), TDP1 (p =
   0.0305), NSD2 (p = 0.0318), PARP1 (p = 0.0155) and BARD1 (p = 0.0128)
   had a significant decrease in expression upon shifting cells to LG in
   comparison to HG ([125]Figure 9a–i). On the contrary, changes in the
   RAD1, LIG1 and COPS8 gene levels were not significantly between HG- and
   LG-cultured cells in our wet lab experiments despite being differently
   expressed in the microarray analysis ([126]Figure S2). Overall, the
   bioinformatics analysis and wet lab experiments showed a great level of
   concordance, supporting the deleterious effects of HG on genome
   integrity and cellular functions.

Figure 7.

   [127]Figure 7
   [128]Open in a new tab

   Normalized expression of the differentially expressed DDR genes in
   MDA-MB-231 analyzed using the microarray dataset. The genes analyzed
   are represented in (a–i). Boxplots represent normalized counts in LG
   (grey) and HG (red). HG, high glucose; LG, 50 mM 2-DG added to inhibit
   glucose uptake.

Figure 8.

   [129]Figure 8
   [130]Open in a new tab

   Heatmap of DNA repair genes identified in high- and low-glucose levels
   in MDA-MB-231 cells. Expression of the genes was normalized and log2
   transformed. Both rows and columns are clustered using correlation
   distance and average linkage. The color intensity reflects the
   expression levels where positive values indicate upregulation and
   negative values indicate downregulation. HG, high glucose; LG, 50 mM
   2-DG added to inhibit glucose uptake.

Figure 9.

   [131]Figure 9
   [132]Open in a new tab

   qPCR analysis of DDR genes in HG- vs. LG-treated MDA-MB-231 cells.
   MDA-MB-231 cells cultured in LG conditions (5.55 mM glucose) resulted
   in a significant downregulation of genes presented in (a–i) in
   comparison to HG (25 mM glucose)-cultured cells. GAPDH was used as a
   housekeeping gene for the RQ calculations. Data from three or four
   independent biological replicates of each treatment are presented. Each
   bar represents the mean ± SEM. Results were analyzed using a paired
   Student’s t-test where *** = p < 0.001, ** = p < 0.01, * = p < 0.05.

4. Discussion

   Hyperglycemia promotes oxidative stress and DNA damage, which are
   significant factors contributing to disease development and progression
   [[133]43,[134]44]. Since the discovery of the Warburg effect over a
   century ago, the role of glucose in cancer formation and progression
   has drawn substantial attention. Hyperglycemia results in an increased
   prevalence and mortality associated with many cancers, including breast
   and colorectal [[135]45,[136]46,[137]47,[138]48,[139]49]. It was
   reported that HG significantly increases mutations in
   phosphoribosyltransferase and thymidine kinase loci in human
   lymphoblastoid cell lines and Lac1 in the mouse embryo, thus affecting
   genomic stability [[140]13,[141]50,[142]51]. In addition, it causes DNA
   lesions and strand breaks and alters the DNA damage response in renal
   and prostate cancers [[143]17,[144]52]. Chemo- and radiation-resistance
   were also noted in normal renal epithelial cells and renal cell
   carcinoma after high glucose exposure and attributed to altered DNA
   damage response and reduced repair, though DNA repair protein
   expression changes were not examined [[145]17]. Additionally,
   alterations in the XRCC1 gene and protein expression were reported
   following glucose concentration changes in breast cancer cell lines and
   hepatocytes [[146]53,[147]54]. XRCC1 is an essential protein in DNA
   repair and is known to be involved in the single-strand break (SSB) and
   base excision repair (BER) pathways. Particularly, XRCC1 is recruited
   to the repair of oxidative DNA breaks by BER pathways [[148]55]. It was
   also found that high glucose exposure drives XRCC1 expression through
   increased STAT3 activation, resulting in resistance to DNA damaging
   agents [[149]56]. A recent report also observed that the expression of
   γH2AX protein, which corresponds to DNA double-strand breaks, was
   increased in high-glucose conditions [[150]17]. Despite previous
   efforts establishing a link between hyperglycemia and DNA damage, the
   impact of high-glucose concentration on DNA repair genes is
   understudied [[151]17].

   In this article, bioinformatics analysis of a microarray dataset of
   normal and malignant breast cell lines, MCF10A and MDA-MB-231,
   respectively, was used to study the impact of high glucose on DDR
   pathways. WGCNA followed by pathway enrichment analysis revealed an
   over-representation of DNA damage response pathways upon changing
   glucose levels. We also interestingly detected significant DNA damage
   as DNA double-strand breaks, single-strand breaks, alkali-labile sites,
   DNA-DNA/DNA-protein cross-linking, incomplete excision repair sites and
   oxidative base alterations using the alkaline comet assay
   [[152]39,[153]40,[154]41,[155]42] in cells cultured in HG, which
   explains the need for a strong DDR response to control this damage.
   While the dysfunction of DNA repair proteins through their loss or
   mutations has gathered significant research focus, factors driving the
   overexpression of DNA repair proteins, such as hyperglycemia, are not
   well understood. Therefore, we tried to select specific DDR genes to
   propose possible consequences on specific cellular pathways in HG
   conditions.

   In our experiments, the most significant upregulated genes included
   FANCD2, a player in Fanconi anemia (FA) [[156]57], and several genes
   playing a role in the HR-mediated repair of double-strand breaks (DSBs)
   such as BARD1, BRCA2, RAD51 and DNA2 [[157]58,[158]59]. Other genes
   identified as DEGs included TDP1, which catalyzes the excision of
   stalled topoisomerase I-DNA complex
   [[159]60,[160]61,[161]62,[162]63,[163]64], the mismatch repair gene
   MSH6 [[164]65,[165]66] and the Poly (ADP-ribose) polymerase-1 (PARP1)
   that mediates multiple repair pathways [[166]67,[167]68]. Most of the
   genes analyzed produced consistent results between the microarray
   analysis and the qPCR analysis. However, some differences were
   observed, for example, in the LIG1, RAD1 and COPS8 genes. The minor
   differences between the expression levels in the bioinformatics
   analysis and wet lab results can be attributed to different factors.
   Firstly, despite the usage of the same MDA-MB-231 cell line, some
   variations might exist between different labs [[168]69]. In addition,
   increasing the number of clones analyzed in the microarray analysis
   would produce better results. Performing RNA-Sequencing could even
   produce more solid results [[169]70]. However, overall, both the
   bioinformatics analysis and the wet lab data greatly support an
   elevation in DDR genes in the presence of high glucose.

   Our results propose a model where growing cells in HG increases the
   levels of DNA damage and consequently the expression of DDR genes to
   fix this damage. Yet, despite this elevation, the damage seems to
   accumulate in HG-cultured cells to an extent that is not completely
   repaired by the overexpressed DDR genes/proteins. On the contrary,
   shifting cells to LG conditions decreases the levels of DNA damage and,
   accordingly, the need for DDR ([170]Figure 10).

Figure 10.

   [171]Figure 10
   [172]Open in a new tab

   A concluding model showing the HG impact on DNA damage. In the presence
   of high glucose, the DNA accumulates high damage which requires an
   increase in the recruitment of DNA damage response genes. This is in
   contrast to LG conditions that showed a decrease in DNA damage “Created
   with BioRender.com”.

   As previously mentioned, hyperglycemia is suggested to have a profound
   impact on cancer progression. We have also previously observed an
   upregulation in 36 DDR genes in ductal and lobular breast carcinomas in
   comparison to normal breast cells [[173]70]. Therefore, we expected
   that the increase in the expression levels of DDR genes would be
   specific to the breast cancer cell line (MDA-MB-231). However, to our
   surprise, MCF10A cells cultured in high glucose resulted in an
   elevation in DDR gene expression. This indicates that the negative
   effects of glucose upregulation affect normal cells as well.

   Our results provide novel insights into possible consequences on DDR
   pathways in patients who suffer from uncontrolled diabetes and an
   increase in blood glucose levels. It also opens new questions regarding
   the extent of damage caused to normal cells vs. cancer cells. This link
   may concern the development or progression of cancer as well as whether
   diabetes’ relationship with cancer may be influenced by cellular
   reactions to DNA damage. Our findings concur with others that
   demonstrate elevated levels of DNA damage in diabetic patients’ white
   blood cells [[174]71,[175]72,[176]73,[177]74]. To put it another way,
   we can speculate that the increase in blood sugar levels in patients
   with uncontrolled diabetes may lead to a higher risk of developing
   cancer [[178]75]. Metabolite-induced DNA damage, DDR and persistent DNA
   damage signaling are common soil for several complications of
   diabetes-like cancer. Recognition of this common soil may lead to novel
   therapies and better treatment modalities for DM-cancer patients. Our
   findings suggest that measuring the degree of endogenous DNA damage
   using the comet assay may predict the risk of developing cancer in
   diabetic patients. Combining the transcriptome analysis of DDR with
   proteome analysis to confirm that the gene upregulation is directly
   reflected on the protein level would also produce more accurate
   conclusions on the status of repair in patients’ cells.

5. Conclusions

   In this study, we showed that cells exposed to high-glucose conditions
   manifested an increase in the transcription of multiple DDR genes and
   exhibit significant levels of DNA damage. These effects were
   interestingly reversed to a great extent upon exposure of cells to LG
   conditions ([179]Figure 10). Although we should not hide from the fact
   that the effect appears cancer-independent, the consequences could be
   more aggravated and more relevant to cancer cells. Our data provide new
   insights into the effects of increased sugar levels on DDR and genome
   stability. This intriguingly raises new questions that can link
   hyperglycemia and metabolic disorders such as diabetes mellitus to DNA
   damage and cancer progression.

Acknowledgments