Abstract

   Alkylating agents are a key component of cancer chemotherapy. Several
   cellular mechanisms are known to be important for its survival,
   particularly DNA repair and xenobiotic detoxification, yet genomic
   screens indicate that additional cellular components may be involved.
   Elucidating these components has value in either identifying key
   processes that can be modulated to improve chemotherapeutic efficacy or
   may be altered in some cancers to confer chemoresistance. We therefore
   set out to reevaluate our prior Drosophila RNAi screening data by
   comparison to gene expression arrays in order to determine if we could
   identify any novel processes in alkylation damage survival. We noted a
   consistent conservation of alkylation survival pathways across
   platforms and species when the analysis was conducted on a
   pathway/process level rather than at an individual gene level. Better
   results were obtained when combining gene lists from two datasets (RNAi
   screen plus microarray) prior to analysis. In addition to previously
   identified DNA damage responses (p53 signaling and Nucleotide Excision
   Repair), DNA-mRNA-protein metabolism (transcription/translation) and
   proteasome machinery, we also noted a highly conserved cross-species
   requirement for NRF2, glutathione (GSH)-mediated drug detoxification
   and Endoplasmic Reticulum stress (ER stress)/Unfolded Protein Responses
   (UPR) in cells exposed to alkylation. The requirement for GSH, NRF2 and
   UPR in alkylation survival was validated by metabolomics, protein
   studies and functional cell assays. From this we conclude that
   RNAi/gene expression fusion is a valid strategy to rapidly identify key
   processes that may be extendable to other contexts beyond damage
   survival.

Introduction

   Alkylation is a reactive process donating carbon-hydrogen moieties to
   various classes of biomolecules. Clinically, the most commonly used
   alkylating agents are nitrogen mustards and other chemotherapeutics
   such as cyclophosphamide, ifosfamide and temozolomide. On the other
   hand, prototypical alkylating agents such as methyl-methanesulfonate
   (MMS) and ethyl-methanesulfonate are advantageous to in vitro studies
   as they do not require bioactivation [[38]1]. Previously, we used RNA
   interference (RNAi) screening in Drosophila cells to identify genes and
   pathways necessary to survive MMS [[39]1]. Here, we wanted to ask
   whether it is possible to extend those studies and identify additional
   pathways that are conserved from fly to mammals by integrating RNAi
   screening data with MMS-induced gene expression changes.

   Towards this goal we used data from our aforementioned Drosophila
   genome-wide RNAi screen to identify genes required to survive MMS
   [[40]1] and generated complementary gene expression data in fruitfly
   cells (Kc167), mouse embryonic fibroblasts (MEFs) and human cancer
   cells (MDA-MB231 cells). This rich dataset provided the opportunity to
   determine the feasibility of fusing phenome and transcriptome data in
   response to a single stimulus thereby revealing key biological process
   on a systems level. Noteworthy, this type of complementary analysis has
   been a longstanding challenge in the field; different publications have
   reported no significant overlap between the genes that confer a
   phenotype (the phenome) when knocked out/down and those that are
   transcriptionally regulated (the transcriptome) in the same context
   [[41]2, [42]3]. With our datasets, no gene enrichment (direct ortholog
   comparison) was seen either across species for gene expressions or
   within species between gene expression response and RNAi screen data.
   However, a consistent conservation of alkylation responses across
   platforms and species was observed at a pathway/process level, and a
   stronger enrichment stood out when combining the two datasets (RNAi
   screen plus microarray) prior to analysis. With this, we were able
   identify the same pathways we previously reported and validated when we
   evaluated our RNAi screening results [[43]1] as well as additional
   responses. Our analysis indicated significant changes in DNA-to-protein
   metabolism (transcription, translation) and proteasome-mediated
   degradation, glutathione (GSH) biosynthesis and fatty acid metabolism
   as well as a highly conserved NRF2 and endoplasmic reticulum stress (ER
   stress)/unfolded protein responses (UPR). The GSH pathway metabolic
   changes were confirmed by the use of metabolomics in fly and mouse
   cells. We then went on to evaluate the conservation of response of
   these pathways and whether modulating them impacted MMS survival in
   mammalian cells.

Materials and Methods

Cell lines and tissue cultures

   The D. melanogaster embryonic cell line Kc167 was grown in Schneider
   medium (Invitrogen, Carlsbad, CA) supplemented with 10%
   heat-inactivated fetal bovine serum (FBS), penicillin and streptomycin
   at 22°C in a humidified incubator. Primary mouse embryonic fibroblasts
   (MEFs) were obtained by harvesting 14.5-day C57BL/6J embryos as
   previously described [[44]1]. Briefly, fetal liver and head were
   removed and the remainder of the embryo was mechanically disaggregated
   in plating medium. A suspension of single-cells was plated in DMEM
   (plus 10% FBS, 100U/mL penicillin and 100μg/mL streptomycin). MEFs were
   grown for at least three passages before experiments. MDA-MB231 and
   A549 cells lines were obtained from ATCC and grown in DMEM and RPMI,
   respectively, supplemented with 10% FBS plus antibiotics in a 37°C
   humidified incubator at 5% O[2] and 95% CO[2] atmosphere. Cells were
   kept and treated in the exponential phase of growing at 60–70%
   confluence. Buthionine sulfoximine (BSO, 2 mM) and N-acetyl-cysteine
   (NAC, 7.5 mM) were from Sigma-Aldrich (USA) and were pre-incubated for
   8 h before MMS (40 μg/mL, IC25-50 range at 72 h for all cell lines)
   treatments.

Microarray and RNA sequencing gene expression profiling protocols

   For microarrays, 7.5x10^6 Kc167 Drosophila cells or 5x10^6 wild-type
   MEFs were seeded in T-75 cell culture flask in complete growth medium
   one day before treatments. On the following day, the medium was
   replenished with fresh growth medium or medium consisting of MMS at 40
   μg/mL. Cells were harvested and pelleted at different times post
   treatment (0, 1, 8, 24 and 72 h), washed using ice cold phosphate
   buffered saline (PBS) and the RNA was extracted using the RNeasy kit
   protocol (Qiagen). RNA integrity was assessed by denaturing
   formaldehyde agarose gel electrophoresis or by microanalysis (Agilent
   Bioanalyzer, Santa Clara, CA). Microarray service was provided by
   Expression Analysis Inc., Durham, NC. Affymetrix GeneChip Drosophila
   Genome 2.0 Array was used for the experiment performed using Kc167
   cells, and Affymetrix GeneChip Mouse Genome 430A 2.0 Array was used for
   experiment performed using MEFs. The microarray service provided by
   Expression Analysis Inc., included group comparison between
   experimental control and MMS treatment and time course analysis based
   on permutation analysis of differential expression (PADE). For
   statistical comparison, each experiment was performed in quadruplicate.

   For MDA-MB231 cells gene expression profiling was performed by RNA
   sequencing protocol using Illumina HiSeq 2000 system (Illumina, San
   Diego, CA). RNA from 3x10^6 cells was harvested 8 and 24 h post 40
   μg/mL MMS using the RNeasy protocol (Qiagen), and the purity was
   determined using Agilent 2100 BioAnalyzer. Total RNA samples (1–2 μg)
   were used for sequencing library preparation according to Illumina
   TruSeq Total RNA Sample Preparation Guide (Illumina Cat. #:
   RS-122-2201). Each library was bar-coded and then pooled for cluster
   generation and sequencing run with 100bp single-end (SE) sequencing
   protocol. Short read sequences from RNAseq were first aligned to UCSC
   hg19 genome build using TopHat2 algorithm and then quantified for gene
   expression by HTSeq [[45]4] to obtain raw read counts per gene and then
   converted to RPKM (Read Per Kilobase of gene length per Million reads
   of the library) according to gene length and total mapped read count
   per sample. Log2-transfromed RPKM measurement were used as gene
   expression level, and genes with low-read counts (determined by
   examining read counts in non-exonic regions) were removed.
   Additionally, genes with low expression (RPKM < 2) in both MMS and
   untreated conditions were filtered out. Differentially expressed genes
   (DEGs) at a p<0.05 (T-test control vs MMS-treated) and fold>2 or <-2 in
   at least one time point were selected as MMS-altered genes. Repository
   information: [46]GSE57801;
   [47]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=apmtuguyplcnbgh
   &acc=GSE57801

RNAi screening dataset

   Genes whose knockdown caused sensitivity to MMS in Kc167 cells were
   obtained from our previously published wide-genome RNAi screening in D.
   melanogaster Kc167 cells as detailed in [[48]1]. This RNAi screening
   tested 13,826 open reading frames, of which 996 FBgn (FlyBase gene ID,
   denotes known genes) affected MMS survival; 537 of which were further
   examined with 202 validating [[49]1]. Microarray detected 12,363 FBgn,
   with 95% of these had a matched probe with the RNAi screening platform
   therefore making fusion of RNAi and microarray gene lists feasible.

Pathway Enrichment Analysis (PEA)

   For determination of pathways associated with MMS-induced gene
   expression changes in fly Kc167 cells, DEGs from 8, 24 and 72 h
   treatments were combined into a single gene list prior to PEA. In
   mammalian cells (MEF and MDA-MB231), the DEG list comprised both 8 and
   24 h MMS induced gene expression changes. Differentially expressed
   genes (DEGs) were analyzed using the Ingenuity Systems (IPA, Ingenuity
   Pathway Analyzer, Qiagen), and enrichment significance was calculated
   using the right-tailed Fisher Exact Test. Ingenuity analysis was
   complemented with the publicly available DAVID Bioinformatics
   Functional Annotation tools set as: i) Pathways; ii) KEGG and Reactome
   databases. In DAVID, pathway terms in the output were considered as
   significantly enriched at a p<0.05 and FDR<10%. Uninformative pathway
   terms such as disease-related or cell-line specific pathways (for
   example: “Prostate cancer” and “Melanocyte Development and Pigmentation
   Signaling”) were excluded. Both fly and human orthologues were used to
   determine pathway enrichments in DAVID and Ingenuity tools as
   appropriate for each database. In some figures, Ingenuity canonical
   pathway charts were used to facilitate visualization of MMS-induced
   alterations with NRF2 and ER stress pathways.

Gene/Protein Interaction Networks and Landscape Analysis

   Protein interaction network was generated using the STRING database
   [Parameters: i) input: MMS-induced genes and survival hits identified
   by PEA with each pathway, and converted to human orthologs; ii)
   Prediction methods: databases, gene fusion, experiments, textmining and
   co-occurrence; iii) confidence level 0.400 (medium)]. Fly gene/protein
   interaction networks were built by associating orthologs belonging to
   NRF2/oxidative stress and UPR-ER stress pathways selected from
   literature review (Pubmed, Ingenuity and Qiagen databases) as detailed
   in [50]S3 Table. In summary: i) NRF2-GSH pathway: components of the
   transcription factor core signaling, NRF2 transcriptional targets,
   genes involved in GSH synthesis and GSH-mediated detoxification; ii) ER
   stress/UPR: chaperones and heat shock factors, ER sensors, and
   ER-to-Nucleus signaling transducers. Briefly, the network was generated
   using the STRING database [Parameters: i) input: fly orthologs; ii)
   Prediction methods: databases, gene fusion, experiments, textmining and
   co-occurrence; iii) confidence level 0.700 (high)]. Network parameters
   were saved and the links (interaction strength) were handled by Medusa
   software [[51]5]. Nodes colors include information on MMS-induced
   changes in gene expression (microarray) and gene survival role in RNAi
   screening. Interactomes were further analyzed by ViaComplex software
   [[52]6], which plots the gene expression (means of each replicates per
   gene) value over the network/interactome, and distributes the
   microarray signal through network objects (nodes and links), thus
   building Z-axis/3D topology landscapes.

Metabolomics

   Five replicates of untreated controls or 40 μg/mL MMS-treated Kc167
   cells were harvested after 8 and 24 h incubation. The cell number was
   adjusted to 9x10^7 and provided as a 100 μL of packed cell pellet for
   GC/MS and LC/MS metabolic profiling by mView service provided by
   Metabolon. Data were statistically analyzed using Welch's Two Sample
   t-Tests at a p<0.05 significance.

Cell viability

   Cell viability was assessed using CellTiter-Glo kit (Promega) following
   manufacturer’s instructions.

Western blot

   Protein lysates were prepared using RIPA buffer containing 1 mM PMSF, 1
   mM sodium orthovanadate, 1 mM NaF, and 30 μL/mL aprotinin. The proteins
   (20–30 μg) were resolved in SDS-PAGE, electro-transferred onto
   nitrocellulose membranes (Hybond-ECL, GE Healthcare) and blocked with
   5% BSA. Primary antibodies (1:1000 dilution) included NRF2 (D1Z9C),
   BiP/GRP78 (C50B12) and CHOP (L63F7) from Cell Signaling; beta-actin
   (ab8227) from Abcam and ATF3 (C19) from Santa Cruz. After secondary
   antibody incubation (1:3000, 2 h), the proteins were detected using
   Lumiglo substrate (Cell Signaling Technology, CA).

Small interference RNA (siRNA)

   The siRNA duplexes (30 nM final concentration) targeting human NRF2
   (sc-37030), KEAP1 (sc-43878) and scrambled siRNA-A controls (sc-37007)
   were obtained from Santa Cruz Biotechnology. Reverse transfections were
   performed using the Lipofectamine RNAiMAX Reagent (Invitrogen)
   following manufacturer’s instructions, and protein knockdown was
   confirmed by immunoblot 48 h post transfection.

NRF2 reporter gene assay

   Cignal Antioxidant Response Reporter kit (Qiagen, USA) was used to
   address NRF2 transcriptional activity via Antioxidant Response Elements
   (ARE) activation. Cells were transfected with a mixture of an
   ARE-driven firefly luciferase and constitutively expressing
   Renilla-luciferase constructs (40:1 ratio) for 24 h. Afterwards, the
   cells were treated with alkylation for 8 h and kept for additional 16 h
   in drug-free culture medium prior to assessment using the
   Dual-Luciferase® Reporter Assay (Promega). When used, siRNAs were
   transfected 48 h before ARE-luciferase assessments.

Plasmid Construct Overexpression

   The pcDNA3.1(+)-GRP78/BiP (plasmid 32701, from Dr. Austin; abbreviated
   as GRP78-pcDNA3) were obtained from Addgene. Cells were transfected
   with Lipofectamine 3000 (Invitrogen) and kept for 24–36 h for protein
   expression (validated by Western blot; data not shown). The pCDNA3 and
   pCDNA3-EGFP plasmid constructs were used as empty vector and
   transfection efficiency controls, respectively.

Results

Gene expression and RNAi screening data do not display extensive overlap at a
gene or pathway level

   Our initial goal was to determine whether it is possible to compare
   gene expression and RNAi screen data to identify genes or pathways for
   subsequent validation. Previously we had identified 996 FBgn/genes in
   an RNAi screen for MMS survival using Drosophila cells (pre-validation)
   [[53]1]. 957 of these RNAi hits could be mapped to the genes
   represented on the Affymetrix GeneChip Drosophila Genome 2.0 Array. MMS
   exposure resulted in 1011 significant gene expression changes (703 up
   and 308 down regulated) over 8, 24 and 72 h treatments ([54]Fig 1A),
   with different gene sets showing transient or prolonged mRNA altered
   expression (detailed in [55]S1 Fig and [56]S1 Table). For example, two
   gene subsets, GSH metabolism (Gclc, Gclm, GstD2, GstD3, GstD4, GstD5,
   GstD6, GstD7, GstD10, GstE3, GstE7, GstE9) and heat-shock proteins
   (Hsp23, Hsp26, Hsp27, Hsp67Bc, Hsp70Bc), showed upregulation following
   short exposure to alkylation (8–24 h) but not at 72 h treatment ([57]S1
   Fig).

Fig 1. Gene level overlap and PEA showing the cellular processes associated
with RNAi hits and gene expression alterations in MMS-treated Kc167 cells.

   [58]Fig 1
   [59]Open in a new tab

   (A) Venn diagrams showing the overlap between survival hits (from RNAi
   screening) and alkylation altered gene expressions (from microarrays)
   in MMS treated Kc167 cells. For gene level overlaps, RNAi hits were
   obtained from [[60]1] and only those with matched microarray expression
   changes in at least one time point (8, 24 and 72 h) were used.
   Microarray gene numbers include both MMS up (703) and downregulated
   (308) genes as detailed in Results section. Pathway comparisons only
   show pathways associated with MMS upregulated gene expressions compared
   to RNAi hits. The 26 RNAi hits with downregulated expressions ([61]S1
   Table) were not included in the PEA. (B-C) Antilog P-value
   representation of the pathways associated with the MMS survival RNAi
   hits and MMS upregulated genes as predicted by PEA. Pathways are shown
   as grouped into major biological concepts. Detailed information of
   differentially expressed genes and PEA are provided in [62]S1 and
   [63]S2 Tables, respectively.

   Despite the comparable number of resultant changes, we observed that
   most of the MMS survival hits showed no alteration in gene expression
   ([64]Fig 1A). Only 78 (52 up and 26 down regulated) out of 957 RNAi
   screen hits displayed altered gene expression in at least one time
   point in Kc167 cells ([65]Fig 1A, detailed in [66]S1B–S1D Fig and
   [67]S1 Table). Comparing the genes identified by RNAi or gene
   expression revealed only a minimal overlap (4.1%, e.g. 78/1890 genes
   identified by both assays, [68]Fig 1A; Fisher’s exact, P ≤ 0.73). This
   result indicates that most of the genes that confer survival are not
   dynamically expressed and concurs with prior reports of no enrichment
   between these platforms [[69]2, [70]3]. In the RNAi/microarray overlap,
   29 out of 52 RNAi hits with upregulated expression were previously
   tested and 21 validated (21/29, e.g. 72% validation), including p53,
   the GSH metabolic enzymes Gclc and GstE3, the chaperones Hsp70Bc,
   Hsp70Bb, and the thioredoxin TrxT (from [[71]1]). Of note, this is
   about twice the validation rate observed when just examining RNAi
   screen hits alone [[72]1]. We also noted that of the 78 overlapping
   genes, MMS-induced expressions displayed a robust fold-change in
   microarrays while MMS downregulations were closer to the fold-change
   cut-off ([73]S1C Fig; detailed in [74]S1 Table). Because of this
   observation the pathways associated with these downregulated genes were
   not further investigated. Of note, these 52 up- and 26 downregulated
   genes do not consistently enrich any particular pathway terms when
   analyzed by PEA.

   Previously we had noted that there was a significant conservation for
   the pathways necessary to survive MMS across species, if not at an
   individual gene level [[75]1]. We therefore evaluated whether we might
   see a similar conservation at a pathway level, which was not present at
   an individual gene level. Pathway enrichment analysis (PEA) suggested
   that a majority of the genes altered in each platform participate in
   different cellular processes, which might explain the lack of overlap
   observed at a gene level ([76]Fig 1B and 1C, see PEA genes-in-pathway
   details in [77]S2 Table). Again, no significant overlap (6.1%; 4/66
   pathways, [78]Fig 1A) was observed in the pathways identified from
   either the expression or RNAi screen data ([79]Fig 1A; Fisher’s exact P
   ≤ 0.44). This occurred because while several RNAi survival hits were
   associated with some housekeeping processes such as “basal
   transcription”, “ribosome”, “proteasome”, “cell cycle” as well as DDR
   responses “Nucleotide Excision Repair (NER) and p53 signaling” and some
   signal transduction pathways such as “Notch signaling” (most of which
   we previously validated [[80]1]), the MMS-induced gene expressions
   involved a different set of genes belonging to a variety of metabolic
   pathway covering, mainly “GSH metabolism”, “GSH-mediated
   detoxification”, “Xenobiotic detoxification”, “nucleotides”, “fatty
   acid” and “amino acid” metabolism ([81]Fig 1B and 1C, detailed in
   [82]S1 Table). Four pathway terms were enriched by both platforms
   ([83]Fig 1A); “glutathione metabolism”, “NRF2-mediated Oxidative Stress
   Response”, “p53 signaling”, “unfolded protein response (UPR)” being the
   most enriched responses ([84]Fig 1B and 1C; genes-in-pathway are shown
   in [85]S2 Table), indicating that they are both dynamically expressed
   in response to MMS and essential for its survival. MMS downregulated
   genes were associated with 9 pathways terms that are related to DNA
   replication machinery including DNA polymerases and the MCM
   (minichromosome maintenance protein complex) family of proteins (this
   included fly orthologs for POLE, POLA1, POLA2, MCM2, MCM3, RNASEH2A,
   MCM4, MCM5, MCM6, RFC5, PRIM1, DNA2, MCM7, POLE2, POLD2 ([86]S2
   Table)).

Fusion of gene expression and RNAi data improves pathway detection

   Having observed that neither comparing gene expression or RNAi survival
   at either individual gene or pathway level resulted in extensive
   overlap across platforms, we went on to test if the union of RNAi hits
   and gene expression changes (DEGs) could change the results of our
   analysis or lead to identification of novel responses (schematic in
   [87]Fig 2A). The fusion strategy entails the combined lists of RNAi
   hits with DEG lists from the microarray experiments (upregulated genes
   in this case) prior to running PEA. Pathway terms detected by fusion
   significantly overlapped with the pathways identified independently by
   either DEG or RNAi hits; e.g. 33/41 (Fisher’s exact test p = 0.007) and
   18/29 (Fisher’s exact test p = 0.004) pathways associated with RNAi
   hits and MMS upregulated genes, respectively ([88]Fig 2B, detailed in
   [89]S2 Table and [90]S2 Fig). On the other hand, 8 RNAi and 11
   microarray-identified pathways were not detected in the fusion output
   ([91]Fig 2B). These 8 and 11 pathway terms showed a low enrichment
   p-value in individual microarray/RNAi analysis ([92]S2 Fig) and were
   likely below the PEA threshold when the fused gene list was evaluated.
   In addition, the fusion resulted in 13 additional terms ([93]Fig 2B;
   and detailed in [94]S2 Fig and [95]S2 Table) which were at the
   threshold of enrichment (p-value~0.05) or redundant to other pathway
   terms already noted by microarray or RNAi PEA alone. Based on these
   results we decided to focus on the most consistent/enriched processes.

Fig 2. Microarray/RNAi data fusion.

   [96]Fig 2
   [97]Open in a new tab

   (A) Schematic representation of the fusion strategy for MMS-induced
   gene expression changes and RNAi survival hits followed by Pathway
   Enrichment Analysis (PEA). (B) Pathway level overlap of MMS-induced
   survival responses from analysis of microarray, RNAi survival hits and
   fusion (microarray+RNAi hits) gene lists. (C) Antilog p-value
   representation of the pathways identified by PEA. Pathways are grouped
   into major biological processes, and detailed results are described in
   [98]S2 Table. (D) Protein-protein interaction networks of MMS induced
   genes and hits with the “transcription, translation and proteasome”,
   “DNA damage response” and “NRF2” and “UPR” pathways in Kc167 cells.
   Networks were developed by inputting into the STRING database both
   genes induced by MMS to alter expression and those necessary for
   survival (RNAi hits; converted to human orthologs) as identified by
   PEA. Color legends of edges in STRING interactomes denote “experiments”
   (pink), “databases” (light blue), “co-expressions” (black),
   “textmining” (lime green) and “co-occurrence” (blue) interactions
   between two nodes.

   The greatest benefit of fusing data is the resulting enrichment in
   genes-in-pathway numbers detected within many pathways previously
   identified by RNAi and microarray platforms individually ([99]Table 1,
   [100]Fig 2B and 2C and [101]S2 Table). In contrast, the comparison
   between RNAi and gene expression data distinguishes transcriptionally
   dependent and independent survival responses to alkylation. The
   increased gene numbers by fusion is especially notable for “NRF2
   oxidative stress response”, “p53 pathway”, “GSH metabolism” and “UPR”
   ([102]Fig 2C, [103]Table 1 and [104]S2 Table). This result highlights
   that these pathways have some genes that are essential for survival and
   others that may dynamically expressed in response to MMS but that these
   may not be the same genes. To illustrate this relationship mapping
   these results onto protein-protein interaction networks from the STRING
   database reveal that MMS survival genes (hits and differentially
   expressed) condense into major MMS response modules ([105]Fig 2D; DDR,
   transcription/ribosome, proteasome, NRF2-GSH and UPR).

Table 1. Fusion of RNAi hits and microarray expression changes improves
pathway detection.

   Table showing the number of genes-in-pathway associated with microarray
   DEG (upregulations), RNAi survival hits or fused gene lists in Kc167
   cells treated with MMS. P-value of Fisher-exact test (one-tailed)
   comparison of the proportion of MMS altered/survival genes in each
   platform is also shown. “ND” (not-detected) means a given pathway was
   not detected or was not significantly enriched at a FDR<10%. Legend:
   “Total genes”: total number of genes in the reference pathway;
   “Microarray”: MMS-induced gene expressions; “RNAi screen”: MMS survival
   hits.
   Altered gene numbers per pathway Fisher Exact p-value
   Pathway terms Total genes Micro-array RNAi screen Fusion Fusion vs
   Microarray Fusion vs RNAi
   Microarray and RNAi detected
   NRF2-mediated Oxidative Stress Response 180 13 12 24 0.041 0.026
   p53 Signaling 98 8 11 18 0.028 0.113
   Dme0048:Glutathione metabolism 50 18 8 22 0.271 0.002
   Unfolded protein response 54 4 6 9 0.11 0.29
   Glutathione Biosynthesis 3 1 3 3 0.04 1.00
   RNAi screening and Fusion detected only
   hsa00230:Purine metabolism 153 ND 12 18 p<0.01 0.17
   hsa00240:Pyrimidine metabolism 95 ND 9 13 p<0.01 0.24
   EIF2 Signaling 185 ND 18 23 p<0.01 0.25
   REACT_71:Gene Expression 350 ND 54 60 p<0.01 0.30
   Salvage Pathways of Pyrimidine Ribonucleotides 93 ND 7 10 p<0.01 0.31
   Protein Ubiquitination Pathway 255 ND 20 23 p<0.01 0.38
   hsa03420:Nucleotide excision repair 44 ND 8 10 p<0.01 0.39
   hsa03010:Ribosome 87 ND 11 13 p<0.01 0.41
   REACT_1788:Transcription 131 ND 27 29 p<0.01 0.50
   [106]P00023:General transcription regulation 38 ND 12 12 p<0.01 0.50
   REACT_1675:mRNA Processing 32 ND 13 13 p<0.01 0.50
   REACT_11045:Signaling by Wnt 61 ND 9 10 p<0.01 0.50
   [107]P00045:Notch signaling pathway 50 ND 8 8 p<0.01 0.50
   Nucleotide Excision Repair Pathway 35 ND 9 10 p<0.01 0.50
   hsa03050:Proteasome 47 ND 8 9 p<0.01 0.50
   hsa03022:Basal transcription factors 35 ND 13 13 p<0.01 0.50
   Assembly of RNA Polymerase II Complex 50 ND 19 19 p<0.01 0.50
   γ-glutamyl Cycle 15 ND 3 4 p<0.01 0.50
   REACT_383: DNA replication 97 ND 11 13 p<0.01 0.50
   Microarray and Fusion detected only
   Xenobiotic Metabolism Signaling 271 12 ND 21 0.07 p<0.01
   REACT_1698:Metabolism of nucleotides 77 6 ND 12 0.10 p<0.01
   PTEN Signaling 118 6 ND 11 0.15 p<0.01
   hsa00561:Glycerolipid metabolism 45 6 ND 9 0.29 p<0.01
   dme00980:Metabolism of xenobiotics by CYP450 64 23 ND 26 0.35 p<0.01
   dme00982:Drug metabolism 66 25 ND 28 0.36 p<0.01
   dme00040:Pentose and glucoronate conversions interconversions 40 10 ND
   12 0.40 p<0.01
   Lysine Degradation 5 2 ND 3 0.50 p<0.01
   PI3K/AKT Signaling 123 6 ND 12 0.11 p<0.01
   Fusion detected only
   Amyloid Processing 51 ND ND 7 p<0.01 p<0.01
   REACT_13:Metabolism of amino acids 163 ND ND 18 p<0.01 p<0.01
   Hsa00330: Arginine and Proline Metabolism 53 ND ND 9 p<0.01 p<0.01
   ERK/MAPK Signaling 187 ND ND 18 p<0.01 p<0.01
   PDGF Signaling 77 ND ND 8 p<0.01 p<0.01
   REACT_17015:Metabolism of proteins 217 ND ND 25 p<0.01 p<0.01
   [108]Open in a new tab

Dynamics of NRF2 and UPR gene expression regulation and essentiality for
surviving alkylation

   In our prior Drosophila RNAi screening we had validated basal
   transcription, ribosome, proteasome, NER/p53 DDR and NOTCH as MMS
   survival responses [[109]1]. Reanalysis of those data and parallel gene
   expression data with newer bioinformatics tools revealed additional
   pathways, including NRF2 signaling and UPR. These pathways were further
   highlighted by additional gene enrichment following the fusion of RNAi
   hits and MMS-induced gene expressions (Figs [110]3A and [111]4A).

Fig 3. Gene/Protein interaction networks of NRF2-GSH pathway in MMS treated
Drosophila cells.

   [112]Fig 3
   [113]Open in a new tab

   (A) Ingenuity canonical pathway charts showing gene expression
   inductions (left), RNAi hits (center) and fusion (right) of MMS
   responses with the NRF2 pathway. All edges are supported by at least
   one reference from the literature and stored in the Ingenuity Knowledge
   Base. Nodes are displayed using various shapes that represent the
   functional classes of the gene product (square: cytokines; diamond:
   enzyme; circle into a circle: complex/group; trapezium: transporter;
   ellipse/oval shape: transcription regulator; triangle: kinase; circle:
   other) (B) Fusion of gene expression profiles and RNAi screening hits
   applied to Viacomplex functional networks shows a landscape of
   overexpressed and lethal components/clusters with the NRF2-GSH pathway
   in Kc167 cells treated with MMS for 8 and 24h. The genes used to build
   NRF2-GSH interactomes, the MMS-induced changes in gene expression and
   their survival role (from RNAi screen) are described in [114]S3 Table.

Fig 4. Gene/Protein interaction networks of UPR/ER stress pathway in MMS
treated Drosophila cells.

   [115]Fig 4
   [116]Open in a new tab

   (A) Ingenuity canonical pathway charts showing gene expression
   inductions (left), RNAi hits (center) and fusion (right) of MMS
   responses with the pathway. Details of the edges and nodes are as
   described for [117]Fig 3. (B) Fusion of gene expression profiles and
   RNAi screening hits applied to Viacomplex functional networks shows a
   landscape of overexpressed and lethal components/clusters with the UPR
   pathway in Kc167 cells treated with MMS for 8 and 24h. The genes used
   to build UPR interactome, the MMS-induced changes in gene expression,
   and their survival role (from RNAi screen) are described in [118]S3
   Table.

   Though these processes can be drawn as linear pathways (Figs [119]3A
   and [120]4A), the interactions between components and complexes are
   more complicated. To determine any particular relationship between the
   components identified in alkylation response, protein interaction
   networks were developed for each pathway (Figs [121]3B and [122]4B; see
   [123]S3 Table for NRF2 and UPR pathway gene expressions). Gene
   expression changes over time within these networks were then examined
   by ViaComplex analysis. Within the NRF2 pathway a highly connected
   cluster comprising glutathione S-transferase (GST) orthologs and GSH
   synthesis enzymes form 8 to 24 h ([124]Fig 3B). Genes such as GstE5,
   GstE3 (GST family), Gclc (GSH synthesis rate-limiting enzyme) and TrxT
   (thioredoxin), were both essential for survival and up-regulated by
   MMS. Others, such as the NRF2 ortholog cnc and its transcriptional
   co-activator maf-S, as well as the NRF2 target genes involved in GSH
   synthesis (Gclm and CG32495/Glutathione synthetase/GSS) and
   thioredoxins (Trx-2, CG8993, dhd and CG8517) were not transcriptionally
   regulated but enhanced MMS toxicity when depleted ([125]Fig 3B and
   [126]S3 Table).

   Similar to our analysis of the NRF2 pathway, we also examine the
   interaction and dynamic relationship with the UPR pathway. Of note, the
   Drosophila UPR pathway response to MMS appears more dependent upon
   heat-shock proteins than activation of ER stress mediators as seen in
   mammalian systems ([127]Fig 4B and [128]S3 Table) [[129]7–[130]10]. MMS
   up-regulated a well-connected cluster involving Hsp67Bc, Hsp70Bc,
   Hsp23, Hsp26 and Hsp27 chaperones. Knockdown of the key heat shock
   transcription factor, hsf (heat-shock-factor, human HSF1 ortholog), or
   its direct transcriptional targets Hsp83 and Hsp70Bc, sensitized cells
   to alkylation. Hsf regulates a variety of heat shock proteins such as
   Hsp67Bc [[131]11], a chaperone known to protect against protein
   misfolding in fly [[132]12, [133]13]. Of note, HSF1 and HSP12/HSP26
   induction were also reported in MMS-treated S. cerevisiae [[134]14].
   Knockdown of some components of classical ER stress machinery also
   potentiated MMS toxicity in fly cells, including Ire-1 (IRE1alpha
   ortholog) and the DNAJ chaperones CG14650 and CG6693 ([135]Fig 4B and
   [136]S3 Table).

NRF2-GSH and UPR are evolutionary conserved alkylation survival programs

   Having observed an enrichment of pathways involved in MMS when fusing
   RNAi and gene expression responses in fly cells, we wanted to determine
   whether we could extend this strategy to identify mammalian MMS
   survival pathways. Towards this objective we evaluated the integration
   of our findings from fly cells with MMS induced gene expression
   responses in normal primary mammalian MEFs and human cancer cells. We
   first examined the degree of overlap between MMS gene expression
   changes across species but found there was no consistency in the
   orthologs of differentially expressed genes (DEGs); there was only ~
   0.2% overlap irrespective on comparing fly (red font) human (black
   font) or mouse (data not shown) DEG orthologs ([137]Fig 5A). Among
   ~1680 up-regulated orthologs across species only
   glutamate–cysteine-ligase catalytic subunit (GCLC), glutamine
   synthetase (GLUL) and GADD45B overlapped. In contrast though, pathway
   analysis was very informative and we were able to confirm that 7
   pathway terms that can be simplified into NRF2 and GSH metabolism, p53
   and GADD45 DNA damage responses and UPR/ER stress pathways are
   significantly conserved across species ([138]Fig 5A; upregulated genes
   are detailed in [139]S4 Table). MMS-induced genes with NRF2 and UPR/ER
   stress pathways in MDA-MB231 are represented in [140]Fig 5B and
   MMS-induced fold-changes with NRF2 and UPR pathway markers are shown in
   [141]Fig 5C. Thus, similar to our observation within Drosophila
   comparing fly RNAi screening and microarray data, comparison of gene
   expression responses across species is possible if conducted at a
   pathway/process level, more so than at an individual gene level.

Fig 5. NRF2, glutathione and UPR survival responses are conserved across
species.

   [142]Fig 5
   [143]Open in a new tab

   (A) Venn diagrams showing the overlap between alkylation-induced genes
   expressions and pathways across MDA-MB231, fly Kc167 and MEFs. Black
   and red fonts denote comparisons of human and fruitfly orthologs,
   respectively. The pathways overlapping across the three species are
   also described. Detailed PEA of MMS-induced genes in MEF and MDA-MB231
   are shown in [144]S4 Table. (B) Ingenuity canonical pathway charts
   showing upregulated genes with the NRF2 and ER stress/UPR pathways in
   MMS-treated MDA-MB231 cells. Details of the edges and nodes are as
   described for [145]Fig 3. (C) MMS-induced changes in NRF2 and ER stress
   pathway markers as determined after 8 h alkylation treatment in
   MDA-MB231 and MEFs. (D) Box-plot representation of MMS-induced changes
   in compounds of the GSH metabolism as determined by metabolomics in
   Kc167 and MEFs (see [146]methods). (E) ARE luciferase assays showing
   the relative basal and MMS-induced NRF2 activity in MDA-MB231 and A549
   cells. The effect of KEAP1 and NRF2 siRNAs is also shown as a control.
   (F) Cell viability assays showing the effect of NAC or BSO
   pre-treatments on viability of MDA-MB231, A549 and MEFs treated with
   ~IC50 levels of MMS for 48 h. (G)Cell viability assay showing the
   protective effect of KEAP1 knockdown and GRP78 chaperone overexpression
   upon toxicity of varying MMS levels in MDA-MB231 cells (48 h
   treatment). pcDNA was used as empty vector control; GRP78
   overexpression (~7 fold-induction was validated by immunoblot 24 h
   post-transfection; data not shown); scrambled siRNA controls showed no
   alteration and is not shown. (H) Time course effect of MMS (40 μg/mL)
   on the immunocontent of NRF2, GRP78 and CHOP proteins in MDA-MB231 and
   MEFs as assessed by Western blot. *Different from untreated cells;
   ^#different from untreated and from MMS-treated cells. In (G),
   asterisks denote differences from MMS alone at equivalent
   concentrations (ANOVA-Tukey, p<0.05, n = 3).

   Our comparative gene expression analysis again indicated the importance
   the NRF2-GSH and UPR pathways for alkylation survival. To confirm this
   result we first performed mass-spectrum metabolomics in MEFs following
   MMS exposure. While fly cells were unable to upregulate or maintain
   their GSH and GSSG levels in response to MMS, a 5 to 20-fold increase
   in the GSH precursor gamma-glutamyl-cysteine (gamma-GC) was observed
   ([147]Fig 5D). Gamma-GC is a product of the NRF2 target enzyme GCLC.
   Interestingly, we also observed a ~300-500-fold accumulation of
   methyl-glutathione (methyl-GS), likely a product of phase-II
   detoxification of MMS by GSTs, in MMS-treated fly cells. This suggests
   that de novo GSH biosynthesis is driven by depletion of GSH pools used
   for MMS detoxification. This GSH response is conserved in MEFs, which
   seems to be better able to promote control of GSH synthesis by
   accumulating more GSH and GSSG (2-3-fold increases) and less methyl-GS
   (60 to 100-fold increase at 8 and 24 h, respectively) when compared to
   fly cells ([148]Fig 5D).

   MMS induced NRF2 activation in MDA-MB231 cells was first evaluated by
   using the ARE-luciferase reporter assay ([149]Fig 5E). Confirming this
   response we demonstrated an MMS induced accumulation of NRF2 protein in
   MDA-MB231 and MEFs ([150]Fig 5H–5G). Finally, to demonstrate the
   importance of the NRF2 pathway in MMS survival we found that we were
   able to promote MMS resistance by inducing NRF2; to achieve this we
   depleted the NRF2 negative regulator KEAP1 ([151]Fig 5G; effect of
   KEAP1 siRNA on NRF2 activity is shown in [152]Fig 5E). To complement
   this observation we then used the A549 lung cancer cell line, which is
   known to harbor an inactivating mutation in KEAP1 gene [[153]15]. A549
   cells display a constitutively increased ARE-luciferase reporter
   activity as compared to MDA-MB231 ([154]Fig 5E) and an MMS IC50 level
   twice (75 μg/mL) than those observed in MDA-MB231 or MEF (~35 μg/mL).

   To further demonstrate the importance of the NRF2/UPR pathway in
   alkylation survival we modulated the GSH response. GSH de novo
   biosynthesis by the GCLC/GCLM enzyme complex can be inhibited by BSO.
   BSO treatment caused substantial potentiation of MMS toxicity in normal
   MEF, MDA-MB231 as well as A549 cancer cell lines ([155]Fig 5F).
   Conversely, supplementing glutathione levels by providing the rate
   limiting precursor cysteine in the form of NAC completely blocked MMS
   toxicity in all cell lines tested. As such, our results indicate that
   the metabolite GSH is a major requirement for MMS survival as predicted
   in our cross-species analysis ([156]Fig 5F). To evaluate the ER
   stress/UPR response to MMS, we evaluated the ER stress sensor chaperone
   BIP/GRP78 and the signal transducer CHOP ([157]Fig 5H) in MEFs and
   MDA-MB231. In response to MMS we observed increases in levels of both
   proteins. To demonstrate the importance ER stress in MMS survival we
   overexpressed GRP78, a key ER chaperone that inhibits ER sensor
   activation, which conferred protection against MMS toxicity ([158]Fig
   5G). These fly-to-mammalian data comparisons therefore confirmed the
   biology systems predictions that NRF2-GSH xenobiotics detoxification
   and UPR pathways are cross-species conserved survival responses to
   alkylation.

Discussion

   Forward genetic screens in model organisms have provided the basis of
   much of our foundational knowledge of gene function [[159]16, [160]17].
   The availability of RNAi platforms have provided the potential to
   comprehensively interrogate the role of all genes in a particular
   context. However, these genome scale studies result in additional
   complications, particularly the volume of genes being identified.
   Furthermore, the level of false positives (usually ~40%) and false
   negatives (unknown amount) [[161]1, [162]18, [163]19] is a significant
   impediment to leveraging the full potential of these genomic screens.
   Theoretically, a complementary genomic assay could facilitate
   prioritization of which genes and pathways are key to the phenotype
   being screened. One of the most easily implemented genomic platforms
   currently available is gene expression analysis, be that through
   microarray or sequencing technologies. Conceptually, one might expect
   that the comparison of two or more genomic scale analyses of the same
   biology could help prioritize key genes for follow-up studies. However,
   this type of complementary analysis (gene expression plus RNAi
   screening data) has been explored previously to facilitate genomic
   knockout or RNAi screening analysis without success due to a lack of
   overlap between survival gene hits and those that are transcriptionally
   regulated in the same biological context [[164]2, [165]3].

   Similar to these prior reports [[166]2, [167]3], we did not find a
   significant overlap in the genes that confer a phenotype and those that
   are dynamically expressed in the same context. We also noted that many
   of the pathways significantly enriched in either RNAi screening or gene
   expression analyses also largely differed. However, this did not
   preclude some genes being dynamically expressed in response to MMS in
   pathways identified from RNAi screening or conversely some genes
   conferring sensitivity to MMS in the pathways identified in the gene
   expression analysis. The fusion of the data from these two platforms
   therefore improved the enrichment in a majority of these pathways,
   including those previously validated as being necessary for MMS
   survival [[168]1]. This occurs because fusion of these different data
   adds additional nodes to specific networks/pathways, thus highlighting
   which pathways are most likely to validate following further
   examination. Importantly, while gene level comparison across species
   was uninformative, fusion followed by PEA was a powerful means to
   compare data across evolutionary distant species such as fly to human.
   This observation suggests that different cells, even from different
   species, share similar pathways/processes to respond to alkylation
   damage, though modulating different individual genes (or their
   orthologs) within a conserved pathway to achieve this effect. Inherent
   to these observations is the concept that the regulation of pathway
   activity by the dynamic expression of a gene does not indicate that
   these same genes are essential to that process. Conversely, the
   proteins that are sensitive to RNAi knockdown and are key for pathway
   activity may not be the same genes in that are dynamically expressed.
   This is exemplified by Hsf and cnc/NRF2, two master regulators of the
   UPR and NRF2 responses mostly regulated at the posttranslational level
   [[169]11, [170]20, [171]21], whose knockdown caused sensitivity to MMS,
   but gene expressions were not altered. In addition, fusion provides
   improved gene enrichments by better covering both gene expression
   changes and posttranslational-regulated RNAi hits with a given pathway
   and thus consists an interesting strategy for improve pathway
   identification that may have wider utility.

   To validate the results of our fusion strategy and its utility in
   comparative biology analysis we focused on a relatively underexplored
   component of alkylation damage survival. Alkylating agents are
   electrophilic compounds that can react with cysteine groups such as is
   found in KEAP1. Once these cysteine groups are altered, KEAP1 can no
   longer bind NRF2 and target it for degradation. NRF2 can then
   accumulate and promote transcription of antioxidant and detoxification
   genes following its translocation into the nucleus [[172]11, [173]21].
   We found that NRF2 upregulates GCLC to replenish GSH pools that are
   depleted by the formation of methyl-GSH during MMS detoxification.
   Consequently, depletion of GSH potentiates alkylation toxicity.
   Substantiating our results others have reported that GSH depletion
   potentiates chemotherapy toxicity or that up-regulation of the NRF2 or
   GSH system confers chemoresistance [[174]22–[175]26]. Interestingly,
   the GSH precursor NAC blocks alkylation associated nephrotoxicity
   [[176]27, [177]28], though our data from normal cells such as MEFs and
   the conservation of NRF2-GSH response would predict NAC also to cause
   an undesired protection of cancer cells. GSH also can facilitate
   chemoresistance by serving as a cofactor for MRP2-mediated drug efflux
   [[178]29] or GSTs to detoxify xenobiotics such as alkylators [[179]30].
   Here, we connected NRF2 and GSH in the context of alkylating drugs
   response, and showed that this is a well-conserved process across
   evolutionary distant organisms. This raise concerns about the use of
   NRF2 inhibition in combination cancer therapies due to possible
   off-target toxicity [[180]31].

   Our results also indicated that the UPR is also key in determining
   alkylation survival, and enhancing the folding capability of ER via
   overexpression of the chaperone GRP78 conferred resistance to
   alkylation. Although not herein identified, alkylation of nascent
   proteins within the ER may be the mechanism underlying the ER stress
   induction by alkylators [[181]32]. Of note, studies with the
   alkylation-like chemotherapeutic cisplatin suggest that it may not be
   widespread protein alkylation that induces UPR, but rather conjugation
   of key proteins such as the GRP94, HSP90 and calreticulin within the ER
   [[182]32]. ER stress promotes adaptive responses or cell death,
   depending on the duration of the stimulus and which ER sensors are
   activated [[183]32, [184]33]. While ATF6 and IRE1a ER sensors are first
   responders to unfolded proteins and generally promote production of
   chaperones, ER biogenesis, proteolysis and secretion, thus enhancing ER
   folding capacity [[185]7, [186]8]. On the other hand, PERK responds to
   high and persistent unfolded cargo and inhibits global mRNA translation
   via eIF2α phosphorylation leading to apoptosis via ATF4-CHOP axis
   [[187]9, [188]10]. The complex and differential mechanisms associated
   with ER sensors activation by chemotherapies requires more in depth
   investigation in order to understand how modulating ER stress sensors
   might optimize or hamper alkylation toxicity to normal or tumor
   contexts.

   In summary, our data show that it is possible to fuse fly RNAi and gene
   expression data at a pathway level, but not gene level, in order to
   better identify cellular responses to a cytotoxic stimuli. Further we
   show that this fusion concept and be extended from fly to more complex
   transcriptomes, such as mammalian. RNAi and gene expression combination
   analysis (e.g. fusion) are able to differentiate dynamically from
   non-dynamically expressed survival responses, as well as provide a
   better overview and gene enrichment with pathways. Besides the
   classical and expected DDR pathways, we validated the activation of
   NRF2 and UPR as two conserved alkylation responses, which are possibly
   a consequence of MMS proteotoxicity and a cellular attempt of drug
   detoxification. These DNA repair independent mechanisms are likely
   relevant to both normal and cancer cell responses to alkylating agents
   and possibly other genotoxic chemotherapeutics. It is very likely that
   this fusion strategy can be applied to other contexts where both RNAi
   and gene expression data are available. Further, as we better develop
   pathway analysis tools and develop databases of genes that are either
   capable of dynamic expression or responsive to RNAi, we should be
   better able to use bioinformatics to minimize false positive results
   and improve potential false negative results that need to be retested.

Supporting Information

   S1 Fig. Time course of MMS-induced gene expression alterations in Kc167
   cells.

   (A) Description of MMS upregulated genes over 8, 24 and 72 h treatments
   in fly Kc167 cells. (B) Distribution of the 52 MMS survival hits with
   upregulated gene expressions over 8, 24 and 72 h treatments in fly
   Kc167 cells. In A and B, genes are annotated to their respective time
   points of upregulation. (C) Fold changes of MMS survival hits with
   concomitant up (52 genes) and downregulated (26 genes) expressions over
   8, 24 and 72 h MMS treatment. (D) Representative pathways associated
   with MMS upregulated genes in 8 h compared to combined 8, 24 and 72 h
   gene lists shows better enrichments in combined analysis.

   (TIF)
   [189]Click here for additional data file.^ (1.2MB, tif)
   S2 Fig. Venn Diagrams of pathways associated with MMS survival hits and
   microarray changes.

   Detailed Venn diagrams of Pathway terms associated with MMS induced
   genes (microarray), RNAi survival hits (RNAi screening) and fusion
   (microarray+RNAi screening). In the bottom-right graph, the antilo\g
   p-values of pathway enrichments in each part of Venn diagram are shown.
   Pathway terms overlapping between two platforms show more significant
   p-values than orphan terms.

   (TIF)
   [190]Click here for additional data file.^ (1,015.4KB, tif)
   S1 Table. Differentially expressed genes in MMS-treated Kc167 cells.

   MMS-induced changes in gene expression of fly Kc167 cells (8, 24 and 72
   h) as determined by microarray. Essentiality of each gene for MMS
   survival is also annotated as previously determined by Ravi et al. 2009
   [[191]1].

   (XLSX)
   [192]Click here for additional data file.^ (125KB, xlsx)
   S2 Table. Detailed Pathway Enrichment Analysis (PEA) of MMS-treated
   Kc167 cells.

   Pathway Enrichment Analysis (PEA) of MMS-induced genes (microarray),
   RNAi screening survival hits (from [[193]1]), and fusion of
   microarray/RNAi screening gene lists in Kc167 D. melanogaster cell line
   treated with MMS.

   (XLSX)
   [194]Click here for additional data file.^ (18.8KB, xlsx)
   S3 Table. Alkylation-induced changes in NRF2 and UPR genes in fruitfly
   cells.

   NRF2-GSH and UPR/ER stress pathway gene expression changes in Kc167
   cells treated with 40 μg/mL MMS for 8 and 24 h as determined by
   microarray. Essentiality of each gene for MMS survival is annotated as
   previously determined by Ravi et al. 2009 [[195]1]

   (XLSX)
   [196]Click here for additional data file.^ (19.7KB, xlsx)
   S4 Table. MMS-induced pathways in mouse and human cells.

   Pathway Enrichment Analysis (PEA; Ingenuity) of alkylation-induced gene
   expressions in MDA-MB231 and MEF cells treated with 40 μg/mL MMS for 8
   h as determined by RNA sequencing and microarray, respectively.

   (XLSX)
   [197]Click here for additional data file.^ (11.2KB, xlsx)

Acknowledgments