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. 2016 Apr 21;11(4):e0153970.
doi: 10.1371/journal.pone.0153970. eCollection 2016.

Combined Gene Expression and RNAi Screening to Identify Alkylation Damage Survival Pathways from Fly to Human

Alfeu Zanotto-Filho  1   2 , Ravi Dashnamoorthy  1   3 , Eva Loranc  1 , Luis H T de Souza  2 , José C F Moreira  2 , Uthra Suresh  1   4 , Yidong Chen  1   4 , Alexander J R Bishop  1   5
Affiliations

Combined Gene Expression and RNAi Screening to Identify Alkylation Damage Survival Pathways from Fly to Human

Alfeu Zanotto-Filho et al. PLoS One. .

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Gene level overlap and PEA showing the cellular processes associated with RNAi hits and gene expression alterations in MMS-treated Kc167 cells.
(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 [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 (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 S1 and S2 Tables, respectively.
Fig 2
Fig 2. Microarray/RNAi data fusion.
(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 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.
Fig 3
Fig 3. Gene/Protein interaction networks of NRF2-GSH pathway in MMS treated Drosophila cells.
(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 S3 Table.
Fig 4
Fig 4. Gene/Protein interaction networks of UPR/ER stress pathway in MMS treated Drosophila cells.
(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 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 S3 Table.
Fig 5
Fig 5. NRF2, glutathione and UPR survival responses are conserved across species.
(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 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 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 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).
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