Ravi Dashnamoorthy

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

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.

Multisource biological pathway consolidation

A typical method to discover phenotypic descriptions of an ordered set of differential gene expressions is to identify pathway enrichments. There are many pathways that are highly related or maybe redundant across different databases making their consolidation an essential step when interpreting these results. Two methods of pathway consolidation are explored, one utilizes the gene set of the most enriched pathway to find similar pathways also enriched in a given experiment. The other method uses only the gene members in each pathway, this finds de novo pathway clusters independent of any given experiment. Unique consolidation results from both methods are presented, demonstrating their applications in biological studies.