ILANIT 2020

Mapping global and local co-evolution across 600 species to identify novel homologous recombination repair genes

Dana Sherill-Rofe 1 Dolev Rahat 1,2 Steven Findlay 3 Anna Mellul 1 Irene Unterman 1 Maya Braun 1 Idit Bloch 1 Alon Lalezari 1 Arash Samiei 3 Ruslan Sadreyev 5 Michal Goldberg 4 Alexandre Orthwein 3 Aviad Zick 2 Yuval Tabach 1
1Developmental Biology and Cancer Research, Hebrew University, Israel
2Sharett Institute of Oncology, Hadassah Medical Organisation, Israel
3Segal Cancer Centre, Lady Davis Institute for Medical Research, Canada
4Genetics, Hebrew University
5Department of Molecular Biology, Massachusetts General Hospital, USA

The homologous recombination repair (HRR) pathway repairs DNA double-strand breaks in an error-free manner. Mutations in HRR genes can result in increased mutation rate, genomic rearrangements and are associated with numerous genetic disorders and cancer. Despite intensive research, the HRR pathway is not yet fully mapped.

Phylogenetic profiling analysis, which detects functional linkage between genes using co-evolution, is a powerful approach to identify factors in many pathways. Nevertheless, phylogenetic profiling has limited predictive power when analyzing pathways with complex evolutionary dynamics such as the HRR. To map novel HRR genes systematically, we developed clade phylogenetic profiling (CladePP). CladePP detects local co-evolution across hundreds of genomes and points to the evolutionary scale (e.g. mammals, vertebrates, animals, plants) at which co-evolution occurred. We found that multi-scale co-evolution analysis is significantly more biologically relevant and sensitive to detect gene function.

Using CladePP, we identified dozens of unrecognized genes that co-evolved with the HRR pathway, either globally across all eukaryotes or locally in different clades. We validated eight genes in functional biological assays to have a role in DNA repair at both the cellular and organismal levels. These genes are expected to play a role in the HRR pathway and might lead to a better understanding of missing heredity in HRR associated cancers (e.g. heredity breast and ovarian cancer). Our platform presents an innovative approach to predict gene function, identify novel factors related to different diseases and pathways, and to characterize gene evolution.









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