Cross-species identification of cancer resistance-associated genes that may mediate human cancer risk.

Cancer is a predominant disease across animals. We applied a comparative genomics approach to systematically characterize genes whose conservation levels correlate positively (PC) or negatively (NC) with cancer resistance estimates across 193 vertebrates. Pathway analysis reveals that NC genes are enriched for metabolic functions and PC genes in cell cycle regulation, DNA repair, and immune response, pointing to their corresponding roles in mediating cancer risk. We find that PC genes are less tolerant to loss-of-function (LoF) mutations, are enriched in cancer driver genes, and are associated with germline mutations that increase human cancer risk. Their relevance to cancer risk is further supported via the analysis of mouse functional genomics and cancer mortality of zoo mammals' data. In sum, our study describes a cross-species genomic analysis pointing to candidate genes that may mediate human cancer risk.

Multi-omics data integration analysis identifies the spliceosome as a key regulator of DNA double-strand break repair.

DNA repair by homologous recombination (HR) is critical for the maintenance of genome stability. Germline and somatic mutations in HR genes have been associated with an increased risk of developing breast (BC) and ovarian cancers (OvC). However, the extent of factors and pathways that are functionally linked to HR with clinical relevance for BC and OvC remains unclear. To gain a broader understanding of this pathway, we used multi-omics datasets coupled with machine learning to identify genes that are associated with HR and to predict their sub-function. Specifically, we integrated our phylogenetic-based co-evolution approach (CladePP) with 23 distinct genetic and proteomic screens that monitored, directly or indirectly, DNA repair by HR. This omics data integration analysis yielded a new database (HRbase) that contains a list of 464 predictions, including 76 gold standard HR genes. Interestingly, the spliceosome machinery emerged as one major pathway with significant cross-platform interactions with the HR pathway. We functionally validated 6 spliceosome factors, including the RNA helicase SNRNP200 and its co-factor SNW1. Importantly, their RNA expression correlated with BC/OvC patient outcome. Altogether, we identified novel clinically relevant DNA repair factors and delineated their specific sub-function by machine learning. Our results, supported by evolutionary and multi-omics analyses, suggest that the spliceosome machinery plays an important role during the repair of DNA double-strand breaks (DSBs).

Expanding the MECP2 network using comparative genomics reveals potential therapeutic targets for Rett syndrome.

Inactivating mutations in the Methyl-CpG Binding Protein 2 (MECP2) gene are the main cause of Rett syndrome (RTT). Despite extensive research into MECP2 function, no treatments for RTT are currently available. Here, we used an evolutionary genomics approach to construct an unbiased MECP2 gene network, using 1028 eukaryotic genomes to prioritize proteins with strong co-evolutionary signatures with MECP2. Focusing on proteins targeted by FDA-approved drugs led to three promising targets, two of which were previously linked to MECP2 function (IRAK, KEAP1) and one that was not (EPOR). The drugs targeting these three proteins (Pacritinib, DMF, and EPO) were able to rescue different phenotypes of MECP2 inactivation in cultured human neural cell types, and appeared to converge on Nuclear Factor Kappa B (NF-κB) signaling in inflammation. This study highlights the potential of comparative genomics to accelerate drug discovery, and yields potential new avenues for the treatment of RTT.

ACE2 Co-evolutionary Pattern Suggests Targets for Pharmaceutical Intervention in the COVID-19 Pandemic.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spillover infection in December 2019 has caused an unprecedented pandemic. SARS-CoV-2, as other coronaviruses, binds its target cells through the angiotensin-converting enzyme 2 (ACE2) receptor. Accordingly, this makes ACE2 research essential for understanding the zoonotic nature of coronaviruses and identifying novel drugs. Here we present a systematic analysis of the ACE2 conservation and co-evolution protein network across 1,671 eukaryotes, revealing an unexpected conservation pattern in specific metazoans, plants, fungi, and protists. We identified the co-evolved protein network and pinpointed a list of drugs that target this network by using data integration from different sources. Our computational analysis found widely used drugs such as nonsteroidal anti-inflammatory drugs and vasodilators. These drugs are expected to perturb the ACE2 network affecting infectivity as well as the pathophysiology of the disease.

Titanium Tackles the Endoplasmic Reticulum: A First Genomic Study on a Titanium Anticancer Metallodrug.

PhenolaTi is an advanced non-toxic anticancer chemotherapy; this inert bis(phenolato)bis(alkoxo) Ti(IV) complex demonstrates the intriguing combination of high and wide efficacy with no detected toxicity in animals. Here we unravel the cellular pathways involved in its mechanism of action by a first genome study on Ti(IV)-treated cells, using an attuned RNA sequencing-based available technology. First, phenolaTi induced apoptosis and cell-cycle arrest at the G2/M phase in MCF7 cells. Second, the transcriptome of the treated cells was analyzed, identifying alterations in pathways relating to protein translation, DNA damage, and mitochondrial eruption. Unlike for common metallodrugs, electrophoresis assay showed no inhibition of DNA polymerase activity. Reduced in vitro cytotoxicity with added endoplasmic reticulum (ER) stress inhibitor supported the ER as a putative cellular target. Altogether, this paper reveals a distinct ER-related mechanism by the Ti(IV) anticancer coordination complex, paving the way for wider applicability of related techniques in mechanistic analyses of metallodrugs.

Optimization of co-evolution analysis through phylogenetic profiling reveals pathway-specific signals.

SUMMARY: The exponential growth in available genomic data is expected to reach full sequencing of a million genomes in the coming decade. Improving and developing methods to analyze these genomes and to reveal their utility is of major interest in a wide variety of fields, such as comparative and functional genomics, evolution and bioinformatics. Phylogenetic profiling is an established method for predicting functional interactions between proteins based on similarities in their evolutionary patterns across species. Proteins that function together (i.e. generate complexes, interact in the same pathways or improve adaptation to environmental niches) tend to show coordinated evolution across the tree of life. The normalized phylogenetic profiling (NPP) method takes into account minute changes in proteins across species to identify protein co-evolution. Despite the success of this method, it is still not clear what set of parameters is required for optimal use of co-evolution in predicting functional interactions. Moreover, it is not clear if pathway evolution or function should direct parameter choice. Here, we create a reliable and usable NPP construction pipeline. We explore the effect of parameter selection on functional interaction prediction using NPP from 1028 genomes, both separately and in various value combinations. We identify several parameter sets that optimize performance for pathways with certain biological annotation. This work reveals the importance of choosing the right parameters for optimized function prediction based on a biological context. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are available on GitHub: https://github.com/iditam/CompareNPPs. CONTACT: yuvaltab@ekmd.huji.ac.il. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.