Cell fitness is an omniphenotype

Abstract

Genotype-phenotype relationships are at the heart of biology and medicine. Numerous advances in genotyping and phenotyping have accelerated the pace of disease gene and drug discovery. Though now that there are so many genes and drugs to study, it makes prioritizing them difficult. Also, disease model assays are getting more complex and this is reflected in the growing complexity of research papers and the cost of drug development. Herein we propose a way out of this arms race. We argue for synthetic interaction testing in mammalian cells using cell fitness -which reflect changes in cell number that could be due to a number of factors -as a readout to judge the potential of a genetic or environmental variable of interest (e.g., a gene or drug). That is, if an unknown perturbation of a mammalian gene or drug of interest is combined with a known perturbation and causes a strong cell fitness phenotype relative to that caused by the known perturbation alone, this justifies proceeding with the new gene/drug in more complex models like mouse models where the known perturbation is already validated. This recommendation is backed by the following: 1) human genes essential for cell growth involve nearly all classifications of cellular and molecular processes; 2) Nearly all human genes important in cancer -a disease defined by altered cell number -are also important in other complex diseases; 3) Many drugs affect a patient's condition and the fitness of their cells comparably. Taken together, these findings suggest cell fitness could be a broadly applicable phenotype for understanding gene and drug function.Measuring cell fitness is robust and requires little time and money. These are features that have long been capitalized on by pioneers using model organisms that we hope more mammalian biologists will recognize. (Fig. 4B). PubMed PMIDs are largely chronological, i.e., higher PMIDs mostly mean a more recent citation and vice versa, with the exception of PMIDs < 8M as well as those between 12M and 15M. As of July 9, 2019, citations up to the end of 2017 have been annotated with MeSH terms and gene associations such that cancer vs. non-cancer correlations for multiple time points could be determined. Citations were split every million PMIDs and for the purposes of graphing the results the calendar year was approximated. The FDA-approved drug list was obtained from: https://www.fda.gov/drugs/drug-approvals-anddatabases/drugsfda-data-files. Similar to the cancer vs. non-cancer analysis for genes, all PMIDs for each drug were collected into a tab-delineated file, intersected with the MeSH-PMID file, and the resulting list was separated by those citations that were co-annotated with the MeSH term "neoplasm" and those that were not. Co-citation time series analysis