A team of biostatisticians led by University of Pittsburgh School of Public Health scientists report “Transcriptomic congruence analysis for evaluating model organisms” in PNAS that they’ve developed a framework to determine how much congruence and discordance laboratory animals have with specific human diseases. The tool removes potential bias from scientific interpretation of how translational animal data is for human conditions, according to the researchers.
Mice and other animals have been key to some of the biggest medical breakthroughs in human history. But animals aren’t always good models of human disease, leading to failed experiments and controversy over their usefulness.
“There have been decades of debate about whether animal models mimic humans well and whether they are useful for translational or clinical research,” said senior author George Tseng, ScD, professor and vice chair for research in Pitt Public Health’s Department of Biostatistics. “Our framework is the first to provide quantitative methods and bioinformatic workflow to properly address that debate.”
Tseng and his team tackled the topic after two papers published in PNAS—one in 2013 and one in 2014—that used the same datasets presented contradictory conclusions on the usefulness of mice as models of human diseases that involve inflammation, such as sepsis and burns.
Tseng’s team determined that the previous studies reached different endpoints because the scientific teams—one mostly composed of laboratory-based scientists and the other primarily of clinicians—had used different thresholds, or cut-off points, for their analyses.
“Model organisms are instrumental substitutes for human studies to expedite basic, translational, and clinical research. Despite their indispensable role in mechanistic investigation and drug development, molecular congruence of animal models to humans has long been questioned and debated. Little effort has been made for an objective quantification and mechanistic exploration of a model organism’s resemblance to humans in terms of molecular response under disease or drug treatment,” write the investigators.
“We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative concordance/discordance scores incorporating data variabilities, pathway-centric downstream investigation, knowledge retrieval by text mining, and topological gene module detection for hypothesis generation.
“Instead of a genome-wide vague and dichotomous answer of `poorly’ or ‘greatly’ mimicking humans, CAMO assists researchers to numerically quantify congruence, to dissect true cross-species differences from unwanted biological or cohort variabilities, and to visually identify molecular mechanisms and pathway subnetworks that are best or least mimicked by model organisms, which altogether provides foundations for hypothesis generation and subsequent translational decisions.
“The conclusion drawn by our unbiased, threshold-free framework is much more realistic,” Tseng said. “In the end, you cannot say that the mouse model is totally useless or totally perfect. A mouse model can mimic some biological mechanisms well but others poorly. The issue is whether it mimics the mechanism of interest, such as the drug target. And it even revealed that the data aren’t perfect in some situations—if you have limited information, you can’t draw a conclusion.”
The team is extending their research into cancer to examine which cell-cultured models are good mimics for tumors and psychiatric disorders to learn, for example, whether mice mimic the human circadian rhythm.
“We anticipate CAMO becoming an essential part of preclinical studies to solve all manner of human diseases,” Tseng added.
