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Can We Predict Protein from mRNA Levels?

By August 9, 2017No Comments

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 This week we profile a recent publication in Nature from the laboratories of Drs. Cohen Freue (pictured above), Paul Pavlidis, and Christopher Overall at the University of British Columbia.

Recent advances of genomic, proteomic, and metabolomics technologies have stimulated a large body of biomedical studies focused on the identification of molecular biomarkers that can reduce the challenges and costs associated with the prediction, diagnosis, and management of various diseases. In general, these technologies have been used separately and the integration of the information distilled from these platforms to jointly address biological questions therefore remains limited. To succeed in the “post-genome” era, we need to bring together the existing biological, technological, and experimental wealth of resources with the development and application of tailored statistical and computational methodologies to bring value and knowledge to health care.

Led by Dr. Cohen Freue, an interdisciplinary group from the University of British Columbia, carefully examined the analysis and data of a renowned publication that claimed to find a revolutionary result: “it now becomes possible to predict protein abundance in any given tissue with good accuracy from the measured mRNA abundance” (Wilhelm et al., Nature 509, 582–587). If true, such a claim would be an important step in achieving the task of integration of multiple technologies to mechanistically decipher disease and establish candidate biomarkers. However, in their recent article (Fortelny et al., Nature, 547, E19-E20), the UBC group highlights and corrects an important error in the statistical analyses of Wilhelm et al.’s paper, which has profound implications on the general understanding of biological regulation. With the contribution of David Kepplinger, Ph.D. student in Dr. Cohen Freue’s lab, the group has also built an accompanying web application to better illustrate the results of their analysis and ease the visualization and interact with the data. “Bringing our analyses and conclusions to the attention of the scientific community is essential to correct this error and to assist in directing future research into this topic” – Dr. Cohen Freue.

Dr. Cohen Freue, Assistant Professor in the Department of Statistics and Canada Research Chair II in Statistical Genomics and Proteomics, develops and conducts computational and statistical approaches to find protein biomarkers that may not be detected by classical statistical methods. The identification of such biomarkers could further our understanding of the molecular mechanisms related to various diseases, such as multiple sclerosis, cardiac artery disease, or cancer, and contribute to the development more personalized patient management and to the sustainability of the healthcare system.

Dr. Paul Pavlidis, Professor in the Department of Psychiatry and Centre for Brain Health, integrates genomics and genetics with data on networks, cells, structures, connections and phenotypes, thus merging knowledge from bioinformatics and neuroscience–sometimes referred to as neuroinformatics. The group applies these approaches to increasing understanding of human conditions such as schiozphrenia, depression, autism and Alzheimer’s disease.

Dr. Christopher Overall, Professor in the Department of Oral Biological and Medical Sciences, Canada Research Chair in Protease Proteomics and Systems Biology, has developed world-leading innovative quantitative proteomic techniques to enrich and identify proteolysis products in vivo that are operative in the system under study, that can yield to the discovery of mechanistically informative biomarkers of disease. Resultant new drug targets and new clinical tests for early, accurate patient diagnosis can thereby be translated.

Dr. Nikolaus Fortelny completed his PhD under the supervision by Dr. Pavlidis and Dr. Overall in 2016. Originally focused on understanding signaling networks of proteases, he quickly became interested in statistical prediction approaches to integrate multiple layers of genome-scale molecular measurements. Nikolaus is currently working as a postdoctoral fellow in the lab of Christoph Bock at the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences in Vienna, adding a clinical edge to his research.

Their recent article in Nature demonstrates the joint effort from the three research laboratories to re-open an unsettled debate and advance our current knowledge of a relevant biological topic: Can we predict protein from mRNA levels?