Molecular phenotyping
Molecular phenotyping describes the technique of quantifying pathway reporter genes, i.e. pre-selected genes that are modulated specifically by metabolic and signaling pathways, in order to infer activity of these pathways.[1][2]
In most cases, molecular phenotyping quantifies changes of pathway reporter gene expression to characterize modulation of pathway activities induced by perturbations such as therapeutic agents or stress in a cellular system in vitro. In such contexts, measurements at early time points are often more informative than later observations because they capture the primary response to the perturbation by the cellular system.[3] Integrated with quantified changes of phenotype induced by the perturbation, molecular phenotyping can identify pathways that contribute to the phenotypic changes.
Currently molecular phenotyping uses RNA sequencing and mRNA expression to infer pathway activities. Other technologies and readouts such as mass spectrometry and protein abundance or phosphorylation levels can be potentially used as well.[4]
Application in early drug discovery
Current data suggest that by quantifying pathway reporter gene expression, molecular phenotyping is able to cluster compounds based on pathway profiles and dissect associations between pathway activities and disease phenotypes simultaneously.[5] Furthermore, molecular phenotyping can be applicable to compounds with a range of binding specificities and is able to triage false positives derived from high-content screening assays. Furthermore, molecular phenotyping allows integration of data derived from in vitro and in vivo models as well as patient data into the drug discovery process.
References
- ^ Zhang, JD; Küng, E; Boess, F; Certa, U; Ebeling, M (24 April 2015). "Pathway reporter genes define molecular phenotypes of human cells". BMC Genomics. 16 (1): 342. doi:10.1186/s12864-015-1532-2. PMC 4415216. PMID 25903797.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Zhang, JD; Schindler, T; Küng, E; Ebeling, M; Certa, U (5 July 2014). "Highly sensitive amplicon-based transcript quantification by semiconductor sequencing". BMC Genomics. 15 (1): 565. doi:10.1186/1471-2164-15-565. PMC 4101174. PMID 24997760.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Zhang, JD; Berntenis, N; Roth, A; Ebeling, M (June 2014). "Data mining reveals a network of early-response genes as a consensus signature of drug-induced in vitro and in vivo toxicity". The Pharmacogenomics Journal. 14 (3): 208–16. doi:10.1038/tpj.2013.39. PMC 4034126. PMID 24217556.
- ^ Moffat, JG (18 May 2017). "Turning the Light On in the Phenotypic Drug Discovery Black Box". Cell Chemical Biology. 24 (5): 545–547. doi:10.1016/j.chembiol.2017.05.005. PMID 28525769.
- ^ Drawnel, FM; Zhang, JD; Küng, E; Aoyama, N; Benmansour, F; Araujo Del Rosario, A; Jensen Zoffmann, S; Delobel, F; Prummer, M; Weibel, F; Carlson, C; Anson, B; Iacone, R; Certa, U; Singer, T; Ebeling, M; Prunotto, M (18 May 2017). "Molecular Phenotyping Combines Molecular Information, Biological Relevance, and Patient Data to Improve Productivity of Early Drug Discovery". Cell Chemical Biology. 24 (5): 624–634.e3. doi:10.1016/j.chembiol.2017.03.016. PMID 28434878.