High-Throughput Functional Annotation of Natural Products by Integrated Activity Profiling

Abstract

Determining mechanism of action (MOA) is one of the biggest challenges in natural products discovery. Here, we report a comprehensive platform that uses Similarity Network Fusion (SNF) to improve MOA predictions by integrating data from the cytological profiling high-content imaging platform and the gene expression platform FUSION. The predictive value of the integrative approach was assessed using a library of target-annotated small molecules as benchmarks. Using KS-tests to compare in-class to out-of-class similarity, we found that SNF resulted in improved power to correctly assign MOA over either dataset alone. Furthermore, we integrated untargeted metabolomics of complex natural product fractions to map biological signatures to specific metabolites. Three examples are presented where SNF coupled with metabolomics was used to directly functionally characterize natural products and accelerate identification of bioactive metabolites. Our results support SNF integration of multiple phenotypic screening approaches along with untargeted metabolomics as powerful approach for advancing natural products drug discovery.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

“…Others have interrogated genetic heterogeneity, finding distinct morphological responses to serotonin modulators across breast cancer cell lines 116 . The integration of image and transcriptome data into ensemble approaches has shown promise to improve MOA determination for synthetic small molecules, natural products and identified bioactive metabolites 21 , 117 . Image-based profiles of cells treated with natural products have also been combined with mass spectral features of the same natural products to discover a novel family that causes endoplasmic reticulum stress 118 .…”

Analytical Challenges and Metrological Approaches to Ensuring Dietary Supplement Quality: International Perspectives

“…Agnostic, non-targeted, analytical approaches may address many of these issues with a single method ( Urban, 2016 ). Hight et al (2019) described a method for computational integration of data from three different high-content, high-throughput analyses (UPLC-MS-MS, gene expression analysis, and detailed cell phenotyping) of the same sets of chemically complex natural product fractions. This allowed the simultaneous generation of strong hypotheses regarding the chemical species contributing to the biological activity and the cellular and molecular substrates through which those compounds exert their effects.…”

A Gene Expression High-Throughput Screen (GE-HTS) for Coordinated Detection of Functionally Similar Effectors in Cancer

“…Recent developments in FUSION data analysis include the application of Similarity Network Fusion (SNF) to integrate with other datasets [19]. SNF was first applied to the integration of microRNA, mRNA, and DNA methylation profiling in order to identify novel glioblastoma subtypes [61] and has since been successfully used to integrate orthogonal datasets in other contexts [62,63].…”

“…In this review, we describe the use of a Gene Expression High-Throughput Screening (GE-HTS) technique termed Functional Signature Ontology (FUSION) [12][13][14][15][16][17][18], which offers a methodology for identifying genes critical to tumor cell survival and growth and inhibitors of these processes. FUSION has been used to screen for functional similarity between proteins using 14,355 unique siRNA pools, 344 microRNA mimics, and approximately 1200 natural products fractions in a K-Ras mutant colon cancer cell line and was used to identify novel proteins required for modulation of autophagy and proteins functionally similar to Kinase suppressor of Ras 1 (KSR1) and to determine the target and mechanism of action (MoA) of uncharacterized naturally occurring small molecules [12][13][14][15][16][17][18][19][20].…”