Biochemometrics for Natural Products Research: Comparison of Data Analysis Approaches and Application to Identification of Bioactive Compounds

Abstract

A central challenge of natural products research is assigning bioactive compounds from complex mixtures. The gold standard approach to address this challenge, bioassay-guided fractionation, is often biased towards abundant, rather than bioactive, mixture components. This study evaluated the combination of bioassay-guided fractionation with untargeted metabolite profiling to improve active component identification early in the fractionation process. Key to this methodology was statistical modeling of the integrated biological and chemical datasets (biochemometric analysis). Three data analysis approaches for biochemometric analysis were compared, namely, partial least squares loading vectors, S-plots, and the selectivity ratio. Extracts from the endophytic fungi Alternaria sp. and Pyrenochaeta sp. with antimicrobial activity against Staphylococcus aureus served as test cases. Biochemometric analysis incorporating the selectivity ratio performed best in identifying bioactive ions from these extracts early in the fractionation process, yielding altersetin (3, MIC 0.23 μg/mL) and macrosphelide A (4, MIC 75 μg/mL) as antibacterial constituents from Alternaria sp. and Pyrenochaeta sp., respectively. This study demonstrates the potential of biochemometrics coupled with bioassay-guided fractionation to identify bioactive mixture components. A benefit of this approach is the ability to integrate multiple stages of fractionation and bioassay data into a single analysis.

Integrating Molecular Networking and Biological Assays To Target the Isolation of a Cytotoxic Cyclic Octapeptide, Samoamide A, from an American Samoan Marine Cyanobacterium

“…9–13 The improved access to DNA sequence information of prokaryotes has indicated that a staggering number of uncharacterized biosynthetic gene clusters are present in these organisms, and that there is much molecular diversity left to discover and explore for biologically valuable properties. 14 In turn, this has encouraged many researchers to pursue a “bottom-up” genetics-based discovery program.…”

Integration of Biochemometrics and Molecular Networking to Identify Antimicrobials in Angelica keiskei

“…The traditional workflow for natural product discovery is bioassay-guided fractionation [1–2], in which bioactive extracts and subsequent fractions are chromatographically separated and retested for bioactivity until active compounds have been isolated. Because botanical extracts contain thousands of individual constituents, it is often difficult to assign activity to individual components, thus, the most abundant or easily isolatable compounds are often presumed to be responsible for bioactivity [3–4]. New methods are needed that will enable isolation efforts to be focused on those components most likely to be responsible for the desired biological activity.…”

“…In combination with chromatographic techniques, mass spectrometry can be utilized to analyze hundreds of secondary metabolites simultaneously [3, 15–16]. Using a process called biochemometrics, quantitative chemical information and biological activity data can be incorporated into a statistical model.…”

“…Using a process called biochemometrics, quantitative chemical information and biological activity data can be incorporated into a statistical model. With this statistical modeling approach, it is possible to discovery chemical patterns related to bioactivity [3]. Partial least squares (PLS) analysis can be used in combination with chromatographic and mass spectrometric data to correlate metabolite profiles with biological data [17].…”

“…Partial least squares (PLS) analysis can be used in combination with chromatographic and mass spectrometric data to correlate metabolite profiles with biological data [17]. A recent study from our laboratory showed that selectivity ratio analysis was useful for the identification of trace bioactive constituents in fungal extracts without being confounded by highly abundant compounds [3]. The selectivity ratio compares the correlation and covariance to the residual variance, and provides a quantitative measurement of the ability of a given variable to differentiate between active and inactive groups [18].…”