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PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements
Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.
Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling
Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.
p HBMT1, a BAHD-family monolignol acyltransferase, mediates lignin acylation in poplar
Abstract Poplar (Populus) lignin is naturally acylated with p-hydroxybenzoate ester moieties. However, the enzyme(s) involved in the biosynthesis of the monolignol–p-hydroxybenzoates have remained largely unknown. Here, we performed an in vitro screen of the Populus trichocarpa BAHD acyltransferase superfamily (116 genes) using a wheatgerm cell-free translation system and found five enzymes capable of producing monolignol–p-hydroxybenzoates. We then compared the transcript abundance of the five corresponding genes with p-hydroxybenzoate concentrations using naturally occurring unrelated genotypes of P. trichocarpa and revealed a positive correlation between the expression of p-hydroxybenzoyl-CoA monolig-nol transferase (pHBMT1, Potri.001G448000) and p-hydroxybenzoate levels. To test whether pHBMT1 is responsible for the biosynthesis of monolignol–p-hydroxybenzoates, we overexpressed pHBMT1 in hybrid poplar (Populus alba × P. grandidentata) (35S::pHBMT1 and C4H::pHBMT1). Using three complementary analytical methods, we showed that there was an increase in soluble monolignol–p-hydroxybenzoates and cell-wall-bound monolignol–p-hydroxybenzoates in the poplar transgenics. As these pendent groups are ester-linked, saponification releases p-hydroxybenzoate, a precursor to parabens that are used in pharmaceuticals and cosmetics. This identified gene could therefore be used to engineer lignocellulosic biomass with increased value for emerging biorefinery strategies.