High-throughput metabolomics predicts drug-target relationships for eukaryotic proteins

The metabolic domestication syndrome of budding yeast

Cellular metabolism evolves through changes in the structure and quantitative states of metabolic networks. Here, we explore the evolutionary dynamics of metabolic states by focusing on the collection of metabolite levels, the metabolome, which captures key aspects of cellular physiology. Using a phylogenetic framework, we profiled metabolites in 27 populations of nine budding yeast species, providing a graduated view of metabolic variation across multiple evolutionary time scales. Metabolite levels evolve more rapidly and independently of changes in the metabolic network's structure, providing complementary information to enzyme repertoire. Although metabolome variation accumulates mainly gradually over time, it is profoundly affected by domestication. We found pervasive signatures of convergent evolution in the metabolomes of independently domesticated clades of Saccharomyces cerevisiae. Such recurring metabolite differences between wild and domesticated populations affect a substantial part of the metabolome, including rewiring of the TCA cycle and several amino acids that influence aroma production, likely reflecting adaptation to human niches. Overall, our work reveals previously unrecognized diversity in central metabolism and the pervasive influence of human-driven selection on metabolite levels in yeasts.

Portable capillary LC for in-line UV monitoring and MS detection: Comparable sensitivity and much lower solvent consumption

Pharmaceutical development currently relies on quality separation methods from early discovery through to line-of-site manufacturing. There have been significant advancements made regarding the column particle packing, internal diameter, length connectivity, the understanding of the impact key parameters like void volume, flow rate, and temperature all that affects the resultant separation quality, that is, resolution, peak shape, peak width, run time, and signal-to-noise ratio. There is however a strong need to establish better alternatives to large bulky high-performance liquid chromatography racks either for process analytical reaction monitoring or mass spectrometry analysis in establishing product quality. Compact, portable high-pressure liquid chromatography can be a more efficient alternative to traditional ultra-high pressure liquid chromatography and traditional liquid chromatography. The compact versatile instrument evaluated here allows good separation control with either the on-board column with fixed ultra-violet wavelength cartridge or for use with a high-resolution mass spectrometry. Significant space reduction results in greener lab spaces with improved energy efficiency for smaller labs with lower energy demands. In addition, this compact liquid chromatography was used as a portable reaction monitoring solution to compare forced degradation kinetics and assess portable liquid chromatography-mass spectrometry capability for the analyses required for pharmaceutical drug product testing.

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

BACKGROUND

There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates.

RESULTS

First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors.

CONCLUSIONS

DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .

A screening strategy for bioactive components of Bu-Zhong-Yi-Qi-Tang regulating spleen-qi deficiency based on "endobiotics-targets-xenobiotics" association network

ETHNOPHARMACOLOGICAL RELEVANCE

Bu-Zhong-Yi-Qi-Tang is a famous traditional Chinese medicine formula that has been prevalent in China for over 700 years to treat spleen-qi deficiency related diseases, such as gastrointestinal and respiratory disorders. However, the bioactive components responsible for regulating spleen-qi deficiency remain unclear and have puzzled many researchers.

AIM OF THE STUDY

The current study focuses on efficacy evaluation of regulating spleen-qi deficiency and screening the bioactive components of Bu-Zhong-Yi-Qi-Tang.

MATERIALS AND METHODS

The effects of Bu-Zhong-Yi-Qi-Tang were evaluated through blood routine examination, immune organ index, and biochemical analysis. Metabolomics was utilized to analyze the potential endogenous biomarkers (endobiotics) in the plasma, and the prototypes (xenobiotics) of Bu-Zhong-Yi-Qi-Tang in the bio-samples were characterized using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. Then, these endobiotics were used as "bait" to predict targets based on network pharmacology and to screen potential bioactive components from the absorbed prototypes in the plasma by constructing an "endobiotics-targets-xenobiotics" association network. Further, the anti-inflammatory activities of representative compounds (calycosin and nobiletin) were validated through poly(I:C)-induced pulmonary inflammation mice model.

RESULTS

Bu-Zhong-Yi-Qi-Tang exhibited immunomodulatory and anti-inflammatory activities in spleen-qi deficiency rat, as supported by the observation of increased levels of D-xylose and gastrin in serum, an increase in the thymus index and number of lymphocytes in blood, as well as a reduction in the level of IL-6 in bronchoalveolar lavage fluid. Furthermore, plasma metabolomic analysis revealed a total of 36 Bu-Zhong-Yi-Qi-Tang related endobiotics, which were mainly enriched in primary bile acids biosynthesis, the metabolism of linoleic acid, and the metabolism of phenylalanine pathways. Meanwhile, 95 xenobiotics were characterized in plasma, urine, small intestinal contents, and tissues of spleen-qi deficiency rat after Bu-Zhong-Yi-Qi-Tang treatment. Using an integrated association network, six potential bioactive components of Bu-Zhong-Yi-Qi-Tang were screened. Among them, calycosin was found to significantly reduce the levels of IL-6 and TNF-α in the bronchoalveolar lavage fluid, increase the number of lymphocytes, while nobiletin dramatically decreased the levels of CXCL10, TNF-α, GM-CSF, and IL-6.

CONCLUSION

Our study proposed an available strategy for screening bioactive components of BYZQT regulating spleen-qi deficiency based on "endobiotics-targets-xenobiotics" association network.

Protein-metabolite interactomics of carbohydrate metabolism reveal regulation of lactate dehydrogenase

Metabolic networks are interconnected and influence diverse cellular processes. The protein-metabolite interactions that mediate these networks are frequently low affinity and challenging to systematically discover. We developed mass spectrometry integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS) to identify such interactions. Analysis of 33 enzymes from human carbohydrate metabolism identified 830 protein-metabolite interactions, including known regulators, substrates, and products as well as previously unreported interactions. We functionally validated a subset of interactions, including the isoform-specific inhibition of lactate dehydrogenase by long-chain acyl-coenzyme A. Cell treatment with fatty acids caused a loss of pyruvate-lactate interconversion dependent on lactate dehydrogenase isoform expression. These protein-metabolite interactions may contribute to the dynamic, tissue-specific metabolic flexibility that enables growth and survival in an ever-changing nutrient environment.

Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry

Flow-injection mass spectrometry (FI-MS) enables metabolomics studies with a very high sample-throughput. However, FI-MS is prone to in-source modifications of analytes because samples are directly injected into the electrospray ionization source of a mass spectrometer without prior chromatographic separation. Here, we spiked authentic standards of 160 primary metabolites individually into an Escherichia coli metabolite extract and measured the thus derived 160 spike-in samples by FI-MS. Our results demonstrate that FI-MS can capture a wide range of chemically diverse analytes within 30 s measurement time. However, the data also revealed extensive in-source modifications. Across all 160 spike-in samples, we identified significant increases of 11,013 ion peaks in positive and negative mode combined. To explain these unknown m/z features, we connected them to the m/z feature of the (de-)protonated metabolite using information about mass differences and MS2 spectra. This resulted in networks that explained on average 49 % of all significant features. The networks showed that a single metabolite undergoes compound specific and often sequential in-source modifications like adductions, chemical reactions, and fragmentations. Our results show that FI-MS generates complex MS1 spectra, which leads to an overestimation of significant features, but neutral losses and MS2 spectra explain many of these features.

Lipidomics analysis in drug discovery and development

Despite being a relatively new addition to the Omics' landscape, lipidomics is increasingly being recognized as an important tool for the identification of druggable targets and biochemical markers. In this review we present recent advances of lipid analysis in drug discovery and development. We cover current state of the art technologies which are constantly evolving to meet demands in terms of sensitivity and selectivity. A careful selection of important examples is then provided, illustrating the versatility of lipidomics analysis in the drug discovery and development process. Integration of lipidomics with other omics', stem-cell technologies, and metabolic flux analysis will open new avenues for deciphering pathophysiological mechanisms and the discovery of novel targets and biomarkers.

Dynamic metabolome profiling uncovers potential TOR signaling genes

Although the genetic code of the yeast Saccharomyces cerevisiae was sequenced 25 years ago, the characterization of the roles of genes within it is far from complete. The lack of a complete mapping of functions to genes hampers systematic understanding of the biology of the cell. The advent of high-throughput metabolomics offers a unique approach to uncovering gene function with an attractive combination of cost, robustness, and breadth of applicability. Here, we used flow-injection time-of-flight mass spectrometry to dynamically profile the metabolome of 164 loss-of-function mutants in TOR and receptor or receptor-like genes under a time course of rapamycin treatment, generating a dataset with >7000 metabolomics measurements. In order to provide a resource to the broader community, those data are made available for browsing through an interactive data visualization app hosted at https://rapamycin-yeast.ethz.ch. We demonstrate that dynamic metabolite responses to rapamycin are more informative than steady-state responses when recovering known regulators of TOR signaling, as well as identifying new ones. Deletion of a subset of the novel genes causes phenotypes and proteome responses to rapamycin that further implicate them in TOR signaling. We found that one of these genes, CFF1, was connected to the regulation of pyrimidine biosynthesis through URA10. These results demonstrate the efficacy of the approach for flagging novel potential TOR signaling-related genes and highlight the utility of dynamic perturbations when using functional metabolomics to deliver biological insight.

Virtual screening for small-molecule pathway regulators by image-profile matching

Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small-molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image-profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds.

Metabolomics paves the way for improved drug target identification

Correctly identifying candidate drugs for protein targets is crucial for drug discovery. Despite the importance of this problem for the pharmaceutical industry, chemical screening remains a challenging task, and drug–target misidentification may contribute to failures in drug development. In their recent study, Sauer and colleagues (Holbrook‐Smith et al, 2022) demonstrate proof‐of‐concept for a new way to identify drug–target interactions using high‐throughput metabolomics, potentially paving the way towards a universal method for predicting drug–target relationships.