LKB1-Dependent Regulation of TPI1 Creates a Divergent Metabolic Liability between Human and Mouse Lung Adenocarcinoma.

KRAS is the most frequently mutated oncogene in human lung adenocarcinomas (hLUAD), and activating mutations frequently co-occur with loss-of-function mutations in TP53 or STK11/LKB1. However, mutation of all three genes is rarely observed in hLUAD, even though engineered comutation is highly aggressive in mouse lung adenocarcinoma (mLUAD). Here, we provide a mechanistic explanation for this difference by uncovering an evolutionary divergence in the regulation of triosephosphate isomerase (TPI1). In hLUAD, TPI1 activity is regulated via phosphorylation at Ser21 by the salt inducible kinases (SIK) in an LKB1-dependent manner, modulating flux between the completion of glycolysis and production of glycerol lipids. In mice, Ser21 of TPI1 is a Cys residue that can be oxidized to alter TPI1 activity without a need for SIKs or LKB1. Our findings suggest this metabolic flexibility is critical in rapidly growing cells with KRAS and TP53 mutations, explaining why the loss of LKB1 creates a liability in these tumors. SIGNIFICANCE: Utilizing phosphoproteomics and metabolomics in genetically engineered human cell lines and genetically engineered mouse models (GEMM), we uncover an evolutionary divergence in metabolic regulation within a clinically relevant genotype of human LUAD with therapeutic implications. Our data provide a cautionary example of the limits of GEMMs as tools to study human diseases such as cancers. This article is highlighted in the In This Issue feature, p. 799.

An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies.

The success of precision medicine relies upon collecting data from many individuals at the population level. Although advancing technologies have made such large-scale studies increasingly feasible in some disciplines such as genomics, the standard workflows currently implemented in untargeted metabolomics were developed for small sample numbers and are limited by the processing of liquid chromatography/mass spectrometry data. Here we present an untargeted metabolomics workflow that is designed to support large-scale projects with thousands of biospecimens. Our strategy is to first evaluate a reference sample created by pooling aliquots of biospecimens from the cohort. The reference sample captures the chemical complexity of the biological matrix in a small number of analytical runs, which can subsequently be processed with conventional software such as XCMS. Although this generates thousands of so-called features, most do not correspond to unique compounds from the samples and can be filtered with established informatics tools. The features remaining represent a comprehensive set of biologically relevant reference chemicals that can then be extracted from the entire cohort's raw data on the basis of m/z values and retention times by using Skyline. To demonstrate applicability to large cohorts, we evaluated >2000 human plasma samples with our workflow. We focused our analysis on 360 identified compounds, but we also profiled >3000 unknowns from the plasma samples. As part of our workflow, we tested 14 different computational approaches for batch correction and found that a random forest-based approach outperformed the others. The corrected data revealed distinct profiles that were associated with the geographic location of participants.

A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer.

The thousands of features commonly observed when performing untargeted metabolomics with quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometers often correspond to only a few hundred unique metabolites of biological origin, which is in the range of what can be assayed in a single targeted metabolomics experiment by using a triple quadrupole (QqQ) mass spectrometer. A major benefit of performing targeted metabolomics with QqQ mass spectrometry is the affordability of the instruments relative to high-resolution QTOF and Orbitrap platforms. Optimizing targeted methods to profile hundreds of metabolites on a QqQ mass spectrometer, however, has historically been limited by the availability of authentic standards, particularly for "unknowns" that have yet to be structurally identified. Here, we report a strategy to develop multiple reaction monitoring (MRM) methods for QqQ instruments on the basis of high-resolution spectra, thereby enabling us to use data from untargeted metabolomics to design targeted experiments without the need for authentic standards. We demonstrate that using high-resolution fragmentation data alone to design MRM methods results in the same quantitative performance as when methods are optimized by measuring authentic standards on QqQ instruments, as is conventionally done. The approach was validated by showing that Orbitrap ID-X data can be used to establish MRM methods on a Thermo TSQ Altis and two Agilent QqQs for hundreds of metabolites, including unknowns, without a dependence on standards. Finally, we highlight an application where metabolite profiling was performed on an ID-X and a QqQ by using the strategy introduced here, with both data sets yielding the same result. The described approach therefore allows us to use QqQ instruments, which are often associated with targeted metabolomics, to profile knowns and unknowns at a comprehensive scale that is typical of untargeted metabolomics.

Quantitative rotational-echo double resonance for Carbon-13 spin clusters.

By using only half of the total evolution time for dephasing pulses, C{N} rotational-echo double resonance (REDOR) for clusters of 13C spins (RDX) results in the same universal REDOR behavior as observed for isolated 13C-15N pairs. RDX combines Hahn echoes with solid echoes to suppress interference from scalar J couplings. This is crucial for long evolution times. The modified version (which we call RDX24) makes RDX quantitative for 13C clusters. We apply this scheme to human embryonic kidney cells labeled in culture by L-[13C5 -15N2]-glutamine. We quantitatively characterize three separate nitrogen isotopic enrichments for: (i) the alpha nitrogens of glutamine residues in proteins (including the residues of the five amino acids synthesized from glutamine); (ii) the alpha nitrogens of the five amino-acid residues synthesized from glucose, together with those of the nine essential amino acids added to the growth medium; and (iii) the side-chain nitrogens of glutamine (and of asparagine derived from glutamine).

DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution.

Chimeric MS/MS spectra contain fragments from multiple precursor ions and therefore hinder compound identification in metabolomics. Historically, deconvolution of these chimeric spectra has been challenging and relied on specific experimental methods that introduce variation in the ratios of precursor ions between multiple tandem mass spectrometry (MS/MS) scans. DecoID provides a complementary, method-independent approach where database spectra are computationally mixed to match an experimentally acquired spectrum by using LASSO regression. We validated that DecoID increases the number of identified metabolites in MS/MS datasets from both data-independent and data-dependent acquisition without increasing the false discovery rate. We applied DecoID to publicly available data from the MetaboLights repository and to data from human plasma, where DecoID increased the number of identified metabolites from data-dependent acquisition data by over 30% compared to direct spectral matching. DecoID is compatible with any user-defined MS/MS database and provides automated searching for some of the largest MS/MS databases currently available.

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity.

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.

Nicotinamide mononucleotide increases muscle insulin sensitivity in prediabetic women.

In rodents, obesity and aging impair nicotinamide adenine dinucleotide (NAD+) biosynthesis, which contributes to metabolic dysfunction. Nicotinamide mononucleotide (NMN) availability is a rate-limiting factor in mammalian NAD+ biosynthesis. We conducted a 10-week, randomized, placebo-controlled, double-blind trial to evaluate the effect of NMN supplementation on metabolic function in postmenopausal women with prediabetes who were overweight or obese. Insulin-stimulated glucose disposal, assessed by using the hyperinsulinemic-euglycemic clamp, and skeletal muscle insulin signaling [phosphorylation of protein kinase AKT and mechanistic target of rapamycin (mTOR)] increased after NMN supplementation but did not change after placebo treatment. NMN supplementation up-regulated the expression of platelet-derived growth factor receptor ß and other genes related to muscle remodeling. These results demonstrate that NMN increases muscle insulin sensitivity, insulin signaling, and remodeling in women with prediabetes who are overweight or obese (clinicaltrial.gov NCT03151239).

Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics.

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is common to detect thousands of features from a biological extract. Although it is impractical to collect non-chimeric MS/MS data for each in a single chromatographic run, this is generally unnecessary because most features do not correspond to unique metabolites of biological relevance. Here we show that relatively simple data-processing strategies that can be applied on the fly during acquisition of data with an Orbitrap ID-X, such as blank subtraction and well-established adduct or isotope calculations, decrease the number of features to target for MS/MS analysis by up to an order of magnitude for various types of biological matrices. We demonstrate that annotating these non-biological contaminants and redundancies in real time during data acquisition enables comprehensive MS/MS data to be acquired on each remaining feature at a single collision energy. To ensure that an appropriate collision energy is applied, we introduce a method using a series of hidden ion-trap scans in an Orbitrap ID-X to find an optimal value for each feature that can then be applied in a subsequent high-resolution Orbitrap scan. Data from 100 metabolite standards indicate that this real-time optimization of collision energies leads to more informative MS/MS patterns compared to using a single fixed collision energy alone. As a benchmark to evaluate the overall workflow, we manually annotated unique biological features by independently subjecting E. coli samples to a credentialing analysis. While credentialing led to a more rigorous reduction in feature number, on-the-fly annotation with blank subtraction on an Orbitrap ID-X did not inappropriately discard unique biological metabolites. Taken together, our results reveal that optimal fragmentation data can be obtained in a single LC/MS/MS run for >90% of the unique biological metabolites in a sample when features are annotated during acquisition and collision energies are selected by using parallel mass spectrometry detection.

Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity.

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that scarce medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we performed untargeted metabolomics profiling of 341 patients with plasma samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we then built a predictive model of disease severity. We determined that the levels of 25 metabolites measured at the time of hospital admission successfully predict future disease severity. Through analysis of longitudinal samples, we confirmed that these prognostic markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validated that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

Chemical Discovery in the Era of Metabolomics.

Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.

Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software.

Small-molecule drugs and toxicants commonly interact with more than a single protein target, each of which may have unique effects on cellular phenotype. Although untargeted metabolomics is often applied to understand the mode of action of these chemicals, simple pairwise comparisons of treated and untreated samples are insufficient to resolve the effects of disrupting two or more independent protein targets. Here, we introduce a workflow for dose-response metabolomics to evaluate chemicals that potentially affect multiple proteins with different potencies. Our approach relies on treating samples with various concentrations of compound prior to analysis with mass spectrometry-based metabolomics. Data are then processed with software we developed called TOXcms, which statistically evaluates dose-response trends for each metabolomic signal according to user-defined tolerances and subsequently groups those that follow the same pattern. Although TOXcms was built upon the XCMS framework, it is compatible with any metabolomic data-processing software. Additionally, to enable correlation of dose responses beyond those that can be measured by metabolomics, TOXcms also accepts data from respirometry, cell death assays, other omic platforms, etc. In this work, we primarily focus on applying dose-response metabolomics to find off-target effects of drugs. Using metformin and etomoxir as examples, we demonstrate that each group of dose-response patterns identified by TOXcms signifies a metabolic response to a different protein target with a unique drug binding affinity. TOXcms is freely available on our laboratory website at http://pattilab.wustl.edu/software/toxcms .

Antibody cross-reactivity accounts for widespread appearance of m1A in 5'UTRs.

N1-methyladenosine (m1A) was proposed to be a highly prevalent modification in mRNA 5'UTRs based on mapping studies using an m1A-binding antibody. We developed a bioinformatic approach to discover m1A and other modifications in mRNA throughout the transcriptome by analyzing preexisting ultra-deep RNA-Seq data for modification-induced misincorporations. Using this approach, we detected appreciable levels of m1A only in one mRNA: the mitochondrial MT-ND5 transcript. As an alternative approach, we also developed an antibody-based m1A-mapping approach to detect m1A at single-nucleotide resolution, and confirmed that the commonly used m1A antibody maps sites to the transcription-start site in mRNA 5'UTRs. However, further analysis revealed that these were false-positives caused by binding of the antibody to the m7G-cap. A different m1A antibody that lacks cap-binding cross-reactivity does not show enriched binding in 5'UTRs. These results demonstrate that high-stoichiometry m1A sites are exceedingly rare in mRNAs and that previous mappings of m1A to 5'UTRs were the result of antibody cross-reactivity to the 5' cap.

FTO controls reversible m6Am RNA methylation during snRNA biogenesis.

Small nuclear RNAs (snRNAs) are core spliceosome components and mediate pre-mRNA splicing. Here we show that snRNAs contain a regulated and reversible nucleotide modification causing them to exist as two different methyl isoforms, m1 and m2, reflecting the methylation state of the adenosine adjacent to the snRNA cap. We find that snRNA biogenesis involves the formation of an initial m1 isoform with a single-methylated adenosine (2'-O-methyladenosine, Am), which is then converted to a dimethylated m2 isoform (N6,2'-O-dimethyladenosine, m6Am). The relative m1 and m2 isoform levels are determined by the RNA demethylase FTO, which selectively demethylates the m2 isoform. We show FTO is inhibited by the oncometabolite D-2-hydroxyglutarate, resulting in increased m2-snRNA levels. Furthermore, cells that exhibit high m2-snRNA levels show altered patterns of alternative splicing. Together, these data reveal that FTO controls a previously unknown central step of snRNA processing involving reversible methylation, and suggest that epitranscriptomic information in snRNA may influence mRNA splicing.

Untargeted Metabolite Profiling of Cerebrospinal Fluid Uncovers Biomarkers for Severity of Late Infantile Neuronal Ceroid Lipofuscinosis (CLN2, Batten Disease).

Late infantile neuronal ceroid lipofuscinosis (CLN2 disease) is a rare lysosomal storage disorder caused by a monogenetic deficiency of tripeptidyl peptidase-1 (TPP1). Despite knowledge that lipofuscin is the hallmark disease product, the relevant TPP1 substrate and its role in neuronal physiology/pathology is unknown. We hypothesized that untargeted metabolite profiling of cerebrospinal fluid (CSF) could be used as an effective tool to identify disease-associated metabolic disruptions in CLN2 disease, offering the potential to identify biomarkers that inform on disease severity and progression. Accordingly, a mass spectrometry-based untargeted metabolite profiling approach was employed to differentiate CSF from normal vs. CLN2 deficient individuals. Of 1,433 metabolite features surveyed, 29 linearly correlated with currently employed disease severity scores. With tandem mass spectrometry 8 distinct metabolite identities were structurally confirmed based on retention time and fragmentation pattern matches, vs. standards. These putative CLN2 biomarkers include 7 acetylated species - all attenuated in CLN2 compared to controls. Because acetate is the major bioenergetic fuel for support of mitochondrial respiration, deficient acetylated species in CSF suggests a brain energy defect that may drive neurodegeneration. Targeted analysis of these metabolites in CSF of CLN2 patients offers a powerful new approach for monitoring CLN2 disease progression and response to therapy.

Development of Highly Potent GAT1 Inhibitors: Synthesis of Nipecotic Acid Derivatives by Suzuki-Miyaura Cross-Coupling Reactions.

A new series of potent and selective mGAT1 inhibitors has been identified, featuring a nipecotic acid residue and an N-butenyl linker with a 2-biphenyl residue at the ω-position. Docking, combined with MD calculations, revealed a binding mode for the new compounds similar to that of tiagabine, the only mGAT1 inhibitor currently approved as antiepileptic drug. For the synthesis, a Suzuki-Miyaura cross-coupling reaction was used as a key step by which variously substituted biaryl subunits were assembled. Biological evaluation revealed several compounds that possess binding affinities and inhibitory potencies toward mGAT1, together with subtype selectivities against mGAT2-mGAT4 that were similar to or even higher than those for tiagabine. A derivative carrying the 2',4'-dichloro-2-biphenyl moiety attached to N-but-3-enylnipecotic acid at the terminal position of the linker chain was found to be the most potent binder, with the racemic form of the compound displaying a binding affinity of 8.05±0.13 (pKi ), while the R enantiomer exhibited an affinity value of 8.33±0.06 (pKi ).

Focused pseudostatic hydrazone libraries screened by mass spectrometry binding assay: optimizing affinities toward γ-aminobutyric acid transporter 1.

Mass spectrometric (MS) binding assays, a powerful tool to determine affinities of single drug candidates toward chosen targets, were recently demonstrated to be suitable for the screening of compound libraries generated with reactions of dynamic combinatorial chemistry when rendering libraries pseudostatic. Screening of small hydrazone libraries targeting γ-aminobutyric acid transporter 1 (GAT1), the most abundant γ-aminobutyric acid (GABA) transporter in the central nervous system, revealed two nipecotic acid derived binders with submicromolar affinities. Starting from the biphenyl carrying hit as lead structure, the objective of the present study was to discover novel high affinity GAT1 binders by screening of biphenyl focused pseudostatic hydrazone libraries formed from hydrazine 10 and 36 biphenylcarbaldehydes 11c-al. Hydrazone 12z that carried a 2',4'-dichlorobiphenyl residue was found to be the most potent binder with low nanomolar affinity (pK(i) = 8.094 ± 0.098). When stable carba analogues of representative hydrazones were synthesized and evaluated, the best binder 13z was again displaying the 2',4'-dichlorobiphenyl moiety (pK(i) = 6.930 ± 0.021).

Library screening by means of mass spectrometry (MS) binding assays-exemplarily demonstrated for a pseudostatic library addressing γ-aminobutyric acid (GABA) transporter 1 (GAT1).

In the present study, the application of mass spectrometry (MS) binding assays as a tool for library screening is reported. For library generation, dynamic combinatorial chemistry (DCC) was used. These libraries can be screened by means of MS binding assays when appropriate measures are taken to render the libraries pseudostatic. That way, the efficiency of MS binding assays to determine ligand binding in compound screening with the ease of library generation by DCC is combined. The feasibility of this approach is shown for γ-aminobutyric acid (GABA) transporter 1 (GAT1) as a target, representing the most important subtype of the GABA transporters. For the screening, hydrazone libraries were employed that were generated in the presence of the target by reacting various sets of aldehydes with a hydrazine derivative that is delineated from piperidine-3-carboxylic acid (nipecotic acid), a common fragment of known GAT1 inhibitors. To ensure that the library generated is pseudostatic, a large excess of the nipecotic acid derivative is employed. As the library is generated in a buffer system suitable for binding and the target is already present, the mixtures can be directly analyzed by MS binding assays-the process of library generation and screening thus becoming simple to perform. The binding affinities of the hits identified by deconvolution were confirmed in conventional competitive MS binding assays performed with single compounds obtained by separate synthesis. In this way, two nipecotic acid derivatives exhibiting a biaryl moiety, 1-{2-[2'-(1,1'-biphenyl-2-ylmethylidene)hydrazine]ethyl}piperidine-3-carboxylic acid and 1-(2-{2'-[1-(2-thiophenylphenyl)methylidene]hydrazine}ethyl)piperidine-3-carboxylic acid, were found to be potent GAT1 ligands exhibiting pK(i) values of 6.186 ± 0.028 and 6.229 ± 0.039, respectively. This method enables screening of libraries, whether generated by conventional chemistry or DCC, and is applicable to all kinds of targets including membrane-bound targets such as G protein coupled receptors (GPCRs), ion channels and transporters. As such, this strategy displays high potential in the drug discovery process.