Metabolic Communication by SGLT2 Inhibition.

BACKGROUND: SGLT2 (sodium-glucose cotransporter 2) inhibitors (SGLT2i) can protect the kidneys and heart, but the underlying mechanism remains poorly understood. METHODS: To gain insights on primary effects of SGLT2i that are not confounded by pathophysiologic processes or are secondary to improvement by SGLT2i, we performed an in-depth proteomics, phosphoproteomics, and metabolomics analysis by integrating signatures from multiple metabolic organs and body fluids after 1 week of SGLT2i treatment of nondiabetic as well as diabetic mice with early and uncomplicated hyperglycemia. RESULTS: Kidneys of nondiabetic mice reacted most strongly to SGLT2i in terms of proteomic reconfiguration, including evidence for less early proximal tubule glucotoxicity and a broad downregulation of the apical uptake transport machinery (including sodium, glucose, urate, purine bases, and amino acids), supported by mouse and human SGLT2 interactome studies. SGLT2i affected heart and liver signaling, but more reactive organs included the white adipose tissue, showing more lipolysis, and, particularly, the gut microbiome, with a lower relative abundance of bacteria taxa capable of fermenting phenylalanine and tryptophan to cardiovascular uremic toxins, resulting in lower plasma levels of these compounds (including p-cresol sulfate). SGLT2i was detectable in murine stool samples and its addition to human stool microbiota fermentation recapitulated some murine microbiome findings, suggesting direct inhibition of fermentation of aromatic amino acids and tryptophan. In mice lacking SGLT2 and in patients with decompensated heart failure or diabetes, the SGLT2i likewise reduced circulating p-cresol sulfate, and p-cresol impaired contractility and rhythm in human induced pluripotent stem cell-derived engineered heart tissue. CONCLUSIONS: SGLT2i reduced microbiome formation of uremic toxins such as p-cresol sulfate and thereby their body exposure and need for renal detoxification, which, combined with direct kidney effects of SGLT2i, including less proximal tubule glucotoxicity and a broad downregulation of apical transporters (including sodium, amino acid, and urate uptake), provides a metabolic foundation for kidney and cardiovascular protection.

A ketogenic diet can mitigate SARS-CoV-2 induced systemic reprogramming and inflammation.

The ketogenic diet (KD) has demonstrated benefits in numerous clinical studies and animal models of disease in modulating the immune response and promoting a systemic anti-inflammatory state. Here we investigate the effects of a KD on systemic toxicity in mice following SARS-CoV-2 infection. Our data indicate that under KD, SARS-CoV-2 reduces weight loss with overall improved animal survival. Muted multi-organ transcriptional reprogramming and metabolism rewiring suggest that a KD initiates and mitigates systemic changes induced by the virus. We observed reduced metalloproteases and increased inflammatory homeostatic protein transcription in the heart, with decreased serum pro-inflammatory cytokines (i.e., TNF-α, IL-15, IL-22, G-CSF, M-CSF, MCP-1), metabolic markers of inflammation (i.e., kynurenine/tryptophane ratio), and inflammatory prostaglandins, indicative of reduced systemic inflammation in animals infected under a KD. Taken together, these data suggest that a KD can alter the transcriptional and metabolic response in animals following SARS-CoV-2 infection with improved mice health, reduced inflammation, and restored amino acid, nucleotide, lipid, and energy currency metabolism.

Hydroxyproline metabolism enhances IFN-γ-induced PD-L1 expression and inhibits autophagic flux.

The immune checkpoint protein PD-L1 plays critical roles in both immune system homeostasis and tumor progression. Impaired PD-1/PD-L1 function promotes autoimmunity and PD-L1 expression within tumors promotes immune evasion. If and how changes in metabolism or defined metabolites regulate PD-L1 expression is not fully understood. Here, using a metabolomics activity screening-based approach, we have determined that hydroxyproline (Hyp) significantly and directly enhances adaptive (i.e., IFN-γ-induced) PD-L1 expression in multiple relevant myeloid and cancer cell types. Mechanistic studies reveal that Hyp acts as an inhibitor of autophagic flux, which allows it to regulate this negative feedback mechanism, thereby contributing to its overall effect on PD-L1 expression. Due to its prevalence in fibrotic tumors, these findings suggest that hydroxyproline could contribute to the establishment of an immunosuppressive tumor microenvironment and that Hyp metabolism could be targeted to pharmacologically control PD-L1 expression for the treatment of cancer or autoimmune diseases.

Changes in Plasma Metabolic Signature upon Acute and Chronic Morphine Administration in Morphine-Tolerant Mice.

Morphine administration causes system-level metabolic changes. Here, we show that morphine-tolerant mice exhibited distinct plasma metabolic signatures upon acute and chronic administration. We utilized a mouse model of morphine tolerance by exposing mice to increasing doses of the drug over 4 days. We collected plasma samples from mice undergoing acute or chronic morphine or saline injections and analyzed them using targeted GC-MS-based metabolomics to profile approximately 80 metabolites involved in the central carbon, amino acid, nucleotide, and lipid metabolism. Our findings reveal distinct alterations in plasma metabolite concentrations in response to acute or chronic morphine intake, and these changes were linked to the development of tolerance to morphine's analgesic effects. We identified several metabolites that had been differentially affected by acute versus chronic morphine use, suggesting that metabolic changes may be mitigated by prolonged exposure to the drug. Morphine-tolerant mice showed a restoration of amino acid and glycolytic metabolites. Additionally, we conducted reconstructed metabolic network analysis on the first 30 VIP-ranked metabolites from the PLSDA of the saline, acute, and morphine-tolerant mice groups, which uncovered four interaction networks involving the amino acid metabolism, the TCA cycle, the glutamine-phenylalanine-tyrosine pathway, and glycolysis. These pathways were responsible for the metabolic differences observed following distinct morphine administration regimens. Overall, this study provides a valuable resource for future investigations into the role of metabolites in morphine-induced analgesia and associated effects following acute or chronic use in mice.

Metabolomics- and systems-biology-guided discovery of metabolite lead compounds and druggable targets.

Metabolomics enables the comprehensive and unbiased analysis of metabolites and lipids in biological systems. In conjunction with high-throughput activity screening, big data and synthetic biology, metabolomics can guide the discovery of lead compounds with pharmacological activity from natural sources and the gut microbiome. In combination with other omics, metabolomics can further unlock the elucidation of compound toxicity, the mode of action and novel druggable targets of disease. Here, we discuss the workflows, limitations and future opportunities to leverage metabolomics and big data in conjunction with systems and synthetic biology for streamlining the discovery and development of molecules of pharmaceutical interest.

Accelerated lysine metabolism conveys kidney protection in salt-sensitive hypertension.

Hypertension and kidney disease have been repeatedly associated with genomic variants and alterations of lysine metabolism. Here, we combined stable isotope labeling with untargeted metabolomics to investigate lysine's metabolic fate in vivo. Dietary 13C6 labeled lysine was tracked to lysine metabolites across various organs. Globally, lysine reacts rapidly with molecules of the central carbon metabolism, but incorporates slowly into proteins and acylcarnitines. Lysine metabolism is accelerated in a rat model of hypertension and kidney damage, chiefly through N-alpha-mediated degradation. Lysine administration diminished development of hypertension and kidney injury. Protective mechanisms include diuresis, further acceleration of lysine conjugate formation, and inhibition of tubular albumin uptake. Lysine also conjugates with malonyl-CoA to form a novel metabolite Nε-malonyl-lysine to deplete malonyl-CoA from fatty acid synthesis. Through conjugate formation and excretion as fructoselysine, saccharopine, and Nε-acetyllysine, lysine lead to depletion of central carbon metabolites from the organism and kidney. Consistently, lysine administration to patients at risk for hypertension and kidney disease inhibited tubular albumin uptake, increased lysine conjugate formation, and reduced tricarboxylic acid (TCA) cycle metabolites, compared to kidney-healthy volunteers. In conclusion, lysine isotope tracing mapped an accelerated metabolism in hypertension, and lysine administration could protect kidneys in hypertensive kidney disease.

Mass Spectrometry Imaging of Metabolites by Nanostructure Initiator Mass Spectrometry with Fluorinated Gold Nanoparticles.

Nanostructure initiator mass spectrometry (NIMS) with fluorinated gold nanoparticles (f-AuNPs) enables the detection and spatial localization of a breath of polar metabolites and lipids with high spatial resolution and ultrasensitivity. Here we describe the methods and procedures for the synthesis and application of f-AuNPs for NIMS of small molecule metabolites and lipids in biological tissues, encompassing sample preparation, mass spectrometric detection, and data analysis and interpretation.

Plasticity of Extrachromosomal and Intrachromosomal BRAF Amplifications in Overcoming Targeted Therapy Dosage Challenges.

Focal amplifications (FA) can mediate targeted therapy resistance in cancer. Understanding the structure and dynamics of FAs is critical for designing treatments that overcome plasticity-mediated resistance. We developed a melanoma model of dual MAPK inhibitor (MAPKi) resistance that bears BRAFV600 amplifications through either extrachromosomal DNA (ecDNA)/double minutes (DM) or intrachromosomal homogenously staining regions (HSR). Cells harboring BRAFV600E FAs displayed mode switching between DMs and HSRs, from both de novo genetic changes and selection of preexisting subpopulations. Plasticity is not exclusive to ecDNAs, as cells harboring HSRs exhibit drug addiction-driven structural loss of BRAF amplicons upon dose reduction. FA mechanisms can couple with kinase domain duplications and alternative splicing to enhance resistance. Drug-responsive amplicon plasticity is observed in the clinic and can involve other MAPK pathway genes, such as RAF1 and NRAS. BRAF FA-mediated dual MAPKi-resistant cells are more sensitive to proferroptotic drugs, extending the spectrum of ferroptosis sensitivity in MAPKi resistance beyond cases of dedifferentiation. SIGNIFICANCE: Understanding the structure and dynamics of oncogene amplifications is critical for overcoming tumor relapse. BRAF amplifications are highly plastic under MAPKi dosage challenges in melanoma, through involvement of de novo genomic alterations, even in the HSR mode. Moreover, BRAF FA-driven, dual MAPKi-resistant cells extend the spectrum of resistance-linked ferroptosis sensitivity. This article is highlighted in the In This Issue feature, p. 873.

Cognitive analysis of metabolomics data for systems biology.

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.

Metabolic reprogramming and epigenetic changes of vital organs in SARS-CoV-2-induced systemic toxicity.

Extrapulmonary manifestations of COVID-19 are associated with a much higher mortality rate than pulmonary manifestations. However, little is known about the pathogenesis of systemic complications of COVID-19. Here, we create a murine model of SARS-CoV-2-induced severe systemic toxicity and multiorgan involvement by expressing the human ACE2 transgene in multiple tissues via viral delivery, followed by systemic administration of SARS-CoV-2. The animals develop a profound phenotype within 7 days with severe weight loss, morbidity, and failure to thrive. We demonstrate that there is metabolic suppression of oxidative phosphorylation and the tricarboxylic acid (TCA) cycle in multiple organs with neutrophilia, lymphopenia, and splenic atrophy, mirroring human COVID-19 phenotypes. Animals had a significantly lower heart rate, and electron microscopy demonstrated myofibrillar disarray and myocardial edema, a common pathogenic cardiac phenotype in human COVID-19. We performed metabolomic profiling of peripheral blood and identified a panel of TCA cycle metabolites that served as biomarkers of depressed oxidative phosphorylation. Finally, we observed that SARS-CoV-2 induces epigenetic changes of DNA methylation, which affects expression of immune response genes and could, in part, contribute to COVID-19 pathogenesis. Our model suggests that SARS-CoV-2-induced metabolic reprogramming and epigenetic changes in internal organs could contribute to systemic toxicity and lethality in COVID-19.

Enhanced in-Source Fragmentation Annotation Enables Novel Data Independent Acquisition and Autonomous METLIN Molecular Identification.

Electrospray ionization (ESI) in-source fragmentation (ISF) has traditionally been minimized to promote precursor molecular ion formation, and therefore its value in molecular identification is underappreciated. In-source annotation algorithms have been shown to increase confidence in putative identifications by using ubiquitous in-source fragments. However, these in-source annotation algorithms are limited by ESI sources that are generally designed to minimize ISF. In this study, enhanced in-source fragmentation annotation (eISA) was created by tuning the ISF conditions to generate in-source fragmentation patterns comparable with higher energy fragments generated at higher collision energies as deposited in the METLIN MS/MS library, without compromising the intensity of precursor ions (median loss ≤10% in both positive and negative ionization modes). The analysis of 50 molecules was used to validate the approach in comparison to MS/MS spectra produced via data dependent acquisition (DDA) and data independent acquisition (DIA) mode with quadrupole time-of-flight mass spectrometry (QTOF-MS). Enhanced ISF as compared to QTOF DDA enabled higher peak intensities for the precursor ions (median: 18 times in negative mode and 210 times in positive mode), with the eISA fragmentation patterns consistent with METLIN for over 90% of the molecules with respect to fragment relative intensity and m/z. eISA also provides higher peak intensity as opposed to QTOF DIA for over 60% of the precursor ions in negative mode (median increase: 20%) and for 88% of the precursor ions in positive mode (median increase: 80%). Molecular identification with eISA was also successfully validated from the analysis of a metabolic extract from macrophages. An interesting side benefit of enhanced ISF is that it significantly improved molecular identification confidence with low resolution single quadrupole mass-spectrometry-based untargeted LC/MS experiments. Overall, enhanced ISF allowed for eISA to be used as a more sensitive alternative to other QTOF DIA and DDA approaches, and further, it enabled the acquisition of ESI TOF and ESI single quadrupole mass spectrometry instrumentation spectra with improved molecular identification confidence.

Charting Metabolism Heterogeneity by Nanostructure Imaging Mass Spectrometry: From Biological Systems to Subcellular Functions.

The study of metabolism heterogeneity is essential to understand the role of metabolites in supporting and regulating biological functions. To this end, several mass spectrometry imaging (MSI) approaches have been proposed for the detection of small molecule metabolites. However, high noise from the ionization matrix and low metabolome coverage hinder their applicability for untargeted metabolomics studies across space. In this context, nanostructure imaging (/initiator) mass spectrometry (NIMS) and NIMS with fluorinated gold nanoparticles (f-AuNPs) are attractive strategies for comprehensive MSI of metabolites in biological systems, which can provide heterogeneous metabolome coverage, ultrahigh sensitivity, and high lateral resolution. In particular, NIMS with f-AuNPs permits the simultaneous detection of polar metabolites and lipids in a single and cohesive analytical session, thus allowing the systems-level interpretation of metabolic changes. In this Perspective article, we discuss the use of NIMS and f-AuNPs in the exploration of metabolism heterogeneity and provide a critical outlook on future applications of this technology for revealing the metabolic architecture that supports biological functions in health and disease, from whole organisms to tissues, single cells, and subcellular compartments.

Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology.

Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.

Metabolic rewiring of the hypertensive kidney.

Hypertension is a persistent epidemic across the developed world that is closely associated with kidney disease. Here, we applied a metabolomic, phosphoproteomic, and proteomic strategy to analyze the effect of hypertensive insults on kidneys. Our data revealed the metabolic aspects of hypertension-induced glomerular sclerosis, including lipid breakdown at early disease stages and activation of anaplerotic pathways to regenerate energy equivalents to counter stress. For example, branched-chain amino acids and proline, required for collagen synthesis, were depleted in glomeruli at early time points. Furthermore, indicators of metabolic stress were reflected by low amounts of ATP and NADH and an increased abundance of oxidized lipids derived from lipid breakdown. These processes were specific to kidney glomeruli where metabolic signaling occurred through mTOR and AMPK signaling. Quantitative phosphoproteomics combined with computational modeling suggested that these processes controlled key molecules in glomeruli and specifically podocytes, including cytoskeletal components and GTP-binding proteins, which would be expected to compete for decreasing amounts of GTP at early time points. As a result, glomeruli showed increased expression of metabolic enzymes of central carbon metabolism, amino acid degradation, and lipid oxidation, findings observed in previously published studies from other disease models and patients with glomerular damage. Overall, multilayered omics provides an overview of hypertensive kidney damage and suggests that metabolic or dietary interventions could prevent and treat glomerular disease and hypertension-induced nephropathy.

Effects of transdermal administration of testosterone gel on the urinary steroid profile in hypogonadal men: Implications in antidoping analysis.

Testosterone is one of the most abused pseudo-endogenous anabolic steroids in sport doping. The current method adopted to detect the abuse of testosterone and other pseudo-endogenous steroids (endogenous steroids when administered exogenously) is first based on the longitudinal monitoring of several urinary biomarkers, which constitute the so called "steroidal module" of the Athlete Biological Passport (ABP): atypical samples undergo a confirmation analysis based on the measurement of the 13C/12C isotopic ratio of selected target compounds, to distinguish their endogenous or exogenous origin. At the same time, testosterone administration can be allowed in athletes diagnosed with hypogonadism, provided they are granted a therapeutic use exemption by the relevant medical authority. In this pilot study we have investigated whether the approach based on the preliminary determination of the urinary steroid profile, in the format considered in the steroidal module of the ABP, also integrated with the inclusion of the sulfo-conjugates and of additional target steroids, can retain its validity also in the case of hypogonadal athletes. We have studied the effects of a single low dose (40 mg) of testosterone gel (T-gel) on the urinary concentration of the markers of steroidal module of the ABP, as well as on some additional steroid markers. The study was based on the analysis of urinary samples from 19 non-hospitalized hypogonadal men, 10 of them with late-onset hypogonadism (LOH), collected before, after 4 h and after 24 h the transdermal self-administration of 40 mg of T-gel. None of the patient had any co-morbidities possibly affecting the urinary excretion of the steroidal markers. The steroidal markers were quantified by gas chromatography coupled to tandem mass spectrometry (GC-MS/MS) after the enzymatic hydrolysis of the respective glucuro-conjugates and the chemical hydrolysis of the respective sulfo-conjugates. Targeted GC-MS/MS analysis was carried out operating in electron impact (EI) ionization mode, with acquisition in multiple reaction monitoring (MRM) mode. Our preliminary results show that, as expected, the treatment with T-gel leads, in all hypogonadal men, to an increase of the urinary concentration of the glucuro-conjugate metabolites of testosterone and its main metabolites, with special relevance to those with 5α-reduction. Furthermore, samples collected from non-LOH hypogonadal men showed an increase also in the levels of epitestosterone glucuronide, testosterone sulfate and epitestosterone sulfate. Apart from their biochemical and pharmacological relevance, these outcomes could be leveraged to refine the analytical strategy currently followed in the antidoping field for the analysis of the urinary steroidal markers, with potential implications also in other forensic and/or clinical investigations.

Palbociclib and Fulvestrant Act in Synergy to Modulate Central Carbon Metabolism in Breast Cancer Cells.

The aims of this study were to determine whether combination chemotherapeutics exhibit a synergistic effect on breast cancer cell metabolism. Palbociclib, is a selective inhibitor of cyclin-dependent kinases 4 and 6, and when patients are treated in combination with fulvestrant, an estrogen receptor antagonist, they have improved progression-free survival. The mechanisms for this survival advantage are not known. Therefore, we analyzed metabolic and transcriptomic changes in MCF-7 cells following single and combination chemotherapy to determine whether selective metabolic pathways are targeted during these different modes of treatment. Individually, the drugs caused metabolic disruption to the same metabolic pathways, however fulvestrant additionally attenuated the pentose phosphate pathway and the production of important coenzymes. A comprehensive effect was observed when the drugs were applied together, confirming the combinatory therapy's synergism in the cell model. This study also highlights the power of merging high-dimensional datasets to unravel mechanisms involved in cancer metabolism and therapy.

Fluorinated Gold Nanoparticles for Nanostructure Imaging Mass Spectrometry.

Nanostructure imaging mass spectrometry (NIMS) with fluorinated gold nanoparticles (f-AuNPs) is a nanoparticle assisted laser desorption/ionization approach that requires low laser energy and has demonstrated high sensitivity. Here we describe NIMS with f-AuNPs for the comprehensive analysis of metabolites in biological tissues. F-AuNPs assist in desorption/ionization by laser-induced release of the fluorocarbon chains with minimal background noise. Since the energy barrier required to release the fluorocarbons from the AuNPs is minimal, the energy of the laser is maintained in the low µJ/pulse range, thus limiting metabolite in-source fragmentation. Electron microscopy analysis of tissue samples after f-AuNP NIMS shows a distinct "raising" of the surface as compared to matrix assisted laser desorption ionization ablation, indicative of a gentle desorption mechanism aiding in the generation of intact molecular ions. Moreover, the use of perfluorohexane to distribute the f-AuNPs on the tissue creates a hydrophobic environment minimizing metabolite solubilization and spatial dislocation. The transfer of the energy from the incident laser to the analytes through the release of the fluorocarbon chains similarly enhances the desorption/ionization of metabolites of different chemical nature, resulting in heterogeneous metabolome coverage. We performed the approach in a comparative study of the colon of mice exposed to three different diets. F-AuNP NIMS allows the direct detection of carbohydrates, lipids, bile acids, sulfur metabolites, amino acids, nucleotide precursors as well as other small molecules of varied biological origins. Ultimately, the diversified molecular coverage obtained provides a broad picture of a tissue's metabolic organization.

Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology.

Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of information collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multimodal analysis streamlines the extraction of biological information from the metabolomics data as well as the contextualization within proteomics and transcriptomics data sets. As a proof of concept, this multimodal analysis approach was applied to a colorectal cancer (CRC) study, in which complementary liquid chromatography-mass spectrometry (LC-MS) data were combined with proteomic and transcriptomic data. Our approach provided a highly resolved overview of colon cancer metabolic dysregulation, with an average 17% increase of detected dysregulated metabolites per pathway and an increase in metabolic pathway prediction confidence. Moreover, 95% of the altered metabolic pathways matched with the dysregulated genes and proteins, providing additional validation at a systems level. The analysis platform is currently available via the XCMS Online ( XCMSOnline.scripps.edu ).