NMR Spectroscopy-Based Metabolic Profiling of Biospecimens

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Sleep deprivation and aging are metabolically linked across tissues

STUDY OBJECTIVES

Insufficient sleep is a concerning hallmark of modern society because sleep deprivation (SD) is a risk factor for neurodegenerative and cardiometabolic disorders. SD imparts an aging-like effect on learning and memory, although little is known about possible common molecular underpinnings of SD and aging. Here, we examine this question by profiling metabolic features across different tissues after acute SD in young adult and aged mice.

METHODS

Young adult and aged mice were subjected to acute SD for 5 hours. Blood plasma, hippocampus, and liver samples were subjected to UPLC-MS/MS-based metabolic profiling.

RESULTS

SD preferentially impacts peripheral plasma and liver profiles (e.g. ketone body metabolism) whereas the hippocampus is more impacted by aging. We further demonstrate that aged animals exhibit SD-like metabolic features at baseline. Hepatic alterations include parallel changes in nicotinamide metabolism between aging and SD in young animals. Overall, metabolism in young adult animals is more impacted by SD, which in turn induces aging-like features. A set of nine metabolites was classified (79% correct) based on age and sleep status across all four groups.

CONCLUSIONS

Our metabolic observations demonstrate striking parallels to previous observations in studies of learning and memory and define a molecular metabolic signature of sleep loss and aging.

Deep phenotyping of the lipidomic response in COVID-19 and non-COVID-19 sepsis

BACKGROUND

Lipids may influence cellular penetrance by viral pathogens and the immune response that they evoke. We deeply phenotyped the lipidomic response to SARs-CoV-2 and compared that with infection with other pathogens in patients admitted with acute respiratory distress syndrome to an intensive care unit (ICU).

METHODS

Mass spectrometry was used to characterise lipids and relate them to proteins, peripheral cell immunotypes and disease severity.

RESULTS

Circulating phospholipases (sPLA2, cPLA2 (PLA2G4A) and PLA2G2D) were elevated on admission in all ICU groups. Cyclooxygenase, lipoxygenase and epoxygenase products of arachidonic acid (AA) were elevated in all ICU groups compared with controls. sPLA2 predicted severity in COVID-19 and correlated with TxA2, LTE4 and the isoprostane, iPF2α-III, while PLA2G2D correlated with LTE4. The elevation in PGD2, like PGI2 and 12-HETE, exhibited relative specificity for COVID-19 and correlated with sPLA2 and the interleukin-13 receptor to drive lymphopenia, a marker of disease severity. Pro-inflammatory eicosanoids remained correlated with severity in COVID-19 28 days after admission. Amongst non-COVID ICU patients, elevations in 5- and 15-HETE and 9- and 13-HODE reflected viral rather than bacterial disease. Linoleic acid (LA) binds directly to SARS-CoV-2 and both LA and its di-HOME products reflected disease severity in COVID-19. In healthy marines, these lipids rose with seroconversion. Eicosanoids linked variably to the peripheral cellular immune response. PGE2, TxA2 and LTE4 correlated with T cell activation, as did PGD2 with non-B non-T cell activation. In COVID-19, LPS stimulated peripheral blood mononuclear cell PGF2α correlated with memory T cells, dendritic and NK cells while LA and DiHOMEs correlated with exhausted T cells. Three high abundance lipids - ChoE 18:3, LPC-O-16:0 and PC-O-30:0 - were altered specifically in COVID. LPC-O-16:0 was strongly correlated with T helper follicular cell activation and all three negatively correlated with multi-omic inflammatory pathways and disease severity.

CONCLUSIONS

A broad based lipidomic storm is a predictor of poor prognosis in ARDS. Alterations in sPLA2, PGD2 and 12-HETE and the high abundance lipids, ChoE 18:3, LPC-O-16:0 and PC-O-30:0 exhibit relative specificity for COVID-19 amongst such patients and correlate with the inflammatory response to link to disease severity.

Modulation of the Immune Response to Severe Acute Respiratory Syndrome Coronavirus 2 Vaccination by Nonsteroidal Anti-Inflammatory Drugs

Evidence is scarce to guide the use of nonsteroidal anti-inflammatory drugs (NSAIDs) to mitigate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine-related adverse effects, given the possibility of blunting the desired immune response. In this pilot study, we deeply phenotyped a small number of volunteers who did or did not take NSAIDs concomitant with SARS-CoV-2 immunizations to seek initial information on the immune response. A SARS-CoV-2 vaccine-specific receptor binding domain (RBD) IgG antibody response and efficacy in the evoked neutralization titers were evident irrespective of concomitant NSAID consumption. Given the sample size, only a large and consistent signal of immunomodulation would have been detectable, and this was not apparent. However, the information gathered may inform the design of a definitive clinical trial. Here we report a series of divergent omics signals that invites additional hypotheses testing. SIGNIFICANCE STATEMENT: The impact of nonsteroidal anti-inflammatory drugs (NSAIDs) on the immune response elicited by repeat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunizations was profiled by immunophenotypic, proteomic, and metabolomic approaches in a clinical pilot study of small sample size. A SARS-CoV-2 vaccine-specific immune response was evident irrespective of concomitant NSAID consumption. The information gathered may inform the design of a definitive clinical trial.

NMR-based metabolomic analysis for the effects of moxibustion on imiquimod-induced psoriatic mice

ETHNOPHARMACOLOGICAL RELEVANCE

Moxibustion is a traditional medical intervention of traditional Chinese medicine. It refers to the direct or indirect application of ignited moxa wool made of mugwort leaves to acupuncture points or other specific parts of the body for either treating or preventing diseases. Moxibustion has been proven to be effective in treating skin lesions of psoriasis.

AIM OF THE STUDY

This study was performed to elucidate molecular mechanisms underlying the effects of moxibustion treatment on imiquimod-induced psoriatic mice.

MATERIALS AND METHODS

We established an imiquimod (IMQ)-induced psoriatic mice (Model) and assessed the effects of moxibustion (Moxi) treatment on skin lesions of psoriatic mice by the PASI scores and expressions of inflammation-related factors relative to normal control mice (NC). We then performed nuclear magnetic resonance (NMR)-based metabolomic analysis on the skin tissues of the NC, Model and Moxi-treated mice to address metabolic differences among the three groups.

RESULTS

Moxi mice showed reduced PASI scores and decreased expressions of the pro-inflammatory cytokines IL-8, IL-17A and IL-23 relative to Model mice. Compared with the Model group, the NC and Moxi groups shared 9 characteristic metabolites and 4 significantly altered metabolic pathways except for taurine and hypotaurine metabolism uniquely identified in the NC group. To a certain extent, moxibustion treatment improved metabolic disorders of skin lesions of psoriatic mice by decreasing glucose, valine, asparagine, aspartate and alanine-mediated cell proliferation and synthesis of scaffold proteins, alleviating histidine-mediated hyperproliferation of blood vessels, and promoting triacylglycerol decomposition.

CONCLUSIONS

This study reveals the molecular mechanisms underlying the effects of moxibustion treatment on the skin lesions of psoriasis, potentially improving the clinical efficacy of moxibustion.

Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

Metabolomics-based technologies map in vivo biochemical changes that may be used as early indicators of pathological abnormalities prior to the development of clinical symptoms in neurological conditions. Metabolomics may also reveal biochemical pathways implicated in tissue dysfunction and damage and thus assist in the development of novel targeted therapeutics for neuroinflammation and neurodegeneration. Metabolomics holds promise as a non-invasive, high-throughput and cost-effective tool for early diagnosis, follow-up and monitoring of treatment response in multiple sclerosis (MS), in combination with clinical and imaging measures. In this review, we offer evidence in support of the potential of metabolomics as a biomarker and drug discovery tool in MS. We also use pathway analysis of metabolites that are described as potential biomarkers in the literature of MS biofluids to identify the most promising molecules and upstream regulators, and show novel, still unexplored metabolic pathways, whose investigation may open novel avenues of research.

1H Nuclear Magnetic Resonance Spectroscopy-Based Methods for the Quantification of Proteins in Urine

We described several postprocessing methods to measure protein concentrations in human urine from existing ¹H nuclear magnetic resonance (NMR) metabolomic spectra: (1) direct spectral integration, (2) integration of NCD spectra (NCD = 1D NOESY-CPMG), (3) integration of SMolESY-filtered 1D NOESY spectra (SMolESY = Small Molecule Enhancement SpectroscopY), (4) matching protein patterns, and (5) TSP line integral and TSP linewidth. Postprocessing consists of (a) removal of the metabolite signals (demetabolization) and (b) extraction of the protein integral from the demetabolized spectra. For demetabolization, we tested subtraction of the spin-echo 1D spectrum (CPMG) from the regular 1D spectrum and low-pass filtering of 1D NOESY by its derivatives (c-SMolESY). Because of imperfections in the demetabolization, in addition to direct integration, we extracted protein integrals by the piecewise comparison of demetabolized spectra with the reference spectrum of albumin. We analyzed 42 urine samples with protein content known from the bicinchoninic acid (BCA) assay. We found excellent correlation between the BCA assay and the demetabolized NMR integrals. We have provided conversion factors for calculating protein concentrations in mg/mL from spectral integrals in mM. Additionally, we found the trimethylsilyl propionate (TSP, NMR standard) spectral linewidth and the TSP integral to be good indicators of protein concentration. The described methods increase the information content of urine NMR metabolomics spectra by informing clinical studies of protein concentration.

Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (¹H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel ¹H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the ¹H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).

Treatment of Insomnia with Zaleplon in HIV+ Significantly Improves Sleep and Depression

More than 50% of individuals who are HIV positive report insomnia, which can reduce HIV treatment adherence, impair quality of life, and contribute to metabolic dysfunction. Major depressive disorder is also highly comorbid in this population, leading to further impairment. There is evidence that treating insomnia may improve not only sleep, but depression and metabolic function, as well. The present study aimed to examine the effects of pharmacotherapeutic treatment of insomnia on sleep, depression, and metabolic functioning in individuals with HIV. 20 individuals with asymptomatic seropositive HIV and comorbid insomnia and depression were administered zaleplon for 6 weeks. Insomnia severity was assessed using the Insomnia Severity Index and Epworth Sleepiness Scale, and depression severity was assessed using the Quick Inventory of Depression, both prior to treatment and 6 weeks post treatment. Metabolomic changes were assessed using a comprehensive platform measuring ~2000 lipid features and polar metabolites. Linear mixed effects models demonstrated that 6 weeks of treatment with zaleplon significantly improved symptoms of both insomnia and depression. Metabolomic analyses also demonstrated that changes in insomnia severity were associated with significant changes in key branched chain amino acid metabolites. Our results show that improvement in insomnia is associated with reduced depressive symptoms and beneficial metabolomic changes. Additionally, changes in key branched chain amino acid metabolites following treatment may serve as useful biomarkers of treatment response.

Circadian and Sleep Metabolomics Across Species

Under normal circadian function, metabolic control is temporally coordinated across tissues and behaviors with a 24-h period. However, circadian disruption results in negative consequences for metabolic homeostasis including energy or redox imbalances. Yet, circadian disruption has become increasingly prevalent within today's society due to many factors including sleep loss. Metabolic consequences of both have been revealed by metabolomics analyses of circadian biology and sleep. Specifically, two primary analytical platforms, mass spectrometry and nuclear magnetic resonance spectroscopy, have been used to study molecular clock and sleep influences on overall metabolic rhythmicity. For example, human studies have demonstrated the prevalence of metabolic rhythms in human biology, as well as pan-metabolome consequences of sleep disruption. However, human studies are limited to peripheral metabolic readouts primarily through minimally invasive procedures. For further tissue- and organism-specific investigations, a number of model systems have been studied, based upon the conserved nature of both the molecular clock and sleep across species. Here we summarize human studies as well as key findings from metabolomics studies using mice, Drosophila, and zebrafish. While informative, a limitation in existing literature is a lack of interpretation regarding dynamic synthesis or catabolism within metabolite pools. To this extent, future work incorporating isotope tracers, specific metabolite reporters, and single-cell metabolomics may provide a means of exploring dynamic activity in pathways of interest.