Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Primary Human M2 Macrophage Subtypes Are Distinguishable by Aqueous Metabolite Profiles.

The complexity of macrophage (MΦ) plasticity and polarization states, which include classically activated pro-inflammatory (M1) and alternatively activated anti-inflammatory (M2) MΦ phenotypes, is becoming increasingly appreciated. Within the M2 MΦ polarization state, M2a, M2b, M2c, and M2d MΦ subcategories have been defined based on their expression of specific cell surface receptors, secreted cytokines, and specialized immune effector functions. The importance of immunometabolic networks in mediating the function and regulation of MΦ immune responses is also being increasingly recognized, although the exact mechanisms and extent of metabolic modulation of MΦ subtype phenotypes and functions remain incompletely understood. In this study, proton (1H) nuclear magnetic resonance (NMR) metabolomics was employed to determine the polar metabolomes of M2 MΦ subtypes and to investigate the relationship between aqueous metabolite profiles and M2 MΦ functional phenotypes. Results from this study demonstrate that M2a MΦs are most distinct from M2b, M2c, and M2d MΦ subtypes, and that M2b MΦs display several metabolic traits associated with an M1-like MΦ phenotype. The significance of metabolome differences for metabolites implicated in glycolysis, the tricarboxylic acid (TCA) cycle, phospholipid metabolism, and creatine-phosphocreatine cycling is discussed. Altogether, this study provides biochemical insights into the role of metabolism in mediating the specialized effector functions of distinct M2 MΦ subtypes and supports the concept of a continuum of macrophage activation states rather than two well-separated and functionally distinct M1/M2 MΦ classes, as originally proposed within a classical M1/M2 MΦ framework.

Metabolomic profiling of human bladder tissue extracts.

INTRODUCTION: Bladder cancer is a common malignancy affecting the urinary tract and effective biomarkers and for which monitoring therapeutic interventions have yet to be identified. OBJECTIVES: Major aim of this work was to perform metabolomic profiling of human bladder cancer and adjacent normal tissue and to evaluate cancer biomarkers. METHODS: This study utilized nuclear magnetic resonance (NMR) and high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS) methods to investigate polar metabolite profiles in tissue samples from 99 bladder cancer patients. RESULTS: Through NMR spectroscopy, six tissue metabolites were identified and quantified as potential indicators of bladder cancer, while LDI-MS allowed detection of 34 compounds which distinguished cancer tissue samples from adjacent normal tissue. Thirteen characteristic tissue metabolites were also found to differentiate bladder cancer tumor grades and thirteen metabolites were correlated with tumor stages. Receiver-operating characteristics analysis showed high predictive power for all three types of metabolomics data, with area under the curve (AUC) values greater than 0.853. CONCLUSION: To date, this is the first study in which bladder human normal tissues adjacent to cancerous tissues are analyzed using both NMR and MS method. These findings suggest that the metabolite markers identified in this study may be useful for the detection and monitoring of bladder cancer stages and grades.

Targeted and untargeted urinary metabolic profiling of bladder cancer.

Bladder cancer (BC) is frequent cancer affecting the urinary tract and is one of the most prevalent malignancies worldwide. No biomarkers that can be used for effective monitoring of therapeutic interventions for this cancer have been identified to date. This study investigated polar metabolite profiles in urine samples from 100 BC patients and 100 normal controls (NCs) using nuclear magnetic resonance (NMR) and two methods of high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS). Five urine metabolites were identified and quantified using NMR spectroscopy to be potential indicators of bladder cancer. Twenty-five LDI-MS-detected compounds, predominantly peptides and lipids, distinguished urine samples from BC and NCs individuals. Level changes of three characteristic urine metabolites enabled BC tumor grades to be distinguished, and ten metabolites were reported to correlate with tumor stages. Receiver-Operating Characteristics analysis showed high predictive power for all three types of metabolomics data, with the area under the curve (AUC) values greater than 0.87. These findings suggest that metabolite markers identified in this study may be useful for the non-invasive detection and monitoring of bladder cancer stages and grades.

Acute stress reduces population-level metabolic and proteomic variation.

BACKGROUND: Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. RESULTS: We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. CONCLUSIONS: RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

A Comprehensive NMR Analysis of Serum and Fecal Metabolites in Familial Dysautonomia Patients Reveals Significant Metabolic Perturbations.

Central metabolism has a profound impact on the clinical phenotypes and penetrance of neurological diseases such as Alzheimer's (AD) and Parkinson's (PD) diseases, Amyotrophic Lateral Sclerosis (ALS) and Autism Spectrum Disorder (ASD). In contrast to the multifactorial origin of these neurological diseases, neurodevelopmental impairment and neurodegeneration in Familial Dysautonomia (FD) results from a single point mutation in the ELP1 gene. FD patients represent a well-defined population who can help us better understand the cellular networks underlying neurodegeneration, and how disease traits are affected by metabolic dysfunction, which in turn may contribute to dysregulation of the gut-brain axis of FD. Here, 1H NMR spectroscopy was employed to characterize the serum and fecal metabolomes of FD patients, and to assess similarities and differences in the polar metabolite profiles between FD patients and healthy relative controls. Findings from this work revealed noteworthy metabolic alterations reflected in energy (ATP) production, mitochondrial function, amino acid and nucleotide catabolism, neurosignaling molecules, and gut-microbial metabolism. These results provide further evidence for a close interconnection between metabolism, neurodegeneration, and gut microbiome dysbiosis in FD, and create an opportunity to explore whether metabolic interventions targeting the gut-brain-metabolism axis of FD could be used to redress or slow down the progressive neurodegeneration observed in FD patients.

Arsenic Exposure Causes Global Changes in the Metalloproteome of Escherichia coli.

Arsenic is a toxic metalloid with differential biological effects, depending on speciation and concentration. Trivalent arsenic (arsenite, AsIII) is more toxic at lower concentrations than the pentavalent form (arsenate, AsV). In E. coli, the proteins encoded by the arsRBC operon are the major arsenic detoxification mechanism. Our previous transcriptional analyses indicate broad changes in metal uptake and regulation upon arsenic exposure. Currently, it is not known how arsenic exposure impacts the cellular distribution of other metals. This study examines the metalloproteome of E. coli strains with and without the arsRBC operon in response to sublethal doses of AsIII and AsV. Size exclusion chromatography coupled with inductively coupled plasma mass spectrometry (SEC-ICPMS) was used to investigate the distribution of five metals (56Fe, 24Mg, 66Zn, 75As, and 63Cu) in proteins and protein complexes under native conditions. Parallel analysis by SEC-UV-Vis spectroscopy monitored the presence of protein cofactors. Together, these data reveal global changes in the metalloproteome, proteome, protein cofactors, and soluble intracellular metal pools in response to arsenic stress in E. coli. This work brings to light one outcome of metal exposure and suggests that metal toxicity on the cellular level arises from direct and indirect effects.

Copper deficiency is an independent risk factor for mortality in patients with advanced liver disease.

BACKGROUND AND AIM: Copper is an essential trace metal serving as a cofactor in innate immunity, metabolism, and iron transport. We hypothesize that copper deficiency may influence survival in patients with cirrhosis through these pathways. METHODS: We performed a retrospective cohort study involving 183 consecutive patients with cirrhosis or portal hypertension. Copper from blood and liver tissues was measured using inductively coupled plasma mass spectrometry. Polar metabolites were measured using nuclear magnetic resonance spectroscopy. Copper deficiency was defined by serum or plasma copper below 80 µg/dL for women or 70 µg/dL for men. RESULTS: The prevalence of copper deficiency was 17% (N=31). Copper deficiency was associated with younger age, race, zinc and selenium deficiency, and higher infection rates (42% vs. 20%, p=0.01). Serum copper correlated positively with albumin, ceruloplasmin, hepatic copper, and negatively with IL-1ß. Levels of polar metabolites involved in amino acids catabolism, mitochondrial transport of fatty acids, and gut microbial metabolism differed significantly according to copper deficiency status. During a median follow-up of 396 days, mortality was 22.6% in patients with copper deficiency compared with 10.5% in patients without. Liver transplantation rates were similar (32% vs. 30%). Cause-specific competing risk analysis showed that copper deficiency was associated with a significantly higher risk of death before transplantation after adjusting for age, sex, MELD-Na, and Karnofsky score (HR: 3.40, 95% CI, 1.18-9.82, p=0.023). CONCLUSIONS: In advanced cirrhosis, copper deficiency is relatively common and is associated with an increased infection risk, a distinctive metabolic profile, and an increased risk of death before transplantation.

Gut microbiome dysbiosis drives metabolic dysfunction in Familial dysautonomia.

Familial dysautonomia (FD) is a rare genetic neurologic disorder caused by impaired neuronal development and progressive degeneration of both the peripheral and central nervous systems. FD is monogenic, with >99.4% of patients sharing an identical point mutation in the elongator acetyltransferase complex subunit 1 (ELP1) gene, providing a relatively simple genetic background in which to identify modifiable factors that influence pathology. Gastrointestinal symptoms and metabolic deficits are common among FD patients, which supports the hypothesis that the gut microbiome and metabolome are altered and dysfunctional compared to healthy individuals. Here we show significant differences in gut microbiome composition (16 S rRNA gene sequencing of stool samples) and NMR-based stool and serum metabolomes between a cohort of FD patients (~14% of patients worldwide) and their cohabitating, healthy relatives. We show that key observations in human subjects are recapitulated in a neuron-specific Elp1-deficient mouse model, and that cohousing mutant and littermate control mice ameliorates gut microbiome dysbiosis, improves deficits in gut transit, and reduces disease severity. Our results provide evidence that neurologic deficits in FD alter the structure and function of the gut microbiome, which shifts overall host metabolism to perpetuate further neurodegeneration.

Distinct Metabolic States Are Observed in Hypoglycemia Induced in Mice by Ricin Toxin or by Fasting.

Hypoglycemia may be induced by a variety of physiologic and pathologic stimuli and can result in life-threatening consequences if untreated. However, hypoglycemia may also play a role in the purported health benefits of intermittent fasting and caloric restriction. Previously, we demonstrated that systemic administration of ricin toxin induced fatal hypoglycemia in mice. Here, we examine the metabolic landscape of the hypoglycemic state induced in the liver of mice by two different stimuli: systemic ricin administration and fasting. Each stimulus produced the same decrease in blood glucose and weight loss. The polar metabolome was studied using 1H NMR, quantifying 59 specific metabolites, and untargeted LC-MS on approximately 5000 features. Results were analyzed by multivariate analyses, using both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), to identify global metabolic patterns, and by univariate analyses (ANOVA) to assess individual metabolites. The results demonstrated that while there were some similarities in the responses to the two stimuli including decreased glucose, ADP, and glutathione, they elicited distinct metabolic states. The metabolite showing the greatest difference was O-phosphocholine, elevated in ricin-treated animals and known to be affected by the pro-inflammatory cytokine TNF-α. Another difference was the alternative fuel source utilized, with fasting-induced hypoglycemia primarily ketotic, while the response to ricin-induced hypoglycemia involves protein and amino acid catabolism.

Parenteral Exposure of Mice to Ricin Toxin Induces Fatal Hypoglycemia by Cytokine-Mediated Suppression of Hepatic Glucose-6-Phosphatase Expression.

Ricin toxin is an agent of biodefense concern and we have been developing countermeasures for ricin threats. In doing so, we sought biomarkers of ricin toxicosis and found that in mice parenteral injection of ricin toxin causes profound hypoglycemia, in the absence of other clinical laboratory abnormalities. We now seek to identify the mechanisms underlying this hypoglycemia. Within the first hours following injection, while still normoglycemic, lymphopenia and pro-inflammatory cytokine secretion were observed, particularly tumor necrosis factor (TNF)-α. The cytokine response evolved over the next day into a complex storm of both pro- and anti-inflammatory cytokines. Evaluation of pancreatic function and histology demonstrated marked islet hypertrophy involving predominantly ß-cells, but only mildly elevated levels of insulin secretion, and diminished hepatic insulin signaling. Drops in blood glucose were observed even after destruction of ß-cells with streptozotocin. In the liver, we observed a rapid and persistent decrease in the expression of glucose-6-phosphatase (G6Pase) RNA and protein levels, accompanied by a drop in glucose-6-phosphate and increase in glycogen. TNF-α has previously been reported to suppress G6Pase expression. In humans, a genetic deficiency of G6Pase results in glycogen storage disease, type-I (GSD-1), a hallmark of which is potentially fatal hypoglycemia.

Longitudinal analysis of the Five Sisters hot springs in Yellowstone National Park reveals a dynamic thermoalkaline environment.

Research focused on microbial populations of thermoalkaline springs has been driven in a large part by the lure of discovering functional enzymes with industrial applications in high-pH and high temperature environments. While several studies have focused on understanding the fundamental ecology of these springs, the small molecule profiles of thermoalkaline springs have largely been overlooked. To better understand how geochemistry, small molecule composition, and microbial communities are connected, we conducted a three-year study of the Five Sisters (FS) springs that included high-resolution geochemical measurements, 16S rRNA sequencing of the bacterial and archaeal community, and mass spectrometry-based metabolite and extracellular small molecule characterization. Integration of the four datasets facilitated a comprehensive analysis of the interwoven thermoalkaline spring system. Over the course of the study, the microbial population responded to changing environmental conditions, with archaeal populations decreasing in both relative abundance and diversity compared to bacterial populations. Decreases in the relative abundance of Archaea were associated with environmental changes that included decreased availability of specific nitrogen- and sulfur-containing extracellular small molecules and fluctuations in metabolic pathways associated with nitrogen cycling. This multi-factorial analysis demonstrates that the microbial community composition is more closely correlated with pools of extracellular small molecules than with the geochemistry of the thermal springs. This is a novel finding and suggests that a previously overlooked component of thermal springs may have a significant impact on microbial community composition.

NMR and Metabolomics-A Roadmap for the Future.

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

18ß-Glycyrrhetinic Acid Induces Metabolic Changes and Reduces Staphylococcus aureus Bacterial Cell-to-Cell Interactions.

The rise in bacterial resistance to common antibiotics has raised an increased need for alternative treatment strategies. The natural antibacterial product, 18ß-glycyrrhetinic acid (GRA) has shown efficacy against community-associated methicillin-resistant Staphylococcus aureus (MRSA), although its interactions against planktonic and biofilm modes of growth remain poorly understood. This investigation utilized biochemical and metabolic approaches to further elucidate the effects of GRA on MRSA. Prolonged exposure of planktonic MRSA cell cultures to GRA resulted in increased production of staphyloxanthin, a pigment known to exhibit antioxidant and membrane-stabilizing functions. Then, 1D 1H NMR analyses of intracellular metabolite extracts from MRSA treated with GRA revealed significant changes in intracellular polar metabolite profiles, including increased levels of succinate and citrate, and significant reductions in several amino acids, including branch chain amino acids. These changes reflect the MRSA response to GRA exposure, including potentially altering its membrane composition, which consumes branched chain amino acids and leads to significant energy expenditure. Although GRA itself had no significant effect of biofilm viability, it seems to be an effective biofilm disruptor. This may be related to interference with cell-cell aggregation, as treatment of planktonic MRSA cultures with GRA leads to a significant reduction in micro-aggregation. The dispersive nature of GRA on MRSA biofilms may prove valuable for treatment of such infections and could be used to increase susceptibility to complementary antibiotic therapeutics.

Metabolomic and elemental profiling of blood serum in bladder cancer.

Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, isobutyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metabolites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC.

Isolation and Characterization of Lignocellulose-Degrading Geobacillus thermoleovorans from Yellowstone National Park.

The microbial degradation of lignocellulose in natural ecosystems presents numerous biotechnological opportunities, including biofuel production from agricultural waste and feedstock biomass. To explore the degradation potential of specific thermophiles, we have identified and characterized extremophilic microorganisms isolated from hot springs environments that are capable of biodegrading lignin and cellulose substrates under thermoalkaline conditions, using a combination of culturing, genomics, and metabolomics techniques. Organisms that can use lignin and cellulose as a sole carbon source at 60 to 75°C were isolated from sediment slurry of thermoalkaline hot springs (71 to 81°C and pH 8 to 9) of Yellowstone National Park. Full-length 16S rRNA gene sequencing indicated that these isolates were closely related to Geobacillus thermoleovorans. Interestingly, most of these isolates demonstrated biofilm formation on lignin, a phenotype that is correlated with increased bioconversion. Assessment of metabolite level changes in two Geobacillus isolates from two representative springs were undertaken to characterize the metabolic responses associated with growth on glucose versus lignin carbon source as a function of pH and temperature. Overall, results from this study support that thermoalkaline springs harbor G. thermoleovorans microorganisms with lignocellulosic biomass degradation capabilities and potential downstream biotechnological applications. IMPORTANCE Since lignocellulosic biomass represents a major agro-industrial waste and renewable resource, its potential to replace nonrenewable petroleum-based products for energy production is considerable. Microbial ligninolytic and cellulolytic enzymes are of high interest in biorefineries for the valorization of lignocellulosic biomass, as they can withstand the extreme conditions (e.g., high temperature and high pH) required for processing. Of great interest is the ligninolytic potential of specific Geobacillus thermoleovorans isolates to function at a broad range of pH and temperatures, since lignin is the bottleneck in the bioprocessing of lignocellulose. In this study, results obtained from G. thermoleovorans isolates originating from YNP springs are significant because very few microorganisms from alkaline thermal environments have been discovered to have lignin- and cellulose-biodegrading capabilities, and this work opens new avenues for the biotechnological valorization of lignocellulosic biomass at an industrial scale.

1H NMR based metabolic profiling distinguishes the differential impact of capture techniques on wild bighorn sheep.

Environmental metabolomics has the potential to facilitate the establishment of a new suite of tools for assessing the physiological status of important wildlife species. A first step in developing such tools is to evaluate the impacts of various capture techniques on metabolic profiles as capture is necessary to obtain the biological samples required for assays. This study employed 1H nuclear magnetic resonance (NMR)-based metabolite profiling of 562 blood serum samples from wild bighorn sheep to identify characteristic molecular serum makers of three capture techniques (dart, dropnet, and helicopter-based captures) to inform future sampling protocols for metabolomics studies, and to provide insights into the physiological impacts of capture. We found that different capture techniques induce distinct changes in amino acid serum profiles, the urea cycle, and glycolysis, and attribute the differences in metabolic patterns to differences in physical activity and stress caused by the different capture methods. These results suggest that when designing experiments involving the capture of wild animals, it may be prudent to employ a single capture technique to reduce confounding factors. Our results also supports administration of tranquilizers as soon as animals are restrained to mitigate short-term physiological and metabolic responses when using pursuit and physical restraint capture techniques.

Metabolomic and elemental profiling of human tissue in kidney cancer.

INTRODUCTION: Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite advances in treatment, no specific biomarker is currently in use to guide therapeutic interventions. OBJECTIVES: Major aim of this work was to perform metabolomic and elemental profiling of human kidney cancer and normal tissue and to evaluate cancer biomarkers. METHODS: Metabolic and elemental profiling of tumor and adjacent normal human kidney tissue from 50 patients with kidney cancer was undertaken using three different analytical methods. RESULTS: Five potential tissue biomarkers of kidney cancer were identified and quantified using with high-resolution nuclear magnetic resonance spectroscopy. The contents of selected chemical elements in tissues was analyzed using inductively coupled plasma optical emission spectrometry. Eleven mass spectral features differentiating between kidney cancer and normal tissues were detected using silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry. CONCLUSIONS: Our results, derived from the combination of ICP-OES, LDI MS and 1H NMR methods, suggest that tissue biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer.

An emerging view of the diversity, ecology and function of Archaea in alkaline hydrothermal environments.

The described diversity within the domain Archaea has recently expanded due to advances in sequencing technologies, but many habitats that likely harbor novel lineages of archaea remain understudied. Knowledge of archaea within natural and engineered hydrothermal systems, such as hot springs and engineered subsurface habitats, has been steadily increasing, but the majority of the work has focused on archaea living in acidic or circumneutral environments. The environmental pressures exerted by the combination of high temperatures and high pH likely select for divergent communities and distinct metabolic pathways from those observed in acidic or circumneutral systems. In this review, we examine what is currently known about the archaea found in thermoalkaline environments, focusing on the detection of novel lineages and knowledge of the ecology, metabolic pathways and functions of these populations and communities. We also discuss the potential of emerging multi-omics approaches, including proteomics and metabolomics, to enhance our understanding of archaea within extreme thermoalkaline systems.