Genetic manipulation of gut microbes enables single-gene interrogation in a complex microbiome.

Hundreds of microbiota genes are associated with host biology/disease. Unraveling the causal contribution of a microbiota gene to host biology remains difficult because many are encoded by nonmodel gut commensals and not genetically targetable. A general approach to identify their gene transfer methodology and build their gene manipulation tools would enable mechanistic dissections of their impact on host physiology. We developed a pipeline that identifies the gene transfer methods for multiple nonmodel microbes spanning five phyla, and we demonstrated the utility of their genetic tools by modulating microbiome-derived short-chain fatty acids and bile acids in vitro and in the host. In a proof-of-principle study, by deleting a commensal gene for bile acid synthesis in a complex microbiome, we discovered an intriguing role of this gene in regulating colon inflammation. This technology will enable genetically engineering the nonmodel gut microbiome and facilitate mechanistic dissection of microbiota-host interactions.

Strain-level fitness in the gut microbiome is an emergent property of glycans and a single metabolite.

The human gut microbiota resides within a diverse chemical environment challenging our ability to understand the forces shaping this ecosystem. Here, we reveal that fitness of the Bacteroidales, the dominant order of bacteria in the human gut, is an emergent property of glycans and one specific metabolite, butyrate. Distinct sugars serve as strain-variable fitness switches activating context-dependent inhibitory functions of butyrate. Differential fitness effects of butyrate within the Bacteroides are mediated by species-level variation in Acyl-CoA thioesterase activity and nucleotide polymorphisms regulating an Acyl-CoA transferase. Using in vivo multi-omic profiles, we demonstrate Bacteroides fitness in the human gut is associated together, but not independently, with Acyl-CoA transferase expression and butyrate. Our data reveal that each strain of the Bacteroides exists within a unique fitness landscape based on the interaction of chemical components unpredictable by the effect of each part alone mediated by flexibility in the core genome.

A Cardiovascular Disease-Linked Gut Microbial Metabolite Acts via Adrenergic Receptors.

Using untargeted metabolomics (n = 1,162 subjects), the plasma metabolite (m/z = 265.1188) phenylacetylglutamine (PAGln) was discovered and then shown in an independent cohort (n = 4,000 subjects) to be associated with cardiovascular disease (CVD) and incident major adverse cardiovascular events (myocardial infarction, stroke, or death). A gut microbiota-derived metabolite, PAGln, was shown to enhance platelet activation-related phenotypes and thrombosis potential in whole blood, isolated platelets, and animal models of arterial injury. Functional and genetic engineering studies with human commensals, coupled with microbial colonization of germ-free mice, showed the microbial porA gene facilitates dietary phenylalanine conversion into phenylacetic acid, with subsequent host generation of PAGln and phenylacetylglycine (PAGly) fostering platelet responsiveness and thrombosis potential. Both gain- and loss-of-function studies employing genetic and pharmacological tools reveal PAGln mediates cellular events through G-protein coupled receptors, including α2A, α2B, and ß2-adrenergic receptors. PAGln thus represents a new CVD-promoting gut microbiota-dependent metabolite that signals via adrenergic receptors.

Functional Metabolomics Describes the Yeast Biosynthetic Regulome.

Genome-metabolism interactions enable cell growth. To probe the extent of these interactions and delineate their functional contributions, we quantified the Saccharomyces amino acid metabolome and its response to systematic gene deletion. Over one-third of coding genes, in particular those important for chromatin dynamics, translation, and transport, contribute to biosynthetic metabolism. Specific amino acid signatures characterize genes of similar function. This enabled us to exploit functional metabolomics to connect metabolic regulators to their effectors, as exemplified by TORC1, whose inhibition in exponentially growing cells is shown to match an interruption in endomembrane transport. Providing orthogonal information compared to physical and genetic interaction networks, metabolomic signatures cluster more than half of the so far uncharacterized yeast genes and provide functional annotation for them. A major part of coding genes is therefore participating in gene-metabolism interactions that expose the metabolism regulatory network and enable access to an underexplored space in gene function.

Biosynthesis of selenium-containing small molecules in diverse microorganisms.

Selenium is an essential micronutrient in diverse organisms. Two routes are known for its insertion into proteins and nucleic acids, via selenocysteine and 2-selenouridine, respectively1. However, despite its importance, pathways for specific incorporation of selenium into small molecules have remained elusive. Here we use a genome-mining strategy in various microorganisms to uncover a widespread three-gene cluster that encodes a dedicated pathway for producing selenoneine, the selenium analogue of the multifunctional molecule ergothioneine2,3. We elucidate the reactions of all three proteins and uncover two novel selenium-carbon bond-forming enzymes and the biosynthetic pathway for production of a selenosugar, which is an unexpected intermediate en route to the final product. Our findings expand the scope of biological selenium utilization, suggest that the selenometabolome is more diverse than previously thought, and set the stage for the discovery of other selenium-containing natural products.

Methyl-Metabolite Depletion Elicits Adaptive Responses to Support Heterochromatin Stability and Epigenetic Persistence.

S-adenosylmethionine (SAM) is the methyl-donor substrate for DNA and histone methyltransferases that regulate epigenetic states and subsequent gene expression. This metabolism-epigenome link sensitizes chromatin methylation to altered SAM abundance, yet the mechanisms that allow organisms to adapt and protect epigenetic information during life-experienced fluctuations in SAM availability are unknown. We identified a robust response to SAM depletion that is highlighted by preferential cytoplasmic and nuclear mono-methylation of H3 Lys 9 (H3K9) at the expense of broad losses in histone di- and tri-methylation. Under SAM-depleted conditions, H3K9 mono-methylation preserves heterochromatin stability and supports global epigenetic persistence upon metabolic recovery. This unique chromatin response was robust across the mouse lifespan and correlated with improved metabolic health, supporting a significant role for epigenetic adaptation to SAM depletion in vivo. Together, these studies provide evidence for an adaptive response that enables epigenetic persistence to metabolic stress.

Plasma proteometabolome in lung cancer: exploring biomarkers through bidirectional Mendelian randomization and colocalization analysis.

Unlike other cancers with widespread screening (breast, colorectal, cervical, prostate, and skin), lung nodule biopsies for positive screenings have higher morbidity with clinical complications. Development of non-invasive diagnostic biomarkers could thereby significantly enhance lung cancer management for at-risk patients. Here, we leverage Mendelian Randomization (MR) to investigate the plasma proteome and metabolome for potential biomarkers relevant to lung cancer. Utilizing bidirectional MR and co-localization analyses, we identify novel associations, highlighting inverse relationships between plasma proteins SFTPB and KDELC2 in lung adenocarcinoma (LUAD) and positive associations of TCL1A with lung squamous cell carcinoma (LUSC) and CNTN1 with small cell lung cancer (SCLC). Additionally, our work reveals significant negative correlations between metabolites such as theobromine and paraxanthine, along with paraxanthine-related ratios, in both LUAD and LUSC. Conversely, positive correlations are found in caffeine/paraxanthine and arachidonate (20:4n6)/paraxanthine ratios with these cancer types. Through single-cell sequencing data of normal lung tissue, we further explore the role of lung tissue-specific protein SFTPB in carcinogenesis. These findings offer new insights into lung cancer etiology, potentially guiding the development of diagnostic biomarkers and therapeutic approaches.

A reference map of potential determinants for the human serum metabolome.

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites-in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.

RefMetaPlant: a reference metabolome database for plants across five major phyla.

Plants are unique with tremendous chemical diversity and metabolic complexity, which is highlighted by estimates that green plants collectively produce metabolites numbering in the millions. Plant metabolites play crucial roles in all aspects of plant biology, like growth, development, stress responses, etc. However, the lack of a reference metabolome for plants, and paucity of high-quality standard compound spectral libraries and related analytical tools, have hindered the discovery and functional study of phytochemicals in plants. Here, by leveraging an advanced LC-MS platform, we generated untargeted mass spectral data from >150 plant species collected across the five major phyla. Using a self-developed computation protocol, we constructed reference metabolome for 153 plant species. A 'Reference Metabolome Database for Plants' (RefMetaPlant) was built to encompass the reference metabolome, integrated standard compound mass spectral libraries for annotation, and related query and analytical tools like 'LC-MS/MS Query', 'RefMetaBlast' and 'CompoundLibBlast' for searches and profiling of plant metabolome and metabolite identification. Analogous to a reference genome in genomic research, RefMetaPlant provides a powerful platform to support plant genome-scale metabolite analysis to promote knowledge/data sharing and collaboration in the field of metabolomics. RefMetaPlant is freely available at https://www.biosino.org/RefMetaDB/.

PMhub 1.0: a comprehensive plant metabolome database.

The Plant Metabolome Hub (PMhub), available at https://pmhub.org.cn, is a valuable resource designed to provide scientists with comprehensive information on plant metabolites. It offers extensive details about their reference spectra, genetic foundations, chemical reactions, metabolic pathways and biological functions. The PMhub contains chemical data for 188 837 plant metabolites gathered from various sources, with 1 467 041 standard/in-silico high-resolution tandem mass-spectrometry (HRMS/MS) spectra corresponding to these metabolites. Beyond its extensive literature-derived data, PMhub also boasts a sizable collection of experimental metabolites; it contains 144 366 detected features in 10 typical plant species, with 16 423 successfully annotated by using standard/in-silico HRMS/MS data, this collection is further supplemented with thousands of features gathered from reference metabolites. For each metabolite, the PMhub enables the reconstructed of a simulated network based on structural similarities and existing metabolic pathways. Unlike previous plant-specific metabolome databases, PMhub not only contains a vast amount of metabolic data but also assembles the corresponding genomic and/or transcriptomic information, incorporating multiple methods for the comprehensive genetic analysis of metabolites. To validate the practicality, we verified a synthetic pathway for N-p-coumaroyltyramine by in vitro enzymatic activity experiments. In summary, the robust functionality provided by the PMhub will make it an indispensable tool for studying plant metabolomics.

The Genetics of Plant Metabolism.

Plant metabolic studies have traditionally focused on the role and regulation of the enzymes catalyzing key reactions within specific pathways. Within the past 20 years, reverse genetic approaches have allowed direct determination of the effects of the deficiency, or surplus, of a given protein on the biochemistry of a plant. In parallel, top-down approaches have also been taken, which rely on screening broad, natural genetic diversity for metabolic diversity. Here, we compare and contrast the various strategies that have been adopted to enhance our understanding of the natural diversity of metabolism. We also detail how these approaches have enhanced our understanding of both specific and global aspects of the genetic regulation of metabolism. Finally, we discuss how such approaches are providing important insights into the evolution of plant secondary metabolism.

Transcriptome and metabolome atlas reveals contributions of sphingosine and chlorogenic acid to cold tolerance in Citrus.

Citrus is one of the most important fruit crop genera in the world, but many Citrus species are vulnerable to cold stress. Ichang papeda (Citrus ichangensis), a cold-hardy citrus species, holds great potential for identifying valuable metabolites that are critical for cold tolerance in Citrus. However, the metabolic changes and underlying mechanisms that regulate Ichang papeda cold tolerance remain largely unknown. In this study, we compared the metabolomes and transcriptomes of Ichang papeda and HB pummelo (Citrus grandis "Hirado Buntan", a cold-sensitive species) to explore the critical metabolites and genes responsible for cold tolerance. Metabolomic analyses led to the identification of common and genotype-specific metabolites, consistent with transcriptomic alterations. Compared to HB pummelo under cold stress, Ichang papeda accumulated more sugars, flavonoids, and unsaturated fatty acids, which are well-characterized metabolites involved in stress responses. Interestingly, sphingosine and chlorogenic acid substantially accumulated only in Ichang papeda. Knockdown of CiSPT (C. ichangensis serine palmitoyltransferase) and CiHCT2 (C. ichangensis hydroxycinnamoyl-CoA: shikimate hydroxycinnamoyltransferase2), two genes involved in sphingosine and chlorogenic acid biosynthesis, dramatically decreased endogenous sphingosine and chlorogenic acid levels, respectively. This reduction in sphingosine and chlorogenic acid notably compromised the cold tolerance of Ichang papeda, whereas exogenous application of these metabolites increased plant cold tolerance. Taken together, our findings indicate that greater accumulation of a spectrum of metabolites, particularly sphingosine and chlorogenic acid, promotes cold tolerance in cold-tolerant citrus species. These findings broaden our understanding of plant metabolic alterations in response to cold stress and provide valuable targets that can be manipulated to improve Citrus cold tolerance.

Genome scale metabolic network modelling for metabolic profile predictions.

Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

MiMeDB: the Human Microbial Metabolome Database.

The Human Microbial Metabolome Database (MiMeDB) (https://mimedb.org) is a comprehensive, multi-omic, microbiome resource that connects: (i) microbes to microbial genomes; (ii) microbial genomes to microbial metabolites; (iii) microbial metabolites to the human exposome and (iv) all of these 'omes' to human health. MiMeDB was established to consolidate the growing body of data connecting the human microbiome and the chemicals it produces to both health and disease. MiMeDB contains detailed taxonomic, microbiological and body-site location data on most known human microbes (bacteria and fungi). This microbial data is linked to extensive genomic and proteomic sequence data that is closely coupled to colourful interactive chromosomal maps. The database also houses detailed information about all the known metabolites generated by these microbes, their structural, chemical and spectral properties, the reactions and enzymes responsible for these metabolites and the primary exposome sources (food, drug, cosmetic, pollutant, etc.) that ultimately lead to the observed microbial metabolites in humans. Additional, extensively referenced data about the known or presumptive health effects, measured biosample concentrations and human protein targets for these compounds is provided. All of this information is housed in richly annotated, highly interactive, visually pleasing database that has been designed to be easy to search, easy to browse and easy to navigate. Currently MiMeDB contains data on 626 health effects or bioactivities, 1904 microbes, 3112 references, 22 054 reactions, 24 254 metabolites or exposure chemicals, 648 861 MS and NMR spectra, 6.4 million genes and 7.6 billion DNA bases. We believe that MiMeDB represents the kind of integrated, multi-omic or systems biology database that is needed to enable comprehensive multi-omic integration.

Altered Metabolism during the Dark Period in Drosophila Short Sleep Mutants.

Sleep is regulated via circadian mechanisms, but effects of sleep disruption on physiological rhythms, in particular metabolic cycling, remain unclear. To examine this question, we probed diurnal metabolic alterations of two Drosophila short sleep mutants, fumin and sleepless. Samples were collected with high temporal sampling (every 2 h) over 24 h under a 12:12 light:dark cycle, and profiling was done using an ion-switching LCMS/MS method. Fewer metabolites with 24 h oscillations were noted with short sleep (50 and 46 in fumin and sleepless, BH. Q < 0.2 by RAIN analysis) compared to a wild-type control (iso31, 63 with BH. Q < 0.2), and peak phases of the sleep mutants were consolidated into two major phase peaks at mid-day and middle of night. Overall, altered nicotinate/nicotinamide, alanine/aspartate/glutamate, acetylcholine, glyoxylate/dicarboxylate, and TCA cycle metabolism were observed in the short sleep mutants, indicative of increased energetic demand and oxidative stress compared to wild type. Both changes in cycling and discriminant models suggest unique alterations in the dark period indicative of constrained metabolic networks. Thus, we conclude that sleep loss alters metabolic function uniquely throughout the day, and further examination of specific mechanisms is warranted.

Integrated metabolome, full-length sequencing, and transcriptome analyses unveil the molecular mechanisms of color formation of the canary yellow and red bracts of Bougainvillea × buttiana 'Chitra'.

Bougainvillea is a typical tropical flower of great ornamental value due to its colorful bracts. The molecular mechanism behind color formation is not well-understood. Therefore, this research conducted metabolome analysis, transcriptome analysis, and multi-flux full-length sequencing in two color bracts of Bougainvillea × buttiana 'Chitra' to investigate the significantly different metabolites (SDMs) and differentially expressed genes (DEGs). Overall, 261 SDMs, including 62 flavonoids and 26 alkaloids, were detected, and flavonols and betalains were significantly differentially accumulated among the two bracts. Furthermore, the complete-length transcriptome of Bougainvillea × buttiana was also developed, which contained 512 493 non-redundant isoforms. Among them, 341 210 (66.58%) displayed multiple annotations in the KOG, GO, NR, KEGG, Pfam, Swissprot, and NT databases. RNA-seq findings revealed that 3610 DEGs were identified between two bracts. Co-expression analysis demonstrated that the DEGs and SDMs involved in flavonol metabolism (such as CHS, CHI, F3H, FLS, CYP75B1, kaempferol, and quercetin) and betacyanin metabolism (DODA, betanidin, and betacyanins) were the main contributors for the canary yellow and red bract formation, respectively. Further investigation revealed that several putative transcription factors (TFs) might interact with the promoters of the genes mentioned above. The expression profiles of the putative TFs displayed that they may positively and negatively regulate the structural genes' expression profiles. The data revealed a potential regulatory network between important genes, putative TFs, and metabolites in the flavonol and betacyanin biosynthesis of Bougainvillea × buttiana 'Chitra' bracts. These findings will serve as a rich genetic resource for future studies that could create new color bracts.

Integration of transcriptome and metabolome reveals the accumulation of related metabolites and gene regulation networks during quinoa seed development.

Quinoa seeds are gluten- and cholesterol-free, contain all amino acids required by the human body, have a high protein content, provide endocrine regulation, protein supplementation, and cardiovascular protection effects. However, metabolite accumulation and transcriptional regulatory networks in quinoa seed development are not well understood. Four key stages of seed development in Dianli-3260 and Dianli-557 were thus analyzed and 849 metabolites were identified, among which sugars, amino acids, and lipids were key for developmental processes, and their accumulation showed a gradual decrease. Transcriptome analysis identified 40,345 genes, of which 20,917 were differential between the M and F phases, including 8279 and 12,638 up- and down-regulated genes, respectively. Grain development processes were mainly enriched in galactose metabolism, pentose and glucuronate interconversions, the biosynthesis of amino acids, and carbon metabolism pathways, in which raffinose, phosphoenolpyruvate, series and other metabolites are significantly enriched, gene-LOC110689372, Gene-LOC110710556 and gene-LOC110714584 are significantly expressed, and these metabolites and genes play an important role in carbohydrate metabolism, lipid and Amino acid synthesis of quinoa. This study provides a theoretical basis to expand our understanding of the molecular and metabolic development of quinoa grains.

The interplay of metabolic and epigenetic players in disease development.

Epigenetic modifications and their alterations can cause variation in gene expression patterns which can ultimately affect a healthy individual. Until a few years ago, it was thought that the epigenome affects the transcriptome which can regulate the proteome and the metabolome. Recent studies have shown that the metabolome independently also plays a major role in regulating the epigenome bypassing the need for transcriptomic control. Alternatively, an imbalanced metabolome, stemming from transcriptome abnormalities, can further impact the transcriptome, creating a self-perpetuating cycle of interconnected occurrences. As a result, external factors such as nutrient intake and diet can have a direct impact on the metabolic pools and its reprogramming can change the levels and activity of epigenetic modifiers. Thus, the epigenetic landscape steers toward a diseased condition. In this review, we have discussed how different metabolites and dietary patterns can bring about changes in different arms of the epigenetic machinery such as methylation, acetylation as well as RNA mediated epigenetic mechanisms. We checked for limiting metabolites such as αKG, acetyl-CoA, ATP, NAD+, and FAD, whose abundance levels can lead to common diseases such as cancer, neurodegeneration etc. This gives a clearer picture of how an integrated approach including both epigenetics and metabolomics can be used for therapeutic purposes.

Genetic investigation into the broad health implications of caffeine: evidence from phenome-wide, proteome-wide and metabolome-wide Mendelian randomization.

BACKGROUND: Caffeine is one of the most utilized drugs in the world, yet its clinical effects are not fully understood. Circulating caffeine levels are influenced by the interplay between consumption behaviour and metabolism. This study aimed to investigate the effects of circulating caffeine levels by considering genetically predicted variation in caffeine metabolism. METHODS: Leveraging genetic variants related to caffeine metabolism that affect its circulating levels, we investigated the clinical effects of plasma caffeine in a phenome-wide association study (PheWAS). We validated novel findings using a two-sample Mendelian randomization framework and explored the potential mechanisms underlying these effects in proteome-wide and metabolome-wide Mendelian randomization. RESULTS: Higher levels of genetically predicted circulating caffeine among caffeine consumers were associated with a lower risk of obesity (odds ratio (OR) per standard deviation increase in caffeine = 0.97, 95% confidence interval (CI) CI: 0.95-0.98, p = 2.47 × 10-4), osteoarthrosis (OR = 0.97, 95% CI: 0.96-0.98, P=1.10 × 10-8) and osteoarthritis (OR: 0.97, 95% CI: 0.96 to 0.98, P = 1.09 × 10-6). Approximately one third of the protective effect of plasma caffeine on osteoarthritis risk was estimated to be mediated through lower bodyweight. Proteomic and metabolomic perturbations indicated lower chronic inflammation, improved lipid profiles, and altered protein and glycogen metabolism as potential biological mechanisms underlying these effects. CONCLUSIONS: We report novel evidence suggesting that long-term increases in circulating caffeine may reduce bodyweight and the risk of osteoarthrosis and osteoarthritis. We confirm prior genetic evidence of a protective effect of plasma caffeine on risk of overweight and obesity. Further clinical study is warranted to understand the translational relevance of these findings before clinical practice or lifestyle interventions related to caffeine consumption are introduced.

Physiological, transcriptomic and metabolomic insights of three extremophyte woody species living in the multi-stress environment of the Atacama Desert.

MAIN CONCLUSIONS: In contrast to Neltuma species, S. tamarugo exhibited higher stress tolerance, maintaining photosynthetic performance through enhanced gene expression and metabolites. Differentially accumulated metabolites include chlorophyll and carotenoids and accumulation of non-nitrogen osmoprotectants. Plant species have developed different adaptive strategies to live under extreme environmental conditions. Hypothetically, extremophyte species present a unique configuration of physiological functions that prioritize stress-tolerance mechanisms while carefully managing resource allocation for photosynthesis. This could be particularly challenging under a multi-stress environment, where the synthesis of multiple and sequential molecular mechanisms is induced. We explored this hypothesis in three phylogenetically related woody species co-occurring in the Atacama Desert, Strombocarpa tamarugo, Neltuma alba, and Neltuma chilensis, by analyzing their leaf dehydration and freezing tolerance and by characterizing their photosynthetic performance under natural growth conditions. Besides, the transcriptomic profiling, biochemical analyses of leaf pigments, and metabolite analysis by untargeted metabolomics were conducted to study gene expression and metabolomic landscape within this challenging multi-stress environment. S. tamarugo showed a higher photosynthetic capacity and leaf stress tolerance than the other species. In this species, a multifactorial response was observed, which involves high photochemical activity associated with a higher content of chlorophylls and ß-carotene. The oxidative damage of the photosynthetic apparatus is probably attenuated by the synthesis of complex antioxidant molecules in the three species, but S. tamarugo showed the highest antioxidant capacity. Comparative transcriptomic and metabolomic analyses among the species showed the differential expression of genes involved in the biosynthetic pathways of key stress-related metabolites. Moreover, the synthesis of non-nitrogen osmoprotectant molecules, such as ciceritol and mannitol in S. tamarugo, would allow the nitrogen allocation to support its high photosynthetic capacity without compromising leaf dehydration tolerance and freezing stress avoidance.