High-throughput classification of yeast mutants for functional genomics using metabolic footprinting

Profile of key metabolites and identification of HMGCS1-DHEA pathway in porcine Sertoli cells treated by Vitamin C

Vitamin C (Ascorbic acid, AA), as vital micro-nutrient, plays an essential role for male animal reproduction. Previously, we showed that vitamin C reprogrammed the transcriptome and proteome to change phenotypes of porcine immature Sertoli cells (iSCs). Here, we used LC-MS-based non-targeted metabolomics to further investigate the metabolic effects of vitamin C on porcine iSCs. The results identified 43 significantly differential metabolites (DMs) (16 up and 27 down) as induced by vitamin C (L-ascorbic acid 2-phosphate sesquimagnesium salt hydrate, AA2P) treatment of porcine iSCs, which were mainly enriched in steroid related and protein related metabolic pathways. ELISA (Enzyme-Linked ImmunoSorbent Assay) showed that significantly differential metabolites of Dehydroepiandrosterone (DHEA) (involved in steroid hormone biosynthesis) and Desmosterol (involved in steroid degradation) were significantly increased, which were partially consistent with metabolomic results. Further integrative analysis of metabolomics, transcriptomics and proteomics data identified the strong correlation between the key differential metabolite of Dehydroepiandrosterone and 6 differentially expressed genes (DEGs)/proteins (DEPs) (HMGCS1, P4HA1, STON2, LOXL2, EMILIN2 and CCN3). Further experiments validated that HMGCS1 could positively regulate Dehydroepiandrosterone level. These data indicate that vitamin C could modulate the metabolism profile, and HMGCS1-DHEA could be the pathway to mediate effects exerted by vitamin C on porcine iSCs.

Hydrogen-Rich Gas Enhanced Sprint-Interval Performance: Metabolomic Insights into Underlying Mechanisms

(1) Background: The diversity of blood biomarkers used to assess the metabolic mechanisms of hydrogen limits a comprehensive understanding of its effects on improving exercise performance. This study evaluated the impact of hydrogen-rich gas (HRG) on metabolites following sprint-interval exercise using metabolomics approaches, aiming to elucidate its underlying mechanisms of action. (2) Methods: Ten healthy adult males participated in the Wingate Sprint-interval test (SIT) following 60 min of HRG or placebo (air) inhalation. Venous blood samples were collected for metabolomic analysis both before and after gas inhalation and subsequent to completing the SIT. (3) Results: Compared with the placebo, HRG inhalation significantly improved mean power, fatigue index, and time to peak for the fourth sprint and significantly reduced the attenuation values of peak power, mean power, and time to peak between the first and fourth. Metabolomic analysis highlighted the significant upregulation of acetylcarnitine, propionyl-L-carnitine, hypoxanthine, and xanthine upon HRG inhalation, with enrichment pathway analysis suggesting that HRG may foster fat mobilization by enhancing coenzyme A synthesis, promoting glycerophospholipid metabolism, and suppressing insulin levels. (4) Conclusions: Inhaling HRG before an SIT enhances end-stage anaerobic sprint capabilities and mitigates fatigue. Metabolomic analysis suggests that HRG may enhance ATP recovery during interval stages by accelerating fat oxidation, providing increased energy replenishment for late-stage sprints.

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

Identification of primary metabolites in fungal species of Trichophyton mentagrophyte and Trichophyton rubrum by NMR spectroscopy

BACKGROUND

Superficial mycoses are fungal infections limited to the outermost layers of the skin and its appendages. The chief causative agents of these mycoses are dermatophytes and yeasts. The diagnosis of dermatophytosis can be made by direct mycological examination with potassium hydroxide (10%-30%) of biological material obtained from patients with suspected mycosis, providing results more rapid than fungal cultures, which may take days or weeks. This information, together with clinical history and laboratory diagnosis, ensures that the appropriate treatment is initiated promptly. However, false negative results are obtained in 5%-15%, by conventional methods of diagnosis of dermatophytosis.

OBJECTIVES

To study the metabolic profiles of the commonly occurring dermatophytes by NMR spectroscopy.

PATIENTS/MATERIALS

We have used 1D and 2D Nuclear Magnetic Resonance (NMR) experiments along with Human Metabolome Database (HMDB) and Chenomx database search for identification of primary metabolites in the methanol extract of two fungal species: Trichophyton mentagrophyte (T. mentagrophyte) and Trichophyton rubrum (T. rubrum). Both standard strains and representative number of clinical isolates of these two species were investigated. Further, metabolic profiles obtained were analysed using multivariate analysis.

RESULTS

We have identified 23 metabolites in the T. mentagrophyte and another 23 metabolites in T. rubrum. Many important metabolites like trehalose, proline, mannitol, acetate, GABA and several other amino acids were detected, which provide the necessary components for fungal growth and metabolism. Altered metabolites were defined between Trichophyton mentagrophyte and T. rubrum strains.

CONCLUSION

We have detected many metabolites in the two fungal species T. mentagrophyte and T. rubrum by using NMR spectroscopy. NMR spectroscopy provides a holistic snapshot of the metabolome of an organism. Key metabolic differences were identified between the two fungal strains. We need to perform more studies on metabolite profiling of the samples from these species for their rapid diagnosis and prompt treatment.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Lomatogonium rotatum extract alleviates diabetes mellitus induced by a high-fat, high-sugar diet and streptozotocin in rats

BACKGROUND

Lomatogonium rotatum (LR) is traditionally used in Mongolian folk medicine as a hypoglycemic agent, but its evidence-based pharmacological effects and me-chanisms of action have not been fully elucidated.

AIM

To emphasize the hypoglycemic action mechanism of LR in a type 2 diabetic rat model and examine potential biomarkers to obtain mechanistic understanding regarding serum metabolite modifications.

METHODS

A high-fat, high-sugar diet and streptozotocin injection-induced type 2 diabetic rat model was established. The chemical composition of the LR was identified by high performance liquid chromatography. LR extract administrated as oral gavage at 0.5 g/kg, 2.5 g/kg, and 5 g/kg for 4 wk. Anti-diabetic effects of LR extract were evaluated based on histopathological examination as well as the measurement of blood glucose, insulin, glucagon-like peptide 1 (GLP-1), and lipid levels. Serum metabolites were analyzed using an untargeted metabolomics approach.

RESULTS

According to a chemical analysis, swertiamarin, sweroside, hesperetin, coumarin, 1.7-dihydroxy-3,8-dimethoxyl xanthone, and 1-hydroxy-2,3,5 trimethoxanone are the principal active ingredients in LR. An anti-diabetic experiment revealed that the LR treatment significantly increased plasma insulin and GLP-1 levels while effectively lowering blood glucose, total cholesterol, triglycerides, low-density lipoprotein cholesterol, and oral glucose tolerance test compared to the model group. Furthermore, untargeted metabolomic analysis of serum samples detected 236 metabolites, among which 86 were differentially expressed between the model and the LR group. It was also found that LR considerably altered the levels of metabolites such as vitamin B6, mevalonate-5P, D-proline, L-lysine, and taurine, which are involved in the regulation of the vitamin B6 metabolic pathway, selenium amino acid metabolic pathway, pyrimidine metabolic pathway, and arginine and proline metabolic pathways.

CONCLUSION

These findings indicated that LR may have a hypoglycemic impact and that its role may be related to changes in the serum metabolites and to facilitate the release of insulin and GLP-1, which lower blood glucose and lipid profiles.

High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics

Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS² database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.

Metabolic Footprinting-Based DNA-AuNP Encoders for Extracellular Metabolic Response Profiling

Metabolic footprinting as a convenient and non-invasive cell metabolomics strategy relies on monitoring the whole extracellular metabolic process. It covers nutrient consumption and metabolite secretion of in vitro cell culture, which is hindered by low universality owing to pre-treatment of the cell medium and special equipment. Here, we report the design and a variety of applicability, for quantifying extracellular metabolism, of fluorescently labeled single-stranded DNA (ssDNA)-AuNP encoders, whose multi-modal signal response is triggered by extracellular metabolites. We constructed metabolic response profiling of cells by detecting extracellular metabolites in different tumor cells and drug-induced extracellular metabolites. We further assessed the extracellular metabolism differences using a machine learning algorithm. This metabolic response profiling based on the DNA-AuNP encoder strategy is a powerful complement to metabolic footprinting, which significantly applies potential non-invasive identification of tumor cell heterogeneity.

High-throughput metabolomics for the design and validation of a diauxic shift model

Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.

Identification of potential serum biomarkers for congenital heart disease children with pulmonary arterial hypertension by metabonomics

BACKGROUND

Pulmonary arterial hypertension is a common complication in patients with congenital heart disease. In the absence of early diagnosis and treatment, pediatric patients with PAH has a poor survival rate. Here, we explore serum biomarkers for distinguishing children with pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) from CHD.

METHODS

Samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and 22 metabolites were further quantified by ultra-high-performance liquid chromatography-tandem mass spectroscopy.

RESULTS

Serum levels of betaine, choline, S-Adenosyl methionine (SAM), acetylcholine, xanthosine, guanosine, inosine and guanine were significantly altered between CHD and PAH-CHD. Logistic regression analysis showed that combination of serum SAM, guanine and N-terminal pro-brain natriuretic peptide (NT-proBNP), yielded the predictive accuracy of 157 cases was 92.70% with area under the curve of the receiver operating characteristic curve value of 0.9455.

CONCLUSION

We demonstrated that a panel of serum SAM, guanine and NT-proBNP is potential serum biomarkers for screening PAH-CHD from CHD.

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

Nontargeted metabolomics reveals differences in the metabolite profiling among methicillin-resistant and methicillin-susceptible Staphylococcus aureus in response to antibiotics

Staphylococcus aureus (S. aureus) causes infections and can be fatal. In the long-term struggle against antibiotics, S. aureus has acquired resistance toward antibiotics and become more difficult to kill. Metabolomics could directly reflect the responses of S. aureus toward antibiotics, which is effective for studying the resistance mechanism of S. aureus. In this study, based on a nontargeted metabolic figure printing technique, the metabolome of a pair of isogenic methicillin-susceptible and resistant S. aureus strains ATCC25923 (MSSA) and ATCC43300 (MRSA) treated with or without oxacillin was characterized. 7 and 29 significantly changed metabolites in MRSA and MSSA were identified by combined accurate mass and mass fragmentation analysis. Pathway enrichment analysis suggested that DNA repair and flavin biosynthesis are the universal pathways of both MSSA and MRSA under antibiotic stress. MRSA systematically and effectively fights against oxacillin through precise control of energy production, PBP2a substrate biosynthesis and antioxidant function. In contrast, MSSA lacks effective defense pathways against oxacillin. The different metabolome responses of MSSA and MRSA toward antibiotics provide us with new insights into how S. aureus develops antibiotic resistance.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes – the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system—the Microbial Containment Device (MCD)—that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Determination of Intra- and Extracellular Metabolic Adaptations of 3D Cell Cultures upon Challenges in Real-Time by NMR

NMR flow devices provide longitudinal real-time quantitative metabolome characterisation of living cells. However, discrimination of intra- and extracellular contributions to the spectra represents a major challenge in metabolomic NMR studies. The present NMR study demonstrates the possibility to quantitatively measure both metabolic intracellular fingerprints and extracellular footprints on human control fibroblasts by using a commercially available flow tube system with a standard 5 mm NMR probe. We performed a comprehensive 3D cell culture system characterisation. Diffusion NMR was employed for intra- and extracellular metabolites separation. In addition, complementary extracellular footprints were determined. The implemented perfused NMR bioreactor system allowed the determination of 35 metabolites and intra- and extracellular separation of 19 metabolites based on diffusion rate differences. We show the reliability and sensitivity of NMR diffusion measurements to detect metabolite concentration changes in both intra- and extracellular compartments during perfusion with different selective culture media, and upon complex I inhibition with rotenone. We also demonstrate the sensitivity of extracellular footprints to determine metabolic variations at different flow rates. The current method is of potential use for the metabolomic characterisation of defect fibroblasts and for improving physiological comprehension.

A Defined Medium for Cultivation and Exometabolite Profiling of Soil Bacteria

Exometabolomics is an approach to assess how microorganisms alter, or react to their environments through the depletion and production of metabolites. It allows the examination of how soil microbes transform the small molecule metabolites within their environment, which can be used to study resource competition and cross-feeding. This approach is most powerful when used with defined media that enable tracking of all metabolites. However, microbial growth media have traditionally been developed for the isolation and growth of microorganisms but not metabolite utilization profiling through Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). Here, we describe the construction of a defined medium, the Northen Lab Defined Medium (NLDM), that not only supports the growth of diverse soil bacteria but also is defined and therefore suited for exometabolomic experiments. Metabolites included in NLDM were selected based on their presence in R2A medium and soil, elemental stoichiometry requirements, as well as knowledge of metabolite usage by different bacteria. We found that NLDM supported the growth of 108 of the 110 phylogenetically diverse (spanning 36 different families) soil bacterial isolates tested and all of its metabolites were trackable through LC–MS/MS analysis. These results demonstrate the viability and utility of the constructed NLDM medium for growing and characterizing diverse microbial isolates and communities.

Metabolic control of oocyte development

Well balanced and timed metabolism is essential for oocyte development. The effects of extrinsic nutrients on oocyte maturation have been widely reported. In contrast, intrinsic control of oogenesis by intracellular metabolites and metabolic enzymes has received little attention. The comprehensive characterization of metabolic patterns could lead to more complete understanding of regulatory mechanisms underlying oocyte development. A cell's metabolic state is integrated with epigenetic regulation. Epigenetic modifications in germ cells are therefore sensitive to parental environmental exposures. Nevertheless, direct genetic evidence for metabolites involvement in epigenetic establishment during oocyte development is still lacking. Moreover, metabolic disorder-induced epigenetic perturbations during oogenesis might mediate the inter/transgenerational effects of environmental insults. The molecular mechanisms responsible for this deserve further investigation. Here, we summarize the findings on metabolic regulation in oocyte maturation, and how it contributes to oocyte epigenetic modification. Finally, we propose a mouse model that metabolic disorder in oocyte serves as a potential factor mediating the maternal environment effects on offspring health.

Transformation of sulfamethoxazole by sulfidated nanoscale zerovalent iron activated persulfate: Mechanism and risk assessment using environmental metabolomics

The threat caused by the misuse of antibiotics to ecology and human health has been aroused an extensive attention. Developing cost-effective techniques for removing antibiotics needs to put on the agenda. In current research, the degradation mechanism of sulfamethoxazole (SMX) by sulfidated nanoscale zerovalent iron (S-nZVI) driven persulfate, together with the potential risk of intermediates were studied. The degradation of SMX followed a pseudo-first order kinetics reaction with k_obs at 0.1176 min^-1. Both SO₄^•- and •OH were responsible for the degradation of SMX, and SO₄^•- was the predominant free radical. XPS analysis demonstrated that reduced sulfide species promoted the conversion of Fe (III) to Fe (II), resulting in the higher transformation rate of SMX. Six intermediates products were generated through hydroxylation, dehydration condensation, nucleophilic reaction, and hydrolysis. The risk of intermediates products is subsequently assessed using E. coli as a model microorganism. After E.coli exposure to intermediates for 24 h, the upmetabolism of carbohydrate, nucleotide, citrate acid cycle and downmetabolism of glutathione, sphingolipid, galactose by metabolomics analysis identified that SMX was effectively detoxified by oxidation treatment. These findings not only clarified the superiority of S-nZVI/persulfate, but also generated a novel insight into the security of advanced oxidation processes.

High-throughput metabolomics predicts drug-target relationships for eukaryotic proteins

Chemical probes are important tools for understanding biological systems. However, because of the huge combinatorial space of targets and potential compounds, traditional chemical screens cannot be applied systematically to find probes for all possible druggable targets. Here, we demonstrate a novel concept for overcoming this challenge by leveraging high-throughput metabolomics and overexpression to predict drug-target interactions. The metabolome profiles of yeast treated with 1,280 compounds from a chemical library were collected and compared with those of inducible yeast membrane protein overexpression strains. By matching metabolome profiles, we predicted which small molecules targeted which signaling systems and recovered known interactions. Drug-target predictions were generated across the 86 genes studied, including for difficult to study membrane proteins. A subset of those predictions were tested and validated, including the novel targeting of GPR1 signaling by ibuprofen. These results demonstrate the feasibility of predicting drug-target relationships for eukaryotic proteins using high-throughput metabolomics.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Why Has Metabolomics So Far Not Managed to Efficiently Contribute to the Improvement of Assisted Reproduction Outcomes? The Answer through a Review of the Best Available Current Evidence

Metabolomics emerged to give clinicians the necessary information on the competence, in terms of physiology and function, of gametes, embryos, and the endometrium towards a targeted infertility treatment, namely, assisted reproduction techniques (ART). Our minireview aims to investigate the current status of the use of metabolomics in assisted reproduction, the potential flaws in its use, and to propose specific solutions towards the improvement of ART outcomes through the use of the intervention. We used published reports assessing the role of metabolomic investigation of the endometrium, oocytes, and embryos in improving clinical outcomes in women undergoing ART. We initially found that there is no evidence to support that fertility outcomes can be improved through metabolomics profiling. In contrast, it may be helpful for understanding and appraising the nutritional environment of oocytes and embryos. The causes include the different infertility populations, the difference between animals and humans, technical limitations, and the great heterogeneity in the variables employed. Suggested steps include the standardization of variables of the method itself, the universal creation of a panel where all biomarkers are stored concerning specific infertile populations with different phenotypes or etiologies, specific bioinformatics contribution, significant computing power for data processing, and importantly, properly conducted trials.

The trend in application of omics in type 2 diabetes researches; A bibliometric study

AIMS

Due to the importance of omics approaches in diabetes diagnosis, we were assumed to study the scientific activities on omics and type 2 diabetes worldwide.

METHOD

Bibliometric approach was utilized to evaluate the documents on proteomics, lipidomics, and metabolomics in patients with type 2 diabetes in the Scopus database from the beginning to 2020. The articles were screened by two reviewers and the number of publications and citations on omics and type 2 diabetes, top-ranked journals, top-cited articles, country co-contributions, co-authorships, author keywords, and terms were analyzed.

RESULTS

The scientific publications in this field consisted of 551 original articles, of which the USA shares the most percent, followed by China and Germany. The frequent keywords showed that the following hotspots were of interest: "Metabolomics, proteomics, and lipidomics as biomarkers for diabetes", "Omics and diabetic nephropathy", "The application of omics in obesity, insulin resistance, and type 2 diabetes".

CONCLUSION

This study showed an increasing trend in applying omics in type 2 diabetes researches and determined the leading producers in this field. Besides, the research hotspots and the main subjects of documents were provided for future research and policy decision-making.

A simple strategy to monitor the temporal and spatial distribution of alkaloids in sacred lotus leaves

Owing to the high degree of diversity of metabolite pools and complexity of spatial and temporal distributions within biological tissues, currently available methods for metabolite characterization face large challenges. In this study, the temporal and spatial distributions of the alkaloid components of the medicinal plant lotus (Nelumbo nucifera) were investigated over various growth phases. The results showed that alkaloid biosynthesis in lotus leaf is regulated by development and that there is maximum accumulation of alkaloids when the lotus leaf was completely expanded. Furthermore, alkaloid content tended to be stable in mature lotus leaves. However, there was significant variation in the alkaloid content of lotus leaves with different genotypes, suggesting that genetic background is an important factor that affects the temporal and spatial distributions of alkaloids in sacred lotus leaves. The dynamic contents of alkaloids during the growth and development of lotus leaves provide insight into basic biological differences when sampling.

Untargeted Plasma Metabolomics Unravels a Metabolic Signature for Tissue Sensitivity to Glucocorticoids in Healthy Subjects: Its Implications in Dietary Planning for a Healthy Lifestyle

In clinical practice, differences in glucocorticoid sensitivity among healthy subjects may influence the outcome and any adverse effects of glucocorticoid therapy. Thus, a fast and accurate methodology that could enable the classification of individuals based on their tissue glucocorticoid sensitivity would be of value. We investigated the usefulness of untargeted plasma metabolomics in identifying a panel of metabolites to distinguish glucocorticoid-resistant from glucocorticoid-sensitive healthy subjects who do not carry mutations in the human glucocorticoid receptor (NR3C1) gene. Applying a published methodology designed for the study of glucocorticoid sensitivity in healthy adults, 101 healthy subjects were ranked according to their tissue glucocorticoid sensitivity based on 8:00 a.m. serum cortisol concentrations following a very low-dose dexamethasone suppression test. Ten percent of the cohort, i.e., 11 participants, on each side of the ranking, with no NR3C1 mutations or polymorphisms, were selected, respectively, as the most glucocorticoid-sensitive and most glucocorticoid-resistant of the cohort to be analyzed and compared with untargeted blood plasma metabolomics using gas chromatography–mass spectrometry (GC–MS). The acquired metabolic profiles were evaluated using multivariate statistical analysis methods. Nineteen metabolites were identified with significantly lower abundance in the most sensitive compared to the most resistant group of the cohort, including fatty acids, sugar alcohols, and serine/threonine metabolism intermediates. These results, combined with a higher glucose, sorbitol, and lactate abundance, suggest a higher Cori cycle, polyol pathway, and inter-tissue one-carbon metabolism rate and a lower fat mobilization rate at the fasting state in the most sensitive compared to the most resistant group. In fact, this was the first study correlating tissue glucocorticoid sensitivity with serine/threonine metabolism. Overall, the observed metabolic signature in this cohort implies a worse cardiometabolic profile in the most glucocorticoid-sensitive compared to the most glucocorticoid-resistant healthy subjects. These findings offer a metabolic signature that distinguishes most glucocorticoid-sensitive from most glucocorticoid-resistant healthy subjects to be further validated in larger cohorts. Moreover, they support the correlation of tissue glucocorticoid sensitivity with insulin resistance and metabolic syndrome-associated pathways, further emphasizing the need for nutritionists and doctors to consider the tissue glucocorticoid sensitivity in dietary and exercise planning, particularly when these subjects are to be treated with glucocorticoids.

Need for speed: evaluation of dilute and shoot-mass spectrometry for accelerated metabolic phenotyping in bioprocess development

With the utilization of small-scale and highly parallelized cultivation platforms embedded in laboratory robotics, microbial phenotyping and bioprocess development have been substantially accelerated, thus generating a bottleneck in bioanalytical bioprocess sample analytics. While microscale cultivation platforms allow the monitoring of typical process parameters, only limited information about product and by-product formation is provided without comprehensive analytics. The use of liquid chromatography mass spectrometry can provide such a comprehensive and quantitative insight, but is often limited by analysis runtime and throughput. In this study, we developed and evaluated six methods for amino acid quantification based on two strong cation exchanger columns and a dilute and shoot approach in hyphenation with either a triple-quadrupole or a quadrupole time-of-flight mass spectrometer. Isotope dilution mass spectrometry with 13C15N labeled amino acids was used to correct for matrix effects. The versatility of the methods for metabolite profiling studies of microbial cultivation supernatants is confirmed by a detailed method validation study. The methods using chromatography columns showed a linear range of approx. 4 orders of magnitude, sufficient response factors, and low quantification limits (7-443 nM) for single analytes. Overall, relative standard deviation was comparable for all analytes, with < 8% and < 11% for unbuffered and buffered media, respectively. The dilute and shoot methods with an analysis time of 1 min provided similar performance but showed a factor of up to 35 times higher throughput. The performance and applicability of the dilute and shoot method are demonstrated using a library of Corynebacterium glutamicum strains producing L-histidine, obtained from random mutagenesis, which were cultivated in a microscale cultivation platform.

Generic multicriteria approach to determine the best precipitation agent for removal of biomacromolecules prior to non-targeted metabolic analysis

The removal of biomacromolecules from biofluids decreases the sample complexity and lower electrospray suppression effects. Furthermore, it can increase the analysis sensitivity, precision, and selectivity. Often removal approaches evaluate the model based on a single criterion, like protein removed or response of one of few specific metabolites. In this study, we used a multicriteria approach to test the effect of using the solvents methanol and acetonitrile (organic solvent precipitation), trichloroacetic acid (acidic precipitation) and ammonium sulphate (salting out) to remove biomacromolecules from a downstream recovery process from a bacillus fermentation. The downstream recovery process intermediates were analysed using reversed-phase ultra-high-pressure liquid chromatography with electrospray ionisation and high-resolution time-of-flight mass spectrometry detection. To evaluate the pre-treatment agents the following multicriteria was applied i) practical considerations, ii) total amino acid in the precipitated pellet, iii) putative identification of the molecules removed or created by the different treatments, iv) coherence between high quality extracted ion chromatograms (repeatability of DW-CODA) and v) replicate consistency from principal component analysis score values obtained by using the CHEMometric analysis of sections of Selected Ion Chromatograms (CHEMSIC) method. This study presents a generic workflow to find the best pre-treatment for removing bio-macromolecules from biofluids with a multicriteria approach. In our case, the best protein removal strategy for downstream recovery intermediates was acetonitrile precipitation. This method showed high precision, created few artefact peaks compared to simple sample dilution, and mainly removed small peptides.

Pinpointing secondary metabolites that shape the composition and function of the plant microbiome

One of the major questions in contemporary plant science involves determining the functional mechanisms that plants use to shape their microbiome. Plants produce a plethora of chemically diverse secondary metabolites, many of which exert bioactive effects on microorganisms. Several recent publications have unequivocally shown that plant secondary metabolites affect microbiome composition and function. These studies have pinpointed that the microbiome can be influenced by a diverse set of molecules, including: coumarins, glucosinolates, benzoxazinoids, camalexin, and triterpenes. In this review, we summarize the role of secondary metabolites in shaping the plant microbiome, highlighting recent literature. A body of knowledge is now emerging that links specific plant metabolites with distinct microbial responses, mediated via defined biochemical mechanisms. There is significant potential to boost agricultural sustainability via the targeted enhancement of beneficial microbial traits, and here we argue that the newly discovered links between root chemistry and microbiome composition could provide a new set of tools for rationally manipulating the plant microbiome.

Streamlining the Analysis of Dynamic 13C-Labeling Patterns for the Metabolic Engineering of Corynebacterium glutamicum as l-Histidine Production Host

Today’s possibilities of genome editing easily create plentitudes of strain mutants that need to be experimentally qualified for configuring the next steps of strain engineering. The application of design-build-test-learn cycles requires the identification of distinct metabolic engineering targets as design inputs for subsequent optimization rounds. Here, we present the pool influx kinetics (PIK) approach that identifies promising metabolic engineering targets by pairwise comparison of up- and downstream ¹³C labeling dynamics with respect to a metabolite of interest. Showcasing the complex l-histidine production with engineered Corynebacterium glutamicuml-histidine-on-glucose yields could be improved to 8.6 ± 0.1 mol% by PIK analysis, starting from a base strain. Amplification of purA, purB, purH, and formyl recycling was identified as key targets only analyzing the signal transduction kinetics mirrored in the PIK values.

An In Vitro Model to Investigate the Role of Helicobacter pylori in Type 2 Diabetes, Obesity, Alzheimer's Disease and Cardiometabolic Disease

Helicobacter pylori (Hp) is a Gram-negative bacterium colonizing the human stomach. Nuclear Magnetic Resonance (NMR) analysis of intracellular human gastric carcinoma cells (MKN-28) incubated with the Hp cell filtrate (Hpcf) displays high levels of amino acids, including the branched chain amino acids (BCAA) isoleucine, leucine, and valine. Polymerase chain reaction (PCR) Array Technology shows upregulation of mammalian Target Of Rapamycin Complex 1 (mTORC1), inflammation, and mitochondrial dysfunction. The review of literature indicates that these traits are common to type 2 diabetes, obesity, Alzheimer’s diseases, and cardiometabolic disease. Here, we demonstrate how Hp may modulate these traits. Hp induces high levels of amino acids, which, in turn, activate mTORC1, which is the complex regulating the metabolism of the host. A high level of BCAA and upregulation of mTORC1 are, thus, directly regulated by Hp. Furthermore, Hp modulates inflammation, which is functional to the persistence of chronic infection and the asymptomatic state of the host. Finally, in order to induce autophagy and sustain bacterial colonization of gastric mucosa, the Hp toxin VacA localizes within mitochondria, causing fragmentation of these organelles, depletion of ATP, and oxidative stress. In conclusion, our in vitro disease model replicates the main traits common to the above four diseases and shows how Hp may potentially manipulate them.

An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines

INTRODUCTION

It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites.

OBJECTIVES

Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines.

METHODS

We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the 'exometabolome' or metabolic footprint).

RESULTS

Our method measures some 4000-5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other.

CONCLUSION

Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.

Efficient ammonia production from food by-products by engineered Escherichia coli

Ammonia is used as a fertilizer for agriculture, chemical raw material, and carrier for transporting hydrogen, and with economic development, the demand for ammonia has increased. The Haber-Bosch process, which is the main method for producing ammonia, can produce ammonia with high efficiency. However, since it consumes a large amount of fossil energy, it is necessary to develop an alternative method for producing ammonia with less environmental impact. Ammonia production from food by-products is an appealing production process owing to unused resource usage, including waste, and mild reaction conditions. However, when food by-products and biomass are used as feedstocks, impurities often reduce productivity. Using metabolic profiling, glucose was identified as a potential inhibitor of ammonia production from impure food by-products. We constructed the recombinant Escherichia coli, in which glucose uptake was reduced by ptsG gene disruption and amino acid catabolism was promoted by glnA gene disruption. Ammonia production efficiency from okara, a food by-product, was improved in this strain; 35.4 mM ammonia was produced (47% yield). This study might provide a strategy for efficient ammonia production from food by-products.

Amino acids levels in early pregnancy predict subsequent gestational diabetes

BACKGROUND

We aimed to estimate the performance of amino acids levels in predicting the risk of subsequent gestational diabetes mellitus (GDM).

METHODS

A total of 431 women at 12 to 16 weeks of gestation in the Department of Obstetrics and Gynecology of the Second Affiliated Hospital of Soochow University were recruited. High-performance liquid chromatography electrospray tandem mass spectrometry was used to measure amino acids levels in maternal blood at 12 to 16 weeks of gestation. At 24 to 28 weeks of gestation, all participants were administered 75-g oral glucose tolerance tests for the diagnosis of GDM.

RESULTS

Alanine, isoleucine, and tyrosine levels in early pregnancy were significantly different between women who developed GDM and those who remained normal glucose tolerant. Logistic regressions showed that after adjustments for age, parity, body mass index, family history of diabetes, γ-glutamyltranspeptidase, triglycerides, fasting glucose and fasting insulin levels, alanine (odds ratio [OR], 1.46; 95% CI, 1.05-2.04; P = .027), isoleucine (OR, 1.48; 95% CI, 1.12-1.96; P = .0062), and tyrosine (OR, 1.46; 95% CI, 1.07-2.03; P = .020) levels in early pregnancy were independently associated with subsequent GDM. The addition of isoleucine and tyrosine into the conventional model improved the area under curve from 0.692 to 0.737 (P = .036) and significantly increased the net reclassification improvement (+13.7%, P = .0025).

CONCLUSIONS

The present study suggests that elevated isoleucine, tyrosine, and alanine levels are independently and significantly associated with subsequent incidence of GDM. New models including conventional risk factors, isoleucine and tyrosine levels in early pregnancy might help physicians identify high-risk population of GDM.

Metabolic Profiling in Blastocoel Fluid and Blood Plasma of Diabetic Rabbits

Metabolic disorders of the mother adversely affect early embryo development, causing changes in maternal metabolism and consequent alterations in the embryo environment in the uterus. The goal of this study was to analyse the biochemical profiles of embryonic fluids and blood plasma of rabbits with and without insulin-dependent diabetes mellitus (DT1), to identify metabolic changes associated with maternal diabetes mellitus in early pregnancy. Insulin-dependent diabetes was induced by alloxan treatment in female rabbits 10 days before mating. On day 6 post-coitum, plasma and blastocoel fluid (BF) were analysed by ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) (Metabolon Inc. Durham, NC, USA). Metabolic datasets comprised a total of 284 and 597 compounds of known identity in BF and plasma, respectively. Diabetes mellitus had profound effects on maternal and embryonic metabolic profiles, with almost half of the metabolites changed. As predicted, we observed an increase in glucose and a decrease in 1,5-anhydroglucitol in diabetic plasma samples. In plasma, fructose, mannose, and sorbitol were elevated in the diabetic group, which may be a way of dealing with excess glucose. In BF, metabolites of the pentose metabolism were especially increased, indicating the need for ribose-based compounds relevant to DNA and RNA metabolism at this very early stage of embryo development. Other changes were more consistent between BF and plasma. Both displayed elevated acylcarnitines, body3-hydroxybutyrate, and multiple compounds within the branched chain amino acid metabolism pathway, suggesting that lipid beta-oxidation is occurring at elevated levels in the diabetic group. This study demonstrates that maternal and embryonic metabolism are closely related. Maternal diabetes mellitus profoundly alters the metabolic profile of the preimplantation embryo with changes in all subclasses of metabolites.

The Association Between Leucine and Diabetic Nephropathy in Different Gender: A Cross-Sectional Study in Chinese Patients With Type 2 Diabetes

OBJECTIVE

This study aimed to evaluate how leucine are associated with diabetic nephropathy (DN) in type 2 diabetes (T2D) patients and the gender difference of this association.

METHODS

We retrieved 1,031 consecutive patients with T2D who meet the inclusion and exclusion criteria from the same tertiary care center and extracted clinical information from electronic medical record. Plasma leucine was measured by liquid chromatography-mass spectrometer. Restricted cubic spline (RCS) was conducted to examine potential non-linear relationship between leucine and the risk of DN. Logistic regression was used to obtain odds ratio (OR) and confidence interval (CI). Additive interaction was used to estimate the interaction effect between leucine and gender for DN.

RESULTS

We found there was a negative correlation between leucine and the risk of DN. After stratifying all patients by gender, this relationship only remained significant in women (OR:0.57, CI:0.41-0.79).

CONCLUSIONS

In conclusion, T2D patients with high levels of leucine have a lower risk of developing DN in female.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

Marine Metabolomics: a Method for Nontargeted Measurement of Metabolites in Seawater by Gas Chromatography-Mass Spectrometry

Nontargeted approaches using metabolomics to analyze metabolites that occur in the oceans is less developed than those for terrestrial and limnic ecosystems. One of the challenges in marine metabolomics is that salt limits metabolite analysis in seawater to methods requiring salt removal. Building on previous sample preparation methods for metabolomics, we developed SeaMet, which overcomes the limitations of salt on metabolite detection. Considering that the oceans contain the largest dissolved organic matter pool on Earth, describing the marine metabolome using nontargeted approaches is critical for understanding the drivers behind element cycles, biotic interactions, ecosystem function, and atmospheric CO₂ storage. Our method complements both targeted marine metabolomic investigations as well as other “omics” (e.g., genomics, transcriptomics, and proteomics) approaches by providing an avenue for studying the chemical interaction between marine microbes and their habitats.

Microbial communities exchange molecules with their environment, which plays a major role in regulating global biogeochemical cycles and climate. While extracellular metabolites are commonly measured in terrestrial and limnic ecosystems, the presence of salt in marine habitats limits the nontargeted analyses of the ocean exometabolome using mass spectrometry (MS). Current methods require salt removal prior to sample measurements, which can alter the molecular composition of the metabolome and limit the types of compounds detected by MS. To overcome these limitations, we developed a gas chromatography MS (GC-MS) method that avoids sample altering during salt removal and that detects metabolites down to nanomolar concentrations from less than 1 ml of seawater. We applied our method (SeaMet) to explore marine metabolomes in vitro and in vivo. First, we measured the production and consumption of metabolites during the culture of a heterotrophic bacterium, Marinobacter adhaerens. Our approach revealed successional uptake of amino acids, while sugars were not consumed. These results show that exocellular metabolomics provides insights into nutrient uptake and energy conservation in marine microorganisms. We also applied SeaMet to explore the in situ metabolome of coral reef and mangrove sediment porewaters. Despite the fact that these ecosystems occur in nutrient-poor waters, we uncovered high concentrations of sugars and fatty acids, compounds predicted to play a key role for the abundant and diverse microbial communities in coral reef and mangrove sediments. Our data demonstrate that SeaMet advances marine metabolomics by enabling a nontargeted and quantitative analysis of marine metabolites, thus providing new insights into nutrient cycles in the oceans.

IMPORTANCE Nontargeted approaches using metabolomics to analyze metabolites that occur in the oceans is less developed than those for terrestrial and limnic ecosystems. One of the challenges in marine metabolomics is that salt limits metabolite analysis in seawater to methods requiring salt removal. Building on previous sample preparation methods for metabolomics, we developed SeaMet, which overcomes the limitations of salt on metabolite detection. Considering that the oceans contain the largest dissolved organic matter pool on Earth, describing the marine metabolome using nontargeted approaches is critical for understanding the drivers behind element cycles, biotic interactions, ecosystem function, and atmospheric CO₂ storage. Our method complements both targeted marine metabolomic investigations as well as other “omics” (e.g., genomics, transcriptomics, and proteomics) approaches by providing an avenue for studying the chemical interaction between marine microbes and their habitats.

Stable Redox-Cycling Nitroxide Tempol Has Antifungal and Immune-Modulatory Properties

Invasive mycoses remain underdiagnosed and difficult to treat. Hospitalized individuals with compromised immunity increase in number and constitute the main risk group for severe fungal infections. Current antifungal therapy is hampered by slow and insensitive diagnostics and frequent toxic side effects of standard antifungal drugs. Identification of new antifungal compounds with high efficacy and low toxicity is therefore urgently required. We investigated the antifungal activity of tempol, a cell-permeable nitroxide. To narrow down possible mode of action we used RNA-seq technology and metabolomics to probe for pathways specifically disrupted in the human fungal pathogen Candida albicans due to tempol administration. We found genes upregulated which are involved in iron homeostasis, mitochondrial stress, steroid synthesis, and amino acid metabolism. In an ex vivo whole blood infection, tempol treatment reduced C. albicans colony forming units and at the same time increased the release of pro-inflammatory cytokines, such as interleukin 8 (IL-8, monocyte chemoattractant protein-1, and macrophage migration inhibitory factor). In a systemic mouse model, tempol was partially protective with a significant reduction of fungal burden in the kidneys of infected animals during infection onset. The results obtained propose tempol as a promising new antifungal compound and open new opportunities for the future development of novel therapies.

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.

Does the Chemodiversity of Bacterial Exometabolomes Sustain the Chemodiversity of Marine Dissolved Organic Matter?

Marine dissolved organic matter (DOM) is a complex mixture of chemical compounds. At 750 Pg C, it is one of the biggest pools of reduced carbon on Earth. It has been proposed that the diversity of DOM is responsible for its recalcitrance. We hypothesize that the chemodiversity of marine DOM is a reflection of the chemodiversity of bacterial exometabolomes. To test this, we incubated two model strains of the Roseobacter group; Phaeobacter inhibens and Dinoroseobacter shibae in pure culture using three different simple organic compounds as sole carbon sources (glutamate, glucose, and acetate and succinate for P. inhibens and D. shibae, respectively). The exometabolome of the model organisms was characterized using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS) and ecological diversity measures. We detected thousands of molecular masses in the exometabolomes of P. inhibens and D. shibae (21,105 and 9,386, respectively), reflecting the capability of single bacterial strains to diversify simple organic compounds. The chemical composition of the exometabolomes changed with growth phase and also differed according to the strain incubated and the utilized substrate. We mimicked a higher diversity of substrates, bacterial species and heterogeneous growth (different growth phases) to approach the complexity of natural environments, by computationally creating combinations of detected exometabolomes. We compared the chemodiversity of these combinations, indicative for chemodiversity of freshly produced microbial DOM to that of refractory DOM from one of the oldest oceanic water masses (North Equatorial Pacific Intermediate Water). Some combinations of exometabolomes showed higher richness than the deep ocean refractory DOM, and all the combinations showed higher functional diversity. About 15% of the 13,509 molecular formulae detected in exometabolomes and refractory oceanic DOM were shared, i.e., occurred in Roseobacter exometabolomes and in deep water samples. This overlap provides further support for our hypothesis that marine bacteria from the Roseobacter group contribute to the sustainability of marine DOM chemodiversity and stability.

Single embryo transfer: Why and how to identify the embryo with the best developmental potential

Multiple pregnancies with higher risk of preterm birth and the associated higher morbidity have been a major obstacle from the early days of in vitro fertilization. A good strategy to avoid multiple pregnancies is elective single embryo transfer and cryopreservation of spare embryos. Important factors in adopting this strategy are good counselling of the patients and the selection of embryos with high implantation potential. Technical advances in embryo selection have been described during recent years, time lapse monitoring and genetic assessment of the embryos being the most important achievements. With these studies we have gained new information on early embryos. However, at present, there is insufficient evidence to recommend the routine use of these new techniques. The ultimate goal of infertility treatment is a healthy baby.

Raman profiling of embryo culture medium to identify aneuploid and euploid embryos

OBJECTIVE

To develop and validate Raman metabolic footprint analysis to determine chromosome euploidy and aneuploidy in embryos fertilized in vitro.

DESIGN

Retrospective study.

SETTING

Academic hospital.

PATIENT(S)

Unselected assisted reproductive technology population.

INTERVENTION(S)

To establish the analysis protocol, spent embryo culture medium samples with known genetic outcomes from 87 human embryos were collected and measured with the use of Raman spectroscopy. Individual Raman spectra were analyzed to find biologic components contributing to either euploidy or aneuploidy. To validate the protocol via machine-learning algorithms, additional 1,107 Raman spectra from 123 embryo culture media (61 euploidy and 62 aneuploidy) were analyzed.

MAIN OUTCOME MEASURE(S)

Raman-based footprint profiling of spent culture media and preimplantation genetic testing for aneuploidy (PGT-A).

RESULT(S)

Mean-centered Raman spectra and principal component analysis showed differences in the footprints of euploid and aneuploid embryos growing in culture medium. Significant differences in Raman bands associated with small RNAs and lipids were also observed. Stacking classification based on k-nearest-neighbor, random forests, and extreme-gradient-boosting algorithms achieved an overall accuracy of 95.9% in correctly assigning either euploidy or aneuploidy based on Raman spectra, which was validated by PGT-A sequencing results.

CONCLUSION(S)

This study suggests that chromosomal abnormalities in embryos should lead to changes of metabolic footprints in embryo growth medium that can be detected by Raman spectroscopy. The ploidy status of embryos was analyzed by means of Raman-based footprint profiling of spent culture media and was consistent with PGT-A testing performed by next-generation sequencing.

Yeast Systems Biology: The Continuing Challenge of Eukaryotic Complexity

Research on yeast has produced a plethora of tools and resources that have been central to the progress of systems biology. This chapter reviews these resources, explains the innovations that have been made since the first edition of this book, and introduces the constituent chapters of the current edition. The value of these resources not only in building and testing models of the functional networks of the yeast cell, but also in providing a foundation for network studies on the molecular basis of complex human diseases is considered. The gaps in this vast compendium of data, including enzyme kinetic characteristics, biomass composition, transport processes, and cell-cell interactions are discussed, as are the interactions between yeast cells and those of other species. The relevance of these studies to both traditional and advanced biotechnologies and to human medicine is considered, and the opportunities and challenges in using unicellular yeasts to model the systems of multicellular organisms are presented.

Profiling the Metabolism of Human Cells by Deep 13C Labeling

Studying metabolic activities in living cells is crucial for understanding human metabolism, but facile methods for profiling metabolic activities in an unbiased, hypothesis-free manner are still lacking. To address this need, we here introduce the deep-labeling method, which combines a custom ¹³C medium with high-resolution mass spectrometry. A proof-of-principle study on human cancer cells demonstrates that deep labeling can identify hundreds of endogenous metabolites as well as active and inactive pathways. For example, protein and nucleic acids were almost exclusively de novo synthesized, while lipids were partly derived from serum; synthesis of cysteine, carnitine, and creatine was absent, suggesting metabolic dependencies; and branched-chain keto acids (BCKAs) were formed and metabolized to short-chain acylcarnitines, but did not enter the tricarboxylic acid cycle. Remarkably, BCKAs could substitute for essential amino acids to support growth. The deep-labeling method may prove useful to map metabolic phenotypes across a range of cell types and conditions.