Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

Introduction The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. Objectives Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient’s infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). Methods High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. Results The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74–0.91) and 0.76 (CI 0.67–0.86). Conclusion Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Evolution of Natural Lifespan Variation and Molecular Strategies of Extended Lifespan

To understand the genetic basis and the selective forces acting on longevity, it is useful to employ ecologically diverse individuals of the same species, widely different in lifespan. This way, we may capture the experiment of Nature that modifies the genotype arriving at different lifespans. Here, we analyzed 76 ecologically diverse wild yeast isolates and discovered wide diversity of lifespan. We sequenced the genomes of these organisms and analyzed how their replicative lifespan is shaped by nutrients and transcriptional and metabolite patterns. By identifying genes, proteins and metabolites that correlate with longevity across these isolates, we found that long-lived strains elevate intermediary metabolites, differentially regulate genes involved in NAD metabolism and adjust control of epigenetic landscape through conserved, rare histone modifier. Our data further offer insights into the evolution and mechanisms by which caloric restriction regulates lifespan by modulating the availability of nutrients without decreasing fitness.

The Pentose Phosphate Pathway in Cancer: Regulation and Therapeutic Opportunities

<b><i>Background:</i></b> Tumorigenesis is associated with deregulation of nutritional requirements, intermediary metabolites production, and microenvironment interactions. Unlike their normal cell counterparts, tumor cells rely on aerobic glycolysis, through the Warburg effect. <b><i>Summary:</i></b> The pentose phosphate pathway (PPP) is a major glucose metabolic shunt that is upregulated in cancer cells. The PPP comprises an oxidative and a nonoxidative phase and is essential for nucleotide synthesis of rapidly dividing cells. The PPP also generates nicotinamide adenine dinucleotide phosphate, which is required for reductive metabolism and to counteract oxidative stress in tumor cells. This article reviews the regulation of the PPP and discusses inhibitors that target its main pathways. <b><i>Key Message:</i></b> Exploiting the metabolic vulnerability of the PPP offers potential novel therapeutic opportunities and improves patients’ response to cancer therapy.

Purification and Biochemical Characterization of Taxadiene Synthase from Bacillus koreensis and Stenotrophomonas maltophilia

Taxadiene synthase (TDS) is the rate-limiting enzyme of Taxol biosynthesis that cyclizes the geranylgeranyl pyrophosphate into taxadiene. Attenuating Taxol productivity by fungi is the main challenge impeding its industrial application; it is possible that silencing the expression of TDS is the most noticeable genomic feature associated with Taxol-biosynthetic abolishing in fungi. As such, the characterization of TDS with unique biochemical properties and autonomous expression that is independent of transcriptional factors from the host is the main challenge. Thus, the objective of this study was to kinetically characterize TDS from endophytic bacteria isolated from different plants harboring Taxol-producing endophytic fungi. Among the recovered 23 isolates, Bacillus koreensis and Stenotrophomonas maltophilia achieved the highest TDS activity. Upon using the Plackett–Burman design, the TDS productivity achieved by B. koreensis (18.1 µmol/mg/min) and S. maltophilia (14.6 µmol/mg/min) increased by ~2.2-fold over the control. The enzyme was purified by gel-filtration and ion-exchange chromatography with ~15 overall folds and with molecular subunit structure 65 and 80 kDa from B. koreensis and S. maltophilia, respectively. The chemical identity of taxadiene was authenticated from the GC-MS analyses, which provided the same mass fragmentation pattern of authentic taxadiene. The tds gene was screened by PCR with nested primers of the conservative active site domains, and the amplicons were sequenced, displaying a higher similarity with tds from T. baccata and T. brevifolia. The highest TDS activity by both bacterial isolates was recorded at 37–40 °C. The Apo-TDSs retained ~50% of its initial holoenzyme activities, ensuring their metalloproteinic identity. The activity of purified TDS was completely restored upon the addition of Mg2+, confirming the identity of Mg2+ as a cofactor. The TDS activity was dramatically reduced upon the addition of DTNB and MBTH, ensuring the implementation of cysteine-reactive thiols and ammonia groups on their active site domains. This is the first report exploring the autonomous robust expression TDS from B. koreensis and S. maltophilia with a higher affinity to cyclize GGPP into taxadiene, which could be a novel platform for taxadiene production as intermediary metabolites of Taxol biosynthesis.

Metabolic and Proteomic Defects in Human Hypertrophic Cardiomyopathy

Rationale: Impaired cardiac energetics in hypertrophic cardiomyopathy (HCM) is thought to result from increased ATP utilization at the sarcomere and is believed to be central to pathophysiology. However, the precise defects in cardiac metabolism and substrate availability in human HCM have not been defined. Objective: The purpose of this study is to define major disease pathways and determine the pool sizes of intermediary metabolites in human HCM. Methods and Results: We conducted paired proteomic and metabolomic analyses of septal myectomy samples from patients with HCM and compared results to non-failing control human hearts. Increased abundance of extracellular matrix and intermediate filament / Z-disc proteins, and decreased abundance of proteins involved in fatty acid oxidation and cardiac energetics was evident in HCM compared to controls. Acyl carnitines, byproducts of fatty acid oxidation, were markedly depleted in HCM samples. Conversely, the ketone body 3-hydroxybutyrate, lactate, and the 3 branched chain amino acids, were all significantly increased in HCM hearts, suggesting that they may serve as alternate fuel sources for the production of ATP. ATP, nicotinamide adenine dinucleotide (NADH), NADP and NADPH, and acetyl CoA were also severely depleted in HCM hearts. Based on measurements from human skinned muscle fibers, the magnitude of observed reduction in ATP content in the HCM hearts would be expected to decrease the rate of cross-bridge detachment, implying a direct effect of energy depletion on myofilament function that could contribute to diastolic dysfunction. Conclusions: HCM hearts display profound deficits in cardiac energetics, marked by depletion of fatty acid derivatives and compensatory increases in other metabolites that could serve as alternate fuel sources. These results lend support to the paradigm that energy depletion contributes to the pathophysiology of HCM and also have important therapeutic implications for the future design of metabolic modulators to treat HCM.

Protein Acetylation at the Interface of Genetics, Epigenetics and Environment in Cancer

Metabolic reprogramming is an emerging hallmark of cancer and is driven by abnormalities of oncogenes and tumor suppressors. Accelerated metabolism causes cancer cell aggression through the dysregulation of rate-limiting metabolic enzymes as well as by facilitating the production of intermediary metabolites. However, the mechanisms by which a shift in the metabolic landscape reshapes the intracellular signaling to promote the survival of cancer cells remain to be clarified. Recent high-resolution mass spectrometry-based proteomic analyses have spotlighted that, unexpectedly, lysine residues of numerous cytosolic as well as nuclear proteins are acetylated and that this modification modulates protein activity, sublocalization and stability, with profound impact on cellular function. More importantly, cancer cells exploit acetylation as a post-translational protein for microenvironmental adaptation, nominating it as a means for dynamic modulation of the phenotypes of cancer cells at the interface between genetics and environments. The objectives of this review were to describe the functional implications of protein lysine acetylation in cancer biology by examining recent evidence that implicates oncogenic signaling as a strong driver of protein acetylation, which might be exploitable for novel therapeutic strategies against cancer.

2.2-Diphenic Acid: A Reliable Biomarker Of Phenanthrene Biodegradation

Phenanthrene is among the 16 priority pollutant and its mitigation in the environment has been a global concern. It serves as a model compound when it comes to biodgradation study of polyaromatic hydrocarbons (PAHs) because it has both the Bay- and K-region found in most PAH pollutants. Like other PAH pollutants, different means are available for its remediation in the environment, including microbial biodegradation. Diverse species of bacteria and fungi metabolize phenanthrenes as their sole source of carbon and energy. However, bacteria are more diverse in comparison to fungi. This has been shown in published pathways of phenanthrene biodegradation implicating various intermediary metabolites, including 2,2-diphenic acid, which is a downline metabolite of 9,10-dihydroxyphenanthrene. Though the 2,2-diphenic acid has been widely demonstrated to produce carbon (iv) oxide and linked to phthalate, only few has traced salicylic acid as its downstream molecule. 2,2-diphenic acid mounts equivalent position to 1-hydroxy-2-naphthoic acid, metabolite that ends the phenanthrene metabolic pathway. This is because they both produce phthalic acid and salicylic acid. As a product of bacteria and fungi during phenanthrene degradation, 2,2-diphenic acid can serve as a dependable biomarker of phenanthrene metabolism in a polluted habitat, where microbial community exist freely.

Multiomics-Identified Intervention to Restore Ethanol-Induced Dysregulated Proteostasis and Secondary Sarcopenia in Alcoholic Liver Disease

BACKGROUND/AIMS: Signaling and metabolic perturbations contribute to dysregulated skeletal muscle protein homeostasis and secondary sarcopenia in response to a number of cellular stressors including ethanol exposure. Using an innovative multiomics-based curating of unbiased data, we identified molecular and metabolic therapeutic targets and experimentally validated restoration of protein homeostasis in an ethanol-fed mouse model of liver disease. METHODS: Studies were performed in ethanol-treated differentiated C2C12 myotubes and physiological relevance established in an ethanol-fed mouse model of alcohol-related liver disease (mALD) or pair-fed control C57BL/6 mice. Transcriptome and proteome from ethanol treated-myotubes and gastrocnemius muscle from mALD and pair-fed mice were analyzed to identify target pathways and molecules. Readouts including signaling responses and autophagy markers by immunoblots, mitochondrial oxidative function and free radical generation, and metabolic studies by gas chromatography-mass spectrometry and sarcopenic phenotype by imaging. RESULTS: Multiomics analyses showed that ethanol impaired skeletal muscle mTORC1 signaling, mitochondrial oxidative pathways, including intermediary metabolite regulatory genes, interleukin-6, and amino acid degradation pathways are β-hydroxymethyl-butyrate targets. Ethanol decreased mTORC1 signaling, increased autophagy flux, impaired mitochondrial oxidative function with decreased tricarboxylic acid cycle intermediary metabolites, ATP synthesis, protein synthesis and myotube diameter that were reversed by HMB. Consistently, skeletal muscle from mALD had decreased mTORC1 signaling, reduced fractional and total muscle protein synthesis rates, increased autophagy markers, lower intermediary metabolite concentrations, and lower muscle mass and fiber diameter that were reversed by β-hydroxymethyl-butyrate treatment. CONCLUSION: An innovative multiomics approach followed by experimental validation showed that β-hydroxymethyl-butyrate restores muscle protein homeostasis in liver disease.

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, existing metabolomics studies are either underpowered, measure only a restricted subset of metabolites (‘targeted metabolomics’), compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.We here provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict patient’s infection severity (i.e. mild or severe) and potential outcome (i.e. discharged or deceased).High resolution untargeted LC-MS/MS analysis was performed on patient serum using both positive and negative ionization. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model. The predictors were selected for their relevant biological function and include cytosine (reflecting viral load), kynurenine (reflecting host inflammatory response), nicotinuric acid, and multiple short chain acylcarnitines (energy metabolism) among others.Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.