Small molecule metabolites: discovery of biomarkers and therapeutic targets.

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Emerging Roles of SIRT5 in Metabolism, Cancer, and SARS-CoV-2 Infection.

Sirtuin 5 (SIRT5) is a predominantly mitochondrial enzyme catalyzing the removal of glutaryl, succinyl, malonyl, and acetyl groups from lysine residues through a NAD+-dependent deacylase mechanism. SIRT5 is an important regulator of cellular homeostasis and modulates the activity of proteins involved in different metabolic pathways such as glycolysis, tricarboxylic acid (TCA) cycle, fatty acid oxidation, electron transport chain, generation of ketone bodies, nitrogenous waste management, and reactive oxygen species (ROS) detoxification. SIRT5 controls a wide range of aspects of myocardial energy metabolism and plays critical roles in heart physiology and stress responses. Moreover, SIRT5 has a protective function in the context of neurodegenerative diseases, while it acts as a context-dependent tumor promoter or suppressor. In addition, current research has demonstrated that SIRT5 is implicated in the SARS-CoV-2 infection, although opposing conclusions have been drawn in different studies. Here, we review the current knowledge on SIRT5 molecular actions under both healthy and diseased settings, as well as its functional effects on metabolic targets. Finally, we revise the potential of SIRT5 as a therapeutic target and provide an overview of the currently reported SIRT5 modulators, which include both activators and inhibitors.

Absolute oral bioavailability and possible metabolic pathway of panduratin A from Boesenbergia rotunda extract in beagle dogs.

CONTEXT: Attempts are ongoing to develop medications to fight against the COVID-19 pandemic. Our previous study revealed the in vitro anti-SARS-CoV-2 activity of fingerroot [Boesenbergia rotunda (L.) Mansf. (Zingiberaceae)] and its phytochemical, panduratin A. OBJECTIVE: To investigate the pharmacokinetic profiles of panduratin A as a pure compound and in a fingerroot extract formulation in beagle dogs. MATERIALS AND METHODS: A total of 12 healthy dogs were randomly divided into three groups, a single dose of 1 mg/kg panduratin A by intravenous and multiple doses of 5 and 10 mg/kg panduratin A fingerroot extract formulation by oral administration for seven consecutive days. The plasma concentration of panduratin A was determined by LCMS. RESULTS: The peak concentrations of a single dose of 5 and 10 mg/kg panduratin A fingerroot extract formulation were 12,416 ± 2,326 and 26,319 ± 8,221 µg/L, respectively. Increasing the oral dose of fingerroot extract formulation, equivalent to panduratin A 5-10 mg/kg, showed dose proportionality, with an approximately 2-fold increase in Cmax and AUC. The absolute oral bioavailability of panduratin A in the fingerroot extract formulation was approximately 7-9%. The majority of panduratin A was biotransformed into several products via oxidation and glucuronidation, and predominantly excreted via the faecal route. CONCLUSION: The oral formulation of fingerroot extract was safe in beagle dogs, and increasing dose showed dose proportionality in terms of the systemic exposure of panduratin A. This information will support the phytopharmaceutical product development of fingerroot extract against the COVID-19 pandemic.

A validated LC-MS/MS method for clinical pharmacokinetics and presumptive phase II metabolic pathways following oral administration of Andrographis paniculata extract.

Andrographis paniculata, a medicinal plant in Thailand national list of essential medicines, has been proposed for treatment of patients with mild to moderate coronavirus disease 2019. This study aims to develop a highly selective and sensitive liquid chromatography triple quadrupole tandem mass spectrometry method for quantitative determination of major diterpenoids in plasma and urine with application in pharmacokinetics. Chromatographic separation was performed on C18 column using a gradient mobile phase of water and acetonitrile. Mass spectrometry was analyzed using multiple reaction monitoring with negative ionization mode. This validated analytical method was very sensitive, less time consuming in analysis, and allowed the reliability and reproducibility on its application. The clinical pharmacokinetics was evaluated after single oral administration of A. paniculata extract (calculated as 60 mg of andrographolide). The disposition kinetics demonstrated that major diterpenoids could enter into systemic circulation, but they are mostly biotransformed (phase II) into conjugated glucuronide and sulfate metabolites. These metabolites are predominantly found in plasma and then extremely eliminated, in part through urinary excretion. The successful application of this analytical method supports its suitable uses in further clinical benefits after oral administration of A. paniculata.

Association of Midgut Bacteria and Their Metabolic Pathways with Zika Infection and Insecticide Resistance in Colombian Aedes aegypti Populations.

INTRODUCTION: Aedes aegypti is the vector of several arboviruses such as dengue, Zika, and chikungunya. In 2015-16, Zika virus (ZIKV) had an outbreak in South America associated with prenatal microcephaly and Guillain-Barré syndrome. This mosquito's viral transmission is influenced by microbiota abundance and diversity and its interactions with the vector. The conditions of cocirculation of these three arboviruses, failure in vector control due to insecticide resistance, limitations in dengue management during the COVID-19 pandemic, and lack of effective treatment or vaccines make it necessary to identify changes in mosquito midgut bacterial composition and predict its functions through the infection. Its study is fundamental because it generates knowledge for surveillance of transmission and the risk of outbreaks of these diseases at the local level. METHODS: Midgut bacterial compositions of females of Colombian Ae. aegypti populations were analyzed using DADA2 Pipeline, and their functions were predicted with PICRUSt2 analysis. These analyses were done under the condition of natural ZIKV infection and resistance to lambda-cyhalothrin, alone and in combination. One-step RT-PCR determined the percentage of ZIKV-infected females. We also measured the susceptibility to the pyrethroid lambda-cyhalothrin and evaluated the presence of the V1016I mutation in the sodium channel gene. RESULTS: We found high ZIKV infection rates in Ae. aegypti females from Colombian rural municipalities with deficient water supply, such as Honda with 63.6%. In the face of natural infection with an arbovirus such as Zika, the diversity between an infective and non-infective form was significantly different. Bacteria associated with a state of infection with ZIKV and lambda-cyhalothrin resistance were detected, such as the genus Bacteroides, which was related to functions of pathogenicity, antimicrobial resistance, and bioremediation of insecticides. We hypothesize that it is a vehicle for virus entry, as it is in human intestinal infections. On the other hand, Bello, the only mosquito population classified as susceptible to lambda-cyhalothrin, was associated with bacteria related to mucin degradation functions in the intestine, belonging to the Lachnospiraceae family, with the genus Dorea being increased in ZIKV-infected females. The Serratia genus presented significantly decreased functions related to phenazine production, potentially associated with infection control, and control mechanism functions for host defense and quorum sensing. Additionally, Pseudomonas was the genus principally associated with functions of the degradation of insecticides related to tryptophan metabolism, ABC transporters with a two-component system, efflux pumps, and alginate synthesis. CONCLUSIONS: Microbiota composition may be modulated by ZIKV infection and insecticide resistance in Ae. aegypti Colombian populations. The condition of resistance to lambda-cyhalothrin could be inducing a phenome of dysbiosis in field Ae. aegypti affecting the transmission of arboviruses.

Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity.

The clinical outcome and disease severity in coronavirus disease 2019 (COVID-19) are heterogeneous, and the progression or fatality of the disease cannot be explained by a single factor like age or comorbidities. In this study, we used system-wide network-based system biology analysis using whole blood RNA sequencing, immunophenotyping by flow cytometry, plasma metabolomics, and single-cell-type metabolomics of monocytes to identify the potential determinants of COVID-19 severity at personalized and group levels. Digital cell quantification and immunophenotyping of the mononuclear phagocytes indicated a substantial role in coordinating the immune cells that mediate COVID-19 severity. Stratum-specific and personalized genome-scale metabolic modeling indicated monocarboxylate transporter family genes (e.g., SLC16A6), nucleoside transporter genes (e.g., SLC29A1), and metabolites such as α-ketoglutarate, succinate, malate, and butyrate could play a crucial role in COVID-19 severity. Metabolic perturbations targeting the central metabolic pathway (TCA cycle) can be an alternate treatment strategy in severe COVID-19.

Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models.

COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs.

DeepRF: A deep learning method for predicting metabolic pathways in organisms based on annotated genomes.

The rapid increase of metabolomics has led to an increasing focus on metabolic pathway modeling and reconstruction. In particular, reconstructing an organism's metabolic network based on its genome sequence is a key challenge in systems biology. The method used to address this problem predicts the presence or absence of metabolic pathways from known pathways in a reference database. However, this method is based on manual metabolic pathway construction and cannot be used for large genome sequencing data. To address such problems, we apply a supervised machine learning approach consisting of deep neural networks to learn feature representations of metabolic pathways and feed these representations into random forests to predict metabolic pathways. The supervised learning model, DeepRF, predicts all known and unknown metabolic pathways in an organism. Evaluation of DeepRF on over 318,016 instances shows that the model can predict metabolic pathways with high-performance metrics accuracy (>97%), recall (>95%), and precision (>99%). Comparing DeepRF with other methods in the literature shows that DeepRF produces more reliable results than other methods.

Luteolin Potentially Treating Prostate Cancer and COVID-19 Analyzed by the Bioinformatics Approach: Clinical Findings and Drug Targets.

Coronavirus disease 2019 (COVID-19) is a serious epidemic, characterized by potential mutation and can bring about poor vaccine efficiency. It is evidenced that patients with malignancies, including prostate cancer (PC), may be highly vulnerable to the SARS-CoV-2 infection. Currently, there are no existing drugs that can cure PC and COVID-19. Luteolin can potentially be employed for COVID-19 treatment and serve as a potent anticancer agent. Our present study was conducted to discover the possible drug target and curative mechanism of luteolin to serve as treatment for PC and COVID-19. The differential gene expression of PC cases was determined via RNA sequencing. The application of network pharmacology and molecular docking aimed to exhibit the drug targets and pharmacological mechanisms of luteolin. In this study, we found the top 20 up- and downregulated gene expressions in PC patients. Enrichment data demonstrated anti-inflammatory effects, where improvement of metabolism and enhancement of immunity were the main functions and mechanism of luteolin in treating PC and COVID-19, characterized by associated signaling pathways. Additional core drug targets, including MPO and FOS genes, were computationally identified accordingly. In conclusion, luteolin may be a promising treatment for PC and COVID-19 based on bioinformatics findings, prior to future clinical validation and application.

The Situation of Small Molecules Targeting Key Proteins in combatting SARS-CoV-2: Synthesis, Metabolic Pathway, Mechanism of Action, and Potential Therapeutic Applications.

Due to the high mortality rate of the 2019 coronavirus disease (COVID-19) pandemic, there is an immediate need to discover drugs that can help before a vaccine becomes available. Given that the process of producing new drugs is so long, the strategy of repurposing existing drugs is one of the promising options for the urgent treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19 disease. Although FDA has approved Remdesivir for the use in hospitalized adults and pediatric patients suffering from COVID-19, no fully effective and reliable drug has been yet identified worldwide to treat COVID-19 specifically. Thus, scientists are still trying to find antivirals specific to COVID-19. This work reviews the chemical structure, metabolic pathway, and mechanism of action of the existing drugs with potential therapeutic applications for COVID-19. Furthermore, we summarized the molecular docking stimulation of the medications related to key protein targets. These already established drugs could be further developed, and after their testing through clinical trials, they could be used as suitable therapeutic options for patients suffering from COVID-19.

Integrated histopathological, lipidomic, and metabolomic profiles reveal mink is a useful animal model to mimic the pathogenicity of severe COVID-19 patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted on mink farms between minks and humans in many countries. However, the systemic pathological features of SARS-CoV-2-infected minks are mostly unknown. Here, we demonstrated that minks were largely permissive to SARS-CoV-2, characterized by severe and diffuse alveolar damage, and lasted at least 14 days post inoculation (dpi). We first reported that infected minks displayed multiple organ-system lesions accompanied by an increased inflammatory response and widespread viral distribution in the cardiovascular, hepatobiliary, urinary, endocrine, digestive, and immune systems. The viral protein partially co-localized with activated Mac-2+ macrophages throughout the body. Moreover, we first found that the alterations in lipids and metabolites were correlated with the histological lesions in infected minks, especially at 6 dpi, and were similar to that of patients with severe and fatal COVID-19. Particularly, altered metabolic pathways, abnormal digestion, and absorption of vitamins, lipids, cholesterol, steroids, amino acids, and proteins, consistent with hepatic dysfunction, highlight metabolic and immune dysregulation. Enriched kynurenine in infected minks contributed to significant activation of the kynurenine pathway and was related to macrophage activation. Melatonin, which has significant anti-inflammatory and immunomodulating effects, was significantly downregulated at 6 dpi and displayed potential as a targeted medicine. Our data first illustrate systematic analyses of infected minks to recapitulate those observations in severe and fetal COVID-19 patients, delineating a useful animal model to mimic SARS-CoV-2-induced systematic and severe pathophysiological features and provide a reliable tool for the development of effective and targeted treatment strategies, vaccine research, and potential biomarkers.

Decreased TMPRSS2 expression by SARS-CoV-2 predicts the poor prognosis of lung cancer patients through metabolic pathways and immune infiltration.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread around the world and became a global pandemic in 2020. One promising drug target for SARS-CoV-2 is the transmembrane protease serine 2 (TMPRSS2). This study was designed to explore the expression status, prognostic significance and molecular functions of TMPRSS2 in lung cancer. TMPRSS2 expression was investigated using the TIMER, Oncomine, UALCAN, GEO, HPA and TCGA databases. The prognostic value of TMPRSS2 was examined using Cox regression and a nomogram. KEGG, GO and GSEA were performed to investigate the cellular function of TMPRSS2 in lung cancer. The relationship between TMPRSS2 and immune infiltration was determined using the TIMER and CIBERSORT algorithms. TMPRSS2 mRNA and protein expression was significantly reduced in lung cancer. Decreased TMPRSS2 expression and increased DNA methylation of TMPRSS2 were associated with various clinicopathological parameters in patients with lung cancer. Low TMPRSS2 mRNA expression also correlated with poor outcome in lung cancer patients. Moreover, a nomogram was constructed and exhibited good predictive power for the overall survival of lung cancer patients. KEGG and GO analyses and GSEA implied that multiple immune- and metabolism-related pathways were significantly linked with TMPRSS2 expression. Intriguingly, TMPRSS2 expression associated with immune cell infiltration in lung cancer. More importantly, TMPRSS2 expression was markedly decreased in SARS-CoV-infected cells. These findings indicate that TMPRSS2 may be a promising prognostic biomarker and therapeutic target for lung cancer through metabolic pathways and immune cell infiltration.

Molecular Networking for Drug Toxicities Studies: The Case of Hydroxychloroquine in COVID-19 Patients.

Using drugs to treat COVID-19 symptoms may induce adverse effects and modify patient outcomes. These adverse events may be further aggravated in obese patients, who often present different illnesses such as metabolic-associated fatty liver disease. In Rennes University Hospital, several drug such as hydroxychloroquine (HCQ) have been used in the clinical trial HARMONICOV to treat COVID-19 patients, including obese patients. The aim of this study is to determine whether HCQ metabolism and hepatotoxicity are worsened in obese patients using an in vivo/in vitro approach. Liquid chromatography high resolution mass spectrometry in combination with untargeted screening and molecular networking were employed to study drug metabolism in vivo (patient's plasma) and in vitro (HepaRG cells and RPTEC cells). In addition, HepaRG cells model were used to reproduce pathophysiological features of obese patient metabolism, i.e., in the condition of hepatic steatosis. The metabolic signature of HCQ was modified in HepaRG cells cultured under a steatosis condition and a new metabolite was detected (carboxychloroquine). The RPTEC model was found to produce only one metabolite. A higher cytotoxicity of HCQ was observed in HepaRG cells exposed to exogenous fatty acids, while neutral lipid accumulation (steatosis) was further enhanced in these cells. These in vitro data were compared with the biological parameters of 17 COVID-19 patients treated with HCQ included in the HARMONICOV cohort. Overall, our data suggest that steatosis may be a risk factor for altered drug metabolism and possibly toxicity of HCQ.

Bioinformatics and in-silico findings reveal medical features and pharmacological targets of biochanin A against colorectal cancer and COVID-19.

Severe mortality due to the COVID-19 pandemic resulted from the lack of effective treatment. Although COVID-19 vaccines are available, their side effects have become a challenge for clinical use in patients with chronic diseases, especially cancer patients. In the current report, we applied network pharmacology and systematic bioinformatics to explore the use of biochanin A in patients with colorectal cancer (CRC) and COVID-19 infection. Using the network pharmacology approach, we identified two clusters of genes involved in immune response (IL1A, IL2, and IL6R) and cell proliferation (CCND1, PPARG, and EGFR) mediated by biochanin A in CRC/COVID-19 condition. The functional analysis of these two gene clusters further illustrated the effects of biochanin A on interleukin-6 production and cytokine-cytokine receptor interaction in CRC/COVID-19 pathology. In addition, pathway analysis demonstrated the control of PI3K-Akt and JAK-STAT signaling pathways by biochanin A in the treatment of CRC/COVID-19. The findings of this study provide a therapeutic option for combination therapy against COVID-19 infection in CRC patients.

Metabolic reprograming shapes neutrophil functions in severe COVID-19.

To better understand the mechanisms at the basis of neutrophil functions during SARS-CoV-2, we studied patients with severe COVID-19 pneumonia. They had high blood proportion of degranulated neutrophils and elevated plasma levels of myeloperoxidase (MPO), elastase, and MPO-DNA complexes, which are typical markers of neutrophil extracellular traps (NET). Their neutrophils display dysfunctional mitochondria, defective oxidative burst, increased glycolysis, glycogen accumulation in the cytoplasm, and increase glycogenolysis. Hypoxia-inducible factor 1α (ΗΙF-1α) is stabilized in such cells, and it controls the level of glycogen phosphorylase L (PYGL), a key enzyme in glycogenolysis. Inhibiting PYGL abolishes the ability of neutrophils to produce NET. Patients displayed significant increases of plasma levels of molecules involved in the regulation of neutrophils' function including CCL2, CXCL10, CCL20, IL-18, IL-3, IL-6, G-CSF, GM-CSF, IFN-γ. Our data suggest that metabolic remodelling is vital for the formation of NET and for boosting neutrophil inflammatory response, thus, suggesting that modulating ΗΙF-1α or PYGL could represent a novel approach for innovative therapies.

Metabolites with SARS-CoV-2 Inhibitory Activity Identified from Human Microbiome Commensals.

The COVID-19 pandemic has highlighted the need to identify additional antiviral small molecules to complement existing therapies. Although increasing evidence suggests that metabolites produced by the human microbiome have diverse biological activities, their antiviral properties remain poorly explored. Using a cell-based SARS-CoV-2 infection assay, we screened culture broth extracts from a collection of phylogenetically diverse human-associated bacteria for the production of small molecules with antiviral activity. Bioassay-guided fractionation uncovered three bacterial metabolites capable of inhibiting SARS-CoV-2 infection. This included the nucleoside analogue N6-(Δ2-isopentenyl)adenosine, the 5-hydroxytryptamine receptor agonist tryptamine, and the pyrazine 2,5-bis(3-indolylmethyl)pyrazine. The most potent of these, N6-(Δ2-isopentenyl)adenosine, had a 50% inhibitory concentration (IC50) of 2 µM. These natural antiviral compounds exhibit structural and functional similarities to synthetic drugs that have been clinically examined for use against COVID-19. Our discovery of structurally diverse metabolites with anti-SARS-CoV-2 activity from screening a small fraction of the bacteria reported to be associated with the human microbiome suggests that continued exploration of phylogenetically diverse human-associated bacteria is likely to uncover additional small molecules that inhibit SARS-CoV-2 as well as other viral infections. IMPORTANCE The continued prevalence of COVID-19 and the emergence of new variants has once again put the spotlight on the need for the identification of SARS-CoV-2 antivirals. The human microbiome produces an array of small molecules with bioactivities (e.g., host receptor ligands), but its ability to produce antiviral small molecules is relatively underexplored. Here, using a cell-based screening platform, we describe the isolation of three microbiome-derived metabolites that are able to prevent SARS-CoV-2 infection in vitro. These molecules display structural similarities to synthetic drugs that have been explored for the treatment of COVID-19, and these results suggest that the microbiome may be a fruitful source of the discovery of small molecules with antiviral activities.

Risks and Benefits of UV Radiation.

While UV radiation is a skin carcinogen, this should not obscure the growing evidence that sunlight has significant health benefits, including impacts on cardiovascular and metabolic health. Epidemiological and mechanistic evidences for the importance of different wavelengths of sunlight, including blue light and UV radiation, are presented.

RNA-Seq Analysis Reveals an Essential Role of the Tyrosine Metabolic Pathway and Inflammation in Myopia-Induced Retinal Degeneration in Guinea Pigs.

Myopia is the second leading cause of visual impairment globally. Myopia can induce sight-threatening retinal degeneration and the underlying mechanism remains poorly defined. We generated a model of myopia-induced early-stage retinal degeneration in guinea pigs and investigated the mechanism of action. Methods: The form-deprivation-induced myopia (FDM) was induced in the right eyes of 2~3-week-old guinea pigs using a translucent balloon for 15 weeks. The left eye remained untreated and served as a self-control. Another group of untreated age-matched animals was used as naïve controls. The refractive error and ocular biometrics were measured at 3, 7, 9, 12 and 15 weeks post-FDM induction. Visual function was evaluated by electroretinography. Retinal neurons and synaptic structures were examined by confocal microscopy of immunolabelled retinal sections. The total RNAs were extracted from the retinas and processed for RNA sequencing analysis. Results: The FDM eyes presented a progressive axial length elongation and refractive error development. After 15 weeks of intervention, the average refractive power was -3.40 ± 1.85 D in the FDM eyes, +2.94 ± 0.59 D and +2.69 ± 0.56 D in the self-control and naïve control eyes, respectively. The a-wave amplitude was significantly lower in FDM eyes and these eyes had a significantly lower number of rods, secretagogin+ bipolar cells, and GABAergic amacrine cells in selected retinal areas. RNA-seq analysis showed that 288 genes were upregulated and 119 genes were downregulated in FDM retinas compared to naïve control retinas. In addition, 152 genes were upregulated and 12 were downregulated in FDM retinas compared to self-control retinas. The KEGG enrichment analysis showed that tyrosine metabolism, ABC transporters and inflammatory pathways were upregulated, whereas tight junction, lipid and glycosaminoglycan biosynthesis were downregulated in FDM eyes. Conclusions: The long-term (15-week) FDM in the guinea pig models induced an early-stage retinal degeneration. The dysregulation of the tyrosine metabolism and inflammatory pathways may contribute to the pathogenesis of myopia-induced retinal degeneration.

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets.

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.