Metabolic and mitochondria alterations induced by SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10.

Antiviral signaling, immune response and cell metabolism are dysregulated by SARS-CoV-2, the causative agent of COVID-19. Here, we show that SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10 induce a significant mitochondrial and metabolic reprogramming in A549 lung epithelial cells. While ORF9b, ORF9c and ORF10 induced largely overlapping transcriptomes, ORF3a induced a distinct transcriptome, including the downregulation of numerous genes with critical roles in mitochondrial function and morphology. On the other hand, all four ORFs altered mitochondrial dynamics and function, but only ORF3a and ORF9c induced a marked alteration in mitochondrial cristae structure. Genome-Scale Metabolic Models identified both metabolic flux reprogramming features both shared across all accessory proteins and specific for each accessory protein. Notably, a downregulated amino acid metabolism was observed in ORF9b, ORF9c and ORF10, while an upregulated lipid metabolism was distinctly induced by ORF3a. These findings reveal metabolic dependencies and vulnerabilities prompted by SARS-CoV-2 accessory proteins that may be exploited to identify new targets for intervention.

AI delivers Michaelis constants as fuel for genome-scale metabolic models.

Michaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely available. A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic, genome-wide modeling of metabolism.

Trabectedin and Lurbinectedin Modulate the Interplay between Cells in the Tumour Microenvironment-Progresses in Their Use in Combined Cancer Therapy.

Trabectedin (TRB) and Lurbinectedin (LUR) are alkaloid compounds originally isolated from Ecteinascidia turbinata with proven antitumoral activity. Both molecules are structural analogues that differ on the tetrahydroisoquinoline moiety of the C subunit in TRB, which is replaced by a tetrahydro-ß-carboline in LUR. TRB is indicated for patients with relapsed ovarian cancer in combination with pegylated liposomal doxorubicin, as well as for advanced soft tissue sarcoma in adults in monotherapy. LUR was approved by the FDA in 2020 to treat metastatic small cell lung cancer. Herein, we systematically summarise the origin and structure of TRB and LUR, as well as the molecular mechanisms that they trigger to induce cell death in tumoral cells and supporting stroma cells of the tumoral microenvironment, and how these compounds regulate immune cell function and fate. Finally, the novel therapeutic venues that are currently under exploration, in combination with a plethora of different immunotherapeutic strategies or specific molecular-targeted inhibitors, are reviewed, with particular emphasis on the usage of immune checkpoint inhibitors, or other bioactive molecules that have shown synergistic effects in terms of tumour regression and ablation. These approaches intend to tackle the complexity of managing cancer patients in the context of precision medicine and the application of tailor-made strategies aiming at the reduction of undesired side effects.

A Phase I-II multicenter trial with Avelumab plus autologous dendritic cell vaccine in pre-treated mismatch repair-proficient (MSS) metastatic colorectal cancer patients; GEMCAD 1602 study.

BACKGROUND: Immune check-point blockade (ICB) has shown clinical benefit in mismatch repair-deficient/microsatellite instability high metastatic colorectal cancer (mCRC) but not in mismatch repair-proficient/microsatellite stable patients. Cancer vaccines with autologous dendritic cells (ADC) could be a complementary therapeutic approach to ICB as this combination has the potential to achieve synergistic effects. METHODS: This was a Phase I/II multicentric study with translational sub-studies, to evaluate the safety, pharmacodynamics and anti-tumor effects of Avelumab plus ADC vaccine in heavily pre-treated MSS mCRC patients. Primary objective was to determine the maximum tolerated dose and the efficacy of the combination. The primary end-point was 40% progression-free survival at 6 months with a 2 Simon Stage. RESULTS: A total of 28 patients were screened and 19 pts were included. Combined therapy was safe and well tolerated. An interim analysis (Simon design first-stage) recommended early termination because only 2/19 (11%) patients were disease free at 6 months. Median PFS was 3.1 months [2.1-5.3 months] and overall survival was 12.2 months [3.2-23.2 months]. Stimulation of immune system was observed in vitro but not clinically. The evaluation of basal RNA-seq noted significant changes between pre and post-therapy liver biopsies related to lipid metabolism and transport, inflammation and oxidative stress pathways. CONCLUSIONS: The combination of Avelumab plus ADC vaccine is safe and well tolerated but exhibited modest clinical activity. Our study describes, for the first-time, a de novo post-therapy metabolic rewiring, that could represent novel immunotherapy-induced tumor vulnerabilities.

Defining the metabolic signatures associated with human macrophage polarisation.

Macrophages are essential components of the innate immune system that play both homeostatic roles in healthy organs, and host defence functions against pathogens after tissue injury. To accomplish their physiological role, macrophages display different profiles of gene expression, immune function, and metabolic phenotypes that allow these cells to participate in different steps of the inflammatory reaction, from the initiation to the resolution phase. In addition, significant differences exist in the phenotype of macrophages depending on the tissue in which they are present and on the mammalian species. From a metabolic point of view, macrophages are essentially glycolytic cells; however, their metabolic fluxes are dependent on the functional polarisation of these cells. This metabolic and cellular plasticity offers the possibility to interfere with the activity of macrophages to avoid harmful effects due to persistent activation or the release of molecules that delay tissue recovery after injury.

Noninvasive prediction models of intra-amniotic infection in women with preterm labor.

BACKGROUND: Among women with preterm labor, those with intra-amniotic infection present the highest risk of early delivery and the most adverse outcomes. The identification of intra-amniotic infection requires amniocentesis, perceived as too invasive by women and physicians. Noninvasive methods for identifying intra-amniotic infection and/or early delivery are crucial to focus early efforts on high-risk preterm labor women while avoiding unnecessary interventions in low-risk preterm labor women. OBJECTIVE: This study modeled the best performing models, integrating biochemical data with clinical and ultrasound information to predict a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days. STUDY DESIGN: From 2015 to 2020, data from a cohort of women, who underwent amniocentesis to rule in or rule out intra-amniotic infection or inflammation, admitted with a diagnosis of preterm labor at <34 weeks of gestation at the Hospital Clinic and Hospital Sant Joan de Déu, Barcelona, Spain, were used. At admission, transvaginal ultrasound was performed, and maternal blood and vaginal samples were collected. Using high-dimensional biology, vaginal proteins (using multiplex immunoassay), amino acids (using high-performance liquid chromatography), and bacteria (using 16S ribosomal RNA gene amplicon sequencing) were explored to predict the composite outcome. We selected ultrasound, maternal blood, and vaginal predictors that could be tested with rapid diagnostic techniques and developed prediction models employing machine learning that was applied in a validation cohort. RESULTS: A cohort of 288 women with preterm labor at <34 weeks of gestation, of which 103 (35%) had a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days, were included in this study. The sample was divided into derivation (n=116) and validation (n=172) cohorts. Of note, 4 prediction models were proposed, including ultrasound transvaginal cervical length, maternal C-reactive protein, vaginal interleukin 6 (using an automated immunoanalyzer), vaginal pH (using a pH meter), vaginal lactic acid (using a reflectometer), and vaginal Lactobacillus genus (using quantitative polymerase chain reaction), with areas under the receiving operating characteristic curve ranging from 82.2% (95% confidence interval, ±3.1%) to 85.2% (95% confidence interval, ±3.1%), sensitivities ranging from 76.1% to 85.9%, and specificities ranging from 75.2% to 85.1%. CONCLUSION: The study results have provided proof of principle of how noninvasive methods suitable for point-of-care systems can select high-risk cases among women with preterm labor and might substantially aid in clinical management and outcomes while improving the use of resources and patient experience.

Metabolomics in Cell Biology.

Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.

Circulating Neurofilament Light Chain Levels Increase with Age and Are Associated with Worse Physical Function and Body Composition in Men but Not in Women.

This study aimed to assess the relationship between age-related changes in Neurofilament Light Chain (NFL), a marker of neuronal function, and various factors including muscle function, body composition, and metabolomic markers. The study included 40 participants, aged 20 to 85 years. NFL levels were measured, and muscle function, body composition, and metabolomic markers were assessed. NFL levels increased significantly with age, particularly in men. Negative correlations were found between NFL levels and measures of muscle function, such as grip strength, walking speed, and chair test performance, indicating a decline in muscle performance with increasing NFL. These associations were more pronounced in men. NFL levels also negatively correlated with muscle quality in men, as measured by 50 kHz phase angle. In terms of body composition, NFL was positively correlated with markers of fat mass and negatively correlated with markers of muscle mass, predominantly in men. Metabolomic analysis revealed significant associations between NFL levels and specific metabolites, with gender-dependent relationships observed. This study provides insights into the relationship between circulating serum NFL, muscle function, and aging. Our findings hint at circulating NFL as a potential early marker of age-associated neurodegenerative processes, especially in men.

Integrating systemic and molecular levels to infer key drivers sustaining metabolic adaptations.

Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response.

TKTL1 Knockdown Impairs Hypoxia-Induced Glucose-6-phosphate Dehydrogenase and Glyceraldehyde-3-phosphate Dehydrogenase Overexpression.

Increased expression of transketolase (TKT) and its isoform transketolase-like-1 (TKTL1) has been related to the malignant leukemia phenotype through promoting an increase in the non-oxidative branch of the pentose phosphate pathway (PPP). Recently, it has also been described that TKTL1 can have a role in survival under hypoxic conditions and in the acquisition of radio resistance. However, TKTL1's role in triggering metabolic reprogramming under hypoxia in leukemia cells has never been characterized. Using THP-1 AML cells, and by combining metabolomics and transcriptomics techniques, we characterized the impact of TKTL1 knockdown on the metabolic reprogramming triggered by hypoxia. Results demonstrated that TKTL1 knockdown results in a decrease in TKT, glucose-6-phosphate dehydrogenase (G6PD) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) activities and impairs the hypoxia-induced overexpression of G6PD and GAPDH, all having significant impacts on the redox capacity of NADPH- and NADH-related cells. Moreover, TKTL1 knockdown impedes hypoxia-induced transcription of genes encoding key enzymes and transporters involved in glucose, PPP and amino acid metabolism, rendering cells unable to switch to enhanced glycolysis under hypoxia. Altogether, our results show that TKTL1 plays a key role in the metabolic adaptation to hypoxia in THP-1 AML cells through modulation of G6PD and GAPDH activities, both regulating glucose/glutamine consumption and the transcriptomic overexpression of key players of PPP, glucose and amino acids metabolism.

Biogenic Amine Levels Markedly Increase in the Aqueous Humor of Individuals with Controlled Type 2 Diabetes.

The composition of the aqueous humor of patients with type 2 diabetes is relevant to understanding the underlying causes of eye-related comorbidities. Information on the composition of aqueous humor in healthy subjects is limited due to the lack of adequate controls. To carry out a metabolomics study, 31 samples of aqueous humor from healthy subjects without ocular pathology, submitted to refractive surgery and seven samples from patients with type 2 diabetes without signs of ocular pathology related to diabetes were used. The level of 25 molecules was significantly (p < 0.001) altered in the aqueous humor of the patient group. The concentration of a single molecule, N-acetylornithine, makes it possible to discriminate between control and diabetes (sensitivity and specificity equal to 1). In addition, receptor operating characteristic curve and principal component analysis for the above-mentioned six molecules yielded significantly (p < 0.001) altered in the aqueous humor of the patient group. In addition, receptor operating characteristic curve and principal component analysis for six compounds yielded cut-off values and remarkable sensitivity, specificity, and segregation ability. The altered level of N-acetylornithine may be due to an increased amount of acetate in diabetes. It is of interest to further investigate whether this alteration is related to the pathogenesis of the disease. The increase in the amino form of pyruvate, alanine, in diabetes is also relevant because it could be a means of reducing the formation of lactate from pyruvate.

Metformin lowers glucose 6-phosphate in hepatocytes by activation of glycolysis downstream of glucose phosphorylation.

The chronic effects of metformin on liver gluconeogenesis involve repression of the G6pc gene, which is regulated by the carbohydrate-response element-binding protein through raised cellular intermediates of glucose metabolism. In this study we determined the candidate mechanisms by which metformin lowers glucose 6-phosphate (G6P) in mouse and rat hepatocytes challenged with high glucose or gluconeogenic precursors. Cell metformin loads in the therapeutic range lowered cell G6P but not ATP and decreased G6pc mRNA at high glucose. The G6P lowering by metformin was mimicked by a complex 1 inhibitor (rotenone) and an uncoupler (dinitrophenol) and by overexpression of mGPDH, which lowers glycerol 3-phosphate and G6P and also mimics the G6pc repression by metformin. In contrast, direct allosteric activators of AMPK (A-769662, 991, and C-13) had opposite effects from metformin on glycolysis, gluconeogenesis, and cell G6P. The G6P lowering by metformin, which also occurs in hepatocytes from AMPK knockout mice, is best explained by allosteric regulation of phosphofructokinase-1 and/or fructose bisphosphatase-1, as supported by increased metabolism of [3-3H]glucose relative to [2-3H]glucose; by an increase in the lactate m2/m1 isotopolog ratio from [1,2-13C2]glucose; by lowering of glycerol 3-phosphate an allosteric inhibitor of phosphofructokinase-1; and by marked G6P elevation by selective inhibition of phosphofructokinase-1; but not by a more reduced cytoplasmic NADH/NAD redox state. We conclude that therapeutically relevant doses of metformin lower G6P in hepatocytes challenged with high glucose by stimulation of glycolysis by an AMP-activated protein kinase-independent mechanism through changes in allosteric effectors of phosphofructokinase-1 and fructose bisphosphatase-1, including AMP, Pi, and glycerol 3-phosphate.

Exploratory and confirmatory analysis to investigate the presence of vaginal metabolome expression of microbial invasion of the amniotic cavity in women with preterm labor using high-performance liquid chromatography.

BACKGROUND: Although the influence of microbial invasion of the amniotic cavity on the development of spontaneous preterm delivery is unquestionable, the use of an invasive procedure to diagnose the status of an infection limits its clinical translation. OBJECTIVE: This study aimed to use exploratory and confirmatory analyses to investigate the presence of vaginal metabolome expression of microbial invasion of the amniotic cavity in women diagnosed as having preterm labor using high-performance liquid chromatography. STUDY DESIGN: In 140 women with singleton pregnancies and a diagnosis of preterm labor at <34 weeks' gestation, we analyzed vaginal amino acid concentrations using high-performance liquid chromatography. Vaginal samples were collected shortly after the amniocentesis performed at admission to rule out microbial invasion of the amniotic cavity. Data were normalized for the median of all the amino acid concentrations evaluated. Microbial invasion of the amniotic cavity was defined as a positive aerobic or anaerobic amniotic fluid culture for the presence of bacteria or yeast or Ureaplasma species or Mycoplasma hominis in the mycoplasma culture or a positive polymerase chain reaction result for 16S rRNA gene sequence. Exploratory analysis was performed in half of the sample and confirmatory analysis in the other half. We compared vaginal amino acid concentrations between women with and without microbial invasion of the amniotic cavity in both cohorts. The area under the curve with 95% confidence interval values were calculated for vaginal amino acids with significant differences. RESULTS: In the exploratory cohort (2014-2015), 17 of 76 women (22.3%) had microbial invasion of the amniotic cavity compared with 14 of 72 (19.4%) in the confirmatory cohort (2016-2017). In the exploratory cohort, we found significantly higher amino acid concentrations of vaginal taurine, lysine, and cysteine and significantly lower concentrations of vaginal glutamate, aspartate, and the aspartate to asparagine ratio. These significant differences were confirmed in the confirmatory cohort. The area under the curve of these vaginal amino acids to predict microbial invasion of the amniotic cavity ranged between 0.72 and 0.79, with cysteine being the amino acid with the best performance with an area under the curve of 0.79 (95% confidence interval, 0.71-0.88). CONCLUSION: We found the vaginal metabolome expression of microbial invasion of the amniotic cavity in women with preterm labor and intact membranes. These findings might open the possibility to develop noninvasive diagnostic tools of microbial invasion of the amniotic cavity with the aim of selecting women who would most likely benefit from an amniocentesis for this indication.

Generation of a Novel In Vitro Model to Study Endothelial Dysfunction from Atherothrombotic Specimens.

PURPOSE: Endothelial dysfunction is central to the pathogenesis of acute coronary syndrome. The study of diseased endothelium is very challenging due to inherent difficulties in isolating endothelial cells from the coronary vascular bed. We sought to isolate and characterise coronary endothelial cells from patients undergoing thrombectomy for myocardial infarction to develop a patient-specific in vitro model of endothelial dysfunction. METHODS: In a prospective cohort study, 49 patients underwent percutaneous coronary intervention with thrombus aspiration. Specimens were cultured, and coronary endothelial outgrowth (CEO) cells were isolated. CEO cells, endothelial cells isolated from peripheral blood, explanted coronary arteries, and umbilical veins were phenotyped and assessed functionally in vitro and in vivo. RESULTS: CEO cells were obtained from 27/37 (73%) atherothrombotic specimens and gave rise to cells with cobblestone morphology expressing CD146 (94 ± 6%), CD31 (87 ± 14%), and von Willebrand factor (100 ± 1%). Proliferation of CEO cells was impaired compared to both coronary artery and umbilical vein endothelial cells (population doubling time, 2.5 ± 1.0 versus 1.6 ± 0.3 and 1.2 ± 0.3 days, respectively). Cell migration was also reduced compared to umbilical vein endothelial cells (29 ± 20% versus 85±19%). Importantly, unlike control endothelial cells, dysfunctional CEO cells did not incorporate into new vessels or promote angiogenesis in vivo. CONCLUSIONS: CEO cells can be reliably isolated and cultured from thrombectomy specimens in patients with acute coronary syndrome. Compared to controls, patient-derived coronary endothelial cells had impaired capacity to proliferate, migrate, and contribute to angiogenesis. CEO cells could be used to identify novel therapeutic targets to enhance endothelial function and prevent acute coronary syndromes.

Quantitative Proteomic Approach Reveals Altered Metabolic Pathways in Response to the Inhibition of Lysine Deacetylases in A549 Cells under Normoxia and Hypoxia.

Growing evidence is showing that acetylation plays an essential role in cancer, but studies on the impact of KDAC inhibition (KDACi) on the metabolic profile are still in their infancy. Here, we analyzed, by using an iTRAQ-based quantitative proteomics approach, the changes in the proteome of KRAS-mutated non-small cell lung cancer (NSCLC) A549 cells in response to trichostatin-A (TSA) and nicotinamide (NAM) under normoxia and hypoxia. Part of this response was further validated by molecular and biochemical analyses and correlated with the proliferation rates, apoptotic cell death, and activation of ROS scavenging mechanisms in opposition to the ROS production. Despite the differences among the KDAC inhibitors, up-regulation of glycolysis, TCA cycle, oxidative phosphorylation and fatty acid synthesis emerged as a common metabolic response underlying KDACi. We also observed that some of the KDACi effects at metabolic levels are enhanced under hypoxia. Furthermore, we used a drug repositioning machine learning approach to list candidate metabolic therapeutic agents for KRAS mutated NSCLC. Together, these results allow us to better understand the metabolic regulations underlying KDACi in NSCLC, taking into account the microenvironment of tumors related to hypoxia, and bring new insights for the future rational design of new therapies.

Metabolomics: The Stethoscope for the Twenty-First Century.

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

Interoperable and scalable data analysis with microservices: applications in metabolomics.

MOTIVATION: Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. RESULTS: We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. AVAILABILITY AND IMPLEMENTATION: The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Luminescent PtII and PtIV Platinacycles with Anticancer Activity Against Multiplatinum-Resistant Metastatic CRC and CRPC Cell Models.

Platinum-based chemotherapy persists to be the only effective therapeutic option against a wide variety of tumours. Nevertheless, the acquisition of platinum resistance is utterly common, ultimately cornering conventional platinum drugs to only palliative in many patients. Thus, encountering alternatives that are both effective and non-cross-resistant is urgent. In this work, we report the synthesis, reduction studies, and luminescent properties of a series of cyclometallated (C,N,N')PtIV compounds derived from amine-imine ligands, and their remarkable efficacy at the high nanomolar range and complete lack of cross-resistance, as an intrinsic property of the platinacycle, against multiplatinum-resistant colorectal cancer (CRC) and castration-resistant prostate cancer (CRPC) metastatic cell lines generated for this work. We have also determined that the compounds are effective and selective for a broader cancer panel, including breast and lung cancer. Additionally, selected compounds have been further evaluated, finding a shift in their antiproliferative mechanism towards more cytotoxic and less cytostatic than cisplatin against cancer cells, being also able to oxidize cysteine residues and inhibit topoisomerase II, thereby holding great promise as future improved alternatives to conventional platinum drugs.

p13CMFA: Parsimonious 13C metabolic flux analysis.

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).

Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer.

BACKGROUND: Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods. RESULTS: Our study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype. CONCLUSIONS: The method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications.