Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will ...facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development.
Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed.
Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.
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Genome-scale metabolic model (GEM) has been established as an important tool to study cellular metabolism at a systems level by predicting intracellular fluxes. With the advent of ...generic human GEMs, they have been increasingly applied to a range of diseases, often for the objective of predicting effective metabolic drug targets. Cancer is a representative disease where the use of GEMs has proved to be effective, partly due to the massive availability of patient-specific RNA-seq data. When using a human GEM, so-called context-specific GEM needs to be developed first by using cell-specific RNA-seq data. Biological validity of a context-specific GEM highly depends on both model extraction method (MEM) and model simulation method (MSM). However, while MEMs have been thoroughly examined, MSMs have not been systematically examined, especially, when studying cancer metabolism. In this study, the effects of pairwise combinations of three MEMs and five MSMs were evaluated by examining biological features of the resulting cancer patient-specific GEMs. For this, a total of 1,562 patient-specific GEMs were reconstructed, and subjected to machine learning-guided and biological evaluations to draw robust conclusions. Noteworthy observations were made from the evaluation, including the high performance of two MEMs, namely rank-based ‘task-driven Integrative Network Inference for Tissues’ (tINIT) or ‘Gene Inactivity Moderated by Metabolism and Expression’ (GIMME), paired with least absolute deviation (LAD) as a MSM, and relatively poorer performance of flux balance analysis (FBA) and parsimonious FBA (pFBA). Insights from this study can be considered as a reference when studying cancer metabolism using patient-specific GEMs.
Loop detectors are probably the widest-used technology for traffic state estimation. Previous research has shown that loop detector positions within the link significantly affect the estimation of ...the macroscopic fundamental diagram (MFD) of a given network. This paper examines the biases produced by the positioning of loop detectors on the MFD, using both analytical and simulation methods, as well as empirical data from UTD19. We confirm earlier results that a uniform distribution of loop detector positions reduces the bias. We discovered that: (i) subsets of the MFD determined by the loop detector position can help estimate whether the loop detector MFD will have a bias; (ii) non-uniform distribution of loop detectors is more likely to cause a discrepancy in the position subsets of the MFD, particularly if detectors in the network are positioned more downstream with a greater variation; and (iii) a lower ratio of link length to green signal time increases the possibility of bias in loop detector MFD, while the impact of the aggregation interval was found to be negligible. This research opens the possibility for the bias of MFD induced by the loop detector data to be corrected by only using itself.
•We examined the biases produced by the loop detector positioning on the MFD.•Subsetting MFD by the loop detector position can aid in estimating bias presence.•The logistic regression model is used to estimate factors that influences MFD bias.•We investigated the impact of link length and aggregation interval on MFD bias through simulation.
Abstract
Developing a computational model that can predict the effects of small molecules on biological status of a cell can be very useful for various applications, including candidate drug compound ...screening and toxicity testing in silico. In this study, we developed a deep learning model that predicts the effects of small molecules on gene expression levels of a cell. This deep learning model takes two inputs: the structure of a chemical, presented in SMILES, and a protein sequence encoded by a gene of interest. The model processes these two input data using a fingerprinting method for the input chemical structure and a convolutional neural network for the input protein sequence. As a result, the model predicts whether a given molecule upregulates or downregulates a gene of interest where the molecule and gene are both provided as inputs. The deep learning model was developed by using so called L1000 profiles covering gene expression levels of various cancer cell lines under a large number of perturbation conditions 1. L1000 is a cost-effective transcriptome technology that can accurately infer the expression levels of 9,196 genes on the basis of expression levels of 978 ‘landmark' genes that are directly measured. In this study, the deep learning model was developed for three different cancer cell lines, HA1E (kidney), HCC515 (lung), and HEPG2 (liver), by using L1000 profiles covering expression levels of the landmark genes individually perturbed with 5,213 molecules for the HA1E cell line, 5,171 molecules for the HCC515, and 3,529 for HEPG2. The deep learning models developed in this study will be useful for a wide range of studies that examine the effects of small molecules, for example in drug development and toxicity testing. 1 Subramanian et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437-1452 (2017)
Citation Format: Junhyeok Jeon, Sang Mi Lee, GaRyoung Lee, Hyun Uk Kim. Predicting the effects of small molecules on transcriptome of cancer cell lines using deep learning abstract. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3380.
•Atomic dispersion of NZVI was enlarged by supporting of coffee grounds.•Enlarged atomic dispersion of NZVI-Coffee ground enhanced heavy metal removal.•Heavy metal adsorption by NZVI-Coffee ground ...was mostly spontaneous.
Nanoscale zero-valent iron (NZVI) is recognized as an excellent adsorbent for metallic contaminants. Nevertheless, NZVI itself tends to agglomerate, so that its performance deterioriates without supporting materials. The use of exhausted coffee grounds as a supporting material for NZVI is expected to resolve this problem and provide the social benefits of waste minimization and resource recycling. In this study, NZVI was supported on exhausted coffee grounds (NZVI-Coffee ground) to enhance its dispersion. The aims of this study were to characterize NZVI-Coffee ground with a focus on atomic dispersion, evaluate NZVI-Coffee ground as an adsorbent for typical metallic contaminants and arsenic, and assess the effects of solution chemistry on the adsorption process. In order to achieve these goals, characterization, adsorption kinetics, adsorption equilibrium, and the effects of pH and temperature on adsorption were studied. Pb(II), Cd(II), As(III), and As(V) were selected as target contaminants. The characterization study showed that atomic dispersion was enhanced four-fold by supporting NZVI on coffee grounds. The enhanced dispersion resulted in rapid kinetic characteristics and large adsorption capacity. The optimum pH for adsorption of Pb(II) and Cd(II) was 4–6, and that for As(III) and As(V) was 2–4. The pH effect can be explained by surface protonation/deprotonation and adsorbate speciation. Only the adsorption of Pb(II) was an exothermic process; those of other species were endothermic. In every tested case, the adsorption process was spontaneous. According to the results, NZVI-Coffee ground is an effective adsorbent for the removal of aqueous phase Pb(II), Cd(II), As(III), and As(V).
Kynurenine pathway has a potential to convert L-tryptophan into multiple medicinal molecules. This study aims to explore the biosynthetic potential of kynurenine pathway for the efficient production ...of actinocin, an antitumor precursor selected as a proof-of-concept target molecule. Kynurenine pathway is first constructed in Escherichia coli by testing various combinations of biosynthetic genes from four different organisms. Metabolic engineering strategies are next performed to improve the production by inhibiting a competing pathway, and enhancing intracellular supply of a cofactor S-adenosyl-L-methionine, and ultimately to produce actinocin from glucose. Metabolome analysis further suggests additional gene overexpression targets, which finally leads to the actinocin titer of 719 mg/L. E. coli strain engineered to produce actinocin is further successfully utilized to produce 350 mg/L of kynurenic acid, a neuroprotectant, and 1401 mg/L of 3-hydroxyanthranilic acid, an antioxidant, also from glucose. These competitive production titers demonstrate the biosynthetic potential of kynurenine pathway as a source of multiple medicinal molecules. The approach undertaken in this study can be useful for the sustainable production of molecules derived from kynurenine pathway, which are otherwise chemically synthesized.
Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because ...these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.
•This review discusses the medical application of metabolic flux analysis (MFA).•Constraint-based reconstruction and analysis (COBRA) involves metabolic simulation.•Isotope-based metabolic flux analysis (iMFA) allows more accurate flux estimation.•COBRA and iMFA have been applied to various medical problems, including cancers.•MFA bridges metabolic engineering and biomedical science as a synergistic interface.
커피찌꺼기로 지지된 나노영가철을 이용한 폐수 중 납과 카드뮴 흡착 박만호(Man Ho Park); 이가령(Garyoung Lee); 박현수(Hyonsu Park) ...
Daehan hwan'gyeong gonghag hoeji,
2018, Letnik:
40, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Nano zero valent iron (NZVI) is an emerging adsorbent for heavy metal removal with its high reactivity and reduction potential. However, NZVI tends to aggregate to bigger particles, thus surface area ...and reactivity could be decreased in applications. In this study, NZVI is synthesized while attached on coffee ground to prevent agglomeration. Then batch adsorption tests for $Pb^{2+}$ and $Cd^{2+}$ in wastewater were studied. Adsorption isotherm under pH 6 and $20^{\circ}C$ revealed that maximum adsorption capacity from the Langmuir model was 814.95 mg/g and 196.06 mg/g for $Pb^{2+}$ and $Cd^{2+}$ respectively. Based on mechanism of removing $Pb^{2+}$ involves reduction, adsorption isotherm did not fit well in experiments data. Time to reach equilibrium was 1 hour and 8 hours for $Pb^{2+}$ and $Cd^{2+}$, respectively. Pseudo 2nd order kinetic model explain well kinetics of heavy metal adsorption, thus adsorption is likely to be chemi-sorption. According to the mass transfer mechanism study, 80% of $Pb^{2+}$ and 60% of $Cd^{2+}$ were transported rapidly by surface diffusion and residuals are transported by interparticle diffusion. High adsorption capactiy for $Pb^{2+}$ and $Cd^{2+}$ would be related with suppression of aggregation, hence NZVI-coffee ground showed the outstanding potential on industrial wastewater treatment facilities with high concentration of heavy metals. 나노영가철은 높은 반응성과 환원력으로 중금속 흡착제로 많은 관심을 받고 있다. 그러나 나노영가철은 강력한 표면에너지와 자성으로 인해 큰 입자로 응집되는 성질이 있고 이는 현장 적용 시 표면적과 반응성을 감소시키는 문제가 있다. 본 연구에서는 나노영가철의 입자 간 응집 문제를 해결하기 위해 나노영가철이 커피찌꺼기에 지지되도록 합성시켰으며 합성한 재료를 이용하여 $Pb^{2+}$과 $Cd^{2+}$에 대한 회분식 흡착실험을 진행하였다. pH 6, $20^{\circ}C$조건에서 등온흡착실험 결과 Langmuir 모형으로부터 $Pb^{2+}$과 $Cd^{2+}$에 대해 각각 814.95 mg/g, 196.06 mg/g의 최대흡착능을 가진 것으로 확인되었다. $Pb^{2+}$의 경우 환원반응 기작이 작용하여 실험 결과를 등온흡착곡선 모형으로 설명하기 어려운 것으로 나타났다. 반응 평형 시간은 $Pb^{2+}$에 대해 1시간, $Cd^{2+}$에 대해 8시간이 소요되었다. 유사 2차반응속도 모형이 반응속도를 잘 설명할 수 있는 것으로 나타났기에 흡착 형태는 화학적 흡착으로 추정된다. 물질 전달 메커니즘 분석 결과 $Pb^{2+}$과 $Cd^{2+}$은 각각 80%와 60%가 표면 확산을 통해 빠른 속도로 전달된 이후 나머지는 입자 내부 확산을 통해 전달됨을 확인하였다. 커피찌꺼기로 지지되는 나노영가철은 입자 간 응집이 억제되어 높은 중금속 흡착능과 제거율을 가진 것으로 생각되며, 나노영가철-커피찌꺼기는 고농도 중금속 폐액이 발생하는 산업현장에서 적용가능성이 뛰어날 것으로 판단된다.
Abstract
Cancer metabolism has been extensively explored to identify biomarkers and novel drug targets for effective cancer treatment. In particular, it is expected that metabolism-derived data ...somehow reflects unique features associated with a cancer patient’s prognosis. Here, we introduce a computational framework that predicts metabolic drug targets by considering metabolites associated with poor prognosis. For inputs, the computational framework requires genome-scale metabolic models (GEMs) and survival data that represent each of cancer patients with diverse prognoses. GEM is a computational model that allows predicting fluxes of entire metabolic reactions in a patient-specific manner. By using this computational framework, a risk score, which reflects the prognosis of cancer patients, was first established on the basis of metabolites significantly associated with survival. Finally, metabolic reaction targets were predicted that could reduce the risk score (i.e., improving the prognosis) upon their knockdown in silico. In this study, this framework was applied to bladder cancer patients as a proof-of-concept demonstration, and predicted 13 metabolic reaction targets. These metabolic reaction targets were expected to be also effective to bladder cancer patients with poor prognosis. The computational framework developed in this study is distinct from previous drug targeting methods because it also considers unique metabolic features associated with cancer patients with poor prognosis in order to identify drug targets that are effective to both low-risk and high-risk groups of cancer patients.
Citation Format: Sang Mi Lee, GaRyoung Lee, Hyun Uk Kim. Prediction of metabolic drug targets for cancer patients with poor prognosis by using genome-scale metabolic models and survival analyses abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3149.
Abstract
Metabolic reprogramming is considered a hallmark of cancers, which plays an important role in cancer progression and development, partly as a consequence of somatic mutations. A ...representative product of metabolic reprogramming in cancers is oncometabolites that show abnormal accumulation in a cancer cell, induce malignancy, and are generated upon mutations in a metabolic gene, often IDH1, SDH or FH. Identification of novel mutation-associated metabolites will facilitate developing biomarkers and treatment strategies for cancers, as in the case of ivosidenib, an FDA approved drug for treating acute myeloid leukemia (AML) having the IDH1 mutant. To this end, we develop a computational workflow that predicts so-called metabolite-gene-pathway sets (MGPs) that present metabolites and metabolic pathways significantly associated with gene mutations in cancers. The computational workflow uses cancer patients’ mutation data and a computational model of cellular metabolism called a genome-scale metabolic models (GEM). In this study, the computational workflow was demonstrated using 943 cancer patient-specific GEMs representing 24 different cancer types based on the Pan-Cancer Analysis of Whole Genomes (PCAWG) data 1, which, as a result, predicted 4,135 MGPs for these multiple cancer types. The computational workflow was shown to generate biologically meaningful MGPs on the basis of multi-omics data from 17 AML samples collected in this study as well as previous published studies. For the AML samples, metabolites of the MGPs predicted using the computational workflow showed significantly different intracellular concentrations, depending on mutation of genes involved in the MGP. Moreover, for the 115 MGPs predicted for CNS-GBM/Oligo that are associated with mutation of CIC, EGFR, IDH1 or TP53, 69% of the MGPs were supported by previous studies. This two-level validation indeed showed that it is possible to characterize metabolic pathways and their metabolites that are affected in response to somatic mutations in a cancer cell. Our computational workflow and its prediction outcomes will help better understand the mutation-associated metabolic reprogramming in cancers, and serve as a valuable resource for further extended studies on cancer metabolism. 1 The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82-93
Citation Format: Sang Mi Lee, GaRyoung Lee, Sungyoung Lee, Hyojin Song, Sung Soo Yoon, Hongseok Yun, Youngil Koh, Hyun Uk Kim. A computational model of cellular metabolism with mutation data predicts metabolites associated with somatic mutations in cancers abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2733.
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