Pathway-flux based biomarker for immunotherapy response in melanoma.

e22064 Background: Immune checkpoint therapy (ICT) refers to therapeutic interventions that specifically target immune evasion mechanisms to restore the host immunity with anti-tumor ability. The ICT has revolutionized the immune-based treatment across > 30 different cancers including solid tumors and hematopoietic malignancies, with an ORR of 30% and a 7%-12% grades 3-5 irAEs in average. However, a substantial unmet point is the development of a biomarker with which response of ICT can be predicted before treatment for individual patients. Methods: In order to face this challenge this study developed an advanced genome-scale pathway flux analysis (GPFA) to evaluate the strength of signaling transduction and metabolic flux in immune system. The input of GPFA is the gene expression profiles of individual objects and the output of GPFA can be summarized in a index system, IM.Index, with following definition: IM.Index = 1.78E-4 * Σ flux(P) + 2.37E-4 * Σ flux(P) p ∈ signaling transduction p ∈ energy metabolism. Subsequently, the IM.Index was applied to analyze genetic data of two independent cohorts of melanoma patients treated with anti-PD-1 therapy (nivolumab a. pembrolizumab). Results: The IM.Index predicted the response of anti-PD-1 therapy (nivolumab) in the first cohort with an odds ratio (OR) of 3.14 (95%CI: 1.16-8.45; p = 3.10E-3; AUC = 0.82) and with a sensitivity 89% and specificity 76%. The prediction on overall survival (OS) of this cohort achieved an hazard ratio (HR) of 1.53 (95%CI: 1.22-1.92; p = 7.8E-3). Subsequently, the prediction result for the anti-PD-1 therapy (pembrolizumab) in the second cohort achieved an OR of 2.12 (95%CI: 1.22-3.66; p = 4.50E-4; AUC = 0.87) and the OS prediction in this cohort reached an HR of 1.24 (95%CI: 1.04-1.47; p = 1.40E-2). Comparison with other four potential biomarkers (TMB, TNB, neo-peptide load and cytolytic score) related to immunotherapy showed a comparative outcome of the IM.Index regarding diagnosis and prognosis in melanoma. For instance, IM.Index showed a superior performance on objective response rate (ORR) of 70% and AUC of 0.83. Conclusions: In conclusion this study demonstrated that a pathway flux analysis at a genome-scale may be explorative in biomarker research in immunotherapy, since this type of analysis could reflect the strength or functional status of the immune system. The IM.Index developed in this study may also be applied to investigation the treatment response of immunotherapy in other types of cancer.

Download Full-text

Genome-scale Pathway Flux Analysis Predicts Efficacy of anti-PD-1 Therapy in Melanoma

10.21203/rs.3.rs-18122/v1 ◽

2020 ◽

Author(s):

Qi Mei ◽

Shangming Du ◽

Kathrin Halfter ◽

Xiaoyu Li ◽

Guokun Zhang ◽

...

Keyword(s):

Immune Cells ◽

Biomarker Discovery ◽

Objective Response ◽

Predictive Ability ◽

Flux Analysis ◽

Cellular Functions ◽

A Genome ◽

Metabolic Remodeling ◽

Genome Scale ◽

Pathway Flux

Abstract Background: Currently, predicting treatment efficacy to immunotherapy has been under extensive investigation. However, a putative biomarker for immunotherapy in melanoma remains to be found.Methods: Utilizing genetic data from two independent melanoma patient cohorts treated with anti-PD-1 therapy, the study described herein conducted a genome-scale pathway flux analysis (GPFA) and related statistical methods to determine whether specific pathways could be identified that are relevant to immunotherapy efficacy.Results: The analysis results highlighted three mechanisms responsible for the efficacy of immunotherapy in melanoma including 1) proper cellular functions in immune cells; 2) angiogenesis for the development and differentiation of immune cells; 3) energy metabolic remodeling to meet the activation of immune cells. Based on these discoveries, a pathway flux-based biomarker (IM.Index) was developed and assessed to validate its predictive ability with odds ratio (OR) of 3.14 (95%CI: 1.16-8.45; p=3.10E-3), sensitivity 76% and specificity 89%. The IM.Index achieved an objective response rate (ORR) of 70%. Comparison to other four putative biomarkers (TMB, NAL, neo-peptide load and cytolytic score) showed a comparative outcome with an hazard ratio (HR) of 1.83 (95%CI: 1.26-2.67; p=1.62E-3) and area under curve (AUC) of 0.82.Conclusion: These results indicate a translational potential of IM.Index, as a biomarker, for anti-PD-1 therapy in melanoma and the GPFA might pave a new path for biomarker discovery in immunotherapy.

Download Full-text

Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

Bioscience Reports ◽

10.1042/bsr20170224 ◽

2018 ◽

Vol 38 (6) ◽

Cited By ~ 11

Author(s):

Georg Basler ◽

Alisdair R. Fernie ◽

Zoran Nikoloski

Keyword(s):

Large Scale ◽

Metabolic Flux ◽

Cell Types ◽

Flux Analysis ◽

Metabolic Labeling ◽

Modeling Framework ◽

Metabolomics Data ◽

Technological Advances ◽

A Genome ◽

Genome Scale

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

Download Full-text

13C metabolic flux analysis at a genome-scale

Metabolic Engineering ◽

10.1016/j.ymben.2015.08.006 ◽

2015 ◽

Vol 32 ◽

pp. 12-22 ◽

Cited By ~ 50

Author(s):

Saratram Gopalakrishnan ◽

Costas D. Maranas

Keyword(s):

Metabolic Flux Analysis ◽

Metabolic Flux ◽

Flux Analysis ◽

13C Metabolic Flux Analysis ◽

A Genome ◽

Genome Scale

Download Full-text

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Metabolites ◽

10.3390/metabo5030521 ◽

2015 ◽

Vol 5 (3) ◽

pp. 521-535 ◽

Cited By ~ 10

Author(s):

Saratram Gopalakrishnan ◽

Costas Maranas

Keyword(s):

Metabolic Flux Analysis ◽

Metabolic Flux ◽

Flux Analysis ◽

A Genome ◽

Genome Scale ◽

Scale Challenges

Download Full-text

Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

10.21203/rs.2.16651/v2 ◽

2020 ◽

Author(s):

Piyush Nanda ◽

Pradipta Patra ◽

Manali Das ◽

Amit Ghosh

Keyword(s):

Ethyl Acetate ◽

Metabolic Flux ◽

Carbon Sources ◽

Growth Dynamics ◽

Dry Weight ◽

Metabolic Model ◽

Flux Analysis ◽

Specific Rate ◽

A Genome ◽

Genome Scale

Abstract Background Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to optimal fermentation only in oxygen limiting conditions that render it a suitable host for industrial-scale protein production. Ethyl acetate, an important industrial chemical, has been demonstrated to be a major overflow metabolite during aerobic batch cultivation with a specific rate of 0.12 g per g dry weight per hour. Here, we reconstruct a genome-scale metabolic model of the yeast to better explain the observed phenotypes and aid further hypothesis generation. Results We report the first genome-scale metabolic model, iPN730, using Build Fungal Model in KBase workspace. The inconsistencies in the draft model were semi-automatically corrected using literature and published datasets. The curated model comprises of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments (organelles). The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. The URC pyrimidine degradation pathway incorporated into the model enables it to grow on uracil or urea as the sole nitrogen source. Conclusion The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism, particularly that of pyrimidine metabolism and ethyl acetate production. Metabolic flux analysis using the model will enable hypotheses generation to gain a deeper understanding of metabolism in weakly Crabtree positive yeast and in fungal biodiversity in general.

Download Full-text

Metabolic control of nitrogen fixation in rhizobium-legume symbioses

Science Advances ◽

10.1126/sciadv.abh2433 ◽

2021 ◽

Vol 7 (31) ◽

pp. eabh2433

Author(s):

Carolin C. M. Schulte ◽

Khushboo Borah ◽

Rachel M. Wheatley ◽

Jason J. Terpolilli ◽

Gerhard Saalbach ◽

...

Keyword(s):

Metabolic Flux ◽

Tricarboxylic Acid Cycle ◽

Oxygen Demand ◽

Metabolic Model ◽

Redox Balance ◽

Ammonia Assimilation ◽

Nodule Formation ◽

Flux Analysis ◽

Carbon And Nitrogen ◽

Genome Scale

Rhizobia induce nodule formation on legume roots and differentiate into bacteroids, which catabolize plant-derived dicarboxylates to reduce atmospheric N2 into ammonia. Despite the agricultural importance of this symbiosis, the mechanisms that govern carbon and nitrogen allocation in bacteroids and promote ammonia secretion to the plant are largely unknown. Using a metabolic model derived from genome-scale datasets, we show that carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules. Catabolism of dicarboxylates induces not only a higher oxygen demand but also a higher NADH/NAD+ ratio than sugars. Modeling and 13C metabolic flux analysis indicate that oxygen limitation restricts the decarboxylating arm of the tricarboxylic acid cycle, which limits ammonia assimilation into glutamate. By tightly controlling oxygen supply and providing dicarboxylates as the energy and electron source donors for N2 fixation, legumes promote ammonia secretion by bacteroids. This is a defining feature of rhizobium-legume symbioses.

Download Full-text

Bayesian metabolic flux analysis reveals intracellular flux couplings

Bioinformatics ◽

10.1093/bioinformatics/btz315 ◽

2019 ◽

Vol 35 (14) ◽

pp. i548-i557 ◽

Cited By ~ 3

Author(s):

Markus Heinonen ◽

Maria Osmala ◽

Henrik Mannerström ◽

Janne Wallenius ◽

Samuel Kaski ◽

...

Keyword(s):

Steady State ◽

Metabolic Flux Analysis ◽

Metabolic Flux ◽

Reaction Network ◽

Reaction Rates ◽

Supplementary Information ◽

Flux Analysis ◽

Metabolic Reaction ◽

Metabolic Systems ◽

Genome Scale

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

Download Full-text

Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

Scientific Reports ◽

10.1038/s41598-019-55041-w ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Javad Aminian-Dehkordi ◽

Seyyed Mohammad Mousavi ◽

Arezou Jafari ◽

Ivan Mijakovic ◽

Sayed-Amir Marashi

Keyword(s):

Experimental Data ◽

Metabolic Network ◽

Bacillus Megaterium ◽

In Silico ◽

Metabolic Model ◽

Growth Behavior ◽

Industrial Biotechnology ◽

Flux Analysis ◽

A Genome ◽

Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

Download Full-text

State-of-the-Art Genetic Modalities to Engineer Cyanobacteria for Sustainable Biosynthesis of Biofuel and Fine-Chemicals to Meet Bio–Economy Challenges

Life ◽

10.3390/life9030054 ◽

2019 ◽

Vol 9 (3) ◽

pp. 54 ◽

Cited By ~ 4

Author(s):

Aqib Zafar Khan ◽

Muhammad Bilal ◽

Shahid Mehmood ◽

Ashutosh Sharma ◽

Hafiz M. N. Iqbal

Keyword(s):

High Throughput ◽

Metabolic Flux Analysis ◽

Large Scale ◽

Metabolic Flux ◽

Flux Analysis ◽

Fine Chemicals ◽

Carbon Regulation ◽

Regulation Mechanisms ◽

Large Scale Cultivation ◽

Genome Scale

In recent years, metabolic engineering of microorganisms has attained much research interest to produce biofuels and industrially pertinent chemicals. Owing to the relatively fast growth rate, genetic malleability, and carbon neutral production process, cyanobacteria has been recognized as a specialized microorganism with a significant biotechnological perspective. Metabolically engineering cyanobacterial strains have shown great potential for the photosynthetic production of an array of valuable native or non-native chemicals and metabolites with profound agricultural and pharmaceutical significance using CO2 as a building block. In recent years, substantial improvements in developing and introducing novel and efficient genetic tools such as genome-scale modeling, high throughput omics analyses, synthetic/system biology tools, metabolic flux analysis and clustered regularly interspaced short palindromic repeats (CRISPR)-associated nuclease (CRISPR/cas) systems have been made for engineering cyanobacterial strains. Use of these tools and technologies has led to a greater understanding of the host metabolism, as well as endogenous and heterologous carbon regulation mechanisms which consequently results in the expansion of maximum productive ability and biochemical diversity. This review summarizes recent advances in engineering cyanobacteria to produce biofuel and industrially relevant fine chemicals of high interest. Moreover, the development and applications of cutting-edge toolboxes such as the CRISPR-cas9 system, synthetic biology, high-throughput “omics”, and metabolic flux analysis to engineer cyanobacteria for large-scale cultivation are also discussed.

Download Full-text