The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome

Diener, Christian; Muñoz-Gonzalez, Felipe; Encarnación, Sergio; Resendis‐Antonio, Osbaldo

doi:10.1038/srep28415

Cited by 13 publications

(16 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a different approach, Diener and colleagues used k -cone analysis to quantify enzyme regulation in HeLa cells from the intracellular metabolome 24 . This strategy assumes that the metabolic network is at a steady state and uses stoichiometric models to characterize the relationship between fluxes, which are represented by mass action laws.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Inference of cancer mechanisms through computational systems analysis

Voit

2017

Mol. BioSyst.

View full text Add to dashboard Cite

Large amounts of metabolomics data have been accumulated to study metabolic alterations in cancer that allow cancer cells to synthesize molecular materials necessary for cell growth and proliferation. Although metabolic reprogramming in cancer was discovered almost a century ago, the underlying biochemical mechanisms are still unclear. We show that metabolomics data can be used to infer likely biochemical mechanisms associated with cancer. The proposed inference method is data-driven and quite generic; its efficacy is demonstrated with the analysis of changes in purine metabolism of human renal cell carcinoma. The method and results are essentially unbiased and tolerate noise in the data well. The proposed method correctly identified and accurately quantified primary enzymatic alterations in cancer, and these account for over 80% of the metabolic alterations in the investigated carcinoma. Interestingly, the two primary action sites are not the most sensitive reaction steps in purine metabolism, which implies that sensitivity analysis is not a valid approach for identifying cancer targets. The proposed method exhibits statistically high precision and robustness even for analyses of moderately incomplete metabolomics data. By permitting analyses of individual metabolic profiles, the method may become a tool of personalized precision medicine.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Inference of cancer mechanisms through computational systems analysis

Voit

2017

Mol. BioSyst.

View full text Add to dashboard Cite

show abstract

“…With the aim of disentangling this complex metabolic communication and surveying the metabolic pathways that actively participate in the community, metabolomics–embracing the massive quantitative measurement of intracellular or extracellular metabolites in biological samples such as human stool (Weir et al, 2013)–has been established as the more suitable HT technology to characterize the phenotype and dynamic response of living systems (Nicholson and Lindon, 2008; Marcobal et al, 2013; Diener et al, 2016). …”

Section: Characterization Of the Microbiome Using High-throughput Tecmentioning

confidence: 99%

“…Currently, the HMDB has more than 40,000 metabolite entries (Wishart et al, 2012). The enrichment of these valuable tools can provide a better understanding of the characteristics of health and disease states when combined with other clinical and modeling approaches to fill the gap between the genotype and phenotype relationship (Diener et al, 2016). …”

Section: Characterization Of the Microbiome Using High-throughput Tecmentioning

confidence: 99%

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

et al. 2016

Self Cite

View full text Add to dashboard Cite

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

show abstract

“…Generally, it has been shown recently that HeLa cells have some peculiarities in their metabolism [49,50]. A lower proteolytic activity can lead to a slower degradation of the 8L6 NPs inside the cells and, consequently, might cause increased cytotoxicity.…”

Section: Cell Compatibility Study Of the Npsmentioning

confidence: 99%

Biodegradable Nanoparticles Made of Amino-Acid-Based Ester Polymers: Preparation, Characterization, and In Vitro Biocompatibility Study

Kantaria

Kobauri

et al. 2016

Applied Sciences

View full text Add to dashboard Cite

Abstract:A systematic study of fabricating nanoparticles (NPs) by cost-effective polymer deposition/solvent displacement (nanoprecipitation) method has been carried out. Five amino acid based biodegradable (AABB) ester polymers (four neutral and one cationic), four organic solvents miscible with water, and eight surfactants were tested for the fabrication of the goal NPs. Depending on the nature of the AABB polymers, organic solvents and surfactants, as well as on the fabrication conditions, the size (Mean Particle Diameter) of the NPs could be tuned within 42 ÷ 398 nm, the zeta-potential within 12.5 ÷ +28 mV. The stability (resuspendability) of the NPs upon storage (at room temperature and refrigerated) was tested as well. In Vitro biocompatibility study of the NPs was performed with four different stable cell lines: A549, HeLa (human); RAW264.7, Hepa 1-6 (murine). Comparing the NPs parameters, their stability upon storage, and the data of biological examinations the best were found: As the AABB polymer, a poly(ester amide) composed of L-leucine, 1,6-hexanediol and sebacic acid-8L6, as a solvent (organic phase-DMSO), and as a surfactant, Tween 20.

show abstract

The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome

Cited by 13 publications

References 66 publications

Inference of cancer mechanisms through computational systems analysis

Inference of cancer mechanisms through computational systems analysis

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

Biodegradable Nanoparticles Made of Amino-Acid-Based Ester Polymers: Preparation, Characterization, and In Vitro Biocompatibility Study

Contact Info

Product

Resources

About