Biosynthetic energy cost for amino acids decreases in cancer evolution

Zhang, Hong; Wang, Yirong; Li, Jun; Chen, Han; He, Xionglei; Zhang, Huiwen; Liang, Han; Lü, Jian

doi:10.1038/s41467-018-06461-1

Cited by 36 publications

(45 citation statements)

References 75 publications

(114 reference statements)

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“…These data were consistent with a recent report in which amino acid and polyamine metabolism-associated gene promoters were hypermethylated, resulting in the lower expression of these genes in cisplatin-resistant bladder cancer cell lines 28 . Because amino acid metabolism is closely related to cancer progression 56 , 57 , several clinical trials are currently being conducted (e.g., ADI-PEG 20 treatment) that are tailored toward depleting the amino acid supply to cancer cells that are highly dependent on amino acid metabolism 58 , 59 . Although it would be interesting to investigate whether amino acid depletion therapy improves cisplatin efficacy in bladder cancer, the mechanistic role of amino acid metabolism in bladder cancer resistance requires further clarification.…”

Section: Discussionmentioning

confidence: 99%

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

et al. 2020

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Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.

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Section: Discussionmentioning

confidence: 99%

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

et al. 2020

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“…However, one may question why tumor cells cannot simply use glutamine-derived carbon and nitrogen to synthesize asparagine through asparagine synthetase (ASNS), an enzyme highly expressed in solid tumor cells [51], if glutamine itself is not limiting. A clue may be found from a recent report showing that the relative biosynthetic energy cost for asparagine is the highest among the nine NEAAs (Asn, Asp, Gln, Ser, Arg, Pro, Glu, Ala, Gly) that are synthesized from glycolysis and TCA cycle-derived carbon source in humans [52]. This is likely because of the high energy cost to maintain the carbon flux from glucose to go through the TCA cycle to generate glutamate, glutamine and aspartate when exogenous glutamine is absent.…”

Section: What Is the Critical Limiting Metabolite During Glutaminementioning

confidence: 99%

Starve Cancer Cells of Glutamine: Break the Spell or Make a Hungry Monster?

Jiang

Srivastava

Zhang

2019

Cancers

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Distinct from normal differentiated tissues, cancer cells reprogram nutrient uptake and utilization to accommodate their elevated demands for biosynthesis and energy production. A hallmark of these types of reprogramming is the increased utilization of, and dependency on glutamine, a nonessential amino acid, for cancer cell growth and survival. It is well-accepted that glutamine is a versatile biosynthetic substrate in cancer cells beyond its role as a proteinogenic amino acid. In addition, accumulating evidence suggests that glutamine metabolism is regulated by many factors, including tumor origin, oncogene/tumor suppressor status, epigenetic alternations and tumor microenvironment. However, despite the emerging understanding of why cancer cells depend on glutamine for growth and survival, the contribution of glutamine metabolism to tumor progression under physiological conditions is still under investigation, partially because the level of glutamine in the tumor environment is often found low. Since targeting glutamine acquisition and utilization has been proposed to be a new therapeutic strategy in cancer, it is central to understand how tumor cells respond and adapt to glutamine starvation for optimized therapeutic intervention. In this review, we first summarize the diverse usage of glutamine to support cancer cell growth and survival, and then focus our discussion on the influence of other nutrients on cancer cell adaptation to glutamine starvation as well as its implication in cancer therapy.

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“…The total metabolic demands for synthesizing proteins are not only for the polymerization reaction but also the formation of the amino acids themselves. To take one recent example, see the paper of Zhang et al 29 , where the energy cost of proteins per amino acid in cancer cells was evaluated by averaging the contributions for amino acids making up the protein sequences. That paper refers to other papers that quantify the energetic cost for proteins using amino acid contributions in bacteria (E. coli) and yeast (S. cerevisiae) 8,9 .…”

Section: Conceptual Backgroundmentioning

confidence: 99%

Distinct trends in chemical composition of proteins from metagenomes in redox and salinity gradients

Tan

2020

Preprint

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Thermodynamic influences on the chemical compositions of proteins have remained enigmatic despite much work that demonstrates the impact of environmental conditions on amino acid frequencies. Here, we show that the dehydrating effect of salinity is apparent in protein sequences inferred from metagenomes and metatranscriptomes. The stoichiometric hydration state (n H 2 O ), derived from the number of water molecules in theoretical reactions to form proteins from a particular set of basis species (glutamine, glutamic acid, cysteine, O 2 , H 2 O), decreases along salinity gradients including the Baltic Sea, Amazon River and ocean plume, and other samples from freshwater and marine environments. Analysis of other metagenomic datasets shows that differences in carbon oxidation state, rather than stoichiometric hydration state, are a stronger indicator of redox gradients than salinity gradients. These compositional metrics can help to identify thermodynamic effects in the distribution of proteins along chemical gradients at scales from geologic systems to cells.

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Biosynthetic energy cost for amino acids decreases in cancer evolution

Cited by 36 publications

References 75 publications

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

Starve Cancer Cells of Glutamine: Break the Spell or Make a Hungry Monster?

Distinct trends in chemical composition of proteins from metagenomes in redox and salinity gradients

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