Proteomic indicators of oxidation and hydration state in colorectal cancer

Abstract

New integrative approaches are needed to harness the potential of rapidly growing datasets of protein expression and microbial community composition in colorectal cancer. Chemical and thermodynamic models offer theoretical tools to describe populations of biomacromolecules and their relative potential for formation in different microenvironmental conditions. The average oxidation state of carbon (ZC) can be calculated as an elemental ratio from the chemical formulas of proteins, and water demand per residue () is computed by writing the overall formation reactions of proteins from basis species. Using results reported in proteomic studies of clinical samples, many datasets exhibit higher mean ZC or of proteins in carcinoma or adenoma compared to normal tissue. In contrast, average protein compositions in bacterial genomes often have lower ZC for bacteria enriched in fecal samples from cancer patients compared to healthy donors. In thermodynamic calculations, the potential for formation of the cancer-related proteins is energetically favored by changes in the chemical activity of H2O and fugacity of O2 that reflect the compositional differences. The compositional analysis suggests that a systematic change in chemical composition is an essential feature of cancer proteomes, and the thermodynamic descriptions show that the observed proteomic transformations in host tissue could be promoted by relatively high microenvironmental oxidation and hydration states.

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

“…Either methionine or cysteine would provide the required sulfur for the system, but cysteine is relevant as a constituent of the glutathione molecule, which has important roles in cellular redox chemistry 32 . These considerations support the proposal of the amino acids glutamine, glutamic acid, and cysteine (collectively abbreviated QEC) as a biologically relevant set of basis species for describing the chemical compositions of proteins 16 . These three amino acids are among the top eight amino acids ranked by number of reactions in a metabolic model for By reducing the covariation between Z C and n H 2 O , the QEC basis species yields a more convenient projection than a common "default" choice of inorganic species, such as CO 2 , NH 3 , H 2 S, H 2 O, and O 2 , which commonly appear in overall catabolic reactions 30 (see Methods for details).…”

“…Z C is computed from the chemical formulas of organic molecules, and takes values between the extremes of -4 (CH 4 ) and +4 (CO 2 ), although the range for particular classes of biomolecules is much smaller 15 . n H 2 O is derived from the number of water molecules in theoretical formation reactions of proteins from basis species 16,17 . Through analysis of representative metagenomic and metatranscriptomic datasets, we show that Z C and n H 2 O are most closely aligned with environmental redox and salinity gradients, respectively.…”

Chemical composition and the potential for proteomic transformation in cancer, hypoxia, and hyperosmotic stress

“…Sequences of proteins in other organisms and of human proteins not contained in the reference proteome were downloaded from UniProt or other sources. Amino acid compositions were computed using functions in the CHNOSZ package (Dick, 2008) or the ProtParam tool on the UniProt website (see Dick, 2016). The amino acid compositions are stored in *.csv files in Dataset S1.…”

“…Zhu and Anderson, 2002). Dick (2016) proposed using C 5 H 10 N 2 O 3 , C 5 H 9 NO 4 , C 3 H 7 NO 2 S, O 2 , and H 2 O as a basis for assessing compositional differences in proteomes. The first three formulas correspond to glutamine, glutamic acid, and cysteine.…”

“…Publicly available datasets reporting a minimum of ca. 30 up-and 30 down-expressed proteins for tissue samples of CRC, and one meta-analysis of serum biomarkers, were compiled recently (Dick, 2016). These same datasets are listed in Table 1, with one newer addition (dataset ; Liu et al, 2016).…”