Effects of codon distributions and tRNA competition on protein translation

Translation is a central cellular process and the complexity of its mechanism necessitates mathematical frameworks to better understand system properties and make quantitative predictions. We have developed a gene sequence-specific mechanistic model for translation which accounts for all the elementary steps of translation elongation. Included in our model is the nonspecific binding of tRNAs to the ribosomal A site, and we find that the competitive, nonspecific binding of the tRNAs is the rate-limiting step in the elongation cycle for every codon. By introducing our model in terms of the Michaelis-Menten kinetic framework, we determine that these results are due to the tRNAs that do not recognize the ribosomal A site codon acting as competitive inhibitors to the tRNAs that do recognize the ribosomal A site codon. We present the results of a sensitivity analysis to determine the contribution of elongation cycle kinetic parameters of each codon on the overall translation rate, and observe that the translation rates of mRNAs are controlled by segments of rate-limiting codons that are sequence-specific. Along these lines, we find that the relative position of codons along the mRNA determines the optimal protein synthesis rate.     

Oxidation of methoxyphenethylamines by cytochrome P450 2D6. Analysis of rate-limiting steps

Cytochrome P450 (P450) 2D6 is involved in the oxidation of a large fraction ( approximately 30%) of drugs used by humans and also catalyzes the O-demethylation of the model substrates 3- and 4-methoxyphenethylamine followed by subsequent ring hydroxylation to dopamine. Burst kinetics were not observed; rate-limiting step(s) must occur prior to product formation. Rates of reduction of ferric P450 2D6 were stimulated by 3- or 4-methoxyphenethylamine or the inhibitor quinidine; reduction is not the most rate-limiting step. The non-competitive intramolecular deuterium isotope effect, an estimate of the intrinsic isotope effect, for 4-methoxyphenethylamine O-demethylation was 9.6. Intermolecular non-competitive deuterium isotope effects of 3.1-3.8 were measured for k(cat) and k(cat)/K(m) for both O-demethylation reactions, implicating at least partially rate-limiting C-H bond breaking. Simulation of steady-state kinetic data yielded a catalytic mechanism dominated by the rates of (i) Fe(2+)O(2)(-) protonation (plus O-O bond scission) and (ii) C-H bond breaking, consistent with the appearance of the spectral intermediates in the steady state, attributed to iron-oxygen complexes. However, all the rates of individual steps (or rates of combined steps) are considerably higher than k(cat), and the contributions of several steps must be considered in understanding rates of the P450 2D6 reactions.     

Thermodynamic analysis of trinitrotoluene biodegradation and mineralization pathways

Biodegradation of 2,4,6-trinitrotoluene (TNT) proceeds through several different metabolic pathways. However, the reaction steps which are considered rate-controlling have not been fully determined. Glycolysis and other biological pathways contain biochemical reactions which are acutely rate-limiting due to enzyme control. These rate-limiting steps also have large negative Gibbs free energy changes. Because xenobiotic compounds such as TNT can be used by biological systems as nitrogen, carbon, and energy sources, it is likely that their degradation pathways also contain acutely rate-limiting steps. Identification of these rate-controlling reactions will enhance and better direct genetic engineering techniques to increase specific enzyme levels.This article identifies likely rate-controlling steps (or sets of steps) in reported TNT biodegradation pathways by estimating the Gibbs free energy change for each step and for the overall pathways. The biological standard Gibbs free energy change of reaction was calculated for each pathway step using a group contribution method specifically tailored for biomolecules. The method was also applied to hypothetical "pathways" constructed to mineralize TNT using several different microorganisms. Pathways steps that have large negative Gibbs free energy changes are postulated to be potentially rate-controlling. The microorganisms which utilize degradation pathways with the largest overall (from TNT to citrate) negatiave Gibbs free energy changes were also determined. Such microorganisms can extract more energy from the starting substrate and are thus assumed to have a competitive advantage over other microorganisms. Results from this modeling-based research are consistent with much experimental work available in the literature. (c) 1996 John Wiley & Sons, Inc.     

Angiotensin II facilitates breast cancer cell migration and metastasis

Breast cancer metastasis is a leading cause of death by malignancy in women worldwide. Efforts are being made to further characterize the rate-limiting steps of cancer metastasis, i.e. extravasation of circulating tumor cells and colonization of secondary organs. In this study, we investigated whether angiotensin II, a major vasoactive peptide both produced locally and released in the bloodstream, may trigger activating signals that contribute to cancer cell extravasation and metastasis. We used an experimental in vivo model of cancer metastasis in which bioluminescent breast tumor cells (D3H2LN) were injected intra-cardiacally into nude mice in order to recapitulate the late and essential steps of metastatic dissemination. Real-time intravital imaging studies revealed that angiotensin II accelerates the formation of metastatic foci at secondary sites. Pre-treatment of cancer cells with the peptide increases the number of mice with metastases, as well as the number and size of metastases per mouse. In vitro, angiotensin II contributes to each sequential step of cancer metastasis by promoting cancer cell adhesion to endothelial cells, trans-endothelial migration and tumor cell migration across extracellular matrix. At the molecular level, a total of 102 genes differentially expressed following angiotensin II pre-treatment were identified by comparative DNA microarray. Angiotensin II regulates two groups of connected genes related to its precursor angiotensinogen. Among those, up-regulated MMP2/MMP9 and ICAM1 stand at the crossroad of a network of genes involved in cell adhesion, migration and invasion. Our data suggest that targeting angiotensin II production or action may represent a valuable therapeutic option to prevent metastatic progression of invasive breast tumors.     

Effects of acetylation, polymerase phosphorylation, and DNA unwinding in glucocorticoid receptor transactivation

Varying the concentration of selected factors alters the induction properties of steroid receptors by changing the position of the dose-response curve (or the value for half-maximal induction=EC(50)) and the amount of partial agonist activity of antisteroids. We now describe a rudimentary mathematical model that predicts a simple Michaelis-Menten curve for the multi-step process of steroid-regulated gene induction. This model suggests that steps far downstream from receptor binding to steroid can influence the EC(50) of agonist-complexes and partial agonist activity of antagonist-complexes. We therefore asked whether inhibitors of three possible downstream steps can reverse the effects of increased concentrations of two factors: glucocorticoid receptors (GRs) and Ubc9. The downstream steps (with inhibitors in parentheses) are protein deacetylation (TSA and VPA), DNA unwinding (CPT), and CTD phosphorylation of RNA polymerase II (DRB and H8). None of the inhibitors mimic or prevent the effects of added GRs. However, inhibitors of DNA unwinding and CTD phosphorylation do reverse the effects of Ubc9 with high GR concentrations. These results support our earlier conclusion that different rate-limiting steps operate at low and high GR concentrations versus high GR with Ubc9. The present data also suggest that downstream steps can modulate the EC(50) of GR-mediated induction, thus both supporting the utility of our mathematical model and widening the field of biochemical processes that can modify the EC(50).     

Bacterial Sec Protein Transport Is Rate-limited by Precursor Length: A Single Turnover Study

An in vitro real-time single turnover assay for the Escherichia coli Sec transport system was developed based on fluorescence dequenching. This assay corrects for the fluorescence quenching that occurs when fluorescent precursor proteins are transported into the lumen of inverted membrane vesicles. We found that 1) the kinetics were well fit by a single exponential, even when the ATP concentration was rate-limiting; 2) ATP hydrolysis occurred during most of the observable reaction period; and 3) longer precursor proteins transported more slowly than shorter precursor proteins. If protein transport through the SecYEG pore is the rate-limiting step of transport, which seems likely, these conclusions argue against a model in which precursor movement through the SecYEG translocon is mechanically driven by a series of rate-limiting, discrete translocation steps that result from conformational cycling of the SecA ATPase. Instead, we propose that precursor movement results predominantly from Brownian motion and that the SecA ATPase regulates pore accessibility.     

Testing the generalizability of cfDNA fragmentomic features across different studies for cancer early detection

cfDNA fragmentomic features have been used in cancer detection models; however, the generalizability of the models needs to be tested. We proposed a type of cfDNA fragmentomic feature named chromosomal arm-level fragment size distribution (ARM-FSD), evaluated and compared its performance and generalizability for lung cancer and pan-cancer detection with existing cfDNA fragmentomic features (as reference) by using cohorts from different institutions. The ARM-FSD lung cancer model outperformed the reference model by ∼10% when being tested by two external cohorts (AUC: 0.97 vs. 0.86; 0.87 vs. 0.76). For pan-cancer detection, the performance of the ARM-FSD based model is consistently higher than the reference (AUC: 0.88 vs. 0.75, 0.98 vs. 0.63) in a pan-cancer and a lung cancer external validation cohort, indicating that ARM-FSD model produces stable performance across multiple cohorts. Our study reveals ARM-FSD based models have a higher generalizability, and highlights the necessity of cross-study validation for predictive model development.     

Genetic tumor archeology: microdissection and genetic heterogeneity in squamous and basal cell carcinoma

Carcinogenesis is a multi-step series of somatic genetic events. The complexity of this multi-hit process makes it difficult to determine each single event and the definitive outcome of such events. To investigate the genetic alterations in cancer-related genes, sensitive and reliable detection methods are of major importance for generating relevant results. Another critical issue is the quality of starting material which largely affects the outcome of the analysis. Microdissection of cells defined under the microscope ensures a selection of representative material for subsequent genetic analysis. Skin cancer provides an advantageous model for studying the development of cancer. Detectable lesions occur early during tumor progression, facilitating molecular analysis of the cell populations from both preneoplastic and neoplastic lesions. Alterations of the p53 tumor suppressor gene are very common in non-melanoma skin cancer, and dysregulation of p53 pathways appear to be an early event in the tumor development. A high frequency of epidermal p53 clones has been detected in chronically sun-exposed skin. The abundance of clones containing p53 mutated keratinocytes adjacent to basal cell (BCC) and squamous cell carcinoma (SCC) suggests a role in human skin carcinogenesis. Studies using p53 mutations as a clonality marker have suggested a direct link between actinic keratosis, SCC in situ and invasive SCC. Microdissection-based studies have also shown that different parts of individual BCC tumors can share a common p53 mutation yet differ with respect to additional alterations within the p53 gene, consistent with subclonal development within tumors. Here, we present examples of using well-defined cell populations, including single cells, from complex tissue in combination with molecular tools to reveal features involved in skin carcinogenesis.     

A growth and division model for retinoblastoma

A stochastic growth and division model for studying a two hit cancer is developed and applied to retinoblastoma. Retinoblastoma occurs if both genes coding for a tumor suppressor protein on homologous chromosomes become defective. Germinal cases occur when a patient or carrier, born with one defective gene, suffers a second insult to any progeny retinal cell. Somatic cases are far less likely as two hits to the same cell during development are required. Details of the disease, germinal or somatic, unilateral or bilateral, in combination with case data allow for the estimation of the two parameters of the model: mutation rate, estimated at p=7x10(-7) per chromosome per cell division, and carrier frequency, estimated at f=40 per million. The model indicates that carriers of the disease arise from similar mutations to germ cells; in particular, heridary transmission can occur for only a generation or two before dying out. The results show that a stochastic simulation of a multi-hit cancer is feasible and may predict tumor growth dynamics. A simulation run will have to consist of a few million cells in order to observe even a small number of mutations. And several dozens such runs will have to be simulated.     

Metabolic control analysis indicates a change of strategy in the treatment of cancer

Much of the search for the “magic cancer bullet” or “block buster” has followed the expectation of a single gene or protein as “the rate-limiting step” for tumor persistence. Examples continue to abound: EGFR, VEGFR, Akt/PI3K, HIF-1α, PHD, PDK, or FAS continue to be targeted individually. However, many such attempts to block a metabolic or signal transduction pathway by targeting, specifically, a single rate-limiting molecule have proven to be unsuccessful. Metabolic control analysis (MCA) of cancer cells has generated a generic explanation for this phenomenon: several steps share the control of energy metabolism (for glycolysis: glucose transporter, hexokinase, glycogen synthesis and ATP demand; for oxidative phosphorylation: respiratory complex I and ATP demand), i.e., there is no single “rate-limiting step”. Targeting a type of step that does not exist is unlikely to be a successful paradigm for continued research into drug targeting of cancer.MCA establishes how to determine, quantitatively, the degrees of control that the various enzymes in the intracellular network exert on vital flux (or function) and on the concentration of important metabolites, substituting for the intuitive, qualitative and most often erroneous concept of single rate-limiting step. Moreover, MCA helps to understand (i) the underlying mechanisms by which a given enzyme exerts high or low control, (ii) why the control of the pathway is shared by several pathway enzymes and transporters and (iii) what are the better sets of drug targets. Indeed, by applying MCA it should now be possible to identify the group of proteins (and genes) that should be modified to achieve a successful modulation of the intracellular networks of biotechnological or clinical relevance. The challenge is to move away from the design of drugs that specifically inhibit a single controlling step, towards unspecific drugs or towards drug mixtures, which may have multiple target sites in the most exacerbated, unique and controlling pathways in cancer cells. Successful nonspecific drugs should still be specific for the networks of cancer cells over those of normal cells and to establish such cell-type specificity within molecular non-specificity will continue to require sophisticated analyses. Clinical practice has anticipated the latter strategy of mixtures of drugs: combinations of anti-neoplastic drugs are already administered with encouraging results. Therefore, the most promising strategy for cancer treatment seems to be that of a multi-targeted, MCA-advised, therapy.     

The biosynthesis of the selectin-ligand sialyl Lewis x in colorectal cancer tissues is regulated by fucosyltransferase VI and can be inhibited by an RNA interference-based approach

Sialyl Lewis x (sLex) is a selectin ligand whose overexpression in epithelial cancers mediates metastasis formation. The molecular basis of sLex biosynthesis in colon cancer tissues is still unclear. The prerequisite for therapeutic approaches aimed at sLex down-regulation in cancer, is the identification of rate-limiting steps in its biosynthesis. We have studied the role of α1,3-fucosyltransferases (Fuc-Ts) potentially involved in sLex biosynthesis in specimens of normal and cancer colon as well as in experimental systems. We found that: (i) in colon cancer, but not in normal mucosa where the antigen was poorly expressed, sLex correlated with a Fuc-T which, like Fuc-TVI, was active on 3'sialyllactosamine at a low concentration (Fuc-T(SLN)); (ii) competitive RT-PCR analysis revealed that the level of Fuc-T mRNA expression in both normal and cancer colon was Fuc-TVI>Fuc-TIII>Fuc-TIV; Fuc-TV and Fuc-TVII expression was negligible; (iii) sLex was expressed only by the gastrointestinal cell lines displaying both Fuc-TVI mRNA and Fuc-T(SLN) activity, but not by those expressing only Fuc-TIII mRNA; (iv) transfection with Fuc-TVI cDNA, but not with Fuc-TIII cDNA, induced sLex expression in gastrointestinal cell lines; (v) Fuc-TVI knock-down with specific siRNA induced down-regulation of Fuc-TVI mRNA and Fuc-T(SLN) activity and a dramatic inhibition of sLex expression. These data indicate that in colon cancer tissues Fuc-TVI is a key regulator of sLex biosynthesis which can be the target of RNA-interference-based gene knock-down approaches.     

Heterogeneity in multistage carcinogenesis and mixture modeling

Carcinogenesis is commonly described as a multistage process, in which stem cells are transformed into cancer cells via a series of mutations. In this article, we consider extensions of the multistage carcinogenesis model by mixture modeling. This approach allows us to describe population heterogeneity in a biologically meaningful way. We focus on finite mixture models, for which we prove identifiability. These models are applied to human lung cancer data from several birth cohorts. Maximum likelihood estimation does not perform well in this application due to the heavy censoring in our data. We thus use analytic graduation instead. Very good fits are achieved for models that combine a small high risk group with a large group that is quasi immune.     

A length-based hierarchical model of brown trout (Salmo trutta fario) growth and production

We present a hierarchical Bayesian model (HBM) to estimate the growth parameters, production, and production over biomass ratio (P/B) of resident brown trout (Salmo trutta fario) populations. The data which are required to run the model are removal sampling and air temperature data which are conveniently gathered by freshwater biologists. The model is the combination of eight submodels: abundance, weight, biomass, growth, growth rate, time of emergence, water temperature, and production. Abundance is modeled as a mixture of Gaussian cohorts; cohorts centers and standard deviations are related by a von Bertalanffy growth function; time of emergence and growth rate are functions of water temperature; water temperature is predicted from air temperature; biomass, production, and P/B are subsequently computed. We illustrate the capabilities of the model by investigating the growth and production of a brown trout population (Neste d'Oueil, Pyrénées, France) by using data collected in the field from 2005 to 2010.     

Platelet Transport Rates and Binding Kinetics at High Shear over a?Thrombus

Thrombus formation over a ruptured atherosclerotic plaque cap can occlude an artery with fatal consequences. We describe a computational model of platelet transport and binding to interpret rate-limiting steps seen in experimental thrombus formation over a collagen-coated stenosis. The model is used to compute shear rates in stenoses with growing boundaries. In the model, moving erythrocytes influence platelet transport based on shear-dependent enhanced diffusivity and a nonuniform platelet distribution. Adhesion is modeled as platelet-platelet binding kinetics. The results indicate that observed thrombus growth rates are limited by platelet transport to the wall for shear rates up to 6000 s⁻¹. Above 7000 s⁻¹, the thrombus growth rate is likely limited by binding kinetics (10⁻⁴ m/s). Thrombus growth computed from these rate-limiting steps match the thrombus location and occlusion times for experimental conditions if a lag time for platelet activation is included. Using fitted parameters, the model is then used to predict thrombus size and shape at a higher Reynolds number flow consistent with coronary artery disease.