Replication errors characteristics

There are many endogenous processes that change the information content of the DNA. These processes are inseparable from the performance and characteristics of an organism, and they are influenced by external factors to only a small extent. DNA replication errors... 

Goggins M, Offerhaus GJ, Hilgers W, et al. Pancreatic adenocarcinomas with DNA replication errors (RER+) are associated with wild-type K-ras and characteristic histopathology. Poor differentiation, a syncytial growth pattern, and pushing borders suggest RER+. Am J Pathol. 1998 152 1501-1507. 

Deficiency in the DNA mismatch repair (MMR) pathway defines a variant subset of colorectal cancers (Lynch and Smyrk, 1996 Kinzler and Vogelstein, 1996). Replication errors (RER) are responsible for their main characteristic, the large-scale accumulation of frameshift mutations affecting microsatellite tracts (Lynch and Smyrk, 1996 Kinzler and Vogelstein, 1996), a phenomenon that is often referred to as microsatellite instability. The cancers without MMR deficiency will thereafter be called the common cancers, whereas the cancers with MMR deficiency will be alluded to as variant cancers. The RER phenotype can be acquired early in carcinogenesis, evidence... 

The design of the information resident in the SSR must be appropriate for its self-replication. Its characteristics must be complete and adequate for the task. It must cover all relevant aspects that intervene during replication (e.g., materials, parts, processes, procedures, plans, spatial structures, error and recovery handling, etc.). Completeness means also that the information designed and stored in the SSR is correctly correlated with the SSR environment. That means, for example, that the SSR design should be based on an accurate and exhaustive list of raw materials and raw parts that exist in the SSR environment together with the material identification... 

One common characteristic of many advanced scientific techniques, as indicated in Table 2, is that they are applied at the measurement frontier, where the net signal (S) is comparable to the residual background or blank (B) effect. The problem is compounded because (a) one or a few measurements are generally relied upon to estimate the blank—especially when samples are costly or difficult to obtain, and (b) the uncertainty associated with the observed blank is assumed normal and random and calculated either from counting statistics or replication with just a few degrees of freedom. (The disastrous consequences which may follow such naive faith in the stability of the blank are nowhere better illustrated than in trace chemical analysis, where S B is often the rule [10].) For radioactivity (or mass spectrometric) counting techniques it can be shown that the smallest detectable non-Poisson random error component is approximately 6, where ... 

The process of mutation by tautomerization is similar to the excited-state process described here. If a misprint induced by a tautomer takes place during replication, then an error is recorded. Because reaction path calculations of DNA base pairs show similar potential-energy characteristics to those discussed here, we anticipate being able to explore the relevance of tautomerization dynamics to mutagenesis. In this area, we are currently examining these and other systems, also in solutions. 

Statistics should follow the technical scrutiny, not the other way round. A statistical analysis of data of an interlaboratory study cannot explain deviating results nor can alone give information on the accuracy of the results. Statistics only treat a population of data and provide information on the statistical characteristics of this population. The results of the statistical treatment may give rise to discussions on particular data not belonging to the rest of the population, but outlying data can sometimes be closer to the true value than the bulk of the population (Griepink et al., 1993). If no systematic errors affect the population of data, various statistical tests may be applied to the results, which can be treated either as individual data or as means of laboratory means. When different methods are applied, the statistical treatment is usually based on the mean values of replicate determinations. Examples of statistical tests used for certification purposes are described elsewhere (Horwitz, 1991). Together with the technical evaluation of the results, the statistical evaluation forms the basis for the conclusions to be drawn and the possible actions to be taken. 

At large values of the accuracy of replication (g 1) we observe a quasispecies characteristic for direct replication, /n 2/ predominantly. The master sequence Iq is most frequent, followed by some one-error mutants, two-error mutants, and so on. 

Our studies have revealed that the structures of aromatic amine-DNA adducts are determined primarily by their size and coplanarity, as well as the nature of the adduct linkage (C8, N2, etc). It has been shown that aromatic amine lesions exist primarily in three well-defined conformational categories (S, B, W) and their population balance is strongly influenced by the sequences surrounding the lesion site. It is believed that the S/B/W ratios of aromatic amine adducts, not the subtle structural differences at the lesion site (e.g., rotamers, C8 versus N2 linkage, etc), determine the nature of the conformation-specific repair and mutational outcomes. As such, the available data points towards a new paradigm lesion bypass (either error-free or error-prone) depends on various factors, including the thermodynamic and conformational characteristics of the lesion at the replication fork,... 

Method precision (random error, variation) and accuracy (systematic error, mean bias) for LBAs should be evaluated by analyzing validation samples (QC samples) that are prepared in a biological matrix that is judged scientifically to be representative of the anticipated study samples [18]. This topic has been reviewed in other publications [3 6,9,10,20]. These performance characteristics should be evaluated during the method development phase, taking into consideration the factors known to vary in the method (e.g., analysts, instruments, reagents, different days, etc.). Several concentrations are required during the method development phase and are assayed in replicates. Factors known to vary between runs (e.g., analyst, instrument, and day)... 

The primary performance measures of a ligand-binding assay are bias/trueness and precision. These measures along with the total error are then used to derive and evaluate several other performance characteristics such as sensitivity (LLOQ), dynamic range, and dilutional linearity. Estimation of the primary performance measures (bias, precision, and total error) requires relevant data to be generated from a number of independent runs (also termed as experiments or assay s). Within each run, a number of concentration levels of the analyte of interest are tested with two or more replicates at each level. The primary performance measures are estimated independently at each level of the analyte concentration. This is carried out within the framework of the analysis of variance (ANOVA) model with the experimental runs included as a random effect [23]. Additional terms such as analyst, instmment, etc., may be included in this model depending on the design of the experiment. This ANOVA model allows us to estimate the overall mean of the calculated concentrations and the relevant variance components such as the within-run variance and the between-run variance. 

Neoplasms can undergo permanent stable changes in their phenotype, a process referred to as progression (33). Little is known about the basis for this alteration in the characteristics of neoplasms. It could result from gene amplification or a change in their chromosomal complement. Another hypothesis (34) is that decreased fidelity of DNA polymerases in tumor cells leads to errors in the replication of DNA, thereby introducing new mutations. 

Characteristic 4 means that, in principle, it is always possible to reduce the random error of an analysis to something that approaches 0. Unfortunately, it is seldom practical to achieve this goal, however, because to do so requires performing 20 or more rcplicaie analyses, Ordinarily, wc can only afford the time for two or three replicated measurements, and a significant random error i.s to be expected for the mean ol such a snia number of replicates. 

A nested analysis of variance (ANOVA) was conducted for two control samples analyzed In 37 assays. Variability among replicate wells (within plates) accounted for more than 90% of the total variability for both controls. Among day and among plate (within day) variability constituted 5-10% and less than 1% of the total, respectively. This interwell variability is a characteristic of microplate ELISAs which Is not widely documented, but It Is known to those who work In this field. This variability Is due to several compounded factors. Including Intrinsic variability In the binding characteristics of the plates, pipetting error, thermal variations across the plate and Interwell variability of washing. The relative contributions of these factors have not been studied adequately. 

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