Statistical procedures to test for linearity and estimate threshold doses for tumor induction with nonlinear dose-response relationships in bioassays for carcinogenicity

DeepRibo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns

Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub)-species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network utilizing features extracted from ribosome profiling information and binding site sequence patterns that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes. The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and ribosome binding translation initiation sequence region into one model. DeepRibo is designed as a single model trained on a variety of ribosome profiling experiments, used for the identification of open reading frames in prokaryotes without a priori knowledge of the translational landscape. Through extensive validation of the model trained on various sets of data, multiple species sequence similarity, mass spectrometry and Edman degradation verified proteins, the effectiveness of DeepRibo is highlighted.

The additive to background assumption in cancer risk assessment: A reappraisal

The assumption that chemical and radiation induced cancers act in a manner that is additive to background was proposed in the mid-1970s. It was adopted by the U.S. Environmental Protection Agency (EPA) in 1986 and then subsequently by other regulatory agencies worldwide for cancer risk assessment. It ensured that cancer risks at low doses act in a linear fashion. The additive to background process assumes that the mechanism(s) resulting in induced (i.e., treatment related) and spontaneous (i.e., control group) cancers are identical. This assumption could not be properly evaluated due to inadequate mechanistic data when it was proposed in the 1970s. Using the findings of modern molecular toxicology, including oncogene activation/mutation, gene regulation, and molecular pathway analyses, the additive to background assumption was evaluated in the present paper. Based on published studies with 45 carcinogens over 13 diverse mammalian models and for a broad range of tumor types compelling evidence indicates that carcinogen-induced tumors are mediated in general via mechanisms that are not identical to those affecting the occurrence of the same type of spontaneous tumors in appropriate control groups. These findings, which challenge a fundamental assumption of the additive to background concept, have significant implications for cancer risk assessment policy, regulatory agency practices, as well as fundamental concepts of cancer biology.

Quantitative assessment of the dose-response of alkylating agents in DNA repair proficient and deficient ames tester strains

Mutagenic and clastogenic effects of some DNA damaging agents such as methyl methanesulfonate (MMS) and ethyl methanesulfonate (EMS) have been demonstrated to exhibit a nonlinear or even "thresholded" dose-response in vitro and in vivo. DNA repair seems to be mainly responsible for these thresholds. To this end, we assessed several mutagenic alkylators in the Ames test with four different strains of Salmonella typhimurium: the alkyl transferases proficient strain TA1535 (Ogt+/Ada+), as well as the alkyl transferases deficient strains YG7100 (Ogt+/Ada-), YG7104 (Ogt-/Ada+) and YG7108 (Ogt-/Ada-). The known genotoxins EMS, MMS, temozolomide (TMZ), ethylnitrosourea (ENU) and methylnitrosourea (MNU) were tested in as many as 22 concentration levels. Dose-response curves were statistically fitted by the PROAST benchmark dose model and the Lutz-Lutz "hockeystick" model. These dose-response curves suggest efficient DNA-repair for lesions inflicted by all agents in strain TA1535. In the absence of Ogt, Ada is predominantly repairing methylations but not ethylations. It is concluded that the capacity of alkyl-transferases to successfully repair DNA lesions up to certain dose levels contributes to genotoxicity thresholds.

Statistical model to estimate a threshold dose and its confidence limits for the analysis of sublinear dose-response relationships, exemplified for mutagenicity data

Strongly sublinear dose-response relationships (slope increasing with dose) raise the question about a putative threshold dose below which no biologically relevant effect would be expected. A mathematical threshold with a break in the curve at the threshold dose is generally rejected for consequences of genotoxicity such as mutation, because proportionality between low dose and the rate of DNA-adduct formation is a reasonable hypothesis. In view of an increasing database for distinct deviation from linearity for mutagenicity, we offer a statistical model to analyze continuous response data and estimate a threshold dose together with its confidence limits, thereby taking data quality and degree of sublinearity into account. The simplest mathematical threshold model is a hockey stick defined by a low-dose part with slope zero at background level a to a theoretical break point at threshold dose td, followed by a linear increase above td with slope b. The function is y (dose d)=a+bx(d-td)x1([d>td]). Using the free statistics software package "R", we make a procedure available to estimate the parameters a, b, and td. Confidence intervals are calculated for all parameters at a significance level that can be defined by the user. If the lower limit of the confidence interval for td is >0, linearity is rejected. The procedure is illustrated by two examples. A small data set with three replicates per dose group, indicating a threshold for the induction of thymidine kinase mutants in L5178Y tk(+/-) mouse lymphoma cells treated with methyl methanesulfonate, did not achieve significance. On the other hand, the large data set reported in this issue (Gocke et al.) on lacZ mutants in bone marrow cells of transgenic mice treated with ethyl methanesulfonate strongly favoured the hockey stick model. The question of a theoretically expected linear dose-related increase below the threshold dose is addressed by linear regression of the data below the break point and estimation of an upper limit of the slope. The question of biological relevance of the resulting slope is discussed against the normal variation of background measures in the control group.

Modeling nonlinear dose-response relationships in epidemiologic studies: statistical approaches and practical challenges

Non-linear dose response relationships pose statistical challenges for their discovery. Even when an initial linear approximation is followed by other approaches, the results may be misleading and, possibly, preclude altogether the discovery of the nonlinear relationship under investigation. We review a variety of straightforward statistical approaches for detecting nonlinear relationships and discuss several factors that hinder their detection. Our specific context is that of epidemiologic studies of exposure-outcome associations and we focus on threshold and J-effect dose response relationships. The examples presented reveal that no single approach is universally appropriate; rather, these (and possibly other) nonlinearities require for their discovery a variety of both graphical and numeric techniques.

Nonmonotonic Dose-Response Relationships: Mechanistic Basis, Kinetic Modeling, and Implications for Risk Assessment

Dose-response curves for the first interaction of a chemical with a biochemical target molecule are usually monotonic; i.e., they increase or decrease over the entire dose range. However, for reactions of a complex biological system to a toxicant, nonmonotonic (biphasic) dose-effect relationships can be observed, showing a decrease at low dose followed by an increase at high dose, or vice versa. We present four examples to demonstrate that nonmonotonic dose-response relationships can result from superimposition of monotonic dose responses of component biological reactions. Examples include (i) a membrane-receptor model with receptor subtypes of different ligand affinity and opposing downstream effects (adenosine receptors A1 vs. A2), (ii) androgen receptor-mediated gene expression driven by homodimers, but not mixed-ligand dimers, (iii) repair of background DNA damage by enzymatic activity induced by adducts formed by a xenobiotic, (iv) rate of mutation as a consequence of DNA damage times rate of cell division, the latter being modulated by cell-cycle delay at low-level DNA damage, and cell-cycle acceleration due to regenerative hyperplasia at cytotoxic dose levels. Quantitative analyses based on biological models are shown, and factors that affect the degree of nonmonotonicity are identified. It is noted that threshold-type dose-response curves could in fact be nonmonotonic. Our analysis should promote a scientific discussion of biphasic dose responses and the concept termed "hormesis," and of default procedures for low-dose extrapolation in toxicological risk assessment.