Simulating galactic cosmic ray effects: Synergy modeling of murine tumor prevalence after exposure to two one-ion beams in rapid sequence.

Health risks from galactic cosmic rays (GCR) in space travel above low earth orbit remain a concern. For many years accelerator experiments investigating space radiation induced prevalence of murine Harderian gland (HG) tumorigenesis have been performed to help estimate GCR risks. Most studies used acute, relatively low fluence, exposures. Results on a broad spectrum of individual ions and linear energy transfers (LETs) have become available. However, in space, the crew are exposed simultaneously to many different GCR. Recent upgrades at the Brookhaven NASA Space Radiation Laboratory (NSRL) now allow mixtures in the form of different one-ion beams delivered in rapid sequence. This paper uses the results of three two-ion mixture experiments to illustrate conceptual, mathematical, computational, and statistical aspects of synergy analyses and also acts as an interim report on the mixture experiments' results. The results were interpreted using the following: (a) accumulated data from HG one-ion accelerator experiments; (b) incremental effect additivity synergy theory rather than simple effect additivity synergy theory; (c) parsimonious models for one-ion dose-effect relations; and (d), computer-implemented numerical methods encapsulated in freely available open source customized software. The main conclusions are the following. As yet, the murine HG tumorigenesis experimental studies show synergy in only one case out of three. Moreover, some theoretical arguments suggest GCR-simulating mixed beams are not likely to be synergistic. However, more studies relevant to possible synergy are needed by various groups that are studying various endpoints. Especially important is the possibility of synergy among high-LET radiations, since individual high-LET ions have large relative biological effectiveness for many endpoints. Selected terminology, symbols, and abbreviations. DER - dose-effect relation; E(d) - DER of a one-ion beam, where d is dose; HG prevalence p - in this paper, p is the number of mice with at least one Harderian gland tumor divided by the number of mice that are at risk of developing Harderian gland tumors (so that in this paper prevalence p can never, conceptually speaking, be greater than 1); IEA - incremental effect additivity synergy theory; synergy level - a specification, exemplified in Fig. 5, of how clear-cut an observed synergy is; mixmix principle - a consistency condition on a synergy theory which insures that the synergy theory treats mixtures of agent mixtures in a mathematically self-consistent way; NTE - non-targeted effect(s); NSNA - neither synergy nor antagonism; SEA - simple effect additivity synergy theory; TE - targeted effect(s); ß* - ion speed relative to the speed of light, with 0 < ß* < 1; SLI - swift light ion(s).

Synergy theory for murine Harderian gland tumours after irradiation by mixtures of high-energy ionized atomic nuclei.

Experimental studies reporting murine Harderian gland (HG) tumourigenesis have been a NASA concern for many years. Studies used particle accelerators to produce beams that, on beam entry, consist of a single isotope also present in the galactic cosmic ray (GCR) spectrum. In this paper synergy theory is described, potentially applicable to corresponding mixed-field experiments, in progress, planned, or hypothetical. The "obvious" simple effect additivity (SEA) approach of comparing an observed mixture dose-effect relationship (DER) to the sum of the components' DERs is known from other fields of biology to be unreliable when the components' DERs are highly curvilinear. Such curvilinearity may be present at low fluxes such as those used in the one-ion HG experiments due to non-targeted ('bystander') effects, in which case a replacement for SEA synergy theory is needed. This paper comprises in silico modeling of published experimental data using a recently introduced, arguably optimal, replacement for SEA: incremental effect additivity (IEA). Customized open-source software is used. IEA is based on computer numerical integration of non-linear ordinary differential equations. To illustrate IEA synergy theory, possible rapidly-sequential-beam mixture experiments are discussed, including tight 95% confidence intervals calculated by Monte-Carlo sampling from variance-covariance matrices. The importance of having matched one-ion and mixed-beam experiments is emphasized. Arguments are presented against NASA over-emphasizing accelerator experiments with mixed beams whose dosing protocols are standardized rather than being adjustable to take biological variability into account. It is currently unknown whether mixed GCR beams sometimes have statistically significant synergy for the carcinogenesis endpoint. Synergy would increase risks for prolonged astronaut voyages in interplanetary space.

The effect of DNA degradation bias in passive sampling devices on metabarcoding studies of arthropod communities and their associated microbiota.

PCR amplification bias is a well-known problem in metagenomic analysis of arthropod communities. In contrast, variation of DNA degradation rates is a largely neglected source of bias. Differential degradation of DNA molecules could cause underrepresentation of taxa in a community sequencing sample. Arthropods are often collected by passive sampling devices, like malaise traps. Specimens in such a trap are exposed to varying periods of suboptimal storage and possibly different rates of DNA degradation. Degradation bias could thus be a significant issue, skewing diversity estimates. Here, we estimate the effect of differential DNA degradation on the recovery of community diversity of Hawaiian arthropods and their associated microbiota. We use a simple DNA size selection protocol to test for degradation bias in mock communities, as well as passively collected samples from actual Malaise traps. We compare the effect of DNA degradation to that of varying PCR conditions, including primer choice, annealing temperature and cycle number. Our results show that DNA degradation does indeed bias community analyses. However, the effect of this bias is of minor importance compared to that induced by changes in PCR conditions. Analyses of the macro and microbiome from passively collected arthropod samples are thus well worth pursuing.