Does premature aging of the mtDNA mutator mouse prove that mtDNA mutations are involved in natural aging?

Recent studies have demonstrated that transgenic mice with an increased rate of somatic point mutations in mitochondrial DNA (mtDNA mutator mice) display a premature aging phenotype reminiscent of human aging. These results are widely interpreted as implying that mtDNA mutations may be a central mechanism in mammalian aging. However, the levels of mutations in the mutator mice typically are more than an order of magnitude higher than typical levels in aged humans. Furthermore, most of the aging-like features are not specific to the mtDNA mutator mice, but are shared with several other premature aging mouse models, where no mtDNA mutations are involved. We conclude that, although mtDNA mutator mouse is a very useful model for studies of phenotypes associated with mtDNA mutations, the aging-like phenotypes of the mouse do not imply that mtDNA mutations are necessarily involved in natural mammalian aging. On the other hand, the fact that point mutations in aged human tissues are much less abundant than those causing premature aging in mutator mice does not mean that mtDNA mutations are not involved in human aging. Thus, mtDNA mutations may indeed be relevant to human aging, but they probably differ by origin, type, distribution, and spectra of affected tissues from those observed in mutator mice.     

When man got his mtDNA deletions?

Somatic mtDNA mutations and deletions in particular are known to clonally expand within cells, eventually reaching detrimental intracellular concentrations. The possibility that clonal expansion is a slow process taking a lifetime had prompted an idea that founder mutations of mutant clones that cause mitochondrial dysfunction in the aged tissue might have originated early in life. If, conversely, expansion was fast, founder mutations should predominantly originate later in life. This distinction is important: indeed, from which mutations should we protect ourselves – those of early development/childhood or those happening at old age? Recently, high-resolution data describing the distribution of mtDNA deletions have been obtained using a novel, highly efficient method (Taylor et al., 2014). These data have been interpreted as supporting predominantly early origin of founder mutations. Re-analysis of the data implies that the data actually better fit mostly late origin of founders, although more research is clearly needed to resolve the controversy.mtDNA mutations, and in particular deletions, progressively increase with age and are suspected culprits of several age-related degenerative processes. Because there are hundreds or even thousands of mtDNA genomes per cell, increase in mutational load may include not only the increase in the number of cells containing mutant genomes, but also increase in the fraction of mutant mtDNA in each cell. Studies of mutational composition of individual cells showed that accumulation of mutations within a cell usually does not occur via accrual of random hits. Instead, mtDNA mutations ‘clonally expand’, that is, a single initial mutation multiplies within cell, replaces normal mtDNAs, eventually takes over the cell, and may impair its mitochondrial function. Expansion is possible because mtDNA molecules in a cell are persistently replicated, even in nondividing cells, where some of them are destroyed and replaced by replication of others. Half-life of murine mtDNA is on the order of several weeks (Korr et al., 1998). The result of clonal expansion is that different cells typically contain different types of mutations, while mutant genomes within a cell carry the same mutation. Mechanisms of expansion are still debated; possibilities range from neutral genetic drift to selection within the ‘population’ of intracellular mitochondria. In this commentary, we do not assume any particular mechanism and concentrate on the kinetics of expansion.Because expansion takes time, it is possible that founder mutations of expanded mutant clones that compromise mitochondria at old age might have occurred early in life. Indeed, if expansion was a slow process taking about a lifetime to conclude (Fig. ​(Fig.1A,1A, upper panel), then only those mutations that were generated early in life would have enough time to reach harmful intracellular concentration. In an utmost version of this scenario, there is little de novo mutagenesis and increase in mutations with age is mostly driven by clonal expansion of early founder mutations. The ‘slow’ scenario implies that, as far as mtDNA mutagenesis is concerned, we need to preserve mtDNA during early years or even during development and to be less worried about mutations that arise in older individuals.Open in a separate windowFigure 1The ‘slow’ and the ‘fast’ expansion scenarios and the predicted changes in the diversity and extent of expansion of mtDNA mutations with age. Diversity and extent of expansion can be directly measured and used to distinguish between the two scenarios. mtDNA molecules with different deletions are depicted by small circles of different bright colors. Wild-type mtDNA molecules and cells that never get mutations are not shown for simplicity. Of course in a real tissue, mutant cells are surrounded by a great majority of nonmutant cells.If, on the other hand, clonal expansion was rapid (Fig. ​(Fig.1B1B upper panel), then expanding mutations would swiftly fill up the cells in which founders had arisen and therefore stop expanding. Consequently, overall mutation load would soon ‘plateau off’ if mutants were not continuously occurring. So, in this scenario, the observed persistent increase in mutations with age must be driven by de novo mutagenesis, and most impairment in this scenario is caused by ‘late in life’ mutations, which therefore should be of primary concern. Despite importance of this question, there is still no consensus on which scenario is correct (Payne et al., 2011), (Khrapko, 2011), although the ‘early mutations’ hypothesis appeared more than a decade ago (Elson et al., 2001), (Khrapko et al., 2003).Figure ​Figure11 schematically depicts two characteristics of the mutational dynamics that distinguish between the two scenarios. First, the diversity, that is, number of different types of deletions (Supplemental Note 0), remains constant in the slow scenario (Fig. ​(Fig.1A),1A), but steadily increases in the fast scenario (Fig. ​(Fig.1B).1B). Second, the extent of expansion, that is, the average number of mutant mtDNA molecules per clonal expansion, should increase steadily throughout the lifespan in the slow, early mutations scenario. In contrast, in the fast scenario, extent of expansion should increase rapidly early in life, up to the point when the earliest mutations had enough time to expand to the limits of their host cells and increase much slower thereafter.Recent paper by Taylor et al. (Taylor et al., 2014) describes a new method called Digital Deletion Detection, based on enrichment of deletions by wild-type specific restriction digestion, massive single-molecule PCR in microdroplets, followed by next generation sequencing of the PCR products. This ‘3D’ approach for the first time provided a detailed frequency distribution for a large set of different deleted mtDNA molecules in human brain as a function of age. These data are sufficient to estimate diversity and level of expansion of mutations and therefore promise to help to distinguish between the fast and the slow scenarios. The authors found that a) the number of different types of deletions per sample (used as proxy of diversity) does not increase with age, while b) the ratio of total deletion frequency over the number of deletion types (used as proxy of expansion) does steadily increase with age. Consequently, the authors concluded that ‘diversity of unique deletions remains constant’ and that the ‘data supported the hypothesis that expansion of pre-existing mutations is the primary factor contributing to age-related accumulation of mtDNA deletions’, that is, the slow expansion scenario. We believe, however, that these data deserve more detailed analysis and more cautious interpretation.A striking feature of the data (Taylor et al., 2014) is that the types of deletions found in any two samples are almost completely different (Supplemental Note 1). The same pattern has been previously observed in muscle (Nicholas et al., 2009). To explain this, consider that deletions originate mostly from individual cells each containing clonal expansion of deletion of a certain type. Because there are very many potential types of deletions and much fewer clonal expansions per sample, only a small proportion of possible types of deletions are found in each sample, which explains why two samples typically have almost no deletions in common. Similarly, any two cells with clonal expansions from the same sample usually carry different types of deletion. With this in mind, we will reconsider interpretation of the data.First, consider diversity of deletions. Unfortunately, number of deletion types per sample normalized against the total number of deletions used by Taylor et al. as proxy of diversity is not an adequate measure. First, normalization against the total number of sampled deletion molecules is not justified because in a sample with clonal expansions, the number of types of deletions is not proportional to the number of sequenced molecules (Supplemental Note 2). Instead, the number of deletion types per sample is proportional to the size of the sample (i.e., the size of the tissue piece actually used for DNA isolation). Indeed, increasing sample size means including proportionally more cells with expansions. As discussed above, these additional cells contain different deletion types, so the number of deletion types will also increase roughly proportionally to the sample size. Sample size must be factored out of a rational measure of deletion diversity. The best proxy of sample size available in the original study (Taylor et al., 2014) is the number of mtDNA copies isolated from each sample. Thus, to factor out the sample size, we used the number of deletion types per 10¹⁰ mtDNA, (Fig. ​(Fig.2A).2A). This corrected measure shows rather strong (P < 0.0003) increase in diversity of mtDNA deletions with age (Supplemental Note 3), which fits the ‘fast’ expansion scenario (Fig. ​(Fig.1B1B).Open in a separate windowFigure 2The observed changes in diversity and extent of expansion of mtDNA mutations in brain with age in Taylor et al. data. (A) Diversity of mtDNA deletions (number of deletion types per 10¹⁰ mtDNA) shows strong increase with age (P < 0.0003), corroborating the ‘fast’ expansion scenario (Fig. ​(Fig.1B).1B). (B) The extent of expansion shows excessive variance and does not seem to support any of the two scenarios (neither ‘fast’ nor ‘slow’) to any significant extent. Interpretation of these data requires more detailed analysis.Next, we revisited the extent of expansion of clonal mutations. As a measure of expansion, we used the average of the actual numbers of deleted molecules per deletion type, same as in Fig 1A,B. Note that this measure is different from ‘expansion index’ (Taylor et al., 2014), defined as deletion frequency per deletion type. This is essentially the same measure we use, additionally divided by the number of all mtDNA molecules in the sample. Unfortunately, ‘expansion index’ so defined systematically increases with decreased sample size. This is because deletion frequency is not expected to systematically increase with sample size, while the number of deletion types is, as shown in the previous paragraph. Thus, in particular because old samples in this set tend to be smaller (Supplemental Fig. 4A), this measure is biased.Extent of expansion of mtDNA mutations is plotted versus age in Fig. ​Fig.2B.2B. Which theoretical expansion pattern, the ‘slow’ (Fig. ​(Fig.1A)1A) or the ‘fast’ (Fig. ​(Fig.1B),1B), better fits the actual data (Fig. ​(Fig.2B)?2B)? It looks like either fit is poor: the data are notoriously variable. We conclude that it is necessary to look beyond the coarse average measure of the expansion to interpret the data and explain the excessive variance (Supplemental Note 4).The characteristic biphasic shape of the predicted ‘fast’ plot (Fig. ​(Fig.1B)1B) results from the early large expanded mutations, which are absent in the ‘slow’ scenario (Fig. ​(Fig.1A).1A). We therefore used the data (Taylor et al., 2014) to estimate the size of expansions (Supplemental Note 5, Supplementary Table S1) and in particular, to look for large expansions in young tissue. Indeed, young samples do contain large clonal expansions, and there are four expansions more than 1000 copies in samples 30 years and younger (Table S1). This is consistent with our own observations of large expansions of deletions in single neurons of the young brain using a different approach – single-molecule amplification (Kraytsberg et al., 2006). In other words, although rapid expansion pattern in Fig. ​Fig.2B2B is obscured by large variance of the data, the hallmarks of fast expansion, that is, large early mutant expansions, are present in the tissue.An aspect of the data, however, is at odds either with the fast or the slow scenario. The distribution of expansion sizes at any age is rather gradual; that is, there is a large proportion of expansions of intermediate sizes, ranging from smallest detectable (typically about 10 molecules) up to those more than 1000 molecules. In contrast, according to the ‘slow’ expansion scenario, all expansions should be of approximately the same size, which should increase with age, turning ‘large’ at approximately the same time. The fast scenario, also in contradiction with observations, predicts that proportion of mutants contained in expansions of intermediate sizes markedly decreases with age (Supplemental Note 6).If neither of the scenarios fits the data, what kind of mutational dynamics could be responsible for the observed distribution (Taylor et al., 2014)? We believe that most plausible is a ‘mixed’ scenario, where expansions are fast in some cells and slow in other (‘fast’ and ‘slow expanders’, correspondingly), probably with the whole spectrum of expansion rates in between. Expansion rates may differ between cell types or between cells of the same type differing in individual activity, stress, levels of ROS, length of deletion (Fukui & Moraes, 2009), etc. An example of such a difference is given by myoblasts, which, unlike their descendant myofibers, support only very slow, if any, expansion of mtDNA deletions (Moraes et al., 1989).What does this mean with respect to the question in the title of this commentary – when do mtDNA deletions arise? In fact, if we accept the mixed scenario, then it follows that the share of late mutations is at least significant. Indeed, if late mutations played little role, then accumulation of mutations should have been markedly decelerating with age. This is because ‘fast expander’ cells are saturated with mutations early in life and increase in mutation load at older age is driven by progressively ‘slower expanders’, meaning slower increase in mutational load. In contrast with this prediction, accumulation of deletions observed in most tissues appears to aggressively accelerate with age and is traditionally approximated with an exponent. This is also true for the Taylor et al. (2014), which are better fit by accelerating curves than they are by linear function (Fig. S6). The fraction of deleted mtDNA increases over the lifespan by up to four orders of magnitude in highly affected brain areas such as substantia nigra and about three in less affected, such as cortex (Meissner et al., 2008). In principle, even such a dramatic increase in mutant fraction might be entirely driven by expansion of early founder mutations in slow scenario. Neurons contain thousands of mtDNA copies, so expansion alone could potentially sustain about four orders of magnitude increase in mutant fraction from single founder mutants mtDNA to fully mutant cells. However, accelerated accumulation of (expanded) mutations in mixed scenario can only be explained by generation of de novo mutations at older ages.The reality is probably more complicated than idealized scenarios considered above. For example, cells with expanded mutations may die preferentially. If true, this would make fast scenario/late origin even more plausible. Indeed, that would mean that the actual number of mutations that have reached full expansion at any age is higher than observed (extra mutations being those that had died), implying that mutations expand faster than it appears. Other refinements of the model are certainly possible. However, notable variability of the data makes testing hypotheses, in particular, complex ones, difficult. Excessive variability of data on mtDNA deletions has been observed before, for example Meissner et al., 2008, but have never been duly explored. Lack of replicate analyses hampers understanding of the source of variance and of the shape of the frequency distributions of mutations. The latter are indispensable for interpreting the data. Future studies seeking to explain dynamics of mutations with age must include multiple replicate measurements (Supplemental Note 7).In conclusion, re-analysis of the data (Taylor et al., 2014) challenges the authors’ inference that diversity of unique deletions remains constant with age and that expansion of pre-existing deletions is the primary factor contributing to age-related accumulation of mtDNA deletions. The data are more consistent with increasing diversity of deletions and significant impact of mutagenesis at older age. However, the issue is far from being solved, in part because of high variability of the data, and it awaits more detailed studies (Supplemental Note 7).     

DPP-4 inhibition with linagliptin ameliorates the progression of premature aging in klotho−/− mice

Background

The potential of anti-aging effect of DPP-4 inhibitors is unknown. This study was performed to determine whether linagliptin, a DPP-4 inhibitor, could protect against premature aging in klotho?/? mice.

Methods

Klotho?/? mice exhibit multiple phenotypes resembling human premature aging, including extremely shortened life span, cognitive impairment, hippocampal neurodegeneration, hair loss, muscle atrophy, hypoglycemia, etc. To investigate the effect of linagliptin on these aging-related phenotypes, male klotho?/? mice were divided into two groups: (1) control group fed the standard diet, and (2) linagliptin group fed the standard diet containing linagliptin. Treatment with linagliptin was performed for 4 weeks. The effect of linagliptin on the above mentioned aging-related phenotypes was examined.

Results

Body weight of klotho?/? mice was greater in linagliptin group than in control group (11.1 ± 0.3 vs 9.9 ± 0.3 g; P < 0.01), which was associated with greater gastrocnemius muscle weight (P < 0.01) and greater kidney weight (P < 0.05) in linagliptin group. Thus, linagliptin significantly prevented body weight loss in klotho?/? mice. Survival rate of klotho?/? mice was greater in linagliptin group (93%) compared to control group (67%), although the difference did not reach statistical significance (P = 0.08). None of linagliptin-treated klotho?/? mice had alopecia during the treatment (P < 0.05 vs control klotho?/? mice). Latency of klotho?/? mice in passive avoidance test was larger in linagliptin group than in control group (P < 0.05), indicating the amelioration of cognitive impairment by linagliptin. Cerebral blood flow of klotho?/? mice was larger in linagliptin group than in control group (P < 0.01), being associated with greater cerebral phospho-eNOS levels (P < 0.05) in linagliptin group. Neuronal cell number in hippocampal CA1 region was greater in linagliptin group than in control group (P < 0.05). Linagliptin group had greater cerebral phospho-Akt (P < 0.05) and phospho-CREB (P < 0.05) than control group. Thus, linagliptin ameliorated brain aging in klotho?/? mice. The degree of hypoglycemia in klotho?/? mice was less in linagliptin group than in control group, as estimated by the findings of OGTT.

Conclusions

Out work provided the evidence that DPP-4 inhibition with linagliptin slowed the progression of premature aging in klotho?/? mice, and provided a novel insight into the potential role of DPP-4 in the mechanism of premature aging.

     

Do latitudinal and bioclimatic gradients drive parasitism in Odonata?

Prevalence of parasites in wild animals may follow ecogeographic patterns, under the influence of climatic factors and macroecological features. One of the largest scale biological patterns on Earth is the latitudinal diversity gradient; however, latitudinal gradients may also exist regarding the frequency of interspecific interactions such as the prevalence of parasitism in host populations. Dragonflies and damselflies (order Odonata) are hosts of a wide range of ecto- and endoparasites, interactions that can be affected by environmental factors that shape their occurrence and distribution, such as climatic variation, ultraviolet radiation and vegetation structure. Here, we retrieved data from the literature on parasites of Odonata, represented by 90 populations infected by ectoparasites (water mites) and 117 populations infected by endoparasites (intestinal gregarines). To test whether there is a latitudinal and bioclimatic gradient in the prevalence of water mites and gregarines parasitizing Odonata, we applied Bayesian phylogenetic comparative models. We found that prevalence of ectoparasites was partially associated with latitude, showing the opposite pattern from our expectations – prevalence was reduced at lower latitudes. Prevalence of endoparasites was not affected by latitude. While prevalence of water mites was also positively associated with vegetation biomass and climatic stability, we found no evidence of the effect of bioclimatic variables on the prevalence of gregarines. Our study suggests that infection by ectoparasites of dragonflies and damselflies is driven by latitudinal and bioclimatic variables. We add evidence of the role of global-scale biological patterns in shaping biodiversity, suggesting that parasitic organisms may prove reliable sources of information about climate change and its impact on ecological interactions.     

Do infectious diseases drive MHC diversity?

The primary function of the major histocompatibility complex (MHC) is to allow the immune system to identify infectious pathogens and eliminate them. Infectious diseases are now thought to be the main selection force that drives and maintains the extraordinary diversity of the MHC.     

Can predation by invasive mice drive seabird extinctions?

The house mouse, Mus musculus, is one of the most widespread and well-studied invasive mammals on islands. It was thought to pose little risk to seabirds, but video evidence from Gough Island, South Atlantic Ocean shows house mice killing chicks of two IUCN-listed seabird species. Mouse-induced mortality in 2004 was a significant cause of extremely poor breeding success for Tristan albatrosses, Diomedea dabbenena (0.27 fledglings/pair), and Atlantic petrels, Pterodroma incerta (0.33). Population models show that these levels of predation are sufficient to cause population decreases. Unlike many other islands, mice are the only introduced mammals on Gough Island. However, restoration programmes to eradicate rats and other introduced mammals from islands are increasing the number of islands where mice are the sole alien mammals. If these mouse populations are released from the ecological effects of predators and competitors, they too may become predatory on seabird chicks.     

Do recessive lethals have dominant deleterious effects in mice?

Offspring to the 7th, 8th and 9th generations of our mouse population were used in sib and non-sib matings to determine the rates of intra-uterine death. Families with 3 or more pairs of offspring tested in sib matings were divided according to the rate of intra-uterine death into classes below 10% or above 15%. It was supposed that the latter to a considerable degree carried recessive lethals and were for that reason called “lethal”, while those with less than 10% death were called “non-lethal”. Fertility and intra-uterine death were studied in non-sib matings from these two classes of families and in matings of sons with females from the inbred CBA strain. Breeding performance was also studied. A significant difference between groups was observed in only one of the comparisons made. The “lethal” group was superior to the “non-lethal” in the number of females made pregnant in tests of sons. In the other comparisons the “lethal” group more often showed a better performance than the “non-lethal” group. It is concluded that the present data do not indicate any overall deleterious effects of recessive lethals in heterozygotes nor do they indicate any clear heterotic effects.     

The mitotic checkpoint in cancer and aging: what have mice taught us?

The spindle assembly checkpoint is a cellular surveillance mechanism that functions to ensure faithful chromosome segregation during mitosis. Failure of this checkpoint can result in aneuploidy, a state of having abnormal numbers of chromosomes. Most human cancers consist of aneuploid cells, but it is unclear if the aneuploidy is a cause or a consequence of tumorigenesis. Over recent years, mouse models for spindle assembly checkpoint failure have been generated to investigate the biological relevance of the different spindle assembly checkpoint genes and the pathologies associated with chromosome number instability. Most of these models exhibit susceptibility to carcinogenesis. Moreover, one model has led to the identification of the spindle checkpoint protein BubR1 as a regulator of the normal aging process.     

Do complex population histories drive higher estimates of substitution rate in phylogenetic reconstructions?

Our curiosity about biodiversity compels us to reconstruct the evolutionary past of species. Molecular evolutionary theory now allows parameterization of mathematically sophisticated and detailed models of DNA evolution, which have resulted in a wealth of phylogenetic histories. But reconstructing how species and population histories have played out is critically dependent on the assumptions we make, such as the clock-like accumulation of genetic differences over time and the rate of accumulation of such differences. An important stumbling block in the reconstruction of evolutionary history has been the discordance in estimates of substitution rate between phylogenetic and pedigree-based studies. Ancient genetic data recovered directly from the past are intermediate in time scale between phylogenetics-based and pedigree-based calibrations of substitution rate. Recent analyses of such ancient genetic data suggest that substitution rates are closer to the higher, pedigree-based estimates. In this issue, Navascués & Emerson (2009) model genetic data from contemporary and ancient populations that deviate from a simple demographic history (including changes in population size and structure) using serial coalescent simulations. Furthermore, they show that when these data are used for calibration, we are likely to arrive at upwardly biased estimates of mutation rate.     

Do mtDNA Mutations Participate in the Pathogenesis of Sporadic Parkinson’s Disease?

The pathogenesis of sporadic Parkinson’s disease (PD) remains enigmatic. Mitochondrial complex-I defects are known to occur in the substantia nigra (SN) of PD patients and are also debated in some extracerebral tissues. Early sequencing efforts of the mitochondrial DNA (mtDNA) did not reveal specific mutations, but a long lasting discussion was devoted to the issue of randomly distributed low level point mutations, caused by oxidative stress. However, a potential functional impact remained a matter of speculation, since heteroplasmy (mutational load) at any base position analyzed, remained far below the relevant functional threshold. A clearly age-dependent increase of the ‘common mtDNA deletion’ had been demonstrated in most brain regions by several authors since 1992. However, heteroplasmy did hardly exceed 1% of total mtDNA. It became necessary to exploit PCR techniques, which were able to detect any deletion in a few microdissected dopaminergic neurons of the SN. In 2006, two groups published biochemically relevant loads of somatic mtDNA deletions in these neurons. They seem to accumulate to relevant levels in the SN dopaminergic neurons of aged individuals in general, but faster in those developing PD. It is reasonable to assume that this accumulation causes mitochondrial dysfunction of the SN, although it cannot be taken as a final proof for an early pathogenetic role of this dysfunction. Recent studies demonstrate a distribution of deletion breakpoints, which does not differ between PD, aging and classical mitochondrial disorders, suggesting a common, but yet unknown mechanism.     

A double whammy for aging? Rapamycin extends lifespan and inhibits cancer in inbred female mice

Comment on: Anisimov VN, et al. Cell Cycle 2011; 10:4230-6.     

Do lamellipodia have the mechanical capacity to drive convergent extension?

Convergent extension (CE), a kinematic motif associated with several important morphogenetic movements in embryos, entails narrowing of a tissue in one in-plane direction and elongation in the other. Although the cell elongation and intercalation which accompany this process have been investigated and relevant genes and biochemical pathways have been studied in multiple organisms, a fundamental question that has not yet been answered is "Do the lamellipodia thought to drive these motions actually have the mechanical capacity to do so?" Here, we address this and a number of related issues using a state-of-the-art computational model which can replicate cell motions, changes in cell shape and tissue deformations. The model is based on the cell-level finite element approach of Chen and Brodland, but has additional features which allow it to model lamellipodium formation and contraction. In studying CE, computational models provide an important complement to molecular approaches because they reveal the "mechanical pathways" through which gene products must ultimately act in order to produce physical movements. The model shows that lamellipodia can drive CE, that they do so through cell intercalations and that the elongated cells characteristic of CE arise only when adjacent tissues resist convergence, a result which we confirm experimentally.     

Do house mice use UV cues when foraging?

Research on the behavioural ecology of ultraviolet (UV-A, wavelengths of 320–400 nm, hereafter: UV) sensitivity in terrestrial vertebrates has mainly focused on sexual signalling and foraging in birds and reptiles, whereas the fact that some rodents are also sensitive to UV light has been somewhat ignored. Here, we present the results of two behavioural experiments, which tested whether rodents use UV cues in foraging. In the first experiment we asked whether the colour contrast in the UV waveband is used as a foraging cue. House mice were offered UV-reflecting and UV-absorbing artificial food items in two different illuminations where UV light was either present or absent. The food items were offered to two groups of mice, one group on a UV-reflecting and the other on a UV-absorbing background. The second experiment investigated more specifically whether UV cues are especially important in dawn and dusk when short wavelengths are high in the proportion of available light. House mice showed no preference between the food items regardless of illumination or background. Therefore, our results indicate that house mice do not use UV cues in foraging.     

Do induced sperm-head abnormalities in mice specifically identify mammalian mutagens rather than carcinogens?

The results of testing 54 compounds including 19 carcinogen/non-carcinogen pairs from a wide range of chemical classes are reported. Many carcinogens did not induce increases in abnormal sperm heads. In contrast compounds known to induce transmissible genetic damage in whole animals invariably induced dose-dependent large increases in the incidence of abnormal sperm heads. The test may be useful in assisting discrimination between compounds that only cause mutations in isolated cell systems from those which constitute a real genetic hazard for whole mammals.