What can we teach Drosophila? What can they teach us?

A number of single gene mutations dramatically reduce the ability of fruit flies to learn or to remember. Cloning of the affected genes implicated the adenylyl cyclase second-messenger system as key in learning and memory. The expression patterns of these genes, in combination with other data, indicates that brain structures called mushroom bodies are crucial for olfactory learning. However, the mushroom bodies are not dedicated solely to olfactory processing; they also mediate higher cognitive functions in the fly, such as visual context generalization. Molecular genetic manipulations, coupled with behavioral studies of the fly, will identify rudimentary neural circuits that underly multisensory learning and perhaps also the circuits that mediate more-complex brain functions, such as attention.     

What can we teach Drosophila? What can they teach us?

A number of single gene mutations dramatically reduce the ability of fruit flies to learn or to remember. Cloning of the affected genes implicated the adenylyl cyclase second-messenger system as key in learning and memory. The expression patterns of these genes, in combination with other data, indicates that brain structures called mushroom bodies are crucial for olfactory learning. However, the mushroom bodies are not dedicated solely to olfactory processing; they also mediate higher cognitive functions in the fly, such as visual context generalization. Molecular genetic manipulations, coupled with behavioral studies of the fly, will identify rudimentary neural circuits that underly multisensory learning and perhaps also the circuits that mediate more-complex brain functions, such as attention.     

What can we teach Lymnaea and what can Lymnaea teach us?

This review describes the advantages of adopting a molluscan complementary model, the freshwater snail Lymnaea stagnalis, to study the neural basis of learning and memory in appetitive and avoidance classical conditioning; as well as operant conditioning of its aerial respiratory and escape behaviour. We firstly explored ‘what we can teach Lymnaea’ by discussing a variety of sensitive, solid, easily reproducible and simple behavioural tests that have been used to uncover the memory abilities of this model system. Answering this question will allow us to open new frontiers in neuroscience and behavioural research to enhance our understanding of how the nervous system mediates learning and memory. In fact, from a translational perspective, Lymnaea and its nervous system can help to understand the neural transformation pathways from behavioural output to sensory coding in more complex systems like the mammalian brain. Moving on to the second question: ‘what can Lymnaea teach us?’, it is now known that Lymnaea shares important associative learning characteristics with vertebrates, including stimulus generalization, generalization of extinction and discriminative learning, opening the possibility to use snails as animal models for neuroscience translational research.     

What can microbial genetics teach sociobiology?

Progress in our understanding of sociobiology has occurred with little knowledge of the genetic mechanisms that underlie social traits. However, several recent studies have described microbial genes that affect social traits, thereby bringing genetics to sociobiology. A key finding is that simple genetic changes can have marked social consequences, and mutations that affect cheating and recognition behaviors have been discovered. The study of these mutants confirms a central theoretical prediction of social evolution: that genetic relatedness promotes cooperation. Microbial genetics also provides an important new perspective: that the genome-to-phenome mapping of social organisms might be organized to constrain the evolution of social cheaters. This constraint can occur both through pleiotropic genes that link cheating to a personal cost and through the existence of phoenix genes, which rescue cooperative systems from selfish and destructive strategies. These new insights show the power of studying microorganisms to improve our understanding of the evolution of cooperation.     

What could arsenic bacteria teach us about life?

In this paper, I discuss the recent discovery of alleged arsenic bacteria in Mono Lake, California, and the ensuing debate in the scientific community about the validity and significance of these results. By situating this case in the broader context of projects that search for anomalous life forms, I examine the methodology and upshots of challenging biochemical constraints on living things. I distinguish between a narrower and a broader sense in which we might challenge or change our knowledge of life as the result of such a project, and discuss two different kinds of projects that differ in their potential to overhaul our knowledge of life. I argue that the arsenic bacteria case, while potentially illuminating, is the kind of constraint-challenging project that could not—in spite of what was said when it was presented to the public—change our knowledge of life in the deeper sense.     

What can we do about osteoarthritis?

Osteoarthritis is complex in genetics, pathogenesis, monitoring and treatment. Current treatment of osteoarthritis does not influence progression. Much could be gained by more effective 'low-tech-low-cost' treatment. However, many patients have rapidly progressive disease, multiple joint involvement, and severe disease. We need to clarify the genetics of osteoarthritis, identify those at risk for progression and severe disease, and identify molecular processes critical for joint survival and failure. Will saving the cartilage improve patient pain and function? Effective outcome measures are needed to accelerate testing of new treatments. Further improvement is needed in joint implant technology to decrease costs, wear and loosening.     

What do unicellular organisms teach us about DNA methylation?

DNA methylation is an epigenetic hallmark that has been studied intensively in mammals and plants. However, knowledge of this phenomenon in unicellular organisms is scanty. Examining epigenetic regulation, and more specifically DNA methylation, in these organisms represents a unique opportunity to better understand their biology. The determination of their methylation status is often complicated by the presence of several differentiation stages in their life cycle. This article focuses on some recent advances that have revealed the unexpected nature of the epigenetic determinants present in protozoa. The role of the enigmatic DNA methyltransferase Dnmt2 in unicellular organisms is discussed.     

What can dolphins tell us about primate evolution?

Fifty-five million years ago, a furry, hoofed mammal about the size of a dog ventured into the shallow brackish remnant of the Tethys Sea and set its descendants on a path that would lead to their complete abandonment of the land. These early ancestors of cetaceans (dolphins, porpoises, and whales) thereafter set on an evolutionary course that is arguably the most unusual of any mammal that ever lived. Primates and cetaceans, because of their adaptation to exclusively different physical environments, have had essentially nothing to do with each other throughout their evolution as distinct orders. In fact, the closest phylogenetic relatives of cetaceans are even-toed ungulates.     

What can observational data reveal about metacommunity processes?

A key challenge for community ecology is to understand to what extent observational data can be used to infer the underlying community assembly processes. As different processes can lead to similar or even identical patterns, statistical analyses of non‐manipulative observational data never yield undisputable causal inference on the underlying processes. Still, most empirical studies in community ecology are based on observational data, and hence understanding under which circumstances such data can shed light on assembly processes is a central concern for community ecologists. We simulated a spatial agent‐based model that generates variation in metacommunity dynamics across multiple axes, including the four classic metacommunity paradigms as special cases. We further simulated a virtual ecologist who analysed snapshot data sampled from the simulations using eighteen output metrics derived from beta‐diversity and habitat variation indices, variation partitioning and joint species distribution modelling. Our results indicated two main axes of variation in the output metrics. The first axis of variation described whether the landscape has patchy or continuous variation, and thus was essentially independent of the properties of the species community. The second axis of variation related to the level of predictability of the metacommunity. The most predictable communities were niche‐based metacommunities inhabiting static landscapes with marked environmental heterogeneity, such as metacommunities following the species sorting paradigm or the mass effects paradigm. The most unpredictable communities were neutral‐based metacommunities inhabiting dynamics landscapes with little spatial heterogeneity, such as metacommunities following the neutral or patch sorting paradigms. The output metrics from joint species distribution modelling yielded generally the highest resolution to disentangle among the simulated scenarios. Yet, the different types of statistical approaches utilized in this study carried complementary information, and thus our results suggest that the most comprehensive evaluation of metacommunity structure can be obtained by combining them.     

What can genetics tell us about population connectivity?

Genetic data are often used to assess ‘population connectivity’ because it is difficult to measure dispersal directly at large spatial scales. Genetic connectivity, however, depends primarily on the absolute number of dispersers among populations, whereas demographic connectivity depends on the relative contributions to population growth rates of dispersal vs. local recruitment (i.e. survival and reproduction of residents). Although many questions are best answered with data on genetic connectivity, genetic data alone provide little information on demographic connectivity. The importance of demographic connectivity is clear when the elimination of immigration results in a shift from stable or positive population growth to negative population growth. Otherwise, the amount of dispersal required for demographic connectivity depends on the context (e.g. conservation or harvest management), and even high dispersal rates may not indicate demographic interdependence. Therefore, it is risky to infer the importance of demographic connectivity without information on local demographic rates and how those rates vary over time. Genetic methods can provide insight on demographic connectivity when combined with these local demographic rates, data on movement behaviour, or estimates of reproductive success of immigrants and residents. We also consider the strengths and limitations of genetic measures of connectivity and discuss three concepts of genetic connectivity that depend upon the evolutionary criteria of interest: inbreeding connectivity, drift connectivity, and adaptive connectivity. To conclude, we describe alternative approaches for assessing population connectivity, highlighting the value of combining genetic data with capture‐mark‐recapture methods or other direct measures of movement to elucidate the complex role of dispersal in natural populations.     

What can DNA tell us about biological invasions?

It is often hoped that population genetics can answer questions about the demographic and geographic dynamics of recent biological invasions. Conversely, invasions with well-known histories are sometimes billed as opportunities to test methods of population genetic inference. In both cases, underappreciated limitations constrain the usefulness of genetic methods. The most significant is that human-caused invasions have occurred on historical timescales that are orders of magnitude smaller than the timescales of mutation and genetic drift for most multicellular organisms. Analyses based on the neutral theory of molecular evolution cannot resolve such rapid dynamics. Invasion histories cannot be reconstructed in the same way as biogeographic changes occurring over millenia. Analyses assuming equilibrium between mutation, drift, gene flow, and selection will rarely be applicable, and even methods designed for explicitly non-equilibrium questions often require longer timescales than the few generations of most invasions of current concern. We identified only a few population genetic questions that are tractable over such short timescales. These include comparison of alternative hypotheses for the geographic origin of an invasion, testing for bottlenecks, and hybridization between native and invasive species. When proposing population genetic analysis of a biological invasion, we recommend that biologists ask (i) whether the questions to be addressed will materially affect management practice or policy, and (ii) whether the proposed analyses can really be expected to address important population genetic questions. Despite our own enthusiasm for population genetic research, we conclude that genetic analysis of biological invasions is justified only under exceptional circumstances.     

Recognition memory: can you teach an old dogma new tricks?

Familiarity and recollection are components of recognition memory. Whether these underlie two separate processes or a single process differing only in memory strength is a matter of continued debate. In this issue of Neuron, Haskins et al. provide further evidence in support of a dual-process perspective, whereas Shrager et al. provide evidence supporting a single-process viewpoint.     

Mice without telomerase: what can they teach us about human cancer?

Unicellular organisms, human cells and mice have provided insights into the processes of senescence, crisis, genomic instability and cancer in humans. Here, Artandi and DePinho discuss how studies in mice have uncovered a complex interplay between the ARF-p53 pathway, genomic instability due to telomere dysfunction, and the suppression or promotion of cancer.