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The evolution of sterile worker castes in eusocial insects was a major problem in evolutionary theory until Hamilton developed a method called inclusive fitness. He used it to show that sterile castes could evolve via kin selection, in which a gene for altruistic sterility is favored when the altruism sufficiently benefits relatives carrying the gene. Inclusive fitness theory is well supported empirically and has been applied to many other areas, but a recent paper argued that the general method of inclusive fitness was wrong and advocated an alternative population genetic method. The claim of these authors was bolstered by a new model of the evolution of eusociality with novel conclusions that appeared to overturn some major results from inclusive fitness. Here we report an expanded examination of this kind of model for the evolution of eusociality and show that all three of its apparently novel conclusions are essentially false. Contrary to their claims, genetic relatedness is important and causal, workers are agents that can evolve to be in conflict with the queen, and eusociality is not so difficult to evolve. The misleading conclusions all resulted not from incorrect math but from overgeneralizing from narrow assumptions or parameter values. For example, all of their models implicitly assumed high relatedness, but modifying the model to allow lower relatedness shows that relatedness is essential and causal in the evolution of eusociality. Their modeling strategy, properly applied, actually confirms major insights of inclusive fitness studies of kin selection. This broad agreement of different models shows that social evolution theory, rather than being in turmoil, is supported by multiple theoretical approaches. It also suggests that extensive prior work using inclusive fitness, from microbial interactions to human evolution, should be considered robust unless shown otherwise., Mathematical modelling shows that the evolution of sterile castes requires genetic relatedness but also involves conflicts between kin; these results contradict recent claims but agree with inclusive fitness theory., Author Summary The evolution of sterile worker castes in social insects has fascinated biologists ever since Darwin; how can selection favor a trait that decreases reproductive fitness? W. D. Hamilton solved this dilemma in the 1960s with a theory showing that reproductive altruism could evolve if it increased the worker’s inclusive fitness, which included effects that it had on increasing the fitness of its relatives. This solution to a crucial evolutionary problem, sometimes called kin selection, was challenged in a recent paper. The paper generated much controversy, but no one has contested its new theoretical model of the evolution of eusociality, which appeared to overturn much of what was previously thought to be true from kin selection theory. Here we examine this model in greater depth, showing that its apparently novel conclusions are overgeneralized from narrow and often inappropriate assumptions. Instead, this modeling strategy yields results that confirm important insights from kin selection and inclusive fitness, such as the importance of relatedness and the existence of conflicts in social insect colonies.

Contrary to the current paradigm that nearly all memory T cells proliferate in response to antigenic stimulation, this paper shows that an important population of CD4 T lymphocytes achieves memory/effector status independent of antigenic stimulation., Memory phenotype (CD44bright, CD25negative) CD4 spleen and lymph node T cells (MP cells) proliferate rapidly in normal or germ-free donors, with BrdU uptake rates of 6% to 10% per day and Ki-67 positivity of 18% to 35%. The rapid proliferation of MP cells stands in contrast to the much slower proliferation of lymphocytic choriomeningitis virus (LCMV)-specific memory cells that divide at rates ranging from, Author Summary The class of immune cells called CD4 T lymphocytes consists of two major cell types: naïve cells that have not yet participated in an immune response and memory cells, which are cells that have responded to antigen, expanded in number, and acquired new characteristics. These two cell types can be distinguished from one another because they display different cell surface marker proteins. In this paper, we argue that many—probably most—of the cells researchers generally characterize as memory cells on the basis of their surface markers are not authentic memory cells. True memory cells—the ones produced, for example, when we immunize a child against a disease—divide very slowly, whereas the bulk of the cells we generally characterize as memory cells divide very rapidly. Mice that have never been exposed to antigens have as many of these “memory-like” cells as normal mice have, implying that these cells arise by a process that does not require foreign antigen. Analysis of the sequence of the antigen recognition receptors on these “memory-like” cells indicates that their replication does not derive from a few cells or clones undergoing multiple rounds of proliferation, thus their division cannot be explained by conventional, antigen-driven clonal expansion. We conclude that this large population of “memory-like” cells has arisen by a mechanism independent of a response to foreign antigen, and that these cells may have a crucial biological function.

It has been said that a week is a long time in politics. But in human disease gene mapping, 10 years can seem a very short time indeed. It once seemed so simple: find a family with a number of affected individuals and narrow down regions of the genome shared by affected individuals but not their unaffected siblings. This process (family linkage analysis) was lengthy but had notable success with some diseases, including hereditary breast cancers caused by the BRCA1 and BRCA2 genes. Yet, many diseases known to have a genetic component (because they tend to run in families or siblings show a high concordance) do not follow a simple Mendelian pattern of inheritance and cannot be dissected in this way. Instead, researchers tried an “association” approach, starting with a large number of unrelated individuals, to find gene variants, or alleles, that are more common in affected than in unaffected controls. For such a strategy to work, the diseases must be influenced by variants that are quite common in the populations. Some readers might recall the heated debates about the common disease, common variant (CD-CV) hypothesis. Using arguments based on population genetics (such as the rate of creation and purging of deleterious alleles, the genetic bottleneck in the human population and subsequent population expansion) [1], the CD-CV hypothesis proposed that in common diseases with a genetic component, some predisposing alleles are relatively common and a combination of alleles or environmental effects was required before disease occurred, much like being dealt a bad hand from a common deck of cards. Under this hypothesis, disease-associated alleles might be found by using common gene variants, such as single nucleotide polymorphisms (SNPs), as a guide and comparing affected individuals with controls. Others cast doubt on this idea and suggested that common diseases are unlikely to be caused by common alleles and more likely to be caused by rarer ones; they too deployed arguments based on population genetics and suggested that association studies using common genetic variants might not be successful [2],[3]. As with all scientific debates, there seemed only one way out: collect the data and see. Well, 10 years and several millions of dollars later we have a lot of data, but are we any the wiser? Do we understand the allelic spectrum of disease any better than we did 10 years ago? There have now been over 700 genome-wide association studies (GWAS) published linking many variants to over a hundred diseases [4]. Many of these results are robust in that they can be replicated in several populations, leaving little doubt that common variants can contribute to common diseases. The problem is that the effect of these variants on disease is often rather modest, so that people with the disease-predisposing alleles are only slightly more likely to get the disease than those without. Larger and larger studies reveal more disease genes, usually with smaller and smaller effect on overall disease risk. The “missing heritability” problem then arises because, even in aggregate, these loci typically fall somewhat short of explaining the entire genetic component of disease risk. So where are the genes accounting for major predisposition to disease? One possible explanation is that GWAS do not directly reveal the disease-causative DNA variant, but rather a common DNA variant (usually an SNP) that is close enough to be genetically linked to it (almost always inherited together) and common enough to be on the genotyping microarrays. This has spurred more effort (and more expense) to find rarer and rarer SNPs by sequencing more genomes and make even larger arrays in the hope that the new SNPs may be in even closer linkage with the causative allele. Alternatively, it's possible that the disease-predisposing variants are not SNPs at all, but other changes in the genome, such as a duplicated or deleted gene or region—a so-called copy number variant (CNV)—or a result of epigenetic marks in the chromatin; neither of these would show up using the current generation of microarrays that look just at SNPs. A paper published recently in PLoS Biology [5] from the lab of David Goldstein put the cat amongst the CD-CV pigeons by suggesting that rather than common diseases being caused by common alleles, maybe rare alleles each with a large effect on disease might be creating “synthetic associations” in the GWAS signal by occurring, by chance, more often with one common allele than another. The paper used statistical reasoning to suggest that such synthetic associations are possible—but are they likely? Given how much time and money has been invested in surveying SNPs and attempting to match them up to diseases, the relative importance of such synthetic associations would have important implications on the direction of future research. The paper got a lot of publicity, even making the New York Times [6]. Now, some might say that no one likes the implication that they have been barking up the wrong scientific tree, still less perhaps that such a critique garnered a lot of publicity. But the issue is best settled by discussion—and data—which is why in this issue of PLoS Biology we publish two critiques of the original article [7],[8] together with a response from the original authors [9]. The critiques argue that although rare variants could in theory create synthetic associations, this is not a likely explanation for the missing heritability. Perhaps with further advances in ever cheaper sequencing technologies and the ability to sequence whole genomes from affected individuals we will, before the next 10 years are up, finally have a better understanding of the missing pieces of the genetic causes of common disease.

It is curious that Charles Darwin, perhaps medicine's most famous dropout, provided the impetus for a subject that figures so rarely in medical education. Indeed, even the iconic textbook example of evolution—antibiotic resistance—is rarely described as “evolution” in relevant papers published in medical journals [1]. Despite potentially valid reasons for this oversight (e.g., that authors of papers in medical journals would regard the term as too general), it propagates into the popular press when those papers are reported on, feeding the wider perception of evolution's irrelevance in general, and to medicine in particular [1]. Yet an understanding of how natural selection shapes vulnerability to disease can provide fundamental insights into medicine and health and is no less relevant than an understanding of physiology or biochemistry. One reason that evolution doesn't figure prominently in the medical community is that although it makes sense to have evolution taught as part of medicine, that doesn't make it essential. As explained at a meeting on evolution and medicine I recently attended in York, United Kingdom (the Society for the Study of Human Biology and the Biosocial Society's 2006 symposium, “Medicine and Evolution”), medicine is primarily focused on problem-solving and proximate causation, and ultimate explanations can seem irrelevant to clinical practice. Crudely put, does a mechanic need to understand the origins, history, and technological advances that have gone into the modern motor vehicle in order to fix it? Randolph Nesse (University of Michigan) and colleagues think otherwise [2], and have been campaigning for evolution to be recognized and taught as a basic science to all medical students (see also the Evolution and Medicine Network, http://www.evolutionandmedicine.org). It has been more than 10 years since he and George Williams published their classic book Why We Get Sick: The New Science of Darwinian Medicine [3]. Other landmark texts linking evolution to health have been written since then, with new editions on the way [4–6], and the research field is blossoming. Still, as Nesse mentioned at the start of the York meeting, there are only a handful of medical schools in the United States and in the United Kingdom with an evolutionary biologist listed as such on the faculty. The most obvious examples of evolutionary biology's importance to medical understanding are related to infectious disease [7]. As Jon Laman (Erasmus University, The Netherlands) pointed out at the meeting, the immune system provides the perfect platform to explain the medical relevance of the exquisite evolutionary relationships between pathogens and their hosts. Understanding how virulence evolves, for example, can help predict the potential, sometimes counterintuitive (and controversial) negative consequences of imperfect vaccination [8,9]. But evolution can also tell us that the origin of HIV was precipitated by a jump across the primate species barrier [10] and enables us to predict the imminent arrival of avian flu and the mutations most likely to be responsible for that evolutionary leap from birds to humans [11]. Where epidemiological and population genetic processes occur on the same time scale, the emerging field of “phylodyamics” can also inform us about the timing and progression of pathogen adaptation more generally [12]. The relevance of evolution to medicine is, however, much broader. Participants at the York meeting discussed not only how vulnerability to cancer is an inevitable but unfortunate consequence of imperfect human engineering and natural selection (Mel Greaves, Institute of Cancer Research, UK), but how life history theory can potentially explain patterns of pregnancy loss (Virginia Vitzthum, Indiana University), how a comparative approach applied to different human cultures and different primates can improve rates of breastfeeding (Helen Ball, University of Durham), whether clinical depression has an adaptive origin (Lewis Wolpert, University College London), and if suicide attempts are really just evolutionary bargaining chips in intense social disputes (Ed Hagen, Humboldt University). As with any emerging field, ideas change and the science is challenged. The thrifty gene concept [13]—that some populations (e.g., from Polynesia) are particularly susceptible to type 2 diabetes and heart disease because of past selection pressure specifically during times of famine—no longer enjoys the support it once had [14]. Tessa Pollard (University of Durham, UK) explained that the so-called Syndrome X is now considered to be the result of more general exposure to a rapid change in lifestyle as Western society encroached on these populations during the mid-20th century. The relationship between changing environment, diet, and susceptibility to disease, however, is also far from clear. Many diet-related conditions that typify industrialized populations—e.g., obesity, hypertension, and tooth decay—have been explained as resulting from an evolutionary mismatch between our over-refined, fat-filled contemporary diet and the environment to which humans were once ideally adapted. Sarah Elton (Hull York Medical School, UK) cautioned that while this analogy (the “environment of evolutionary adaptedness”) has been useful as a research tool and has led to public health campaigns for better diets (more seeds, nuts, fish oil, etc.), recreating such a typical “Stone Age diet” as a benchmark can be misleading. Human ecology in the past was at least as variable as human (and other primate) ecology is today. Surprisingly, an evolutionary framework to study human variation can be seen as counterproductive. George Ellison (St. George's Medical School, UK) provided an example, although not concerning evolutionary medicine, about a statistically flawed study leading to spurious conclusions about regional variation in IQ (which I won't promulgate here). However, bad papers are published in all subjects and are a failure of scientists and the peer-review system, not the science. These should not provide an excuse to dismiss the relevance of evolution to medicine (or to any other life science). Even at a very basic level, medical students can draw insights from evolution they cannot obtain from other core sciences on their course. Paul O'Higgins (Hull York Medical School) noted that it is much easier for medics to learn the nerves involved in the brachial plexus (the nerves supplying the arm) if they first understand the origin of the pentadactyl limb. It is not the case, however, that all clinicians fail to see the relevance of evolution. Gillian Bentley (now at University of Durham) conducted a series of interviews with leading biologists and clinicians when she was based at Imperial College London. What was surprising was not the positive endorsement of evolution by the geneticists and evolutionary biologists but the enthusiasm of practicing medical doctors for the topic, whether involved in the active birth movement or dealing with major trauma in intensive care. Indeed, several local clinicians attended the York meeting and helped lead the discussions. Ironically, the hardest task in adding evolutionary/Darwinian medicine to medical curricula may well be soliciting support from medical students. Although Paul O'Higgins thought a comparison of the brachial plexus to the pentadactyl limb was helpful, not all his students agreed—complaints were lodged that he was forcing evolution on them. That lack of support was also reflected in the participation of only three medical students at the York meeting (albeit enthusiastic ones), despite being widely publicized. It is not clear whether this is because medical students are more overburdened than most or because of a more deep-rooted resistance to the subject, reflecting wider political and religious prejudice against evolution. But evolutionary medicine isn't and shouldn't be controversial, and the best way to challenge prejudice is through education. As the oft-quoted Theodosius Dobzhansky wrote in 1973, “Nothing in biology makes sense except in the light of evolution” [15]. The time has clearly come for medicine to explicitly integrate evolutionary biology into its theoretical and practical underpinnings The medical students of Charles Darwin's day did not have the advantage of such a powerful framework to inform their thinking; we shouldn't deprive today's budding medical talent of the potential insights to be gained at the intersection of these two great disciplines.

At some time almost 4 billion years ago, nature likely was faced with a chemical dilemma. Nucleic acids had emerged as replicable information carriers and primitive catalysts (Joyce 2002), yet their functional potential was constrained by their structural homogeneity and lack of reactive groups. These properties rendered nucleic acids well suited for storing information, but flawed for mediating the diverse chemistries required to sustain and improve increasingly complex biological systems. It is tempting to speculate that translation emerged as the solution to this dilemma. Translation, defined here as the conversion of an informationcarrying molecule into a corresponding encoded structure, enabled the expanded functional potential of proteins to be explored using powerful evolutionary methods that depend on the unique ability of nucleic acids to replicate. A small but growing number of researchers have begun to tackle a modern version of this dilemma. While proteins and nucleic acids can be manipulated using powerful molecular biology techniques that enable their directed evolution, the size, fragility, and relatively limited functional group diversity of biological macromolecules make them poorly suited for solving many problems in the chemical sciences. Ideally, researchers would like to apply evolution-based approaches to the discovery of functional synthetic, rather than biological, molecules. A solution analogous to nature's translation of mRNA into protein could, in principle, address this contemporary problem (Orgel 1995; Gartner and Liu 2001). If a laboratory system were developed that could translate amplifiable information carriers such as DNA into arbitrary synthetic molecules, the evolution of synthetic molecules using iterated cycles of translation, selection, amplification, and diversification would be possible. The translation of DNA into synthetic molecules is conceptually distinct from the use of DNA simply as a tag during the solidphase synthesis of a molecule that is part of a combinatorial library (Brenner and Lerner 1992). The latter process uses DNA to record the history of a series of chemical reactions by cosynthesizing a portion of a DNA oligonucleotide during each step of a molecule's solidphase synthesis. As a result, the identity of compounds that pass screening can be inferred by PCR amplification and sequencing of the DNA associated with a given bead (Needels et al. 1993). The resulting DNA, however, cannot redirect the synthesis of active compounds. In contrast, the translation of DNA into synthetic molecules uses the sequence of nucleotides in a strand of DNA to direct the synthesis of a nascent molecule. As a result, a complete cycle of translation, selection, and amplification can be applied to the discovery of synthetic molecules in a manner that is analogous to the processes that take place during biological evolution. DNA-templated organic synthesis (DTS) has emerged as one way to translate DNA sequences into a variety of complex synthetic small molecules (Gartner and Liu 2001; Gartner et al. 2002; Li and Liu 2004). In this approach, starting materials covalently linked to DNA templates approximately 20–50 nucleotides in length are combined in very dilute solutions with reagents that are covalently linked to complementary DNA oligonucleotides. Upon Watson-Crick base pairing, the proximity of the synthetic reactive groups elevates their effective molarity by several orders of magnitude, inducing a chemical reaction. Because reactions do not take place between reactants linked to mismatched (noncomplementary) DNA, DTS generates synthetic products in a manner that is programmed by the sequence of bases in the template strand. In a series of three papers in this issue of PLoS Biology, Harbury and co-workers describe an elegant new approach to translating DNA into synthetic peptides called “DNA display.” Their approach uses DNA hybridization to separate mixtures of DNA sequences into spatially distinct locations. The first paper (Halpin and Harbury 2004a) reports the development of resin-linked oligonucleotides that efficiently and sequence-specifically capture DNA containing complementary subsequences. This immobilization process is efficient enough to be iterated, so that DNA sequences specifying multiple amino acids can be routed to the appropriate miniature resin-filled columns during each step. In the second paper (Halpin and Harbury 2004b), Harbury and coworkers detail solid-phase peptide synthesis performed on unprotected DNA 340mers bound to DEAE Sepharose. Optimization of amino acid side-chain-protecting groups and peptide coupling conditions enabled a variety of amino acids to undergo efficient peptide coupling to bound oligonucleotides containing an amine group. The third paper (Halpin et al. 2004) integrates the routing and peptide synthesis described above into the translation of a library of 106 DNA 340mers into a corresponding library of up to 106 synthetic pentapeptides. To achieve chemical translation, the DNA library was subjected to iterated cycles of routing and solidphase peptide synthesis. After each routing step, the appropriate amino acid was coupled to each DNA-linked subpopulation. DNA routing was therefore used to achieve the splitting step of “split-and-pool” combinatorial peptide synthesis. The completed library of peptide–DNA conjugates was then subjected to in vitro selection based on the ability to bind an antibody with known affinity for the [Leu]enkephalin pentapeptide Tyr-Gly-Gly-Phe-Leu. After two rounds of routing, synthesis, and selection, followed by DNA sequencing, the remaining oligonucleotides predominantly encoded the Tyr-Gly-Gly-Phe-Leu sequence or close variants thereof. This result demonstrates that the DNA display method is capable of facilitating the discovery of functional molecules by enabling in vitro selection methods to be applied to molecules generated by split-and-pool combinatorial synthesis. The fundamental distinctions between DTS and DNA display approaches to chemical translation imply that these two strategies will be applicable to different types of synthetic structures. Because the DNA display approach separates the DNA hybridization step from the chemical synthesis step, it does not require the coupling of synthetic reagents to oligonucleotides (beyond the starting material), and can use reaction conditions such as high temperatures or high pH that may not be compatible with DNA hybridization. These features suggest that DNA display may be able to access structures that cannot be created by DTS. Likewise, because DTS approaches use effective molarity rather than intermolecular reactivity to direct organic synthesis, they enable modes of controlling reactivity (such as using otherwise incompatible reactions in a single solution [Calderone et al. 2002]) and classes of chemical reactions (such as heterocoupling of substrates that preferentially homocouple) that cannot be accessed using split-and-pool synthesis. In principle, these two approaches are complementary, and it is tantalizing to envision the use of both DNA display and DTS to direct different steps during a single chemical translation. In order for either approach to fully realize its potential of truly evolving libraries of diverse synthetic molecules, rather than simply enriching libraries that already contain at the outset the “most fit” molecule, researchers must develop sophisticated library syntheses that generate remarkable complexity (vast numbers of different compounds) in a relatively modest number of DNA-compatible synthetic steps. True evolution takes place when the theoretical complexity of a population exceeds the number of different molecules that can be created in a single library translation step, and when diversification is required to access compounds in later generations that are more fit than any member of the starting pool. To my knowledge, no synthetic library to date contains this degree of complexity (indeed, the total size of the Chemical Abstracts Service database of known chemical substances is presently less than 108 compounds). However, because so few copies of a DNA-linked synthetic molecule are required for in vitro selection (Doyon et al. 2003)—compared with the relatively large quantities of material that are required for conventional screening approaches—these chemical translation methods offer the first hope of achieving such synthetic complexity without requiring an impractical amount of material or storage space. For comparison, a conventional-format synthetic library containing 100 µg of each of 108 different structures would represent 10 kg of material, not including the mass of beads or plates associated with the library, while a chemically translated library containing 10,000 copies of 108 different species represents less than 1 µg of total material. While significant remaining challenges face efforts to develop and apply chemical translation, the promise of marrying evolution and organic synthesis is an irresistible combination for some researchers. The work of Harbury and co-workers described in this issue represents the latest approach to the very ancient problem of translating replicable information into functional structures.

The reproducibility crisis triggered worldwide initiatives to improve rigor, reproducibility, and transparency in biomedical research. There are many examples of scientists, journals, and funding agencies adopting responsible research practices. The QUEST (Quality-Ethics-Open Science-Translation) Center offers a unique opportunity to examine the role of institutions. The Berlin Institute of Health founded QUEST to increase the likelihood that research conducted at this large academic medical center would be trustworthy, useful for scientists and society, and ethical. QUEST researchers perform “science of science” studies to understand problems with standard practices and develop targeted solutions. The staff work with institutional leadership and local scientists to incentivize and support responsible practices in research, funding, and hiring. Some activities described in this paper focus on the institution, whereas others may benefit the national and international scientific community. Our experience, approaches, and recommendations will be informative for faculty leadership, administrators, and researchers interested in improving scientific practice., This Community Page article describes an ongoing large-scale structured initiative at the Berlin Institute of Health which aims at improving the robustness, reproducibility and transparency of research in an academic biomedical setting.

The centrifuge is an essential tool for many aspects of research and medical diagnostics. However, conventional centrifuges are often inaccessible outside of standard laboratory settings, such as remote field sites, because they require a constant external power source and can be prohibitively costly in resource-limited settings and Science, technology, engineering, and mathematics (STEM)-focused programs. Here we present the 3D-Fuge, a 3D-printed hand-powered centrifuge, as a novel alternative to standard benchtop centrifuges. Based on the design principles of a paper-based centrifuge, this 3D-printed instrument increases the volume capacity to 2 mL and can reach hand-powered centrifugation speeds up to 6,000 rpm. The 3D-Fuge devices presented here are capable of centrifugation of a wide variety of different solutions such as spinning down samples for biomarker applications and performing nucleotide extractions as part of a portable molecular lab setup. We introduce the design and proof-of-principle trials that demonstrate the utility of low-cost 3D-printed centrifuges for use in remote field biology and educational settings., This Community Page article describes a low-cost 3D-printed centrifuge to enable sequencing in remote field conditions and lowering the barrier to synthetic biology research in high schools to broaden participation in hands-on STEM.

For most experimental biologists, handling the avalanche of data generated is similar to self-learn how to drive. Although that might be doable, it is preferable and safer to learn good practices. One way to achieve this is to build local communities of practice by bringing together scientists that perform code-intensive research to spread know-how and good practices. Here, we indicate important challenges and issues that stand in the way of establishing these local communities of practice. For a given researcher working for an academic institution, their capacity to conduct data-intensive research will be arbitrarily relying on the presence of well-trained bioinformaticians in their neighborhood. In this paper, we propose a model to build a local community of practice for scientific programmers. First, Software/Data Carpentry (SWC) programming workshops designed for researchers new to computational biology can be organized. However, while they provide an immediate solution for learning, more regular long-term assistance is also needed. Researchers need persisting, local support to continue learning and to solve programming issues that hamper their research progress. The solution we describe here is to implement a study group where researchers can meet-up and help each other in a "safe-learning atmosphere". Based on our experience, we describe two examples of building local communities of practice: one in the Netherlands at the Amsterdam Science Park and one in the United States at the University of Wisconsin-Madison. The current challenge is to make these local communities self-sustainable despite the high turnover of researchers at any institution and the lack of academic reward (e.g. publication). Here, we present some lessons learned from our experience. We believe that our local communities of practice will prove useful for other scientists that want to set up similar structures of researchers involved in scientific programming and data science.

Staphylococcus aureus is a nearly ubiquitous bacterium. Most of the time, it lives quietly and unnoticed as a member of the microbiota on your skin and in your nose. However, S. aureus—or “staph” as it is commonly known—is an opportunistic pathogen, switching from friend to foe when it gets the chance. Some staph infections can be mild, local infections while in other instances it can lead to severe, life-threatening cases of pneumonia and bacteremia (blood infection) (Fig 1). Fig 1 Methicillin-resistant S. aureus (MRSA). When staph goes from being a mild-mannered microbiota member (a state known as carriage) to an active infection, what governs whether it will be a minor or a major infection? Previous research has implicated the secretion of toxins by staph as one of the determinants. These toxins are responsible for damaging local tissue, releasing nutrients to aid in bacteria growth, and blocking the host immune response. Toxins are also critical to the transmission of staph both within and between hosts. Thus, it seems to make inherent sense that strains of staph that secrete lots of toxins might be more likely to cause severe infections, and most research to date, particularly in mouse models, has supported this idea. However, the idea that high toxin secretion causes severe infection is now in doubt, based on a study published in PLOS Biology by Maisem Laabei, Daniel Wilson, Mario Recker, Ruth Massey, and coworkers. Their work, using functional genomics and mathematical modeling, suggests that it is the low toxin–expressing strains that are most likely to establish severe infections. In their paper “Evolutionary Trade-Offs Underlie the Multi-Faceted Virulence of Staphylococcus aureus,” the authors use two collections of clinical staph samples to investigate the link between toxicity and virulence. The first collection is a series of samples taken from a single patient as they progressed from carriage to a severe bacteremia infection over the course of several months. When the authors looked at the toxicity of these samples, they found that during the carriage phase the staph strains secreted more toxins than during the bacteremia phase. The second collection were all isolates of the same strain of staph (the infamous USA300 strain) taken from multiple patients with varying degrees of infection. Again, the authors found that carriage and mild infections were far more likely to be caused by high toxin producing strains and that severe infections were more often established by low toxin producing strains. These counterintuitive findings beg the question of why low toxin strains might be better able to establish severe infections. While the underlying health of the patient certainly contributes to the susceptibility of developing a severe infection, it does not explain why patients are more likely to succumb to bacteremia with the less toxic strains of staph. The authors were also able to rule out several of the more obvious theories, such as that low toxin–producing strains are better at invading cells or eluding the immune system than high toxin–producing strains. The only difference that the authors were able to find between the low- and high-toxin strains was their ability to grow in human serum. The bloodstream is a very hard place for bacteria to grow, with the entire immune system present and actively policing this niche. Additionally, serum contains many antimicrobial peptides and can neutralize staph-produced toxins. Since producing toxins is known to be energetically demanding, and serum induces toxin production, the authors hypothesized that the cost of toxin production might disadvantage these strains in this challenging environment. To test this, they grew high- and low-toxin strains in a rich broth with and without added human serum. They saw that in the absence of serum, strains had equivalent fitness, but when serum was added, the low-toxin strains were able to outperform the high-toxin strains. This suggests that low toxin–producing strains are able to establish blood infections because they do not waste energy producing toxins and thus have more energy for defense and growth. But while these findings suggest an answer to why the low-toxin strains are better able to establish a severe infection, they raise another question: why do high toxin–producing strains exist at all? If low-toxin strains are fitter than high-toxin strains, then evolutionary pressure should have purged all high-toxin strains long ago. To understand this conundrum, the authors created a simple mathematical model. Their model accounts for the observations that highly toxic strains have increased transmissibility and are better at progressing from carriage to a mild infection. On the other hand, while low toxin strains are better at establishing bacteremia, they have much worse transmissibility, i.e., a blood infection is very rarely passed on to another person. Their modeling suggests that the lower transmission rate of less toxic strains could contribute to the maintenance of highly toxic strains at the population level. The results of this study paint a complex picture of staph virulence. There appears to be a genetic tug of war between highly toxic strains, which are better able to transmit between hosts, and less toxic strains, which are better at infecting a single host. Add to this that a severe infection by a low-toxin strain is a transmission dead-end, and that this (theoretically) leads to an evolutionary selection for higher levels of toxicity—and thus transmissibility—in the population. This trade-off between virulence and toxin expression can at least partially explain why staph can be both a constant constituent of the microbiota and a deadly pathogen.

Parameterisation of a mechanistic population model with data from a 51-year study on great tits suggests that phenotypic plasticity is crucial for viability of bird populations under current climate change scenarios., Predictions about the fate of species or populations under climate change scenarios typically neglect adaptive evolution and phenotypic plasticity, the two major mechanisms by which organisms can adapt to changing local conditions. As a consequence, we have little understanding of the scope for organisms to track changing environments by in situ adaptation. Here, we use a detailed individual-specific long-term population study of great tits (Parus major) breeding in Wytham Woods, Oxford, UK to parameterise a mechanistic model and thus directly estimate the rate of environmental change to which in situ adaptation is possible. Using the effect of changes in early spring temperature on temporal synchrony between birds and a critical food resource, we focus in particular on the contribution of phenotypic plasticity to population persistence. Despite using conservative estimates for evolutionary and reproductive potential, our results suggest little risk of population extinction under projected local temperature change; however, this conclusion relies heavily on the extent to which phenotypic plasticity tracks the changing environment. Extrapolating the model to a broad range of life histories in birds suggests that the importance of phenotypic plasticity for adjustment to projected rates of temperature change increases with slower life histories, owing to lower evolutionary potential. Understanding the determinants and constraints on phenotypic plasticity in natural populations is thus crucial for characterising the risks that rapidly changing environments pose for the persistence of such populations., Author Summary Predictions about the effect of climate change on organisms often ignore the possibility that organisms can evolve, or that they have an inbuilt capacity to cope with changing conditions. In order to understand the potential for existing populations to adapt to climate change, and the relative risks of extinction, such processes need to be modelled together with projected changes in climate. In this paper, we use data from a long-term study (51 years) of a small bird, the great tit, to model how birds can match the time they breed each year with the time their food is most abundant, and how this match can change with a changing climate. We found that evolution offers the chance for short-lived birds to adapt at the rate of climate change that is expected over the next century, but that the most important way that birds can cope with climate change is their evolved ability to adjust their behaviour depending on the environment they experience (“plasticity”). We extrapolated the model to other bird species, to estimate their relative vulnerability to changing climate. The model shows that longer-lived species (which also tend to have fewer offspring and take longer to reach sexual maturity) are more vulnerable to extinction because their evolutionary potential is lower. For such species, the importance of close adjustment to their environment becomes even greater. Hence, knowledge of the causes and limits of individual adjustment to the environment are crucial to predict the fate of populations under climate change.

The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation—especially along the veterinary/ecological research interface—remains., "One Health" is a global initiative fostering interdisciplinary collaborations to address health challenges. Although an empirical analysis of published disease models shows a rapid growth in cross-disciplinary "One Health" collaborations, the continued segregation of medical, veterinary, and ecological communities poses an ongoing challenge., Author Summary A cohesive reaction to emerging threats requires the efficient dissemination of knowledge and methodologies across multiple disciplinary boundaries. A “One Health” approach has been advocated to facilitate cross-disciplinary communication and research in responding to challenges in human, animal, and environmental health. We empirically describe collaboration networks surrounding one aspect of this One Health initiative: infectious disease transmission models. A systematic literature review and social network analysis showed that dynamic disease modelling grew faster than most biological and natural science disciplines over the last 25 y, yet the field remains somewhat compartmentalized. Many researchers contribute to a broad, cross-disciplinary group of journals, but publications from veterinarians and ecologists remain segregated into relatively discrete communities. While the veterinary and ecological communities regularly reference the broad, cross-disciplinary group, they only rarely cite each other; furthermore, the highest-tiered medical journals contributed very few papers to our search. Therefore, our results are somewhat mixed: while One Health may have precipitated a great deal of research interest in infectious disease, it has not fully overcome the barriers that segregate its contributing disciplines.

Why the autosomal recombination rate differs between female and male meiosis in most species has been a genetic enigma since the early study of meiosis. Some hypotheses have been put forward to explain this widespread phenomenon and, up to now, only one fact has emerged clearly: In species in which meiosis is achiasmate in one sex, it is the heterogametic one. This pattern, known as the Haldane-Huxley rule, is thought to be a side effect, on autosomes, of the suppression of recombination between the sex chromosomes. However, this rule does not hold for heterochiasmate species (i.e., species in which recombination is present in both sexes but varies quantitatively between sexes) and does not apply to species lacking sex chromosomes, such as hermaphroditic plants. In this paper, we show that in plants, heterochiasmy is due to a male-female difference in gametic selection and is not influenced by the presence of heteromorphic sex chromosomes. This finding provides strong empirical support in favour of a population genetic explanation for the evolution of heterochiasmy and, more broadly, for the evolution of sex and recombination., Measuring the opportunity for natural selection on gametes provides the first empirical evidence for a theory explaining why recombination at meiosis varies between males and females

​ForFor eons, viruses and their host organisms have been locked in an evolutionary arms race, with hosts—sometimes under threat of death—continually perfecting adaptive countermeasures to thwart viral threats. Such adaptations often come about via mutation in single amino acids of host proteins. The mutations become fixed in the host population as resistant individuals and their offspring outperform susceptible individuals. When this happens, the mutation is said to have undergone positive selection. In this issue of PLoS Biology, Maulik Patel, Harmit Malik, and colleagues explain how selective pressures from ancient viruses related to hepatitis C virus (HCV) sometimes forced adaptations in a protein, MAVS, that is critical for primate antiviral responses. Hepatitis C virus cleaves antiviral factor MAVS to evade host detection (left diagram, top). Adaptive changes (red shields) close to the cleavage site provide protection from this antagonism and have occurred in multiple primate species via convergent ... When cells detect the presence of RNA viruses such as HCV, MAVS (short for Mitochondrial antiviral signaling) acts to kick-start the production of potent antiviral factors that can stop viral replication. Unfortunately for humans (the only primates naturally infected by HCV), the virus can trump this defense using the viral protein NS3, which chops up and inactivates MAVS. This is part of the reason HCV is such a nasty virus for humans, causing liver disease in many of the approximately 200 million infected people worldwide. For insight into why human MAVS is so vulnerable to HCV, the researchers examined MAVS proteins in other primates. Statistical analyses of MAVS amino acid sequences from 21 primate species, including humans, indicated that MAVS has undergone positive selection in several primate species at some point in the past. This suggests many primates have probably faced infection by viruses that attack MAVS. What kinds of changes did these viruses provoke in MAVS? Might some of these changes be aimed at protecting against a viral protein similar to HCV NS3? To address these questions, the authors first showed that all the primate MAVS could support production of antiviral factors. Then they examined whether any could do so when HCV NS3 is present. They found that three primate species (rhesus macaques, spider monkeys, and dusky titis) possess MAVS that is very resistant to suppression by HCV NS3. These “resistant” MAVS each exhibit a mutation substituting a different amino acid for the valine that normally appears at position 506 in MAVS of all the other primates tested (including humans). The researchers showed that mutations at position 506 cause resistance because when they changed the mutations back to valine, resistance disappeared. Surprisingly, none of the three primates with resistant MAVS are closely related to each other, so they must each have independently evolved their protective mutations at position 506. Nonetheless, these mutations all likely work in the same way: by stopping NS3 from binding tightly to MAVS and thereby preventing the inactivation of MAVS. That's why NS3-resistant MAVS, like that found in rhesus macaques, or in human MAVS where “resistant” mutations have been experimentally introduced, controls HCV replication in cell culture so much better than does the normal human MAVS protein. Together, these data support the idea that mutations at position 506 happened in primates that were under pressure from viruses that, like HCV, could cleave MAVS. These mutations weren't driven by HCV (because in nature, HCV only infects humans), but instead, something similar. In fact, there are a few viruses distantly related to HCV that infect wild primates. These viruses possess their own NS3-like proteins, which Patel and colleagues showed could interfere with the ability of normal human MAVS to stimulate antiviral factor production. Therefore, the ability to antagonize MAVS is likely a key feature of this viral family. Patel and colleagues point out that today, no HCV-like viruses are found in primate species that have NS3-resistant MAVS, so we don't actually know much about the viruses that drove positive selection at position 506. Nonetheless, we can infer that they possessed a protein similar to NS3. We can also deduce that they also last plagued their hosts long ago: rhesus macaques share their protective mutation with other macaque species but not with their next closest primate relatives (baboons). Thus, the mutation probably appeared around the time the two primates split off from each other, 5–8 million years ago. Although humans still struggle with HCV, the evidence presented in this paper suggests that some primates have won their evolutionary arms race. Sometimes, such vanquished viruses leave behind quiescent “fossils” integrated into their hosts' DNA. But no such fossils have yet been found and these ancient viruses may now be extinct. In that case, the only way to learn anything about them is to study the armaments (mutations) they inspired in their host's genomes, using methods like those employed by Patel et al. Patel MR, Loo Y-M, Horner SM, Gale M Jr, Malik HS (2012) Convergent Evolution of Escape from Hepaciviral Antagonism in Primates. doi:10.1371/journal.pbio.1001282

As a group, parasites are extraordinarily diverse. Even closely related parasites may behave very differently, infecting different host species, causing different pathologies, or infecting different tissues. For example, Escherichia coli bacteria, a typically harmless inhabitant of the human gut, can, in different forms, cause diarrhea, intestinal bleeding, urinary tract infections, kidney bleeding, meningitis, and other diseases [1]. Underlying this diversity is evolution. It is widely appreciated that parasites are prone to rapid evolution, and because of their often short generation times and large population sizes, parasites may evolve far more rapidly than their hosts. Attempts to understand parasite evolution, and the relevance of that evolution to disease, go back at least half a century to the first observations of drug resistance evolution in bacteria [2]. However, the application of evolutionary theory to parasites remains fertile ground for original research [3]. Indeed, evolutionary biology and parasitology have undergone such rapid advances in recent years that it has been difficult to keep abreast of both. Some recent papers, including the study of Babayan et al. in this issue of PloS Biology [4], apply results from one branch of evolutionary theory—life history theory—to the characteristics of pathogens of medical interest such as parasitic roundworms (nematodes) and malaria [5]. Babayan et al. propose that the life history of parasitic microfilarial worms shows evidence of adaptive “plasticity.” Specifically, they propose that worm development inside a mammalian host changes in response to the host's immunity, and that the parasite's response matches predictions from life history theory. Most basically, life history theory addresses the birth and death schedule of an organism in the context of its environment: how is natural selection expected to shape an organism's age of first reproduction, its fecundity, and survival? (See [6]–[9] for reviews.) Body size and other phenotypic traits are also often considered in the theory. As a typical example, a population that loses half its individuals each year to predation is expected to evolve to begin reproducing at a younger age than a population losing only 10% of its individuals to predation annually. This occurs even though early reproduction has costs that would reduce lifetime fecundity if predation is low. This early maturity increases the chance that that an individual survives to maturity, a feature that is increasingly important with an increasing mortality rate. As might be expected intuitively, increases in juvenile mortality select for earlier maturation, while increases in adult mortality do not have this effect [10],[11]. The specific life history that will evolve by natural selection depends on details such as the different mortality rates and fecundity schedules that accrue to individuals with different ages of maturation. In order to avoid telling just-so stories about the evolution of life history traits, it is important to make predictions and then test them. It is hard to make predictions for a single species, in a single environment, at one point in time. The difficulty is that we do not know what life history options are available to the organism, and unless those options are known, prediction is hard. To escape this dilemma, life history theory applications have developed almost completely in a comparative context, predicting how birth and death schedules should vary across populations of the same species in different environments. If population P1 inhabits environment E1 and population P2 inhabits environment E2, the theory leads to straightforward relative predictions based on the differences between E1 and E2. These predictions are easy because the birth–death constraints of the organism will be the same for population P1 as for P2, so even though we cannot predict the exact birth–death schedules favored in E1 and E2, we can predict the direction of the differences (whether the population in E1 should mature earlier or produce more offspring early in life, for example). The situation encountered in the study of Babayan et al. is slightly more complex. Their problem does not involve two separate populations of the same parasite in different environments, but instead involves one parasite population responding to different environments. It is of course ubiquitous for organisms to live in variable environments: the seeds of a single plant may encounter a range of soil moistures and sunlight availabilities to germinate; a parasite may encounter hosts having different levels of immunity. One possible response of the organism is to evolve a compromise life history, one that produces the best average response to all conditions. Another possibility is that the organism evolves a plastic response: in each environment the organism displays a different life history suited to that environment. Whether a fixed or plastic response is optimal depends on the costs of sensing and regulation versus the benefits of plasticity. An approach taken to determine if an organism displays adaptive phenotypic plasticity is to compare the responses in the two environments and see if the differences observed are consistent with what might be expected if the organism had optimized to the two environments.

Recent advances in the study of natural products made by bacteria have laid the foundation for engineering these molecules and for developing cost-effective ways to manufacture them. In our lab, we study a number of natural products that are synthesized by harmless soil bacteria of the Streptomyces genus. Whereas our primary interest in these molecules is due to their antibiotic properties, many of these natural products have distinct colors [1]. (The reasons for why Streptomyces make antibiotics or pigments remain mysterious.) This article is intended to make the case to the scientific and educational communities that Streptomyces-derived natural products are an untapped source of useful biopigments. By sharing some of our own experiences in harnessing these pigments to create paint and paintings, we also hope to inspire others to explore the potential of Streptomyces-derived pigments in art, industry, and perhaps most importantly, the classroom. The pedagogical value of bacterial pigments is highlighted by the wide range of concepts and methods in chemistry, biology, and art that can be introduced to students in this context (see Box 1). Teachers can incorporate bacterial pigments into their lessons while introducing fundamental scientific principles ranging from the physics of color to the chemistry behind paints that fade in sunlight. Painting with living bacteria (Box 2) or extracting pigments from bacterial cultures (Box 3) provides a visual and kinesthetic activity to support key aspects of scientific investigations and methods learned in the classroom. Because the methods to do so are safe, inexpensive, and easily implementable in the everyday world, it is possible to use biopigments as a vehicle to introduce school children to science via art and vice versa. While many of these concepts and techniques are appropriate for the advanced high school or undergraduate classroom, even elementary school children can use bacterial paints prepared by their teacher to create art, an activity that may teach children at a young age that bacteria are a source of valuable materials rather than merely agents of disease. Box 1: Concepts at a Glance Leads into chemistry, microbiology, and biotechnology Chemical composition of paint (solubility and states of matter)¥, ‡ Structures of pigment molecules (electromagnetic radiation, electron configuration, valence bonds, molecular orbital theory)‡ Culturing Streptomyces and extracting their pigments (sterile culture techniques, natural product extraction techniques, solubility)‡ Painting Streptomyces on agar plates (bacterial growth control)¥,‡ Engineering bacteria to make new pigments (metabolic engineering of microbial systems)‡ Scaling up the production of bacterial pigments (large scale bioprocessing techniques, recombinant DNA technology)‡ UV absorber and radical scavengers as additives to paints (chemical structure and reactivity, radical reactions)‡ Leads into fine arts The perception of color (electromagnetic radiation, the eye as a spectrometer)¥, ‡ Paint constituents (pigments, binders, solvents, surfactants, additives)‡ Sources of pigments‡ Making paints from pigments (grinding pigments, suspending in binder)¥, ‡ History of pigments (art history)*, ¥, ‡ Fun stuff Drawing on paper with bacteria-derived paint*, ¥, ‡ Creating living art by painting with bacteria on agar medium*, ¥, ‡ ‡ = for undergraduate or advanced placement high school courses; ¥ = for high school courses; * = for elementary school courses

The completion of the human genome project has ushered in a new era in which biology has become an information science. In this new era, sharing of information is quickly becoming a critical aspect of scientific discovery. As directors of National Institutes of Health (NIH) institutes dedicated to neuroscience, we recognize several areas of research where sharing of primary data will be necessary for us to reach our scientific goals, including brain-mapping, genetics, and clinical trials. Progress in each of these areas will require not only new tools for sharing information but a change in our scientific culture. Here we describe some of the recent progress in efforts to map the brain as an example of the potential and the challenge of sharing data in an era when neurobiology, like genomics, is becoming an information science. In parallel to the worldwide effort to map the human genome, investigators in neuroscience have used a range of techniques to map the brain. The efforts share some superficial similarities: the genome has 3 × 109 bases and the human brain has roughly 100 × 109 neurons; both the genome and the brain have embedded modules of functional units (genes versus circuits) that can be mapped in space; and localization of both genes and circuits requires computational power that can be distributed across laboratories. But the analogy breaks down quickly. Whereas fundamental genome data can be addressed as unidimensional text of four letters in varying order, a comprehensive map of the brain includes molecular, cellular, system, and behavioral data—all of which are dynamic, interacting, and interdependent. For example, brain circuitry is organized in three-dimensional space constantly changing in time, with each neuron having 103–104 synapses and with many of those synapses capable of plasticity that may, in turn, have significant functional consequences. As we emerge from the “decade of the brain,” we are entering a decade for which data-sharing will be the currency for progress in neuroscience. As a testament to the complexity of brain data, a century after the classic age of neurohistology, there are continuing arguments about the taxonomy of neurons, depending on location, morphology, neurochemistry, or RNA profile. For instance, a population of neurons in a small region of the brain, the dorsal raphe, is the main source of the neurochemical serotonin that has been implicated in stress responsiveness and mood disorders. These serotonergic neurons can be subdivided according to rostralcaudal location, axon thickness, or projections (Mamounas et al. 1991; Lowry 2002). However, what we recognize by immunochemical stain as a single shared phenotype in an anatomically distinct region may consist of a heterogeneous population of cells with diverse RNA profiles. In this sense, the strategy for brain-mapping might borrow a page from astronomy, with its maps of galaxies with mixed elements, as well as from the experience of the genome project. Indeed, advances in human brain-mapping, like discoveries in astronomy, have until recently largely depended on the tools available. The postmortem studies of the early 20th century provided delineation of cortical areas through light microscopic histology and gross connectional information. Neurochemical techniques in the last three decades yielded maps with cellular and subcellular resolution, identifying populations of cells usually by one or two neurochemical phenotypes. During the same period, electrophysiological approaches revealed the exquisite distribution of function across the brain, within particular brain subdivisions, and within neurons themselves. In the past two decades, direct study of the intact, functioning human brain in healthy and disordered states has been made possible by a variety of neuroimaging modalities. These studies have provided both structural and functional topography at increasing resolution, as well as neurochemical data and, most recently, information regarding neural connectivity (Behrens et al. 2003). The advent of techniques for mapping RNA profiles now permits analysis of several thousand species of RNA even in a single neuron, resulting in exponential increases in information. As these approaches are combined with the experimental behavioral and clinical sciences, opportunities abound for understanding this complex organ and treating its pathologies. The challenge now is to integrate this information into a coherent, accessible form that permits hierarchical analysis from RNA to protein to morphology to connectivity to function in a universal language while preserving fidelity. While earlier comprehensive maps in simpler nervous systems, such as the classic lineage maps of invertebrates (Stern and Fraser 2001), could be completed by single labs, more ambitious projects like a transcriptional map of the mouse brain, the Human Genome Project, and other goal-directed or large-scale research endeavors (Nass and Stillman 2003) will require collaboration of scientists who add value to the enterprise by working in multidisciplinary teams; coordination of efforts to attain a goal; and computation through the use of informatics, models, and simulations. The keystone in this new paradigm is, of course, meaningful data-sharing. Several initiatives serving the brain and behavioral research communities are advancing cooperative research. The Gensat Project (www.gensat.org) will soon provide developmental and whole-brain maps of several hundred genes in the mouse nervous system using a bacterial artificial chromosome (BAC) transgenic strategy with fluorescent reporters to provide subcellular resolution. A digital atlas of the mouse brain and associated informatics tools have been developed to organize, visualize, and analyze such gene expression (and other spatial) data generated by researchers (http://www.loni.ucla.edu/MAP/index.html). We now have the capability to map the transcriptional expression of virtually the entire mouse genome in the adult and the developing mouse brain, registering these data to a common, digital atlas. Like the galaxy maps generated by the Hubble telescope, this transcriptional atlas will provide important temporal as well as spatial information, revealing genes that may be expressed only at critical stages of brain development. Similarly, the Human Brain Project (http://www.nimh.nih.gov/neuroinformatics/index.cfm) is an informatics effort funded through several federal agencies to develop databases, analytical and computational simulations, and other resources to assist human brain-mapping as well as other large-scale coordinated neuroscience programs. While there are several initiatives at NIH aimed at overcoming the informatics barriers to sharing data and facilitating collaboration, coordination, and computation, we recognize that not all of the impediments to data-sharing are technical. The advent of neurobiology as an information science also demonstrates that the academic culture in which our science develops and the publication culture in which our science is communicated will need to change. Promotion decisions at major universities largely depend on the quality and quantity of first-authored or senior-authored papers. Multidisciplinary studies require teams of investigators in which hierarchical schemes for authorship may fail to reflect accurately the magnitude of each individual's contributions. Similarly, contributions to a research database may represent important scientific and scholarly achievements, but generally are underrecognized by promotions committees counting peer-reviewed publications. Indeed, the nature of publication itself needs to change in an era when some of the most important contributions will emerge from comprehensive descriptions of new landscapes (analogous to new genomes and new galaxies) rather than tests of specific hypotheses. These cultural issues are not peculiar to brain and behavioral science, of course, and have recently been considered broadly at the NIH (http://www.becon.nih.gov/symposium2003.htm). Scientific publication, as we have known it in print, is slow and expensive, with access limited to those with either the funds to purchase an individual subscription or the proximity to a library with an institutional subscription. Data-sharing also means open-access publishing so that data, whether from mapping efforts or from hypothesis-driven experiments, become available quickly and freely to the scientific community. As we emerge from the “decade of the brain,” we are entering a decade for which data-sharing will be the currency for progress in neuroscience. Efforts driven by collaboration, coordination, and computation should yield the data, tools, and resources that neuroscientists will need in the coming decades. We hope that new electronic publications with open access will accelerate this change and provide the vehicle for disseminating the most exciting discoveries in neuroscience in a rapid, respected, and ready format. A Constellation of Neurons