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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.

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.

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