Effects of thymic selection of the T cell repertoire on HLA-class I associated control of HIV infection

Without therapy, most people infected with human immunodeficiency virus (HIV) ultimately progress to AIDS. Rare individuals (‘elite controllers’) maintain very low levels of HIV RNA without therapy, thereby making disease progression and transmission unlikely. Certain HLA class I alleles are markedly enriched in elite controllers, with the highest association observed for HLA-B57 (ref. 1). Because HLA molecules present viral peptides that activate CD8+ T cells, an immune-mediated mechanism is probably responsible for superior control of HIV. Here we describe how the peptide-binding characteristics of HLA-B57 molecules affect thymic development such that, compared to other HLA-restricted T cells, a larger fraction of the naive repertoire of B57-restricted clones recognizes a viral epitope, and these T cells are more cross-reactive to mutants of targeted epitopes. Our calculations predict that such a T-cell repertoire imposes strong immune pressure on immunodominant HIV epitopes and emergent mutants, thereby promoting efficient control of the virus. Supporting these predictions, in a large cohort of HLA-typed individuals, our experiments show that the relative ability of HLA-B alleles to control HIV correlates with their peptide-binding characteristics that affect thymic development. Our results provide a conceptual framework that unifies diverse empirical observations, and have implications for vaccination strategies.Mark and Lisa Schwartz FoundationNational Institutes of Health (U.S.) (Director’s Pioneer award)Philip T. and Susan M. Ragon FoundationJane Coffin Childs Memorial Fund for Medical ResearchBill & Melinda Gates FoundationNational Institute of Allergy and Infectious Diseases (U.S.)National Institutes of Health (U.S.) (contract no. HHSN261200800001E)National Institutes of Health (U.S.). Intramural Research ProgramNational Cancer Institute (U.S.)Center for Cancer Research (National Cancer Institute (U.S.)

Growth, entropy and commutativity of algebras satisfying prescribed relations

In 1964, Golod and Shafarevich found that, provided that the number of relations of each degree satisfy some bounds, there exist infinitely dimensional algebras satisfying the relations. These algebras are called Golod-Shafarevich algebras. This paper provides bounds for the growth function on images of Golod-Shafarevich algebras based upon the number of defining relations. This extends results from [32], [33]. Lower bounds of growth for constructed algebras are also obtained, permitting the construction of algebras with various growth functions of various entropies. In particular, the paper answers a question by Drensky [7] by constructing algebras with subexponential growth satisfying given relations, under mild assumption on the number of generating relations of each degree. Examples of nil algebras with neither polynomial nor exponential growth over uncountable fields are also constructed, answering a question by Zelmanov [40]. Recently, several open questions concerning the commutativity of algebras satisfying a prescribed number of defining relations have arisen from the study of noncommutative singularities. Additionally, this paper solves one such question, posed by Donovan and Wemyss in [8].Comment: arXiv admin note: text overlap with arXiv:1207.650

Is drinking water a risk factor for endemic cryptosporidiosis? A case-control study in the immunocompetent general population of the San Francisco Bay Area

BACKGROUND: Cryptosporidiosis, caused by Cryptosporidium, is an enteric illness that has received much attention as an infection of immunocompromised persons as well as in community outbreaks (frequently waterborne). There are, however, no studies of the risk factors for sporadic community-acquired cryptosporidiosis in the immunocompetent US population. We undertook a case-control study in the San Francisco Bay Area as part of a national study sponsored by the Centers for Disease Control and Prevention to ascertain the major routes of transmission for endemic cryptosporidiosis, with an emphasis on evaluating risk from drinking water. METHODS: Cases were recruited from a population-based, active surveillance system and age-matched controls were recruited using sequential random-digit dialing. Cases (n = 26) and controls (n = 62) were interviewed by telephone using a standardized questionnaire that included information about the following exposures: drinking water, recreational water, food items, travel, animal contact, and person-to-person fecal contact, and (for adults) sexual practices. RESULTS: In multivariate conditional logistic regression analyses no significant association with drinking water was detected. The major risk factor for cryptosporidiosis in the San Francisco Bay Area was travel to another country (matched odds ratio [95% confidence interval]: 24.1 [2.6, 220]). CONCLUSION: The results of this study do not support the hypothesis that drinking water is an independent risk factor for cryptosporidiosis among the immunocompetent population. These findings should be used to design larger studies of endemic cryptosporidiosis to elucidate the precise mechanisms of transmission, whether waterborne or other

Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines

Chemokines and their role in airway hyper-reactivity

Airway hyper-reactivity is a characteristic feature of many inflammatory lung diseases and is defined as an exaggerated degree of airway narrowing. Chemokines and their receptors are involved in several pathological processes that are believed to contribute to airway hyper-responsiveness, including recruitment and activation of inflammatory cells, collagen deposition and airway wall remodeling. These proteins are therefore thought to represent important therapeutic targets in the treatment of airway hyper-responsiveness. This review highlights the processes thought to be involved in airway hyper-responsiveness in allergic asthma, and the role of chemokines in these processes. Overall, the application of chemokines to the prevention or treatment of airway hyper-reactivity has tremendous potential

A Multi-Component Model of the Developing Retinocollicular Pathway Incorporating Axonal and Synaptic Growth

During development, neurons extend axons to different brain areas and produce stereotypical patterns of connections. The mechanisms underlying this process have been intensively studied in the visual system, where retinal neurons form retinotopic maps in the thalamus and superior colliculus. The mechanisms active in map formation include molecular guidance cues, trophic factor release, spontaneous neural activity, spike-timing dependent plasticity (STDP), synapse creation and retraction, and axon growth, branching and retraction. To investigate how these mechanisms interact, a multi-component model of the developing retinocollicular pathway was produced based on phenomenological approximations of each of these mechanisms. Core assumptions of the model were that the probabilities of axonal branching and synaptic growth are highest where the combined influences of chemoaffinity and trophic factor cues are highest, and that activity-dependent release of trophic factors acts to stabilize synapses. Based on these behaviors, model axons produced morphologically realistic growth patterns and projected to retinotopically correct locations in the colliculus. Findings of the model include that STDP, gradient detection by axonal growth cones and lateral connectivity among collicular neurons were not necessary for refinement, and that the instructive cues for axonal growth appear to be mediated first by molecular guidance and then by neural activity. Although complex, the model appears to be insensitive to variations in how the component developmental mechanisms are implemented. Activity, molecular guidance and the growth and retraction of axons and synapses are common features of neural development, and the findings of this study may have relevance beyond organization in the retinocollicular pathway

Cocaine Is Low on the Value Ladder of Rats: Possible Evidence for Resilience to Addiction

International audienceBACKGROUND:Assessing the relative value of cocaine and how it changes with chronic drug use represents a long-standing goal in addiction research. Surprisingly, recent experiments in rats--by far the most frequently used animal model in this field--suggest that the value of cocaine is lower than previously thought.METHODOLOGY/PRINCIPAL FINDINGS:Here we report a series of choice experiments that better define the relative position of cocaine on the value ladder of rats (i.e., preference rank-ordering of different rewards). Rats were allowed to choose either taking cocaine or drinking water sweetened with saccharin--a nondrug alternative that is not biologically essential. By systematically varying the cost and concentration of sweet water, we found that cocaine is low on the value ladder of the large majority of rats, near the lowest concentrations of sweet water. In addition, a retrospective analysis of all experiments over the past 5 years revealed that no matter how heavy was past cocaine use most rats readily give up cocaine use in favor of the nondrug alternative. Only a minority, fewer than 15% at the heaviest level of past cocaine use, continued to take cocaine, even when hungry and offered a natural sugar that could relieve their need of calories.CONCLUSIONS/SIGNIFICANCE:This pattern of results (cocaine abstinence in most rats; cocaine preference in few rats) maps well onto the epidemiology of human cocaine addiction and suggests that only a minority of rats would be vulnerable to cocaine addiction while the large majority would be resilient despite extensive drug use. Resilience to drug addiction has long been suspected in humans but could not be firmly established, mostly because it is difficult to control retrospectively for differences in drug self-exposure and/or availability in human drug users. This conclusion has important implications for preclinical research on the neurobiology of cocaine addiction and for future medication development

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli