Peanut oral immunotherapy transiently expands circulating Ara h 2–specific B cells with a homologous repertoire in unrelated subjects

Background Peanut oral immunotherapy (PNOIT) induces persistent tolerance to peanut in a subset of patients and induces specific antibodies that might play a role in clinical protection. However, the contribution of induced antibody clones to clinical tolerance in PNOIT is unknown. Objective We hypothesized that PNOIT induces a clonal, allergen-specific B-cell response that could serve as a surrogate for clinical outcomes. Methods We used a fluorescent Ara h 2 multimer for affinity selection of Ara h 2–specific B cells and subsequent single-cell immunoglobulin amplification. The diversity of related clones was evaluated by means of next-generation sequencing of immunoglobulin heavy chains from circulating memory B cells with 2x250 paired-end sequencing on the Illumina MiSeq platform. Results Expression of class-switched antibodies from Ara h 2–positive cells confirms enrichment for Ara h 2 specificity. PNOIT induces an early and transient expansion of circulating Ara h 2–specific memory B cells that peaks at week 7. Ara h 2–specific sequences from memory cells have rates of nonsilent mutations consistent with affinity maturation. The repertoire of Ara h 2–specific antibodies is oligoclonal. Next-generation sequencing–based repertoire analysis of circulating memory B cells reveals evidence for convergent selection of related sequences in 3 unrelated subjects, suggesting the presence of similar Ara h 2–specific B-cell clones. Conclusions Using a novel affinity selection approach to identify antigen-specific B cells, we demonstrate that the early PNOIT-induced Ara h 2–specific B-cell receptor repertoire is oligoclonal and somatically hypermutated and shares similar clonal groups among unrelated subjects consistent with convergent selection. Key words Immunotherapy; antigen-specific B cells; peanut allergy; food allergy; antibody repertoire Abbreviations used APC, Allophycocyanin; BCR, B-cell receptor; CDR, Complementarity-determining region; NGS, Next-generation sequencing; OIT, Oral immunotherapy; PNOIT, Peanut oral immunotherapyNational Institute of Allergy and Infectious Diseases (U.S.) (NIAID U19 AI087881)National Institute of Allergy and Infectious Diseases (U.S.) (NIAID U19 AI095261)United States. National Institutes of Health (1S10RR023440-01A1)National Institute of Allergy and Infectious Diseases (U.S.) (NIAID F32 AI104182)United States. National Institutes of Health (UL1 TR001102

Genome sequencing highlights the dynamic early history of dogs

To identify genetic changes underlying dog domestication and reconstruct their early evolutionary history, we generated high-quality genome sequences from three gray wolves, one from each of the three putative centers of dog domestication, two basal dog lineages (Basenji and Dingo) and a golden jackal as an outgroup. Analysis of these sequences supports a demographic model in which dogs and wolves diverged through a dynamic process involving population bottlenecks in both lineages and post-divergence gene flow. In dogs, the domestication bottleneck involved at least a 16-fold reduction in population size, a much more severe bottleneck than estimated previously. A sharp bottleneck in wolves occurred soon after their divergence from dogs, implying that the pool of diversity from which dogs arose was substantially larger than represented by modern wolf populations. We narrow the plausible range for the date of initial dog domestication to an interval spanning 11-16 thousand years ago, predating the rise of agriculture. In light of this finding, we expand upon previous work regarding the increase in copy number of the amylase gene (AMY2B) in dogs, which is believed to have aided digestion of starch in agricultural refuse. We find standing variation for amylase copy number variation in wolves and little or no copy number increase in the Dingo and Husky lineages. In conjunction with the estimated timing of dog origins, these results provide additional support to archaeological finds, suggesting the earliest dogs arose alongside hunter-gathers rather than agriculturists. Regarding the geographic origin of dogs, we find that, surprisingly, none of the extant wolf lineages from putative domestication centers is more closely related to dogs, and, instead, the sampled wolves form a sister monophyletic clade. This result, in combination with dog-wolf admixture during the process of domestication, suggests that a re-evaluation of past hypotheses regarding dog origins is necessary

Genome Sequencing Highlights Genes Under Selection and the Dynamic Early History of Dogs

Abstract To identify genetic changes underlying dog domestication and reconstruct their early evolutionary history, we analyzed novel high-quality genome sequences of three gray wolves, one from each of three putative centers of dog domestication, two ancient dog lineages (Basenji and Dingo) and a golden jackal as an outgroup. We find dogs and wolves diverged through a dynamic process involving population bottlenecks in both lineages and post-divergence gene flow, which confounds previous inferences of dog origins. In dogs, the domestication bottleneck was severe involving a 17 to 49-fold reduction in population size, a much stronger bottleneck than estimated previously from less intensive sequencing efforts. A sharp bottleneck in wolves occurred soon after their divergence from dogs, implying that the pool of diversity from which dogs arose was far larger than represented by modern wolf populations. Conditional on mutation rate, we narrow the plausible range for the date of initial dog domestication to an interval from 11 to 16 thousand years ago. This period predates the rise of agriculture and, along with new evidence from variation in amylase copy number, implies that the earliest dogs arose alongside hunter-gathers rather than agriculturists. Regarding the geographic origin of dogs, we find that surprisingly, none of the extant wolf lineages from putative domestication centers are more closely related to dogs, and the sampled wolves instead form a sister monophyletic clade. This result, in combination with our finding of dogwolf admixture during the process of domestication, suggests a re-evaluation of past hypotheses of dog origin is necessary. Finally, we also detect signatures of selection, including evidence for selection on genes implicated in morphology, metabolism, and neural development. Uniquely, we find support for selective sweeps at regulatory sites suggesting gene regulatory changes played a critical role in dog domestication

A Unified Model of Transcription Elongation: What Have We Learned from Single-Molecule Experiments?

The transcription of the genetic information encoded in DNA into RNA is performed by RNA polymerase (RNAP), a complex molecular motor, highly conserved across species. Despite remarkable progress in single-molecule techniques revealing important mechanistic details of transcription elongation (TE) with up to base-pair resolution, some of the results and interpretations of these studies are difficult to reconcile, and have not yet led to a minimal unified picture of transcription. We propose a simple model that accounts quantitatively for many of the experimental observations. This model belongs to the class of isothermal ratchet models of TE involving the thermally driven stochastic backward and forward motion (backtracking and forward tracking) of RNAP along DNA between single-nucleotide incorporation events. We uncover two essential features for the success of the model. The first is an intermediate state separating the productive elongation pathway from nonelongating backtracked states. The rates of entering and exiting this intermediate state modulate pausing by RNAP. The second crucial ingredient of the model is the cotranscriptional folding of the RNA transcript, sterically inhibiting the extent of backtracking. This model resolves several apparent differences between single-molecule studies and provides a framework for future work on TE

Cannabis microbiome sequencing reveals several mycotoxic fungi native to dispensary grade Cannabis flowers [version 2; referees: 2 approved]

The Center for Disease Control estimates 128,000 people in the U.S. are hospitalized annually due to food borne illnesses. This has created a demand for food safety testing targeting the detection of pathogenic mold and bacteria on agricultural products. This risk extends to medical Cannabis and is of particular concern with inhaled, vaporized and even concentrated Cannabis products . As a result, third party microbial testing has become a regulatory requirement in the medical and recreational Cannabis markets, yet knowledge of the Cannabis microbiome is limited. Here we describe the first next generation sequencing survey of the fungal communities found in dispensary based Cannabis flowers by ITS2 sequencing, and demonstrate the sensitive detection of several toxigenic Penicillium and Aspergillus species, including P. citrinum and P. paxilli, that were not detected by one or more culture-based methods currently in use for safety testing

Bayesian inference in nonlinear univariate time series: Investigation of GSTUR and SB models.

In the literature, many statistical models have been used to investigate the existence of a deterministic time trend, changing persistence and nonlinearity in macroeconomic and financial data. Good understanding of these properties in a univariate time series model is crucial when making forecasts. Forecasts are used in various ways, such as helping to control risks in financial institutions and to assist in setting monetary policies in central banks. Hence, evaluating the forecast capacities of statistical models, quantifying and reducing forecast uncertainties are the main concerns of forecast practitioners. In this thesis, we propose two flexible parametric models that allow for autoregressive parameters to be time varying. One is a novel Generalised Stochastic Unit Root (GSTUR) model and the other is a Stationary Bilinear (SR) model. Bayesian inference in these two models are developed using methods on the frontier of numerical analysis. Programs, including model estimation with Markov chain Monte Carlo (MCMC), model comparison with Bayes Factors, model forecasting and Forecast Model Averaging, are developed and made available to meet the demand of economic modelers. With an application to the S&P 500 series, we found strong evidences of a deterministic trend when we allow the persistence to change with time. By fitting the GSTUR model to monthly UK/US real exchange rate data, the Purchasing Power Parity (PPP) theory is revisited. Our findings of a changing persistence in the data suggest that the GSTUR model may reconcile the empirical findings of nonstationarity in real exchange rates with the PPP theory. The forecasting capacities of a group of nonlinear and linear models are evaluated with an application to UK inflation rates. We propose a GSTUR model to be applied with data, which contains as much information as possible, for forecasting near-term inflation rates