Complex hybrid origin of genetic caste determination in harvester ants

Caste differentiation and division of labour are the hallmarks of insect societies and at the root of their ecological success. Kin selection predicts that caste determination should result from environmentally induced differences in gene expression, a prediction largely supported by empirical data. However, two exceptional cases of genetically determined caste differentiation have recently been found in harvester ants. Here we show that genetic caste determination evolved in these populations after complex hybridization events. We identified four distinct genetic lineages, each consisting of unique blends of the genomes of the parental species, presumably Pogonomyrmex barbatus and P. rugosus. Crosses between lineages H1 and H2 and between J1 and J2 give rise to workers, whereas queens develop from within-lineage matings. Although historical gene flow is evident, genetic exchange among lineages and between lineages and the parental species no longer occurs. This unusual system of caste determination seems to be evolutionarily stable

Computational Stem Cell Biology: Open Questions and Guiding Principles

Computational biology is enabling an explosive growth in our understanding of stem cells and our ability to use them for disease modeling, regenerative medicine, and drug discovery. We discuss four topics that exemplify applications of computation to stem cell biology: cell typing, lineage tracing, trajectory inference, and regulatory networks. We use these examples to articulate principles that have guided computational biology broadly and call for renewed attention to these principles as computation becomes increasingly important in stem cell biology. We also discuss important challenges for this field with the hope that it will inspire more to join this exciting area

Novel evolutionary algorithm identifies interactions driving infestation of triatoma dimidiata, a chagas disease vector

Chagas disease is a lethal, neglected tropical disease. Unfortunately, aggressive insecticide-spraying campaigns have not been able to eliminate domestic infestation of Triatoma dimidiata, the native vector in Guatemala. To target interventions toward houses most at risk of infestation, comprehensive socioeconomic and entomologic surveys were conducted in two towns in Jutiapa, Guatemala. Given the exhaustively large search space associated with combinations of risk factors, traditional statistics are limited in their ability to discover risk factor interactions. Two recently developed statistical evolutionary algorithms, specifically designed to accommodate risk factor interactions and heterogeneity, were applied to this large combinatorial search space and used in tandem to identify sets of risk factor combinations associated with infestation. The optimal model includes 10 risk factors in what is known as a third-order disjunctive normal form (i.e., infested households have chicken coops AND deteriorated bedroom walls OR an accumulation of objects AND dirt floors AND total number of occupants 3 5 AND years of electricity 3 5 OR poor hygienic condition ratings AND adobe walls AND deteriorated walls AND dogs). Houses with dirt floors and deteriorated walls have been reported previously as risk factors and align well with factors currently targeted by Ecohealth interventions to minimize infestation. However, the tandem evolutionary algorithms also identified two new socioeconomic risk factors (i.e., households having many occupants and years of electricity 3 5). Identifying key risk factors may help with the development of new Ecohealth interventions and/or reduce the survey time needed to identify houses most at risk

Lock-in detection using a cryogenic low noise looped current preamplifier for the readout of resistive bolometers

We implemented a low noise current preamplifier for the readout of resistive bolometers. We tested the apparatus on thermometer resistances ranging from 10 Ohm to 500 Mohm. The use of current preamplifier overcomes constraints introduced by the readout time constant due to the thermometer resistance and the input capacitance. Using cold JFETs, this preamplifier board is shown to have very low noise: the Johnson noise of the source resistor (1 fA/Hz1/2) dominated in our noise measurements. We also implemented a lock-in chain using this preamplifier. Because of fast risetime, compensation of the phase shift may be unnecessary. If implemented, no tuning is necessary when the sensor impedance changes. Transients are very short, and thus low-passing or sampling of the signal is simplified. In case of spurious noise, the modulation frequency can be chosen in a much wider frequency range, without requiring a new calibration of the apparatus.Comment: 18 pages, 7 figures, Accepted in NIM

Uncovering vector, parasite, blood meal and microbiome patterns from mixed-DNA specimens of the Chagas disease vector Triatoma dimidiata

Chagas disease, considered a neglected disease by the World Health Organization, is caused by the protozoan parasite Trypanosoma cruzi, and transmitted by \u3e140 triatomine species across the Americas. In Central America, the main vector is Triatoma dimidiata, an opportunistic blood meal feeder inhabiting both domestic and sylvatic ecotopes. Given the diversity of interacting biological agents involved in the epidemiology of Chagas disease, having simultaneous information on the dynamics of the parasite, vector, the gut microbiome of the vector, and the blood meal source would facilitate identifying key biotic factors associated with the risk of T. cruzi transmission. In this study, we developed a RADseq-based analysis pipeline to study mixed-species DNA extracted from T. dimidiata abdomens. To evaluate the efficacy of the method across spatial scales, we used a nested spatial sampling design that spanned from individual villages within Guatemala to major biogeographic regions of Central America. Information from each biotic source was distinguished with bioinformatics tools and used to evaluate the prevalence of T. cruzi infection and predominant Discrete Typing Units (DTUs) in the region, the population genetic structure of T. dimidiata, gut microbial diversity, and the blood meal history. An average of 3.25 million reads per specimen were obtained, with approximately 1% assigned to the parasite, 20% to the vector, 11% to bacteria, and 4% to putative blood meals. Using a total of 6,405 T. cruzi SNPs, we detected nine infected vectors harboring two distinct DTUs: TcI and a second unidentified strain, possibly TcIV. Vector specimens were sufficiently variable for population genomic analyses, with a total of 25,710 T. dimidiata SNPs across all samples that were sufficient to detect geographic genetic structure at both local and regional scales. We observed a diverse microbiotic community, with significantly higher bacterial species richness in infected T. dimidiata abdomens than those that were not infected. Unifrac analysis suggests a common assemblage of bacteria associated with infection, which co-occurs with the typical gut microbial community derived from the local environment. We identified vertebrate blood meals from five T. dimidiata abdomens, including chicken, dog, duck and human; however, additional detection methods would be necessary to confidently identify blood meal sources from most specimens. Overall, our study shows this method is effective for simultaneously generating genetic data on vectors and their associated parasites, along with ecological information on feeding patterns and microbial interactions that may be followed up with complementary approaches such as PCR-based parasite detection, 18S eukaryotic and 16S bacterial barcoding

Are CSR disclosures value relevant? Cross-country evidence

Using proprietary data that rate corporate social responsibility (CSR) disclosures of firms in 21 countries, this study examines how the strength of nation-level institutions affects the extent of CSR disclosures. We then examine the valuation implications of CSR disclosures and consider how the relation between CSR disclosures and firm value varies across countries. In contrast to prior studies, we separate CSR disclosures into an expected and unexpected portion where the unexpected portion is a proxy for the incremental information contained in CSR disclosures. We observe a positive relation between unexpected CSR disclosure and firm value measured by Tobin's Q. We also find that, while countries with strong nation-level institutions promote more CSR disclosures, the valuation of a unit increase in unexpected CSR disclosures is higher when nation-level institutions are weak.http://www.tandfonline.com/loi/rear202017-09-30hb2017Accountin

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

Radiation-associated sarcoma of the skull base after irradiation for pituitary adenoma

Secondary, radiation-induced neoplasms represent a significant long-term risk after radiation treatment, and radiation-induced sarcomas (RAS) have an especially poor prognosis. These have rarely been reported after irradiation for pituitary adenomas

Integrating GWAS and Transcriptomics to Identify the Molecular Underpinnings of Thermal Stress Responses in \u3cem\u3eDrosophila melanogaster\u3c/em\u3e

Thermal tolerance of an organism depends on both the ability to dynamically adjust to a thermal stress and preparatory developmental processes that enhance thermal resistance. However, the extent to which standing genetic variation in thermal tolerance alleles influence dynamic stress responses vs. preparatory processes is unknown. Here, using the model species Drosophila melanogaster, we used a combination of Genome Wide Association mapping (GWAS) and transcriptomic profiling to characterize whether genes associated with thermal tolerance are primarily involved in dynamic stress responses or preparatory processes that influence physiological condition at the time of thermal stress. To test our hypotheses, we measured the critical thermal minimum (CTmin) and critical thermal maximum (CTmax) of 100 lines of the Drosophila Genetic Reference Panel (DGRP) and used GWAS to identify loci that explain variation in thermal limits. We observed greater variation in lower thermal limits, with CTmin ranging from 1.81 to 8.60°C, while CTmax ranged from 38.74 to 40.64°C. We identified 151 and 99 distinct genes associated with CTmin and CTmax, respectively, and there was strong support that these genes are involved in both dynamic responses to thermal stress and preparatory processes that increase thermal resistance. Many of the genes identified by GWAS were involved in the direct transcriptional response to thermal stress (72/151 for cold; 59/99 for heat), and overall GWAS candidates were more likely to be differentially expressed than other genes. Further, several GWAS candidates were regulatory genes that may participate in the regulation of stress responses, and gene ontologies related to development and morphogenesis were enriched, suggesting many of these genes influence thermal tolerance through effects on development and physiological status. Overall, our results suggest that thermal tolerance alleles can influence both dynamic plastic responses to thermal stress and preparatory processes that improve thermal resistance. These results also have utility for directly comparing GWAS and transcriptomic approaches for identifying candidate genes associated with thermal tolerance