Caffeine intake, plasma caffeine level, and kidney function: a Mendelian randomization study

Caffeine is a psychoactive substance widely consumed worldwide, mainly via sources such as coffee and tea. The effects of caffeine on kidney function remain unclear. We leveraged the genetic variants in the CYP1A2 and AHR genes via the two-sample Mendelian randomization (MR) framework to estimate the association of genetically predicted plasma caffeine and caffeine intake on kidney traits. Genetic association summary statistics on plasma caffeine levels and caffeine intake were taken from genome-wide association study (GWAS) meta-analyses of 9876 and of >47,000 European ancestry individuals, respectively. Genetically predicted plasma caffeine levels were associated with a decrease in estimated glomerular filtration rate (eGFR) measured using either creatinine or cystatin C. In contrast, genetically predicted caffeine intake was associated with an increase in eGFR and a low risk of chronic kidney disease. The discrepancy is likely attributable to faster metabolizers of caffeine consuming more caffeine-containing beverages to achieve the same pharmacological effect. Further research is needed to distinguish whether the observed effects on kidney function are driven by the harmful effects of higher plasma caffeine levels or the protective effects of greater intake of caffeine-containing beverages, particularly given the widespread use of drinks containing caffeine and the increasing burden of kidney disease

'Against the World': Michael Field, female marriage and the aura of amateurism'

This article considers the case of Katherine Bradley and Edith Cooper, an aunt and niece who lived and wrote together as ‘Michael Field’ in the fin-de-siècle Aesthetic movement. Bradley’s bold statement that she and Cooper were ‘closer married’ than the Brownings forms the basis for a discussion of their partnership in terms of a ‘female marriage’, a union that is reflected, as I will argue, in the pages of their writings. However, Michael Field’s exclusively collaborative output, though extensive, was no guarantee for success. On the contrary, their case illustrates the notion, valid for most products of co-authorship, that the jointly written work is always surrounded by an aura of amateurism. Since collaboration defied the ingrained notion of the author as the solitary producer of his or her work, critics and readers have time and again attempted to ‘parse’ the collaboration by dissecting the co-authored work into its constituent halves, a treatment that the Fields too failed to escape

Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data

Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs

Long-term trends in economic inequality: The case of the Florentine state, c. 1300-1800

This article provides an overview of economic inequality, particularly of wealth, in the Florentine state (Tuscany) from the early fourteenth to the late eighteenth century. Regional studies of this kind are rare, and this is only the second-ever attempt at covering such a long period. Consistent with recent research conducted on other European areas, during the early modern period we find clear indications of a tendency for economic inequality to grow continually, a finding that for Tuscany cannot be explained as the consequence of economic growth. Furthermore, the exceptionally old sources we use allow us to demonstrate that a phase of declining inequality, lasting about one century, was triggered by the Black Death from 1348 to 1349. This finding challenges earlier scholarship and significantly alters our understanding of the economic consequences of the Black Death

Cell based therapy for the management of chronic pain

The management of chronic pain, particularly neuropathic pain, still has significant unmet needs. In addition to inadequate symptomatic relief, there are concerns about adverse effects and addiction associated with treatments. The transplantation of cells that secrete neuroactive substances with analgesic properties into the central nervous system has only become of practical interest in more recent years, but provides a novel strategy to challenge current approaches in treating chronic pain. This review covers pre-clinical and clinical studies from both allogeneic and xenogeneic sources for management of chronic refractory pain

An essential function for the ATR-Activation-Domain (AAD) of TopBP1 in mouse development and cellular senescence

ATR activation is dependent on temporal and spatial interactions with partner proteins. In the budding yeast model, three proteins – Dpb11TopBP1, Ddc1Rad9 and Dna2 - all interact with and activate Mec1ATR. Each contains an ATR activation domain (ADD) that interacts directly with the Mec1ATR:Ddc2ATRIP complex. Any of the Dpb11TopBP1, Ddc1Rad9 or Dna2 ADDs is sufficient to activate Mec1ATR in vitro. All three can also independently activate Mec1ATR in vivo: the checkpoint is lost only when all three AADs are absent. In metazoans, only TopBP1 has been identified as a direct ATR activator. Depletion-replacement approaches suggest the TopBP1-AAD is both sufficient and necessary for ATR activation. The physiological function of the TopBP1 AAD is, however, unknown. We created a knock-in point mutation (W1147R) that ablates mouse TopBP1-AAD function. TopBP1-W1147R is early embryonic lethal. To analyse TopBP1-W1147R cellular function in vivo, we silenced the wild type TopBP1 allele in heterozygous MEFs. AAD inactivation impaired cell proliferation, promoted premature senescence and compromised Chk1 signalling following UV irradiation. We also show enforced TopBP1 dimerization promotes ATR-dependent Chk1 phosphorylation. Our data suggest that, unlike the yeast models, the TopBP1-AAD is the major activator of ATR, sustaining cell proliferation and embryonic development

Neural cytoskeleton capabilities for learning and memory

This paper proposes a physical model involving the key structures within the neural cytoskeleton as major players in molecular-level processing of information required for learning and memory storage. In particular, actin filaments and microtubules are macromolecules having highly charged surfaces that enable them to conduct electric signals. The biophysical properties of these filaments relevant to the conduction of ionic current include a condensation of counterions on the filament surface and a nonlinear complex physical structure conducive to the generation of modulated waves. Cytoskeletal filaments are often directly connected with both ionotropic and metabotropic types of membrane-embedded receptors, thereby linking synaptic inputs to intracellular functions. Possible roles for cable-like, conductive filaments in neurons include intracellular information processing, regulating developmental plasticity, and mediating transport. The cytoskeletal proteins form a complex network capable of emergent information processing, and they stand to intervene between inputs to and outputs from neurons. In this manner, the cytoskeletal matrix is proposed to work with neuronal membrane and its intrinsic components (e.g., ion channels, scaffolding proteins, and adaptor proteins), especially at sites of synaptic contacts and spines. An information processing model based on cytoskeletal networks is proposed that may underlie certain types of learning and memory

Accelerated search for biomolecular network models to interpret high-throughput experimental data

<p>Abstract</p> <p>Background</p> <p>The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.</p> <p>Results</p> <p>Optimal parameters for the evolutionary search were identified based on artificial data, and the algorithm showed scalable and consistent performance for as many as 150 variables. The method was tested on previously published human cell cycle gene expression microarray data sets. The evolutionary search method was found to converge to the results of exhaustive search. The randomized evolutionary search was able to converge on a set of similar best-fitting network models on different training data sets after 30 generations running 30 models per generation. Consistent results were found regardless of which of the published data sets were used to train or verify the quantitative predictions of the best-fitting models for cell cycle gene dynamics.</p> <p>Conclusion</p> <p>Our results demonstrate the capability of scalable evolutionary search for fuzzy network models to address the problem of inferring models based on complex, noisy biomolecular data sets. This approach yields multiple alternative models that are consistent with the data, yielding a constrained set of hypotheses that can be used to optimally design subsequent experiments.</p

Surface and Temporal Biosignatures

Recent discoveries of potentially habitable exoplanets have ignited the prospect of spectroscopic investigations of exoplanet surfaces and atmospheres for signs of life. This chapter provides an overview of potential surface and temporal exoplanet biosignatures, reviewing Earth analogues and proposed applications based on observations and models. The vegetation red-edge (VRE) remains the most well-studied surface biosignature. Extensions of the VRE, spectral "edges" produced in part by photosynthetic or nonphotosynthetic pigments, may likewise present potential evidence of life. Polarization signatures have the capacity to discriminate between biotic and abiotic "edge" features in the face of false positives from band-gap generating material. Temporal biosignatures -- modulations in measurable quantities such as gas abundances (e.g., CO2), surface features, or emission of light (e.g., fluorescence, bioluminescence) that can be directly linked to the actions of a biosphere -- are in general less well studied than surface or gaseous biosignatures. However, remote observations of Earth's biosphere nonetheless provide proofs of concept for these techniques and are reviewed here. Surface and temporal biosignatures provide complementary information to gaseous biosignatures, and while likely more challenging to observe, would contribute information inaccessible from study of the time-averaged atmospheric composition alone.Comment: 26 pages, 9 figures, review to appear in Handbook of Exoplanets. Fixed figure conversion error

Fundamentals of neurogastroenterology: Basic science

This review examines the fundamentals of neurogastroenterology that may underlie the pathophysiology of functional GI disorders (FGIDs). It was prepared by an invited committee of international experts and represents an abbreviated version of their consensus document that will be published in its entirety in the forthcoming book and online version entitled Rome IV. It emphasizes recent advances in our understanding of the enteric nervous system, sensory physiology underlying pain, and stress signaling pathways. There is also a focus on neuroimmmune signaling and intestinal barrier function, given the recent evidence implicating the microbiome, diet, and mucosal immune activation in FGIDs. Together, these advances provide a host of exciting new targets to identify and treat FGIDs, and new areas for future research into their pathophysiology