Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to reduce preventable all-cause readmissions or death

We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks. We used the framework to formulate a survival model for using medical claims to predict hospital readmission and death that focuses on discharge placement, adjusting for confounding in estimating causal local average treatment effects. We trained the model on a 5% sample of Medicare beneficiaries from 2008 and 2011, based on their 2009-2011 inpatient episodes (approximately 1.2 million), and then tested the model on 2012 episodes (approximately 400 thousand). The model scored an out-of-sample AUROC of approximately 0.75 on predicting all-cause readmissions-defined using official Centers for Medicare and Medicaid Services (CMS) methodology-or death within 30-days of discharge, being competitive against XGBoost and a Bayesian deep neural network, demonstrating that one need-not sacrifice interpretability for accuracy. Crucially, as a regression model, it provides what blackboxes cannot-its exact gold-standard global interpretation, explicitly defining how the model performs its internal "reasoning" for mapping the input data features to predictions. In doing so, we identify relative risk factors and quantify the effect of discharge placement. We also show that the posthoc explainer SHAP provides explanations that are inconsistent with the ground truth model reasoning that our model readily admits.

Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models

We introduce a set of gradient-flow-guided adaptive importance sampling (IS) transformations to stabilize Monte-Carlo approximations of point-wise leave one out cross-validated (LOO) predictions for Bayesian classification models. One can leverage this methodology for assessing model generalizability by for instance computing a LOO analogue to the AIC or computing LOO ROC/PRC curves and derived metrics like the AUROC and AUPRC. By the calculus of variations and gradient flow, we derive two simple nonlinear single-step transformations that utilize gradient information to shift a model's pre-trained full-data posterior closer to the target LOO posterior predictive distributions. In doing so, the transformations stabilize importance weights. Because the transformations involve the gradient of the likelihood function, the resulting Monte Carlo integral depends on Jacobian determinants with respect to the model Hessian. We derive closed-form exact formulae for these Jacobian determinants in the cases of logistic regression and shallow ReLU-activated artificial neural networks, and provide a simple approximation that sidesteps the need to compute full Hessian matrices and their spectra. We test the methodology on an n ≪ p dataset that is known to produce unstable LOO IS weights.

A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of "Paxlovid rebound," a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

Distributing task-related neural activity across a cortical network through task-independent connections

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. Task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.

Multiple models for outbreak decision support in the face of uncertainty

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Phase transitions may explain why SARS-CoV-2 spreads so fast and why new variants are spreading faster

The novel coronavirus SARS CoV-2 responsible for the COVID-19 pandemic and SARS CoV-1 responsible for the SARS epidemic of 2002-2003 share an ancestor yet evolved to have much different transmissibility and global impact ¹. A previously developed thermodynamic model of protein conformations hypothesized that SARS CoV-2 is very close to a new thermodynamic critical point, which makes it highly infectious but also easily displaced by a spike-based vaccine because there is a tradeoff between transmissibility and robustness ². The model identified a small cluster of four key mutations of SARS CoV-2 that predicts much stronger viral attachment and viral spreading compared to SARS CoV-1. Here we apply the model to the SARS-CoV-2 variants Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1) and Delta (B.1.617.2)³ and predict, using no free parameters, how the new mutations will not diminish the effectiveness of current spike based vaccines and may even further enhance infectiousness by augmenting the binding ability of the virus.

Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.

A data-driven model for COVID-19 pandemic - Evolution of the attack rate and prognosis for Brazil

We introduce a compartmental model SEIAHRV (Susceptible, Exposed, Infected, Asymptomatic, Hospitalized, Recovered, Vaccinated) with age structure for the spread of the SARAS-CoV virus. In order to model current different vaccines we use compartments for individuals vaccinated with one and two doses without vaccine failure and a compartment for vaccinated individual with vaccine failure. The model allows to consider any number of different vaccines with different efficacies and delays between doses. Contacts among age groups are modeled by a contact matrix and the contagion matrix is obtained from a probability of contagion p c per contact. The model uses known epidemiological parameters and the time dependent probability p c is obtained by fitting the model output to the series of deaths in each locality, and reflects non-pharmaceutical interventions. As a benchmark the output of the model is compared to two good quality serological surveys, and applied to study the evolution of the COVID-19 pandemic in the main Brazilian cities with a total population of more than one million. We also discuss with some detail the case of the city of Manaus which raised special attention due to a previous report of We also estimate the attack rate, the total proportion of cases (symptomatic and asymptomatic) with respect to the total population, for all Brazilian states since the beginning of the COVID-19 pandemic. We argue that the model present here is relevant to assessing present policies not only in Brazil but also in any place where good serological surveys are not available.

Pupal behavior emerges from unstructured muscle activity in response to neuromodulation in Drosophila

Identifying neural substrates of behavior requires defining actions in terms that map onto brain activity. Brain and muscle activity naturally correlate via the output of motor neurons, but apart from simple movements it has been difficult to define behavior in terms of muscle contractions. By mapping the musculature of the pupal fruit fly and comprehensively imaging muscle activation at single-cell resolution, we here describe a multiphasic behavioral sequence in Drosophila. Our characterization identifies a previously undescribed behavioral phase and permits extraction of major movements by a convolutional neural network. We deconstruct movements into a syllabary of co-active muscles and identify specific syllables that are sensitive to neuromodulatory manipulations. We find that muscle activity shows considerable variability, with sequential increases in stereotypy dependent upon neuromodulation. Our work provides a platform for studying whole-animal behavior, quantifying its variability across multiple spatiotemporal scales, and analyzing its neuromodulatory regulation at cellular resolution.

How do we find out how the brain works? One way is to use imaging techniques to visualise an animal’s brain in action as it performs simple behaviours: as the animal moves, parts of its brain light up under the microscope. For laboratory animals like fruit flies, which have relatively small brains, this lets us observe their brain activity right down to the level of individual brain cells.

The brain directs movements via collective activity of the body’s muscles. Our ability to track the activity of individual muscles is, however, more limited than our ability to observe single brain cells: even modern imaging technology still cannot monitor the activity of all the muscle cells in an animal’s body as it moves about. Yet this is precisely the information that scientists need to fully understand how the brain generates behaviour.

Fruit flies perform specific behaviours at certain stages of their life cycle. When the fly pupa begins to metamorphose into an adult insect, it performs a fixed sequence of movements involving a set number of muscles, which is called the pupal ecdysis sequence. This initial movement sequence and the rest of metamorphosis both occur within the confines of the pupal case, which is a small, hardened shell surrounding the whole animal. Elliott et al. set out to determine if the fruit fly pupa’s ecdysis sequence could be used as a kind of model, to describe a simple behaviour at the level of individual muscles.

Imaging experiments used fly pupae that were genetically engineered to produce an activity-dependent fluorescent protein in their muscle cells. Pupal cases were treated with a chemical to make them transparent, allowing easy observation of their visually ‘labelled’ muscles. This yielded a near-complete record of muscle activity during metamorphosis.

Initially, individual muscles became active in small groups. The groups then synchronised with each other over the different regions of the pupa’s body to form distinct movements, much as syllables join to form words. This synchronisation was key to progression through metamorphosis and was co-ordinated at each step by specialised nerve cells that produce or respond to specific hormones.

These results reveal how the brain might direct muscle activity to produce movement patterns. In the future, Elliott et al. hope to compare data on muscle activity with comprehensive records of brain cell activity, to shed new light on how the brain, muscles, and other factors work together to control behaviour.

Dynamic imaging of nascent RNA reveals general principles of transcription dynamics and stochastic splice site selection

The activities of RNA polymerase and the spliceosome are responsible for the heterogeneity in the abundance and isoform composition of mRNA in human cells. However, the dynamics of these megadalton enzymatic complexes working in concert on endogenous genes have not been described. Here, we establish a quasi-genome-scale platform for observing synthesis and processing kinetics of single nascent RNA molecules in real time. We find that all observed genes show transcriptional bursting. We also observe large kinetic variation in intron removal for single introns in single cells, which is inconsistent with deterministic splice site selection. Transcriptome-wide footprinting of the U2AF complex, nascent RNA profiling, long-read sequencing, and lariat sequencing further reveal widespread stochastic recursive splicing within introns. We propose and validate a unified theoretical model to explain the general features of transcription and pervasive stochastic splice site selection.

Training Spiking Neural Networks in the Strong Coupling Regime

Recurrent neural networks trained to perform complex tasks can provide insight into the dynamic mechanism that underlies computations performed by cortical circuits. However, due to a large number of unconstrained synaptic connections, the recurrent connectivity that emerges from network training may not be biologically plausible. Therefore, it remains unknown if and how biological neural circuits implement dynamic mechanisms proposed by the models. To narrow this gap, we developed a training scheme that, in addition to achieving learning goals, respects the structural and dynamic properties of a standard cortical circuit model: strongly coupled excitatory-inhibitory spiking neural networks. By preserving the strong mean excitatory and inhibitory coupling of initial networks, we found that most of trained synapses obeyed Dale's law without additional constraints, exhibited large trial-to-trial spiking variability, and operated in inhibition-stabilized regime. We derived analytical estimates on how training and network parameters constrained the changes in mean synaptic strength during training. Our results demonstrate that training recurrent neural networks subject to strong coupling constraints can result in connectivity structure and dynamic regime relevant to cortical circuits.

A Quantitative Evaluation of COVID-19 Epidemiological Models

Quantifying how accurate epidemiological models of COVID-19 forecast the number of future cases and deaths can help frame how to incorporate mathematical models to inform public health decisions. Here we analyze and score the predictive ability of publicly available COVID-19 epidemiological models on the COVID-19 Forecast Hub. Our score uses the posted forecast cumulative distributions to compute the log-likelihood for held-out COVID-19 positive cases and deaths. Scores are updated continuously as new data become available, and model performance is tracked over time. We use model scores to construct ensemble models based on past performance. Our publicly available quantitative framework may aid in improving modeling frameworks, and assist policy makers in selecting modeling paradigms to balance the delicate trade-offs between the economy and public health.

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10^-8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10^-5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (r_g ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|r_g| ≈ 0.1-0.3) and positive genetic correlations with physical activity (r_g ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (r_g ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Dissecting transcriptional amplification by MYC

Supraphysiological MYC levels are oncogenic. Originally considered a typical transcription factor recruited to E-boxes (CACGTG), another theory posits MYC a global amplifier increasing output at all active promoters. Both models rest on large-scale genome-wide "-omics'. Because the assumptions, statistical parameter and model choice dictates the '-omic' results, whether MYC is a general or specific transcription factor remains controversial. Therefore, an orthogonal series of experiments interrogated MYC's effect on the expression of synthetic reporters. Dose-dependently, MYC increased output at minimal promoters with or without an E-box. Driving minimal promoters with exogenous (glucocorticoid receptor) or synthetic transcription factors made expression more MYC-responsive, effectively increasing MYC-amplifier gain. Mutations of conserved MYC-Box regions I and II impaired amplification, whereas MYC-box III mutations delivered higher reporter output indicating that MBIII limits over-amplification. Kinetic theory and experiments indicate that MYC activates at least two steps in the transcription-cycle to explain the non-linear amplification of transcription that is essential for global, supraphysiological transcription in cancer.

A mathematical model for persistent post-CSD vasoconstriction

Cortical spreading depression (CSD) is the propagation of a relatively slow wave in cortical brain tissue that is linked to a number of pathological conditions such as stroke and migraine. Most of the existing literature investigates the dynamics of short term phenomena such as the depolarization and repolarization of membrane potentials or large ion shifts. Here, we focus on the clinically-relevant hour-long state of neurovascular malfunction in the wake of CSDs. This dysfunctional state involves widespread vasoconstriction and a general disruption of neurovascular coupling. We demonstrate, using a mathematical model, that dissolution of calcium that has aggregated within the mitochondria of vascular smooth muscle cells can drive an hour-long disruption. We model the rate of calcium clearance as well as the dynamical implications on overall blood flow. Based on reaction stoichiometry, we quantify a possible impact of calcium phosphate dissolution on the maintenance of F0F1-ATP synthase activity.

Global prediction of unreported SARS-CoV2 infection from observed COVID-19 cases

Estimation of infectiousness and fatality of the SARS-CoV-2 virus in the COVID-19 global pandemic is complicated by ascertainment bias resulting from incomplete and non-representative samples of infected individuals. We developed a strategy for overcoming this bias to obtain more plausible estimates of the true values of key epidemiological variables. We fit mechanistic Bayesian latent-variable SIR models to confirmed COVID-19 cases, deaths, and recoveries, for all regions (countries and US states) independently. Bayesian averaging over models, we find that the raw infection incidence rate underestimates the true rate by a factor, the case ascertainment ratio CARt that depends upon region and time. At the regional onset of COVID-19, the predicted global median was 13 infections unreported for each case confirmed (CARt = 0.07 C.I. (0.02, 0.4)). As the infection spread, the median CARt rose to 9 unreported cases for every one diagnosed as of April 15, 2020 (CARt = 0.1 C.I. (0.02, 0.5)). We also estimate that the median global initial reproduction number R0 is 3.3 (C.I (1.5, 8.3)) and the total infection fatality rate near the onset is 0.17% (C.I. (0.05%, 0.9%)). However the time-dependent reproduction number Rt and infection fatality rate as of April 15 were 1.2 (C.I. (0.6, 2.5)) and 0.8% (C.I. (0.2%,4%)), respectively. We find that there is great variability between country- and state-level values. Our estimates are consistent with recent serological estimates of cumulative infections for the state of New York, but inconsistent with claims that very large fractions of the population have already been infected in most other regions. For most regions, our estimates imply a great deal of uncertainty about the current state and trajectory of the epidemic.

Before and beyond the Wilson-Cowan equations

The Wilson-Cowan equations represent a landmark in the history of computational neuroscience. Along with the insights Wilson and Cowan offered for neuroscience, they crystallized an approach to modeling neural dynamics and brain function. Although their iconic equations are used in various guises today, the ideas that led to their formulation and the relationship to other approaches are not well known. Here, we give a little context to some of the biological and theoretical concepts that lead to the Wilson-Cowan equations and discuss how to extend beyond them.

Unintentional error in formula preparation and its simulated impact on infant weight and adiposity

BACKGROUND

Accelerated weight gain in infancy is a public health issue and is likely due to feeding behaviours.

OBJECTIVES

To test the accuracy of individuals to dispense infant formula as compared with recommended serving sizes and to estimate the effect of dispensing inaccuracy on infant growth.

METHODS

Fifty-three adults dispensed infant formula powder for three servings of 2, 4, 6, and 8 fl oz bottles, in random order. The weight of dispensed infant formula powder was compared with the recommended serving size weight on the nutrition label. A novel mathematical model was used to estimate the impact of formula dispensing on infant weight and adiposity.

RESULTS

Nineteen percent of bottles (20 of 636) prepared contained the recommended amount of infant formula powder. Three percent were underdispensed, and 78% of bottles were overdispensed, resulting in 11% additional infant formula powder. Mathematical modelling feeding 11% above energy requirements exclusively for 6 months for male and female infants suggested infants at the 50th percentile for weight at birth would reach the 75th percentile with increased adiposity by 6 months.

CONCLUSIONS

Inaccurate measurement of infant formula powder and overdispensing, which is highly prevalent, specifically, may contribute to rapid weight gain and increased adiposity in formula-fed infants.

Dynamical modeling of multi-scale variability in neuronal competition

Variability is observed at multiple-scales in the brain and ubiquitous in perception. However, the nature of perceptual variability is an open question. We focus on variability during perceptual rivalry, a form of neuronal competition. Rivalry provides a window into neural processing since activity in many brain areas is correlated to the alternating perception rather than a constant ambiguous stimulus. It exhibits robust properties at multiple scales including conscious awareness and neuron dynamics. The prevalent theory for spiking variability is called the balanced state; whereas, the source of perceptual variability is unknown. Here we show that a single biophysical circuit model, satisfying certain mutual inhibition architectures, can explain spiking and perceptual variability during rivalry. These models adhere to a broad set of strict experimental constraints at multiple scales. As we show, the models predict how spiking and perceptual variability changes with stimulus conditions.

Asymmetric cell division-dominant neutral drift model for normal intestinal stem cell homeostasis

The normal intestinal epithelium is continuously regenerated at a rapid rate from actively cycling Lgr5-expressing intestinal stem cells (ISCs) that reside at the crypt base. Recent mathematical modeling based on several lineage-tracing studies in mice shows that the symmetric cell division-dominant neutral drift model fits well with the observed in vivo growth of ISC clones and suggests that symmetric divisions are central to ISC homeostasis. However, other studies suggest a critical role for asymmetric cell division in the maintenance of ISC homeostasis in vivo. Here, we show that the stochastic branching and Moran process models with both a symmetric and asymmetric division mode not only simulate the stochastic growth of the ISC clone in silico but also closely fit the in vivo stem cell dynamics observed in lineage-tracing studies. In addition, the proposed model with highest probability for asymmetric division is more consistent with in vivo observations reported here and by others. Our in vivo studies of mitotic spindle orientations and lineage-traced progeny pairs indicate that asymmetric cell division is a dominant mode used by ISCs under normal homeostasis. Therefore, we propose the asymmetric cell division-dominant neutral drift model for normal ISC homeostasis. NEW & NOTEWORTHY The prevailing mathematical model suggests that intestinal stem cells (ISCs) divide symmetrically. The present study provides evidence that asymmetric cell division is the major contributor to ISC maintenance and thus proposes an asymmetric cell division-dominant neutral drift model. Consistent with this model, in vivo studies of mitotic spindle orientation and lineage-traced progeny pairs indicate that asymmetric cell division is the dominant mode used by ISCs under normal homeostasis.

Intrinsic Dynamics of a Human Gene Reveal the Basis of Expression Heterogeneity

Transcriptional regulation in metazoans occurs through long-range genomic contacts between enhancers and promoters, and most genes are transcribed in episodic "bursts" of RNA synthesis. To understand the relationship between these two phenomena and the dynamic regulation of genes in response to upstream signals, we describe the use of live-cell RNA imaging coupled with Hi-C measurements and dissect the endogenous regulation of the estrogen-responsive TFF1 gene. Although TFF1 is highly induced, we observe short active periods and variable inactive periods ranging from minutes to days. The heterogeneity in inactive times gives rise to the widely observed "noise" in human gene expression and explains the distribution of protein levels in human tissue. We derive a mathematical model of regulation that relates transcription, chromosome structure, and the cell's ability to sense changes in estrogen and predicts that hypervariability is largely dynamic and does not reflect a stable biological state.

Finite-size effects for spiking neural networks with spatially dependent coupling

We study finite-size fluctuations in a network of spiking deterministic neurons coupled with nonuniform synaptic coupling. We generalize a previously developed theory of finite-size effects for globally coupled neurons with a uniform coupling function. In the uniform coupling case, mean-field theory is well defined by averaging over the network as the number of neurons in the network goes to infinity. However, for nonuniform coupling it is no longer possible to average over the entire network if we are interested in fluctuations at a particular location within the network. We show that if the coupling function approaches a continuous function in the infinite system size limit, then an average over a local neighborhood can be defined such that mean-field theory is well defined for a spatially dependent field. We then use a path-integral formalism to derive a perturbation expansion in the inverse system size around the mean-field limit for the covariance of the input to a neuron (synaptic drive) and firing rate fluctuations due to dynamical deterministic finite-size effects.

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies

To infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with environmental variables that also affect the trait. In this study, we study the properties of linkage disequilibrium (LD) Score regression, a recently developed method for using summary statistics from genome-wide association studies to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are unbiased or conservative under fairly general conditions. This robustness holds in the case of the parent genotype affecting the offspring phenotype through some environmental mechanism, despite the resulting correlation over SNPs between LD Scores and the degree of confounding. Additionally, we demonstrate that LD Score regression can produce reasonably robust estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.

An Approach to Greater Specificity for Glucocorticoids

Glucocorticoid steroids are among the most prescribed drugs each year. Nonetheless, the many undesirable side effects, and lack of selectivity, restrict their greater usage. Research to increase glucocorticoid specificity has spanned many years. These efforts have been hampered by the ability of glucocorticoids to both induce and repress gene transcription and also by the lack of success in defining any predictable properties that control glucocorticoid specificity. Correlations of transcriptional specificity have been observed with changes in steroid structure, receptor and chromatin conformation, DNA sequence for receptor binding, and associated cofactors. However, none of these studies have progressed to the point of being able to offer guidance for increased specificity. We summarize here a mathematical theory that allows a novel and quantifiable approach to increase selectivity. The theory applies to all three major actions of glucocorticoid receptors: induction by agonists, induction by antagonists, and repression by agonists. Simple graphical analysis of competition assays involving any two factors (steroid, chemical, peptide, protein, DNA, etc.) yields information (1) about the kinetically described mechanism of action for each factor at that step where the factor acts in the overall reaction sequence and (2) about the relative position of that step where each factor acts. These two pieces of information uniquely provide direction for increasing the specificity of glucocorticoid action. Consideration of all three modes of action indicate that the most promising approach for increased specificity is to vary the concentrations of those cofactors/pharmaceuticals that act closest to the observed end point. The potential for selectivity is even greater when varying cofactors/pharmaceuticals in conjunction with a select class of antagonists.

Learning recurrent dynamics in spiking networks

Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity in a network of excitatory and inhibitory neurons respecting Dale's law, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain.

High risk of conversion to diabetes in first-degree relatives of individuals with young-onset type 2 diabetes: a 12-year follow-up analysis

AIM

Family history of diabetes is an established risk factor for Type 2 diabetes, but the impact of a family history of young-onset diabetes (onset < 40 years) on future risk of diabetes among first-degree relatives is unclear. In this prospective study, we examined the influence of family history of late- versus young-onset diabetes on the development of diabetes in a young to middle-aged Chinese population.

METHODS

Some 365 siblings identified through probands with Type 2 diabetes and 452 participants from a community-based health awareness project (aged 18-55 years) who underwent metabolic assessment during the period 1998-2002 were followed to 2012-2013 to determine their glycaemic status. Multivariate logistic regression was performed to investigate the association of family history of diabetes presented at different age categories with development of diabetes.

RESULTS

In this cohort, 53.4% (n = 167) of participants with a family history of young-onset diabetes, 30.1% (n = 68) of those with a family history of late-onset diabetes and 14.4% (n = 40) of those without a family history developed diabetes. Using logistic regression, family history of diabetes presented at ages ≥ 50, 40-49, 30-39 and < 30 years, increased conversion to diabetes with respective odds ratios of 2.4, 5.8, 9.4 and 7.0 (P < 0.001 for all), after adjustment for socio-economic status, smoking, obesity, hypertension and dyslipidaemia. Among participants without diabetes at baseline, risk association of family history of late-onset diabetes with incident diabetes was not sustained, whereas that of family history of young-onset diabetes remained robust on further adjustment for baseline glycaemic measurements.

CONCLUSIONS

First-degree relatives of people with Type 2 diabetes, especially relatives of those with young-onset diabetes, are at high risk for diabetes.

Quality of care in patients with diabetic kidney disease in Asia: The Joint Asia Diabetes Evaluation (JADE) Registry

AIMS

Diabetic kidney disease independently predicts cardiovascular disease and premature death. We examined the burden of chronic kidney disease (CKD, defined as an estimated GFR < 60 ml/min/1.73 m(2) ) and quality of care in a cross-sectional survey of adults (age ≥ 18 years) with Type 2 diabetes across Asia.

METHODS

The Joint Asia Diabetes Evaluation programme is a disease-management programme implemented using an electronic portal that systematically captures clinical characteristics of all patients enrolled. Between July 2007 and December 2012, data on 28 110 consecutively enrolled patients (China: 3415, Hong Kong: 15 196, India: 3714, Korea: 1651, Philippines: 3364, Vietnam: 692, Taiwan: 78) were analysed.

RESULTS

In this survey, 15.9% of patients had CKD, 25.0% had microalbuminuria and 12.5% had macroalbuminuria. Patients with CKD were less likely to achieve HbA1c < 53 mmol/mol (7.0%) (36.0% vs. 42.3%) and blood pressure < 130/80 mmHg (20.8% vs. 35.3%), and were more likely to have retinopathy (26.2% vs. 8.7%), sensory neuropathy (29.0% vs. 7.7%), cardiovascular disease (26.6% vs. 8.7%) and self-reported hypoglycaemia (18.9% vs. 8.2%). Despite high frequencies of albuminuria (74.8%) and dyslipidaemia (93.0%) among CKD patients, only 49.0% were using renin-angiotensin system inhibitors and 53.6% were on statins. On logistic regression, old age, male gender, tobacco use, long disease duration, high HbA1c , blood pressure and BMI, and low LDL cholesterol were independently associated with CKD (all P < 0.05).

CONCLUSIONS

The poor control of risk factors, suboptimal use of organ-protective drugs and high frequencies of hypoglycaemia highlight major treatment gaps in patients with diabetic kidney disease in Asia.

Impact of Masked Replacement of Sugar-Sweetened with Sugar-Free Beverages on Body Weight Increases with Initial BMI: Secondary Analysis of Data from an 18 Month Double-Blind Trial in Children

Background

Substituting sugar-free for sugar-sweetened beverages reduces weight gain. This effect may be more pronounced in children with a high body mass index (BMI) because their sensing of kilocalories might be compromised. We investigated the impact of sugar-free versus sugary drinks separately in children with a higher and a lower initial BMI z score, and predicted caloric intakes and degree of compensation in the two groups.

Methods and Findings

This is a secondary, explorative analysis of our double-blind randomized controlled trial (RCT) which showed that replacement of one 250-mL sugary drink per day by a sugar—free drink for 18 months significantly reduced weight gain. In the 477 children who completed the trial, mean initial weights were close to the Dutch average. Only 16% were overweight and 3% obese. Weight changes were expressed as BMI z-score, i.e. as standard deviations of the BMI distribution per age and sex group. We designated the 239 children with an initial BMI z-score below the median as ‘lower BMI’ and the 238 children above the median as ‘higher BMI’. The difference in caloric intake from experimental beverages between treatments was 86 kcal/day both in the lower and in the higher BMI group. We used a multiple linear regression and the coefficient of the interaction term (initial BMI group times treatment), indicated whether children with a lower BMI responded differently from children with a higher BMI. Statistical significance was defined as p ≤ 0.05. Relative to the sugar sweetened beverage, consumption of the sugar—free beverage for 18 months reduced the BMI z-score by 0.05 SD units within the lower BMI group and by 0.21 SD within the higher BMI group. Body weight gain was reduced by 0.62 kg in the lower BMI group and by 1.53 kg in the higher BMI group. Thus the treatment reduced the BMI z-score by 0.16 SD units more in the higher BMI group than in the lower BMI group (p = 0.04; 95% CI -0.31 to -0.01). The impact of the intervention on body weight gain differed by 0.90 kg between BMI groups (p = 0.09; 95% CI -1.95 to 0.14). In addition, we used a physiologically-based model of growth and energy balance to estimate the degree to which children had compensated for the covertly removed sugar kilocalories by increasing their intake of other foods. The model predicts that children with a lower BMI had compensated 65% (95% CI 28 to 102) of the covertly removed sugar kilocalories, whereas children with a higher BMI compensated only 13% (95% CI -37 to 63).

Conclusions

The children with a BMI above the median might have a reduced tendency to compensate for changes in caloric intake. Differences in these subconscious compensatory mechanisms may be an important cause of differences in the tendency to gain weight. If further research bears this out, cutting down on the intake of sugar-sweetened drinks may benefit a large proportion of children, especially those who show a tendency to become overweight.

Trial Registration

ClinicalTrials.gov NCT00893529

Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory

It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli. However, it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory, where maintenance of a percept when the stimulus is absent is required. This challenges the universality of the basic canonical cortical circuit. Here, we show that by including an overlooked realistic small nonspecific background neural activity, the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena. The background activity induces a mutual-inhibition mechanism for short-term memory, which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary. We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion, which is a prototypical intermittent rivalry stimulus.

When the brain is presented with an ambiguous stimulus like the Necker cube or what is known as the quartet illusion, the perception will alternate or rival between the possible interpretations. There are neurons in the brain whose activity is correlated with the perception and not the stimulus. Hence, perceptual rivalry provides a unique probe of cortical function and could possibly serve as a diagnostic tool for cognitive disorders such as autism. A mathematical model based on the known biology of the brain has been developed to account for perceptual rivalry when the stimulus is static. The basic model also accounts for other neural responses to stimuli that do not elicit rivalry. However, these models cannot explain illusions where the stimulus is intermittently switched on and off and the same perception returns after an off period because there is no built-in mechanism to hold the memory. Here, we show that the inclusion of experimentally observed low-level background neural activity is sufficient to explain rivalry for static inputs, and rivalry for intermittent inputs. We validate the model with new experiments.

Modeling glucose and free fatty acid kinetics in glucose and meal tolerance test

BACKGROUND

Quantitative evaluation of insulin regulation on plasma glucose and free fatty acid (FFA) in response to external glucose challenge is clinically important to assess the development of insulin resistance (World J Diabetes 1:36-47, 2010). Mathematical minimal models (MMs) based on insulin modified frequently-sampled intravenous glucose tolerance tests (IM-FSIGT) are widely applied to ascertain an insulin sensitivity index (IEEE Rev Biomed Eng 2:54-96, 2009). Furthermore, it is important to investigate insulin regulation on glucose and FFA in postprandial state as a normal physiological condition. A simple way to calculate the appearance rate (Ra) of glucose and FFA would be especially helpful to evaluate glucose and FFA kinetics for clinical applications.

METHODS

A new MM is developed to simulate the insulin modulation of plasma glucose and FFA, combining IM-FSIGT with a mixed meal tolerance test (MT). A novel simple functional form for the appearance rate (Ra) of glucose or FFA in the MT is developed. Model results are compared with two other models for data obtained from 28 non-diabetic women (13 African American, 15 white).

RESULTS

The new functional form for Ra of glucose is an acceptable empirical approximation to the experimental Ra for a subset of individuals. When both glucose and FFA are included in FSIGT and MT, the new model is preferred using the Bayes Information Criterion (BIC).

CONCLUSIONS

Model simulations show that the new MM allows consistent application to both IM-FSIGT and MT data, balancing model complexity and data fitting. While the appearance of glucose in the circulation has an important effect on FFA kinetics in MT, the rate of appearance of FFA can be neglected for the time-period modeled.

Uncovering the Genetic Architectures of Quantitative Traits

The aim of a genome-wide association study (GWAS) is to identify loci in the human genome affecting a phenotype of interest. This review summarizes some recent work on conceptual and methodological aspects of GWAS. The average effect of gene substitution at a given causal site in the genome is the key estimand in GWAS, and we argue for its fundamental importance. Implicit in the definition of average effect is a linear model relating genotype to phenotype. The fraction of the phenotypic variance ascribable to polymorphic sites with nonzero average effects in this linear model is called the heritability, and we describe methods for estimating this quantity from GWAS data. Finally, we show that the theory of compressed sensing can be used to provide a sharp estimate of the sample size required to identify essentially all sites contributing to the heritability of a given phenotype.

Kinetically Defined Mechanisms and Positions of Action of Two New Modulators of Glucocorticoid Receptor-regulated Gene Induction

Most of the steps in, and many of the factors contributing to, glucocorticoid receptor (GR)-regulated gene induction are currently unknown. A competition assay, based on a validated chemical kinetic model of steroid hormone action, is now used to identify two new factors (BRD4 and negative elongation factor (NELF)-E) and to define their sites and mechanisms of action. BRD4 is a kinase involved in numerous initial steps of gene induction. Consistent with its complicated biochemistry, BRD4 is shown to alter both the maximal activity (Amax) and the steroid concentration required for half-maximal induction (EC50) of GR-mediated gene expression by acting at a minimum of three different kinetically defined steps. The action at two of these steps is dependent on BRD4 concentration, whereas the third step requires the association of BRD4 with P-TEFb. BRD4 is also found to bind to NELF-E, a component of the NELF complex. Unexpectedly, NELF-E modifies GR induction in a manner that is independent of the NELF complex. Several of the kinetically defined steps of BRD4 in this study are proposed to be related to its known biochemical actions. However, novel actions of BRD4 and of NELF-E in GR-controlled gene induction have been uncovered. The model-based competition assay is also unique in being able to order, for the first time, the sites of action of the various reaction components: GR < Cdk9 < BRD4 ≤ induced gene < NELF-E. This ability to order factor actions will assist efforts to reduce the side effects of steroid treatments.

Increased food energy supply as a major driver of the obesity epidemic: a global analysis

OBJECTIVE

We investigated associations between changes in national food energy supply and in average population body weight.

METHODS

We collected data from 24 high-, 27 middle- and 18 low-income countries on the average measured body weight from global databases, national health and nutrition survey reports and peer-reviewed papers. Changes in average body weight were derived from study pairs that were at least four years apart (various years, 1971-2010). Selected study pairs were considered to be representative of an adolescent or adult population, at national or subnational scale. Food energy supply data were retrieved from the Food and Agriculture Organization of the United Nations food balance sheets. We estimated the population energy requirements at survey time points using Institute of Medicine equations. Finally, we estimated the change in energy intake that could theoretically account for the observed change in average body weight using an experimentally-validated model.

FINDINGS

In 56 countries, an increase in food energy supply was associated with an increase in average body weight. In 45 countries, the increase in food energy supply was higher than the model-predicted increase in energy intake. The association between change in food energy supply and change in body weight was statistically significant overall and for high-income countries (P < 0.001).

CONCLUSION

The findings suggest that increases in food energy supply are sufficient to explain increases in average population body weight, especially in high-income countries. Policy efforts are needed to improve the healthiness of food systems and environments to reduce global obesity.

Kinetically-defined component actions in gene repression

Gene repression by transcription factors, and glucocorticoid receptors (GR) in particular, is a critical, but poorly understood, physiological response. Among the many unresolved questions is the difference between GR regulated induction and repression, and whether transcription cofactor action is the same in both. Because activity classifications based on changes in gene product level are mechanistically uninformative, we present a theory for gene repression in which the mechanisms of factor action are defined kinetically and are consistent for both gene repression and induction. The theory is generally applicable and amenable to predictions if the dose-response curve for gene repression is non-cooperative with a unit Hill coefficient, which is observed for GR-regulated repression of AP1LUC reporter induction by phorbol myristate acetate. The theory predicts the mechanism of GR and cofactors, and where they act with respect to each other, based on how each cofactor alters the plots of various kinetic parameters vs. cofactor. We show that the kinetically-defined mechanism of action of each of four factors (reporter gene, p160 coactivator TIF2, and two pharmaceuticals [NU6027 and phenanthroline]) is the same in GR-regulated repression and induction. What differs is the position of GR action. This insight should simplify clinical efforts to differentially modulate factor actions in gene induction vs. gene repression.

Path integral methods for stochastic differential equations

Stochastic differential equations (SDEs) have multiple applications in mathematical neuroscience and are notoriously difficult. Here, we give a self-contained pedagogical review of perturbative field theoretic and path integral methods to calculate moments of the probability density function of SDEs. The methods can be extended to high dimensional systems such as networks of coupled neurons and even deterministic systems with quenched disorder.

Second-generation PLINK: rising to the challenge of larger and richer datasets

BACKGROUND

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format.

FINDINGS

To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, [Formula: see text]-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0).

CONCLUSIONS

The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

Second-generation PLINK: rising to the challenge of larger and richer datasets

Background

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1’s primary data format.

Findings

To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $O\left (\sqrt {n}\right)$ \end{document}On-time/constant-space Hardy-Weinberg equilibrium and Fisher’s exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0).

Conclusions

The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

Electronic supplementary material

The online version of this article (doi:10.1186/s13742-015-0047-8) contains supplementary material, which is available to authorized users.

Theory of partial agonist activity of steroid hormones

The different amounts of residual partial agonist activity (PAA) of antisteroids under assorted conditions have long been useful in clinical applications but remain largely unexplained. Not only does a given antagonist often afford unequal induction for multiple genes in the same cell but also the activity of the same antisteroid with the same gene changes with variations in concentration of numerous cofactors. Using glucocorticoid receptors as a model system, we have recently succeeded in constructing from first principles a theory that accurately describes how cofactors can modulate the ability of agonist steroids to regulate both gene induction and gene repression. We now extend this framework to the actions of antisteroids in gene induction. The theory shows why changes in PAA cannot be explained simply by differences in ligand affinity for receptor and requires action at a second step or site in the overall sequence of reactions. The theory also provides a method for locating the position of this second site, relative to a concentration limited step (CLS), which is a previously identified step in glucocorticoid-regulated transactivation that always occurs at the same position in the overall sequence of events of gene induction. Finally, the theory predicts that classes of antagonist ligands may be grouped on the basis of their maximal PAA with excess added cofactor and that the members of each class differ by how they act at the same step in the overall gene induction process. Thus, this theory now makes it possible to predict how different cofactors modulate antisteroid PAA, which should be invaluable in developing more selective antagonists.

Metabolic profiles and treatment gaps in young-onset type 2 diabetes in Asia (the JADE programme): a cross-sectional study of a prospective cohort

BACKGROUND

The prevalence of diabetes is increasing in young adults in Asia, but little is known about metabolic control or the burden of associated complications in this population. We assessed the prevalence of young-onset versus late-onset type 2 diabetes, and associated risk factors and complication burdens, in the Joint Asia Diabetes Evaluation (JADE) cohort.

METHODS

JADE is an ongoing prospective cohort study. We enrolled adults with type 2 diabetes from 245 outpatient clinics in nine Asian countries or regions. We classified patients as having young-onset diabetes if they were diagnosed before the age of 40 years, and as having late-onset diabetes if they were diagnosed at 40 years or older. Data for participants' first JADE assessment was extracted for cross-sectional analysis. We compared clinical characteristics, metabolic risk factors, and the prevalence of complications between participants with young-onset diabetes and late-onset diabetes.

FINDINGS

Between Nov 1, 2007, and Dec 21, 2012, we enrolled 41,029 patients (15,341 from Hong Kong, 9107 from India, 7712 from Philippines, 5646 from China, 1751 from South Korea, 705 from Vietnam, 385 from Singapore, 275 from Thailand, 107 from Taiwan). 7481 patients (18%) had young-onset diabetes, with age at diagnosis of mean 32·9 years [SD 5·7] versus 53·9 years [9·0] with late-onset diabetes (n=33,548). Those with young-onset diabetes had longer disease duration (median 10 years [IQR 3-18]) than those with late-onset diabetes (5 years [2-11]). Fewer patients with young-onset diabetes achieved HbA1c concentrations lower than 7% compared to those with late-onset diabetes (27% vs 42%; p<0·0001) Patients with young-onset diabetes had higher mean concentrations of HbA1c (mean 8·32% [SD 2·03] vs 7·69% [1·82]; p<0·0001), LDL cholesterol (2·78 mmol/L [0·96] vs 2·74 [0·93]; p=0·009), and a higher prevalence of retinopathy (1363 [20%] vs 5714 (18%); p=0·011) than those with late-onset diabetes, but were less likely to receive statins (2347 [31%] vs 12,441 [37%]; p<0·0001) and renin-angiotensin-system inhibitors (1868 [25%] vs 9665 [29%]; p=0·006).

INTERPRETATION

In clinic-based settings across Asia, one in five adult patients had young-onset diabetes. Compared with patients with late-onset diabetes, metabolic control in those with young-onset diabetes was poor, and fewer received organ-protective drugs. Given the risk conferred by long-term suboptimum metabolic control, our findings suggest an impending epidemic of young-onset diabetic complications.

FUNDING

The Asia Diabetes Foundation (ADF) and Merck.

Kinetic competition during the transcription cycle results in stochastic RNA processing

Synthesis of mRNA in eukaryotes involves the coordinated action of many enzymatic processes, including initiation, elongation, splicing, and cleavage. Kinetic competition between these processes has been proposed to determine RNA fate, yet such coupling has never been observed in vivo on single transcripts. In this study, we use dual-color single-molecule RNA imaging in living human cells to construct a complete kinetic profile of transcription and splicing of the β-globin gene. We find that kinetic competition results in multiple competing pathways for pre-mRNA splicing. Splicing of the terminal intron occurs stochastically both before and after transcript release, indicating there is not a strict quality control checkpoint. The majority of pre-mRNAs are spliced after release, while diffusing away from the site of transcription. A single missense point mutation (S34F) in the essential splicing factor U2AF1 which occurs in human cancers perturbs this kinetic balance and defers splicing to occur entirely post-release.

DOI:http://dx.doi.org/10.7554/eLife.03939.001

To make a protein, part of a DNA sequence is copied to make a messenger RNA (or mRNA) molecule in a process known as transcription. The enzyme that builds an mRNA molecule first binds to a start point on a DNA strand, and then uses the DNA sequence to build a ‘pre-mRNA’ molecule until a stop signal is reached.

To make the final mRNA molecule, sections called introns are removed from the pre-mRNA molecules, and the parts left behind—known as exons—are then joined together. This process is called splicing. However, it is not fully understood how the splicing process is coordinated with the other stages of transcription. For example, does splicing occur after the pre-mRNA molecule is completed or while it is still being built? And what controls the order in which these processes occur?

One theory about how the different mRNA-making processes are coordinated is called kinetic competition. This theory states that the fastest process is the most likely to occur, even if the other processes use less energy and so might be expected to be preferred. Alternatively, the different steps may be started and stopped by ‘checkpoints’ that cause the different processes to follow on from each other in a set order.

Coulon et al. used fluorescence microscopy to investigate how mRNA molecules are made during the transcription of a human gene that makes a hemoglobin protein. To make the RNA visible, two different fluorescent markers were introduced into the pre-mRNA that cause different regions of the mRNA to glow in different colors. Coulon et al. made the introns fluoresce red and the exons glow green. Unspliced pre-mRNA molecules contain both introns and exons and so fluoresce in both colors, whereas spliced mRNA molecules contain only exons and so only glow with a green color.

By looking at both the red and green fluorescence signals at the same time, Coulon et al. could see when an intron was spliced out of the pre-mRNA. This revealed that in normal cells, splicing can occur either before or after the RNA is released from where it is transcribed. Thus, splicing and transcription does not follow a set pattern, suggesting that checkpoints do not control the sequence of events. Instead, the fact that a spliced mRNA molecule can be formed in different ways suggests kinetic competition controls the process.

In some cancer cells, there are defects in the cellular machinery that controls splicing. When looking at cells with such a defect, Coulon et al. found that splicing only occurred after transcription was completed. This study thus provides insight into the complex workings of mRNA synthesis and establishes a blueprint for understanding how splicing is impaired in diseases such as cancer.

DOI:http://dx.doi.org/10.7554/eLife.03939.002

Applying compressed sensing to genome-wide association studies

Background

The aim of a genome-wide association study (GWAS) is to isolate DNA markers for variants affecting phenotypes of interest. This is constrained by the fact that the number of markers often far exceeds the number of samples. Compressed sensing (CS) is a body of theory regarding signal recovery when the number of predictor variables (i.e., genotyped markers) exceeds the sample size. Its applicability to GWAS has not been investigated.

Results

Using CS theory, we show that all markers with nonzero coefficients can be identified (selected) using an efficient algorithm, provided that they are sufficiently few in number (sparse) relative to sample size. For heritability equal to one (h² = 1), there is a sharp phase transition from poor performance to complete selection as the sample size is increased. For heritability below one, complete selection still occurs, but the transition is smoothed. We find for h² ∼ 0.5 that a sample size of approximately thirty times the number of markers with nonzero coefficients is sufficient for full selection. This boundary is only weakly dependent on the number of genotyped markers.

Conclusion

Practical measures of signal recovery are robust to linkage disequilibrium between a true causal variant and markers residing in the same genomic region. Given a limited sample size, it is possible to discover a phase transition by increasing the penalization; in this case a subset of the support may be recovered. Applying this approach to the GWAS analysis of height, we show that 70-100% of the selected markers are strongly correlated with height-associated markers identified by the GIANT Consortium.

Research resource: modulators of glucocorticoid receptor activity identified by a new high-throughput screening assay

Glucocorticoid steroids affect almost every type of tissue and thus are widely used to treat a variety of human pathological conditions. However, the severity of numerous side effects limits the frequency and duration of glucocorticoid treatments. Of the numerous approaches to control off-target responses to glucocorticoids, small molecules and pharmaceuticals offer several advantages. Here we describe a new, extended high-throughput screen in intact cells to identify small molecule modulators of dexamethasone-induced glucocorticoid receptor (GR) transcriptional activity. The novelty of this assay is that it monitors changes in both GR maximal activity (A(max)) and EC(50) (the position of the dexamethasone dose-response curve). Upon screening 1280 chemicals, 10 with the greatest changes in the absolute value of A(max) or EC(50) were selected for further examination. Qualitatively identical behaviors for 60% to 90% of the chemicals were observed in a completely different system, suggesting that other systems will be similarly affected by these chemicals. Additional analysis of the 10 chemicals in a recently described competition assay determined their kinetically defined mechanism and site of action. Some chemicals had similar mechanisms of action despite divergent effects on the level of the GR-induced product. These combined assays offer a straightforward method of identifying numerous new pharmaceuticals that can alter GR transactivation in ways that could be clinically useful.

Conditions for the validity of SNP-based heritability estimation

The heritability of a trait (h(2)) is the proportion of its population variance caused by genetic differences, and estimates of this parameter are important for interpreting the results of genome-wide association studies (GWAS). In recent years, researchers have adopted a novel method for estimating a lower bound on heritability directly from GWAS data that uses realized genetic similarities between nominally unrelated individuals. The quantity estimated by this method is purported to be the contribution to heritability that could in principle be recovered from association studies employing the given panel of SNPs (h(2)(SNP)). Thus far, the validity of this approach has mostly been tested empirically. Here, we provide a mathematical explication and show that the method should remain a robust means of obtaining h(2)(SNP)) under circumstances wider than those under which it has so far been derived.

Short and long-term energy intake patterns and their implications for human body weight regulation

Adults consume millions of kilocalories over the course of a few years, but the typical weight gain amounts to only a few thousand kilocalories of stored energy. Furthermore, food intake is highly variable from day to day and yet body weight is remarkably stable. These facts have been used as evidence to support the hypothesis that human body weight is regulated by active control of food intake operating on both short and long time scales. Here, we demonstrate that active control of human food intake on short time scales is not required for body weight stability and that the current evidence for long term control of food intake is equivocal. To provide more data on this issue, we emphasize the urgent need for developing new methods for accurately measuring energy intake changes over long time scales. We propose that repeated body weight measurements can be used along with mathematical modeling to calculate long-term changes in energy intake and thereby quantify adherence to a diet intervention and provide dynamic feedback to individuals that seek to control their body weight.

Genetic variants for type 2 diabetes and new-onset cancer in Chinese with type 2 diabetes

BACKGROUND

Diabetes is associated with an increased risk of cancer. This study aimed to evaluate associations between recently reported type 2 diabetes (T2D) susceptibility genetic variants and cancer risk in a prospective cohort of Chinese patients with T2D.

METHODS

Seven single nucleotide polymorphisms (SNP) in IGF2BP2, CDKAL1, SLC30A8, CDKN2A/B, HHEX and TCF7L2, all identified from genome-wide association studies of T2D, were genotyped in 5900 T2D patients [age mean ± SD = 57 ± 13 years, % males = 46] without any known cancer at baseline. Associations between new-onset of cancer and SNPs were tested by Cox proportional hazard models with adjustment of conventional risk factors.

RESULTS

During the mean follow-up period of 8.5 ± 3.3 years, 429 patients (7.3%) developed cancer. Of the T2D-related SNPs, the G-alleles of HHEX rs7923837 (hazard ratio [HR] (95% C.I.) = 1.34 (1.08-1.65); P = 6.7 ×10(-3) under dominant model) and TCF7L2 rs290481 (HR (95% C.I.) = 1.16 (1.01-1.33); P = 0.040 under additive model) were positively associated with cancer risk, while the G-allele of CDKAL1 rs7756992 was inversely associated (HR (95% C.I.) = 0.80 (0.65-1.00); P = 0.048 under recessive model). The risk alleles of these significant SNPs exhibited combined effect on increasing cancer risk (per-allele HR (95% C.I.) = 1.25 (1.12-1.39); P = 4.8 × 10(-5)). The adjusted cancer risk was 2.41 (95% C.I. 1.23-4.69) for patients with four risk alleles comparing to patients without risk allele.

CONCLUSIONS

T2D-related variants HHEX rs7923837, TCF7L2 rs290481 and CDKAL1 rs7756992 increased cancer risk in patients with diabetes.

IMPACT

Our findings provide novel insights into the pathogenesis of cancer in diabetes.