Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations

Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.     

Inbred strains of zebrafish exhibit variation in growth performance and myostatin expression following fasting

Although the zebrafish (Danio rerio) has been widely utilized as a model organism for several decades, there is little information available on physiological variation underlying genetic variation among the most commonly used inbred strains. This study evaluated growth performance using physiological and molecular markers of growth in response to fasting in six commonly used zebrafish strains [AB, TU, TL, SJA, WIK, and petstore (PET) zebrafish]. Fasting resulted in a standard decrease in whole blood glucose levels, a typical vertebrate glucose metabolism pattern, in AB, PET, TL, and TU zebrafish strains. Alternatively, fasting did not affect glucose levels in SJA and WIK zebrafish strains. Similarly, fasting had no effect on myostatin mRNA levels in AB, PET, TU, and WIK zebrafish strains, but decreased myostatin-1 and ? 2 mRNA levels in SJA zebrafish. Consistent with previous work, fasting increased myostatin-2 mRNA levels in TL zebrafish. These data demonstrate that variation is present in growth performance between commonly used inbred strains of zebrafish. These data can help future research endeavors by highlighting the attributes of each strain with regard to growth performance so that the most fitting strain may be utilized.     

Photosynthetic rapid light curves (RLC) of Thalassia testudinum exhibit diurnal variation

The use of pulse-amplitude-modulated fluorometry (PAM) and rapid light curves (RLC) were evaluated for monitoring the physiological condition of the seagrass, Thalassia testudinum, at the landscape scale in Florida Bay, USA. PAM fluorometry provides rapid, non-invasive, and quantitative physiological information on the state of photosynthesis. Yet, previous studies of effective and maximum quantum yields have shown that problems arise when expanding measurements from the organismal scale to the landscape scale, mainly due to temporal and irradiance-induced changes in photophysiology. Here, the magnitude of diurnal and spatial variation of photosynthetic characteristics among 10 sample basins and between two sample years was investigated using RLCs. Because RLCs measure effective quantum yields over a range of changing actinic irradiances, we hypothesized that the response parameters might be less sensitive to diurnal light history effects. Our results indicate that the RLC parameters, alpha and ETR_max, significantly changed diurnally, as was previously found for both maximum and effective quantum yields, but the diurnal patterns were variable among the 10 basins. Both among-basin and between-year comparisons were confounded by diurnal variation and statistical analyses comparing morning, mid-day, and afternoon time periods were unable to definitively discern which time of day was best suited for assessing the relative photophysiological status of T. testudinum. However, pooling RLC data at the basin scale revealed among-basin differences and landscape scale trends that were consistent with basin-level morphometric variation in this seagrass. Thus, PAM fluorometry may be useful as a landscape scale monitoring tool within certain constraints. When using this approach over large spatial and temporal scales, diurnal variability must be considered.     

Scalable and flexible inference framework for stochastic dynamic single-cell models

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability.     

GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data

To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms.     

Experimental verification of the behavioral foundation of bacterial transport parameters using microfluidics

We present novel microfluidic experiments to quantify population-scale transport parameters (chemotactic sensitivity χ₀ and random motility μ) of a population of bacteria. Previously, transport parameters have been derived theoretically from single-cell swimming behavior using probabilistic models, yet the mechanistic foundations of this upscaling process have not been verified experimentally. We designed a microfluidic capillary assay to generate and accurately measure gradients of chemoattractant (α-methylaspartate) while simultaneously capturing the swimming trajectories of individual Escherichia coli bacteria using videomicroscopy and cell tracking. By measuring swimming speed and bias in the swimming direction of single cells for a range of chemoattractant concentrations and concentration gradients, we directly computed the chemotactic velocity V_C and the associated chemotactic sensitivity χ₀. We then show how μ can also be readily determined using microfluidics but that a population-scale microfluidic approach is experimentally more convenient than a single-cell analysis in this case. Measured values of both χ₀ [(12.4 ± 2.0) × 10⁻⁴ cm² s⁻¹] and μ [(3.3 ± 0.8) × 10⁻⁶ cm² s⁻¹] are comparable to literature results. This microscale approach to bacterial chemotaxis lends experimental support to theoretical derivations of population-scale transport parameters from single-cell behavior. Furthermore, this study shows that microfluidic platforms can go beyond traditional chemotaxis assays and enable the quantification of bacterial transport parameters.     

菹草种群内外水质日变化

对菹草(Potamogeton crispus)种群内外水体进行了昼夜连续监测,分析菹草种群内外水质的日变化趋势.结果显示,种群内DO自日出后增加,日落后持续下降,且与水温变化一致,于19:30左右出现1次明显低谷,后略有恢复,后一直降至日出前后;开阔水域DO变化与水温呈现一定相关性,但变化幅度较小;交界处DO含量兼有种群内与开阔水域的变化特点.种群内水体pH值自日出前后升高,日落时达最高值,后开始下降,直至次日出前后;交界处与种群变化趋势一致;开阔水域pH总体变化幅度较小,白天高于夜间,总体DO含量及pH分布:种群区＞交界处＞开阔水域.种群及交界处TN含量均在日出前后达最高,日出后递减,开阔水域夜间含量较高,日出前后达最高;各点NH+4-N变化规律不显著;种群内水体TP含量夜间较高,日出前后达最高值,日出后递减,日落后递增;开阔水域TP含量夜间较高,白天略低;交界处正午前后达最低值,傍晚达最高值,TN、TP总体分布:种群＜交界处＜开阔水域.菹草种群存在对水体DO、pH等环境因子均产生重要影响,继而影响水体内源性氮磷的迁移,其中对pH影响较小,其变化未能影响水体氮磷迁移,而DO昼夜变化较大,对水体内源性氮磷的迁移起重要影响.     

Estimating Allee Dynamics before They Can Be Observed: Polar Bears as a Case Study

Allee effects are an important component in the population dynamics of numerous species. Accounting for these Allee effects in population viability analyses generally requires estimates of low-density population growth rates, but such data are unavailable for most species and particularly difficult to obtain for large mammals. Here, we present a mechanistic modeling framework that allows estimating the expected low-density growth rates under a mate-finding Allee effect before the Allee effect occurs or can be observed. The approach relies on representing the mechanisms causing the Allee effect in a process-based model, which can be parameterized and validated from data on the mechanisms rather than data on population growth. We illustrate the approach using polar bears (Ursus maritimus), and estimate their expected low-density growth by linking a mating dynamics model to a matrix projection model. The Allee threshold, defined as the population density below which growth becomes negative, is shown to depend on age-structure, sex ratio, and the life history parameters determining reproduction and survival. The Allee threshold is thus both density- and frequency-dependent. Sensitivity analyses of the Allee threshold show that different combinations of the parameters determining reproduction and survival can lead to differing Allee thresholds, even if these differing combinations imply the same stable-stage population growth rate. The approach further shows how mate-limitation can induce long transient dynamics, even in populations that eventually grow to carrying capacity. Applying the models to the overharvested low-density polar bear population of Viscount Melville Sound, Canada, shows that a mate-finding Allee effect is a plausible mechanism for slow recovery of this population. Our approach is generalizable to any mating system and life cycle, and could aid proactive management and conservation strategies, for example, by providing a priori estimates of minimum conservation targets for rare species or minimum eradication targets for pests and invasive species.     

Diurnal lighting patterns and habitat alter opsin expression and colour preferences in a killifish

Spatial variation in lighting environments frequently leads to population variation in colour patterns, colour preferences and visual systems. Yet lighting conditions also vary diurnally, and many aspects of visual systems and behaviour vary over this time scale. Here, we use the bluefin killifish (Lucania goodei) to compare how diurnal variation and habitat variation (clear versus tannin-stained water) affect opsin expression and the preference to peck at different-coloured objects. Opsin expression was generally lowest at midnight and dawn, and highest at midday and dusk, and this diurnal variation was many times greater than variation between habitats. Pecking preference was affected by both diurnal and habitat variation but did not correlate with opsin expression. Rather, pecking preference matched lighting conditions, with higher preferences for blue at noon and for red at dawn/dusk, when these wavelengths are comparatively scarce. Similarly, blue pecking preference was higher in tannin-stained water where blue wavelengths are reduced. In conclusion, L. goodei exhibits strong diurnal cycles of opsin expression, but these are not tightly correlated with light intensity or colour. Temporally variable pecking preferences probably result from lighting environment rather than from opsin production. These results may have implications for the colour pattern diversity observed in these fish.     

群体基因组结构变异检测工作流

结构变异作为人类基因组上的一种大规模的变异类型,对分子与细胞进程、调节功能、基因表达调控、个体表型具有重要的影响,检测群体中基因组结构变异有助于绘制群体基因组变异图谱,刻画群体遗传进化特征,为疾病诊治、精准医疗的发展提供支撑。本研究提出一种面向高通量测序的群体基因组结构变异检测工作流,该工作流通过使用多种高性能基因组结构变异检测算法实现全面、精准的结构变异挖掘,使用多层融合与过滤获得高精度群体结构变异候选集合,利用基因型重新校正、变异修剪、类型校对,最终完整绘制群体基因组结构变异图谱。基于该工作流对由267个样本组成的人群进行群体结构变异检测,检测出了96 202个结构变异,其变异种类和频率分布与其他国际基因组计划相符,这些结果证明了本工作流具有良好的群体结构变异检测能力。同时,工作流通过并行的方式在内存可控的基础上显著降低了分析时间,为大规模人群基因组结构变异的高效检测提供了重要支撑。     

A statistical-reaction-diffusion approach for analyzing expansion processes

In this article, we propose a method for analyzing the spatial variations in the range expansion of the pine processionary moth (PPM), an invasive species in France. Based on binary measurements - the presence or absence of PPM nests - the proposed method allows us to infer the local effect of the environment on PPM population expansion. This effect is estimated at each position x using a parameter F(x) that corresponds to the local PPM fitness. The data type and the two stage PPM life cycle make estimating this parameter difficult. To overcome these difficulties we adopt a mechanistic-statistical approach that combines a statistical model for the observation process with a hierarchical,reaction-diffusion based mechanistic model for the expansion process. Bayesian inference of the parameter F(x) reveals that PPM fitness is spatially heterogeneous and highlights the existence of large regions associated with lower fitness. The factors underlying this lower fitness are yet to be determined.     

Chapter 13: Mining Electronic Health Records in the Genomics Era

Abstract: The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.

What to Learn in This Chapter

• Describe the types of information available in Electronic Health Records (EHRs), and the relative sensitivity and positive predictive value of each
• Describe the difference between unstructured and structured information in the EHR
• Describe methods for developing accurate phenotype algorithms that integrate structured and unstructured EHR information, and the roles played by billing codes, laboratory values, medication data, and natural language processing
• Describe recent uses of EHR-derived phenotypes to study genome-phenome relationships
• Describe the cost advantages unique to EHR-linked biobanks, and the ability to reuse genetic data for many studies
• Understand the role of EHRs to enable phenome-wide association studies of genetic variants

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

Effect of pedal rate on diurnal variations in cardiorespiratory variables

Recently, it was observed that the freely chosen pedal rate of elite cyclists was significantly lower at 06:00 than at 18:00 h, and that ankle kinematics during cycling exhibits diurnal variation. The modification of the pedaling technique and pedal rate observed throughout the day could be brought about to limit the effect of diurnal variation on physiological variables. Imposing a pedal rate should limit the subject's possibility of adaptation and clarify the influence of time of day on physiological variables. The purpose of this study was to determine whether diurnal variation in cardiorespiratory variables depends on pedal rate. Ten male cyclists performed a submaximal 15 min exercise on a cycle ergometer (50% W_max). Five test sessions were performed at 06:00, 10:00, 14:00, 18:00, and 22:00 h. The exercise bout was divided into three equivalent 5 min periods during which different pedal rates were imposed (70 rev · min^-1, 90 rev · min^-1 and 120 rev · min^-1). No significant diurnal variation was observed in heart rate and oxygen consumption, whatever the pedal rate. A significant diurnal variation was observed in minute ventilation (p=0.01). In addition, the amplitude of the diurnal variation in minute ventilation depended on pedal rate: the higher the pedal rate, the greater the amplitude of its diurnal variation (p=0.03). The increase of minute ventilation throughout the day is mainly due to variation in breath frequency (p=0.01)—the diurnal variation of tidal volume (all pedal rate conditions taken together) being non-significant—but the effect of pedal rate×time of day interaction on minute ventilation specific to the higher pedal rate conditions (p=0.03) can only be explained by the increase of tidal volume throughout the day. Even though an influence of pedal rate on diurnal rhythms in overall physiological variables was not also evidenced, high pedal rate should have been imposed when diurnal variations of physiological variables in cycling were studied.     

Effect of Pedal Rate on Diurnal Variations in Cardiorespiratory Variables

Recently, it was observed that the freely chosen pedal rate of elite cyclists was significantly lower at 06:00 than at 18:00 h, and that ankle kinematics during cycling exhibits diurnal variation. The modification of the pedaling technique and pedal rate observed throughout the day could be brought about to limit the effect of diurnal variation on physiological variables. Imposing a pedal rate should limit the subject's possibility of adaptation and clarify the influence of time of day on physiological variables. The purpose of this study was to determine whether diurnal variation in cardiorespiratory variables depends on pedal rate. Ten male cyclists performed a submaximal 15 min exercise on a cycle ergometer (50% W_max). Five test sessions were performed at 06:00, 10:00, 14:00, 18:00, and 22:00 h. The exercise bout was divided into three equivalent 5 min periods during which different pedal rates were imposed (70 rev · min^?1, 90 rev · min^?1 and 120 rev · min^?1). No significant diurnal variation was observed in heart rate and oxygen consumption, whatever the pedal rate. A significant diurnal variation was observed in minute ventilation (p=0.01). In addition, the amplitude of the diurnal variation in minute ventilation depended on pedal rate: the higher the pedal rate, the greater the amplitude of its diurnal variation (p=0.03). The increase of minute ventilation throughout the day is mainly due to variation in breath frequency (p=0.01)—the diurnal variation of tidal volume (all pedal rate conditions taken together) being non‐significant—but the effect of pedal rate×time of day interaction on minute ventilation specific to the higher pedal rate conditions (p=0.03) can only be explained by the increase of tidal volume throughout the day. Even though an influence of pedal rate on diurnal rhythms in overall physiological variables was not also evidenced, high pedal rate should have been imposed when diurnal variations of physiological variables in cycling were studied.     

A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus×giganteus: description, parameterization and validation

Biomass based bioenergy is promoted as a major sustainable energy source which can simultaneously decrease net greenhouse gas emissions. Miscanthus × giganteus ( M. × giganteus ), a C₄ perennial grass with high nitrogen, water, and light use efficiencies, is regarded as a promising energy crop for biomass production. Mathematical models which can accurately predict M. × giganteus biomass production potential under different conditions are critical to evaluate the feasibility of its production in different environments. Although previous models based on light-conversion efficiency have been shown to provide good predictions of yield, they cannot easily be used in assessing the value of physiological trait improvement or ecosystem processes. Here, we described in detail the physical and physiological processes of a previously published generic mechanistic eco-physiological model, WIMOVAC, adapted and parameterized for M. × giganteus . Parameterized for one location in England, the model was able to realistically predict daily field diurnal photosynthesis and seasonal biomass at a range of other sites from European studies. The model provides a framework that will allow incorporation of further mechanistic information as it is developed for this new crop.     

An Accessible Method for Implementing Hierarchical Models with Spatio-Temporal Abundance Data

A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.     

Development of a dynamic continuous-discrete-continuous model describing the lag phase of individual bacterial cells

AIMS: A previous model for adaptation and growth of individual bacterial cells was not dynamic in the lag phase, and could not be used to perform simulations of growth under non-isothermal conditions. The aim of the present study was to advance this model by adding a continuous adaptation step, prior to the discrete step, to form a continuous-discrete-continuous (CDC) model. METHODS AND RESULTS: The revised model uses four parameters: N(0), initial population; N(max), maximum population; p0, mean initial individual cell physiological state; SD(p0), standard deviation of the distribution of individual physiological states. A truncated normal distribution was used to generate tables of distributions to allow fitting of the CDC model to viable count data for Listeria monocytogenes grown at 5 degrees C to 35 degrees C. The p0 values increased with increasing SD(p0) and were, on average, greater than the corresponding population physiological states (h0); p0 and h0 were equivalent for individual cells. CONCLUSION: The CDC model has improved the ability to simulate the behaviour of individual bacterial cells by using a physiological state parameter and a distribution function to handle inter-cell variability. The stages of development of this model indicate the importance of physiological state parameters over the population lag concept, and provide a potential approach for making growth models more mechanistic by incorporating actual physiological events. SIGNIFICANCE AND IMPACT OF THE STUDY: Individual cell behaviour is important in modelling bacterial growth in foods. The CDC model provides a means of improving existing growth models, and increases the value of mathematical modelling to the food industry.     

Effect of feeding regimen on diurnal variation of breathing movements in late-gestation fetal sheep

There is a diurnal variation in the mean incidence and amplitude of fetal breathing movements (FBMs) in sheep after approximately 120 days gestation. To determine whether this variation is caused by diurnal fluctuations in plasma glucose or prostaglandin (PG) concentrations, we studied two groups of pregnant sheep fed either once daily at 1100 h or every 2 h for 24 h. Maternal and fetal blood samples were taken every 2 h from 0900 to 0900 h the next day for assay of plasma glucose and PGE2 and PGF2 alpha concentrations. FBMs were recorded throughout the 24 h. The mean fetal plasma glucose concentrations of the once-daily and multifed groups were not different, but there was a significant difference between the two groups in the 24-h pattern of fetal glucose concentrations. In the once-daily fed group, plasma glucose concentrations reached a peak 8 h after maternal feeding and then declined, whereas in the multifed group, fetal plasma glucose concentrations reached a plateau and were constant from 1300 to 0900 h the next day. Fetal plasma PGE2 and PGF2 alpha concentrations did not show a significant change with time of day in either group. There was a significant diurnal variation in the incidence and amplitude of FBMs in each of the two feeding groups, and the 24-h pattern of FBMs did not differ significantly between groups. Therefore it would appear that the diurnal variation of FBMs is not a consequence of the maternal feeding regimen or diurnal changes in plasma glucose or PG concentrations.     

SIRT1 Polymorphism,Long-Term Survival and Glucose Tolerance in the General Population

Mutations that increase activity of Sir2 (silent information regulator 2) are associated with extended lifespan of yeast, fruit flies and worms. SIRT1, the human homolog of Sir2, that controls numerous physiological processes including the glucose metabolism, is considered a candidate gene for predicting variation in human lifespan. Whereas the role of Sir2 has been extensively investigated in model organisms, less is known about the relation between SIRT1 and lifespan in humans. In the current study we included 1,390 subjects from a general population-based cohort with 18 years of follow-up to investigate associations between variation in single nucleotide polymorphisms (SNPs) in the SIRT1 gene and human survival. Additionally in 535 male subjects with available data we investigated associations between SIRT1 and glucose tolerance. Carriers of the minor allele of rs12778366 had a significantly reduced mortality risk compared to the wild types: Hazard Ratio 0.69 (95% CI 0.50 to 0.96; p = 0.025). The directions of the effect were the same in females and males, never and ever smokers and the effect was significantly protective in overweight/obese subjects. Carriers of the minor allele of SNP rs12778366 had better glucose tolerance indicated by 0.34 mmol/l lower glucose levels compared to wild type subjects (p = 0.03). This study shows that SIRT1 affects human long-term survival and therefore may be an important factor in modulating lifespan not only in lower organisms, but also in humans.     

Genetic and physiological bases for phenological responses to current and predicted climates

We are now reaching the stage at which specific genetic factors with known physiological effects can be tied directly and quantitatively to variation in phenology. With such a mechanistic understanding, scientists can better predict phenological responses to novel seasonal climates. Using the widespread model species Arabidopsis thaliana, we explore how variation in different genetic pathways can be linked to phenology and life-history variation across geographical regions and seasons. We show that the expression of phenological traits including flowering depends critically on the growth season, and we outline an integrated life-history approach to phenology in which the timing of later life-history events can be contingent on the environmental cues regulating earlier life stages. As flowering time in many plants is determined by the integration of multiple environmentally sensitive gene pathways, the novel combinations of important seasonal cues in projected future climates will alter how phenology responds to variation in the flowering time gene network with important consequences for plant life history. We discuss how phenology models in other systems—both natural and agricultural—could employ a similar framework to explore the potential contribution of genetic variation to the physiological integration of cues determining phenology.