Search

Natural selection generally favours phenotypic variability in camouflaged organisms, whereas aposematic organisms are expected to evolve a more uniform warning coloration. However, no comprehensive analysis of the phenotypic consequences of predator selection in aposematic and cryptic species exists. Using state-of-the-art image analysis, we examine 2800 wing images of 82 moth species accessed via three online museum databases. We test whether anti-predator strategy (i.e., camouflage or aposematism) explains intraspecific variation in wing colour and pattern across northern hemisphere moths. In addition, we test two mutually non-exclusive, ecological hypotheses to explain variation in colour pattern: diel-activity or dietary-niche. In this work, taking into account phylogenetic relationships, moth phenotypic variability is best explained by anti-predator strategy with camouflaged moths being more variable in wing patterning than aposematic species.

BACKGROUND: The harlequin ladybird Harmonia axyridis (Coleoptera: Coccinellidae), native to Asia, has been introduced to other major continents where it has caused serious negative impacts on local biodiversity. Though notable advances to understand its invasion success have been made during the past decade, especially with then newer molecular tools, the conclusions reached remain to be confirmed with more advanced genomic analyses and especially using more samples from larger geographical regions across the native range. Furthermore, although H. axyridis is one of the best studied invasive insect species with respect to life history traits (often comparing invasive and native populations), the traits responsible for its colonization success in non-native areas warrant more research. RESULTS: Our analyses of genome-wide nuclear population structure indicated that an eastern Chinese population could be the source of all non-native populations and revealed several putatively adaptive candidate genomic loci involved in body color variation, visual perception, and hemolymph synthesis. Our estimates of evolutionary history indicate (1) asymmetric migration with varying population sizes across its native and non-native range, (2) a recent admixture between eastern Chinese and American populations in Europe, (3) signatures of a large progressive, historical bottleneck in the common ancestors of both populations and smaller effective sizes of the non-native population, and (4) the southwest origin and subsequent dispersal routes within its native range in China. In addition, we found that while two mitochondrial haplotypes-Hap1 and Hap2 were dominant in the native range, Hap1 was the only dominant haplotype in the non-native range. Our laboratory observations in both China and USA found statistical yet slight differences between Hap1 and Hap2 in some of life history traits. CONCLUSIONS: Our study on H. axyridis provides new insights into its invasion processes into other major continents from its native Asian range, reconstructs a geographic range evolution across its native region China, and tentatively suggests that its invasiveness may differ between mitochondrial haplotypes.

BackgroundTo assess the long-term biological coefficient of variation within individuals (CVI) and between individuals (CVG), effect of aging and cholesterol lowering drugs on blood levels of lipids in HIV-1-infected and -uninfected men.MethodsBloods were analyzed every six months over 17 years for total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) in 140 HIV-uninfected (38-66 years old) and 90 HIV-treated infected (48-64 years old) white Caucasian men to examine CVI, CVG, and the effect of cholesterol lowering drugs (CLDs) on lipid levels, and estimated changes per year of biomarkers.ResultsWith exception of HDL-C, the long term CVI compared with CVG were higher for serum levels of TC, TGs, and LDL-C in both HIV-1 infected and uninfected men not taking CLDs. Excluding results of TGs in HIV positive men, the CVI compared with CVG were lower for serum levels of TC, HDL-C, and LDL-C in both groups not taking CLDs. There were significant (p

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.

OBJECTIVE: Major negative life events, such as trauma exposure, can play a key role in igniting or exacerbating psychopathology. However, few disorders are diagnosed with respect to precipitating events, and the role of these events in the unfolding of new psychopathology is not well understood. The authors conducted a multisite transdiagnostic longitudinal study of trauma exposure and related mental health outcomes to identify neurobiological predictors of risk, resilience, and different symptom presentations. METHODS: A total of 146 participants (discovery cohort: N=69; internal replication cohort: N=77) were recruited from emergency departments within 72 hours of a trauma and followed for the next 6 months with a survey, MRI, and physiological assessments. RESULTS: Task-based functional MRI 2 weeks after a motor vehicle collision identified four clusters of individuals based on profiles of neural activity reflecting threat reactivity, reward reactivity, and inhibitory engagement. Three clusters were replicated in an independent sample with a variety of trauma types. The clusters showed different longitudinal patterns of posttrauma symptoms. CONCLUSIONS: These findings provide a novel characterization of heterogeneous stress responses shortly after trauma exposure, identifying potential neuroimaging-based biotypes of trauma resilience and psychopathology.

The coronavirus disease (Covid-19) pandemic is the most serious event of the year 2020, causing considerable global morbidity and mortality. The goal of this review is to provide a comprehensive summary of reported associations between inter-individual immunogenic variants and disease susceptibility or symptoms caused by the coronavirus strains severe acute respiratory syndrome-associated coronavirus, severe acute respiratory syndrome-associated coronavirus-2, and two of the main respiratory viruses, respiratory syncytial virus and influenza virus. The results suggest that the genetic background of the host could affect the levels of proinflammatory and anti-inflammatory cytokines and might modulate the progression of Covid-19 in affected patients. Notably, genetic variations in innate immune components such as toll-like receptors and mannose-binding lectin 2 play critical roles in the ability of the immune system to recognize coronavirus and initiate an early immune response to clear the virus and prevent the development of severe symptoms. This review provides promising clues related to the potential benefits of using immunotherapy and immune modulation for respiratory infectious disease treatment in a personalized manner.

BackgroundSmokers manifest varied phenotypes of pulmonary impairment.Research questionWhich pulmonary phenotypes are associated with coronary artery disease (CAD) in smokers?Study design and methodsWe analyzed data from the University of Pittsburgh COPD Specialized Center for Clinically Oriented Research (SCCOR) cohort (n = 481) and the Genetic Epidemiology of COPD (COPDGene) cohort (n = 2,580). Participants were current and former smokers with > 10 pack-years of tobacco exposure. Data from the two cohorts were analyzed separately because of methodologic differences. Lung hyperinflation was assessed by plethysmography in the SCCOR cohort and by inspiratory and expiratory CT scan lung volumes in the COPDGene cohort. Subclinical CAD was assessed as the coronary artery calcium score, whereas clinical CAD was defined as a self-reported history of CAD or myocardial infarction (MI). Analyses were performed in all smokers and then repeated in those with airflow obstruction (FEV1 to FVC ratio, < 0.70).ResultsPulmonary phenotypes, including airflow limitation, emphysema, lung hyperinflation, diffusion capacity, and radiographic measures of airway remodeling, showed weak to moderate correlations (r < 0.7) with each other. In multivariate models adjusted for pulmonary phenotypes and CAD risk factors, lung hyperinflation was the only phenotype associated with calcium score, history of clinical CAD, or history of MI (per 0.2 higher expiratory and inspiratory CT scan lung volume; coronary calcium: OR, 1.2; 95% CI, 1.1-1.5; P = .02; clinical CAD: OR, 1.6; 95% CI, 1.1-2.3; P = .01; and MI in COPDGene: OR, 1.7; 95% CI, 1.0-2.8; P = .05). FEV1 and emphysema were associated with increased risk of CAD (P < .05) in models adjusted for CAD risk factors; however, these associations were attenuated on adjusting for lung hyperinflation. Results were the same in those with airflow obstruction and were present in both cohorts.InterpretationLung hyperinflation is associated strongly with clinical and subclinical CAD in smokers, including those with airflow obstruction. After lung hyperinflation was accounted for, FEV1 and emphysema no longer were associated with CAD. Subsequent studies should consider measuring lung hyperinflation and examining its mechanistic role in CAD in current and former smokers.

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.

Determining the proportion of a population at risk of inadequate or excessive nutrient intake is a crucial step in planning and managing nutrition intervention programs. Multiple days of 24-h dietary intake data per subject allow for adjustment of modeled usual nutrient intake distributions for the proportion of total variance in intake attributable to within-individual variation (WIV:total). When only single-day dietary data are available, an external adjustment factor can be used; however, WIV:total may vary by population, and use of incorrect WIV:total ratios may influence the accuracy of prevalence estimates and subsequent program impacts. WIV:total values were compiled from publications and from reanalyses of existing datasets to describe variation in WIV:total across populations and settings. The potential impact of variation in external WIV:total on estimates of prevalence of inadequacy was assessed through simulation analyses using the National Cancer Institute 1-d method. WIV:total values were extracted from 40 publications from 24 countries, and additional values were calculated from 15 datasets from 12 nations. Wide variation in WIV:total (from 0.02 to 1.00) was observed in publications and reanalyses. Few patterns by population characteristics were apparent, but WIV:total varied by age in children (< vs. >1 y) and between rural and urban settings. Simulation analyses indicated that estimates of the prevalence of inadequate intake are sensitive to the selected ratio in some cases. Selection of an external WIV:total estimate should consider comparability between the reference and primary studies with regard to population characteristics, study design, and statistical methods. Given wide variation in observed ratios with few discernible patterns, the collection of ≥2 days of intake data in at least a representative subsample in population dietary studies is strongly encouraged. In the case of single-day dietary studies, sensitivity analyses are recommended to determine the robustness of prevalence estimates to changes in the variance ratio.

Determining the proportion of a population at risk of inadequate or excessive nutrient intake is a crucial step in planning and managing nutrition intervention programs. Multiple days of 24-h dietary intake data per subject allow for adjustment of modeled usual nutrient intake distributions for the proportion of total variance in intake attributable to within-individual variation (WIV:total). When only single-day dietary data are available, an external adjustment factor can be used; however, WIV:total may vary by population, and use of incorrect WIV:total ratios may influence the accuracy of prevalence estimates and subsequent program impacts. WIV:total values were compiled from publications and from reanalyses of existing datasets to describe variation in WIV:total across populations and settings. The potential impact of variation in external WIV:total on estimates of prevalence of inadequacy was assessed through simulation analyses using the National Cancer Institute 1-d method. WIV:total values were extracted from 40 publications from 24 countries, and additional values were calculated from 15 datasets from 12 nations. Wide variation in WIV:total (from 0.02 to 1.00) was observed in publications and reanalyses. Few patterns by population characteristics were apparent, but WIV:total varied by age in children (< vs. >1 y) and between rural and urban settings. Simulation analyses indicated that estimates of the prevalence of inadequate intake are sensitive to the selected ratio in some cases. Selection of an external WIV:total estimate should consider comparability between the reference and primary studies with regard to population characteristics, study design, and statistical methods. Given wide variation in observed ratios with few discernible patterns, the collection of ≥2 days of intake data in at least a representative subsample in population dietary studies is strongly encouraged. In the case of single-day dietary studies, sensitivity analyses are recommended to determine the robustness of prevalence estimates to changes in the variance ratio.

Mixed-effects models are becoming common in psychological science. Although they have many desirable features, there is still untapped potential. It is customary to view homogeneous variance as an assumption to satisfy. We argue to move beyond that perspective, and to view modeling within-person variance as an opportunity to gain a richer understanding of psychological processes. The technique to do so is based on the mixed-effects location scale model that can simultaneously estimate mixed-effects submodels to both the mean (location) and within-person variance (scale). We develop a framework that goes beyond assessing the submodels in isolation of one another and introduce a novel Bayesian hypothesis test for mean-variance correlations in the distribution of random effects. We first present a motivating example, which makes clear how the model can characterize mean-variance relations. We then apply the method to reaction times (RTs) gathered from 2 cognitive inhibition tasks. We find there are more individual differences in the within-person variance than the mean structure, as well as a complex web of structural mean-variance relations. This stands in contrast to the dominant view of within-person variance (i.e., "noise"). The results also point toward paradoxical within-person, as opposed to between-person, effects: several people had slower and less variable incongruent responses. This contradicts the typical pattern, wherein larger means tend to be associated with more variability. We conclude with future directions, spanning from methodological to theoretical inquires, that can be answered with the presented methodology. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.

Conservation efforts have led to the recovery of the endangered mountain gorilla populations. Due to their limited potential for spatial expansion, population densities increased, which may alter the epidemiology of infectious diseases. Recently, clinical gastrointestinal illnesses linked to helminth infections have been recorded in both gorilla populations. To understand drivers and patterns of helminth infections we quantified strongylid and tapeworm infections across both Virunga Massif and Bwindi populations using fecal egg counts. We assessed the impact of age, sex, group size, season and spatial differences used as a proxy, which reflects observed variation in the occurrence of gastrointestinal problems, vegetation types, gorilla subpopulation growth and associated social structure on helminth infections. We revealed striking geographic differences in strongylid infections with higher egg counts mostly in areas with high occurrences of gastrointestinal disease. Increased helminth egg counts were also associated with decreasing group size in some areas. Observed spatial differences may reflect mutual effects of variations in subpopulation growth rates, gorilla social structure, and vegetation associated with altitude across mountain gorilla habitat. Helminth infection intensities in Virunga gorillas were lowest in the youngest and the oldest animals. Elucidating parasite infection patterns of endangered species with low genetic diversity is crucial for their conservation management.

BackgroundLittle is known about the concordance of atopy with asthma COPD overlap. Among individuals with COPD, a better understanding of the phenotypes characterized by asthma overlap and atopy is needed to better target therapies.Research questionWhat is the overlap between atopy and asthma status among individuals with COPD, and how are categories defined by the presence of atopy and asthma status associated with clinical and radiologic phenotypes and outcomes in the Genetic Epidemiology of COPD Study (COPDGene) and Subpopulation and Intermediate Outcome Measures in COPD Study (SPIROMICS)?Study design and methodsFour hundred three individuals with COPD from SPIROMICS and 696 individuals from COPDGene with data about specific IgEs to 10 common allergens and mixes (simultaneous assessment of combination of allergens in similar category) were included. Comparison groups were defined by atopic and asthma status (neither, atopy alone, atopic asthma, nonatopic asthma, with atopy defined as any positive specific IgE (≥0.35 KU/L) to any of the 10 allergens or mixes and asthma defined as self-report of doctor-diagnosed current asthma). Multivariable regression analyses (linear, logistic, and zero inflated negative binomial where appropriate) adjusted for age, sex, race, lung function, smoking status, pack-years smoked, and use of inhaled corticosteroids were used to determine characteristics of groups and relationship with outcomes (exacerbations, clinical outcomes, CT metrics) separately in COPDGene and SPIROMICS, and then adjusted results were combined using meta-analysis.ResultsThe prevalence of atopy was 35% and 36% in COPD subjects from SPIROMICS and COPDGene, respectively, and less than 50% overlap was seen between atopic status with asthma in both cohorts. In meta-analysis, individuals with nonatopic asthma had the most impaired symptom scores (effect size for St. George's Respiratory Questionnaire total score, 4.2; 95% CI, 0.4-7.9; effect size for COPD Assessment Test score, 2.8; 95% CI, 0.089-5.4), highest risk for exacerbations (incidence rate ratio, 1.41; 95% CI, 1.05-1.88) compared with the group without atopy or asthma. Those with atopy and atopic asthma were not at increased risk for adverse outcomes.InterpretationAsthma and atopy had incomplete overlap among former and current smokers with COPD in COPDGene and SPIROMICS. Nonatopic asthma was associated with adverse outcomes and exacerbation risk in COPD, whereas groups having atopy alone and atopic asthma had less risk.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2020. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

Genome-wide association studies of neurological diseases have identified thousands of variants associated with disease phenotypes. However, most of these variants do not alter coding sequences, making it difficult to assign their function. Here, we present a multi-omic epigenetic atlas of the adult human brain through profiling of single-cell chromatin accessibility landscapes and three-dimensional chromatin interactions of diverse adult brain regions across a cohort of cognitively healthy individuals. We developed a machine-learning classifier to integrate this multi-omic framework and predict dozens of functional SNPs for Alzheimer's and Parkinson's diseases, nominating target genes and cell types for previously orphaned loci from genome-wide association studies. Moreover, we dissected the complex inverted haplotype of the MAPT (encoding tau) Parkinson's disease risk locus, identifying putative ectopic regulatory interactions in neurons that may mediate this disease association. This work expands understanding of inherited variation and provides a roadmap for the epigenomic dissection of causal regulatory variation in disease.

Genome-wide association studies of neurological diseases have identified thousands of variants associated with disease phenotypes. However, most of these variants do not alter coding sequences, making it difficult to assign their function. Here, we present a multi-omic epigenetic atlas of the adult human brain through profiling of single-cell chromatin accessibility landscapes and three-dimensional chromatin interactions of diverse adult brain regions across a cohort of cognitively healthy individuals. We developed a machine-learning classifier to integrate this multi-omic framework and predict dozens of functional SNPs for Alzheimer's and Parkinson's diseases, nominating target genes and cell types for previously orphaned loci from genome-wide association studies. Moreover, we dissected the complex inverted haplotype of the MAPT (encoding tau) Parkinson's disease risk locus, identifying putative ectopic regulatory interactions in neurons that may mediate this disease association. This work expands understanding of inherited variation and provides a roadmap for the epigenomic dissection of causal regulatory variation in disease.

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disorder with complex biology and significant clinical heterogeneity. Many preclinical and early phase ALS clinical trials have yielded promising results that could not be replicated in larger phase 3 confirmatory trials. One reason for the lack of reproducibility may be ALS biological and clinical heterogeneity. Therefore, in this review, we explore sources of ALS heterogeneity that may reduce statistical power to evaluate efficacy in ALS trials. We also review efforts to manage clinical heterogeneity, including use of validated disease outcome measures, predictive biomarkers of disease progression, and individual clinical risk stratification. We propose that personalized prognostic models with use of predictive biomarkers may identify patients with ALS for whom a specific therapeutic strategy may be expected to be more successful. Finally, the rapid application of emerging clinical and biomarker strategies may reduce heterogeneity, increase trial efficiency, and, in turn, accelerate ALS drug development.

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disorder with complex biology and significant clinical heterogeneity. Many preclinical and early phase ALS clinical trials have yielded promising results that could not be replicated in larger phase 3 confirmatory trials. One reason for the lack of reproducibility may be ALS biological and clinical heterogeneity. Therefore, in this review, we explore sources of ALS heterogeneity that may reduce statistical power to evaluate efficacy in ALS trials. We also review efforts to manage clinical heterogeneity, including use of validated disease outcome measures, predictive biomarkers of disease progression, and individual clinical risk stratification. We propose that personalized prognostic models with use of predictive biomarkers may identify patients with ALS for whom a specific therapeutic strategy may be expected to be more successful. Finally, the rapid application of emerging clinical and biomarker strategies may reduce heterogeneity, increase trial efficiency, and, in turn, accelerate ALS drug development.

As social animals, many primates use acoustic communication to maintain relationships. Vocal individuality has been documented in a diverse range of primate species and call types, many of which have presumably different functions. Auditory recognition of ones neighbors may confer a selective advantage if identifying conspecifics decreases the need to participate in costly territorial behaviors. Alternatively, vocal individuality may be nonadaptive and the result of a unique combination of genetics and environment. Pair-bonded primates, in particular, often participate in coordinated vocal duets that can be heard over long distances by neighboring conspecifics. In contrast to adult calls, infant vocalizations are short-range and used for intragroup communication. Here, we provide two separate but complementary analyses of vocal individuality in distinct call types of coppery titi monkeys (Plecturocebus cupreus) to test whether individuality occurs in call types from animals of different age classes with presumably different functions. We analyzed 600 trill vocalizations from 30 infants and 169 pulse-chirp duet vocalizations from 30 adult titi monkeys. We predicted that duet contributions would exhibit a higher degree of individuality than infant trills, given their assumed function for long-distance, intergroup communication. We estimated 7 features from infant trills and 16 features from spectrograms of adult pulse-chirps, then used discriminant function analysis with leave-one-out cross-validation to classify individuals. We correctly classified infants with 48% accuracy and adults with 83% accuracy. To further investigate variance in call features, we used a multivariate variance components model to estimate variance partitioning in features across two levels: within- and between-individuals. Between-individual variance was the most important source of variance for all features in adults, and three of four features in infants. We show that pulse-chirps of adult titi monkey duets are individually distinct, and infant trills are less individually distinct, which may be due to the different functions of the vocalizations.

As social animals, many primates use acoustic communication to maintain relationships. Vocal individuality has been documented in a diverse range of primate species and call types, many of which have presumably different functions. Auditory recognition of one's neighbors may confer a selective advantage if identifying conspecifics decreases the need to participate in costly territorial behaviors. Alternatively, vocal individuality may be nonadaptive and the result of a unique combination of genetics and environment. Pair-bonded primates, in particular, often participate in coordinated vocal duets that can be heard over long distances by neighboring conspecifics. In contrast to adult calls, infant vocalizations are short-range and used for intragroup communication. Here, we provide two separate but complementary analyses of vocal individuality in distinct call types of coppery titi monkeys (Plecturocebus cupreus) to test whether individuality occurs in call types from animals of different age classes with presumably different functions. We analyzed 600 trill vocalizations from 30 infants and 169 pulse-chirp duet vocalizations from 30 adult titi monkeys. We predicted that duet contributions would exhibit a higher degree of individuality than infant trills, given their assumed function for long-distance, intergroup communication. We estimated 7 features from infant trills and 16 features from spectrograms of adult pulse-chirps, then used discriminant function analysis with leave-one-out cross-validation to classify individuals. We correctly classified infants with 48% accuracy and adults with 83% accuracy. To further investigate variance in call features, we used a multivariate variance components model to estimate variance partitioning in features across two levels: within- and between-individuals. Between-individual variance was the most important source of variance for all features in adults, and three of four features in infants. We show that pulse-chirps of adult titi monkey duets are individually distinct, and infant trills are less individually distinct, which may be due to the different functions of the vocalizations.

Clinical benefit of immune checkpoint blockade in glioblastoma (GBM) is rare, and we hypothesize that tumor clonal evolution and the immune microenvironment are key determinants of response. Here, we present a detailed molecular characterization of the intratumoral and immune heterogeneity in an IDH wild-type, MGMT-negative GBM patient who plausibly benefited from anti-PD-1 therapy with an unusually long 25-mo overall survival time. We leveraged multiplex immunohistochemistry, RNA-seq, and whole-exome data from the primary tumor and three resected regions of recurrent disease to survey regional tumor-immune interactions, genomic instability, mutation burden, and expression profiles. We found significant regional heterogeneity in the neoantigenic and immune landscape, with a differential T-cell signature among recurrent sectors, a uniform loss of focal amplifications in EGFR, and a novel subclonal EGFR mutation. Comparisons with recently reported correlates of checkpoint blockade in GBM and with TCGA-GBM revealed appreciable intratumoral heterogeneity that may have contributed to a differential PD-1 blockade response.

Changes in affect over time have been associated with health outcomes. However, previously utilized measurement methods focus on variability of affect (e.g., standard deviation, root mean squared successive difference) and ignore the more complex temporal patterns of affect over time. These patterns may be an important feature in understanding how the dynamics of affect relate to health. Recurrence quantification analysis (RQA) may help alleviate this problem by assessing temporal characteristics unassessed by past methods. RQA metrics, such as determinism and recurrence, can provide a measure of the predictability of affect over time, indexing how often patterns within affective experiences repeat. In Study 1, we first contrasted RQA metrics with commonly used measures of variability to demonstrate that RQA can further differentiate among patterns of affect. In Study 2, we analyzed the associations between these new metrics and health, namely, depressive and somatic symptoms. We found that RQA metrics predicted health above and beyond mean levels and variability of affect over time. The most desirable health outcomes were observed in people who had high mean positive affect, low mean negative affect, low affect variability, and high affect predictability. These studies are the first to demonstrate the utility of RQA for determining how temporal patterns in affective experiences are important for health outcomes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the "single disease" approach appears untenable, as does the notion of individualizing each single patient's care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.

BackgroundCleft lip and palate is one of the most common human birth defects, but the underlying etiology is poorly understood. The A/WySn mouse is a spontaneously occurring model of multigenic clefting in which 20% to 30% of individuals develop an orofacial cleft. Recent work has shown altered methylation at a specific retrotransposon insertion downstream of the Wnt9b locus in clefting animals, which results in decreased Wnt9b expression.ResultsUsing a newly developed protocol that allows us to measure morphology, gene expression, and DNA methylation in the same embryo, we relate gene expression in an individual embryo directly to its three-dimensional morphology for the first time. We find that methylation at the retrotransposon relates to Wnt9b expression and morphology. IAP methylation relates to shape of the nasal process in a manner consistent with clefting. Embryos with low IAP methylation exhibit increased among-individual variance in facial shape.ConclusionsMethylation and gene expression relate nonlinearly to nasal process morphology. Individuals at one end of a continuum of phenotypic states display a clinical phenotype and increased phenotypic variation. Variable penetrance and expressivity in this model is likely determined both by among-individual variation in methylation and changes in phenotypic robustness along the underlying liability distribution for orofacial clefting.

BackgroundCytokines, chemokines, adipocytokines, soluble cell receptors, and immune activation markers play an important role in immune responsiveness and can provide prognostic value since they reflect underlying conditions and disease states. This study was undertaken to investigate the components of biological variation for various laboratory tests of blood immunological biomarkers.ResultsEstimates of intra-individual coefficient of variation (CVI) and inter-individual coefficient of variation (CVG) were examined for blood immunological biomarkers. Biomarkers with CVI  40% were adiponectin, IL-1ra, leptin, MIP-1β, sCD163, and sIL-2Rα.ConclusionThe biological variations of biomarkers have important monitoring value for longitudinal investigation and are essential for quality specification of tests that are performed in the laboratory. The CVI was relatively small while CVG was comparatively large and mean values of each biomarker vary between subjects. The individuality of biomarkers significantly influences reference interval values. A majority of the biomarkers in this study had strong individuality and the result of each biomarker should be cautiously interpreted if using established reference interval values. Comparison of a patient's test result with previous ones may be more useful than the usage of conventional reference values.

PURPOSE:Evidence exists regarding the beneficial effects of diets rich in plant-based foods regarding the prevention of cardiometabolic diseases. These plant-based foods are an exclusive and abundant source of a variety of biologically active phytochemicals, including polyphenols, carotenoids, glucosinolates and phytosterols, with known health-promoting effects through a wide range of biological activities, such as improvements in endothelial function, platelet function, blood pressure, blood lipid profile and insulin sensitivity. We know that an individual's physical/genetic makeup may influence their response to a dietary intervention, and thereby may influence the benefit/risk associated with consumption of a particular dietary constituent. This inter-individual variation in responsiveness has also been described for dietary plant bioactives but has not been explored in depth. To address this issue, the European scientific experts involved in the COST Action POSITIVe systematically analyzed data from published studies to assess the inter-individual variation in selected clinical biomarkers associated with cardiometabolic risk, in response to the consumption of plant-based bioactives (poly)phenols and phytosterols. The present review summarizes the main findings resulting from the meta-analyses already completed. RESULTS:Meta-analyses of randomized controlled trials conducted within POSITIVe suggest that age, sex, ethnicity, pathophysiological status and medication may be responsible for the heterogeneity in the biological responsiveness to (poly)phenol and phytosterol consumption and could lead to inconclusive results in some clinical trials aiming to demonstrate the health effects of specific dietary bioactive compounds. However, the contribution of these factors is not yet demonstrated consistently across all polyphenolic groups and cardiometabolic outcomes, partly due to the heterogeneity in trial designs, low granularity of data reporting, variety of food vectors and target populations, suggesting the need to implement more stringent reporting practices in the future studies. Studies investigating the effects of genetic background or gut microbiome on variability were limited and should be considered in future studies. CONCLUSION:Understanding why some bioactive plant compounds work effectively in some individuals but not, or less, in others is crucial for a full consideration of these compounds in future strategies of personalized nutrition for a better prevention of cardiometabolic disease. However, there is also still a need for the development of a substantial evidence-base to develop health strategies, food products or lifestyle solutions that embrace this variability.

To assess the intra-individual and inter-individuals biological variation and the effect of aging on lymphocyte T-cells subsets.We assessed lymphocyte phenotypes (CD3, CD4, and CD8 T-cells) in 89 HIV-1-infected and 88 uninfected white non-Hispanic men every 6 months, to examine the biological variation for those measurements, and the average change in lymphocyte phenotype over 34 years.The markers showed significant intra-individuality in HIV-infected and uninfected individuals with index of individuality of

To assess the intra-individual and inter-individuals biological variation and the effect of aging on lymphocyte T-cells subsets.We assessed lymphocyte phenotypes (CD3, CD4, and CD8 T-cells) in 89 HIV-1-infected and 88 uninfected white non-Hispanic men every 6 months, to examine the biological variation for those measurements, and the average change in lymphocyte phenotype over 34 years.The markers showed significant intra-individuality in HIV-infected and uninfected individuals with index of individuality of

To assess the intra-individual and inter-individuals biological variation and the effect of aging on lymphocyte T-cells subsets.We assessed lymphocyte phenotypes (CD3, CD4, and CD8 T-cells) in 89 HIV-1-infected and 88 uninfected white non-Hispanic men every 6 months, to examine the biological variation for those measurements, and the average change in lymphocyte phenotype over 34 years.The markers showed significant intra-individuality in HIV-infected and uninfected individuals with index of individuality of

BackgroundCytokines, chemokines, adipocytokines, soluble cell receptors, and immune activation markers play an important role in immune responsiveness and can provide prognostic value since they reflect underlying conditions and disease states. This study was undertaken to investigate the components of biological variation for various laboratory tests of blood immunological biomarkers.ResultsEstimates of intra-individual coefficient of variation (CVI) and inter-individual coefficient of variation (CVG) were examined for blood immunological biomarkers. Biomarkers with CVI  40% were adiponectin, IL-1ra, leptin, MIP-1β, sCD163, and sIL-2Rα.ConclusionThe biological variations of biomarkers have important monitoring value for longitudinal investigation and are essential for quality specification of tests that are performed in the laboratory. The CVI was relatively small while CVG was comparatively large and mean values of each biomarker vary between subjects. The individuality of biomarkers significantly influences reference interval values. A majority of the biomarkers in this study had strong individuality and the result of each biomarker should be cautiously interpreted if using established reference interval values. Comparison of a patient's test result with previous ones may be more useful than the usage of conventional reference values.

Little is known about the nature or extent of everyday variability in voice quality. This paper describes a series of principal component analyses to explore within- and between-talker acoustic variation and the extent to which they conform to expectations derived from current models of voice perception. Based on studies of faces and cognitive models of speaker recognition, the authors hypothesized that a few measures would be important across speakers, but that much of within-speaker variability would be idiosyncratic. Analyses used multiple sentence productions from 50 female and 50 male speakers of English, recorded over three days. Twenty-six acoustic variables from a psychoacoustic model of voice quality were measured every 5 ms on vowels and approximants. Across speakers the balance between higher harmonic amplitudes and inharmonic energy in the voice accounted for the most variance (females = 20%, males = 22%). Formant frequencies and their variability accounted for an additional 12% of variance across speakers. Remaining variance appeared largely idiosyncratic, suggesting that the speaker-specific voice space is different for different people. Results further showed that voice spaces for individuals and for the population of talkers have very similar acoustic structures. Implications for prototype models of voice perception and recognition are discussed.

ObjectiveIntraindividual cognitive variability (IIV), a measure of within-person variability across cognitive measures at a single time point, is associated with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Little is known regarding brain changes underlying IIV, or the relationship between IIV and functional ability. Therefore, we investigated the association between IIV and cerebral atrophy in AD-vulnerable regions and everyday functioning in nondemented older adults.Method736 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (285 cognitively normal [CN]; 451 MCI) underwent neuropsychological testing and serial MRI over 2 years. Linear mixed effects models examined the association between baseline IIV and change in entorhinal cortex thickness, hippocampal volume, and everyday functioning.ResultsAdjusting for age, sex, apolipoprotein E genotype, amyloid-β positivity, and mean level of cognitive performance, higher baseline IIV predicted faster rates of entorhinal and hippocampal atrophy, as well as functional decline. Higher IIV was associated with both entorhinal and hippocampal atrophy among MCI participants but selective vulnerability of the entorhinal cortex among CN individuals.ConclusionsIIV was associated with more widespread medial temporal lobe (MTL) atrophy in individuals with MCI relative to CN, suggesting that IIV may be tracking advancing MTL pathologic changes across the continuum of aging, MCI, and dementia. Findings suggest that cognitive dispersion may be a sensitive marker of neurodegeneration and functional decline in nondemented older adults. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Listeria monocytogenes hijacks host actin to promote its intracellular motility and intercellular spread. While L. monocytogenes virulence hinges on cell-to-cell spread, little is known about the dynamics of bacterial spread in epithelia at a population level. Here, we use live microscopy and statistical modeling to demonstrate that L. monocytogenes cell-to-cell spread proceeds anisotropically in an epithelial monolayer in culture. We show that boundaries of infection foci are irregular and dominated by rare pioneer bacteria that spread farther than the rest. We extend our quantitative model for bacterial spread to show that heterogeneous spreading behavior can improve the chances of creating a persistent L. monocytogenes infection in an actively extruding epithelium. Thus, our results indicate that L. monocytogenes cell-to-cell spread is heterogeneous, and that rare pioneer bacteria determine the frontier of infection foci and may promote bacterial infection persistence in dynamic epithelia. Editorial note:This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Interest in autonomic arousal in autism spectrum disorder (ASD) is increasing; however, reliability of these measures in ASD is unknown, and previously reported associations with social and cognitive abilities are inconsistent. This study assesses heart rate (HR) and HR variability (HRV) in preschoolers with ASD or typical development (TD) while they passively watched naturalistic videos. Measurement reliability, group differences, and the relationship with social and cognitive abilities were evaluated. Seventy one ASD and 66 TD children (2-4 years) provided cardiac data from two sessions. Test-retest intraclass correlations of HR and HRV over a 3-week period were moderate to good in both groups. Groups did not differ in mean level of HR or HRV. Intra-individual variability of HR between video segments within a session was higher in the ASD group, but intraclass correlations of this metric were low. Higher HR related to better language skills in TD children, but not after accounting for age and nonverbal ability. Higher HRV related to better expressive and receptive language in ASD children after controlling for age and nonverbal ability. HR/HRV were not related to social or executive functioning skills and did not explain any additional variance in abilities at a 12-month follow-up visit. In summary, variation in language abilities is associated with HR in the TD group and HRV in the ASD group. While preliminary, these results are promising for consideration of autonomic control as a biomarker for individual differences in ASD and may help us understand the mechanisms that contribute to communication skills. Autism Research 2019, 12: 39-52. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Cardiac activity, such as heart rate and heart rate variability, is linked to a wide range of psychological functions. This study shows that there is an association between heart rate and heart rate variability and language skills in young children with autism spectrum disorder (ASD). These results may help us understand what underlies individual differences in developmental abilities in young children with ASD.

BackgroundClinicians often use population-based reference intervals (RIs) when interpreting patient results. However, this method can present problems if the analyte in question has wide variability from person to person.MethodsWe examined the biological variation of routine hematologic markers in 82 white non-Hispanic men every 6 months during a 30-year period, to determine the usefulness of population-based RIs and age-related decline of hematological markers.ResultsMany of these markers showed significant person-to-person differences (index of individuality

Listeria monocytogenes hijacks host actin to promote its intracellular motility and intercellular spread. While L. monocytogenes virulence hinges on cell-to-cell spread, little is known about the dynamics of bacterial spread in epithelia at a population level. Here, we use live microscopy and statistical modeling to demonstrate that L. monocytogenes cell-to-cell spread proceeds anisotropically in an epithelial monolayer in culture. We show that boundaries of infection foci are irregular and dominated by rare pioneer bacteria that spread farther than the rest. We extend our quantitative model for bacterial spread to show that heterogeneous spreading behavior can improve the chances of creating a persistent L. monocytogenes infection in an actively extruding epithelium. Thus, our results indicate that L. monocytogenes cell-to-cell spread is heterogeneous, and that rare pioneer bacteria determine the frontier of infection foci and may promote bacterial infection persistence in dynamic epithelia. This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

ObjectiveBronchodilator responsiveness (BDR) is prevalent in COPD, but its clinical implications remain unclear. We explored the significance of BDR, defined by post-bronchodilator change in FEV1 (BDRFEV1) as a measure reflecting the change in flow and in FVC (BDRFVC) reflecting the change in volume.MethodsWe analyzed 2974 participants from a multicenter observational study designed to identify varying COPD phenotypes (SPIROMICS). We evaluated the association of BDR with baseline clinical characteristics, rate of prospective exacerbations and mortality using negative binomial regression and Cox proportional hazards models.ResultsA majority of COPD participants exhibited BDR (52.7%). BDRFEV1 occurred more often in earlier stages of COPD, while BDRFVC occurred more frequently in more advanced disease. When defined by increases in either FEV1 or FVC, BDR was associated with a self-reported history of asthma, but not with blood eosinophil counts. BDRFVC was more prevalent in subjects with greater emphysema and small airway disease on CT. In a univariate analysis, BDRFVC was associated with increased exacerbations and mortality, although no significance was found in a model adjusted for post-bronchodilator FEV1.ConclusionWith advanced airflow obstruction in COPD, BDRFVC is more prevalent in comparison to BDRFEV1 and correlates with the extent of emphysema and degree of small airway disease. Since these associations appear to be related to the impairment of FEV1, BDRFVC itself does not define a distinct phenotype nor can it be more predictive of outcomes, but it can offer additional insights into the pathophysiologic mechanism in advanced COPD.Clinical trials registrationClinicalTrials.gov: NCT01969344T4.

ObjectiveBronchodilator responsiveness (BDR) is prevalent in COPD, but its clinical implications remain unclear. We explored the significance of BDR, defined by post-bronchodilator change in FEV1 (BDRFEV1) as a measure reflecting the change in flow and in FVC (BDRFVC) reflecting the change in volume.MethodsWe analyzed 2974 participants from a multicenter observational study designed to identify varying COPD phenotypes (SPIROMICS). We evaluated the association of BDR with baseline clinical characteristics, rate of prospective exacerbations and mortality using negative binomial regression and Cox proportional hazards models.ResultsA majority of COPD participants exhibited BDR (52.7%). BDRFEV1 occurred more often in earlier stages of COPD, while BDRFVC occurred more frequently in more advanced disease. When defined by increases in either FEV1 or FVC, BDR was associated with a self-reported history of asthma, but not with blood eosinophil counts. BDRFVC was more prevalent in subjects with greater emphysema and small airway disease on CT. In a univariate analysis, BDRFVC was associated with increased exacerbations and mortality, although no significance was found in a model adjusted for post-bronchodilator FEV1.ConclusionWith advanced airflow obstruction in COPD, BDRFVC is more prevalent in comparison to BDRFEV1 and correlates with the extent of emphysema and degree of small airway disease. Since these associations appear to be related to the impairment of FEV1, BDRFVC itself does not define a distinct phenotype nor can it be more predictive of outcomes, but it can offer additional insights into the pathophysiologic mechanism in advanced COPD.Clinical trials registrationClinicalTrials.gov: NCT01969344T4.

Interindividual variation in the composition of the human gut microbiome was examined in relation to demographic and anthropometric traits, and to changes in dietary saturated fat intake and protein source. One hundred nine healthy men and women aged 21 to 65, with BMIs of 18 to 36, were randomized, after a two-week baseline diet, to high (15% total energy [E])- or low (7%E)-saturated-fat groups and randomly received three diets (four weeks each) in which the protein source (25%E) was mainly red meat (beef, pork) (12%E), white meat (chicken, turkey) (12%E), and nonmeat sources (nuts, beans, soy) (16%E). Taxonomic characterization using 16S ribosomal DNA was performed on fecal samples collected at each diet completion. Interindividual differences in age, body fat (%), height, ethnicity, sex, and alpha diversity (Shannon) were all significant factors, and most samples clustered by participant in the PCoA ordination. The dietary interventions did not significantly alter the overall microbiome community in ordination space, but there was an effect on taxon abundance levels. Saturated fat had a greater effect than protein source on taxon differential abundance, but protein source had a significant effect once the fat influence was removed. Higher alpha diversity predicted lower beta diversity between the experimental and baseline diets, indicating greater resistance to change in people with higher microbiome diversity. Our results suggest that interindividual differences outweighed the influence of these specific dietary changes on the microbiome and that moderate changes in saturated fat level and protein source correspond to modest changes in the microbiome.IMPORTANCE The microbiome has proven to influence health and disease, but how combinations of external factors affect the microbiome is relatively unknown. Diet can cause changes, but this is usually achieved by altering macronutrient ratios and has not focused on dietary protein source or saturated fat intake levels. In addition, each individual's unique microbiome profile can be an important factor during studies, and it has even been shown to affect therapeutic outcomes. We show here that the effects of individual differences outweighed the effect of experimental diets and that protein source is less influential than saturated fat level. This suggests that fat and protein composition, separate from macronutrient ratio and carbohydrate composition, is an important consideration in dietary studies.

Interindividual variation in the composition of the human gut microbiome was examined in relation to demographic and anthropometric traits, and to changes in dietary saturated fat intake and protein source. One hundred nine healthy men and women aged 21 to 65, with BMIs of 18 to 36, were randomized, after a two-week baseline diet, to high (15% total energy [E])- or low (7%E)-saturated-fat groups and randomly received three diets (four weeks each) in which the protein source (25%E) was mainly red meat (beef, pork) (12%E), white meat (chicken, turkey) (12%E), and nonmeat sources (nuts, beans, soy) (16%E). Taxonomic characterization using 16S ribosomal DNA was performed on fecal samples collected at each diet completion. Interindividual differences in age, body fat (%), height, ethnicity, sex, and alpha diversity (Shannon) were all significant factors, and most samples clustered by participant in the PCoA ordination. The dietary interventions did not significantly alter the overall microbiome community in ordination space, but there was an effect on taxon abundance levels. Saturated fat had a greater effect than protein source on taxon differential abundance, but protein source had a significant effect once the fat influence was removed. Higher alpha diversity predicted lower beta diversity between the experimental and baseline diets, indicating greater resistance to change in people with higher microbiome diversity. Our results suggest that interindividual differences outweighed the influence of these specific dietary changes on the microbiome and that moderate changes in saturated fat level and protein source correspond to modest changes in the microbiome.IMPORTANCE The microbiome has proven to influence health and disease, but how combinations of external factors affect the microbiome is relatively unknown. Diet can cause changes, but this is usually achieved by altering macronutrient ratios and has not focused on dietary protein source or saturated fat intake levels. In addition, each individual's unique microbiome profile can be an important factor during studies, and it has even been shown to affect therapeutic outcomes. We show here that the effects of individual differences outweighed the effect of experimental diets and that protein source is less influential than saturated fat level. This suggests that fat and protein composition, separate from macronutrient ratio and carbohydrate composition, is an important consideration in dietary studies.

BackgroundSorghum bicolor is the fifth most commonly grown cereal worldwide and is remarkable for its drought and abiotic stress tolerance. For these reasons and the large size of biomass varieties, it has been proposed as a bioenergy crop. However, little is known about the genes underlying sorghum's abiotic stress tolerance and biomass yield.ResultsTo uncover the genetic basis of drought tolerance in sorghum at a genome-wide level, we undertook a high-density phenomics genome wide association study (GWAS) in which 648 diverse sorghum lines were phenotyped at two locations in California once per week by drone over the course of a growing season. Biomass, height, and leaf area were measured by drone for individual field plots, subjected to two drought treatments and a well-watered control. The resulting dataset of ~ 171,000 phenotypic data-points was analyzed along with 183,989 genotype by sequence markers to reveal 213 high-quality, replicated, and conserved GWAS associations.ConclusionsThe genomic intervals defined by the associations include many strong candidate genes, including those encoding heat shock proteins, antifreeze proteins, and other domains recognized as important to plant stress responses. The markers identified by our study can be used for marker assisted selection for drought tolerance and biomass. In addition, our results are a significant step toward identifying specific sorghum genes controlling drought tolerance and biomass yield.

BackgroundRecent advances in sequencing technologies and bioinformatics tools have allowed for large-scale microbiome studies that are rapidly advancing medical research. However, small changes in technique or analysis can significantly alter the results and lead to conflicting findings. Quantifying the technical versus biological variation expected in targeted 16S rRNA gene sequencing studies and how this variation changes with input biomass is critical to guide meaningful interpretation of the current literature and plan future research.ResultsData were compiled from 469 sequencing libraries across 19 separate targeted 16S rRNA gene sequencing runs over a 2.5-year time period. Following removal of contaminant sequences identified from negative controls, 244 samples retained sufficient reads for further analysis. Coefficients of variation for intra- and inter-assay variation from repeated measurements of a bacterial mock community ranged from 8.7 to 37.6% (intra) and 15.6 to 80.5% (inter) for all but one genus of bacteria whose relative abundance was greater than 1%. Intra- versus inter-assay Bray-Curtis pairwise distances for a single stool sample were 0.11 versus 0.31, whereas intra-assay variation from repeat stool samples from the same donor was greater at 0.38 (Wilcoxon p = 0.001). A dilution series of the bacterial mock community was used to assess the effect of input biomass on variability. Pairwise distances increased with more dilute samples, and estimates of relative abundance became unreliable below approximately 100 copies of the 16S rRNA gene per microliter. Using this data, we created a prediction model to estimate the expected variation in microbiome measurements for given input biomass and relative abundance values.ConclusionsWell-controlled microbiome studies are sufficiently robust to capture small biological effects and can achieve levels of variability consistent with clinical assays. Relative abundance is negatively associated with measures of variability and has a stronger effect on variability than does absolute biomass, suggesting that it is feasible to detect differences in bacterial populations in very low-biomass samples. Further, by quantifying the effect of biomass and relative abundance on compositional variability, we developed a tool for defining the expected variance in a given microbiome study.

BACKGROUND:Some studies have shown that body weight variability is a risk factor for cardiovascular events, but this has not been studied in subjects with diabetes mellitus. METHODS AND RESULTS:We measured intraindividual variations in body weight from baseline and follow-up visits in 6408 subjects with type 2 diabetes mellitus from 3 clinical trials. The primary end point, any coronary event, was a composite of coronary heart disease death, myocardial infarction, resuscitated cardiac arrest, coronary revascularization, and unstable or new-onset angina. After adjustment for risk factors, baseline lipid levels, mean body weight, and weight change, each increase of 1 SD in body weight variability, measured as average successive variability and used as a time-dependent covariate, was associated with an increase in the risk of any coronary event (hazard ratio, 1.08; 95% CI, 1.01-1.14; P=0.017), major coronary event (hazard ratio, 1.12; 95% CI, 1.04-1.20; P=0.002), any cardiovascular event (hazard ratio, 1.08; 95% CI, 1.03-1.14; P=0.0015), and death (hazard ratio, 1.16; 95% CI, 1.10-1.22; P

BACKGROUND:Some studies have shown that body weight variability is a risk factor for cardiovascular events, but this has not been studied in subjects with diabetes mellitus. METHODS AND RESULTS:We measured intraindividual variations in body weight from baseline and follow-up visits in 6408 subjects with type 2 diabetes mellitus from 3 clinical trials. The primary end point, any coronary event, was a composite of coronary heart disease death, myocardial infarction, resuscitated cardiac arrest, coronary revascularization, and unstable or new-onset angina. After adjustment for risk factors, baseline lipid levels, mean body weight, and weight change, each increase of 1 SD in body weight variability, measured as average successive variability and used as a time-dependent covariate, was associated with an increase in the risk of any coronary event (hazard ratio, 1.08; 95% CI, 1.01-1.14; P=0.017), major coronary event (hazard ratio, 1.12; 95% CI, 1.04-1.20; P=0.002), any cardiovascular event (hazard ratio, 1.08; 95% CI, 1.03-1.14; P=0.0015), and death (hazard ratio, 1.16; 95% CI, 1.10-1.22; P

Transcription is an episodic process characterized by probabilistic bursts, but how the transcriptional noise from these bursts is modulated by cellular physiology remains unclear. Using simulations and single-molecule RNA counting, we examined how cellular processes influence cell-to-cell variability (noise). The results show that RNA noise is higher in the cytoplasm than the nucleus in ∼85% of genes across diverse promoters, genomic loci, and cell types (human and mouse). Measurements show further amplification of RNA noise in the cytoplasm, fitting a model of biphasic mRNA conversion between translation- and degradation-competent states. This multi-state translation-degradation of mRNA also causes substantial noise amplification in protein levels, ultimately accounting for ∼74% of intrinsic protein variability in cell populations. Overall, the results demonstrate how noise from transcriptional bursts is intrinsically amplified by mRNA processing, leading to a large super-Poissonian variability in protein levels.

BackgroundSorghum bicolor is the fifth most commonly grown cereal worldwide and is remarkable for its drought and abiotic stress tolerance. For these reasons and the large size of biomass varieties, it has been proposed as a bioenergy crop. However, little is known about the genes underlying sorghum's abiotic stress tolerance and biomass yield.ResultsTo uncover the genetic basis of drought tolerance in sorghum at a genome-wide level, we undertook a high-density phenomics genome wide association study (GWAS) in which 648 diverse sorghum lines were phenotyped at two locations in California once per week by drone over the course of a growing season. Biomass, height, and leaf area were measured by drone for individual field plots, subjected to two drought treatments and a well-watered control. The resulting dataset of ~ 171,000 phenotypic data-points was analyzed along with 183,989 genotype by sequence markers to reveal 213 high-quality, replicated, and conserved GWAS associations.ConclusionsThe genomic intervals defined by the associations include many strong candidate genes, including those encoding heat shock proteins, antifreeze proteins, and other domains recognized as important to plant stress responses. The markers identified by our study can be used for marker assisted selection for drought tolerance and biomass. In addition, our results are a significant step toward identifying specific sorghum genes controlling drought tolerance and biomass yield.

The behavioural composition of a group and the dynamics of social interactions can both influence how social animals work collectively. For example, individuals exhibiting certain behavioural tendencies may have a disproportionately large impact on the group, and so are referred to as keystone individuals, while interactions between individuals can facilitate information transmission about resources. Despite the potential impact of both behavioural composition and interactions on collective behaviour, the relationship between consistent behaviours (also known as personalities) and social interactions remains poorly understood. Here, we use stochastic actor-oriented models to uncover the interdependencies between boldness and social interactions in the social spider Stegodyphus dumicola We find that boldness has no effect on the likelihood of forming social interactions, but interactions do affect boldness, and lead to an increase in the boldness of the shyer individual. Furthermore, spiders tend to interact with the same individuals as their neighbours. In general, boldness decreases over time, but once an individual's boldness begins to increase, this increase accelerates, suggesting a positive feedback mechanism. These dynamics of interactions and boldness result in skewed boldness distributions of a few bold individuals and many shy individuals, as observed in nature. This group behavioural composition facilitates efficient collective behaviours, such as rapid collective prey attack. Thus, by examining the relationship between behaviour and interactions, we reveal the mechanisms that underlie the emergence of adaptive group composition and collective behaviour.

BackgroundRecent advances in sequencing technologies and bioinformatics tools have allowed for large-scale microbiome studies that are rapidly advancing medical research. However, small changes in technique or analysis can significantly alter the results and lead to conflicting findings. Quantifying the technical versus biological variation expected in targeted 16S rRNA gene sequencing studies and how this variation changes with input biomass is critical to guide meaningful interpretation of the current literature and plan future research.ResultsData were compiled from 469 sequencing libraries across 19 separate targeted 16S rRNA gene sequencing runs over a 2.5-year time period. Following removal of contaminant sequences identified from negative controls, 244 samples retained sufficient reads for further analysis. Coefficients of variation for intra- and inter-assay variation from repeated measurements of a bacterial mock community ranged from 8.7 to 37.6% (intra) and 15.6 to 80.5% (inter) for all but one genus of bacteria whose relative abundance was greater than 1%. Intra- versus inter-assay Bray-Curtis pairwise distances for a single stool sample were 0.11 versus 0.31, whereas intra-assay variation from repeat stool samples from the same donor was greater at 0.38 (Wilcoxon p = 0.001). A dilution series of the bacterial mock community was used to assess the effect of input biomass on variability. Pairwise distances increased with more dilute samples, and estimates of relative abundance became unreliable below approximately 100 copies of the 16S rRNA gene per microliter. Using this data, we created a prediction model to estimate the expected variation in microbiome measurements for given input biomass and relative abundance values.ConclusionsWell-controlled microbiome studies are sufficiently robust to capture small biological effects and can achieve levels of variability consistent with clinical assays. Relative abundance is negatively associated with measures of variability and has a stronger effect on variability than does absolute biomass, suggesting that it is feasible to detect differences in bacterial populations in very low-biomass samples. Further, by quantifying the effect of biomass and relative abundance on compositional variability, we developed a tool for defining the expected variance in a given microbiome study.