Decanalization and the origin of complex disease

R2ROC: an efficient method of comparing two or more correlated AUC from out-of-sample prediction using polygenic scores

Polygenic risk scores (PRSs) enable early prediction of disease risk. Evaluating PRS performance for binary traits commonly relies on the area under the receiver operating characteristic curve (AUC). However, the widely used DeLong's method for comparative significance tests suffer from limitations, including computational time and the lack of a one-to-one mapping between test statistics based on AUC and R 2 . To overcome these limitations, we propose a novel approach that leverages the Delta method to derive the variance and covariance of AUC values, enabling a comprehensive and efficient comparative significance test. Our approach offers notable advantages over DeLong's method, including reduced computation time (up to 150-fold), making it suitable for large-scale analyses and ideal for integration into machine learning frameworks. Furthermore, our method allows for a direct one-to-one mapping between AUC and R 2 values for comparative significance tests, providing enhanced insights into the relationship between these measures and facilitating their interpretation. We validated our proposed approach through simulations and applied it to real data comparing PRSs for diabetes and coronary artery disease (CAD) prediction in a cohort of 28,880 European individuals. The PRSs were derived using genome-wide association study summary statistics from two distinct sources. Our approach enabled a comprehensive and informative comparison of the PRSs, shedding light on their respective predictive abilities for diabetes and CAD. This advancement contributes to the assessment of genetic risk factors and personalized disease prediction, supporting better healthcare decision-making.

Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axis

While genetic factors, behavior, and environmental exposures form a complex web of interrelated associations in type 2 diabetes (T2D), their interaction is poorly understood. Here, using data from ~500K participants of the UK Biobank, we identify the genetic determinants of a "polyexposure risk score" (PXS) a new risk factor that consists of an accumulation of 25 associated individual-level behaviors and environmental risk factors that predict longitudinal T2D incidence. PXS-T2D had a non-zero heritability (h2 = 0.18) extensive shared genetic architecture with established clinical and biological determinants of T2D, most prominently with body mass index (genetic correlation [rg] = 0.57) and Homeostatic Model Assessment for Insulin Resistance (rg = 0.51). Genetic loci associated with PXS-T2D were enriched for expression in the brain. Biobank scale data with genetic information illuminates how complex and cumulative exposures and behaviors as a whole impact T2D risk but whose biology have been elusive in genome-wide studies of T2D.

Disentangling Dual Threats: Premature Coronary Artery Disease and Early-Onset Type 2 Diabetes Mellitus in South Asians

South Asian individuals (SAs) face heightened risks of premature coronary artery disease (CAD) and early-onset type 2 diabetes mellitus (T2DM), with grave health, societal, and economic implications due to the region's dense population. Both conditions, influenced by cardiometabolic risk factors such as insulin resistance, hypertension, and central adiposity, manifest earlier and with unique thresholds in SAs. Epidemiological, demographic, nutritional, environmental, sociocultural, and economic transitions in SA have exacerbated the twin epidemic. The coupling of premature CAD and T2DM arises from increased obesity due to limited adipose storage, early-life undernutrition, distinct fat thresholds, reduced muscle mass, and a predisposition for hepatic fat accumulation from certain dietary choices cumulatively precipitating a decline in insulin sensitivity. As T2DM ensues, the β-cell adaptive responses are suboptimal, precipitating a transition from compensatory hyperinsulinemia to β-cell decompensation, underscoring a reduced functional β-cell reserve in SAs. This review delves into the interplay of these mechanisms and highlights a prediabetes endotype tied to elevated vascular risk. Deciphering these mechanistic interconnections promises to refine stratification paradigms, surpassing extant risk-prediction strategies.

DI/cle, a Measure Consisting of Insulin Sensitivity, Secretion, and Clearance, Captures Diabetic States

CONTEXT

Insulin clearance is implicated in regulation of glucose homeostasis independently of insulin sensitivity and insulin secretion.

OBJECTIVE

To understand the relation between blood glucose and insulin sensitivity, secretion, and clearance.

METHODS

We performed a hyperglycemic clamp, a hyperinsulinemic-euglycemic clamp, and an oral glucose tolerance test (OGTT) in 47, 16, and 49 subjects with normal glucose tolerance (NGT), impaired glucose tolerance (IGT), and type 2 diabetes mellitus (T2DM), respectively. Mathematical analyses were retrospectively performed on this dataset.

RESULTS

The disposition index (DI), defined as the product of insulin sensitivity and secretion, showed a weak correlation with blood glucose levels, especially in IGT (r = 0.04; 95% CI, -0.63 to 0.44). However, an equation relating DI, insulin clearance, and blood glucose levels was well conserved regardless of the extent of glucose intolerance. As a measure of the effect of insulin, we developed an index, designated disposition index/clearance, (DI/cle) that is based on this equation and corresponds to DI divided by the square of insulin clearance. DI/cle was not impaired in IGT compared with NGT, possibly as a result of a decrease in insulin clearance in response to a reduction in DI, whereas it was impaired in T2DM relative to IGT. Moreover, DI/cle estimated from a hyperinsulinemic-euglycemic clamp, OGTT, or a fasting blood test were significantly correlated with that estimated from 2 clamp tests (r = 0.52; 95% CI, 0.37 to 0.64, r = 0.43; 95% CI, 0.24 to 0.58, r = 0.54; 95% CI, 0.38 to 0.68, respectively).

CONCLUSION

DI/cle can serve as a new indicator for the trajectory of changes in glucose tolerance.

Hsp90 shapes adaptation by controlling the fitness consequences of regulatory variation

The essential stress-responsive chaperone Hsp90 impacts development and adaptation from microbes to humans. Yet despite evidence of its role in evolution, pathogenesis, and oncogenic transformation, the molecular mechanisms by which Hsp90 alters the consequences of mutations remain vigorously debated. Here we exploit the power of nucleotide-resolution genetic mapping in Saccharomyces cerevisiae to uncover more than 1,000 natural variant-to-phenotype associations governed by this molecular chaperone. Strikingly, Hsp90 more frequently modified the phenotypic effects of cis-regulatory variation than variants that altered protein sequence. Moreover, these interactions made the largest contribution to Hsp90-dependent heredity. Nearly all interacting variants-both regulatory and protein-coding-fell within clients of Hsp90 or targets of its direct binding partners. Hsp90 activity affected mutations in evolutionarily young genes, segmental deletions, and heterozygotes, highlighting its influence on variation central to evolutionary novelty. Reconciling the diverse epistatic effects of this chaperone, synthetic transcriptional regulation and reconstructions of natural alleles by genome editing revealed a central role for Hsp90 in regulating the fundamental relationship between activity and phenotype. Our findings establish that non-coding variation is a core driver of Hsp90's influence on heredity, offering a mechanistic explanation for the chaperone's strong effects on evolution and development across species.

Insulin Resistance in Adolescents and Youth With Type 1 Diabetes: A Review of Problems and Solutions

Though insulin resistance (IR) was previously considered a feature of only type 2 Diabetes (T2DM), its development in type 1 Diabetes (T1DM) is not an uncommon occurrence, the causes of which are multifactorial (gender, pubertal status, diabetes duration, ethnicity, genetics, adiposity, glycemic control, chronic inflammation). Despite improvements in glucose, blood pressure and lipid profile, vascular complications (coronary artery disease and nephropathy) continue to remain common causes of morbidity and mortality in T1DM. Aggressive glycemic control reduces but does not eliminate the risk of IR. IR accelerates the development of micro and macrovascular complications, many of which can be potentially reversed if diagnosed and managed early. Lack of endogenous insulin production makes estimation of insulin sensitivity in T1DM difficult. As hyperinsulinemic-euglycemic clamp studies are cumbersome and invasive, the use of prediction equations for calculating estimated insulin sensitivity may prove to be useful. Along with intensive insulin therapy, dietary modifications and increasing physical activity, the role of Metformin in managing IR in T1DM is becoming increasingly popular. Metformin adjunct therapy in T1DM has been shown to improve insulin sensitivity, glycemic control, lipid profile, body composition, vascular smooth muscle function, thereby reducing the risk of vascular complications, as well as reversal of early vascular dysfunction. However, further studies to assess long-term efficacy and safety of Metformin use in adolescents and youth with T1DM are needed. This review aims at revisiting the pathophysiology of IR in T1DM and techniques of identifying those at risk so as to put into action various strategies for management of the same.

Applying an evolutionary mismatch framework to understand disease susceptibility

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Mechanisms of robustness in gene regulatory networks involved in neural development

The functions of living organisms are affected by different kinds of perturbation, both internal and external, which in many cases have functional effects and phenotypic impact. The effects of these perturbations become particularly relevant for multicellular organisms with complex body patterns and cell type heterogeneity, where transcriptional programs controlled by gene regulatory networks determine, for example, the cell fate during embryonic development. Therefore, an essential aspect of development in these organisms is the ability to maintain the functionality of their genetic developmental programs even in the presence of genetic variation, changing environmental conditions and biochemical noise, a property commonly termed robustness. We discuss the implication of different molecular mechanisms of robustness involved in neurodevelopment, which is characterized by the interplay of many developmental programs at a molecular, cellular and systemic level. We specifically focus on processes affecting the function of gene regulatory networks, encompassing transcriptional regulatory elements and post-transcriptional processes such as miRNA-based regulation, but also higher order regulatory organization, such as gene network topology. We also present cases where impairment of robustness mechanisms can be associated with neurodevelopmental disorders, as well as reasons why understanding these mechanisms should represent an important part of the study of gene regulatory networks driving neural development.

Significance tests for R2 of out-of-sample prediction using polygenic scores

The coefficient of determination (R²) is a well-established measure to indicate the predictive ability of polygenic scores (PGSs). However, the sampling variance of R² is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGSs based on different discovery samples, the sampling covariance of R² is required to test the difference between them. Here, we show how to estimate the variance and covariance of R² values to assess the 95% CI and p value of the R² difference. We apply this approach to real data calculating PGSs in 28,880 European participants derived from UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGSs compared to BBJ PGSs (p value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGSs significantly improves the predictive ability, compared to a model of UKBB PGS only (p value 3.5e-05 for cholesterol and 1.3e-28 for BMI). We also show that the predictive ability of regulatory SNPs is significantly enriched over non-regulatory SNPs for cholesterol (p value 8.9e-26 for UKBB and 3.8e-17 for BBJ). We suggest that the proposed approach (available in R package r2redux) should be used to test the statistical significance of difference between pairs of PGSs, which may help to draw a correct conclusion about the comparative predictive ability of PGSs.

Dietary stress remodels the genetic architecture of lifespan variation in outbred Drosophila

Evolutionary theory suggests that lifespan-reducing alleles should be purged from the gene pool, and yet decades of genome-wide association and model organism studies have shown that they persist. One potential explanation is that alleles that regulate lifespan do so only in certain environmental contexts. We exposed outbred Drosophila to control and high-sugar diets and genotyped more than 10,000 adult flies to track allele frequency changes over the course of a single adult lifespan. We identified thousands of lifespan-associated alleles associated with early versus late-life trade-offs, late-onset effects and genotype-by-environment interactions. Remarkably, a third of lifespan-associated genetic variation had environmentally dependent effects on lifespan. We find that lifespan-reducing alleles are often recently derived, have stronger effects on a high-sugar diet and show signatures of selection in wild Drosophila populations, consistent with the evolutionary mismatch hypothesis. Our results provide insight into the highly polygenic and context-dependent genetic architecture of lifespan variation and the evolutionary processes that shape this key trait.

The translational genetics of ADHD and related phenotypes in model organisms

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder resulting from the interaction between genetic and environmental risk factors. It is well known that ADHD co-occurs frequently with other psychiatric disorders due, in part, to shared genetics factors. Although many studies have contributed to delineate the genetic landscape of psychiatric disorders, their specific molecular underpinnings are still not fully understood. The use of animal models can help us to understand the role of specific genes and environmental stimuli-induced epigenetic modifications in the pathogenesis of ADHD and its comorbidities. The aim of this review is to provide an overview on the functional work performed in rodents, zebrafish and fruit fly and highlight the generated insights into the biology of ADHD, with a special focus on genetics and epigenetics. We also describe the behavioral tests that are available to study ADHD-relevant phenotypes and comorbid traits in these models. Furthermore, we have searched for new models to study ADHD and its comorbidities, which can be useful to test potential pharmacological treatments.

Genetic architecture of natural variation of cardiac performance from flies to humans

Deciphering the genetic architecture of human cardiac disorders is of fundamental importance but their underlying complexity is a major hurdle. We investigated the natural variation of cardiac performance in the sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP). Genome-wide associations studies (GWAS) identified genetic networks associated with natural variation of cardiac traits which were used to gain insights as to the molecular and cellular processes affected. Non-coding variants that we identified were used to map potential regulatory non-coding regions, which in turn were employed to predict transcription factors (TFs) binding sites. Cognate TFs, many of which themselves bear polymorphisms associated with variations of cardiac performance, were also validated by heart-specific knockdown. Additionally, we showed that the natural variations associated with variability in cardiac performance affect a set of genes overlapping those associated with average traits but through different variants in the same genes. Furthermore, we showed that phenotypic variability was also associated with natural variation of gene regulatory networks. More importantly, we documented correlations between genes associated with cardiac phenotypes in both flies and humans, which supports a conserved genetic architecture regulating adult cardiac function from arthropods to mammals. Specifically, roles for PAX9 and EGR2 in the regulation of the cardiac rhythm were established in both models, illustrating that the characteristics of natural variations in cardiac function identified in Drosophila can accelerate discovery in humans.

Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach

Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank.

Analysis of Common SNPs across Continents Reveals Major Genomic Differences between Human Populations

Common alleles tend to be more ancient than rare alleles. These common SNPs appeared thousands of years ago and reflect intricate human evolution including various adaptations, admixtures, and migration events. Eighty-four thousand abundant region-specific alleles (ARSAs) that are common in one continent but absent in the rest of the world have been characterized by processing 3100 genomes from 230 populations. Also computed were 17,446 polymorphic sites with regional absence of common alleles (RACAs), which are widespread globally but absent in one region. A majority of these region-specific SNPs were found in Africa. America has the second greatest number of ARSAs (3348) and is even ahead of Europe (1911). Surprisingly, East Asia has the highest number of RACAs (10,524) and the lowest number of ARSAs (362). ARSAs and RACAs have distinct compositions of ancestral versus derived alleles in different geographical regions, reflecting their unique evolution. Genes associated with ARSA and RACA SNPs were identified and their functions were analyzed. The core 100 genes shared by multiple populations and associated with region-specific natural selection were examined. The largest part of them (42%) are related to the nervous system. ARSA and RACA SNPs are important for both association and human evolution studies.

Phenotypic decanalization driven by social determinants could explain variance patterns for glycemia in adult urban Argentinian population

Type 2 diabetes, one of the major causes of death and disability worldwide, is characterized by problems in the homeostasis of blood glucose. Current preventive policies focus mainly on individual behaviors (diet, exercise, salt and alcohol consumption). Recent hypotheses state that the higher incidence of metabolic disease in some human populations may be related to phenotypic decanalization causing a heightened phenotypic variance in response to unusual or stressful environmental conditions, although the nature of these conditions is under debate. Our aim was to explore variability patterns of fasting blood glucose to test phenotypic decanalization as a possible explanation of heightened prevalence for type 2 diabetes in some groups and to detect variables associated with its variance using a nation-wide survey of Argentinian adult population. We found patterns of higher local variance for fasting glycemia associated with lower income and educational attainment. We detected no meaningful association of glycemia or its variability with covariates related to individual behaviors (diet, physical activity, salt or alcohol consumption). Our results were consistent with the decanalization hypothesis for fasting glycemia, which appears associated to socioeconomic disadvantage. We therefore propose changes in public policy and discuss the implications for data gathering and further analyses.

Canalization of the Polygenic Risk for Common Diseases and Traits in the UK Biobank Cohort

Since organisms develop and thrive in the face of constant perturbations due to environmental and genetic variation, species may evolve resilient genetic architectures. We sought evidence for this process, known as canalization, through a comparison of the prevalence of phenotypes as a function of the polygenic score (PGS) across environments in the UK Biobank cohort study. Contrasting seven diseases and three categorical phenotypes with respect to 151 exposures in 408,925 people, the deviation between the prevalence-risk curves was observed to increase monotonically with the PGS percentile in one-fifth of the comparisons, suggesting extensive PGS-by-Environment (PGS×E) interaction. After adjustment for the dependency of allelic effect sizes on increased prevalence in the perturbing environment, cases where polygenic influences are greater or lesser than expected are seen to be particularly pervasive for educational attainment, obesity, and metabolic condition type-2 diabetes. Inflammatory bowel disease analysis shows fewer interactions but confirms that smoking and some aspects of diet influence risk. Notably, body mass index has more evidence for decanalization (increased genetic influence at the extremes of polygenic risk), whereas the waist-to-hip ratio shows canalization, reflecting different evolutionary pressures on the architectures of these weight-related traits. An additional 10 % of comparisons showed evidence for an additive shift of prevalence independent of PGS between exposures. These results provide the first widespread evidence for canalization protecting against disease in humans and have implications for personalized medicine as well as understanding the evolution of complex traits. The findings can be explored through an R shiny app at https://canalization-gibsonlab.shinyapps.io/rshiny/.

The genetic control paradigm in biology: What we say, and what we are entitled to mean

We comment on the article by Keith Baverstock (2021) and provide critiques of the concepts of genetic control, genetic blueprint and genetic program.

Manifold medicine: A schema that expands treatment dimensionality

Drug discovery currently focuses on identifying new druggable targets and drug repurposing. Here, we illustrate a third domain of drug discovery: the dimensionality of treatment regimens. We formulate a new schema called ‘Manifold Medicine’, in which disease states are described by vectorial positions on several body-wide axes. Thus, pathological states are represented by multidimensional ‘vectors’ that traverse the body-wide axes. We then delineate the manifold nature of drug action to provide a strategy for designing manifold drug cocktails by design using state-of-the-art biomedical and technological innovations. Manifold Medicine offers a roadmap for translating knowledge gained from next-generation technologies into individualized clinical practice.

Modeling the effects of EMT-immune dynamics on carcinoma disease progression

During progression from carcinoma in situ to an invasive tumor, the immune system is engaged in complex sets of interactions with various tumor cells. Tumor cell plasticity alters disease trajectories via epithelial-to-mesenchymal transition (EMT). Several of the same pathways that regulate EMT are involved in tumor-immune interactions, yet little is known about the mechanisms and consequences of crosstalk between these regulatory processes. Here we introduce a multiscale evolutionary model to describe tumor-immune-EMT interactions and their impact on epithelial cancer progression from in situ to invasive disease. Through simulation of patient cohorts in silico, the model predicts that a controllable region maximizes invasion-free survival. This controllable region depends on properties of the mesenchymal tumor cell phenotype: its growth rate and its immune-evasiveness. In light of the model predictions, we analyze EMT-inflammation-associated data from The Cancer Genome Atlas, and find that association with EMT worsens invasion-free survival probabilities. This result supports the predictions of the model, and leads to the identification of genes that influence outcomes in bladder and uterine cancer, including FGF pathway members. These results suggest new means to delay disease progression, and demonstrate the importance of studying cancer-immune interactions in light of EMT.

Language evolution: examining the link between cross-modality and aggression through the lens of disorders

We demonstrate how two linguistic phenomena, figurative language (implicating cross-modality) and derogatory language (implicating aggression), both demand a precise degree of (dis)inhibition in the same cortico-subcortical brain circuits, in particular cortico-striatal networks, whose connectivity has been significantly enhanced in recent evolution. We examine four cognitive disorders/conditions that exhibit abnormal patterns of (dis)inhibition in these networks: schizophrenia (SZ), autism spectrum disorder (ASD), synaesthesia and Tourette's syndrome (TS), with the goal of understanding why the two phenomena altered reactive aggression and altered cross-modality cluster together in these disorders. Our proposal is that enhanced cross-modality (necessary to support language, in particular metaphoricity) was a result, partly a side-effect, of self-domestication (SD). SD targeted the taming of reactive aggression, but reactive impulses are controlled by the same cortico-subcortical networks that are implicated in cross-modality. We further add that this biological process of SD did not act alone, but was engaged in an intense feedback loop with the cultural emergence of early forms of language/grammar, whose high degree of raw metaphoricity and verbal aggression also contributed to increased brain connectivity and cortical control. Consequently, in conjunction with linguistic expressions serving as approximations/'fossils' of the earliest stages of language, these cognitive disorders/conditions serve as confident proxies of brain changes in language evolution, helping us reconstruct certain crucial aspects of early prehistoric languages and cognition, as well as shed new light on the nature of the disorders. This article is part of the theme issue 'Reconstructing prehistoric languages'.

Race/ethnicity and challenges for optimal insulin therapy

AIMS

We aimed to review insulin dosing recommendations, insulin regulation and its determinants, glycaemic response to carbohydrates, and the efficacy and safety of insulin therapy in different races/ethnicities.

METHODS

We searched for articles in PubMed and Google Scholar databases up to 31 March 2021, with the following keywords: "ethnicity", "diabetes", "insulin", "history of insulin", "insulin therapy", "food/rice", "carbohydrate intake", "insulin resistance", "BMI", "insulin dosing", "insulin sensitivity", "insulin response", "glycaemic index", "glycaemic response", "efficacy and safety", with interposition of the Boolean operator "AND".In addition, we reviewed the reference lists of the articles found.

RESULTS

The differential effect of race/ethnicity has not yet been considered in current insulin therapy guidelines. Nevertheless, body size and composition, body mass index, fat distribution, diet, storage, and energy expenditure vary significantly across populations. Further, insulin sensitivity, insulin response, and glycaemicresponse to carbohydrates differ by ethnicity. These disparities may lead to different insulin requirements, adversely impacting the efficacy and safety of insulin therapy among ethnic groups.

CONCLUSIONS

Race/ethnicity affects glucose metabolism and insulin regulation.Until now, international guidelines addressing racial/ethnic-specific clinical recommendations are limited. Comprehensive updated insulin therapy guidelines by ethnicity are urgently needed.

Growing points in attachment disorganization: looking back to advance forward

In this special issue paper we reflect on the next generation of attachment research with a focus on disorganization, a central but still poorly understood topic in this area. We suggest that progress will be facilitated by a return to attachment theory's evolutionary roots, and to the emphasis on biological function that inspired Bowlby's original thinking. Increased interdisciplinary cross-fertilization and collaborations would enable novel and generative research on some of the long-standing questions surrounding attachment disorganization. Accordingly, we present an agenda for future research that encompasses contributions of modern ethology and neurobiology, novel hypotheses based on the concept of adaptive decanalization, connections with neurodevelopmental vulnerability and risk for mental disorders such as schizophrenia, and the possibility of sex differences in the behavioral manifestations of attachment disorganization. We believe that these avenues of theory and research offer exciting potential for innovative work in attachment disorganization in the years ahead.

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.

A model of developmental canalization, applied to human cranial form

Developmental mechanisms that canalize or compensate perturbations of organismal development (targeted or compensatory growth) are widely considered a prerequisite of individual health and the evolution of complex life, but little is known about the nature of these mechanisms. It is even unclear if and how a “target trajectory” of individual development is encoded in the organism’s genetic-developmental system or, instead, emerges as an epiphenomenon. Here we develop a statistical model of developmental canalization based on an extended autoregressive model. We show that under certain assumptions the strength of canalization and the amount of canalized variance in a population can be estimated, or at least approximated, from longitudinal phenotypic measurements, even if the target trajectories are unobserved. We extend this model to multivariate measures and discuss reifications of the ensuing parameter matrix. We apply these approaches to longitudinal geometric morphometric data on human postnatal craniofacial size and shape as well as to the size of the frontal sinuses. Craniofacial size showed strong developmental canalization during the first 5 years of life, leading to a 50% reduction of cross-sectional size variance, followed by a continual increase in variance during puberty. Frontal sinus size, by contrast, did not show any signs of canalization. Total variance of craniofacial shape decreased slightly until about 5 years of age and increased thereafter. However, different features of craniofacial shape showed very different developmental dynamics. Whereas the relative dimensions of the nasopharynx showed strong canalization and a reduction of variance throughout postnatal development, facial orientation continually increased in variance. Some of the signals of canalization may owe to independent variation in developmental timing of cranial components, but our results indicate evolved, partly mechanically induced mechanisms of canalization that ensure properly sized upper airways and facial dimensions.

Developmental mechanisms that canalize or compensate perturbations of organismal development are a prerequisite of individual health and the evolution of complex life. However, surprisingly little is known about these mechanisms, partly because the “target trajectories” of individual development cannot be directly observed. Here we develop a statistical model of developmental canalization that allows one to estimate the strength of canalization and the amount of canalized variance in a population even if the target trajectories are unobserved. We applied these approaches to data on human postnatal craniofacial growth. Whereas overall craniofacial size was strongly canalized during the first 5 years of age, frontal sinus size did not show any signs of canalization. The relative dimensions of the nasopharynx showed strong canalization and a reduction of variance throughout postnatal development, whereas other shape features, such as facial orientation, continually increased in variance. Our results indicate evolved, partly mechanically induced mechanisms of canalization that ensure properly sized upper airways and facial dimensions.

Genomic Variation, Evolvability, and the Paradox of Mental Illness

Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.

The influence of evolutionary history on human health and disease

Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual's DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.

Contrasting effects of Western vs Mediterranean diets on monocyte inflammatory gene expression and social behavior in a primate model

Dietary changes associated with industrialization increase the prevalence of chronic diseases, such as obesity, type II diabetes, and cardiovascular disease. This relationship is often attributed to an 'evolutionary mismatch' between human physiology and modern nutritional environments. Western diets enriched with foods that were scarce throughout human evolutionary history (e.g. simple sugars and saturated fats) promote inflammation and disease relative to diets more akin to ancestral human hunter-gatherer diets, such as a Mediterranean diet. Peripheral blood monocytes, precursors to macrophages and important mediators of innate immunity and inflammation, are sensitive to the environment and may represent a critical intermediate in the pathway linking diet to disease. We evaluated the effects of 15 months of whole diet manipulations mimicking Western or Mediterranean diet patterns on monocyte polarization in a well-established model of human health, the cynomolgus macaque (Macaca fascicularis). Monocyte transcriptional profiles differed markedly between diets, with 40% of transcripts showing differential expression (FDR < 0.05). Monocytes from Western diet consumers were polarized toward a more proinflammatory phenotype. The Western diet shifted the co-expression of 445 gene pairs, including small RNAs and transcription factors associated with metabolism and adiposity in humans, and dramatically altered behavior. For example, Western-fed individuals were more anxious and less socially integrated. These behavioral changes were also associated with some of the effects of diet on gene expression, suggesting an interaction between diet, central nervous system activity, and monocyte gene expression. This study provides new molecular insights into an evolutionary mismatch and uncovers new pathways through which Western diets alter monocyte polarization toward a proinflammatory phenotype.

Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders

The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case-control pairs (ASD: n = 100, mean age = 11.43 years, IQ = 110.58; controls: n = 100, mean age = 11.43 years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.

The evolution of group differences in changing environments

The selection pressures that have shaped the evolution of complex traits in humans remain largely unknown, and in some contexts highly contentious, perhaps above all where they concern mean trait differences among groups. To date, the discussion has focused on whether such group differences have any genetic basis, and if so, whether they are without fitness consequences and arose via random genetic drift, or whether they were driven by selection for different trait optima in different environments. Here, we highlight a plausible alternative: that many complex traits evolve under stabilizing selection in the face of shifting environmental effects. Under this scenario, there will be rapid evolution at the loci that contribute to trait variation, even when the trait optimum remains the same. These considerations underscore the strong assumptions about environmental effects that are required in ascribing trait differences among groups to genetic differences.

Genetic assimilation and accommodation: Models and mechanisms

Genetic assimilation and genetic accommodation are mechanisms by which novel phenotypes are produced and become established in a population. Novel characters may be fixed and canalized so they are insensitive to environmental variation, or can be plastic and adaptively responsive to environmental variation. In this review we explore the various theories that have been proposed to explain the developmental origin and evolution of novel phenotypes and the mechanisms by which canalization and phenotypic plasticity evolve. These theories and models range from conceptual to mathematical and have taken different views of how genes and environment contribute to the development and evolution of the properties of phenotypes. We will argue that a deeper and more nuanced understanding of genetic accommodation requires a recognition that phenotypes are not static entities but are dynamic system properties with no fixed deterministic relationship between genotype and phenotype. We suggest a mechanistic systems-view of development that allows one to incorporate both genes and environment in a common model, and that enables both quantitative analysis and visualization of the evolution of canalization and phenotypic plasticity.

Disease Ionomics: Understanding the Role of Ions in Complex Disease

Ionomics is a novel multidisciplinary field that uses advanced techniques to investigate the composition and distribution of all minerals and trace elements in a living organism and their variations under diverse physiological and pathological conditions. It involves both high-throughput elemental profiling technologies and bioinformatic methods, providing opportunities to study the molecular mechanism underlying the metabolism, homeostasis, and cross-talk of these elements. While much effort has been made in exploring the ionomic traits relating to plant physiology and nutrition, the use of ionomics in the research of serious diseases is still in progress. In recent years, a number of ionomic studies have been carried out for a variety of complex diseases, which offer theoretical and practical insights into the etiology, early diagnosis, prognosis, and therapy of them. This review aims to give an overview of recent applications of ionomics in the study of complex diseases and discuss the latest advances and future trends in this area. Overall, disease ionomics may provide substantial information for systematic understanding of the properties of the elements and the dynamic network of elements involved in the onset and development of diseases.

Evolution of Plasticity in Response to Ethanol between Sister Species with Different Ecological Histories (Drosophila melanogaster and D. simulans)

AbstractWhen populations evolve adaptive reaction norms in response to novel environments, it can occur through a process termed genetic accommodation. Under this model, the initial response to the environment is widely variable between genotypes as a result of cryptic genetic variation, which is then refined by selection to a single adaptive response. Here, I empirically test these predictions from genetic accommodation by measuring reaction norms in individual genotypes and across several time points. I compare two species of Drosophila that differ in their adaptation to ethanol (D. melanogaster and D. simulans). Both species are human commensals with a recent cosmopolitan expansion, but only D. melanogaster is adapted to ethanol exposure. Using gene expression as a phenotype and an approach that combines information about expression and alternative splicing, I find that D. simulans exhibits cryptic genetic variation in the response to ethanol, while D. melanogaster has almost no genotype-specific variation in reaction norm. This is evidence for adaptation to ethanol through genetic accommodation, suggesting that the evolution of phenotypic plasticity could be an important contributor to the ability to exploit novel resources.

Expression correlation attenuates within and between key signaling pathways in chronic kidney disease

BACKGROUND

Compared to the conventional differential expression approach, differential coexpression analysis represents a different yet complementary perspective into diseased transcriptomes. In particular, global loss of transcriptome correlation was previously observed in aging mice, and a most recent study found genetic and environmental perturbations on human subjects tended to cause universal attenuation of transcriptome coherence. While methodological progresses surrounding differential coexpression have helped with research on several human diseases, there has not been an investigation of coexpression disruptions in chronic kidney disease (CKD) yet.

METHODS

RNA-seq was performed on total RNAs of kidney tissue samples from 140 CKD patients. A combination of differential coexpression methods were employed to analyze the transcriptome transition in CKD from the early, mild phase to the late, severe kidney damage phase.

RESULTS

We discovered a global expression correlation attenuation in CKD progression, with pathway Regulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. Moreover, the pathway Signaling events mediated by focal adhesion kinase displayed significantly weakened crosstalk with seven pathways, including Regulation of nuclear SMAD2/3 signaling. Well-known relevant genes, such as ACTN4, were characterized with widespread correlation disassociation with partners from a wide array of signaling pathways.

CONCLUSIONS

Altogether, our analysis reported a global expression correlation attenuation within and between key signaling pathways in chronic kidney disease, and presented a list of vanishing hub genes and disrupted correlations within and between key signaling pathways, illuminating on the pathophysiological mechanisms of CKD progression.

Schizophrenia: Developmental Variability Interacts with Risk Factors to Cause the Disorder: Nonspecific Variability-Enhancing Factors Combine with Specific Risk Factors to Cause Schizophrenia

A new etiological model is proposed for schizophrenia that combines variability-enhancing nonspecific factors acting during development with more specific risk factors. This model is better suited than the current etiological models of schizophrenia, based on the risk factors paradigm, for predicting and/or explaining several important findings about schizophrenia: high co-morbidity rates, low specificity of many risk factors, and persistence in the population of the associated genetic polymorphisms. Compared with similar models, e.g., de-canalization, common psychopathology factor, sexual-selection, or differential sensitivity to the environment, this proposal is more general and integrative. Recently developed research methods have proven the existence of genetic and environmental factors that enhance developmental variability. Applying such methods to newly collected or already available data can allow for testing the hypotheses upon which this model is built. If validated, this model may change the understanding of the etiology of schizophrenia, the research models, and preventionbrk paradigms.

Canalization and Robustness in Human Genetics and Disease

Canalization refers to the evolution of populations such that the number of individuals who deviate from the optimum trait, or experience disease, is minimized. In the presence of rapid cultural, environmental, or genetic change, the reverse process of decanalization may contribute to observed increases in disease prevalence. This review starts by defining relevant concepts, drawing distinctions between the canalization of populations and robustness of individuals. It then considers evidence pertaining to three continuous traits and six domains of disease. In each case, existing genetic evidence for genotype-by-environment interactions is insufficient to support a strong inference of decanalization, but we argue that the advent of genome-wide polygenic risk assessment now makes an empirical evaluation of the role of canalization in preventing disease possible. Finally, the contributions of both rare and common variants to congenital abnormality and adult onset disease are considered in light of a new kerplunk model of genetic effects.

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Pleiotropy-when a single mutation affects multiple traits-is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background, and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

Increased β-Cell Responsivity Independent of Insulin Sensitivity in Healthy African American Adults

BACKGROUND

Primary insulin hypersecretion predicts type 2 diabetes (T2DM) independent of insulin resistance. Enhanced β-cell glucose responsivity contributes to insulin hypersecretion. African Americans (AAs) are at a higher risk for T2DM than non-Hispanic Whites (NHWs). Whether AAs manifest primary insulin hypersecretion is an important topic that has not been examined systematically.

OBJECTIVE

To examine if nondiabetic AA adults have a higher β-cell glucose responsivity compared with NHWs.

METHODS

Healthy nondiabetic AA (n = 18) and NHW (n=18) subjects were prospectively recruited. Indices of β-cell function, acute C-peptide secretion (X0); basal (Φ B), first-phase (Φ 1), second-phase (Φ 2), and total β-cell responsivity to glucose (Φ TOT), were derived from modeling of insulin, C-peptide, and glucose concentrations during an intravenous glucose tolerance test. Insulin sensitivity was assessed by the hyperinsulinemic-euglycemic glucose clamp technique.

RESULTS

Glucose disposal rate (GDR) during clamp was similar in AAs and NHWs (GDR: [AA] 12.6 ± 3.2 vs [NHW] 12.6 ± 4.2 mg/kg fat free mass +17.7/min, P = .49). Basal insulin secretion rates were similar between the groups. AA had significantly higher X0 (4423 ± 593 vs 1807 ± 176 pmol/L, P = .007), Φ 1 [377.5 ± 59.0 vs 194.5 ± 26.6 (109) P = 0.03], and Φ TOT [76.7 ± 18.3 vs 29.6 ± 4.7 (109/min), P = 0.03], with no significant ethnic differences in Φ B and Φ 2.

CONCLUSIONS

Independent of insulin sensitivity, AAs showed significantly higher first-phase and total β-cell responsivity than NHWs. We propose that this difference reflects increased β-cell responsivity specifically to first-phase readily releasable insulin secretion. Future studies are warranted to identify mechanisms leading to primary β-cell hypersensitivity in AAs.

Homeostatic plasticity fails at the intersection of autism-gene mutations and a novel class of common genetic modifiers

We identify a set of common phenotypic modifiers that interact with five independent autism gene orthologs (RIMS1, CHD8, CHD2, WDFY3, ASH1L) causing a common failure of presynaptic homeostatic plasticity (PHP) in Drosophila. Heterozygous null mutations in each autism gene are demonstrated to have normal baseline neurotransmission and PHP. However, PHP is sensitized and rendered prone to failure. A subsequent electrophysiology-based genetic screen identifies the first known heterozygous mutations that commonly genetically interact with multiple ASD gene orthologs, causing PHP to fail. Two phenotypic modifiers identified in the screen, PDPK1 and PPP2R5D, are characterized. Finally, transcriptomic, ultrastructural and electrophysiological analyses define one mechanism by which PHP fails; an unexpected, maladaptive up-regulation of CREG, a conserved, neuronally expressed, stress response gene and a novel repressor of PHP. Thus, we define a novel genetic landscape by which diverse, unrelated autism risk genes may converge to commonly affect the robustness of synaptic transmission.

Dynamic genetic architecture of yeast response to environmental perturbation shed light on origin of cryptic genetic variation

Cryptic genetic variation could arise from, for example, Gene-by-Gene (G-by-G) or Gene-by-Environment (G-by-E) interactions. The underlying molecular mechanisms and how they influence allelic effects and the genetic variance of complex traits is largely unclear. Here, we empirically explored the role of environmentally influenced epistasis on the suppression and release of cryptic variation by reanalysing a dataset of 4,390 haploid yeast segregants phenotyped on 20 different media. The focus was on 130 epistatic loci, each contributing to segregant growth in at least one environment and that together explained most (69–100%) of the narrow sense heritability of growth in the individual environments. We revealed that the epistatic growth network reorganised upon environmental changes to alter the estimated marginal (additive) effects of the individual loci, how multi-locus interactions contributed to individual segregant growth and the level of expressed genetic variance in growth. The estimated additive effects varied most across environments for loci that were highly interactive network hubs in some environments but had few or no interactors in other environments, resulting in changes in total genetic variance across environments. This environmentally dependent epistasis was thus an important mechanism for the suppression and release of cryptic variation in this population. Our findings increase the understanding of the complex genetic mechanisms leading to cryptic variation in populations, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits in breeding and evolution.

Many biological traits are polygenic, with complex interplay between underlying genes and the surrounding environment. As a result, individuals with the same allele might have distinctive phenotypes due to differences in the polygenic background and/or the environment. Such differences often create additional genetic variation that is highly relevant to quantitative and evolutionary genetics by limiting our ability to accurately predict the phenotypes in medical or agricultural applications and providing opportunities for long term evolution. Previously, yeast growth regulating genes were found to be organised in large interacting networks. Here, we found that these networks were reorganised upon environmental changes, and that this resulted in altered effect sizes of individual genes, and how the whole network contributed to growth and the level of total genetic variance, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits.

Cell-cycle dependence of transcriptome gene modules: comparison of regression lines

The transcriptome consists of various gene modules that can be mutually dependent, and ignoring these dependencies may lead to misinterpretation. The most important problem is module dependence on cell-cycle activity. Using meta-analysis of over 30 000 single-cell transcriptomes, we show gene module dependencies on cell-cycle signature, which can be consistently observed in various normal and cancer cells. Transcript levels of receptors, plasma membrane, and differentiation-related genes are negatively regressed on cell-cycle signature. Pluripotency, stress response, DNA repair, chromatin remodeling, proteasomal protein degradation, protein network connectivity, and unicellular evolutionary origin are regressed positively. These effects cannot be explained by partial overlap of corresponding gene sets because they remain if the overlapped genes were removed. We propose a visual analysis of gene module-specific regression lines as complement to an uncurated enrichment analysis. The different lines for a same gene module indicate different cell conditions. The approach is tested on several problems (polyploidy, pluripotency, cancer, phylostratigraphy). Intriguingly, we found variation in cell-cycle activity, which is independent of cell progression through the cycle. The upregulation of G2/M checkpoint genes with downregulation of G2/M transition and cytokinesis is revealed in polyploid cells. A temporal increase in cell-cycle activity at transition from pluripotent to more differentiated state is found in human embryonic stem cells. The upregulation of unicellular interactome cluster in human cancers is shown in single cells with control for cell-cycle activity. The greater scatter around regression line in cancer cells suggests greater heterogeneity caused by deviation from a line of normal cells.

Unexpected cell type-dependent effects of autophagy on polyglutamine aggregation revealed by natural genetic variation in C. elegans

BACKGROUND

Monogenic protein aggregation diseases, in addition to cell selectivity, exhibit clinical variation in the age of onset and progression, driven in part by inter-individual genetic variation. While natural genetic variants may pinpoint plastic networks amenable to intervention, the mechanisms by which they impact individual susceptibility to proteotoxicity are still largely unknown.

RESULTS

We have previously shown that natural variation modifies polyglutamine (polyQ) aggregation phenotypes in C. elegans muscle cells. Here, we find that a genomic locus from C. elegans wild isolate DR1350 causes two genetically separable aggregation phenotypes, without changing the basal activity of muscle proteostasis pathways known to affect polyQ aggregation. We find that the increased aggregation phenotype was due to regulatory variants in the gene encoding a conserved autophagy protein ATG-5. The atg-5 gene itself conferred dosage-dependent enhancement of aggregation, with the DR1350-derived allele behaving as hypermorph. Surprisingly, increased aggregation in animals carrying the modifier locus was accompanied by enhanced autophagy activation in response to activating treatment. Because autophagy is expected to clear, not increase, protein aggregates, we activated autophagy in three different polyQ models and found a striking tissue-dependent effect: activation of autophagy decreased polyQ aggregation in neurons and intestine, but increased it in the muscle cells.

CONCLUSIONS

Our data show that cryptic natural variants in genes encoding proteostasis components, although not causing detectable phenotypes in wild-type individuals, can have profound effects on aggregation-prone proteins. Clinical applications of autophagy activators for aggregation diseases may need to consider the unexpected divergent effects of autophagy in different cell types.