The metabolome as a link in the genotype-phenotype map for peroxide resistance in the fruit fly, Drosophila melanogaster

Combined metabolomic and transcriptomic analysis reveals the characteristics of the lignan in Isatis indigotica Fortune

Isatis indigotica Fortune is a plant species containing lignan compounds of significant economic value. Its root plays a crucial role in treating viruses and exhibits antitumor, anti-inflammatory, antibacterial, and other biological activities. Now, I. indigotica has been included in Isatis tinctoria Linnaeus. In this study, the roots of diploid I. indigotica, tetraploid I. indigotica, and Isatis tinctoria Linnaeus were analyzed using metabolome and transcriptome analysis. The metabolomic analysis detected 48 lignan metabolites, including Lirioresinol A, Vladinol A, Syringaresinol, Arctigenin, Acanthoside B, and Sesamin as characteristic compounds, without significant variations among the remaining metabolites. The transcriptomic analysis identified 41 differentially expressed phenylpropanoid synthase genes, which were further analyzed for variations in lignan transcriptome profiles across different samples. RT-qPCR analysis also revealed differential genes expression related to lignan biosynthesis pathway among the three sample groups. The analysis of transcription factors showed that the AP2-EREBP family (Iin24319), MYB family (Iin24843), and WRKY family (Iin08158) displayed expression patterns similar to Iin14549. Phylogenetic analyses also indicate that Iin14549 may play a role in lignan synthesis. These transcription factor families exhibited high expression in tetraploid I. indigotica, moderate expression in diploid I. indigotica, and low expression in I. tinctoria. The findings of this study can serve as a reference for improving the quality of I. indigotica and developing germplasms with high lignan content. Additionally, these results lay a foundation for the functional characterization of UGTs in lignan biosynthesis pathway.

Using Drosophila to identify naturally occurring genetic modifiers of amyloid beta 42- and tau-induced toxicity

Alzheimer's disease is characterized by 2 pathological proteins, amyloid beta 42 and tau. The majority of Alzheimer's disease cases in the population are sporadic and late-onset Alzheimer's disease, which exhibits high levels of heritability. While several genetic risk factors for late-onset Alzheimer's disease have been identified and replicated in independent studies, including the ApoE ε4 allele, the great majority of the heritability of late-onset Alzheimer's disease remains unexplained, likely due to the aggregate effects of a very large number of genes with small effect size, as well as to biases in sample collection and statistical approaches. Here, we present an unbiased forward genetic screen in Drosophila looking for naturally occurring modifiers of amyloid beta 42- and tau-induced ommatidial degeneration. Our results identify 14 significant SNPs, which map to 12 potential genes in 8 unique genomic regions. Our hits that are significant after genome-wide correction identify genes involved in neuronal development, signal transduction, and organismal development. Looking more broadly at suggestive hits (P < 10-5), we see significant enrichment in genes associated with neurogenesis, development, and growth as well as significant enrichment in genes whose orthologs have been identified as significantly or suggestively associated with Alzheimer's disease in human GWAS studies. These latter genes include ones whose orthologs are in close proximity to regions in the human genome that are associated with Alzheimer's disease, but where a causal gene has not been identified. Together, our results illustrate the potential for complementary and convergent evidence provided through multitrait GWAS in Drosophila to supplement and inform human studies, helping to identify the remaining heritability and novel modifiers of complex diseases.

KL1 Domain of Longevity Factor Klotho Mimics the Metabolome of Cognitive Stimulation and Enhances Cognition in Young and Aging Mice

Cognitive deficits are a major biomedical challenge-and engagement of the brain in stimulating tasks improves cognition in aged individuals (Wilson et al., 2002; Gates et al., 2011) and rodents (Aidil-Carvalho et al., 2017), through unknown mechanisms. Whether cognitive stimulation alters specific metabolic pathways in the brain is unknown. Understanding which metabolic processes are involved in cognitive stimulation is important because it could lead to pharmacologic intervention that promotes biological effects of a beneficial behavior, toward the goal of effective medical treatments for cognitive deficits. Here we show using male mice that cognitive stimulation induced metabolic remodeling of the mouse hippocampus, and that pharmacologic treatment with the longevity hormone α-klotho (KL), mediated by its KL1 domain, partially mimicked this alteration. The shared, metabolic signature shared between cognitive stimulation and treatment with KL or KL1 closely correlated with individual mouse cognitive performance, indicating a link between metabolite levels and learning and memory. Importantly, the treatment of mice with KL1, an endogenous circulating factor that more closely mimicked cognitive stimulation than KL, acutely increased synaptic plasticity, a substrate of cognition. KL1 also improved cognition, itself, in young mice and countered deficits in old mice. Our data show that treatments or interventions mimicking the hippocampal metabolome of cognitive stimulation can enhance brain functions. Further, we identify the specific domain by which klotho promotes brain functions, through KL1, a metabolic mimic of cognitive stimulation.SIGNIFICANCE STATEMENT Cognitive deficits are a major biomedical challenge without truly effective pharmacologic treatments. Engaging the brain through cognitive tasks benefits cognition. Mimicking the effects of such beneficial behaviors through pharmacological treatment represents a highly valuable medical approach to treating cognitive deficits. We demonstrate that brain engagement through cognitive stimulation induces metabolic remodeling of the hippocampus that was acutely recapitulated by the longevity factor klotho, mediated by its KL1 domain. Treatment with KL1, a close mimic of cognitive stimulation, enhanced cognition and countered cognitive aging. Our findings shed light on how cognition metabolically alters the brain and provide a plausible therapeutic intervention for mimicking these alterations that, in turn, improves cognition in the young and aging brain.

The metabolome as a biomarker of aging in Drosophila melanogaster

Many biomarkers have been shown to be associated not only with chronological age but also with functional measures of biological age. In human populations, it is difficult to show whether variation in biological age is truly predictive of life expectancy, as such research would require longitudinal studies over many years, or even decades. We followed adult cohorts of 20 Drosophila Genetic Reference Panel (DGRP) strains chosen to represent the breadth of lifespan variation, obtain estimates of lifespan, baseline mortality, and rate of aging, and associate these parameters with age‐specific functional traits including fecundity and climbing activity and with age‐specific targeted metabolomic profiles. We show that activity levels and metabolome‐wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. This study with 20 genotypes and 87 metabolites, while relatively small in scope, establishes strong proof of principle for the fly as a powerful experimental model to test hypotheses about biomarkers and aging and provides further evidence for the potential value of metabolomic profiles as biomarkers of aging.

Extending human healthspan and longevity: a symposium report

For many years, it was believed that the aging process was inevitable and that age-related diseases could not be prevented or reversed. The geroscience hypothesis, however, posits that aging is, in fact, malleable and, by targeting the hallmarks of biological aging, it is indeed possible to alleviate age-related diseases and dysfunction and extend longevity. This field of geroscience thus aims to prevent the development of multiple disorders with age, thereby extending healthspan, with the reduction of morbidity toward the end of life. Experts in the field have made remarkable advancements in understanding the mechanisms underlying biological aging and identified ways to target aging pathways using both novel agents and repurposed therapies. While geroscience researchers currently face significant barriers in bringing therapies through clinical development, proof-of-concept studies, as well as early-stage clinical trials, are underway to assess the feasibility of drug evaluation and lay a regulatory foundation for future FDA approvals in the future.

Evolution of natural lifespan variation and molecular strategies of extended lifespan in yeast

To understand the genetic basis and selective forces acting on longevity, it is useful to examine lifespan variation among closely related species, or ecologically diverse isolates of the same species, within a controlled environment. In particular, this approach may lead to understanding mechanisms underlying natural variation in lifespan. Here, we analyzed 76 ecologically diverse wild yeast isolates and discovered a wide diversity of replicative lifespan (RLS). Phylogenetic analyses pointed to genes and environmental factors that strongly interact to modulate the observed aging patterns. We then identified genetic networks causally associated with natural variation in RLS across wild yeast isolates, as well as genes, metabolites, and pathways, many of which have never been associated with yeast lifespan in laboratory settings. In addition, a combined analysis of lifespan-associated metabolic and transcriptomic changes revealed unique adaptations to interconnected amino acid biosynthesis, glutamate metabolism, and mitochondrial function in long-lived strains. Overall, our multiomic and lifespan analyses across diverse isolates of the same species shows how gene-environment interactions shape cellular processes involved in phenotypic variation such as lifespan.

Natural variation in the transcriptional response of Drosophila melanogaster to oxidative stress

Broadly distributed species must cope with diverse and changing environmental conditions, including various forms of stress. Cosmopolitan populations of Drosophila melanogaster are more tolerant to oxidative stress than those from the species’ ancestral range in sub-Saharan Africa, and the degree of tolerance is associated with an insertion/deletion polymorphism in the 3′ untranslated region of the Metallothionein A (MtnA) gene that varies clinally in frequency. We examined oxidative stress tolerance and the transcriptional response to oxidative stress in cosmopolitan and sub-Saharan African populations of D. melanogaster, including paired samples with allelic differences at the MtnA locus. We found that the effect of the MtnA polymorphism on oxidative stress tolerance was dependent on the genomic background, with the deletion allele increasing tolerance only in a northern, temperate population. Genes that were differentially expressed under oxidative stress included MtnA and other metallothioneins, as well as those involved in glutathione metabolism and other genes known to be part of the oxidative stress response or the general stress response. A gene coexpression analysis revealed further genes and pathways that respond to oxidative stress including those involved in additional metabolic processes, autophagy, and apoptosis. There was a significant overlap among the genes induced by oxidative and cold stress, which suggests a shared response pathway to these two stresses. Interestingly, the MtnA deletion was associated with consistent changes in the expression of many genes across all genomic backgrounds, regardless of the expression level of the MtnA gene itself. We hypothesize that this is an indirect effect driven by the loss of microRNA binding sites within the MtnA 3′ untranslated region.

A Metabolomic Aging Clock Using Human Cerebrospinal Fluid

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that, in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid (CSF) from healthy human subjects and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer's and Parkinson's disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently overpredict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small data set (n = 85) and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of CSF.

Metabolic response to point mutations reveals principles of modulation of in vivo enzyme activity and phenotype

The relationship between sequence variation and phenotype is poorly understood. Here, we use metabolomic analysis to elucidate the molecular mechanism underlying the filamentous phenotype of E. coli strains that carry destabilizing mutations in dihydrofolate reductase (DHFR). We find that partial loss of DHFR activity causes reversible filamentation despite SOS response indicative of DNA damage, in contrast to thymineless death (TLD) achieved by complete inhibition of DHFR activity by high concentrations of antibiotic trimethoprim. This phenotype is triggered by a disproportionate drop in intracellular dTTP, which could not be explained by drop in dTMP based on the Michaelis-Menten-like in vitro activity curve of thymidylate kinase (Tmk), a downstream enzyme that phosphorylates dTMP to dTDP. Instead, we show that a highly cooperative (Hill coefficient 2.5) in vivo activity of Tmk is the cause of suboptimal dTTP levels. dTMP supplementation rescues filamentation and restores in vivo Tmk kinetics to Michaelis-Menten. Overall, this study highlights the important role of cellular environment in sculpting enzymatic kinetics with system-level implications for bacterial phenotype.

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.