Age-associated microRNA expression in human peripheral blood is associated with all-cause mortality and age-related traits

Recent studies provide evidence of correlations of DNA methylation and expression of protein-coding genes with human aging. The relations of microRNA expression with age and age-related clinical outcomes have not been characterized thoroughly. We explored associations of age with whole-blood microRNA expression in 5221 adults and identified 127 microRNAs that were differentially expressed by age at P \u3c 3.3 x 10(-4) (Bonferroni-corrected). Most microRNAs were underexpressed in older individuals. Integrative analysis of microRNA and mRNA expression revealed changes in age-associated mRNA expression possibly driven by age-associated microRNAs in pathways that involve RNA processing, translation, and immune function. We fitted a linear model to predict \u27microRNA age\u27 that incorporated expression levels of 80 microRNAs. MicroRNA age correlated modestly with predicted age from DNA methylation (r = 0.3) and mRNA expression (r = 0.2), suggesting that microRNA age may complement mRNA and epigenetic age prediction models. We used the difference between microRNA age and chronological age as a biomarker of accelerated aging (Deltaage) and found that Deltaage was associated with all-cause mortality (hazards ratio 1.1 per year difference, P = 4.2 x 10(-5) adjusted for sex and chronological age). Additionally, Deltaage was associated with coronary heart disease, hypertension, blood pressure, and glucose levels. In conclusion, we constructed a microRNA age prediction model based on whole-blood microRNA expression profiling. Age-associated microRNAs and their targets have potential utility to detect accelerated aging and to predict risks for age-related diseases. Wiley and Sons Ltd

DCC gene network in the prefrontal cortex is associated with total brain volume in childhood

BACKGROUND: Genetic variation in the guidance cue DCC gene is linked to psychopathologies involving dysfunction in the prefrontal cortex. We created an expression-based polygenic risk score (ePRS) based on the DCC coexpression gene network in the prefrontal cortex, hypothesizing that it would be associated with individual differences in total brain volume. METHODS: We filtered single nucleotide polymorphisms (SNPs) from genes coexpressed with DCC in the prefrontal cortex obtained from an adult postmortem donors database (BrainEAC) for genes enriched in children 1.5 to 11 years old (BrainSpan). The SNPs were weighted by their effect size in predicting gene expression in the prefrontal cortex, multiplied by their allele number based on an individual's genotype data, and then summarized into an ePRS. We evaluated associations between the DCC ePRS and total brain volume in children in 2 community-based cohorts: the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) and University of California, Irvine (UCI) projects. For comparison, we calculated a conventional PRS based on a genome-wide association study of total brain volume. RESULTS: Higher ePRS was associated with higher total brain volume in children 8 to 10 years old (β = 0.212, p = 0.043; n = 88). The conventional PRS at several different thresholds did not predict total brain volume in this cohort. A replication analysis in an independent cohort of newborns from the UCI study showed an association between the ePRS and newborn total brain volume (β = 0.101, p = 0.048; n = 80). The genes included in the ePRS demonstrated high levels of coexpression throughout the lifespan and are primarily involved in regulating cellular function. LIMITATIONS: The relatively small sample size and age differences between the main and replication cohorts were limitations. CONCLUSION: Our findings suggest that the DCC coexpression network in the prefrontal cortex is critically involved in whole brain development during the first decade of life. Genes comprising the ePRS are involved in gene translation control and cell adhesion, and their expression in the prefrontal cortex at different stages of life provides a snapshot of their dynamic recruitment

Co-expression network analysis identifies possible hub genes in aging of the human prefrontal cortex

Introducción: el envejecimiento es el principal factor de riesgo para el desarrollo de enfermedades crónicas como el cáncer, la diabetes, el Parkinson y el Alzheimer. El sistema nervioso central es particularmente susceptible al deterioro funcional progresivo asociado con la edad, entre las regiones cerebrales con mayor compromiso se encuentra la corteza prefrontal (cpf). Estudios de transcriptómica de esta región han identificado como características fundamentales del proceso de envejecimiento la disminución de la función sináptica y la activación de las células de la neuroglia. No es claro cuáles son las causas iniciales, ni los mecanismos moleculares subyacentes a estas alteraciones. El objetivo de este estudio fue identificar genes clave en la desregulación transcriptómica en el envejecimiento de la cpf para avanzar en el conocimiento de este proceso. Materiales y métodos: se hizo un análisis de coexpresión de genes de los transcriptomas de 45 personas entre 60 y 80 años con el de 38 personas entre 20 y 40 años. Las redes fueron visualizadas y analizadas usando Cytoscape, se usó citoHubba para determinar qué genes tenían las mejores características topológicas en las redes de coexpresión. Resultados: se identificaron cinco genes con características topológicas altas. Cuatro de ellos —hpca, cacng3, ca10, plppr4— reprimidos y uno sobreexpresado —cryab—. Conclusión: los cuatro genes reprimidos se expresan preferencialmente en neuronas y regulan la función sináptica y la plasticidad neuronal, mientras el gen sobreexpresado es típico de células de la glía y se expresa como respuesta a daño neuronal facilitando la mielinización y la regeneración neuronal.Introduction: Aging is the main risk factor for the development of chronic diseases such as cancer, diabetes, Parkinson’s disease, and Alzheimer’s disease. The central nervous system is particularly susceptible to progressive functional deterioration associated with age, among the brain regions the prefrontal cortex (PFC) has one of the highest involvements. Transcriptomics studies of this brain region have identified the decrease in synaptic function and activation of neuroglia cells as fundamental characteristics of the aging process. The aim of this study was to identify hub genes in the transcriptomic deregulation in the PFC aging to advance in the knowledge of this process. Materials and methods: A gene co-expression analysis was carried out for 45 people 60 to 80 years old compared with 38 people 20 to 40 years old. The networks were visualized and analyzed using Cytoscape; citoHubba was used to determine which genes had the best topological characteristics in the co-expression networks. Results: Five genes with high topological characteristics were identified. Four of them —HPCA, CACNG3, CA10, PLPPR4— were repressed and one was over-expressed —CRYAB—. Conclusion: The four repressed genes are expressed preferentially in neurons and regulate the synaptic function and the neuronal plasticity, while the overexpressed gene is typical of glial cells and is expressed as a response to neuronal damage, facilitating myelination and neuronal regeneration

Global gene expression profiling of healthy human brain and its application in studying neurological disorders

The human brain is the most complex structure known to mankind and one of the greatest challenges in modern biology is to understand how it is built and organized. The power of the brain arises from its variety of cells and structures, and ultimately where and when different genes are switched on and off throughout the brain tissue. In other words, brain function depends on the precise regulation of gene expression in its sub-anatomical structures. But, our understanding of the complexity and dynamics of the transcriptome of the human brain is still incomplete. To fill in the need, we designed a gene expression model that accurately defines the consistent blueprint of the brain transcriptome; thereby, identifying the core brain specific transcriptional processes conserved across individuals. Functionally characterizing this model would provide profound insights into the transcriptional landscape, biological pathways and the expression distribution of neurotransmitter systems. Here, in this dissertation we developed an expression model by capturing the similarly expressed gene patterns across congruently annotated brain structures in six individual brains by using data from the Allen Brain Atlas (ABA). We found that 84% of genes are expressed in at least one of the 190 brain structures. By employing hierarchical clustering we were able to show that distinct structures of a bigger brain region can cluster together while still retaining their expression identity. Further, weighted correlation network analysis identified 19 robust modules of coexpressing genes in the brain that demonstrated a wide range of functional associations. Since signatures of local phenomena can be masked by larger signatures, we performed local analysis on each distinct brain structure. Pathway and gene ontology enrichment analysis on these structures showed, striking enrichment for brain region specific processes. Besides, we also mapped the structural distribution of the gene expression profiles of genes associated with major neurotransmission systems in the human. We also postulated the utility of healthy brain tissue gene expression to predict potential genes involved in a neurological disorder, in the absence of data from diseased tissues. To this end, we developed a supervised classification model, which achieved an accuracy of 84% and an AUC (Area Under the Curve) of 0.81 from ROC plots, for predicting autism-implicated genes using the healthy expression model as the baseline. This study represents the first use of healthy brain gene expression to predict the scope of genes in autism implication and this generic methodology can be applied to predict genes involved in other neurological disorders

The Endothelial Cell Response to Inflammation, the Functional Role of the Endothelial-enriched Protein KANK3 and the Adipose Tissue Transcriptome

A compilation of three complementary projects explores various facets of endothelial cell biology and transcriptomics, illuminating the intricate dynamics underlying cellular responses to specific stimuli across different tissues. The first project examines how endothelial cells react to the inflammatory molecule tumour necrosis factor (TNF), by studying these cells over time after TNF exposure. We identified distinct gene expression patterns and revealed two central temporal phases of gene upregulation in the endothelial response. The induction of interferon response genes, without de novo interferon production, was further investigated. An online resource was developed for comprehensive data exploration (www.endothelial-response.org). The second project analysed adipose tissue to define cell type enriched transcripts and differences between the sexes and depot types. We found mesothelial cells to be the main driver for heterogeneity between subcutaneous and visceral adipose tissue. This data is accessible through the Human Protein Atlas. The third project focuses on KANK3, which was predicted to be an endothelial enriched gene in the previous study, and others from the group. Our findings show that KANK3 is endothelial specific in multiple tissues through the body, inhibition of KANK3 in endothelial cells affects cell motility, expression of blood clotting proteins on gene and protein level, and thrombin generation. Together, these projects enhance our understanding of endothelial cell responses to inflammation and detail the functional investigation of an uncharacterised endothelial protein. Each project offers a different perspective, by examining temporal responses, functional changes, and tissue-wide patterns. This multifaceted approach deepens our insights into cell biology and furthers our understanding of critical health processes

Epigenetics, heritability and longitudinal analysis

© 2018 The Author(s). Background: Longitudinal data and repeated measurements in epigenome-wide association studies (EWAS) provide a rich resource for understanding epigenetics. We summarize 7 analytical approaches to the GAW20 data sets that addressed challenges and potential applications of phenotypic and epigenetic data. All contributions used the GAW20 real data set and employed either linear mixed effect (LME) models or marginal models through generalized estimating equations (GEE). These contributions were subdivided into 3 categories: (a) quality control (QC) methods for DNA methylation data; (b) heritability estimates pretreatment and posttreatment with fenofibrate; and (c) impact of drug response pretreatment and posttreatment with fenofibrate on DNA methylation and blood lipids. Results: Two contributions addressed QC and identified large statistical differences with pretreatment and posttreatment DNA methylation, possibly a result of batch effects. Two contributions compared epigenome-wide heritability estimates pretreatment and posttreatment, with one employing a Bayesian LME and the other using a variance-component LME. Density curves comparing these studies indicated these heritability estimates were similar. Another contribution used a variance-component LME to depict the proportion of heritability resulting from a genetic and shared environment. By including environmental exposures as random effects, the authors found heritability estimates became more stable but not significantly different. Two contributions investigated treatment response. One estimated drug-associated methylation effects on triglyceride levels as the response, and identified 11 significant cytosine-phosphate-guanine (CpG) sites with or without adjusting for high-density lipoprotein. The second contribution performed weighted gene coexpression network analysis and identified 6 significant modules of at least 30 CpG sites, including 3 modules with topological differences pretreatment and posttreatment. Conclusions: Four conclusions from this GAW20 working group are: (a) QC measures are an important consideration for EWAS studies that are investigating multiple time points or repeated measurements; (b) application of heritability estimates between time points for individual CpG sites is a useful QC measure for DNA methylation studies; (c) drug intervention demonstrated strong epigenome-wide DNA methylation patterns across the 2 time points; and (d) new statistical methods are required to account for the environmental contributions of DNA methylation across time. These contributions demonstrate numerous opportunities exist for the analysis of longitudinal data in future epigenetic studies

Sex/gender differences and autism: setting the scene for future research.

OBJECTIVE: The relationship between sex/gender differences and autism has attracted a variety of research ranging from clinical and neurobiological to etiological, stimulated by the male bias in autism prevalence. Findings are complex and do not always relate to each other in a straightforward manner. Distinct but interlinked questions on the relationship between sex/gender differences and autism remain underaddressed. To better understand the implications from existing research and to help design future studies, we propose a 4-level conceptual framework to clarify the embedded themes. METHOD: We searched PubMed for publications before September 2014 using search terms "'sex OR gender OR females' AND autism." A total of 1,906 articles were screened for relevance, along with publications identified via additional literature reviews, resulting in 329 articles that were reviewed. RESULTS: Level 1, "Nosological and diagnostic challenges," concerns the question, "How should autism be defined and diagnosed in males and females?" Level 2, "Sex/gender-independent and sex/gender-dependent characteristics," addresses the question, "What are the similarities and differences between males and females with autism?" Level 3, "General models of etiology: liability and threshold," asks the question, "How is the liability for developing autism linked to sex/gender?" Level 4, "Specific etiological-developmental mechanisms," focuses on the question, "What etiological-developmental mechanisms of autism are implicated by sex/gender and/or sexual/gender differentiation?" CONCLUSIONS: Using this conceptual framework, findings can be more clearly summarized, and the implications of the links between findings from different levels can become clearer. Based on this 4-level framework, we suggest future research directions, methodology, and specific topics in sex/gender differences and autism.Dr. Lai has received grant or research support from the William Binks Autism Neuroscience Fellowship, the European Autism Interventions— A Multicentre Study for Developing New Medications (EU-AIMS), and Wolfson College, Cambridge University. Dr. Lombardo has received grant or research support from the British Academy, the Wellcome Trust, and Jesus College, Cambridge University. Dr. Auyeung has received grant or research support from the Wellcome Trust. Dr. Chakrabarti has received grant or research support from the UK Medical Research Council. Dr. Baron-Cohen has received grant or research support from the Wellcome Trust, the EU-AIMS, the UK Medical Research Council, and the Autism Research Trust.This is the final published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S0890856714007254#