A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status

Open Access ArticleBACKGROUND: Diagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health. RESULTS: One hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83-0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is 'up-regulated' in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case-control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA 'disease signature', the healthy ageing RNA classifier is diagnostic for AD. CONCLUSIONS: We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.European CommissionAlzheimer’s Research UKJohn and Lucille van Geest FoundationNational Institute for Health Research (NIHR)European Medical Information Framework (EMIF)Medical Research Council (MRC)Wallenberg FoundationKarolinska InstitutetSwedish Medical Research CouncilSwedish Society for Medical Research (SSMF

Genomic analysis of macrophage gene signatures during idiopathic pulmonary fibrosis development

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, irreversible lung disease. After diagnosis, the interstitial condition commonly presents 3-5 years of life expectancy if untreated. Despite the limited capacity of recapitulating IPF, animal models have been useful for identifying related pathways relevant for drug discovery and diagnostic tools development. Using these techniques, several immune-related mechanisms have been implicated to IPF. For instance, subpopulations of macrophages and monocytes-derived cells are recognized as centrally active in pulmonary immunological processes. One of the most used technologies is high-throughput gene expression analysis, which has been available for almost two decades now. The “omics” revolution has presented major impacts on macrophage and pulmonary fibrosis research. The present study aims to investigate macrophage dynamics within the context of IPF at the transcriptomic level. Using publicly available gene-expression data, we applied modern data science approaches to (1) understand longitudinal profiles within IPF models; (2) investigate correlation between macrophage genomic dynamics and IPF development; and (3) apply longitudinal profiles uncovered through multivariate data analysis to the development of new sets of predictors able to classify IPF and control samples accordingly. Principal Component Analysis and Hierarchical Clustering showed that our pipeline was able to construct a complex set of biomarker candidates that together outperformed gene expression alone in separating treatment groups in an IPF animal model dataset. We further assessed the predictive performance of our candidates on publicly available gene expression data from IPF patients. Once again, the constructed biomarker candidates were significantly differentiated between IPF and control samples. The data presented in this work strongly suggest that longitudinal data analysis holds major unappreciated potentials for translational medicine research

Sensitive periods for the effect of childhood adversity on DNA methylation: Results from a prospective, longitudinal study

Background: Exposure to "early life" adversity is known to predict DNA methylation (DNAm) patterns that may be related to psychiatric risk. However, few studies have investigated whether adversity has time-dependent effects based on the age at exposure.Methods: Using a two-stage structured life course modeling approach (SLCMA), we tested the hypothesis that there are sensitive periods when adversity induced greater DNAm changes. We tested this hypothesis in relation to two alternatives: an accumulation hypothesis, in which the effect of adversity increases with the number of occasions exposed, regardless of timing, and a recency model, in which the effect of adversity is stronger for more proximal events. Data came from the Accessible Resource for Integrated Epigenomics Studies (ARIES), a subsample of mother-child pairs from the Avon Longitudinal Study of Parents and Children (ALSPAC; n=691-774).Results: After covariate adjustment and multiple testing correction, we identified 38 CpG sites that were differentially methylated at age 7 following exposure to adversity. Most loci (n=35) were predicted by the timing of adversity, namely exposures before age 3. Neither theaccumulation nor recency of the adversity explained considerable variability in DNAm. A standard EWAS of lifetime exposure (vs. no exposure) failed to detect these associations.Conclusions: The developmental timing of adversity explains more variability in DNAm than the accumulation or recency of exposure. Very early childhood appears to be a sensitive period when exposure to adversity predicts differential DNAm patterns. Classification of individuals as exposed vs. unexposed to “early life” adversity may dilute observed effects

Genome-Wide Analyses of Gene Expression during Mouse Endochondral Ossification

Endochondral ossification is a complex process involving a series of events that are initiated by the establishment of a chondrogenic template and culminate in its replacement through the coordinated activity of osteoblasts, osteoclasts and endothelial cells. Comprehensive analyses of in vivo gene expression profiles during these processes are essential to obtain a complete understanding of the regulatory mechanisms involved.To address these issues, we completed a microarray screen of three zones derived from manually segmented embryonic mouse tibiae. Classification of genes differentially expressed between each respective zone, functional categorization as well as characterization of gene expression patterns, cytogenetic loci, signaling pathways and functional motifs both confirmed reported data and provided novel insights into endochondral ossification. Parallel comparisons of the microdissected tibiae data set with our previously completed micromass culture screen further corroborated the suitability of micromass cultures for modeling gene expression in chondrocyte development. The micromass culture system demonstrated striking similarities to the in vivo microdissected tibiae screen; however, the micromass system was unable to accurately distinguish gene expression differences in the hypertrophic and mineralized zones of the tibia.These studies allow us to better understand gene expression patterns in the growth plate and endochondral bones and provide an important technical resource for comparison of gene expression in diseased or experimentally-manipulated cartilages. Ultimately, this work will help to define the genomic context in which genes are expressed in long bones and to understand physiological and pathological ossification

Transcriptional Profiling of Organ‐Specific Autoimmunity in Human

Our understanding of the pathogenesis of organ‐specific autoinflammation has been restricted by limited access to the target organs. Peripheral blood, however, as a preferred transportation route for immune cells, provides a window to assess the entire immune system throughout the body. Transcriptional profiling with RNA stabilizing blood collection tubes reflects in vivo expression profiles at the time the blood is drawn, allowing detection of the disease activity in different samples or within the same sample over time. The main objective of this Ph.D. study was to apply gene‐expression microarrays in the characterization of peripheral blood transcriptional profiles in patients with autoimmune diseases. To achieve this goal a custom cDNA microarray targeted for gene‐expression profiling of human immune system was designed and produced. Sample collection and preparation was then optimized to allow gene‐expression profiling from whole‐blood samples. To overcome challenges resulting from minute amounts of sample material, RNA amplification was successfully applied to study pregnancy related immunosuppression in patients with multiple sclerosis (MS). Furthermore, similar sample preparation was applied to characterize longitudinal genome‐wide expression profiles in children with type 1 diabetes (T1D) associated autoantibodies and eventually clinical T1D. Blood transcriptome analyses, using both the ImmunoChip cDNA microarray with targeted probe selection and genome‐wide Affymetrix U133 Plus 2.0 oligonucleotide array, enabled monitoring of autoimmune activity. Novel disease related genes and general autoimmune signatures were identified. Notably, down‐regulation of the HLA class Ib molecules in peripheral blood was associated with disease activity in both MS and T1D. Taken together, these studies demonstrate the potential of peripheral blood transcriptional profiling in biomedical research and diagnostics. Imbalances in peripheral blood transcriptional activity may reveal dynamic changes that are relevant for the disease but might be completely missed in conventional cross‐sectional studies.Geenien ilmentyminen ihmisen kudos‐spesifisissä autoimmuunisairauksissa Kohdekudosten hankala saatavuus on rajoittanut kudos‐spesifisten autoimmuunisairauksien tutkimusta. Immuunijärjestelmää voidaan kuitenkin tarkastella myös potilaan verestä, joka toimii immuunijärjestelmän solujen tärkeimpänä kuljetusreittinä. Käyttämällä erityisesti RNA‐molekyylien säilyttämiseksi tarkoitettuja näytteenottoputkia, voidaan tarkastella geenien ilmentymistä elimistössä näytteenottohetkellä ja siten seurata immuunijärjestelmän aktiivisuutta. Tämän väitöskirjatyön tavoitteena oli tarkastella DNA‐mikrosirujen avulla geenien ilmentymistä potilaiden veressä immuunijärjestelmän aktiivisuuden muuttuessa. Tätä tarkoitusta varten suunniteltiin ja valmistettiin keskeiset immuunijärjestelmän geenit sisältävä cDNA‐mikrosiru, jota käytettiin raskauden aikaansaaman immuunivasteen heikkenemisen tarkasteluun MS‐potilailla. Tutkimusta varten optimoitiin verinäytteiden keruu‐ ja RNA‐eristysmenetelmät, ja koska verinäytteiden RNA‐määrät olivat pieniä, eristetty RNA monistettiin ennen analysointia DNAmikrosiruilla. Samaa näytteenkäsittelymenetelmää käytettiin myös kerättäessä näytesarjoja lapsista, joilla oli jo havaittu tyypin 1 diabetekseen yhdistettyjä autovasta‐aineita. Näytesarjat lapsista, jotka myöhemmin sairastuivat tyypin 1 diabetekseen, analysoitiin kaupallisella koko genomin kattavalla sirulla. Tutkimuksissa löydettiin aikaisemmin autoimmuunijärjestelmään yhdistettyjen geenien lisäksi uusia löydöksiä sekä itse suunniteltua ja valmistettua ImmunoChip cDNA‐mikrosirua että koko genomin kattavaa Affymetrix U133 Plus 2.0 oligonukleotidisirua käytettäessä. Erityisen merkillepantavaa oli luokan 1b HLA geenien hiljeneminen sekä MS‐taudin että tyypin 1 diabeteksen aktiivisuuden lisääntyessä. Väitöskirjatyön tutkimukset osoittivat, että immuunijärjestelmän aktiivisuutta voidaan seurata potilaiden verinäytteissä ilmenevien geenien kautta, ja veren soluissa ilmenevien geenien tarkastelua voidaan hyödyntää biolääketieteen tutkimuksessa ja diagnostiikassa. Lisäksi, geenien ilmentymisen seuraaminen saman potilaan peräkkäisissä näytteissä voi paljastaa toiminnallisia muutoksia, jotka perinteisessä poikkileikkaustutkimuksessa saattaisivat jäädä kokonaan huomioimattaSiirretty Doriast

Between destiny and disease: genetics and molecular pathways of CNS aging

Human brain aging is associated with robust "normal" functional, structural, and molecular changes that underlie changes in cognition, memory, mood and motor function, amongst other processes. Normal aging is also a requirement for onset of many neurological diseases, ranging from later onset neurodegenerative diseases such as Alzheimer's(AD) and Parkinson's diseases(PD), to earlier onset psychiatric disorders such as bipolar disorder(BPD) and schizophrenia(SCHZ). Understanding the molecular mechanisms and genetic underpinnings of normal age-related brain changes would have profound consequences for prevention and treatment of age-related impairments and disease. Here I introduce current knowledge of these functional changes, their structural and molecular underpinnings, their genetic modulators, and the contribution of normal aging to age-related neurological disease. I then present my contribution to this field in the form of three papers on genetic modulation of mammalian brain molecular aging. These studies demonstrate and investigate mechanisms underlying the causal modulation of molecular brain aging rates by Brain Derived Neurotrophic Factor (BDNF) and Serotonin (5-HT) in knock-out (KO) mice, and associative modulation by the putative longevity gene, Sirtuin 5, in humans (novel low-expressing promoter polymorphism (Sirt5prom2)). In humans we additionally investigate the potential mechanism(s) underlying neurological disease gating by normal aging, providing supporting evidence for molecular aging being a genetically controlled "transcriptional program" that progressively promotes age-regulated neurological diseases. In the discussion, I place these studies in a broader context within the field, detailing their implications and future directions

Morphogenetic Principles of Brain Organisation in Health and Disease

Non-invasive neuroimaging methods, such as MRI, provide a window into the structure of the mammalian brain. However, despite the ubiquity of these methods, the biological interpretation of the information obtained using these tools remains elusive. In order to accurately link this macroscale data to microscale measurements, it is critical that the construct validity is high. This thesis provides novel analyses, pipelines and methods to: i) generate and validate maps of brain organisation obtained via MRI, and ii) demonstrate the utility of these methods in capturing elements of cognition and psychopathology. First, in Chapter 1, I review some of the neuroscientific context for the new methods presented, from cytoarchitecture to gene expression to connectomes. Chapters 2-4 introduce a new method, “Morphometric Similarity Mapping”, which captures the brain organisation of an individual by mapping the relationships of multiple features of the cerebral cortex. Chapter 2 focuses on the development of the analysis pipeline and the graph theoretical features of the resulting morphometric similarity networks (MSNs), with an emphasis on reproducibility. Chapter 3 highlights the generalisability of MSNs to the macaque monkey, linking MSNs to ex vivo tract tracing experiments and presenting new tools for processing non-human imaging data; as well as evidence that MSN topography is organised by cytoarchitectonic features. Chapter 4 is focused on determining the transcriptomic correlates of MSNs using publicly available gene expression maps, and on applying MSNs to examine the relationship between brain organisation and intelligence. Chapter 5 is dedicated to rigorous evaluation of the applicability of MSNs to measure specific disease-relevant phenotypes in 8 rare genetic disorder cohorts. This includes the validation of novel methods for utilising data from both single-cell sequencing technologies and differential gene expression experiments (in multiple tissue types) in analysing neuroimaging and bulk transcriptomic brain maps. Chapter 6 provides a brief summary and presents some ongoing and future projects expanding on this original work. It also importantly discusses a general framework of comparing brain maps, including MSNs and gene expression, as well as other canonical maps of brain structure and function. Altogether, this thesis presents and evaluates novel methods and applications for integrating multimodal neuroimaging data with genetic data derived from multiple tissue types and through various acquisition strategies. It also includes tools for performing these analyses in non-human primates, and pipelines for statistically comparing brain maps. These results not only provide insight into the manifestation of brain-related changes due to various components of human variation, but also provides a framework for evaluating this variation at multiple biological scales purely from non-invasive neuroimaging data

Preclinical Models for Neuroblastoma: Establishing a Baseline for Treatment

Preclinical models of pediatric cancers are essential for testing new chemotherapeutic combinations for clinical trials. The most widely used genetic model for preclinical testing of neuroblastoma is the TH-MYCN mouse. This neuroblastoma-prone mouse recapitulates many of the features of human neuroblastoma. Limitations of this model include the low frequency of bone marrow metastasis, the lack of information on whether the gene expression patterns in this system parallels human neuroblastomas, the relatively slow rate of tumor formation and variability in tumor penetrance on different genetic backgrounds. As an alternative, preclinical studies are frequently performed using human cell lines xenografted into immunocompromised mice, either as flank implant or orthtotopically. Drawbacks of this system include the use of cell lines that have been in culture for years, the inappropriate microenvironment of the flank or difficult, time consuming surgery for orthotopic transplants and the absence of an intact immune system.Here we characterize and optimize both systems to increase their utility for preclinical studies. We show that TH-MYCN mice develop tumors in the paraspinal ganglia, but not in the adrenal, with cellular and gene expression patterns similar to human NB. In addition, we present a new ultrasound guided, minimally invasive orthotopic xenograft method. This injection technique is rapid, provides accurate targeting of the injected cells and leads to efficient engraftment. We also demonstrate that tumors can be detected, monitored and quantified prior to visualization using ultrasound, MRI and bioluminescence. Finally we develop and test a "standard of care" chemotherapy regimen. This protocol, which is based on current treatments for neuroblastoma, provides a baseline for comparison of new therapeutic agents.The studies suggest that use of both the TH-NMYC model of neuroblastoma and the orthotopic xenograft model provide the optimal combination for testing new chemotherapies for this devastating childhood cancer

Interpretable machine learning for genomics

High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines