Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

Traumatic Brain Injury Induces Genome-Wide Transcriptomic, Methylomic, and Network Perturbations in Brain and Blood Predicting Neurological Disorders.

The complexity of the traumatic brain injury (TBI) pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing), and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain

Functional genomic characterisation of animal models of AD: relevance to human dementia

The onset and progression of Alzheimer’s disease (AD) is characterised by increasing intracellular aggregation of hyperphosphorylated tau protein and the accumulation of amyloid beta (Aβ) in the neocortex. Despite recent success in identifying genetic risk factors for AD, the transcriptional and epigenomic mechanisms involved in disease progression are not fully understood. The main aim of this project was to evaluate transcriptional and epigenomic differences associated with the development of tau and amyloid pathology. To achieve this, I used transgenic mice harbouring human tau (rTg4510) and amyloid precursor protein (J20) mutations. I profiled transcriptional and epigenomic variation in brains from rTg4510 and J20 mice, collected at four time points carefully selected to span from early to late stages of neuropathology in each model. I identified robust gene expression and methylomic changes in both models, including genes associated with familial AD from genetic studies of human patients, and genes annotated to both common and rare variants identified in genome-wide association and exome-sequencing studies of late-onset sporadic AD. I quantified neuropathological burden across multiple brain regions in the same individual mice, identifying genomic changes paralleling the development of tau pathology in rTg4510 mice and amyloid pathology in J20 mice. Furthermore, I compared gene co-expression networks identified in my rTg4510 and J20 samples to those identified in AD human brains, finding considerable overlap with disease-associated co-expression modules (or clusters of genes) identified in the human cortex. In summary, this project represents the most systematic analysis of transcriptional and methylomic variation in mouse models of tau and amyloid pathology, providing further support for an immune-response component in the accumulation of AD-associated neuropathology, and highlighting novel molecular pathways involved in AD progression

Transcription factor networks play a key role in human brain evolution and disorders

Although the human brain has been studied over past decades at morphological and histological levels, much remains unknown about its molecular and genetic mechanisms. Furthermore, when compared with our closest relative the chimpanzee, the human brain strikingly shows great morphological changes that have been often associated with our cognitive specializations and skills. Nevertheless, such drastic changes in the human brain may have arisen not only through morphological changes but also through changes in the expression levels of genes and transcripts. Gene regulatory networks are complex and large-scale sets of protein interactions that play a fundamental role at the core of cellular and tissue functions. Among the most important players of such regulatory networks are transcription factors (TFs) and the transcriptional circuitries in which TFs are the central nodes. Over past decades, several studies have focused on the functional characterization of brain-specific TFs, highlighting their pathways, interactions, and target genes implicated in brain development and often disorders. However, one of the main limitations of such studies is the data collection which is generally based on an individual experiment using a single TF. To understand how TFs might contribute to such human-specific cognitive abilities, it is necessary to integrate the TFs into a system level network to emphasize their potential pathways and circuitry. This thesis proceeds with a novel systems biology approach to infer the evolution of these networks. Using human, chimpanzee, and rhesus macaque, we spanned circa 35 million years of evolution to infer ancestral TF networks and the TF-TF interactions that are conserved or shared in important brain regions. Additionally, we developed a novel method to integrate multiple TF networks derived from human frontal lobe next-generation sequencing data into a high confidence consensus network. In this study, we also integrated a manually curated list of TFs important for brain function and disorders. Interestingly, such “Brain-TFs” are important hubs of the consensus network, emphasizing their biological role in TF circuitry in the human frontal lobe. This thesis describes two major studies in which DNA microarray and RNA-sequencing (RNA-seq) datasets have been mined, directing the TFs and their potential target genes into co-expression networks in human and non-human primate brain genome-wide expression datasets. In a third study we functionally characterized ZEB2, a TF implicated in brain development and linked with Mowat-Wilson syndrome, using human, chimpanzee, and orangutan cell lines. This work introduces not only an accurate analysis of ZEB2 targets, but also an analysis of the evolution of ZEB2 binding sites and the regulatory network controlled by ZEB2 in great apes, spanning circa 16 million years of evolution. In summary, those studies demonstrated the critical role of TFs on the gene regulatory networks of human frontal lobe evolution and functions, emphasizing the potential relationships between TF circuitries and such cognitive skills that make humans unique

Whole transcriptome sequencing of the aging rat brain reveals dynamic RNA changes in the dark matter of the genome

Brain aging frequently underlies cognitive decline and is a major risk factor for neurodegenerative conditions. The exact molecular mechanisms underlying brain aging, however, remain unknown. Whole transcriptome sequencing provides unparalleled depth and sensitivity in gene expression profiling. It also allows non-coding RNA and splice variant detection/comparison across phenotypes. Using RNA-seq to sequence the cerebral cortex transcriptome in 6-, 12- and 28-month-old rats, age-related changes were studied. Protein-coding genes related to MHC II presentation and serotonin biosynthesis were differentially expressed (DE) in aging. Relative to protein-coding genes, more non-coding genes were DE over the three age-groups. RNA-seq quantifies not only levels of whole genes but also of their individual transcripts. Over the three age-groups, 136 transcripts were DE, 37 of which were so-called dark matter transcripts that do not map to known exons. Fourteen of these transcripts were identified as novel putative long non-coding RNAs. Evidence of isoform switching and changes in usage were found. Promoter and coding sequence usage were also altered, hinting of possible changes to mitochondrial transport within neurons. Therefore, in addition to changes in the expression of protein-coding genes, changes in transcript expression, isoform usage, and non-coding RNAs occur with age. This study demonstrates dynamic changes in RNA with age at various genomic levels, which may reflect changes in regulation of transcriptional networks and provides non-coding RNA gene candidates for further studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11357-012-9410-1) contains supplementary material, which is available to authorized users

Systematic review of gene expression studies in people with Lewy body dementia

Objectives:Lewy body dementia (LBD) is the second most prevalent neurodegenerative dementia, and it causes more morbidity and mortality than Alzheimer's disease. Several genetic associations of LBD have been reported, and their functional implications remain uncertain. Hence, we aimed to do a systematic review of all gene expression studies that investigated people with LBD for improving our understanding of LBD molecular pathology and for facilitating discovery of novel biomarkers and therapeutic targets for LBD.Methods:We systematically reviewed five online databases (PROSPERO protocol: CRD42017080647) and assessed the functional implications of all reported differentially expressed genes (DEG) using Ingenuity Pathway Analyses.Results:We screened 3,809 articles and identified 31 eligible studies. 1,242 statistically significant (p[less than]0.05) DEGs including 70 microRNAs have been reported in people with LBD. Expression levels of alternatively spliced transcripts of SNCA, SNCB, PRKN, APP, RELA, and ATXN2 significantly differ in LBD. Several mitochondrial genes and genes involved in ubiquitin proteasome system and autophagy lysosomal pathway were significantly downregulated in LBD. Evidence supporting chronic neuroinflammation in LBD was inconsistent. Our functional analyses highlighted the importance of RNA-mediated gene silencing, neuregulin signalling, and neurotrophic factors in the molecular pathology of LBD.Conclusions:α-synuclein aggregation, mitochondrial dysfunction, defects in molecular networks clearing misfolded proteins, and RNA-mediated gene silencing contribute to neurodegeneration in LBD. Larger longitudinal transcriptomic studies investigating biological fluids of people living with LBD are needed for molecular subtyping and staging of LBD. Diagnostic biomarker potential and therapeutic promise of identified DEGs warrant further research

A novel ultra-deep sequencing and computational analysis framework for non-coding small RNAs

Small non-coding RNAs (sncRNAs) are essential players in all pathological and pathophysiological processes. The high evolutionary conservation, especially for microRNAs (miRNAs) from nematodes to humans make them very interesting research objects. To advance the translation and application of sncRNAs in human healthcare, it is mandatory to have a profound understanding of their expression in health and diseases, especially in the context of aging. With such knowledge we can then model the contribution of non-coding RNAs to challenging diseases such as Alzheimer’s disease and other neurodegenerative disorders. Two factors adding to an improved understanding of non-coding RNAs are high-resolution experimental approaches that measure the molecules in an as least as possible biased manner and advanced computational analysis. The latter topic covers two aspects, primary data analysis such as genome mapping but more importantly also statistical analysis with respect to the biological function and altered molecular pathways. In my phD thesis, I contributed to the unbiased measurement of small non-coding RNAs by using combinatorial probe-anchor synthesis (cPAS) sequencing [1]. Using the new sequencing approach called DNBSEQ now, we were able to demonstrate that cPAS is not only more accurate as compared to microarrays but also that it generates a physiological distribution of non-coding RNAs that is partially lost in other sequencing approaches. Further, cPAS demonstrated a great technical reproducibility, making it of potential use for medical application. Available as high-throughput approach now, the work in my thesis is fundamental to characterize thousands of samples with millions of reads each in a reproducible and affordable manner, even limiting the hands-on time of technicians. Based on the success of the initial cPAS sequencing I worked on the advanced and even less biased analysis using the CoolMPS technology [2]. The key difference in this sequencing approach is that the detection signal is not generated by a chemically modified nucleotide incorporated in synthesized DNA but that a highly specific secondary antibody emits a light signal to sequence DNA or RNA [3]. This improved the sequencing quality significantly, at the same time lowering the sequencing cost. As very first application for the sequencing of sncRNAs, we selected Alzheimer’s disease, generating data of sufficient quality for application as clinical biomarker. As specimen types, we intentionally selected whole blood, containing the information from white blood cells, red blood cells, free circulating sncRNAs in plasma and extracellular vesicles. Notably, I also contributed to make the molecular measurements feasible as home-sampling [4], a topic that gains rapid traction not only because of the recent Sars-Cov2 pandemic. Independent on the technology, it is essential to extract the relevant biological information from small non-coding RNA data. Using data from neurological disorders but also from other diseases such as lung cancer and from controls we first modeled how aging affects the molecular patterns [5]. Our results clearly suggest a dependency of small non-coding RNAs from the age of patients, calling for age specific diagnostic tests. One challenge is however to differentiate between causative and correlated effects. Finally, we thus collected our and others knowledge on one specific class of small non-coding RNAs, microRNAs, and model how these molecules regulate the gene expression. We included this information to miRTargetLink2 [6], a web server that can model specific gene regulatory effects in one disease such as Alzheimer’s disease but that also can be applied to any other biomedical research question. Using miRTargetLink2, others and we now can answer highly complex questions – e.g., which genes are targeted by sets of miRNAs or which miRNAs target gene sets in a disease within minutes. In sum, the advanced and least biased measurement of non-coding RNAs by deep sequencing and the advanced computational analysis developed in this work contributes to advance our understanding of the molecules in health and diseases. Further, the framework can be applied by other researchers in the context of any physiological or pathological processes in humans, mice and other animals.Kleine nicht-kodierende RNAs (small non-coding RNAs, sncRNAs) sind wesentliche Akteure in allen pathologischen und pathophysiologischen Prozessen. Ihr hoher Grad an evolutionärer Konservierung, von Fadenwürmern bis hin zum Menschen, machen sie zum interessanten Forschungsgegenstand. Um die Translation von sncRNAs zum Patientenwohl zu ermöglichen ist es zwingend notwendig, ein tiefes Verständnis ihrer Expression in Gesundheit und Krankheit zu haben, insbesondere im Kontext des Alterns. Mit diesem Wissen können wir dann den Beitrag von nicht-kodierenden RNAs zu herausfordernden Krankheiten wie der Alzheimer- oder Parkinson-Krankheit modellieren. Zwei Faktoren, die zu einem verbesserten Verständnis der nicht-kodierenden RNAs beitragen, sind hochauflösende experimentelle Ansätze, die die Moleküle auf eine möglichst unvoreingenommene Weise messen, und fortgeschrittene rechnerische Analysen. Letzteres umfasst zwei Aspekte, zum einen die primäre Datenanalyse wie das Genom-Mapping, aber vor allem auch die statistische Analyse im Hinblick auf die biologische Funktion und veränderte molekulare Pfade. In meiner Doktorarbeit habe ich einen Beitrag zur unvoreingenommenen Messung von kleinen nicht-kodierenden RNAs mit Hilfe der kombinatorischen Sonden-Anker- Synthese (cPAS) Sequenzierung geleistet [1]. Mit dem neuen Sequenzieransatz DNBSEQ konnten wir zeigen, dass cPAS im Vergleich zu Microarrays nicht nur genauer ist, sondern auch eine physiologische Verteilung der nicht-kodierenden RNAs erzeugt, die bei anderen Sequenzieransätzen teilweise verloren geht. Weiterhin zeigte cPAS eine große technische Reproduzierbarkeit, was es für den medizinischen Einsatz interessant macht. Die Methode ist inzwischen als Hochdurchsatz Methode verfügbar und erlaubt es Kohorten mit tausenden Patienten, jeweils mit Millionen an Datenpunkten, Zeit- und Kosten-effizient zu messen. Basierend auf dem Erfolg der ersten cPAS-Sequenzierung arbeitete ich an der weiterentwickelten und noch weniger verzerrten Analyse mit der CoolMPS-Technologie [2]. Der entscheidende Unterschied bei diesem Sequenzieransatz ist, dass das Detektionssignal nicht durch ein chemisch modifiziertes Nukleotid erzeugt wird, das in die synthetisierte DNA eingebaut ist, sondern dass ein hochspezifischer sekundärer Antikörper ein Lichtsignal zur Sequenzierung von DNA oder RNA aussendet [3]. Dadurch konnte die Sequenzierqualität deutlich verbessert und gleichzeitig die Sequenzierkosten gesenkt werden. Als erste Anwendung für die Sequenzierung wählten wir die Alzheimer-Krankheit und generierten Daten von ausreichender Qualität für die Anwendung als klinischer Biomarker. Unsere Resultate beruhen dabei auf der Analyse von Vollblutproben, die sowohl das Muster von Weißen Blutkörperchen, Roten Blutkörperchen als auch frei zirkulierender und Vesikel-gebundener Moleküle widerspiegeln. Insbesondere habe ich auch dazu beigetragen, die molekularen Messungen als Home-Sampling möglich zu machen [4], ein Thema, das nicht Zuletzt wegen der Sars-Cov-2 Pandemie schnell an Bedeutung gewinnt. Unabhängig von der Technologie ist es wichtig, die relevanten biologischen Informationen aus den nicht-kodierenden RNA-Daten zu extrahieren. Anhand von Daten von neurologischen Erkrankungen, aber auch von anderen Krankheiten wie Lungenkrebs und von Kontrollen haben wir zunächst modelliert, wie das Altern die molekularen Muster beeinflusst [5]. Unsere Resultate deuten eindeutig auf eine Abhängigkeit der kleinen nicht-kodierenden RNAs vom Alter der Patienten hin, was nach altersspezifischen diagnostischen Tests verlangt. Eine Kern-Herausforderung ist es allerdings, zwischen Ursächlichen und Korrelierten Effekten zu unterscheiden. Daher bündeln wir unser Wissen und das von anderen Forschern über eine bestimmte Klasse von nicht-kodierenden RNAs, die microRNAs, und wie diese Moleküle die Genexpression regulieren. Diese Informationen fügten wir miRTargetLink [6] hinzu, einem Webserver, der spezifische genregulatorische Effekte bei einer Krankheit wie der Alzheimer-Krankheit modellieren kann, der aber auch auf jede andere biomedizinische Forschungsfrage angewendet werden kann. Mit Hilfe von miRTargetLink können andere und wir nun innerhalb von Minuten hochkomplexe Fragen beantworten - z. B. welche Gene von Sets von miRNAs angegriffen werden oder welche miRNAs bei einer Krankheit auf Gensets zielen. Zusammenfassend lässt sich sagen, dass die fortschrittliche und am wenigsten verzerrte Messung von nicht-kodierenden RNAs durch Deep Sequencing und die fortschrittliche rechnerische Analyse, die in dieser Arbeit entwickelt wurde, dazu beiträgt, unser Verständnis dieser Moleküle in Gesundheit und Krankheit zu verbessern. Darüber hinaus kann das Framework von anderen Forschern im Zusammenhang mit beliebigen physiologischen oder pathologischen Prozessen bei Menschen, Mäusen und anderen Tieren angewendet werden

Gene expression meta-analysis of Parkinson’s disease and its relationship with Alzheimer’s disease

Abstract Parkinson’s disease (PD) and Alzheimer’s disease (AD) are the most common neurodegenerative diseases and have been suggested to share common pathological and physiological links. Understanding the cross-talk between them could reveal potentials for the development of new strategies for early diagnosis and therapeutic intervention thus improving the quality of life of those affected. Here we have conducted a novel meta-analysis to identify differentially expressed genes (DEGs) in PD microarray datasets comprising 69 PD and 57 control brain samples which is the biggest cohort for such studies to date. Using identified DEGs, we performed pathway, upstream and protein-protein interaction analysis. We identified 1046 DEGs, of which a majority (739/1046) were downregulated in PD. YWHAZ and other genes coding 14–3-3 proteins are identified as important DEGs in signaling pathways and in protein-protein interaction networks (PPIN). Perturbed pathways also include mitochondrial dysfunction and oxidative stress. There was a significant overlap in DEGs between PD and AD, and over 99% of these were differentially expressed in the same up or down direction across the diseases. REST was identified as an upstream regulator in both diseases. Our study demonstrates that PD and AD share significant common DEGs and pathways, and identifies novel genes, pathways and upstream regulators which may be important targets for therapy in both diseases

Dexmedetomidine attenuates surgery-induced cognitive deficit and hippocampal Mapt expression in aged mice

Background Postoperative cognitive dysfunction is a significant complication, but the mechanisms underlying this condition and its impact on the brain network are not fully elucidated. The goals of this study were to verify how the transcriptional network in the hippocampus of aged mice changes following sevoflurane/surgery-induced stress and to substantiate how dexmedetomidine influences the cognitive function and mRNA changes in the hippocampus.Materials and methods We first performed transcriptome analysis to confirm the changes of mRNA expression in the Naïve and Ope (surgery under sevoflurane) groups. Then, the mice were divided into four groups: Naïve, Sevo (sevoflurane exposure), Ope, and Dex (dexmedetomidine injection before surgery). We selected the Mapt gene, the upregulated expression of which has been observed in our transcriptome analysis and in neurodegenerative disorders, as the target gene and investigated whether the changes in its expression occurred in the hippocampus using quantitative rev transcription polymerase chain reaction (qRT-PCR). The cognitive function of mice was evaluated via the Barnes Maze test.Results In the qRT-PCR analysis, Mapt expression was significantly upregulated (2.60 ± 0.77 in mice from the Ope group vs. 1.00 ± 0.11 in mice from the Naïve group [mean ± SD for fold change]; p<0.01). Dexmedetomidine significantly attenuated the sevoflurane/surgery-induced upregulation of Mapt expression in the hippocampus (0.88 ± 0.32; p<0.01). Sevoflurane/surgery-induced stress also increased the time to identify the target box, and dexmedetomidine treatment inhibited the time extension 7, 14, and 28 days after the surgery.Conclusions Dexmedetomidine attenuates the sevoflurane/surgery-induced cognitive deficit and Mapt expression in the hippocampus of aged mice