Systemic inflammatory reactions have been postulated to exacerbate neurodegenerative diseases via microglial activation. We now demonstrate in vivo that repeated systemic challenge of mice over four consecutive days with bacterial LPS maintained an elevated microglial inflammatory phenotype and induced loss of dopaminergic neurons in the substantia nigra. The same total cumulative LPS dose given within a single application did not induce neurodegeneration. Whole-genome transcriptome analysis of the brain demonstrated that repeated systemic LPS application induced an activation pattern involving the classical complement system and its associated phagosome pathway. Loss of dopaminergic neurons induced by repeated systemic LPS application was rescued in complement C3-deficient mice, confirming the involvement of the complement system in neurodegeneration. Our data demonstrate that a phagosomal inflammatory response of microglia is leading to complement-mediated loss of dopaminergic neurons.
Chronic diseases are diseases of long duration and slow progression. Major NCDs (cardiovascular diseases, cancer, chronic respiratory diseases, diabetes, rheumatologic diseases and mental health) represent the predominant health problem of the Century. The prevention and control of NCDs are the priority of the World Health Organization 2008 Action Plan, the United Nations 2010 Resolution and the European Union 2010 Council. The novel trend for the management of NCDs is evolving towards integrative, holistic approaches. NCDs are intertwined with ageing. The European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) has prioritised NCDs. To tackle them in their totality in order to reduce their burden and societal impact, it is proposed that NCDs should be considered as a single expression of disease with different risk factors and entities. An innovative integrated health system built around systems medicine and strategic partnerships is proposed to combat NCDs. It includes (i) understanding the social, economic, environmental, genetic determinants, as well as the molecular and cellular mechanisms underlying NCDs; (ii) primary care and practice-based interprofessional collaboration; (iii) carefully phenotyped patients; (iv) development of unbiased and accurate biomarkers for comorbidities, severity and follow up of patients; (v) socio-economic science; (vi) development of guidelines; (vii) training; and (viii) policy decisions. The results could be applicable to all countries and adapted to local needs, economy and health systems. This paper reviews the complexity of NCDs intertwined with ageing. It gives an overview of the problem and proposes two practical examples of systems medicine (MeDALL) applied to allergy and to NCD co-morbidities (MACVIA-LR, Reference Site of the European Innovation Partnership on Active and Healthy Ageing).
Non-Communicable Diseases (NCDs) are among the most pressing global health problems of the twenty-first century. Their rising incidence and prevalence is linked to severe morbidity and mortality, and they are putting economic and managerial pressure on healthcare systems around the world. Moreover, NCDs are impeding healthy aging by negatively affecting the quality of life of a growing number of the global population. NCDs result from the interaction of various genetic, environmental and habitual factors, and cluster in complex ways, making the complex identification of resulting phenotypes not only difficult, but also a top research priority. The degree of complexity required to interpret large patient datasets generated by advanced high-throughput functional genomics assays has now increased to the point that novel computational biology approaches are essential to extract information that is relevant to the clinical decision-making process. Consequently, system-level models that interpret the interactions between extensive tissues, cellular and molecular measurements and clinical features are also being created to identify new disease phenotypes, so that disease definition and treatment are optimized, and novel therapeutic targets discovered. Likewise, Systems Medicine (SM) platforms applied to extensively-characterized patients provide a basis for more targeted clinical trials, and represent a promising tool to achieve better prevention and patient care, thereby promoting healthy aging globally. The present paper: (1) reviews the novel systems approaches to NCDs; (2) discusses how to move efficiently from Systems Biology to Systems Medicine; and (3) presents the scientific and clinical background of the San Raffaele Systems Medicine Platform.
Febrile seizures affect 2-4% of all children and have a strong genetic component. Recurrent mutations in three main genes (SCN1A, SCN1B and GABRG2) have been identified that cause febrile seizures with or without epilepsy. Here we report the identification of mutations in STX1B, encoding syntaxin-1B, that are associated with both febrile seizures and epilepsy. Whole-exome sequencing in independent large pedigrees identified cosegregating STX1B mutations predicted to cause an early truncation or an in-frame insertion or deletion. Three additional nonsense or missense mutations and a de novo microdeletion encompassing STX1B were then identified in 449 familial or sporadic cases. Video and local field potential analyses of zebrafish larvae with antisense knockdown of stx1b showed seizure-like behavior and epileptiform discharges that were highly sensitive to increased temperature. Wild-type human syntaxin-1B but not a mutated protein rescued the effects of stx1b knockdown in zebrafish. Our results thus implicate STX1B and the presynaptic release machinery in fever-associated epilepsy syndromes.
Immunoresponsive gene 1 (Irg1) is highly expressed in mammalian macrophages during inflammation, but its biological function has not yet been elucidated. Here, we identify Irg1 as the gene coding for an enzyme producing itaconic acid (also known as methylenesuccinic acid) through the decarboxylation of cis-aconitate, a tricarboxylic acid cycle intermediate. Using a gain-and-loss-of-function approach in both mouse and human immune cells, we found Irg1 expression levels correlating with the amounts of itaconic acid, a metabolite previously proposed to have an antimicrobial effect. We purified IRG1 protein and identified its cis-aconitate decarboxylating activity in an enzymatic assay. Itaconic acid is an organic compound that inhibits isocitrate lyase, the key enzyme of the glyoxylate shunt, a pathway essential for bacterial growth under specific conditions. Here we show that itaconic acid inhibits the growth of bacteria expressing isocitrate lyase, such as Salmonella enterica and Mycobacterium tuberculosis. Furthermore, Irg1 gene silencing in macrophages resulted in significantly decreased intracellular itaconic acid levels as well as significantly reduced antimicrobial activity during bacterial infections. Taken together, our results demonstrate that IRG1 links cellular metabolism with immune defense by catalyzing itaconic acid production.
Parkinsons disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map .
Since the discovery of dopamine as a neurotransmitter in the 1950s, Parkinsons disease (PD) research has generated a rich and complex body of knowledge, revealing PD to be an age-related multifactorial disease, influenced by both genetic and environmental factors. The tremendous complexity of the disease is increased by a nonlinear progression of the pathogenesis between molecular, cellular and organic systems. In this minireview, we explore the complexity of PD and propose a systems-based approach, organizing the available information around cellular disease hallmarks. We encourage our peers to adopt this cell-based view with the aim of improving communication in interdisciplinary research endeavors targeting the molecular events, modulatory cell-to-cell signaling pathways and emerging clinical phenotypes related to PD.
Systems Biology is about combining theory, technology, and targeted experiments in a way that drives not only data accumulation but knowledge as well. The challenge in Systems Biomedicine is to furthermore translate mechanistic insights in biological systems to clinical application, with the central aim of improving patients quality of life. The challenge is to find theoretically well-chosen models for the contextually correct and intelligible representation of multi-scale biological systems. In this review, we discuss the current state of Systems Biology, highlight the emergence of Systems Biomedicine, and highlight some of the topics and views that we think are important for the efficient application of Systems Theory in Biomedicine.
We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.
The development of disease may be characterized as a pathological shift of homeostasis; the main goal of contemporary drug treatment is, therefore, to return the pathological homeostasis back to the normal physiological range. From the view point of systems biology, homeostasis emerges from the interactions within the network of biomolecules (e.g. DNA, mRNA, proteins), and, hence, understanding how drugs impact upon the entire network should improve their efficacy at returning the network (body) to physiological homeostasis. Large, mechanism-based computer models, such as the anticipated human whole body models (silicon or virtual human), may help in the development of such network-targeting drugs. Using the philosophical concept of weak and strong emergence, we shall here take a more general look at the paradigm of network-targeting drugs, and propose our approaches to scale the strength of strong emergence. We apply these approaches to several biological examples and demonstrate their utility to reveal principles of bio-modeling. We discuss this in the perspective of building the silicon human.
Animal models with high predictive power are a prerequisite for translational research. The closer the similarity of a model to Parkinsons disease (PD), the higher is the predictive value for clinical trials. An ideal PD model should present behavioral signs and pathology that resemble the human disease. The increasing understanding of PD stratification and etiology, however, complicates the choice of adequate animal models for preclinical studies. An ultimate mouse model, relevant to address all PD-related questions, is yet to be developed. However, many of the existing models are useful in answering specific questions. An appropriate model should be chosen after considering both the context of the research and the model properties. This review addresses the validity, strengths, and limitations of current PD mouse models for translational research.
The "4D Biology Workshop for Health and Disease", held on 16-17th of March 2010 in Brussels, aimed at finding the best organising principles for large-scale proteomics, interactomics and structural genomics/biology initiatives, and setting the vision for future high-throughput research and large-scale data gathering in biological and medical science. Major conclusions of the workshop include the following. (i) Development of new technologies and approaches to data analysis is crucial. Biophysical methods should be developed that span a broad range of time/spatial resolution and characterise structures and kinetics of interactions. Mathematics, physics, computational and engineering tools need to be used more in biology and new tools need to be developed. (ii) Database efforts need to focus on improved definitions of ontologies and standards so that system-scale data and associated metadata can be understood and shared efficiently. (iii) Research infrastructures should play a key role in fostering multidisciplinary research, maximising knowledge exchange between disciplines and facilitating access to diverse technologies. (iv) Understanding disease on a molecular level is crucial. System approaches may represent a new paradigm in the search for biomarkers and new targets in human disease. (v) Appropriate education and training should be provided to help efficient exchange of knowledge between theoreticians, experimental biologists and clinicians. These conclusions provide a strong basis for creating major possibilities in advancing research and clinical applications towards personalised medicine.
The recent explosion of biological data and the concomitant proliferation of distributed databases make it challenging for biologists and bioinformaticians to discover the best data resources for their needs, and the most efficient way to access and use them. Despite a rapid acceleration in uptake of syntactic and semantic standards for interoperability, it is still difficult for users to find which databases support the standards and interfaces that they need. To solve these problems, several groups are developing registries of databases that capture key metadata describing the biological scope, utility, accessibility, ease-of-use and existence of web services allowing interoperability between resources. Here, we describe some of these initiatives including a novel formalism, the Database Description Framework, for describing database operations and functionality and encouraging good database practise. We expect such approaches will result in improved discovery, uptake and utilization of data resources. Database URL: http://www.casimir.org.uk/casimir_ddf.
The tremendous amount of the data obtained from the study of complex biological systems changes our view on the pathogenesis of human diseases. Instead of looking at individual components of biological processes, we focus our attention more on the interaction and dynamics of biological systems. A network representation and analysis of the physiology and pathophysiology of biological systems is an effective way to study their complex behavior. Specific perturbations can trigger cascades of failures, which lead to the malfunctioning of cellular networks and as a result to the development of specific diseases. In this review we discuss recent developments in the field of disease network analysis and highlight some of the topics and views that we think are important for understanding network-based disease mechanisms.
The vitamin D hormone 1,25-dihydroxyvitamin D(3) [1,25(OH)(2) D(3) ], the biologically active form of vitamin D, is not only essential for mineral metabolism but may have important functions beyond calcium homoeostasis. By gene targeting, we have recently generated mice expressing a functionally inactive mutant vitamin D receptor (VDR). After a change in environmental conditions from specific pathogen free (SPF) conditions to a modified barrier system, a high percentage of aged mutant, but not wild-type, mice developed a haematological disorder characterized by splenomegaly, granulocytosis, thrombocytosis and dysplastic changes with displacement of erythropoiesis in bone marrow during the following months. All cases were associated with very high serum levels of the acute phase reaction protein serum amyloid A (SAA). Serological testing of affected mice revealed antibodies against murine hepatitis virus (MHV). However, electron microscopy of spleen and bone marrow cells did not reveal virus particles, and clinical signs of infectious diseases were absent. We hypothesize that a non-functioning VDR is associated with a latent defect in the regulation of myeloid cell differentiation and proliferation. Under the conditions of environmental stress, this latent defect may predispose to a deregulation of myelopoiesis in the form of a leukaemoid reaction accompanied by dysplastic changes. Thus, 1,25(OH)(2) D(3) may be an important inhibitory factor in the onset and progression of myeloproliferative and myelodysplastic diseases.
Over 800 million people worldwide are infected with hepatitis viruses, human immunodeficiency virus (HIV), and malaria, resulting in more than 5 million deaths annually. Here we discuss the potential and challenges of humanized mouse models for developing effective and affordable therapies and vaccines, which are desperately needed to combat these diseases.
Reverse engineering of gene networks aims at revealing the structure of the gene regulation network in a biological system by reasoning backward directly from experimental data. Many methods have recently been proposed for reverse engineering of gene networks by using gene transcript expression data measured by microarray. Whereas the potentials of the methods have been well demonstrated, the assumptions and limitations behind them are often not clearly stated or not well understood. In this review, we first briefly explain the principles of the major methods, identify the assumptions behind them and pinpoint the limitations and possible pitfalls in applying them to real biological questions. With regard to applications, we then discuss challenges in the experimental verification of gene networks generated from reverse engineering methods. We further propose an optimal experimental design for allocating sampling schedule and possible strategies for reducing the limitations of some of the current reverse engineering methods. Finally, we examine the perspectives for the development of reverse engineering and urge the need to move from revealing network structure to the dynamics of biological systems.
Human FOXP3(+)CD25(+)CD4(+) regulatory T cells (Tregs) are essential to the maintenance of immune homeostasis. Several genes are known to be important for murine Tregs, but for human Tregs the genes and underlying molecular networks controlling the suppressor function still largely remain unclear. Here, we describe a strategy to identify the key genes directly from an undirected correlation network which we reconstruct from a very high time-resolution (HTR) transcriptome during the activation of human Tregs/CD4(+) T-effector cells. We show that a predicted top-ranked new key gene PLAU (the plasminogen activator urokinase) is important for the suppressor function of both human and murine Tregs. Further analysis unveils that PLAU is particularly important for memory Tregs and that PLAU mediates Treg suppressor function via STAT5 and ERK signaling pathways. Our study demonstrates the potential for identifying novel key genes for complex dynamic biological processes using a network strategy based on HTR data, and reveals a critical role for PLAU in Treg suppressor function.
Healthy functioning is an emergent property of the network of interacting biomolecules that comprise an organism. It follows that disease (a network shift that causes malfunction) is also an emergent property, emerging from a perturbation of the network. On the one hand, the biomolecular network of every individual is unique and this is evident when similar disease-producing agents cause different individual pathologies. Consequently, a personalized model and approach for every patient may be required for therapies to become effective across mankind. On the other hand, diverse combinations of internal and external perturbation factors may cause a similar shift in network functioning. We offer this as an explanation for the multi-factorial nature of most diseases: they are "systems biology diseases," or "network diseases." Here we use neurodegenerative diseases, like Parkinsons disease (PD), as an example to show that due to the inherent complexity of these networks, it is difficult to understand multi-factorial diseases with simply our "naked brain." When describing interactions between biomolecules through mathematical equations and integrating those equations into a mathematical model, we try to reconstruct the emergent properties of the system in silico. The reconstruction of emergence from interactions between huge numbers of macromolecules is one of the aims of systems biology. Systems biology approaches enable us to break through the limitation of the human brain to perceive the extraordinarily large number of interactions, but this also means that we delegate the understanding of reality to the computer. We no longer recognize all those essences in the systems design crucial for important physiological behavior (the so-called "design principles" of the system). In this paper we review evidence that by using more abstract approaches and by experimenting in silico, one may still be able to discover and understand the design principles that govern behavioral emergence.
A sustained neuroinflammatory response is the hallmark of many neurodegenerative diseases, including Parkinsons disease, Alzheimers disease, amyotrophic lateral sclerosis, multiple sclerosis, and HIV-associated neurodegeneration. A specific subset of T cells, currently recognized as FOXP3(+) CD25(+) CD4(+) regulatory T cells (Tregs), are pivotal in suppressing autoimmunity and maintaining immune homeostasis by mediating self-tolerance at the periphery as shown in autoimmune diseases and cancers. A growing body of evidence shows that Tregs are not only important for maintaining immune balance at the periphery but also contribute to self-tolerance and immune privilege in the central nervous system. In this article, we first review the current status of knowledge concerning the development and the suppressive function of Tregs. We then discuss the evidence supporting a dysfunction of Tregs in several neurodegenerative diseases. Interestingly, a dysfunction of Tregs is mainly observed in the early stages of several neurodegenerative diseases, but not in their chronic stages, pointing to a causative role of inflammation in the pathogenesis of neurodegenerative diseases. Furthermore, we provide an overview of a number of molecules, such as hormones, neuropeptides, neurotransmitters, or ion channels, that affect the dysfunction of Tregs in neurodegenerative diseases. We also emphasize the effects of the intestinal microbiome on the induction and function of Tregs and the need to study the crosstalk between the enteric nervous system and Tregs in neurodegenerative diseases. Finally, we point out the need for a systems biology approach in the analysis of the enormous complexity regulating the function of Tregs and their potential role in neurodegenerative diseases.
Personalized medicine is a term for a revolution in medicine that envisions the individual patient as the central focus of healthcare in the future. The term "personalized medicine", however, fails to reflect the enormous dimensionality of this new medicine that will be predictive, preventive, personalized, and participatory-a vision of medicine we have termed P4 medicine. This reflects a paradigm change in how medicine will be practiced that is revolutionary rather than evolutionary. P4 medicine arises from the confluence of a systems approach to medicine and from the digitalization of medicine that creates the large data sets necessary to deal with the complexities of disease. We predict that systems approaches will empower the transition from conventional reactive medical practice to a more proactive P4 medicine focused on wellness, and will reverse the escalating costs of drug development an will have enormous social and economic benefits. Our vision for P4 medicine in 10 years is that each patient will be associated with a virtual data cloud of billions of data points and that we will have the information technology for healthcare to reduce this enormous data dimensionality to simple hypotheses about health and/or disease for each individual. These data will be multi-scale across all levels of biological organization and extremely heterogeneous in type - this enormous amount of data represents a striking signal-to-noise (S/N) challenge. The key to dealing with this S/N challenge is to take a "holistic systems approach" to disease as we will discuss in this article.
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