Deciphering complex mechanisms in neurodegenerative diseases: the advent of systems biology

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Neurodegenerative disorders: From clinicopathology convergence to systems biology divergence

Neurodegenerative diseases are multifactorial. This means that several genetic, epigenetic, and environmental factors contribute to their emergence. Therefore, for the future management of these highly prevalent diseases, it is necessary to change perspective. If a holistic viewpoint is assumed, the phenotype (the clinicopathological convergence) emerges from the perturbation of a complex system of functional interactions among proteins (systems biology divergence). The systems biology top-down approach starts with the unbiased collection of sets of data generated through one or more -omics techniques and has the aim to identify the networks and the components that participate in the generation of a phenotype (disease), often without any available a priori knowledge. The principle behind the top-down method is that the molecular components that respond similarly to experimental perturbations are somehow functionally related. This allows the study of complex and relatively poorly characterized diseases without requiring extensive knowledge of the processes under investigation. In this chapter, the use of a global approach will be applied to the comprehension of neurodegeneration, with a particular focus on the two most prevalent ones, Alzheimer's and Parkinson's diseases. The final purpose is to distinguish disease subtypes (even with similar clinical manifestations) to launch a future of precision medicine for patients with these disorders.

Gold nanoparticles application to the treatment of brain dysfunctions related to metabolic diseases: evidence from experimental studies

Nanotechnology is an emerging and expanding technology worldwide. The manipulation of materials on a nanometric scale generates new products with unique properties called nanomaterials. Due to its significant expansion, nanotechnology has been applied in several fields of study, including developing materials for biomedical applications, i.e., nanomedicine. The use of nanomaterials, including nanoparticles, in nanomedicine, is promising and has been associated with pharmacokinetics, bioavailability, and therapeutic advantages. In this regard, it is worth mentioning the Gold Nanoparticles (AuNPs). AuNPs' biomedical application is extensively investigated due to their high biocompatibility, simple preparation, catalytic, and redox properties. Experimental studies have pointed out critical therapeutic actions related to AuNPs in different pathophysiological contexts, mainly due to their anti-inflammatory and antioxidant effects. Thus, in this review, we will discuss the main experimental findings related to the therapeutic properties of AuNPs in metabolic, neurodegenerative diseases, and ultimately brain dysfunctions related to metabolic diseases.

Forgetful, sad and old: Do vascular cognitive impairment and depression share a common pre-disease network and how is it impacted by ageing?

Vascular cognitive impairment (VCI) and depression frequently coexist in geriatric populations and reciprocally increase disease risks. We assert that a shared pre-disease state of the psycho-immune-neuroendocrine (PINE) network model mechanistically explains bidirectional associations between VCI and depression. Five pathophysiological sub-networks are identified that are shared by VCI and depression: neuroinflammation, kynurenine pathway imbalance, hypothalamic-pituitary-adrenal (HPA) axis overactivity, impaired neurotrophic support and cerebrovascular dysfunction. These do not act independently, and their complex interactions necessitate a systems biology approach to better define disease pathogenesis. The PINE network is already established in the context of non-communicable diseases (NCDs) such as depression, hypertension, atherosclerosis, coronary heart disease and type 2 diabetes mellitus. We build on previous literature to specifically explore mechanistic links between MDD and VCI in the context of PINE pathways and discuss key mechanistic commonalities linking these comorbid conditions and identify a common pre-disease state which precedes transition to VCI and MDD. We expand the model to incorporate bidirectional interactions with biological ageing. Diathesis factors for both VCI and depression feed into this network and the culmination of shared mechanisms (on an ageing substrate) lead to a critical network transition to one or both disease states. A common pre-disease state underlying VCI and depression can provide clinicians a unique opportunity for early risk assessment and intervention in disease development. Establishing the mechanistic elements and systems biology of this network can reveal early warning or predictive biomarkers together with novel therapeutic targets. Integrative studies are recommended to elucidate the dynamic networked biology of VCI and depression over time.

The pathophysiology of major depressive disorder through the lens of systems biology: Network analysis of the psycho-immune-neuroendocrine physiome

BACKGROUND/AIMS

The psycho-immune-neuroendocrine (PINE) network is a predominantly physiological (metabolomic) model constructed from the literature, inter-linking multiple biological processes associated with major depressive disorder (MDD), thereby integrating putative mechanistic pathways for MDD into a single network.

MATERIAL AND METHODS

Previously published metabolomic pathways for the PINE network based on literature searches conducted in 1991-2021 were used to construct an edge table summarizing all physiological pathways in pairs of origin nodes and target nodes. The Gephi software program was used to calculate network metrics from the edge table, including total degree and centrality measures, to ascertain key network nodes and construct a directed network graph.

RESULTS

An edge table and directional network graph of physiological relationships in the PINE network is presented. The network has properties consistent with complex biological systems, with analysis yielding key network nodes comprising pro-inflammatory cytokines (TNF- α, IL6 and IL1), glucocorticoids and corticotropin releasing hormone (CRH). These may represent central structural and regulatory elements in the context of MDD.

CONCLUSION

The identified hubs have a high degree of connection and are known to play roles in the progression from health to MDD. These nodes represent strategic targets for therapeutic intervention or prevention. Future work is required to build a weighted and dynamic simulation of the network PINE.

Nanoparticle-based colorimetric sensors to detect neurodegenerative disease biomarkers

Neurodegenerative disorders (NDDs) are progressive, incurable health conditions that primarily affect brain cells, and result in loss of brain mass and impaired function. Current sensing technologies for NDD detection are limited by high cost, long sample preparation, and/or require skilled personnel. To overcome these limitations, optical sensors, specifically colorimetric sensors, have garnered increasing attention towards the development of a cost-effective, simple, and rapid alternative approach. In this review, we evaluate colorimetric sensing strategies of NDD biomarkers (e.g. proteins, neurotransmitters, bio-thiols, and sulfide), address the limitations and challenges of optical sensor technologies, and provide our outlook on the future of this field.

Non-Rodent Genetic Animal Models for Studying Tauopathy: Review of Drosophila, Zebrafish, and C. elegans Models

Tauopathy refers to a group of progressive neurodegenerative diseases, including frontotemporal lobar degeneration and Alzheimer's disease, which correlate with the malfunction of microtubule-associated protein Tau (MAPT) due to abnormal hyperphosphorylation, leading to the formation of intracellular aggregates in the brain. Despite extensive efforts to understand tauopathy and develop an efficient therapy, our knowledge is still far from complete. To find a solution for this group of devastating diseases, several animal models that mimic diverse disease phenotypes of tauopathy have been developed. Rodents are the dominating tauopathy models because of their similarity to humans and established disease lines, as well as experimental approaches. However, powerful genetic animal models using Drosophila, zebrafish, and C. elegans have also been developed for modeling tauopathy and have contributed to understanding the pathophysiology of tauopathy. The success of these models stems from the short lifespans, versatile genetic tools, real-time in-vivo imaging, low maintenance costs, and the capability for high-throughput screening. In this review, we summarize the main findings on mechanisms of tauopathy and discuss the current tauopathy models of these non-rodent genetic animals, highlighting their key advantages and limitations in tauopathy research.

Exploring the Potential of Neuroproteomics in Alzheimer's Disease

Alzheimer's disease (AD) is progressive brain amyloidosis that damages brain regions associated with memory, thinking, behavioral and social skills. Neuropathologically, AD is characterized by intraneuronal hyperphosphorylated tau inclusions as neurofibrillary tangles (NFTs), and buildup of extracellular amyloid-beta (Aβ) peptide as senile plaques. Several biomarker tests capturing these pathologies have been developed. However, for the full clinical expression of the neurodegenerative events of AD, there exist other central molecular pathways. In terms of understanding the unidentified underlying processes for the progression and development of AD, a complete comprehension of the structure and composition of atypical aggregation of proteins is essential. Presently, to aid the prognosis, diagnosis, detection, and development of drug targets in AD, neuroproteomics is elected as one of the leading essential tools for the efficient exploratory discovery of prospective biomarker candidates estimated to play a crucial role. Therefore, the aim of this review is to present the role of neuroproteomics to analyze the complexity of AD.

Preclinical Marmoset Model for Targeting Chronic Inflammation as a Strategy to Prevent Alzheimer's Disease

Due to the aging population, modern society is facing an increasing prevalence of neurological diseases such as Alzheimer’s disease (AD). AD is an age-related chronic neurodegenerative disorder for which no satisfying therapy exists. Understanding the mechanisms underlying the onset of AD is necessary to find targets for protective treatment. There is growing awareness of the essential role of the immune system in the early AD pathology. Amyloidopathy, the main feature of early-stage AD, has a deregulating effect on the immune function. This is reciprocal as the immune system also affects amyloidopathy. It seems that the inflammatory reaction shows a heterogeneous pattern depending on the stage of the disease and the variation between individuals, making not only the target but also the timing of treatment important. The lack of relevant translational animal models that faithfully reproduce clinical and pathogenic features of AD is a major cause of the delay in developing new disease-modifying therapies and their optimal timing of administration. This review describes the communication between amyloidopathy and inflammation and the possibility of using nonhuman primates as a relevant animal model for preclinical AD research.

A Mechanistic Review on Medicinal Mushrooms-Derived Bioactive Compounds: Potential Mycotherapy Candidates for Alleviating Neurological Disorders

According to the World Health Organization, neurological and neurodegenerative diseases are highly debilitating and pose the greatest threats to public health. Diseases of the nervous system are caused by a particular pathological process that negatively affects the central and peripheral nervous systems. These diseases also lead to the loss of neuronal cell function, which causes alterations in the nervous system structure, resulting in the degeneration or death of nerve cells throughout the body. This causes problems with movement (ataxia) and mental dysfunction (dementia), both of which are commonly observed symptoms in Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis. Medicinal mushrooms are higher fungi with nutraceutical properties and are low in calories and fat. They are also a rich source of nutrients and bioactive compounds such as carbohydrates, proteins, fibers, and vitamins that have been used in the treatment of many ailments. Medicinal mushrooms such as Pleurotus giganteus, Ganoderma lucidium, and Hericium erinaceus are commonly produced worldwide for use as health supplements and medicine. Medicinal mushrooms and their extracts have a large number of bioactive compounds, such as polysaccharide β-glucan, or polysaccharide-protein complexes, like lectins, lactones, terpenoids, alkaloids, antibiotics, and metal-chelating agents. This review will focus on the role of the medicinal properties of different medicinal mushrooms that contain bioactive compounds with a protective effect against neuronal dysfunction. This information will facilitate the development of drugs against neurodegenerative diseases.

The brain-adipocyte-gut network: Linking obesity and depression subtypes

Major depressive disorder (MDD) and obesity are dominant and inter-related health burdens. Obesity is a risk factor for MDD, and there is evidence MDD increases risk of obesity. However, description of a bidirectional relationship between obesity and MDD is misleading, as closer examination reveals distinct unidirectional relationships in MDD subtypes. MDD is frequently associated with weight loss, although obesity promotes MDD. In contrast, MDD with atypical features (MDD-AF) is characterised by subsequent weight gain and obesity. The bases of these distinct associations remain to be detailed, with conflicting findings clouding interpretation. These associations can be viewed within a systems biology framework-the psycho-immune neuroendocrine (PINE) network shared between MDD and metabolic disorders. Shared PINE subsystem perturbations may underlie increased MDD in overweight and obese people (obesity-associated depression), while obesity in MDD-AF (depression-associated obesity) involves more complex interactions between behavioural and biomolecular changes. In the former, the chronic PINE dysfunction triggering MDD is augmented by obesity-dependent dysregulation in shared networks, including inflammatory, leptin-ghrelin, neuroendocrine, and gut microbiome systems, influenced by chronic image-associated psychological stress (particularly in younger or female patients). In MDD-AF, behavioural dysregulation, including hypersensitivity to interpersonal rejection, fundamentally underpins energy imbalance (involving hyperphagia, lethargy, hypersomnia), with evolving obesity exaggerating these drivers via positive feedback (and potentially augmenting PINE disruption). In both settings, sex and age are important determinants of outcome, associated with differences in emotional versus cognitive dysregulation. A systems biology approach is recommended for further research into the pathophysiological networks underlying MDD and linking depression and obesity.

Matrix metalloproteinases in the CNS: interferons get nervous

Matrix metalloproteinases (MMPs) have been investigated in context of chronic inflammatory diseases and demonstrated to degrade multiple components of the extracellular matrix (ECM). However, following several disappointing MMP clinical trials, recent studies have demonstrated unexpected novel functions of MMPs in viral infections and autoimmune inflammatory diseases in unanticipated locations. Thus, MMPs play additional functions in inflammation than just ECM degradation. They can regulate the activity of chemokines and cytokines of the immune response by precise proteolytic processing resulting in activation or inactivation of signaling pathways. MMPs have been demonstrated to cleave multiple substrates of the central nervous systems (CNS) and contribute to promoting and dampening diseases of the CNS. Initially, believed to be solely promoting pathologies, more than 10 MMPs to date have been shown to have protective functions. Here, we present some of the beneficial and destructive roles of MMPs in CNS pathologies and discuss strategies for the use of MMP inhibitors.

Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine - most notably, oncology and cardiovascular diseases - allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic-industry co-development pathway from identification and assay development to validation for clinical use.

Health, pre-disease and critical transition to disease in the psycho-immune-neuroendocrine network: Are there distinct states in the progression from health to major depressive disorder?

The psycho-immune-neuroendocrine (PINE) network is a regulatory network of interrelated physiological pathways that have been implicated in major depressive disorder (MDD). A model of disease progression for MDD is presented where the stable, healthy state of the PINE network (PINE physiome) undergoes progressive pathophysiological changes to an unstable but reversible pre-disease state (PINE pre-diseasome) with chronic stress. The PINE network may then undergo critical transition to a stable, possibly irreversible disease state of MDD (PINE pathome). Critical transition to disease is heralded by early warning signs which are detectible by biomarkers specific to the PINE network and may be used as a screening test for MDD. Critical transition to MDD may be different for each individual, as it is reliant on diathesis, which comprises genetic predisposition, intrauterine and developmental factors. Finally, we propose the PINE pre-disease state may form a "universal pre-disease state" for several non-communicable diseases (NCDs), and critical transition of the PINE network may lead to one of several frequently associated disease states (influenced by diathesis), supporting the existence of a common Chronic Illness Risk Network (CIRN). This may provide insight into both the puzzle of multifinality and the growing clinical challenge of multimorbidity.

MicroRNA-150 targets PU.1 and regulates macrophage differentiation and function in experimental autoimmune encephalomyelitis

PU.1 is a transcription factor which is expressed in myeloid cells. Herein, we investigated the expression of PU.1 and its potentially targeting miRNAs in the central nervous system (CNS) of mice with experimental autoimmune encephalitis (EAE) and in cultured primary macrophages. PU.1 levels where highly induced in EAE spinal cords and in activated macrophages; this was associated with a significant reduction in miR-150-5p levels at chronic phase of disease and in activated cells. Luciferase assays confirmed the PU.1-miR-150-5p interaction. Overexpression of miR-150-5p in macrophages decreased the expression of proinflammatory cytokines and shifted the polarization of macrophages away from the M1-like phenotype.

Evolving Relevance of Neuroproteomics in Alzheimer's Disease

Substantial progress in the understanding of the biology of Alzheimer's disease (AD) has been achieved over the past decades. The early detection and diagnosis of AD and other age-related neurodegenerative diseases, however, remain a challenging scientific frontier. Therefore, the comprehensive discovery (relating to all individual, converging or diverging biochemical disease mechanisms), development, validation, and qualification of standardized biological markers with diagnostic and prognostic functions with a precise performance profile regarding specificity, sensitivity, and positive and negative predictive value are warranted.Methodological innovations in the area of exploratory high-throughput technologies, such as sequencing, microarrays, and mass spectrometry-based analyses of proteins/peptides, have led to the generation of large global molecular datasets from a multiplicity of biological systems, such as biological fluids, cells, tissues, and organs. Such methodological progress has shifted the attention to the execution of hypothesis-independent comprehensive exploratory analyses (opposed to the classical hypothesis-driven candidate approach), with the aim of fully understanding the biological systems in physiology and disease as a whole. The systems biology paradigm integrates experimental biology with accurate and rigorous computational modelling to describe and foresee the dynamic features of biological systems. The use of dynamically evolving technological platforms, including mass spectrometry, in the area of proteomics has enabled to rush the process of biomarker discovery and validation for refining significantly the diagnosis of AD. Currently, proteomics-which is part of the systems biology paradigm-is designated as one of the dominant matured sciences needed for the effective exploratory discovery of prospective biomarker candidates expected to play an effective role in aiding the early detection, diagnosis, prognosis, and therapy development in AD.

Proteomics and lipidomics in the human brain

Proteomics and lipidomics are powerful tools to the large-scale study of proteins and lipids, respectively. Several methods can be employed with particular benefits and limitations in the study of human brain. This is a review of the rationale use of current techniques with particular attention to limitations and pitfalls inherent to each one of the techniques, and more importantly, to their use in the study of post-mortem brain tissue. These aspects are cardinal to avoid false interpretations, errors and unreal expectancies. Other points are also stressed as exemplified in the analysis of human neurodegenerative diseases which are manifested by disease-, region-, and stage-specific modifications commonly in the context of aging. Information about certain altered protein clusters and proteins oxidatively damaged is summarized for Alzheimer and Parkinson diseases.

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

BACKGROUND

Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion.

METHODS

In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window".

RESULTS

The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set.

CONCLUSIONS

Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.

MicroRNA-181 Variants Regulate T Cell Phenotype in the Context of Autoimmune Neuroinflammation

Background

Recent studies have revealed that multiple sclerosis (MS) lesions have distinct microRNA (miRNA) expression profiles. miR-181 family members show altered expression in MS tissues although their participation in MS pathogenesis remains uncertain. Herein, we investigated the involvement of miR-181a and miR-181b in the pathogenesis of MS and its animal model, experimental autoimmune encephalomyelitis (EAE).

Methods

miR-181a and -b levels were measured in the central nervous system (CNS) of patients with MS and mice with EAE as well as relevant leukocyte cultures by real-time RT-PCR. To examine the role of the miRNAs in leukocyte differentiation and function, miR-181a and -b mimic sequences were transfected into cultured primary macrophages and purified CD4⁺ T cells which were then analyzed by RT-PCR and flow cytometry. Luciferase reporter assays were performed to investigate the interaction of miR-181a and -b with the 3′-UTR of potential target transcripts, and the expression of target genes was measured in the CNS of EAE mice, activated lymphocytes, and macrophages.

Results

Expression analyses revealed a significant decrease in miR-181a and -b levels in brain white matter from MS patients as well as in spinal cords of EAE mice during the acute and chronic phases of disease. Suppression of miR-181a was observed following antigen-specific or polyclonal activation of lymphocytes as well as in macrophages following LPS treatment. Overexpression of miR-181a and -b mimic sequences reduced proinflammatory gene expression in macrophages and polarization toward M1 phenotype. miR-181a and -b mimic sequences inhibited Th1 generation in CD4⁺ T cells and miR-181a mimic sequences also promoted Treg differentiation. Luciferase assays revealed Suppressor of mothers against decapentaplegic 7 (Smad7), as a direct target of miR-181a and -b.

Conclusion

Our data highlight the anti-inflammatory actions of miR-181a and -b in the context of autoimmune neuroinflammation. miR-181a and -b influence differentiation of T helper cell and activation of macrophages, providing potential therapeutic options for controlling inflammation in MS.

Suppressed oligodendrocyte steroidogenesis in multiple sclerosis: Implications for regulation of neuroinflammation

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS). Neurosteroids are reported to exert anti-inflammatory effects in several neurological disorders. We investigated the expression and actions of the neurosteroid, dehydroepiandrosterone (DHEA), and its more stable 3β-sulphated ester, DHEA-S, in MS and associated experimental models. CNS tissues from patients with MS and animals with experimental autoimmune encephalomyelitis (EAE) displayed reduced DHEA concentrations, accompanied by diminished expression of the DHEA-synthesizing enzyme CYP17A1 in oligodendrocytes (ODCs), in association with increased expression of inflammatory genes including interferon (IFN)-γ and interleukin (IL)-1β. CYP17A1 was expressed variably in different human neural cell types but IFN-γ exposure selectively reduced CYP17A1 detection in ODCs. DHEA-S treatment reduced IL-1β and -6 release from activated human myeloid cells with minimal effect on lymphocyte viability. Animals with EAE receiving DHEA-S treatment showed reduced Il1b and Ifng transcript levels in spinal cord compared to vehicle-treated animals with EAE. DHEA-S treatment also preserved myelin basic protein immunoreactivity and reduced axonal loss in animals with EAE, relative to vehicle-treated EAE animals. Neurobehavioral deficits were reduced in DHEA-S-treated EAE animals compared with vehicle-treated animals with EAE. Thus, CYP17A1 expression in ODCs and its product DHEA were downregulated in the CNS during inflammatory demyelination while DHEA-S provision suppressed neuroinflammation, demyelination, and axonal injury that was evident as improved neurobehavioral performance. These findings indicate that DHEA production is an immunoregulatory pathway within the CNS and its restoration represents a novel treatment approach for neuroinflammatory diseases.

Big Data in traumatic brain injury; promise and challenges

Traumatic brain injury (TBI) is a spectrum disease of overwhelming complexity, the research of which generates enormous amounts of structured, semi-structured and unstructured data. This resulting big data has tremendous potential to be mined for valuable information regarding the "most complex disease of the most complex organ". Big data analyses require specialized big data analytics applications, machine learning and artificial intelligence platforms to reveal associations, trends, correlations and patterns not otherwise realized by current analytical approaches. The intersection of potential data sources between experimental TBI and clinical TBI research presents inherent challenges for setting parameters for the generation of common data elements and to mine existing legacy data that would allow highly translatable big data analyses. In order to successfully utilize big data analyses in TBI, we must be willing to accept the messiness of data, collect and store all data and give up causation for correlation. In this context, coupling the big data approach to established clinical and pre-clinical data sources will transform current practices for triage, diagnosis, treatment and prognosis into highly integrated evidence-based patient care.

PRECISION MEDICINE - The Golden Gate for Detection, Treatment and Prevention of Alzheimer's Disease

During this decade, breakthrough conceptual shifts have commenced to emerge in the field of Alzheimer's disease (AD) recognizing risk factors and the non-linear dynamic continuum of complex pathophysiologies amongst a wide dimensional spectrum of multi-factorial brain proteinopathies/neurodegenerative diseases. As is the case in most fields of medicine, substantial advancements in detecting, treating and preventing AD will likely evolve from the generation and implementation of a systematic precision medicine strategy. This approach will likely be based on the success found from more advanced research fields, such as oncology. Precision medicine will require integration and transfertilization across fragmented specialities of medicine and direct reintegration of Neuroscience, Neurology and Psychiatry into a continuum of medical sciences away from the silo approach. Precision medicine is biomarker-guided medicine on systems-levels that takes into account methodological advancements and discoveries of the comprehensive pathophysiological profiles of complex multi-factorial neurodegenerative diseases, such as late-onset sporadic AD. This will allow identifying and characterizing the disease processes at the asymptomatic preclinical stage, where pathophysiological and topographical abnormalities precede overt clinical symptoms by many years to decades. In this respect, the uncharted territory of the AD preclinical stage has become a major research challenge as the field postulates that early biomarker guided customized interventions may offer the best chance of therapeutic success. Clarification and practical operationalization is needed for comprehensive dissection and classification of interacting and converging disease mechanisms, description of genomic and epigenetic drivers, natural history trajectories through space and time, surrogate biomarkers and indicators of risk and progression, as well as considerations about the regulatory, ethical, political and societal consequences of early detection at asymptomatic stages. In this scenario, the integrated roles of genome sequencing, investigations of comprehensive fluid-based biomarkers and multimodal neuroimaging will be of key importance for the identification of distinct molecular mechanisms and signaling pathways in subsets of asymptomatic people at greatest risk for progression to clinical milestones due to those specific pathways. The precision medicine strategy facilitates a paradigm shift in Neuroscience and AD research and development away from the classical "one-size-fits-all" approach in drug discovery towards biomarker guided "molecularly" tailored therapy for truly effective treatment and prevention options. After the long and winding decade of failed therapy trials progress towards the holistic systems-based strategy of precision medicine may finally turn into the new age of scientific and medical success curbing the global AD epidemic.

A systems biology-led insight into the role of the proteome in neurodegenerative diseases

INTRODUCTION

Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases.

AREAS COVERED

Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer's disease, Parkinson's disease and other neurodegenerative disorders, as well as of affected human tissues. Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.

Application of Systems Theory in Longitudinal Studies on the Origin and Progression of Alzheimer's Disease

This chapter questions the prevailing "implicit" assumption that molecular mechanisms and the biological phenotype of dominantly inherited early-onset alzheimer's disease (EOAD) could serve as a linear model to study the pathogenesis of sporadic late-onset alzheimer's disease (LOAD). Now there is growing evidence to suggest that such reductionism may not be warranted; these suppositions are not adequate to explain the molecular complexities of LOAD. For example, the failure of some recent amyloid-centric clinical trials, which were largely based on the extrapolations from EOAD biological phenotypes to the molecular mechanisms in the pathogenesis of LOAD, might be due to such false assumptions. The distinct difference in the biology of LOAD and EOAD is underscored by the presence of EOAD cases without evidence of familial clustering or Mendelian transmission and, conversely, the discovery and frequent reports of such clustering and transmission patterns in LOAD cases. The primary thesis of this chapter is that a radically different way of thinking is required for comprehensive explanations regarding the distinct complexities in the molecular pathogenesis of inherited and sporadic forms of Alzheimer's disease (AD). We propose using longitudinal analytical methods and the paradigm of systems biology (using transcriptomics, proteomics, metabolomics, and lipidomics) to provide us a more comprehensive insight into the lifelong origin and progression of different molecular mechanisms and neurodegeneration. Such studies should aim to clarify the role of specific pathophysiological and signaling pathways such as neuroinflammation, altered lipid metabolism, apoptosis, oxidative stress, tau hyperphosphorylation, protein misfolding, tangle formation, and amyloidogenic cascade leading to overproduction and reduced clearance of aggregating amyloid-beta (Aβ) species. A more complete understanding of the distinct difference in molecular mechanisms, signaling pathways, as well as comparability of the various forms of AD is of paramount importance. The development of knowledge and technologies for early detection and characterization of the disease across all stages will improve the predictions regarding the course of the disease, prognosis, and response to treatment. No doubt such advances will have a significant impact on the clinical management of both EOAD and LOAD patients. The approach propped here, combining longitudinal studies with the systems biology paradigm, will create a more effective and comprehensive framework for development of prevention therapies in AD.

Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks

Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.

Systems biology, emergence and antireductionism

This study explores the conceptual history of systems biology and its impact on philosophical and scientific conceptions of reductionism, antireductionism and emergence. Development of systems biology at the beginning of 21st century transformed biological science. Systems biology is a new holistic approach or strategy how to research biological organisms, developed through three phases. The first phase was completed when molecular biology transformed into systems molecular biology. Prior to the second phase, convergence between applied general systems theory and nonlinear dynamics took place, hence allowing the formation of systems mathematical biology. The second phase happened when systems molecular biology and systems mathematical biology, together, were applied for analysis of biological data. Finally, after successful application in science, medicine and biotechnology, the process of the formation of modern systems biology was completed.

Systems and molecular reductionist views on organisms were completely opposed to each other. Implications of systems and molecular biology on reductionist–antireductionist debate were quite different. The analysis of reductionism, antireductionism and emergence issues, in the era of systems biology, revealed the hierarchy between methodological, epistemological and ontological antireductionism. Primarily, methodological antireductionism followed from the systems biology. Only after, epistemological and ontological antireductionism could be supported.

Rationale for the use of multifunctional drugs as neuroprotective agents for glaucoma

Glaucoma, the leading cause globally of irreversible blindness, is a neurodegenerative disease characterized by progressive retinal ganglion cell death. To date, no drug has been shown to prevent the retinal ganglion cell loss associated with glaucoma. Multiple mechanisms lead to ganglion cell death in glaucoma, suggesting that a neuroprotectant that has a single mode of action, like memantine, would have a limited positive effect at slowing down ganglion cell death. Conversely, simultaneously targeting several factors may be the best therapeutic approach to improve outcomes. Multifunctional drugs are fast gaining acceptance as a strategy for the treatment of complex disorders of the central nervous system, such as Parkinson's disease, Alzheimer's disease and other progressive neurodegenerative diseases. In this paper, we review the current literature on multifunctional drugs and propose a rationale for the use of multifunctional drugs in glaucomatous optic neuropathy.

Assembly and interrogation of Alzheimer's disease genetic networks reveal novel regulators of progression

Alzheimer's disease (AD) is a complex multifactorial disorder with poorly characterized pathogenesis. Our understanding of this disease would thus benefit from an approach that addresses this complexity by elucidating the regulatory networks that are dysregulated in the neural compartment of AD patients, across distinct brain regions. Here, we use a Systems Biology (SB) approach, which has been highly successful in the dissection of cancer related phenotypes, to reverse engineer the transcriptional regulation layer of human neuronal cells and interrogate it to infer candidate Master Regulators (MRs) responsible for disease progression. Analysis of gene expression profiles from laser-captured neurons from AD and controls subjects, using the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe), yielded an interactome consisting of 488,353 transcription-factor/target interactions. Interrogation of this interactome, using the Master Regulator INference algorithm (MARINa), identified an unbiased set of candidate MRs causally responsible for regulating the transcriptional signature of AD progression. Experimental assays in autopsy-derived human brain tissue showed that three of the top candidate MRs (YY1, p300 and ZMYM3) are indeed biochemically and histopathologically dysregulated in AD brains compared to controls. Our results additionally implicate p53 and loss of acetylation homeostasis in the neurodegenerative process. This study suggests that an integrative, SB approach can be applied to AD and other neurodegenerative diseases, and provide significant novel insight on the disease progression.

Paths to Alzheimer's disease prevention: from modifiable risk factors to biomarker enrichment strategies

Alzheimer's disease (AD) represents an increasing worldwide healthcare epidemic. Secondary preventive disease-modifying treatments under clinical development are considered most effective when initiated as early as possible in the pathophysiological course and progression of the disease. Major targets are to enhance clearance and to reduce cerebral accumulation of amyloid, decrease hyperphosphorylation of tau and the generation of neurofibrillary tangles, reduce inflammation, and finally progressive neurodegeneration. Comprehensive sets of biological markers are needed to characterize the pathophysiological mechanisms, indicate effects of treatment and to facilitate early characterisation and detection of AD during the prodromal or even at asymptomatic stages. No primary or secondary preventive treatments for AD have been approved. Epidemiological research, however, has provided evidence of specifically modifiable risk and protective factors. Among them are vascular, lifestyle and psychological risk factors that may act both independently and by potentiating each other. These factors may be substantially impacted by single or multi-domain strategies to prevent or postpone the onset of AD-related pathophysiology. Researchers have recently started the European Dementia Prevention Initiative (EDPI), an international consortium to improve strategies for preventing dementia. EDPI, in particular, includes the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) which aims at optimizing the early identification of subjects at increased risk of late-life cognitive deterioration, and at the evaluation of multi-domain intervention strategies. The ongoing discussion on new diagnostic criteria provided by the International Working Group (IWG), as well as by the recommendations summoned by the National Institute on Aging and Alzheimer's Association (NIA-AA) initiative, has inspired the creation of novel study designs and the definition of earlier target populations for trials in pre- and asymptomatic at-risk and prodromal stages of AD. As a result, a number of promising international prevention trials are currently ongoing. In this review, we critically discuss the main paths to AD prevention through control of modifiable risk factors and lifestyle changes. We will also review the role of biomarkers to identify subgroups of patients who would most likely benefit from secondary prevention strategies, and to evaluate the benefit of treatment in such patients.

Yizhijiannao Granule and a combination of its effective monomers, icariin and Panax notoginseng saponins, inhibit early PC12 cell apoptosis induced by beta-amyloid (25-35)

One of our previous studies showed that Yizhijiannao Granule, a compound Chinese medicine, effectively improved the clinical symptoms of Alzheimer's disease. In the present study, we established a model of Alzheimer's disease using beta-amyloid (25–35) in PC12 cells, and treated the cells with Yizhijiannao Granule and its four monomers, i.e., icariin, catechin, Panax notoginseng saponins, and eleutheroside E. Flow cytometry showed that Yizhijiannao Granule-containing serum, icariin, Panax notoginseng saponins, and icariin + Panax notoginseng saponins were protective against beta-amyloid (25–35)-induced injury in PC12 cells. Icariin in combination with Panax notoginseng saponins significantly inhibited early apoptosis of PC12 cells with beta-amyloid (25–35)-induced injury compared to icariin or Panax notoginseng saponins alone. The effects of icariin + Panax notoginseng saponins were similar to the effects of Yizhijiannao Granule. The findings indicate that two of the effective monomers of Yizhijiannao Granule, icariin and Panax notoginseng saponins, can synergistically inhibit early apoptosis of PC12 cells induced by beta-amyloid (25–35).

Systems biology, complexity, and the impact on antiepileptic drug discovery

The number of available anticonvulsant drugs increased in the period spanning over more than a century, amounting to the current panoply of nearly two dozen so-called antiepileptic drugs (AEDs). However, none of them actually prevents/reduces the post-brain insult development of epilepsy in man, and in no less than a third of patients with epilepsy, the seizures are not drug-controlled. Plausibly, the enduring limitation of AEDs' efficacy derives from the insufficient understanding of epileptic pathology. This review pinpoints the unbalanced reductionism of the analytic approaches that overlook the intrinsic complexity of epilepsy and of the drug resistance in epilepsy as the core conceptual flaw hampering the discovery of truly antiepileptogenic drugs. A rising awareness of the complexity of epileptic pathology is, however, brought about by the emergence of nonreductionist systems biology (SB) that considers the networks of interactions underlying the normal organismic functions and of SB-based systems (network) pharmacology that aims to restore pathological networks. By now, the systems pharmacology approaches of AED discovery are fairly meager, but their forthcoming development is both a necessity and a realistic prospect, explored in this review.

Phenotypic assays for β-amyloid in mouse embryonic stem cell-derived neurons

Given the complex nature of Alzheimer's disease (AD), a cell-based model that recapitulates the physiological properties of the target neuronal population would be extremely valuable for discovering improved drug candidates and chemical probes to uncover disease mechanisms. We established phenotypic neuronal assays for the biogenesis and synaptic action of amyloid β peptide (Aβ) based on embryonic stem cell-derived neurons (ESNs). ESNs enriched with pyramidal neurons were robust, scalable, and amenable to a small-molecule screening assay, overcoming the apparent limitations of neuronal models derived from human pluripotent cells. Small-molecule screening of clinical compounds identified four compounds capable of reducing Aβ levels in ESNs derived from the Tg2576 mouse model of AD. Our approach is therefore highly suitable for phenotypic screening in AD drug discovery and has the potential to identify therapeutic candidates with improved efficacy and safety potential.

Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

Cerebrospinal fluid analysis in Alzheimer's disease: technical issues and future developments

Alzheimer's disease (AD) is a leading cause of morbidity, mortality, and a major epidemic worldwide. Although clinical assessment continues to remain the keystone for patient management and clinical trials, such evaluation has important limitations. In this context, cerebrospinal fluid (CSF) biomarkers are important tools to better identify high-risk individuals, to diagnose AD promptly and accurately, especially at the prodromal mild cognitive impairment stage of the disease, and to effectively prognosticate and treat AD patients. Recent advances in functional genomics, proteomics, metabolomics, and bioinformatics will hopefully revolutionize unbiased inquiries into several putative CSF markers of cerebral pathology that may be concisely informative with regard to the various stages of AD progression through years and decades. Moreover, the identification of efficient drug targets and development of optimal therapeutic strategies for AD will increasingly rely on a better understanding and integration of the systems biology paradigm, which will allow predicting the series of events and resulting responses of the biological network triggered by the introduction of new therapeutic compounds. In this scenario, unbiased systems biology-based diagnostic and prognostic models in AD will consist of relevant comprehensive panels of molecules and key branches of the disease-affected cellular neuronal network. Such characteristic and unbiased biomarkers will more accurately and comprehensively reflect pathophysiology from the early asymptomatic and presymptomatic to the final prodromal and symptomatic clinical stages in individual patients (and their individual genetic disease predisposition), ultimately increasing the chances of success of future disease modifying and preventive treatments.

Systems biology approaches for discovering biomarkers for traumatic brain injury

The rate of traumatic brain injury (TBI) in service members with wartime injuries has risen rapidly in recent years, and complex, variable links have emerged between TBI and long-term neurological disorders. The multifactorial nature of TBI secondary cellular response has confounded attempts to find cellular biomarkers for its diagnosis and prognosis or for guiding therapy for brain injury. One possibility is to apply emerging systems biology strategies to holistically probe and analyze the complex interweaving molecular pathways and networks that mediate the secondary cellular response through computational models that integrate these diverse data sets. Here, we review available systems biology strategies, databases, and tools. In addition, we describe opportunities for applying this methodology to existing TBI data sets to identify new biomarker candidates and gain insights about the underlying molecular mechanisms of TBI response. As an exemplar, we apply network and pathway analysis to a manually compiled list of 32 protein biomarker candidates from the literature, recover known TBI-related mechanisms, and generate hypothetical new biomarker candidates.

The anti-tumor histone deacetylase inhibitor SAHA and the natural flavonoid curcumin exhibit synergistic neuroprotection against amyloid-beta toxicity

With the trend of an increasing aged population worldwide, Alzheimer's disease (AD), an age-related neurodegenerative disorder, as one of the major causes of dementia in elderly people is of growing concern. Despite the many hard efforts attempted during the past several decades in trying to elucidate the pathological mechanisms underlying AD and putting forward potential therapeutic strategies, there is still a lack of effective treatments for AD. The efficacy of many potential therapeutic drugs for AD is of main concern in clinical practice. For example, large bodies of evidence show that the anti-tumor histone deacetylase (HDAC) inhibitor, suberoylanilidehydroxamic acid (SAHA), may be of benefit for the treatment of AD; however, its extensive inhibition of HDACs makes it a poor therapeutic. Moreover, the natural flavonoid, curcumin, may also have a potential therapeutic benefit against AD; however, it is plagued by low bioavailability. Therefore, the integrative effects of SAHA and curcumin were investigated as a protection against amyloid-beta neurotoxicity in vitro. We hypothesized that at low doses their synergistic effect would improve therapeutic selectivity, based on experiments that showed that at low concentrations SAHA and curcumin could provide comprehensive protection against Aβ_25–35-induced neuronal damage in PC12 cells, strongly implying potent synergism. Furthermore, network analysis suggested that the possible mechanism underlying their synergistic action might be derived from restoration of the damaged functional link between Akt and the CBP/p300 pathway, which plays a crucial role in the pathological development of AD. Thus, our findings provided a feasible avenue for the application of a synergistic drug combination, SAHA and curcumin, in the treatment of AD.

Two diseases, one approach: multitarget drug discovery in Alzheimer's and neglected tropical diseases

Multitarget drug discovery may represent a promising therapeutic approach to treat Alzheimer's and neglected tropical diseases.

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: a long-range point of view beyond 2020

Recent advances in understanding the molecular mechanisms underlying various paths toward the pathogenesis of Alzheimer's disease (AD) has begun to provide new insight for interventions to modify disease progression. The evolving knowledge gained from multidisciplinary basic research has begun to identify new concepts for treatments and distinct classes of therapeutic targets; as well as putative disease-modifying compounds that are now being tested in clinical trials. There is a mounting consensus that such disease modifying compounds and/or interventions are more likely to be effectively administered as early as possible in the cascade of pathogenic processes preceding and underlying the clinical expression of AD. The budding sentiment is that "treatments" need to be applied before various molecular mechanisms converge into an irreversible pathway leading to morphological, metabolic and functional alterations that characterize the pathophysiology of AD. In light of this, biological indicators of pathophysiological mechanisms are desired to chart and detect AD throughout the asymptomatic early molecular stages into the prodromal and early dementia phase. A major conceptual development in the clinical AD research field was the recent proposal of new diagnostic criteria, which specifically incorporate the use of biomarkers as defining criteria for preclinical stages of AD. This paradigm shift in AD definition, conceptualization, operationalization, detection and diagnosis represents novel fundamental opportunities for the modification of interventional trial designs. This perspective summarizes not only present knowledge regarding biological markers but also unresolved questions on the status of surrogate indicators for detection of the disease in asymptomatic people and diagnosis of AD.

Inferring the effective TOR-dependent network: a computational study in yeast

Background

Calorie restriction (CR) is one of the most conserved non-genetic interventions that extends healthspan in evolutionarily distant species, ranging from yeast to mammals. The target of rapamycin (TOR) has been shown to play a key role in mediating healthspan extension in response to CR by integrating different signals that monitor nutrient-availability and orchestrating various components of cellular machinery in response. Both genetic and pharmacological interventions that inhibit the TOR pathway exhibit a similar phenotype, which is not further amplified by CR.

Results

In this paper, we present the first comprehensive, computationally derived map of TOR downstream effectors, with the objective of discovering key lifespan mediators, their crosstalk, and high-level organization. We adopt a systematic approach for tracing information flow from the TOR complex and use it to identify relevant signaling elements. By constructing a high-level functional map of TOR downstream effectors, we show that our approach is not only capable of recapturing previously known pathways, but also suggests potential targets for future studies.

Information flow scores provide an aggregate ranking of relevance of proteins with respect to the TOR signaling pathway. These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways. We propose a novel statistical framework for integrating information flow scores, the set of differentially expressed genes in response to rapamycin treatment, and the transcriptional regulatory network. We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway. This network is hypothesized to mediate life-span extension in response to TOR inhibition.

Conclusions

Our approach, unlike experimental methods, is not limited to specific aspects of cellular response. Rather, it predicts transcriptional changes and post-translational modifications in response to TOR inhibition. The constructed effective response network greatly enhances understanding of the mechanisms underlying the aging process and helps in identifying new targets for further investigation of anti-aging regimes. It also allows us to identify potential network biomarkers for diagnosis and prognosis of age-related pathologies.

The cell adhesion molecule L1 regulates the expression of FGF21 and enhances neurite outgrowth

L1 plays a role in neural development. However, it remains unclear how L1 plays this role. In the present study, we have shown extensive outgrowth of long neurites in cerebellar neurons after treatment with either L1 or L1 antibody. Notably, the mRNA level of FGF21 was significantly increased in both L1 and L1 antibody treated neurons compared to control group. Consistently, the neurite outgrowth promoted by L1 was strongly inhibited by siRNA against FGF21 gene or a treatment of cells with FGFR inhibitor. These results demonstrate that FGF21/FGFR signaling promotes the neurite outgrowth in a L1-dependent manner.

Systems biology as a comparative approach to understand complex gene expression in neurological diseases

Systems biology interdisciplinary approaches have become an essential analytical tool that may yield novel and powerful insights about the nature of human health and disease. Complex disorders are known to be caused by the combination of genetic, environmental, immunological or neurological factors. Thus, to understand such disorders, it becomes necessary to address the study of this complexity from a novel perspective. Here, we present a review of integrative approaches that help to understand the underlying biological processes involved in the etiopathogenesis of neurological diseases, for example, those related to autism and autism spectrum disorders (ASD) endophenotypes. Furthermore, we highlight the role of systems biology in the discovery of new biomarkers or therapeutic targets in complex disorders, a key step in the development of personalized medicine, and we demonstrate the role of systems approaches in the design of classifiers that can shorten the time for behavioral diagnosis of autism.