Systems biology and its application to the understanding of neurological diseases

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.

Computational systems biology in disease modeling and control, review and perspectives

Omics-based approaches have become increasingly influential in identifying disease mechanisms and drug responses. Considering that diseases and drug responses are co-expressed and regulated in the relevant omics data interactions, the traditional way of grabbing omics data from single isolated layers cannot always obtain valuable inference. Also, drugs have adverse effects that may impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is applied to predict potential molecular interactions by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines' therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among gene, protein and drug molecules for modeling disease mechanisms and drug responses. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are also discussed for its clinical uses in this work.

Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis

Background

Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects.

Methods

Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies.

Results

Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS.

Conclusions

Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00925-8.

Identification and Validation of a Novel Immune-Related Four-lncRNA Signature for Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, the prognosis of patients with which is associated with both lncRNAs and cancer immunity. In this study, we collected gene expression data of 585 LUAD patients from The Cancer Genome Atlas (TCGA) database and 605 subjects from the Gene Expression Omnibus (GEO) database. LUAD patients were divided into high and low immune-cell-infiltrated groups according to the single sample gene set enrichment analysis (ssGSEA) algorithm to identify differentially expressed genes (DEGs). Based on the 49 immune-related DE lncRNAs, a four-lncRNA prognostic signature was constructed by applying least absolute shrinkage and selection operator (LASSO) regression, univariate Cox regression, and stepwise multivariate Cox regression in sequence. Kaplan–Meier curve, ROC analysis, and the testing GEO datasets verified the effectiveness of the signature in predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression suggested that the signature was an independent prognostic factor. The correlation analysis revealed that the infiltration immune cell subtypes were related to these lncRNAs.

Gene Regulatory Network of Dorsolateral Prefrontal Cortex: a Master Regulator Analysis of Major Psychiatric Disorders

Despite the strong genetic component of psychiatric disorders, traditional genetic studies have failed to find individual genes of large effect size. Thus, alternative methods, using bioinformatics, have been proposed to solve these biological puzzles. Of these, here we employ systems biology-based approaches to identify potential master regulators (MRs) of bipolar disorder (BD), schizophrenia (SZ), and major depressive disorder (MDD), their association with biological processes and their capacity to differentiate disorders' phenotypes. High-throughput gene expression data was used to reconstruct standard human dorsolateral prefrontal cortex regulatory transcriptional network, which was then queried for regulatory units and MRs associated with the psychiatric disorders of interest. Furthermore, the activity status (active or repressed) of MR candidates was obtained and used in cluster analysis to characterize disease phenotypes. Finally, we explored the biological processes modulated by the MRs using functional enrichment analysis. Thirty-one, thirty-four, and fifteen MR candidates were identified in BD, SZ, and MDD, respectively. The activity state of these MRs grouped the illnesses in three clusters: MDD only, mostly BD, and a third one with BD and SZ. While BD and SZ share several biological processes related to ion transport and homeostasis, synapse, and immune function, SZ showed peculiar enrichment of processes related to cytoskeleton and neuronal structure. Meanwhile, MDD presented mostly processes related to glial development and fatty acid metabolism. Our findings suggest notable differences in functional enrichment between MDD and BD/SZ. Furthermore, similarities between BD and SZ may impose particular challenges in attempts to discriminate these pathologies based solely on their transcriptional profiles. Nevertheless, we believe that systems-oriented approaches are promising strategies to unravel the pathophysiology peculiarities underlying mental illnesses and reveal therapeutic targets.

Identification of Biomarkers in Neuropsychiatric Disorders Based on Systems Biology and Epigenetics

Clinically useful biomarkers are available for some neuropsychiatric disorders like fragile X syndrome, Rett syndrome, and Huntington’s disease. Despite many decades of research on the pathogenesis of neuropsychiatric disorders like schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), the exact pathogenesis of these disorders remains unclear, and there are no clinically useful biomarkers for these disorders. However, there is increasing evidence that abnormal epigenetic mechanisms of gene expression contribute to the pathogenesis of SZ, BD, and MDD. Both systems (or network) biology and epigenetics (a component of systems biology) attempt to make sense of biological systems that are highly dynamic and multi-compartmental. This article suggests that systems biology, emphasizing the epigenetic component of systems biology, could help identify clinically useful biomarkers in neuropsychiatric disorders like SZ, BD, and MDD.

Prediction of patient's response to OnabotulinumtoxinA treatment for migraine

Migraine affects the daily life of millions of people around the world. The most well-known disabling symptom associated with this illness is the intense headache. Nowadays, there are treatments that can diminish the level of pain. OnabotulinumtoxinA (BoNT-A) has become a very popular medication for treating migraine headaches in those cases in which other medication is not working, typically in chronic migraines. Currently, the positive response to Botox treatment is not clearly understood, yet understanding the mechanisms that determine the effectiveness of the treatment could help with the development of more effective treatments.

To solve this problem, this paper sets up a realistic scenario of electronic medical records of migraineurs under BoNT-A treatment where some clinical features from real patients are labeled by doctors. Medical registers have been preprocessed. A label encoding method based on simulated annealing has been proposed. Two methodologies for predicting the results of the first and the second infiltration of the BoNT-A based treatment are contempled. Firstly, a strategy based on the medical HIT6 metric is described, which achieves an accuracy over 91%. Secondly, when this value is not available, several classifiers and clustering methods have been performed in order to predict the reduction and adverse effects, obtaining an accuracy of 85%. Some clinical features as Greater occipital nerves (GON), chronic migraine time evolution and others have been detected as relevant features when examining the prediction models. The GON and the retroocular component have also been described as important features according to doctors.

Using Systems Biology and Mathematical Modeling Approaches in the Discovery of Therapeutic Targets for Spinal Muscular Atrophy

Systems biology uses a combination of experimental and mathematical approaches to investigate the complex and dynamic interactions with a given system or biological process. Systems biology integrates genetics, signal transduction, biochemistry and cell biology with mathematical modeling. It can be used to identify novel pathways implicated in diseases as well as to understand the mechanisms by which a specific gene is regulated. This review describes the development of mathematical models for the regulation of an endogenous modifier gene, SMN2, in spinal muscular atrophy-an early-onset motor neuron disease that is a leading genetic cause of infant mortality worldwide-by cAMP signaling. These mathematical models not only can aid in understanding how SMN2 expression is regulated but they can also be used to examine the best ways to manipulate cAMP signaling to maximally increase SMN2 expression. These models will lead to the development of therapeutic strategies for treating SMA. This systems biology approach can also be applied to other neurological diseases, particularly those in which a disease-causing gene or a modifier gene has been identified.

Dynamic graph metrics: Tutorial, toolbox, and tale

The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.

Dynamic graph metrics: Tutorial, toolbox, and tale

The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.

Dynamics and heterogeneity of brain damage in multiple sclerosis

Multiple Sclerosis (MS) is an autoimmune disease driving inflammatory and degenerative processes that damage the central nervous system (CNS). However, it is not well understood how these events interact and evolve to evoke such a highly dynamic and heterogeneous disease. We established a hypothesis whereby the variability in the course of MS is driven by the very same pathogenic mechanisms responsible for the disease, the autoimmune attack on the CNS that leads to chronic inflammation, neuroaxonal degeneration and remyelination. We propose that each of these processes acts more or less severely and at different times in each of the clinical subgroups. To test this hypothesis, we developed a mathematical model that was constrained by experimental data (the expanded disability status scale [EDSS] time series) obtained from a retrospective longitudinal cohort of 66 MS patients with a long-term follow-up (up to 20 years). Moreover, we validated this model in a second prospective cohort of 120 MS patients with a three-year follow-up, for which EDSS data and brain volume time series were available. The clinical heterogeneity in the datasets was reduced by grouping the EDSS time series using an unsupervised clustering analysis. We found that by adjusting certain parameters, albeit within their biological range, the mathematical model reproduced the different disease courses, supporting the dynamic CNS damage hypothesis to explain MS heterogeneity. Our analysis suggests that the irreversible axon degeneration produced in the early stages of progressive MS is mainly due to the higher rate of myelinated axon degeneration, coupled to the lower capacity for remyelination. However, and in agreement with recent pathological studies, degeneration of chronically demyelinated axons is not a key feature that distinguishes this phenotype. Moreover, the model reveals that lower rates of axon degeneration and more rapid remyelination make relapsing MS more resilient than the progressive subtype. Therefore, our results support the hypothesis of a common pathogenesis for the different MS subtypes, even in the presence of genetic and environmental heterogeneity. Hence, MS can be considered as a single disease in which specific dynamics can provoke a variety of clinical outcomes in different patient groups. These results have important implications for the design of therapeutic interventions for MS at different stages of the disease.

Multiple Sclerosis (MS) is an autoimmune disease in which inflammatory and degenerative processes damage the brain. We tested the hypothesis that the variability in disease progression and the clinical heterogeneity observed in MS is driven by a single mechanism, namely the autoimmune attack on the CNS that provokes this chronic inflammation and degeneration. As such, it is the difference in the intensity of these processes at distinct times that is responsible for establishing each of the disease subtypes defined to date. Mathematical models of brain damage and disease course were generated that were fitted to clinical data. We found that the phenotypes of the different MS subtypes were reproduced by the model, supporting the concept of a common pathogenesis and thus, that of a single disease in which specific dynamics can produce a variety of clinical outcomes in different groups of patients. These results are likely to be helpful when designing new therapies for this disease.

Lost in elimination: mechanisms of axonal loss

Axonal loss is an important process both dur­ing development and diseases of the ner­vous system. While the molecular mecha­nisms that mediate axonal loss are largely elusive, modern imaging technology affords an increasingly clear view of the cellular processes that allow nerve cells to shed individiual axon branches or even dismantle entire parts of their axonal projections. The present review discusses the characteristics of post-traumatic Wallerian degeneration, the process of axonal loss currently best understood. Subsequently, the properties of a number of recently discovered axonal loss phenome­na are described. These phenomena explain some of the axonal loss that occurs locally after axon transection, during neuro-inflammatory insults, and as part of normal neurode­velopment.

Towards the Molecular Foundations of Glutamatergic-targeted Antidepressants

BACKGROUND

Depression affects over 120 million individuals of all ages and is the leading cause of disability worldwide. The lack of objective diagnostic criteria, together with the heterogeneity of the depressive disorder itself, makes it challenging to develop effective therapies. The accumulation of preclinical data over the past 20 years derived from a multitude of models using many divergent approaches, has fueled the resurgence of interest in targeting glutamatergic neurotransmission for the treatment of major depression.

OBJECTIVE

The emergence of mechanistic studies are advancing our understanding of the molecular underpinnings of depression. While clearly far from complete and conclusive, they offer the potential to lead to the rational design of more specific therapeutic strategies and the development of safer and more effective rapid acting, long lasting antidepressants.

METHODS

The development of comprehensive omics-based approaches to the dysregulation of synaptic transmission and plasticity that underlies the core pathophysiology of MDD are reviewed to illustrate the fundamental elements.

RESULTS

This review frames the rationale for the conceptualization of depression as a "pathway disease". As such, it culminates in the call for the development of novel state-of-the-art "-omics approaches" and neurosystems biological techniques necessary to advance our understanding of spatiotemporal interactions associated with targeting glutamatergic-triggered signaling in the CNS.

CONCLUSION

These technologies will enable the development of novel psychiatric medications specifically targeted to impact specific, critical intracellular networks in a more focused manner and have the potential to offer new dimensions in the area of translational neuropsychiatry.

Clinical implications of omics and systems medicine: focus on predictive and individualized treatment

Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High-throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole-genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide a brief introduction to systems medicine and discuss how it may contribute to the clinical implementation of individualized treatment, using clinically relevant examples.

Transcriptome Analysis of Ullrich Congenital Muscular Dystrophy Fibroblasts Reveals a Disease Extracellular Matrix Signature and Key Molecular Regulators

Background

Collagen VI related myopathies encompass a range of phenotypes with involvement of skeletal muscle, skin and other connective tissues. They represent a severe and relatively common form of congenital disease for which there is no treatment. Collagen VI in skeletal muscle and skin is produced by fibroblasts.

Aims & Methods

In order to gain insight into the consequences of collagen VI mutations and identify key disease pathways we performed global gene expression analysis of dermal fibroblasts from patients with Ullrich Congenital Muscular Dystrophy with and without vitamin C treatment. The expression data were integrated using a range of systems biology tools. Results were validated by real-time PCR, western blotting and functional assays.

Findings

We found significant changes in the expression levels of almost 600 genes between collagen VI deficient and control fibroblasts. Highly regulated genes included extracellular matrix components and surface receptors, including integrins, indicating a shift in the interaction between the cell and its environment. This was accompanied by a significant increase in fibroblasts adhesion to laminin. The observed changes in gene expression profiling may be under the control of two miRNAs, miR-30c and miR-181a, which we found elevated in tissue and serum from patients and which could represent novel biomarkers for muscular dystrophy. Finally, the response to vitamin C of collagen VI mutated fibroblasts significantly differed from healthy fibroblasts. Vitamin C treatment was able to revert the expression of some key genes to levels found in control cells raising the possibility of a beneficial effect of vitamin C as a modulator of some of the pathological aspects of collagen VI related diseases.

Prognostic models based on patient snapshots and time windows: Predicting disease progression to assisted ventilation in Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating disease and the most common neurodegenerative disorder of young adults. ALS patients present a rapidly progressive motor weakness. This usually leads to death in a few years by respiratory failure. The correct prediction of respiratory insufficiency is thus key for patient management. In this context, we propose an innovative approach for prognostic prediction based on patient snapshots and time windows. We first cluster temporally-related tests to obtain snapshots of the patient's condition at a given time (patient snapshots). Then we use the snapshots to predict the probability of an ALS patient to require assisted ventilation after k days from the time of clinical evaluation (time window). This probability is based on the patient's current condition, evaluated using clinical features, including functional impairment assessments and a complete set of respiratory tests. The prognostic models include three temporal windows allowing to perform short, medium and long term prognosis regarding progression to assisted ventilation. Experimental results show an area under the receiver operating characteristics curve (AUC) in the test set of approximately 79% for time windows of 90, 180 and 365 days. Creating patient snapshots using hierarchical clustering with constraints outperforms the state of the art, and the proposed prognostic model becomes the first non population-based approach for prognostic prediction in ALS. The results are promising and should enhance the current clinical practice, largely supported by non-standardized tests and clinicians' experience.

Network-Based Association Study of Obesity and Type 2 Diabetes with Gene Expression Profiles

The increased prevalence of obesity and type 2 diabetes (T2D) has become an important factor affecting the health of the human. Obesity is commonly considered as a major risk factor for the development of T2D. However, the molecular mechanisms of the disease relations are not well discovered yet. In this study, the combination of multiple differential expression profiles and a comprehensive biological network of obesity and T2D allowed us to identify and compare the disease-responsive active modules and subclusters. The results demonstrated that the connection between obesity and T2D mainly relied on several pathways involved in the digestive metabolism, immunization, and signal transduction, such as adipocytokine, chemokine signaling pathway, T cell receptor signaling pathway, and MAPK signaling pathways. The relationships of almost all of these pathways with obesity and T2D have been verified by the previous reports individually. We also found that the different parts in the same pathway are activated in obesity and T2D. The association of cancer, obesity, and T2D was identified too here. As a conclusion, our network-based method not only gives better support for the close connection between obesity and T2D, but also provides a systemic view in understanding the molecular functions underneath the links. It should be helpful in the development of new therapies for obesity, T2D, and the associated diseases.

Integrative biomarker discovery in neurodegenerative diseases

Data mining has been widely applied in biomarker discovery resulting in significant findings of different clinical and biological biomarkers. With developments in technology, from genomics to proteomics analysis, a deluge of data has become available, as well as standardized data repositories. Nonetheless, researchers are still facing important challenges in analyzing the data, especially when considering the complexity of pathways involved in biological processes and diseases. Data from single sources appear unable to explain complex processes, such as those involved in brain-related disorders, including Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis, thus raising the need for a more comprehensive perspective. A possible solution relies on data and model integration, where several data types are combined to provide complementary views. This in turn can result in the discovery of previously unknown biomarkers by unraveling otherwise hidden relationships between data from different sources, and/or validate such composite biomarkers in more powerful predictive models.

Visualizing the indefinable: three-dimensional complexity of 'infectious diseases'

Background

The words ‘infection’ and ‘inflammation’ lack specific definitions. Here, such words are not defined. Instead, the ability to visualize host-microbial interactions was explored.

Methods

Leukocyte differential counts and four bacterial species (Staphylococcus aureus, Streptococcus dysgalactiae, Staphylococcus chromogenes, and Escherichia coli) were determined or isolated in a cross-sectional and randomized study conducted with 611 bovine milk samples. Two paradigms were evaluated: (i) the classic one, which measures non-structured (count or percent) data; and (ii) a method that, using complex data structures, detects and differentiates three-dimensional (3D) interactions among lymphocytes (L), macrophages (M), and neutrophils (N).

Results

Classic analyses failed to differentiate bacterial-positive (B+) from –negative (B−) observations: B− and B+ data overlapped, even when statistical significance was achieved. In contrast, the alternative approach showed distinct patterns, such as perpendicular data inflections, which discriminated microbial-negative/mononuclear cell-predominating (MCP) from microbial-positive/phagocyte-predominating (PP) subsets. Two PP subcategories were distinguished, as well as PP/culture-negative (false-negative) and MCP/culture-positive (false-positive) observations. In 3D space, MCP and PP subsets were perpendicular to one another, displaying ≥91% specificity or sensitivity. Findings supported five inferences: (i) disease is not always ruled out by negative bacterial tests; (ii) low total cell counts can coexist with high phagocyte percents; (iii) neither positive bacterial isolation nor high cell counts always coincide with PP profiles; (iv) statistical significance is not synonymous with discrimination; and (v) hidden relationships cannot be detected when simple (non-structured) data formats are used and statistical analyses are performed before data subsets are identified, but can be uncovered when complexity is investigated.

Conclusions

Pattern recognition-based assessments can detect host-microbial interactions usually unobserved. Such cutoff-free, confidence interval-free, gold standard-free approaches provide interpretable information on complex entities, such as ‘infection’ and ‘inflammation’, even without definitions. To investigate disease dynamics, combinations of observational and experimental longitudinal studies, on human and non-human infections, are recommended.

Toward defining the anatomo-proteomic puzzle of the human brain: An integrative analysis

The human brain is exceedingly complex, constituted by billions of neurons and trillions of synaptic connections that, in turn, define ∼900 neuroanatomical subdivisions in the adult brain (Hawrylycz et al. An anatomically comprehensive atlas of the human brain transcriptome. Nature 2012, 489, 391-399). The human brain transcriptome has revealed specific regional transcriptional signatures that are regulated in a spatiotemporal manner, increasing the complexity of the structural and molecular organization of this organ (Kang et al. Spatio-temporal transcriptome of the human brain. Nature 2011, 478, 483-489). During the last decade, neuroproteomics has emerged as a powerful approach to profile neural proteomes using shotgun-based MS, providing complementary information about protein content and function at a global level. Here, we revise recent proteome profiling studies performed in human brain, with special emphasis on proteome mapping of anatomical macrostructures, specific subcellular compartments, and cerebrospinal fluid. Moreover, we have performed an integrative functional analysis of the protein compilation derived from these large-scale human brain proteomic studies in order to obtain a comprehensive view of human brain biology. Finally, we also discuss the potential contribution of our meta-analysis to the Chromosome-centric Human Proteome Project initiative.

Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology

Background

In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS).

Methods

The MS Ontology was created using scientific literature and expert review under the Protégé OWL environment. We developed a dictionary with semantic synonyms and translations to different languages for mining EMR. The MS Ontology was integrated with other ontologies and dictionaries (diseases/comorbidities, gene/protein, pathways, drug) into the text-mining tool SCAIView. We analyzed the EMRs from 624 patients with MS using the MS ontology dictionary in order to identify drug usage and comorbidities in MS. Testing competency questions and functional evaluation using F statistics further validated the usefulness of MS ontology.

Results

Validation of the lexicalized ontology by means of named entity recognition-based methods showed an adequate performance (F score = 0.73). The MS Ontology retrieved 80% of the genes associated with MS from scientific abstracts and identified additional pathways targeted by approved disease-modifying drugs (e.g. apoptosis pathways associated with mitoxantrone, rituximab and fingolimod). The analysis of the EMR from patients with MS identified current usage of disease modifying drugs and symptomatic therapy as well as comorbidities, which are in agreement with recent reports.

Conclusion

The MS Ontology provides a semantic framework that is able to automatically extract information from both scientific literature and EMR from patients with MS, revealing new pathogenesis insights as well as new clinical information.

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).

Modules, networks and systems medicine for understanding disease and aiding diagnosis

Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.

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.

Signaling networks in MS: a systems-based approach to developing new pharmacological therapies

The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.

How implementation of systems biology into clinical trials accelerates understanding of diseases

Systems biology comprises a series of concepts and approaches that have been used successfully both to delineate novel biological mechanisms and to drive translational advances. The goal of systems biology is to re-integrate putatively critical elements extracted from multi-modality datasets in order to understand how interactions among multiple components form functional networks at the organism/patient-level, and how dysfunction of these networks underlies a particular disease. Due to the genetic and environmental diversity of human subjects, identification of critical elements related to a particular disease process from cross-sectional studies requires prohibitively large cohorts. Alternatively, implementation of systems biology principles to interventional clinical trials represents a unique opportunity to gain predictive understanding of complex diseases in comparatively small cohorts of patients. This paper reviews systems biology principles applicable to translational research, focusing on lessons from systems approaches to inflammation applied to multiple sclerosis. We suggest that employing systems biology methods in the design and execution of biomarker-supported, proof-of-principle clinical trials provides a singular opportunity to merge therapeutic development with a basic understanding of disease processes. The ultimate goal is to develop predictive computational models of the disease, which will revolutionize diagnostic process and provide mechanistic understanding necessary for personalized therapeutic approaches. Added, biologically meaningful information can be derived from diagnostic tests, if they are interpreted in functional relationships, rather than as independent measurements. Such systems biology based diagnostics will transform disease taxonomies from phenotypical to molecular and will allow physicians to select optimal therapeutic regimens for individual patients.

Application of quantitative proteomics technologies to the biomarker discovery pipeline for multiple sclerosis

Multiple sclerosis is an inflammatory-mediated demyelinating disorder most prevalent in young Caucasian adults. The various clinical manifestations of the disease present several challenges in the clinic in terms of diagnosis, monitoring disease progression and response to treatment. Advances in MS-based proteomic technologies have revolutionized the field of biomarker research and paved the way for the identification and validation of disease-specific markers. This review focuses on the novel candidates discovered by the application of quantitative proteomics to relevant disease-affected tissues in both the human context and within the animal model of the disease known as experimental autoimmune encephalomyelitis. The role of targeted MS approaches for biomarker validation studies, such as multiple reaction monitoring will also be discussed.

Integrative biological analysis for neuropsychopharmacology

Although advances in psychotherapy have been made in recent years, drug discovery for brain diseases such as schizophrenia and mood disorders has stagnated. The need for new biomarkers and validated therapeutic targets in the field of neuropsychopharmacology is widely unmet. The brain is the most complex part of human anatomy from the standpoint of number and types of cells, their interconnections, and circuitry. To better meet patient needs, improved methods to approach brain studies by understanding functional networks that interact with the genome are being developed. The integrated biological approaches--proteomics, transcriptomics, metabolomics, and glycomics--have a strong record in several areas of biomedicine, including neurochemistry and neuro-oncology. Published applications of an integrated approach to projects of neurological, psychiatric, and pharmacological natures are still few but show promise to provide deep biological knowledge derived from cells, animal models, and clinical materials. Future studies that yield insights based on integrated analyses promise to deliver new therapeutic targets and biomarkers for personalized medicine.

Systems Biology and Ratio-Based, Real-Time Disease Surveillance

Most infectious disease surveillance methods are not well fit for early detection. To address such limitation, here we evaluated a ratio- and Systems Biology-based method that does not require prior knowledge on the identity of an infective agent. Using a reference group of birds experimentally infected with West Nile virus (WNV) and a problem group of unknown health status (except that they were WNV-negative and displayed inflammation), both groups were followed over 22 days and tested with a system that analyses blood leucocyte ratios. To test the ability of the method to discriminate small data sets, both the reference group (n = 5) and the problem group (n = 4) were small. The questions of interest were as follows: (i) whether individuals presenting inflammation (disease-positive or D+) can be distinguished from non-inflamed (disease-negative or D-) birds, (ii) whether two or more D+ stages can be detected and (iii) whether sample size influences detection. Within the problem group, the ratio-based method distinguished the following: (i) three (one D- and two D+) data classes; (ii) two (early and late) inflammatory stages; (iii) fast versus regular or slow responders; and (iv) individuals that recovered from those that remained inflamed. Because ratios differed in larger magnitudes (up to 48 times larger) than percentages, it is suggested that data patterns are likely to be recognized when disease surveillance methods are designed to measure inflammation and utilize ratios.

The effect of network biology on drug toxicology

INTRODUCTION

The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity.

AREAS COVERED

This review describes the strengths and limitations of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity.

EXPERT OPINION

There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The next step for network biology will be to better integrate differential and quantitative data.

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.

Bioresponsive hydrogels

Bioresponsive hydrogels are emerging with technological significance in targeted drug delivery, biosensors, and regenerative medicine. Their ability to respond to specific biologically derived stimuli creates a design challenge in effectively linking the conferred biospecificity with an engineered response tailored to the needs of a particular application. Moreover, the fundamental phenomena governing the response must support an appropriate dynamic range, limit of detection, and the potential for feedback control. The design of these systems is inherently complicated due to the high interdependency of the governing phenomena that guide sensing, transduction, and actuation of the hydrogel. Future advancements in bioresponsive hydrogels will out of necessity contain control loops similar to synthetic metabolic pathways. The use of these materials will continue to expand as they become coupled and integrated with new technologies.

Feedback-based, system-level properties of vertebrate-microbial interactions

Background

Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered.

Methods

To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions.

Results

In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D–, or microbial-negative) groups: D+ and D– data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D– data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections.

Conclusions

More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.

A clinical/translational perspective: can a developmental hormone play a role in the treatment of traumatic brain injury?

Despite decades of laboratory research and clinical trials, a safe and effective treatment for traumatic brain injury (TBI) has yet to be put into successful clinical use. I suggest that much of the problem can be attributed to a reductionist perspective and attendant research strategy directed to finding or designing drugs that target a single receptor mechanism, gene, or brain locus. This approach fails to address the complexity of TBI, which leads to a cascade of systemic toxic events in the brain and throughout the body that may persist over long periods of time. Attention is now turning to pleiotropic drugs: drugs that act on multiple genomic, proteomic and metabolic pathways to enhance morphological and functional outcomes after brain injury. Of the various agents now in clinical trials, the neurosteroid progesterone (PROG) is gaining attention despite the widespread assumption that it is "just a female hormone" with limited, if any, neuroprotective properties. This perspective should change. PROG is also a powerful developmental hormone that plays a critical role in protecting the fetus during gestation. I argue here that development, neuroprotection and cellular repair have a number of properties in common. I discuss evidence that PROG is pleiotropically neuroprotective and may be a useful therapeutic and neuroprotective agent for central nervous system injury and some neurodegenerative diseases.

Immunologic privilege in the central nervous system and the blood-brain barrier

The brain is in many ways an immunologically and pharmacologically privileged site. The blood-brain barrier (BBB) of the cerebrovascular endothelium and its participation in the complex structure of the neurovascular unit (NVU) restrict access of immune cells and immune mediators to the central nervous system (CNS). In pathologic conditions, very well-organized immunologic responses can develop within the CNS, raising important questions about the real nature and the intrinsic and extrinsic regulation of this immune privilege. We assess the interactions of immune cells and immune mediators with the BBB and NVU in neurologic disease, cerebrovascular disease, and intracerebral tumors. The goals of this review are to outline key scientific advances and the status of the science central to both the neuroinflammation and CNS barriers fields, and highlight the opportunities and priorities in advancing brain barriers research in the context of the larger immunology and neuroscience disciplines. This review article was developed from reports presented at the 2011 Annual Blood-Brain Barrier Consortium Meeting.

Development of biomarkers to chart all Alzheimer's disease stages: the royal road to cutting the therapeutic Gordian Knot

The aim of this perspective article is to stimulate radical shifts in thinking and foster further discussion on the effective discovery, development, validation, and qualification process of biological markers derived from all available technical modalities that meet the complex conceptual and pathophysiological challenges across all stages of the complex, nonlinear, dynamic, and chronically progressive sporadic Alzheimer's disease (AD). This perspective evaluates the current state of the science regarding a broad spectrum of hypothesis-driven and exploratory technologies and "markers" as candidates for all required biomarker functions, in particular, surrogate indicators of adaptive to maladaptive and compensatory to decompensatory, reversible to irreversible brain "systems failure." We stress the future importance of the systems biology (SB) paradigm (next to the neural network paradigm) for substantial progress in AD research. SB represents an integrated and deeper investigation of interacting biomolecules within cells and organisms. This approach has only recently become feasible as high-throughput technologies and mass spectrometric analyses of proteins and lipids, together with rigorous bioinformatics, have evolved. Existing high-content data derived from clinically and experimentally derived neural tissues point to convergent pathophysiological pathways during the course of AD, transcending traditional descriptive studies to reach a more integrated and comprehensive understanding of AD pathophysiology, derived systems biomarkers, and "druggable" system nodes. The discussion is continued on the premise that the lack of integration of advanced biomarker technologies and transfertilization from more mature translational research fields (e.g., oncology, immunology, cardiovascular), which satisfy regulatory requirements for an accurate, sensitive, and well-validated surrogate marker of specific pathophysiological processes and/or clinical outcomes, is a major rate-limiting factor for the successful development and approval of effective treatments for AD prevention. We consider the conceptual, scientific, and technical challenges for the discovery-development-validation-qualification process of biomarker tools and analytical algorithms for detection of the earliest pathophysiological processes in asymptomatic individuals at elevated risk during preclinical stages of AD. The most critical need for rapid translation of putative markers into validated (performance) and standardized (harmonized standard operating procedures) biomarker tools that fulfill regulatory requirements (qualify for use in treatment trials: e.g., safety, target engagement, mechanism of action, enrichment, stratification, secondary and primary outcome, surrogate outcome) is the availability of a large-scale worldwide comprehensive longitudinal database that includes the following cohorts: (a) healthy aging, (b) people at elevated risks (genetic/epigenetic/lifestyle/comorbid conditions), and (c) asymptomatic-preclinical/prodromal-mild cognitive impairment/syndromal mild, moderate, or severe AD. Our proposal, as initial strategic steps for integrating markers into future development of diagnostic and therapy trial technologies, is to work toward: (a) creating the essential research and development infrastructure as an international shared resource, (b) building the organizational structure for managing such a multinational shared resource, and (c) establishing an integrated transsectoral multidisciplinary global network of collaborating investigators to help build and use the shared research resource.

Ensemble transcript interaction networks: a case study on Alzheimer's disease

Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD.

The impact of network biology in pharmacology and toxicology

With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological environment. By studying drug action across multiple scales of complexity, from molecular to cellular and tissue level, network-based computational methods have the potential to improve our understanding of the impact of chemicals in human health. In this review we present the available large-scale databases and tools that allow integration and analysis of such information for understanding the properties of small molecules in the context of cellular networks. With the recent advances in the omics area, global integrative approaches are necessary to cope with the massive amounts of data, and biomedical researchers are urged to implement new types of analyses that can lead to new therapeutic interventions with increased safety and efficacy compared with existing medications.

Data integration and systems biology approaches for biomarker discovery: challenges and opportunities for multiple sclerosis

New "omic" technologies and their application to systems biology approaches offer new opportunities for biomarker discovery in complex disorders, including multiple sclerosis (MS). Recent studies using massive genotyping, DNA arrays, antibody arrays, proteomics, glycomics, and metabolomics from different tissues (blood, cerebrospinal fluid, brain) have identified many molecules associated with MS, defining both susceptibility and functional targets (e.g., biomarkers). Such discoveries involve many different levels in the complex organizational hierarchy of humans (DNA, RNA, protein, etc.), and integrating these datasets into a coherent model with regard to MS pathogenesis would be a significant step forward. Given the dynamic and heterogeneous nature of MS, validating biomarkers is mandatory. To develop accurate markers of disease prognosis or therapeutic response that are clinically useful, combining molecular, clinical, and imaging data is necessary. Such an integrative approach would pave the way towards better patient care and more effective clinical trials that test new therapies, thus bringing the paradigm of personalized medicine in MS one step closer.

Progesterone and vitamin d hormone as a biologic treatment of traumatic brain injury in the aged

There is growing recognition that traumatic brain injury is a highly variable and complex systemic disorder that is refractory to therapies that target individual mechanisms. It is even more complex in elderly persons, in whom frailty, previous comorbidities, altered metabolism, and a long history of medication use are likely to complicate the secondary effects of brain trauma. Progesterone, one of the few neuroprotective agents that has shown promise for the treatment of acute brain injury, is now in national and international phase 3 multicenter trials. New findings show that vitamin D hormone (VDH) and VDH deficiency in the aging process (and across the developmental spectrum) may interact with progesterone and treatment for traumatic brain injury. In this article we review the use of progesterone and VDH as biologics-based therapies along with recent studies demonstrating that the combination of progesterone and VDH may promote better functional outcomes than either treatment independently.

Systems biology impact on antiepileptic drug discovery

Systems biology (SB), a recent trend in bioscience research to consider the complex interactions in biological systems from a holistic perspective, sees the disease as a disturbed network of interactions, rather than alteration of single molecular component(s). SB-relying network pharmacology replaces the prevailing focus on specific drug-receptor interaction and the corollary of rational drug design of "magic bullets", by the search for multi-target drugs that would act on biological networks as "magic shotguns". Epilepsy being a multi-factorial, polygenic and dynamic pathology, SB approach appears particularly fit and promising for antiepileptic drug (AED) discovery. In fact, long before the advent of SB, AED discovery already involved some SB-like elements. A reported SB project aimed to find out new drug targets in epilepsy relies on a relational database that integrates clinical information, recordings from deep electrodes and 3D-brain imagery with histology and molecular biology data on modified expression of specific genes in the brain regions displaying spontaneous epileptic activity. Since hitting a single target does not treat complex diseases, a proper pharmacological promiscuity might impart on an AED the merit of being multi-potent. However, multi-target drug discovery entails the complicated task of optimizing multiple activities of compounds, while having to balance drug-like properties and to control unwanted effects. Specific design tools for this new approach in drug discovery barely emerge, but computational methods making reliable in silico predictions of poly-pharmacology did appear, and their progress might be quite rapid. The current move away from reductionism into network pharmacology allows expecting that a proper integration of the intrinsic complexity of epileptic pathology in AED discovery might result in literally anti-epileptic drugs.

Sieving treatment biomarkers from blood gene-expression profiles: a pharmacogenomic update on two types of multiple sclerosis therapy

Interferon-β (IFN-β) and glatiramer acetate are routinely used to inhibit disease activity in multiple sclerosis, but their mechanisms of action are incompletely understood. Individual treatment responses vary and candidate molecular markers that predict them have yet to be established. Why some patients respond poorly to a certain treatment while others respond well is addressed by the pharmacogenomic approach, which postulates that the molecular response to treatment correlates with the clinical effects, and thus seeks biological markers to estimate prognosis, guide therapy, comprehend the drugs' mechanisms of action and offer insights into disease pathogenesis. A poor clinical response can be owing to genetic variants in drug receptors or signaling components, or the appearance of neutralizing antibodies that interfere with the drug's binding efficacy. Independently, such mechanisms could lead to inadequate, that is to say unchanged, molecular responses, or exceedingly increased or decreased changes. By means of DNA microarray studies, various research groups endeavour to establish a clinically relevant relationship between the biological response to these drugs and treatment effects. Molecular profiles obtained in this way differ in the pattern and number of modulated genes, suggesting the existence of an individual 'drug-response fingerprint'. To further unravel the underlying regulatory interaction structure of the genes responsive to these immunotherapies represents a daunting but inevitable task. In this article, we focus on longitudinal ex vivo transcriptomic studies in multiple sclerosis and its therapy. We will discuss recurrently reported biomarker candidates, emphasizing those of immunologically meaning, and review studies with network module outputs.

Modeling the effector - regulatory T cell cross-regulation reveals the intrinsic character of relapses in Multiple Sclerosis

Background

The relapsing-remitting dynamics is a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). Although current understanding of both cellular and molecular mechanisms involved in the pathogenesis of autoimmune diseases is significant, how their activity generates this prototypical dynamics is not understood yet. In order to gain insight about the mechanisms that drive these relapsing-remitting dynamics, we developed a computational model using such biological knowledge. We hypothesized that the relapsing dynamics in autoimmunity can arise through the failure in the mechanisms controlling cross-regulation between regulatory and effector T cells with the interplay of stochastic events (e.g. failure in central tolerance, activation by pathogens) that are able to trigger the immune system.

Results

The model represents five concepts: central tolerance (T-cell generation by the thymus), T-cell activation, T-cell memory, cross-regulation (negative feedback) between regulatory and effector T-cells and tissue damage. We enriched the model with reversible and irreversible tissue damage, which aims to provide a comprehensible link between autoimmune activity and clinical relapses and active lesions in the magnetic resonances studies in patients with Multiple Sclerosis. Our analysis shows that the weakness in this negative feedback between effector and regulatory T-cells, allows the immune system to generate the characteristic relapsing-remitting dynamics of autoimmune diseases, without the need of additional environmental triggers. The simulations show that the timing at which relapses appear is highly unpredictable. We also introduced targeted perturbations into the model that mimicked immunotherapies that modulate effector and regulatory populations. The effects of such therapies happened to be highly dependent on the timing and/or dose, and on the underlying dynamic of the immune system.

Conclusion

The relapsing dynamic in MS derives from the emergent properties of the immune system operating in a pathological state, a fact that has implications for predicting disease course and developing new therapies for MS.

What do 'omic technologies have to offer periodontal clinical practice in the future?

BACKGROUND AND OBJECTIVE

Periodontal diseases are the most common chronic inflammatory diseases of humans and a major cause of tooth loss. Inflammatory periodontitis is also a complex multifactorial disease involving many cell types, cell products and interactions. It is associated with a dysregulated inflammatory response, which fails to resolve, and which also fails to re-establish a beneficial periodontal microbiota. There is a rich history of biomarker research within the field of periodontology, but exemplary improvements in analytical platform technologies offer exciting opportunities for discovery. These include the 'omic technologies, such as genomics, transcriptomics, proteomics and metabolomics, which provide information on global scales that can match the complexity of the disease. This narrative review focuses on the recent advances made in in vivo human periodontal research by use of 'omic technologies.

MATERIAL AND METHODS

The Medline database was searched to identify articles currently available on 'omic technologies with regard to periodontal research.

RESULTS

One hundred and sixty-one articles focusing on biomarkers of and 'omic advances in periodontal research were analysed for their contributions to the understanding of periodontal diseases.

CONCLUSION

The data generated by the use of 'omic technologies have huge potential to inform paradigm shifts in our understanding of periodontal diseases, but data management, analysis and interpretation require a thoughtful and systematic bioinformatics approach, to ensure meaningful conclusions can be made.