Network medicine in drug design: implications for neuroinflammation

Microglia-mediated inflammatory destruction of neuro-cardiovascular dysfunction after stroke

Stroke, a serious systemic inflammatory disease, features neurological deficits and cardiovascular dysfunction. Neuroinflammation is characterized by the activation of microglia after stroke, which disrupts the cardiovascular-related neural network and the blood-brain barrier. Neural networks activate the autonomic nervous system to regulate the cardiac and blood vessels. Increased permeability of the blood-brain barrier and the lymphatic pathways promote the transfer of the central immune components to the peripheral immune organs and the recruitment of specific immune cells or cytokines, produced by the peripheral immune system, and thus modulate microglia in the brain. In addition, the spleen will also be stimulated by central inflammation to further mobilize the peripheral immune system. Both NK cells and Treg cells will be generated to enter the central nervous system to suppress further inflammation, while activated monocytes infiltrate the myocardium and cause cardiovascular dysfunction. In this review, we will focus on microglia-mediated inflammation in neural networks that result in cardiovascular dysfunction. Furthermore, we will discuss neuroimmune regulation in the central-peripheral crosstalk, in which the spleen is a vital part. Hopefully, this will benefit in anchoring another therapeutic target for neuro-cardiovascular dysfunction.

Multiple mechanisms underlying neuroprotection by secretory phospholipase A2 preconditioning in a surgically induced brain injury rat model

BACKGROUND

Intra-operative bleeding, post-operative brain edema and neuroinflammation are major complications in patients with surgical brain injury (SBI). Phospholipase A2 (PLA2) is the upstream enzyme which initiates the PLA2, 5-lipoxygenase (5-LOX) and leukotriene B4 (LTB4) inflammatory pathway. We hypothesized PLA2preconditioning (PPC) prior to SBI can activate endogenous anti-inflammatory responses to protect against SBI. This study evaluated if PPC can ameliorate neurosurgical complications and elucidated PPC-mediated possible protective mechanisms in a rat SBI model.

METHODS

Total 105 adult male Sprague Dawley rats were used for this study. SBI was induced by partial resection of the right frontal lobe. PLA2 or 0.9% NaCl was injected via rats' tail vein for 3 consecutive days prior to SBI. For mechanism study, a selective PLA2 inhibitor, Manoalide and 5-LOX inhibitor, Zileuton were injected intravenously with PPC to elucidate the role of PLA2 and 5-LOX in PPC-mediated anti-inflammatory effects. Brain water content (BWC) and lung water content, neurological tests, ELISA, western blot, immunohistochemistry, white blood cells (WBC) count, and spectrophotometric assay for intra-operative hemorrhage volume were evaluated.

RESULTS

First, PPC reduced brain water content, intra-operative bleeding, and improved neurological function after SBI. Second, PPC decreased 5-LOX expression and brain leukocyte infiltration, while increasing glial fibrillary acidic protein (GFAP) expression in the peri-resection brain tissue after SBI. Third, PPC induced peripheral inflammation represented by mild pulmonary inflammation and increased peripheral blood WBC count and LTB4 level. Lastly, PPC increased blood glucose concentration and glucocorticoid levels after SBI. In addition, PPC mediated above-mentioned changes were partially reversed by administration of PLA2 inhibitor, Manoalide and 5-LOX inhibitor, Zileuton.

CONCLUSIONS

PPC conferred neuroprotection against SBI via multi-target involvement induced anti-inflammatory mechanisms.

Genetic tests in major psychiatric disorders-integrating molecular medicine with clinical psychiatry-why is it so difficult?

With the advent of post-genomic era, new technologies create extraordinary possibilities for diagnostics and personalized therapy, transforming todays' medicine. Rooted in both medical genetics and clinical psychiatry, the paper is designed as an integrated source of information of the current and potential future application of emerging genomic technologies as diagnostic tools in psychiatry, moving beyond the classical concept of patient approach. Selected approaches are presented, starting from currently used technologies (next-generation sequencing (NGS) and microarrays), followed by newer options (reverse phenotyping). Next, we describe an old concept in a new light (endophenotypes), subsequently coming up with a sophisticated and complex approach (gene networks) ending by a nascent field (computational psychiatry). The challenges and barriers that exist to translate genomic research to real-world patient assessment are further discussed. We emphasize the view that only a paradigm shift can bring a fundamental change in psychiatric practice, allowing to disentangle the intricacies of mental diseases. All the diagnostic methods, as described, are directed at uncovering the integrity of the system including many types of relations within a complex structure. The integrative system approach offers new opportunity to connect genetic background with specific diseases entities, or concurrently, with symptoms regardless of a diagnosis. To advance the field, we propose concerted cross-disciplinary effort to provide a diagnostic platform operating at the general level of genetic pathogenesis of complex-trait psychiatric disorders rather than at the individual level of a specific disease.

Identification of Major Signaling Pathways in Prion Disease Progression Using Network Analysis

Prion diseases are transmissible neurodegenerative diseases that arise due to conformational change of normal, cellular prion protein (PrPC) to protease-resistant isofrom (rPrPSc). Deposition of misfolded PrpSc proteins leads to an alteration of many signaling pathways that includes immunological and apoptotic pathways. As a result, this culminates in the dysfunction and death of neuronal cells. Earlier works on transcriptomic studies have revealed some affected pathways, but it is not clear which is (are) the prime network pathway(s) that change during the disease progression and how these pathways are involved in crosstalks with each other from the time of incubation to clinical death. We perform network analysis on large-scale transcriptomic data of differentially expressed genes obtained from whole brain in six different mouse strain-prion strain combination models to determine the pathways involved in prion diseases, and to understand the role of crosstalks in disease propagation. We employ a notion of differential network centrality measures on protein interaction networks to identify the potential biological pathways involved. We also propose a crosstalk ranking method based on dynamic protein interaction networks to identify the core network elements involved in crosstalk with different pathways. We identify 148 DEGs (differentially expressed genes) potentially related to the prion disease progression. Functional association of the identified genes implicates a strong involvement of immunological pathways. We extract a bow-tie structure that is potentially dysregulated in prion disease. We also propose an ODE model for the bow-tie network. Predictions related to diseased condition suggests the downregulation of the core signaling elements (PI3Ks and AKTs) of the bow-tie network. In this work, we show using transcriptomic data that the neuronal dysfunction in prion disease is strongly related to the immunological pathways. We conclude that these immunological pathways occupy influential positions in the PFNs (protein functional networks) that are related to prion disease. Importantly, this functional network involvement is prevalent in all the five different mouse strain-prion strain combinations that we studied. We also conclude that the dysregulation of the core elements of the bow-tie structure, which belongs to PI3K-Akt signaling pathway, leads to dysregulation of the downstream components corresponding to other biological pathways.

Phenotype-dependent alteration of pathways and networks reveals a pure synergistic mechanism for compounds treating mouse cerebral ischemia

AIM

Our previous studies have showed that ursodeoxycholic acid (UA) and jasminoidin (JA) effectively reduce cerebral infarct volume in mice. In this study we explored the pure synergistic mechanism of these compounds in treatment of mouse cerebral ischemia, which was defined as synergistic actions specific for phenotype variations after excluding interference from ineffective compounds.

METHODS

Mice with focal cerebral ischemia were treated with UA, JA or a combination JA and UA (JU). Concha margaritifera (CM) was taken as ineffective compound. Cerebral infarct volume of the mice was determined, and the hippocampi were taken for microarray analysis. Particular signaling pathways and biological functions were enriched based on differentially expressed genes, and corresponding networks were constructed through Ingenuity Pathway Analysis.

RESULTS

In phenotype analysis, UA, JA, and JU significantly reduced the ischemic infarct volume with JU being superior to UA or JA alone, while CM was ineffective. As a result, 4 pathways enriched in CM were excluded. Core pathways in the phenotype-positive groups (UA or JA) were involved in neuronal homeostasis and neuropathology. JU-contributing pathways included all UA-contributing and the majority (71.7%) of JA-contributing pathways, and 10 new core pathways whose effects included inflammatory immunity, apoptosis and nervous system development. The functions of JU group included all functions of JA group, the majority (93.1%) of UA-contributing functions, and 3 new core functions, which focused on physiological system development and function.

CONCLUSION

The pure synergism between UA and JA underlies 10 new core pathways and 3 new core functions, which are involved in inflammation, immune responses, apoptosis and nervous system development.

Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine.

B-cell lymphoma gene regulatory networks: biological consistency among inference methods

Despite the development of numerous gene regulatory network (GRN) inference methods in the last years, their application, usage and the biological significance of the resulting GRN remains unclear for our general understanding of large-scale gene expression data in routine practice. In our study, we conduct a structural and a functional analysis of B-cell lymphoma GRNs that were inferred using 3 mutual information-based GRN inference methods: C3Net, BC3Net and Aracne. From a comparative analysis on the global level, we find that the inferred B-cell lymphoma GRNs show major differences. However, on the edge-level and the functional-level—that are more important for our biological understanding—the B-cell lymphoma GRNs were highly similar among each other. Also, the ranks of the degree centrality values and major hub genes in the inferred networks are highly conserved as well. Interestingly, the major hub genes of all GRNs are associated with the G-protein-coupled receptor pathway, cell-cell signaling and cell cycle. This implies that hub genes of the GRNs can be highly consistently inferred with C3Net, BC3Net, and Aracne, representing prominent targets for signaling pathways. Finally, we describe the functional and structural relationship between C3Net, BC3Net and Aracne gene regulatory networks. Our study shows that these GRNs that are inferred from large-scale gene expression data are promising for the identification of novel candidate interactions and pathways that play a key role in the underlying mechanisms driving cancer hallmarks. Overall, our comparative analysis reveals that these GRNs inferred with considerably different inference methods contain large amounts of consistent, method independent, biological information.

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

Systems biology for molecular life sciences and its impact in biomedicine

Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.