A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining

Whole blood ratio of CDK1/CX3CR1 mRNA expression combined to lactate refines the prediction of ICU mortality in septic patients in the Sepsis-3 era: a proof-of-concept study

Background

Transcriptomics biomarkers have been widely used to predict mortality in patients with sepsis. However, the association between mRNA levels and outcomes shows substantial variability over the course of sepsis, limiting their predictive performance. We aimed to: (a) identify and validate an mRNA biomarker signature whose association with all-cause intensive care unit (ICU) mortality is consistent at several timepoints; and (b) evaluate how this mRNA signature could be used in association with lactate levels for predictive and prognostic enrichment in sepsis.

Methods

We conducted a gene expression analysis study at two timepoints (day 1 and day 2-3 following ICU admission) using microarray data from adult septic patients to identify candidate biomarkers predictive of all-cause ICU mortality. We validated mRNA biomarkers using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) on an external validation cohort. The predictive performance of the mRNA biomarkers combination was assessed in association with lactate level to refine ICU mortality prediction.

Main results

Among 180 chips from 100 septic patients, we identified 39 upregulated and 2 downregulated differentially expressed genes (DEGs) in survivors vs. non-survivors, both at day 1 and days 2-3 following ICU admission. We combined CDK1, the hub gene of upregulated DEGs, and CX3CR1 and IL1b to compute expression ratios. The CDK1/CX3CR1 ratio had the best performance to predict all-cause ICU mortality, with an area under the ROC curve (AUROC) of 0.77 (95% confidence interval [CI] 0.88-0.66) at day 1 and of 0.82 (95% CI 0.91-0.72) at days 2-3 after ICU admission. This performance was better than that of each individual mRNA biomarker. In the external validation cohort, the predictive performance of the CDK1/CX3CR1 ratio, measured using RT-qPCR, was similar to that of lactate when measure at day 1, and higher when measured at days 2-3. Combining lactate levels and the CDK1/CX3CR1 ratio, we identify 3 groups of patients with an increasing risk of ICU-mortality, ranging from 9 to 60% with an intermediate-risk group mortality rate of 28%.

Conclusion

With stable predictive performance over the first 3 days following ICU admission, the CDK1/CX3CR1 ratio identifies three groups of septic patients with increasing ICU mortality risk. In combination with lactate, this novel biomarker strategy may be useful for sepsis patient stratification for personalized medicine trials and ICU management.

Chronic inflammation: Cross linking insights from Ayurvedic Sciences, a silver lining to systems biology and personalized medicine

Precision in personalized medicine is a crucial subject that needs comprehensive discussion and scientific validation. Traditional healthcare approaches like the Ayurvedic Sciences are often contextually linked with personalized medicine. However, it is unfortunate that this knowledge concerning Ayurveda and personalized medicine is restricted to applying systems biology techniques to 'prakriti' the phenotypic expression and characterization detailed in the literature. There are other significant constructs besides prakruti that interest an Ayurvedic physician, which accounts for crafting precision in evidence-based medicinal practices. There is this influential model of Ayurvedic healthcare practice wherein the physician maps specific personalized characters in addition to prakruti to deduce the host responses to endogenous and exposome conditions. Subsequently, tailored protocols are administered that bring about holistic, personalized outcomes. The review aimed to determine the effective methods for integrating Systems Biology, Ayurvedic Sciences, and Personalized Medicine (precision medicinebased). Ayurveda adopts a holistic approach, considering multiple variables and their interconnections, while the modern reductionist approach focuses on understanding complex details of smaller parts through rigorous experimentation. Despite seeming extremes, ongoing research on lifestyle, gut health, and spiritual well-being highlights the evolving intersection between traditional Ayurvedic practices and modern science. The current focus is on developing the fundamental concept of Ayurveda Biology by incorporating Systems Biology techniques. Challenges in this integration include understanding diverse data types, bridging interdisciplinary knowledge gaps, and addressing technological limitations and ethical concerns. Overcoming these challenges will require interdisciplinary collaboration, innovative methodologies, substantial investment in technology, and cultural sensitivity to preserve Ayurveda's core principles while leveraging modern scientific advancements. The focus of discussions and debates on such collaborations should be breakthrough clinical models, such as chronic inflammation, which can be objectively related to specific stages of disease manifestations described in Ayurveda. Validating patient characteristics with systems biology approaches, particularly in shared pathologies like chronic inflammation, is crucial for bringing prediction and precision to personalized medicine.

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Although the genetic correlations between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated. Yet, we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations (Brahman; BB, Tropical Composite; TC) to develop a novel framework termed correlation scan (CS). This framework was used to identify local regions associated with the genetic correlations between male and female fertility traits. Animals were genotyped with bovine high-density single nucleotide polymorphisms (SNPs) chip assay. The data used consisted of ~1000 individual records measured through frequent ovarian scanning for age at first corpus luteum (AGECL) and a laboratory assay for serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP effects in a 100-SNPs sliding window in each chromosome to identify local genomic regions that either drive or antagonize the genetic correlations between traits. We used Fisher's Z-statistics through a permutation method to confirm which regions of the genome harboured significant correlations. About 30% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two populations. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. For BB, the most important chromosome in terms of local regions is often located on bovine chromosome (BTA) 14. However, the important regions are spread across few different BTA's in TC. Quantitative trait loci (QTLs) and functional enrichment analysis revealed many significant windows co-localized with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to chromosome X. These results suggest regions of chromosome X for further investigation into the trade-offs between male and female fertility. We compared the CS with two other recently proposed methods that map local genomic correlations. Some genomic regions were significant across methods. Yet, many significant regions identified with the CS were overlooked by other methods.

Multiple-Molecule Drug Design Based on Systems Biology Approaches and Deep Neural Network to Mitigate Human Skin Aging

Human skin aging is affected by various biological signaling pathways, microenvironment factors and epigenetic regulations. With the increasing demand for cosmetics and pharmaceuticals to prevent or reverse skin aging year by year, designing multiple-molecule drugs for mitigating skin aging is indispensable. In this study, we developed strategies for systems medicine design based on systems biology methods and deep neural networks. We constructed the candidate genomewide genetic and epigenetic network (GWGEN) via big database mining. After doing systems modeling and applying system identification, system order detection and principle network projection methods with real time-profile microarray data, we could obtain core signaling pathways and identify essential biomarkers based on the skin aging molecular progression mechanisms. Afterwards, we trained a deep neural network of drug–target interaction in advance and applied it to predict the potential candidate drugs based on our identified biomarkers. To narrow down the candidate drugs, we designed two filters considering drug regulation ability and drug sensitivity. With the proposed systems medicine design procedure, we not only shed the light on the skin aging molecular progression mechanisms but also suggested two multiple-molecule drugs for mitigating human skin aging from young adulthood to middle age and middle age to old age, respectively.

Identification of potential biomarkers of peripheral blood mononuclear cell in hepatocellular carcinoma using bioinformatic analysis: A protocol for systematic review and meta-analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is the cause of an overwhelming number of cancer-related deaths across the world. Developing precise and noninvasive biomarkers is critical for diagnosing HCC. Our research was designed to explore potentially useful biomarkers of host peripheral blood mononuclear cell (PBMC) in HCC by integrating comprehensive bioinformatic analysis.

METHODS

Gene expression data of PBMC in both healthy individuals and patients with HCC were extracted from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to annotate the function of DEGs. Protein-protein interaction analysis was performed to screen the hub genes from DEGs. cBioportal database analysis was performed to assess the prognostic significance of hub genes. The Cancer Cell Line Encyclopedia (CCLE) and The Human Protein Atlas (HPA) database analyses were performed to confirm the expression levels of the hub genes in HCC cells and tissue.

RESULTS

A total of 95 DEGs were screened. Results of the GO analysis revealed that DEGs were primarily involved in platelet degranulation, cytoplasm, and protein binding. Results of the KEGG analysis indicated that DEGs were primarily enriched in focal adhesion. Five genes, namely, myosin light chain kinase (MYLK), interleukin 1 beta (IL1B), phospholipase D1 (PLD1), cortactin (CTTN), and moesin (MSN), were identified as hub genes. A search in the CCLE and HPA database showed that the expression levels of these hub genes were remarkably increased in the HCC samples. Survival analysis revealed that the overexpression of MYLK, IL1B, and PLD1 may have a significant effect on HCC survival. The aberrant high expression levels of MYLK, IL1B, and PLD1 strongly indicated worse prognosis in patients with HCC.

CONCLUSIONS

The identified hub genes may be closely linked with HCC tumorigenicity and may act as potentially useful biomarkers for the prognostic prediction of HCC in PBMC samples.

Integrated Network and Gene Ontology Analysis Identifies Key Genes and Pathways for Coronary Artery Diseases

BACKGROUND

The prevalence of Coronary Artery Disease (CAD) in developing countries is on the rise, owing to rapidly changing lifestyle. Therefore, it is imperative that the underlying genetic and molecular mechanisms be understood to develop specific treatment strategies. Comprehensive disease network and Gene Ontology (GO) studies aid in prioritizing potential candidate genes for CAD and also give insights into gene function by establishing gene and disease pathway relationships.

METHODS

In the present study, CAD-associated genes were collated from different data sources and protein-protein interaction network was constructed using STRING. Highly interconnected network clusters were inferred and GO analysis was performed.

RESULTS

Interrelation between genes and pathways were analyzed on ClueGO and 38 candidates were identified from 1475 CAD-associated genes, which were significantly enriched in CAD-related pathways such as metabolism and regulation of lipid molecules, platelet activation, macrophage derived foam cell differentiation, and blood coagulation and fibrin clot formation.

DISCUSSION

Integrated network and ontology analysis enables biomarker prioritization for common complex diseases such as CAD. Experimental validation and future studies on the prioritized genes may reveal valuable insights into CAD development mechanism and targeted treatment strategies.

Molecular Mechanism of Aflatoxin-Induced Hepatocellular Carcinoma Derived from a Bioinformatics Analysis

Exposure to aflatoxin is considered to be one of the causes of hepatocellular carcinoma (HCC). With the development of bioinformation, we sought to reveal the occurrence and development of aflatoxin-induced HCC through data research. We identified differentially expressed genes (DEGs) of datasets GSE127791 (Aflatoxin-treated pluripotent stem cell derived human hepatocytes vs. controls) and GSE64041 (liver carcinoma with unknown cause vs. non-cancerous tissue) by GEO2R to find the common DEGs. Gene ontology (GO) and KEGG path enrichment analysis were used to annotate the function of DEGs. Hub genes were screened from identified DEGs by protein-protein interaction (PPI) network analysis. The prognostic value of hub genes in cancer databases were evaluated. We obtained 132 common DEGs and 11 hub genes. According to cluster analysis and protein co-expression networks, we screened out the key genes, histidine-rich glycoprotein (HRG) and phosphoenolpyruvate carboxykinase 2 (PCK2). Oncomine database and survival curve analysis showed that the decline in HRG and PCK2 expression in the development of HCC indicated poor prognosis. We speculated that the decreased expression of HRG and PCK2 after aflatoxin exposure to hepatocyte may be related to aflatoxin induced hepatocyte injury and carcinogenesis. In addition, the decreased expression of HRG and PCK2 in the occurrence and development of HCC suggests a poor prognosis of HCC.

A Biomolecular Network Driven Proteinic Interaction in HCV Clearance

Hepatitis C virus infection causes chronic liver disease that leads to cancer-related mortality. Presently around 30% of the HCV (infected) affected population get rid of the infection through spontaneous disease clearance. This phenomenon is conducted by a set of reported immune candidate genes. Hence, this study focuses only on these immune-response related genes with aid of network approach, where the idea is to disseminate the network for better understanding of key functional genes and their transcription control activity. Based on the network analysis the IFNG, TNF, IFNB1, STAT1, NFKB1, STAT3, SOCS1, and MYD88 genes are prioritized as hub genes along with their common transcription factors (TFs), IRF9, NFKB1, and STAT1. The dinucleotide frequency of TF binding elements indicated GG-rich motifs in these regulatory elements. On the other hand, gene enrichment report suggests the regulation of response to interferon gamma signaling pathway, which plays central role in the spontaneous HCV clearance. Therefore, our study tends to prioritize the genes, TFs, and their regulatory pathway towards HCV clearance. Even so, the resultant hub genes and their TFs and TF binding elements could be crucial in underscoring the clearance activity in specific populations.

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.)

Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.

Microglial brain region-dependent diversity and selective regional sensitivities to aging

Microglia have critical roles in neural development, homeostasis and neuroinflammation and are increasingly implicated in age-related neurological dysfunction. Neurodegeneration often occurs in disease-specific, spatially restricted patterns, the origins of which are unknown. We performed to our knowledge the first genome-wide analysis of microglia from discrete brain regions across the adult lifespan of the mouse, and found that microglia have distinct region-dependent transcriptional identities and age in a regionally variable manner. In the young adult brain, differences in bioenergetic and immunoregulatory pathways were the major sources of heterogeneity and suggested that cerebellar and hippocampal microglia exist in a more immune-vigilant state. Immune function correlated with regional transcriptional patterns. Augmentation of the distinct cerebellar immunophenotype and a contrasting loss in distinction of the hippocampal phenotype among forebrain regions were key features during aging. Microglial diversity may enable regionally localized homeostatic functions but could also underlie region-specific sensitivities to microglial dysregulation and involvement in age-related neurodegeneration.

Temporal Genetic Modifications after Controlled Cortical Impact--Understanding Traumatic Brain Injury through a Systematic Network Approach

Traumatic brain injury (TBI) is a primary injury caused by external physical force and also a secondary injury caused by biological processes such as metabolic, cellular, and other molecular events that eventually lead to brain cell death, tissue and nerve damage, and atrophy. It is a common disease process (as opposed to an event) that causes disabilities and high death rates. In order to treat all the repercussions of this injury, treatment becomes increasingly complex and difficult throughout the evolution of a TBI. Using high-throughput microarray data, we developed a systems biology approach to explore potential molecular mechanisms at four time points post-TBI (4, 8, 24, and 72 h), using a controlled cortical impact (CCI) model. We identified 27, 50, 48, and 59 significant proteins as network biomarkers at these four time points, respectively. We present their network structures to illustrate the protein–protein interactions (PPIs). We also identified UBC (Ubiquitin C), SUMO1, CDKN1A (cyclindependent kinase inhibitor 1A), and MYC as the core network biomarkers at the four time points, respectively. Using the functional analytical tool MetaCore™, we explored regulatory mechanisms and biological processes and conducted a statistical analysis of the four networks. The analytical results support some recent findings regarding TBI and provide additional guidance and directions for future research.

Dynamic network-based relevance score reveals essential proteins and functional modules in directed differentiation

The induction of stem cells toward a desired differentiation direction is required for the advancement of stem cell-based therapies. Despite successful demonstrations of the control of differentiation direction, the effective use of stem cell-based therapies suffers from a lack of systematic knowledge regarding the mechanisms underlying directed differentiation. Using dynamic modeling and the temporal microarray data of three differentiation stages, three dynamic protein-protein interaction networks were constructed. The interaction difference networks derived from the constructed networks systematically delineated the evolution of interaction variations and the underlying mechanisms. A proposed relevance score identified the essential components in the directed differentiation. Inspection of well-known proteins and functional modules in the directed differentiation showed the plausibility of the proposed relevance score, with the higher scores of several proteins and function modules indicating their essential roles in the directed differentiation. During the differentiation process, the proteins and functional modules with higher relevance scores also became more specific to the neuronal identity. Ultimately, the essential components revealed by the relevance scores may play a role in controlling the direction of differentiation. In addition, these components may serve as a starting point for understanding the systematic mechanisms of directed differentiation and for increasing the efficiency of stem cell-based therapies.

On the crucial cerebellar wound healing-related pathways and their cross-talks after traumatic brain injury in Danio rerio

Upon injury, the direct damage and the subsequent secondary injury in the brain often result in chronic neurological disorders. Due to multifactorial nature of secondary injury and subsequent complex cellular responses, much of the underlying mechanisms are unclear. This study used an adult zebrafish cerebellum injury model to investigate the phenotypes and the secondary injury responses for recovery mechanisms of injured brain. Using the time course microarray analysis, a candidate protein-protein interaction (PPI) network was refined as cerebellar wound healing PPI network by dynamic modeling and big data mining. Pathway enrichment and ontological analysis were incorporated into the refined network to highlight the main molecular scheme of cerebellar wound healing. Several significant pathways, including chemokine, Phosphatidylinositide 3-kinases, and axon guidance signaling pathway and their cross-talks through PI3K, PAK2, and PLXNA3 were identified to coordinate for neurogenesis and angiogenesis, which are essential for the restoration of the injured brain. Our finding provides an insight into the molecular restoration mechanisms after traumatic brain injury, and open up new opportunity to devise the treatment for traumatic brain injury in human.

Network analysis of inflammatory genes and their transcriptional regulators in coronary artery disease

Network analysis is a novel method to understand the complex pathogenesis of inflammation-driven atherosclerosis. Using this approach, we attempted to identify key inflammatory genes and their core transcriptional regulators in coronary artery disease (CAD). Initially, we obtained 124 candidate genes associated with inflammation and CAD using Polysearch and CADgene database for which protein-protein interaction network was generated using STRING 9.0 (Search Tool for the Retrieval of Interacting Genes) and visualized using Cytoscape v 2.8.3. Based on betweenness centrality (BC) and node degree as key topological parameters, we identified interleukin-6 (IL-6), vascular endothelial growth factor A (VEGFA), interleukin-1 beta (IL-1B), tumor necrosis factor (TNF) and prostaglandin-endoperoxide synthase 2 (PTGS2) as hub nodes. The backbone network constructed with these five hub genes showed 111 nodes connected via 348 edges, with IL-6 having the largest degree and highest BC. Nuclear factor kappa B1 (NFKB1), signal transducer and activator of transcription 3 (STAT3) and JUN were identified as the three core transcription factors from the regulatory network derived using MatInspector. For the purpose of validation of the hub genes, 97 test networks were constructed, which revealed the accuracy of the backbone network to be 0.7763 while the frequency of the hub nodes remained largely unaltered. Pathway enrichment analysis with ClueGO, KEGG and REACTOME showed significant enrichment of six validated CAD pathways - smooth muscle cell proliferation, acute-phase response, calcidiol 1-monooxygenase activity, toll-like receptor signaling, NOD-like receptor signaling and adipocytokine signaling pathways. Experimental verification of the above findings in 64 cases and 64 controls showed increased expression of the five candidate genes and the three transcription factors in the cases relative to the controls (p<0.05). Thus, analysis of complex networks aid in the prioritization of genes and their transcriptional regulators in complex diseases.

Construction of the influenza A virus infection-induced cell-specific inflammatory regulatory network based on mutual information and optimization

Background

Influenza A virus (IAV) infection-induced inflammatory regulatory networks (IRNs) are extremely complex and dynamic. Specific biological experiments for investigating the interactions between individual inflammatory factors cannot provide a detailed and insightful multidimensional view of IRNs. Recently, data from high-throughput technologies have permitted system-level analyses. The construction of large and cell-specific IRNs from high-throughput data is essential to understanding the pathogenesis of IAV infection.

Results

In this study, we proposed a computational method, which combines nonlinear ordinary differential equation (ODE)-based optimization with mutual information, to construct a cell-specific optimized IRN during IAV infection by integrating gene expression data with a prior knowledge of network topology. Moreover, we used the average relative error and sensitivity analysis to evaluate the effectiveness of our proposed approach. Furthermore, from the optimized IRN, we confirmed 45 interactions between proteins in biological experiments and identified 37 new regulatory interactions and 8 false positive interactions, including the following interactions: IL1β regulates TLR3, TLR3 regulates IFN-β and TNF regulates IL6. Most of these regulatory interactions are statistically significant by Z-statistic. The functional annotations of the optimized IRN demonstrated clearly that the defense response, immune response, response to wounding and regulation of cytokine production are the pivotal processes of IAV-induced inflammatory response. The pathway analysis results from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) showed that 8 pathways are enriched significantly. The 5 pathways were validated by experiments, and 3 other pathways, including the intestinal immune network for IgA production, the cytosolic DNA-sensing pathway and the allograft rejection pathway, are the predicted novel pathways involved in the inflammatory response.

Conclusions

Integration of knowledge-driven and data-driven methods allows us to construct an effective IRN during IAV infection. Based on the constructed network, we have identified new interactions among inflammatory factors and biological pathways. These findings provide new insight into our understanding of the molecular mechanisms in the inflammatory network in response to IAV infection. Further characterization and experimental validation of the interaction mechanisms identified from this study may lead to a novel therapeutic strategy for the control of infections and inflammatory responses.

Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

On the increase in network robustness and decrease in network response ability during the aging process: a systems biology approach via microarray data

Aging, an extremely complex and system-level process, has attracted much attention in medical research, especially since chronic diseases are quite prevalent in the elderly population. These may be the result of both gene mutations that lead to intrinsic perturbations and environmental changes that may stimulate signaling in the body. Therefore, analysis of network robustness to tolerate intrinsic perturbations and network response ability of gene networks to respond to external stimuli during the aging process may provide insight into the systematic changes caused by aging. We first propose novel methods to estimate network robustness and measure network response ability of gene regulatory networks by using their corresponding microarray data in the aging process. Then, we find that an aging-related gene network is more robust to intrinsic perturbations in the elderly than the young, and therefore is less responsive to external stimuli. Finally, we find that the response abilities of individual genes, especially FOXOs, NF-κB, and p53, are significantly different in the young versus the aged subjects. These observations are consistent with experimental findings in the aged population, e.g., elevated incidence of tumorigenesis and diminished resistance to oxidative stress. The proposed method can also be used for exploring and analyzing the dynamic properties of other biological processes via corresponding microarray data to provide useful information on clinical strategy and drug target selection.

Kaposi's sarcoma: a computational approach through protein-protein interaction and gene regulatory networks analysis

Interactomic data for Kaposi's Sarcoma Associated Herpes virus (KSHV)-the causative agent of vascular origin tumor called Kaposi's sarcoma-is relatively modest to date. The objective of this study was to assign functions to the previously uncharacterized ORFs in the virus using computational approaches and subsequently fit them to the host interactome landscape on protein, gene, and cellular level. On the basis of expression data, predicted RNA interference data, reported experimental data, and sequence based functional annotation we also tried to hypothesize the ORFs role in lytic and latent cycle during viral infection. We studied 17 previously uncharacterized ORFs in KSHV and the host-virus interplay seems to work in three major functional pathways-cell division, transport, metabolic and enzymatic in general. Studying the host-virus crosstalk for lytic phase predicts ORF 10 and ORF 11 as a predicted virus hub whereas PCNA is predicted as a host hub. On the other hand, ORF31 has been predicted as a latent phase inducible protein. KSHV invests a lion's share of its coding potential to suppress host immune response; various inflammatory mediators such as IFN-γ, TNF, IL-6, and IL-8 are negatively regulated by the ORFs while Il-10 secretion is stimulated in contrast. Although, like any other computational prediction, the study requires further validation, keeping into account the reproducibility and vast sample size of the systems biology approach the study allows us to propose an integrated network for host-virus interaction with good confidence. We hope that the study, in the long run, would help us identify effective dug against potential molecular targets.

Identification of follistatin-like 1 by expression cloning as an activator of the growth differentiation factor 15 gene and a prognostic biomarker in acute coronary syndrome

BACKGROUND

Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine and biomarker that is produced after myocardial infarction and that is related to prognosis in acute coronary syndrome (ACS). We hypothesized that secreted proteins that activate GDF15 production may represent new ACS biomarkers.

METHODS

We expressed clones from an infarcted mouse heart cDNA library in COS1 cells and assayed for activation of a luciferase reporter gene controlled by a 642-bp fragment of the mouse growth differentiation factor 15 (GDF15) gene promoter. We measured the circulating concentrations of follistatin-like 1 (FSTL1) and GDF15 in 1369 patients with ACS.

RESULTS

One cDNA clone that activated the GDF15 promoter-luciferase reporter encoded the secreted protein FSTL1. Treatment with FSTL1 activated GDF15 production in cultured cardiomyocytes. Transgenic production of FSTL1 stimulated GDF15 production in the murine heart, whereas cardiomyocyte-selective deletion of FSTL1 decreased production of GDF15 in cardiomyocytes, indicating that FSTL1 is sufficient and required for GDF15 production. In ACS, FSTL1 emerged as the strongest independent correlate of GDF15 (partial R(2) = 0.26). A total of 106 patients died of a cardiovascular cause during a median follow-up of 252 days. Patients with an FSTL1 concentration in the top quartile had a 3.7-fold higher risk of cardiovascular death compared with patients in the first 3 quartiles (P < 0.001). FSTL1 remained associated with cardiovascular death after adjustment for clinical, angiographic, and biochemical variables.

CONCLUSIONS

Our study is the first to use expression cloning for biomarker discovery upstream of a gene of interest and to identify FSTL1 as an independent prognostic biomarker in ACS.

Mechanistic explanations how cell-mediated immune activation, inflammation and oxidative and nitrosative stress pathways and their sequels and concomitants play a role in the pathophysiology of unipolar depression

This paper reviews that cell-mediated-immune (CMI) activation and inflammation contribute to depressive symptoms, including anhedonia; anxiety-like behaviors; fatigue and somatic symptoms, e.g. illness behavior or malaise; and mild cognitive impairment (MCI). These effects are in part mediated by increased levels of pro-inflammatory cytokines (PICs), e.g. interleukin-1 (IL-1), IL-6 and tumor necrosis factor (TNF)α, and Th-1-derived cytokines, such as IL-2 and interferon (IFN)γ. Moreover, new pathways, i.e. concomitants and sequels of CMI activation and inflammation, were detected in depression: (1) Induction of indoleamine 2,3-dioxygenase (IDO) by IFNγ and some PICs is associated with depleted plasma tryptophan, which may interfere with brain 5-HT synthesis, and increased production of anxiogenic and depressogenic tryptophan catabolites. (2) Increased bacterial translocation may cause depression-like behaviors by activating the cytokine network, oxidative and nitrosative stress (O&NS) pathways and IDO. (3) Induction of O&NS causes damage to membrane ω3 PUFAs, functional proteins, DNA and mitochondria, and autoimmune responses directed against intracellular molecules that may cause dysfunctions in intracellular signaling. (4) Decreased levels of ω3 PUFAs and antioxidants, such as coenzyme Q10, glutathione peroxidase or zinc, are associated with an increased inflammatory potential; more oxidative damage; the onset of specific symptoms; and changes in the expression or functions of brain 5-HT and N-methyl-d-aspartate receptors. (5) All abovementioned factors cause neuroprogression, that is a combination of neurodegeneration, neuronal apoptosis, and lowered neurogenesis and neuroplasticity. It is concluded that depression may be the consequence of a complex interplay between CMI activation and inflammation and their sequels/concomitants which all together cause neuroprogression that further shapes the depression phenotype. Future research should employ high throughput technologies to collect genetic and gene expression and protein data from patients with depression and analyze these data by means of systems biology methods to define the dynamic interactions between the different cell signaling networks and O&NS pathways that cause depression.

Constructing gene regulatory networks for long term photosynthetic light acclimation in Arabidopsis thaliana

BACKGROUND

Photosynthetic light acclimation is an important process that allows plants to optimize the efficiency of photosynthesis, which is the core technology for green energy. However, currently little is known about the molecular mechanisms behind the regulation of the photosynthetic light acclimation response. In this study, a systematic method is proposed to investigate this mechanism by constructing gene regulatory networks from microarray data of Arabidopsis thaliana.

METHODS

The potential TF-gene regulatory pairs of photosynthetic light acclimation have been obtained by data mining of literature and databases. Following the identification of these potential TF-gene pairs, they have been refined using Pearson's correlation, allowing the construction of a rough gene regulatory network. This rough gene regulatory network is then pruned using time series microarray data of Arabidopsis thaliana via the maximum likelihood system identification method and Akaike's system order detection method to approach the real gene regulatory network of photosynthetic light acclimation.

RESULTS

By comparing the gene regulatory networks under the PSI-to-PSII light shift and the PSII-to-PSI light shift, it is possible to identify important transcription factors for the different light shift conditions. Furthermore, the robustness of the gene network, in particular the hubs and weak linkage points, are also discussed under the different light conditions to gain further insight into the mechanisms of photosynthesis.

CONCLUSIONS

This study investigates the molecular mechanisms of photosynthetic light acclimation for Arabidopsis thaliana from the physiological level. This has been achieved through the construction of gene regulatory networks from the limited data sources and literature via an efficient computation method. If more experimental data for whole-genome ChIP-chip data and microarray data with multiple sampling points becomes available in the future, the proposed method will be improved on by constructing the whole-genome gene regulatory network. These advances will greatly improve our understanding of the mechanisms of the photosynthetic system.

Signal integration, crosstalk mechanisms and networks in the function of inflammatory cytokines

Infection or cell damage triggers the release of pro-inflammatory cytokines such as interleukin(IL)-1α or β and tumor necrosis factor (TNF)α which are key mediators of the host immune response. Following their identification and the elucidation of central signaling pathways, recent results show a highly complex crosstalk between various cytokines and their signaling effectors. The molecular mechanisms controlling signaling thresholds, signal integration and the function of feed-forward and feedback loops are currently revealed by combining methods from biochemistry, genetics and in silico analysis. Increasing evidence is mounted that defects in information processing circuits or their components can be causative for chronic or overshooting inflammation. As progress in biosciences has always benefitted from the use of well-studied model systems, research on inflammatory cytokines may function as a paradigm to reveal general principles of signal integration, crosstalk mechanisms and signaling networks.

Proteins and an inflammatory network expressed in colon tumors

The adenomatous polyposis coli (APC) protein is crucial to homeostasis of normal intestinal epithelia because it suppresses the β-catenin/TCF pathway. Consequently, loss or mutation of the APC gene causes colorectal tumors in humans and mice. Here, we describe our use of multidimensional protein identification technology (MudPIT) to compare protein expression in colon tumors to that of adjacent healthy colon tissue from Apc(Min/+) mice. Twenty-seven proteins were found to be up-regulated in colon tumors and 25 were down-regulated. As an extension of the proteomic analysis, the differentially expressed proteins were used as "seeds" to search for coexpressed genes. This approach revealed a coexpression network of 45 genes that is up-regulated in colon tumors. Members of the network include the antibacterial peptide cathelicidin (CAMP), Toll-like receptors (TLRs), IL-8, and triggering receptor expressed on myeloid cells 1 (TREM1). The coexpression network is associated with innate immunity and inflammation, and there is significant concordance between its connectivity in humans versus mice (Friedman: p value = 0.0056). This study provides new insights into the proteins and networks that are likely to drive the onset and progression of colon cancer.

Dynamic cross-talk analysis among TNF-R, TLR-4 and IL-1R signalings in TNFalpha-induced inflammatory responses

Background

Development in systems biology research has accelerated in recent years, and the reconstructions for molecular networks can provide a global view to enable in-depth investigation on numerous system properties in biology. However, we still lack a systematic approach to reconstruct the dynamic protein-protein association networks at different time stages from high-throughput data to further analyze the possible cross-talks among different signaling/regulatory pathways.

Methods

In this study we integrated protein-protein interactions from different databases to construct the rough protein-protein association networks (PPANs) during TNFα-induced inflammation. Next, the gene expression profiles of TNFα-induced HUVEC and a stochastic dynamic model were used to rebuild the significant PPANs at different time stages, reflecting the development and progression of endothelium inflammatory responses. A new cross-talk ranking method was used to evaluate the potential core elements in the related signaling pathways of toll-like receptor 4 (TLR-4) as well as receptors for tumor necrosis factor (TNF-R) and interleukin-1 (IL-1R).

Results

The highly ranked cross-talks which are functionally relevant to the TNFα pathway were identified. A bow-tie structure was extracted from these cross-talk pathways, suggesting the robustness of network structure, the coordination of signal transduction and feedback control for efficient inflammatory responses to different stimuli. Further, several characteristics of signal transduction and feedback control were analyzed.

Conclusions

A systematic approach based on a stochastic dynamic model is proposed to generate insight into the underlying defense mechanisms of inflammation via the construction of corresponding signaling networks upon specific stimuli. In addition, this systematic approach can be applied to other signaling networks under different conditions in different species. The algorithm and method proposed in this study could expedite prospective systems biology research when better experimental techniques for protein expression detection and microarray data with multiple sampling points become available in the future.

Integrated cellular network of transcription regulations and protein-protein interactions

Background

With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway.

Results

In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated.

Conclusions

We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

Protopia: a protein-protein interaction tool

BACKGROUND

Protein-protein interactions can be considered the basic skeleton for living organism self-organization and homeostasis. Impressive quantities of experimental data are being obtained and computational tools are essential to integrate and to organize this information. This paper presents Protopia, a biological tool that offers a way of searching for proteins and their interactions in different Protein Interaction Web Databases, as a part of a multidisciplinary initiative of our institution for the integration of biological data http://asp.uma.es.

RESULTS

The tool accesses the different Databases (at present, the free version of Transfac, DIP, Hprd, Int-Act and iHop), and results are expressed with biological protein names or databases codes and can be depicted as a vector or a matrix. They can be represented and handled interactively as an organic graph. Comparison among databases is carried out using the Uniprot codes annotated for each protein.

CONCLUSION

The tool locates and integrates the current information stored in the aforementioned databases, and redundancies among them are detected. Results are compatible with the most important network analysers, so that they can be compared and analysed by other world-wide known tools and platforms. The visualization possibilities help to attain this goal and they are especially interesting for handling multiple-step or complex networks.