CASCADE_SCAN: mining signal transduction network from high-throughput data based on steepest descent method

Signaling pathways of dental implants' osseointegration: a narrative review on two of the most relevant; NF-κB and Wnt pathways

INTRODUCTION

Cell signaling pathways are the biological reactions that control cell functions and fate. They also directly affect the body reactions to implanted biomaterials. It is well-known that dental implants success depends on a successful integration with the alveolar bone: "osseointegration" which events comprise early and later responses to the implanted biomaterials. The early events are mainly immune-inflammatory responses to the implant considered by its microenvironment as a foreign body. Later reactions are osteogenic aiming to regulate bone formation and remodeling. All these events are controlled by the cell signaling pathways in an incredible harmonious coordination.

AIM

The number of pathways having a role in osseointegration is so big to be reviewed in a single article. So the aim of this review was to study only two of the most relevant ones: the inflammatory Nuclear Factor Kappa B (NF-κB) pathway regulating the early osseointegration events and the osteogenic Wnt pathway regulating later events.

METHODS

We conducted a literature review using key databases to provide an overview about the NF-κB and Wnt cell signaling pathways and their mutual relationship with dental implants. A simplified narrative approach was conducted to explain these cell signaling pathways, their mode of activation and how they are related to the cellular events of osseointegration.

RESULTS AND CONCLUSION

NF-κB and Wnt cell signaling pathways are important cross-talking pathways that are affected by the implant's material and surface characteristics. The presence of the implant itself in the bone alters the intracellular events of both pathways in the adjacent implant's cellular microenvironment. Both pathways have a great role in the success or failure of osseointegration. Such knowledge can offer a new hope to treat failed implants and enhance osseointegration in difficult cases. This is consistent with advances in Omics technologies that can change the paradigm of dental implant therapy.

Sub-zero microRNA expression in the liver of the frozen hatchling painted turtle, Chrysemys picta marginata

The Midland painted turtle (Chrysemys picta marginata) are the highest known vertebrate species to experience and survive freezing and sub-zero temperatures. Painted turtles typically hatch from their eggs in the fall and remain underground in their nests until the following spring. While in these nests over the winter, hatchling turtles withstand over 50 % of their total extracellular body water freezing. Herein, the expression of microRNAs (miRNAs) was investigated in response to freezing stress in the hatchling painted turtle liver. A total of 204 known miRNAs were identified to be expressed in turtles, with 17 being upregulated and 13 being downregulated during freezing. KEGG and GO analyses suggested that upregulated miRNAs inhibit genes of cell cycle and Focal adhesion and Adherens junction, suggesting their role in downregulation of central metabolic processes necessary for metabolic rate depression (MRD) and maintaining the tissue homeostasis. Only 9 of the 36 enriched KEGG pathways were less targeted by miRNAs during freezing, including linoleic acid metabolism and multiple signaling pathways. These predicted upregulated pathways likely promote homeoviscous adaptation and expression of pro-survival/protective proteins for metabolic adaptations necessary for defence of liver during MRD. Overall, miRNA-seq analysis of liver revealed a strong role of miRNA in the adaptive strategy that not only enables hatchlings to substantially suppress their nonessential energy needs but also makes them flexible enough to restore and protect their basal organ functions by activating pro-survival processes.

Interactions and effects of food additive dye Allura red on pepsin structure and protease activity; experimental and computational supports

Background and purpose:

Today, color additives such as Allura red (AR) are widely used in different kinds of food products. Pepsin is a globular protein that is secreted as a digestive protease from the main cells in the stomach. Because of the important role of pepsin in protein digestion and because of its importance in digestive diseases the study of the interactions of pepsin with chemical food additives is important.

Experimental approach:

In this study, the interactions between AR and pepsin were investigated by different computational and experimental approaches such as ultraviolet and fluorescence spectroscopy along with computational molecular modeling.

Findings/Results:

The experimental results of fluorescence indicated that AR can strongly quench the fluorescence of pepsin through a static quenching. Thermodynamic analysis of the binding phenomena suggests that van der Waals forces and hydrogen bonding played a major role in the complex formation. The results of synchronous fluorescence spectra and furrier transformed infra-red (FTIR) experiments showed that there are no significant structural changes in the protein conformation. Also, examined pepsin protease activity revealed that the activity of pepsin was increased upon ligand binding. In agreement with the experimental results, the computational results showed that hydrogen bonding and van der Waals interactions occurred between AR and binding sites.

Conclusion and implications:

From the pharmaceutical point of view, this interaction can help us to get a deeper understanding of the effect of this synthetic dye on food digestion.

Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks

Identification of new drug and cell therapy targets for disease treatment will be facilitated by a detailed molecular understanding of normal and disease development. Human pluripotent stem cells can provide a large in vitro source of human cell types and, in a growing number of instances, also three-dimensional multicellular tissues called organoids. The application of stem cell technology to discovery and development of new therapies will be aided by detailed molecular characterisation of cell identity, cell signalling pathways and target gene networks. Big data or 'omics' techniques-particularly transcriptomics and proteomics-facilitate cell and tissue characterisation using thousands to tens-of-thousands of genes or proteins. These gene and protein profiles are analysed using existing and/or emergent bioinformatics methods, including a growing number of methods that compare sample profiles against compendia of reference samples. This review assesses how compendium-based analyses can aid the application of stem cell technology for new therapy development. This includes via robust definition of differentiated stem cell identity, as well as elucidation of complex signalling pathways and target gene networks involved in normal and diseased states.

Identification of active signaling pathways by integrating gene expression and protein interaction data

Background

Signaling pathways are the key biological mechanisms that transduce extracellular signals to affect transcription factor mediated gene regulation within cells. A number of computational methods have been developed to identify the topological structure of a specific signaling pathway using protein-protein interaction data, but they are not designed for identifying active signaling pathways in an unbiased manner. On the other hand, there are statistical methods based on gene sets or pathway data that can prioritize likely active signaling pathways, but they do not make full use of active pathway structure that link receptor, kinases and downstream transcription factors.

Results

Here, we present a method to simultaneously predict the set of active signaling pathways, together with their pathway structure, by integrating protein-protein interaction network and gene expression data. We evaluated the capacity for our method to predict active signaling pathways for dental epithelial cells, ocular lens epithelial cells, human pluripotent stem cell-derived lens epithelial cells, and lens fiber cells. This analysis showed our approach could identify all the known active pathways that are associated with tooth formation and lens development.

Conclusions

The results suggest that SPAGI can be a useful approach to identify the potential active signaling pathways given a gene expression profile. Our method is implemented as an open source R package, available via https://github.com/VCCRI/SPAGI/.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0655-x) contains supplementary material, which is available to authorized users.

The Genome-Scale Integrated Networks in Microorganisms

The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.

A biosystems approach to identify the molecular signaling mechanisms of TMEM30A during tumor migration

Understanding the molecular mechanisms underlying cell migration, which plays an important role in tumor growth and progression, is critical for the development of novel tumor therapeutics. Overexpression of transmembrane protein 30A (TMEM30A) has been shown to initiate tumor cell migration, however, the molecular mechanisms through which this takes place have not yet been reported. Thus, we propose the integration of computational and experimental approaches by first predicting potential signaling networks regulated by TMEM30A using a) computational biology methods, b) our previous mass spectrometry results of the TMEM30A complex in mouse tissue, and c) a number of migration-related genes manually collected from the literature, and subsequently performing molecular biology experiments including the in vitro scratch assay and real-time quantitative polymerase chain reaction (qPCR) to validate the reliability of the predicted network. The results verify that the genes identified in the computational signaling network are indeed regulated by TMEM30A during cell migration, indicating the effectiveness of our proposed method and shedding light on the regulatory mechanisms underlying tumor migration, which facilitates the understanding of the molecular basis of tumor invasion.

The beta-3 adrenergic agonist (CL-316,243) restores the expression of down-regulated fatty acid oxidation genes in type 2 diabetic mice

Background

The hallmark of Type 2 diabetes (T2D) is hyperglycemia, although there are multiple other metabolic abnormalities that occur with T2D, including insulin resistance and dyslipidemia. To advance T2D prevention and develop targeted therapies for its treatment, a greater understanding of the alterations in metabolic tissues associated with T2D is necessary. The aim of this study was to use microarray analysis of gene expression in metabolic tissues from a mouse model of pre-diabetes and T2D to further understand the metabolic abnormalities that may contribute to T2D. We also aimed to uncover the novel genes and pathways regulated by the insulin sensitizing agent (CL-316,243) to identify key pathways and target genes in metabolic tissues that can reverse the diabetic phenotype.

Methods

Male MKR mice on an FVB/n background and age matched wild-type (WT) FVB/n mice were used in all experiments. Skeletal muscle, liver and fat were isolated from prediabetic (3 week old) and diabetic (8 week old) MKR mice. Male MKR mice were treated with CL-316,243. Skeletal muscle, liver and fat were isolated after the treatment period. RNA was isolated from the metabolic tissues and subjected to microarray and KEGG database analysis.

Results

Significant decreases in the expression of mitochondrial and peroxisomal fatty acid oxidation genes were found in the skeletal muscle and adipose tissue of adult MKR mice, and the liver of pre-diabetic MKR mice, compared to WT controls. After treatment with CL-316,243, the circulating glucose and insulin concentrations in the MKR mice improved, an increase in the expression of peroxisomal fatty acid oxidation genes was observed in addition to a decrease in the expression of retinaldehyde dehydrogenases. These genes were not previously known to be regulated by CL-316,243 treatment.

Conclusions

This study uncovers novel genes that may contribute to pharmacological reversal of insulin resistance and T2D and may be targets for treatment. In addition, it explains the lower free fatty acid levels in MKR mice after treatment with CL-316,243 and furthermore, it provides biomarker genes such as ACAA1 and HSD17b4 which could be further probed in a future study.

Electronic supplementary material

The online version of this article (doi:10.1186/s12986-015-0003-8) contains supplementary material, which is available to authorized users.

Dissecting the mechanism of carotid atherosclerosis from the perspective of regulation

Carotid atherosclerosis is a chronic inflammatory disease of the arterial wall. The present study aimed to identify changes in the gene expression and regulatory factors for atherosclerotic plaques of carotid atherosclerosis from an early to an advanced stage. The original data were downloaded from the NCBI GEO database under accession no. GSE28829. Differentially expressed genes (DEGs) were detected by the Robust Multiarray Average (RMA). The enriched Gene Ontology (GO) terms and pathways for DEGs using DAVID were subsequently identified. The transcriptional and microRNA (miRNA) regulatory network were constructed for the DEGs. Cis-regulatory signals were also investigated. More genes were activated in the advanced stage compared with the early stage. IGHG1 and SPP1 were upregulated, while MYBL1 and PLD were downregulated. The upregulated genes in the advanced stage were involved in atherosclerosis‑related GO terms such as immune, vascular and cell movement homeostasis. The DEGs were significantly enriched in cell adhesion molecules (CAMs) and the focal adhesion pathway. MMP9 and CFL2 played key roles in the transcriptional regulatory network. Moreover, miR-328 was identified as one of the hubs in the miRNA regulatory network. The results may therefore be used to determine the mechanism involved in carotid atherosclerosis.

Bioinformatics analysis of biomarkers and transcriptional factor motifs in Down syndrome

In this study, biomarkers and transcriptional factor motifs were identified in order to investigate the etiology and phenotypic severity of Down syndrome. GSE 1281, GSE 1611, and GSE 5390 were downloaded from the gene expression ominibus (GEO). A robust multiarray analysis (RMA) algorithm was applied to detect differentially expressed genes (DEGs). In order to screen for biological pathways and to interrogate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, the database for annotation, visualization, and integrated discovery (DAVID) was used to carry out a gene ontology (GO) function enrichment for DEGs. Finally, a transcriptional regulatory network was constructed, and a hypergeometric distribution test was applied to select for significantly enriched transcriptional factor motifs. CBR1, DYRK1A, HMGN1, ITSN1, RCAN1, SON, TMEM50B, and TTC3 were each up-regulated two-fold in Down syndrome samples compared to normal samples; of these, SON and TTC3 were newly reported. CBR1, DYRK1A, HMGN1, ITSN1, RCAN1, SON, TMEM50B, and TTC3 were located on human chromosome 21 (mouse chromosome 16). The DEGs were significantly enriched in macromolecular complex subunit organization and focal adhesion pathways. Eleven significantly enriched transcription factor motifs (PAX5, EGR1, XBP1, SREBP1, OLF1, MZF1, NFY, NFKAPPAB, MYCMAX, NFE2, and RP58) were identified. The DEGs and transcription factor motifs identified in our study provide biomarkers for the understanding of Down syndrome pathogenesis and progression.

A dedicated database system for handling multi-level data in systems biology

Background

Advances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging.

Methods

To overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase.

Results and conclusion

In this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.

System approaches reveal the molecular networks involved in neural stem cell differentiation

The self-renewal and multipotent potentials in neural stem cells (NSCs) maintain the normal physiological functions of central nervous system (CNS). The abnormal differentiation of NSCs would lead to CNS disorders. However, the mechanisms of how NSCs differentiate into astrocytes, oligodendrocytes (OLs) and neurons are still unclear, which is mainly due to the complexity of differentiation processes and the limitation of the cell separation method. In this study, we modeled the dynamics of neural cell interactions in a systemic approach by mining the high-throughput genomic and proteomic data, and identified 8615 genes that are involved in various biological processes and functions with significant changes during the differentiation processes. A total of 1559 genes are specifically expressed in neural cells, in which 242 genes are NSC specific, 215 are astrocyte specific, 551 are OL specific, and 563 are neuron specific. In addition, we proposed 57 transcriptional regulators specifically expressed in NSCs may play essential roles in the development courses. These findings provide more comprehensive analysis for better understanding the endogenous mechanisms of NSC fate determination.