Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome

Background

Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40–50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.

Results

Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.

Conclusion

We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.

Reviewers

This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers’ comments section.

     

miR-1260b promotes cell migration and invasion of hepatocellular carcinoma by targeting the regulator of G-protein signaling 22

Objectives

To investigate whether miR-1260b can regulate migration and invasion of hepatocellular carcinoma (HCC) by targeting RGS22.

Results

miR-1260b was up-regulated in HCC tissues compared with their corresponding non-cancerous tissues. Over-expression of miR-1260b increased migration and invasion of HepG2 and SMMC-7721 cells associated with HCC. Regulator of G-protein signaling 22 (RGS22) was identified as a directly target of miR-1260b and was inhibited by miR-1260b. Knockdown of RGS22 increased proliferation of HCC cells.

Conclusions

The new identified miR-1260b/RGS22 axis provides useful therapeutic methods for treatment of HCC deepening on our understanding of underlying mechanisms of HCC tumorigenesis.

     

Normalization of TAM post-receptor signaling reveals a cell invasive signature for Axl tyrosine kinase

Background

Tyro3, Axl, and Mertk (TAMs) are a family of three conserved receptor tyrosine kinases that have pleiotropic roles in innate immunity and homeostasis and when overexpressed in cancer cells can drive tumorigenesis.

Methods

In the present study, we engineered EGFR/TAM chimeric receptors (EGFR/Tyro3, EGFR/Axl, and EGF/Mertk) with the goals to interrogate post-receptor functions of TAMs, and query whether TAMs have unique or overlapping post-receptor activation profiles. Stable expression of EGFR/TAMs in EGFR-deficient CHO cells afforded robust EGF inducible TAM receptor phosphorylation and activation of downstream signaling.

Results

Using a series of unbiased screening approaches, that include kinome-view analysis, phosphor-arrays, RNAseq/GSEA analysis, as well as cell biological and in vivo readouts, we provide evidence that each TAM has unique post-receptor signaling platforms and identify an intrinsic role for Axl that impinges on cell motility and invasion compared to Tyro3 and Mertk.

Conclusion

These studies demonstrate that TAM show unique post-receptor signatures that impinge on distinct gene expression profiles and tumorigenic outcomes.

     

Lack of interaction between ErbB2 and insulin receptor substrate signaling in breast cancer

Background

ErbB2 Receptor Tyrosine Kinase 2 (ErbB2, HER2/Neu) is amplified in breast cancer and associated with poor prognosis. Growing evidence suggests interplay between ErbB2 and insulin-like growth factor (IGF) signaling. For example, ErbB2 inhibitors can block IGF-induced signaling while, conversely, IGF1R inhibitors can inhibit ErbB2 action. ErbB receptors can bind and phosphorylate insulin receptor substrates (IRS) and this may be critical for ErbB-mediated anti-estrogen resistance in breast cancer. Herein, we examined crosstalk between ErbB2 and IRSs using cancer cell lines and transgenic mouse models.

Methods

MMTV-ErbB2 and MMTV-IRS2 transgenic mice were crossed to create hemizygous MMTV-ErbB2/MMTV-IRS2 bigenic mice. Signaling crosstalk between ErbB2 and IRSs was examined in vitro by knockdown or overexpression followed by western blot analysis for downstream signaling intermediates and growth assays.

Results

A cross between MMTV-ErbB2 and MMTV-IRS2 mice demonstrated no enhancement of ErbB2 mediated mammary tumorigenesis or metastasis by elevated IRS2. Substantiating this, overexpression or knockdown of IRS1 or IRS2 in MMTV-ErbB2 mammary cancer cell lines had little effect upon ErbB2 signaling. Similar results were obtained in human mammary epithelial cells (MCF10A) and breast cancer cell lines.

Conclusion

Despite previous evidence suggesting that ErbB receptors can bind and activate IRSs, our findings indicate that ErbB2 does not cooperate with the IRS pathway in these models to promote mammary tumorigenesis.

     

An efficient algorithm for identifying primary phenotype attractors of a large-scale Boolean network

Background

Boolean network modeling has been widely used to model large-scale biomolecular regulatory networks as it can describe the essential dynamical characteristics of complicated networks in a relatively simple way. When we analyze such Boolean network models, we often need to find out attractor states to investigate the converging state features that represent particular cell phenotypes. This is, however, very difficult (often impossible) for a large network due to computational complexity.

Results

There have been some attempts to resolve this problem by partitioning the original network into smaller subnetworks and reconstructing the attractor states by integrating the local attractors obtained from each subnetwork. But, in many cases, the partitioned subnetworks are still too large and such an approach is no longer useful. So, we have investigated the fundamental reason underlying this problem and proposed a novel efficient way of hierarchically partitioning a given large network into smaller subnetworks by focusing on some attractors corresponding to a particular phenotype of interest instead of considering all attractors at the same time. Using the definition of attractors, we can have a simplified update rule with fixed state values for some nodes. The resulting subnetworks were small enough to find out the corresponding local attractors which can be integrated for reconstruction of the global attractor states of the original large network.

Conclusions

The proposed approach can substantially extend the current limit of Boolean network modeling for converging state analysis of biological networks.

     

PTEN at the interface of immune tolerance and tumor suppression

Background

PTEN is well known to function as a tumor suppressor that antagonizes oncogenic signaling and maintains genomic stability. The PTEN gene is frequently deleted or mutated in human cancers and the wide cancer spectrum associated with PTEN deficiency has been recapitulated in a variety of mouse models of Pten deletion or mutation. Pten mutations are highly penetrant in causing various types of spontaneous tumors that often exhibit resistance to anticancer therapies including immunotherapy. Recent studies demonstrate that PTEN also regulates immune functionality.

Objective

To understand the multifaceted functions of PTEN as both a tumor suppressor and an immune regulator.

Methods

This review will summarize the emerging knowledge of PTEN function in cancer immunoediting. In addition, the mechanisms underlying functional integration of various PTEN pathways in regulating cancer evolution and tumor immunity will be highlighted.

Results

Recent preclinical and clinical studies revealed the essential role of PTEN in maintaining immune homeostasis, which significantly expands the repertoire of PTEN functions. Mechanistically, aberrant PTEN signaling alters the interplay between the immune system and tumors, leading to immunosuppression and tumor escape.

Conclusion

Rational design of personalized anti-cancer treatment requires mechanistic understanding of diverse PTEN signaling pathways in modulation of the crosstalk between tumor and immune cells.

     

From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes

Background

The protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function.

Results

Recent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation.

Conclusions

The proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction.

     

Type 2 diabetes increases oocyte mtDNA mutations which are eliminated in the offspring by bottleneck effect

Background

Diabetes induces many complications including reduced fertility and low oocyte quality, but whether it causes increased mtDNA mutations is unknown.

Methods

We generated a T2D mouse model by using high-fat-diet (HFD) and Streptozotocin (STZ) injection. We examined mtDNA mutations in oocytes of diabetic mice by high-throughput sequencing techniques.

Results

T2D mice showed glucose intolerance, insulin resistance, low fecundity compared to the control group. T2D oocytes showed increased mtDNA mutation sites and mutation numbers compared to the control counterparts. mtDNA mutation examination in F1 mice showed that the mitochondrial bottleneck could eliminate mtDNA mutations.

Conclusions

T2D mice have increased mtDNA mutation sites and mtDNA mutation numbers in oocytes compared to the counterparts, while these adverse effects can be eliminated by the bottleneck effect in their offspring. This is the first study using a small number of oocytes to examine mtDNA mutations in diabetic mothers and offspring.

     

Association of a novel point mutation in MSH2 gene with familial multiple primary cancers

Background

Multiple primary cancers (MPC) have been identified as two or more cancers without any subordinate relationship that occur either simultaneously or metachronously in the same or different organs of an individual. Lynch syndrome is an autosomal dominant genetic disorder that increases the risk of many types of cancers. Lynch syndrome patients who suffer more than two cancers can also be considered as MPC; patients of this kind provide unique resources to learn how genetic mutation causes MPC in different tissues.

Methods

We performed a whole genome sequencing on blood cells and two tumor samples of a Lynch syndrome patient who was diagnosed with five primary cancers. The mutational landscape of the tumors, including somatic point mutations and copy number alternations, was characterized. We also compared Lynch syndrome with sporadic cancers and proposed a model to illustrate the mutational process by which Lynch syndrome progresses to MPC.

Results

We revealed a novel pathologic mutation on the MSH2 gene (G504 splicing) that associates with Lynch syndrome. Systematical comparison of the mutation landscape revealed that multiple cancers in the proband were evolutionarily independent. Integrative analysis showed that truncating mutations of DNA mismatch repair (MMR) genes were significantly enriched in the patient. A mutation progress model that included germline mutations of MMR genes, double hits of MMR system, mutations in tissue-specific driver genes, and rapid accumulation of additional passenger mutations was proposed to illustrate how MPC occurs in Lynch syndrome patients.

Conclusion

Our findings demonstrate that both germline and somatic alterations are driving forces of carcinogenesis, which may resolve the carcinogenic theory of Lynch syndrome.

     

A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles

Introduction

It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.

Objectives

This work simplifies this process.

Methods

A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.

Results

The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.

Conclusion

This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.

     

Feed-forward neural network model for hunger and satiety related VAS score prediction

Background

An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding.

Methods

A multilayer feed-forward neural network was trained with sets of experimental data relating concentration-time courses of plasma satiety hormones to Visual Analog Scales (VAS) scores. The network successfully predicted VAS responses from sets of satiety hormone data obtained in experiments using different food compositions.

Results

The correlation coefficients for the predicted VAS responses for test sets having i) a full set of three satiety hormones, ii) a set of only two satiety hormones, and iii) a set of only one satiety hormone were 0.96, 0.96, and 0.89, respectively. The predicted VAS responses discriminated the satiety effects of high satiating food types from less satiating food types both in orally fed and ileal infused forms.

Conclusions

From this application of artificial neural networks, one may conclude that neural network models are very suitable to describe situations where behavior is complex and incompletely understood. However, training data sets that fit the experimental conditions need to be available.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

RAC3 influences the chemoresistance of colon cancer cells through autophagy and apoptosis inhibition

Background

RAC3 coactivator overexpression has been implicated in tumorigenesis, contributing to inhibition of apoptosis and autophagy. Both mechanisms are involved in resistance to treatment with chemotherapeutic agents. The aim of this study was to investigate its role in chemoresistance of colorectal cancer.

Methods

The sensitivity to 5-fluorouracil and oxaliplatin in colon cancer cells HT-29, HCT 116 and Lovo cell lines, expressing high or low natural levels of RAC3, was investigated using viability assays.

Results

In HCT 116 cells, we found that although 5-fluorouracil was a poor inducer of apoptosis, autophagy was strongly induced, while oxaliplatin has shown a similar ability to induce both of them. However, in HCT 116 cells expressing a short hairpin RNA for RAC3, we found an increased sensitivity to both drugs if it is compared with control cells. 5-Fluorouracil and oxaliplatin treatment lead to an enhanced caspase 3-dependent apoptosis and produce an increase of autophagy. In addition, both process have shown to be trigged faster than in control cells, starting earlier after stimulation.

Conclusions

Our results suggest that RAC3 expression levels influence the sensitivity to chemotherapeutic drugs. Therefore, the knowledge of RAC3 expression levels in tumoral samples could be an important contribution to design new improved therapeutic strategies in the future.

     

Profiling the interactome of protein kinase C ζ by proteomics and bioinformatics

Background

Protein kinase C ζ (PKCζ), an isoform of the atypical protein kinase C, is a pivotal regulator in cancer. However, the molecular and cellular mechanisms whereby PKCζ regulates tumorigenesis and metastasis are still not fully understood. In this study, proteomics and bioinformatics analyses were performed to establish a protein-protein interaction (PPI) network associated with PKCζ, laying a stepping stone to further understand the diverse biological roles of PKCζ.

Methods

Protein complexes associated with PKCζ were purified by co-immunoprecipitation from breast cancer cell MDA-MB-231 and identified by LC-MS/MS. Two biological replicates and two technical replicates were analyzed. The observed proteins were filtered using the CRAPome database to eliminate the potential false positives. The proteomics identification results were combined with PPI database search to construct the interactome network. Gene ontology (GO) and pathway analysis were performed by PANTHER database and DAVID. Next, the interaction between PKCζ and protein phosphatase 2 catalytic subunit alpha (PPP2CA) was validated by co-immunoprecipitation, Western blotting and immunofluorescence. Furthermore, the TCGA database and the COSMIC database were used to analyze the expressions of these two proteins in clinical samples.

Results

The PKCζ centered PPI network containing 178 nodes and 1225 connections was built. Network analysis showed that the identified proteins were significantly associated with several key signaling pathways regulating cancer related cellular processes.

Conclusions

Through combining the proteomics and bioinformatics analyses, a PKCζ centered PPI network was constructed, providing a more complete picture regarding the biological roles of PKCζ in both cancer regulation and other aspects of cellular biology.

     

<Emphasis FontCategory="SansSerif">Investigation on changes of modularity and robustness by edge-removal mutations in signaling networks</Emphasis>

Background

Biological networks consisting of molecular components and interactions are represented by a graph model. There have been some studies based on that model to analyze a relationship between structural characteristics and dynamical behaviors in signaling network. However, little attention has been paid to changes of modularity and robustness in mutant networks.

Results

In this paper, we investigated the changes of modularity and robustness by edge-removal mutations in three signaling networks. We first observed that both the modularity and robustness increased on average in the mutant network by the edge-removal mutations. However, the modularity change was negatively correlated with the robustness change. This implies that it is unlikely that both the modularity and the robustness values simultaneously increase by the edge-removal mutations. Another interesting finding is that the modularity change was positively correlated with the degree, the number of feedback loops, and the edge betweenness of the removed edges whereas the robustness change was negatively correlated with them. We note that these results were consistently observed in randomly structure networks. Additionally, we identified two groups of genes which are incident to the highly-modularity-increasing and the highly-robustness-decreasing edges with respect to the edge-removal mutations, respectively, and observed that they are likely to be central by forming a connected component of a considerably large size. The gene-ontology enrichment of each of these gene groups was significantly different from the rest of genes. Finally, we showed that the highly-robustness-decreasing edges can be promising edgetic drug-targets, which validates the usefulness of our analysis.

Conclusions

Taken together, the analysis of changes of robustness and modularity against edge-removal mutations can be useful to unravel novel dynamical characteristics underlying in signaling networks.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Inferring gene regulatory networks from asynchronous microarray data with AIRnet

Background

Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. Microarray data available for many biological experiments is limited to a small number of arrays with little or no time series guarantees. When several samples are averaged to examine differences in mean value between a diseased and normal state, information from individual samples that could indicate a gene relationship can be lost.

Results

Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inference using more practical assumptions about the microarray data. By learning correlation patterns for the changes in microarray values from all pairs of samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments.

Conclusions

By focussing on the changes between microarray samples, instead of absolute values, increased information can be gleaned from expression data.

     

A genetic algorithm-based boolean delay model of intracellular signal transduction in inflammation

Background

Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed.

Results

In present study, we employed a genetic algorithm-based Boolean model to represent NF-κB signaling pathway. We were able to capture feedback and crosstalk characteristics to enhance our understanding on the acute and chronic inflammatory response. Key network components affecting the response dynamics were identified.

Conclusions

We designed an effective algorithm to elucidate the process of immune response using comprehensive knowledge about network structure and limited experimental data on dynamic responses. This approach can potentially be implemented for large-scale analysis on cellular processes and organism behaviors.

     

Stimulation of mouse vibrissal follicle growth by recombinant human fibroblast growth factor 20

Objectives

To explore potential effects of recombinant human fibroblast growth factor 20 (rhFGF20) in the growth of cultured mouse vibrissal follicles.

Results

The growth of cultured mouse vibrissal follicles was significantly induced by rhFGF20 in a dose dependent pattern in the in vitro vibrissal follicle organ culture model. However, too high concentration of rhFGF20 could inhibit the growth of vibrissal follicles. We further demonstrated that rhFGF20 stimulated the proliferation of hair matrix cells and activated Wnt/β-catenin signaling pathway.

Conclusions

The rhFGF20 might be a potential therapeutic agent to treat hair loss disorders.