Exploring pathways from gene co-expression to network dynamics

Leucine rich repeat LGI family member 3: Integrative analyses support its prognostic association with pancreatic adenocarcinoma

Leucine rich repeat LGI family member 3 (LGI3) is a member of the LGI protein family. Previous studies of our group have reported that LGI3 is expressed in adipose tissue, skin and brain, and serves as a multifunctional cytokine. LGI3 may also be involved in cytokine networks in various cancers. This study aimed to analyze differentially expressed genes in pancreatic adenocarcinoma (PAC) tissues and PAC cohort data in order to evaluate the prognostic role of LGI3. The expression microarray and the PAC cohort data were analyzed by bioinformatic methods for differential expression, protein-protein interactions, functional enrichment and pathway analyses, gene co-expression network analysis, and prognostic association analysis. Results showed that LGI3 expression was significantly reduced in PAC tissues. Nineteen upregulated genes and 31 downregulated genes in PAC tissues were identified as LGI3-regulated genes. Protein-protein interaction network analysis demonstrated that 92% (46/50) of the LGI3-regulated genes that were altered in PACs belonged to a protein-protein interaction network cluster. Functional enrichment and gene co-expression network analyses demonstrated that these genes in the network cluster were associated with various processes including inflammatory and immune responses, metabolic processes, cell differentiation, and angiogenesis. PAC cohort analyses revealed that low expression levels of LGI3 were significantly associated with poor PAC prognosis. Analysis of favorable or unfavorable prognostic gene products in PAC showed that 93 LGI3-regulated genes were differentially associated with PAC prognosis. LGI3 expression was correlated with the tumor-infiltration levels of various immune cells. Taken together, these results suggested that LGI3 may be a potential prognostic marker of PAC.

Unraveling Plant-Pathogen Interactions in Cereals Using RNA-seq

Over the past two decades, there have been significant advancements in the realm of transcriptomics, or the study of genes and their expression. Modern RNA sequencing technologies and high-performance computing are creating a "big data" revolution that provides new opportunities to explore the interactions between cereals and pathogens that affect grain yield and food safety. These data are being used to annotate genes and gene variants, as well as identify differentially expressed genes and create global gene co-expression networks. Moreover, these data can unravel the complex interactions between pathogen and host and identify genes and pathways involved in these interactions. This information can then be used for disease mitigation and the development of crops with superior resistance.

Evolution of higher mesenchymal CD44 expression in the human lineage

CD44 is an extracellular matrix receptor implicated in cancer progression. CD44 increases the invasibility of skin (SF) and endometrial stromal fibroblasts (ESF) by cancer and trophoblast cells. We reasoned that the evolution of CD44 expression can affect both, the fetal–maternal interaction through CD44 in ESF as well as vulnerability to malignant cancer through expression in SF. We studied the evolution of CD44 expression in mammalian SF and ESF and demonstrate that in the human lineage evolved higher CD44 expression. Isoform expression in cattle and human is very similar suggesting that differences in invasibility are not due to the nature of expressed isoforms. We then asked whether the concerted gene expression increase in both cell types is due to shared regulatory mechanisms or due to cell type-specific factors. Reporter gene experiments with cells and cis-regulatory elements from human and cattle show that the difference of CD44 expression is due to cis effects as well as cell type-specific trans effects. These results suggest that the concerted expression increase is likely due to selection acting on both cell types because the evolutionary change in cell type-specific factors requires selection on cell type-specific functions. This scenario implies that the malignancy enhancing effects of elevated CD44 expression in humans likely evolved as a side-effect of positive selection on a yet unidentified other function of CD44. A possible candidate is the anti-fibrotic effect of CD44 but there are no reliable data showing that humans and primates are less fibrotic than other mammals.

Androgen Deprivation Induces Transcriptional Reprogramming in Prostate Cancer Cells to Develop Stem Cell-Like Characteristics

Enzalutamide, an antiandrogen, is approved for therapy of castration resistant prostate cancer. Clinical applications have shown that approximately 30% of patients acquire resistance after a short period of treatment. However, the molecular mechanisms underlying this resistance is not completely understood. To identify transcriptomic signatures associated with acquisition of drug resistance we profiled gene expression of paired enzalutamide sensitive and resistant human prostate cancer LNCaP (lymph node carcinoma of the prostate) and C4-2B cells. Overlapping genes differentially regulated in the enzalutamide resistant cells were ranked by Ingenuity Pathway Analysis and their functional validation was performed using ingenuity knowledge database followed by confirmation to correlate transcript with protein expression. Analysis revealed that genes associated with cancer stem cells, such as POU5F1 (OCT4), SOX2, NANOG, BMI1, BMP2, CD44, SOX9, and ALDH1 were markedly upregulated in enzalutamide resistant cells. Amongst the pathways enriched in the enzalutamide-resistant cells were those associated with RUNX2, hedgehog, integrin signaling, and molecules associated with elastic fibers. Further examination of a patient cohort undergoing ADT and its comparison with no-ADT group demonstrated high expression of POU5F1 (OCT4), ALDH1, and SOX2 in ADT specimens, suggesting that they may be clinically relevant therapeutic targets. Altogether, our approach exhibits the potential of integrative transcriptomic analyses to identify critical genes and pathways of antiandrogen resistance as a promising approach for designing novel therapeutic strategies to circumvent drug resistance.

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis

Genes central to drug absorption, distribution, metabolism and elimination (ADME) also regulate numerous endogenous molecules. The Remote Sensing and Signaling Hypothesis argues that an ADME gene-centered network-including SLC and ABC "drug" transporters, "drug" metabolizing enzymes (DMEs), and regulatory genes-is essential for inter-organ communication via metabolites, signaling molecules, antioxidants, gut microbiome products, uremic solutes, and uremic toxins. By cross-tissue co-expression network analysis, the gut, liver, and kidney (GLK) formed highly connected tissue-specific clusters of SLC transporters, ABC transporters, and DMEs. SLC22, SLC25 and SLC35 families were network hubs, having more inter-organ and intra-organ connections than other families. Analysis of the GLK network revealed key physiological pathways (e.g., involving bile acids and uric acid). A search for additional genes interacting with the network identified HNF4α, HNF1α, and PXR. Knockout gene expression data confirmed ~60-70% of predictions of ADME gene regulation by these transcription factors. Using the GLK network and known ADME genes, we built a tentative gut-liver-kidney "remote sensing and signaling network" consisting of SLC and ABC transporters, as well as DMEs and regulatory proteins. Together with protein-protein interactions to prioritize likely functional connections, this network suggests how multi-specificity combines with oligo-specificity and mono-specificity to regulate homeostasis of numerous endogenous small molecules.

Leucine rich repeat LGI family member 3: Integrative analyses reveal its prognostic association with non-small cell lung cancer

Leucine rich repeat LGI family member 3 (LGI3) is a member of the LGI protein family. Our previous studies reported that LGI3 was expressed in adipose tissues, brain and skin, where it served roles as a multifunctional cytokine and pro-inflammatory adipokine. It was hypothesized that LGI3 may be involved in cytokine networks in cancer. The present study aimed to analyze differentially expressed genes in non-small cell lung cancer (NSCLC) tissues and NSCLC cohort data, to evaluate the prognostic role of LGI3. Expression microarray and NSCLC cohort data were statistically analyzed by bioinformatic methods, and protein-protein interactions, functional enrichment and pathway, gene coexpression network (GCN) and prognostic association analyses were performed. The results demonstrated that the expression levels of LGI3 and its receptor a disintegrin and metalloproteinase domain-containing protein 22 were significantly decreased in NSCLC tissues. A total of two upregulated genes and 11 downregulated genes in NSCLC tissues were identified as LGI3-regulated genes. Protein-protein interaction network analysis demonstrated that all LGI3-regulated genes that were altered in NSCLC were involved in a protein-protein interaction network cluster. Functional enrichment, Kyoto Encyclopedia of Genes and Genomes pathway and GCN analyses demonstrated the association of these genes with the immune and inflammatory responses, angiogenesis, the tumor necrosis factor pathway, and chemokine and peroxisome proliferator-activated receptor signaling pathways. Analysis of NSCLC cohorts revealed that low expression levels of LGI3 was significantly associated with poor prognosis of NSCLC. Analysis of the somatic mutations of the LGI3 gene in NSCLC revealed that the amino acid residues altered in NSCLC included two single nucleotide polymorphism sites and three phylogenetically coevolved amino acid residues. Taken together, these results suggest that LGI3 may be a potential prognostic marker of NSCLC.

Informed walks: whispering hints to gene hunters inside networks' jungle

Background

Systemic approaches offer a different point of view on the analysis of several types of molecular associations as well as on the identification of specific gene communities in several cancer types. However, due to lack of sufficient data needed to construct networks based on experimental evidence, statistical gene co-expression networks are widely used instead. Many efforts have been made to exploit the information hidden in these networks. However, these approaches still need to capitalize comprehensively the prior knowledge encrypted into molecular pathway associations and improve their efficiency regarding the discovery of both exclusive subnetworks as candidate biomarkers and conserved subnetworks that may uncover common origins of several cancer types.

Methods

In this study we present the development of the Informed Walks model based on random walks that incorporate information from molecular pathways to mine candidate genes and gene-gene links. The proposed model has been applied to TCGA (The Cancer Genome Atlas) datasets from seven different cancer types, exploring the reconstructed co-expression networks of the whole set of genes and driving to highlighted sub-networks for each cancer type. In the sequel, we elucidated the impact of each subnetwork on the indication of underlying exclusive and common molecular mechanisms as well as on the short-listing of drugs that have the potential to suppress the corresponding cancer type through a drug-repurposing pipeline.

Conclusions

We have developed a method of gene subnetwork highlighting based on prior knowledge, capable to give fruitful insights regarding the underlying molecular mechanisms and valuable input to drug-repurposing pipelines for a variety of cancer types.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0473-6) contains supplementary material, which is available to authorized users.

Chemogenomic analysis of neuronal differentiation with pathway changes in PC12 cells

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database creates networks from interrelations between molecular biology and underlying chemical elements. This allows for analysis of biologic networks, genomic information, and higher-order functional information at a system level. Through high throughput experiments and system biology analysis, we investigated the genes and pathways associated with NGF induced neuronal differentiation. We performed microarray experiments and used the KEGG database, system biology analysis, and annotation of pathway functions to study NGF-induced differentiation in PC12 cells. We identified 2020 NGF-induced genes with altered expressions over time. Cross-matching with the KEGG database revealed 830 genes; among which, 395 altered genes were found to have a 2-fold increase in gene expression over a two-hour period. We then identified 191 associated biologic pathways in the KEGG database; the top 15 pathways showed correlation with neural differentiation. These included the neurotrophin pathways, mitogen-activated protein kinase (MAPK) pathways, genes associated with axonal guidance and the Wnt pathways. The activation of these pathways synchronized with nerve growth factor (NGF)-induced differentiation in PC12 cells. In summary, we have established a model system that allows one to systematically characterize the functional pathway changes in a group of neuronal population after an external stimulus.

Discovering gene re-ranking efficiency and conserved gene-gene relationships derived from gene co-expression network analysis on breast cancer data

Systemic approaches are essential in the discovery of disease-specific genes, offering a different perspective and new tools on the analysis of several types of molecular relationships, such as gene co-expression or protein-protein interactions. However, due to lack of experimental information, this analysis is not fully applicable. The aim of this study is to reveal the multi-potent contribution of statistical network inference methods in highlighting significant genes and interactions. We have investigated the ability of statistical co-expression networks to highlight and prioritize genes for breast cancer subtypes and stages in terms of: (i) classification efficiency, (ii) gene network pattern conservation, (iii) indication of involved molecular mechanisms and (iv) systems level momentum to drug repurposing pipelines. We have found that statistical network inference methods are advantageous in gene prioritization, are capable to contribute to meaningful network signature discovery, give insights regarding the disease-related mechanisms and boost drug discovery pipelines from a systems point of view.

Weighted frequent gene co-expression network mining to identify genes involved in genome stability

Gene co-expression network analysis is an effective method for predicting gene functions and disease biomarkers. However, few studies have systematically identified co-expressed genes involved in the molecular origin and development of various types of tumors. In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. We compared the results with networks found in multiple normal tissue types and discovered 18 tightly connected frequent networks in cancers, with highly enriched functions on cancer-related activities. Most networks identified also formed physically interacting networks. In contrast, only 6 networks were found in normal tissues, which were highly enriched for housekeeping functions. The largest cancer network contained many genes with genome stability maintenance functions. We tested 13 selected genes from this network for their involvement in genome maintenance using two cell-based assays. Among them, 10 were shown to be involved in either homology-directed DNA repair or centrosome duplication control including the well-known cancer marker MKI67. Our results suggest that the commonly recognized characteristics of cancers are supported by highly coordinated transcriptomic activities. This study also demonstrated that the co-expression network directed approach provides a powerful tool for understanding cancer physiology, predicting new gene functions, as well as providing new target candidates for cancer therapeutics.

Comparing statistical methods for constructing large scale gene networks

The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structure; (2) GeneNet and SPACE (Sparse PArtial Correlation Estimation) performed well in identifying a few connections with high specificity.

Integrating fragmented software applications into holistic solutions: focus on drug discovery

INTRODUCTION

Current advances in software development and global molecular profiling technologies allow the development of holistic software solutions for drug discovery. Such solutions must streamline in silico drug and therapy development by integrating all types of data into one knowledge base and also by enabling continuous analysis workflows uninterrupted by manual restructuring of inputs and outputs from workflow components. They must provide a collaborative environment for data sharing between multiple users and allow importing of all types of experimental data for subsequent analysis.

AREAS COVERED

The reader is provided with a review of disparate software applications currently used in drug development and a discussion of existing organizational challenges for development of holistic software solutions. The reader is also provided with a proposed conceptual framework for integration of software components and some details for its implementation are suggested.

EXPERT OPINION

Holistic solutions can undoubtedly affect the speed, quality and cost of drug development and personalized therapy. However, it must be constantly evolved to rapidly adopt new experimental and statistical methods, incorporate advances in software technologies and allow perpetual optimization of its components. Perpetual improvements in data structure, data quality, statistical algorithms and other mathematical approaches for computer modeling can gradually shift financial and cultural emphasis in the pharmaceutical industry away from traditional experimental approaches and towards computational approaches.

The tumor microenvironment: the making of a paradigm

What has been will be again, what has been done will be done again; there is nothing new under the sun

(Ecclesiastes 1:9)

Stephen Paget was the conceptual father of the role played by the Tumor Microenvironment (TME) in tumor progression. The focus of this essay is the developmental phase of the post Paget TME research. Attempts will be made to highlight some of the pioneering work of scientists from the late sixties through the eighties of last century who laid the foundations for the contemporary scientific achievements of TME research but whose ground breaking studies are rarely cited. This review should serve as a small tribute to their great work.