Discovery and validation of a glioblastoma co-expressed gene module

Influence of Zika virus on the cytotoxicity, cell adhesion, apoptosis and inflammatory markers of glioblastoma cells

Glioblastoma (GBM) is one of the most common types of brain tumor in adults. Despite the availability of treatments for this disease, GBM remains one of the most lethal and difficult types of tumors to treat, and thus, a majority of patients die within 2 years of diagnosis. Infection with Zika virus (ZIKV) inhibits cell proliferation and induces apoptosis, particularly in developing neuronal cells, and thus could potentially be considered an alternative for GBM treatment. In the present study, two GBM cell lines (U-138 and U-251) were infected with ZIKV at different multiplicities of infection (0.1, 0.01 and 0.001), and cell viability, migration, adhesion, induction of apoptosis, interleukin levels and CD14/CD73 cell surface marker expression were analyzed. The present study demonstrated that ZIKV infection promoted loss of cell viability and increased apoptosis in U-138 cells, as measured by MTT and triplex assay, respectively. Changes in cell migration, as determined by wound healing assay, were not observed; however, the GBM cell lines exhibited an increase in cell adhesion when compared with non-tumoral cells (Vero). The Luminex immunoassay showed a significant increase in the expression levels of IL-4 specifically in U-251 cells (MOI 0.001) following exposure to ZIKV. There was no significant change in the expression levels of IFN-γ upon ZIKV infection in the cell lines tested. Furthermore, a marked increase in the percentage of cells expressing the CD14 surface marker was observed in both GBM cell lines compared with in Vero cells; and significantly increased CD73 expression was observed particularly in U-251 cells, when compared with uninfected cells. These findings indicate that ZIKV infection could lead to reduced cell viability, elevated CD73 expression, improved cellular adherence, and higher rates of apoptosis in glioblastoma cells. Further studies are required to explore the potential use of ZIKV in the treatment of GBM.

Addressing noise in co-expression network construction

Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The ‘one-size-fits-all’ approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow.

Integration of RNA-Seq and proteomics data identifies glioblastoma multiforme surfaceome signature

Background

Glioblastoma multiforme (GBM) is a highly lethal, stage IV brain tumour with a prevalence of approximately 2 per 10,000 people globally. The cell surface proteins or surfaceome serve as information gateway in many oncogenic signalling pathways and are important in modulating cancer phenotypes. Dysregulation in surfaceome expression and activity have been shown to promote tumorigenesis. The expression of GBM surfaceome is a case in point; OMICS screening in a cell-based system identified that this sub-proteome is largely perturbed in GBM. Additionally, since these cell surface proteins have ‘direct’ access to drugs, they are appealing targets for cancer therapy. However, a comprehensive GBM surfaceome landscape has not been fully defined yet. Thus, this study aimed to define GBM-associated surfaceome genes and identify key cell-surface genes that could potentially be developed as novel GBM biomarkers for therapeutic purposes.

Methods

We integrated the RNA-Seq data from TCGA GBM (n = 166) and GTEx normal brain cortex (n = 408) databases to identify the significantly dysregulated surfaceome in GBM. This was followed by an integrative analysis that combines transcriptomics, proteomics and protein-protein interaction network data to prioritize the high-confidence GBM surfaceome signature.

Results

Of the 2381 significantly dysregulated genes in GBM, 395 genes were classified as surfaceome. Via the integrative analysis, we identified 6 high-confidence GBM molecular signature, HLA-DRA, CD44, SLC1A5, EGFR, ITGB2, PTPRJ, which were significantly upregulated in GBM. The expression of these genes was validated in an independent transcriptomics database, which confirmed their upregulated expression in GBM. Importantly, high expression of CD44, PTPRJ and HLA-DRA is significantly associated with poor disease-free survival. Last, using the Drugbank database, we identified several clinically-approved drugs targeting the GBM molecular signature suggesting potential drug repurposing.

Conclusions

In summary, we identified and highlighted the key GBM surface-enriched repertoires that could be biologically relevant in supporting GBM pathogenesis. These genes could be further interrogated experimentally in future studies that could lead to efficient diagnostic/prognostic markers or potential treatment options for GBM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08591-0.

Named Data Networking for Genomics Data Management and Integrated Workflows

Advanced imaging and DNA sequencing technologies now enable the diverse biology community to routinely generate and analyze terabytes of high resolution biological data. The community is rapidly heading toward the petascale in single investigator laboratory settings. As evidence, the single NCBI SRA central DNA sequence repository contains over 45 petabytes of biological data. Given the geometric growth of this and other genomics repositories, an exabyte of mineable biological data is imminent. The challenges of effectively utilizing these datasets are enormous as they are not only large in the size but also stored in geographically distributed repositories in various repositories such as National Center for Biotechnology Information (NCBI), DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and NASA’s GeneLab. In this work, we first systematically point out the data-management challenges of the genomics community. We then introduce Named Data Networking (NDN), a novel but well-researched Internet architecture, is capable of solving these challenges at the network layer. NDN performs all operations such as forwarding requests to data sources, content discovery, access, and retrieval using content names (that are similar to traditional filenames or filepaths) and eliminates the need for a location layer (the IP address) for data management. Utilizing NDN for genomics workflows simplifies data discovery, speeds up data retrieval using in-network caching of popular datasets, and allows the community to create infrastructure that supports operations such as creating federation of content repositories, retrieval from multiple sources, remote data subsetting, and others. Named based operations also streamlines deployment and integration of workflows with various cloud platforms. Our contributions in this work are as follows 1) we enumerate the cyberinfrastructure challenges of the genomics community that NDN can alleviate, and 2) we describe our efforts in applying NDN for a contemporary genomics workflow (GEMmaker) and quantify the improvements. The preliminary evaluation shows a sixfold speed up in data insertion into the workflow. 3) As a pilot, we have used an NDN naming scheme (agreed upon by the community and discussed in Section 4) to publish data from broadly used data repositories including the NCBI SRA. We have loaded the NDN testbed with these pre-processed genomes that can be accessed over NDN and used by anyone interested in those datasets. Finally, we discuss our continued effort in integrating NDN with cloud computing platforms, such as the Pacific Research Platform (PRP). The reader should note that the goal of this paper is to introduce NDN to the genomics community and discuss NDN’s properties that can benefit the genomics community. We do not present an extensive performance evaluation of NDN—we are working on extending and evaluating our pilot deployment and will present systematic results in a future work.

The Role of Network Science in Glioblastoma

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

Racial Disparities in Malignant Primary Brain Tumor Survival in Texas From 1995 to 2013

BACKGROUND

Differences among the top five races in Texas will be explored to determine if racial, geographic, and healthcare disparities exist in patients undergoing treatment for a primary malignant brain tumor.

METHODS

Data were obtained from the Texas Cancer Registry from 1995 to 2013. SAS 9.3 (SAS Institute, Inc., Cary, NC) and SEER*Stat 8.3.2 (National Cancer Institute, Bethesda, MD) software were used to analyze death from malignant brain tumors and cause-specific survival. Survival rates were compared using Kaplan-Meier curves and Log-Rank tests. Hazard ratios were estimated using the Cox proportional hazards regression model.

RESULTS

Median survival was highest among Asians at 92 months (95% CI: 72, 142) and least among Whites at 20 months (95% CI: 19, 21). Patients living in the Upper Gulf Coast region of Texas had the longest survival time at 31 months (95% CI 29-35%), while those patients in the Texas Panhandle had the shortest survival time at 18 months (95% CI 14-23%). Patients with a poverty index of 0-5% had the highest median survival time of 32 months (95% CI 29-35%), as compared to patients with a poverty index of 10-20% who had a median survival of 22 months (95% CI 21-24%).

CONCLUSIONS

Ethnic minorities and higher socioeconomic class demonstrated survival advantage. White males had the worst survival of those with primary malignant brain tumors. Other significant factors affecting a patient's survival rate included geographic location, poverty index, sex, and age, thus suggesting a potential genetic and environmental influence.

Identifying Temporally Regulated Root Nodulation Biomarkers Using Time Series Gene Co-Expression Network Analysis

Root nodulation results from a symbiotic relationship between a plant host and Rhizobium bacteria. Synchronized gene expression patterns over the course of rhizobial infection result in activation of pathways that are unique but overlapping with the highly conserved pathways that enable mycorrhizal symbiosis. We performed RNA sequencing of 30 Medicago truncatula root maturation zone samples at five distinct time points. These samples included plants inoculated with Sinorhizobium medicae and control plants that did not receive any Rhizobium. Following gene expression quantification, we identified 1,758 differentially expressed genes at various time points. We constructed a gene co-expression network (GCN) from the same data and identified link community modules (LCMs) that were comprised entirely of differentially expressed genes at specific time points post-inoculation. One LCM included genes that were up-regulated at 24 h following inoculation, suggesting an activation of allergen family genes and carbohydrate-binding gene products in response to Rhizobium. We also identified two LCMs that were comprised entirely of genes that were down regulated at 24 and 48 h post-inoculation. The identity of the genes in these modules suggest that down-regulating specific genes at 24 h may result in decreased jasmonic acid production with an increase in cytokinin production. At 48 h, coordinated down-regulation of a specific set of genes involved in lipid biosynthesis may play a role in nodulation. We show that GCN-LCM analysis is an effective method to preliminarily identify polygenic candidate biomarkers of root nodulation and develop hypotheses for future discovery.

Linking Binary Gene Relationships to Drivers of Renal Cell Carcinoma Reveals Convergent Function in Alternate Tumor Progression Paths

Renal cell carcinoma (RCC) subtypes are characterized by distinct molecular profiles. Using RNA expression profiles from 1,009 RCC samples, we constructed a condition-annotated gene coexpression network (GCN). The RCC GCN contains binary gene coexpression relationships (edges) specific to conditions including RCC subtype and tumor stage. As an application of this resource, we discovered RCC GCN edges and modules that were associated with genetic lesions in known RCC driver genes, including VHL, a common initiating clear cell RCC (ccRCC) genetic lesion, and PBRM1 and BAP1 which are early genetic lesions in the Braided Cancer River Model (BCRM). Since ccRCC tumors with PBRM1 mutations respond to targeted therapy differently than tumors with BAP1 mutations, we focused on ccRCC-specific edges associated with tumors that exhibit alternate mutation profiles: VHL-PBRM1 or VHL-BAP1. We found specific blends molecular functions associated with these two mutation paths. Despite these mutation-associated edges having unique genes, they were enriched for the same immunological functions suggesting a convergent functional role for alternate gene sets consistent with the BCRM. The condition annotated RCC GCN described herein is a novel data mining resource for the assignment of polygenic biomarkers and their relationships to RCC tumors with specific molecular and mutational profiles.