Effects of threshold on the topology of gene co-expression networks

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

CLSPN is a potential biomarker associated with poor prognosis in low-grade gliomas based on a multi-database analysis

BACKGROUND

The oncogene CLSPN, also known as claspin, has regulatory effects in a variety of tumours; however, it is not clear whether CLSPN is a therapeutic target in low-grade gliomas (LGG). In this study, the prognostic value of CLSPN in LGG and its role as an immunotherapeutic target were evaluated.

METHODS

Transcriptome and methylation data for thousands of patients with glioma were collected from various databases, including The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gene Expression Omnibus. Subsequently, a series of bioinformatics methods were used to evaluate the relationships between CLSPN and prognosis, clinical features, methylation status, immune cells, and molecular signaling pathways in LGG.

RESULTS

CLSPN expression levels were positively correlated with major malignant characteristics of LGG, and low expression of CLSPN was associated with a better prognosis. The methylation sites cg04263115 and cg06100291 negatively regulated the expression of CLSPN, and increased methylation levels at these sites were related to a longer survival time in patients with LGG. CLSPN was positively correlated with tumour-infiltrating immune cells and showed high copy number variation in these cells. There was a positive regulatory relationship between CLSPN expression and programmed death-1 (PD-1) and programmed cell death ligand 1 (PD-L1). A gene set enrichment analysis revealed that CLSPN activates a variety of cancer signaling pathways.

CONCLUSION

CLSPN was identified as an independent risk factor for LGG with excellent prognostic value.

From diversity to complexity: Microbial networks in soils

Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research. Analysing microbial networks in soils opens a window to a better understanding of the complexity of microbial communities. However, this approach is unfortunately often used to draw conclusions which are far beyond the scientific evidence it can provide, which has damaged its reputation for soil microbial analysis. In this Perspectives Paper, we would like to sharpen the view for the real potential of microbial co-occurrence analysis in soils, and at the same time raise awareness regarding its limitations and the many ways how it can be misused or misinterpreted.

Phenotype correlations reveal the relationships of physiological systems underlying human ageing

Ageing is characterized by degeneration and loss of function across multiple physiological systems. To study the mechanisms and consequences of ageing, several metrics have been proposed in a hierarchical model, including biological, phenotypic and functional ageing. In particular, phenotypic ageing and interconnected changes in multiple physiological systems occur in all ageing individuals over time. Recently, phenotypic age, a new ageing measure, was proposed to capture morbidity and mortality risk across diverse subpopulations in US cohort studies. Although phenotypic age has been widely used, it may overlook the complex relationships among phenotypic biomarkers. Considering the correlation structure of these phenotypic biomarkers, we proposed a composite phenotype analysis (CPA) strategy to analyse 71 biomarkers from 2074 individuals in the Rugao Longitudinal Ageing Study. CPA grouped these biomarkers into 18 composite phenotypes according to their internal correlation, and these composite phenotypes were mostly consistent with prior findings. In addition, compared with prior findings, this strategy exhibited some different yet important implications. For example, the indicators of kidney and cardiovascular functions were tightly connected, implying internal interactions. The composite phenotypes were further verified through associations with functional metrics of ageing, including disability, depression, cognitive function and frailty. Compared to age alone, these composite phenotypes had better predictive performances for functional metrics of ageing. In summary, CPA could reveal the hidden relationships of physiological systems and identify the links between physiological systems and functional ageing metrics, thereby providing novel insights into potential mechanisms underlying human ageing.

Improved gene co-expression network quality through expression dataset down-sampling and network aggregation

Large-scale gene co-expression networks are an effective methodology to analyze sets of co-expressed genes and discover new gene functions or associations. Distances between genes are estimated according to their expression profiles and are visualized in networks that may be further partitioned to reveal communities of co-expressed genes. Creating expression profiles is now eased by the large amounts of publicly available expression data (microarrays and RNA-seq). Although many distance calculation methods have been intensively compared and reviewed in the past, it is unclear how to proceed when many samples reflecting a wide range of different conditions are available. Should as many samples as possible be integrated into network construction or be partitioned into smaller sets of more related samples? Previous studies have indicated a saturation in network performances to capture known associations once a certain number of samples is included in distance calculations. Here, we examined the influence of sample size on co-expression network construction using microarray and RNA-seq expression data from three plant species. We tested different down-sampling methods and compared network performances in recovering known gene associations to networks obtained from full datasets. We further examined how aggregating networks may help increase this performance by testing six aggregation methods.

Detection and visualization of communities in mass spectrometry imaging data

Background

The spatial distribution and colocalization of functionally related metabolites is analysed in order to investigate the spatial (and functional) aspects of molecular networks. We propose to consider community detection for the analysis of m/z-images to group molecules with correlative spatial distribution into communities so they hint at functional networks or pathway activity. To detect communities, we investigate a spectral approach by optimizing the modularity measure. We present an analysis pipeline and an online interactive visualization tool to facilitate explorative analysis of the results. The approach is illustrated with synthetical benchmark data and two real world data sets (barley seed and glioblastoma section).

Results

For the barley sample data set, our approach is able to reproduce the findings of a previous work that identified groups of molecules with distributions that correlate with anatomical structures of the barley seed. The analysis of glioblastoma section data revealed that some molecular compositions are locally focused, indicating the existence of a meaningful separation in at least two areas. This result is in line with the prior histological knowledge. In addition to confirming prior findings, the resulting graph structures revealed new subcommunities of m/z-images (i.e. metabolites) with more detailed distribution patterns. Another result of our work is the development of an interactive webtool called GRINE (Analysis of GRaph mapped Image Data NEtworks).

Conclusions

The proposed method was successfully applied to identify molecular communities of laterally co-localized molecules. For both application examples, the detected communities showed inherent substructures that could easily be investigated with the proposed visualization tool. This shows the potential of this approach as a complementary addition to pixel clustering methods.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2890-6) contains supplementary material, which is available to authorized users.

Analysis of cyclin E co-expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

Objective

To explore genes potentially co-expressed with cyclin E in gastric cancer and discover possible targets for gastric cancer treatment.

Methods

The Cancer Genome Atlas (TCGA) stomach adenocarcinoma sequencing data were used to predict genes co-expressed with cyclin E. Co-expression genes predicted by cBioPortal online analysis with Pearson correlation coefficient ≥0.4 were analyzed by gene ontology (GO) enrichment annotation using the PANTHER online platform (Ver. 7). Interactions between proteins encoded by these genes were analyzed using the STRING online platform (Ver. 10.5) and Cytoscape software (Ver. 3.5.1). Genes displaying a high degree of connection were analyzed by transcription factor enrichment prediction using FunRich software (Ver. 3). The significant transcription factor and cyclin E expression levels and their impact on gastric cancer progression were analyzed by Western blotting and Kaplan–Meier survival curve analysis.

Results

After filtering the co-expression gene prediction results, 78 predicted genes that included 73 protein coding genes and 5 non-coding genes with Pearson correlation coefficient ≥0.4 were selected. The expressions of the genes were considered to be correlated with cyclin E expression. Among the 78 genes co-expressed with cyclin E, 19 genes at the central of the regulatory network associated with cyclin E were discovered. Nuclear transcription factor Y subunit alpha (NF-YA) was identified as a significant transcription factor associated with cyclin E co-expressing genes. Analysis of specimen donors’ clinical records revealed that high expression of NF-YA tended to be associated with increased cyclin E expression. The expression of both was associated with progression of gastric cancer. Western blotting results showed that compared with normal tissues, NF-YA and cyclin E were highly expressed in tumor tissues (P < 0.001). Survival curve analysis clearly demonstrated relatively poor overall survival of gastric cancer patients with high cyclin E or high NF-YA expression level, compared to patients with low cyclin E or NF-YA expression (P < 0.05).

Conclusions

NF-YA may promote gastric cancer progression by increasing the transcription of cyclin E and other cell cycle regulatory genes. NF-YA might be a potential therapeutically useful prognostic factor for gastric cancer.