NEArender: an R package for functional interpretation of 'omics' data via network enrichment analysis

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.

Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis

Functional analysis of gene sets derived from experiments is typically done by pathway annotation. Although many algorithms exist for analyzing the association between a gene set and a pathway, an issue which is generally ignored is that gene sets often represent multiple pathways. In such cases an association to a pathway is weakened by the presence of genes associated with other pathways. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each module. We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering can be beneficial by increasing the sensitivity of pathway analysis methods and by providing deeper insights of biological mechanisms related to the phenotype under study. However, keeping a high specificity is a challenge. For ANUBIX, clustering caused a minor loss of specificity, while for BinoX and NEAT it caused an unacceptable loss of specificity. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We show examples of this approach and conclude that clustering can improve overall pathway annotation performance, but should only be used if the used enrichment method has a low false positive rate.

Individualized discovery of rare cancer drivers in global network context

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become ‘hard currency’ of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in each tumour genome – as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.

PIGNON: a protein-protein interaction-guided functional enrichment analysis for quantitative proteomics

Background

Quantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline often heavily relies on arbitrary thresholds of significance. However, a functional annotation may be dysregulated in a given experimental condition, while none, or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches.

Results

Herein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of the differential expression of individual proteins, but rather maps protein differential expression levels onto a protein–protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering significance of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein–protein interaction network and differentially expressed across different breast cancer subtypes. PIGNON identified functional annotations that are dysregulated and clustered within the network between the HER2+, triple negative and hormone receptor positive subtypes. We show that PIGNON’s results are complementary to those of state-of-the-art functional enrichment analyses and that it highlights functional annotations missed by standard approaches. Furthermore, PIGNON detects functional annotations that have been previously associated with specific breast cancer subtypes.

Conclusion

PIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different experimental conditions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04042-6.

PyGNA: a unified framework for geneset network analysis

BACKGROUND

Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying networks associated with complex diseases and phenotypes.

RESULTS

Here we introduce an integrated statistical framework to test network properties of single and multiple genesets under different interaction models. We implemented this framework as an open-source software, called Python Geneset Network Analysis (PyGNA). Our software is designed for easy integration into existing analysis pipelines and to generate high quality figures and reports. We also developed PyGNA to take advantage of multi-core systems to generate calibrated null distributions on large datasets. We then present the results of extensive benchmarking of the tests implemented in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrated to study biological networks. PyGNA is available at http://github.com/stracquadaniolab/pygna and can be easily installed using the PyPi or Anaconda package managers, and Docker.

CONCLUSIONS

We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.

Pathway-specific model estimation for improved pathway annotation by network crosstalk

Pathway enrichment analysis is the most common approach for understanding which biological processes are affected by altered gene activities under specific conditions. However, it has been challenging to find a method that efficiently avoids false positives while keeping a high sensitivity. We here present a new network-based method ANUBIX based on sampling random gene sets against intact pathway. Benchmarking shows that ANUBIX is considerably more accurate than previous network crosstalk based methods, which have the drawback of modelling pathways as random gene sets. We demonstrate that ANUBIX does not have a bias for finding certain pathways, which previous methods do, and show that ANUBIX finds biologically relevant pathways that are missed by other methods.

Protein profiling and network enrichment analysis in individuals before and after the onset of rheumatoid arthritis

Background

Antibodies and upregulated cytokines and chemokines predate the onset of rheumatoid arthritis (RA) symptoms. We aimed to identify the pathways related to the early processes leading to RA development, as well as potential novel biomarkers, using multiple protein analyses.

Methods

A case-control study was conducted within the Biobank of northern Sweden. The plasma samples from 118 pre-symptomatic individuals (207 samples; median predating time 4.1 years), 79 early RA patients, and 74 matched controls were analyzed. The levels of 122 unique proteins with an acknowledged relationship to autoimmunity were analyzed using 153 antibodies and a bead-based multiplex system (FlexMap3D; Luminex Corp.). The data were analyzed using multifactorial linear regression model, random forest, and network enrichment analysis (NEA) based on the 10 most significantly differentially expressed proteins for each two-by-two group comparison, using the MSigDB collection of hallmarks.

Results

There was a high agreement between the different statistical methods to identify the most significant proteins. The adipogenesis and interferon alpha response hallmarks differentiated pre-symptomatic individuals from controls. These two hallmarks included proteins involved in innate immunity. Between pre-symptomatic individuals and RA patients, three hallmarks were identified as follows: apical junction, epithelial mesenchymal transition, and TGF-β signaling, including proteins suggestive of cell interaction, remodulation, and fibrosis. The adipogenesis and heme metabolism hallmarks differentiated RA patients from controls.

Conclusions

We confirm the importance of interferon alpha signaling and lipids in the early phases of RA development. Network enrichment analysis provides a tool for a deeper understanding of molecules involved at different phases of the disease progression.

EviNet: a web platform for network enrichment analysis with flexible definition of gene sets

The new web resource EviNet provides an easily run interface to network enrichment analysis for exploration of novel, experimentally defined gene sets. The major advantages of this analysis are (i) applicability to any genes found in the global network rather than only to those with pathway/ontology term annotations, (ii) ability to connect genes via different molecular mechanisms rather than within one high-throughput platform, and (iii) statistical power sufficient to detect enrichment of very small sets, down to individual genes. The users’ gene sets are either defined prior to upload or derived interactively from an uploaded file by differential expression criteria. The pathways and networks used in the analysis can be chosen from the collection menu. The calculation is typically done within seconds or minutes and the stable URL is provided immediately. The results are presented in both visual (network graphs) and tabular formats using jQuery libraries. Uploaded data and analysis results are kept in separated project directories not accessible by other users. EviNet is available at https://www.evinet.org/.

Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data

Despite the widening range of high-throughput platforms and exponential growth of generated data volume, the validation of biomarkers discovered from large-scale data remains a challenging field. In order to tackle cancer heterogeneity and comply with the data dimensionality, a number of network and pathway approaches were invented but rarely systematically applied to this task. We propose a new method, called NEAmarker, for finding sensitive and robust biomarkers at the pathway level. scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways. These dimensions are then correlated with phenotype variables. The method was first tested using in vitro data from three anti-cancer drug screens and then on clinical data of The Cancer Genome Atlas. It proved superior to the single-gene and alternative enrichment analyses in terms of (1) universal applicability to different data types with a possibility of cross-platform integration, (2) consistency of the discovered correlates between independent drug screens, and (3) ability to explain differential survival of treated patients. Our new screen of anti-cancer compounds validated the performance of multivariate models of drug sensitivity. The previously proposed methods of enrichment analysis could achieve comparable levels of performance in certain tests. However, only our method could discover predictors of both in vitro response and patient survival given administration of the same drug.

Expression of scavenger receptor MARCO defines a targetable tumor-associated macrophage subset in non-small cell lung cancer

Tumor-associated macrophages (TAMs) are attractive targets for immunotherapy. Recently, studies in animal models showed that treatment with an anti-TAM antibody directed against the scavenger receptor MARCO resulted in suppression of tumor growth and metastatic dissemination. Here we investigated the expression of MARCO in relation to other macrophage markers and immune pathways in a non-small cell lung cancer (NSCLC) cohort (n = 352). MARCO, CD68, CD163, MSR1 and programmed death ligand-1 (PD-L1) were analyzed by immunohistochemistry and immunofluorescence, and associations to other immune cells and regulatory pathways were studied in a subset of cases (n = 199) with available RNA-seq data. We observed a large variation in macrophage density between cases and a strong correlation between CD68 and CD163, suggesting that the majority of TAMs present in NSCLC exhibit a protumor phenotype. Correlation to clinical data only showed a weak trend toward worse survival for patients with high macrophage infiltration. Interestingly, MARCO was expressed on a distinct subpopulation of TAMs, which tended to aggregate in close proximity to tumor cell nests. On the transcriptomic level, we found a positive association between MARCO gene expression and general immune response pathways including strong links to immunosuppressive TAMs, T-cell infiltration and immune checkpoint molecules. Indeed, a higher macrophage infiltration was seen in tumors expressing PD-L1, and macrophages residing within tumor cell nests co-expressed MARCO and PD-L1. Thus, MARCO is a potential new immune target for anti-TAM treatment in a subset of NSCLC patients, possibly in combination with available immune checkpoint inhibitors.

Spatially resolved transcriptome profiling in model plant species

Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study high-resolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes high-throughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.

The joint NETTAB/Integrative Bioinformatics 2015 Meeting: aims, topics and outcomes

The 15^th International NETTAB workshop and the 11^th Integrative Bioinformatics Symposium were held together in Bari, on October 14-16, 2016, as Joint NETTAB/IB 2015 Meeting. A special topic for the meeting was "Bioinformatics for ncRNA", but the traditional topics of both meetings series were also included in the event.About 60 scientific contributions were presented, including six keynote lectures, one special guest lecture, and many oral communications and posters. A "Two-Day Hands-on Tutorial" event was organised before the workshop.Selected full papers from some of the best works presented in Bari were submitted either to the Journal of Integrative Bioinformatics or to a purpose Call for a Supplement of BMC Bioinformatics.Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been either accepted for publication in this Supplement or published in the Journal of Integrative Bioinformatics, for a more complete presentation of the outcomes of the meeting.

RhoA knockout fibroblasts lose tumor-inhibitory capacity in vitro and promote tumor growth in vivo

Fibroblasts are a main player in the tumor-inhibitory microenvironment. Upon tumor initiation and progression, fibroblasts can lose their tumor-inhibitory capacity and promote tumor growth. The molecular mechanisms that underlie this switch have not been defined completely. Previously, we identified four proteins overexpressed in cancer-associated fibroblasts and linked to Rho GTPase signaling. Here, we show that knocking out the Ras homolog family member A (RhoA) gene in normal fibroblasts decreased their tumor-inhibitory capacity, as judged by neighbor suppression in vitro and accompanied by promotion of tumor growth in vivo. This also induced PC3 cancer cell motility and increased colony size in 2D cultures. RhoA knockout in fibroblasts induced vimentin intermediate filament reorganization, accompanied by reduced contractile force and increased stiffness of cells. There was also loss of wide F-actin stress fibers and large focal adhesions. In addition, we observed a significant loss of α-smooth muscle actin, which indicates a difference between RhoA knockout fibroblasts and classic cancer-associated fibroblasts. In 3D collagen matrix, RhoA knockout reduced fibroblast branching and meshwork formation and resulted in more compactly clustered tumor-cell colonies in coculture with PC3 cells, which might boost tumor stem-like properties. Coculturing RhoA knockout fibroblasts and PC3 cells induced expression of proinflammatory genes in both. Inflammatory mediators may induce tumor cell stemness. Network enrichment analysis of transcriptomic changes, however, revealed that the Rho signaling pathway per se was significantly triggered only after coculturing with tumor cells. Taken together, our findings in vivo and in vitro indicate that Rho signaling governs the inhibitory effects by fibroblasts on tumor-cell growth.