Link splice-isoform expression to cancer metabolism with GEMsplice

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Metabolic models rely on genes and proteins expression to estimate or predict a metabolic cell phenotype. In the case of cancer, it is now admitted that metabolism dysregulations play a crucial role in cancer onset and proliferation. However, most metabolic models only rely on gene expression, and do not account for splice-isoform expression and/or alteration.

To solve this gap, Claudio Angione developed GEMsplice, a desktop application that allows to link splice-isoform gene expression data to cancer metabolism. Here, he describes the features and benefits of GEMsplice.

Solving the gap in cancer metabolism models

Despite being often perceived as the main contributors to cell fate and physiology, genes alone cannot predict the cellular phenotype. A genome-scale analysis of cancer metabolism captures many effects that cannot be identified using standard transcriptomic analysis.

However, although metabolic models have been successfully integrated with transcriptomic data to provide a mechanistic link between genotype and phenotype in cancer, there is no method for integration of splice isoform expression levels into such models. As a result, transcriptomic data in metabolic models can only be integrated at the gene level. Expression data at the splice-isoform level is currently neglected or simply averaged within the same gene to approximate the expression at the gene level.

This issue has been outlined in a number of recent reviews, and recently acknowledged by the scientific community as one of the main issues of metabolic modelling. In fact, the incorporation of splice isoforms is needed to understand complex diseases like cancer, where alternative splicing plays a crucial role.

GEMsplice features

GEMsplice is the first method for the incorporation of splice-isoform expression data into genome-scale metabolic models. It is validated by generating cancer-versus-normal predictions on metabolic pathways and by comparing them with available literature on pathways affected by breast cancer.

GEMsplice uses gene expression and transcript level information to incorporate them into the model (Figure 1). As a result, it exploits the full potential of next-generation sequencing in the context of genome-scale metabolic reconstructions. A set of phenotype-specific RNA-Seq transcript expression levels in a variety of breast cancer types and stages from the Cancer RNA-Seq Nexus dataset (Li et al., 2016), including data from TCGA, GEO and SRA, are then mapped onto the model using constraint-based modeling. Cancer-specific metabolic models are finally generated and investigated using multilevel linear programming, leading to phenotype prediction for different types of breast cancer (Figure 1).

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Figure 1: GEMsplice incorporates RNA-Seq data into genome-scale metabolic models at the splice-isoform level.

GEMsplice is freely available for academic use on Github.

With respect to state-of-the-art methods, GEMsplice will enable for the first time computational analyses of metabolism at transcript level with splice-isoform resolution.

References:

Claudio Angione. (2018). Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism. Bioinformatics.

How playing games can help scientific research

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Ever felt that saving princess Zelda or “catching ‘em all” was not rewarding enough? If so, why don’t you help Science and play serious games?

In their wider definition, serious games are games designed for other purposes than pure entertainment. In scientific research, serious games have been developed to make the community of gamers participate in the resolution of complex problems that cannot be solved by machines.

Serious games in biomedical research

Indeed, humans are very efficient at recognising patterns, which is rather a difficult task for computers and algorithms. Serious games combine a pleasing interface, a challenging and entertaining problem to solve, and a will to help scientific research.  Problem to solve can be broke down into smaller tasks, which multiplied by the number of players can lead to great results.

How serious games can make a difference

A notable example of how serious games can make science go forward is the resolution of the crystal structure of the M-PMV virus retroviral protease. Stuck for more than 10 years on the resolution of its structure, researchers used the online protein-folding game “Foldit” and its community of gamers. After 3 weeks, the 3D structure of the protein was solved and published in Nature Structural & Molecular Biology.

Popular serious games also include “Phylo”, where players try to improve multiple sequence alignments by moving blocks, “EyeWire”, dedicated to 3D reconstruction of neurons, or “EteRNA”, where players design RNA sequences that fold into target secondary structures.

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Player interface from the serious games Phylo (up) and EyeWire (down).

Serious games are also used in human and public health, to raise awareness on specific diseases, or to help patients and their family deal with a medical condition. Their use is now spreading to a lot of different disciplines such as teaching, politics, ecology, etc.

If you want to contribute to research and science while having fun, check out these games:

  • MalariaSpot, to quantify malaria parasites on thick blood smears.
  • Dizeez, a multiple-choice quiz to catalog gene-disease associations.
  • The Cure, where you use your knowledge to make informed decisions about the best combinations of variables (e.g. genes) to build predictive patterns.
  • GenESP, a gene annotation game where players contribute their knowledge of gene function and disease relevance.

If you want to produce, publish, or promote your own game, visit Science Game Lab, a platform for the promotion of scientific games with a purpose.

Game on!

References:

 

Map functional networks of ncRNAs with circlncRNAnet

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Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) lack protein-coding potential but have nonetheless emerged as key determinants in gene regulation, acting to fine-tune transcriptional and signaling output. These noncoding RNA transcripts are known to affect expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation.

To fully capture, from a network perspective, the functional implications of lncRNAs or circRNAs of interest, Dr. Bertrand Chin-MingTan and his team have implemented an integrative bioinformatics approach to examine in silico the functional networks of non-coding RNAs. Here, they present their web server tool “circlncRNAnet” and discuss its main features.

In-depth analyses of non-coding RNA biology

The main purpose for implementing this web server is to provide biologists with a user-friendly, “one-stop” web tool to study from a network perspective the biology of lncRNAs or circRNAs of interest.

Despite their lack of protein-coding potential, lncRNAs and circRNAs have emerged as key determinant in gene regulation, acting to fine-tune transcriptional and signaling output. Given the widespread regulatory roles and target spectrum of non-coding RNAs, complete understanding of their biological relevance depends on integrative analyses of systems data at multiple levels. However, only a handful of available databases have been reported in this field, and they are limited in the scope of reference data and analytic modules. Through an integrated and streamlined design, circlncRNAnet is aimed to broaden the understanding of ncRNA candidates by testing in silico several hypotheses of ncRNA-based functions on the basis of large-scale RNA-seq data.

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Overall design and analytic workflow of circlncRNAnet.

Main functionalities 

  • This web server is implemented with several features representing advances in the bioinformatics of ncRNAs:
    circlncRNAnet is designed with the flexibility of accepting private or public data. To further support efficient analyses and presentation, we have extensively curated public data into reference annotations for the circlncRNAnet workflow.
  • Multi-layer modules and algorithms then provide outputs on expression profiles, co-expression networks & pathways, and molecular interactomes (i.e. microRNAs, RNA-binding proteins, and transcription factors), which are dynamically and interactively displayed according to user-defined criteria.
  • Users may apply circlncRNAnet to obtain, in real time, multiple lines of functionally relevant information on the circRNAs/lncRNAs of their interest. The overall workflow takes only a few minutes, as compared to hours of manual efforts of independent database searches and analyses.
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Schematic showing example outputs of circlncRNAnet analyses of lncRNA-based networks in colorectal cancer. After dataset upload, the server executes differential expression and expression correlation analyses. The web server allows the user to select query genes and correlation criteria. From Shao-Min Wu et al.

CiclncRNAnet is freely available at: http://app.cgu.edu.tw/circlnc/

Reference :

(Wu et al., 2017). circlncRNAnet: An integrated web-based resource for mapping functional networks of long or circular forms of non-coding RNAs. Gigascience.