FunMod: a Cytoscape plugin for identifying functional modules in undirected protein-protein networks

A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine

Motivation

Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on ‘multiple network analysis’ in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases.

Results

By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered.

Availability

The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55].

Supplementary Information

A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.

Computational systems biology approaches for Parkinson's disease

Parkinson's disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.

Global Gene Expression Analysis Reveals Crosstalk between Response Mechanisms to Cold and Drought Stresses in Cassava Seedlings

Abiotic stress negatively impacts cassava (Manihot esculenta) growth and yield. Several molecular mechanisms of plant response to cold and drought have been identified and described in the literature, however, little is known about the crosstalk of the responses of cassava to these two stresses. To elucidate this question, transcriptome analysis of cassava seedlings under cold or PEG-simulated drought stress treatment was performed. Our results showed that 6103 and 7462 transcripts were significantly regulated by cold and drought stress, respectively. Gene Ontology annotation revealed that the abscisic and jasmonic acid signaling pathways shared between the two stresses responses. We further identified 2434 common differentially expressed genes (DEGs), including 1130 up-regulated and 841 down-regulated DEGs by the two stresses. These co-induced or co-suppressed genes are grouped as stress signal perception and transduction, transcription factors (TFs), metabolism as well as transport facilitation according to the function annotation. Furthermore, a large proportion of well characterized protein kinases, TF families and ubiquitin proteasome system related genes, such as RLKs, MAPKs, AP2/ERFBPs, WRKYs, MYBs, E2 enzymes and E3 ligases, including three complexes of interacting proteins were shown as key points of crosstalk between cold and drought stress signaling transduction pathways in a hierarchical manner. Our research provides valuable information and new insights for genetically improving the tolerance of crops to multiple abiotic stresses.

NET-GE: a novel NETwork-based Gene Enrichment for detecting biological processes associated to Mendelian diseases

Background

Enrichment analysis is a widely applied procedure for shedding light on the molecular mechanisms and functions at the basis of phenotypes, for enlarging the dataset of possibly related genes/proteins and for helping interpretation and prioritization of newly determined variations. Several standard and Network-based enrichment methods are available. Both approaches rely on the annotations that characterize the genes/proteins included in the input set; network based ones also include in different ways physical and functional relationships among different genes or proteins that can be extracted from the available biological networks of interactions.

Results

Here we describe a novel procedure based on the extraction from the STRING interactome of sub-networks connecting proteins that share the same Gene Ontology(GO) terms for Biological Process (BP). Enrichment analysis is performed by mapping the protein set to be analyzed on the sub-networks, and then by collecting the corresponding annotations. We test the ability of our enrichment method in finding annotation terms disregarded by other enrichment methods available. We benchmarked 244 sets of proteins associated to different Mendelian diseases, according to the OMIM web resource. In 143 cases (58%), the network-based procedure extracts GO terms neglected by the standard method, and in 86 cases (35%), some of the newly enriched GO terms are not included in the set of annotations characterizing the input proteins. We present in detail six cases where our network-based enrichment provides an insight into the biological basis of the diseases, outperforming other freely available network-based methods.

Conclusions

Considering a set of proteins in the context of their interaction network can help in better defining their functions. Our novel method exploits the information contained in the STRING database for building the minimal connecting network containing all the proteins annotated with the same GO term. The enrichment procedure is performed considering the GO-specific network modules and, when tested on the OMIM-derived benchmark sets, it is able to extract enrichment terms neglected by other methods. Our procedure is effective even when the size of the input protein set is small, requiring at least two input proteins.