Applications of molecular networks in biomedicine

A review of causal discovery methods for molecular network analysis

BACKGROUND

With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions.

METHODS

We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade.

RESULTS

In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area.

CONCLUSION

Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View

Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete “diseases”; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease. On the contrary, phenotypes, by definition, are directly observable and can be closer to the molecular basis of the pathology. These clinical phenotypes are also important for personalised medicine, as they can help stratify patients and design personalised interventions. For these reasons, network and systemic approaches to pathologies are gradually incorporating phenotypic information. This review covers the current landscape of phenotype-centred network approaches to study different aspects of human diseases.

Systematic analysis to identify transcriptome-wide dysregulation of Alzheimer's disease in genes and isoforms

Alzheimer's disease (AD) is one of the most common neurodegeneration diseases caused by multiple factors. The mechanistic insight of AD remains limited. To disclose molecular mechanisms of AD, many studies have been proposed from transcriptome analyses. However, no analysis across multiple levels of transcription has been conducted to discover co-expression networks of AD. We performed gene-level and isoform-level analyses of RNA sequencing (RNA-seq) data from 544 brain tissues of AD patients, mild cognitive impaired (MCI) patients, and healthy controls. Gene and isoform levels of co-expression modules were constructed by RNA-seq data. The associations of modules with AD were evaluated by integrating cognitive scores of patients, Genome-wide association studies (GWAS), alternative splicing analysis, and dementia-related genes expressed in brain tissues. Totally, 29 co-expression modules were found with expressions significantly correlated with the cognitive scores. Among them, two isoform modules were enriched with AD-associated SNPs and genes whose mRNA splicing displayed significant alteration in relation to AD disease. These two modules were further found enriched with dementia-related genes expressed in four brain regions of 125 AD patients. Analyzing expressions of these two modules revealed expressions of 39 isoforms (corresponding to 35 genes) significantly correlated with cognitive scores of AD patients, in which 38 isoforms were significantly up-regulated in AD patients comparing to controls, and 33 isoforms (corresponding to 29 genes) were not reported as AD-related previously. Employing the co-expression modules and the drug-induced gene expression data from Connectivity Map (CMAP), 12 drugs were predicted as significant in restoring the gene expression of AD patients towards health, which include nine drugs reported for relieving AD. In comparison, four of the top 12 significant drugs were known for relieving AD if the drug prediction was performed by the genes expressed significantly different in AD and healthy controls. Analysis of multiple levels of the transcriptomic organization is useful in suggesting AD-related co-expression networks and discovering drugs.

Modelling bioactivities of combinations of whole extracts of edibles with a simplified theoretical framework reveals the statistical role of molecular diversity and system complexity in their mode of action and their nearly certain safety

Network pharmacology and polypharmacology are emerging as novel drug discovery paradigms. The many discovery, safety and regulatory issues they raise may become tractable with polypharmacological combinations of natural compounds found in whole extracts of edible and mixes thereof. The primary goal of this work is to get general insights underlying the innocuity and the emergence of beneficial and toxic activities of combinations of many compounds in general and of edibles in particular. A simplified model of compounds’ interactions with an organism and of their desired and undesired effects is constructed by considering the departure from equilibrium of interconnected biological features. This model allows to compute the scaling of the probability of significant effects relative to nutritional diversity, organism complexity and synergy resulting from mixing compounds and edibles. It allows also to characterize massive indirect perturbation mode of action drugs as a potential novel multi-compound-multi-target pharmaceutical class, coined Ediceuticals when based on edibles. Their mode of action may readily target differentially organisms’ system robustness as such based on differential complexity for discovering nearly certainly safe novel antimicrobials, antiviral and anti-cancer treatments. This very general model provides also a theoretical framework to several pharmaceutical and nutritional observations. In particular, it characterizes two classes of undesirable effects of drugs, and may question the interpretation of undesirable effects in healthy subjects. It also formalizes nutritional diversity as such as a novel statistical supra-chemical parameter that may contribute to guide nutritional health intervention. Finally, it is to be noted that a similar formalism may be further applicable to model whole ecosystems in general.