From a network medicine perspective, hereditary diseases can be seen as perturbations of “disease modules” in the interactome. An important effort in our lab has been aimed at quantifying similarity between hereditable diseases at molecular level by bringing together the existing information that is scattered across the vast corpus of biomedical literature. In other words, we obtain a number that accurately quantifies distance between disease modules in the interactome.
Quantifying disease similarity at molecular level enables the transfer of knowledge between similar diseases, providing hypotheses for causal genes discovery and even suggestions for drug repositioning. This is particularly important for hereditary diseases for which no disease gene is currently known – about 30% of them. For these orphan diseases, our measure can help pinpoint the location of their molecular perturbations. Our measure can also be used for differential diagnosis, aiding medical practitioners in identifying putative alternative diagnosis that are obscured by the complexity and multiplicity of the symptoms.
We have also developed a novel network-based approach to prioritize gene-disease associations that can also predict genes for diseases with no known molecular basis by exploiting our phenotypic measure. Our method, which uses semi-supervised learning for the prediction, can accurately predict disease genes for molecularly uncharacterised diseases and also gives excellent results for molecularly characterized diseases, when compared with state-of-the-art methods. Moreover, it can also be used for disease module prediction.
In collaboration with the lab of Giorgio Valentini at the University of Milan, we have developed a network-based approach for modelling patients’ biomolecular profiles for clinical phenotype/outcome prediction. Our method builds the profiles in a graph-structured patient space rather than the more typical biomarker space. We construct a network of patients based on their functional or genetic similarities, and then we apply a semi-supervised transductive approach to predict phenotype/clinical outcomes. Extensive tests show that our approach accurately predicts phenotype/outcome in patients with several diseases and provide interpretable results, thus leading to an explainable patient stratification based on their biomolecular characteristics.
In our lab research in Network Medicine has been funded by the BBSRC (grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1)
Moreover, we have recently begun to develop similar matrix completion approaches for tackling the problem of prediction of drug-target interactions, an important step in the drug discovery and repositioning process. Our final aim is to extend the druggable genome and preliminary results show that our models can be very effective at predicting drug-target associations involving previously unknown targets.
In the context of computational pharmacology, we have developed pipelines for repositioning drug combinations for neglected tropical diseases. Currently we are focusing on Chagas disease (among the top 10 target diseases in the Gates Foundation), which is caused by the protozoan parasite Trypanosoma cruzi (T. cruzi) and is endemic throughout Latin America. About 6 to 7 million people are infected with T. cruzi, around 40 million are at risk of infection. and no drug is effective against the parasite in the chronic phase of the disease. This project aims at identifying drug combinations with antitrypanosomal effects. We developed a method that exploits concepts from comparative genomics for the prediction of FDA approved drugs which could be effective against T. cruzi. Our method selects FDA approved drugs which target enzymes in model organisms that are evolutionarily related to enzymes in T. cruzi pathways and could therefore be effective at disrupting its metabolic pathways. We have identified 384 FDA approved drugs and the drug combinations are selected following a multi-objective optimisation approach. These are being tested in vitro by Dr Celeste Vega at CEDIC, our collaborating lab in Asuncion, Paraguay.
Over the last decade, genome-wide ligation-based assays such as Hi-C have provided an unprecedented opportunity to investigate the 3D organization of the genome. Results of a typical Hi-C experiment are summarized by a chromosomal contact map, a matrix whose elements reflect the population-averaged co-location frequencies of genomic loci, which can be viewed as a measurement of the spatial proximity between genomic loci.
We realized that there are two different components contributing to the overall contact frequency observed between a pair of genes in the contact map. The first component is related to their genomic distance, i.e., the distance between genes due to the fact they are positioned sequentially on the 1D DNA strand. The second component depends on cell specific arrangements of the genes in 3D. Since all human cells have an identical 1D genome, it is the second component that has a role in gene regulation.
We developed a network-based framework that effectively extracts the 3D component of the gene proximity signal. We show that such component can be used for in-depth analysis of the interplay between the spatial positioning of genes and their regulation in different human cells, and that such interplay is consistently easier to detect and quantify than when using the contact frequency obtained directly from the Hi-C data. In other words, our procedure can be thought of as a de-noising procedure that is able to extract the 3D component of the signal from the mixture of 1D and 3D signal components that constitutes the experimental Hi-C data.