8 research outputs found

    Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)

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    Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and-to best of our knowledge-no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts:Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects linksandnodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as anRpackage in CRAN (https://CRAN. R-project.org/package=CoDiNA)

    Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19

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    The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development

    Novel methods for constructing, combining and comparing co-expression networks: Towards uncovering the molecular basis of human cognition

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    Network analyses, such as gene co-expression networks are an important approach for the systems-level study of biological data. For example, understanding patterns of \linebreak co-regulation in mental disorders can contribute to the development of new therapies and treatments. In a gene regulatory process a particular TF or ncRNA can up- or down-regulate other genes, therefore it is important to explicitly consider both positive and negative interactions. Although exists a variety of software and libraries for constructing and investigating such networks, none considers the sign of interaction. It is also required that the represented networks have high accuracy, where the interactions found have to be relevant and not found by chance or background noise. Another issue derived from building co-expression networks is the reproducibility of those. When constructing independent networks for the same phenotype, though, using different expression datasets, the output network can be remarkably distinct due to biological or technical noise in the data. However, most of the times the interest is not only to characterise a network but to compare its features to others. A series of questions arise from understanding phenotypes using co-expression networks: i) how to construct highly accurate networks; ii) how to combine multiple networks derived from different platforms; iii) how to compare multiple networks. For answering those questions, i) I improved the wTO method to construct highly accurate networks, where now each interaction in a network receives a probability. This method showed to be much more efficient in finding correct interactions than other well-known methods; ii) I developed a method that is able to combine multiple networks into one building a CN. This method enables the correction for background noise; iii) I developed a completely novel method for the comparison of multiple co-expression networks, CoDiNA. This method identifies genes specific to at least one network. It is natural that after associating genes to phenotypes, an inference whether those genes are enriched for a particular disorder is needed. I also present here a tool, RichR, that enables enrichment analysis and background correction. I applied the methods proposed here in two important studies. In the first one, the aim was to understand the neurogenesis process and how certain genes would affect it. The combination of the methods shown here pointed one particular TF, ZN787, as playing an important role in this process. Moreover, the application of this toolset to networks derived from brain samples of individuals with cognitive disorders identified genes and network connections that are specific to certain disorders, but also found an overlap between neurodegenerative disorders and brain development and between evolutionary changes and psychological disorders. CoDiNA also pointed out that there are genes involved in those disorders that are not only human-specific

    wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool

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    Abstract Background Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network. Results Here, we present an R package for calculating the weighted topological overlap (wTO), that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. To graphically inspect the resulting networks, the R package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set. Conclusion In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL ‚ąí2 Open Source License (https://cran.r-project.org/web/packages/wTO/)
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