Novel network pharmacology methods for drug mechanism of action identification, pre-clinical drug screening and drug repositioning

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Here we present two methodology innovations on network pharmacology modeling. (1) We hypothesize that the gene network associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. We proposed a novel in vivo genetic interaction between genes as ‘synergistic outcome determination’, in a similar way to ‘synthetic lethality’. We scanned above genetic interactions based on microarray profiling for cancer prognosis, and identified a cluster of important yet epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to this network, we could define a perturbation index for each drug based upon its characteristic perturbation pattern. Finally, by using this index, we significantly discriminated successful drugs from the candidate pool, and revealed the mechanisms of drug combinations. Thus, the prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies. Part of this work was published, and we will present new results on this project. (2) MicroRNAs (miRNAs) play a key role in the regulation of the transcriptome and have been identified as a key mediator in human disease and drug response. we introduced a novel concept, the Context-specific MiRNA activity (CoMi activity), to reflect a miRNA’s regulation effect on a context specific gene set .Using breast cancer as an example, we examined the CoMi activity based on a Gene Ontology (GO) term as context. Interestingly, we found that chemotherapeutic drug treatment can counteract the dis-regulated CoMi activity in the cancer-specific network. For instance, 100% of down-regulated CoMi activities in a “core” breast cancer network contains apoptosis-related GO terms that could be counteracted by Paclitaxel treatment. By defining a Stability Index for in silico drug screening, we found CoMi activity signatures strikingly outperformed the traditional CMAP method or mRNA-based signatures. Thus, the dynamic remodeling of context-specific miRNAs regulation network could reveal the hidden miRNAs that act as key mediators of drug action and facilitate in silico cancer drug screening

SAveRUNNER: a network-based algorithm for drug repurposing and its application to COVID-19

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity, comorbidity, or for their association to drugs tentatively repurposed to treat COVID-19. Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments, as well as a new combination therapy of 5 drugs, actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies, and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.Comment: 42 pages, 9 figure

ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification

Motivation: Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process. Results: Here, we present ClinOmicsTrailbc, a comprehensive visual analytics tool for breast cancer decision support that provides a holistic assessment of standard-of-care targeted drugs, candidates for drug repositioning and immunotherapeutic approaches. To this end, our tool analyzes and visualizes clinical markers and (epi-)genomics and transcriptomics datasets to identify and evaluate the tumor’s main driver mutations, the tumor mutational burden, activity patterns of core cancerrelevant pathways, drug-specific biomarkers, the status of molecular drug targets and pharmacogenomic influences. In order to demonstrate ClinOmicsTrailbc’s rich functionality, we present three case studies highlighting various ways in which ClinOmicsTrailbc can support breast cancer precision medicine. ClinOmicsTrailbc is a powerful integrated visual analytics tool for breast cancer research in general and for therapy stratification in particular, assisting oncologists to find the best possible treatment options for their breast cancer patients based on actionable, evidence-based results. Availability and implementation: ClinOmicsTrailbc can be freely accessed at https://clinomicstrail. bioinf.uni-sb.de

Evolution of Translational Bioinformatics: lessons learned from TBC 2016

Translational bioinformatics (TBI) is a relatively young discipline that spans a wide spectrum from data to diagnostics and therapeutics. TBI involves applying novel methods to the storage, analysis, and interpretation of a massive volume of omics data, and it bridges the gap between bench research and real-world application to human health. The Translational Bioinformatics Conference (TBC) series has aimed to highlight the multidisciplinary nature of TBI, and it provides an opportunity to bring researchers together to exchange ideas between biology, informatics, technology, and clinical fields worldwide.10.1186/s12920-017-0262-

Kernel-Elastic Autoencoder for Molecular Design

We introduce the Kernel-Elastic Autoencoder (KAE), a self-supervised generative model based on the transformer architecture with enhanced performance for molecular design. KAE is formulated based on two novel loss functions: modified maximum mean discrepancy and weighted reconstruction. KAE addresses the long-standing challenge of achieving valid generation and accurate reconstruction at the same time. KAE achieves remarkable diversity in molecule generation while maintaining near-perfect reconstructions on the independent testing dataset, surpassing previous molecule-generating models. KAE enables conditional generation and allows for decoding based on beam search resulting in state-of-the-art performance in constrained optimizations. Furthermore, KAE can generate molecules conditional to favorable binding affinities in docking applications as confirmed by AutoDock Vina and Glide scores, outperforming all existing candidates from the training dataset. Beyond molecular design, we anticipate KAE could be applied to solve problems by generation in a wide range of applications

Domestic Violence and Health Care: Opening Pandora¿s Box ¿ Challenges and Dilemmas

In this article we take a critical stance toward the rational progressive narrative surrounding the integration of domestic violence within health care. Whilst changes in recent UK policy and practice have resulted in several tangible benefits, it is argued that there may be hidden dilemmas and challenges. We suggest that the medical model of care and its discursive practices position women as individually accountable for domestic violence-related symptoms and injuries. This may not only be ineffective in terms of service provision but could also have the potential to reduce the political significance of domestic violence as an issue of concern for all women. Furthermore, it is argued that the use of specific metaphors enables practitioners to distance themselves from interactions that may prove to be less comfortable and provide less than certain outcomes. Our analysis explores the possibilities for change that might currently be available. This would appear to involve a consideration of alternative discourses and the reformulation of power relations and subject positions in health care

Recent advances in malaria genomics and epigenomics

Malaria continues to impose a significant disease burden on low- and middle-income countries in the tropics. However, revolutionary progress over the last 3 years in nucleic acid sequencing, reverse genetics, and post-genome analyses has generated step changes in our understanding of malaria parasite (Plasmodium spp.) biology and its interactions with its host and vector. Driven by the availability of vast amounts of genome sequence data from Plasmodium species strains, relevant human populations of different ethnicities, and mosquito vectors, researchers can consider any biological component of the malarial process in isolation or in the interactive setting that is infection. In particular, considerable progress has been made in the area of population genomics, with Plasmodium falciparum serving as a highly relevant model. Such studies have demonstrated that genome evolution under strong selective pressure can be detected. These data, combined with reverse genetics, have enabled the identification of the region of the P. falciparum genome that is under selective pressure and the confirmation of the functionality of the mutations in the kelch13 gene that accompany resistance to the major frontline antimalarial, artemisinin. Furthermore, the central role of epigenetic regulation of gene expression and antigenic variation and developmental fate in P. falciparum is becoming ever clearer. This review summarizes recent exciting discoveries that genome technologies have enabled in malaria research and highlights some of their applications to healthcare. The knowledge gained will help to develop surveillance approaches for the emergence or spread of drug resistance and to identify new targets for the development of antimalarial drugs and perhaps vaccines

A bovine lymphosarcoma cell line infected with theileria annulata exhibits an irreversible reconfiguration of host cell gene expression

Theileria annulata, an intracellular parasite of bovine lymphoid cells, induces substantial phenotypic alterations to its host cell including continuous proliferation, cytoskeletal changes and resistance to apoptosis. While parasite induced modulation of host cell signal transduction pathways and NFκB activation are established, there remains considerable speculation on the complexities of the parasite directed control mechanisms that govern these radical changes to the host cell. Our objectives in this study were to provide a comprehensive analysis of the global changes to host cell gene expression with emphasis on those that result from direct intervention by the parasite. By using comparative microarray analysis of an uninfected bovine cell line and its Theileria infected counterpart, in conjunction with use of the specific parasitacidal agent, buparvaquone, we have identified a large number of host cell gene expression changes that result from parasite infection. Our results indicate that the viable parasite can irreversibly modify the transformed phenotype of a bovine cell line. Fifty percent of genes with altered expression failed to show a reversible response to parasite death, a possible contributing factor to initiation of host cell apoptosis. The genes that did show an early predicted response to loss of parasite viability highlighted a sub-group of genes that are likely to be under direct control by parasite infection. Network and pathway analysis demonstrated that this sub-group is significantly enriched for genes involved in regulation of chromatin modification and gene expression. The results provide evidence that the Theileria parasite has the regulatory capacity to generate widespread change to host cell gene expression in a complex and largely irreversible manner