AsthmaMap: An expert‐driven computational representation of disease mechanisms

Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis

Macrophages play an essential role in rheumatoid arthritis. Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in rheumatoid arthritis is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we used a newly developed version of the BMA tool deployed to a high-performance computing cluster to identify the models' steady states. The identified attractors are then validated using gene expression data sets and prior knowledge. We successfully applied our framework to generate and calibrate the M1 and M2 macrophage Boolean models for rheumatoid arthritis. Using KO simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages and GSK3B as a promising target that could promote anti-inflammatory macrophages in rheumatoid arthritis.

In silico investigation of molecular networks linking gastrointestinal diseases, malnutrition, and sarcopenia

Malnutrition (MN) is a common primary or secondary complication in gastrointestinal diseases. The patient’s nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels. Although extensive knowledge of individual molecular factors is available, their regulatory interplay is not yet fully understood. A comprehensive overall picture of pathological mechanisms and resulting phenotypes is lacking. In silico approaches that convert existing knowledge into computationally readable formats can help unravel mechanisms, underlying such complex molecular processes. From public literature, we manually compiled experimental evidence for molecular interactions involved in the development of sarcopenia into a knowledge base, referred to as the Sarcopenia Map. We integrated two diseases, namely liver cirrhosis (LC), and intestinal dysfunction, by considering their effects on nutrition and blood secretome. We demonstrate the performance of our model by successfully simulating the impact of changing dietary frequency, glycogen storage capacity, and disease severity on the carbohydrate and muscle systems. We present the Sarcopenia Map as a publicly available, open-source, and interactive online resource, that links gastrointestinal diseases, MN, and sarcopenia. The map provides tools that allow users to explore the information on the map and perform in silico simulations.

Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps

Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases that capture molecular interactions, disease-related processes, and disease phenotypes with standardized representations in large-scale molecular interaction maps. Various tools are available for disease map analysis, but an intuitive solution to perform in silico experiments on the maps in a wide range of contexts and analyze high-dimensional data is currently missing. To this end, we introduce a two-dimensional enrichment analysis (2DEA) approach to infer downstream and upstream elements through the statistical association of network topology parameters and fold changes from molecular perturbations. We implemented our approach in a plugin suite for the MINERVA platform, providing an environment where experimental data can be mapped onto a disease map and predict potential regulatory interactions through an intuitive graphical user interface. We show several workflows using this approach and analyze two RNA-seq datasets in the Atlas of Inflammation Resolution (AIR) to identify enriched downstream processes and upstream transcription factors. Our work improves the usability of disease maps and increases their functionality by facilitating multi-omics data integration and exploration.

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

CyFi-MAP: an interactive pathway-based resource for cystic fibrosis

Cystic fibrosis (CF) is a life-threatening autosomal recessive disease caused by more than 2100 mutations in the CF transmembrane conductance regulator (CFTR) gene, generating variability in disease severity among individuals with CF sharing the same CFTR genotype. Systems biology can assist in the collection and visualization of CF data to extract additional biological significance and find novel therapeutic targets. Here, we present the CyFi-MAP-a disease map repository of CFTR molecular mechanisms and pathways involved in CF. Specifically, we represented the wild-type (wt-CFTR) and the F508del associated processes (F508del-CFTR) in separate submaps, with pathways related to protein biosynthesis, endoplasmic reticulum retention, export, activation/inactivation of channel function, and recycling/degradation after endocytosis. CyFi-MAP is an open-access resource with specific, curated and continuously updated information on CFTR-related pathways available online at https://cysticfibrosismap.github.io/ . This tool was developed as a reference CF pathway data repository to be continuously updated and used worldwide in CF research.

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation

Background

Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task.

Results

We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance.

Conclusions

Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00279-2.

The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.

Automated inference of Boolean models from molecular interaction maps using CaSQ

Motivation

Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner.

Results

We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability.

Availability and implementation

The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq.

Supplementary information

Supplementary data are available at Bioinformatics online.

Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer's disease

Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challenges, calling for adequate knowledge models. Disease maps provide curated knowledge about disorders' pathophysiology at the molecular level adapted to omics measurements. However, the expressiveness of disease maps could be increased to help in avoiding ambiguities and misinterpretations and to reinforce their interoperability with other knowledge resources. Ontology is an adequate framework to overcome this limitation, through their axiomatic definitions and logical reasoning properties. We introduce the Disease Map Ontology (DMO), an ontological upper model based on systems biology terms. We then propose to apply DMO to Alzheimer's disease (AD). Specifically, we use it to drive the conversion of AlzPathway, a disease map devoted to AD, into a formal ontology: Alzheimer DMO. We demonstrate that it allows one to deal with issues related to redundancy, naming, consistency, process classification and pathway relationships. Furthermore, we show that it can store and manage multi-omics data. Finally, we expand the model using elements from other resources, such as clinical features contained in the AD Ontology, resulting in an enriched model called ADMO-plus. The current versions of DMO, ADMO and ADMO-plus are freely available at http://bioportal.bioontology.org/ontologies/ADMO.

Three Major Efforts to Phenotype Asthma: Severe Asthma Research Program, Asthma Disease Endotyping for Personalized Therapeutics, and Unbiased Biomarkers for the Prediction of Respiratory Disease Outcome

The SARP, ADEPT, and U-BIOPRED programs are all significant efforts in characterizing asthma and reporting clusters that will assist in designing personalized therapies for asthma, and especially severe asthma. Key aspects of the design of these programs are summarized and major findings are reported in this review.

Omics for the future in asthma

Asthma is a common, complex, multifaceted disease. It comprises multiple phenotypes, which might benefit from treatment with different types of innovative targeted therapies. Refining these phenotypes and understanding their underlying biological structure would help to apply precision medicine approaches. Using different omics methods, such as (epi)genomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, allowed to view and investigate asthma from diverse angles. Technological advancement led to a large increase in the application of omics studies in the asthma field. Although the use of omics technologies has reduced the gap between bench to bedside, several design and methodological challenges still need to be tackled before omics can be applied in asthma patient care. Collaborating under a centralized harmonized work frame (such as in consortia, under consistent methodologies) could help worldwide research teams to tackle these challenges. In this review, we discuss the transition of single biomarker research to multi-omics studies. In addition, we deliberate challenges such as the lack of standardization of sampling and analytical methodologies and validation of findings, which comes in between omics and personalized patient care. The future of omics in asthma is encouraging but not completely clear with some unanswered questions, which have not been adequately addressed before. Therefore, we highlight these questions and emphasize on the importance of fulfilling them.

RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis

Rheumatoid arthritis (RA) is a progressive, inflammatory autoimmune disease of unknown aetiology. The complex mechanism of aetiopathogenesis, progress and chronicity of the disease involves genetic, epigenetic and environmental factors. To understand the molecular mechanisms underlying disease phenotypes, one has to place implicated factors in their functional context. However, integration and organization of such data in a systematic manner remains a challenging task. Molecular maps are widely used in biology to provide a useful and intuitive way of depicting a variety of biological processes and disease mechanisms. Recent large-scale collaborative efforts such as the Disease Maps Project demonstrate the utility of such maps as versatile tools to organize and formalize disease-specific knowledge in a comprehensive way, both human and machine-readable. We present a systematic effort to construct a fully annotated, expert validated, state-of-the-art knowledge base for RA in the form of a molecular map. The RA map illustrates molecular and signalling pathways implicated in the disease. Signal transduction is depicted from receptors to the nucleus using the Systems Biology Graphical Notation (SBGN) standard representation. High-quality manual curation, use of only human-specific studies and focus on small-scale experiments aim to limit false positives in the map. The state-of-the-art molecular map for RA, using information from 353 peer-reviewed scientific publications, comprises 506 species, 446 reactions and 8 phenotypes. The species in the map are classified to 303 proteins, 61 complexes, 106 genes, 106 RNA entities, 2 ions and 7 simple molecules. The RA map is available online at ramap.elixir-luxembourg.org as an open-access knowledge base allowing for easy navigation and search of molecular pathways implicated in the disease. Furthermore, the RA map can serve as a template for omics data visualization.

What did we learn from multiple omics studies in asthma?

More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired-presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies.