A computational approach for mapping heme biology in the context of hemolytic disorders

Humayun, Farah; Domingo‐Fernándéz, Daniel; George, Ajay Abisheck Paul; Hopp, Marie-Thérèse; Syllwasschy, Benjamin Franz; Detzel, Milena S.; Hoyt, Charles Tapley; Hofmann-Apitius, Martin; Imhof, Diana

doi:10.1101/804906

Cited by 4 publications

(4 citation statements)

References 58 publications

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“…A major advantage of this strategy is that the knowledge graph is computable, meaning downstream machine learning tasks can be carried out for knowledge discovery. Furthermore, knowledge graphs support hypothesis generation by enabling researchers to assess whether their hypotheses are compatible with existing knowledge (Humayun et al, 2019).…”

Section: Challenges Of Hypothetical Modelsmentioning

confidence: 99%

Challenges of Integrative Disease Modeling in Alzheimer's Disease

Khatami

Robinson

Birkenbihl

et al. 2020

Front. Mol. Biosci.

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Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.

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Section: Challenges Of Hypothetical Modelsmentioning

confidence: 99%

Challenges of Integrative Disease Modeling in Alzheimer's Disease

Khatami

Robinson

Birkenbihl

et al. 2020

Front. Mol. Biosci.

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“…The process of discriminating "known knowns" from "unknown knowns" can be supported by knowledge graphs (KGs), as they provide a means to capture, represent and formalize structured information (Nelson et al, 2019). Furthermore, although these KGs were originally developed to describe interactions between entities, they are complemented by a broad range of algorithms that have been proven to partially automate the process of knowledge discovery (Cowen et al, 2017;Humayun et al, 2020). Importantly, novel machine learning techniques can generate latent, low-dimensional representations of the KG which can then be utilized for downstream tasks such as clustering or classification (Hamilton et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

Domingo‐Fernándéz

Baksi²,

Schultz

et al. 2020

Bioinformatics

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Summary The COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats. Availability The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de. Supplementary information Supplementary data are available online.

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“…More recently, other compounds have been described to interact and stimulate TLR4 including hyaluronic acid, the dust mite protein Der p 2, nickel and various endogenous molecules released from injured cells, that are collectively termed danger-associated molecular patterns (DAMPs) ( 4 – 7 ). In particular, the red blood cell-derived product heme has been implicated in TLR4 signaling and has been proposed to be a DAMP that affects inflammatory responses in a variety of pathophysiological conditions ( 8 – 15 ). Heme is an iron-containing tetrapyrrole with important functions in various biological processes as a prosthetic moiety of hemoproteins in its covalent or non-covalent bound form ( 16 , 17 ).…”

Section: Introductionmentioning

confidence: 99%

“…Many of the pro-inflammatory effects of heme have been associated with activation of TLR4 signaling, as initially demonstrated in macrophages ( 10 ). However, TLR4 signaling by heme appears to involve highly complex regulatory mechanisms, which are dependent on the applied models and experimental conditions ( 15 , 21 ). For example, conflicting findings on potential heme-dependent pro-inflammatory effects have been reported in kidney injury models applying TAK-242, a specific inhibitor of TLR4 signaling, and TLR4 knockout mice ( 22 – 25 ).…”

Section: Introductionmentioning

confidence: 99%

TLR4 Signaling by Heme and the Role of Heme-Binding Blood Proteins

2020

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Toll-like receptors (TLRs), also known as pattern recognition receptors, respond to exogenous pathogens and to intrinsic danger signals released from damaged cells and tissues. The tetrapyrrole heme has been suggested to be an agonist for TLR4, the receptor for the pro-inflammatory bacterial component lipopolysaccharide (LPS), synonymous with endotoxin. Heme is a double-edged sword with contradictory functions. On the one hand, it has vital cellular functions as the prosthetic group of hemoproteins including hemoglobin, myoglobin, and cytochromes. On the other hand, if released from destabilized hemoproteins, non-protein bound or “free” heme can have pro-oxidant and pro-inflammatory effects, the mechanisms of which are not fully understood. In this review, the complex interactions between heme and TLR4 are discussed with a particular focus on the role of heme-binding serum proteins in handling extracellular heme and its impact on TLR4 signaling. Moreover, the role of heme as a direct and indirect trigger of TLR4 activation and species-specific differences in the regulation of heme-dependent TLR4 signaling are highlighted.

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A computational approach for mapping heme biology in the context of hemolytic disorders

Abstract: 20

Cited by 4 publications

References 58 publications

Challenges of Integrative Disease Modeling in Alzheimer's Disease

Challenges of Integrative Disease Modeling in Alzheimer's Disease

COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

TLR4 Signaling by Heme and the Role of Heme-Binding Blood Proteins

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