Multiscale networks in multiple sclerosis

Kennedy, Keith; Rosbo, Nicole Kerlero de; Uccelli, Antonio; Cellerino, Maria; Ivaldi, Federico; Contini, Paola; Palma, Raffaele De; Harbo, Hanne F.; Berge, Tone; Bos, Steffan D.; Høgestøl, Einar August; Brune-Ingebretsen, Synne; Benavent, Sigrid de Rodez; Paul, Friedemann; Brandt, Alexander U.; Bäcker-Koduah, Priscilla; Behrens, Janina; Kuchling, Joseph; Asseyer, Susana E; Scheel, Michael; Chien, Claudia; Zimmermann, Hanna; Motamedi, Seyedamirhosein; Kauer-Bonin, Josef; Sáez-Rodríguez, Julio; Rinas, Melanie; Alexopoulos, Leonidas G.; Andorrà, Magí; Llufriú, Sara; Sáiz, Albert; Blanco, Yolanda; Martínez‐Heras, Eloy; Solana, Elisabeth; Pulido-Valdeolivas, Irene; Martínez-Lapiscina, Elena H.; García-Ojalvo, Jordi; Villoslada, Pablo

doi:10.1101/2023.02.26.530153

Cited by 3 publications

(3 citation statements)

References 57 publications

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“…One promising treatment-sensitive biomarker for disease activity in pwMS currently being studied is the assessment of high-frequency serum neurofilament light chain levels (130). ML and AI techniques are also increasingly employed in the quest for new MS biomarkers [see, e.g., (131)]. One more reason, to finally leave the traditional pathway and to go digital in MS care.…”

Section: Walking Along the Pathway: Ms Treatment Could Change For The...mentioning

confidence: 99%

Building digital patient pathways for the management and treatment of multiple sclerosis

Wenk,

Voigt,

Inojosa

et al. 2024

Front. Immunol.

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Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and use of big data from not only a cross-sectional, but also a longitudinal perspective. For each patient with MS, there is a vast amount of multimodal data being accumulated over time. But for the application of AI and related technologies, these data need to be available in a machine-readable format and need to be collected in a standardized and structured manner. Through the use of mobile electronic devices and the internet it has also become possible to provide healthcare services from remote and collect information on a patient’s state of health outside of regular check-ups on site. Against this background, we argue that the concept of pathways in healthcare now could be applied to structure the collection of information across multiple devices and stakeholders in the virtual sphere, enabling us to exploit the full potential of AI technology by e.g., building digital twins. By going digital and using pathways, we can virtually link patients and their caregivers. Stakeholders then could rely on digital pathways for evidence-based guidance in the sequence of procedures and selection of therapy options based on advanced analytics supported by AI as well as for communication and education purposes. As far as we aware of, however, pathway modelling with respect to MS management and treatment has not been thoroughly investigated yet and still needs to be discussed. In this paper, we thus present our ideas for a modular-integrative framework for the development of digital patient pathways for MS treatment.

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Section: Walking Along the Pathway: Ms Treatment Could Change For The...mentioning

confidence: 99%

Building digital patient pathways for the management and treatment of multiple sclerosis

Wenk,

Voigt,

Inojosa

et al. 2024

Front. Immunol.

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“…We noted proteins such as NTRK1 (neurotrophic receptor tyrosine kinase 1), APP (Amyloid-beta precursor protein), MAPK1, and MAP2K1. These proteins play pivotal roles in signal transduction pathways and glial cell differentiation (24)(25)(26)(27)(28)(29) . Despite variability in the expression profiles across species, the data suggested that PDRN might exert distinct influences on the MAP kinase pathway, thereby affecting ADORA2A interactions and neuronal cell differentiation (Fig.…”

Section: Network Of Differentially Expressed Adora2a Interactome Asso...mentioning

confidence: 99%

Elucidating Interactome Dynamics of the A2A Adenosine Receptor

Lee,

Son,

Kim

et al. 2024

Preprint

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Polydeoxyribonucleotide (PDRN) is a tissue regeneration substance that resembles human DNA and is present in human cells, mullets, salmons, and flatfish. It stimulates physiological regeneration and metabolic activity. The regenerative and metabolic effects of PDRN are attributed to the activation of Adenosine A2A receptors (ADORA2A), which increases the production of angiogenesis factors and growth factors. Activation of ADORA2A leads to the activation of ADORA2A-interacting proteins with similar functions. To investigate the changes and dynamics of proteins in the presence of PDRN, we conducted selected reaction monitoring-mass spectrometry (SRM-MS) to quantify 491 proteins, 3,852 peptides. Through peptide-level analysis, we founded 374 proteins and 1,193 peptides demonstrating both up-regulation and down-regulation in expression. We conducted gene ontology (GO) analysis and physical network analysis and discovered a novel network of proteins related to neuronal differentiation among the ADORA2A-interacting protein. Based on network analysis, we found indirect interactions with MAPK1 and MP2K1, which are known to influence neuronal cell differentiation and suggest the formation of a network involving MAPK signaling transduction. Finally, using AlphaFold multimer, we were able to predict interaction sites among ADORA2A-interacting proteins in the network associated with neuronal cell differentiation. Specifically, we predicted five interaction sites between ADORA2A and NTRK1 (which interacts with ADORA2A), forming the edge. Thus, we provided indicators for further research using ADORA2A present in robust network and highlighted the potential of PDRN to impact neuronal differentiation.

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“… 286 We propose using clinical phenotype clusters (e.g., AIS B-severe neuropathic pain-prominent dysautonomia) to serve as anchors for molecular network assessments, critical transition events, and the generation of Physiome models. We suggest an initial approach to SCI systems-medicine via multi-variate modeling casting a wide variable net across different scales, 287 including demographics factors (age/sex), AIS, MRI injury severity, multi-organ injury, serum biomarkers, genetic characterization (GWAS) to identify relevant SNPs, peripheral blood inflammatory response and immune depression markers, autonomic instability and blood pressure, complications, and microbiome changes. 288 Serial measures are important to understand how phenotypes such as neuropathic pain and metabolic syndrome evolve, the impact of allostatic stress, and to assess epigenetic markers, including aging.…”

Section: Modeling Of Sci As a Multi-system Diseasementioning

confidence: 99%

Development of a Systems Medicine Approach to Spinal Cord Injury

Guest

Kelly-Hedrick

Williamson

et al. 2023

Journal of Neurotrauma

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Traumatic spinal cord injury (SCI) causes a sudden onset multi-system disease, permanently altering homeostasis with multiple complications. Consequences include aberrant neuronal circuits, multiple organ system dysfunctions, and chronic phenotypes such as neuropathic pain and metabolic syndrome. Reductionist approaches are used to classify SCI patients based on residual neurological function. Still, recovery varies due to interacting variables, including individual biology, comorbidities, complications, therapeutic side effects, and socioeconomic influences for which data integration methods are lacking. Infections, pressure sores, and heterotopic ossification are known recovery modifiers. However, the molecular pathobiology of the disease-modifying factors altering the neurological recovery-chronic syndrome trajectory is mainly unknown, with significant data gaps between intensive early treatment and chronic phases. Changes in organ function such as gut dysbiosis, adrenal dysregulation, fatty liver, muscle loss, and autonomic dysregulation disrupt homeostasis, generating progression-driving allostatic load. Interactions between interdependent systems produce emergent effects, such as resilience, that preclude single mechanism interpretations. Due to many interacting variables in individuals, substantiating the effects of treatments to improve neurological outcomes is difficult. Acute injury outcome predictors, including blood and cerebrospinal fluid biomarkers, neuroimaging signal changes, and autonomic system abnormalities, often do not predict chronic SCI syndrome phenotypes. In systems medicine, network analysis of bioinformatics data is used to derive molecular control modules. To better understand the evolution from acute SCI to chronic SCI multi-system states, we propose a topological phenotype framework integrating bioinformatics, physiological data, and allostatic load tested against accepted established recovery metrics. This form of correlational phenotyping may reveal critical nodal points for intervention to improve recovery trajectories. This study examines the limitations of current classifications of SCI and how these can evolve through systems medicine.

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Multiscale networks in multiple sclerosis

Cited by 3 publications

References 57 publications

Building digital patient pathways for the management and treatment of multiple sclerosis

Building digital patient pathways for the management and treatment of multiple sclerosis

Elucidating Interactome Dynamics of the A2A Adenosine Receptor

Development of a Systems Medicine Approach to Spinal Cord Injury

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