Deep brain stimulation-associated brain tissue imprints: a new in vivo approach to biological research in human Parkinson’s disease

Zaccaria, A.; Bouamrani, Ali; Chabardès, Stéphan; Atifi, Michèle El; Seigneuret, Éric; Lobrinus, Johannes Alexander; Dubois‐Dauphin, Michel; Berger, François; Burkhard, Pierre

doi:10.1186/s13024-016-0077-4

Cited by 10 publications

(35 citation statements)

References 40 publications

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“…Nevertheless, there is very encouraging progress in these disease areas, including from a number of large research projects that are characterizing basic mechanisms (eg, the Human Brain Project), as well as the development of innovative solutions for sampling issues. [28][29][30] Thus, the virtual patient Omics, networks, and the virtual patient -Lehrach Dialogues in Clinical Neuroscience - Vol 18 . No.…”

Section: B a S I C R E S E A R C Hmentioning

confidence: 99%

“…Owing to rapid progress in single-cell sequencing, molecular data from hard-to-sample areas could be generated from the few cells clinging to tools used to introduce electrodes into affected areas, eg, in brain-stimulation therapies. 28 In parallel, surrogate tissues could be developed from individual patients by reprogramming induced pluripotent stem (iPS) cells; eg, skin cells could be developed from neuronal cells for further molecular analysis. 29,30 Such data would be complemented by imaging and increasingly powerful sensor data, the latter providing large amounts of information, possibly on a continuous basis.…”

Section: Model-driven Personalized Therapy In Other Disease Areasmentioning

confidence: 99%

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Omics approaches to individual variation: modeling networks and the virtual patient

Lehrach

2016

Dialogues in Clinical Neuroscience

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Every human is unique. We differ in our genomes, environment, behavior, disease history, and past and current medical treatment—a complex catalog of differences that often leads to variations in the way each of us responds to a particular therapy. We argue here that true personalization of drug therapies will rely on “virtual patient” models based on a detailed characterization of the individual patient by molecular, imaging, and sensor techniques. The models will be based, wherever possible, on the molecular mechanisms of disease processes and drug action but can also expand to hybrid models including statistics/machine learning/artificial intelligence-based elements trained on available data to address therapeutic areas or therapies for which insufficient information on mechanisms is available. Depending on the disease, its mechanisms, and the therapy, virtual patient models can be implemented at a fairly high level of abstraction, with molecular models representing cells, cell types, or organs relevant to the clinical question, interacting not only with each other but also the environment. In the future, “virtual patient/insilico self” models may not only become a central element of our health care system, reducing otherwise unavoidable mistakes and unnecessary costs, but also act as “guardian angels” accompanying us through life to protect us against dangers and to help us to deal intelligently with our own health and wellness.

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Section: B a S I C R E S E A R C Hmentioning

confidence: 99%

Section: Model-driven Personalized Therapy In Other Disease Areasmentioning

confidence: 99%

Omics approaches to individual variation: modeling networks and the virtual patient

Lehrach

2016

Dialogues in Clinical Neuroscience

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“…One possible strategy to address this in the future could be to use the non-lesional access to the brain during deep brain stimulation (DBS) surgery, by capturing cells spontaneously adhering to the DBS stylet for omics profiling (Zaccaria et al 2016), and correlating these profiles to corresponding molecular measurements in blood samples. Using pathway and network analysis approaches discussed in this review, blood–brain correlations could not only be assessed at the level of individual biomolecules but also via pathway or sub-network activity scores to establish more robust correlations.…”

Section: Introductionmentioning

confidence: 99%

“…Since the midbrain ( substantia nigra ) is regarded as the main affected tissue in PD and only post-mortem omics data are available for this brain region, one of the main challenges for omics-based biomarker modeling is to identify reliable surrogate markers in peripheral tissues or body fluids. One possible strategy to address this in the future could be to use the non-lesional access to the brain during deep brain stimulation (DBS) surgery, by capturing cells spontaneously adhering to the DBS stylet for omics profiling (Zaccaria et al 2016 ), and correlating these profiles to corresponding molecular measurements in blood samples. Using pathway and network analysis approaches discussed in this review, blood–brain correlations could not only be assessed at the level of individual biomolecules but also via pathway or sub-network activity scores to establish more robust correlations.…”

Section: Introductionmentioning

confidence: 99%

Computational systems biology approaches for Parkinson’s disease

Glaab

2017

Cell Tissue Res

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Parkinson’s disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.

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“…Shotgun proteomic and transcriptomic analyses provided for the first time molecular information from DBS-associated brain samples, and confirmed the compatibility of this new type of sample with poly-omic approaches. 4 As a new event, one teaching session was dedicated to the "Swiss Young Neurologists," which was very well accepted by the audience and should be continued in 2018. Clinical algorithms to approach myoclonus as well as essential diagnostics and therapies of common forms of adult-onset dystonia were covered by the presentations of Dr. Georg Kägi and Prof. Joseph-Andre Ghika, respectively.…”

mentioning

confidence: 99%