| Literature DB >> 30584155 |
Alberto J Espay1, Anthony E Lang2.
Abstract
Parkinson disease has been considered for practical purposes a heterogeneous clinico-pathological entity. The operational definition requires clinical ascertainment of a levodopa-responsive parkinsonism with no "atypical" features, and pathological criteria based on the finding, usually at postmortem, of aggregates of α-synuclein in Lewy bodies and Lewy neurites. The underlying assumption has been that a molecular-biological disorder, targetable for disease modification as a whole, underlies this clinico-pathologic, convergent model of disease. The 2020s will be expected to mark the beginning of the end for this model, especially if therapeutic success in a specific molecular subtype, such as PD-GBA, is not translated to "sporadic PD". The complex and dynamic biological abnormalities of aging, which have informed the evolution of other fields in medicine into divergent, systems-biology models, will also provide the template for the development of disease modifying therapies for neurodegenerative disorders. In the 2020s and 2030s we will no longer ask whether any given molecule may be neuroprotective in early Parkinson disease but, rather, which subtype (which endophenotype) among the Parkinson diseases would be the best mechanistic recipient for such molecule and which would not. The next breakthrough in Parkinson's research will be conceptual: the recognition that discoveries in a subtype of PD will apply only or largely to that subtype and not construed to represent "a piece" that seamlessly inserts into, and helps explains, a unifying "Parkinson's puzzle". Successful neuroprotection for each PD subtype will likely require pharmacotherapeutic combinations ("drug cocktails") to harness synergistic potential benefits when more than the dominant pathogenic mechanism is targeted, as identified from forthcoming population-based unbiased biomarker discovery programs.Entities:
Keywords: Parkinson disease; clinico-pathologic model; precision medicine; reductionism; systems biology
Mesh:
Substances:
Year: 2018 PMID: 30584155 PMCID: PMC6311362 DOI: 10.3233/JPD-181465
Source DB: PubMed Journal: J Parkinsons Dis ISSN: 1877-7171 Impact factor: 5.568
Fig.1A) Reductionism: the Duck of Vaucanson. This engraving of the Canard Digérateur or “Digesting Duck” illustrated the famous mechanical duck made by Jacques de Vaucanson in the 18th century, which supposedly ate grain and excreted droppings in front of an audience unaware such stool pellets were not manufactured by the contraption but placed surreptitiously. The “Duck of Vaucanson” served to illustrate Descartes view (in De Homine, 1662) that all animals could be reductively explained as automata. We have reductively attempted to explain the large range of clinical, pathologic, genetic, and molecular findings into a single “Duck of Parkinson,” represented by each of the wheels, levers, and pipes of a single contraption (Copyright in public domain via Wikimedia Commons). B) Systems biology: Looks like a duck, walks like a duck, but there are differences. Modifications of the Duck of Vaucanson illustrate the systems biology model of Parkinson diseases. It acknowledges distinctive self-regulating “mechanical” systems within each of these “birds” (distinct pathophysiological processes), yet sharing enough external and internal duck-like features to belong to the same “family” but with a phenotype shaped by the “pond where they swim” (colored circles surrounding the ducks). Each “pond” includes a combination of genetic, molecular, and environmental traits that combine in systems biology networks (shades of yellow, blue, green, and red). These “Parkinson ducks” represent an oversimplification of more complicated interactions (e.g., mosaicism might have an effect on all genetic factors, mono- and polygenic; similarly, microbiota on metagenomics, etc.). LRRK2: leucine-rich repeat kinase 2 gene; SNCA: alpha-synuclein gene; GBA: glucocerebrosidase gene; P-tau181: tau phosphorylated at threonine 181; α-syn fibrils: oligomeric (presumably “toxic”) forms of α-synuclein; Aβ1-42: amyloid beta 1-42; NF-L: neurofilament light chain.
Reductionism and related ideas that will die
| Few principles must explain many natural phenomena. Mathematics can explain natural patterns. | Mathematics isn’t physics. We can only construct approximate models.* | |
| People and events must belong to discrete categories. | There exists a continuous spectrum of intermediates. | |
| Events must be organized into chains or causes and effects. A gene seems to cause a trait like height or a disease such as cancer. | Complex dynamical systems of living organisms have patterns of information flow that defy our tools for storytelling. | |
| The vast biological diversity can be ordered based on the description of their similarities and differences. | Taxonomies do not equate with basic biological processes, impeding discovery of treatments. | |
| Single-cell sequencing technology works because all 37 trillion cells have the same copy of one’s genome. | A high proportion of brain cells have structural DNA variants (mosaicism). | |
| Skin color, hair form, cranial shape cluster into some diseases. Racial groups may give order to biology. | Racial patterns are complex genetic mixtures created by the sharing of similar exposures. | |
| You can separate one from the other like Newtonian space and time: heritability is immutable. | As Einsteinian spacetime, they are intertwined. Heritability is affected by the environment. | |
| Larger | Significant effects on low | |
| A complex system is nothing but the sum of its parts and can be reduced to its individual constituents. Exceptions to this model are physiological “noise” obscuring the “true” signal. | “Noise” turn into profiles of unique biological systems or subsystems evolving in humans into intricate phenotypes that cannot be reduced. | |
Inspired from “This Idea Must Die: Scientific Theories That Are Blocking Progress” [15]. *Even the most sacred unifications are approximations: equations describing electricity and magnetism are perfectly symmetric only in an empty space. from Marcelo Gleiser (Theoretical physicist); from Richard Dawkins (Evolutionary biologist); from W. Daniel Hillis (Physicist); (“Numbering Nature”) from Kurt Gray (Social psychologist); from Eric J. Topol (Professor of genomics); from Nina Jablonski (Biological anthropologist); from Timo Hannay (Director of Digital Science); and from Melanie Swan (Applied genomics expert).