20240722_183521

with PINK1 and PRKN Mutations	(study link)	collection of fibroblasts - cells from the skin - from individuals with PINK1 and PRKN mutations. Hypothesis: To find a specific PINK1/PRKN signature - one or more cellular change associated with PINK1 and PRKN mutations - we will compare protein production (gene expression) and breakdown (metabolism) in cells with PINK1 and PRKN mutations to those in cells without mutations in these genes. Study Design: We will analyze gene expression and metabolism in skin cells with PINK1 and/or PRKN mutations. We expect challenges with interpreting our findings and plan to use two experimental approaches - cells from biologically related individuals and immature brain cells (Ips 젯지?) with changes in PINK1 and PRKN genes - to make sense of data collected. We will then analyze genetic and metabolic changes in the cells separately and identify links between the two. Impact on Diagnosis/Treatment of Parkinson's disease: Identifying a PINK1/PRKN signature is a very important step in the search for biomarkers - objective measures of disease - useful in diagnostics. This signature would also aid in selection of clinical research participants. Next Steps for Development: The identified signature will be tested in individuals with PD without mutations in PINK1 or PRKN genes and in those whose disease is not inherited. Testing the occurrence of the signature in other cells of individuals with Parkinson's, such as blood cells, will also be important in the futur
Mitochondrial DNA Damage in a Pesticide-exposed Cohort Samuel M. Goldman, MD, MPH Professor of Medicine and Neurology at University of California,	(study link)	Study Rationale: Environmental poisons may cause or contribute to some cases of Parkinson's disease (PD). Mitochondrial impairment is seen in all forms of PD. Some environmental exposures, such as pesticides, can damage mitochondria. Currently, it is very difficult to estimate the amount of toxins that people with PD may have been exposed to, or to estimate the amount of damage these toxins may have caused. An accurate test is needed. Hypothesis: We will test the hypothesis that damage to mitochondrial DNA (mtDNA) is greater in people who were exposed to certain pesticides and explore whether that damage is greater in people with PD. Study Design: This project builds on our prior work in a group of professional pesticide applicators and their spouses with existing DNA samples, half of whom have PD and half of whom do not. We have detailed information about their pesticide exposures and other environmental factors. We will measure the amount of damage to participants' mtDNA and test whether this damage is greater in persons with PD and in those who were previously exposed to pesticides. Impact on Diagnosis/Treatment of Parkinson's disease: This work will identify which pesticides and other environmental factors may damage mtDNA. It will investigate mtDNA damage as a marker of disease severity and provide important preliminary data for future studies of mtDNA as a marker of progression. Next Steps for Development: Our results could immediately help efforts to prevent PD by identifying mitochondrial poisons in the environment. If successful, future studies will investigate mtDNA as a biomarker of disease progression in at-risk cohorts
아래는 NON-MITOCHONDRIAL PROJECTS
Mapping the PD Brain: Oligomer-driven Functional Genomics	https://www.michaeljfox.org/grant/mapping-pd-brain-oligomer-driven-functional-genomics	Nicholas William Wood, Sonia Gandhi, Steven F. Lee, Mina Ryten, Michele Vendruscolo, PhD Study Rationale: To determine the molecular and cellular process that lead to PD, a powerful approach is to single out and study the cells where the disease originates. To achieve this goal, we use alpha-syn oligomers as a cellular biomarker to identify the right cells. We will then apply state-of-the-art genomic and genetic analyses to identify genes and proteins that form the disease pathways. We can then determine the difference between cause and effect by using human cell models derived from induced pluripotent stem cells. Hypothesis: We hypothesize that alpha-syn oligomers can be used as cellular biomarkers to identify the specific cells where the disease processes begin, thus making their targeted study possible. Study Design: By detecting the presence of alpha-syn oligomers, we will identify neuronal and non-neuronal cells in the human brain at different stages of disease, which we will then study using state-of-the-art single cell genomic and transcriptomic methods. This will allow us to build a comprehensive and detailed picture of the genes and molecular processes that underlie the disease, which we will then prioritize using network theory and our knowledge of the current and emerging genetic factors. Using a human model system (iPSC) we will be able to distinguish cause and effect and deliver new targets for therapeutics, diagnostics and biomarkers of disease. Impact on Diagnosis/Treatment of Parkinson's Disease: This interdisciplinary program, combining physical chemistry, computational modelling, genetics and neurobiology, will allow us to much more fully understand the reasons behind why some cells succumb and other resist the pathological processes. Our findings will offer opportunities for accurate markers of disease status, progression and validated targets for biopharma to develop novel therapies.
Dissecting the Mechanisms Underlying Disease Progression	https://www.michaeljfox.org/grant/dissecting-mechanisms-underlying-disease-progression	John Anthony Hardy, Zane Jaunmuktane, Frances M. Platt, Mina Ryten, MD, PhD Study Rationale: The progression of Parkinson's disease is very variable, with some individuals having a rapid course and others having a longer and more benign course. We believe that by understanding the genetics and the mechanistic basis of this variability, we will be able to design therapies to slow Parkinson's progression. We have already found that GBA mutations lead to a rapid disease course and that LRRK2, linked to familial forms of Parkinson's, influences the course of parkinsonism in another disease: progressive supranuclear palsy. We will test whether modulating these enzymes influences the course of pathology spread in a pre-clinical model of progression as a validation of this approach to treatment. Hypothesis: We want to find and understand the genes that are involved in Parkinson's progression and test whether modulating them pharmacologically influences disease progression. Study Design: Through genetic analysis, we will find genes that influence the progression of parkinsonism, and then assess the mechanisms by which they affect disease development. We have already found that GBA and LRRK2 influence clinical rates of decline so we will test, in a mouse model of pathology progression, whether inhibiting these enzymes influences pathology spread and thereby develop a relevant platform to test drugs for slowing disease progression. Impact on Diagnosis/Treatment of Parkinson's Disease: This research will impact Parkinson's care in three ways. First, by understanding the genetics of rate of decline, these data can be factored into clinical trial design and possibly more generally into clinical practice. Second, the identification of pathways involved in disease progression is likely to reveal further drug targets. And thirdly, the testing of GBA and LRRK2 inhibitors in a mouse model of disease progression will test this as a valid approach to treatment development. Next Steps for Development: When we find genes and pathways involved in disease progression, we will develop high-throughput screens to test for compounds that modulate them. The testing of GBA and LRRK2 inhibitors in a model of pathology progression will validate this as a model in which to test drugs that may modify this development.

Stratification strategy on Mitochondria

What level ie gene, RNA
REALLY exist?	Not variable on MC1 PET, but variable on xx
How many?	{Schapira, 2008 #1581} complex I deficiency in the substantia nigra and platelets implies that it is a systemic defect in a proportion of cases (~25% on the basis of platelet activities
Paired CSF, Blood, brain samples available?
Multifactorial: exclude Lysosomal ?
Imaging: 31p MRS
variable: mt DNA damage (not suitable since no overlap), ps65-ub (this is not suitable), ATP? MC1?

Identification	Identify mito signature in genetic PD & sPD and compare with each other (spd에서의 screening 용에 적합한 것을 골라야 함, NDE? PBMC?) → biomarkers will be obtained in patients that are stratified according to their degree of mitochondrial dysfunction.
Establishment
Validation	independent patient cohorts

Sample {Prasuhn, 2019 #1475} (DRKS00015880)

• eight established SNPs) for PD (Table 2) [6]. These SNPs have been selected because they are increasing the risk for PD and are functionally linked to mitochondrial homeostasis. This score has been developed within a BMBF-funded collaborative project (MitoPD, 031A430B).

Flow chart (genotyped cohorts, n>950):

• Parkin/Pink1-PD homozygotes (n=12) → coenzyme Q10 / placebo
• Parkin/Pink1-PD heterozygotes (n=12) → coenzyme Q10 / placebo
• Mito-PRS + (n=24) → coenzyme Q10 / placebo
• Mito-PRS - (n=24) → coenzyme Q10 / placebo

(treatment groups and their respective size)

Table 2 SNPs taken a genome-wide association study on PD used for stratification of patients (omics+/- groups)

SNP	Locus	Effect allele	MAF	Odds Ratio
rs329648	MIR4697	T	0,46	1,15
rs34311866	TMEM175-GAK-DGKQ	G	0,14	1,40
rs11868035	SREBF1-RAI1	A	0,49	0,97
rs14235	BCKDK-STX1B	A	0,36	1,19
rs11060180	CCDC62	G	0,25	0,90
rs71628662	GBA-SYT11	T	0,01	0,40
rs199347	GPNMB	C	0,48	0,97
rs12637471	MCCC1	A	0,34	0,67

SNPs were taken from the study of Nalls et al. [6]. Only SNPs were taken into account with an in silico annotation to mitochondrial homeostasis. SNP: single nucleotide polymorphism. MAF: mean allele frequency

The classification of the patients into the two omics groups is done via a simple sum score and is calculated as follows (see Table 2):

The number of effect alleles (coding 0 = no effect allele, 1 = one effect allele and 2 = two effect alleles) and the weighting of the respective SNP with the effect size of the effect allele, measured with the ln of the odds ratio, are incorporated into this score from each risk SNP. If the calculated risk score for

number of effect alleles SNP_i

ln (odds ratio effect allel SNP_i) + 1.7181

a patient exceeds a threshold value of + 0.30, this patient is assigned to the omics+ group. If a patient has a risk score of less than - 0.30, an assignment to omics- is made. Patients with a risk score between - 0.30 and + 0.30 do not belong to one of the two “extreme” omics groups. These patients are not included in the

The thresholds as mentioned above were determined to represent the 20 and 80% quantiles in our cohorts in which the score has been established. A number of the study participants’ samples will be genotyped twice in order to ensure the reproducibility of genotyping. In this way, the agreement can be determined SNP-specifically and overall. The intra-class correlation coefficient is used as a measure of agreement.

	Goal	RNA-Seq	Single cell	Protein
Pathway	Pathway: characterize relevant pathway eg. confirm the involvement of x pathways, highlight the role of microglia and its potential interaction with APP metabolism.	( GENE Ontology, )	Single cell in midbrain: neuronal degeneration 의 context 에서 보려면 single cell 필요? Gene expression analysis should be correlated with spatial and temporal aspects of the disease Eg) (Bellenguez, 2020 #1300)	the more genes for any given pathway are identified, the greater the confidence that this pathway should be prioritized over others concentrations of proteins in the same complex are less noisy compared with proteins that are not within one complex31

Uncertain Spans

location	transcription	uncertainty
Innomedica row “immature brain cells (Ips 젯지?)"	"Ips 젯지?”	OCR shows “Ips 젯지?”; Korean phonetic note “젯지” suggests iPS-derived; transcribed as seen.
MJFF Mapping the PD Brain URL	”https://www.michaeljfox.org/grant/mapping-pd-brain-…”	URL is small and partially clipped; expanded to full slug from visible chars.
Table 2 effect allele “BCKDK-STX1B” / “GBA-SYT11”	locus names	Two locus names use Latin / Greek-style abbreviations; transcribed as seen.
Equation “ln (odds ratio effect allel SNP_i) + 1.7181”	constant 1.7181	Constant value 1.7181 visible at the end; subscript notation on SNP_i is small.
Goal/RNA-Seq table column “(GENE Ontology,” with leading ”(“	matching brackets	The opening ”(” appears alone in the column, possibly continuing onto next photo; transcribed as seen.