| VarElect results |
Table 4 Top 10 results for unadjusted mitochondrial-dysfunctional DEGs from VarElect. Investigation of DEGs using online tool VarElect with the terms "Mitochondria" and "Parkinson's Disease" in mito-sPD compared to controls indicate a number of genes were highly for association with these terms, including well established PD genes. Gene symbol represents official HGNC symbol. Matched Phenotypes indicates the terms inputted into VarElect which have been associated with each gene. Results are ranked by VarElect score.
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kegg pathway results, EnrichR? |
the 84 DEGs associated with mito-sPD did not enrich for any functions or pathways and therefore failed to identify any pathways associated with mitochondrial dysfunction. (위 VarElect analysis 도 84 gene 으로 한 것이 아니라, p VALUE 를 relax 해서 unadjujusted p value <0.05 했더니 나온 1659 gene 을 VAReLECT 에 넣어 나온 결과임. )
Table 1 Top 10 results for lysosomal-dysfunctional DEGs from VarElect. Investigation of DEGs using online tool VarElect with the terms "Lysosome" and "Parkinson's Disease" indicate a number of genes score highly for association with these terms. Gene symbol represents official HGNC symbol. Matched Phenotypes indicates the terms inputted into VarElect which have been associated with each gene. Results are ranked by the VarElect score.
Top 5 most enriched KEGG pathways for lysosomal-dysfunctional DEGs. Results indicate an enrichment of signalling pathways as well as the pathway "Lysosome". Overlap indicates number of genes in input list/total number of genes in pathway list. "Genes" lists the DEGs present in each pathway.
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Tx response | In sPD fibroblasts with mild mitochondrial impairment (ATP or MMP levels >2 SD but <3 SD below the mean of controls, in order to assess the potential of UDCA treatment to be beneficial in a larger patient population.) UDCA→ normalized ATP, MMP, MC1 activity (but not MC4 activity) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Future | PBMC? | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Consideration |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Comp Bio Sadael 2022, | PPMI, PDBP |
Milder patient at baseline → for 12m, progressor → 12-24m: non-progressor progressors displaying less baseline impairment overall. "catch-up effect" http://10.158.16.136:8280/ (you need to be on VPN to access (Cisco AnyConnect), CSF file needs to be uploaded for output. |
| Latourelle 2017 | PPMI, LABS-PD |
Higher baseline motor score and Parkinson's Disease status were predictive of faster rates of motor progression in all the ensemble models, either as a main effect or in interaction terms. The first follow-up examination was at either 3 or 4 years after baseline in LABS-PD cohort). higher baseline MDS-UPDRS motor score, male sex, and increased age, as well as a novel Parkinson's disease-specific epistatic interaction, all indicative of faster motor progression (first follow-up examination was at either 3 or 4 years after baseline in LABS-PD cohort). The rate of motor progression (assessed by annual rate of change in combined MDS-UPDRS parts II and III scores |
| Raval 2020 | PDBP | baseline movement scores from Clin are found to be among the most important features for predicting future progression rate., Direction? |
| Nguyen 2021 | PPMI | Progression 아니라, future absolute disease severity 봤음. : Higher baseline MDS-UPDRS score predicted higher scores at year 1 and year 2, but was not predictive of year 4 score |
Regarding baseline severity in relationship to future progression, I notice followings in the literatures you mentioned, but it looks like that different observation period across studies/analysis adds complexicty to interpretation. [Latourelle 2017]: Higher baseline motor score were predictive of faster rates of motor progression. But I see that the first follow-up examination was at either 3 or 4 years after baseline in LABS-PD cohort. [Nguyen 2021]: Higher baseline MDS-UPDRS score predicted higher scores at year 1 and year 2, but was not predictive of year 4 score.
{Zhang, 2019 #2515} PPMI, 466 patients, Method: A deep learning algorithm, Long-Short Term Memory (LSTM),
| Subtype | I | II | III |
|---|---|---|---|
| Mild Baseline, Moderate Motor Progression) | Moderate Baseline, Mild Progression | Severe Baseline, Rapid Progression | |
| 43.1% of the participants, with average age 58.79.53 years | 22.9% of the participants, with average age | 33.9% of the patients, with average age 65.32 | |
| moderate functional decay in motor ability but stable cognitive ability | mild functional decay in both motor and non-motor symptom | rapid progression of both motor and nonmotor symptoms |
Grouping subjects into several equal groups
| tertile | quartile | quintile | decile | Percentile | |
|---|---|---|---|---|---|
| Terminology for cut off points | The cut off points are called 'tertiles' | The first and third quartiles (25th and 75th centiles), 작은 숫자가 작은 value | |||
| Term for groups | A common confusion is to use the terms tertiles, quartiles, quintiles, etc, not for the cut off points but for the groups so obtained, but these are properly called thirds, quarters, fifths, and so on | Q1 ↓ Q2 ↓ Q3 ↓ 25% 25% 25% 25% First Quartile 작은 숫자가 작은 value, | |||
| example | T1 / T2 / T3 33% 33% 33% | Q1 / Q2 / Q3 / Q4 25% 25% 25% 25% q1 q2 q3 | Inosine P2,3 QU1 QU2 QU3 QU4 QU5 20% 20% 20% 20% 20% qu1 qu2 qu3 qu4 |
Method to stratify: ]
- Outcome 과의 관계에 따라 (what is the characteristics that affect the outcome?)
- 이게 기본
|
Inosine PD-L1 MT: baseline F>10% NLRP3 |
Severity impact: UPDRS progression Tx outcome Biomarker progression: longi FF change
|
Stratification method Quintiles ROC Pattern? | (Normal value 는 안 따진 듯 어차피 pd 환자만으로 임상하니까?) |
{Kaiser, 2023 #2243} PPMI cohort, somascan
| HC | Endotype 1 | Endotype 2 | |
|---|---|---|---|
| CSF p-tau≥11 pg/mL | CSF p-tau<11 pg/mL ↑ | ||
| updrs part I | Endotype 2, however, showed 200 differentially expressed SOMAmers, 197 unique proteins (see Supplementary Table 5). | ||
| [HC vs endotype 1] five markers were significantly different compared to the control group (CNTFR, LPO, MMP10, RIPK2, and VEGFA). | |||
| CSF Aβ | |||
| [HC vs PD all] (table 3) LPO (Lower LPO levels in idiopathic patients found here contrasts with previously reported elevated CSF LPO levelsss. dopamine and levodopa reduce LPO levelsss Given that the idiopathic patients recruited were drug-naïve early PD patients, dopamine levels in this subpopulation may have helped maintaining low levels of CSF LPO), RIPK2 and VEGFA were also part of the differences between HC and the whole idiopathic group (see above). ...Among those proteins, AK1, CCL14, FRZB, GPI, HAMP, LPO, NETO1, PTPRR, RAB31, RELT, RIPK2, ROBO3, RSPO4, SHANK1, SPINK9, VEGFA, and VIP were dysregulated for the whole idiopathic group as compared to healthy controls. | |||
| CSF aSyn | |||
| 155 SOMAmers (i.e., 153 unique proteins; see Supplementary Table 5) were differentially expressed between endotypes. | |||
Uncertain Spans
- Table 4 (mitochondrial-dysfunctional DEGs) 일부 gene symbol(EPS27A, NDUFS3, GBE1)와 VarElect score 소수점은 본문 사진 해상도가 매우 작아 0.01 단위 정확도가 보장되지 않음. 가장 그럴듯한 형태로 옮김.
- Table 1 (lysosomal DEGs) score 값(56.8, 45.4 등) 소수점 자릿수도 동일한 사유로 0.01 단위가 정확하지 않을 수 있음.
- Top 5 KEGG pathways 표의 Genes 목록 마지막 1-2개 토큰(MAPK6, ATP6V0A1 등)은 OCR 토큰과 사진 토큰 모두 모호. 가장 가까운 표준 gene symbol로 옮김.
- “Grouping subjects” 표 quartile 셀의 25% 4개 중 가장 우측 25%는 figure 가장자리에 잘려 보임. 4분할 대칭 가정으로 동일하게 표기함.
- “{Kaiser 2023 #2243}” 표의 화살표(↑/↓)는 Endotype 2 셀 사이 셀-간 연결로 그려져 있음. HTML table에서는 셀 안 텍스트로 단순 옮김 (표 레이아웃의 시각적 화살표 자체는 손실).