OSCR

A microbiome quantitative trait locus in SLC39A8 modulates disease severity in synucleinopathy-induced models of Parkinson's disease.

Code ↔ Paper

15 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 15 matches
  1. [1] § Materials and methods › Statistical analysis ↔ src/Figure_S4_ASO_PFF_Behaviors.R, lines 212–258 · score 0.83 · clasping scores, body weight, grip strength, Wire Hang, open field, nest
  2. [2] § Materials and methods › 16S rRNA gene sequencing ↔ src/PFF/PFF_DADA2.R, lines 61–115 · score 0.78 · assignTaxonomy, Silva v138, DNA, assignment, database, DADA2
  3. [3] § Materials and methods › 16S rRNA gene sequencing ↔ src/ASO/ASO_DADA2.R, lines 1–59 · score 0.76 · amplicon sequence variants, DADA2, PhiX, quality, trimmed, raw
  4. [4] § Materials and methods › Statistical analysis ↔ src/PFF/PFF_RSJensen_Beta_Diversity.R, lines 84–142 · score 0.74 · Jensen Shannon, beta diversity, distance matrices, scores, cecum, colon
  5. [5] § Results › SLC39A8 A393T variant modulates motor behavior in synucleinopathy models of PD ↔ src/Figure_S1_Spontaneous_Aggregated.R, lines 30–118 · score 0.65 · distance traveled, location memory, open field, S1, Rotarod, MUT
  6. [6] § Results › SLC39A8 A393T variant modulates motor behavior in synucleinopathy models of PD ↔ src/Figure_S3_MPTP_Behaviors.R, lines 31–69 · score 0.65 · distance traveled, location memory, open field, spent, MPTP, behaviors
  7. [7] § Materials and methods › Tail suspension test ↔ src/Figure_S3_MPTP_Behaviors.R, lines 31–69 · score 0.64 · spent mobile, tail suspension, MPTP, behavior
  8. [8] § Results › SLC39A8 A393T variant modulates motor behavior in synucleinopathy models of PD ↔ src/Figure_2_Behavior.R, lines 73–130 · score 0.61 · forelimb grip strength, buried food pellet, behavior, MPTP, Figure 2, latency
  9. [9] § Results › SLC39A8 A393T variant modulates motor behavior in synucleinopathy models of PD ↔ src/ASO/ASO_Behavior_PCA.R, lines 84–121 · score 0.58 · hindlimb clasp, buried food pellet, wire hang, pole, Tg, behavior
  10. [10] § Materials and methods › Novel object recognition and object location memory ↔ src/Figure_S1_Spontaneous_Aggregated.R, lines 30–118 · score 0.58 · location memory, open field, OLM, training, day
  11. [11] § Results › SLC39A8 A393T variant modulates motor behavior in synucleinopathy models of PD ↔ src/Figure_2_Behavior.R, lines 73–130 · score 0.57 · forelimb grip strength, buried food pellet, weight, behaviors, Boxplots, MPTP
  12. [12] § Results › SLC39A8 A393T variant modulates motor behavior in synucleinopathy models of PD ↔ src/Figure_S4_ASO_PFF_Behaviors.R, lines 212–258 · score 0.57 · grip strength, wire hang, open field, nesting, clasp, Tg
  13. [13] § Materials and methods › Immunofluorescence analysis ↔ src/Figure_Correlate_PFF_Puncta.R, lines 41–94 · score 0.55 · 3.5–10 pixel, 3.5 pixel, Particle, threshold, puncta, contralateral
  14. [14] § Materials and methods › Immunofluorescence analysis ↔ src/Figure_4_Synucleinopathy.R, lines 26–81 · score 0.54 · 3.5–10 pixel, 3.5 pixel, Particle, threshold, contralateral, puncta
  15. [15] § Materials and methods › Wire hang test ↔ src/ASO/ASO_Wire_Hang.R, lines 1–54 · score 0.51 · wire hang, insincere, scored, ASO

Paper

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The authors' code

R · 260 lines · 9.2 KB · MIT · 2 matches

  1. library(ggplot2)
  2. library(dplyr)
  3. library(cowplot)
  4. library(here)
  5. library(tidyr)
  6. library(ggbeeswarm)
  8. ## Environment --
  9. here::i_am("Rscripts/Figure_S4_ASO_PFF_Behaviors.R")
  11. ## Functions --
  12. generate_boxplots <- function(input_data, X, Y, min,max){
  13. data<-as.data.frame(input_data)
  14. #Ensure correct ordering of levels
  15. #data$Genotype <- data$SLC_Genotype
  16. data$SLC_Genotype <- factor(data$SLC_Genotype, levels = c("WT", "HET", "MUT"))
  18. ggplot(data=data,aes(x={{X}},y={{Y}}, fill={{X}})) +
  19. #geom_violin(alpha=0.25,position=position_dodge(width=.75),size=1,color="black",draw_quantiles=c(0.5))+
  20. geom_boxplot(alpha=0.25)+
  21. #geom_quasirandom(alpha=0.1)+
  22. scale_fill_viridis_d()+
  23. geom_point(size=1,position=position_jitter(width=0.25),alpha=0.3)+
  24. theme_cowplot(16) +
  25. ylim(min,max)+
  26. theme(legend.position = "none")
  29. }
  31. ## ASO Pole Test --
  32. data<-readr::read_csv(here("data", "ASO", "ASO Pole Test - All_Cohorts_Assign_Maximum_Time.csv"))
  33. data <- data %>%
  34. rowwise() %>%
  35. mutate(Average_Tturn = mean(c(Trial_1_Tturn, Trial_2_Tturn, Trial_3_Tturn, Trial_4_Tturn, Trial_5_Tturn), na.rm = TRUE)) %>%
  36. mutate(Fastest_Tturn = pmin(Trial_1_Tturn, Trial_2_Tturn, Trial_3_Tturn, Trial_4_Tturn, Trial_5_Tturn, na.rm = TRUE))
  38. data <- data %>%
  39. rowwise() %>%
  40. mutate(Average_Ttotal = mean(c(Trial_1_Ttotal, Trial_2_Ttotal, Trial_3_Ttotal, Trial_4_Ttotal, Trial_5_Ttotal), na.rm = TRUE)) %>%
  41. mutate(Fastest_Ttotal = pmin(Trial_1_Ttotal, Trial_2_Ttotal, Trial_3_Ttotal, Trial_4_Ttotal, Trial_5_Ttotal, na.rm = TRUE))
  43. data <- data %>%
  44. mutate(Best_Performance = case_when(
  45. any(Trial_1_Mode == "C" | Trial_2_Mode == "C" | Trial_3_Mode == "C" | Trial_4_Mode == "C" | Trial_5_Mode == "C") ~ "C",
  46. any(Trial_1_Mode == "S" | Trial_2_Mode == "S" | Trial_3_Mode == "S" | Trial_4_Mode == "S" | Trial_5_Mode == "S") ~ "S",
  47. TRUE ~ "S"
  48. ))
  49. data$SLC_Genotype <- factor(data$SLC_Genotype, levels =c("WT","HET","MUT"))
  51. pole_tg_pos <- data %>% filter(ASO_Tg=="Positive")
  52. write.csv(pole_tg_pos,here("data/ASO/Fig_S4A_B.csv"))
  53. pos_slc_avg_turn <- generate_boxplots(pole_tg_pos, SLC_Genotype, Average_Tturn,0,20) +
  54. ylab("Average time to turn (s)")+
  55. xlab("")+
  56. labs(title= "ASO Pole Test - Tturn")+
  57. theme(plot.title = element_text(hjust = 0.5))
  59. pos_slc_avg_total <- generate_boxplots(pole_tg_pos, SLC_Genotype, Average_Tturn,0,20) +
  60. ylab("Average time to descend (s)")+
  61. xlab("")+
  62. ggtitle("ASO Pole Test - Ttotal")+
  63. theme(plot.title = element_text(hjust = 0.5))
  65. summary_table <- pole_tg_pos %>%
  66. group_by(Best_Performance, SLC_Genotype) %>%
  67. count()
  69. category_counts <- summary_table %>%
  70. pivot_wider(names_from = Best_Performance, values_from = n, values_fill = 0)
  72. result <- fisher.test(category_counts[, c("C", "S")])
  74. ## ASO Wire Hang --
  75. data<-readr::read_csv(here("data", "ASO", "Final_ASO_Wire_Hang.csv"))
  76. wirehang_tg_pos <- data %>% filter(ASO_Tg=="Positive")
  77. wirehang <-generate_boxplots(wirehang_tg_pos, SLC_Genotype, Time,0,200)+
  78. ylab("Total Hang Time (s)")+
  79. xlab("")+
  80. ggtitle("ASO Wire Hang")+
  81. theme(plot.title = element_text(hjust = 0.5))
  83. ## ASO Hindlimb Clasp --
  84. data<- readr::read_csv(here("data", "ASO", "ASO_Hindlimb_Clasping .csv"))
  85. data$SLC_Genotype <- factor(data$SLC_Genotype, levels=c("WT", "HET", "MUT"))
  86. clasp_tg_pos <- data %>% filter(ASO_Tg=="Positive")
  88. clasp_score <- ggplot(data=clasp_tg_pos,aes(x=SLC_Genotype,y=Score_Truncated, color=SLC_Genotype)) +
  89. stat_summary(aes(x=SLC_Genotype, y=Score_Truncated), fun=median, geom="crossbar", colour="black")+
  90. geom_beeswarm(cex = 3,priority = "density",size=3)+
  91. scale_color_viridis_d(option = "D")+
  92. theme_cowplot(16) +
  93. theme(legend.position = "none")+
  94. ggtitle("ASO Hindlimb Clasping")+
  95. ylab("Score")+
  96. xlab("")+
  97. theme(plot.title = element_text(hjust = 0.5))
  99. clasp_score
  101. clasp_wt_het <- clasp_tg_pos %>% filter(SLC_Genotype!="MUT")
  102. wilcox.test(Score~SLC_Genotype, clasp_wt_het)
  104. clasp_wt_mut <- clasp_tg_pos %>% filter(SLC_Genotype!="HET")
  105. wilcox.test(Score~SLC_Genotype, clasp_wt_mut)
  107. ## ASO Weights --
  108. bw <- readr::read_csv(here("data", "ASO","ASO Rotarod - Rotarod.csv"))
  109. bw_tg_pos <- bw %>% filter(ASO_Tg=="Positive") %>%
  110. select(c("MouseID","Weight", "SLC_Genotype", "Sex"))
  111. bw_tg_pos <- unique(bw_tg_pos)
  112. write.csv(bw_tg_pos, here("data/ASO/Fig_S4D.csv"))
  114. summary(bw_tg_pos$Weight)
  115. weight <- generate_boxplots(bw_tg_pos, SLC_Genotype, Weight,0,53) +
  116. ggtitle("ASO Body Weight")+
  117. ylab("Weight (g)")+
  118. xlab("")+
  119. theme(plot.title = element_text(hjust = 0.5))
  120. weight
  122. ## PFF Hindlimb Clasp
  123. pff_clasp<- readr::read_csv(here("data", "PFF", "PFF_Hindlimb_Clasp.csv"))
  124. pff_clasp$SLC_Genotype <- factor(pff_clasp$SLC_Genotype, levels=c("WT", "HET", "MUT"))
  126. pff_clasp_score <- ggplot(data=pff_clasp,aes(x=SLC_Genotype,y=Score_Severe_HB, color=SLC_Genotype)) +
  127. stat_summary(aes(x=SLC_Genotype, y=Score_Severe_HB), fun=median, geom="crossbar", colour="black")+
  128. geom_beeswarm(cex = 3,priority = "density",size=3)+
  129. scale_color_viridis_d(option = "D")+
  130. theme_cowplot(16) +
  131. theme(legend.position = "none")+
  132. ggtitle("PFF Hindlimb Clasping")+
  133. ylab("Score")+
  134. xlab("")+
  135. theme(plot.title = element_text(hjust = 0.5))
  136. pff_clasp_score
  138. clasp_wt_het <- pff_clasp %>% filter(SLC_Genotype!="MUT")
  139. wilcox.test(Score_Severe_HB~SLC_Genotype, clasp_wt_het)
  141. clasp_wt_mut <- pff_clasp %>% filter(SLC_Genotype!="HET")
  142. wilcox.test(Score_Severe_HB~SLC_Genotype, clasp_wt_mut)
  145. ## PFF Nesting
  146. pff_nest<- readr::read_csv(here("data", "PFF", "PFF_Nesting_Score.csv"))
  147. pff_nest$SLC_Genotype <- factor(pff_nest$SLC_Genotype, levels=c("WT", "HET", "MUT"))
  149. pff_nest_score <- ggplot(data=pff_nest,aes(x=SLC_Genotype,y=Score_Severe, color=SLC_Genotype)) +
  150. stat_summary(aes(x=SLC_Genotype, y=Score_Severe), fun=median, geom="crossbar", colour="black")+
  151. geom_beeswarm(cex = 3,priority = "density",size=3)+
  152. scale_color_viridis_d(option = "D")+
  153. theme_cowplot(16) +
  154. theme(legend.position = "none")+
  155. ggtitle("PFF Nest Construction")+
  156. ylim(0,5)+
  157. ylab("Score")+
  158. xlab("")+
  159. theme(plot.title = element_text(hjust = 0.5))
  160. pff_nest_score
  162. nest_wt_het <- pff_nest %>% filter(SLC_Genotype!="MUT")
  163. wilcox.test(Score_Severe~SLC_Genotype, nest_wt_het)
  165. nest_wt_mut <- pff_nest %>% filter(SLC_Genotype!="HET")
  166. wilcox.test(Score_Severe~SLC_Genotype, nest_wt_mut)
  168. ## PFF Wire Hang --
  169. wire_hang <- readr::read_csv(here("data", "PFF", "PFF_Wire_Hang - Wire_Hang.csv"))
  170. wire_hang <- wire_hang %>% filter(DPI==90)
  171. wire_hang$DPI <- as.character(wire_hang$DPI)
  172. wire_hang$DPI <- plyr::revalue(wire_hang$DPI, c("90"="90 DPI"))
  174. summary(wire_hang$Total_Hang_Time)
  175. pff_wire_hang<-generate_boxplots(wire_hang, SLC_Genotype, Total_Hang_Time,0,650)+
  176. facet_wrap(~DPI)+
  177. ggtitle("PFF Wire Hang")+
  178. ylab("Total Hang Time (s)")+
  179. xlab("")+
  180. theme(plot.title = element_text(hjust = 0.5))
  182. pff_wire_hang
  184. ## PFF wire hang --
  185. wire_hang <- readr::read_csv(here("data", "PFF", "PFF_Wire_Hang - Wire_Hang.csv"))
  186. wire_hang$DPI <- as.character(wire_hang$DPI)
  187. wire_hang$DPI <- plyr::revalue(wire_hang$DPI, c("90"="90_DPI","150"="150_DPI", "120" = "120_DPI", "180"="180_DPI"))
  188. wire_hang$DPI <- factor(wire_hang$DPI, levels=c("90_DPI", "120_DPI","150_DPI","180_DPI"))
  190. pff_wire_hang<-generate_boxplots(wire_hang, SLC_Genotype, Total_Hang_Time,0,1000)+
  191. facet_wrap(~DPI,nrow=1)+
  192. ggtitle("PFF Wire Hang")+
  193. ylab("Hang Time (s)")+
  194. xlab("")+
  195. theme(plot.title = element_text(hjust = 0.5))
  197. pff_wire_hang
  199. ## PFF Weights --
  200. pff_bw <- readr::read_csv(here("data/PFF/PFF Rotarod - PFF_Rotarod_Analysis.csv"))
  201. pff_bw <- unique(pff_bw %>% select(c("MouseID","Weight","SLC_Genotype","Sex")))
  202. write.csv(pff_bw, here("data/PFF/Fig_S4H.csv"))
  204. pff_weight <- generate_boxplots(pff_bw, SLC_Genotype, Weight,0, 40)+
  205. ggtitle("PFF Body Weight")+
  206. ylab("Weight (g)")+
  207. xlab("")+
  208. theme(plot.title = element_text(hjust = 0.5))
  210. pff_weight
  212. ## Full figure S3 --
  214. top <- plot_grid(pos_slc_avg_turn, pos_slc_avg_total, wirehang, weight,
  215. labels=c("A","B","C","D"), nrow=1)
  217. middle <- plot_grid(clasp_score,pff_wire_hang,
  218. labels=c("E","F"))
  219. bottom <- plot_grid(pff_clasp_score, pff_weight, pff_nest_score,
  220. labels=c("G","H","I"), nrow=1)
  221. top
  222. middle
  223. bottom
  225. ### Accompanying statistics ---
  227. ## PFF Wire Hang --
  228. hang_bw <- merge(pff_bw, wire_hang, by="MouseID")
  229. hang_bw$SLC_Genotype <- factor(hang_bw$SLC_Genotype, levels=c("WT","HET","MUT"))
  230. lm <- lm(Total_Hang_Time~ Weight + Sex + SLC_Genotype, data = hang_bw)
  231. summary(lm)
  233. ## PFF grip strength --
  234. pff_bw <- pff_bw %>% select(c("MouseID","Weight"))
  235. grip_bw <- merge(pff_bw, grip, by="MouseID")
  236. grip_bw$SLC_Genotype <- factor(grip_bw$SLC_Genotype, levels=c("WT","HET","MUT"))
  237. lm <- lm(Average ~ Sex + SLC_Genotype, data = grip_bw)
  238. summary(lm)
  241. ## Body Weight --
  242. bw$SLC_Genotype <- factor(bw$SLC_Genotype, levels=c("WT","HET", "MUT"))
  243. lm_day1 <- lm(Weight ~ Sex + SLC_Genotype, data = bw)
  244. summary(lm_day1)
  246. bw_tg_pos$SLC_Genotype <- factor(bw_tg_pos$SLC_Genotype, levels=c("WT","HET", "MUT"))
  247. lm_day1 <- lm(Weight ~ Sex + SLC_Genotype, data = bw_tg_pos)
  248. summary(lm_day1)
  250. ## Open Field --
  251. of$SLC_Genotype <- factor(of$SLC_Genotype, levels=c("WT","HET", "MUT"))
  252. lm_day1 <- lm(Center_Time ~ Sex + SLC_Genotype, data = of)
  253. summary(lm_day1)
  255. of_bw <- merge(bw, of,by="MouseID")
  256. of_bw$SLC_Genotype.x <- factor(of_bw$SLC_Genotype.x, levels = c("WT", "HET", "MUT"))
  257. lm_day1 <- lm(Distance ~ Weight+SLC_Genotype.x + Sex.x, data = of_bw)
  258. summary(lm_day1)

Figure_S4_ASO_PFF_Behaviors.R at commit dfa4c08, under MIT · at the source

Overview

Authors: Julianne C Yang1, Jamilla Situ1, Ryan Troutman1, Ruowei Zhu2, Margaret Black1, Heidi Buri1, Arjun Gutta1, Fengrui Tian1, Allyson Kang1, Ezinne Aja1,3, Amber Zeng1, Rochelle W Lai4, Jia Tan5, Fengting Liang1, Caitlyn Brahim1, Grace Murphy1, Aaron Ahdoot1, Chao Peng2,6,7,8,9,10, Jonathan P Jacobs1,3,11
ORCID iDs: Julianne C Yang, Ezinne Aja, Rochelle W Lai, Aaron Ahdoot, Jonathan P Jacobs
  1. The Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, 100 Medical Plaza, Los Angeles, CA 90095, United States
  2. Department of Neurology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, 300 Medical Plaza, Los Angeles, CA 90095, United States
  3. Goodman-Luskin Microbiome Center, University of California, Los Angeles,10833 Le Conte Ave, Los Angeles, CA 90095, United States
  4. Division of Cardiology, Department of Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, 100 Medical Plaza, Los Angeles, CA 90095, United States
  5. Department of Pediatric Endocrinology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, 100 Medical Plaza, Los Angeles, CA 90095, United States
  6. California NanoSystems Institute, University of California, Los Angeles, 570 Westwood Plaza, Los Angeles, CA 90095, United States
  7. Brain Research Institute, University of California, Los Angeles, 695 Charles E Young Dr S, Los Angeles, CA 90095, United States
  8. Department of Molecular and Medical Pharmacology, University of California, Los Angeles, 650 Charles E Young Dr S, Los Angeles, CA 90095, United States
  9. Howard and Irene Levine Family Center for Movement Disorder, University of California, Los Angeles, 300 Medical Plaza, Los Angeles, CA 90095, United States
  10. Mary S. Easton Center for Alzheimer's Research and Care, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, CA 90095, United States
  11. Division of Gastroenterology, Hepatology and Parenteral Nutrition, Veterans Affairs Greater Los Angeles Healthcare System, 11301 Wilshire Blvd, Los Angeles, CA 90073, United States
Institutions: University of California, Los Angeles (United States); California NanoSystems Institute (United States); VA Greater Los Angeles Healthcare System (United States)
Journal: Human molecular genetics, volume 35, issue 6, article ddag024
Dates: received 8 September 2025; accepted 17 March 2026; published online 8 April 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/hmg/ddag024 · PMID 41955304 · PMCID PMC13069889 · OpenAlex W7153073466
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics, Connectivity, Machine learning
Keywords: Gene-microbiome interaction, gut microbiome, mbQTL, microbiome quantitative trait locus, Parkinson’s disease, SLC39A8
MeSH: Cation Transport Proteins*, Gastrointestinal Microbiome*, Parkinson Disease*, Quantitative Trait Loci*, Synucleinopathies*, alpha-Synuclein, Animals, Disease Models, Animal, Dopaminergic Neurons, Humans, Male, Mice, Mice, Transgenic, Polymorphism, Single Nucleotide (* major topic)
Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex
Funding: CSRD VA (IK2 CX001717)
Citations: cited by 1 paper (Europe PMC); 58 references in the paper

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor deficits, dopaminergic neuron loss, and α-synuclein (α-syn) aggregation. While rare mutations underlie familial PD, around 85% of cases are idiopathic. Emerging evidence implicates common genetic variants and the gut microbiome in PD risk, but their interaction has not been studied. We previously demonstrated that the PD-protective SLC39A8 variant rs13107325 (human A391T, corresponding to A393T in mouse) is associated with microbial compositional shifts in humans and reshapes the microbiome in SLC39A8 A393T knock-in mice. Here, we test whether this SNP modifies PD phenotypes in two α-synucleinopathy mouse models. In the human α-synuclein overexpression model, A393T carrier mice show reduced motor deficits, consistent with a protective role. However, in the α-synuclein preformed fibril (PFF) injection model, A393T carriers exhibit worsened motor deficits, increased dopaminergic terminal loss, and enhanced α-synuclein pathology spread. SNP- and model-specific microbiome changes correlated with motor outcomes. These included enrichment of Lactobacillus and Lactobacillaceae HT002 genera in A393T carriers with α-synuclein overexpression, and enrichment of Erysipelatoclostridium in PFF-injected A393T carriers. These findings suggest that SLC39A8 A393T-induced microbiome alterations are associated with differential disease outcomes depending on context. Our results are consistent with a model in which susceptibility gene SNPs may influence PD progression via the gut microbiome, though direct causal effects remain to be tested.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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julianneyang/pdbehavior

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: dfa4c089aefe24fa7d7ff4a142b41af43d7956b6, 14 December 2025
Languages: R (65)
Size: 1,134 files, 65 scripts
Software Heritage: not archived
Found in: the text, “16S rRNA gene sequencing”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (58 files), ggplot2 (57 files), tidyverse (57 files), nlme (20 files), ggpubr (12 files), rstatix (9 files), circlize (2 files), emmeans (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
67 files
At the source: github.com/julianneyang/pdbehavior/

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Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 6 keywords, 14 MeSH terms, 1 funder, 53 references.

Cite

This paper

Yang, J. C., Situ, J., Troutman, R., Zhu, R., Black, M., Buri, H., Gutta, A., Tian, F., Kang, A., Aja, E., Zeng, A., Lai, R. W., Tan, J., Liang, F., Brahim, C., Murphy, G., Ahdoot, A., Peng, C., & Jacobs, J. P. (2026). A microbiome quantitative trait locus in SLC39A8 modulates disease severity in synucleinopathy-induced models of Parkinson's disease. Human molecular genetics, 35(6), ddag024. https://doi.org/10.1093/hmg/ddag024

BibTeX

@article{yang2026microbiome,
author = {Yang, Julianne C and Situ, Jamilla and Troutman, Ryan and Zhu, Ruowei and Black, Margaret and Buri, Heidi and Gutta, Arjun and Tian, Fengrui and Kang, Allyson and Aja, Ezinne and Zeng, Amber and Lai, Rochelle W and Tan, Jia and Liang, Fengting and Brahim, Caitlyn and Murphy, Grace and Ahdoot, Aaron and Peng, Chao and Jacobs, Jonathan P},
title = {{A microbiome quantitative trait locus in SLC39A8 modulates disease severity in synucleinopathy-induced models of Parkinson's disease}},
journal = {Human molecular genetics},
year = {2026},
month = mar,
volume = {35},
number = {6},
pages = {ddag024},
publisher = {Oxford University Press},
issn = {0964-6906},
doi = {10.1093/hmg/ddag024},
url = {https://doi.org/10.1093/hmg/ddag024},
pmid = {41955304},
pmcid = {PMC13069889}
}

RIS

TY - JOUR
AU - Yang, Julianne C
AU - Situ, Jamilla
AU - Troutman, Ryan
AU - Zhu, Ruowei
AU - Black, Margaret
AU - Buri, Heidi
AU - Gutta, Arjun
AU - Tian, Fengrui
AU - Kang, Allyson
AU - Aja, Ezinne
AU - Zeng, Amber
AU - Lai, Rochelle W
AU - Tan, Jia
AU - Liang, Fengting
AU - Brahim, Caitlyn
AU - Murphy, Grace
AU - Ahdoot, Aaron
AU - Peng, Chao
AU - Jacobs, Jonathan P
TI - A microbiome quantitative trait locus in SLC39A8 modulates disease severity in synucleinopathy-induced models of Parkinson's disease
T2 - Human molecular genetics
J2 - Hum Mol Genet
PY - 2026
DA - 2026/03/01
VL - 35
IS - 6
SP - ddag024
SN - 0964-6906
PB - Oxford University Press
DO - 10.1093/hmg/ddag024
UR - https://doi.org/10.1093/hmg/ddag024
LA - en
ER -

CSL-JSON

{
"id": "10.1093/hmg/ddag024",
"type": "article-journal",
"title": "A microbiome quantitative trait locus in SLC39A8 modulates disease severity in synucleinopathy-induced models of Parkinson's disease",
"container-title": "Human molecular genetics",
"author": [
{
"family": "Yang",
"given": "Julianne C"
},
{
"family": "Situ",
"given": "Jamilla"
},
{
"family": "Troutman",
"given": "Ryan"
},
{
"family": "Zhu",
"given": "Ruowei"
},
{
"family": "Black",
"given": "Margaret"
},
{
"family": "Buri",
"given": "Heidi"
},
{
"family": "Gutta",
"given": "Arjun"
},
{
"family": "Tian",
"given": "Fengrui"
},
{
"family": "Kang",
"given": "Allyson"
},
{
"family": "Aja",
"given": "Ezinne"
},
{
"family": "Zeng",
"given": "Amber"
},
{
"family": "Lai",
"given": "Rochelle W"
},
{
"family": "Tan",
"given": "Jia"
},
{
"family": "Liang",
"given": "Fengting"
},
{
"family": "Brahim",
"given": "Caitlyn"
},
{
"family": "Murphy",
"given": "Grace"
},
{
"family": "Ahdoot",
"given": "Aaron"
},
{
"family": "Peng",
"given": "Chao"
},
{
"family": "Jacobs",
"given": "Jonathan P"
}
],
"container-title-short": "Hum Mol Genet",
"volume": "35",
"issue": "6",
"page": "ddag024",
"DOI": "10.1093/hmg/ddag024",
"PMID": "41955304",
"PMCID": "PMC13069889",
"ISSN": "0964-6906",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/hmg/ddag024",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
1
]
]
}
}

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