1. Use Case 2: HDL-bound small RNA in Lupus 1 Data and background slides kindly provided by Kasey Vickers, Vanderbilt University. Use Case 2: High-Density Lipoproteins – small RNA Signatures in Systemic Erythematosus Lupus Organized and Hosted by the Data Management and Resource Repository (DMRR) Wednesday, Nov 5 th, 2014 6:00 – 8:30 pm

2. Use Case 2: HDL-bound small RNA in Lupus 2 1.5 million Americans Systemic Inflammation Increased production of autoantibodies against multiple antigens: dsDNA, histones, HDL, apolipoproteins, phospholipids, RBC Background: Systemic Lupus Erythematosus (SLE) 9 out of 10 SLE patients are women Presents between ages 20-40, with 15-20% of cases presenting before 18 years of age Increased frequency in African American and Hispanic women

3. We sought to ask if HDL-small RNAs contribute to SLE- accelerated atherosclerosis. Thus, this use case focuses on non-vesicular exRNA, whereas most exRNA work to date has focused on vesicular exRNAs. We therefore wish to use the short-RNA Seq pipeline in the Genboree Workbench to ask if HDL-small RNAs are altered in SLE subjects. Results: We found that 0.25% of reads isolated from HDL particles map to miRNAs, which is lower than other strategies that resulted in 1.63%, 3.2%, and 1.8% mapped miRNAs. 3 Use Case 2: HDL-bound small RNA in Lupus

4. Biological Samples to Be Analyzed Patient NumberSampleInput File NameBiosample Metadata # in KB #1Plasma (Control) 2572_KCV_1_25_VK.fastq.gz EXR-KCVSLE01-BS #2Plasma (Control) 2572_KCV_1_26_CC.fastq.gz EXR-KCVSLE02-BS #3Plasma (Control) 2572_KCV_1_27_AE.fastq.gz EXR-KCVSLE03-BS #4Plasma (Disease) 2572_KCV_1_28_CHL001.fastq.gz EXR-KCVSLE04-BS #5Plasma (Disease) 2572_KCV_1_29_CHL002.fastq.gz EXR-KCVSLE05-BS #6Plasma (Disease) 2572_KCV_1_30_CHL003.fastq.gz EXR-KCVSLE06-BS Input files are located in the Data Selector in the following Group  Database  Folder: Group: exRNA Metadata Standards Database: Use Case 2: Small RNA Profiles in Lupus Folder: 1. Inputs (FASTQ) Use Case 2: HDL-bound small RNA in Lupus 4 1 mL of plasma => anti-apoA-I IP column => Library from 102ug total HDL protein

5. Genboree Workbench – Getting Started Getting Started –http://genboree.org/theCommons/projects/pub lic-commons/wiki/Getting_startedhttp://genboree.org/theCommons/projects/pub lic-commons/wiki/Getting_started Genboree Workbench Icons Explanation –http://genboree.org/theCommons/projects/pub lic-commons/wiki/genboree_iconshttp://genboree.org/theCommons/projects/pub lic-commons/wiki/genboree_icons FAQs –http://genboree.org/theCommons/ezfaq/index/ public-commonshttp://genboree.org/theCommons/ezfaq/index/ public-commons 5

6. Genboree Workbench – Create Database Create a Genboree Workbench Database –http://genboree.org/theCommons/ezfaq/show/ public-commons?faq_id=491http://genboree.org/theCommons/ezfaq/show/ public-commons?faq_id=491 hg19 6 Note: - You will be using this newly created Genboree Workbench Database to hold the output of tool runs. This will be the database that we’re referring to when we say ‘your database’. Note: - You will be using this newly created Genboree Workbench Database to hold the output of tool runs. This will be the database that we’re referring to when we say ‘your database’.

7. 7 Running the Pipeline: Select Input Files Note: You will input (1) fastq file per tool run. So, for each fastq file you wish to analyze, you will need to repeat the process shown on the next 3 slides. Note: You will input (1) fastq file per tool run. So, for each fastq file you wish to analyze, you will need to repeat the process shown on the next 3 slides.

8. 8 Running the Pipeline: Select Output Database Note: Drag Your newly created database to Output Targets. Note: Drag Your newly created database to Output Targets.

9. 9 Running the Pipeline: Select Tool

10. 10 Running the Pipeline: Submit Job

11. 11 Post-processing: Select Input Files Note: These zip files will be in your database, but will be in the folder that you named: Files/smallRNAseqPipeline/[your analysis name]/ Note: These zip files will be in your database, but will be in the folder that you named: Files/smallRNAseqPipeline/[your analysis name]/

12. 12 Post-processing: Select Output Database Note: Drag Your newly created database to Output Targets. Note: Drag Your newly created database to Output Targets.

13. 13 Post-processing: Select Tool

14. 14 Post-processing: Submit Job

15. 15 Post-processing: Begin Analysis (Excel) Note: The processed files to the left will be in your database, in the folder that you named: Files/processPipelineRuns/[your analysis name]/ Note: The processed files to the left will be in your database, in the folder that you named: Files/processPipelineRuns/[your analysis name]/

16. 16 inputclippedrRNAnot_rRNAgenome miRNA sense miRNA antisense tRNA sense tRNA antisense piRNA sense piRNA antisense snoRNA sense snoRNA antisense miRNA plantVirus sense C115053616127367851015767117210184510397121080605521339415354158 C21370276611552643919246106333974095507110213551611468016382153 C31442140112327187982075113451124349297118541590821538619414181 D47263611615597249277456631982180483567422911575741520280 D591168277761935622480713945527430287262136741851041424495 D6173809211471991511924841352743152232211354716830217911949413167 inputclippedrRNAnot_rRNAgenome miRNA sense miRNA antisense tRNA sense tRNA antisense piRNA sense piRNA antisense snoRNA sense snoRNA antisense miRNA plantVirus sense C1334%282%23%260%100%0.2684%0.0000%1.3425%0.0029%0.0021%0.0003%0.0008%0.0001%0.0035% C2335%282%22%260%100%0.2691%0.0001%1.3469%0.0036%0.0020%0.0004%0.0009%0.0000%0.0037% C3332%283%23%261%100%0.2725%0.0000%1.3584%0.0035%0.0020%0.0004%0.0009%0.0001%0.0042% D4333%282%23%260%100%0.2602%0.0001%1.3353%0.0034% 0.0007%0.0009%0.0001%0.0037% D5332%283%23%260%100%0.2647%0.0000%1.3394%0.0031%0.0038%0.0005%0.0009%0.0001%0.0035% D6333%282%23%259%100%0.2594%0.0000%1.3077%0.0034%0.0023%0.0009%0.0008%0.0001%0.0032% not_rRNAgenome Mapped Fraction Unmapped Fraction C111721018451039738%62% C210633397409550739%61% C311345112434929738%62% D45663198218048339%61% D57139455274302838%62% D613527431522322139%61% Use Case 2: Pipeline Results – miRNA and Unmapped Read Fractions

17. 17 ✓ ✓ ✓ LEGEND Expressed with similar fold-change Expressed with opposite fold-change Related miR expressed with similar fold-change Related miR expressed with opposite fold-change Not expressed LEGEND Expressed with similar fold-change Expressed with opposite fold-change Related miR expressed with similar fold-change Related miR expressed with opposite fold-change Not expressed ✓ ✓ Use Case 2: Pipeline Results – HDL-miRNA Changes Associated with SLE

18. Gene Symbol parametric p-valueFDRSLEControls Fold ChangePotential role in SLEref(s) hsa-mir-142-3p<1E-07 0.220.0663.28 TGF-  signaling Carlsen hsa-mir-106a<1E-07 3.945.540.71 TGF-  signaling, BMPR2 Carlsen hsa-mir-17<1E-07 3.264.620.71Targets CXCR5, expression is down regulated by Bcl6 (Tfh cells), TGF-  signaling, BMPR2 Yu et al, 2009 hsa-mir-20a1.0E-078.35E-070.81.20.66Targets CXCR5, expression is down regulated by Bcl6 (Tfh cells), associates with active lupus nephritis Yu et al, 2009, Carlsen hsa-mir-92a0.00090.00250.610.78 TGF-  signaling, BMPR2 Carlsen hsa-mir-2230.00180.00468.1411.510.71associates with active lupus nephritis Carlsen hsa-mir-146b0.06510.1130.0840.10.84targets AP1 (transcription factor for IL-2) Curtale 2010 Carlsen, AL A & R 2013 18 Use Case 2: Known Plasma miRNA Changes from Literature ✓ ✓

19.  ~40% of reads isolated from HDL particles map to the human genome.  ~0.25% of reads isolated from HDL particles map to miRNAs.  miR-486-3p is more highly expressed in HDL particles of lupus patients than in controls. Use Case 2: Summary 19