Use Case 2: HDL-bound small RNA in Lupus

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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 Managementand Resource Repository (DMRR)

Wednesday, Nov 5th, 20146:00 – 8:30 pm

Use Case 2: HDL-bound small RNA in Lupus

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• 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

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.

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Use Case 2: HDL-bound small RNA in Lupus

Biological Samples to Be AnalyzedPatient Number Sample Input File Name Biosample

Metadata # in KB#1 Plasma (Control) 2572_KCV_1_25_VK.fastq.gz EXR-KCVSLE01-BS

#2 Plasma (Control) 2572_KCV_1_26_CC.fastq.gz EXR-KCVSLE02-BS

#3 Plasma (Control) 2572_KCV_1_27_AE.fastq.gz EXR-KCVSLE03-BS

#4 Plasma (Disease) 2572_KCV_1_28_CHL001.fastq.gz EXR-KCVSLE04-BS

#5 Plasma (Disease) 2572_KCV_1_29_CHL002.fastq.gz EXR-KCVSLE05-BS

#6 Plasma (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 StandardsDatabase: Use Case 2: Small RNA Profiles in Lupus Folder: 1. Inputs (FASTQ)

Use Case 2: HDL-bound small RNA in Lupus

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1 mL of plasma => anti-apoA-I IP column => Library from 102ug total HDL protein

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Genboree Workbench – Getting Started

• Getting Started– http://

genboree.org/theCommons/projects/public-commons/wiki/Getting_started

• Genboree Workbench Icons Explanation– http://

genboree.org/theCommons/projects/public-commons/wiki/genboree_icons

• FAQs– http://

genboree.org/theCommons/ezfaq/index/public-commons

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Genboree Workbench – Create Database

• Create a Genboree Workbench Database– http://

genboree.org/theCommons/ezfaq/show/public-commons?faq_id=491

• hg19

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’.

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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.

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Running the Pipeline: Select Output Database

Note: Drag Your newly created

database to Output Targets.

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Running the Pipeline: Select Tool

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Running the Pipeline: Submit Job

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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]/

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Post-processing: Select Output Database

Note: Drag Your newly created

database to Output Targets.

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Post-processing: Select Tool

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Post-processing: Submit Job

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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]/

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input clipped rRNA not_rRNA genomemiRNA sense

miRNA antisense

tRNA sense

tRNA antisense

piRNA sense

piRNA antisense

snoRNA sense

snoRNA antisense

miRNA plantVirus sense

C1 15053616 12736785 1015767 11721018 4510397 12108 0 60552 133 94 15 35 4 158C2 13702766 11552643 919246 10633397 4095507 11021 3 55161 146 80 16 38 2 153C3 14421401 12327187 982075 11345112 4349297 11854 1 59082 153 86 19 41 4 181D4 7263611 6155972 492774 5663198 2180483 5674 2 29115 75 74 15 20 2 80D5 9116827 7761935 622480 7139455 2743028 7262 1 36741 85 104 14 24 4 95D6 17380921 14719915 1192484 13527431 5223221 13547 1 68302 179 119 49 41 3 167

input clipped rRNA not_rRNA genomemiRNA sense

miRNA antisense

tRNA sense

tRNA antisense

piRNA sense

piRNA antisense

snoRNA sense

snoRNA antisense

miRNA plantVirus sense

C1 334% 282% 23% 260% 100% 0.2684% 0.0000% 1.3425% 0.0029% 0.0021% 0.0003% 0.0008% 0.0001% 0.0035%C2 335% 282% 22% 260% 100% 0.2691% 0.0001% 1.3469% 0.0036% 0.0020% 0.0004% 0.0009% 0.0000% 0.0037%C3 332% 283% 23% 261% 100% 0.2725% 0.0000% 1.3584% 0.0035% 0.0020% 0.0004% 0.0009% 0.0001% 0.0042%D4 333% 282% 23% 260% 100% 0.2602% 0.0001% 1.3353% 0.0034% 0.0034% 0.0007% 0.0009% 0.0001% 0.0037%D5 332% 283% 23% 260% 100% 0.2647% 0.0000% 1.3394% 0.0031% 0.0038% 0.0005% 0.0009% 0.0001% 0.0035%D6 333% 282% 23% 259% 100% 0.2594% 0.0000% 1.3077% 0.0034% 0.0023% 0.0009% 0.0008% 0.0001% 0.0032%

not_rRNA genomeMapped Fraction

Unmapped Fraction

C1 11721018 4510397 38% 62%C2 10633397 4095507 39% 61%C3 11345112 4349297 38% 62%D4 5663198 2180483 39% 61%D5 7139455 2743028 38% 62%D6 13527431 5223221 39% 61%

Use Case 2: Pipeline Results –miRNA and Unmapped Read Fractions

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✓✓✓

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

miRNA FC p-value Pipeline FC

hsa-mir-486-3p 2.283 0.05387 3.09

hsa-mir-184 2.236 0.06184 -1.62

hsa-mir-93-5p 1.709 0.02616 1.06

hsa-mir-720 -3.783 0.009731 ---

Gene Symbolparametricp-value FDR SLE Controls

Fold Change Potential role in SLE ref(s)

hsa-mir-142-3p <1E-07 <1E-07 0.22 0.066 3.28 TGF-b signaling Carlsen

hsa-mir-106a <1E-07 <1E-07 3.94 5.54 0.71 TGF-b signaling, BMPR2 Carlsen

hsa-mir-17 <1E-07 <1E-07 3.26 4.62 0.71 Targets CXCR5, expression is down regulated by Bcl6 (Tfh cells), TGF-b signaling, BMPR2

Yu et al, 2009

hsa-mir-20a 1.0E-07 8.35E-07 0.8 1.2 0.66 Targets CXCR5, expression is down regulated by Bcl6 (Tfh cells), associates with active lupus nephritis

Yu et al, 2009, Carlsen

hsa-mir-92a 0.0009 0.0025 0.61 0.78 0.78 TGF-b signaling, BMPR2 Carlsen

hsa-mir-223 0.0018 0.0046 8.14 11.51 0.71 associates with active lupus nephritis

Carlsen

hsa-mir-146b 0.0651 0.113 0.084 0.1 0.84 targets AP1 (transcription factor for IL-2)

Curtale 2010

Carlsen, AL A & R 201318

Use Case 2: Known Plasma miRNA Changes from Literature

✓✓

~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

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